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1

Landslide Prediction Based on Neural Network Modelling  

Science Journals Connector (OSTI)

The opportunities of artificial neural networks model application to landslide forecasting are considered, namely prediction of landslide types and parameters of landslide damage area. The data collected by ob...

Yuri Aleshin; Isakbek Torgoev

2013-01-01T23:59:59.000Z

2

Enhanced oil recovery data base analysis by simplified predictive models  

SciTech Connect (OSTI)

The U.S. Department of Energy, Bartlesville Energy Technology Center (BETC), has been developing computerized data bases and simplified predictive models to be used to predict enhanced oil recovery (EOR) potential in the U.S. The development phase of this work is nearing completion whereupon the models and data bases will be made available to the public. This paper describes the overall development phase for the models and data bases with analyses of selected EOR projects using the predictive models. Examples of model outputs are discussed and brief descriptions of the predictive algorithms are given.

Ray, R.M.; Wesson, T.C.

1982-11-01T23:59:59.000Z

3

Predictive clothing insulation model based on outdoor air and indoor operative temperatures  

E-Print Network [OSTI]

2012) Predictive clothing insulation model based on outdoorPredictive clothing insulation model based on outdoor airpredictive models of clothing insulation have been developed

Schiavon, Stefano; Lee, Kwang Ho

2012-01-01T23:59:59.000Z

4

A neural network based model for urban noise prediction  

Science Journals Connector (OSTI)

Noise is a global problem. In 1972 the World Health Organization (WHO) classified noise as a pollutant. Since then most industrialized countries have enacted laws and local regulations to prevent and reduce acoustic environmental pollution. A further aim is to alert people to the dangers of this type of pollution. In this context urban planners need to have tools that allow them to evaluate the degree of acoustic pollution. Scientists in many countries have modeled urban noise using a wide range of approaches but their results have not been as good as expected. This paper describes a model developed for the prediction of environmental urban noise using Soft Computing techniques namely Artificial Neural Networks (ANN). The model is based on the analysis of variables regarded as influential by experts in the field and was applied to data collected on different types of streets. The results were compared to those obtained with other models. The study found that the ANN system was able to predict urban noise with greater accuracy and thus was an improvement over those models. The principal component analysis (PCA) was also used to try to simplify the model. Although there was a slight decline in the accuracy of the results the values obtained were also quite acceptable.

N. Genaro; A. Torija; A. Ramos-Ridao; I. Requena; D. P. Ruiz; M. Zamorano

2010-01-01T23:59:59.000Z

5

Productivity prediction model based on Bayesian analysis and productivity console  

E-Print Network [OSTI]

in poor planning and defies effective control of time and budgets in project management. In this research, we have built a productivity prediction model which uses productivity data from an ongoing project to reevaluate the initial productivity estimate...

Yun, Seok Jun

2005-08-29T23:59:59.000Z

6

PREDICTIVE MODELS  

SciTech Connect (OSTI)

PREDICTIVE MODELS is a collection of five models - CFPM, CO2PM, ICPM, PFPM, and SFPM - used in the 1982-1984 National Petroleum Council study of enhanced oil recovery (EOR) potential. Each pertains to a specific EOR process designed to squeeze additional oil from aging or spent oil fields. The processes are: 1) chemical flooding; 2) carbon dioxide miscible flooding; 3) in-situ combustion; 4) polymer flooding; and 5) steamflood. CFPM, the Chemical Flood Predictive Model, models micellar (surfactant)-polymer floods in reservoirs, which have been previously waterflooded to residual oil saturation. Thus, only true tertiary floods are considered. An option allows a rough estimate of oil recovery by caustic or caustic-polymer processes. CO2PM, the Carbon Dioxide miscible flooding Predictive Model, is applicable to both secondary (mobile oil) and tertiary (residual oil) floods, and to either continuous CO2 injection or water-alternating gas processes. ICPM, the In-situ Combustion Predictive Model, computes the recovery and profitability of an in-situ combustion project from generalized performance predictive algorithms. PFPM, the Polymer Flood Predictive Model, is switch-selectable for either polymer or waterflooding, and an option allows the calculation of the incremental oil recovery and economics of polymer relative to waterflooding. SFPM, the Steamflood Predictive Model, is applicable to the steam drive process, but not to cyclic steam injection (steam soak) processes. The IBM PC/AT version includes a plotting capability to produces a graphic picture of the predictive model results.

Ray, R.M. (DOE Bartlesville Energy Technology Center, Bartlesville, OK (United States))

1988-10-01T23:59:59.000Z

7

Predictive Modeling of Large-Scale Commercial Water Desalination Plants: Data-Based Neural Network and Model-Based Process  

E-Print Network [OSTI]

Predictive Modeling of Large-Scale Commercial Water Desalination Plants: Data-Based Neural Network for developing predictive models for large-scale commercial water desalination plants by (1) a data (MSF) and reverse osmosis (RO) desalination plants in the world. Our resulting neural network

Liu, Y. A.

8

Dynamic predictive clothing insulation models based on outdoor air and indoor operative temperatures  

E-Print Network [OSTI]

predictive clothing insulation models based on outdoor airrange of the clothing insulation calculated for eachbuilding). Figure 8 Clothing insulation versus dress code [

Schiavon, Stefano; Lee, Kwang Ho

2012-01-01T23:59:59.000Z

9

PREDICTIVE MODELS  

SciTech Connect (OSTI)

PREDICTIVE MODELS is a collection of five models - CFPM, CO2PM, ICPM, PFPM, and SFPM - used in the 1982-1984 National Petroleum Council study of enhanced oil recovery (EOR) potential. Each pertains to a specific EOR process designed to squeeze additional oil from aging or spent oil fields. The processes are: 1) chemical flooding, where soap-like surfactants are injected into the reservoir to wash out the oil; 2) carbon dioxide miscible flooding, where carbon dioxide mixes with the lighter hydrocarbons making the oil easier to displace; 3) in-situ combustion, which uses the heat from burning some of the underground oil to thin the product; 4) polymer flooding, where thick, cohesive material is pumped into a reservoir to push the oil through the underground rock; and 5) steamflood, where pressurized steam is injected underground to thin the oil. CFPM, the Chemical Flood Predictive Model, models micellar (surfactant)-polymer floods in reservoirs, which have been previously waterflooded to residual oil saturation. Thus, only true tertiary floods are considered. An option allows a rough estimate of oil recovery by caustic or caustic-polymer processes. CO2PM, the Carbon Dioxide miscible flooding Predictive Model, is applicable to both secondary (mobile oil) and tertiary (residual oil) floods, and to either continuous CO2 injection or water-alternating gas processes. ICPM, the In-situ Combustion Predictive Model, computes the recovery and profitability of an in-situ combustion project from generalized performance predictive algorithms. PFPM, the Polymer Flood Predictive Model, is switch-selectable for either polymer or waterflooding, and an option allows the calculation of the incremental oil recovery and economics of polymer relative to waterflooding. SFPM, the Steamflood Predictive Model, is applicable to the steam drive process, but not to cyclic steam injection (steam soak) processes.

Ray, R.M. (DOE Bartlesville Energy Technology Technology Center, Bartlesville, OK (United States))

1986-12-01T23:59:59.000Z

10

Interval Methods for Sensitivity-Based Model-Predictive Control of  

E-Print Network [OSTI]

Interval Methods for Sensitivity-Based Model-Predictive Control of Solid Oxide Fuel Cell Systems and experiment for the thermal subprocess of a high-temperature solid oxide fuel cell system. Keywords: Interval analysis, model-predictive control, sensitivity analysis, tracking control, solid oxide fuel cells AMS

Kearfott, R. Baker

11

Letters: Neural network based hybrid computing model for wind speed prediction  

Science Journals Connector (OSTI)

This paper proposes a Neural Network based hybrid computing model for wind speed prediction in renewable energy systems. Wind energy is one of the renewable energy sources which lower the cost of electricity production. Due to the fluctuation and nonlinearity ... Keywords: Hybrid Model, Multilayer Perceptron, Neural Networks, Self Organizing Maps, Wind Speed Prediction

K. Gnana Sheela; S. N. Deepa

2013-12-01T23:59:59.000Z

12

Selecting Building Predictive Control Based on Model Uncertainty  

E-Print Network [OSTI]

S. Pr´?vara et al. “Building Modeling as a Crucial Part forThe details of building thermal modeling and estimation ofModeling and Optimal Control Algorithm Design for HVAC Systems in Energy Efficient Buildings”.

Maasoumy, Mehdi

2014-01-01T23:59:59.000Z

13

Predictive Models of Biohydrogen and Biomethane Production Based on the Compositional and Structural Features of Lignocellulosic Materials  

Science Journals Connector (OSTI)

Predictive Models of Biohydrogen and Biomethane Production Based on the Compositional and Structural Features of Lignocellulosic Materials ...

Florian Monlau; Cecilia Sambusiti; Abdellatif Barakat; Xin Mei Guo; Eric Latrille; Eric Trably; Jean-Philippe Steyer; Hélène Carrere

2012-10-10T23:59:59.000Z

14

Validated Model-Based Performance Prediction of Multi-Core Software Routers  

E-Print Network [OSTI]

Terms--measurement, simulation, intra-node model, re- source contention, model validation, software components. Leveraged by high flexibility and low costs of software developments in comparison with hardwareValidated Model-Based Performance Prediction of Multi-Core Software Routers Torsten Meyer1

Carle, Georg

15

A Chemical Class-Based Approach to Predictive Model Generation  

Science Journals Connector (OSTI)

The table shows, for each trial, the number of molecules used in the classification, the number of classes generated, the number of molecules successfully classified (i.e., the number that do not become singletons), the average number of molecules per class, and the class redundancy (the number of classes, on average, in which each molecule appears). ... Given the greater apparent reliability of the reduced-default versus the full-default results, the class-based approach appears even more favorable. ... rules that classify objects into similar categories or, in this case, structures into groups of mols. ...

David W. Miller

2003-01-24T23:59:59.000Z

16

Water Research 38 (2004) 33313339 Testing a surface tension-based model to predict the salting  

E-Print Network [OSTI]

Water Research 38 (2004) 3331­3339 Testing a surface tension-based model to predict the salting out associated with transferring solutes from water to a salt solution to the difference in surface tensions likely reflects the inability of the simple surface tension model to account for all interactions among

Herbert, Bruce

17

A Physically Based Analytical Model to Predict Quantized Eigen Energies and Wave Functions Incorporating Penetration Effect  

E-Print Network [OSTI]

We propose a physically based analytical compact model to calculate Eigen energies and Wave functions which incorporates penetration effect. The model is applicable for a quantum well structure that frequently appears in modern nano-scale devices. This model is equally applicable for both silicon and III-V devices. Unlike other models already available in the literature, our model can accurately predict all the eigen energies without the inclusion of any fitting parameters. The validity of our model has been checked with numerical simulations and the results show significantly better agreement compared to the available methods.

Nadim Chowdhury; Imtiaz Ahmed; Zubair Al Azim; Md. Hasibul Alam; Iftikhar Ahmad Niaz; Quazi D. M. Khosru

2014-04-14T23:59:59.000Z

18

Towards QoS Prediction Based on Composition Structure Analysis and Probabilistic Environment Models  

E-Print Network [OSTI]

Towards QoS Prediction Based on Composition Structure Analysis and Probabilistic Environment Models Dragan Ivanovi´c Universidad Polit´ecnica de Madrid idragan@clip.dia.fi.upm.es Peerachai Kaowichakorn Universidad Polit´ecnica de Madrid p.kaowichakorn@gmail.com Manuel Carro Universidad Polit´ecnica de Madrid

Politécnica de Madrid, Universidad

19

Scenario-Based Fault-Tolerant Model Predictive Control for Diesel-Electric Marine Power Plant  

E-Print Network [OSTI]

Scenario-Based Fault-Tolerant Model Predictive Control for Diesel-Electric Marine Power Plant where diesel gener- ator sets (a diesel engine connected to a generator) produce electrical power, which Email: torstein.bo@itk.ntnu.no, tor.arne.johansen@itk.ntnu.no Abstract--Diesel-electric propulsion

Johansen, Tor Arne

20

MRI based diffusion and perfusion predictive model to estimate stroke Stephen E. Rosea,  

E-Print Network [OSTI]

MRI based diffusion and perfusion predictive model to estimate stroke evolution Stephen E. Rosea and perfusion images acquired in the acute stage of stroke. The validity of this methodology was tested on novel patient data including data acquired from an independent stroke clinic. Regions-of-interest (ROIs

McLachlan, Geoff

Note: This page contains sample records for the topic "model based predictive" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


21

Control of Airborne Wind Energy Systems Based on Nonlinear Model Predictive Control & Moving Horizon Estimation  

E-Print Network [OSTI]

tethered to the ground at a high velocity across the wind direction. Power can be generated by a, the first option is considered. Because it involves a much lighter structure, a major advantage of powerControl of Airborne Wind Energy Systems Based on Nonlinear Model Predictive Control & Moving

22

C H A P T E R Patch-based Models to Predict  

E-Print Network [OSTI]

3 2 7 C H A P T E R 26 Patch-based Models to Predict Species Occurrence: Lessons from Salmonid localized peaks of abundance (Maurer 1999). This is particularly obvi- ous in stream ecosystems, where patch. To be most useful, patches should be clearly defined by associations between a biological response (e.g., re

23

OPERATOR INTERACTION WITH MODEL-BASED PREDICTIVE CONTROLLERS IN PETROCHEMICAL REFINING  

E-Print Network [OSTI]

. These differences in level may explain why we observed refinery operators asking questions of the automation not. This could also explain why we did not observe refinery operators having difficultyOPERATOR INTERACTION WITH MODEL-BASED PREDICTIVE CONTROLLERS IN PETROCHEMICAL REFINING Greg A

Virginia, University of

24

Website link prediction using a Markov chain model based on multiple time periods  

Science Journals Connector (OSTI)

Growing size and complexity of many websites have made navigation through these sites increasingly difficult. Attempting to automatically predict the next page for a website user to visit has many potential benefits, for example in site navigation, automatic tour generation, adaptive web applications, recommendation systems, web server optimisation, web search and web pre-fetching. This paper describes an approach to link prediction using a Markov chain model based on an exponentially smoothed transition probability matrix which incorporates site usage statistics collected over multiple time periods. The improved performance of this approach compared to earlier methods is also discussed.

Shantha Jayalal; Chris Hawksley; Pearl Brereton

2007-01-01T23:59:59.000Z

25

Prediction of demand trends of coking coal in China based on grey linear regression composition model  

Science Journals Connector (OSTI)

The scarce of coking coal resources in China results in its short supply. By establishing a grey linear regression composition model, this paper has greatly improved the inadequacy of grey system prediction model and regression analysis method in trend prediction and finished the prediction of demand trends of coking coal in China with this model. As result of the prediction, it is estimated that in the next decade, the demand for coking coal in China will experience a growth trend; China's demand for coking coal will reach more than 1.535 billion tons by 2015, reach the maximum of 1.639 billion tons by 2020 and drop in 2025.

Hai-Dong Zhou; Qiang Wu; Min Fang; Zhong-Bao Ren; Li-Fei Jin

2013-01-01T23:59:59.000Z

26

The development of design factors for heat-strengthened and tempered glass based on the glass failure prediction model  

E-Print Network [OSTI]

THE DEVELOPMENT OF DESIGN FACTORS FOR HEAT-STRENGTHENED AND TEMPERED GLASS BASED ON THE GLASS FAILURE PREDICTION MODEL A Thesis by Timothy Andrew Oakes Submitted to the Office of Graduate Studies of Texas A&M University in partial... fulfillment of the requirements for the degree of MASTER OF SCIENCE Decypber 199$ Major Subject: Civil Engineering THE DEVELOPMENT OF DESIGN FACTORS FOR HEAT-STRENGTHENED AND TEMPERED GLASS BASED ON THE GLASS FAILURE PREDICTION MODEL A Thesis...

Oakes, Timothy Andrew

1991-01-01T23:59:59.000Z

27

Towards Accurate and Practical Predictive Models of Active-Vision-Based Visual Search  

E-Print Network [OSTI]

which permit increasingly realistic and accurate predictions for visual human-computer interaction tasks not practical. For as long as human-computer interaction has been studied, researchers have been working@cs.uoregon.edu ABSTRACT Being able to predict the performance of interface designs using models of human cognition

Hornof, Anthony

28

A novel exergy-event based model predictive control strategy for energy saving  

Science Journals Connector (OSTI)

Abstract Industrial processes are among the biggest energy consumers and also the highest energy-wasting fields and many applied methods have been proposed to save energy more in these processes. The main purpose of event-based control is to reduce the computing load and communication efforts only after an event occurs which could save energy. Energy consumption and control performance are major challenges in event-based control. Exergy is the basic criterion to analyze a process in terms of the energy associated with control performance. MPC a widely used in industry, is utilized to solve an optimal control problem based on control performance aiming to save more energy. Moreover, MPC enables the controller to cope explicitly with MIMO plants and constraints such as state constraints and actuator constraints. The exergy-event based strategy under the general model predictive control (MPC) framework from viewpoint of energy saving is investigated in this paper. It will be shown that the proposed architecture is guaranteed to reduce the energy and exergy losses, the computational burden and communication effort of the whole system.

Mohsen Hadian; M.H. Asheri; Karim Salahshoor

2014-01-01T23:59:59.000Z

29

A heuristic model to predict earthworm biomass in agroecosystems based on selected management and soil properties  

Science Journals Connector (OSTI)

Earthworm burrows can be significant preferential flow paths for water and contaminants to move to subsurface drainage networks and groundwater. Thus earthworm biomass could serve as an indicator of such transport potential, and therefore, inform risk assessments associated with water contamination resulting from land application of fertilizer amendments. In this study, we evaluated relationships and interactions between earthworm biomass, soil properties (bulk density, particle size, organic matter, surface residue), land management (crop type, tillage approach), and soil hydraulic properties (field saturated hydraulic conductivity and air-entry tension) for the purpose of building regionally based models to predict earthworm biomass. Data were collected from 43 fields distributed throughout eastern Ontario, Canada. Earthworm biomass was measured using “hot mustard” methods (early autumn) and in situ soil hydraulic properties were determined using pressure infiltrometers (late summer/early fall). Classification and Regression Tree (CART) data mining techniques were used to develop tree-structured models to predict biomass from site environmental data. CART regression tree models had coefficients of determination between 0.50 (not including soil hydraulic properties) and 0.55 (including soil hydraulic properties). Both regression trees split all earthworm biomass data (N = 243) into two groupings defined on the basis of tillage treatment. No-tilled field biomass averaged 192.1 g m?2 (S.D. = 71.5 g m?2), and biomass data for conventionally tilled sites subdivided into terminal groupings on the basis of “higher surface residue cover” (biomass average = 107.9 g m?2 (S.D. = 81.1 g m?2) and ‘lower surface residue cover’ (62.4 g m?2 (S.D. = 54.6 g m?2)) classes. Soil physical and hydraulic data were not important predictors of biomass for tilled datasets; whereas they were more important for no-tilled datasets. For both regression trees, no-till biomass stratified into terminal biomass groupings defined on the basis of bulk density, clay content, and silt content; and for the model including soil hydraulic properties, additionally by soil air-entry tension and surface residue cover. However, bulk density was deemed in the model to be a proxy for years a field was in no-tillage; a positive relationship existed between bulk density and biomass. Overall, the terminal tree groups with the highest average earthworm biomasses were for no-till soils with bulk densities >1.4 g cm?3 (longer term no-tillage). Regression tree variance reductions associated with the in situ measurements of field saturated hydraulic conductivity and air-entry tension were insignificant or small. Generally, empirical models predicting earthworm biomass at large spatial scales in agroecosystems using soils and land management information, should consider utilizing variables that express tillage practice, surface residue coverage, years in no-tillage, and soil particle size; however, variable interactions should be considered.

G. Ouellet; D.R. Lapen; E. Topp; M. Sawada; M. Edwards

2008-01-01T23:59:59.000Z

30

A dynamic prediction model for gas–water effective permeability based on coalbed methane production data  

Science Journals Connector (OSTI)

Abstract An understanding of the relative permeability of gas and water in coal reservoirs is vital for coalbed methane (CBM) development. In this work, a prediction model for gas–water effective permeability is established to describe the permeability variation within coal reservoirs during production. The effective stress and matrix shrinkage effects are taken into account by introducing the Palmer and Mansoori (PM) absolute permeability model. The endpoint relative permeability is calibrated through experimentation instead of through the conventional Corey relative permeability model, which is traditionally employed for the simulation of petroleum reservoirs. In this framework, the absolute permeability model and the relative permeability model are comprehensively coupled under the same reservoir pressure and water saturation conditions through the material balance equation. Using the Qinshui Basin as an example, the differences between the actual curve that is measured with the steady-state method and the simulation curve are compared. The model indicates that the effective permeability is expressed as a function of reservoir pressure and that the curve shape is controlled by the production data. The results illustrate that the PM–Corey dynamic prediction model can accurately reflect the positive and negative effects of coal reservoirs. In particular, the model predicts the matrix shrinkage effect, which is important because it can improve the effective permeability of gas production and render the process more economically feasible.

H. Xu; D.Z. Tang; S.H. Tang; J.L. Zhao; Y.J. Meng; S. Tao

2014-01-01T23:59:59.000Z

31

System reliability prediction model based on evidential reasoning algorithm with nonlinear optimization  

Science Journals Connector (OSTI)

In this paper, a novel reliability prediction technique based on the evidential reasoning (ER) algorithm is developed and applied to forecast reliability in turbocharger engine systems. The focus of this study is to examine the feasibility and validity ... Keywords: Evidential reasoning, Forecasting, Nonlinear optimization, Reliability

Chang-Hua Hu; Xiao-Sheng Si; Jian-Bo Yang

2010-03-01T23:59:59.000Z

32

Artificial Neural Networks Modelling of PID and Model Predictive Controlled Waste Water Treatment Plant Based on the Benchmark Simulation Model No.1  

Science Journals Connector (OSTI)

The paper presents techniques for the design and training of Artificial Neural Networks (ANN) models for the dynamic simulation of the controlled Benchmark Simulation Model no. 1 (BSM1) Waste Water Treatment Plant (WWTP). The developed ANN model of the WWTP and its associated control system is used for the assessment of the plant behaviour in integrated urban waste water system simulations. Both embedded PID (Proportional-Integral-Derivative) control and Model Predictive Control (MPC) structures for the WWTP are investigated. The control of the Dissolved Oxygen (DO) mass concentration in the aerated reactors and nitrate (NO) mass concentration in the anoxic compartments are presented. The ANN based simulators reveal good accuracy for predicting important process variables and an important reduction of the simulation time, compared to the first principle WWTP simulator.

Vasile-Mircea Cristea; Cristian Pop; Paul Serban Agachi

2009-01-01T23:59:59.000Z

33

A dynamic prediction model for gas-water effective permeability in unsaturated coalbed methane reservoirs based on production data  

Science Journals Connector (OSTI)

Abstract Effective permeability of gas and water in coalbed methane (CBM) reservoirs is vital during CBM development. However, few studies have investigated it for unsaturated CBM reservoirs rather than saturated CBM reservoirs. In this work, the dynamic prediction model (PM-Corey model) for average gas-water effective permeability in two-phase flow in saturated CBM reservoirs was improved to describe unsaturated CBM reservoirs. In the improved effective permeability model, Palmer et al. absolute permeability model segmented based on critical desorption pressure and Chen et al. relative permeability model segmented based on critical water saturation were introduced and coupled comprehensively under conditions with the identical reservoir pressures and the identical water saturations through production data and the material balance equations (MBEs) in unsaturated CBM reservoirs. Taking the Hancheng CBM field as an example, the differences between the saturated and unsaturated effective permeability curves were compared. The results illustrate that the new dynamic prediction model could characterize not only the stage of two-phase flow but also the stage of single-phase water drainage. Also, the new model can accurately reflect the comprehensive effects of the positive and negative effects (the matrix shrinking effect and the effective stress effect) and the gas Klinkenberg effect of coal reservoirs, especially for the matrix shrinkage effect and the gas Klinkenberg effect, which can improve the effective permeability of gas production and render the process more economically. The new improved model is more realistic and practical than previous models.

Junlong Zhao; Dazhen Tang; Hao Xu; Yanjun Meng; Yumin Lv; Shu Tao

2014-01-01T23:59:59.000Z

34

Prediction of PWSCC in nickel base alloys using crack growth rate models  

SciTech Connect (OSTI)

The Ford/Andresen slip-dissolution SCC model, originally developed for stainless steel components in BWR environments, has been applied to Alloy 600 and Alloy X-750 tested in deaerated pure water chemistry. A method is described whereby the crack growth rates measured in compact tension specimens can be used to estimate crack growth in a component. Good agreement was found between model prediction and measured SCC in X-750 threaded fasteners over a wide range of temperatures, stresses, and material conditions. Most data support the basic assumption of this model that cracks initiate early in life. The evidence supporting a particular SCC mechanism is mixed. Electrochemical repassivation data and estimates of oxide fracture strain indicate that the slip-dissolution model can account for the observed crack growth rates, provided primary rather than secondary creep rates are used. However, approximately 100 cross-sectional TEM foils of SCC cracks including crack tips reveal no evidence of enhanced plasticity or unique dislocation patterns at the crack tip or along the crack to support a classic slip-dissolution mechanism. No voids, hydrides, or microcracks are found in the vicinity of the crack tips creating doubt about classic hydrogen related mechanisms. The bulk oxide films exhibit a surface oxide which is often different than the oxides found within a crack. Although bulk chromium concentration affects the rate of SCC, analytical data indicates the mechanism does not result from chromium depletion at the grain boundaries. The overall findings support a corrosion/dissolution mechanism but not one necessarily related to slip at the crack tip.

Thompson, C.D.; Krasodomski, H.T.; Lewis, N.; Makar, G.L. [Knolls Atomic Power Lab., Schenectady, NY (United States)

1995-12-31T23:59:59.000Z

35

Performance Comparison of Two Fuzzy Based Models in Predicting Carbon Dioxide Emissions  

Science Journals Connector (OSTI)

Many studies have been carried out worldwide to predict carbon dioxide (CO2) emissions using various methods. Most of the methods...2 emissions are not immediately known. This paper offers...2 emissions in Malays...

Herrini Mohd Pauzi; Lazim Abdullah

2014-01-01T23:59:59.000Z

36

Optimization of numerical weather/wave prediction models based on information geometry and computational techniques  

Science Journals Connector (OSTI)

The last years a new highly demanding framework has been set for environmental sciences and applied mathematics as a result of the needs posed by issues that are of interest not only of the scientific community but of today's society in general: global warming renewable resources of energy natural hazards can be listed among them. Two are the main directions that the research community follows today in order to address the above problems: The utilization of environmental observations obtained from in situ or remote sensing sources and the meteorological-oceanographic simulations based on physical-mathematical models. In particular trying to reach credible local forecasts the two previous data sources are combined by algorithms that are essentially based on optimization processes. The conventional approaches in this framework usually neglect the topological-geometrical properties of the space of the data under study by adopting least square methods based on classical Euclidean geometry tools. In the present work new optimization techniques are discussed making use of methodologies from a rapidly advancing branch of applied Mathematics the Information Geometry. The latter prove that the distributions of data sets are elements of non-Euclidean structures in which the underlying geometry may differ significantly from the classical one. Geometrical entities like Riemannian metrics distances curvature and affine connections are utilized in order to define the optimum distributions fitting to the environmental data at specific areas and to form differential systems that describes the optimization procedures. The methodology proposed is clarified by an application for wind speed forecasts in the Kefaloniaisland Greece.

2014-01-01T23:59:59.000Z

37

A lightning summary and decision model for thunderstorm prediction at Whiteman Air Force Base, Missouri  

E-Print Network [OSTI]

with other meteorological considerations. A preferred track for springtime thunderstorms was located between the base and the Ozark Mountains. No preferred track was found during the other seasons. Although diurnal distributions of lightning flashes showed...

Bass, Randall Gerald

2012-06-07T23:59:59.000Z

38

PREDICTIVE MODELS. Enhanced Oil Recovery Model  

SciTech Connect (OSTI)

PREDICTIVE MODELS is a collection of five models - CFPM, CO2PM, ICPM, PFPM, and SFPM - used in the 1982-1984 National Petroleum Council study of enhanced oil recovery (EOR) potential. Each pertains to a specific EOR process designed to squeeze additional oil from aging or spent oil fields. The processes are: 1 chemical flooding; 2 carbon dioxide miscible flooding; 3 in-situ combustion; 4 polymer flooding; and 5 steamflood. CFPM, the Chemical Flood Predictive Model, models micellar (surfactant)-polymer floods in reservoirs, which have been previously waterflooded to residual oil saturation. Thus, only true tertiary floods are considered. An option allows a rough estimate of oil recovery by caustic or caustic-polymer processes. CO2PM, the Carbon Dioxide miscible flooding Predictive Model, is applicable to both secondary (mobile oil) and tertiary (residual oil) floods, and to either continuous CO2 injection or water-alternating gas processes. ICPM, the In-situ Combustion Predictive Model, computes the recovery and profitability of an in-situ combustion project from generalized performance predictive algorithms. PFPM, the Polymer Flood Predictive Model, is switch-selectable for either polymer or waterflooding, and an option allows the calculation of the incremental oil recovery and economics of polymer relative to waterflooding. SFPM, the Steamflood Predictive Model, is applicable to the steam drive process, but not to cyclic steam injection (steam soak) processes. The IBM PC/AT version includes a plotting capability to produces a graphic picture of the predictive model results.

Ray, R.M. [DOE Bartlesville Energy Technology Center, Bartlesville, OK (United States)

1992-02-26T23:59:59.000Z

39

Journal Article: Simplified Protein Models: Predicting Folding...  

Office of Scientific and Technical Information (OSTI)

Simplified Protein Models: Predicting Folding Pathways and Structure Using Amino Acid Sequences Citation Details Title: Simplified Protein Models: Predicting Folding Pathways and...

40

Model Predictive Control Wind Turbines  

E-Print Network [OSTI]

Model Predictive Control of Wind Turbines Martin Klauco Kongens Lyngby 2012 IMM-MSc-2012-65 #12;Summary Wind turbines are the biggest part of the green energy industry. Increasing interest control strategies. Control strategy has a significant impact on the wind turbine operation on many levels

Note: This page contains sample records for the topic "model based predictive" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


41

PREDICTIVE MODELS. Enhanced Oil Recovery Model  

SciTech Connect (OSTI)

PREDICTIVE MODELS is a collection of five models - CFPM, CO2PM, ICPM, PFPM, and SFPM - used in the 1982-1984 National Petroleum Council study of enhanced oil recovery (EOR) potential. Each pertains to a specific EOR process designed to squeeze additional oil from aging or spent oil fields. The processes are: 1 chemical flooding, where soap-like surfactants are injected into the reservoir to wash out the oil; 2 carbon dioxide miscible flooding, where carbon dioxide mixes with the lighter hydrocarbons making the oil easier to displace; 3 in-situ combustion, which uses the heat from burning some of the underground oil to thin the product; 4 polymer flooding, where thick, cohesive material is pumped into a reservoir to push the oil through the underground rock; and 5 steamflood, where pressurized steam is injected underground to thin the oil. CFPM, the Chemical Flood Predictive Model, models micellar (surfactant)-polymer floods in reservoirs, which have been previously waterflooded to residual oil saturation. Thus, only true tertiary floods are considered. An option allows a rough estimate of oil recovery by caustic or caustic-polymer processes. CO2PM, the Carbon Dioxide miscible flooding Predictive Model, is applicable to both secondary (mobile oil) and tertiary (residual oil) floods, and to either continuous CO2 injection or water-alternating gas processes. ICPM, the In-situ Combustion Predictive Model, computes the recovery and profitability of an in-situ combustion project from generalized performance predictive algorithms. PFPM, the Polymer Flood Predictive Model, is switch-selectable for either polymer or waterflooding, and an option allows the calculation of the incremental oil recovery and economics of polymer relative to waterflooding. SFPM, the Steamflood Predictive Model, is applicable to the steam drive process, but not to cyclic steam injection (steam soak) processes.

Ray, R.M. [DOE Bartlesville Energy Technology Technology Center, Bartlesville, OK (United States)

1992-02-26T23:59:59.000Z

42

Prediction of pure water stress corrosion cracking (PWSCC) in nickel base alloys using crack growth rate models  

SciTech Connect (OSTI)

The Ford/Andresen slip dissolution SCC model, originally developed for stainless steel components in BWR environments, has been applied to Alloy 600 and Alloy X-750 tested in deaerated pure water chemistry. A method is described whereby the crack growth rates measured in compact tension specimens can be used to estimate crack growth in a component. Good agreement was found between model prediction and measured SCC in X-750 threaded fasteners over a wide range of temperatures, stresses, and material condition. Most data support the basic assumption of this model that cracks initiate early in life. The evidence supporting a particular SCC mechanism is mixed. Electrochemical repassivation data and estimates of oxide fracture strain indicate that the slip dissolution model can account for the observed crack growth rates, provided primary rather than secondary creep rates are used. However, approximately 100 cross-sectional TEM foils of SCC cracks including crack tips reveal no evidence of enhanced plasticity or unique dislocation patterns at the crack tip or along the crack to support a classic slip dissolution mechanism. No voids, hydrides, or microcracks are found in the vicinity of the crack tips creating doubt about classic hydrogen related mechanisms. The bulk oxide films exhibit a surface oxide which is often different than the oxides found within a crack. Although bulk chromium concentration affects the rate of SCC, analytical data indicates the mechanism does not result from chromium depletion at the grain boundaries. The overall findings support a corrosion/dissolution mechanism but not one necessarily related to slip at the crack tip.

Thompson, C.D.; Krasodomski, H.T.; Lewis, N.; Makar, G.L.

1995-02-22T23:59:59.000Z

43

A real time model to forecast 24 hours ahead, ozone peaks and exceedance levels. Model based on artificial neural networks, neural classifier and weather predictions.  

E-Print Network [OSTI]

on artificial neural networks, neural classifier and weather predictions. Application in an urban atmosphere - will be solved. Keywords: Artificial neural network; Multilayer Perceptron; ozone modelling; statistical stepwise and Software 22, 9 (2007) 1261-1269" DOI : 10.1016/j.envsoft.2006.08.002 #12;Abstract A neural network combined

Paris-Sud XI, Université de

44

Modeling and Predicting Pointing Errors in Two Dimensions  

E-Print Network [OSTI]

to complement Fitts' law's predictive model of pointing speed. However, their model was based on one-dimensional time, error prediction, error rates. ACM Classification Keywords: H.5.2 [Information interfaces and presentation]: User interfaces ­ theory and methods; H.1.2 [Models and principles]: User/machine systems

Anderson, Richard

45

Coordinating Regulation and Demand Response in Electric Power Grids: Direct and Price-Based Tracking Using Multirate Economic Model Predictive Control  

Science Journals Connector (OSTI)

?Based on “Coordinating regulation and demand response in electric power grids using multirate model...

Haitham Hindi; Daniel Greene…

2012-01-01T23:59:59.000Z

46

Standard Model Predictions for the Muon $(g-2)/2$  

E-Print Network [OSTI]

The current status of the Standard Model predictions for the muon anomalous magnetic moment is described. Various contributions expected in the Standard Model are discussed. After the reevaluation of the leading-order hadronic term based on the new \\ep data, the theoretical prediction is more than three standard deviations lower than the experimental value.

S. I. Eidelman

2009-04-21T23:59:59.000Z

47

Title: Development of Statistical and Data Drive Models to Predict Flares for Space Weather Predictions  

E-Print Network [OSTI]

D and civilian assets in both space and ground. The current state of predictability of solar flares is basedTitle: Development of Statistical and Data Drive Models to Predict Solar Flares for Space Weather Collaborator: Dr. K. S. Balasubramaniam, Air Force Research Laboratory Summary: Solar flares impact Do

Johnson, Eric E.

48

Model predictive torque control of a Switched Reluctance Motor  

Science Journals Connector (OSTI)

The strongly nonlinear magnetic characteristic of Switched Reluctance Motors (SRMs) makes their torque control a challenging task. In contrast to standard current-based control schemes, we use Model Predictive Control (MPC) and directly manipulate the ...

Helfried Peyrl; Georgios Papafotiou; Manfred Morari

2009-02-01T23:59:59.000Z

49

Model accurately predicts directional borehole trajectory  

SciTech Connect (OSTI)

Theoretical investigations and field data analyses helped develop a new method of predicting the rate of inclination change in a deviated well bore to help reduce the frequency and magnitude of doglegs. Predicting borehole dogleg severity is one of the main problems in directional drilling. Predicting the tendency and magnitude of borehole deviation and comparing them to the planned well path makes it possible to improve bottom hole assembly (BHA) design and to reduce the number of correction runs. The application of adaptation models for predicting the rate of inclination change if measurement-while-drilling systems are used results in improved accuracy of prediction, and therefore a reduction in correction runs.

Mamedbekov, O.K. (Azerbaijan State Petroleum Academy, Baku (Azerbaijan))

1994-08-29T23:59:59.000Z

50

Machine learning based prediction for peptide drift times in...  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Machine learning based prediction for peptide drift times in ion mobility spectrometry . Machine learning based prediction for peptide drift times in ion mobility spectrometry ....

51

Including Ocean Model Uncertainties in Climate Predictions  

E-Print Network [OSTI]

Including Ocean Model Uncertainties in Climate Predictions Chris Brierley, Alan Thorpe, Mat Collins's to perform the integrations Currently uses a `slab' ocean #12;An Ocean Model Required to accurately model transient behaviour Will have its own uncertainties Requires even more computing power Create new models

Jones, Peter JS

52

Reduced-order residential home modeling for model predictive control  

Science Journals Connector (OSTI)

Abstract Building simulation software packages such as EnergyPlus are useful energy modeling tools. These software packages, however, are often not amenable to model-based control due to model complexity or difficulties connecting control algorithms with the software. We present a method for automatically generating input/output data from an EnergyPlus residential home model using the OpenStudio software suite. These input/output data are used to create a simple reduced-order model that can be evaluated in fractions of a second. The reduced-order model is implemented in a model predictive controller to minimize the home's electricity costs during summer months in Austin, Texas, USA. The controller optimally precools the home in the morning and turns down or off the air conditioning system in the afternoon. For this example, electricity prices were taken from actual market prices in the Austin area. The optimal precooling strategy given by the model predictive controller reduces peak energy consumption from the air conditioning unit by an average of 70% and reduces operating costs by 60%. Precooling, however, consumes more total energy versus not precooling. Reducing peak energy consumption by 1 kWh results, on average, in an increase of 0.63 kWh in overall energy consumption.

Wesley J. Cole; Kody M. Powell; Elaine T. Hale; Thomas F. Edgar

2014-01-01T23:59:59.000Z

53

Influence Of Three Dynamic Predictive Clothing Insulation Models On Building Energy Use, HVAC Sizing And Thermal Comfort  

E-Print Network [OSTI]

Predictive Clothing Insulation Models based on Outdoor AirPREDICTIVE CLOTHING INSULATION MODELS ON BUILDING ENERGYthat the clothing insulation is equal to a constant value of

Schiavon, Stefano; Lee, Kwang Ho

2013-01-01T23:59:59.000Z

54

Predictive Models of Forest Dynamics  

Science Journals Connector (OSTI)

...currently highly uncertain (Fig. 1), making vegetation dynamics one of the largest sources of uncertainty in Earth system models. Reducing this uncertainty requires work on several fronts. For example, physiological parameters need to be...

Drew Purves; Stephen Pacala

2008-06-13T23:59:59.000Z

55

ASSESSMENT OF ECONOMIC PERFORMANCE OF MODEL PREDICTIVE  

E-Print Network [OSTI]

ASSESSMENT OF ECONOMIC PERFORMANCE OF MODEL PREDICTIVE CONTROL THROUGH VARIANCE/CONSTRAINT TUNING advanced process control (APC) strategies to deal with multivariable constrained control problems with an ultimate objective towards economic optimization. Any attempt to evaluate MPC performance should therefore

Huang, Biao

56

Intelligent fault prediction system based on internet of things  

Science Journals Connector (OSTI)

Fault prediction is the key technology to ensure the safe operation of large equipment. Based on the investigation of current and developing research of fault prediction, an intelligent fault prediction system based on internet of things is proposed ... Keywords: Fault prediction, Intelligent computer information processing, Internet of things, Mechanical equipment groups, Predictive maintenance

Xiaoli Xu; Tao Chen; Mamoru Minami

2012-09-01T23:59:59.000Z

57

Satellite Collision Modeling with Physics-Based Hydrocodes: Debris Generation Predictions of the Iridium-Cosmos Collision Event and Other Impact Events  

SciTech Connect (OSTI)

Satellite collision debris poses risks to existing space assets and future space missions. Predictive models of debris generated from these hypervelocity collisions are critical for developing accurate space situational awareness tools and effective mitigation strategies. Hypervelocity collisions involve complex phenomenon that spans several time- and length-scales. We have developed a satellite collision debris modeling approach consisting of a Lagrangian hydrocode enriched with smooth particle hydrodynamics (SPH), advanced material failure models, detailed satellite mesh models, and massively parallel computers. These computational studies enable us to investigate the influence of satellite center-of-mass (CM) overlap and orientation, relative velocity, and material composition on the size, velocity, and material type distributions of collision debris. We have applied our debris modeling capability to the recent Iridium 33-Cosmos 2251 collision event. While the relative velocity was well understood in this event, the degree of satellite CM overlap and orientation was ill-defined. In our simulations, we varied the collision CM overlap and orientation of the satellites from nearly maximum overlap to partial overlap on the outermost extents of the satellites (i.e, solar panels and gravity boom). As expected, we found that with increased satellite overlap, the overall debris cloud mass and momentum (transfer) increases, the average debris size decreases, and the debris velocity increases. The largest predicted debris can also provide insight into which satellite components were further removed from the impact location. A significant fraction of the momentum transfer is imparted to the smallest debris (< 1-5mm, dependent on mesh resolution), especially in large CM overlap simulations. While the inclusion of the smallest debris is critical to enforcing mass and momentum conservation in hydrocode simulations, there seems to be relatively little interest in their disposition. Based on comparing our results to observations, it is unlikely that the Iridium 33-Cosmos 2251 collision event was a large mass-overlap collision. We also performed separate simulations studying the debris generated by the collision of 5 and 10 cm spherical projectiles on the Iridium 33 satellite at closing velocities of 5, 10, and 15 km/s. It is important to understand the vulnerability of satellites to small debris threats, given their pervasiveness in orbit. These studies can also be merged with probabilistic conjunction analysis to better understand the risk to space assets. In these computational studies, we found that momentum transfer, kinetic energy losses due to dissipative mechanisms (e.g., fracture), fragment number, and fragment velocity increases with increasing velocity for a fixed projectile size. For a fixed velocity, we found that the smaller projectile size more efficiently transfers momentum to the satellite. This latter point has an important implication: Eight (spaced) 5 cm debris objects can impart more momentum to the satellite, and likely cause more damage, than a single 10 cm debris object at the same velocity. Further studies are required to assess the satellite damage induced by 1-5 cm sized debris objects, as well as multiple debris objects, in this velocity range.

Springer, H K; Miller, W O; Levatin, J L; Pertica, A J; Olivier, S S

2010-09-06T23:59:59.000Z

58

In silico modeling to predict drug-induced phospholipidosis  

SciTech Connect (OSTI)

Drug-induced phospholipidosis (DIPL) is a preclinical finding during pharmaceutical drug development that has implications on the course of drug development and regulatory safety review. A principal characteristic of drugs inducing DIPL is known to be a cationic amphiphilic structure. This provides evidence for a structure-based explanation and opportunity to analyze properties and structures of drugs with the histopathologic findings for DIPL. In previous work from the FDA, in silico quantitative structure–activity relationship (QSAR) modeling using machine learning approaches has shown promise with a large dataset of drugs but included unconfirmed data as well. In this study, we report the construction and validation of a battery of complementary in silico QSAR models using the FDA's updated database on phospholipidosis, new algorithms and predictive technologies, and in particular, we address high performance with a high-confidence dataset. The results of our modeling for DIPL include rigorous external validation tests showing 80–81% concordance. Furthermore, the predictive performance characteristics include models with high sensitivity and specificity, in most cases above ? 80% leading to desired high negative and positive predictivity. These models are intended to be utilized for regulatory toxicology applied science needs in screening new drugs for DIPL. - Highlights: • New in silico models for predicting drug-induced phospholipidosis (DIPL) are described. • The training set data in the models is derived from the FDA's phospholipidosis database. • We find excellent predictivity values of the models based on external validation. • The models can support drug screening and regulatory decision-making on DIPL.

Choi, Sydney S.; Kim, Jae S.; Valerio, Luis G., E-mail: luis.valerio@fda.hhs.gov; Sadrieh, Nakissa

2013-06-01T23:59:59.000Z

59

Stimulation Prediction Models | Open Energy Information  

Open Energy Info (EERE)

Stimulation Prediction Models Stimulation Prediction Models Jump to: navigation, search Geothermal ARRA Funded Projects for Stimulation Prediction Models Loading map... {"format":"googlemaps3","type":"ROADMAP","types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"limit":200,"offset":0,"link":"all","sort":[""],"order":[],"headers":"show","mainlabel":"","intro":"","outro":"","searchlabel":"\u2026 further results","default":"","geoservice":"google","zoom":false,"width":"600px","height":"350px","centre":false,"layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","icon":"","visitedicon":"","forceshow":true,"showtitle":true,"hidenamespace":false,"template":false,"title":"","label":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"locations":[{"text":"

60

NETL: IEP - Air Quality Research: Predictive Modeling and Evaluation  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Predictive Modeling and Evaluation Predictive Modeling and Evaluation Predictive Modeling and Evaluation Map Click on a Project Name to Get More Information It is likely that most or all State Implementation Plans pertaining to PM2.5 will be developed with the aid of some type of atmospheric modeling to predict the reductions in PM2.5 attainable via reductions in power plant emissions. The accuracy of such predictions depends on how accurately the models represent the actual emissions and atmospheric chemistry/transport phenomena. Modeling studies supported by the NETL fine PM program include: (1) receptor-based (source apportionment) modeling pertinent to electric power sources; (2) model evaluation using ambient PM mass measurements; (3) methods for estimating the lifetime and transport distances of primary and secondary PM; (4) quantifying the relationships between PM (nitric acid and sulfate) and NOx and SO2 emissions in the modeling domain; and (5) quantifying the contribution of primary and secondary organic aerosol emissions from power sources to observed organic PM.

Note: This page contains sample records for the topic "model based predictive" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


61

Towards predictive modeling of near-edge structures in electron energy-loss spectra of AlN-based ternary alloys  

Science Journals Connector (OSTI)

Although the analysis of electron energy loss near-edge structure provides a tool for experimentally probing the density of unoccupied states, a detailed comparison with simulations is necessary in order to understand the origin of individual peaks. This paper presents a density functional theory based technique for predicting the N K edge for ternary (quasibinary) nitrogen alloys by adopting a core hole approach, a methodology that has been successful for binary nitride compounds. It is demonstrated that by using the spectra of binary compounds for optimizing the core hole charge (0.35?e for cubic Ti1-xAlxN and 0.45?e for wurtzite AlxGa1-xN), the predicted spectra evolutions of the ternary alloys agree well with the experiments. The spectral features are subsequently discussed in terms of the electronic structure and bonding of the alloys.

D. Holec; R. Rachbauer; D. Kiener; P. D. Cherns; P. M. F. J. Costa; C. McAleese; P. H. Mayrhofer; C. J. Humphreys

2011-04-20T23:59:59.000Z

62

The evolutionary development of roughness prediction models  

Science Journals Connector (OSTI)

The vigorous expansion of wind energy power generation over the last decade has also entailed innovative improvements to surface roughness prediction models applied to high-torque milling operations. Artificial neural networks are the most widely used ... Keywords: Dimensionality reduction, Genetic algorithm, High-torque milling, Surface roughness

Maciej Grzenda; Andres Bustillo

2013-05-01T23:59:59.000Z

63

Numerical Weather Prediction (NWP) and hybrid ARMA/ANN model to predict global radiation  

E-Print Network [OSTI]

Numerical Weather Prediction (NWP) and hybrid ARMA/ANN model to predict global radiation Cyril a hybrid ARMA/ANN model and data issued from a numerical weather prediction model (ALADIN). We particularly@gmail.com #12;Abstract. We propose in this paper an original technique to predict global radiation using

Paris-Sud XI, Université de

64

Predicting Time-Delays under Real-Time Scheduling for Linear Model Predictive Control  

E-Print Network [OSTI]

Predicting Time-Delays under Real-Time Scheduling for Linear Model Predictive Control Zhenwu Shi prediction of time-delays caused by real-time scheduling. Then, a model predictive controller is designed, the interaction between real-time scheduling and control design has received interest in the literature

Zhang, Fumin

65

Numerical Weather Prediction (NWP) and hybrid ARMA/ANN model to predict global radiation  

E-Print Network [OSTI]

We propose in this paper an original technique to predict global radiation using a hybrid ARMA/ANN model and data issued from a numerical weather prediction model (ALADIN). We particularly look at the Multi-Layer Perceptron. After optimizing our architecture with ALADIN and endogenous data previously made stationary and using an innovative pre-input layer selection method, we combined it to an ARMA model from a rule based on the analysis of hourly data series. This model has been used to forecast the hourly global radiation for five places in Mediterranean area. Our technique outperforms classical models for all the places. The nRMSE for our hybrid model ANN/ARMA is 14.9% compared to 26.2% for the na\\"ive persistence predictor. Note that in the stand alone ANN case the nRMSE is 18.4%. Finally, in order to discuss the reliability of the forecaster outputs, a complementary study concerning the confidence interval of each prediction is proposed

Voyant, Cyril; Paoli, Christophe; Nivet, Marie Laure

2012-01-01T23:59:59.000Z

66

Development and Validation of an Advanced Stimulation Prediction Model for  

Open Energy Info (EERE)

Validation of an Advanced Stimulation Prediction Model for Validation of an Advanced Stimulation Prediction Model for Enhanced Geothermal Systems Geothermal Project Jump to: navigation, search Last modified on July 22, 2011. Project Title Development and Validation of an Advanced Stimulation Prediction Model for Enhanced Geothermal Systems Project Type / Topic 1 Recovery Act: Enhanced Geothermal Systems Component Research and Development/Analysis Project Type / Topic 2 Stimulation Prediction Models Project Description The proposal is in response to DOE FOA DE-PS36-08GO99018/DE-FOA-0000075, specifically: the Topic Area: Stimulation Prediction Models - "To develop and validate models to predict a reservoir's response to stimulation and/or to quantitatively compare existing stimulation prediction models," and the Target Specification: "Development of stimulation prediction models capable of accurately predicting the location, spacing, orientation, and flow properties of created fractures."

67

PNNL: Mechanistic-Based Ductility Prediction for Complex Mg Castings...  

Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

and Vehicle Technologies Program Annual Merit Review and Peer Evaluation Meeting lm057sun2012o.pdf More Documents & Publications PNNL: Mechanistic-Based Ductility Prediction...

68

On the Predictive Uncertainty of a Distributed Hydrologic Model  

E-Print Network [OSTI]

We use models to simulate the real world mainly for prediction purposes. However, since any model is a simplification of reality, there remains a great deal of uncertainty even after the calibration of model parameters. The model’s identifiability...

Cho, Huidae

2009-05-15T23:59:59.000Z

69

LLNL-TR-411072 A Predictive Model  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

072 072 A Predictive Model of Fragmentation using Adaptive Mesh Refinement and a Hierarchical Material Model A. E. Koniges, N. D. Masters, A. C. Fisher, R. W. Anderson, D. C. Eder, D. Benson, T. B. Kaiser, B. T. Gunney, P. Wang, B. R. Maddox, J. F. Hansen, D. H. Kalantar, P. Dixit, H. Jarmakani, M. A. Meyers March 5, 2009 -2- Disclaimer This document was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor Lawrence Livermore National Security, LLC, nor any of their employees makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights.

70

A minimal and predictive $T_7$ lepton flavor 331 model  

E-Print Network [OSTI]

We present a model based on the $SU(3)_{C}\\otimes SU(3)_{L}\\otimes U(1)_{X}$ gauge group having an extra $T_{7}\\otimes Z_{3}\\otimes Z_{14}$ flavor group, where the light active neutrino masses arise via double seesaw mechanism and the observed charged lepton mass hierarchy is a consequence of the $Z_{14}$ symmetry breaking at very high energy. In our minimal and predictive $T_7$ lepton flavor 331 model, the spectrum of neutrinos includes very light active neutrinos and heavy and very heavy sterile neutrinos. The obtained neutrino mixing parameters and neutrino mass squared splittings are compatible with the neutrino oscillation experimental data, for both normal and inverted hierarchies. The model predicts CP conservation in neutrino oscillations.

Hernández, A E Cárcamo

2015-01-01T23:59:59.000Z

71

Explicit-Ready Nonlinear Model Predictive Control for Turbocharged Spark-Ignited Engines  

E-Print Network [OSTI]

Explicit-Ready Nonlinear Model Predictive Control for Turbocharged Spark- Ignited Engines J. El with saturated actuators. In this context, the need for model-based control laws is greater than ever with saturated actuators. In this paper, we evaluate the benefits of a nonlinear model predictive control (NMPC

Paris-Sud XI, Université de

72

Open problem: Dynamic Relational Models for Improved Hazardous Weather Prediction  

E-Print Network [OSTI]

. Current weather radar detection and prediction sys- tems primarily rely on numerical models. We proposeOpen problem: Dynamic Relational Models for Improved Hazardous Weather Prediction Amy McGovern1, #12;Dynamic Relational Models for Improved Hazardous Weather Prediction Radar velocity Radar

McGovern, Amy

73

Estimating Predictive Variance for Statistical Gas Distribution Modelling  

SciTech Connect (OSTI)

Recent publications in statistical gas distribution modelling have proposed algorithms that model mean and variance of a distribution. This paper argues that estimating the predictive concentration variance entails not only a gradual improvement but is rather a significant step to advance the field. This is, first, since the models much better fit the particular structure of gas distributions, which exhibit strong fluctuations with considerable spatial variations as a result of the intermittent character of gas dispersal. Second, because estimating the predictive variance allows to evaluate the model quality in terms of the data likelihood. This offers a solution to the problem of ground truth evaluation, which has always been a critical issue for gas distribution modelling. It also enables solid comparisons of different modelling approaches, and provides the means to learn meta parameters of the model, to determine when the model should be updated or re-initialised, or to suggest new measurement locations based on the current model. We also point out directions of related ongoing or potential future research work.

Lilienthal, Achim J.; Asadi, Sahar; Reggente, Matteo [AASS Research Center, Oerebro University (Sweden)

2009-05-23T23:59:59.000Z

74

ORRECTEDPROOF Please cite this article in press as: Furman, K. C., Androulakis, I. P., A novel MINLP-based representation of the original complex model for predicting  

E-Print Network [OSTI]

refinery operations models or to use in combination with models for designer gasoline. RFG and boutique is extremely difficult to implement within refinery operations models or to use in25 combination with models

Androulakis, Ioannis (Yannis)

75

Offshore pile driving noise—Prediction through comprehensive model development  

Science Journals Connector (OSTI)

Offshore wind energy is one of the most potent among renewables and thus the worldwide number of offshore wind turbines increases rapidly. The foundations of the wind turbines are typically fastened to the seabed by impact pile driving which comes along with a significant amount of waterborne noise. To protect the marine biosphere the use of noise mitigation systems like bubble curtains or cofferdams may become necessary. In this context the model-based prediction of underwater sound pressure levels as well as the design and optimization of effective sound mitigation measures by using numerical models is one of today’s challenges. The current work presents a modeling approach that consists of a near field finite element model and a far field propagation model. Furthermore it has been found necessary to generate a benchmark to allow for a qualitative and quantitative comparison between the manifold modeling approaches that are currently developed at various institutes and companies.

Marcel Ruhnau; Stephan Lippert

2013-01-01T23:59:59.000Z

76

Near quantitative agreement of model free DFT- MD predictions...  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Near quantitative agreement of model free DFT- MD predictions with XAFS observations of the hydration structure of highly Near quantitative agreement of model free DFT- MD...

77

Adaptive neurofuzzy inference system-based pollution severity prediction of polymeric insulators in power transmission lines  

Science Journals Connector (OSTI)

This paper presents the prediction of pollution severity of the polymeric insulators used in power transmission lines using adaptive neurofuzzy inference system (ANFIS) model. In this work, laboratory-based pollution performance tests were carried out ...

C. Muniraj; S. Chandraseka

2011-01-01T23:59:59.000Z

78

Predictive model-based for the critical submergence of horizontal intakes in open channel flows with different clearance bottoms using CART, ANN and linear regression approaches  

Science Journals Connector (OSTI)

This study presents the development of classification and regression tree (CART), artificial neural network (ANN) and linear regression approaches to predict the critical submergence in an open channel flow for different clearance bottoms. To use the ... Keywords: ANN, CART, Critical submergence

Mohammad Karim Ayoubloo; H. Md. Azamathulla; Ebrahim Jabbari; Morteza Zanganeh

2011-08-01T23:59:59.000Z

79

Analysing earthquake slip models with the spatial prediction comparison test  

Science Journals Connector (OSTI)

......slip models with the spatial prediction comparison test Ling Zhang 1 P. Martin Mai 1 Kiran K.S. Thingbaijam...performance of the spatial prediction comparison test (SPCT), a statistical test developed to compare spatial (random) fields by......

Ling Zhang; P. Martin Mai; Kiran K.S. Thingbaijam; Hoby N.T. Razafindrakoto; Marc G. Genton

2015-01-01T23:59:59.000Z

80

Activity Prediction for Agent-based Home Energy Management  

E-Print Network [OSTI]

, the main chal- lenge is to predict the energy consumption activities of home- owners, so that the agent canActivity Prediction for Agent-based Home Energy Management Ngoc Cuong Truong, Long Tran on real­world data from a prominent database of home energy usage. We also show that the computational

Southampton, University of

Note: This page contains sample records for the topic "model based predictive" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


81

Model Predictive Control for Energy Efficient Buildings  

E-Print Network [OSTI]

Learning Control for Thermal Energy Storage Systems”. In:Predictive Control of Thermal Energy Storage in Buildingmaking use of building thermal energy storage, and this work

Ma, Yudong

2012-01-01T23:59:59.000Z

82

Wind Speed Prediction Via Time Series Modeling.  

E-Print Network [OSTI]

??Projected construction of nearby wind farms motivates this study of statistical forecasting of wind speed, for which accurate prediction is critically important to the fluid… (more)

Alexander, Daniel

2009-01-01T23:59:59.000Z

83

A high-resolution, cloud-assimilating numerical weather prediction model for solar irradiance forecasting  

E-Print Network [OSTI]

Multiscale Numerical Weather Prediction Model.   Progress assimilating numerical weather prediction model for solar customizable  numerical weather prediction model that is 

Mathiesen, Patrick; Collier, Craig; Kleissl, Jan

2013-01-01T23:59:59.000Z

84

Defect site prediction based upon statistical analysis of fault signatures  

E-Print Network [OSTI]

Good failure analysis is the ability to determine the site of a circuit defect quickly and accurately. We propose a method for defect site prediction that is based on a site's probability of excitation, making no assumptions about the type...

Trinka, Michael Robert

2004-09-30T23:59:59.000Z

85

Segmentation of speech based on adaptive pitch prediction  

E-Print Network [OSTI]

SEGMENTATION OF SPEECH BASED ON ADAPTIVE PITCH PREDICTION A Thesis by JAN ERIK 8DEGARD Submitted to the 0%ce of Graduate Studies of Texas ARM University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE August... 1990 Major Subject: Electrical Engineering SEGMENTATION OF SPEECH BASED OiN ADAPTIVE PITCH PREDICTION A Thesis by JAN ERII( 8DEGARD Approved as to style and content, by: Shiping Li (Chair of Committee) D. R. Halverson J. H. Painter (Ivlernber...

Ødega?rd, Jan Erik

2012-06-07T23:59:59.000Z

86

Fast prediction and evaluation of gravitational waveforms using surrogate models  

E-Print Network [OSTI]

[Abridged] We propose a solution to the problem of quickly and accurately predicting gravitational waveforms within any given physical model. The method is relevant for both real-time applications and in more traditional scenarios where the generation of waveforms using standard methods can be prohibitively expensive. Our approach is based on three offline steps resulting in an accurate reduced-order model that can be used as a surrogate for the true/fiducial waveform family. First, a set of m parameter values is determined using a greedy algorithm from which a reduced basis representation is constructed. Second, these m parameters induce the selection of m time values for interpolating a waveform time series using an empirical interpolant. Third, a fit in the parameter dimension is performed for the waveform's value at each of these m times. The cost of predicting L waveform time samples for a generic parameter choice is of order m L + m c_f online operations where c_f denotes the fitting function operation count and, typically, m standard ways. Surrogate model building for other waveform models follow the same steps and have the same low online scaling cost. For expensive numerical simulations of binary black hole coalescences we thus anticipate large speedups in generating new waveforms with a surrogate. As waveform generation is one of the dominant costs in parameter estimation algorithms and parameter space exploration, surrogate models offer a new and practical way to dramatically accelerate such studies without impacting accuracy.

Scott E. Field; Chad R. Galley; Jan S. Hesthaven; Jason Kaye; Manuel Tiglio

2014-02-28T23:59:59.000Z

87

Markovian Models for Electrical Load Prediction in Smart Buildings  

E-Print Network [OSTI]

Markovian Models for Electrical Load Prediction in Smart Buildings Muhammad Kumail Haider, Asad,13100004,ihsan.qazi}@lums.edu.pk Abstract. Developing energy consumption models for smart buildings is important develop parsimo- nious Markovian models of smart buildings for different periods in a day for predicting

California at Santa Barbara, University of

88

Supporting technology for enhanced oil recovery: Polymer predictive model  

SciTech Connect (OSTI)

The Polymer Flood Predictive Model (PFPM) was developed by Scientific Software-Intercomp for the National Petroleum Council's (NPC) 1984 survey of US enhanced oil recovery potential (NPC, 1984). The PFPM is switch-selectable for either polymer or waterflooding, and an option in the model allows the calculation of the incremental oil recovery and economics of polymer relative to waterflooding. The architecture of the PFPM is similar to that of the other predictive models in the series: in-situ combustion, steam drive (Aydelotte and Pope, 1983), chemical flooding (Paul et al., 1982) and CO/sub 2/ miscible flooding (Paul et al., 1984). In the PFPM, an oil rate versus time function for a single pattern is computed and then is passed to the economic calculations. Data for reservoir and process development, operating costs, and a pattern schedule (if multiple patterns are desired) allow the computation of discounted cash flow and other measures of profitability. The PFPM is a three-dimensional (stratified, five-spot), two-phase (water and oil) model which computes water from breakthrough and oil recovery using fractional flow theory, and models areal and vertical sweeps using a streamtube approach. A correlation based on numerical simulation results is used to model the polymer slug size effect. The physical properties of polymer fluids, such as adsorption, permeability reduction, and non-Newtonian effects, are included in the model. Pressure drop between the injector and producer is kept constant, and the injectivity at each time step is calculated based on the mobility in each streamtube. Heterogeneity is accounted for by either entering detailed layer data or using the Dykstra-Parsons coefficient for a reservoir with a log-normal permeability distribution. 24 refs., 27 figs., 59 tabs.

Not Available

1986-12-01T23:59:59.000Z

89

Building a Statistical Model toBuilding a Statistical Model to Predict Reactor TemperaturesPredict Reactor Temperatures  

E-Print Network [OSTI]

Building a Statistical Model toBuilding a Statistical Model to Predict Reactor Temperatures.scarrott@lancaster.ac.uk g.tunnicliffe-wilson@lancaster.ac.uk #12;OutlineOutline l Objectives l Data l Statistical Model l temperatures ­ Physical model ­ Statistical model l How to identify and model physical effects? l How to model

Scarrott, Carl

90

Economic and Distributed Model Predictive Control of Nonlinear Systems  

E-Print Network [OSTI]

R. Amrit. Optimizing process economic performance us- ing2 Economic Model Predictive Control of Nonlinear Processof MPC and economic optimization of processes (e.g. , [2,

Heidarinejad, Mohsen

2012-01-01T23:59:59.000Z

91

Reliability updating and prediction of bridge structures based on proof loads and monitored data  

Science Journals Connector (OSTI)

Abstract Bridge deterioration with time and ever increasing traffic loads raise concerns about reliability of aging bridges. One of the ways to predict reliability of aging bridges is to build reasonable resistance prediction model and load effect prediction model. In this paper, to obtain the predicted resistance, by the truncated method or Bayesian method, the initial resistance probability model is updated with the structural proof loads which are greatly less than the resistance of a bridge, reduce uncertainty in the bridge resistance and so increase the bridge reliability; to predict the time-variant load effects which is treated as a time series, the Bayesian dynamic models (BDMs) are introduced and adopted to predict the structural load effects based on the monitored data (everyday monitored extreme stresses). Finally, with the predicted resistance and load effects, the structural reliability indices are solved and predicted with First Order Second Moment method (FOSM), and three numerical examples are provided to illustrate the feasibility and application of the built prediction model in this paper.

Liu Yuefei; Lu Dagang; Fan Xueping

2014-01-01T23:59:59.000Z

92

Data Assimilation for Idealised Mathematical Models of Numerical Weather Prediction  

E-Print Network [OSTI]

Data Assimilation for Idealised Mathematical Models of Numerical Weather Prediction Supervisors). Background: Numerical Weather Prediction (NWP) has seen significant gains in accuracy in recent years due is directed at achieving real-world impact in numerical weather prediction by addressing fundamental issues

Wirosoetisno, Djoko

93

Climate Prediction: The Limits of Ocean Models  

E-Print Network [OSTI]

We identify three major areas of ignorance which limit predictability in current ocean GCMs. One is the very crude representation of subgrid-scale mixing processes. These processes are parameterized with coefficients whose ...

Stone, Peter H.

94

Biodiesel Density: Experimental Measurements and Prediction Models  

Science Journals Connector (OSTI)

Density is an important biodiesel parameter, with impact on fuel quality. Predicting density is of high relevance for a correct formulation of an adequate blend of raw materials that optimize the cost of biodiesel fuel production while allowing the ...

Maria Jorge Pratas; Samuel V. D. Freitas; Mariana B. Oliveira; Sílvia C. Monteiro; Álvaro S. Lima; João A. P. Coutinho

2011-04-19T23:59:59.000Z

95

Metabolism Site Prediction Based on Xenobiotic Structural Formulas and PASS Prediction Algorithm  

Science Journals Connector (OSTI)

The external validation was made with evaluation sets containing data on biotransformations for 57 cardiovascular drugs. ... (13) The combination of particular methods that covers supplementary aspects of CYP interaction with substrates often provide more accurate predictions than individual approaches. ... Earlier, we developed a method to predict the biotransformation of xenobiotics on the basis of structural formulas using MNA (multilevel neighborhoods of atom)(23) and RMNA (reacting multilevel neighborhoods of atom) descriptors and a fragment data set(24, 25) based on the algorithm of the PASS program (prediction of activity spectra for substances). ...

Anastasia V. Rudik; Alexander V. Dmitriev; Alexey A. Lagunin; Dmitry A. Filimonov; Vladimir V. Poroikov

2014-01-13T23:59:59.000Z

96

Connecting Peptide Physicochemical and Antimicrobial Properties by a Rational Prediction Model  

E-Print Network [OSTI]

Connecting Peptide Physicochemical and Antimicrobial Properties by a Rational Prediction Model Marc network approach, based on the AMP's physicochemical characteristics, that is able not only to identify active peptides but also to assess its antimicrobial potency. The physicochemical properties considered

Pompeu Fabra, Universitat

97

Spectral barotropic model for the prediction of synoptic currents in the open part of the ocean  

Science Journals Connector (OSTI)

We consider a quasigeostrophic spectral model used for the prediction of synoptic currents in the barotropic ocean. The spectral method is based on the expansion of the current function in a double series in cosi...

I. I. Karpatovich

2006-07-01T23:59:59.000Z

98

Grid-based modeling in "Wissensnetz Energiemeteorologie" Jan Ploski1  

E-Print Network [OSTI]

-Grid) for running numerical weather prediction models. Based on experience with our introductory implementation resources of the German Grid [3] for running NWP (Numerical Weather Prediction) models. This paper its prediction quality and on overcoming the technical challenges to establish numerical weather

Heinemann, Detlev

99

predictive-models | netl.doe.gov  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

are available. The five recovery processes modeled are Steamflood, In-Situ Combustion, Polymer, Chemical Flooding, and CO2 Miscible Flooding. The models are available...

100

Nonlinear Model Predictive Control of Municipal Solid Waste Combustion Plants  

E-Print Network [OSTI]

. Also, the energy that results from waste combustion is often used to produce heat and/or electricityNonlinear Model Predictive Control of Municipal Solid Waste Combustion Plants M. Leskens , R.h.Bosgra@tudelft.nl, p.m.j.vandenhof@tudelft.nl Keywords : nonlinear model predictive control, municipal solid waste

Van den Hof, Paul

Note: This page contains sample records for the topic "model based predictive" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


101

Web Page Rank Prediction with Markov Models Michalis Vazirgiannis  

E-Print Network [OSTI]

Web Page Rank Prediction with Markov Models Michalis Vazirgiannis INRIA Futurs Orsay, France a method for predicting the rank- ing position of a Web page. Assuming a set of successive past top-k rankings, we study the evolution of Web pages in terms of ranking trend sequences used for Markov Models

Paris-Sud XI, Université de

102

Model Predictive Control of a Kaibel Distillation Column  

E-Print Network [OSTI]

Model Predictive Control of a Kaibel Distillation Column Martin Kvernland Ivar Halvorsen Sigurd (e-mail: skoge@ntnu.no) Abstract: This is a simulation study on controlling a Kaibel distillation column with model predictive control (MPC). A Kaibel distillation column has several advantages compared

Skogestad, Sigurd

103

Forecasting wave height probabilities with numerical weather prediction models  

E-Print Network [OSTI]

Forecasting wave height probabilities with numerical weather prediction models Mark S. Roulstona; Numerical weather prediction 1. Introduction Wave forecasting is now an integral part of operational weather methods for generating such forecasts from numerical model output from the European Centre for Medium

Stevenson, Paul

104

The Dynamics of Deterministic Chaos in Numerical Weather Prediction Models  

E-Print Network [OSTI]

Atmospheric weather systems are coherent structures consisting of discrete cloud cells forming patterns of rows/streets, mesoscale clusters and spiral bands which maintain their identity for the duration of their appreciable life times in the turbulent shear flow of the planetary Atmospheric Boundary Layer. The existence of coherent structures (seemingly systematic motion) in turbulent flows has been well established during the last 20 years of research in turbulence. Numerical weather prediction models based on the inherently non-linear Navier-Stokes equations do not give realistic forecasts because of the following inherent limitations: (1) the non-linear governing equations for atmospheric flows do not have exact analytic solutions and being sensitive to initial conditions give chaotic solutions characteristic of deterministic chaos (2) the governing equations do not incorporate the dynamical interactions and co-existence of the complete spectrum of turbulent fluctuations which form an integral part of the large coherent weather systems (3) limitations of available computer capacity necessitates severe truncation of the governing equations, thereby generating errors of approximations (4) the computer precision related roundoff errors magnify the earlier mentioned uncertainties exponentially with time and the model predictions become unrealistic. The accurate modelling of weather phenomena therefore requires alternative concepts and computational techniques. In this paper a universal theory of deterministic chaos applicable to the formation of coherent weather structures in the ABL is presented.

A. Mary Selvam

2003-10-07T23:59:59.000Z

105

Location-based vehicular moving predictions for wireless communication  

Science Journals Connector (OSTI)

In cellular networks, an important practical issue is how to ensure high-quality service. We assume all Base Stations (BSs) have a Geographic Information System (GIS) that has a street map with all BSs and all vehicles have a Global Positioning System (GPS), which acquires data for vehicle speed, direction, and location and sends this information to the nearest BS periodically when communicating within the BS. Based on this vehicle information, a BS checks whether an intersection has a traffic light, determines the traffic light pattern, predicts where vehicles may move, determines whether vehicles may move into another region, and informs the BS to reserve resources for a hand-off. Based on GPS and GIS information, the proposed method, TSDMP, has a lower prediction error rate in predicting traffic light patterns and vehicle movements than other techniques.

Hsin-Te Wu; Wen-Shyong Hsieh

2012-01-01T23:59:59.000Z

106

Project Profile: Predictive Physico-Chemical Modeling of Intrinsic Degradation Mechanisms for Advanced Reflector Materials  

Broader source: Energy.gov [DOE]

NREL, under the Physics of Reliability: Evaluating Design Insights for Component Technologies in Solar (PREDICTS) Program will be developing a physics-based computational degradation model to assess the kinetic oxidation rates; realistic model light attenuation and transport; and multi-layer treatment with variable properties Simulation based experimental design.

107

EVALUATION OF NUMERICAL WEATHER PREDICTION IN MODELING CLOUD-RADIATION INTERACTIONS OVER THE SOUTHERN GREAT PLAINS  

E-Print Network [OSTI]

EVALUATION OF NUMERICAL WEATHER PREDICTION IN MODELING CLOUD- RADIATION INTERACTIONS OVER.bnl.gov ABSTRACT Numerical weather prediction (NWP) is the basis for present-day weather forecasts, and NWP- and satellite- based observations over the Southern Great Plains to evaluate how well cloud

Johnson, Peter D.

108

Cancer growth: Predictions of a realistic model  

Science Journals Connector (OSTI)

Simulations of avascular cancer growth are performed using experimental values of the relevant parameters. This permits a realistic assessment of the influence of these parameters on cancer growth dynamics. In general, an early exponential growth phase is followed by a linear regime (as observed in recent experiments), while the thickness of the viable cell layer remains approximately constant. Contrary to some predictions, a transition to latency is not observed.

S. A. Menchón and C. A. Condat

2008-08-08T23:59:59.000Z

109

24 More Years of Numerical Weather Prediction: A Model Performance Model  

E-Print Network [OSTI]

24 More Years of Numerical Weather Prediction: A Model Performance Model Gerard Cats May 26, 2008 Abstract For two formulations of currently usual numerical weather prediction models the evolution in such a model is much 1 #12;24 More Years of Numerical Weather Prediction Gerard Cats higher than in a sis

Stoffelen, Ad

110

Predicting the Energy Output of Wind Farms Based on Weather Data: Important Variables and their Correlation  

E-Print Network [OSTI]

Wind energy plays an increasing role in the supply of energy world-wide. The energy output of a wind farm is highly dependent on the weather condition present at the wind farm. If the output can be predicted more accurately, energy suppliers can coordinate the collaborative production of different energy sources more efficiently to avoid costly overproductions. With this paper, we take a computer science perspective on energy prediction based on weather data and analyze the important parameters as well as their correlation on the energy output. To deal with the interaction of the different parameters we use symbolic regression based on the genetic programming tool DataModeler. Our studies are carried out on publicly available weather and energy data for a wind farm in Australia. We reveal the correlation of the different variables for the energy output. The model obtained for energy prediction gives a very reliable prediction of the energy output for newly given weather data.

Vladislavleva, Katya; Neumann, Frank; Wagner, Markus

2011-01-01T23:59:59.000Z

111

Supporting technology for enhanced oil recovery: Chemical flood predictive model  

SciTech Connect (OSTI)

The Chemical Flood Predictive Model (CFPM) was developed by Scientific Software-Intercomp for the US Department of Energy and was used in the National Petroleum Council's (NPC) 1984 survey of US enhanced oil recovery potential (NPC, 1984). The CFPM models micellar (surfactant)-polymer (MP) floods in reservoirs which have been previously waterflooded to residual oil saturation. Thus, only true tertiary floods are considered. An option is available in the model which allows a rough estimate of oil recovery by caustic (alkaline) or caustic-polymer processes. This ''caustic'' option, added for the NPC survey, is not modeled as a separate process. Rather, the caustic and caustic-polymer oil recoveries are computed simply as 15% and 40%, respectively, of the MP oil recovery. In the CFPM, an oil rate versus time function for a single pattern is computed and the results are passed to the economic routines. To estimate multi-pattern project behavior, a pattern development schedule must be specified. After-tax cash flow is computed by combining revenues with capital costs for drilling, conversion and upgrading of wells, chemical handling costs, fixed and variable operating costs, injectant costs, depreciation, royalties, severance, state, federal, and windfall profit taxes, cost and price inflation rates, and the discount rate. A lumped parameter uncertainty routine is used to estimate risk, and allows for variation in computed project performance within an 80% confidence interval. The CFPM uses theory and the results of numerical simulation to predict MP oil recovery in five-spot patterns. Oil-bank and surfactant breakthrough and project life are determined from fractional flow theory. A Koval-type factor, based on the Dykstra-Parsons (1950) coefficient, is used to account for the effects of reservoir heterogeneity on surfactant and oil bank velocities. 18 refs., 17 figs., 27 tabs.

Ray, R.M.; Munoz, J.D.

1986-12-01T23:59:59.000Z

112

MIT Emissions Prediction and Policy Analysis (EPPA) Model | Open Energy  

Open Energy Info (EERE)

MIT Emissions Prediction and Policy Analysis (EPPA) Model MIT Emissions Prediction and Policy Analysis (EPPA) Model Jump to: navigation, search LEDSGP green logo.png FIND MORE DIA TOOLS This tool is part of the Development Impacts Assessment (DIA) Toolkit from the LEDS Global Partnership. Tool Summary LAUNCH TOOL Name: MIT Emissions Prediction and Policy Analysis (EPPA) Model Agency/Company /Organization: Massachusetts Institute of Technology (MIT) Topics: Analysis Tools Complexity/Ease of Use: Not Available Website: dspace.mit.edu/handle/1721.1/29790 Cost: Free Related Tools IGES GHG Calculator For Solid Waste Energy and Power Evaluation Program (ENPEP) Regional Economic Models, Inc. (REMI) Model ... further results The part of the MIT Integrated Global Systems Model (IGSM) that represents human systems; a recursive-dynamic multi-regional general equilibrium model

113

A predictive ocean oil spill model  

SciTech Connect (OSTI)

This is the final report of a two-year, Laboratory-Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). Initially, the project focused on creating an ocean oil spill model and working with the major oil companies to compare their data with the Los Alamos global ocean model. As a result of this initial effort, Los Alamos worked closely with the Eddy Joint Industry Project (EJIP), a consortium oil and gas producing companies in the US. The central theme of the project was to use output produced from LANL`s global ocean model to look in detail at ocean currents in selected geographic areas of the world of interest to consortium members. Once ocean currents are well understood this information could be used to create oil spill models, improve offshore exploration and drilling equipment, and aid in the design of semi-permanent offshore production platforms.

Sanderson, J.; Barnette, D. [Sandia National Labs., Albuquerque, NM (United States); Papodopoulos, P. [Oak Ridge National Lab., TN (United States); Schaudt, K. [Marathon Oil Co., Littleton, CO (United States); Szabo, D. [Mobil Research and Development Corp., Dallas, TX (United States)

1996-07-01T23:59:59.000Z

114

Conformal Higgs model: predicted dark energy density  

E-Print Network [OSTI]

Postulated universal Weyl conformal scaling symmetry provides an alternative to the $\\Lambda$CDM paradigm for cosmology. Recent applications to galactic rotation velocities, Hubble expansion, and a model of dark galactic halos explain qualitative phenomena and fit observed data without invoking dark matter. Significant revision of theory relevant to galactic collisions and clusters is implied, but not yet tested. Dark energy is found to be a consequence of conformal symmetry for the Higgs scalar field of electroweak physics. The present paper tests this implication. The conformal Higgs model acquires a gravitational effect described by a modified Friedmann cosmic evolution equation, shown to fit cosmological data going back to the cosmic microwave background epoch. The tachyonic mass parameter of the Higgs model becomes dark energy in the Friedmann equation. A dynamical model of this parameter, analogous to the Higgs mechanism for gauge boson mass, is derived and tested here. An approximate calculation yields a result consistent with the empirical magnitude inferred from Hubble expansion.

R. K. Nesbet

2014-11-03T23:59:59.000Z

115

Interactive software for model predictive control with simultaneous identification  

E-Print Network [OSTI]

This thesis is a unified practical framework in the theory of Model Predictive Control with Simultaneous Identification. The ability to change and visualize parameters on-line makes this toolbox attractive for control engineers, and for anyone...

Echeverria Del Rio, Pablo

2000-01-01T23:59:59.000Z

116

Standard Model Prediction of the Muon Anomalous Magnetic Moment  

E-Print Network [OSTI]

I review the present Standard Model prediction of the muon anomalous magnetic moment. The discrepancy with its experimental determination is (25.5 +- 8.0) x 10^-10, i.e., 3.2 standard deviations.

Joaquim Prades

2010-02-18T23:59:59.000Z

117

Hospital Readmission in General Medicine Patients: A Prediction Model  

E-Print Network [OSTI]

J Med. 1985;313: JGIM Hasan et al. : Hospital ReadmissionA Prediction Model Omar Hasan, MBBS, MPH 1,2 , David O.online December 15, 2009 Hasan et al. : Hospital Readmission

2010-01-01T23:59:59.000Z

118

Carbon-cycle models for better long-term predictions | EMSL  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Carbon-cycle models for better long-term predictions Carbon-cycle models for better long-term predictions Reduced variation among models should improve precision Improved...

119

Energy price prediction multi-step ahead using hybrid model in the Brazilian market  

Science Journals Connector (OSTI)

Abstract This paper proposes a new hybrid approach for short-term energy price prediction. This approach combines auto-regressive integrated moving average (ARIMA) and neural network (NN) models in a cascaded structure and uses explanatory variables. A two step procedure is applied. In the first step, the selected explanatory variables are predicted. In the second one, the energy prices are forecasted by using the explanatory variables prediction. Further, the proposed model considers a multi-step ahead price prediction (12 weeks-ahead) and is applied to Brazilian market, which adopts a cost-based centralized dispatch with unique characteristics of price behavior. The results show good ability to predict spikes and satisfactory accuracy according to error measures and tail loss test when compared with traditional techniques. Thus, the model can be an attractive tool to mitigate risks in purchasing power.

José C. Reston Filho; Carolina de M. Affonso; Roberto C.L. de Oliveira

2014-01-01T23:59:59.000Z

120

NETL: Predictive Modeling and Evaluation - CMU Regional Modeling Study  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Regional Source-Receptor Modeling Study Regional Source-Receptor Modeling Study The Pittsburgh Air Quality Study (PAQS) [PDF-744KB] is comprised of three inter-related components: 1) ambient PM measurements, 2) source characterization, and 3) deterministic and statistical air quality modeling. This effort will permit clarification of the contribution of coal-fired power plants to fine ambient PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 µm). The resources from the Department of Energy (DOE) will be leveraged with resources from the Environmental Protection Agency (EPA) and other organizations. Clarkson University (Hopke group) will apply advanced receptor models to identify the nature, location and contribution of the sources of particulate matter observed by the measurements made as part of the PAQS. Several forms of factor analysis including Positive Matrix Factorization (PMF) and UNMIX will be applied in order to identify the composition and contributions of the sources. Potential Source Contribution Function analysis as well as Residence Time Weighted Concentration analysis will be applied to the determination of the locations of the likely major contributing sources. The aforementioned factor analysis methods will also be applied to the spatially distributed data both on a single species and multiple species basis and to compare these results with those obtained utilizing the back-trajectory-based methods. The availability of highly time resolved data should permit greater source resolution and will be examined to determine how much increased source specificity can be obtained from the increased time resolution in the data. Assistance will be provided with the multivariate calibration that will permit the use of single-particle mass spectrometry data to estimate ambient concentrations of particulate species. These analyses should provide a better understanding of the source/receptor relationships that lead to the observed particle concentrations in the Pittsburgh area.

Note: This page contains sample records for the topic "model based predictive" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


121

The Isospin Model prediction for multi-pion tau decays  

E-Print Network [OSTI]

The predictions of an isospin model are compared with the branching ratios of the 5 and 6 pion decays of the tau lepton. In both cases, the isospin model suggests that the tau favours decays in which there is an omega resonance. Recent measurements of such tau decays confirm this hypothesis. If the decay of the tau to 7 pions also proceeds through an intermediate omega, then the isospin model predicts that the branching ratio of the tau to seven charged pions should be small when compared with other 7 pion decays. New limits on this mode appear to support this argument.

Randall J. Sobie

1998-10-19T23:59:59.000Z

122

Penetration rate prediction for percussive drilling via dry friction model  

E-Print Network [OSTI]

Penetration rate prediction for percussive drilling via dry friction model Anton M. Krivtsov a. Similarly, an increased weight on bit in downhole drilling does not improve the penetration rates when hard- tration rate is presented. The inherent nonlinearity of the discontinuous impact process is modelled

Krivtsov, Anton M.

123

A NEW MODEL FOR PERFORMANCE PREDICTION OF HARD ROCK TBMS.  

E-Print Network [OSTI]

methods to accu- rately predict the penetration rate of a TBM in a given geology. These models are mainly, and the penetration rate. A good example of this is the Norwegian (NTH) hard rock diagnostic system and predictor penetration rate. This group of models 1.ResearchAssociakandGraduacStudentinMiningErrg.Dept. 2.Directorof

124

The origins of computer weather prediction and climate modeling  

Science Journals Connector (OSTI)

Numerical simulation of an ever-increasing range of geophysical phenomena is adding enormously to our understanding of complex processes in the Earth system. The consequences for mankind of ongoing climate change will be far-reaching. Earth System Models ... Keywords: Climate modelling, History of NWP, Numerical weather prediction

Peter Lynch

2008-03-01T23:59:59.000Z

125

Development of Chemical Model to Predict the Interactions between  

Open Energy Info (EERE)

Model to Predict the Interactions between Model to Predict the Interactions between Supercritical CO2 and Fluid, Rocks in EGS Reservoirs Geothermal Project Jump to: navigation, search Last modified on July 22, 2011. Project Title Development of Chemical Model to Predict the Interactions between Supercritical CO2 and Fluid, Rocks in EGS Reservoirs Project Type / Topic 1 Recovery Act: Enhanced Geothermal Systems Component Research and Development/Analysis Project Type / Topic 2 Supercritical Carbon Dioxide / Reservoir Rock Chemical Interactions Project Description In order to develop this model, databases will be assembled and/or updated for thermodynamic and kinetic rate laws for water/brine/rock/CO2 interactions at the pressures and temperatures common to EGS systems. In addition to a literature search, extrapolation of existing data and experimental laboratory work will be conducted to calibrate and verify the datasets.

126

Development of an Ocean Model Adjoint for Decadal Prediction | Argonne  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Development of an Ocean Model Adjoint for Decadal Prediction Development of an Ocean Model Adjoint for Decadal Prediction Development of an Ocean Model Adjoint for Decadal Prediction This project will develop an adjoint of the Parallel Ocean Program (POP; version 2.0.1) using automatic differentiation (AD) techniques. We have already had success with AD on sea ice models and will use this knowledge with POP. It is now unequivocal that the Earth's climate system is warming. The most recent IPCC assessment concludes that the increased temperatures in the latter 20th century are very likely due to anthropogenic greenhouse gases, and continued greenhouse emissions will likely result in even larger increases during the 21st century. Even if controls could be put on greenhouse emissions immediately, there is still some climate change that

127

Evaluating Case-based Decision Theory: Predicting Empirical Patterns of Human Classification Learning  

E-Print Network [OSTI]

Evaluating Case-based Decision Theory: Predicting Empirical Patterns of Human Classification which calculates an agent's optimal behavior according to Case- based Decision Theory (Gilboa behavior of this program (and therefore Case-based Decision Theory) correctly predicts the empirically

Tesfatsion, Leigh

128

A support vector regression model for predicting tunnel boring machine penetration rates  

Science Journals Connector (OSTI)

Abstract With widespread increasing applications of mechanized tunneling in almost all ground conditions, prediction of tunnel boring machine (TBM) performance is required for time planning, cost control and choice of excavation method in order to make tunneling economical. Penetration rate is a principal measure of full-face TBM performance and is used to evaluate the feasibility of the machine and predict advance rate of excavation. This research aims at developing a regression model to predict penetration rate of TBM in hard rock conditions based on a new artificial intelligence (AI) algorithm namely support vector regression (SVR). For this purpose, the Queens Water Tunnel, in New York City, was selected as a case study to test the proposed model. In order to find out the optimum values of the parameters and prevent over-fitting, 80% of the total data were selected randomly for training set and the rest were kept for testing the model. According to the results, it can be said that the proposed model is a useful and reliable means to predict TBM penetration rate provided that a suitable dataset exists. From the prediction results of training and testing samples, the squared correlation coefficient (R2) between the observed and predicted values of the proposed model was obtained 0.99 and 0.95, respectively, which shows a high conformity between predicted and actual penetration rate.

Satar Mahdevari; Kourosh Shahriar; Saffet Yagiz; Mohsen Akbarpour Shirazi

2014-01-01T23:59:59.000Z

129

Statistical prediction of biomethane potentials based on the composition of lignocellulosic biomass  

Science Journals Connector (OSTI)

Abstract Mixture models are introduced as a new and stronger methodology for statistical prediction of biomethane potentials (BPM) from lignocellulosic biomass compared to the linear regression models previously used. A large dataset from literature combined with our own data were analysed using canonical linear and quadratic mixture models. The full model to predict BMP (R2 > 0.96), including the four biomass components cellulose (xC), hemicellulose (xH), lignin (xL) and residuals (xR = 1 ? xC ? xH ? xL) had highly significant regression coefficients. It was possible to reduce the model without substantially affecting the quality of the prediction, as the regression coefficients for xC, xH and xR were not significantly different based on the dataset. The model was extended with an effect of different methods of analysing the biomass constituents content (DA) which had a significant impact. In conclusion, the best prediction of BMP is pBMP = 347xC+H+R ? 438xL + 63DA.

Sune Tjalfe Thomsen; Henrik Spliid; Hanne Østergård

2014-01-01T23:59:59.000Z

130

An electrochemical model for prediction of CO{sub 2} corrosion  

SciTech Connect (OSTI)

A predictive model of CO{sub 2} corrosion, based on modelling of individual electrochemical reactions occurring in a water CO{sub 2} system, is presented. The model takes into account the following electrochemical reactions: hydrogen ion reduction, carbonic acid reduction, direct water reduction, oxygen reduction and anodic dissolution of iron. The required electrochemical parameters in the model such as: exchange current densities and Tafel slopes for different reactions are determined from experiments conducted in glass cells. In those experiments the corrosion process was monitored with the following electrochemical measuring techniques: polarization resistance, potentiodynamic sweep, electrochemical impedance in addition to weight loss measurements. The model has been calibrated for two different mild steels over a wide range of parameters: t = 20--80C, pH 3--6, p(CO{sub 2})= 0--1 bar, {omega} = 0--5,000 rpm. In its present stage of development the model applies for the case of uniform corrosion with no protective films present. Performance of the model is validated by comparing the predictions with results from independent loop experiments. The predictions made with the present model were also compared with performance of other CO{sub 2} corrosion prediction models. Compared to the previous largely empirical models, the present model gives a much clearer picture of the corrosion mechanisms and of the effect of key parameters.

Nesic, S.; Postlethwaite, J. [Inst. for Energiteknikk, Kjeller (Norway); Olsen, S. [Statoil, Trondheim (Norway)

1995-10-01T23:59:59.000Z

131

Shadow prediction model for the International Space Station Alpha  

SciTech Connect (OSTI)

A Fortran computer model, SHADOW5, was developed to predict shadows on the solar arrays of the International Space Station Alpha (ISSA) for general flight modes. This shadow model was incorporated into the EPSOP-F (Electrical Power System On-Orbit Performance) program to conduct ISSA power analyses for various operating conditions. This paper describes the mathematical methods of the model and shows the typical results predicted with the model. Vector analyses with coordinate transformations were used to trace the shadows between the potential shadowing and shadowed components of the station during the sun portion of the orbit. Including the space shuttle orbiter, 40 components were modeled. The basic shapes of the components were assumed to be either planar or cylindrical. The elemental areas obtained from the Cartesian grid lines allocated on the component surfaces were projected in the sun vector direction to reconstruct shadows on the shadowed planar surface. Comparison of predicted results with other models showed good agreement. Ease of preparing input data and relatively short CPU time make this model suitable for shadow analyses required for the many design and flight configurations of the space station.

Chung, D.K. [Rockwell International, Canoga Park, CA (United States). Rocketdyne Division

1995-12-31T23:59:59.000Z

132

Principles of models based engineering  

SciTech Connect (OSTI)

This report describes a Models Based Engineering (MBE) philosophy and implementation strategy that has been developed at Los Alamos National Laboratory`s Center for Advanced Engineering Technology. A major theme in this discussion is that models based engineering is an information management technology enabling the development of information driven engineering. Unlike other information management technologies, models based engineering encompasses the breadth of engineering information, from design intent through product definition to consumer application.

Dolin, R.M.; Hefele, J.

1996-11-01T23:59:59.000Z

133

Predicting mesh density for adaptive modelling of the global atmosphere  

Science Journals Connector (OSTI)

...under investigation for atmospheric modelling for some time...atmosphere, using the shallow water equations-a necessary...to solve the shallow water equations on fixed meshes...discussed in 3. The mesh generator and the predictive adaptive...Solving the shallow water equations on polygons...

2009-01-01T23:59:59.000Z

134

The origins of computer weather prediction and climate modeling  

SciTech Connect (OSTI)

Numerical simulation of an ever-increasing range of geophysical phenomena is adding enormously to our understanding of complex processes in the Earth system. The consequences for mankind of ongoing climate change will be far-reaching. Earth System Models are capable of replicating climate regimes of past millennia and are the best means we have of predicting the future of our climate. The basic ideas of numerical forecasting and climate modeling were developed about a century ago, long before the first electronic computer was constructed. There were several major practical obstacles to be overcome before numerical prediction could be put into practice. A fuller understanding of atmospheric dynamics allowed the development of simplified systems of equations; regular radiosonde observations of the free atmosphere and, later, satellite data, provided the initial conditions; stable finite difference schemes were developed; and powerful electronic computers provided a practical means of carrying out the prodigious calculations required to predict the changes in the weather. Progress in weather forecasting and in climate modeling over the past 50 years has been dramatic. In this presentation, we will trace the history of computer forecasting through the ENIAC integrations to the present day. The useful range of deterministic prediction is increasing by about one day each decade, and our understanding of climate change is growing rapidly as Earth System Models of ever-increasing sophistication are developed.

Lynch, Peter [Meteorology and Climate Centre, School of Mathematical Sciences, University College Dublin, Belfield (Ireland)], E-mail: Peter.Lynch@ucd.ie

2008-03-20T23:59:59.000Z

135

MBGP IN MODELLING AND PREDICTION Carlos OliverMorales  

E-Print Network [OSTI]

. Universitaria O4510 Mexico City, MEXICO Abstract. The paper describes a hybrid approach for dynamic system), relative humidity (H), solar radiation (R) and wind speed (V) and direction (D) were recorded. The time An alternative representation in GP for dynamic system modelling and prediction was presented. This MB

Fernandez, Thomas

136

MB GP IN MODELLING AND PREDICTION Carlos Oliver-Morales  

E-Print Network [OSTI]

. Universitaria O4510 Mexico City, MEXICO Abstract. The paper describes a hybrid approach for dynamic system), relative humidity (H), solar radiation (R) and wind speed (V) and direction (D) were recorded. The time system modelling and prediction was presented. This MB-GP approach has used small values of GP parameters

Fernandez, Thomas

137

A Simple Empirical Model for Decadal Climate Prediction  

Science Journals Connector (OSTI)

Decadal climate prediction is a challenging aspect of climate research. It has been and will be tackled by various modeling groups. This study proposes a simple empirical forecasting system for the near-surface temperature that can be used as a ...

Oliver Krueger; Jin-Song Von Storch

2011-02-01T23:59:59.000Z

138

Image-based building modeling.  

E-Print Network [OSTI]

??Image-based modeling is the process of converting 2D images of the real world into digital 3D models in computer. Among myriad kinds of objects in… (more)

Xiao, Jianxiong

2009-01-01T23:59:59.000Z

139

Predicting solar cycle 24 with a solar dynamo model  

E-Print Network [OSTI]

Whether the upcoming cycle 24 of solar activity will be strong or not is being hotly debated. The solar cycle is produced by a complex dynamo mechanism. We model the last few solar cycles by `feeding' observational data of the Sun's polar magnetic field into our solar dynamo model. Our results fit the observed sunspot numbers of cycles 21-23 extremely well and predict that cycle~24 will be about 35% weaker than cycle~23.

Arnab Rai Choudhuri; Piyali Chatterjee; Jie Jiang

2007-01-18T23:59:59.000Z

140

A novel EDAs based method for HP model protein folding  

Science Journals Connector (OSTI)

The protein structure prediction (PSP) problem is one of the most important problems in computational biology. This paper proposes a novel Estimation of Distribution Algorithms (EDAs) based method to solve the PSP problem on HP model. Firstly, a composite ...

Benhui Chen; Long Li; Jinglu Hu

2009-05-01T23:59:59.000Z

Note: This page contains sample records for the topic "model based predictive" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


141

Development and Testing of Model Predictive Control for a Campus Chilled  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Development and Testing of Model Predictive Control for a Campus Chilled Development and Testing of Model Predictive Control for a Campus Chilled Water Plant with Thermal Storage Title Development and Testing of Model Predictive Control for a Campus Chilled Water Plant with Thermal Storage Publication Type Conference Proceedings Year of Publication 2010 Authors Coffey, Brian, Philip Haves, Michael Wetter, Brandon Hencey, Francesco Borrelli, Yudong Ma, and Sorin Bengea Conference Name 2010 ACEEE Summer Study on Energy Efficiency in Buildings Date Published 2010 Publisher Omnipress Conference Location Asilomar, California, USA ISBN 0-918249-60-0 Abstract A Model Predictive Control (MPC) implementation was developed for a university campus chilled water plant. The plant includes three water-cooled chillers and a two million gallon chilled water storage tank. The tank is charged during the night to minimize on-peak electricity consumption and take advantage of the lower ambient wet bulb temperature. A detailed model of the chilled water plant and simplified models of the campus buildings were developed using the equation-based modeling language Modelica. Steady state models of the chillers, cooling towers and pumps were developed, based on manufacturers' performance data, and calibrated using measured data collected and archived by the control system. A dynamic model of the chilled water storage tank was also developed and calibrated. A semi-empirical model was developed to predict the temperature and flow rate of the chilled water returning to the plant from the buildings. These models were then combined and simplified for use in a MPC algorithm that determines the optimal chiller start and stop times and set-points for the condenser water temperature and the chilled water supply temperature. The paper describes the development and testing of the MPC implementation and discusses lessons learned and next steps in further research

142

An electrochemical model for prediction of corrosion of mild steel in aqueous carbon dioxide solutions  

SciTech Connect (OSTI)

A predictive model was developed for uniform carbon dioxide corrosion, based on modeling of individual electrochemical reactions in a water-CO{sub 2} system. The model takes into account the electrochemical reactions of hydrogen ion reduction, carbonic acid reduction, direct water reduction, oxygen reduction, and anodic dissolution of iron. The required electrochemical parameters (e.g., exchange current densities and Tafel slopes) for different reactions were determined from experiments conducted in glass cells. The corrosion process was monitored using polarization resistance, potentiodynamic sweep, electrochemical impedance, and weight-loss measurements. The model was calibrated for two mild steels over a range of parameters: temperature (t) = 20 C to 80 C, pH = 3 to 6, partial pressure of CO{sub 2} (P{sub CO{sub 2}}) = 0 bar to 1 bar (0 kPa to 100 kPa), and {omega} = 0 rpm to 5,000 rpm (v{sub p} = 0 m/s to 2.5 m/s). The model was applicable for uniform corrosion with no protective films present. Performance of the model was validated by comparing predictions to results from independent loop experiments. Predictions also were compared to those of other CO{sub 2} corrosion prediction models. Compared to the previous largely empirical models, the model gave a clearer picture of the corrosion mechanisms by considering the effects of pH, temperature, and solution flow rate on the participating anodic and cathodic reactions.

Nesic, S. [Inst. for Energiteknikk, Kjeller (Norway); Postlethwaite, J. [Univ. of Saskatchewan, Saskatoon (Canada); Olsen, S. [Statoil, Trondheim (Norway)

1996-04-01T23:59:59.000Z

143

NETL: Predictive Modeling and Evaluation - TVA Model Comparison  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Ozone/PM2.5 Formation & Transport Model Comparison Ozone/PM2.5 Formation & Transport Model Comparison Future regulatory actions for mitigating PM2.5 concentrations will rely, to some extent, on results from large-scale atmospheric models. The most efficient approach to evaluating regulatory actions is to use an integrated approach that examines multiple air quality impacts simultaneously. This is because of the strong linkage between PM2.5 levels, visibility degradation, ozone and acidic deposition. Thus, regional modeling of the impacts on PM2.5 levels from proposed emission reductions should be evaluated in terms of computed impacts not only on PM2.5 levels, but on ozone and acidic deposition as well. TVA is an active participant in the only ongoing assessment of this type, which is being done for the Southern Appalachian Mountains Initiative (SAMI) as part of its integrated assessment in the southeastern United States. SAMI, with its focus on protecting PSD Class I areas, is using a model called URM that can examine all the aforementioned phenomena at once. In addition, URM has the capability, which SAMI intends to use, of efficiently examining the sensitivity of model outputs to changes in emissions across the entire modeling domain. Finally, SAMI will use URM to test various emission management options (EMOs) for mitigating impacts in the southern Appalachians. These EMOs will include controls on various source sectors, including energy.

144

Artificial neural network models for predicting condition of offshore oil and gas pipelines  

Science Journals Connector (OSTI)

Abstract Pipelines daily transport and distribute huge amounts of oil and gas across the world. They are considered the safest method of transporting oil and gas because of their limited number of failures. However, pipelines are subject to deterioration and degradation. It is therefore important that pipelines be effectively monitored to optimize their operation and to reduce their failures to an acceptable safety limit. Numerous models have been developed recently to predict pipeline conditions. Nevertheless, most of these models have used corrosion features alone to assess the condition of pipelines. Hence, this paper presents the development of models that evaluate and predict the condition of offshore oil and gas pipelines based on several factors besides corrosion. The models were developed using artificial neural network (ANN) technique based on historical inspection data collected from three existing offshore oil and gas pipelines in Qatar. The models were able to successfully predict pipeline conditions with an average percent validity above 97% when applied to the validation data set. The models are expected to help pipeline operators to assess and predict the condition of existing oil and gas pipelines and hence prioritize the planning of their inspection and rehabilitation.

Mohammed S. El-Abbasy; Ahmed Senouci; Tarek Zayed; Farid Mirahadi; Laya Parvizsedghy

2014-01-01T23:59:59.000Z

145

Predictive Models of Li-ion Battery Lifetime (Presentation)  

SciTech Connect (OSTI)

Predictive models of Li-ion battery reliability must consider a multiplicity of electrochemical, thermal and mechanical degradation modes experienced by batteries in application environments. Complicating matters, Li-ion batteries can experience several path dependent degradation trajectories dependent on storage and cycling history of the application environment. Rates of degradation are controlled by factors such as temperature history, electrochemical operating window, and charge/discharge rate. Lacking accurate models and tests, lifetime uncertainty must be absorbed by overdesign and warranty costs. Degradation models are needed that predict lifetime more accurately and with less test data. Models should also provide engineering feedback for next generation battery designs. This presentation reviews both multi-dimensional physical models and simpler, lumped surrogate models of battery electrochemical and mechanical degradation. Models are compared with cell- and pack-level aging data from commercial Li-ion chemistries. The analysis elucidates the relative importance of electrochemical and mechanical stress-induced degradation mechanisms in real-world operating environments. Opportunities for extending the lifetime of commercial battery systems are explored.

Smith, K.; Wood, E.; Santhanagopalan, S.; Kim, G.; Shi, Y.; Pesaran, A.

2014-09-01T23:59:59.000Z

146

Development and validation of regression models to predict monthly heating demand for residential buildings  

Science Journals Connector (OSTI)

The present research work concerns development of regression models to predict the monthly heating demand for single-family residential sector in temperate climates, with the aim to be used by architects or design engineers as support tools in the very first stage of their projects in finding efficiently energetic solutions. Another interest to use such simplified models is to make it possible a very quick parametric study in order to optimize the building structure versus environmental or economic criteria. All the energy prediction models were based on an extended database obtained by dynamic simulations for 16 major cities of France. The inputs for the regression models are the building shape factor, the building envelope U-value, the window to floor area ratio, the building time constant and the climate which is defined as function of the sol-air temperature and heating set-point. If the neural network (NN) methods could give precise representations in predicting energy use, with the advantage that they are capable of adjusting themselves to unexpected pattern changes in the incoming data, the multiple regression analysis was also found to be an efficient method, nevertheless with the requirement that an extended database should be used for the regression. The validation is probably the most important level when trying to find prediction models, so 270 different scenarios are analysed in this research work for different inputs of the models. It has been established that the energy equations obtained can do predictions quite well, a maximum deviation between the predicted and the simulated is noticed to be 5.1% for Nice climate, with an average error of 2%. In this paper, we also show that is possible to predict the building heating demand even for more complex scenarios, when the construction is adjacent to non-heated spaces, basements or roof attics.

Tiberiu Catalina; Joseph Virgone; Eric Blanco

2008-01-01T23:59:59.000Z

147

Prediction modeling of physiological responses and human performance in the heat  

Science Journals Connector (OSTI)

Over the last two decades, our laboratory has been establishing the data base and developing a series of predictive equations for deep body temperature, heart rate and sweat loss responses of clothed soldiers performing physical work at various environmental extremes. Individual predictive equations for rectal temperature, heart rate and sweat loss as a function of the physical work intensity, environmental conditions and particular clothing ensemble have been published in the open literature. In addition, important modifying factors such as energy expenditure, state of heat acclimation and solar heat load have been evaluated and appropriate predictive equations developed. Currently, we have developed a comprehensive model which is programmed on a Hewlett-Packard 41 CV hand held calculator. The primary physiological inputs are deep body (rectal) temperature and sweat loss while the predicted outputs are the expected physical work-rest cycle, the maximum single physical work time if appropriate, and the associated water requirements. This paper presents the mathematical basis employed in the development of the various individual predictive equations of our heat stress model. In addition, our current heat stress prediction model as programmed on the HP 41 CV is discussed from the standpoint of propriety in meeting the Army's needs and therefore assisting in military mission accomplishment.

Kent B. Pandolf; Leander A. Stroschein; Lawrence L. Drolet; Richard R. Gonzalez; Michael N. Sawka

1986-01-01T23:59:59.000Z

148

Predictive models for power dissipation in optical transceivers  

E-Print Network [OSTI]

Power dissipation in optical networks is a significant problem for the telecommunications industry. The optical transceiver was selected as a representative device of the network, and a component based power model is ...

Butler, Katherine, 1981-

2004-01-01T23:59:59.000Z

149

A Predictive Maintenance Policy Based on the Blade of Offshore Wind Wenjin Zhu, Troyes University of Technology  

E-Print Network [OSTI]

A Predictive Maintenance Policy Based on the Blade of Offshore Wind Turbine Wenjin Zhu, Troyes, Paris-Erdogan law, rotor blade, wind turbine SUMMARY & CONCLUSIONS Based on the modeling and the better quality of the wind resource in the sea, the installation of wind turbines is shifting from

McCalley, James D.

150

Predicting Land-Ice Retreat and Sea-Level Rise with the Community Earth System Model  

SciTech Connect (OSTI)

Coastal stakeholders need defensible predictions of 21st century sea-level rise (SLR). IPCC assessments suggest 21st century SLR of {approx}0.5 m under aggressive emission scenarios. Semi-empirical models project SLR of {approx}1 m or more by 2100. Although some sea-level contributions are fairly well constrained by models, others are highly uncertain. Recent studies suggest a potential large contribution ({approx}0.5 m/century) from the marine-based West Antarctic Ice Sheet, linked to changes in Southern Ocean wind stress. To assess the likelihood of fast retreat of marine ice sheets, we need coupled ice-sheet/ocean models that do not yet exist (but are well under way). CESM is uniquely positioned to provide integrated, physics based sea-level predictions.

Lipscomb, William [Los Alamos National Laboratory

2012-06-19T23:59:59.000Z

151

Reynolds-stress model prediction of 3-D duct flows  

E-Print Network [OSTI]

The paper examines the impact of different modelling choices in second-moment closures by assessing model performance in predicting 3-D duct flows. The test-cases (developing flow in a square duct [Gessner F.B., Emery A.F.: {\\em ASME J. Fluids Eng.} {\\bf 103} (1981) 445--455], circular-to-rectangular transition-duct [Davis D.O., Gessner F.B.: {\\em AIAA J.} {\\bf 30} (1992) 367--375], and \\tsn{S}-duct with large separation [Wellborn S.R., Reichert B.A., Okiishi T.H.: {\\em J. Prop. Power} {\\bf 10} (1994) 668--675]) include progressively more complex strains. Comparison of experimental data with selected 7-equation models (6 Reynolds-stress-transport and 1 scale-determining equations), which differ in the closure of the velocity/pressure-gradient tensor $\\Pi_{ij}$, suggests that rapid redistribution controls separation and secondary-flow prediction, whereas, inclusion of pressure-diffusion modelling improves reattachment and relaxation behaviour.

Gerolymos, G A

2014-01-01T23:59:59.000Z

152

Prediction-based Strategies for Energy Saving in Object Tracking Sensor Networks  

E-Print Network [OSTI]

Prediction-based Strategies for Energy Saving in Object Tracking Sensor Networks Yingqi Xu Julian are sometimes predictable, we pro- pose a Prediction-based Energy Saving scheme, called PES, to re- duce on PES through extensive simulation. Our results show that PES can save significant energy under various

Giles, C. Lee

153

Rotation-based model trees for classification  

Science Journals Connector (OSTI)

Structurally, a model tree is a regression method that takes the form of a decision tree with linear regression functions instead of terminal class values at its leaves. In this study, model trees were coupled with a rotation-based ensemble for solving classification problems. In order to apply this regression technique to classification problems, we considered the conditional class probability function and sought a model-tree approximation to it. During classification, the class whose model tree generated the greatest approximated probability value was chosen as the predicted class. We performed a comparison with other well-known ensembles of decision trees on standard benchmark data sets, and the performance of the proposed technique was greater in most cases.

S.B. Kotsiantis

2010-01-01T23:59:59.000Z

154

Air handler sound power prediction method based on ARI Standard 260  

Science Journals Connector (OSTI)

A method of predicting air handler sound power based on ratings for a product line is described. The method provides octave band sound power levels based on ratings obtained using Air?Conditioning and Refrigeration Institute (ARI) Standard 260 Sound Rating Of Ducted Air Moving And Conditioning Equipment. Detailed sound power information for HVAC equipment is not always available but it is important in accurately predicting noise levels in acoustically sensitive spaces. To address this need a rating program was undertaken using ARI 260. This standard is a reverberant room technique for sound rating ducted air conditioning equipment using a reference sound source substitution method. Since sound travels from the source to receiver along numerous paths this standard differentiates between sound power emanating from several common paths called components. Components for this project included ducted discharge free inlet plus casing ducted inlet and casing. The standard provides guidance on adequate number of fan sizes appurtenances and operating characteristics. The intent of the project was to provide a model to predict sound power by unit size component operating condition and unit configuration. Good agreement was found between predicted levels and measured data.

2000-01-01T23:59:59.000Z

155

A displacement-based method for predicting plasticity-induced fatigue crack closure  

SciTech Connect (OSTI)

A numerical method for predicting closure and its effects on thermomechanical crack growth has been developed. A finite element model, using linear-elastic fracture mechanics shape functions, is employed to predict crack tip displacements. The effective changes in stress intensity, and therefore crack growth, are obtained from the minimum and maximum crack tip displacement predictions. When a flaw is loaded in Mode 1, a ligament of material ahead of the flaw yields, and a maximum crack tip displacement is computed. Upon unloading, plastically deformed material from prior plastic zones acts to limit the minimum displacements of the crack tip. The material is modeled as elastic-perfectly plastic. The yield strength of the material is varied based on the degree of constraint. The upper limit of constraint is a plane strain condition while the lowest constraint is a plane stress condition. The level of constraint is predicted by relating the stress intensity to the thickness of the component. Temperatures also affect yield strength, along with stiffness, and can cause the plastic zone to expand due to creep. During variable-amplitude loadings, and/or temperature changes, the irregular shape of the wake can be accommodated with this numerical procedure. The method has proven to accurately account for load interaction effects such as delayed retardation, crack arrest, initial accelerations following overloads, and the transient growth and stabilization of closure level with number of overloads.

Pawlik, M.E.; Saff, C.R.

1999-07-01T23:59:59.000Z

156

Model Predictive Control of Integrated Gasification Combined Cycle Power Plants  

SciTech Connect (OSTI)

The primary project objectives were to understand how the process design of an integrated gasification combined cycle (IGCC) power plant affects the dynamic operability and controllability of the process. Steady-state and dynamic simulation models were developed to predict the process behavior during typical transients that occur in plant operation. Advanced control strategies were developed to improve the ability of the process to follow changes in the power load demand, and to improve performance during transitions between power levels. Another objective of the proposed work was to educate graduate and undergraduate students in the application of process systems and control to coal technology. Educational materials were developed for use in engineering courses to further broaden this exposure to many students. ASPENTECH software was used to perform steady-state and dynamic simulations of an IGCC power plant. Linear systems analysis techniques were used to assess the steady-state and dynamic operability of the power plant under various plant operating conditions. Model predictive control (MPC) strategies were developed to improve the dynamic operation of the power plants. MATLAB and SIMULINK software were used for systems analysis and control system design, and the SIMULINK functionality in ASPEN DYNAMICS was used to test the control strategies on the simulated process. Project funds were used to support a Ph.D. student to receive education and training in coal technology and the application of modeling and simulation techniques.

B. Wayne Bequette; Priyadarshi Mahapatra

2010-08-31T23:59:59.000Z

157

Domain boundary prediction based on profile domain linker propensity index  

Science Journals Connector (OSTI)

Successful prediction of protein domain boundaries provides valuable information not only for the computational structure prediction of multi-domain proteins but also for the experimental structure determination. In this work, a novel index at the profile ... Keywords: Domain, Domain linker, Profile

Qiwen Dong; Xiaolong Wang; Lei Lin; Zhiming Xu

2006-04-01T23:59:59.000Z

158

Predictive Modeling of fMRI Brain States using Functional Canonical Correlation Analysis  

E-Print Network [OSTI]

Predictive Modeling of fMRI Brain States using Functional Canonical Correlation Analysis S Abstract. We present a novel method for predictive modeling of human brain states from functional for prediction of naturalistic stimuli from unknown fMRI data shows that the method nds highly predictive brain

Smeulders, Arnold

159

Prediction of siRNA knockdown efficiency using artificial neural network models  

SciTech Connect (OSTI)

Selective knockdown of gene expression by short interference RNAs (siRNAs) has allowed rapid validation of gene functions and made possible a high throughput, genome scale approach to interrogate gene function. However, randomly designed siRNAs display different knockdown efficiencies of target genes. Hence, various prediction algorithms based on siRNA functionality have recently been constructed to increase the likelihood of selecting effective siRNAs, thereby reducing the experimental cost. Toward this end, we have trained three Back-propagation and Bayesian neural network models, previously not used in this context, to predict the knockdown efficiencies of 180 experimentally verified siRNAs on their corresponding target genes. Using our input coding based primarily on RNA structure thermodynamic parameters and cross-validation method, we showed that our neural network models outperformed most other methods and are comparable to the best predicting algorithm thus far published. Furthermore, our neural network models correctly classified 74% of all siRNAs into different efficiency categories; with a correlation coefficient of 0.43 and receiver operating characteristic curve score of 0.78, thus highlighting the potential utility of this method to complement other existing siRNA classification and prediction schemes.

Ge Guangtao [Department of Computer Science, Tufts University, 161 College Avenue, Medford, MA 02155 (United States) and Whitehead Institute for Biomedical Research, 9 Cambridge Center, Cambridge, MA 02142 (United States)]. E-mail: guge@eecs.tufts.edu; Wong, G.William [Whitehead Institute for Biomedical Research, 9 Cambridge Center, Cambridge, MA 02142 (United States)]. E-mail: wong@wi.mit.edu; Luo Biao [RNAi Consortium, Broad Institute, Massachusetts Institute of Technology, 320 Bent Street, Cambridge, MA 02142 (United States)]. E-mail: bluo@broad.mit.edu

2005-10-21T23:59:59.000Z

160

Predictions  

Science Journals Connector (OSTI)

...Web jet aircraft, rocketry, space travel fax machines and mobile...London, UK/Bridgeman Art Library. Predictions generated by natural processes in astronomical space, and it is thought to supply...to be used in early 1999 as public transport in that city, is...

2001-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "model based predictive" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


161

Crucial stages of protein folding through a solvable model: Predicting target sites  

E-Print Network [OSTI]

Crucial stages of protein folding through a solvable model: Predicting target sites for enzyme. Keywords: Protein-folding modeling; prediction of key folding sites; HIV-1 protease; drug resistance One

Cecconi, Fabio

162

Modeling the spread of bird flu and predicting outbreak diversity  

Science Journals Connector (OSTI)

Avian influenza, commonly known as bird flu, is an epidemic caused by H5N1 virus that primarily affects birds like chickens, wild water birds, etc. On rare occasions, these can infect other species including pigs and humans. In the span of less than a year, the lethal strain of bird flu is spreading very fast across the globe mainly in South East Asia, parts of Central Asia, Africa and Europe. In order to study the patterns of spread of epidemic, we made an investigation of outbreaks of the epidemic in one week, that is from February 13–18, 2006, when the deadly virus surfaced in India. We have designed a statistical transmission model of bird flu taking into account the factors that affect the epidemic transmission such as source of infection, social and natural factors and various control measures are suggested. For modeling the general intensity coefficient f ( r ) , we have implemented the recent ideas given in the article Fitting the Bill, Nature [R. Howlett, Fitting the bill, Nature 439 (2006) 402], which describes the geographical spread of epidemics due to transportation of poultry products. Our aim is to study the spread of avian influenza, both in time and space, to gain a better understanding of transmission mechanism. Our model yields satisfactory results as evidenced by the simulations and may be used for the prediction of future situations of epidemic for longer periods. We utilize real data at these various scales and our model allows one to generalize our predictions and make better suggestions for the control of this epidemic.

Ranjit Kumar Upadhyay; Nitu Kumari; V. Sree Hari Rao

2008-01-01T23:59:59.000Z

163

Energy Savings Through Application of Model Predictive Control to an Air Separation Facility  

E-Print Network [OSTI]

Energy Savings Through Application of Model Predictive Control to an Air Separation Facility Thomas C. Hanson PauiF. Scharf Manager Senior Engineering Associate Process Development Process Control Technology Praxair, Inc., Tonawanda, New York...TM based operator interface was developed by Praxair to make the system usable in our operations. 174 ESL-IE-96-04-24 Proceedings from the Eighteenth Industrial Energy Technology Conference, Houston, TX, April 17-18, 1996 Benefits overview MPC has...

Hanson, T. C.; Scharf, P. F.

164

Solar energy prediction using linear and non-linear regularization models: A study on AMS (American Meteorological Society) 2013–14 Solar Energy Prediction Contest  

Science Journals Connector (OSTI)

Abstract In 2013, American Meteorological Society Committees on AI (artificial intelligence) Applications organized a short-term solar energy prediction competition aiming at predicting total daily solar energy received at 98 solar farms based on the outputs of various weather patterns of a numerical weather prediction model. In this paper, a methodology to solve this problem has been explained and the performance of ordinary LSR (least-square regression), regularized LSR and ANN (artificial neural network) models has been compared. In order to improve the generalization capability of the models, more experiments like variable segmentation, subspace feature sampling and ensembling of models have been conducted. It is observed that model accuracy can be improved by proper selection of input data segments. Further improvements can be obtained by ensemble of forecasts of different models. It is observed that the performance of an ensemble of ANN and LSR models is the best among all the proposed models in this work. As far as the competition is concerned, Gradient Boosting Regression Tree has turned out to be the best algorithm. The proposed ensemble of ANN and LSR model is able to show a relative improvement of 7.63% and 39.99% as compared to benchmark Spline Interpolation and Gaussian Mixture Model respectively.

S.K. Aggarwal; L.M. Saini

2014-01-01T23:59:59.000Z

165

Model Predictive Control of HVAC Systems: Implementation and Testing at the University of California, Merced  

E-Print Network [OSTI]

13]  Wetter,  M..  2009.   “Modelica?based  Modeling  and 14]  Wetter,  M..  2009.   “Modelica?based  Modeling  and modeling  language  Modelica.   Steady  state  models  of 

Haves, Phillip

2010-01-01T23:59:59.000Z

166

Hybrid fuzzy predictive control based on genetic algorithms for the temperature control of a batch reactor .  

E-Print Network [OSTI]

??In this paper we describe the design of hybrid fuzzy predictive control based on a genetic algorithm (GA). We also present a simulation test of… (more)

Causa, Javier

2008-01-01T23:59:59.000Z

167

A Scenario-based Predictive Control Approach to Building HVAC Management Systems  

E-Print Network [OSTI]

A Scenario-based Predictive Control Approach to Building HVAC Management Systems Alessandra Parisio and Air Conditioning (HVAC) systems while minimizing the overall energy use. The strategy uses

Johansson, Karl Henrik

168

Real-time solar wind prediction based on SDO/AIA coronal hole data  

E-Print Network [OSTI]

We present an empirical model based on the visible area covered by coronal holes close to the central meridian in order to predict the solar wind speed at 1 AU with a lead time up to four days in advance with a 1hr time resolution. Linear prediction functions are used to relate coronal hole areas to solar wind speed. The function parameters are automatically adapted by using the information from the previous 3 Carrington Rotations. Thus the algorithm automatically reacts on the changes of the solar wind speed during different phases of the solar cycle. The adaptive algorithm has been applied to and tested on SDO/AIA-193A observations and ACE measurements during the years 2011-2013, covering 41 Carrington Rotations. The solar wind speed arrival time is delayed and needs on average 4.02 +/- 0.5 days to reach Earth. The algorithm produces good predictions for the 156 solar wind high speed streams peak amplitudes with correlation coefficients of cc~0.60. For 80% of the peaks, the predicted arrival matches within ...

Rotter, T; Temmer, M; Vrsnak, B

2015-01-01T23:59:59.000Z

169

Bayesian System Identification and Response Predictions Robust to Modeling Uncertainty  

E-Print Network [OSTI]

uncertainties, both prior (e.g. design based on reliability or life-cycle cost optimization), & posterior (e reliability of treating excitation uncertainty under wind and earthquakes (random vibrations, stochastic in the development and use of Bayesian methods in the last decade or so · Allows analysis that is robust to modeling

Beck, James L.

170

Design of spatial experiments: Model fitting and prediction  

SciTech Connect (OSTI)

The main objective of the paper is to describe and develop model oriented methods and algorithms for the design of spatial experiments. Unlike many other publications in this area, the approach proposed here is essentially based on the ideas of convex design theory.

Fedorov, V.V.

1996-03-01T23:59:59.000Z

171

Evaluating the ability of a numerical weather prediction model to forecast tracer concentrations during ETEX 2  

E-Print Network [OSTI]

Evaluating the ability of a numerical weather prediction model to forecast tracer concentrations an operational numerical weather prediction model to forecast air quality are also investigated. These potential a numerical weather prediction (NWP) model independently of the CTM. The NWP output is typically archived

Dacre, Helen

172

USING LEARNING MACHINES TO CREATE SOLAR RADIATION MAPS FROM NUMERICAL WEATHER PREDICTION MODELS,  

E-Print Network [OSTI]

USING LEARNING MACHINES TO CREATE SOLAR RADIATION MAPS FROM NUMERICAL WEATHER PREDICTION MODELS simulation by means of a Numerical Weather Prediction Model (NWP), Skiron. After that, we have made spatial solar resource map. 2.1. Meteorological simulation The numerical weather prediction model used is SKIRON

Paris-Sud XI, Université de

173

Probabilistic fatigue life prediction model for alloys with defects: applied to A206  

SciTech Connect (OSTI)

Presented here is a model for the prediction of fatigue life based on the statistical distribution of pores, intermetallic particles and grains. This has been applied to a cast Al alloy A206, before and after friction stir processing (FSP). The model computes the probability to initiate a small crack based on the probability of finding combinations of defects and grains on the surface. The crack initiation and propagation life of small cracks due to these defect and grain combinations are computed and summed to obtain the total fatigue life. The defect and grain combinations are ranked according to total fatigue life and the failure probability computed. Bending fatigue experiments were carried out on A206 before and after FSP. FSP eliminated the porosity, broke down the particles and refined the microstructure. The model predicted the fatigue life of A206 before and after FSP well. The cumulative probability distribution vs. fatigue life was fitted to a three parameter Weibull distribution function. The scatter reduced after FSP and the threshold of fatigue life increased. The potential improvement in the fatigue life of A206 for a microstructure consisting of a finer distribution of particle sizes after FSP was predicted using the model.

Kapoor, Rajeev; Sree Hari Rao, V.; Mishra, Rajiv S.; Baumann, John A.; Grant, Glenn J.

2011-05-31T23:59:59.000Z

174

Hybrid coupled models of the tropical Paci c | II ENSO prediction  

E-Print Network [OSTI]

Hybrid coupled models of the tropical Paci#12;c | II ENSO prediction by Youmin Tang 1 , William W: ytang@cims.nyu.edu #12; Abstract Two hybrid coupled models (HCMs), a dynamical ocean model coupled Introduction Models for ENSO prediction can be categorized into purely statistical models, hybrid coupled

Hsieh, William

175

ZEPHYR THE PREDICTION MODELS T.S. Nielsen, H. Madsen, H. Aa. Nielsen  

E-Print Network [OSTI]

models and methods for predicting the wind power output from wind farms. The system is being developed Modelling (IMM) as the modelling team and all the Danish utilities as partners and users. The new models INTRODUCTION Historically there has been two models used in Denmark to predict the power production from wind

176

RESIDUA UPGRADING EFFICIENCY IMPROVEMENT MODELS: COKE FORMATION PREDICTABILITY MAPS  

SciTech Connect (OSTI)

The dispersed particle solution model of petroleum residua structure was used to develop predictors for pyrolytic coke formation. Coking Indexes were developed in prior years that measure how near a pyrolysis system is to coke formation during the coke formation induction period. These have been demonstrated to be universally applicable for residua regardless of the source of the material. Coking onset is coincidental with the destruction of the ordered structure and the formation of a multiphase system. The amount of coke initially formed appears to be a function of the free solvent volume of the original residua. In the current work, three-dimensional coke make predictability maps were developed at 400 C, 450 C, and 500 C (752 F, 842 F, and 932 F). These relate residence time and free solvent volume to the amount of coke formed at a particular pyrolysis temperature. Activation energies for two apparent types of zero-order coke formation reactions were estimated. The results provide a new tool for ranking residua, gauging proximity to coke formation, and predicting initial coke make tendencies.

John F. Schabron; A. Troy Pauli; Joseph F. Rovani Jr.

2002-05-01T23:59:59.000Z

177

Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization – Extreme learning machine approach  

Science Journals Connector (OSTI)

Abstract This paper presents a novel approach for short-term wind speed prediction based on a Coral Reefs Optimization algorithm (CRO) and an Extreme Learning Machine (ELM), using meteorological predictive variables from a physical model (the Weather Research and Forecast model, WRF). The approach is based on a Feature Selection Problem (FSP) carried out with the CRO, that must obtain a reduced number of predictive variables out of the total available from the WRF. This set of features will be the input of an ELM, that finally provides the wind speed prediction. The CRO is a novel bio-inspired approach, based on the simulation of reef formation and coral reproduction, able to obtain excellent results in optimization problems. On the other hand, the ELM is a new paradigm in neural networks’ training, that provides a robust and extremely fast training of the network. Together, these algorithms are able to successfully solve this problem of feature selection in short-term wind speed prediction. Experiments in a real wind farm in the USA show the excellent performance of the CRO–ELM approach in this FSP wind speed prediction problem.

S. Salcedo-Sanz; A. Pastor-Sánchez; L. Prieto; A. Blanco-Aguilera; R. García-Herrera

2014-01-01T23:59:59.000Z

178

Adaptive model predictive process control using neural networks  

DOE Patents [OSTI]

A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data.

Buescher, Kevin L. (Los Alamos, NM); Baum, Christopher C. (Mazomanie, WI); Jones, Roger D. (Espanola, NM)

1997-01-01T23:59:59.000Z

179

Adaptive model predictive process control using neural networks  

DOE Patents [OSTI]

A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.

Buescher, K.L.; Baum, C.C.; Jones, R.D.

1997-08-19T23:59:59.000Z

180

Predictive Model for Environmental Assessment in Additive Manufacturing Process  

Science Journals Connector (OSTI)

Abstract Additive Manufacturing is an innovative way to produce parts. However its environmental impact is unknown. To ensure the development of additive manufacturing processes it seems important to develop the concept of DFSAM (Design for Sustainable Additive Manufacturing). In fact, one of the objectives of environmental sustainable manufacturing is to minimize the whole flux consumption (electricity, material and fluids) during manufacturing step. To achieve this goal, it is interesting to get a predictive model of consumptions, integrated in the design step, allowing to evaluate the product's environmental impact during the manufacturing step. This paper presents a new methodology for electric, fluids and raw material consumptions assessment for additive manufacturing processes, in particular for a direct metal deposition process. The methodology will help engineers to design parts optimized for additive manufacturing with an environmental point of view.

Florent Le Bourhis; Olivier Kerbrat; Lucas Dembinski; Jean-Yves Hascoet; Pascal Mognol

2014-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "model based predictive" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


181

A multivariate quadrature based moment method for LES based modeling of supersonic combustion  

E-Print Network [OSTI]

function (PDF) approach is a powerful technique for large eddy simulation (LES) based modeling of scramjet and robust scramjet engine is critical for the1 realization of hypersonic flight. Availability of predictive computational models will provide2 a fast and efficient means for designing and optimizing scramjet engines

Raman, Venkat

182

Hybrid powertrain optimization with trajectory prediction based on inter-vehicle-communication and vehicle-infrastructure-integration  

Science Journals Connector (OSTI)

Abstract Recent advances in Inter-Vehicle Communications (IVC) and Vehicle-Infrastructure Integration (VII) paved ways to real-time information sharing among vehicles, which are beneficial for vehicle energy management strategies (EMS). This is especially valuable for power-split hybrid electrical vehicles (HEV) in order to determine the optimal power-split between two different power sources at any particular time. Certainly, researches in this area have been done, but tradeoffs between optimality, driving-cycle sensitivity, speed of calculation and charge-sustaining (CS) conditions have not been cohesively addressed before. In light of this, a combined approach of a time-efficient powertrain optimization strategy, utilizing trajectory prediction based on IVC and VII is proposed. First, Gipps’ car following model for traffic prediction is used to predict the interactions between vehicles, combined with the cell-transmission-model (CTM) for the leading vehicle trajectory prediction. Secondly, a computationally efficient charge-sustaining (CS) HEV powertrain optimization strategy is analytically derived and simulated, based on the Pontryagin’s Minimum Principle and a CS-condition constraint. A 3D lookup-map, generated offline to interpolate the optimizing parameters based on the predicted speed, is also utilized to speed up the calculations. Simulations are conducted for 6-mile and 15-mile cases with different prediction update timings to test the performance of the proposed strategy against a Rule-Based (RB) control strategy. Results for accurate-prediction cases show 9.6% average fuel economy improvements in miles-per-gallon (MPG) over RB for the 6-mile case and 7% improvements for the 15-mile case. Prediction-with-error cases show smaller average MPG’s improvements, with 1.6% to 4.3% improvements for the 6-mile case and 2.6% to 3.4% improvements for the 15-mile case.

Mohd Azrin Mohd Zulkefli; Jianfeng Zheng; Zongxuan Sun; Henry X. Liu

2014-01-01T23:59:59.000Z

183

HEART MOTION PREDICTION BASED ON ADAPTIVE ESTIMATION ALGORITHMS FOR ROBOTIC-ASSISTED  

E-Print Network [OSTI]

HEART MOTION PREDICTION BASED ON ADAPTIVE ESTIMATION ALGORITHMS FOR ROBOTIC-ASSISTED BEATING HEART of the Graduate School of Engineering and Science ii #12;ABSTRACT HEART MOTION PREDICTION BASED ON ADAPTIVE ESTIMATION ALGORITHMS FOR ROBOTIC-ASSISTED BEATING HEART SURGERY Eser Erdem Tuna M.S. in Electrical

Cavusoglu, Cenk

184

An Association Rule-based CLIPS Program for Interactive Prediction of MSC Differentiation in vitro  

E-Print Network [OSTI]

@liverpool.ac.uk Abstract-- In this paper, a software toolkit has been developed for in silica prediction the rules obtained from previous experimental data via data mining techniques, based on which the prediction (Classification based on Multiple Association Rules) [10] has been successfully used to obtain rules with useful

Coenen, Frans

185

Predictive Materials Modeling for Li-Air Battery Systems | Argonne...  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Related Publications: Predicted thermochemistry for chemical conversions of 5-hydroxymethylfurfural Computational Studies of the Thermochemistry for Conversion of Glucose to...

186

Dispersion modeling for prediction of emission factors for cattle feedyards  

E-Print Network [OSTI]

. , 45 SUMMARY AND CONCLUSIONS PROPOSED FUTURE RESEARCH . 47 . 49 REFERENCES APPENDICES APPENDIX A PREDICTED AVERAGE YEARLY CONCENTRATIONS OF PMio UTILIZING AMARILLO WEATHER DATA 51 54 . . 55 APPENDIX B PREDICTED AVERAGE YEARLY... CONCENTRATIONS OF PM)0 UTILIZING LUBBOCK WEATHER DATA 59 VII TABLE OF CONTENTS (Coutinued) Page APPENDIX C PREDICTED AVERAGE YEARLY CONCENTRATIONS OF PM|0 UTILIZING SAN ANGELO WEATHER DATA . . 63 APPENDIX D PREDICTED AVERAGE YEARLY CONCENTRATIONS OF PM|0...

Parnell, Sarah Elizabeth

2012-06-07T23:59:59.000Z

187

NETL: Predictive Modeling and Evaluation - Evaluation of the Emission,  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Evaluation of the Emission, Transport, and Deposition of Mercury, Arsenic, and Fine Particulate Matter from Coal Based Power Plants in the Ohio River Valley Region Evaluation of the Emission, Transport, and Deposition of Mercury, Arsenic, and Fine Particulate Matter from Coal Based Power Plants in the Ohio River Valley Region Ohio University, in collaboration with CONSOL Energy, Advanced Technology Systems, Inc (ATS) and Atmospheric Environmental Research, Inc. (AER) as subcontractors will evaluate the impact of emissions from coal-fired power plants in the Ohio River Valley region as they relate to the transport and deposition of mercury, arsenic, and associated fine particulate matter. This evaluation will involve two interrelated areas of effort: regional-scale modeling analysis and ambient air monitoring. The scope of work for the modeling analysis will include (1) development of updated inventories of mercury and arsenic emissions from coal plants and other important sources in the modeled domain; (2) adapting an existing 3-D atmospheric chemical transport model to incorporate recent advancements in the understanding of mercury transformations in the atmosphere; (3) analyses of the flux of Hg0, RGM, arsenic, and fine particulate matter in the different sectors of the study region to identify key transport mechanisms; (4) comparison of cross correlations between species from the model results to observations in order to evaluate characteristics of specific air masses associated with long-range transport from a specified source region; and (5) evaluation of the sensitivity of these correlations to emissions from regions along the transport path. This will be accomplished by multiple model runs with emissions simulations switched on and off from the various source regions.

188

Physically-based demand modeling  

E-Print Network [OSTI]

for d1fferent values of insulation or control tempera- ture. Also, the results of var1ous load management. scenarios may be evaluated. 26 REFERENCES LZ] D. P. Lijesen and J. Rosing, MAdaptive Forecasting of Hourly Loads Based on Load Measurement...) Terry Marshall Calloway, B. S, , Northeast Louisiana University B. S. , Louisiana Tech University Chairman of Advisory Committee: Dr. C. W. Brice, III This thesis proposes a new methodology for modeling short-term (one hour to one day) air...

Calloway, Terry Marshall

1980-01-01T23:59:59.000Z

189

Model-based safety assessments  

SciTech Connect (OSTI)

Sandia National Laboratories performs systems analysis of high risk, high consequence systems. In particular, Sandia is responsible for the engineering of nuclear weapons, exclusive of the explosive physics package. In meeting this responsibility, Sandia has developed fundamental approaches to safety and a process for evaluating safety based on modeling and simulation. These approaches provide confidence in the safety of our nuclear weapons. Similar concepts may be applied to improve the safety of other high consequence systems.

Carlson, D.D.; Jones, T.R.

1998-04-01T23:59:59.000Z

190

Forecasting a Moving Target: Ensemble Models for ILI Case Count Predictions Prithwish Chakraborty  

E-Print Network [OSTI]

with official flu estimates. We also compare the prediction accuracy between model-level fusion of differentForecasting a Moving Target: Ensemble Models for ILI Case Count Predictions Prithwish Chakraborty using neighbor- hood embedding to predict flu case counts. Comparing our proposed ensemble method

Ryder, Barbara G.

191

Network Flow Modeling Via Lattice-Boltzmann Based Channel Conductance  

SciTech Connect (OSTI)

Lattice-Boltzmann (LB) computations of single phase, pore-to-pore conductance are compared to models in which such conductances are computed via standard pore body-channel-pore body series resistance (SR), with the conductance of each individual element (pore body, channel) based on geometric shape factor measurements. The LB computations, based upon actual channel geometry derived from X-ray computed tomographic imagery, reveal that the variation in conductance for channels having similar shape factor is much larger than is adequately captured by the geometric models. Fits to the dependence of median value of conductance versus shape factor from the LB-based computations show a power law dependence of higher power than that predicted by the geometric models. We introduce two network flow models based upon the LB conductance computations: one model is based upon LB computations for each pore-to-pore connection; the second is based upon a power law fit to the relationship between computed conductance and throat shape factor. Bulk absolute permeabilities for Fontainebleau sandstone images are computed using the SR-based network models and the two LB-based models. Both LB-based network models produce bulk absolute permeability values that fit published data more accurately than the SR-based models.

Sholokhova, Y.; Kim, D; Lindquist, W

2009-01-01T23:59:59.000Z

192

Application of the cumulative risk model in predicting school readiness in Head Start children  

E-Print Network [OSTI]

This study investigates the degree to which the cumulative risk index predicted school readiness in a Head Start population. In general, the reviewed studies indicated the cumulative risk model was efficacious in predicting adverse developmental...

Rodriguez-Escobar, Olga Lydia

2009-05-15T23:59:59.000Z

193

The MIT Emissions Prediction and Policy Analysis (EPPA) Model: Version 4  

E-Print Network [OSTI]

The Emissions Prediction and Policy Analysis (EPPA) model is the part of the MIT Integrated Global Systems Model (IGSM) that represents the human systems. EPPA is a recursive-dynamic multi-regional general equilibrium model ...

Paltsev, Sergey.

194

The MIT Emissions Prediction and Policy Analysis (EPPA) model : revisions, sensitivities, and comparisons of results  

E-Print Network [OSTI]

The Emissions Prediction and Policy Analysis (EPPA) model is a component of the MIT Integrated Earth Systems Model (IGSM). Here, we provide an overview of the model accessible to a broad audience and present the detailed ...

Babiker, Mustafa H.M.; Reilly, John M.; Mayer, Monika.; Eckaus, Richard S.; Sue Wing, Ian.; Hyman, Robert C.

195

A Forward Looking Version of the MIT Emissions Prediction and Policy Analysis (EPPA) Model  

E-Print Network [OSTI]

This paper documents a forward looking multi-regional general equilibrium model developed from the latest version of the recursive-dynamic MIT Emissions Prediction and Policy Analysis (EPPA) model. The model represents ...

Babiker, Mustafa M.H.

196

Modeling and life prediction methodology for titanium matrix composites subjected to mission profiles  

SciTech Connect (OSTI)

Titanium matrix composites (TMCs) are being evaluated as structural materials for elevated temperature applications in future generation hypersonic vehicles. In such applications, TMC components are subjected to complex thermomechanical loading profiles at various elevated temperatures. Therefore, thermomechanical fatigue (TMF) testing, using a simulated mission profile, is essential for evaluation and development of life prediction methodologies. The objective of the research presented in this paper was to evaluate the TMF response of the [0/90]{sub 2s} SCS-6/TIMETAL-21S subjected to a generic hypersonic flight profile and its portions with a temperature ranging from {minus}130 to 816 C. It was found that the composite modulus, prior to rapid degradation, had consistent values for all the profiles tested. The accumulated minimum strain was also found to be the same for all the profiles tested. A micromechanics-based analysis was used to predict the stress-strain response of the laminate and of the constituents in each ply during thermomechanical loading conditions by using only constituent properties as input. The fiber was modeled as elastic with transverse orthotropic and temperature-dependent properties. The matrix was modeled using a thermoviscoplastic constitutive relationship. In the analysis, the composite modulus degradation was assumed to result from matrix cracking and was modeled by reducing the matrix modulus. Fatigue lives of the composite subjected to the complex generic hypersonic flight profiles were well correlated using the predicted stress in 0{degree} fibers.

Mirdamadi, M. [Analytical Services and Materials Inc., Hampton, VA (United States); Johnson, W.S. [Georgia Inst. of Tech., Atlanta, GA (United States). School of Materials Science and Engineering

1996-12-31T23:59:59.000Z

197

Modeling and life prediction methodology for Titanium Matrix Composites subjected to mission profiles  

SciTech Connect (OSTI)

Titanium matrix composites (TMC) are being evaluated as structural materials for elevated temperature applications in future generation hypersonic vehicles. In such applications, TMC components are subjected to complex thermomechanical loading profiles at various elevated temperatures. Therefore, thermomechanical fatigue (TMF) testing, using a simulated mission profile, is essential for evaluation and development of life prediction methodologies. The objective of the research presented in this paper was to evaluate the TMF response of the (0/90)2s SCS-6/Timetal-21S subjected to a generic hypersonic flight profile and its portions with a temperature ranging from -130 C to 816 C. It was found that the composite modulus, prior to rapid degradation, had consistent values for all the profiles tested. A micromechanics based analysis was used to predict the stress-strain response of the laminate and of the constituents in each ply during thermomechanical loading conditions by using only constituent properties as input. The fiber was modeled as elastic with transverse orthotropic and temperature dependent properties. The matrix was modeled using a thermoviscoplastic constitutive relation. In the analysis, the composite modulus degradation was assumed to result from matrix cracking and was modeled by reducing the matrix modulus. Fatigue lives of the composite subjected to the complex generic hypersonic flight profile were well correlated using the predicted stress in 0 degree fibers.

Mirdamadi, M.; Johnson, W.S.

1994-08-01T23:59:59.000Z

198

Predictive Materials Modeling for Li-Air Battery Systems | Argonne...  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Studies of the Thermochemistry for Conversion of Glucose to Levulinic Acid Predicted thermochemistry for chemical conversions of 5-hydroxymethylfurfural Catalyst: Nichols Romero...

199

Model Predictive Control for the Operation of Building Cooling Systems  

E-Print Network [OSTI]

predictive control of thermal energy storage in buildingpredictive control of thermal energy storage in buildingsystems which use thermal energy storage. In particular the

Ma, Yudong

2010-01-01T23:59:59.000Z

200

Failure Predictions for VHTR Core Components using a Probabilistic Contiuum Damage Mechanics Model  

SciTech Connect (OSTI)

The proposed work addresses the key research need for the development of constitutive models and overall failure models for graphite and high temperature structural materials, with the long-term goal being to maximize the design life of the Next Generation Nuclear Plant (NGNP). To this end, the capability of a Continuum Damage Mechanics (CDM) model, which has been used successfully for modeling fracture of virgin graphite, will be extended as a predictive and design tool for the core components of the very high- temperature reactor (VHTR). Specifically, irradiation and environmental effects pertinent to the VHTR will be incorporated into the model to allow fracture of graphite and ceramic components under in-reactor conditions to be modeled explicitly using the finite element method. The model uses a combined stress-based and fracture mechanics-based failure criterion, so it can simulate both the initiation and propagation of cracks. Modern imaging techniques, such as x-ray computed tomography and digital image correlation, will be used during material testing to help define the baseline material damage parameters. Monte Carlo analysis will be performed to address inherent variations in material properties, the aim being to reduce the arbitrariness and uncertainties associated with the current statistical approach. The results can potentially contribute to the current development of American Society of Mechanical Engineers (ASME) codes for the design and construction of VHTR core components.

Fok, Alex

2013-10-30T23:59:59.000Z

Note: This page contains sample records for the topic "model based predictive" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


201

IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 8, NO. 6, DECEMBER 2000 665 Fuzzy Model Predictive Control  

E-Print Network [OSTI]

. Index Terms--Control system design, fuzzy logic, model predic- tive control. I. INTRODUCTION MODELIEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 8, NO. 6, DECEMBER 2000 665 Fuzzy Model Predictive Control computational effort that may prohibit its on-line applications. In this paper, a fuzzy model predictive control

Huang, Yinlun

202

Matchstick: a room-to-room thermal model for predicting indoor temperature from wireless sensor data  

Science Journals Connector (OSTI)

In this paper we present a room-to-room thermal model used to accurately predict temperatures in residential buildings. We evaluate the accuracy of this model with ground truth data from four occupied family homes (two in the UK and two in the US). The ... Keywords: forced air, home automation, prediction, radiators, thermal modelling, underfloor heating

Carl Ellis; Mike Hazas; James Scott

2013-04-01T23:59:59.000Z

203

Evaluation of Location-Specific Predictions by a Detailed Simulation Model of Aedes aegypti Populations  

E-Print Network [OSTI]

Evaluation of Location-Specific Predictions by a Detailed Simulation Model of Aedes aegypti Buster is a stochastic, spatially explicit simulation model of Aedes aegypti populations, designed of Location-Specific Predictions by a Detailed Simulation Model of Aedes aegypti Populations. PLoS ONE 6(7): e

Lloyd, Alun

204

Pharmacokinetic modeling: Prediction and evaluation of route dependent dosimetry of bisphenol A in monkeys with extrapolation to humans  

SciTech Connect (OSTI)

A physiologically based pharmacokinetic (PBPK) model was developed for bisphenol A (BPA) in adult rhesus monkeys using intravenous (iv) and oral bolus doses of 100 {mu}g d6-BPA/kg (). This calibrated PBPK adult monkey model for BPA was then evaluated against published monkey kinetic studies with BPA. Using two versions of the adult monkey model based on monkey BPA kinetic data from and , the aglycone BPA pharmacokinetics were simulated for human oral ingestion of 5 mg d16-BPA per person (Voelkel et al., 2002). Voelkel et al. were unable to detect the aglycone BPA in plasma, but were able to detect BPA metabolites. These human model predictions of the aglycone BPA in plasma were then compared to previously published PBPK model predictions obtained by simulating the Voelkel et al. kinetic study. Our BPA human model, using two parameter sets reflecting two adult monkey studies, both predicted lower aglycone levels in human serum than the previous human BPA PBPK model predictions. BPA was metabolized at all ages of monkey (PND 5 to adult) by the gut wall and liver. However, the hepatic metabolism of BPA and systemic clearance of its phase II metabolites appear to be slower in younger monkeys than adults. The use of the current non-human primate BPA model parameters provides more confidence in predicting the aglycone BPA in serum levels in humans after oral ingestion of BPA. -- Highlights: Black-Right-Pointing-Pointer A bisphenol A (BPA) PBPK model for the infant and adult monkey was constructed. Black-Right-Pointing-Pointer The hepatic metabolic rate of BPA increased with age of the monkey. Black-Right-Pointing-Pointer The systemic clearance rate of metabolites increased with age of the monkey. Black-Right-Pointing-Pointer Gut wall metabolism of orally administered BPA was substantial across all ages of monkeys. Black-Right-Pointing-Pointer Aglycone BPA plasma concentrations were predicted in humans orally given oral doses of deuterated BPA.

Fisher, Jeffrey W., E-mail: jeffrey.fisher@fda.hhs.gov; Twaddle, Nathan C.; Vanlandingham, Michelle; Doerge, Daniel R.

2011-11-15T23:59:59.000Z

205

A turbulence model for buoyant flows based on vorticity generation.  

SciTech Connect (OSTI)

A turbulence model for buoyant flows has been developed in the context of a k-{var_epsilon} turbulence modeling approach. A production term is added to the turbulent kinetic energy equation based on dimensional reasoning using an appropriate time scale for buoyancy-induced turbulence taken from the vorticity conservation equation. The resulting turbulence model is calibrated against far field helium-air spread rate data, and validated with near source, strongly buoyant helium plume data sets. This model is more numerically stable and gives better predictions over a much broader range of mesh densities than the standard k-{var_epsilon} model for these strongly buoyant flows.

Domino, Stefan Paul; Nicolette, Vernon F.; O'Hern, Timothy John; Tieszen, Sheldon R.; Black, Amalia Rebecca

2005-10-01T23:59:59.000Z

206

Sequence-Based Prediction of Protein Folding Rates Using Contacts, Secondary Structures and Support Vector Machines  

E-Print Network [OSTI]

Sequence-Based Prediction of Protein Folding Rates Using Contacts, Secondary Structures and Support, Columbia, Missouri * Corresponding author: chengji@missouri.edu Abstract Predicting protein folding rate is useful for understanding protein folding process and guiding protein design. Here we developed a method

Cheng, Jianlin Jack

207

Sequential Expectations: The Role of Prediction-Based Learning in Language  

E-Print Network [OSTI]

October 2009; accepted 3 November 2009 Abstract Prediction-based processes appear to play an important prediction learning and natural lan- guage processing. This paper builds upon existing statistical learning differences for developing prob- abilistic sequential expectations. Across three interrelated experiments

208

Predictions from the macrohomogeneous model of an aerospace Ni-Cd battery  

SciTech Connect (OSTI)

The mathematical porous-electrode model developed at Texas A and M University has been combined with a planar model for the surface active layer to formulate a pseudo two-dimensional model for a sealed nickel-cadmium cell. The porous electrode model is based on a macrohomogeneous description of the electrodes and takes into account various processes such as mass transport in the liquid phase and porosity and conductivity changes in the solid phase. The planar electrode model describes the processes occurring across the surface layer of active material, i.e., solid-state diffusion of protons and conductivity changes in the nickel oxide, and the charge-transfer across the film-electrolyte interface. Also, various routines have been added to the pseudo two-dimensional model thus integrated, to allow predictions for any nickel-cadmium battery under any desired charge-discharge schedule. From a comparison with the experimental data of an aerospace cell, the model parameters describing charge-discharge behavior of a Ni-Cd cell have been optimized to obtain a closer prediction with the experimental data. Upon optimizing the model parameters, the performance of the aerospace nickel-cadmium cell has been simulated under various experimental conditions, i.e., at different rates and temperatures. Also, generic Ragone plots for the cell and typical Tafel plots for cadmium and nickel electrodes at different states of charge have been constructed form the simulations. Finally, this model has been made available for any interested user through COSMIC NASA`s Computer Management and Information Center, along with documentation in six volumes describing the code, principles, and operating instructions.

Ratnakumar, B.V.; Timmerman, P.; Sanchez, C.; Di Stefano, S.; Halpert, G. [California Inst. of Tech., Pasadena, CA (United States). Jet Propulsion Lab.

1996-03-01T23:59:59.000Z

209

E-Print Network 3.0 - area prediction models Sample Search Results  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

for the area-specific model, and recalibrate confidence interval... predictive logis- tic regression ... Source: Montana, University of - Cooperative Wildlife Research Unit...

210

Modeling the Number of Ignitions Following an Earthquake: Developing Prediction Limits for Overdispersed Count Data  

Broader source: Energy.gov [DOE]

Modeling the Number of Ignitions Following an Earthquake: Developing Prediction Limits for Overdispersed Count Data Elizabeth J. Kelly and Raymond N. Tell

211

Calendar ageing analysis of a LiFePO4/graphite cell with dynamic model validations: Towards realistic lifetime predictions  

Science Journals Connector (OSTI)

Abstract The present study aims at establishing a methodology for a comprehensive calendar ageing predictive model development, focusing specially on validation procedures. A LFP-based Li-ion cell performance degradation was analysed under different temperature and SOC storage conditions. Five static calendar ageing conditions were used for understanding the ageing trends and modelling the dominant ageing phenomena (SEI growth and the resulting loss of active lithium). The validation process included an additional test under other constant operating conditions (static validation) and other four tests under non–constant impact factors operating schemes within the same experiment (dynamic validation), in response to battery stress conditions in real applications. Model predictions are in good agreement with experimental results as the residuals are always below 1% for experiments run for 300–650 days. The model is able to predict dynamic behaviour close to real operating conditions and the level of accuracy corresponds to a root-mean-square error of 0.93%.

E. Sarasketa-Zabala; I. Gandiaga; L.M. Rodriguez-Martinez; I. Villarreal

2014-01-01T23:59:59.000Z

212

Ecological Modelling 120 (1999) 349358 Use of artificial neural networks for predicting rice crop  

E-Print Network [OSTI]

Ecological Modelling 120 (1999) 349­358 Use of artificial neural networks for predicting rice crop of artificial neural networks (ANN) in predicting presence or absence of flamingo damages from 11 variables B.V. All rights reserved. Keywords: Flamingos; Rice; Damage; Artificial neural networks; Prediction

Lek, Sovan

213

Evaluation of a case-based Reasoning Energy Prediction Tool for Commercial Buildings  

E-Print Network [OSTI]

This paper presents the results of an energy predictor that predicts the energy demand of commercial buildings using Case Based Reasoning (CBR). The proposed approach is evaluated using monitored data in a real office building located in Varennes...

Monfet, D.; Arkhipova, E.; Choiniere, D.

2013-01-01T23:59:59.000Z

214

Prediction-based Iterative Learning Control (PILC) for Uncertain Dynamic Nonlinear Systems Using System Identification Technique  

Science Journals Connector (OSTI)

Prediction-based Iterative Learning Control (PILC) is proposed in this paper for a ... time varying nonlinear uncertain systems. Convergence of PILC is analyzed and the uniform boundedness of ... succeeding itera...

M. Arif; T. Ishihara; H. Inooka

2000-03-01T23:59:59.000Z

215

Vehicle Technologies Office Merit Review 2014: Mechanistic-based Ductility Prediction for Complex Mg Castings  

Broader source: Energy.gov [DOE]

Presentation given by USAMP at 2014 DOE Hydrogen and Fuel Cells Program and Vehicle Technologies Office Annual Merit Review and Peer Evaluation Meeting about mechanistic-based ductility prediction...

216

Robust UAV Coordination for Target Tracking using Output-Feedback Model Predictive Control with Moving Horizon Estimation  

E-Print Network [OSTI]

Robust UAV Coordination for Target Tracking using Output-Feedback Model Predictive Control consider the control of two UAVs tracking an evasive moving ground vehicle. The UAVs are small fixed to maintain visibility. The control inputs to the UAVs are computed based on noisy measurements of the UAVs

Hespanha, João Pedro

217

Predicting Coupled Ocean-Atmosphere Modes with a Climate Modeling Hierarchy -- Final Report  

SciTech Connect (OSTI)

The goal of the project was to determine midlatitude climate predictability associated with tropical-extratropical interactions on interannual-to-interdecadal time scales. Our strategy was to develop and test a hierarchy of climate models, bringing together large GCM-based climate models with simple fluid-dynamical coupled ocean-ice-atmosphere models, through the use of advanced probabilistic network (PN) models. PN models were used to develop a new diagnostic methodology for analyzing coupled ocean-atmosphere interactions in large climate simulations made with the NCAR Parallel Climate Model (PCM), and to make these tools user-friendly and available to other researchers. We focused on interactions between the tropics and extratropics through atmospheric teleconnections (the Hadley cell, Rossby waves and nonlinear circulation regimes) over both the North Atlantic and North Pacific, and the ocean’s thermohaline circulation (THC) in the Atlantic. We tested the hypothesis that variations in the strength of the THC alter sea surface temperatures in the tropical Atlantic, and that the latter influence the atmosphere in high latitudes through an atmospheric teleconnection, feeding back onto the THC. The PN model framework was used to mediate between the understanding gained with simplified primitive equations models and multi-century simulations made with the PCM. The project team is interdisciplinary and built on an existing synergy between atmospheric and ocean scientists at UCLA, computer scientists at UCI, and climate researchers at the IRI.

Michael Ghil, UCLA; Andrew W. Robertson, IRI, Columbia Univ.; Sergey Kravtsov, U. of Wisconsin, Milwaukee; Padhraic Smyth, UC Irvine

2006-08-04T23:59:59.000Z

218

Validation of a CFD Analysis Model for Predicting CANDU-6 Moderator Temperature Against SPEL Experiments  

SciTech Connect (OSTI)

A validation of a 3D CFD model for predicting local subcooling of the moderator in the vicinity of calandria tubes in a CANDU-6 reactor is performed. The small scale moderator experiments performed at Sheridan Park Experimental Laboratory (SPEL) in Ontario, Canada[1] is used for the validation. Also a comparison is made between previous CFD analyses based on 2DMOTH and PHOENICS, and the current analysis for the same SPEL experiment. For the current model, a set of grid structures for the same geometry as the experimental test section is generated and the momentum, heat and continuity equations are solved by CFX-4.3, a CFD code developed by AEA technology. The matrix of calandria tubes is simplified by the porous media approach. The standard k-{epsilon} turbulence model associated with logarithmic wall treatment and SIMPLEC algorithm on the body fitted grid are used. Buoyancy effects are accounted for by the Boussinesq approximation. For the test conditions simulated in this study, the flow pattern identified is the buoyancy-dominated flow, which is generated by the interaction between the dominant buoyancy force by heating and inertial momentum forces by the inlet jets. As a result, the current CFD moderator analysis model predicts the moderator temperature reasonably, and the maximum error against the experimental data is kept at less than 2.0 deg. C over the whole domain. The simulated velocity field matches with the visualization of SPEL experiments quite well. (authors)

Churl Yoon; Bo Wook Rhee; Byung-Joo Min [Korea Atomic Energy Research Institute, 150, Dukjin-Dong, Yusong-Gu, Taejon 305-353 (Korea, Republic of)

2002-07-01T23:59:59.000Z

219

Sensor-based interior modeling  

SciTech Connect (OSTI)

Robots and remote systems will play crucial roles in future decontamination and decommissioning (D&D) of nuclear facilities. Many of these facilities, such as uranium enrichment plants, weapons assembly plants, research and production reactors, and fuel recycling facilities, are dormant; there is also an increasing number of commercial reactors whose useful lifetime is nearly over. To reduce worker exposure to radiation, occupational and other hazards associated with D&D tasks, robots will execute much of the work agenda. Traditional teleoperated systems rely on human understanding (based on information gathered by remote viewing cameras) of the work environment to safely control the remote equipment. However, removing the operator from the work site substantially reduces his efficiency and effectiveness. To approach the productivity of a human worker, tasks will be performed telerobotically, in which many aspects of task execution are delegated to robot controllers and other software. This paper describes a system that semi-automatically builds a virtual world for remote D&D operations by constructing 3-D models of a robot`s work environment. Planar and quadric surface representations of objects typically found in nuclear facilities are generated from laser rangefinder data with a minimum of human interaction. The surface representations are then incorporated into a task space model that can be viewed and analyzed by the operator, accessed by motion planning and robot safeguarding algorithms, and ultimately used by the operator to instruct the robot at a level much higher than teleoperation.

Herbert, M.; Hoffman, R.; Johnson, A.; Osborn, J.

1995-02-01T23:59:59.000Z

220

A simple model to predict train-induced vibration: theoretical formulation and experimental validation  

SciTech Connect (OSTI)

No suitable handy tool is available to predict train-induced vibration on environmental impact assessment. A simple prediction model is proposed which has been calibrated for high speed trains. The model input data are train characteristics, train speed and track properties; model output data are soil time-averaged velocity and velocity level. Model results have been compared with numerous vibration data retrieved from measurement campaigns led along the most important high-speed European rail tracks. Model performances have been tested by comparing measured and predicted vibration values.

Rossi, Federico; Nicolini, Andrea

2003-05-01T23:59:59.000Z

Note: This page contains sample records for the topic "model based predictive" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


221

Prediction of regional seasonal fluctuations in precipitation based on chaos theory  

E-Print Network [OSTI]

In the past decade, the combined effect of flood and drought resulted in the loss of thousands of lives and billions of dollars. Multi season ahead prediction of regional precipitation extremes could significantly reduce losses. However, the evolution of climate is highly sensitive to initial conditions, or chaotic, so practical long term prediction of precipitation in time is impossible. Adding to the difficulty, the climate system is non-stationary; with the energy available to move water and air as tracked by global average surface temperature (GAST) increasing over the last several decades2. Neither purely empirical autoregression, nor global circulation models (GCM) are sufficiently accurate. Here I use statistical methods motivated by chaos theory to predict seasonal fluctuations in regional and local precipitation with high correlation. The change in GAST is accommodated using special runs of a global circulation model to build an initial set of predictive models, while ground data is used to train, co...

LuValle, M

2013-01-01T23:59:59.000Z

222

Hydrodynamic Model with Binary Particle Diameters to Predict Axial Voidage Profile in a CFB Combustor  

Science Journals Connector (OSTI)

A hydrodynamic model with binary particle diameters was developed to better predict axial voidage profile in a CFB combustor. In the model, the CFB is regarded as a superposition of two ... field data of voidage ...

J. J. Li; H. Zhang; H. R. Yang; Y. X. Wu…

2010-01-01T23:59:59.000Z

223

Application of Scale-Selective Data Assimilation to Regional Climate Modeling and Prediction  

Science Journals Connector (OSTI)

A method referred to as scale-selective data assimilation (SSDA) is designed to inject the large-scale components of the atmospheric circulation from a global model into a regional model to improve regional climate simulations and predictions. ...

Shiqiu Peng; Lian Xie; Bin Liu; Fredrick Semazzi

2010-04-01T23:59:59.000Z

224

A unified algorithm for predicting partition coefficients for PBPK modeling of drugs and environmental chemicals  

SciTech Connect (OSTI)

The algorithms in the literature focusing to predict tissue:blood PC (P{sub tb}) for environmental chemicals and tissue:plasma PC based on total (K{sub p}) or unbound concentration (K{sub pu}) for drugs differ in their consideration of binding to hemoglobin, plasma proteins and charged phospholipids. The objective of the present study was to develop a unified algorithm such that P{sub tb}, K{sub p} and K{sub pu} for both drugs and environmental chemicals could be predicted. The development of the unified algorithm was accomplished by integrating all mechanistic algorithms previously published to compute the PCs. Furthermore, the algorithm was structured in such a way as to facilitate predictions of the distribution of organic compounds at the macro (i.e. whole tissue) and micro (i.e. cells and fluids) levels. The resulting unified algorithm was applied to compute the rat P{sub tb}, K{sub p} or K{sub pu} of muscle (n = 174), liver (n = 139) and adipose tissue (n = 141) for acidic, neutral, zwitterionic and basic drugs as well as ketones, acetate esters, alcohols, aliphatic hydrocarbons, aromatic hydrocarbons and ethers. The unified algorithm reproduced adequately the values predicted previously by the published algorithms for a total of 142 drugs and chemicals. The sensitivity analysis demonstrated the relative importance of the various compound properties reflective of specific mechanistic determinants relevant to prediction of PC values of drugs and environmental chemicals. Overall, the present unified algorithm uniquely facilitates the computation of macro and micro level PCs for developing organ and cellular-level PBPK models for both chemicals and drugs.

Peyret, Thomas [DSEST, Universite de Montreal, Canada H3T 1A8 (Canada); Poulin, Patrick [Consultant, 4009 rue Sylvia Daoust, Quebec City, Quebec, G1X 0A6 (Canada); Krishnan, Kannan, E-mail: kannan.krishnan@umontreal.ca [DSEST, Universite de Montreal, H3T 1A8 (Canada)

2010-12-15T23:59:59.000Z

225

StatREC: a graphical user interface tool for visual hypothesis testing of cost prediction models  

Science Journals Connector (OSTI)

Background: During the previous decades there has been noted a significantly increased research interest on the construction of prediction models for accurate estimation of software cost. Despite the development of sophisticated methodologies, ... Keywords: REC curves, graphical comparison, graphical user interface, permutation test, prediction models, software cost estimation

Nikolaos Mittas; Ioannis Mamalikidis; Lefteris Angelis

2012-09-01T23:59:59.000Z

226

Genetic Algorithm for Predicting Protein Folding in the 2D HP Model  

E-Print Network [OSTI]

Genetic Algorithm for Predicting Protein Folding in the 2D HP Model A Parameter Tuning Case Study of a protein, predicting its tertiary structure is known as the protein folding problem. This problem has been. The protein folding problem in the HP model is to find a conformation (a folded sequence) with the lowest

Emmerich, Michael

227

EXPLICIT SIMULATION OF ICE PARTICLE HABITS IN A NUMERICAL WEATHER PREDICTION MODEL  

E-Print Network [OSTI]

EXPLICIT SIMULATION OF ICE PARTICLE HABITS IN A NUMERICAL WEATHER PREDICTION MODEL by Tempei This study develops a scheme for explicit simulation of ice particle habits in Cloud Resolving Models (CRMs is called Spectral Ice Habit Prediction System (SHIPS), which represents a continuous-property approach

Wisconsin at Madison, University of

228

Leveraging Model-Based Tool Integration by Conceptual Modeling Techniques  

Science Journals Connector (OSTI)

In the context of model-based tool integration, model transformation languages are the first choice for realizing model exchange between heterogenous tools. However, the lack of a conceptual view on the integr...

Gerti Kappel; Manuel Wimmer; Werner Retschitzegger…

2011-01-01T23:59:59.000Z

229

Leveraging model-based tool integration by conceptual modeling techniques  

Science Journals Connector (OSTI)

In the context of model-based tool integration, model transformation languages are the first choice for realizing model exchange between heterogenous tools. However, the lack of a conceptual view on the integration problem and appropriate reuse mechanisms ...

Gerti Kappel; Manuel Wimmer; Werner Retschitzegger; Wieland Schwinger

2011-01-01T23:59:59.000Z

230

A Model Based Approach to Increase the Part Accuracy in Robot Based Incremental Sheet Metal Forming  

SciTech Connect (OSTI)

One main influence on the dimensional accuracy in robot based incremental sheet metal forming results from the compliance of the involved robot structures. Compared to conventional machine tools the low stiffness of the robot's kinematic results in a significant deviation of the planned tool path and therefore in a shape of insufficient quality. To predict and compensate these deviations offline, a model based approach, consisting of a finite element approach, to simulate the sheet forming, and a multi body system, modeling the compliant robot structure, has been developed. This paper describes the implementation and experimental verification of the multi body system model and its included compensation method.

Meier, Horst; Laurischkat, Roman; Zhu Junhong [Institute Product and Service Engineering, Chair of Production Systems, Ruhr-University of Bochum, Universitaetsstrasse 150, D-44780 Bochum (Germany)

2011-01-17T23:59:59.000Z

231

Performance of corrosion inhibiting admixtures for structural concrete -- assessment methods and predictive modeling  

SciTech Connect (OSTI)

During the past fifteen years corrosion inhibiting admixtures (CIAs) have become increasingly popular for protection of reinforced components of highway bridges and other structures from damage induced by chlorides. However, there remains considerable debate about the benefits of CIAs in concrete. A variety of testing methods to assess the performance of CIA have been reported in the literature, ranging from tests in simulated pore solutions to long-term exposures of concrete slabs. The paper reviews the published techniques and recommends the methods which would make up a comprehensive CIA effectiveness testing program. The results of this set of tests would provide the data which can be used to rank the presently commercially available CIA and future candidate formulations utilizing a proposed predictive model. The model is based on relatively short-term laboratory testing and considers several phases of a service life of a structure (corrosion initiation, corrosion propagation without damage, and damage to the structure).

Yunovich, M.; Thompson, N.G. [CC Technologies Labs., Inc., Dublin, OH (United States)

1998-12-31T23:59:59.000Z

232

Radial forging force prediction through MR, ANN, and ANFIS models  

Science Journals Connector (OSTI)

The application of finite element method and intelligent systems techniques to predict the applied force during the radial forging process is studied. Radial forging is a unique process used for the precision forging of round and tubular components, ... Keywords: ANFIS, ANN, Forging force, MR, Radial forging

A. Azari, M. Poursina, D. Poursina

2014-09-01T23:59:59.000Z

233

Falsifying Field-based Dark Energy Models  

E-Print Network [OSTI]

We survey the application of specific tools to distinguish amongst the wide variety of dark energy models that are nowadays under investigation. The first class of tools is more mathematical in character: the application of the theory of dynamical systems to select the better behaved models, with appropriate attractors in the past and future. The second class of tools is rather physical: the use of astrophysical observations to crack the degeneracy of classes of dark energy models. In this last case the observations related with structure formation are emphasized both in the linear and non-linear regimes. We exemplify several studies based on our research, such as quintom and quinstant dark energy ones. Quintom dark energy paradigm is a hybrid construction of quintessence and phantom fields, which does not suffer from fine-tuning problems associated to phantom field and additionally it preserves the scaling behavior of quintessence. Quintom dark energy is motivated on theoretical grounds as an explanation for the crossing of the phantom divide, i.e. the smooth crossing of the dark energy state equation parameter below the value -1. On the other hand, quinstant dark energy is considered to be formed by quintessence and a negative cosmological constant, the inclusion of this later component allows for a viable mechanism to halt acceleration. We comment that the quinstant dark energy scenario gives good predictions for structure formation in the linear regime, but fails to do that in the non-linear one, for redshifts larger than one. We comment that there might still be some degree of arbitrariness in the selection of the best dark energy models.

Genly Leon; Yoelsy Leyva; Emmanuel N. Saridakis; Osmel Martin; Rolando Cardenas

2009-12-02T23:59:59.000Z

234

A Parallel Statistical Learning Approach to the Prediction of Building Energy Consumption Based on Large Datasets  

E-Print Network [OSTI]

A Parallel Statistical Learning Approach to the Prediction of Building Energy Consumption Based consumption of buildings based on historical performances is an important approach to achieve energy consumption plays an important role in the total energy consumption of end use. Energy efficiency in building

Paris-Sud XI, Université de

235

Experimental Evaluation of Inventory-Based Discrete-Updating Market Maker for Intra-Firm Prediction  

E-Print Network [OSTI]

an inventory-based updating logic according to the transactions in the market. Laboratory experimentsExperimental Evaluation of Inventory-Based Discrete- Updating Market Maker for Intra-Firm Prediction Market System Using VIPS Hajime Mizuyama1 , Morio Ueda, Katsunobu Asada and Yu Tagaya 1 Department

Boyer, Edmond

236

Image-based Prediction of Landmark Features for Mobile Robot Navigation  

E-Print Network [OSTI]

-based prediction of point and line features for a mobile system operating on a planar surface. Preliminary-based navigation system. The central idea in this design is to constantly track im- age features used as landmarks. This pro- vides constant and accurate control of position, yet avoids the overhead of computing an explicit

Jaffe, Jules

237

Proton Exchange Membrane Fuel Cell degradation prediction based on Adaptive Neuro Fuzzy Inference Systems  

E-Print Network [OSTI]

Proton Exchange Membrane Fuel Cell degradation prediction based on Adaptive Neuro Fuzzy Inference online XX XX XXXX Keywords: Proton Exchange Membrane fuel cell degradation, Prognostic and Health nominal operating condition of a PEM fuel cell stack. It proposes a methodology based on Adaptive Neuro

Paris-Sud XI, Université de

238

Soil-landscape model helps predict potassium supply in vineyards  

E-Print Network [OSTI]

Australia: Winetitles. Marchand DE, Allwardt A. 1981. LateGeologic ages based on Marchand and Allwardt (1981). †

O'Geen, Anthony T; Pettygrove, Stuart; Southard, Randal; Minoshima, Hideomi; Verdegaal, Paul S.

2008-01-01T23:59:59.000Z

239

Vibration-based approach to lifetime prediction of electric motors for reuse  

Science Journals Connector (OSTI)

This paper is concerned with lifetime prediction of components in washing machines. Vibration signals were measured on electric motors during an accelerated lifetime test ranging from 26.7 to 38.5 simulated years. Loose bearings have initiated air-gap eccentricity and rotor-to-stator rubbing, which resulted in a motor breakdown. Significant frequency bands were identified using a spectral comparison based on the constant percentage bandwidth (CPB) spectrum. Increasing trends were extracted from several vibration indicators, such as envelope cepstrum (EC) and a weighted integral of CPB differences. The EC is computed as the real cepstrum of the envelope signal obtained by demodulating the band identified by the CPB comparison. Hence the EC is more sensitive as it employs a priori information provided by historical data. The fault was first detected 9.7 years in advance and confirmed 5.3 years before the breakdown. The indicators can be integrated with a recent methodology based on Weibull analysis and neural network modelling.

Jiri Vass; Robert B. Randall; Sami Kara; Hartmut Kaebernick

2010-01-01T23:59:59.000Z

240

A Novel Ab-initio Genetic-Based Approach for Protein Folding Prediction  

E-Print Network [OSTI]

In this paper, a model based on genetic algorithms for protein folding prediction is proposed. The most important features of the proposed approach are: i) Heuristic secondary structure information is used in the initialization of the genetic algorithm; ii) An enhanced 3D spatial representation called cube-octahedron is used, also, an expansion technique is proposed in order to reduce the computational complexity and spatial constraints; iii) Data preprocessing of geometric features to characterize the cubeoctahedron using twelve basic vectors to define the nodes. Additionally, biological information (torsion angles, bond angles and secondary structure conformations) was pre-processed through an analysis of all possible combinations of the basic vectors which satisfy the biological constrains defined by the spatial representation; and iv) Hashing techniques were used to improve the computational efficiency. The pre-processed information was stored in hash tables, which are intensively used by the genetic algorithm. Some experiments were carried out to validate the proposed model obtaining very promising results.

Sergio R. Duarte; David C. Becerra; Fernando Nino; Yoan J. Pinzón

Note: This page contains sample records for the topic "model based predictive" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


241

Abstract--Eventually, prediction of transformer thermal performance for dynamic loading will be made using models  

E-Print Network [OSTI]

1 Abstract--Eventually, prediction of transformer thermal performance for dynamic loading will be made using models distilled from measure data, rather than models derived from transformer heat for measuring the acceptability of transformer thermal models. For a model to be acceptable, it must have

242

Local approach to fracture based prediction of the T56J and 100 shifts due to irradiation for an A508 pressure vessel steel.  

E-Print Network [OSTI]

Local approach to fracture based prediction of the T56J and TKIc 100 shifts due to irradiation model integrating a description of viscoplasticity, ductile damage and brittle fracture is used to simulate both the impact (Charpy) test and the toughness (CT) fracture test. The model is calibrated

Boyer, Edmond

243

Roadway pollutant dispersion: development of a data base and a model and evaluation of five models  

E-Print Network [OSTI]

ROADWAY POLLUTANT D1SPERSION: DEVELOPMENT OF A DATA 3ASE AND A MODEL AND EVALUATION OF FIVE MODELS A Thesis by NICHOLAS JOSEPH GREEN Submitted to the Graduate College of Texas A&M University in partial fulfillment of the requirement... previous dispersion models, as well as the present model. The emission rates for a portion of the Texas ASM data base included those predicted by MOBILE 1, an EPA computer model, and those calcul- ated by a mass balance technique using experimental data...

Green, Nicholas Joseph

1980-01-01T23:59:59.000Z

244

Copula Based Hierarchical Bayesian Models  

E-Print Network [OSTI]

WITH THE SAME MARGINAL AND CONDITIONAL LINK . 9 III.1. Random effects model . . . . . . . . . . . . . . . . . . . . 12 III.1.1. Logistic link with bridge random effects . . . . . 15 III.1.2. Log-log link with positive stable random effects . 19 III.1.3. Logistic... probabilities for models of various order . . . . . . . . . . . 58 8. Comparison among various mixture-copula models . . . . . . . . . . 59 9. DIC, AAPE and AAD for the two competing models . . . . . . . . . 93 10. Posterior summary of parameters for the two...

Ghosh, Souparno

2010-10-12T23:59:59.000Z

245

Theoretical model for predicting thermodynamic behavior of thermal-lag Stirling engine  

Science Journals Connector (OSTI)

A theoretical model for predicting thermodynamic behavior of thermal-lag Stirling engine is presented in this study. Without a displacer and its link, the thermal-lag engine contains only a moving part (piston) and a static part (regenerative heater) in engine's cylinder and hence, is regarded as a unique type of Stirling engines that featuring rather simple mechanical structure. In this study, a numerical simulation of thermodynamic behavior of the thermal-lag Stirling engine is performed based on the theoretical model developed. Transient variations of temperatures, pressures, pressure difference, and working fluid masses in the individual working spaces of the engine are predicted. Dependence of indicated power and thermal efficiency on engine speed has been investigated. Then, optimal engine speeds at which the engine may reach its maximum power output and/or maximum thermal efficiency is determined. Furthermore, effects of geometrical and operating parameters, such as heating and cooling temperatures, volumes of the chambers, thermal resistances, stroke of piston, and bore size on indicated power output and thermal efficiency are also evaluated.

Chin-Hsiang Cheng; Hang-Suin Yang

2013-01-01T23:59:59.000Z

246

A predictive model of yellow spotted river turtle (Podocnemis unifilis) encounter rates at basking sites in lowland eastern Bolivia  

Science Journals Connector (OSTI)

Abstract This paper develops a model predicting encounter rates of the yellow-spotted river turtle (Podocnemis unifilis) based on human hunting pressure and an ecological classification of potential basking sites. We estimate Poisson regression models for turtles observed in basking surveys. Field surveys were conducted in eastern lowland Bolivia in 2000 and 2011. Our model predicts a significant correlation between turtle encounter rates and two ecological classifications – steep cliff with vegetation and muddy flats that we believe are important habitat types for these turtles. Additionally, our model supports the hypothesis that human population has a significant but less negative impact on observed turtle encounter rates. Analyses of turtle encounter rates and factors that influence it are critical for the conservation of P. unifilis turtles and the broader Amazonian ecological system.

Kristen Conway-Gómez; Michael Reibel; Christopher Mihiar

2014-01-01T23:59:59.000Z

247

Comparison of Predictive Models for Photovoltaic Module Performance: Preprint  

SciTech Connect (OSTI)

This paper examines three models used to estimate the performance of photovoltaic (PV) modules when the irradiances and PV cell temperatures are known. The results presented here were obtained by comparing modeled and measured maximum power (Pm) for PV modules that rely on different technologies.

Marion, B.

2008-05-01T23:59:59.000Z

248

Putting Nonlinear Model Predictive Control Bjarne A. Foss1  

E-Print Network [OSTI]

estimation. Finally, we consider the design of the optimization problem itself and implementation issues. 1 for optimization of suspension PVC polymerization processes has been implemented on two large (140 m3) autoclaves: It contains a rather detailed nonlinear model of the polymerization reactor. The reactor model includes

Foss, Bjarne A.

249

A new spatio-temporal prediction approach based on aggregate queries  

Science Journals Connector (OSTI)

The prediction of spatio-temporal data streams which is based on aggregate queries has been an important research direction in the research field of databases. More and more methods have been proposed to obtain approximate aggregate results. However, they will consume a lot of time and storage space. This paper proposes Dynamic Sketch (DS) index by using modified method of Adaptive Multi-dimensional Histogram (AMH*) to intelligently partition static sketch which can improve the approximate quality of aggregate queries in road networks. Then, based on DS index, this paper proposes a new prediction approach over data streams in road networks using Self-Adaptive Exponential Smoothing (SAES).

Jun Feng; Zhonghua Zhu; Yaqing Shi; Liming Xu

2013-01-01T23:59:59.000Z

250

Distributed Central Pattern Generator Model for Robotics Application Based on Phase Sensitivity Analysis  

Science Journals Connector (OSTI)

A method is presented to predict phase relationships between coupled phase oscillators. As an illustration of how the method can be applied, a distributed Central Pattern Generator (CPG) model based on amplitude ...

Jonas Buchli; Auke Jan Ijspeert

2004-01-01T23:59:59.000Z

251

The use of least squares methods in functional optimization of energy use prediction models  

Science Journals Connector (OSTI)

The least squares method (LSM) is used to optimize the coefficients of a closed-form correlation that predicts the annual energy use of buildings based on key envelope design and thermal parameters. Specifically annual energy use is related to a number parameters like the overall heat transfer coefficients of the wall roof and glazing glazing percentage and building surface area. The building used as a case study is a previously energy-audited mosque in a suburb of Kuwait City Kuwait. Energy audit results are used to fine-tune the base case mosque model in the VisualDOE{trade mark serif} software. Subsequently 1625 different cases of mosques with varying parameters were developed and simulated in order to provide the training data sets for the LSM optimizer. Coefficients of the proposed correlation are then optimized using multivariate least squares analysis. The objective is to minimize the difference between the correlation-predicted results and the VisualDOE-simulation results. It was found that the resulting correlation is able to come up with coefficients for the proposed correlation that reduce the difference between the simulated and predicted results to about 0.81%. In terms of the effects of the various parameters the newly-defined weighted surface area parameter was found to have the greatest effect on the normalized annual energy use. Insulating the roofs and walls also had a major effect on the building energy use. The proposed correlation and methodology can be used during preliminary design stages to inexpensively assess the impacts of various design variables on the expected energy use. On the other hand the method can also be used by municipality officials and planners as a tool for recommending energy conservation measures and fine-tuning energy codes.

2012-01-01T23:59:59.000Z

252

Hybrid principal component analysis and support vector machine model for predicting the cost performance of commercial building projects using pre-project planning variables  

Science Journals Connector (OSTI)

An accurate prediction of project performance in the pre-project planning stage – especially prediction of cost performance – is paramount to project stakeholders. The aim of this study is to propose and validate a hybrid predictive model for cost performance of commercial building projects using 64 variables related to the levels of definition in the pre-project planning stage. The proposed model integrates a support vector regression (SVR) model with principal component analysis (PCA). The proposed method was analyzed and validated based on 84 sets of data from an equal number of commercial building projects. Additionally, the result obtained using the proposed PCA–SVR model was compared with four other data-mining techniques. Experimental results revealed that the proposed PCA–SVR model is able to predict with high accuracy the cost performance of commercial building projects in the pre-project planning stage and is more efficient than the other four models.

Hyojoo Son; Changmin Kim; Changwan Kim

2012-01-01T23:59:59.000Z

253

Predicting the net carbon exchanges of crop rotations in Europe with an agro-ecosystem model  

E-Print Network [OSTI]

Predicting the net carbon exchanges of crop rotations in Europe with an agro-ecosystem model S.Lehuger@art.admin.ch. Fax: (+41) 44 377 72 01. Phone: (+41) 44 377 75 13. hal-00414342,version2-1Sep2010 #12;Abstract Carbon and measuring land-atmosphere carbon exchanges from arable lands are important tasks to predict the influence

Boyer, Edmond

254

NETL: Gasification Systems - Model Based Optimal Sensor Network Design for  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Model Based Optimal Sensor Network Design for Condition Monitoring Model Based Optimal Sensor Network Design for Condition Monitoring Project Number: FE0005712 General Electric (GE) Global Research is developing an advanced model-based optimal sensor network to monitor the condition of the gasification section in an integrated gasification combined cycle (IGCC) plant. The work builds on model-based controls aimed at enhancing efficiency and operational flexibility through increased automation. Within an overall strategy of employing model-based online monitoring and predictive controls, GE Global Research is extending existing models for the gasifier and radiant syngas cooler to include the effects of degradation and fouling on the sensed variables like temperature etc., and will implement an estimation algorithm to assess the extent of gasifier refractory degradation and radiant syngas cooler fouling. An optimization-based solution will be employed to optimally place the hardware sensors utilized in the estimation algorithm in order to achieve the monitoring requirements at the lowest cost. The performance of the sensor placement algorithm and resulting monitoring solution will be demonstrated through simulations using representative test cases. The overall approach is one of the first to be applicable to condition monitoring of critical components in IGCC plants.

255

Prediction of nuclear proteins using SVM and HMM models  

Science Journals Connector (OSTI)

All modules were trained and tested on a non-redundant dataset and evaluated using five-fold cross-validation ... based module/profile was developed for searching exclusively nuclear and non-nuclear domains in a ...

Manish Kumar; Gajendra PS Raghava

2009-01-01T23:59:59.000Z

256

Model-Generated Predictions of Dry Thunderstorm Potential  

Science Journals Connector (OSTI)

Dry thunderstorms (those that occur without significant rainfall at the ground) are common in the interior western United States. Moisture drawn into the area from the Gulfs of Mexico and California is sufficient to form high-based thunderstorms. ...

Miriam L. Rorig; Steven J. McKay; Sue A. Ferguson; Paul Werth

2007-05-01T23:59:59.000Z

257

Development of Chemical Model to Predict the Interactions between...  

Broader source: Energy.gov (indexed) [DOE]

- TOUGH family codes have been widely used in modeling EGS and CCS processes. The fracture-matrix feature can be handled through the MINC module; however, at considerable cost....

258

Model predictive control system and method for integrated gasification combined cycle power generation  

SciTech Connect (OSTI)

Control system and method for controlling an integrated gasification combined cycle (IGCC) plant are provided. The system may include a controller coupled to a dynamic model of the plant to process a prediction of plant performance and determine a control strategy for the IGCC plant over a time horizon subject to plant constraints. The control strategy may include control functionality to meet a tracking objective and control functionality to meet an optimization objective. The control strategy may be configured to prioritize the tracking objective over the optimization objective based on a coordinate transformation, such as an orthogonal or quasi-orthogonal projection. A plurality of plant control knobs may be set in accordance with the control strategy to generate a sequence of coordinated multivariable control inputs to meet the tracking objective and the optimization objective subject to the prioritization resulting from the coordinate transformation.

Kumar, Aditya; Shi, Ruijie; Kumar, Rajeeva; Dokucu, Mustafa

2013-04-09T23:59:59.000Z

259

Validating agent based models through virtual worlds.  

SciTech Connect (OSTI)

As the US continues its vigilance against distributed, embedded threats, understanding the political and social structure of these groups becomes paramount for predicting and dis- rupting their attacks. Agent-based models (ABMs) serve as a powerful tool to study these groups. While the popularity of social network tools (e.g., Facebook, Twitter) has provided extensive communication data, there is a lack of ne-grained behavioral data with which to inform and validate existing ABMs. Virtual worlds, in particular massively multiplayer online games (MMOG), where large numbers of people interact within a complex environ- ment for long periods of time provide an alternative source of data. These environments provide a rich social environment where players engage in a variety of activities observed between real-world groups: collaborating and/or competing with other groups, conducting battles for scarce resources, and trading in a market economy. Strategies employed by player groups surprisingly re ect those seen in present-day con icts, where players use diplomacy or espionage as their means for accomplishing their goals. In this project, we propose to address the need for ne-grained behavioral data by acquiring and analyzing game data a commercial MMOG, referred to within this report as Game X. The goals of this research were: (1) devising toolsets for analyzing virtual world data to better inform the rules that govern a social ABM and (2) exploring how virtual worlds could serve as a source of data to validate ABMs established for analogous real-world phenomena. During this research, we studied certain patterns of group behavior to compliment social modeling e orts where a signi cant lack of detailed examples of observed phenomena exists. This report outlines our work examining group behaviors that underly what we have termed the Expression-To-Action (E2A) problem: determining the changes in social contact that lead individuals/groups to engage in a particular behavior. Results from our work indicate that virtual worlds have the potential for serving as a proxy in allocating and populating behaviors that would be used within further agent-based modeling studies.

Lakkaraju, Kiran; Whetzel, Jonathan H.; Lee, Jina [Sandia National Laboratories, Livermore, CA] [Sandia National Laboratories, Livermore, CA; Bier, Asmeret Brooke; Cardona-Rivera, Rogelio E. [North Carolina State University, Raleigh, NC] [North Carolina State University, Raleigh, NC; Bernstein, Jeremy Ray Rhythm [Gaikai, Inc., Aliso Viejo, CA] [Gaikai, Inc., Aliso Viejo, CA

2014-01-01T23:59:59.000Z

260

Sensors and Actuators B 106 (2005) 122127 Eulerian-Lagrangian model for predicting odor dispersion using  

E-Print Network [OSTI]

mills. This paper de- scribes a paradigm for predicting the trajectory of odorous emissions from a CAFO: sss@acpub.duke.edu (S.S. Schiffman). long-distance dispersal of seeds by wind [1]. It is based

Katul, Gabriel

Note: This page contains sample records for the topic "model based predictive" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


261

Direct comparison of Neural Network, Fuzzy Logic and Model Prediction Variable Structure vortex flow controllers  

E-Print Network [OSTI]

Predictive Variable Structure and Fuzzy Logic based controllers for the same benchmark problem. Evaluation criteria consist of closed-loop system performance, activity level of the VFC nozzles, ease of controller synthesis, time required to synthesize...

Joshi, Praveen Sudhakar

2012-06-07T23:59:59.000Z

262

Model predictive control with application to real-time hardware and guided parafoil  

E-Print Network [OSTI]

Model Predictive Control (MPC) is a control strategy that is suitable for optimizing the performance of constrained systems. Constraints are present in all control systems due to the physical and environmental limits on ...

Alaniz, Abran, 1980-

2004-01-01T23:59:59.000Z

263

Model predictive controller design for the dynamic positioning system of a semi-submersible platform  

Science Journals Connector (OSTI)

This paper researches how to apply the advanced control technology of model predictive control (MPC) to the design of the dynamic positioning system (DPS) of a semi-submersible platform. First, a linear low-frequ...

Hongli Chen; Lei Wan; Fang Wang…

2012-09-01T23:59:59.000Z

264

Use of Rough Sets and Spectral Data for Building Predictive Models of Reaction Rate Constants  

Science Journals Connector (OSTI)

A model for predicting the log of the rate constants for alkaline hydrolysis of organic esters has been developed with the use of gas-phase mid-infrared library spectra and a...

Collette, Timothy W; Szladow, Adam J

1994-01-01T23:59:59.000Z

265

Comparisons of Transport and Dispersion Model Predictions of the URBAN 2000 Field Experiment  

Science Journals Connector (OSTI)

The tracer releases of the “URBAN 2000” urban tracer and meteorological field experiment conducted in Salt Lake City, Utah, in October 2000 provided a wealth of data for comparison with the predictions of transport and dispersion models. ...

Steve Warner; Nathan Platt; James F. Heagy

2004-06-01T23:59:59.000Z

266

Evaluation of Precipitation from Numerical Weather Prediction Models and Satellites Using Values Retrieved from Radars  

Science Journals Connector (OSTI)

Precipitation is evaluated from two weather prediction models and satellites, taking radar-retrieved values as a reference. The domain is over the central and eastern United States, with hourly accumulated precipitation over 21 days for the ...

Slavko Vasi?; Charles A. Lin; Isztar Zawadzki; Olivier Bousquet; Diane Chaumont

2007-11-01T23:59:59.000Z

267

Artificial Neural Networks and Hidden Markov Models for Predicting the Protein Structures: The Secondary Structure  

E-Print Network [OSTI]

1 Artificial Neural Networks and Hidden Markov Models for Predicting the Protein Structures advice on the development of this project #12;2 Artificial Neural Networks and Hidden Markov Models learning methods: artificial neural networks (ANN) and hidden Markov models (HMM) (Rost 2002; Karplus et al

268

Ventilation performance prediction for buildings: Model Assessment Qingyan Chena,b,*  

E-Print Network [OSTI]

1 Ventilation performance prediction for buildings: Model Assessment Qingyan Chena,b,* , Kisup Leeb building, but cannot provide detailed flow information in a room. The zonal model can be useful when a user ventilation systems for buildings requires a suitable model to assess system performance. The performance can

Chen, Qingyan "Yan"

269

Flow Control of Real Time Multimedia Applications Using Model Predictive Control with a Feed Forward Term  

E-Print Network [OSTI]

................................................................... 20 3.2 ARX model structure ................................................................................. 21 3.3 One-step-ahead prediction of accumulation signal in 3% CLR network using ARX model designed by Bhattacharya[9...] ........................... 27 3.4 One-step-ahead estimation of accumulation signal in 3% CLR network using the proposed ARX model. ......................................... 28 4.1 Schematic of MPC strategy...

Duong, Thien Chi

2011-02-22T23:59:59.000Z

270

Modeling, Analysis, Predictions, and Projections Email: oar.cpo.mapp@noaa.gov  

E-Print Network [OSTI]

Earth system models to better simulate the climate system? Can we improve intraseasonal to seasonal mission, MAPP supports the development of advanced Earth system models that can predict climate variations, and the external research community. MAPP Objectives · Improve Earth system models · Achieve an integrated Earth

271

Building a predictive model of indoor concentrations of outdoor PM-2.5 for  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Building a predictive model of indoor concentrations of outdoor PM-2.5 for Building a predictive model of indoor concentrations of outdoor PM-2.5 for a residential research house in Clovis, California Title Building a predictive model of indoor concentrations of outdoor PM-2.5 for a residential research house in Clovis, California Publication Type Report Year of Publication 2002 Authors Fischer, Marc L., Melissa M. Lunden, Tracy L. Thatcher, David Littlejohn, Thomas W. Kirchstetter, Susanne V. Hering, Richard G. Sextro, and Nancy J. Brown Abstract The prevalence of relocatable classrooms (RCs) at schools is rising due to federal and state initiatives to reduce K-3 class size, and limited capital resources. Concerns regarding inadequate ventilation and indoor air and environmental quality (IEQ) in RCs have been raised. Adequate ventilation is an important link between improved IEQ and energy efficiency for schools. Since students and teachers spend the majority of a 7-8 hour school day inside classrooms, indoor contaminant concentrations are assumed to drive personal school-day exposures. We conducted a demonstration project in new relocatable classrooms (RCs) during the 2001-02 school year to address these issues. Four new 24' x 40' (960 ft2) RCs were constructed and sited in pairs at an elementary school campus in each of two participant school districts (SD) in Northern California. Each RC was equipped with two heating, ventilation, and air conditioning (HVAC) systems, one per module. The two HVAC systems were a standard heat pump with intermittent 25-50% outdoor air ventilation and an energy-efficient advanced system, based on indirect-direct evaporative cooling with an integrated natural gas-fired hydronic heating loop and improved particle filtration, providing continuous 100% outdoor air ventilation at = 15 ft3 min-1 occupant-1. Alternate carpets, wall panels, and ceiling panels were installed in two classrooms -- one in each pair -- based on the results of a laboratory study of VOC emissions from standard and alternate materials. Numerous IEQ and outdoor air quality and meteorological parameters were measured either continuously over the school year or as integrated school day samples during the fall cooling and winter heating seasons. Details of the RC designs, the field monitoring methodology including handling, storage, transport and management of chemical samples and data, and analyses to be conducted are presented

272

A mathematical model to predict leaching of hazardous inorganic wastes from solidified/stabilized waste forms  

E-Print Network [OSTI]

A MATHEMATICAL MODEL TO PREDICT LEACHING OF HAZARDOUS INORGANIC WASTES FROM SOLIDIFIED/STABILIZED WASTE FORMS A Thesis by KRISHAN SABHARWAL Submitted to the Office of Graduate Studies of Texas AkM University in partial fulfillment...A MATHEMATICAL MODEL TO PREDICT LEACHING OF HAZARDOUS INORGANIC WASTES FROM SOLIDIFIED/STABILIZED WASTE FORMS A Thesis by KRISHAN SABHARWAL Submitted to the Office of Graduate Studies of Texas AkM University in partial fulfillment...

Sabharwal, Krishan

2012-06-07T23:59:59.000Z

273

ECG Compression Algorithms Comparisons among EZW, Modified EZW and Wavelet Based Linear Prediction  

E-Print Network [OSTI]

ECG Compression Algorithms Comparisons among EZW, Modified EZW and Wavelet Based Linear Prediction 74 6 Recommendation for Future Research 78 List of References 79 Appendices 81 Appendix 1 ECG Signal.............................................87 #12;iv List of Tables 2.1 Variance Comparisons (ECG 16265

Fowler, Mark

274

A Regression-Based Approach for Improving the Association Rule Mining through Predicting  

E-Print Network [OSTI]

rules for credit evaluation in the domain of farmers'credit his- tory. Li et al. [6] specifically of Rules on General Datasets Dien Tuan Le, Fenghui Ren, and Minjie Zhang School of Computer Science to create concrete models in particular domains for predicting the potential number of association rules

Zhang, Minjie

275

Ensemble climate predictions using climate models and observational constraints  

Science Journals Connector (OSTI)

...near-surface temperatures, the estimated...determined the distribution of scaling factors...simulations of future temperature change on the assumption...output from the Sun and from explosive...pattern of future temperature change being based...the probability distribution of beta i , obtained...

2007-01-01T23:59:59.000Z

276

Lurking Pathway Prediction And Pathway ODE Model Dynamic Analysis  

E-Print Network [OSTI]

regulated proteins in the transduction pro- cess. And by modeling the CCL2 pathway in MTB infected cells, J N K , cM Y C and P LC showed as the most significant modules. Hence, the drug treatments inhibit- ing J N K , cM Y C and P LC would effectively...

Zhang, Rengjing

2013-11-18T23:59:59.000Z

277

Should we believe model predictions of future climate change?  

E-Print Network [OSTI]

equations are derived from first principles (e.g. equations of motion, and conservation of energy, mass deficiencies in the attempt to provide useful information to the public and policy-makers. Keywords: climate to communicate what we know and what is uncertain about future climate change? Why are climate model projections

Fischlin, Andreas

278

Predictions from an Ising-like Statistical Mechanical Model on the Dynamic and Thermodynamic Effects of Protein Surface Electrostatics  

Science Journals Connector (OSTI)

Predictions from an Ising-like Statistical Mechanical Model on the Dynamic and Thermodynamic Effects of Protein Surface Electrostatics ...

Athi N. Naganathan

2012-10-05T23:59:59.000Z

279

Mechanism-based Representative Volume Elements (RVEs) for Predicting Property Degradations in Multiphase Materials  

SciTech Connect (OSTI)

Quantitative understanding of the evolving thermal-mechanical properties of a multi-phase material hinges upon the availability of quantitative statistically representative microstructure descriptions. Questions then arise as to whether a two-dimensional (2D) or a three-dimensional (3D) representative volume element (RVE) should be considered as the statistically representative microstructure. Although 3D models are more representative than 2D models in general, they are usually computationally expensive and difficult to be reconstructed. In this paper, we evaluate the accuracy of a 2D RVE in predicting the property degradations induced by different degradation mechanisms with the multiphase solid oxide fuel cell (SOFC) anode material as an example. Both 2D and 3D microstructure RVEs of the anodes are adopted to quantify the effects of two different degradation mechanisms: humidity-induced electrochemical degradation and phosphorus poisoning induced structural degradation. The predictions of the 2D model are then compared with the available experimental measurements and the results from the 3D model. It is found that the 2D model, limited by its inability of reproducing the realistic electrical percolation, is unable to accurately predict the degradation of thermo-electrical properties. On the other hand, for the phosphorus poisoning induced structural degradation, both 2D and 3D microstructures yield similar results, indicating that the 2D model is capable of providing computationally efficient yet accurate results for studying the structural degradation within the anodes.

Xu, Wei; Sun, Xin; Li, Dongsheng; Ryu, Seun; Khaleel, Mohammad A.

2013-02-01T23:59:59.000Z

280

Advanced Models and Controls for Prediction and Extension of Battery Lifetime (Presentation)  

SciTech Connect (OSTI)

Predictive models of capacity and power fade must consider a multiplicity of degradation modes experienced by Li-ion batteries in the automotive environment. Lacking accurate models and tests, lifetime uncertainty must presently be absorbed by overdesign and excess warranty costs. To reduce these costs and extend life, degradation models are under development that predict lifetime more accurately and with less test data. The lifetime models provide engineering feedback for cell, pack and system designs and are being incorporated into real-time control strategies.

Smith, K.; Wood, E.; Santhanagopalan, S.; Kim, G.; Pesaran, A.

2014-02-01T23:59:59.000Z

Note: This page contains sample records for the topic "model based predictive" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


281

A soil moisture availability model for crop stress prediction  

E-Print Network [OSTI]

is composed of three major components, which are, a) calcul ation of evapotranspiration, b) infiltration of moisture into the soil, c) redistribution of the soil moisture. Other edaphic models have been developed by Hill [1974], Bai er and Robertson... inputs could result in the development of moist layers in the lower soil layer that would not be accounted for if the moisture were uniformly redistributed. As the cycle progesses, redistribution and moisture depletion do occur, until there 1s less...

Gay, Roger Franklin

1983-01-01T23:59:59.000Z

282

Prediction and computation of phase equilibria in polar and polarizable mixtures using theory-based equations of state.  

E-Print Network [OSTI]

??The purpose of this dissertation is to contribute to the development of the predictive theory-based equation of state. The specific objectives are threefold: first, to… (more)

Al-Saifi, Nayef Masned

2011-01-01T23:59:59.000Z

283

NONLINEAR MPC BASED ON MULTI-MODEL FOR DISTILLATION COLUMNS  

E-Print Network [OSTI]

-estimation and prediction in a MPC scheme. The controller has been applied to quality control of a FCCU fractionator IFAC Keywords: Nonlinear Model Predictive Control, Multi-Model, FCCU fractionator 1. INTRDUCTION Model is investigated through simulation of a rigorous model of a typical refining unit, a FCCU (Fluidized Catalytic

Foss, Bjarne A.

284

Ranking Docked Models of Protein-Protein Complexes Using Predicted Partner-Specific Protein-Protein  

E-Print Network [OSTI]

-Azhar University, Cairo, Egypt {lixue, rjordan, yasser, ddobbs, honavar}@iastate.edu ABSTRACT Computational protein-size-based and energy-based criteria for 61 out of the 64 docking complexes for which PS-HomPPI produces interface efficiency of docking) [2]. In this study, we test whether knowledge of predicted interface residues can also

Honavar, Vasant

285

Intercomparison of Single-Column Numerical Models for the Prediction of Radiation Fog  

E-Print Network [OSTI]

layers of the atmosphere. Current NWP models poorly forecast the life cycle of fog, and improved NWP models exist in the surface boundary layer before the fog onset, particularly in cases with light winds before improving the analysis and prediction of fog (e.g., Benjamin et al. 2004; Fowler et al. 2006

Ribes, Aurélien

286

CROSS SHORE SANDBAR MIGRATION PREDICTED BY A TIME DOMAIN BOUSSINESQ MODEL INCORPORATING  

E-Print Network [OSTI]

CROSS SHORE SANDBAR MIGRATION PREDICTED BY A TIME DOMAIN BOUSSINESQ MODEL INCORPORATING UNDERTOW Wen Long1 , James T. Kirby2 and T.-J. Hsu3 An existing Boussinesq wave model is modified and erosional cross-shore sediment transport processes. INTRODUCTION Long and Kirby (2003) have used Boussinesq

Kirby, James T.

287

A simplified approach to quantifying predictive and parametric uncertainty in artificial neural network hydrologic models  

E-Print Network [OSTI]

considerable interest in developing methods for uncertainty analysis of artificial neural network (ANN) models and parametric uncertainty in artificial neural network hydrologic models, Water Resour. Res., 43, W10407, doi:10A simplified approach to quantifying predictive and parametric uncertainty in artificial neural

Chaubey, Indrajeet

288

User-click Modeling for Understanding and Predicting Search-behavior  

E-Print Network [OSTI]

. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: General Terms AlgorithmsUser-click Modeling for Understanding and Predicting Search-behavior Yuchen Zhang1 , Weizhu Chen1 advances in search users' click modeling consider both users' search queries and click/skip behavior

Yang, Qiang

289

A Model for Predicting Daily Peak Visitation and Implications for Recreation Management and Water Quality: Evidence  

E-Print Network [OSTI]

A Model for Predicting Daily Peak Visitation and Implications for Recreation Management and Water carrying capacity. Keywords Visitation model Á Recreation management Á Water quality Á River visitation Á Clark, Fort Collins, Colorado 80523, USA 123 Environmental Management DOI 10.1007/s00267-008-9079-5 #12

290

Critical Fracture Stress and Fracture Strain Models for the Prediction of Lower and  

E-Print Network [OSTI]

Critical Fracture Stress and Fracture Strain Models for the Prediction of Lower and Upper Shelf fracture stress and stress modified fracture strain models are utilized to describe the variation of lower and upper shelf fracture toughness with temperature and strain rate for two alloy steels used

Ritchie, Robert

291

Prediction of oxy-coal flame stand-off using high-fidelity thermochemical models  

E-Print Network [OSTI]

Prediction of oxy-coal flame stand-off using high-fidelity thermochemical models and the one Abstract An Eulerian one-dimensional turbulence (ODT) model is applied to simulate oxy-coal combustion temperature and mixing rate on oxy-coal flame is simulated and discussed where flame stand-off is used

292

Atomistic Modeling of Macromolecular Crowding Predicts Modest Increases in Protein Folding and Binding Stability  

E-Print Network [OSTI]

Atomistic Modeling of Macromolecular Crowding Predicts Modest Increases in Protein Folding that macromolecular crowding can increase protein folding stability, but depending on details of the models (e.g., how on the effects of macro- molecular crowding on protein folding and binding stability has been reached. Crowders

Weston, Ken

293

An Efficient Genetic Algorithm for Predicting Protein Tertiary Structures in the 2D HP Model  

E-Print Network [OSTI]

, predicting its tertiary structure is known as the protein folding problem. This problem has been widely genetic algo- rithm for the protein folding problem under the HP model in the two-dimensional square Genetic Algorithm, Protein Folding Problem, 2D HP Model 1. INTRODUCTION Amino acids are the building

Istrail, Sorin

294

Comparison of the predictions of two models with dose measurements in a  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Comparison of the predictions of two models with dose measurements in a Comparison of the predictions of two models with dose measurements in a case of children exposed to the emissions of a lead smelter Title Comparison of the predictions of two models with dose measurements in a case of children exposed to the emissions of a lead smelter Publication Type Journal Article LBNL Report Number LBNL-2397E Year of Publication 2009 Authors Bonnard, Roseline, and Thomas E. McKone Journal Journal of Human and Environmental Risk Assessment Volume 15 Issue 6 Pagination 1203-1226 ISSN 1080-7039 Keywords environmental chemistry, exposure & risk group, exposure and health effects, exposure assessment, ieubk, indoor environment department, lead, multimedia models, probabilistic risk assessment Abstract The predictions of two source-to-dose models are systematically evaluated with observed data collected in a village polluted by a currently operating secondary lead smelter. Both models were built up from several sub-models linked together and run using Monte-Carlo simulation, to calculate the distribution children's blood lead levels attributable to the emissions from the facility. The first model system is composed of the CalTOX model linked to a recoded version of the IEUBK model. This system provides the distribution of the media-specific lead concentrations (air, soil, fruit, vegetables and blood) in the whole area investigated. The second model consists of a statistical model to estimate the lead deposition on the ground, a modified version of the model HHRAP and the same recoded version of the IEUBK model. This system provides an estimate of the concentration of exposure of specific individuals living in the study area. The predictions of the first model system were improvedin terms of accuracy and precision by performing a sensitivity analysis and using field data to correct the default value provided for the leaf wet density. However, in this case study, the first model system tends to overestimate the exposure due to exposed vegetables. The second model was tested for nine children with contrasting exposure conditions. It managed to capture the blood levels for eight of them. In the last case, the exposure of the child by pathways not considered in the model may explain the failure of the model. The interest of this integrated model is to provide outputs with lower variance than the first model system, but at the moment further tests are necessary to conclude about its accuracy.

295

NIST Technical Note 1753 Model Based Enterprise /  

E-Print Network [OSTI]

Lubell Kenway Chen John Horst Simon Frechette Paul Huang http://dx.doi.org/10.6028/NIST.TN.1753 #12;NIST Technical Note 1753 Model Based Enterprise / Technical Data Package Summit Report Joshua Lubell Kenway Chen

Perkins, Richard A.

296

Extension of NORSOK CO2 corrosion prediction model for elbow geometry  

Science Journals Connector (OSTI)

Internal corrosion of flowlines and pipelines is inevitable when transporting oil and gas that contains corrosive species. The consequences of corrosion such as material failure, loss of production, plant shutdown, and environmental pollution result in extra cost that negatively affect the project economics. Early prediction of corrosion severity is, therefore, very important to propose proper measures to avoid or eliminate corrosion. The prediction is normally carried on using a selected model for corrosion prediction. One of these models is NORSOK model, an empirical model developed by NORSOK for CO2 corrosion prediction in straight pipes. Norsk Sokkels Konkuranseposisjon or, in English, The Competitive Standing of the Norwegian Offshore Sector (NORSOK) is number of standards developed by Norwegian industry groups covering different topics that related to offshore industry. In this paper, NORSOK model has been modified to make it applicable to elbows geometries by introducing the equivalent length concept. A friendly graphical user interface computational package is developed for corrosion prediction in both straight pipes and elbows. The package is validated against measured data and acceptable accuracy is attained.

Mysara Eissa Mohyaldin; Noaman Elkhatib; Mokhtar Che Ismail

2013-01-01T23:59:59.000Z

297

Artificial Neural Network Meta Models To Enhance the Prediction and Consistency of Multiphase Reactor Correlations  

Science Journals Connector (OSTI)

Artificial Neural Network Meta Models To Enhance the Prediction and Consistency of Multiphase Reactor Correlations ... Artificial neural networks (ANNs), as correlation tools, have gained wide acceptance in the field because of their inherent ability to map nonlinear relationships that tie up independent variables (either as dimensional inputs, e.g., pressure, diameter, etc., or as dimensionless inputs, e.g., Reynolds, Weber, and Froude numbers, etc.) to the reactor characteristics to be predicted, i.e., dimensional or dimensionless output. ...

Laurentiu A. Tarca; Bernard P. A. Grandjean; Faïçal Larachi

2003-03-19T23:59:59.000Z

298

A computer-based total productive maintenance model for electric motors  

Science Journals Connector (OSTI)

The paper describes the development of a computer-based total productive maintenance (TPM) model to improve electrical motors readiness and uptime while reducing capital overhead. The TPM model includes the consideration of reactive, periodic, and predictive practices. The input data is processed and the generated report details a set of periodic recommendations providing guidelines on recommended actions and their frequency. The details about test results indicating the current condition of the motor as well estimated reactive, periodic, and predictive maintenance cost details are presented. Based on the historic data stored in its database, the model can predict potential problems prior to failure as well as prescribe periodic maintenance actions to maximise motor life. The TPM model will be a useful tool to predict the degradation in motor life due to deterioration in insulation, bearing, rotor bar and stator windings of the motor.

Aruna Muniswamy; Bhaskaran Gopalakrishnan; Subodh Chaudhari; Majid Jaridi; Ed Crowe; Deepak Gupta

2014-01-01T23:59:59.000Z

299

Estimation of the mean depth of boreal lakes for use in numerical weather prediction and climate modelling  

E-Print Network [OSTI]

in the numerical weather prediction (NWP) and climate models through parameterisation. For parameterisation, data. The effect of lakes should be parameterised in numerical weather prediction (NWP) and climate modellingEstimation of the mean depth of boreal lakes for use in numerical weather prediction and climate

Paris-Sud XI, Université de

300

Individual-based modeling of fish: Linking to physical models and water quality.  

SciTech Connect (OSTI)

The individual-based modeling approach for the simulating fish population and community dynamics is gaining popularity. Individual-based modeling has been used in many other fields, such as forest succession and astronomy. The popularity of the individual-based approach is partly a result of the lack of success of the more aggregate modeling approaches traditionally used for simulating fish population and community dynamics. Also, recent recognition that it is often the atypical individual that survives has fostered interest in the individual-based approach. Two general types of individual-based models are distribution and configuration. Distribution models follow the probability distributions of individual characteristics, such as length and age. Configuration models explicitly simulate each individual; the sum over individuals being the population. DeAngelis et al (1992) showed that, when distribution and configuration models were formulated from the same common pool of information, both approaches generated similar predictions. The distribution approach was more compact and general, while the configuration approach was more flexible. Simple biological changes, such as making growth rate dependent on previous days growth rates, were easy to implement in the configuration version but prevented simple analytical solution of the distribution version.

Rose, K.A.

1997-08-01T23:59:59.000Z

Note: This page contains sample records for the topic "model based predictive" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


301

Predictive Theory and Modeling| U.S. DOE Office of Science (SC)  

Office of Science (SC) Website

Predictive Theory and Modeling Predictive Theory and Modeling Basic Energy Sciences (BES) BES Home About Research Facilities Science Highlights Benefits of BES Funding Opportunities Closed Funding Opportunity Announcements (FOAs) Closed Lab Announcements Award Search Peer Review Policies EFRCs FOA Applications from Universities and Other Research Institutions Construction Review EPSCoR DOE Office of Science Graduate Fellowship (DOE SCGF) External link Early Career Research Program Basic Energy Sciences Advisory Committee (BESAC) News & Resources Contact Information Basic Energy Sciences U.S. Department of Energy SC-22/Germantown Building 1000 Independence Ave., SW Washington, DC 20585 P: (301) 903-3081 F: (301) 903-6594 E: sc.bes@science.doe.gov More Information » Funding Opportunities Predictive Theory and Modeling

302

A predictive model for the combustion process in dual fuel engines  

SciTech Connect (OSTI)

A multi-zone model has been developed for the prediction of the combustion processes in dual fuel engines and some of their performance features. The consequences of the interaction between the gaseous and the diesel fuels and the resulting modification to the combustion processes are considered. A reacting zone has been incorporated in the model to describe the partial oxidation of the gaseous fuel-air mixture while detailed kinetic schemes are employed to describe the oxidation of the gaseous fuel, right from the start of compression to the end of the expansion process. The associated formation and concentrations of exhaust emissions are correspondingly established. The model can predict the onset of knock as well as the operating features and emissions for the more demanding case of light load performance. Predicted values for methane operation show good agreement with corresponding experimental values.

Liu, Z.; Karim, G.A. [Univ. of Calgary, Alberta (Canada)

1995-12-31T23:59:59.000Z

303

Handling model uncertainty in model predictive control for energy efficient buildings  

E-Print Network [OSTI]

to apply to other building modeling practices. 2. Wemodel 3.1. Mathematical modeling Building models proposed inMore details of building thermal modeling and estimation of

Maasoumy, Mehdi; Razmara, M; Shahbakhti, M; Sangiovanni-Vincentelli, Alberto

2014-01-01T23:59:59.000Z

304

Comparison of model predicted to observed winds in the coastal zone  

SciTech Connect (OSTI)

Predictions of near-surface (10 to 100 m) wind velocities made by a mesoscale numerical model on a 10 km grid over and near the coastline are checked against observations. Two comparisons are made. The first is between observed and model-estimated mean annual wind power density at locations where surface observations exist in three coastal areas: the Chesapeake Bay, the Apalachee Bay and the South Texas coastal area. The second comparison is made between model predictions over the Delmarva Peninsula and adjacent ocean and observations made over a 120 x 30 km rectangle extending across the peninsula and out to sea. It is concluded that the unbiased error analysis skill ratings of 81% and 76% are attained for two days of prediction-observation comparisons. In the meantime, the skill of the model in duplicating individual coastal wind fields is taken as 78%. In addition, a qualitative comparison is made between the predicted fields of wind and the observed wind field. The predicted wind field unquestionably reproduces the observed field.

Garstang, M.; Pielke, R.A.; Snow, J.W.

1982-06-01T23:59:59.000Z

305

Model-Based Sampling and Inference  

U.S. Energy Information Administration (EIA) Indexed Site

Model-Based Sampling, Inference and Imputation Model-Based Sampling, Inference and Imputation James R. Knaub, Jr., Energy Information Administration, EI-53.1 James.Knaub@eia.doe.gov Key Words: Survey statistics, Randomization, Conditionality, Random sampling, Cutoff sampling Abstract: Picking a sample through some randomization mechanism, such as random sampling within groups (stratified random sampling), or, say, sampling every fifth item (systematic random sampling), may be familiar to a lot of people. These are design-based samples. Estimates of means and totals for an entire population may be inferred from such a sample, along with estimation of the amount of error that might be expected. However, inference based on a sample and its (modeled) relationship to other data may be less familiar. If there is enough

306

Model-Based Testing : The Test of Formal Models  

E-Print Network [OSTI]

Model-Based Testing : The Test of Formal Models Jan Tretmans ESI & Radboud University Nijmegen #12;2 Testing (Software) Testing: checking or measuring some quality characteristics of an executing object by performing experiments in a controlled way w.r.t. a specification tester specification SUT System Under Test

307

Artificial Neural Network Estimator Design for the Inferential Model Predictive Control of an Industrial Distillation Column  

Science Journals Connector (OSTI)

The ANN architecture is a multilayer perceptron (MLP), which is a typical feed-forward (layered) neural network.2 A collection of neurons connected to each other forms the artificial neural network. ... It is shown that the how artificial neural networks can model the column, and demonstrated that the network model is as good or better than a simplified first principles model when used for model predictive control. ... A dynamic, nonlinear, multi-input multi-output application using the recurrent dynamic neuron network (RDNN) model is presented for a two-by-two distn. ...

Alm?la Bahar; Canan Özgen; Kemal Leblebicio?lu; U?ur Hal?c?

2004-08-12T23:59:59.000Z

308

Towards the Prediction of Decadal to Centennial Climate Processes in the Coupled Earth System Model  

SciTech Connect (OSTI)

In this proposal, we have made major advances in the understanding of decadal and long term climate variability. (a) We performed a systematic study of multidecadal climate variability in FOAM-LPJ and CCSM-T31, and are starting exploring decadal variability in the IPCC AR4 models. (b) We develop several novel methods for the assessment of climate feedbacks in the observation. (c) We also developed a new initialization scheme DAI (Dynamical Analogue Initialization) for ensemble decadal prediction. (d) We also studied climate-vegetation feedback in the observation and models. (e) Finally, we started a pilot program using Ensemble Kalman Filter in CGCM for decadal climate prediction.

Zhengyu Liu, J. E. Kutzbach, R. Jacob, C. Prentice

2011-12-05T23:59:59.000Z

309

COBRA-WC model and predictions for a fast-reactor natural-circulation transient. [LMFBR  

SciTech Connect (OSTI)

The COBRA-WC (Whole Core) code has been used to predict the core-wide coolant and rod temperature distribution in a liquid metal fast reactor during the early part (first 220 seconds) of a natural circulation transient. Approximately one-sixth of the core was modeled including bypass flows and the pressure losses above and below the core region. Detailed temperature and flow distributions were obtained for the two test fuel assemblies. The COBRA-WC model, the approach, and predictions of core-wide transient coolant and rod temperatures during a natural circulation transient are presented in this paper.

George, T.L.; Basehore, K.L.; Prather, W.A.

1980-01-01T23:59:59.000Z

310

Microinstability-based model for anomalous thermal confinement in tokamaks  

SciTech Connect (OSTI)

This paper deals with the formulation of microinstability-based thermal transport coefficients (chi/sub j/) for the purpose of modelling anomalous energy confinement properties in tokamak plasmas. Attention is primarily focused on ohmically heated discharges and the associated anomalous electron thermal transport. An appropriate expression for chi/sub e/ is developed which is consistent with reasonable global constraints on the current and electron temperature profiles as well as with the key properties of the kinetic instabilities most likely to be present. Comparisons of confinement scaling trends predicted by this model with the empirical ohmic data base indicate quite favorable agreement. The subject of anomalous ion thermal transport and its implications for high density ohmic discharges and for auxiliary-heated plasmas is also addressed.

Tang, W.M.

1986-03-01T23:59:59.000Z

311

Final Report Coupling in silico microbial models with reactive transport models to predict the fate of contaminants in the subsurface.  

SciTech Connect (OSTI)

This project successfully accomplished its goal of coupling genome-scale metabolic models with hydrological and geochemical models to predict the activity of subsurface microorganisms during uranium bioremediation. Furthermore, it was demonstrated how this modeling approach can be used to develop new strategies to optimize bioremediation. The approach of coupling genome-scale metabolic models with reactive transport modeling is now well enough established that it has been adopted by other DOE investigators studying uranium bioremediation. Furthermore, the basic principles developed during our studies will be applicable to much broader investigations of microbial activities, not only for other types of bioremediation, but microbial metabolism in diversity of environments. This approach has the potential to make an important contribution to predicting the impact of environmental perturbations on the cycling of carbon and other biogeochemical cycles.

Lovley, Derek R.

2012-10-31T23:59:59.000Z

312

Enhanced Prediction Techniques Based on Time-Accurate Simulations for Turbine Blade Internal Cooling  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Performance Computational Fluid-Thermal Science & Engineering Lab Performance Computational Fluid-Thermal Science & Engineering Lab utsr.dkt.oct05 Enhanced Prediction Techniques Based on Time-Accurate Simulations for Turbine Blade Internal Cooling Danesh Tafti SCIES Project 02- 01- SR100 DOE COOPERATIVE AGREEMENT DE-FC26-02NT41431 Tom J. George, Program Manager, DOE/NETL Richard Wenglarz, Manager of Research, SCIES Project Awarded (5/1/02, 36 Month Duration) $ 331,430 Total Contract Value ($331,430 DOE) High Performance Computational Fluid-Thermal Science & Engineering Lab utsr.dkt.oct05 Gas Turbine Need * Need for higher thermal efficiencies result in higher gas temperatures * Cooling technologies critical for increased durability * Reliable prediction tools for design - reduce costs High Performance Computational Fluid-Thermal Science & Engineering Lab

313

Subtask 2.4 - Integration and Synthesis in Climate Change Predictive Modeling  

SciTech Connect (OSTI)

The Energy & Environmental Research Center (EERC) completed a brief evaluation of the existing status of predictive modeling to assess options for integration of our previous paleohydrologic reconstructions and their synthesis with current global climate scenarios. Results of our research indicate that short-term data series available from modern instrumental records are not sufficient to reconstruct past hydrologic events or predict future ones. On the contrary, reconstruction of paleoclimate phenomena provided credible information on past climate cycles and confirmed their integration in the context of regional climate history is possible. Similarly to ice cores and other paleo proxies, acquired data represent an objective, credible tool for model calibration and validation of currently observed trends. It remains a subject of future research whether further refinement of our results and synthesis with regional and global climate observations could contribute to improvement and credibility of climate predictions on a regional and global scale.

Jaroslav Solc

2009-06-01T23:59:59.000Z

314

The impact of global nuclear mass model uncertainties on $r$-process abundance predictions  

E-Print Network [OSTI]

Rapid neutron capture or `$r$-process' nucleosynthesis may be responsible for half the production of heavy elements above iron on the periodic table. Masses are one of the most important nuclear physics ingredients that go into calculations of $r$-process nucleosynthesis as they enter into the calculations of reaction rates, decay rates, branching ratios and Q-values. We explore the impact of uncertainties in three nuclear mass models on $r$-process abundances by performing global monte carlo simulations. We show that root-mean-square (rms) errors of current mass models are large so that current $r$-process predictions are insufficient in predicting features found in solar residuals and in $r$-process enhanced metal poor stars. We conclude that the reduction of global rms errors below $100$ keV will allow for more robust $r$-process predictions.

Mumpower, M; Aprahamian, A

2014-01-01T23:59:59.000Z

315

Prediction of Ice Crystal Number in Community Atmospheric Model (CAM3.0)  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Prediction of Ice Crystal Number in Community Atmospheric Model (CAM3.0) Prediction of Ice Crystal Number in Community Atmospheric Model (CAM3.0) Liu, Xiaohong Pacific Northwest National Laboratory Ghan, Steven Pacific Northwest National Laboratory Wang, M University of Michigan Penner, Joyce University of Michigan Category: Modeling A prognostic equation of ice crystal number concentrations is implemented in the Community Atmospheric Model (CAM3.0) with the aim to study the aerosol effects on climate through changing the ice cloud properties. The microphysical processes affecting the ice number concentration include ice nucleation, secondary production of crystals, and the conversion of ice to snow. For ice nucleation process, Liu and Penner (2005) parameterization of homogeneous nucleation of sulfate and heterogeneous immersion nucleation on

316

Comparison of predictions of the Hybrid Plume Dispersion Model with observations at the Kincaid power plant  

Science Journals Connector (OSTI)

The Hybrid Plume Dispersion Model (HPDM) has been evaluated with observations from a field experiment at the Kincaid power plant. HPDM is a dispersion model for buoyant plumes that employs parameterisations of boundary-layer wind, temperature, and turbulence profiles and Lagrangian time-scales. The model accounts for the bimodal distribution of turbulent velocities in the convective boundary layer and contains an algorithm for calculating the lofting of a buoyant plume against a capping inversion. The model predictions of maximum plume centreline concentrations show a mean bias of less than l0%, a typical error that is about 50% of the mean, and a correlation of about 0.5.

S.R. Hanna; J.C. Chang

1995-01-01T23:59:59.000Z

317

Prediction of Solid Polycyclic Aromatic Hydrocarbons Solubility in Water with the NRTL-PR Model  

E-Print Network [OSTI]

processes of PAH with subcritical water [5,6] since it provides the extractability limit which can be used groups, for the representation of the solubility of solid PAH in subcritical water. These hal-00872639Prediction of Solid Polycyclic Aromatic Hydrocarbons Solubility in Water with the NRTL-PR Model

Paris-Sud XI, Université de

318

A probabilistic model to predict the formation and propagation of crack networks in thermal  

E-Print Network [OSTI]

. In the case of cooling systems in nuclear power plants, observations revealed the presence of thermal crazing loading even if thermal fatigue is multiaxial. However, the first simulations on a uniaxial mechanicalA probabilistic model to predict the formation and propagation of crack networks in thermal fatigue

319

A Novel Virtual Age Reliability Model for Time-to-Failure Prediction  

E-Print Network [OSTI]

counts, devices approaching physical feature size limits and nuclear plant comparable power densityA Novel Virtual Age Reliability Model for Time-to-Failure Prediction Yao Wang, Sorin Cotofana their relatively short operating lifetime. To overcome the MTTF weakness, this paper proposes a novel virtual age

Kuzmanov, Georgi

320

A Predictive Model of Bacterial Foraging by Means of Freely Released Extracellular Enzymes  

E-Print Network [OSTI]

A Predictive Model of Bacterial Foraging by Means of Freely Released Extracellular Enzymes Y T Extracellular enzymes are important agents for microbial foraging and material cycling in diverse natural immobile microbe, of freely releasing extracellular enzymes into a fluid- bathed, stable matrix of both

Jumars, Pete

Note: This page contains sample records for the topic "model based predictive" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


321

Discrepancies in the Prediction of Solar Wind using Potential Field Source Surface Model: An  

E-Print Network [OSTI]

expansion near the Sun and the solar wind speed observed at earth was first noted by Levine, AltschulerDiscrepancies in the Prediction of Solar Wind using Potential Field Source Surface Model between the magnetic flux tube expansion factor (FTE) at the source surface and the solar wind speed

Zhao, Xuepu

322

Pattern-oriented modelling: a ‘multi-scope’ for predictive systems ecology  

Science Journals Connector (OSTI)

...calibration methods [22]. 4. A mini-review The adoption of POM...explicitly refer to POM. This mini-review shows that POM can be...ecology than captured by our mini-review: many highly predictive...model in this field includes a grid of cells that can be empty or...

2012-01-01T23:59:59.000Z

323

Human Leg Model Predicts Ankle Muscle-Tendon Morphology, State, Roles and Energetics in Walking  

E-Print Network [OSTI]

Human Leg Model Predicts Ankle Muscle-Tendon Morphology, State, Roles and Energetics in Walking to be established. Here we develop a computational framework to address how the ankle joint actuation problem-tendon morphology and neural activations enable a metabolically optimal realization of biological ankle mechanics

Herr, Hugh

324

Predictive Inner-Outer Model for Turbulent Boundary Layers Applied to Hypersonic DNS Data  

E-Print Network [OSTI]

Predictive Inner-Outer Model for Turbulent Boundary Layers Applied to Hypersonic DNS Data Clara numerical simulation (DNS) data of supersonic and hypersonic turbulent boundaries with Mach 3 and Mach 7, and Martin12­14 on DNS of hypersonic turbulent boundary layers demonstrates the existence of large scale

Martín, Pino

325

A New Empirical Model for Predicting Single-Sided, Wind-Driven Natural Ventilation in Buildings  

E-Print Network [OSTI]

ventilation rate due to the pulsating flow and eddy penetration of single-sided, wind-driven natural Normal to the opening q Fluctuating flow rate e Eddy penetration Wang, H. and Chen, Q. 2012. "A new buildings. A new empirical model was developed that can predict the mean ventilation rate and fluctuating

Chen, Qingyan "Yan"

326

Randomized Model Predictive Control for HVAC Systems Alessandra Parisio, Damiano Varagnolo, Daniel Risberg,  

E-Print Network [OSTI]

Randomized Model Predictive Control for HVAC Systems Alessandra Parisio, Damiano Varagnolo, Daniel Conditioning (HVAC) sys- tems play a fundamental role in maintaining acceptable ther- mal comfort and Indoor. A possible solu- tion is to develop effective control strategies for HVAC sys- tems, but this is complicated

Johansson, Karl Henrik

327

Exploiting Two Intelligent Models to Predict Water Level: A field study of Urmia lake, Iran  

E-Print Network [OSTI]

Exploiting Two Intelligent Models to Predict Water Level: A field study of Urmia lake, Iran Shahab. The measurements from a single tide gauge at Urmia Lake, Northwest Iran, were used to train and validate the GP of Tabriz, Tabriz, Iran. Tel: 0098-411-3392786 Fax: 0098-411-3345332, (e-mail: sha- hab kvk66@yahoo

Fernandez, Thomas

328

A comparison of various models in predicting ignition delay in single-particle coal combustion  

E-Print Network [OSTI]

A comparison of various models in predicting ignition delay in single-particle coal combustion November 2013 Accepted 7 January 2014 Available online xxxx Keywords: Coal Devolatilization Ignition delay a b s t r a c t In this paper, individual coal particle combustion under laminar conditions

329

Model-predicted distribution of wind-induced internal wave energy in the world's oceans  

E-Print Network [OSTI]

Model-predicted distribution of wind-induced internal wave energy in the world's oceans Naoki 9 July 2008; published 30 September 2008. [1] The distribution of wind-induced internal wave energy-scaled kinetic energy are all consistent with the available observations in the regions of significant wind

Miami, University of

330

Supervisory hybrid model predictive control for voltage stability of power networks  

E-Print Network [OSTI]

on the predicted behavior of a model featuring hybrid dynamics of the loads and the generation system. I (via solar energy or wind energy installations) will start to feed electricity into the network [2 continuous dynamics and discrete events, i.e., power systems exhibit hybrid behavior. Components

Paris-Sud XI, Université de

331

Economic Nonlinear Model Predictive Control for the Optimization of Gas Pipeline Networks  

E-Print Network [OSTI]

Economic Nonlinear Model Predictive Control for the Optimization of Gas Pipeline Networks EWO University Oct 12, 2011 Ajit Gopalakrishnan (CMU) Economic NMPC for gas pipeline optimization Oct 12, 2011 1 Gopalakrishnan (CMU) Economic NMPC for gas pipeline optimization Oct 12, 2011 4 / 24 #12;Natural Gas Industry

Grossmann, Ignacio E.

332

Model Predictive Control for Starvation Prevention in a Hybrid Fuel Cell System1  

E-Print Network [OSTI]

voltage, a control system is necessary for maintaining optimal temperature, membrane humidity and pressure: Schematic of the fuel cell stack and air supply control sys- tem. The fuel cell stack consists of 350 cellsModel Predictive Control for Starvation Prevention in a Hybrid Fuel Cell System1 Ardalan Vahidi 2

Stefanopoulou, Anna

333

Dasatinib (BMS-354825) Pharmacokinetics and Pharmacodynamic Biomarkers in Animal Models Predict Optimal Clinical Exposure  

Science Journals Connector (OSTI)

...Bristol-Myers Squibb Company, Princeton, New Jersey The costs...inhibition correlated with the plasma levels of dasatinib in...inhibition correlated with the plasma levels of dasatinib in...modeling predicted that the plasma concentration of dasatinib...Bristol-Myers Squibb Company, Princeton, New Jersey 08543...

Feng R. Luo; Zheng Yang; Amy Camuso; Richard Smykla; Kelly McGlinchey; Krista Fager; Christine Flefleh; Stephen Castaneda; Ivan Inigo; David Kan; Mei-Li Wen; Robert Kramer; Anne Blackwood-Chirchir; and Francis Y. Lee

2006-12-01T23:59:59.000Z

334

Predicting Protein Folds with Structural Repeats Using a Chain Graph Model  

E-Print Network [OSTI]

Predicting Protein Folds with Structural Repeats Using a Chain Graph Model Yan Liu yanliu, Carnegie Mellon University, Pittsburgh, PA 15213 USA Abstract Protein fold recognition is a key step to to accurately identify protein folds aris- ing from typical spatial arrangements of well-defined secondary

Xing, Eric P.

335

Model Based Torque Control and Estimation for Common Rail Diesel Engine  

Science Journals Connector (OSTI)

A rapid control prototyping based on torque control algorithm using V-cycle mode for common rail diesel engine was developed, and a torque prediction model was present which including a feed-forward mean value engine model and a feedback correction of ... Keywords: common rail diesel engine, control strategies, torque control, torque estimation

Wang Hongrong; Wang Yongfu; Liu Zhi

2010-11-01T23:59:59.000Z

336

Bayesian modeling to unmask and predict influenza A/H1N1pdm dynamics in London  

Science Journals Connector (OSTI)

...unmask and predict influenza...Centre for Outbreak Analysis...age-structured model into a Bayesian...presence of any flu virus (SI...strength of any model used to predict or assess...class of models. Two separate...the swine-flu pandemic...responses to the outbreak: Results...

Paul J. Birrell; Georgios Ketsetzis; Nigel J. Gay; Ben S. Cooper; Anne M. Presanis; Ross J. Harris; André Charlett; Xu-Sheng Zhang; Peter J. White; Richard G. Pebody; Daniela De Angelis

2011-01-01T23:59:59.000Z

337

Model-Inspired Research. TES research uses modeling, prediction, and synthesis to identify  

E-Print Network [OSTI]

in Earth system models (ESMs). TES supports research to advance fundamental understanding of terrestrial-process models, ecosystem models, and the Community Earth System Model). This emphasis on the capture of advanced in Earth system models to increase the quality of climate model projections and to provide the scientific

338

Prediction of gas solubilities using the LCVM equation of state/excess Gibbs free energy model  

SciTech Connect (OSTI)

A recently developed EoS/G{sup E} model, the LCVM one, is applied to the prediction of Henry constants of nine gases (O{sub 2}, N{sub 2}, CO{sub 2}, CO, H{sub 2}S, CH{sub 4}, C{sub 2}H{sub 6}, C{sub 3}H{sub 8}, C{sub 4}H{sub 10}) in a wide range of pure and mixed solvents, including heavy hydrocarbons, polar solvents, and water. When not available, LCVM interaction parameters are estimated by correlating vapor-liquid equilibrium data. LCVM yields very good predictions for Henry constants in pure solvents, with typical errors less than 8%; it also performs very satisfactorily in the prediction of high-pressure vapor-liquid equilibria. On the other hand, widely-used EoS/G{sup E} models, such as PSRK and MHV2, are shown to result in progressively poorer behavior with increasing system asymmetry. Finally, LCVM predictions for Henry constants in mixed solvents are also satisfactory, especially when combined with the method of Catte et al. The results presented here, combined with those from previous ones, render LCVM a valuable tool for predicting vapor-liquid equilibria.

Apostolou, D.A.; Kalospiros, N.S.; Tassios, D.P. [National Technical Univ. of Athens, Zographos (Greece)

1995-03-01T23:59:59.000Z

339

Demand Response-Enabled Model Predictive HVAC Load Control in Buildings using Real-Time Electricity Pricing.  

E-Print Network [OSTI]

??A practical cost and energy efficient model predictive control (MPC) strategy is proposed for HVAC load control under dynamic real-time electricity pricing. The MPC strategy… (more)

Avci, Mesut

2013-01-01T23:59:59.000Z

340

Demonstrating and Validating a Next Generation Model-Based Controller...  

Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

and Validating a Next Generation Model-Based Controller for Fuel Efficient, Low Emissions Diesel Engines Demonstrating and Validating a Next Generation Model-Based Controller for...

Note: This page contains sample records for the topic "model based predictive" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


341

Why Do Continuum Gas-Solids Flow Models Predict Core-Annulus Flow?  

SciTech Connect (OSTI)

Core-annulus flow is an experimentally well established, industrially significant flow pattern of circulating fluidized beds. Several studies reported in the literature have shown that continuum gas-solids flow models are able to predict that flow pattern. But the crucial features of the model that give rise to the core-annulus flow structure have not been identified. To determine those features, we conduct transient simulations and analyze the results. Furthermore we time-average the results and investigate the formulation of time-averaged equations. We use transient, highly resolved, 1-D, grid-independent numerical solutions of a continuum model in this study. We show that the results could be even qualitatively incorrect (high solids concentration at the center of the channel) unless grid-independence is established. This explains why in certain coarse grid computations reported in the literature it was necessary to remove a dissipation term or to increase the particle size. Our simulations verify that the core-annulus structure arises in a time-averaged sense from unsteady gas-solids flow, as observed in experiments. We show that the key term that makes the flow unsteady is the dissipation term in the granular energy equation. Without that term the simulation yields a steady-state solution. The intuition based on steady-state solutions may not be valid. Unlike steady-state solutions, the transient solutions are not unduly sensitive to the restitution coefficient. The effect of restitution coefficient in transient simulations is remarkably different: a smaller restitution coefficient gives a higher average granular temperature. Both the micro-scale (clusters resolved) and meso-scale (clusters time-averaged) phenomena are important, unlike turbulent single-phase flows where the meso-scale (turbulent) stresses dominate. The prediction of core-annulus flow is strongly affected by the parameters used in the (micro-scale) wall boundary conditions; it is essential that the parameters are such that no granular energy is produced at the wall. The normal stress based on kinetic theory (Ps, micro) is an order of magnitude larger than normal stress arising from fluctuations (Ps, meso). Therefore, the granular temperature and solids fraction are approximately inversely correlated, just as shown by a steady-state analysis. However, the gradient of Ps, micro is of the same order of magnitude as the gradient of Ps, meso; those gradients adjust to ensure that the time averaged total Ps gradient in the radial direction is zero. The meso-scale shear stress is larger than the micro-scale shear stress. The meso-scale granular energy production term dominates the corresponding micro-scale term and must be included in time-averaged equations. That term is responsible for the maximum at the center in the granular temperature profile. The micro-scale granular energy production term is identically zero at the center because it is proportional to the gradient of solids velocity, which is zero at the center. The instantaneous gradient of solids velocity at the center, however, is not zero because of the down flow of clusters near the walls; it takes positive and negative values making the time-averaged velocity gradient exactly zero at the center. Therefore, the time-averaged square of the velocity gradient is non-zero at the center, which results in a production term in the time-averaged equations that is non-zero at the center. We find that the predictions are insensitive to the currently available k-å type turbulence models. The traditional k-å type models, based on the experience with single phase flow calculations, may not be adequate because meso-scale terms do not necessarily dominate the micro-scale terms. And certain parameters could behave counter to our intuition based on single phase flows: we compute and confirm with physical arguments that the gas-phase turbulent (meso-scale) viscosity could become negative.

Benyahia, S.; Syamlal, M.; O'Brien, T.J.

2006-11-01T23:59:59.000Z

342

Physics-Based Mathematical Models for Nanotechnology  

E-Print Network [OSTI]

Physics-Based Mathematical Models for Nanotechnology 2008 J. Phys.: Conf. Ser. 107, 011001, doi: 10 for their excellent support during the workshop. Nanotechnology is the study and application of phenomena at or below. This workshop put strong emphasis on discussions of the new mathematics needed in nanotechnology especially

Melnik, Roderick

343

An individual-based instream flow model for coexisting populations of brown and rainbow trout  

SciTech Connect (OSTI)

This report describes an individual-based model for sympatric populations of brown and rainbow trout in a stream habitat. Hatchery rainbow trout are included as a third species. The model provides a tool for predicting flow effects on trout populations by linking the hydraulic component of the Physical Habitat Simulation (PHABSIM) methodology and an individual-based population modeling approach. PHABSIM simulates the spatial distribution of depth and velocity at different flows. The individual-based model simulates the reproduction, foraging, consumption, energetic costs, growth, habitat utilization, movement, and mortality of individual fish, and enables population attributes to be determined from relevant attributes of individual fish. The spatially explicit nature of the model permits evaluation of behavioral responses used by fish to mitigate temporary setbacks in habitat quality. This linked mechanistic modeling approach readily lends itself to the iterative process of making predictions, testing against field data, improving the model, and making more predictions. The model has been applied to a stream segment in the Tule River, California. Physical and biological data from this site are used as input to the model. Calibrating the model to abundance data was relatively easy because values for mortality parameters were not strongly constrained by empirical data. Calibrating the model to observed growth rates and habitat use was more challenging. The primary reason for developing this model has been to provide a new and complementary tool to PHABSIM that can be used in instream-flow assessments.

Van Winkle, W.; Jager, H.I.; Holcomb, B.D.

1996-03-01T23:59:59.000Z

344

Modeling and performance prediction for water production in CBM wells of an Eastern India coalfield  

Science Journals Connector (OSTI)

Dewatering of coal bed methane (CBM) reservoirs is a very important part of methane production. Efficient production depends very much on the proper designing of the wells. In the present study, a comprehensive testing is conducted on 17 wells of a particular block in Eastern India and a general reservoir flow equation is modeled. Prediction of the water flow potential of a particular well using the derived flow equation helps in monitoring the variables of the artificial lift facility. The outcome of work can be used comprehensively to predict the future water and gas flow rates of simulated wells under the designed test.

A. Agarwal; A. Mandal; B. Karmakar; K. Ojha

2013-01-01T23:59:59.000Z

345

Regression Model Predicting Appraised Unit Value of Land in San Francisco County from Number of and Distance to Public Transit Stops using GIS  

E-Print Network [OSTI]

The objective of this study is to develop a quantifying model that predicts the appraised unit value of parcels in San Francisco County based on number of LEED-NC Public Transportation Access (PTA) qualified bus, light rail and commuter rail stops...

Son, Kiyoung

2012-07-16T23:59:59.000Z

346

Low Clouds Contribute to Weather Prediction Model Bias | U.S. DOE Office of  

Office of Science (SC) Website

2 2 » Low Clouds Contribute to Weather Prediction Model Bias Biological and Environmental Research (BER) BER Home About Research Facilities Science Highlights Searchable Archive of BER Highlights External link Benefits of BER Funding Opportunities Biological & Environmental Research Advisory Committee (BERAC) News & Resources Contact Information Biological and Environmental Research U.S. Department of Energy SC-23/Germantown Building 1000 Independence Ave., SW Washington, DC 20585 P: (301) 903-3251 F: (301) 903-5051 E: sc.ber@science.doe.gov More Information » November 2012 Low Clouds Contribute to Weather Prediction Model Bias Long-term measurement records improve the representation of clouds in climate and weather forecast models. Print Text Size: A A A Subscribe

347

A Model for Predicting Magnetic Targeting of Multifunctional Particles in the Microvasculature  

E-Print Network [OSTI]

A mathematical model is presented for predicting magnetic targeting of multifunctional carrier particles that are designed to deliver therapeutic agents to malignant tissue in vivo. These particles consist of a nonmagnetic core material that contains embedded magnetic nanoparticles and therapeutic agents such as photodynamic sensitizers. For in vivo therapy, the particles are injected into the vascular system upstream from malignant tissue, and captured at the tumor using an applied magnetic field. The applied field couples to the magnetic nanoparticles inside the carrier particle and produces a force that attracts the particle to the tumor. In noninvasive therapy the applied field is produced by a permanent magnet positioned outside the body. In this paper a mathematical model is developed for predicting noninvasive magnetic targeting of therapeutic carrier particles in the microvasculature. The model takes into account the dominant magnetic and fluidic forces on the particles and leads to an analytical expr...

Furlani, E J

2006-01-01T23:59:59.000Z

348

Left-right models with light neutrino mass prediction and dominant neutrinoless double beta decay rate  

E-Print Network [OSTI]

In TeV scale left-right symmetric models, new dominant predictions to neutrinoless double beta decay and light neutrino masses are in mutual contradiction because of large contribution to the latter through popular seesaw mechanisms. We show that in a class of left-right models with high-scale parity restoration, these results coexist without any contravention with neutrino oscillation data and the relevant formula for light neutrino masses is obtained via gauged inverse seesaw mechanism. The most dominant contribution to the double beta decay is shown to be via $W^-_L- W^-_R$ mediation involving both light and heavy neutrino exchanges, and the model predictions are found to discriminate whether the Dirac neutrino mass is of quark-lepton symmetric origin or without it. We also discuss associated lepton flavor violating decays.

M. K. Parida; Sudhanwa Patra

2013-01-14T23:59:59.000Z

349

A Threading-Based Method for the Prediction of DNA-Binding Proteins with Application to the Human Genome  

E-Print Network [OSTI]

the human genome; 1,654 proteins are predicted to have DNA-binding function. Comparison with existing Gene) A Threading-Based Method for the Prediction of DNA-Binding Proteins with Application to the Human Genome. PLo have been released, and about 5,000 active genome sequencing projects are on the way [6

Skolnick, Jeff

350

Large-scale binding ligand prediction by improved patch-based method Patch-Surfer2.0  

Science Journals Connector (OSTI)

......Large-scale binding ligand prediction by improved patch-based method Patch-Surfer2.0 Xiaolei Zhu 1 Yi Xiong 1 Daisuke Kihara...Results: We present a computational method named Patch-Surfer2.0, which predicts binding ligands for......

Xiaolei Zhu; Yi Xiong; Daisuke Kihara

2014-10-01T23:59:59.000Z

351

Evaluating case-based decision theory: Predicting empirical patterns of human classification learning  

Science Journals Connector (OSTI)

Abstract We introduce a computer program which calculates an agent?s optimal behavior according to case-based decision theory (Gilboa and Schmeidler, 1995) and use it to test CBDT against a benchmark set of problems from the psychological literature on human classification learning (Shepard et al., 1961). This allows us to evaluate the efficacy of CBDT as an account of human decision-making on this set of problems. We find: (1) The choice behavior of this program (and therefore case-based decision theory) correctly predicts the empirically observed relative difficulty of problems and speed of learning in human data. (2) ‘Similarity’ (how CBDT decision makers extrapolate from memory) is decreasing in vector distance, consistent with evidence in psychology (Shepard, 1987). (3) The best-fitting parameters suggest humans aspire to an 80 – 85 % success rate, and humans may increase their aspiration level during the experiment. (4) Average similarity is rejected in favor of additive similarity.

Andreas Duus Pape; Kenneth J. Kurtz

2013-01-01T23:59:59.000Z

352

QMU as an approach to strengthening the predictive capabilities of complex models.  

SciTech Connect (OSTI)

Complex systems are made up of multiple interdependent parts, and the behavior of the entire system cannot always be directly inferred from the behavior of the individual parts. They are nonlinear and system responses are not necessarily additive. Examples of complex systems include energy, cyber and telecommunication infrastructures, human and animal social structures, and biological structures such as cells. To meet the goals of infrastructure development, maintenance, and protection for cyber-related complex systems, novel modeling and simulation technology is needed. Sandia has shown success using M&S in the nuclear weapons (NW) program. However, complex systems represent a significant challenge and relative departure from the classical M&S exercises, and many of the scientific and mathematical M&S processes must be re-envisioned. Specifically, in the NW program, requirements and acceptable margins for performance, resilience, and security are well-defined and given quantitatively from the start. The Quantification of Margins and Uncertainties (QMU) process helps to assess whether or not these safety, reliability and performance requirements have been met after a system has been developed. In this sense, QMU is used as a sort of check that requirements have been met once the development process is completed. In contrast, performance requirements and margins may not have been defined a priori for many complex systems, (i.e. the Internet, electrical distribution grids, etc.), particularly not in quantitative terms. This project addresses this fundamental difference by investigating the use of QMU at the start of the design process for complex systems. Three major tasks were completed. First, the characteristics of the cyber infrastructure problem were collected and considered in the context of QMU-based tools. Second, UQ methodologies for the quantification of model discrepancies were considered in the context of statistical models of cyber activity. Third, Bayesian methods for optimal testing in the QMU framework were developed. This completion of this project represent an increased understanding of how to apply and use the QMU process as a means for improving model predictions of the behavior of complex systems. 4

Gray, Genetha Anne; Boggs, Paul T.; Grace, Matthew D.

2010-09-01T23:59:59.000Z

353

Use of a predictive model for the impact of cofiring coal/biomass blends on slagging and fouling propensity  

SciTech Connect (OSTI)

The paper describes an investigation of slagging and fouling effects when cofiring coal/biomass blends by using a predictive model for large utility boilers. This model is based on the use a zone computational method to determine the midsection temperature profile throughout a boiler, coupled with a thermo-chemical model, to define and assess the risk of elevated slagging and fouling levels during cofiring of solid fuels. The application of this prediction tool was made for a 618 MW thermal wall-fired pulverized coal boiler, cofired with a typical medium volatile bituminous coal and two substitute fuels, sewage sludge and sawdust. Associated changes in boiler efficiency as well as various heat transfer and thermodynamic parameters of the system were analyzed with slagging and fouling effects for different cofiring ratios. The results of the modeling revealed that, for increased cofiring of sewage sludge, an elevated risk of slagging and high-temperature fouling occurred, in complete contrast to the effects occurring with the utilization of sawdust as a substitute fuel. 30 refs., 9 figs.,1 tab.

Piotr Plaza; Anthony J. Griffiths; Nick Syred; Thomas Rees-Gralton [Cardiff University, Cardiff (United Kingdom). Centre for Research in Energy

2009-07-15T23:59:59.000Z

354

Intelligent-based Structural Damage Detection Model  

SciTech Connect (OSTI)

This paper presents the application of a novel Artificial Neural Network (ANN) model for the diagnosis of structural damage. The ANN model, denoted as the GRNNFA, is a hybrid model combining the General Regression Neural Network Model (GRNN) and the Fuzzy ART (FA) model. It not only retains the important features of the GRNN and FA models (i.e. fast and stable network training and incremental growth of network structure) but also facilitates the removal of the noise embedded in the training samples. Structural damage alters the stiffness distribution of the structure and so as to change the natural frequencies and mode shapes of the system. The measured modal parameter changes due to a particular damage are treated as patterns for that damage. The proposed GRNNFA model was trained to learn those patterns in order to detect the possible damage location of the structure. Simulated data is employed to verify and illustrate the procedures of the proposed ANN-based damage diagnosis methodology. The results of this study have demonstrated the feasibility of applying the GRNNFA model to structural damage diagnosis even when the training samples were noise contaminated.

Lee, Eric Wai Ming; Yu, K.F. [Department of Building and Construction, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong (Hong Kong)

2010-05-21T23:59:59.000Z

355

Energy Band Model Based on Effective Mass  

E-Print Network [OSTI]

In this work, we demonstrate an alternative method of deriving an isotropic energy band model using a one-dimensional definition of the effective mass and experimentally observed dependence of mass on energy. We extend the effective mass definition to anti-particles and particles with zero rest mass. We assume an often observed linear dependence of mass on energy and derive a generalized non-parabolic energy-momentum relation. The resulting non-parabolicity leads to velocity saturation at high particle energies. We apply the energy band model to free relativistic particles and carriers in solid state materials and obtain commonly used dispersion relations and experimentally confirmed effective masses. We apply the model to zero rest mass particles in graphene and propose using the effective mass for photons. Therefore, it appears that the new energy band model based on the effective mass can be applied to relativistic particles and carriers in solid state materials.

Viktor Ariel

2012-09-06T23:59:59.000Z

356

Standardised and transparent model descriptions for agent-based models: Current status and prospects  

Science Journals Connector (OSTI)

Agent-based models are helpful to investigate complex dynamics in coupled human-natural systems. However, model assessment, model comparison and replication are hampered to a large extent by a lack of transparency and comprehensibility in model descriptions. ... Keywords: Agent-based modelling, Domain specific languages, Graphical representations, Model communication, Model comparison, Model design, Model development, Model replication, Standardised protocols

Birgit Müller, Stefano Balbi, Carsten M. Buchmann, Luís De Sousa, Gunnar Dressler, Jürgen Groeneveld, Christian J. Klassert, Quang Bao Le, James D. A. Millington, Henning Nolzen, Dawn C. Parker, J. Gary Polhill, Maja Schlüter, Jule Schulze, Nina Schwarz, Zhanli Sun, Patrick Taillandier, Hanna Weise

2014-05-01T23:59:59.000Z

357

Comparing Large-Scale Hydrological Model Predictions with Observed Streamflow in the Pacific Northwest: Effects of Climate and Groundwater  

Science Journals Connector (OSTI)

Assessing uncertainties in hydrologic models can improve accuracy in predicting future streamflow. Here, simulated streamflows using the Variable Infiltration Capacity (VIC) model at coarse (°) and fine (°) spatial resolutions were evaluated ...

Mohammad Safeeq; Guillaume S. Mauger; Gordon E. Grant; Ivan Arismendi; Alan F. Hamlet; Se-Yeun Lee

2014-12-01T23:59:59.000Z

358

Impact of emissions, chemistry, and climate on atmospheric carbon monoxide : 100-year predictions from a global chemistry-climate model  

E-Print Network [OSTI]

The possible trends for atmospheric carbon monoxide in the next 100 yr have been illustrated using a coupled atmospheric chemistry and climate model driven by emissions predicted by a global economic development model. ...

Wang, Chien.; Prinn, Ronald G.

359

Reduced model prediction of electron temperature profiles in microtearing-dominated National Spherical Torus eXperiment plasmas  

SciTech Connect (OSTI)

A representative H-mode discharge from the National Spherical Torus eXperiment is studied in detail to utilize it as a basis for a time-evolving prediction of the electron temperature profile using an appropriate reduced transport model. The time evolution of characteristic plasma variables such as ?{sub e},??{sub e}{sup ?}, the MHD ? parameter, and the gradient scale lengths of T{sub e}, T{sub i}, and n{sub e} were examined as a prelude to performing linear gyrokinetic calculations to determine the fastest growing micro instability at various times and locations throughout the discharge. The inferences from the parameter evolutions and the linear stability calculations were consistent. Early in the discharge, when ?{sub e} and ?{sub e}{sup ?} were relatively low, ballooning parity modes were dominant. As time progressed and both ?{sub e} and ?{sub e}{sup ?} increased, microtearing became the dominant low-k{sub ?} mode, especially in the outer half of the plasma. There are instances in time and radius, however, where other modes, at higher-k{sub ?}, may, in addition to microtearing, be important for driving electron transport. Given these results, the Rebut-Lallia-Watkins (RLW) electron thermal diffusivity model, which is based on microtearing-induced transport, was used to predict the time-evolving electron temperature across most of the profile. The results indicate that RLW does a good job of predicting T{sub e} for times and locations where microtearing was determined to be important, but not as well when microtearing was predicted to be stable or subdominant.

Kaye, S. M., E-mail: skaye@pppl.gov; Guttenfelder, W.; Bell, R. E.; Gerhardt, S. P.; LeBlanc, B. P.; Maingi, R. [Princeton Plasma Physics Laboratory, Princeton University, Princeton, New Jersey 08543 (United States)

2014-08-15T23:59:59.000Z

360

A new thermodynamic model to predict wax deposition from crude oils  

E-Print Network [OSTI]

Hydrocarbons 5 Comparison of Experimental and Predicted Onset Temperatures using this Model at 1 Atm. 30 31 37 6 Component Data for Oil Mixture l. 7 Characterization for Oil Mixture l. 8 Characterization for Oil Mixture 2. 9 Characterization for Oil... for Flash Calculations . . 34 4 Variation of Onset Temperature with Pressure for Oil Mixture l. . . 5 Variation of Onset Temperature with Pressure for Oil Mixture 2 . . 51 52 6 Wax Precipitation Curves for Oil Mixture 1 at 1 Atm. . . 7 Wax...

Loganathan, Narayanan

1993-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "model based predictive" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


361

A new BML-based RANS modelling for the description of gas turbine typical combustion processes  

Science Journals Connector (OSTI)

The work is concentrated on the formulation and validation of integral models within RANS framework for the numerical prediction of the premixed and partially premixed flames occurring in gas turbine combustors. The premixed combustion modelling is based on the BML approach coupled to the mixing transport providing variable equivalence ratio. Chemistry is described by means of ILDM model solving transport equations for reaction progress variables conditioned on the flame front. Multivariate presumed PDF model is used for the turbulence-chemistry interaction treatment. Turbulence is modelled using the second moment closure (SMC) and the standard ?-? model as well. The influence of non-gradient turbulent transport is investigated comparing the gradient diffusion closure and the solution of the scalar flux transport equations. Different model combinations are assessed simulating several premixed and partially premixed flame configurations and comparing results to the experimental data. The proposed model provides good predictions particularly in combination with SMC.

A. Maltsev; A. Sadiki; J. Janicka

2004-01-01T23:59:59.000Z

362

Room acoustic prediction based on a unified treatment of diffuse and specular reflection  

Science Journals Connector (OSTI)

A new general algorithm for room acoustic prediction is presented. The algorithm based on approximate cone tracing handles diffuse reflection by a splitup of cones incident on diffusing surfaces. The splitup of cones treats the interaction between specular and diffuse reflection in a physically sensible manner. A ‘‘brute?force’’ implementation of such an algorithm inevitably creates an exponential dependence of the calculation time on the number of reflection combinations between diffusely reflecting surfaces and therefore results in extremely long processing times. By exploiting the properties of diffuse reflection the described algorithm displays an essentially linear dependence resulting in processing times short enough also for personal computers. The algorithm adapts its behavior to the properties of each specific environment relies on very few nonverifiable assumptions and is designed to meet the specific requirements of auralization. The splitup of cones creates a myriad of weak diffuse reflections resulting in a very smooth late decay. The algorithm is described in detail and sample predictions are shown.

Bengt?Inge L. Dalenbäck

1996-01-01T23:59:59.000Z

363

Feedforward artificial neural network to improve model predictive control in biological processes  

Science Journals Connector (OSTI)

Artificial neural networks (ANNs) offer the versatility of being able to model the dynamics of a biosystem without requiring a phenomenological model. In addition, model predictive control (MPC) is a member of advanced discrete-time process control algorithms. The recent developments in the biotechnology due to MPC utilising the capability of ANN make the practical application of non-linear process control strategies a reality. This paper reviews the recent enhancement and applications of MPC in various biochemical processes using feedforward artificial neural networks which is also known as neural predictive control. The capability of neural predictive control to handle the common problems associated with biochemical processes, namely optimisation of objective function, optimisation of dynamic behaviour of the system, control of ill-defined non-linear systems, improving the computational efficiency of the strategy, disturbance rejection ability and evaluating the control effectiveness are discussed. The review clearly indicates that enormous work has been carried out involving dynamic behaviour of the bioreactor system which is analysed and optimised revealing that feedforward neural network has evolved as a good bioreactor neuro-controller.

Senthil Kumar Arumugasamy; Zainal Ahmad

2011-01-01T23:59:59.000Z

364

A Modified Johnson-Cook Model for Sheet Metal Forming at Elevated Temperatures and Its Application for Cooled Stress-Strain Curve and Spring-Back Prediction  

SciTech Connect (OSTI)

In this study, a modified Johnson-Cook (J-C) model and an innovated method to determine (J-C) material parameters are proposed to predict more correctly stress-strain curve for tensile tests in elevated temperatures. A MATLAB tool is used to determine material parameters by fitting a curve to follow Ludwick's hardening law at various elevated temperatures. Those hardening law parameters are then utilized to determine modified (J-C) model material parameters. The modified (J-C) model shows the better prediction compared to the conventional one. As the first verification, an FEM tensile test simulation based on the isotropic hardening model for boron sheet steel at elevated temperatures was carried out via a user-material subroutine, using an explicit finite element code, and compared with the measurements. The temperature decrease of all elements due to the air cooling process was then calculated when considering the modified (J-C) model and coded to VUMAT subroutine for tensile test simulation of cooling process. The modified (J-C) model showed the good agreement between the simulation results and the corresponding experiments. The second investigation was applied for V-bending spring-back prediction of magnesium alloy sheets at elevated temperatures. Here, the combination of proposed J-C model with modified hardening law considering the unusual plastic behaviour for magnesium alloy sheet was adopted for FEM simulation of V-bending spring-back prediction and shown the good comparability with corresponding experiments.

Duc-Toan, Nguyen [Department of Mechanical Engineering, Jeju National University, Jeju-Do 690-756 (Korea, Republic of); School of Mechanical Engineering, Hanoi University of Science and Technology, lA-Dai Co Viet Street, Hai Ba Trung District, Hanoi City (Viet Nam); Tien-Long, Banh [School of Mechanical Engineering, Hanoi University of Science and Technology, lA-Dai Co Viet Street, Hai Ba Trung District, Hanoi City (Viet Nam); Young-Suk, Kim [Department of Mechanical Engineering, Kyungpook National University, Daegu 702-701 (Korea, Republic of); Dong-Won, Jung [Department of Mechanical Engineering, Jeju National University, Jeju-Do 690-756 (Korea, Republic of)

2011-08-22T23:59:59.000Z

365

Inclusion of KI67 significantly improves performance of the PREDICT prognostication and prediction model for early breast cancer  

E-Print Network [OSTI]

subclasses with clinical implications. Proc Natl Acad Sci USA 2001, 98(19): 10869-10   74 11   12   3. Curtis C, Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ, Speed D, Lynch 13   AG, Samarajiwa S, Yuan Y, Graf S, Ha G, Haffari G, Bashashati A... WH, Sonke GS, van’t Veer LJ, Rutgers EJT, van de Vijver MJ, Linn SC. 3   Optimized prediction of clinical outcome by the PREDICT plus tool and 70-gene 4   signature in early stage node-negative breast cancer [abstract]. 36th Annual San Antonio 5...

Wishart, G. C.; Rakha, E.; Green, A.; Ellis, I.; Ali, H. R.; Provenzano, E.; Blows, F. M.; Caldas, C.; Pharoah, P. D. P.

2014-01-01T23:59:59.000Z

366

semble Prediction Lizzie S. R. Froude1  

E-Print Network [OSTI]

by numerical weather prediction (NWP). Operational NWP models are based on a set of equations known for Medium Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP will grow rapidly, resulting in a total loss of predictability at higher forecast times. Today's models

Froude, Lizzie

367

Terrestrial Carbon Sinks for the United States Predicted from MODIS Satellite Data and Ecosystem Modeling  

Science Journals Connector (OSTI)

A simulation model based on satellite observations of monthly vegetation cover from the Moderate Resolution Imaging Spectroradiometer (MODIS) was used to estimate monthly carbon fluxes in terrestrial ecosystems of the conterminous United States ...

Christopher Potter; Steven Klooster; Alfredo Huete; Vanessa Genovese

2007-08-01T23:59:59.000Z

368

Developing algorithms for predicting protein-protein interactions of homology modeled proteins.  

SciTech Connect (OSTI)

The goal of this project was to examine the protein-protein docking problem, especially as it relates to homology-based structures, identify the key bottlenecks in current software tools, and evaluate and prototype new algorithms that may be developed to improve these bottlenecks. This report describes the current challenges in the protein-protein docking problem: correctly predicting the binding site for the protein-protein interaction and correctly placing the sidechains. Two different and complementary approaches are taken that can help with the protein-protein docking problem. The first approach is to predict interaction sites prior to docking, and uses bioinformatics studies of protein-protein interactions to predict theses interaction site. The second approach is to improve validation of predicted complexes after docking, and uses an improved scoring function for evaluating proposed docked poses, incorporating a solvation term. This scoring function demonstrates significant improvement over current state-of-the art functions. Initial studies on both these approaches are promising, and argue for full development of these algorithms.

Martin, Shawn Bryan; Sale, Kenneth L.; Faulon, Jean-Loup Michel; Roe, Diana C.

2006-01-01T23:59:59.000Z

369

Development of a Computer Model for Prediction of PCB Degradation Endpoints  

SciTech Connect (OSTI)

Several researchers have demonstrated the transformation if polychlorinated biphenyls (PCBs) by both aerobic and anaerobic bacteria. This transformation, or conversion, is characteristic and often dependent on PCB congener structure and in addition, dictates the products or endpoints. Since transformation is linked to microbial activities, bioremediation has been hailed as a possible solution for PCB-contaminated soils and sediments, and several demonstration activities have verified laboratory results. This paper presents results from mathematical modeling of PCB transformation as a means of predicting possible endpoints of bioremediation. Since transformation can be influenced by both starting composition of the PCBs and microbial activity, this paper systematically evaluates several of the most common transformation patterns. The predicted data are also compared with experimental results. For example, the correlation between laboratory-observed and predicted endpoint data was, in some cases, as good as 0.98 (perfect correlation = 1.0). In addition to predicting chemical endpoints, the possible human effects of the PCBs are discussed through the use of documented dioxin-like toxicity and accumulation in humans before and after transformation.

Just, E.M.; Klasson, T.

1999-12-07T23:59:59.000Z

370

Validation of PV performance models using satellite-based irradiance measurements : a case study.  

SciTech Connect (OSTI)

Photovoltaic (PV) system performance models are relied upon to provide accurate predictions of energy production for proposed and existing PV systems under a wide variety of environmental conditions. Ground based meteorological measurements are only available from a relatively small number of locations. In contrast, satellite-based radiation and weather data (e.g., SUNY database) are becoming increasingly available for most locations in North America, Europe, and Asia on a 10 x 10 km grid or better. This paper presents a study of how PV performance model results are affected when satellite-based weather data is used in place of ground-based measurements.

Stein, Joshua S.; Parkins, Andrew (Clean Power Research); Perez, Richard (University at Albany)

2010-05-01T23:59:59.000Z

371

Integrated cluster analysis and artificial neural network modeling for steam-assisted gravity drainage performance prediction in heterogeneous reservoirs  

Science Journals Connector (OSTI)

Abstract Evaluation of steam-assisted gravity drainage (SAGD) performance that involves detailed compositional simulations is usually deterministic, cumbersome, expensive (manpower and time consuming), and not quite suitable for practical decision making and forecasting, particularly when dealing with high-dimensional data space consisting of large number of operational and geological parameters. Data-driven modeling techniques, which entail comprehensive data analysis and implementation of machine learning methods for system forecast, provide an attractive alternative. In this paper, artificial neural network (ANN) is employed to predict SAGD production in heterogeneous reservoirs, an important application that is lacking in existing literature. Numerical flow simulations are performed to construct a training data set consists of various attributes describing characteristics associated with reservoir heterogeneities and other relevant operating parameters. Empirical Arps decline parameters are tested successfully for parameterization of cumulative production profile and considered as outputs of the ANN models. Sensitivity studies on network configurations are also investigated. Principal components analysis (PCA) is performed to reduce the dimensionality of the input vector, improve prediction quality, and limit over-fitting. In a case study, reservoirs with distinct heterogeneity distributions are fed to the model. It is shown that robustness and accuracy of the prediction capability are greatly enhanced when cluster analysis are performed to identify internal data structures and groupings prior to ANN modeling. Both deterministic and fuzzy-based clustering techniques are compared, and separate ANN model is constructed for each cluster. The model is then tested using a validation data set (cases that have not been used during the training stage). The proposed approach can be integrated directly into most existing reservoir management routines. In addition, incorporating techniques for dimensionality reduction and clustering with ANN demonstrates the viability of this approach for analyzing large field data set. Given that quantitative ranking of operating areas, robust forecasting, and optimization of heavy oil recovery processes are major challenges faced by the industry, the proposed research highlights the significant potential of applying effective data-driven modeling approaches in analyzing other solvent-additive steam injection projects.

Ehsan Amirian; Juliana Y. Leung; Stefan Zanon; Peter Dzurman

2015-01-01T23:59:59.000Z

372

Electrochemistry-based Battery Modeling for Prognostics Matthew Daigle1  

E-Print Network [OSTI]

of discharge (EOD) and end of use- ful life (EOL) events. To implement such technologies, it is crucial efficient, capture the effects of aging, and are of suitable accuracy for reliable EOD prediction the model validity and accurate EOD predictions. 1. INTRODUCTION With electric unmanned aerial vehicles

Daigle, Matthew

373

Machine Learning Based Online Performance Prediction for Runtime Parallelization and Task Scheduling  

SciTech Connect (OSTI)

With the emerging many-core paradigm, parallel programming must extend beyond its traditional realm of scientific applications. Converting existing sequential applications as well as developing next-generation software requires assistance from hardware, compilers and runtime systems to exploit parallelism transparently within applications. These systems must decompose applications into tasks that can be executed in parallel and then schedule those tasks to minimize load imbalance. However, many systems lack a priori knowledge about the execution time of all tasks to perform effective load balancing with low scheduling overhead. In this paper, we approach this fundamental problem using machine learning techniques first to generate performance models for all tasks and then applying those models to perform automatic performance prediction across program executions. We also extend an existing scheduling algorithm to use generated task cost estimates for online task partitioning and scheduling. We implement the above techniques in the pR framework, which transparently parallelizes scripts in the popular R language, and evaluate their performance and overhead with both a real-world application and a large number of synthetic representative test scripts. Our experimental results show that our proposed approach significantly improves task partitioning and scheduling, with maximum improvements of 21.8%, 40.3% and 22.1% and average improvements of 15.9%, 16.9% and 4.2% for LMM (a real R application) and synthetic test cases with independent and dependent tasks, respectively.

Li, J; Ma, X; Singh, K; Schulz, M; de Supinski, B R; McKee, S A

2008-10-09T23:59:59.000Z

374

An Equilibrium-Based Model of Gas Reaction and Detonation  

SciTech Connect (OSTI)

During gaseous diffusion plant operations, conditions leading to the formation of flammable gas mixtures may occasionally arise. Currently, these could consist of the evaporative coolant CFC-114 and fluorinating agents such as F2 and ClF3. Replacement of CFC-114 with a non-ozone-depleting substitute is planned. Consequently, in the future, the substitute coolant must also be considered as a potential fuel in flammable gas mixtures. Two questions of practical interest arise: (1) can a particular mixture sustain and propagate a flame if ignited, and (2) what is the maximum pressure that can be generated by the burning (and possibly exploding) gas mixture, should it ignite? Experimental data on these systems, particularly for the newer coolant candidates, are limited. To assist in answering these questions, a mathematical model was developed to serve as a tool for predicting the potential detonation pressures and for estimating the composition limits of flammability for these systems based on empirical correlations between gas mixture thermodynamics and flammability for known systems. The present model uses the thermodynamic equilibrium to determine the reaction endpoint of a reactive gas mixture and uses detonation theory to estimate an upper bound to the pressure that could be generated upon ignition. The model described and documented in this report is an extended version of related models developed in 1992 and 1999.

Trowbridge, L.D.

2000-04-01T23:59:59.000Z

375

A warranty forecasting model based on piecewise statistical distributions and stochastic simulation  

E-Print Network [OSTI]

industry and has a specific application to automotive electronics. The warranty prediction model is based is demonstrated using a case study of automotive electronics warranty returns. The approach developed b CALCE Electronic Products and Systems Center, Department of Mechanical Engineering, University

Sandborn, Peter

376

The probabilistic life time prediction model of oil pipeline due to local corrosion crack  

Science Journals Connector (OSTI)

Abstract A four-stage probabilistic damage model is proposed basis of cross-scale damage processes to deal with the local corrosion crack of oil pipeline. At first, some key parameters for life time prediction were determined; then the probabilistic damage model is formulated and numerically calculated by using Monte Carlo Simulation (MCS). Furthermore, the model is used to deal with an example in order to check its validity. The results show that the life-span of this pipeline is nearly 20.55 years, and the pipe wall thickness, operating pressure difference and corrosion electric current density are the three key parameters to determine the span-life of this pipeline; the longest examination and repair period should be less than 4.71 years for safety when the surface crack length of 10 mm can be detected reliably.

Jun Hu; Yangyang Tian; Haipeng Teng; Lijun Yu; Maosheng Zheng

2014-01-01T23:59:59.000Z

377

Effective index model predicts modal frequencies of vertical-cavity lasers  

SciTech Connect (OSTI)

Previously, an effective index optical model was introduced for the analysis of lateral waveguiding effects in vertical-cavity surface-emitting lasers. The authors show that the resultant transverse equation is almost identical to the one typically obtained in the analysis of dielectric waveguide problems, such as a step-index optical fiber. The solution to the transverse equation yields the lateral dependence of the optical field and, as is recognized in this paper, the discrete frequencies of the microcavity modes. As an example, they apply this technique to the analysis of vertical-cavity lasers that contain thin-oxide apertures. The model intuitively explains the experimental data and makes quantitative predictions in good agreement with a highly accurate numerical model.

SERKLAND,DARWIN K.; HADLEY,G. RONALD; CHOQUETTE,KENT D.; GEIB,KENT M.; ALLERMAN,ANDREW A.

2000-04-18T23:59:59.000Z

378

Predicting long-term lumen maintenance life of LED light sources using a particle filter-based prognostic approach  

Science Journals Connector (OSTI)

Abstract Lumen degradation is a common failure mode in LED light sources. Lumen maintenance life, defined as the time when the maintained percentages of the initial light output fall below a failure threshold, is a key characteristic for assessing the reliability of LED light sources. Owing to the long lifetime and high reliability of LED lights sources, it is challenging to estimate the lumen maintenance life for LED light sources using traditional life testing that records failure data. This paper describes a particle filter-based (PF-based) prognostic approach based on both Sequential Monte Carlo (SMC) and Bayesian techniques to predict the lumen maintenance life of LED light sources. The lumen maintenance degradation data collected from an accelerated degradation test was used to demonstrate the prediction algorithm and methodology of the proposed PF approach. Its feasibility and prediction accuracy were then validated and compared with the TM-21 standard method that was created by the Illuminating Engineering Society of North America (IESNA). Finally, a robustness study was also conducted to analyze the initialization of parameters impacting the prediction accuracy and the uncertainties of the proposed PF method. The results show that, compared to the TM-21 method, the PF approach achieves better prediction performance, with an error of less than 5% in predicting the long-term lumen maintenance life of LED light sources.

Jiajie Fan; Kam-Chuen Yung; Michael Pecht

2015-01-01T23:59:59.000Z

379

Predicting construction cost and schedule success using artificial neural networks ensemble and support vector machines classification models  

Science Journals Connector (OSTI)

It is commonly perceived that how well the planning is performed during the early stage will have significant impact on final project outcome. This paper outlines the development of artificial neural networks ensemble and support vector machines classification models to predict project cost and schedule success, using status of early planning as the model inputs. Through industry survey, early planning and project performance information from a total of 92 building projects is collected. The results show that early planning status can be effectively used to predict project success and the proposed artificial intelligence models produce satisfactory prediction results.

Yu-Ren Wang; Chung-Ying Yu; Hsun-Hsi Chan

2012-01-01T23:59:59.000Z

380

Wind resource assessment using numerical weather prediction models and multi-criteria decision making technique: case study (Masirah Island, Oman)  

Science Journals Connector (OSTI)

The Authority for Electricity Regulation in Oman has recently announced the implementation of a 500 kW wind farm pilot project in Masirah Island. Detailed wind resource assessment is then required to identify the most suitable location for this project. This paper presents wind resource assessment using nested ensemble numerical weather prediction (NWP) model's approach at 2.8 km resolution and multi-criteria decision making (MCDM) technique. A case study based on the proposed approach is conducted over Masirah Island, Oman. The resource assessment over the island was based on the mean wind speed and wind power distribution over the entire island at different heights. In addition, important criteria such as turbulence intensity and peak hour matching are also considered. The NWP model results were verified against the available 10 m wind data observations from the meteorological station in the northern part of the island. The resource assessment criteria were evaluated using MCDM technique to score the locations over the island based on their suitability for wind energy applications. Two MCDM approaches namely equally weighted and differently weighted criteria were implemented in this paper.

Sultan Al-Yahyai; Yassine Charabi; Abdullah Al-Badi; Adel Gastli

2013-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "model based predictive" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


381

A Stochastic Reactor Based Virtual Engine Model Employing Detailed...  

Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

A Stochastic Reactor Based Virtual Engine Model Employing Detailed Chemistry for Kinetic Studies of In-Cylinder Combustion and Exhaust Aftertreatment A Stochastic Reactor Based...

382

A GIS-based atmospheric dispersion model  

E-Print Network [OSTI]

pollution due to the use of agricultural pesticide is a major concern to- day, regarding both public health dispersion and to propose an useful air pollution prediction tool, using fluid mechanics equations and open un outil de prediction de la pollution de l'air . Ce travail concerne la modélisation de la dérive

Boyer, Edmond

383

Protein secondary structure prediction by combining hidden Markov models and sliding window scores  

Science Journals Connector (OSTI)

Instead of conformation states of single residues, refined conformation states of quintuplets are proposed to reflect conformation correlation. Simple hidden Markov models combined with sliding window scores are used to predict the secondary structure of a protein from its amino acid sequence. Since the length of protein conformation segments varies within a narrow range, we can ignore the duration effect of the length distribution. The window scores for residues are a window version of the Chou-Fasman propensities estimated under an approximation of conditional independency. Different window widths are examined, and the optimal width is found to be 17. A high accuracy of about 70% is achieved.

Wei-Mou Zheng

2005-01-01T23:59:59.000Z

384

Predicted Solar Neutrino Rates in the Q-Nuclear Solar Model  

Science Journals Connector (OSTI)

It is been shown previoulsy that the existence of a tiny abundance of nuclei which have an additional embedded hadronic particle, Q-nuclei, can solve the solar-neutrino problem. We present here detailed predictions of solar-neutrino detection rates for detectors of Cl37, Ga71, and In115. It is found that, while Q-nuclei could reduce the Cl37 detection rate from that of the standard model to the experimental value, they would also produce a dramatic increase in the rates for the Ga71 and In115 detectors.

B. Sur and R. N. Boyd

1985-02-04T23:59:59.000Z

385

Weather Regime Prediction Using Statistical Learning  

E-Print Network [OSTI]

most advanced numerical weather prediction models still havefor numerical weather prediction models. Acknowledgements It

A. Deloncle; R. Berk; F. D'Andrea; M. Ghil

2011-01-01T23:59:59.000Z

386

Vehicle Technologies Office Merit Review 2014: Trip Prediction and Route-Based Vehicle Energy Management  

Broader source: Energy.gov [DOE]

Presentation given by Argonne National Laboratory at 2014 DOE Hydrogen and Fuel Cells Program and Vehicle Technologies Office Annual Merit Review and Peer Evaluation Meeting about trip prediction...

387

Prediction of insulation degradation of distribution power cables based on chemical analysis and electrical measurements.  

E-Print Network [OSTI]

??This thesis deals with the prediction of medium voltage cable insulation condition. Different kinds of electrical measurements and chemical analyses are tested to find out… (more)

Hyvönen, Petri

2008-01-01T23:59:59.000Z

388

A difference based approach to the semiparametric partial linear model  

E-Print Network [OSTI]

A commonly used semiparametric partial linear model is considered. We propose analyzing this model using a difference based approach. The procedure estimates the linear component based on the differences of the observations ...

Wang, Lie

2011-01-01T23:59:59.000Z

389

Be-CoDiS: An epidemiological model to predict the risk of human diseases spread worldwide. Application to the 2014 Ebola Virus Disease epidemic  

E-Print Network [OSTI]

Ebola virus disease is a lethal human and primate disease that currently requires a particular attention from the national and international health authorities due to important outbreaks concurring in some Western African countries and possible spread to other continents, which has already occurred in the USA and Spain. Regarding the emergency of this situation, there is a need of development of decision tools to help the authorities to focus their efforts in important factors that can help to eradicate Ebola. Mathematical modeling and, more precisely, epidemiological modeling can help to predict the possible evolution of the Ebola outbreaks and to give some recommendations in the region to be prioritized for surveillance. In this work, we present a first formulation of a new spatial-temporal epidemiological model, called Be-CoDiS (Between-COuntries Disease Spread), based on the combination of a deterministic Individual-Based model (modelling the interaction between countries, considered as individual) for be...

Benjamin, Ivorra; Diène, Ngom

2014-01-01T23:59:59.000Z

390

Dynamical and Microphysical Evolution during Mixed-Phase Cloud Glaciation Simulated Using the Bulk Adaptive Habit Prediction Model  

Science Journals Connector (OSTI)

A bulk microphysics scheme predicting ice particle habit evolution has been implemented in the Weather Research and Forecasting Model. Large-eddy simulations are analyzed to study the effects of ice habit and number concentration on the bulk ice ...

Kara J. Sulia; Hugh Morrison; Jerry Y. Harrington

2014-11-01T23:59:59.000Z

391

Development of analytical and numerical models predicting the deposition rate of electrically charged particles in turbulent channel flows  

E-Print Network [OSTI]

An analytical model is established to predict an electrostatically charged particle deposition as a function of particle size in fully-developed turbulent pipe flow. The convectivediffusion flux equation is solved for the particle concentration as a...

Ko, Hanseo

2012-06-07T23:59:59.000Z

392

Assimilation of Satellite Cloud and Precipitation Observations in Numerical Weather Prediction Models: Introduction to the JAS Special Collection  

Science Journals Connector (OSTI)

To date, the assimilation of satellite measurements in numerical weather prediction (NWP) models has focused on the clear atmosphere. But satellite observations in the visible, infrared, and microwave provide a great deal of information on clouds ...

Ronald M. Errico; George Ohring; Fuzhong Weng; Peter Bauer; Brad Ferrier; Jean-François Mahfouf; Joe Turk

2007-11-01T23:59:59.000Z

393

Utilization of Smart Materials and Predictive Modeling to Integrate Intracellular Dynamics with Cell Biomechanics and Collective Tissue Behavior  

E-Print Network [OSTI]

Utilization of Smart Materials and Predictive Modeling to Integrate Intracellular Dynamics important structures inside cells. New "smart" material will be used to trigger changes to cell movement Medical University Control of Cell Polarization by Smart Material Substrates Multiscale Imaging Multiscale

Mather, Patrick T.

394

Inference-based Geometric Modeling for the Generation of Complex Cluttered Virtual Environments  

E-Print Network [OSTI]

algorithm is capable of identifying objects of interest amongst a cluttered scene, and reconstructing complete representations of these objects even in the presence of occluded surfaces. This approach incorporates a predictive modeling framework that uses... of parts sampled from the input models. Finally, we a rm our motivation by showing an application of these two ap- proaches. We demonstrate how the constructed environments can be easily used within a physically-based simulation, capable of supporting...

Biggers, Keith Edward

2012-07-16T23:59:59.000Z

395

Node-Based Connection Semantics for Equation-Based Object-Oriented Modeling Languages  

Science Journals Connector (OSTI)

Declarative, Equation-Based Object-Oriented (EOO) modeling languages, like Modelica, support modeling of physical systems by composition of reusable component models. An important application area is modeling ...

David Broman; Henrik Nilsson

2012-01-01T23:59:59.000Z

396

A Framework for Performance Modeling and Prediction Allan Snavely, Laura Carrington, Nicole Wolter of The San Diego Supercomputer Center with  

E-Print Network [OSTI]

a framework for performance modeling and prediction that is faster than cycle-accurate simulation, more combined with a network model. 0-7695-1524-X/02 $17.00 (c) 2002 IEEE #12;2 Existing network simulators can and then combine this information with a network simulator. To model single-processor performance, we separate

Snavely, Allan

397

Standard Model Predictions and New Physics Sensitivity in B->DD Decays  

E-Print Network [OSTI]

An extensive model-independent analysis of B->DD decays is carried out employing SU(3) flavour symmetry, including symmetry-breaking corrections. Several theoretically clean observables are identified which allow for testing the Standard Model. These include the known time-dependent CP asymmetries, the penguin pollution of which can be controlled in this framework, but notably also quasi-isospin relations which are experimentally well accessible and unaffected by symmetry-breaking corrections. Theoretical assumptions can be kept to a minimum and controlled by additional sum rules. Available data are used in global fits to predict the branching ratio for the B0->DsDs decay as well as several CP asymmetries which have not been measured so far, and future prospects are analyzed.

Jung, Martin

2014-01-01T23:59:59.000Z

398

The application of a chemical equilibrium model, SOLTEQ, to predict the chemical speciations in stabilized/solidified waste forms  

E-Print Network [OSTI]

THE APPLICATION OI' A CHEMICAL EQUILIBRIUM MODEL, SOLTEQ, TO PREDICT THK CHEMICAL SPKCIATIONS IN STABILIZED/SOLIDIFIED WASTE FORMS A Thesis by JOO-YANG PARK Submitted to the Office of Graduate Studies of Texas A&M University in partial... fulfillment of the requirements for the degree of MASTER OF SCIENCE December 1994 Major Subject: Civil Engineering THE APPLICATION OF A CHEMICAL EQUILIBRIUM MODEL, SOLTEQ, TO PREDICT THE CHEMICAL SPECIATIONS IN STABILIZED/SOLIDIFIED WASTE FORMS A Thesis...

Park, Joo-Yang

1994-01-01T23:59:59.000Z

399

Development of a rock mass characteristics model for TBM penetration rate prediction  

Science Journals Connector (OSTI)

The TBM tunneling process in hard rock is actually a rock or rock mass breakage process, which determines the efficiency of tunnel boring machine (TBM). On the basis of the rock breakage process, a rock mass conceptual model that identifies the effect of rock mass properties on TBM penetration rate is proposed. During the construction of T05 and T06 tunnels of DTSS project in Singapore, a comprehensive program was performed to obtain the relevant rock mass properties and TBM performance data. A database, including rock mass properties, TBM specifications and the corresponding TBM performance, was established. Combining the rock mass conceptual model for evaluating rock mass boreability with the established database, a statistical prediction model of TBM penetration rate is set up by performing a nonlinear regression analysis. The parametric studies of the new model showed that the rock uniaxial compressive strength and the volumetric joint count have predominantly effects on the penetration rate. These results showed good agreement with the numerical simulations. The model limitations were also discussed.

Q.M. Gong; J. Zhao

2009-01-01T23:59:59.000Z

400

Interface modeling to predict well casing damage for big hill strategic petroleum reserve.  

SciTech Connect (OSTI)

Oil leaks were found in well casings of Caverns 105 and 109 at the Big Hill Strategic Petroleum Reserve site. According to the field observations, two instances of casing damage occurred at the depth of the interface between the caprock and top of salt. This damage could be caused by interface movement induced by cavern volume closure due to salt creep. A three dimensional finite element model, which allows each cavern to be configured individually, was constructed to investigate shear and vertical displacements across each interface. The model contains interfaces between each lithology and a shear zone to examine the interface behavior in a realistic manner. This analysis results indicate that the casings of Caverns 105 and 109 failed by shear stress that exceeded shear strength due to the horizontal movement of the top of salt relative to the caprock, and tensile stress due to the downward movement of the top of salt from the caprock, respectively. The casings of Caverns 101, 110, 111 and 114, located at the far ends of the field, are predicted to be failed by shear stress in the near future. The casings of inmost Caverns 107 and 108 are predicted to be failed by tensile stress in the near future.

Ehgartner, Brian L.; Park, Byoung Yoon

2012-02-01T23:59:59.000Z

Note: This page contains sample records for the topic "model based predictive" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


401

Bioenergetics-Based Modeling of Individual PCB Congeners in  

E-Print Network [OSTI]

Bioenergetics-Based Modeling of Individual PCB Congeners in Nestling Tree Swallows from Two 14850 A bioenergetics-based model was used to simulate the accumulation of total PCBs and 20 PCB of sediment- associated contaminants to sediment-dwelling organisms. A bioenergetics-based model was developed

McCarty, John P.

402

A Debugging Scheme for Declarative Equation Based Modeling Languages  

E-Print Network [OSTI]

and simulation, a number of object-oriented and/or declarative acausal modeling languages have emerged equation based modeling languages. Since these languages usually are based on object- orientationA Debugging Scheme for Declarative Equation Based Modeling Languages Peter Bunus, Peter Fritzson

Zhao, Yuxiao

403

Towards a model based approach for integration testing  

Science Journals Connector (OSTI)

In this paper, we introduce a model based approach for integration test cases generation. The approach is based on UML 2 Testing Profile and follows the Mode-Driven Architecture for generating integration test cases from unit test models. The generated ... Keywords: UTP, integration testing, model based testing, test cases generation

Mohamed Mussa; Ferhat Khendek

2011-07-01T23:59:59.000Z

404

Machine learning based prediction for peptide drift times in ion mobility spectrometry  

Science Journals Connector (OSTI)

......of local structure in proteins using a library of sequence-structure...detects periodicity in protein hydrophobicity. Proc...reliably predicting long disordered regions. Bioinformatics...program for predicting protein antigenic determinants...Nanni L , Lumini A. An ensemble of K-local hyperplanes......

Anuj R. Shah; Khushbu Agarwal; Erin S. Baker; Mudita Singhal; Anoop M. Mayampurath; Yehia M. Ibrahim; Lars J. Kangas; Matthew E. Monroe; Rui Zhao; Mikhail E. Belov; Gordon A. Anderson; Richard D. Smith

2010-07-01T23:59:59.000Z

405

A Debugging Scheme for Declarative Equation Based Modeling Languages  

Science Journals Connector (OSTI)

This paper concerns the static analysis for debugging purposes of programs written in declarative equation based modeling languages. We first give an introduction to declarative equation based languages and the consequences equation based programming ... Keywords: bipartite graphs, debugging, declarative equation based language, graph decomposition techniques, modelica, modeling languages, static analysis

Peter Bunus; Peter Fritzson

2002-01-01T23:59:59.000Z

406

3D Model Retrieval based on Adaptive Views Clustering  

E-Print Network [OSTI]

3D Model Retrieval based on Adaptive Views Clustering Tarik Filali Ansary1 , Mohamed Daoudi2 , Jean.daoudi@univ-tours.fr http://www-rech.enic.fr/miire Abstract. In this paper, we propose a method for 3D model indexing based selection of 2D views from a 3D model, and a probabilistic Bayesian method for 3D model retrieval from

Paris-Sud XI, Université de

407

Transition Prediction for Scramjet Intakes Using the \\gamma-Re_\\theta_t Model Coupled to Two Turbulence Models  

E-Print Network [OSTI]

Due to the thick boundary layers in hypersonic flows, the state of the boundary layer significantly influences the whole flow field as well as surface heat loads. Hence, for engineering applications the efficient numerical prediction of laminar-to-turbulent transition is a challenging and important task. Within the framework of the Reynolds averaged Navier-Stokes equations, Langtry/Menter [1] proposed the -Re?t transition model using two transport equations for the intermittency and Re?t combined with the Shear Stress Transport turbulence model (SST) [2]. The transition model contains two empirical correlations for onset and length of transition. Langtry/Menter [1] designed and validated the correlations for the subsonic and transonic flow regime. For our applications in the hypersonic flow regime, the development of a new set of correlations proved necessary, even when using the same SST turbulence model [3]. Within this paper, we propose a next step and couple the transition model with the SSG/LRR-! Reynold...

Frauholz, Sarah; Müller, Siegfried; Behr, Marek

2014-01-01T23:59:59.000Z

408

Validation of a zero-dimensional model for prediction of \\{NOx\\} and engine performance for electronically controlled marine two-stroke diesel engines  

Science Journals Connector (OSTI)

The aim of this paper is to derive a methodology suitable for energy system analysis for predicting the performance and \\{NOx\\} emissions of marine low speed diesel engines. The paper describes a zero-dimensional model, evaluating the engine performance by means of an energy balance and a two zone combustion model using ideal gas law equations over a complete crank cycle. The combustion process is divided into intervals, and the product composition and flame temperature are calculated in each interval. The \\{NOx\\} emissions are predicted using the extended Zeldovich mechanism. The model is validated using experimental data from two MAN B&W engines; one case being data subject to engine parameter changes corresponding to simulating an electronically controlled engine; the second case providing data covering almost all model input and output parameters. The first case of validation suggests that the model can predict specific fuel oil consumption and \\{NOx\\} emissions within the 95% confidence intervals given by the experimental measurements. The second validation confirms the capability of the model to match measured engine output parameters based on measured engine input parameters with a maximum 5% deviation.

Fabio Scappin; Sigurður H. Stefansson; Fredrik Haglind; Anders Andreasen; Ulrik Larsen

2012-01-01T23:59:59.000Z

409

Society of Petroleum Engineers A New Approach to Predict Bit Life Based on Tooth or Bearing Failures  

E-Print Network [OSTI]

ms m . . . Society of Petroleum Engineers SPE 51082 A New Approach to Predict Bit Life Based. Aminian, SPE, West Virginia U. Copyright 1998, Society of Petroleum Engineers Inc. This paper was prepared by the Society .of Petroleum Engineers and are subject to correction by the author(s). The material, as presented

Mohaghegh, Shahab

410

A Simple Path Loss Prediction Model for HVAC Systems O. K. Tonguz, D. D. Stancil, A. E. Xhafa, A. G. Cepni, P. V. Nikitin  

E-Print Network [OSTI]

1 A Simple Path Loss Prediction Model for HVAC Systems O. K. Tonguz, D. D. Stancil, A. E. Xhafa, A, and air conditioning (HVAC) cylindrical ducts in 2.4-2.5 GHz frequency band. The model we propose predicts the average power loss between a transmitter-receiver pair in an HVAC duct network. This prediction model

Stancil, Daniel D.

411

Stress-induced patterns in ion-irradiated Silicon: a model based on anisotropic plastic flow  

E-Print Network [OSTI]

We present a model for the effect of stress on thin amorphous films that develop atop ion-irradiated silicon, based on the mechanism of ion-induced anisotropic plastic flow. Using only parameters directly measured or known to high accuracy, the model exhibits remarkably good agreement with the wavelengths of experimentally-observed patterns, and agrees qualitatively with limited data on ripple propagation speed. The predictions of the model are discussed in the context of other mechanisms recently theorized to explain the wavelengths, including extensive comparison with an alternate model of stress.

Scott A. Norris

2012-07-24T23:59:59.000Z

412

Thermodynamic model for predicting equilibrium conditions of clathrate hydrates of noble gases + light hydrocarbons: Combination of Van der Waals–Platteeuw model and sPC-SAFT EoS  

Science Journals Connector (OSTI)

Abstract In this communication, equilibrium conditions of clathrate hydrates containing mixtures of noble gases (Argon, Krypton and Xenon) and light hydrocarbons (C1–C3), which form structure I and II, are modeled. The thermodynamic model is based on the solid solution theory of Van der Waals–Platteeuw combined with the simplified Perturbed-Chain Statistical Association Fluid Theory equation of state (sPC-SAFT EoS). In dispersion term of sPC-SAFT EoS, the temperature dependent binary interaction parameters ( k ij ) are adjusted; taking advantage of the well described (vapor + liquid) phase equilibria. Furthermore, the Kihara potential parameters are optimized based on the P–T data of pure hydrate former. Subsequently, these obtained parameters are used to predict the binary gas hydrate dissociation conditions. The equilibrium conditions of the binary gas hydrates predicted by this model agree well with experimental data (overall AAD P  ?  2.17).

Mostafa Abolala; Farshad Varaminian

2015-01-01T23:59:59.000Z

413

Feature-incorporated alignment based ligand-binding residue prediction for carbohydrate-binding modules  

Science Journals Connector (OSTI)

......of hydrogen bonding in the interaction between a xylan binding module and xylan. Biochemistry (2001) 40:5700-5707. Yang...predicted ligand-binding residues residing on the surface in the hypothetical structures were verified to......

Wei-Yao Chou; Wei-I Chou; Tun-Wen Pai; Shu-Chuan Lin; Ting-Ying Jiang; Chuan-Yi Tang; Margaret Dah-Tsyr Chang

2010-04-15T23:59:59.000Z

414

Prediction of disordered regions in proteins based on the meta approach  

Science Journals Connector (OSTI)

......algorithm (the simplest ensemble-leaning algorithm), the ensembles of training subsets are...prediction accuracy of an ensemble predictor, similar to...Several predictors of disordered proteins are now available, but......

Takashi Ishida; Kengo Kinoshita

2008-06-01T23:59:59.000Z

415

A Geographic Primitive-Based Bayesian Framework to Predict Cyclone-Induced Flooding  

Science Journals Connector (OSTI)

The effectiveness of managing cyclone-induced floods is highly dependent on how fast reasonably accurate predictions can be made, which is a particularly difficult task given the multitude of highly variable physical factors. Even with ...

Isuri Wijesundera; Malka N. Halgamuge; Thas Nirmalathas; Thrishantha Nanayakkara

2013-04-01T23:59:59.000Z

416

Optimization Research of Refueled Scheme Based on Fuel Price Prediction of the Voyage Charter  

Science Journals Connector (OSTI)

Fuel cost is the key element of ship owners to control operating costs in the business of voyage charter. While fuel price changes frequently over time and also ocean shipping transport cycle is long, and therefore how to develop the most economical refueling scheme among the ports of call becomes one of the significant issues that the ship owners concerning. This study addresses the refueling scheme optimization problem for voyage charter operators from the perspective of the ship owner. First, an ARMA-based model was proposed to forecast a time serials of the fuel prices. Then, to maximize the shipping operation profit, the non-linear programming model is formulated to solve the optimal refueling scheme where to refuel and how much to refuel. Finally, a case study on a Pacific Ocean-circle route under multi-charter voyage contracts is conducted for a dry bulk cargo ship. The results indicate that the optimal fuel supply program compared with conventional refueling cost saves 263,400 USD, accounting for 14.3% of the total operating profit.

Peng JIA; Xueshan SUN; Zhongzhen YANG

2012-01-01T23:59:59.000Z

417

A high temperature fatigue life prediction computer code based on the total strain version of StrainRange Partitioning (SRP)  

SciTech Connect (OSTI)

A recently developed high-temperature fatigue life prediction computer code is presented and an example of its usage given. The code discussed is based on the Total Strain version of Strainrange Partitioning (TS-SRP). Included in this code are procedures for characterizing the creep-fatigue durability behavior of an alloy according to TS-SRP guidelines and predicting cyclic life for complex cycle types for both isothermal and thermomechanical conditions. A reasonably extensive materials properties database is included with the code.

Mcgaw, M.A.; Saltsman, J.F.

1993-10-01T23:59:59.000Z

418

Optimization of Computational Performance and Accuracy in 3?D Transient CFD Model for CFB Hydrodynamics Predictions  

Science Journals Connector (OSTI)

This work aims to present a pure 3?D CFD model accurate and efficient for the simulation of a pilot scale CFB hydrodynamics. The accuracy of the model was investigated as a function of the numerical parameters in order to derive an optimum model setup with respect to computational cost. The necessity of the in depth examination of hydrodynamics emerges by the trend to scale up CFBCs. This scale up brings forward numerous design problems and uncertainties which can be successfully elucidated by CFD techniques. Deriving guidelines for setting a computational efficient model is important as the scale of the CFBs grows fast while computational power is limited. However the optimum efficiency matter has not been investigated thoroughly in the literature as authors were more concerned for their models accuracy and validity. The objective of this work is to investigate the parameters that influence the efficiency and accuracy of CFB computational fluid dynamics models find the optimum set of these parameters and thus establish this technique as a competitive method for the simulation and design of industrial large scale beds where the computational cost is otherwise prohibitive. During the tests that were performed in this work the influence of turbulence modeling approach time and space density and discretization schemes were investigated on a 1.2 MWth CFB test rig. Using Fourier analysis dominant frequencies were extracted in order to estimate the adequate time period for the averaging of all instantaneous values. The compliance with the experimental measurements was very good. The basic differences between the predictions that arose from the various model setups were pointed out and analyzed. The results showed that a model with high order space discretization schemes when applied on a coarse grid and averaging of the instantaneous scalar values for a 20 sec period adequately described the transient hydrodynamic behaviour of a pilot CFB while the computational cost was kept low. Flow patterns inside the bed such as the core?annulus flow and the transportation of clusters were at least qualitatively captured.

I. Rampidis; A. Nikolopoulos; N. Koukouzas; P. Grammelis; E. Kakaras

2007-01-01T23:59:59.000Z

419

Improving Thermal Model Prediction Through Statistical Analysis of Irradiation and Post-Irradiation Data from AGR Experiments  

SciTech Connect (OSTI)

As part of the Research and Development program for Next Generation High Temperature Reactors (HTR), a series of irradiation tests, designated as Advanced Gas-cooled Reactor (AGR), have been defined to support development and qualification of fuel design, fabrication process, and fuel performance under normal operation and accident conditions. The AGR tests employ fuel compacts placed in a graphite cylinder shrouded by a steel capsule and instrumented with thermocouples (TC) embedded in graphite blocks enabling temperature control. The data representing the crucial test fuel conditions (e.g., temperature, neutron fast fluence, and burnup) while impossible to obtain from direct measurements are calculated by physics and thermal models. The irradiation and post-irradiation examination (PIE) experimental data are used in model calibration effort to reduce the inherent uncertainty of simulation results. This paper is focused on fuel temperature predicted by the ABAQUS code’s finite element-based thermal models. The work follows up on a previous study, in which several statistical analysis methods were adapted, implemented in the NGNP Data Management and Analysis System (NDMAS), and applied for improving qualification of AGR-1 thermocouple data. The present work exercises the idea that the abnormal trends of measured data observed from statistical analysis may be caused by either measuring instrument deterioration or physical mechanisms in capsules that may have shifted the system thermal response. As an example, the uneven reduction of the control gas gap in Capsule 5 revealed by the capsule metrology measurements in PIE helps justify the reduction in TC readings instead of TC drift. This in turn prompts modification of thermal model to better fit with experimental data, thus help increase confidence, and in other word reduce model uncertainties in thermal simulation results of the AGR-1 test.

Dr. Binh T. Pham; Grant L. Hawkes; Jeffrey J. Einerson

2012-10-01T23:59:59.000Z

420

Observing, modeling and predicting the effects of solar radio bursts on radio communications  

Science Journals Connector (OSTI)

The Sun is a source of broadband radio noise which can reach significantly high levels during outbursts associated with the time evolution of the activity cycle. The statistics point out that the maximum occurrence frequency and intensity of solar radio bursts (SRBs) are observed in the proximity of the activity maximum but relevant phenomena can occur also in the raising and declining phases of the cycle. Both theoretical estimations based on extensive statistical analyses carried out in recent years and direct observations performed in the past solar activity cycle indicate that solar radio bursts can interfere wireless communications as well as Global Navigation Satellite Systems (GNSS). In this work we briefly review the theoretical basis and the experimental evidences to date and we show the effectiveness of fast multichannel solar radiopolarimeters like the Trieste Solar Radio System in monitoring and predicting solar radio noise increase in the framework of Space Weather applications.

Mauro Messerotti

2008-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "model based predictive" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


421

Modeling the Number of Ignitions Following an Earthquake: Developing Prediction Limits for Overdispersed Count Data  

Broader source: Energy.gov (indexed) [DOE]

LA-UR-11-01857 LA-UR-11-01857 Approved for public release; distribution I unlimited. Title: Modeling the Number of Ignitions Following an Earthquake: Developing Prediction Limits for Overdispersed Count Data Authors: Elizabeth J. Kelly and Raymond N. Tell Intended Use: Deliverable to SB-TS: Safety Basis Technical Services Los Alamos National Laboratory, an affirmative action/equal opportunity employer, is operated by the Los Alamos National Security, LLC for the National Nuclear Security Administration of the U.S. Department of Energy under contract DE-AC52- 06NA25396. By acceptance of this article, the publisher recognizes that the U.S. Government retains a nonexclusive, royalty-free license to publish or reproduce the published form of this contribution, or to allow others to do so, for U.S.

422

Depositional sequence analysis and sedimentologic modeling for improved prediction of Pennsylvanian reservoirs (Annex 1)  

SciTech Connect (OSTI)

Interdisciplinary studies of the Upper Pennsylvanian Lansing and Kansas City groups have been undertaken in order to improve the geologic characterization of petroleum reservoirs and to develop a quantitative understanding of the processes responsible for formation of associated depositional sequences. To this end, concepts and methods of sequence stratigraphy are being used to define and interpret the three-dimensional depositional framework of the Kansas City Group. The investigation includes characterization of reservoir rocks in oil fields in western Kansas, description of analog equivalents in near-surface and surface sites in southeastern Kansas, and construction of regional structural and stratigraphic framework to link the site specific studies. Geologic inverse and simulation models are being developed to integrate quantitative estimates of controls on sedimentation to produce reconstructions of reservoir-bearing strata in an attempt to enhance our ability to predict reservoir characteristics.

Watney, W.L.

1992-01-01T23:59:59.000Z

423

Short-term production optimization of offshore oil and gas production using nonlinear model predictive control  

Science Journals Connector (OSTI)

The topic of this paper is the application of nonlinear model predictive control (NMPC) for optimizing control of an offshore oil and gas production facility. Of particular interest is the use of NMPC for direct short-term production optimization, where two methods for (one-layer) production optimization in NMPC are investigated. The first method is the unreachable setpoints method where an unreachable setpoint is used in order to maximize oil production. The ideas from this method are combined with the exact penalty function for soft constraints in a second method, named infeasible soft-constraints. Both methods can be implemented within standard NMPC software tools. The case-study first looks into the use of NMPC for ‘conventional’ pressure control, where disturbance rejection of time-varying disturbances (caused, e.g., by the ‘slugging’ phenomenon) is an issue. Then the above two methods for production optimization are employed, where both methods find the economically optimal operating point. Two different types of reservoir models are studied, using rate-independent and rate-dependent gas/oil ratios. These models lead to different types of optimums. The relative merits of the two methods for production optimization, and advantages of the two one-layer approaches compared to a two-layer structure, are discussed.

Anders Willersrud; Lars Imsland; Svein Olav Hauger; Pål Kittilsen

2013-01-01T23:59:59.000Z

424

Modeling Stress Strain Relationships and Predicting Failure Probabilities For Graphite Core Components  

SciTech Connect (OSTI)

This project will implement inelastic constitutive models that will yield the requisite stress-strain information necessary for graphite component design. Accurate knowledge of stress states (both elastic and inelastic) is required to assess how close a nuclear core component is to failure. Strain states are needed to assess deformations in order to ascertain serviceability issues relating to failure, e.g., whether too much shrinkage has taken place for the core to function properly. Failure probabilities, as opposed to safety factors, are required in order to capture the bariability in failure strength in tensile regimes. The current stress state is used to predict the probability of failure. Stochastic failure models will be developed that can accommodate possible material anisotropy. This work will also model material damage (i.e., degradation of mechanical properties) due to radiation exposure. The team will design tools for components fabricated from nuclear graphite. These tools must readily interact with finite element software--in particular, COMSOL, the software algorithm currently being utilized by the Idaho National Laboratory. For the eleastic response of graphite, the team will adopt anisotropic stress-strain relationships available in COMSO. Data from the literature will be utilized to characterize the appropriate elastic material constants.

Duffy, Stephen

2013-09-09T23:59:59.000Z

425

Development of a model for predicting transient hydrogen venting in 55-gallon drums  

SciTech Connect (OSTI)

Remote drum venting was performed on a population of unvented high activity drums (HAD) in the range of 63 to 435 plutonium equivalent Curies (PEC). These 55-gallon Transuranic (TRU) drums will eventually be shipped to the Waste Isolation Pilot Plant (WIPP). As a part of this process, the development of a calculational model was required to predict the transient hydrogen concentration response of the head space and polyethylene liner (if present) within the 55-gallon drum. The drum and liner were vented using a Remote Drum Venting System (RDVS) that provided a vent sampling path for measuring flammable hydrogen vapor concentrations and allow hydrogen to diffuse below lower flammability limit (LFL) concentrations. One key application of the model was to determine the transient behavior of hydrogen in the head space, within the liner, and the sensitivity to the number of holes made in the liner or number of filters. First-order differential mass transport equations were solved using Laplace transformations and numerically to verify the results. the Mathematica 6.0 computing tool was also used as a validation tool and for examining larger than two chamber systems. Results will be shown for a variety of configurations, including 85-gallon and 110-gallon overpack drums. The model was also validated against hydrogen vapor concentration assay measurements.

Apperson, Jason W [Los Alamos National Laboratory; Clemmons, James S [Los Alamos National Laboratory; Garcia, Michael D [Los Alamos National Laboratory; Sur, John C [Los Alamos National Laboratory; Zhang, Duan Z [Los Alamos National Laboratory; Romero, Michael J [Los Alamos National Laboratory

2008-01-01T23:59:59.000Z

426

Variance, Skewness & Kurtosis: results from the APM Cluster Redshift Survey and model predictions  

E-Print Network [OSTI]

We estimate the variance $\\xibar_2$, the skewness $\\xibar_3$ and the kurtosis $\\xibar_4$ in the distribution of density fluctuations in a complete sample from the APM Cluster Redshift Survey with 339 clusters and a mean depth $ \\sim 250\\Mpc$. We are able to measure the statistics of fluctuations in spheres of radius $R \\simeq 5-80 \\Mpc$, with reasonable errorbars. The statistics in the cluster distribution follow the hierarchical pattern $\\xibar_J=S_J~\\xibar_2^{J-1}$ with $S_J$ roughly constant, $S_3 \\simeq 2$ and $S_4 \\sim 8$. We analyse the distribution of clusters taken from N-body simulations of different dark matter models. The results are compared with an alternative method of simulating clusters which uses the truncated Zel'dovich approximation. We argue that this alternative method is not reliable enough for making quantitative predictions of $\\xibar$. The N-body simulation results follow similar hierarchical relations to the observations, with $S_J$ almost unaffected by redshift distortions from peculiar motions. The standard $\\Omega=1$ Cold Dark Matter (CDM) model is inconsistent with either the second, third or fourth order statistics at all scales. However both a hybrid Mixed Dark Matter model and a low density CDM variant agree with the $\\xibar_J$ observations.

Enrique Gaztañaga; Rupert Croft; Gavin Dalton

1995-01-31T23:59:59.000Z

427

Predictive Treatment Management: Incorporating a Predictive Tumor Response Model Into Robust Prospective Treatment Planning for Non-Small Cell Lung Cancer  

SciTech Connect (OSTI)

Purpose: We hypothesized that a treatment planning technique that incorporates predicted lung tumor regression into optimization, predictive treatment planning (PTP), could allow dose escalation to the residual tumor while maintaining coverage of the initial target without increasing dose to surrounding organs at risk (OARs). Methods and Materials: We created a model to estimate the geometric presence of residual tumors after radiation therapy using planning computed tomography (CT) and weekly cone beam CT scans of 5 lung cancer patients. For planning purposes, we modeled the dynamic process of tumor shrinkage by morphing the original planning target volume (PTV{sub orig}) in 3 equispaced steps to the predicted residue (PTV{sub pred}). Patients were treated with a uniform prescription dose to PTV{sub orig}. By contrast, PTP optimization started with the same prescription dose to PTV{sub orig} but linearly increased the dose at each step, until reaching the highest dose achievable to PTV{sub pred} consistent with OAR limits. This method is compared with midcourse adaptive replanning. Results: Initial parenchymal gross tumor volume (GTV) ranged from 3.6 to 186.5 cm{sup 3}. On average, the primary GTV and PTV decreased by 39% and 27%, respectively, at the end of treatment. The PTP approach gave PTV{sub orig} at least the prescription dose, and it increased the mean dose of the true residual tumor by an average of 6.0 Gy above the adaptive approach. Conclusions: PTP, incorporating a tumor regression model from the start, represents a new approach to increase tumor dose without increasing toxicities, and reduce clinical workload compared with the adaptive approach, although model verification using per-patient midcourse imaging would be prudent.

Zhang, Pengpeng, E-mail: zhangp@mskcc.org [Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York (United States); Yorke, Ellen; Hu, Yu-Chi; Mageras, Gig [Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York (United States); Rimner, Andreas [Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, New York (United States); Deasy, Joseph O. [Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York (United States)

2014-02-01T23:59:59.000Z

428

Coupling a Mesoscale Numerical Weather Prediction Model with Large-Eddy Simulation for Realistic Wind Plant Aerodynamics Simulations (Poster)  

SciTech Connect (OSTI)

Wind plant aerodynamics are influenced by a combination of microscale and mesoscale phenomena. Incorporating mesoscale atmospheric forcing (e.g., diurnal cycles and frontal passages) into wind plant simulations can lead to a more accurate representation of microscale flows, aerodynamics, and wind turbine/plant performance. Our goal is to couple a numerical weather prediction model that can represent mesoscale flow [specifically the Weather Research and Forecasting model] with a microscale LES model (OpenFOAM) that can predict microscale turbulence and wake losses.

Draxl, C.; Churchfield, M.; Mirocha, J.; Lee, S.; Lundquist, J.; Michalakes, J.; Moriarty, P.; Purkayastha, A.; Sprague, M.; Vanderwende, B.

2014-06-01T23:59:59.000Z

429

Real-Time Track Prediction of Tropical Cyclones over the North Indian Ocean Using the ARW Model  

E-Print Network [OSTI]

Real-Time Track Prediction of Tropical Cyclones over the North Indian Ocean Using the ARW Model of Technology Bhubaneswar, Odisha, India A. ROUTRAY National Centre for Medium Range Weather Forecasting, Noida The performance of the Advanced Research version of the Weather Research and Forecasting (ARW) model in real

430

Optimization of the GB/SA Solvation Model for Predicting the Structure of Surface Loops in Proteins  

E-Print Network [OSTI]

Optimization of the GB/SA Solvation Model for Predicting the Structure of Surface Loops in ProteinsVed: October 10, 2005; In Final Form: December 1, 2005 Implicit solvation models are commonly optimized the force field is sometimes not considered. In previous studies, we have developed an optimization

Meirovitch, Hagai

431

Commercial Buildings Sector Agent-Based Model | Open Energy Information  

Open Energy Info (EERE)

Commercial Buildings Sector Agent-Based Model Commercial Buildings Sector Agent-Based Model Jump to: navigation, search Tool Summary Name: Commercial Buildings Sector Agent-Based Model Agency/Company /Organization: Argonne National Laboratory Sector: Energy Focus Area: Buildings - Commercial Phase: Evaluate Options Topics: Implementation Resource Type: Technical report User Interface: Website Website: web.anl.gov/renewables/research/building_agent_based_model.html OpenEI Keyword(s): EERE tool, Commercial Buildings Sector Agent-Based Model Language: English References: Building Efficiency: Development of an Agent-based Model of the US Commercial Buildings Sector[1] Model the market-participants, dynamics, and constraints-help decide whether to adopt energy-efficient technologies to meet commercial building

432

MODELING AND CONTROLLING PARALLEL TASKS IN DROPLET-BASED MICROFLUIDIC  

E-Print Network [OSTI]

Chapter 12 MODELING AND CONTROLLING PARALLEL TASKS IN DROPLET-BASED MICROFLUIDIC SYSTEMS Karl F-independent models and algorithms to automate the operation of droplet-based microfluidic systems. In these systems mapping of a biochemical analysis task onto a droplet-based microfluidic system is investigated. Achieving

433

Engineering Process Coordination based on A Service Event Notification Model  

E-Print Network [OSTI]

Engineering Process Coordination based on A Service Event Notification Model Jian Cao1, Jie Wang2 the project lifecycle process. Grid-based engineering service is a potentially useful technology for process coordination. Thus we propose a Grid service based event notification model to support engineering process

Stanford University

434

Cross-language retrieval using link-based language models  

Science Journals Connector (OSTI)

We propose a cross-language retrieval model that is solely based on Wikipedia as a training corpus. The main contributions of our work are: 1. A translation model based on linked text in Wikipedia and a term weighting method associated with it. 2. A ... Keywords: CLIR, LDA, language modeling, wikipedia

Benjamin Roth; Dietrich Klakow

2010-07-01T23:59:59.000Z

435

Position Paper: Model-Based Testing Mark Utting  

E-Print Network [OSTI]

Position Paper: Model-Based Testing Mark Utting The University of Waikato, New Zealand Email: marku@cs.waikato.ac.nz 1 Introduction This position paper gives an overview of model-based testing and discusses how is the automation of black-box test design. It usually in- volves four stages: 1 #12;1. building an abstract model

Utting, Mark

436

PARAMETER ESTIMATION BASED MODELS OF WATER SOURCE HEAT PUMPS  

E-Print Network [OSTI]

PARAMETER ESTIMATION BASED MODELS OF WATER SOURCE HEAT PUMPS By HUI JIN Bachelor of Science validation of the water-to-air heat pump model. It's hard to find any words to express the thanks to my BASED MODELS OF WATER SLOURCE HEAT PUMPS Thesis Approved: Thesis Adviser Dean of the Graduate College ii

437

A Debugging Scheme for Declarative Equation Based Modeling Languages  

E-Print Network [OSTI]

and simulation, a number of object-oriented and/or declarative acausal modeling languages have emerged languages. Since these languages usually are based on object- orientation and acausal physical modelingA Debugging Scheme for Declarative Equation Based Modeling Languages Peter Bunus and Peter Fritzson

Burns, Peter

438

Molecule-based modeling of heavy oil  

Science Journals Connector (OSTI)

A molecular-level kinetics model has been developed for the pyrolysis of heavy residual oil. Resid structure was modeled in terms of three attribute groups: cores, inter-core linkages, and side chains. The con...

Scott R. Horton; Zhen Hou; Brian M. Moreno; Craig A. Bennett…

2013-07-01T23:59:59.000Z

439

MIT Big Data Challenge: Transportation in the City of Boston Model of Prediction Challenge  

E-Print Network [OSTI]

and for periods before and after the prediction interval. When available, the number of MBTA T rides at nearby

Oliva, Aude

440

Predictions of flow through an isothermal serpentine passage with linear eddy-viscosity Reynolds Averaged Navier Stokes models.  

SciTech Connect (OSTI)

Flows with strong curvature present a challenge for turbulence models, specifically eddy viscosity type models which assume isotropy and a linear and instantaneous equilibrium relation between stress and strain. Results obtained from three different codes and two different linear eddy viscosity turbulence models are compared to a DNS simulation in order to gain some perspective on the turbulence modeling capability of SIERRA/Fuego. The Fuego v2f results are superior to the more common two-layer k-e model results obtained with both a commercial and research code in terms of the concave near wall behavior predictions. However, near the convex wall, including the separated region, little improvement is gained using the v2f model and in general the turbulent kinetic energy prediction is fair at best.

Laskowski, Gregory Michael

2005-12-01T23:59:59.000Z

Note: This page contains sample records for the topic "model based predictive" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


441

Impact of Pilot Light Modeling on the Predicted Annual Performance of Residential Gas Water Heaters: Preprint  

SciTech Connect (OSTI)

Modeling residential water heaters with dynamic simulation models can provide accurate estimates of their annual energy consumption, if the units? characteristics and use conditions are known. Most gas storage water heaters (GSWHs) include a standing pilot light. It is generally assumed that the pilot light energy will help make up standby losses and have no impact on the predicted annual energy consumption. However, that is not always the case. The gas input rate and conversion efficiency of a pilot light for a GSWH were determined from laboratory data. The data were used in simulations of a typical GSWH with and without a pilot light, for two cases: 1) the GSWH is used alone; and 2) the GSWH is the second tank in a solar water heating (SWH) system. The sensitivity of wasted pilot light energy to annual hot water use, climate, and installation location was examined. The GSWH used alone in unconditioned space in a hot climate had a slight increase in energy consumption. The GSWH with a pilot light used as a backup to an SWH used up to 80% more auxiliary energy than one without in hot, sunny locations, from increased tank losses.

Maguire, J.; Burch, J.

2013-08-01T23:59:59.000Z

442

Statistical prediction of protein–chemical interactions based on chemical structure and mass spectrometry data  

Science Journals Connector (OSTI)

......acid composition n-peptide composition and the derivatives of these...was applied. fDipeptide composition was used in mapping C and...Using pseudo amino acid composition to predict protein structural...studies of drug-likeness, agrochemical-likeness, and enzyme inhibition......

Nobuyoshi Nagamine; Yasubumi Sakakibara

2007-08-01T23:59:59.000Z

443

User behavior pattern analysis and prediction based on mobile phone sensors  

Science Journals Connector (OSTI)

More and more mobile phones are equipped with multiple sensors today. This creates a new opportunity to analyze users' daily behaviors and evolve mobile phones into truly intelligent personal devices, which provide accurate context-adaptive and individualized ... Keywords: MAST, context-adaptive, individualized, mobile computing, pattern prediction, sensor, user behavior analysis

Jiqiang Song; Eugene Y. Tang; Leibo Liu

2010-09-01T23:59:59.000Z

444

A bridge-functional-based classical mapping method for predicting the correlation functions of uniform electron gases at finite temperature  

SciTech Connect (OSTI)

Efficient and accurate prediction of the correlation functions of uniform electron gases is of great importance for both practical and theoretical applications. This paper presents a bridge-functional-based classical mapping method for calculating the correlation functions of uniform spin-unpolarized electron gases at finite temperature. The bridge functional is formulated by following Rosenfeld's universality ansatz in combination with the modified fundamental measure theory. The theoretical predictions are in good agreement with recent quantum Monte Carlo results but with negligible computational cost, and the accuracy is better than a previous attempt based on the hypernetted-chain approximation. We find that the classical mapping method is most accurate if the effective mass of electrons increases as the density falls.

Liu, Yu; Wu, Jianzhong, E-mail: jwu@engr.ucr.edu [Department of Chemical and Environmental Engineering and Department of Mathematics, University of California, Riverside, California 92521 (United States)] [Department of Chemical and Environmental Engineering and Department of Mathematics, University of California, Riverside, California 92521 (United States)

2014-02-28T23:59:59.000Z

445

Extending the bubble-based EMMS model to CFB riser simulations  

Science Journals Connector (OSTI)

Abstract Experiments have shown that various structures (e.g., the streamer, cluster, void and bubble) can be clearly observed in gas–solid fluidized beds. These structures affect the overall behavior of flow, mass/heat transfer and reactions significantly and should be accounted for in simulations of gas–solid flow. For the purpose of riser flow simulation, the structure-based drag model has already been proposed based on the description of meso-scale clusters, and incorporated into the framework of a two-fluid model to calculate the effective drag force. However, there is still no clear agreement about the characterization of clusters, such as the cluster shape, size and orientation. In contrast, it is easier to describe and measure the characteristic size of visible bubbles. Here, we attempt to upgrade the bubble-based EMMS model which was established for bubbling fluidized beds originally, and then extend it to simulations of heterogeneous gas–solid flows in CFB risers. Numerical analysis on this new model reveals that it is capable of predicting two turning points, where the flow state is transforming rapidly from uniform to heterogeneous distribution. The predicted voidage in the dense phase is qualitatively consistent with experimental data in the literature. To evaluate this new drag, finally, CFB riser simulations have been carried out by coupling of the two-fluid model and the bubble-based EMMS drag. The results show good agreement with experimental data.

Kun Hong; Zhansheng Shi; Atta Ullah; Wei Wang

2014-01-01T23:59:59.000Z

446

Modeling tip zones to predict the throw and length characteristics of faults  

SciTech Connect (OSTI)

A map of faults in a 60 km{sup 2} area of the southern North Sea has been produced from three-dimensional seismic data. The faults shown on the map obey power-law cumulative-frequency distributions for throw (power-law exponent, D, {approx} 2.7) and length (D {approx} 1.1). Simulations have been carried out to correct for sampling biases in the data and to make predictions of the throw the data and to make predictions of the throw and length scaling characteristics of the faults. The most important bias is caused by poor resolution of the small displacement tip zones of faults. The raw data show considerable scatter in their length: throw ratios, but they more closely fit a linar relationship if a length of 500 m is added to each fault, thereby making up for the zones near the fault tips with throws ({approx} 15 m) below seismic resolution. Further variability in the data may be caused by such geological factors as fault interaction. Tip lengths have been extended to simulate the actual fault pattern in the study area. Maps produced by this procedure can be used to estimate the true connectivity of the fault network. Extending the faults results in greater connectivity than shown by the raw data, which may cause greater compartmentalization of the rock mass. This greater compartmentalization has implications for hydrocarbon exploitation if the faults are sealing. A problem with the model, however, is that it does not deal effectively with the interaction of subparallel, noncoplanar faults. To test the reliability of the procedure, we analyzed exposure-scale faults in Somerset, United Kingdom, where the tips are well constrained. Both length-throw relationships and map-pattern connectivity for the simulated fault networks agree closely with the actual data.

Pickering, G.; Sanderson, D.J.; Bull, J.M. [Univ. of Southampton (United Kingdom)] [and others

1997-01-01T23:59:59.000Z

447

Development and verification of simplified prediction models for enhanced oil recovery applications. CO/sub 2/ (miscible flood) predictive model. Final report  

SciTech Connect (OSTI)

A screening model for CO/sub 2/ miscible flooding has been developed consisting of a reservoir model for oil rate and recovery and an economic model. The reservoir model includes the effects of viscous fingering, reservoir heterogeneity, gravity segregation and areal sweep. The economic model includes methods to calculate various profitability indices, the windfall profits tax, and provides for CO/sub 2/ recycle. The model is applicable to secondary or tertiary floods, and to solvent slug or WAG processes. The model does not require detailed oil-CO/sub 2/ PVT data for execution, and is limited to five-spot patterns. A pattern schedule may be specified to allow economic calculations for an entire project to be made. Models of similar architecture have been developed for steam drive, in-situ combustion, surfactant-polymer flooding, polymer flooding and waterflooding. 36 references, 41 figures, 4 tables.

Paul, G.W.

1984-10-01T23:59:59.000Z

448

A Debugging Scheme for Declarative Equation Based Modeling Languages  

Science Journals Connector (OSTI)

This paper concerns the static analysis for debugging purposes of programs written in declarative equation based modeling languages. We first give an introduction to declarative equation based languages and th...

Peter Bunus; Peter Fritzson

2002-01-01T23:59:59.000Z

449

Chemical Kinetic Modeling of Non-Petroleum Based Fuels | Department...  

Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

ft010pitz2011o.pdf More Documents & Publications Chemical Kinetic Modeling of Non-Petroleum Based Fuels Chemical Kinetic Modeling of Fuels Simulation of High Efficiency Clean...

450

Chemical Kinetic Modeling of Non-Petroleum Based Fuels | Department...  

Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

ft010pitz2012o.pdf More Documents & Publications Chemical Kinetic Modeling of Non-Petroleum Based Fuels Chemical Kinetic Modeling of Fuels Chemical Kinetic Research on HCCI &...

451

Tokamak reactor cost model based on STARFIRE/WILDCAT costing  

SciTech Connect (OSTI)

A cost model is presented which is useful for survey and comparative studies of tokamak reactors. The model is heavily based on STARFIRE and WILDCAT costing guidelines, philosophies, and procedures and reproduces the costing for these devices quite accurately.

Evans, K. Jr.

1983-03-01T23:59:59.000Z

452

Design of a component-based integrated environmental modeling framework  

Science Journals Connector (OSTI)

Integrated environmental modeling (IEM) includes interdependent science-based components that comprise an appropriate software modeling system and are responsible for consuming and producing information as part of the system, but moving information from ... Keywords: FRAMES, IEM, Integrated environmental modeling, Multimedia modeling, Risk assessment

Gene Whelan, Keewook Kim, Mitch A. Pelton, Karl J. Castleton, Gerard F. Laniak, Kurt Wolfe, Rajbir Parmar, Justin Babendreier, Michael Galvin

2014-05-01T23:59:59.000Z

453

Node-Based connection semantics for equation-based object-oriented modeling languages  

Science Journals Connector (OSTI)

Declarative, Equation-Based Object-Oriented (EOO) modeling languages, like Modelica, support modeling of physical systems by composition of reusable component models. An important application area is modeling of cyber-physical systems. EOO languages ... Keywords: declarative languages, modeling, simulation

David Broman; Henrik Nilsson

2012-01-01T23:59:59.000Z

454

Do Ecological Niche Model Predictions Reflect the Adaptive Landscape of Species?: A Test Using Myristica malabarica Lam., an Endemic Tree in the Western Ghats, India  

E-Print Network [OSTI]

Ecological niche models (ENM) have become a popular tool to define and predict the “ecological niche” of a species. An implicit assumption of the ENMs is that the predicted ecological niche of a species actually reflects ...

Nagaraju, Shivaprakash K.; Gudasalamani, Ravikanth; Barve, Narayani; Ghazoul, Jaboury; Narayangowda, Ganeshaiah Kotiganahalli; Ramanan, Uma Shaanker

2013-11-29T23:59:59.000Z

455

Topology-Based Vehicle Systems Modelling.  

E-Print Network [OSTI]

??The simulation tools that are used to model vehicle systems have not been advancing as quickly as the growth of research and technology surrounding the… (more)

Yam, Edward

2013-01-01T23:59:59.000Z

456

Agent based modeling of energy networks  

Science Journals Connector (OSTI)

Abstract Attempts to model any present or future power grid face a huge challenge because a power grid is a complex system, with feedback and multi-agent behaviors, integrated by generation, distribution, storage and consumption systems, using various control and automation computing systems to manage electricity flows. Our approach to modeling is to build upon an established model of the low voltage electricity network which is tested and proven, by extending it to a generalized energy model. But, in order to address the crucial issues of energy efficiency, additional processes like energy conversion and storage, and further energy carriers, such as gas, heat, etc., besides the traditional electrical one, must be considered. Therefore a more powerful model, provided with enhanced nodes or conversion points, able to deal with multidimensional flows, is being required. This article addresses the issue of modeling a local multi-carrier energy network. This problem can be considered as an extension of modeling a low voltage distribution network located at some urban or rural geographic area. But instead of using an external power flow analysis package to do the power flow calculations, as used in electric networks, in this work we integrate a multiagent algorithm to perform the task, in a concurrent way to the other simulation tasks, and not only for the electric fluid but also for a number of additional energy carriers. As the model is mainly focused in system operation, generation and load models are not developed.

José María Gonzalez de Durana; Oscar Barambones; Enrique Kremers; Liz Varga

2014-01-01T23:59:59.000Z

457

Modeling and computing based on lattices.  

E-Print Network [OSTI]

??This dissertation presents three studies addressing various modeling and computational aspects of lattice structures. The first study is concerned with characterization of the threshold behavior… (more)

Zhao, Haifeng, 1980-

2011-01-01T23:59:59.000Z

458

Thermal transport property prediction of a CMC laminate from base materials properties and manufacturing porosities  

Science Journals Connector (OSTI)

...aeroengines and heavy-duty gas turbines and for applications...represents a relevant development towards the modelling...following modelling development strategy, which takes account...high-temperature components of gas turbines (Krenkel Gern 1993...

2005-01-01T23:59:59.000Z

459

A multivariate quadrature based moment method for supersonic combustion modeling  

E-Print Network [OSTI]

QMOM is then used for studying an experimental Mach 2.2 supersonic cavity based combustor. Development of predictive is the transported probability density function (PDF) method. Here, the joint-PDF of the gas phase thermochemical. Finally, the stochastic nature of these methods introduces numerical instabilities when coupled

Raman, Venkat

460

Supersonic combustion studies using a multivariate quadrature based method for combustion modeling  

E-Print Network [OSTI]

of predictive models for supersonic combustion is a critical step in design and development of scramjet engines

Raman, Venkat

Note: This page contains sample records for the topic "model based predictive" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


461

Knowledge-based simulation of DNA metabolism: prediction of enzyme action  

Science Journals Connector (OSTI)

......Press January 1991 other ORIGINAL PAPERS Knowledge-based simulation of DNA metabolism...CA 94305, USA We have developed a knowledge-based simulation of DNA metabolism...first step in the development of a large knowledge base to envision the metabolic pathways......

Douglas L. Brutlag; Adam R. Galper; David H. Millis

1991-01-01T23:59:59.000Z

462

A prediction of meander migration based on large-scale flume tests in clay  

E-Print Network [OSTI]

Scour Depth, d, and Different Lengths of the Project Life, L t (Briaud et al. 2003) ......... 47 3.31 Time-Sequence Maps and Extrapolations (Lagasse et al. 2004b).............. 48 3.32 Predicted Position and Radius of Curvature... of the Circle that Defines the Outer Bank of the Hypothetical Channel in Year 4 (Lagasse et al. 2004b)................................................................................. 48 3.33 Cumulative Percentage of Extension Migration (Lagasse et...

Park, Namgyu

2009-05-15T23:59:59.000Z

463

Forecasting GHG emissions using an optimized artificial neural network model based on correlation and principal component analysis  

Science Journals Connector (OSTI)

Abstract The prediction of GHG emissions is very important due to their negative impacts on climate and global warming. The aim of this study was to develop a model for GHG forecasting emissions at the national level using a new approach based on artificial neural networks (ANN) and broadly available sustainability, economical and industrial indicators acting as inputs. The ANN model architecture and training parameters were optimized, with inputs being selected using correlation analysis and principal component analysis. The developed ANN models were compared with the corresponding multiple linear regression (MLR) model, while an ANN model created using transformed inputs (principal components) was compared with a principal component regression (PCR) model. Since the best results were obtained with the ANN model based on correlation analysis, that particular model was selected for the actual 2011 GHG emissions forecasting. The relative errors of the 2010 GHG emissions predictions were used to adjust the ANN model predictions for 2011, which subsequently resulted in the adjusted 2011 predictions having a MAPE value of only 3.60%. Sensitivity analysis showed that gross inland energy consumption had the highest sensitivity to GHG emissions.

Davor Z. Antanasijevi?; Mirjana ?. Risti?; Aleksandra A. Peri?-Gruji?; Viktor V. Pocajt

2014-01-01T23:59:59.000Z

464

A component-based debugging approach for detecting structural inconsistencies in declarative equation based models  

Science Journals Connector (OSTI)

Object-oriented modeling with declarative equation based languages often unconsciously leads to structural inconsistencies. Component-based debugging is a new structural analysis approach that addresses this problem by analyzing the structure of each ... Keywords: component-based debugging, declarative model, modelica, simulation, structural inconsistency

Jian-Wan Ding; Li-Ping Chen; Fan-Li Zhou

2006-05-01T23:59:59.000Z

465

Creating GIS-based spatial interaction models for retail centres in Jeddah City  

Science Journals Connector (OSTI)

Spatial interaction models are used today in facilities planning research for predicting and for allocating flows of demand between origin and destination areas based on the attractiveness of each facility and based on the distance between facilities and demand areas. These models have been adapted to a wide range of application areas including predicting flows of people to shops, offices, schools, and hospitals. The aim of this paper is to use GIS for producing spatial interaction models for two retail centres Jeddah City, Saudi Arabia. These models are created using ArcGIS software and using the interaction function which is available within the network analysis module. To produce these models, detailed geo-database was created that covers location of retail centres, the capacity of each centre, the size of centres demand at the study area, and road network coverage for Jeddah City. The created models can be used by city planners for identifying areas of the city that are poorly served by existing retail centres. In addition, these models can be used to define the impacts of expanding retail supply and or retail demand at the study area.

Abdulkader A. Murad

2014-01-01T23:59:59.000Z

466

A Mechanism-Based Approach to Predict the Relative Biological Effectiveness of Protons and Carbon Ions in Radiation Therapy  

SciTech Connect (OSTI)

Purpose: The physical and potential biological advantages of proton and carbon ions have not been fully exploited in radiation therapy for the treatment of cancer. In this work, an approach to predict proton and carbon ion relative biological effectiveness (RBE) in a representative spread-out Bragg peak (SOBP) is derived using the repair-misrepair-fixation (RMF) model. Methods and Materials: Formulas linking dose-averaged linear-quadratic parameters to DSB induction and processing are derived from the RMF model. The Monte Carlo Damage Simulation (MCDS) software is used to quantify the effects of radiation quality on the induction of DNA double-strand breaks (DSB). Trends in parameters {alpha} and {beta} for clinically relevant proton and carbon ion kinetic energies are determined. Results: Proton and carbon ion RBE are shown to increase as particle energy, dose, and tissue {alpha}/{beta} ratios decrease. Entrance RBE is {approx}1.0 and {approx}1.3 for protons and carbon ions, respectively. For doses in the range of 0.5 to 10 Gy, proton RBE ranges from 1.02 (proximal edge) to 1.4 (distal edge). Over the same dose range, the RBE for carbon ions ranges from 1.5 on the proximal edge to 6.7 on the distal edge. Conclusions: The proposed approach is advantageous because the RBE for clinically relevant particle distributions is guided by well-established physical and biological (track structure) considerations. The use of an independently tested Monte Carlo model to predict the effects of radiation quality on DSB induction also minimizes the number of ad hoc biological parameters that must be determined to predict RBE. Large variations in predicted RBE across an SOBP may produce undesirable biological hot and cold spots. These results highlight the potential for the optimization of physical dose for a uniform biological effect.

Frese, Malte C. [Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, Connecticut (United States); Department of Medical Physics in Radiation Oncology, German Cancer Research Center, Heidelberg (Germany); Yu, Victor K. [Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, Connecticut (United States); Stewart, Robert D. [Department of Radiation Oncology, University of Washington, Seattle, Washington (United States); Carlson, David J., E-mail: david.j.carlson@yale.edu [Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, Connecticut (United States)

2012-05-01T23:59:59.000Z

467

SEMANTIC LEARNING MODEL AND EXTENDED STUDENT MODEL: TOWARDS AN AHAM-BASED ADAPTIVE SYSTEM  

E-Print Network [OSTI]

SEMANTIC LEARNING MODEL AND EXTENDED STUDENT MODEL: TOWARDS AN AHAM-BASED ADAPTIVE SYSTEM Hend hypermedia systems, we distinguish AHAM as the most popular reference model which is based on the Dexter hoc integration of the AHAM's user's model as well as the IMS/LIP and IEEE/PAPI standards. KEY WORDS

Paris-Sud XI, Université de

468

Geothermal field case studies that document the usefulness of models in predicting reservoir and well behavior  

SciTech Connect (OSTI)

The geothermal industry has shown significant interest in case histories that document field production histories and demonstrate the techniques which work best in the characterization and evaluation of geothermal systems. In response to this interest, LBL has devoted a significant art of its geothermal program to the compilation and analysis of data from US and foreign fields (e.g., East Mesa, The Geysers, Susanville, and Long Valley in California; Klamath Falls in Oregon; Valles Caldera, New Mexico; Cerro Prieto and Los Azufres in Mexico; Krafla and Nesjavellir in Iceland; Larderello in Italy; Olkaria in Kenya). In each of these case studies we have been able to test and validate in the field, or against field data, the methodology and instrumentation developed under the Reservoir Technology Task of the DOE Geothermal Program, and to add to the understanding of the characteristics and processes occurring in geothermal reservoirs. Case study results of the producing Cerro Prieto and Olkaria geothermal fields are discussed in this paper. These examples were chosen because they illustrate the value of conceptual and numerical models to predict changes in reservoir conditions, reservoir processes, and well performance that accompany field exploitation, as well as to reduce the costs associated with the development and exploitation of geothermal resources. 14 refs., 6 figs.

Lippmann, M.J.

1989-03-01T23:59:59.000Z

469

Geothermal Field Case Studies that Document the Usefulness of Models in Predicting Reservoir and Well Behavior  

SciTech Connect (OSTI)

The geothermal industry has shown significant interest in case histories that document field production histories and demonstrate the techniques which work best in the characterization and evaluation of geothermal systems. In response to this interest, LBL has devoted a significant part of its geothermal program to the compilation and analysis of data from US and foreign fields (e.g., East Mesa, The Geysers, Susanville, and Long Valley in California; Klamath Fall in Oregon; Valles Caldera, New Mexico; Cerro Prieto and Los Azufres in Mexico; Krafla and Nesjavellir in Iceland; Larderello in Italy; Olkaria in Kenya). In each of these case studies we have been able to test and validate in the field, or against field data, the methodology and instrumentation developed under the Reservoir Technology Task of the DOE Geothermal Program, and to add to the understanding of the characteristics and processes occurring in geothermal reservoirs. Case study results of the producing Cerro Prieto and Olkaria geothermal fields are discussed in this paper. These examples were chosen because they illustrate the value of conceptual and numerical models to predict changes in reservoir conditions, reservoir processes, and well performance that accompany field exploitation, as well as to reduce the costs associated with the development and exploitation of geothermal resources.

Lippmann, Marcelo J.

1989-03-21T23:59:59.000Z

470

Development of a rock mass characteristics model for TBM penetration rate prediction.  

E-Print Network [OSTI]

??With the advances of technology, TBMs are becoming more versatile and TBM tunneling has become a common tunneling method. During project planning, the prediction of… (more)

Gong, Qiuming.

2008-01-01T23:59:59.000Z

471

Prediction of survival of ICU patients using computational intelligence  

Science Journals Connector (OSTI)

This paper presents a computational-intelligence-based model to predict the survival rate of critically ill patients who were admitted to an intensive care unit (ICU). The prediction input variables were based on the first 24h admission physiological ... Keywords: Clinical management, Computational intelligence, Fuzzy systems, ICU, Neural networks, Prediction of survival rate

Yi-Zeng Hsieh, Mu-Chun Su, Chen-Hsu Wang, Pa-Chun Wang

2014-04-01T23:59:59.000Z

472

Lattice and off-lattice side chain models of protein folding: Linear time structure prediction better than 86% of optimal  

SciTech Connect (OSTI)

This paper considers the protein structure prediction problem for lattice and off-lattice protein folding models that explicitly represent side chains. Lattice models of proteins have proven extremely useful tools for reasoning about protein folding in unrestricted continuous space through analogy. This paper provides the first illustration of how rigorous algorithmic analyses of lattice models can lead to rigorous algorithmic analyses of off-lattice models. The authors consider two side chain models: a lattice model that generalizes the HP model (Dill 85) to explicitly represent side chains on the cubic lattice, and a new off-lattice model, the HP Tangent Spheres Side Chain model (HP-TSSC), that generalizes this model further by representing the backbone and side chains of proteins with tangent spheres. They describe algorithms for both of these models with mathematically guaranteed error bounds. In particular, the authors describe a linear time performance guaranteed approximation algorithm for the HP side chain model that constructs conformations whose energy is better than 865 of optimal in a face centered cubic lattice, and they demonstrate how this provides a 70% performance guarantee for the HP-TSSC model. This is the first algorithm in the literature for off-lattice protein structure prediction that has a rigorous performance guarantee. The analysis of the HP-TSSC model builds off of the work of Dancik and Hannenhalli who have developed a 16/30 approximation algorithm for the HP model on the hexagonal close packed lattice. Further, the analysis provides a mathematical methodology for transferring performance guarantees on lattices to off-lattice models. These results partially answer the open question of Karplus et al. concerning the complexity of protein folding models that include side chains.

Hart, W.E.; Istrail, S. [Sandia National Labs., Albuquerque, NM (United States). Algorithms and Discrete Mathematics Dept.

1996-08-09T23:59:59.000Z

473

Improvement of ENSO simulation based on inter-model diversity  

Science Journals Connector (OSTI)

In this study, a new methodology is developed to improve the climate simulation of state-of-the-art coupled Global Climate Models (GCMs), by a post-processing based on the inter-model diversity. Based on the close connection between the ...

Yoo-Geun Ham; Jong-Seong Kug

474

Mobile Model-Based Bridge Lifecycle Management Systems  

E-Print Network [OSTI]

Mobile Model-Based Bridge Lifecycle Management Systems Amin Hammad* , Cheng Zhang, Yongxin Hu, Montreal, Quebec, Canada H3G 1T7, Canada Abstract: This paper discusses the requirements for developing Mobile Model-based Bridge Lifecycle Management Systems (MMBLMSs). These new systems should link all

Hammad, Amin

475

Mobile Model-Based Bridge Lifecycle Management Systems  

E-Print Network [OSTI]

Mobile Model-Based Bridge Lifecycle Management Systems Amin Hammad* Concordia Institute for Information Systems Engineering 1425 René Lévesque Boulevard, Montréal, Québec, H3G 1T7, Canada Cheng Zhang University Abstract: This paper discusses the requirements for developing Mobile Model-based Bridge Lifecycle

Hammad, Amin

476

Institute for Software Technology Model-Based Testing  

E-Print Network [OSTI]

t Institute for Software Technology Model-Based Testing Ausgewählte Kapitel Softwaretechnologie 2 2013/14 B.K. Aichernig Model-Based Testing 1 / 38 #12;t Institute for Software Technology Testing Testing: checking or measuring some quality characteristics of an executing system by performing

477

Analysis and Model-Based Control of Servomechanisms With Friction  

E-Print Network [OSTI]

Analysis and Model-Based Control of Servomechanisms With Friction Evangelos G. Papadopoulos e Engineering, National Technical University of Athens, 15780 Athens, Greece Friction is responsible for several, model-based feedback compensation is studied for servomechanism tracking tasks. Several kinetic friction

Papadopoulos, Evangelos

478

Procedural audio modeling for particle-based environmental effects  

E-Print Network [OSTI]

Procedural audio modeling for particle-based environmental effects Charles Verron, George Drettakis-based graphical models, resulting in syn- chronous audio/graphics environmental effects. The approach velocity) and sound parameters (e.g., distribution of sound atoms, spectral modifications). The joint audio

479

A Momentum-Zonal Model for Predicting Zone Airflow and Temperature Distributions to Enhance Building Load and Energy Simulations  

E-Print Network [OSTI]

ABSTRACT Building load and energy simulation programs based on the complete-mixing air model fail between model complexity and capturing enough of the physics. For building load and energy calculations to building problems over the past 30 years including: complete-mixing, nodal-network models, zonal models

Chen, Qingyan "Yan"

480

Log-normal distribution based EMOS models for probabilistic wind speed forecasting  

E-Print Network [OSTI]

Ensembles of forecasts are obtained from multiple runs of numerical weather forecasting models with different initial conditions and typically employed to account for forecast uncertainties. However, biases and dispersion errors often occur in forecast ensembles, they are usually under-dispersive and uncalibrated and require statistical post-processing. We present an Ensemble Model Output Statistics (EMOS) method for calibration of wind speed forecasts based on the log-normal (LN) distribution, and we also show a regime-switching extension of the model which combines the previously studied truncated normal (TN) distribution with the LN. Both presented models are applied to wind speed forecasts of the eight-member University of Washington mesoscale ensemble, of the fifty-member ECMWF ensemble and of the eleven-member ALADIN-HUNEPS ensemble of the Hungarian Meteorological Service, and their predictive performances are compared to those of the TN and general extreme value (GEV) distribution based EMOS methods an...

Baran, Sándor

2014-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "model based predictive" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


481

Cross-comparison of spacecraft-environment interaction model predictions applied to Solar Probe Plus near perihelion  

SciTech Connect (OSTI)

Five spacecraft-plasma models are used to simulate the interaction of a simplified geometry Solar Probe Plus (SPP) satellite with the space environment under representative solar wind conditions near perihelion. By considering similarities and differences between results obtained with different numerical approaches under well defined conditions, the consistency and validity of our models can be assessed. The impact on model predictions of physical effects of importance in the SPP mission is also considered by comparing results obtained with and without these effects. Simulation results are presented and compared with increasing levels of complexity in the physics of interaction between solar environment and the SPP spacecraft. The comparisons focus particularly on spacecraft floating potentials, contributions to the currents collected and emitted by the spacecraft, and on the potential and density spatial profiles near the satellite. The physical effects considered include spacecraft charging, photoelectron and secondary electron emission, and the presence of a background magnetic field. Model predictions obtained with our different computational approaches are found to be in agreement within 2% when the same physical processes are taken into account and treated similarly. The comparisons thus indicate that, with the correct description of important physical effects, our simulation models should have the required skill to predict details of satellite-plasma interaction physics under relevant conditions, with a good level of confidence. Our models concur in predicting a negative floating potential V{sub fl}??10V for SPP at perihelion. They also predict a “saturated emission regime” whereby most emitted photo- and secondary electron will be reflected by a potential barrier near the surface, back to the spacecraft where they will be recollected.

Marchand, R. [Department of Physics, University of Alberta, Edmonton, Alberta T6G 2E1 (Canada); Miyake, Y.; Usui, H. [Graduate School of System Informatics, Kobe University, Kobe 657-8501 (Japan); Deca, J.; Lapenta, G. [Centre for Mathematical Plasma Astrophysics, Mathematics Department, KU Leuven, Celestijnenlaan 200B bus 2400, 3001 Leuven (Belgium); Matéo-Vélez, J. C. [Department of Space Environment, Onera—The French Aerospace Lab, Toulouse (France); Ergun, R. E.; Sturner, A. [Department of Astrophysical and Planetary Science, University of Colorado, Boulder, Colorado 80309 (United States); Génot, V. [Institut de Recherche en Astrophysique et Planétologie, Université de Toulouse, France and CNRS, IRAP, 9 Av. colonel Roche, BP 44346, 31028 Toulouse cedex 4 (France); Hilgers, A. [ESA, ESTEC, Keplerlaan 1, PO Box 299, 2200 AG Noordwijk (Netherlands); Markidis, S. [High Performance Computing and Visualization Department, KTH Royal Institute of Technology, Stockholm (Sweden)

2014-06-15T23:59:59.000Z

482

Development and Validation of the 3-D Computational Fluid Dynamics Model for CANDU-6 Moderator Temperature Predictions  

SciTech Connect (OSTI)

A computational fluid dynamics (CFD) model for predicting the moderator circulation inside the Canada deuterium uranium (CANDU) reactor vessel has been developed to estimate the local subcooling of the moderator in the vicinity of the Calandria tubes. The buoyancy effect induced by internal heating is accounted for by Boussinesq approximation. The standard k-[curly epsilon] turbulence model associated with logarithmic wall treatment is applied to predict the turbulent jet flows from the inlet nozzles. The matrix of the Calandria tubes in the core region is simplified to porous media, in which anisotropic hydraulic impedance is modeled using an empirical correlation of the frictional pressure loss. The governing equations are solved by CFX-4.4, a commercial CFD code developed by AEA Technology. The CFD model has been successfully verified and validated against experimental data obtained at Stern Laboratories Inc. in Hamilton, Ontario, Canada.

Yoon, Churl; Rhee, Bo Wook; Min, Byung-Joo [Korea Atomic Energy Research Institute (Korea, Republic of)

2004-12-15T23:59:59.000Z

483

Kinetic model for predicting the concentrations of active halogens species in chlorinated saline cooling waters. Final report  

SciTech Connect (OSTI)

A kinetic model has been developed for describing the speciation of chlorine-produced oxidants in seawater as a function of time. The model is applicable under a broad variety of conditions, including all pH range, salinities, temperatures, ammonia concentrations, organic amine concentrations, and chlorine doses likely to be encountered during power plant cooling water chlorination. However, the effects of sunlight are not considered. The model can also be applied to freshwater and recirculating water systems with cooling towers. The results of the model agree with expectation, however, complete verification is not feasible at the present because analytical methods for some of the predicted species are lacking.

Haag, W.R.; Lietzke, M.H.

1981-08-01T23:59:59.000Z

484

Optimization of a simplified sub-model for NO emission prediction by CFD in large 4-stroke marine diesel engines  

Science Journals Connector (OSTI)

A simplified sub-model for NO emission prediction at pressurized conditions has been put forth at Åbo Akademi University [7,9] including NO formation via the thermal NO path (3 reactions) and via the nitrous oxide intermediate paths (2 + 5 reactions). CFD simulations carried out with the sub-model for marine and off-road diesel engines showed, however, that it significantly – by an order of magnitude – over-predicted NO emission as compared to measurements. The objective of this work was to find the reasons to the discrepancy and to suggest and incorporate improvements. By detailed investigations, a number of programming technical errors and chemical kinetic shortcomings were identified. The improved sub-model and its sub-parts were then tested for CFD simulation of a medium-speed, four-stroke, direct-injection marine diesel engine for different loads and fuels. The importance of NO reduction by soot and hydrocarbons was also investigated. All the sub-models correctly predicted the trend of increasing NO emission with increasing load. In absolute amounts, NO emission was over-predicted by a factor of 2 to 4, if no fitting of rate constants was allowed. Including NO reduction by soot and hydrocarbons, decreased NO emission by ca 4–25% for the cases studied.

Pia Kilpinen

2010-01-01T23:59:59.000Z

485

Development of a cell-based stream flow routing model  

E-Print Network [OSTI]

al. (1994) developed a 2.00x2.50 resolution river routing model for a number of World Rivers, coupled with an atmospheric-ocean model. The GCM of NASA/Goddard Institute for Space Studies (GISS) (Hansen et al., 1983) was used to calculate the runoff... resolution of 2.00 X 2.50 using the coarse river network developed by Miller et al. (1994). Input to each of the grid cell was derived from the improved GISS GCM (Hansen et al., 1983), which improved the model prediction of discharge. Costa and Foley (1997...