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1

Enhanced oil recovery data base analysis by simplified predictive models  

Science Conference Proceedings (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

2

Computationally efficient nonlinear predictive control based on neural Wiener models  

Science Conference Proceedings (OSTI)

This paper describes a computationally efficient nonlinear model predictive control (MPC) algorithm based on neural Wiener models and its application. The model contains a linear dynamic part in series with a steady-state nonlinear part which is realised ... Keywords: Linearisation, Model predictive control, Neural networks, Optimisation, Process control, Wiener systems

Maciej ?awry?czuk

2010-12-01T23:59:59.000Z

3

Trajectory Design and Implementation for Multiple Autonomous Underwater Vehicles Based on Ocean Model Predictions  

E-Print Network (OSTI)

Underwater Vehicles Based on Ocean Model Predictions Ryan N.Trajectory Design based on Ocean Model Predictions PredictEffective tracking of ocean features Gather specific in situ

2009-01-01T23:59:59.000Z

4

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

5

HVAC Room Temperature Prediction Control Based on Neural Network Model  

Science Conference Proceedings (OSTI)

HVAC (Heating Ventilating &Air-conditioning) system is a nonlinear complex system with delay. It is very difficult to build a mathematical model of HVAC and implement model-based control. Since a BP (Back Propagation) neural network can fully approximate ... Keywords: BP neural network, predictive control, HVAC, least squares method

Shujiang Li, Shuang Ren, Xiangdong Wang

2013-01-01T23:59:59.000Z

6

Provably Safe and Robust Learning-Based Model Predictive Control  

E-Print Network (OSTI)

Controller design for systems typically faces a trade-off between robustness and performance, and the reliability of linear controllers has caused many control practitioners to focus on the former. However, there is a renewed interest in improving system performance to deal with growing energy and pollution constraints. This paper describes a learning-based model predictive control (MPC) scheme. The MPC provides deterministic guarantees on robustness and safety, and the learning is used to identify richer models of the system to improve controller performance. Our scheme uses a linear model with bounds on its uncertainty to construct invariant sets which help to provide the guarantees, and it can be generalized to other classes of models and to pseudo-spectral methods. This framework allows us to handle state and input constraints and optimize system performance with respect to a cost function. The learning occurs through the use of an oracle which returns the value and gradient of unmodeled dynamics at discr...

Aswani, Anil; Sastry, S Shankar; Tomlin, Claire

2011-01-01T23:59:59.000Z

7

Crude Oil Price Prediction Using Slantlet Denoising Based Hybrid Models  

Science Conference Proceedings (OSTI)

The accurate prediction of crude oil price movement has always been the central issue with profound implications across different levels of the economy. This study conducts empirical investigations into the characteristics of crude oil market and proposes ... Keywords: Slantlet Analysis, ARMA Model, Hybrid Forecasting Algorithm, Rrandom Walk Model, Support Vector Regression

Kaijian He; Kin Keung Lai; Jerome Yen

2009-04-01T23:59:59.000Z

8

PREDICTIVE MODELS  

Science Conference Proceedings (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

9

Structure-Based Predictive model for Coal Char Combustion.  

SciTech Connect

During the third quarter of this project, progress was made on both major technical tasks. Progress was made in the chemistry department at OSU on the calculation of thermodynamic properties for a number of model organic compounds. Modelling work was carried out at Brown to adapt a thermodynamic model of carbonaceous mesophase formation, originally applied to pitch carbonization, to the prediction of coke texture in coal combustion. This latter work makes use of the FG-DVC model of coal pyrolysis developed by Advanced Fuel Research to specify the pool of aromatic clusters that participate in the order/disorder transition. This modelling approach shows promise for the mechanistic prediction of the rank dependence of char structure and will therefore be pursued further. Crystalline ordering phenomena were also observed in a model char prepared from phenol-formaldehyde carbonized at 900{degrees}C and 1300{degrees}C using high-resolution TEM fringe imaging. Dramatic changes occur in the structure between 900 and 1300{degrees}C, making this char a suitable candidate for upcoming in situ work on the hot stage TEM. Work also proceeded on molecular dynamics simulations at Boston University and on equipment modification and testing for the combustion experiments with widely varying flame types at Ohio State.

Hurt, R.; Colo, J [Brown Univ., Providence, RI (United States). Div. of Engineering; Essenhigh, R.; Hadad, C [Ohio State Univ., Columbus, OH (United States). Dept. of Chemistry; Stanley, E. [Boston Univ., MA (United States). Dept. of Physics

1997-09-24T23:59:59.000Z

10

An approach to model validation and model-based prediction -- polyurethane foam case study.  

Science Conference Proceedings (OSTI)

Enhanced software methodology and improved computing hardware have advanced the state of simulation technology to a point where large physics-based codes can be a major contributor in many systems analyses. This shift toward the use of computational methods has brought with it new research challenges in a number of areas including characterization of uncertainty, model validation, and the analysis of computer output. It is these challenges that have motivated the work described in this report. Approaches to and methods for model validation and (model-based) prediction have been developed recently in the engineering, mathematics and statistical literatures. In this report we have provided a fairly detailed account of one approach to model validation and prediction applied to an analysis investigating thermal decomposition of polyurethane foam. A model simulates the evolution of the foam in a high temperature environment as it transforms from a solid to a gas phase. The available modeling and experimental results serve as data for a case study focusing our model validation and prediction developmental efforts on this specific thermal application. We discuss several elements of the ''philosophy'' behind the validation and prediction approach: (1) We view the validation process as an activity applying to the use of a specific computational model for a specific application. We do acknowledge, however, that an important part of the overall development of a computational simulation initiative is the feedback provided to model developers and analysts associated with the application. (2) We utilize information obtained for the calibration of model parameters to estimate the parameters and quantify uncertainty in the estimates. We rely, however, on validation data (or data from similar analyses) to measure the variability that contributes to the uncertainty in predictions for specific systems or units (unit-to-unit variability). (3) We perform statistical analyses and hypothesis tests as a part of the validation step to provide feedback to analysts and modelers. Decisions on how to proceed in making model-based predictions are made based on these analyses together with the application requirements. Updating modifying and understanding the boundaries associated with the model are also assisted through this feedback. (4) We include a ''model supplement term'' when model problems are indicated. This term provides a (bias) correction to the model so that it will better match the experimental results and more accurately account for uncertainty. Presumably, as the models continue to develop and are used for future applications, the causes for these apparent biases will be identified and the need for this supplementary modeling will diminish. (5) We use a response-modeling approach for our predictions that allows for general types of prediction and for assessment of prediction uncertainty. This approach is demonstrated through a case study supporting the assessment of a weapons response when subjected to a hydrocarbon fuel fire. The foam decomposition model provides an important element of the response of a weapon system in this abnormal thermal environment. Rigid foam is used to encapsulate critical components in the weapon system providing the needed mechanical support as well as thermal isolation. Because the foam begins to decompose at temperatures above 250 C, modeling the decomposition is critical to assessing a weapons response. In the validation analysis it is indicated that the model tends to ''exaggerate'' the effect of temperature changes when compared to the experimental results. The data, however, are too few and to restricted in terms of experimental design to make confident statements regarding modeling problems. For illustration, we assume these indications are correct and compensate for this apparent bias by constructing a model supplement term for use in the model-based predictions. Several hypothetical prediction problems are created and addressed. Hypothetical problems are used because no guidance was provided concern

Dowding, Kevin J.; Rutherford, Brian Milne

2003-07-01T23:59:59.000Z

11

PREDICTIVE MODELS  

SciTech Connect

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

12

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

13

Structure-Based Predictive model for Coal Char Combustion.  

Science Conference Proceedings (OSTI)

During the second quarter of this project, progress was made on both major technical tasks. Three parallel efforts were initiated on the modeling of carbon structural evolution. Structural ordering during carbonization was studied by a numerical simulation scheme proposed by Alan Kerstein involving molecular weight growth and rotational mobility. Work was also initiated to adapt a model of carbonaceous mesophase formation, originally developed under parallel NSF funding, to the prediction of coke texture. This latter work makes use of the FG-DVC model of coal pyrolysis developed by Advanced Fuel Research to specify the pool of aromatic clusters that participate in the order/disorder transition. Boston University has initiated molecular dynamics simulations of carbonization processes and Ohio State has begun theoretical treatment of surface reactions. Experimental work has also begun on model compound studies at Brown and on pilot-scale combustion systems with widely varying flame types at OSE. The work on mobility / growth models shows great promise and is discussed in detail in the body of the report.

Hurt, R.; Calo, J. [Brown Univ., Providence, RI (United States). Div. of Engineering; Essenhigh, R.; Hadad, C. [Ohio State Univ., Columbus, OH (United States). Dept. of Chemistry; Stanley, E. [Boston Univ., MA (United States). Dept. of Physics

1997-06-25T23:59:59.000Z

14

STRUCTURE-BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION  

Science Conference Proceedings (OSTI)

Progress was made this period on a number of tasks. A significant advance was made in the incorporation of macrostructural ideas into high temperature combustion models. Work at OSU by R. Essenhigh in collaboration with the University of Stuttgart has led to a theory that the zone I / II transition in char combustion lies within the range of conditions of interest for pulverized char combustion. The group has presented evidence that some combustion data, previously interpreted with zone II models, in fact takes place in the transition from zone II to zone 1. This idea was used at Brown to make modifications to the CBK model (a char kinetics package specially designed for carbon burnout prediction, currently used by a number of research and furnace modeling groups in academia and industry). The resulting new model version, CBK8, shows improved ability to predict extinction behavior in the late stages of combustion, especially for particles with low ash content. The full development and release of CBK8, along with detailed descriptions of the role of the zone 1/2 transition will be reported on in subsequent reports. ABB-CE is currently implementing CBK7 into a special version of the CFD code Fluent for use in the modeling and design of their boilers. They have been appraised of the development, and have expressed interest in incorporating the new feature, realizing full CBK8 capabilities into their combustion codes. The computational chemistry task at OSU continued to study oxidative pathways for PAH, with emphasis this period on heteroatom containing ring compounds. Preliminary XPS studies were also carried out. Combustion experiments were also carried out at OSU this period, leading to the acquisition of samples at various residence times and the measurement of their oxidation reactivity by nonisothermal TGA techniques. Several members of the project team attended the Carbon Conference this period and made contacts with representatives from the new FETC Consortium for Premium Carbon Products from Coal. Possibilities for interactions with this new center will be explored. Also this period, an invited review paper was prepared for the 27th International Symposium on Combustion, to be held in Boulder, Colorado in August. The paper is entitled; "Structure, Properties, and Reactivity of Solid Fuels," and reports on a number of advances made in this collaborative project.

CHRISTOPHER M. HADAD; JOSEPH M. CALO; ROBERT H. ESSENHIGH; ROBERT H. HURT

1998-09-11T23:59:59.000Z

15

Original Article: Simulation-based weight factor selection and FPGA prediction core implementation for finite-set model based predictive control of power electronics  

Science Conference Proceedings (OSTI)

Model-based predictive control (MBPC) for power-electronic converters offers fast and accurate control. Based on the prediction of the future system states the optimal control input sequence is obtained by calculating a cost for each sequence. The control ... Keywords: FPGA, Multilevel inverters, Parallel calculation, Pipelining, Predictive control

Thomas J. Vyncke, Steven Thielemans, Jan A. A. Melkebeek

2013-05-01T23:59:59.000Z

16

STRUCTURE BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION  

SciTech Connect

This report is part on the ongoing effort at Brown University and Ohio State University to develop structure based models of coal combustion. A very fundamental approach is taken to the description of coal chars and their reaction processes, and the results are therefore expected to have broad applicability to the spectrum of carbon materials of interest in energy technologies. This quarter, the project was in a period no-cost extension and discussions were held about the end phase of the project and possible continuations. The technical tasks were essentially dormant this period, but presentations of results were made, and plans were formulated for renewed activity in the fiscal year 2001.

Robert Hurt; Joseph Calo; Robert Essenhigh; Christopher Hadad

2001-06-15T23:59:59.000Z

17

STRUCTURE-BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION  

Science Conference Proceedings (OSTI)

During the past quarter of this project, significant progress continued was made on both major technical tasks. Progress was made at OSU on advancing the application of computational chemistry to oxidative attack on model polyaromatic hydrocarbons (PAHs) and graphitic structures. This work is directed at the application of quantitative ab initio molecular orbital theory to address the decomposition products and mechanisms of coal char reactivity. Previously, it was shown that the ?hybrid? B3LYP method can be used to provide quantitative information concerning the stability of the corresponding radicals that arise by hydrogen atom abstraction from monocyclic aromatic rings. In the most recent quarter, these approaches have been extended to larger carbocyclic ring systems, such as coronene, in order to compare the properties of a large carbonaceous PAH to that of the smaller, monocyclic aromatic systems. It was concluded that, at least for bond dissociation energy considerations, the properties of the large PAHs can be modeled reasonably well by smaller systems. In addition to the preceding work, investigations were initiated on the interaction of selected radicals in the ?radical pool? with the different types of aromatic structures. In particular, the different pathways for addition vs. abstraction to benzene and furan by H and OH radicals were examined. Thus far, the addition channel appears to be significantly favored over abstraction on both kinetic and thermochemical grounds. Experimental work at Brown University in support of the development of predictive structural models of coal char combustion was focused on elucidating the role of coal mineral matter impurities on reactivity. An ?inverse? approach was used where a carbon material was doped with coal mineral matter. The carbon material was derived from a high carbon content fly ash (Fly Ash 23 from the Salem Basin Power Plant. The ash was obtained from Pittsburgh #8 coal (PSOC 1451). Doped samples were then burned in a high temperature flame reactor fitted with rapid quench extractive sampling. It was found that the specific reaction rate decreased with increasing ash content by about an order of magnitude over the ash content range investigated. In this case, it was concluded that at least one of the primary reasons for the resultant observation was that an increasing amount of carbon becomes inaccessible to oxygen by being covered with a fused, ?protective,? ash layer. Progress continued on equipment modification and testing for the combustion experiments with widely varying flame types at OSU.

CHRISTOPHER M. HADAD; JOSEPH M. CALO; ROBERT H. ESSENHIGH; ROBERT H. HURT

1998-06-04T23:59:59.000Z

18

Structure Based Predictive Model for Coal Char Combustion  

SciTech Connect

This unique collaborative project has taken a very fundamental look at the origin of structure, and combustion reactivity of coal chars. It was a combined experimental and theoretical effort involving three universities and collaborators from universities outside the U.S. and from U.S. National Laboratories and contract research companies. The project goal was to improve our understanding of char structure and behavior by examining the fundamental chemistry of its polyaromatic building blocks. The project team investigated the elementary oxidative attack on polyaromatic systems, and coupled with a study of the assembly processes that convert these polyaromatic clusters to mature carbon materials (or chars). We believe that the work done in this project has defined a powerful new science-based approach to the understanding of char behavior. The work on aromatic oxidation pathways made extensive use of computational chemistry, and was led by Professor Christopher Hadad in the Department of Chemistry at Ohio State University. Laboratory experiments on char structure, properties, and combustion reactivity were carried out at both OSU and Brown, led by Principle Investigators Joseph Calo, Robert Essenhigh, and Robert Hurt. Modeling activities were divided into two parts: first unique models of crystal structure development were formulated by the team at Brown (PI'S Hurt and Calo) with input from Boston University and significant collaboration with Dr. Alan Kerstein at Sandia and with Dr. Zhong-Ying chen at SAIC. Secondly, new combustion models were developed and tested, led by Professor Essenhigh at OSU, Dieter Foertsch (a collaborator at the University of Stuttgart), and Professor Hurt at Brown. One product of this work is the CBK8 model of carbon burnout, which has already found practical use in CFD codes and in other numerical models of pulverized fuel combustion processes, such as EPRI's NOxLOI Predictor. The remainder of the report consists of detailed technical discussion organized into chapters whose organization is dictated by the nature of the research performed. Chapter 2 is entitled 'Experimental Work on Char Structure, Properties, and Reactivity', and focuses on fundamental structural studies at Brown using both phenollformaldehyde resin chars as model carbons and real coal chars. This work includes the first known in site high resolution TEM studies of carbonization processes, and some intriguing work on 'memory loss', a form of interaction between annealing and oxidation phenomena in chars. Chapter 3 entitled 'Computational Chemistry of Aromatic Oxidation Pathways' presents in detail the OSU work targeted at understanding the elementary molecular pathways of aromatic oxidation. Chapter 4 describes the 'Mesoscale Structural Models', using a combination of thermodynamic (equilibrium) approaches based on liquid crystal theory and kinetic simulations accounting for the effects of limited layer mobility in many fossil fuel derived carbons containing cross-linking agents. Chapter 5 entitled 'Combustion Modeling' presents work on extinction in the late stages of combustion and the development and features of the CBK8 model.

Robert Hurt; Joseph Calo; Robert Essenhigh; Christopher Hadad

2000-12-30T23:59:59.000Z

19

STRUCTURE-BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION  

SciTech Connect

This report is part on the ongoing effort at Brown University and Ohio State University to develop structure based models of coal combustion. A very fundamental approach is taken to the description of coal chars and their reaction processes, and the results are therefore expected to have broad applicability to the spectrum of carbon materials of interest in energy technologies. This quarter, our work on structure development in carbons continued. A combination of hot stage in situ and ex situ polarized light microscopy was used to identify the preferred orientational of graphene layers at gas interfaces in pitches used as carbon material precursors. The experiments show that edge-on orientation is the equilibrium state of the gas/pitch interface, implying that basal-rich surfaces have higher free energies than edge-rich surfaces in pitch. This result is in agreement with previous molecular modeling studies and TEM observations in the early stages of carbonization. The results may have important implications for the design of tailored carbons with edge-rich or basal-rich surfaces. In the computational chemistry task, we have continued our investigations into the reactivity of large aromatic rings. The role of H-atom abstraction as well as radical addition to monocyclic aromatic rings has been examined, and a manuscript is currently being revised after peer review. We have also shown that OH radical is more effective than H atom in the radical addition process with monocyclic rings. We have extended this analysis to H-atom and OH-radical addition to phenanthrene. Work on combustion kinetics focused on the theoretical analysis of the data previously gathered using thermogravametric analysis.

Robert H. Hurt; Eric M. Suuberg

2000-05-03T23:59:59.000Z

20

Using graph-based program characterization for predictive modeling  

Science Conference Proceedings (OSTI)

Using machine learning has proven effective at choosing the right set of optimizations for a particular program. For machine learning techniques to be most effective, compiler writers have to develop expressive means of characterizing the program being ... Keywords: compiler optimization, graph-based program characterization, iterative compilation, machine learning, support vector machine

Eunjung Park; John Cavazos; Marco A. Alvarez

2012-03-01T23:59:59.000Z

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.


21

Structure-Based Predictive Model for Coal Char Combustion  

Science Conference Proceedings (OSTI)

Progress was made this period on a number of separate experimental and modelling activities. At Brown, the models of carbon nanostructure evolution were expanded to consider high-rank materials with initial anisotropy. The report presents detailed results of Monte Carlo simulations with non-zero initial layer length and with statistically oriented initial states. The expanded simulations are now capable of describing the development of nanostructure during carbonization of most coals. Work next quarter will address the remaining challenge of isotropic coke-forming coals. Experiments at Brown yielded important data on the "memory loss" phenomenon in carbon annealing, and on the effect of mineral matter on high-temperature reactivity. The experimental aspects of the Brown work will be discussed in detail in the next report.

Christopher Hadad; Joseph Calo; Robert Essenhigh; Robert Hurt

1998-04-08T23:59:59.000Z

22

Assessment of emissions prediction capability of RANS based PDF models for lean premixed combustion of methane  

DOE Green Energy (OSTI)

The high computational cost of Large Eddy Simulation (LES) makes Reynolds Averaged Navier-Stokes (RANS) methods the current standard for turbulent combustion modeling. Empirical models for turbulence, turbulence-combustion interaction and chemical kinetics are, however, a major source of uncertainty in RANS based combustion simulation. While Probability Density Function (PDF) based models overcome some of these issues, most commercial codes do not take full advantage of these models. In this study, lean premixed combustion of methane in a bluff-body combustor is simulated using two different reduced chemical mechanisms (ARM9 and ARM19) combined with the composition PDF transport combustion model in the commercial code FLUENT. Two different turbulence models, namely the RNG k-? model and the Reynolds Stress Model (RSM) are used and the results of the simulations are compared to experimental data. For all the models tested, the prediction of temperature and major species (CH4, O2, CO2, CO, H2, and H2O) was good when compared to experiments. While all of the model predictions for the intermediate species OH showed an order magnitude difference (compared to the experiments) close to the bluff body surface; downstream axial locations showed good quantitative and qualitative agreement with the experiments. In a trend similar to the previous study (Nanduri et al., 2007) using the Eddy Dissipation Concept (EDC) model, predicted values for NO emission radial profiles showed an average difference of ±5 ppm when compared to experimental values. The results were also compared to the results of a velocity-composition joint PDF model developed by researchers at the University of Pittsburgh. In terms of emissions (NO and CO) predictions the relatively expensive composition PDF model in FLUENT did not give significant improvement when compared to the computationally cheaper EDC models. However, the velocity-composition joint PFD model used by researchers at the University of Pittsburgh did show significant improvement over EDC models in the prediction of NO. Both of the PDF models resulted in better qualitative and quantitative agreement in H2 prediction, thus showing the promise of PDF based models in simulating lean premixed combustion of fuel blends like hydrogen enriched natural gas.

Parsons, D.R.; Nanduri, J.R.; Celik, Ismail; Strakey, P.A.

2008-01-01T23:59:59.000Z

23

DDoS attack detection method based on linear prediction model  

Science Conference Proceedings (OSTI)

Distributed denial of service (DDoS) attack is one of the major threats to the current Internet. The IP Flow feature value (FFV) algorithm is proposed based on the essential features of DDoS attacks, such as the abrupt traffic change, flow dissymmetry, ... Keywords: ARMA model, attack features, distributed denial of service, linear prediction, network security

Jieren Cheng; Jianping Yin; Chengkun Wu; Boyun Zhang; Yun Liu

2009-09-01T23:59:59.000Z

24

Assessment of RANS-based turbulent combustion models for prediction of gas turbine emissions: turbulence model and reaction mechanism effects  

DOE Green Energy (OSTI)

The goal of this study is to assess current, commonly applied turbulence and combustion models with respect to their performance in gas-turbine combustion (GTC). Reynolds Averaged Navier-Stokes (RANS)-based turbulence and chemistry models are two primary factors influencing the uncertainty in predicting turbulent combustion characteristics, especially for GTC. RANS-based methods are the design tools of choice in the gas turbine industry due to the high computational costs of LES (Large Eddy Simulation). In this study, lean premixed combustion of methane was simulated using two different reduced mechanisms (ARM9 and ARM19) along with the Eddy Dissipation Concept (EDC) turbulent chemistry interaction model to calculate the CO and NOx emissions. The effect of turbulence models was assessed by considering two different models. Both of the models tested performed well in the prediction of temperature and major species profiles. Predicted values of NO emission profiles showed an average difference of ±5 ppm compared to experimental values. Computed intermediate species profiles showed large qualitative and quantitative errors when compared with the experimental data. These discrepancies, especially the intermediate species hydrogen, indicate the challenges these reduced mechanisms and turbulence models can present when modeling pollutant emissions from gas turbine combustors.

Nanduri, J.R.; Celik, I.B.; Strakey, P.A.; Parsons, D.R.

2007-10-01T23:59:59.000Z

25

Introducing Spatial Information into Predictive NF-kB Modelling – An Agent-Based Approach  

E-Print Network (OSTI)

Nature is governed by local interactions among lower-level sub-units, whether at the cell, organ, organism, or colony level. Adaptive system behaviour emerges via these interactions, which integrate the activity of the sub-units. To understand the system level it is necessary to understand the underlying local interactions. Successful models of local interactions at different levels of biological organisation, including epithelial tissue and ant colonies, have demonstrated the benefits of such ‘agent-basedmodelling [1–4]. Here we present an agent-based approach to modelling a crucial biological system – the intracellular NF-kB signalling pathway. The pathway is vital to immune response regulation, and is fundamental to basic survival in a range of species [5–7]. Alterations in pathway regulation underlie a variety of diseases, including atherosclerosis and arthritis. Our modelling of individual molecules, receptors and genes provides a more comprehensive outline of regulatory network mechanisms than previously possible with equation-based approaches [8]. The method also permits consideration of structural parameters in pathway regulation; here we predict that inhibition of NF-kB is directly affected by actin filaments of the cytoskeleton sequestering excess inhibitors, therefore regulating steady-state and feedback

Mark Pogson; Mike Holcombe; Rod Smallwood; Eva Qwarnstrom

2008-01-01T23:59:59.000Z

26

Mining subsidence prediction based on 3D stratigraphic model and visualization  

Science Conference Proceedings (OSTI)

3D phenomenon involved in mining subsidence was Classified, summarized and aggregated, established the hierarchical structure that describing the geologic phenomena and engineering phenomena of stratum structure. Proposed a 3D stratigraphic model that ... Keywords: 3D stratigraphic model, 3D visualization, DEMs-TEN model, mining subsidence prediction

Ruisheng Jia; Yanjun Peng; Hongmei Sun

2011-01-01T23:59:59.000Z

27

Dynamic-Model-Based Seasonal Prediction of Meteorological Drought over the Contiguous United States  

Science Conference Proceedings (OSTI)

A simple method was developed to forecast 3- and 6-month standardized precipitation indices (SPIs) for the prediction of meteorological drought over the contiguous United States based on precipitation seasonal forecasts from the NCEP Climate ...

Jin-Ho Yoon; Kingtse Mo; Eric F. Wood

2012-04-01T23:59:59.000Z

28

A Local Model Networks Based Multivariable Long-Range Predictive Control Strategy for Thermal Power Plants  

Science Conference Proceedings (OSTI)

Load-cycling operation of thermal power plants leads to changes in operating point right across the whole operating range. This results in non-linear variations in most of the plant variables. This paper investigates methods to account for non-linearities ... Keywords: Constrained multivariable control, local model networks, long range predictive control, thermal power plant boiler

G. PRASAD; E. SWIDENBANK; B. W. HOGG

1998-10-01T23:59:59.000Z

29

A support vector regression based prediction model of affective responses for product form design  

Science Conference Proceedings (OSTI)

In this paper, a state-of-the-art machine learning approach known as support vector regression (SVR) is introduced to develop a model that predicts consumers' affective responses (CARs) for product form design. First, pairwise adjectives were used to ... Keywords: Genetic algorithm, Kansei engineering, Neural network, Product form design, Support vector regression

Chih-Chieh Yang; Meng-Dar Shieh

2010-11-01T23:59:59.000Z

30

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

Science Conference Proceedings (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

31

Modeling and implementing an agent-based system for prediction of protein relative solvent accessibility  

Science Conference Proceedings (OSTI)

In this paper, an agent-based system for prediction of relative solvent accessibility (RSA) of proteins is proposed. Since, it is believed that the 3D-structure of most proteins is defined by their sequences, utilizing data mining methods to extract ... Keywords: Data mining, Feature selection methods, Intelligent agents, Physicochemical properties of amino acids

Alireza Meshkin; Nasser Ghasem Aghaee; Mehdi Sadeghi

2011-05-01T23:59:59.000Z

32

Short-term Wind Power Prediction for Offshore Wind Farms -Evaluation of Fuzzy-Neural Network Based Models  

E-Print Network (OSTI)

Short-term Wind Power Prediction for Offshore Wind Farms - Evaluation of Fuzzy-Neural Network Based of wind power capacities are likely to take place offshore. As for onshore wind parks, short-term wind of offshore farms and their secure integration to the grid. Modeling the behavior of large wind farms

Paris-Sud XI, Université de

33

A sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory.  

Science Conference Proceedings (OSTI)

Evidence theory provides an alternative to probability theory for the representation of epistemic uncertainty in model predictions that derives from epistemic uncertainty in model inputs, where the descriptor epistemic is used to indicate uncertainty that derives from a lack of knowledge with respect to the appropriate values to use for various inputs to the model. The potential benefit, and hence appeal, of evidence theory is that it allows a less restrictive specification of uncertainty than is possible within the axiomatic structure on which probability theory is based. Unfortunately, the propagation of an evidence theory representation for uncertainty through a model is more computationally demanding than the propagation of a probabilistic representation for uncertainty, with this difficulty constituting a serious obstacle to the use of evidence theory in the representation of uncertainty in predictions obtained from computationally intensive models. This presentation describes and illustrates a sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory. Preliminary trials indicate that the presented strategy can be used to propagate uncertainty representations based on evidence theory in analysis situations where naive sampling-based (i.e., unsophisticated Monte Carlo) procedures are impracticable due to computational cost.

Johnson, J. D. (Prostat, Mesa, AZ); Oberkampf, William Louis; Helton, Jon Craig (Arizona State University, Tempe, AZ); Storlie, Curtis B. (North Carolina State University, Raleigh, NC)

2006-10-01T23:59:59.000Z

34

Model-free Model-fitting and Predictive Distributions  

E-Print Network (OSTI)

Politis, D.N. (2007a). Model-free vs. model-based volatilityPolitis, D.N. (2007b). Model-free prediction, in Bulletin ofFurthermore, the model-free prediction principle can be

Politis, Dimitris N

2010-01-01T23:59:59.000Z

35

Coarsening of the Sn-Pb Solder Microstructure in Constitutive Model-Based Predictions of Solder Joint Thermal Mechanical Fatigue  

SciTech Connect

Thermal mechanical fatigue (TMF) is an important damage mechanism for solder joints exposed to cyclic temperature environments. Predicting the service reliability of solder joints exposed to such conditions requires two knowledge bases: first, the extent of fatigue damage incurred by the solder microstructure leading up to fatigue crack initiation, must be quantified in both time and space domains. Secondly, fatigue crack initiation and growth must be predicted since this metric determines, explicitly, the loss of solder joint functionality as it pertains to its mechanical fastening as well as electrical continuity roles. This paper will describe recent progress in a research effort to establish a microstructurally-based, constitutive model that predicts TMF deformation to 63Sn-37Pb solder in electronic solder joints up to the crack initiation step. The model is implemented using a finite element setting; therefore, the effects of both global and local thermal expansion mismatch conditions in the joint that would arise from temperature cycling.

Vianco, P.T.; Burchett, S.N.; Neilsen, M.K.; Rejent, J.A.; Frear, D.R.

1999-04-12T23:59:59.000Z

36

A GM-Based Profitable Duration Prediction Model for Chinese Crude Oil Main Production District  

Science Conference Proceedings (OSTI)

In this paper, a grey model (GM) based profitable duration forecasting approach is proposed for Chinese crude oil main production district. In this methodology, the forecasting functions on electricity expenditure and crude oil sales revenue are first ... Keywords: GM, china, crude oil, forecasting, profitable duration

Jinlou Zhao; Yuzhen Han; Lixia Ke

2007-05-01T23:59:59.000Z

37

Model Predictive Control-based Optimal Coordination of Distributed Energy Resources  

SciTech Connect

Distributed energy resources, such as renewable energy resources (wind, solar), energy storage and demand response, can be used to complement conventional generators. The uncertainty and variability due to high penetration of wind makes reliable system operations and controls challenging, especially in isolated systems. In this paper, an optimal control strategy is proposed to coordinate energy storage and diesel generators to maximize wind penetration while maintaining system economics and normal operation performance. The goals of the optimization problem are to minimize fuel costs and maximize the utilization of wind while considering equipment life of generators and energy storage. Model predictive control (MPC) is used to solve a look-ahead dispatch optimization problem and the performance is compared to an open loop look-ahead dispatch problem. Simulation studies are performed to demonstrate the efficacy of the closed loop MPC in compensating for uncertainties and variability caused in the system.

Mayhorn, Ebony T.; Kalsi, Karanjit; Lian, Jianming; Elizondo, Marcelo A.

2013-01-07T23:59:59.000Z

38

The ECPC Coupled Prediction Model  

Science Conference Proceedings (OSTI)

This paper presents a new Experimental Climate Prediction Center (ECPC) Coupled Prediction Model (ECPM). The ECPM includes the Jet Propulsion Laboratory (JPL) version of the Massachusetts Institute of Technology (MIT) ocean model coupled to the ...

E. Yulaeva; M. Kanamitsu; J. Roads

2008-01-01T23:59:59.000Z

39

VBR MPEG Video Traffic Dynamic Prediction Based on the Modeling and Forecast of Time Series  

Science Conference Proceedings (OSTI)

The variable-bit-rate traffic characteristic brings a large complication to the utilization of network resources, especially bandwidth. To solve this problem, this paper proposes a dynamic prediction scheme of MPEG video traffic. We first advance an ... Keywords: MPEG, video trace, forecast, time series, ARMA

Jun Dai; Jun Li

2009-08-01T23:59:59.000Z

40

Trajectory Design and Implementation for Multiple Autonomous Underwater Vehicles Based on Ocean Model Predictions  

E-Print Network (OSTI)

for Multiple Autonomous Underwater Vehicles Based on OceanAUVs) • Autonomous Underwater Vehicles Evolving ocean

2009-01-01T23:59:59.000Z

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.


41

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

E-Print Network (OSTI)

Clothing Insulation Models on Building Energy Use, HVACinsulation for mechanically conditioned buildings andclothing insulation calculated for each building). Figure 8

Schiavon, Stefano; Lee, Kwang Ho

2012-01-01T23:59:59.000Z

42

Midtemperature solar systems test facility predictions for thermal performance based on test data. Polisolar Model POL solar collector with glass reflector surface  

DOE Green Energy (OSTI)

Thermal performance predictions based on test data are presented for the Polisolar Model POL solar collector, with glass reflector surfaces, for three output temperatures at five cities in the United States.

Harrison, T.D.

1981-05-01T23:59:59.000Z

43

Predictive Modeling of Mercury Speciation in Combustion Flue Gases Using GMDH-Based Abductive Networks  

E-Print Network (OSTI)

harmful emissions from coal-fired power plants and developing strategies to reduce them. First principle method of data handling (GMDH) algorithm, with the advantages of simplified and more automated model, and particulate) using a small data set containing six inputs parameters on the composition of the coal used

Abdel-Aal, Radwan E.

44

NETL: Computer Software & Databases - Predictive Models  

NLE Websites -- All DOE Office Websites (Extended Search)

Predictive Models DOEBC-881SP. EOR Predictive Models: Handbook for Personal Computer Versions of Enhanced Oil Recovery Predictive Models. BPO Staff. February 1988. 76 pp. NTIS...

45

Model Predictive Control for Energy Efficient Buildings  

E-Print Network (OSTI)

P. Haves et al. “Model Predictive Control of HVAC Systems:Bilinear Model Predictive Control of a HVAC System Usingof model predictive control algorithms for HVAC systems.

Ma, Yudong

2012-01-01T23:59:59.000Z

46

Nonlinear model predictive control of a reactive distillation column.  

E-Print Network (OSTI)

??Model Predictive Control (MPC) is an optimal-control based method to select control inputs by minimizing the predicted error from setpoint for the future. Industrially popular… (more)

Kawathekar, Rohit

2004-01-01T23:59:59.000Z

47

A model driven development approach based on a reference model for predicting disruptive events in a supply process  

Science Conference Proceedings (OSTI)

Due to the impossibility of predicting with certainty the occurrence of disruptive events, buffers defined to obtain a robust schedule could not absorb all the changes. Then, local modifications of the schedule are usually performed to avoid a new planning ... Keywords: Disruptive event, Event management, Monitoring system, SCEM systems

Erica Fernández; Enrique Salomone; Omar Chiotti

2012-06-01T23:59:59.000Z

48

PREDICTIVE MODELS. Enhanced Oil Recovery Model  

SciTech Connect

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

49

EOF-Based Linear Prediction Algorithm: Theory  

Science Conference Proceedings (OSTI)

This study considers the theory of a general three-dimensional (space and time) statistical prediction/extrapolation algorithm. The predictor is in the form of a linear data filter. The prediction kernel is based on the minimization of prediction ...

Kwang-Y. Kim; Gerald R. North

1998-11-01T23:59:59.000Z

50

PREDICTIVE MODELS. Enhanced Oil Recovery Model  

SciTech Connect

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

51

Prediction of Magnetic Storms by Nonlinear Models  

E-Print Network (OSTI)

The strong correlation between magnetic storms and southward interplanetary magnetic field (IMF) is well known from linear prediction filter studies using the Dst and IMF data. However, the linear filters change significantly from one storm to another and thus are limited in their predicting ability. Previous studies have indicated nonlinearity in the magnetospheric response as the ring current decay rate varies with the Dst value during storms. We present in this letter nonlinear models for the evolution of the Dst based on the OMNI database for 1964-1990. When solar wind data are available in advance, the evolution of storms can be predicted from the Dst and IMF data. Solar wind data, however, are not available most of the time or are available typically an hour or less in advance. Therefore, we have developed nonlinear predictive models based on the Dst data alone. In the absence of solar wind data, these models cannot predict the storm onset, but can predict the storm evolution, an...

J. A. Valdivia; A. S. Sharma; K. Papadopoulos

1996-01-01T23:59:59.000Z

52

Comparing cost prediction models by resampling techniques  

Science Conference Proceedings (OSTI)

The accurate software cost prediction is a research topic that has attracted much of the interest of the software engineering community during the latest decades. A large part of the research efforts involves the development of statistical models based ... Keywords: Accuracy measure, Bootstrap, Confidence interval, Permutation test, Software cost estimation

Nikolaos Mittas; Lefteris Angelis

2008-05-01T23:59:59.000Z

53

Hurricane Track Prediction Using a Statistical Ensemble of Numerical Models  

Science Conference Proceedings (OSTI)

A new statistical ensemble prediction system for tropical cyclone tracks is presented. The system is based on a statistical analysis of the annual performance of numerical track prediction models, assuming that their position errors are ...

Harry C. Weber

2003-05-01T23:59:59.000Z

54

A Nonlinear Artificial Intelligence Ensemble Prediction Model for Typhoon Intensity  

Science Conference Proceedings (OSTI)

A new nonlinear artificial intelligence ensemble prediction (NAIEP) model has been developed for predicting typhoon intensity based on multiple neural networks with the same expected output and using an evolutionary genetic algorithm (GA). The ...

Long Jin; Cai Yao; Xiao-Yan Huang

2008-12-01T23:59:59.000Z

55

Development of Rail Temperature Prediction Model SUMMARY  

E-Print Network (OSTI)

Preventing track buckling is important to the railroad industry’s goal of operational safety. It is a common practice for railroads to impose slow orders during hot weather when the risk of track buckling is high. Numerous factors affect track buckling, but the instantaneous rail temperatures and stress-free (neutral) rail temperatures are the most critical factors. Unfortunately, neither of these two temperatures is easily obtainable. Decisions for slow orders are often based on an arbitrary, ambient temperature limit. The Federal Railroad Administration (FRA) Office of Research and Development has initiated a research project to develop a model for predicting rail temperatures based on real-time meteorological forecast data. The rail temperature prediction model is based on the heat transfer process of a rail exposed to the sun. In developing such a model, a rail-weather station was established, composed of a portable weather station and a short segment of rail track with temperature sensors installed on both rails. The model has proven to be able to predict the maximum rail temperature within a few degrees and within 30 minutes of the actual time when the maximum rail temperature occurs during the day. The model is being validated for three locations where real-time weather data and rail temperature are collected. A prototype webbased

unknown authors

2008-01-01T23:59:59.000Z

56

Training of neural models for predictive control  

Science Conference Proceedings (OSTI)

This paper emphasises the link between neural model training and its role in model predictive control (MPC) algorithms. This role is of fundamental importance since in MPC at each sampling instant a model is used on-line to calculate predictions of future ... Keywords: Identification, Model predictive control, Neural networks, Optimisation

Maciej ?awry?czuk

2010-03-01T23:59:59.000Z

57

Impact of Enthalpy-Based Ensemble Filtering Sea Ice Data Assimilation on Decadal Predictions: Simulation with a Conceptual Pycnocline Prediction Model  

Science Conference Proceedings (OSTI)

The non-Gaussian probability distribution of sea ice concentration makes it difficult to directly assimilate sea ice observations into a climate model. Because of the strong impact of the atmospheric and oceanic forcing on the sea ice state, any ...

S. Zhang; M. Winton; A. Rosati; T. Delworth; B. Huang

2013-04-01T23:59:59.000Z

58

A Consensus Model for Seasonal Hurricane Prediction  

Science Conference Proceedings (OSTI)

The authors apply a procedure called Bayesian model averaging (BMA) for examining the utility of a set of covariates for predicting the distribution of U.S. hurricane counts and demonstrating a consensus model for seasonal prediction. Hurricane ...

Thomas H. Jagger; James B. Elsner

2010-11-01T23:59:59.000Z

59

Distributed Processing of a Regional Prediction Model  

Science Conference Proceedings (OSTI)

This paper describes the parallelization of a mesoscale-cloud-scale numerical weather prediction model and experiments conducted to assess its performance. The model used is the Advanced Regional Prediction System (ARPS), a limited-area ...

Kenneth W. Johnson; Jeff Bauer; Gregory A. Riccardi; Kelvin K. Droegemeier; Ming Xue

1994-11-01T23:59:59.000Z

60

Air Conditioning Load Prediction Based on DE-SVM Algorithm  

Science Conference Proceedings (OSTI)

Based on SVM (Support Vector Machine) theory, and the model to predict air conditioning load was established. In order to optimize the behavior of SVM, the DE (Differential Evolution) algorithm was introduced into classic SVM. The DE-SVM model is applied ... Keywords: Air Conditioning load, DE-SVM, Prediction

Zhonghai Chen; Yong Sun; Guoli Yang; Tengfei Wu; Guizhu Li; Longbiao Xin

2010-04-01T23:59:59.000Z

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.


61

Kinetic Modeling of Halogen-Based Plasma Etching of Complex Oxide Films and its Application to Predictive Feature Profile Simulation.  

E-Print Network (OSTI)

??In this work, a comprehensive framework for predicting etching behavior is developed using the test case of hafnium lanthanate (HfxLayOz) in Cl2/BCl3 chemistry, starting from… (more)

Marchack, Nathan

0294-01-01T23:59:59.000Z

62

Midtemperature Solar Systems Test Facility predictions for thermal performance based on test data. Alpha Solarco Model 104 solar collector with 0. 125-inch Schott low-iron glass reflector surface  

DOE Green Energy (OSTI)

Thermal performance predictions based on test data are presented for the Alpha Solarco Model 104 solar collector, with 0.125-inch Schott low-iron glass reflector surface, for three output temperatures at five cities in the United States.

Harrison, T.D.

1981-04-01T23:59:59.000Z

63

Compatibility of Stand Basal Area Predictions Based on Forecast Combination  

E-Print Network (OSTI)

Compatibility of Stand Basal Area Predictions Based on Forecast Combination Xiongqing Zhang Carr.) in Beijing, forecast combination was used to adjust predicted stand basal areas from these three types of models. The forecast combination method combines information and disperses errors from

Cao, Quang V.

64

Synchrotron-based microanalysis of iron distribution after thermal processing and predictive modeling of resulting solar cell efficiency  

E-Print Network (OSTI)

Synchrotron-based X-ray fluorescence microscopy is applied to study the evolution of iron silicide precipitates during phosphorus diffusion gettering and low-temperature annealing. Heavily Fe-contaminated ingot border ...

Fenning, David P.

2013-04-10T23:59:59.000Z

65

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

66

Niche-based modelling as a tool for predicting the risk of alien plant invasions at a global scale  

E-Print Network (OSTI)

) succulent karoo, (3) Nama-karoo, (4) fynbos, (5) Albany thicket, (6) grassland and (7) savanna. Because, fynbos and succulent karoo. Temperate Europe and the south-eastern part of the USA now appear suitable (succulent karoo, Nama-karoo and dwarf savanna). Species' distribution models Native distribution of South

Schweik, Charles M.

67

A T-EOF Based Prediction Method  

Science Conference Proceedings (OSTI)

A new statistical time series prediction method based on temporal empirical orthogonal function (T-EOF) is introduced in this study. This method first applies singular spectrum analysis (SSA) to extract dominant T-EOFs from historical data. Then, ...

Yung-An Lee

2002-01-01T23:59:59.000Z

68

A predictive model for MSSW student success.  

E-Print Network (OSTI)

??Ph. D. This study tested a hypothetical model for predicting both graduate GPA and graduation of University of Louisville Kent School of Social Work Master… (more)

Napier, Angela Michele

2011-01-01T23:59:59.000Z

69

Modeling the flexibility of alpha helices in protein interfaces : structure based design and prediction of helix-mediated protein-protein interactions  

E-Print Network (OSTI)

Protein-protein interactions play an essential role in many biological functions. Prediction and design of these interactions using computational methods requires models that can be used to efficiently sample structural ...

Apgar, James R. (James Reasoner)

2008-01-01T23:59:59.000Z

70

Standardized Software for Wind Load Forecast Error Analyses and Predictions Based on Wavelet-ARIMA Models - Applications at Multiple Geographically Distributed Wind Farms  

Science Conference Proceedings (OSTI)

Given the multi-scale variability and uncertainty of wind generation and forecast errors, it is a natural choice to use time-frequency representation (TFR) as a view of the corresponding time series represented over both time and frequency. Here we use wavelet transform (WT) to expand the signal in terms of wavelet functions which are localized in both time and frequency. Each WT component is more stationary and has consistent auto-correlation pattern. We combined wavelet analyses with time series forecast approaches such as ARIMA, and tested the approach at three different wind farms located far away from each other. The prediction capability is satisfactory -- the day-ahead prediction of errors match the original error values very well, including the patterns. The observations are well located within the predictive intervals. Integrating our wavelet-ARIMA (‘stochastic’) model with the weather forecast model (‘deterministic’) will improve our ability significantly to predict wind power generation and reduce predictive uncertainty.

Hou, Zhangshuan; Makarov, Yuri V.; Samaan, Nader A.; Etingov, Pavel V.

2013-03-19T23:59:59.000Z

71

Nonlinear predictive models: overview and possibilities in speaker recognition  

Science Conference Proceedings (OSTI)

In this paper we give a brief overview of speaker recognition with special emphasis on nonlinear predictive models, based on neural nets. Main challenges and possibilities for nonlinear feature extraction are described, and experimental results of several ...

Marcos Faundez-Zanuy; Mohamed Chetouani

2007-01-01T23:59:59.000Z

72

Predictive modelling of boiler fouling. Final report.  

SciTech Connect

A spectral element method embodying Large Eddy Simulation based on Re- Normalization Group theory for simulating Sub Grid Scale viscosity was chosen for this work. This method is embodied in a computer code called NEKTON. NEKTON solves the unsteady, 2D or 3D,incompressible Navier Stokes equations by a spectral element method. The code was later extended to include the variable density and multiple reactive species effects at low Mach numbers, and to compute transport of large particles governed by inertia. Transport of small particles is computed by treating them as trace species. Code computations were performed for a number of test conditions typical of flow past a deep tube bank in a boiler. Results indicate qualitatively correct behavior. Predictions of deposition rates and deposit shape evolution also show correct qualitative behavior. These simulations are the first attempts to compute flow field results at realistic flow Reynolds numbers of the order of 10{sup 4}. Code validation was not done; comparison with experiment also could not be made as many phenomenological model parameters, e.g., sticking or erosion probabilities and their dependence on experimental conditions were not known. The predictions however demonstrate the capability to predict fouling from first principles. Further work is needed: use of large or massively parallel machine; code validation; parametric studies, etc.

Chatwani, A

1990-12-31T23:59:59.000Z

73

Numerical Ocean Prediction Models—Goal for the 1980s  

Science Conference Proceedings (OSTI)

Based on the experience of numerical weather prediction during the 1950s and 1960s as a model, a case is presented for the development of an ocean prediction capability during the 1980s. Examples selected from recent research at the Naval ...

Russell L. Elsberry; Roland W. Garwood Jr.

1980-12-01T23:59:59.000Z

74

Redundant CORDIC Rotator Based on Parallel Prediction  

E-Print Network (OSTI)

In this work we present a Cordic rotator, using carry--save arithmetic, based on the prediction of all the coefficients into which the rotation angle is decomposed. The prediction algorithm is based on the use of radix--2 microrotations with multiple shifts in the first iterations and the use of a redundant radix--2 and radix--4 representation for the coefficients in the rest of the microrotations. The use of multiple shifts facilitates the prediction of the coefficients in the case of microrotations where i n=4, being n the precision of the algorithm, and the use of radix--4 microrotations helps to reduce the total number of iterations. The prediction is carried out using the redundant representation of the z coordinate, without any need for conversions to a non--redundant representation. Finally, we present a VLSI architecture based on this algorithm. As the production of the coefficients is very fast, and they are known before starting each microrotation, the resulting architecture...

E. Antelo; J.D. Bruguera; J. Villalba; E.L. Zapata; Elisardo Antelo; Javier D. Bruguera Julio Villalba; Emilio L. Zapata

1995-01-01T23:59:59.000Z

75

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

76

The Influence of Hydrologic Modeling on the Predicted Local Weather: Two-Way Coupling of a Mesoscale Weather Prediction Model and a Land Surface Hydrologic Model  

Science Conference Proceedings (OSTI)

A two-way coupling of the operational mesoscale weather prediction model known as Lokal Modell (LM; German Weather Service) with the land surface hydrologic “TOPMODEL”-Based Land Surface–Atmosphere Transfer Scheme (TOPLATS; Princeton University) ...

G. Seuffert; P. Gross; C. Simmer; E. F. Wood

2002-10-01T23:59:59.000Z

77

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

SciTech Connect

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

78

Predicting software bugs using ARIMA model  

Science Conference Proceedings (OSTI)

The number of software products available in market is increasing rapidly. Many a time, multiple companies develop software products of similar functionalities. Thus the competition among those owning companies is becoming tougher every day. Moreover, ... Keywords: ARIMA models, evaluation approach, information theory, prediction models

Lisham L. Singh; Al Muhsen Abbas; Flaih Ahmad; Srinivasan Ramaswamy

2010-04-01T23:59:59.000Z

79

A Numerical Model for Prediction of Road Temperature and Ice  

Science Conference Proceedings (OSTI)

A numerical model for the prediction of road temperature and ice has been tested on data from a Danish road station. The model is based on the solution of the equation of heat conduction in the ground and the surface energy-balance equation.

Bent H. Sass

1992-12-01T23:59:59.000Z

80

A Predictive Model of Geosynchronous Magnetopause Crossings  

E-Print Network (OSTI)

We have developed a model predicting whether or not the magnetopause crosses geosynchronous orbit at given location for given solar wind pressure Psw, Bz component of interplanetary magnetic field (IMF) and geomagnetic conditions characterized by 1-min SYM-H index. The model is based on more than 300 geosynchronous magnetopause crossings (GMCs) and about 6000 minutes when geosynchronous satellites of GOES and LANL series are located in the magnetosheath (so-called MSh intervals) in 1994 to 2001. Minimizing of the Psw required for GMCs and MSh intervals at various locations, Bz and SYM-H allows describing both an effect of magnetopause dawn-dusk asymmetry and saturation of Bz influence for very large southward IMF. The asymmetry is strong for large negative Bz and almost disappears when Bz is positive. We found that the larger amplitude of negative SYM-H the lower solar wind pressure is required for GMCs. We attribute this effect to a depletion of the dayside magnetic field by a storm-time intensification of t...

Dmitriev, A; Chao, J -K

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.


81

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":"

82

GIS-BASED PREDICTION OF HURRICANE FLOOD INUNDATION  

Science Conference Proceedings (OSTI)

A simulation environment is being developed for the prediction and analysis of the inundation consequences for infrastructure systems from extreme flood events. This decision support architecture includes a GIS-based environment for model input development, simulation integration tools for meteorological, hydrologic, and infrastructure system models and damage assessment tools for infrastructure systems. The GIS-based environment processes digital elevation models (30-m from the USGS), land use/cover (30-m NLCD), stream networks from the National Hydrography Dataset (NHD) and soils data from the NRCS (STATSGO) to create stream network, subbasins, and cross-section shapefiles for drainage basins selected for analysis. Rainfall predictions are made by a numerical weather model and ingested in gridded format into the simulation environment. Runoff hydrographs are estimated using Green-Ampt infiltration excess runoff prediction and a 1D diffusive wave overland flow routing approach. The hydrographs are fed into the stream network and integrated in a dynamic wave routing module using the EPA's Storm Water Management Model (SWMM) to predict flood depth. The flood depths are then transformed into inundation maps and exported for damage assessment. Hydrologic/hydraulic results are presented for Tropical Storm Allison.

JUDI, DAVID [Los Alamos National Laboratory; KALYANAPU, ALFRED [Los Alamos National Laboratory; MCPHERSON, TIMOTHY [Los Alamos National Laboratory; BERSCHEID, ALAN [Los Alamos National Laboratory

2007-01-17T23:59:59.000Z

83

Potential Predictability of Northern America Surface Temperature -- Part I: Information-based vs signal-to-noise based metrics  

Science Conference Proceedings (OSTI)

In this study, the potential predictability of the Northern America (NA) surface air temperature was explored using information-based predictability framework and ENSEMBLE multiple model ensembles. Emphasis was put on the comparison of ...

Y. Tang; D. Chen; X. Yan

84

Model Predictive Control of Thermal Energy Storage in Building...  

NLE Websites -- All DOE Office Websites (Extended Search)

Model Predictive Control of Thermal Energy Storage in Building Cooling Systems Title Model Predictive Control of Thermal Energy Storage in Building Cooling Systems Publication Type...

85

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

NLE Websites -- All DOE Office Websites (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.

86

Uncertainty Assessment of Future Hydroclimatic Predictions: A Comparison of Probabilistic and Scenario-Based Approaches  

Science Conference Proceedings (OSTI)

During the last decade, numerous studies have been carried out to predict future climate based on climatic models run on the global scale and fed by plausible scenarios about anthropogenic forcing to climate. Based on climatic model output, ...

D. Koutsoyiannis; A. Efstratiadis; K. P. Georgakakos

2007-06-01T23:59:59.000Z

87

Numerical Weather Prediction Studies from the FGGE Southern Hemisphere Data Base  

Science Conference Proceedings (OSTI)

The quality of numerical weather prediction available for the Southern Hemisphere from the FGGE data base has been examined. The Australian Numerical Meteorology Research Centre (ANMRC) spectral prediction model has been initialized with analyses ...

W. Bourke; K. Puri; R. Seaman

1982-12-01T23:59:59.000Z

88

Comparison of Information-Based Measures of Forecast Uncertainty in Ensemble ENSO Prediction  

Science Conference Proceedings (OSTI)

In this study, ensemble predictions of the El Niño–Southern Oscillation (ENSO) were conducted for the period 1981–98 using two hybrid coupled models. Several recently proposed information-based measures of predictability, including relative ...

Youmin Tang; Richard Kleeman; Andrew M. Moore

2008-01-01T23:59:59.000Z

89

Combining Modeling and Gaming for Predictive Analytics  

SciTech Connect

Many of our most significant challenges involve people. While human behavior has long been studied, there are recent advances in computational modeling of human behavior. With advances in computational capabilities come increases in the volume and complexity of data that humans must understand in order to make sense of and capitalize on these modeling advances. Ultimately, models represent an encapsulation of human knowledge. One inherent challenge in modeling is efficient and accurate transfer of knowledge from humans to models, and subsequent retrieval. The simulated real-world environment of games presents one avenue for these knowledge transfers. In this paper we describe our approach of combining modeling and gaming disciplines to develop predictive capabilities, using formal models to inform game development, and using games to provide data for modeling.

Riensche, Roderick M.; Whitney, Paul D.

2012-08-22T23:59:59.000Z

90

Predictive modeling for collections of accounts receivable  

Science Conference Proceedings (OSTI)

It is commonly agreed that accounts receivable (AR) can be a source of financial difficulty for firms when they are not efficiently managed and are underperforming. Experience across multiple industries shows that effective management of AR and overall ... Keywords: accounts receivable, invoice to cash, knowledge discovery, order to cash, payment collection, predictive modeling

Sai Zeng; Ioana Boier-Martin; Prem Melville; Conrad Murphy; Christian A. Lang

2007-08-01T23:59:59.000Z

91

Randomized Model Predictive Control for HVAC Systems  

Science Conference Proceedings (OSTI)

Heating, Ventilation and Air Conditioning (HVAC) systems play a fundamental role in maintaining acceptable thermal comfort and Indoor Air Quality (IAQ) levels, essentials for occupants well-being. Since performing this task implies high energy requirements, ... Keywords: Copulas, Learning, Randomized Model Predictive Control, Smart Buildings, Sustainable Control Systems

Alessandra Parisio, Damiano Varagnolo, Daniel Risberg, Giorgio Pattarello, Marco Molinari, Karl H. Johansson

2013-11-01T23:59:59.000Z

92

Regression modeling method of space weather prediction  

E-Print Network (OSTI)

A regression modeling method of space weather prediction is proposed. It allows forecasting Dst index up to 6 hours ahead with about 90% correlation. It can also be used for constructing phenomenological models of interaction between the solar wind and the magnetosphere. With its help two new geoeffective parameters were found: latitudinal and longitudinal flow angles of the solar wind. It was shown that Dst index remembers its previous values for 2000 hours.

Parnowski, Aleksei

2009-01-01T23:59:59.000Z

93

LES based urban dispersal predictions for consequence management  

Science Conference Proceedings (OSTI)

It is unlikely that we will ever have a deterministic predictive framework for the study of flows in urban scale scenarios purely based on computational fluid dynamics. This is due to the inherent difficulty in modeling and validating all relevant physical sub-processes and acquiring all the necessary and relevant boundary condition information. On the other hand, this case is representative of very fundamental ones for which whole-domain scalable laboratory (or field) studies are impossible or very difficult, but for which it is also crucial to develop predictability. In this paper, we discuss a framework for detailed dispersal predictions in urban and regional settings based on effective linkage of strong motion codes - capable of simulating detailed energetic and contaminant sources, and large-eddy simulation - capable of emulating contaminant transport due to wind and turbulence fields in built-up areas. Challenging technical aspects of the simulation approach are outlined and recent progress is reviewed in th is context.

Grinstein, Fernando Franklin [Los Alamos National Laboratory; Bos, Randall [Los Alamos National Laboratory; Dey, Tom [Los Alamos National Laboratory

2008-01-01T23:59:59.000Z

94

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

Science Conference Proceedings (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 (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

95

New model accurately predicts reformate composition  

Science Conference Proceedings (OSTI)

Although naphtha reforming is a well-known process, the evolution of catalyst formulation, as well as new trends in gasoline specifications, have led to rapid evolution of the process, including: reactor design, regeneration mode, and operating conditions. Mathematical modeling of the reforming process is an increasingly important tool. It is fundamental to the proper design of new reactors and revamp of existing ones. Modeling can be used to optimize operating conditions, analyze the effects of process variables, and enhance unit performance. Instituto Mexicano del Petroleo has developed a model of the catalytic reforming process that accurately predicts reformate composition at the higher-severity conditions at which new reformers are being designed. The new AA model is more accurate than previous proposals because it takes into account the effects of temperature and pressure on the rate constants of each chemical reaction.

Ancheyta-Juarez, J.; Aguilar-Rodriguez, E. (Inst. Mexicano del Petroleo, Mexico City (Mexico))

1994-01-31T23:59:59.000Z

96

Model Predictive Control with Repeated Model Fitting for Ramp Metering  

E-Print Network (OSTI)

1 Model Predictive Control with Repeated Model Fitting for Ramp Metering Tom Bellemans, Bart De Schutter, Bart De Moor Abstract--- Ramp metering is a dynamic traffic control measure that has often shown to be very effective. There are several possible methods to determine appropriate ramp metering signals

97

Model predictive control of a Kaibel distillation column.  

E-Print Network (OSTI)

?? Model predictive control (MPC) of a Kaibel distillation column is the main focus of this thesis. A model description together with a model extension… (more)

Kvernland, Martin Krister

2009-01-01T23:59:59.000Z

98

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."

99

Engineering model for predicting rubble motion during blasting  

SciTech Connect

Recent applications of explosives and blasting agents to rubble rock have led to requirements for more elaborate design and analysis methods. In most blasting uses, it is necessary not only to fracture the rock, but also to move the broken rubble in a predictable manner. Many in-situ extraction techniques require rubblization to take place in a confined region where rock motion is a predominate factor in creating a permeable broken bed. In this paper, an engineering model is presented which describes the large rubble motion during blasting. This model is intended to provide the blast designer with a tool for evaluation and further refinement of blasting patterns and timing sequences. In this model the rock medium is represented by a discrete series of circular regions of fractured material. These regions are set in motion by pressure loads from the explosive. The motion of the regions is calculated using a step-wise, explicit, numerical time integration method. Interaction of adjacent regions is based on inelastic impact of spherical bodies. The derivation of this model is presented along with the background for selecting loading pressure based on explosive behavior. Three typical examples, including both cratering and bench geometries, are discussed which illustrate the use of this model to predict rubble motion. This engineering representation appears to provide a practical model for use in predicting rubble motion and a tool for design evaluation of blasting in confined geometries. 15 figures.

Schamaun, J.T.

1982-12-01T23:59:59.000Z

100

Engineering model for predicting rubble motion during blasting  

SciTech Connect

Recent applications of explosives and blasting agents to rubble rock have led to requirements for more elaborate design and analysis methods. In most blasting uses, it is necessary not only to fracture the rock, but also to move the broken rubble in a predictable manner. Many in situ extraction techniques require rubblization to take place in a confined region where rock motion is a predominate factor in creating a permeable broken bed. In this paper, an engineering model is presented which describes the large rubble motion during blasting. This model is intended to provide the blast designer with a tool for evaluation and further refinement of blasting patterns and timing sequences. In this model the rock medium is represented by a discrete series of circular regions of fractured material. These regions are set in motion by pressure loads from the explosive. The motion of the regions is calculated using a step-wise, explicit, numerical time integration method. Interaction of adjacent regions is based on inelastic impact of spherical bodies. The derivation of this model is presented along with the background for selecting loading pressure based on explosive behavior. Three typical examples, including both cratering and bench geometries, are discussed which illustrate the use of this model to predict rubble motion. This engineering representation appears to provide a practical model for use in predicting rubble motion and a tool for design evaluation of blasting in confined geometries. 15 figures, 1 table.

Schamaun, J.T.

1983-01-01T23:59:59.000Z

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.


101

Mathematical modeling to predict residential solid waste generation  

Science Conference Proceedings (OSTI)

One of the challenges faced by waste management authorities is determining the amount of waste generated by households in order to establish waste management systems, as well as trying to charge rates compatible with the principle applied worldwide, and design a fair payment system for households according to the amount of residential solid waste (RSW) they generate. The goal of this research work was to establish mathematical models that correlate the generation of RSW per capita to the following variables: education, income per household, and number of residents. This work was based on data from a study on generation, quantification and composition of residential waste in a Mexican city in three stages. In order to define prediction models, five variables were identified and included in the model. For each waste sampling stage a different mathematical model was developed, in order to find the model that showed the best linear relation to predict residential solid waste generation. Later on, models to explore the combination of included variables and select those which showed a higher R{sup 2} were established. The tests applied were normality, multicolinearity and heteroskedasticity. Another model, formulated with four variables, was generated and the Durban-Watson test was applied to it. Finally, a general mathematical model is proposed to predict residential waste generation, which accounts for 51% of the total.

Ojeda Benitez, Sara [Engineering Institute, UABC, Boulevard Benito Juarez y Calle de la Normal S/N, Col. Insurgentes Este, C.P. 21280, Mexicali, Baja California (Mexico)], E-mail: sojedab@uabc.mx; Lozano-Olvera, Gabriela [Engineering Institute, UABC, Boulevard Benito Juarez y Calle de la Normal S/N, Col. Insurgentes Este, C.P. 21280, Mexicali, Baja California (Mexico); Morelos, Raul Adalberto [CESUES Superior Studies Center, San Luis R.C. Sonora (Mexico); Vega, Carolina Armijo de [Engineering Faculty, UABC, Km 103, Carretera Tijuana-Ensenada, C.P. 22860, Ensenada, Baja California (Mexico)

2008-07-01T23:59:59.000Z

102

Nonlinear model predictive control for dosing daily anticancer agents using a novel saturating-rate cell-cycle model  

Science Conference Proceedings (OSTI)

A nonlinear model predictive control (NMPC) algorithm was developed to dose the chemotherapeutic agent tamoxifen based on a novel saturating-rate, cell-cycle model (SCM). Using daily tumor measurements, the algorithm decreased tumor volume along a specified ... Keywords: Biomedical systems, Cancer, Nonlinear model, Nonlinear model predictive control, Pharmacodynamics, Pharmacokinetics

Jeffry A. Florian, Jr.; Julie L. Eiseman; Robert S. Parker

2008-03-01T23:59:59.000Z

103

LLNL-TR-411072 A Predictive Model  

NLE Websites -- All DOE Office Websites (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.

104

Comprehensive fluence model for absolute portal dose image prediction  

SciTech Connect

Amorphous silicon (a-Si) electronic portal imaging devices (EPIDs) continue to be investigated as treatment verification tools, with a particular focus on intensity modulated radiation therapy (IMRT). This verification could be accomplished through a comparison of measured portal images to predicted portal dose images. A general fluence determination tailored to portal dose image prediction would be a great asset in order to model the complex modulation of IMRT. A proposed physics-based parameter fluence model was commissioned by matching predicted EPID images to corresponding measured EPID images of multileaf collimator (MLC) defined fields. The two-source fluence model was composed of a focal Gaussian and an extrafocal Gaussian-like source. Specific aspects of the MLC and secondary collimators were also modeled (e.g., jaw and MLC transmission factors, MLC rounded leaf tips, tongue and groove effect, interleaf leakage, and leaf offsets). Several unique aspects of the model were developed based on the results of detailed Monte Carlo simulations of the linear accelerator including (1) use of a non-Gaussian extrafocal fluence source function, (2) separate energy spectra used for focal and extrafocal fluence, and (3) different off-axis energy spectra softening used for focal and extrafocal fluences. The predicted energy fluence was then convolved with Monte Carlo generated, EPID-specific dose kernels to convert incident fluence to dose delivered to the EPID. Measured EPID data were obtained with an a-Si EPID for various MLC-defined fields (from 1x1 to 20x20 cm{sup 2}) over a range of source-to-detector distances. These measured profiles were used to determine the fluence model parameters in a process analogous to the commissioning of a treatment planning system. The resulting model was tested on 20 clinical IMRT plans, including ten prostate and ten oropharyngeal cases. The model predicted the open-field profiles within 2%, 2 mm, while a mean of 96.6% of pixels over all IMRT fields was in agreement with the 2%, 3 mm criteria. This model demonstrates accuracy commensurate to existing methods for IMRT pretreatment verification with portal dose image prediction of complex clinical examples (<2%, 3 mm).

Chytyk, K.; McCurdy, B. M. C. [Division of Medical Physics, CancerCare Manitoba, 675 McDermot Avenue, Winnipeg, Manitoba R3E 0V9 (Canada) and Department of Physics and Astronomy, University of Manitoba, Winnipeg, Manitoba R3T 2N2 (Canada); Division of Medical Physics, CancerCare Manitoba, 675 McDermot Avenue, Winnipeg, Manitoba R3E 0V9 (Canada); Department of Physics and Astronomy, University of Manitoba, Winnipeg, Manitoba R3T 2N2 (Canada) and Department of Radiology, University of Manitoba, Winnipeg, Manitoba R3A 1R9 (Canada)

2009-04-15T23:59:59.000Z

105

PHYSIOLOGICALLY BASED MODELING OF HALON ...  

Science Conference Proceedings (OSTI)

... fibrillation, never regained consciousness and died ... 5 min) human inhalation exposures to ... chemicals predicted in humans by physiologically based ...

2011-07-01T23:59:59.000Z

106

Control Engineering Practice 14 (2006) 757767 Model predictive control for ramp metering of motorway traffic  

E-Print Network (OSTI)

Control Engineering Practice 14 (2006) 757­767 Model predictive control for ramp metering metering implemented. Two types of controllers are compared: a traditional ALINEA based controller and a model predictive control based ramp metering controller. In order to evaluate the controllers

107

Experiences with collaborative, distributed predictive human performance modeling  

Science Conference Proceedings (OSTI)

Although predictive human performance modeling has been researched for 30 years in HCI, to our knowledge modeling has been conducted as a solitary task of one modeler or, occasionally, two modelers working in tight face-to-face collaboration. In contrast, ... Keywords: cogtool, efficiency, klm, predictive human performance modeling, usability evaluation

Bonnie John; Sonal Starr; Brian Utesch

2012-05-01T23:59:59.000Z

108

FEM based 3D tumor growth prediction for kidney tumor  

Science Conference Proceedings (OSTI)

It is important to predict the tumor growth so that appropriate treatment can be planned especially in the early stage. In this paper, we propose a finite element method (FEM) based 3D tumor growth prediction system using longitudinal kidney tumor images. ... Keywords: finite element method, kidney tumor, segmentation, tumor growth prediction

Xinjian Chen; Ronald Summers; Jianhua Yao

2010-09-01T23:59:59.000Z

109

A Simple Empirical Model for Predicting the Decay of Tropical Cyclone Winds after Landfall  

Science Conference Proceedings (OSTI)

An empirical model for predicting the maximum wind of landfalling tropical cyclones is developed. The model is based upon the observation that the wind speed decay rate after landfall is proportional to the wind speed. Observations also indicate ...

John Kaplan; Mark DeMaria

1995-11-01T23:59:59.000Z

110

A Beta Regression Model for Improved Solar Radiation Predictions  

Science Conference Proceedings (OSTI)

Predicting global solar radiation is an integral part of much environmental modeling. There are several approaches for predicting global solar radiation at a site where no instrumentation exists. One popular approach uses the difference between ...

Randall Mullen; Lucy Marshall; Brian McGlynn

2013-08-01T23:59:59.000Z

111

A beta regression model for improved solar radiation predictions  

Science Conference Proceedings (OSTI)

Predicting global solar radiation is an integral part of much environmental modeling. There are several approaches for predicting global solar radiation at a site where no instrumentation exists. One popular approach uses the difference between ...

Randall Mullen; Lucy Marshall; Brian McGlynn

112

A Coupled Soil Moisture and Surface Temperature Prediction Model  

Science Conference Proceedings (OSTI)

A model for soil moisture and soil surface temperature prediction for bare soil is considered in this paper. In describing evaporation rate. soil structure and moisture were taken into account as much as possible. Soil moisture prediction was ...

F. Ács; D. T. Mihailovi?; B. Rajkovi?

1991-06-01T23:59:59.000Z

113

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

Science Conference Proceedings (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

114

Comparison of models for predicting landfill methane recovery. Final report  

DOE Green Energy (OSTI)

Landfill methane models are tools used to project methane generation over time from a mass of landfilled waste. These models are used for sizing landfill gas (LFG) collection systems, evaluations and projections of LFG energy uses, and regulatory purposes. The objective of this project was to select various landfill methane models and to provide a comparison of model outputs to actual long-term gas recovery data from a number of well managed and suitable landfills. Another objective was to use these data to develop better estimates of confidence limits that can be assigned to model projections. This project assessed trial model forms against field data from available landfills where methane extraction was maximized, waste filling history was well-documented, and other pertinent site information was of superior quality. Data were obtained from 18 US landfills. Four landfill methane models were compared: a zero-order, a simple first order, a modified first order, and a multi-phase first order model. Models were adjusted for best fit to field data to yield parameter combinations based on the minimized residual errors between predicted and experienced methane recovery. The models were optimized in this way using two data treatments: absolute value of the differences (arithmetic error minimization) and absolute value of the natural log of the ratios (logarithmic error minimization).

Vogt, W.G. [SCS Engineers, Reston, VA (United States); Augenstein, D. [Institute for Environmental Management, Palo Alto, CA (United States)

1997-03-01T23:59:59.000Z

115

Brief A probabilistically constrained model predictive controller  

Science Conference Proceedings (OSTI)

We propose a novel control algorithm, probabilistically constrained predictive control, to deal with the uncertainties of system disturbances. The output is to be controlled in the constrained range with a desired probability. Under the assumption of ... Keywords: Multivariate normal distribution, Nonlinear programming, Predictive control, Probabilistic constraints, Uncertainty

Pu Li; Moritz Wendt; GüNter Wozny

2002-07-01T23:59:59.000Z

116

Identification, Modelling and Prediction of Non-periodic Bursts in Workloads  

Science Conference Proceedings (OSTI)

Non-periodic bursts are prevalent in workloads of large scale applications. Existing workload models do not predict such non-periodic bursts very well because they mainly focus on repeatable base functions. We begin by showing the necessity to include ... Keywords: data management, distributed system, workload modeling, burst prediction

Mario Lassnig; Thomas Fahringer; Vincent Garonne; Angelos Molfetas; Miguel Branco

2010-05-01T23:59:59.000Z

117

Financial health prediction models using artificial neural networks, genetic algorithm and multivariate discriminant analysis: Iranian evidence  

Science Conference Proceedings (OSTI)

The purpose of this study is to design a model to predict financial health of companies. Financial ratios for 180 manufacturing companies quoted in Tehran Stock Exchange for one year (year ended March 21, 2008) have been used. Three models; based on ... Keywords: Artificial neural networks, Discriminant analysis, Financial health prediction, Financial ratios, Genetic algorithm, Iranian company

F. Mokhatab Rafiei; S. M. Manzari; S. Bostanian

2011-08-01T23:59:59.000Z

118

Copula Based Hierarchical Bayesian Models  

E-Print Network (OSTI)

The main objective of our study is to employ copula methodology to develop Bayesian hierarchical models to study the dependencies exhibited by temporal, spatial and spatio-temporal processes. We develop hierarchical models for both discrete and continuous outcomes. In doing so we expect to address the dearth of copula based Bayesian hierarchical models to study hydro-meteorological events and other physical processes yielding discrete responses. First, we present Bayesian methods of analysis for longitudinal binary outcomes using Generalized Linear Mixed models (GLMM). We allow flexible marginal association among the repeated outcomes from different time-points. An unique property of this copula-based GLMM is that if the marginal link function is integrated over the distribution of the random effects, its form remains same as that of the conditional link function. This unique property enables us to retain the physical interpretation of the fixed effects under conditional and marginal model and yield proper posterior distribution. We illustrate the performance of the posited model using real life AIDS data and demonstrate its superiority over the traditional Gaussian random effects model. We develop a semiparametric extension of our GLMM and re-analyze the data from the AIDS study. Next, we propose a general class of models to handle non-Gaussian spatial data. The proposed model can deal with geostatistical data that can accommodate skewness, tail-heaviness, multimodality. We fix the distribution of the marginal processes and induce dependence via copulas. We illustrate the superior predictive performance of our approach in modeling precipitation data as compared to other kriging variants. Thereafter, we employ mixture kernels as the copula function to accommodate non-stationary data. We demonstrate the adequacy of this non-stationary model by analyzing permeability data. In both cases we perform extensive simulation studies to investigate the performances of the posited models under misspecification. Finally, we take up the important problem of modeling multivariate extreme values with copulas. We describe, in detail, how dependences can be induced in the block maxima approach and peak over threshold approach by an extreme value copula. We prove the ability of the posited model to handle both strong and weak extremal dependence and derive the conditions for posterior propriety. We analyze the extreme precipitation events in the continental United States for the past 98 years and come up with a suite of predictive maps.

Ghosh, Souparno

2009-08-01T23:59:59.000Z

119

Expeditious Data Center Sustainability, Flow, and Temperature Modeling: Life-Cycle Exergy Consumption Combined with a Potential Flow Based, Rankine Vortex Superposed, Predictive Method  

E-Print Network (OSTI)

strategy bypasses the vapor- compression cycle of the CRACis based on the same vapor-compression refrigeration cycle4.1, which shows the vapor-compression refrig- eration cycle

Lettieri, David

2012-01-01T23:59:59.000Z

120

An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction  

Science Conference Proceedings (OSTI)

Several studies have demonstrated the superior performance of ensemble classification algorithms, whereby multiple member classifiers are combined into one aggregated and powerful classification model, over single models. In this paper, two rotation-based ... Keywords: AUC, CRM, Customer churn prediction, Database marketing, Ensemble classification, ICA, Lift, RotBoost, Rotation Forest, Rotation-based ensemble classifiers

Koen W. De Bock; Dirk Van den Poel

2011-09-01T23:59:59.000Z

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.


121

Gas Emission Rate Prediction in Fully-Mechanized Excavated Faces Based on Support Vector Machine  

Science Conference Proceedings (OSTI)

In order to ensure safety in coal production, full assurance is given for fully-mechanized excavated faces. Based on the vector supporting machine for regression (SVR), a model is established for predicting the gas emission in fully-mechanized excavated ... Keywords: SVM, Tracking, emission rate, fully-mechanized excavated faces, gas prediction

Wang Changlong; Fu Weihua

2009-11-01T23:59:59.000Z

122

A Comparison of Experimental Data and Model Predictions with ...  

Science Conference Proceedings (OSTI)

A Comparison of Experimental Data and Model Predictions with Constitutive Laws ... Friction Stir Welding and Processing of Advanced Materials for Coal and  ...

123

Predictability Mysteries in Cloud-Resolving Models  

Science Conference Proceedings (OSTI)

The rapid amplification of small-amplitude perturbations by the chaotic nature of the atmospheric dynamics intrinsically limits the skill of deterministic weather forecasts. In this study, limited-area cloud-resolving numerical weather prediction ...

Cathy Hohenegger; Daniel Lüthi; Christoph Schär

2006-08-01T23:59:59.000Z

124

Prediction of Coal /Gas Outbursts Based on Selective Ensemble Learning  

Science Conference Proceedings (OSTI)

For the purpose of achieving accurate and reliable coal /gas outbursts prediction, a coal /gas outbursts prediction algorithm based on selective ensemble learning is presented. The component learners consisted of RS-PNN network, and the redundant component ... Keywords: Coal and gas outburst, selective ensemble learning, RS-PNN classifier, classification

Wang Heng, Shao Liangshan, Liu Shuanhong, Lu Lin

2013-01-01T23:59:59.000Z

125

Designing smart environments: a paradigm based on learning and prediction  

Science Conference Proceedings (OSTI)

We propose a learning and prediction based paradigm for designing smart home environments. The foundation of this paradigm lies in information theory as it manages uncertainties of the inhabitants’ contexts (e.g., locations or activities) in daily ...

Sajal K. Das; Diane J. Cook

2005-12-01T23:59:59.000Z

126

GIS-Based Infrastructure Modeling  

NLE Websites -- All DOE Office Websites (Extended Search)

GIS-Based Infrastructure Modeling Hydrogen Scenario Meeting August 9-10, 2006 Keith Parks, NREL GIS-Based Infrastructure Modeling * Station Analysis - Selection Criteria - Los...

127

A Linearized Convective Overturning Model for Prediction of Thunderstorm Movement  

Science Conference Proceedings (OSTI)

A linearized model of convective overturning in shear for prediction of storm propagation is presented. Good correspondence between the model and observation is found for a number of case studies of real storms. Supercell storms, however, are an ...

Adrian Marroquin; David J. Raymond

1982-01-01T23:59:59.000Z

128

Prediction Models for Annual U.S. Hurricane Counts  

Science Conference Proceedings (OSTI)

The authors build on their efforts to understand and predict coastal hurricane activity by developing statistical seasonal forecast models that can be used operationally. The modeling strategy uses May–June averaged values representing the North ...

James B. Elsner; Thomas H. Jagger

2006-06-01T23:59:59.000Z

129

An Interpretable Stroke Prediction Model using Rules and Bayesian Analysis  

E-Print Network (OSTI)

We aim to produce predictive models that are not only accurate, but are also interpretable to human experts. Our models are decision lists, which consist of a series of if...then... statements (for example, if high blood ...

Letham, Benjamin

2013-11-15T23:59:59.000Z

130

Power flow management of microgrid networks using model predictive control  

Science Conference Proceedings (OSTI)

In this paper, we present a power flow management method for a network of cooperating microgrids within the context of a smart grid by formulating the problem in a model predictive control framework. In order to reliably and economically provide the ... Keywords: Microgrid, Model predictive control, Renewable energy sources, Smart grid, Storage devices

A. Hooshmand; H. A. Malki; J. Mohammadpour

2012-09-01T23:59:59.000Z

131

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

132

Multivariable model predictive control for a gas turbine power plant  

Science Conference Proceedings (OSTI)

In this brief, constrained multi variable model predictive control (MPC) strategy is investigated for a GE9001E gas turbine power plant. So the rotor speed and exhaust gas temperature are controlled manipulating the fuel command and compressor inlet ... Keywords: ARX, gas turbine, identification, modeling, multivariable control, power plant, predictive control

Hadi Ghorbani; Ali Ghaffari; Mehdi Rahnama

2008-05-01T23:59:59.000Z

133

Operational Assimilation of GPS Zenith Total Delay Observations into the Met Office Numerical Weather Prediction Models  

Science Conference Proceedings (OSTI)

Zenith total delay (ZTD) observations derived from ground-based GPS receivers have been assimilated operationally into the Met Office North Atlantic and European (NAE) numerical weather prediction (NWP) model since 2007. Assimilation trials were ...

Gemma V. Bennitt; Adrian Jupp

2012-08-01T23:59:59.000Z

134

A Comprehensive Radiation Scheme for Numerical Weather Prediction Models with Potential Applications in Climate Simulations  

Science Conference Proceedings (OSTI)

A comprehensive scheme for the parameterization of radiative transfer in numerical weather Prediction (NWP) models has been developed. The scheme is based on the solution of the ?-two-stream version of the radiative transfer equation ...

Bodo Ritter; Jean-Francois Geleyn

1992-02-01T23:59:59.000Z

135

Evaluating Mesoscale Model Predictions of Clouds and Radiation with SGP ARM Data over a Seasonal Timescale  

Science Conference Proceedings (OSTI)

This study evaluates the predictions of radiative and cloud-related processes of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5). It is based on extensive comparison of ...

Françoise Guichard; David B. Parsons; Jimy Dudhia; James Bresch

2003-05-01T23:59:59.000Z

136

Development of a fourth generation predictive capability maturity model.  

Science Conference Proceedings (OSTI)

The Predictive Capability Maturity Model (PCMM) is an expert elicitation tool designed to characterize and communicate completeness of the approaches used for computational model definition, verification, validation, and uncertainty quantification associated for an intended application. The primary application of this tool at Sandia National Laboratories (SNL) has been for physics-based computational simulations in support of nuclear weapons applications. The two main goals of a PCMM evaluation are 1) the communication of computational simulation capability, accurately and transparently, and 2) the development of input for effective planning. As a result of the increasing importance of computational simulation to SNL's mission, the PCMM has evolved through multiple generations with the goal to provide more clarity, rigor, and completeness in its application. This report describes the approach used to develop the fourth generation of the PCMM.

Hills, Richard Guy; Witkowski, Walter R.; Urbina, Angel; Rider, William J.; Trucano, Timothy Guy

2013-09-01T23:59:59.000Z

137

Application of a noninhibitory growth model to predict the transient response in a chemostat  

SciTech Connect

A method of adapting a kinetic model based on steady-state chemostat data to predict the transient performance of a chemostat culture is presented. The proposal provides for a time delay which can be considered equivalent to a period of reduced activity of the organism subsequent to the introduction of a step change in operating conditions. The adapted kinetic model gives substantially better performance in predicting the transient response of an experimental system than the unmodified kinetic model.

Chiam, H.F.; Harris, I.J.

1983-06-01T23:59:59.000Z

138

Analytical Modeling and Performance Prediction of Remanufactured ...  

Science Conference Proceedings (OSTI)

The CLP tool assists in remanufacturing of high value, high demand rotorcraft, automotive and wind turbine gears. This paper will summarize the CLP models ...

139

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 cost of generating EOB waveforms in 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 extremely 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

2013-08-16T23:59:59.000Z

140

Compositional Model for Predicting Asphaltenes Precipitation  

Science Conference Proceedings (OSTI)

... depletion, acid stimulation, gas-lift operation, and miscible flooding, just to ... are based on the classical Flory-Huggins polymer- solution theory ...

2006-07-20T23:59:59.000Z

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

A predictive ocean oil spill model  

SciTech Connect

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

142

A Knowledge-Based System for the Diagnosis and Prediction of Short-Term Climatic Changes in the North Atlantic  

Science Conference Proceedings (OSTI)

Understanding and predicting climate change is the key problem in climatology. The most well-accepted current approach to this problem involves the development of general circulation models(GCMs).This approach is based on modeling fundamental ...

Sergei Rodionov; James H. Martin

1996-08-01T23:59:59.000Z

143

BWR Channel Bow Model: Technical Bases, Description, and Qualification  

Science Conference Proceedings (OSTI)

A model has been developed for the prediction of Zircaloy-2 (Zr-2) channel bow, including fast fluence gradient-induced channel bow and control blade shadow corrosion-induced channel bow. This report provides: (1) a description of the channel bow model in its present form, (2) the technical bases for the model formulations, (3) detailed qualification of the model prediction capability by comparison of predictions to the available performance characterization measurements, and ...

2013-05-20T23:59:59.000Z

144

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

145

LHC diphoton Higgs signal predicted by little Higgs models  

Science Conference Proceedings (OSTI)

Little Higgs theory naturally predicts a light Higgs boson whose most important discovery channel at the LHC is the diphoton signal pp{yields}h{yields}{gamma}{gamma}. In this work, we perform a comparative study for this signal in some typical little Higgs models, namely, the littlest Higgs model, two littlest Higgs models with T-parity (named LHT-I and LHT-II), and the simplest little Higgs models. We find that compared with the standard model prediction, the diphoton signal rate is always suppressed and the suppression extent can be quite different for different models. The suppression is mild (Higgs boson predicted by the little Higgs theory through the diphoton channel at the LHC will be more difficult than discovering the standard model Higgs boson.

Wang Lei [Department of Physics, Yantai University, Yantai 264005 (China); Yang Jinmin [Key Laboratory of Frontiers in Theoretical Physics, Institute of Theoretical Physics, Academia Sinica, Beijing 100190 (China)

2011-10-01T23:59:59.000Z

146

MODEL BASED ENTERPRISE  

Science Conference Proceedings (OSTI)

... Model 3564 STEELSHED, inc Model A-G22 HD Model 65-1 DIY Model AB344 ... 23 Bill identifies DIY Model AB344 as his choice and procures the ...

2010-12-21T23:59:59.000Z

147

Preliminary results of model predictive control of shading systems (WIP)  

Science Conference Proceedings (OSTI)

Shades in buildings are widely installed and are an effective technique for managing solar gains and occupant comfort. A model of a typical office space located in Ottawa, Ontario has been created and the model was developed for analysis under variable ... Keywords: energy management system, model predictive control, reactive control, shades

Brent Huchuk; William O'Brien; Cynthia A. Cruickshank

2013-04-01T23:59:59.000Z

148

A Model to Predict the Probability of Precipitation  

Science Conference Proceedings (OSTI)

A model to predict the probability of a specific amount of accumulated precipitation at a point in an area of extended convective precipitation has been developed. The model has been used in conjunction with a large-scale numerical forecast model ...

Ulla Hammarstrand

1980-06-01T23:59:59.000Z

149

Settlement Prediction, Gas Modeling and Slope Stability Analysis  

E-Print Network (OSTI)

Settlement Prediction, Gas Modeling and Slope Stability Analysis in Coll Cardús Landfill Li Yu using mechanical models Simulation of gas generation, transport and extraction in MSW landfill 1 models Simulation of gas generation, transport and extraction in MSW landfill 1) Analytical solution

Politècnica de Catalunya, Universitat

150

Hurricane Track Prediction with a New Barotropic Model  

Science Conference Proceedings (OSTI)

A new barotropic prediction system for the tracks of tropical cyclones is presented. The system (referred to as WBAR) consists of an initialization procedure, a vortex enhancement scheme, and a shallow water model, formulated in a geographical ...

Harry C. Weber

2001-08-01T23:59:59.000Z

151

Forecasting Pacific SSTs: Linear Inverse Model Predictions of the PDO  

Science Conference Proceedings (OSTI)

A linear inverse model (LIM) is used to predict Pacific (30°S–60°N) sea surface temperature anomalies (SSTAs), including the Pacific decadal oscillation (PDO). The LIM is derived from the observed simultaneous and lagged covariance statistics of ...

Michael A. Alexander; Ludmila Matrosova; Cécile Penland; James D. Scott; Ping Chang

2008-01-01T23:59:59.000Z

152

Representing Convective Organization in Prediction Models by a Hybrid Strategy  

Science Conference Proceedings (OSTI)

The mesoscale organization of precipitating convection is highly relevant to next-generation global numerical weather prediction models, which will have an intermediate horizontal resolution (grid spacing about 10 km). A primary issue is how to ...

Mitchell W. Moncrieff; Changhai Liu

2006-12-01T23:59:59.000Z

153

Predictability Associated with Nonlinear Regimes in an Atmospheric Model  

Science Conference Proceedings (OSTI)

Atmospheric regimes are midlatitude flow patterns that persist for periods of time exceeding a few days. Here, the authors analyzed the output of an idealized atmospheric model (QG3) to examine the relationship between regimes and predictability.

John M. Peters; Sergey Kravtsov; Nicholas T. Schwartz

2012-03-01T23:59:59.000Z

154

Performance and prediction: bayesian modelling of fallible choice in chess  

Science Conference Proceedings (OSTI)

Evaluating agents in decision-making applications requires assessing their skill and predicting their behaviour. Both are well developed in Poker-like situations, but less so in more complex game and model domains. This paper addresses both tasks by ...

Guy Haworth; Ken Regan; Giuseppe Di Fatta

2009-05-01T23:59:59.000Z

155

Cloud Predictions Diagnosed from Global Weather Model Forecasts  

Science Conference Proceedings (OSTI)

The U.S. Air Force has a long history of investment in cloud analysis and prediction operations. Their need for accurate cloud cover information has resulted in routine production of global cloud analyses (from their RTNEPH analysis model) and ...

Donald C. Norquist

2000-10-01T23:59:59.000Z

156

Extended-Range Atmospheric Prediction and the Lorenz Model  

Science Conference Proceedings (OSTI)

The physical basis for extended-range prediction is explored using the famous three-component Lorenz convection model, taken as a conceptual representation of the chaotic extratropical circulation, and extended by coupling to a linear oscillator ...

T. N. Palmer

1993-01-01T23:59:59.000Z

157

Rule-Based prediction of rare extreme values  

Science Conference Proceedings (OSTI)

This paper describes a rule learning method that obtains models biased towards a particular class of regression tasks. These tasks have as main distinguishing feature the fact that the main goal is to be accurate at predicting rare extreme values of ...

Rita Ribeiro; Luís Torgo

2006-10-01T23:59:59.000Z

158

LIFETIME PREDICTION FOR MODEL 9975 O-RINGS IN KAMS  

SciTech Connect

The Savannah River Site (SRS) is currently storing plutonium materials in the K-Area Materials Storage (KAMS) facility. The materials are packaged per the DOE 3013 Standard and transported and stored in KAMS in Model 9975 shipping packages, which include double containment vessels sealed with dual O-rings made of Parker Seals compound V0835-75 (based on Viton{reg_sign} GLT). The outer O-ring of each containment vessel is credited for leaktight containment per ANSI N14.5. O-ring service life depends on many factors, including the failure criterion, environmental conditions, overall design, fabrication quality and assembly practices. A preliminary life prediction model has been developed for the V0835-75 O-rings in KAMS. The conservative model is based primarily on long-term compression stress relaxation (CSR) experiments and Arrhenius accelerated-aging methodology. For model development purposes, seal lifetime is defined as a 90% loss of measurable sealing force. Thus far, CSR experiments have only reached this target level of degradation at temperatures {ge} 300 F. At lower temperatures, relaxation values are more tolerable. Using time-temperature superposition principles, the conservative model predicts a service life of approximately 20-25 years at a constant seal temperature of 175 F. This represents a maximum payload package at a constant ambient temperature of 104 F, the highest recorded in KAMS to date. This is considered a highly conservative value as such ambient temperatures are only reached on occasion and for short durations. The presence of fiberboard in the package minimizes the impact of such temperature swings, with many hours to several days required for seal temperatures to respond proportionately. At 85 F ambient, a more realistic but still conservative value, bounding seal temperatures are reduced to {approx}158 F, with an estimated seal lifetime of {approx}35-45 years. The actual service life for O-rings in a maximum wattage package likely lies higher than the estimates due to the conservative assumptions used for the model. For lower heat loads at similar ambient temperatures, seal lifetime is further increased. The preliminary model is based on several assumptions that require validation with additional experiments and longer exposures at more realistic conditions. The assumption of constant exposure at peak temperature is believed to be conservative. Cumulative damage at more realistic conditions will likely be less severe but is more difficult to assess based on available data. Arrhenius aging behavior is expected, but non-Arrhenius behavior is possible. Validation of Arrhenius behavior is ideally determined from longer tests at temperatures closer to actual service conditions. CSR experiments will therefore continue at lower temperatures to validate the model. Ultrasensitive oxygen consumption analysis has been shown to be useful in identifying non-Arrhenius behavior within reasonable test periods. Therefore, additional experiments are recommended and planned to validate the model.

Hoffman, E.; Skidmore, E.

2009-11-24T23:59:59.000Z

159

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

160

On the practice of artificial intelligence based predictive control scheme: a case study  

Science Conference Proceedings (OSTI)

This paper describes a novel artificial intelligence based predictive control scheme for the purpose of dealing with so many complicated systems. In the control scheme proposed here, the system has to be first represented through a multi-Takagi-Sugeno-Kang ... Keywords: Drum-type boiler-turbine system, GPC identifier, GPC scheme, Industrial tubular heat exchanger system, Linear model approximation, Multi-GPC scheme, Multi-TSK fuzzy-based model approach, NLGPC scheme, System operating points

A. H. Mazinan; M. Sheikhan

2012-01-01T23:59:59.000Z

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

NETL: Predictive Modeling and Evaluation - CMU Regional Modeling Study  

NLE Websites -- All DOE Office Websites (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.

162

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

E-Print Network (OSTI)

of numerical weather prediction solar irradiance forecasts of numerical weather prediction for intra?day solar numerical weather prediction model for solar irradiance 

Mathiesen, Patrick; Collier, Craig; Kleissl, Jan

2013-01-01T23:59:59.000Z

163

Development of an Ocean Model Adjoint for Decadal Prediction | Argonne  

NLE Websites -- All DOE Office Websites (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

164

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.

165

Variational Bayesian learning of nonlinear hidden state-space models for model predictive control  

Science Conference Proceedings (OSTI)

This paper studies the identification and model predictive control in nonlinear hidden state-space models. Nonlinearities are modelled with neural networks and system identification is done with variational Bayesian learning. In addition to the robustness ... Keywords: Model predictive control, Neural network, Nonlinear system, Partially observable Markov decision process, State-space method, Stochastic optimal control, Variational methods

Tapani Raiko; Matti Tornio

2009-10-01T23:59:59.000Z

166

Medium Range Prediction by a GFDL Global Spectral Model: Results for Three Winter Cases and Sensitivity to Dissipation  

Science Conference Proceedings (OSTI)

A preliminary evaluation is made of the medium range predictive capability of a GFDL global spectral model of the atmosphere, based upon three winter blocking cases. Analogous forecasts by a GFDL global grid point model provide a background ...

Charles T. Gordon; William P. Stern

1984-02-01T23:59:59.000Z

167

Modelling Monsoons: Understanding and Predicting Current and Future Behaviour  

SciTech Connect

The global monsoon system is so varied and complex that understanding and predicting its diverse behaviour remains a challenge that will occupy modellers for many years to come. Despite the difficult task ahead, an improved monsoon modelling capability has been realized through the inclusion of more detailed physics of the climate system and higher resolution in our numerical models. Perhaps the most crucial improvement to date has been the development of coupled ocean-atmosphere models. From subseasonal to interdecadal timescales, only through the inclusion of air-sea interaction can the proper phasing and teleconnections of convection be attained with respect to sea surface temperature variations. Even then, the response to slow variations in remote forcings (e.g., El Nino-Southern Oscillation) does not result in a robust solution, as there are a host of competing modes of variability that must be represented, including those that appear to be chaotic. Understanding the links between monsoons and land surface processes is not as mature as that explored regarding air-sea interactions. A land surface forcing signal appears to dominate the onset of wet season rainfall over the North American monsoon region, though the relative role of ocean versus land forcing remains a topic of investigation in all the monsoon systems. Also, improved forecasts have been made during periods in which additional sounding observations are available for data assimilation. Thus, there is untapped predictability that can only be attained through the development of a more comprehensive observing system for all monsoon regions. Additionally, improved parameterizations - for example, of convection, cloud, radiation, and boundary layer schemes as well as land surface processes - are essential to realize the full potential of monsoon predictability. Dynamical considerations require ever increased horizontal resolution (probably to 0.5 degree or higher) in order to resolve many monsoon features including, but not limited to, the Mei-Yu/Baiu sudden onset and withdrawal, low-level jet orientation and variability, and orographic forced rainfall. Under anthropogenic climate change many competing factors complicate making robust projections of monsoon changes. Without aerosol effects, increased land-sea temperature contrast suggests strengthened monsoon circulation due to climate change. However, increased aerosol emissions will reflect more solar radiation back to space, which may temper or even reduce the strength of monsoon circulations compared to the present day. A more comprehensive assessment is needed of the impact of black carbon aerosols, which may modulate that of other anthropogenic greenhouse gases. Precipitation may behave independently from the circulation under warming conditions in which an increased atmospheric moisture loading, based purely on thermodynamic considerations, could result in increased monsoon rainfall under climate change. The challenge to improve model parameterizations and include more complex processes and feedbacks pushes computing resources to their limit, thus requiring continuous upgrades of computational infrastructure to ensure progress in understanding and predicting the current and future behavior of monsoons.

Turner, A; Sperber, K R; Slingo, J M; Meehl, G A; Mechoso, C R; Kimoto, M; Giannini, A

2008-09-16T23:59:59.000Z

168

A Finite Mixture Logit Model to Segment and Predict Electronic Payments System Adoption  

Science Conference Proceedings (OSTI)

Despite much hype about electronic payments systems (EPSs), a 2004 survey establishes that close to 80% of between-business payments are still made using paper-based formats. We present a finite mixture logit model to predict likelihood of EPS adoption ... Keywords: clustering analysis, electronic payments systems, finite mixture model, hierarchical logit regression, logistic regression, market segmentation

Ravi Bapna; Paulo Goes; Kwok Kee Wei; Zhongju Zhang

2011-03-01T23:59:59.000Z

169

Predictivity of models with spontaneously broken non-Abelian discrete flavor symmetries  

E-Print Network (OSTI)

In a class of supersymmetric flavor models predictions are based on residual symmetries of some subsectors of the theory such as those of the charged leptons and neutrinos. However, the vacuum expectation values of the so-called flavon fields generally modify the K\\"ahler potential of the setting, thus changing the predictions. We derive simple analytic formulae that allow us to understand the impact of these corrections on the predictions for the masses and mixing parameters. Furthermore, we discuss the effects on the vacuum alignment and on flavor changing neutral currents. Our results can also be applied to non--supersymmetric flavor models.

Chen, Mu-Chun; Omura, Yuji; Ratz, Michael; Staudt, Christian

2013-01-01T23:59:59.000Z

170

Experimental Validation of Stochastic Wireless Urban Channel Model: Estimation and Prediction  

SciTech Connect

Stochastic differential equations (SDE) can be used to describe the time-varying nature of wireless channels. This paper validates a long-term fading channel model for estimation and prediction from solely using measured received signal strength measurements. Such channel models can be used for optimizing wireless networks deployed for industrial automation, public access, and communication. This paper uses two different sets of received signal measurement data to estimate an predict the signal strength based on past measurements. The realworld performance of the estimation and prediction algorithm is demonstrated.

Kuruganti, Phani Teja [ORNL; Ma, Xiao [ORNL; Djouadi, Seddik M [ORNL

2012-01-01T23:59:59.000Z

171

Occupancy Modeling and Prediction for Building Energy Varick L. Erickson, University of California, Merced  

E-Print Network (OSTI)

response HVAC control strategy," in proceedings of the 2nd ACM Workshop on Embedded Sensing SystemsA Occupancy Modeling and Prediction for Building Energy Management Varick L. Erickson, University into building conditioning system for usage based demand control conditioning strategies. Using strategies based

Cerpa, Alberto E.

172

Adaptive time-frequency analysis based on autoregressive modeling  

Science Conference Proceedings (OSTI)

A new adaptive method for discrete time-frequency analysis based on autoregressive (AR) modeling is introduced. The performance of AR modeling often depends upon a good selection of the model order. The predictive least squares (PLS) principle of Rissanen ... Keywords: Adaptive filters, Autoregressive modeling, Least squares methods, Model order, Time-frequency analysis

Antonio H. Costa; Stephan Hengstler

2011-04-01T23:59:59.000Z

173

Lepton Flavor Violation in Predictive Supersymmetric GUT Models  

E-Print Network (OSTI)

There have been many theoretical models constructed which aim to explain the neutrino masses and mixing patterns. While many of the models will be eliminated once more accurate determinations of the mixing parameters, especially $\\sin^2 2\\theta_{13}$, are obtained, charged lepton flavor violation (LFV) experiments are able to differentiate even further among the models. In this paper, we investigate various rare LFV processes, such as $\\ell_{i} \\to \\ell_{j} + \\gamma$ and $\\mu-e$ conversion, in five predictive supersymmetric (SUSY) SO(10) models and their allowed soft-SUSY breaking parameter space in the constrained minimal SUSY standard model. Utilizing the Wilkinson Microwave Anisotropy Probe dark matter constraints, we obtain lower bounds on the branching ratios of these rare processes and find that at least three of the five models we consider give rise to predictions for $\\mu \\to e + \\gamma$ that will be tested by the MEG Collaboration at PSI. In addition, the next generation $\\mu-e$ conversion experiment has sensitivity to the predictions of all five models, making it an even more robust way to test these models. While generic studies have emphasized the dependence of the branching ratios of these rare processes on the reactor neutrino angle $\\theta_{13}$ and the mass of the heaviest right-handed neutrino $M_3$, we find very massive $M_3$ is more significant than large $\\theta_{13}$ in leading to branching ratios near to the present upper limits.

Carl H. Albright; Mu-Chun Chen

2008-02-28T23:59:59.000Z

174

Lepton Flavor Violation in Predictive SUSY-GUT Models  

SciTech Connect

There have been many theoretical models constructed which aim to explain the neutrino masses and mixing patterns. While many of the models will be eliminated once more accurate determinations of the mixing parameters, especially sin{sup 2} 2{theta}{sub 13}, are obtained, charged lepton flavor violation (LFV) experiments are able to differentiate even further among the models. In this paper, they investigate various rare LFV processes, such as {ell}{sub i} {yields} {ell}{sub j} + {gamma} and {mu} - e conversion, in five predictive SUSY SO(10) models and their allowed soft SUSY breaking parameter space in the constrained minimal SUSY standard model (CMSSM). Utilizing the WMAP dark matter constraints, they obtain lower bounds on the branching ratios of these rare processes and find that at least three of the five models they consider give rise to predictions for {mu} {yields} e + {gamma} that will be tested by the MEG collaboration at PSI. in addition, the next generation {mu} - e conversion experiment has sensitivity to the predictions of all five models, making it an even more robust way to test these models. While generic studies have emphasized the dependence of the branching ratios of these rare processes on the reactor neutrino angle, {theta}{sub 13}, and the mass of the heaviest right-handed neutrino, M{sub 3}, they find very massive M{sub 3} is more significant than large {theta}{sub 13} in leading to branching ratios near to the present upper limits.

Albright, Carl H.; /Northern Illinois U. /Fermilab; Chen, Mu-Chun; /UC, Irvine

2008-02-01T23:59:59.000Z

175

Predictability and reduced order modeling in stochastic reaction networks.  

SciTech Connect

Many systems involving chemical reactions between small numbers of molecules exhibit inherent stochastic variability. Such stochastic reaction networks are at the heart of processes such as gene transcription, cell signaling or surface catalytic reactions, which are critical to bioenergy, biomedical, and electrical storage applications. The underlying molecular reactions are commonly modeled with chemical master equations (CMEs), representing jump Markov processes, or stochastic differential equations (SDEs), rather than ordinary differential equations (ODEs). As such reaction networks are often inferred from noisy experimental data, it is not uncommon to encounter large parametric uncertainties in these systems. Further, a wide range of time scales introduces the need for reduced order representations. Despite the availability of mature tools for uncertainty/sensitivity analysis and reduced order modeling in deterministic systems, there is a lack of robust algorithms for such analyses in stochastic systems. In this talk, we present advances in algorithms for predictability and reduced order representations for stochastic reaction networks and apply them to bistable systems of biochemical interest. To study the predictability of a stochastic reaction network in the presence of both parametric uncertainty and intrinsic variability, an algorithm was developed to represent the system state with a spectral polynomial chaos (PC) expansion in the stochastic space representing parametric uncertainty and intrinsic variability. Rather than relying on a non-intrusive collocation-based Galerkin projection [1], this PC expansion is obtained using Bayesian inference, which is ideally suited to handle noisy systems through its probabilistic formulation. To accommodate state variables with multimodal distributions, an adaptive multiresolution representation is used [2]. As the PC expansion directly relates the state variables to the uncertain parameters, the formulation lends itself readily to sensitivity analysis. Reduced order modeling in the time dimension is accomplished using a Karhunen-Loeve (KL) decomposition of the stochastic process in terms of the eigenmodes of its covariance matrix. Subsequently, a Rosenblatt transformation relates the random variables in the KL decomposition to a set of independent random variables, allowing the representation of the system state with a PC expansion in those independent random variables. An adaptive clustering method is used to handle multimodal distributions efficiently, and is well suited for high-dimensional spaces. The spectral representation of the stochastic reaction networks makes these systems more amenable to analysis, enabling a detailed understanding of their functionality, and robustness under experimental data uncertainty and inherent variability.

Najm, Habib N.; Debusschere, Bert J.; Sargsyan, Khachik

2008-10-01T23:59:59.000Z

176

Principles of models based engineering  

SciTech Connect

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

177

PII-39: A Microstructure-strength Calculation Model for Predicting ...  

Science Conference Proceedings (OSTI)

Based on the temperature field simulation by FT-STAR, an MCA model was used ... Model Validation for Microstructural Sensitivities Using High Energy Diffraction Microscopy ... PI-8: Building 3D Microstructure Database using an Advanced ...

178

Forecasting the Skill of a Regional Numerical Weather Prediction Model  

Science Conference Proceedings (OSTI)

It is demonstrated that the skill of short-term regional numerical forecasts can be predicted on a day-to-day basis. This was achieved by using a statistical regression scheme with the model forecast errors (MFE) as the predictands and the ...

L. M. Leslie; K. Fraedrich; T. J. Glowacki

1989-03-01T23:59:59.000Z

179

Rain Attenuation Prediction Model for Lagos at Millimeter Wave Bands  

Science Conference Proceedings (OSTI)

“Rain Attenuation Prediction Model for Lagos at Millimeter Wave bands” is the subject of this work. Lagos (geog. Lat. 6.350N and Long. 3.20E), is a coastal station in the rain forest area in the South-Western Nigeria with an altitude of 380 ...

Abayomi Isiaka Yussuff; Nor Hisham Haji Khamis

180

Reference wind farm selection for regional wind power prediction models  

E-Print Network (OSTI)

1 Reference wind farm selection for regional wind power prediction models Nils Siebert George.siebert@ensmp.fr, georges.kariniotakis@ensmp.fr Abstract Short-term wind power forecasting is recognized today as a major requirement for a secure and economic integration of wind generation in power systems. This paper deals

Paris-Sud XI, Université de

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181

Predictive discrete latent factor models for large scale dyadic data  

Science Conference Proceedings (OSTI)

We propose a novel statistical method to predict large scale dyadic response variables in the presence of covariate information. Our approach simultaneously incorporates the effect of covariates and estimates local structure that is induced by interactions ... Keywords: co-clustering, dyadic data, generalized linear regression, latent factor modeling

Deepak Agarwal; Srujana Merugu

2007-08-01T23:59:59.000Z

182

Hurricane Prediction with a High Resolution Global Model  

Science Conference Proceedings (OSTI)

A global spectral model is used to carry out a number of short to medium range prediction experiments with global datasets. The primary objective of these studies is to examine the formation and motion of the hurricanes/typhoons with a fairly ...

T. N. Krishnamurti; D. Oosterhof; Nancy Dignon

1989-03-01T23:59:59.000Z

183

Behavioral dynamics on the web: Learning, modeling, and prediction  

Science Conference Proceedings (OSTI)

The queries people issue to a search engine and the results clicked following a query change over time. For example, after the earthquake in Japan in March 2011, the query japan spiked in popularity and people issuing the query were more likely ... Keywords: Behavioral analysis, predictive behavioral models

Kira Radinsky; Krysta M. Svore; Susan T. Dumais; Milad Shokouhi; Jaime Teevan; Alex Bocharov; Eric Horvitz

2013-07-01T23:59:59.000Z

184

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

185

Designing intelligent disaster prediction models and systems for debris-flow disasters in Taiwan  

Science Conference Proceedings (OSTI)

Effective disaster prediction relies on using correct disaster decision model to predict the disaster occurrence accurately. This study proposes three effective debris-flow prediction models and an inference engine to predict and decide the debris-flow ... Keywords: Back-propagation network, Debris-flow prediction models, Decision support system, Disaster prevention, Mobile multimedia communications

Hsu-Yang Kung; Chi-Hua Chen; Hao-Hsiang Ku

2012-04-01T23:59:59.000Z

186

NETL: Predictive Modeling and Evaluation - TVA Model Comparison  

NLE Websites -- All DOE Office Websites (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.

187

A physics-based emissions model for aircraft gas turbine combustors  

E-Print Network (OSTI)

In this thesis, a physics-based model of an aircraft gas turbine combustor is developed for predicting NO. and CO emissions. The objective of the model is to predict the emissions of current and potential future gas turbine ...

Allaire, Douglas L

2006-01-01T23:59:59.000Z

188

Predictive model of nucleon-nucleus scattering cross sections  

DOE Green Energy (OSTI)

Nucleon total reaction and neutron total cross sections as well as differential (including spin) observables from 25 to 300 MeV for stable nuclei from 6Li to 238U have been predicted that are in good agreement with measured data. Those predictions have been made using non-local, energy dependent, and complex optical potentials in coordinate space formed by full folding of effective nucleon-nucleon interactions with realistic nuclear ground state densities. By inverse kinematics the same model prescription describes exotic (radioactive) nuclei scattering from hydrogen as a target and the results reveal the extended (neutron) distributions such nuclei can have.

Amos, K. (Ken); Deb, P. (Pradip); Karataglidis, S. (Steven); Madland, D. G.

2001-01-01T23:59:59.000Z

189

Prediction of Cooling of a Nocturnal Environment Using Two Atmospheric Models  

Science Conference Proceedings (OSTI)

A surface energy balance model and a boundary layer model were used to predict nocturnal cooling in an agricultural environment. The results from both models were compared with the observed temperatures to determine which model predicted the ...

Paul H. Heinemann; J. David Martsolf

1988-04-01T23:59:59.000Z

190

Development and Testing of Model Predictive Control for a Campus Chilled  

NLE Websites -- All DOE Office Websites (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

191

Explicit/multi-parametric model predictive control (MPC) of linear discrete-time systems by dynamic and multi-parametric programming  

Science Conference Proceedings (OSTI)

This work presents a new algorithm for solving the explicit/multi-parametric model predictive control (or mp-MPC) problem for linear, time-invariant discrete-time systems, based on dynamic programming and multi-parametric programming techniques. The ... Keywords: Dynamic programming, Explicit Model Predictive Control, Model Predictive Control, Multi-parametric control, Multi-parametric programming

K. I. Kouramas; N. P. Faísca; C. Panos; E. N. Pistikopoulos

2011-08-01T23:59:59.000Z

192

Model Predictive Control of a Permanent Magnet Synchronous Motor with Experimental Validation  

E-Print Network (OSTI)

Model Predictive Control of a Permanent Magnet Synchronous Motor with Experimental Validation Shan to regulate the speed of a permanent magnet synchronous motor where the design is based on a linearized state rejection, constraints, quadratic programming. 1. Introduction Permanent Magnet Synchronous Motors (PMSMs

193

An Energy Based Fatigue Life Prediction Framework for In-Service Structural Components  

Science Conference Proceedings (OSTI)

An energy based fatigue life prediction framework has been developed for calculation of remaining fatigue life of in service gas turbine materials. The purpose of the life prediction framework is to account aging effect caused by cyclic loadings on fatigue strength of gas turbine engines structural components which are usually designed for very long life. Previous studies indicate the total strain energy dissipated during a monotonic fracture process and a cyclic process is a material property that can be determined by measuring the area underneath the monotonic true stress-strain curve and the sum of the area within each hysteresis loop in the cyclic process, respectively. The energy-based fatigue life prediction framework consists of the following entities: (1) development of a testing procedure to achieve plastic energy dissipation per life cycle and (2) incorporation of an energy-based fatigue life calculation scheme to determine the remaining fatigue life of in-service gas turbine materials. The accuracy of the remaining fatigue life prediction method was verified by comparison between model approximation and experimental results of Aluminum 6061-T6. The comparison shows promising agreement, thus validating the capability of the framework to produce accurate fatigue life prediction.

H. Ozaltun; M. H.H. Shen; T. George; C. Cross

2011-06-01T23:59:59.000Z

194

Colour Recognition In Outdoor Images Through Context-Based Models  

E-Print Network (OSTI)

This paper analyzes the variation of the color of objects with respect to existing models of daylight and surface reflectance, and develops context-based models of daylight (based on the CIE model [18]) and hybrid surface reflectance (based on existing hybrid surface reflectance models [21, 25, 32, 35]) called the Normalized Photometric Function. Thereafter, given the time-of-day (which, along with location, is used to calculate the sun-angle [23]), approximate cloud cover and sun-visibility, the color of the incident daylight is predicted, and combined with the reflectance model of the target object to predict the apparent color of the object; image pixels are then classified based on the predicted color. Section 2 describes the causes for the variation in apparent color; section 3 gives a brief literature review; section 4 describes the CIE daylight model and the context-based daylight model developed in this study; section 5 analyzes surface reflectance with respect to existing models and then develops the Normalized Photometric Function (NPF) model; section 6 combines the daylight and NPF models for context-based color prediction; finally, section 7 summarizes the conclusions of the study. 2 Causes for color shift in outdoor scenes

Shashi Buluswar; Red Green; Red Green

1998-01-01T23:59:59.000Z

195

The Influence of Artificial and Physical Factors upon Predictability Estimates Using a Complex Limited-Area Model  

Science Conference Proceedings (OSTI)

Recently, optimistic reports have appeared indicating that mesoscale circulations are more predictable than synoptic scale circulations. These have been based on studies using limited-area meso-?-scale forecast models. Warnings have also appeared ...

Tomislava Vukicevic; Ronald M. Errico

1990-07-01T23:59:59.000Z

196

Homogeneous bubble nucleation predicted by a molecular interaction model  

SciTech Connect

The homogenous bubble nucleation of various hydrocarbons was estimated by the modified classical nucleation theory. In this modification, the kinetic formalism of the classical theory is retained while the surface energy needed for the bubble formation is calculated form the interaction energy between molecules. With a nucleation rate value of J{sub n{sub c}} = 10{sup 22} nuclei/cm{sup 3}s, this modified model gives a very good prediction of the superheat limits of liquids. In another test of the model the complete evaporation time of a butane droplet at its superheat limit is compared with experiments and found to be in good agreement.

Hoyoung Kwak; Sangbum Lee (Chung-Ang Univ., Seoul (Korea))

1991-08-01T23:59:59.000Z

197

Model-Based Software Reuse  

Science Conference Proceedings (OSTI)

This report summarises the presentations and discussions of the First Workshop on Model-based Software Reuse, held in conjunction with the 16th European Conference on Object-Oriented Programming (ECOOP) Malaga, Spain June 10, 2002. This workshop was ...

Andreas Speck; Elke Pulvermüller; Ragnhild Van Der Straeten; Ralf Reussner; Matthias Clauß

2002-06-01T23:59:59.000Z

198

New autoregressive (AR) order selection criteria based on the prediction error estimation  

Science Conference Proceedings (OSTI)

The most important problem in data modeling using the AR model is the order selection. Some AR order selection criteria estimate the prediction error and choose the order that minimizes this estimated prediction error. All of these criteria use the same ... Keywords: Autoregressive model order selection, Least-Squares-Forward method, Prediction error

S. Khorshidi; M. Karimi; A. R. Nematollahi

2011-10-01T23:59:59.000Z

199

Motif discovery through predictive modeling of gene regulation  

Science Conference Proceedings (OSTI)

We present MEDUSA, an integrative method for learning motif models of transcription factor binding sites PSSMs by incorporating promoter sequence and transcriptome gene expression data. We use a modern large-margin machine learning approach, based on ...

Manuel Middendorf; Anshul Kundaje; Mihir Shah; Yoav Freund; Chris H. Wiggins; Christina Leslie

2005-05-01T23:59:59.000Z

200

GIS-BASED 1-D DIFFUSIVE WAVE OVERLAND FLOW MODEL  

Science Conference Proceedings (OSTI)

This paper presents a GIS-based 1-d distributed overland flow model and summarizes an application to simulate a flood event. The model estimates infiltration using the Green-Ampt approach and routes excess rainfall using the 1-d diffusive wave approximation. The model was designed to use readily available topographic, soils, and land use/land cover data and rainfall predictions from a meteorological model. An assessment of model performance was performed for a small catchment and a large watershed, both in urban environments. Simulated runoff hydrographs were compared to observations for a selected set of validation events. Results confirmed the model provides reasonable predictions in a short period of time.

KALYANAPU, ALFRED [Los Alamos National Laboratory; MCPHERSON, TIMOTHY N. [Los Alamos National Laboratory; BURIAN, STEVEN J. [NON LANL

2007-01-17T23:59:59.000Z

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

Southern Hemisphere Medium-Range Forecast Skill and Predictability: A Comparison of Two Operational Models  

Science Conference Proceedings (OSTI)

The skill of two global numerical weather prediction models, the National Centers for Environmental Prediction (NCEP) medium-range forecast model and the European Centre for Medium-Range Weather Forecasts (ECMWF) operational model, has been ...

James A. Renwick; Craig S. Thompson

2001-09-01T23:59:59.000Z

202

A Split Explicit Reformulation of the Regional Numerical Weather Prediction Model of the Japan Meteorological Agency  

Science Conference Proceedings (OSTI)

The split explicit integration scheme for numerical weather prediction models is employed in a version of the regional numerical weather prediction model of the Japan Meteorological Agency. The finite-difference scheme of the model is designed in ...

Dean G. Duffy

1981-05-01T23:59:59.000Z

203

Fuzzy-wavelet based prediction of Earth rotation parameters  

Science Conference Proceedings (OSTI)

Prediction of Earth rotation parameters (ERPs) is of importance especially for near real-time applications including navigation, remote sensing, and hazard monitoring. Therefore, prediction of ERPs at least over a few days in the future is necessary. ... Keywords: Earth rotation, Fuzzy-inference systems, Prediction, Wavelet transform

O. Akyilmaz; H. Kutterer; C. K. Shum; T. Ayan

2011-01-01T23:59:59.000Z

204

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

SciTech Connect

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

205

Automated system for near-real time modelling and prediction of altimeter-derived sea level anomalies  

Science Conference Proceedings (OSTI)

This paper serves as a presentation of a novel geoinformation system and a dedicated service, jointly named as Prognocean and based at the University of Wroclaw (Poland), that aim to predict Sea Level Anomaly (SLA) maps and publish them online. The system ... Keywords: Geoinformation, Modelling, Prediction, Satellite altimetry, Sea level anomaly, System

Tomasz Niedzielski, Bart?Omiej Mizi?Ski

2013-08-01T23:59:59.000Z

206

Model Predictive Control of Integrated Gasification Combined Cycle Power Plants  

SciTech Connect

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

207

Predictive modeling of reactive wetting and metal joining.  

SciTech Connect

The performance, reproducibility and reliability of metal joints are complex functions of the detailed history of physical processes involved in their creation. Prediction and control of these processes constitutes an intrinsically challenging multi-physics problem involving heating and melting a metal alloy and reactive wetting. Understanding this process requires coupling strong molecularscale chemistry at the interface with microscopic (diffusion) and macroscopic mass transport (flow) inside the liquid followed by subsequent cooling and solidification of the new metal mixture. The final joint displays compositional heterogeneity and its resulting microstructure largely determines the success or failure of the entire component. At present there exists no computational tool at Sandia that can predict the formation and success of a braze joint, as current capabilities lack the ability to capture surface/interface reactions and their effect on interface properties. This situation precludes us from implementing a proactive strategy to deal with joining problems. Here, we describe what is needed to arrive at a predictive modeling and simulation capability for multicomponent metals with complicated phase diagrams for melting and solidification, incorporating dissolutive and composition-dependent wetting.

van Swol, Frank B.

2013-09-01T23:59:59.000Z

208

Understanding, Modeling and Predicting Hidden Solder Joint Shape Using Active Thermography  

E-Print Network (OSTI)

Characterizing hidden solder joint shapes is essential for electronics reliability. Active thermography is a methodology to identify hidden defects inside an object by means of surface abnormal thermal response after applying a heat flux. This research focused on understanding, modeling, and predicting hidden solder joint shapes. An experimental model based on active thermography was used to understand how the solder joint shapes affect the surface thermal response (grand average cooling rate or GACR) of electronic multi cover PCB assemblies. Next, a numerical model simulated the active thermography technique, investigated technique limitations and extended technique applicability to characterize hidden solder joint shapes. Finally, a prediction model determined the optimum active thermography conditions to achieve an adequate hidden solder joint shape characterization. The experimental model determined that solder joint shape plays a higher role for visible than for hidden solder joints in the GACR; however, a MANOVA analysis proved that hidden solder joint shapes are significantly different when describe by the GACR. An artificial neural networks classifier proved that the distances between experimental solder joint shapes GACR must be larger than 0.12 to achieve 85% of accuracy classifying. The numerical model achieved minimum agreements of 95.27% and 86.64%, with the experimental temperatures and GACRs at the center of the PCB assembly top cover, respectively. The parametric analysis proved that solder joint shape discriminability is directly proportional to heat flux, but inversely proportional to covers number and heating time. In addition, the parametric analysis determined that active thermography is limited to five covers to discriminate among hidden solder joint shapes. A prediction model was developed based on the parametric numerical data to determine the appropriate amount of energy to discriminate among solder joint shapes for up to five covers. The degree of agreement between the prediction model and the experimental model was determined to be within a 90.6% for one and two covers. The prediction model is limited to only three solder joints, but these research principles can be applied to generate more realistic prediction models for large scale electronic assemblies like ball grid array assemblies having as much as 600 solder joints.

Giron Palomares, Jose

2012-05-01T23:59:59.000Z

209

Coal Mine Safety Investment Prediction Based on Support Vector Machine  

Science Conference Proceedings (OSTI)

Presently, coal mine safety situation in China is still severe. One of the most important reasons is safety investment insufficient. Safety investment prediction can provide decision basis for efficient controlling and guiding safety investment. The ... Keywords: coal mine safety investment, SVM, index system, prediction

Chen Xiang; Cai Weihua; Chen Na

2009-08-01T23:59:59.000Z

210

Study on Oil Drilling Prediction Based on Improved FNN Method  

Science Conference Proceedings (OSTI)

The prediction of exploitable reserves of oil layer is a complicated problem, which involves many geological and crude oil parameters. Considering its intrinsic properties, this paper put forward an improved fuzzy neural network (FFN) method, and compared ... Keywords: fuzzy neural network, exploitable reserve, prediction

Xinyu Geng; Bin Liu; Xiaoyan Huang

2010-12-01T23:59:59.000Z

211

Source of Seasonality and Scale Dependence of Predictability in a Coupled Ocean–Atmosphere Model  

Science Conference Proceedings (OSTI)

The seasonality of predictability of ENSO (related to the so-called spring predictability barrier) is investigated using the Cane–Zebiak coupled model. Observed winds are used to force the ocean component of the model to generate analyzed initial ...

B. N. Goswami; K. Rajendran; D. Sengupta

1997-05-01T23:59:59.000Z

212

Development of a PMV-based thermal comfort modelling  

Science Conference Proceedings (OSTI)

This paper concentrates on the modelling development for a PMV-based thermal comfort system. Operators can define their own expression towards the surroundings by inserting the respective value of PMV and the system will generate the compressor and fan ... Keywords: climatic modelling, predicted mean vote (PMV), thermal comfort

Shazmin Aniza Abdul Shukor; Karl Kohlhof; Zul Azhar Zahid Jamal

2007-05-01T23:59:59.000Z

213

Design of spatial experiments: Model fitting and prediction  

Science Conference Proceedings (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

214

Results from baseline tests of the SPRE I and comparison with code model predictions  

DOE Green Energy (OSTI)

The Space Power Research Engine (SPRE), a free-piston Stirling engine with linear alternator, is being tested at the NASA Lewis Research Center as part of the Civil Space Technology Initiative (CSTI) as a candidate for high capacity space power. This paper presents results of base-line engine tests at design and off-design operating conditions. The test results are compared with code model predictions.

Cairelli, J.E.; Geng, S.M. [National Aeronautics and Space Administration, Cleveland, OH (United States). Lewis Research Center; Skupinski, R.C. [Sverdrup Technology, Inc., Cleveland, OH (United States). NASA Lewis Research Center Group

1994-09-01T23:59:59.000Z

215

Decentralized-coordinated model predictive control for a hydro-power valley  

Science Conference Proceedings (OSTI)

This paper aims at improving control systems for hydro-power production, by combining model predictive control techniques with decomposition-coordination methods for a global optimization over a whole hydro-power valley. It first recalls the model predictive ... Keywords: Case-study validation, Control optimization, Decomposition-coordination, Hydroelectricity, Model predictive control

J. ZáRate FlóRez, J. Martinez, G. BesançOn, D. Faille

2013-05-01T23:59:59.000Z

216

Matchstick: A Room-to-Room Thermal Model for Predicting Indoor Temperature from Wireless Sensor Data  

E-Print Network (OSTI)

Matchstick: A Room-to-Room Thermal Model for Predicting Indoor Temperature from Wireless Sensor present a room-to-room thermal model used to accurately predict temperatures in residential buildings. We that our model can predict future indoor temperature trends with a 90th percentile aggregate error between

Hazas, Mike

217

Optimal Control of Distributed Energy Resources using Model Predictive Control  

Science Conference Proceedings (OSTI)

In an isolated power system (rural microgrid), Distributed Energy Resources (DERs) such as renewable energy resources (wind, solar), energy storage and demand response can be used to complement fossil fueled generators. The uncertainty and variability due to high penetration of wind makes reliable system operations and controls challenging. In this paper, an optimal control strategy is proposed to coordinate energy storage and diesel generators to maximize wind penetration while maintaining system economics and normal operation. The problem is formulated as a multi-objective optimization problem with the goals of minimizing fuel costs and changes in power output of diesel generators, minimizing costs associated with low battery life of energy storage and maintaining system frequency at the nominal operating value. Two control modes are considered for controlling the energy storage to compensate either net load variability or wind variability. Model predictive control (MPC) is used to solve the aforementioned problem and the performance is compared to an open-loop look-ahead dispatch problem. Simulation studies using high and low wind profiles, as well as, different MPC prediction horizons demonstrate the efficacy of the closed-loop MPC in compensating for uncertainties in wind and demand.

Mayhorn, Ebony T.; Kalsi, Karanjit; Elizondo, Marcelo A.; Zhang, Wei; Lu, Shuai; Samaan, Nader A.; Butler-Purry, Karen

2012-07-22T23:59:59.000Z

218

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

219

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

220

Passive millimeter-wave retrieval of global precipitation utilizing satellites and a numerical weather prediction model  

E-Print Network (OSTI)

This thesis develops and validates the MM5/TBSCAT/F([lambda]) model, composed of a mesoscale numerical weather prediction (NWP) model (MM5), a two-stream radiative transfer model (TBSCAT), and electromagnetic models for ...

Surussavadee, Chinnawat

2007-01-01T23:59:59.000Z

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.


221

Elemental Solubility Tendency for the Phases of Uranium by Classical Models Used to Predict Alloy Behavior  

Science Conference Proceedings (OSTI)

Traditional alloy theory models, specifically Darken-Gurry and Miedema’s analyses, that characterize solutes in solid solvents relative to physical properties of the elements have been used to assist in predicting alloy behavior. These models will be applied relative to the three solid phases of uranium: alpha (orthorhombic), beta (tetragonal), and gamma (bcc). These phases have different solubilities for specific alloy additions as a function of temperature. The Darken-Gurry and Miedema models, with modifications based on concepts of Waber, Gschneider, and Brewer will be used to predict the behavior of four types of solutes: 1) Transition metals that are used for various purposes associated with the containment as alloy additions in the uranium fuel 2) Transuranic elements in the uranium 3) Rare earth fission products (lanthanides) 4) Transition metals and other fission products Using these solute map criteria, elemental behavior will be predicted as highly soluble, marginally soluble, or immiscible (compound formers) and will be used to compare solute effects during uranium phase transformations. The overlapping of these solute maps are convenient first approximation tools for predicting alloy behavior.

Van Blackwood; Travis Koenig; Saleem Drera; Brajenda Mishra; Davis Olson; Doug Porter; Robert Mariani

2012-03-01T23:59:59.000Z

222

The North American Multi-Model Ensemble (NMME): Phase-1 Seasonal to Interannual Prediction, Phase-2 Toward Developing Intra-Seasonal Prediction  

Science Conference Proceedings (OSTI)

The recent US National Academies report “Assessment of Intraseasonal to Interannual Climate Prediction and Predictability” was unequivocal in recommending the need for the development of a North American Multi-Model Ensemble (NMME) operational predictive ...

Ben P. Kirtman; Dughong Min; Johnna M. Infanti; James L. Kinter III; Daniel A. Paolino; Qin Zhang; Huug van den Dool; Suranjana Saha; Malaquias Pena Mendez; Emily Becker; Peitao Peng; Patrick Tripp; Jin Huang; David G. DeWitt; Michael K. Tippett; Anthony G. Barnston; Shuhua Li; Anthony Rosati; Siegfried D. Schubert; Michele Rienecker; Max Suarez; Zhao E. Li; Jelena Marshak; Young-Kwon Lim; Joseph Tribbia; Kathleen Pegion; William J. Merryfield; Bertrand Denis; Eric F. Wood

223

Predictability of Precipitation in a Cloud-Resolving Model  

Science Conference Proceedings (OSTI)

An ensemble methodology is developed and tested to objectively isolate and quantify meso-?-scale predictability limitations in numerical weather prediction (NWP). The methodology involves conducting an ensemble of limited-area simulations with ...

André Walser; Daniel Lüthi; Christoph Schär

2004-02-01T23:59:59.000Z

224

The Dark Gravity model predictions for Gravity Probe B  

E-Print Network (OSTI)

The previous version of this article gave erroneous predictions. The correct uptodate predictions can be found in the section devoted to gravitomagnetism in the living review of the Dark Gravity theory: gr-qc/0610079 The most natural prediction is zero frame dragging and the same geodetic effect as predicted by GR. However, a straightforward extension of the theory could lead to the same frame-dragging as in GR.

Frederic Henry-Couannier

2005-09-05T23:59:59.000Z

225

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

NLE Websites -- All DOE Office Websites (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.

226

A GIS based approach to predicting road surface temperatures  

Science Conference Proceedings (OSTI)

Treatment of transport networks of the winter period is a key responsibility of maintenance teams whether in the private or public sector. With tightening budgets and increasing statutory requirements, maintenance teams are looking to improve the treatment ... Keywords: ASP.NET, ArcGIS server, GIS, ice prediction, roads

Richard Fry; Lionel Slade; George Taylor; Ian Davy

2007-11-01T23:59:59.000Z

227

An Agent Based Classification Model  

E-Print Network (OSTI)

The major function of this model is to access the UCI Wisconsin Breast Can- cer data-set[1] and classify the data items into two categories, which are normal and anomalous. This kind of classifi cation can be referred as anomaly detection, which discriminates anomalous behaviour from normal behaviour in computer systems. One popular solution for anomaly detection is Artifi cial Immune Sys- tems (AIS). AIS are adaptive systems inspired by theoretical immunology and observed immune functions, principles and models which are applied to prob- lem solving. The Dendritic Cell Algorithm (DCA)[2] is an AIS algorithm that is developed specifi cally for anomaly detection. It has been successfully applied to intrusion detection in computer security. It is believed that agent-based mod- elling is an ideal approach for implementing AIS, as intelligent agents could be the perfect representations of immune entities in AIS. This model evaluates the feasibility of re-implementing the DCA in an agent-based simulation environ- ...

Gu, Feng; Greensmith, Julie

2009-01-01T23:59:59.000Z

228

Constrained model predictive control implementation for a heavy-duty gas turbine power plant  

Science Conference Proceedings (OSTI)

In this paper, model predictive control (MPC) strategy is implemented to a GE9001E gas turbine power plant. A linear model is developed for the gas turbine using conventional mathematical models and ARX identification procedure. Also a process control ... Keywords: ARX, PID, gas turbine, identification, modeling, multivariable control, power plant, predictive control

Hadi Ghorbani; Ali Ghaffari; Mehdi Rahnama

2008-06-01T23:59:59.000Z

229

Developing an abstraction layer for the visualization of HSMM-based predictive decision support  

E-Print Network (OSTI)

Hidden semi-Markov models (HSMMs) have been previously proposed as real-time operator behavior prediction models that could be used by a supervisor to detect future anomalous behaviors. Because of the disconnect between ...

Huang, Hank Hsin Han

2009-01-01T23:59:59.000Z

230

Model predictive control of a Kaibel distillation column  

E-Print Network (OSTI)

A Kaibel distillation column separates a feed into four products with significant lower energy consumption than a conventional sequence of binary columns. Optimal operation and control of such systems is an important task in order to obtain the potential energy savings. A laboratory column has been built at NTNU, Department of Chemical Engineering. At the time of the diploma work the laboratory column has unfortunately not been available for MPC experiments. In practical operation a control structure based on temperature measurements is chosen for the given case. This structure gives a four-by-four multivariable system. The candidate shall base his work on a model developed by Jens Strandberg. Tasks: 1. Describe the model and extend it to include an efficiency parameter describing insufficient mixing at stages 2. Describe a general linear MPC approach for the system 3. Analyze sensitivity of model errors 4. Evaluate alternative MPC approaches 5. Implement the MPC in MATLAB and illustrate the performance by simulations 6. Prepare a setup for connecting the MPC to the actual laboratory column

Martin Krister Kvernland; Supervisor Ole; Morten Aamo; Co-supervisor Ivar Halvorsen; Sigurd Skogestad Ikp; Jim Morrison Vii

2009-01-01T23:59:59.000Z

231

Generic-model based human-body modeling  

Science Conference Proceedings (OSTI)

This paper presents a generic-model based human-body modeling method which take the anatomical structure of the human body into account. The generic model contains anatomical structure of bones and muscles of the human body. For a given target skin mesh, ... Keywords: anatomically-based modeling, generic model, human body modeling

Xiaomao Wu; Lizhuang Ma; Ke-Sen Huang; Yan Gao; Zhihua Chen

2005-09-01T23:59:59.000Z

232

Quantifying Predictability Variations in a Low-Order Occan-Atmosphere Model: A Dynamical Systems Approach  

Science Conference Proceedings (OSTI)

A dynamical systems approach is used to quantify the predictability of weather and climatic states of a low order, moist general circulation model. The effects on predictability of incorporating a simple oceanic circulation are evaluated. The ...

Jon M. Nese; John A. Dutton

1993-02-01T23:59:59.000Z

233

Prediction of Tropical Atlantic Sea Surface Temperatures Using Linear Inverse Modeling  

Science Conference Proceedings (OSTI)

The predictability of tropical Atlantic sea surface temperature on seasonal to interannual timescales by linear inverse modeling is quantified. The authors find that predictability of Caribbean Sea and north tropical Atlantic sea surface ...

Cécile Penland; Ludmila Matrosova

1998-03-01T23:59:59.000Z

234

A Comparison of General Circulation Model Predictions to Sand Drift and Dune Orientations  

Science Conference Proceedings (OSTI)

The growing concern over climate change and desertification stresses the importance of aeolian process prediction. In this paper the use of a general circulation model to predict current aeolian features is examined. A GCM developed at NASA/...

Dan G. Blumberg; Ronald Greeley

1996-12-01T23:59:59.000Z

235

Southeast US Rainfall prediction in the North American Multi-Model Ensemble  

Science Conference Proceedings (OSTI)

The present study investigates the predictive skill of the North American Multi-Model Ensemble (NMME) System for Intra-seasonal to Interannual (ISI) Prediction with focus on southeast US precipitation. The southeast US is of particular interest ...

Johnna M. Infanti; Ben P. Kirtman

236

Predictability of SST in a Stochastic Climate Model and Its Application to the Kuroshio Extension Region  

Science Conference Proceedings (OSTI)

The influence of deterministic forcing on SST predictability is investigated in a zero-dimensional, stochastic, coupled atmosphere–ocean climate model. The SST anomaly predictability time is found to be very sensitive to the properties of the ...

Robert B. Scott; Bo Qiu

2003-01-01T23:59:59.000Z

237

Implementation of the Cloud Prediction Scheme in the Eta Model at NCEP  

Science Conference Proceedings (OSTI)

An explicit cloud prediction scheme has been developed and incorporated into the Eta Model at the National Centers for Environmental Prediction (NCEP) to improve the cloud and precipitation forecasts. In this scheme, the cloud liquid water and ...

Qingyun Zhao; Thomas L. Black; Michael E. Baldwin

1997-09-01T23:59:59.000Z

238

Use of a Mixed-Layer Model to Investigate Problems in Operational Prediction of Return Flow  

Science Conference Proceedings (OSTI)

Inaccuracy in the numerical prediction of the moisture content of return-flow air over the Gulf of Mexico continues to plague operational forecasters. At the Environmental Modeling Center/National Centers for Environmental Prediction in the ...

John M. Lewis

2007-07-01T23:59:59.000Z

239

The Impact of Tropical Forcing on Extratropical Predictability in a Simple Global Model  

Science Conference Proceedings (OSTI)

The impact of tropical forcing on the predictability of the extratropical atmosphere is studied. Using a two-layer spectral model, numerical experiments and diagnostic analyses have been carried out to examine the enhancement of predictability ...

Jianchun Qin; Walter A. Robinson

1995-11-01T23:59:59.000Z

240

The Nature of Predictability Enhancement in a Low-Order Ocean-Atmosphere Model  

Science Conference Proceedings (OSTI)

A low-order moist general circulation model of the coupled ocean-atmosphere system is reexamined to determine the source of short-term predictability enhancement that occurs when an oceanic circulation is activated. The predictability enhancement ...

Jon M. Nese; Arthur J. Miller; John A. Dutton

1996-09-01T23:59:59.000Z

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.


241

Bayesian methods for discontinuity detection in climate model predictions.  

Science Conference Proceedings (OSTI)

Discontinuity detection is an important component in many fields: Image recognition, Digital signal processing, and Climate change research. Current methods shortcomings are: Restricted to one- or two-dimensional setting, Require uniformly spaced and/or dense input data, and Give deterministic answers without quantifying the uncertainty. Spectral methods for Uncertainty Quantification with global, smooth bases are challenged by discontinuities in model simulation results. Domain decomposition reduces the impact of nonlinearities and discontinuities. However, while gaining more smoothness in each subdomain, the current domain refinement methods require prohibitively many simulations. Therefore, detecting discontinuities up front and refining accordingly provides huge improvement to the current methodologies.

Safta, Cosmin; Debusschere, Bert J.; Najm, Habib N.; Sargsyan, Khachik

2010-06-01T23:59:59.000Z

242

Model Predictive Control for the Operation of Building Cooling Systems  

E-Print Network (OSTI)

predictive control of thermal energy storage in buildingsystems which use thermal energy storage. In particular thepredictive control of thermal energy storage in building

Ma, Yudong

2010-01-01T23:59:59.000Z

243

Model Uncertainty in a Mesoscale Ensemble Prediction System: Stochastic versus Multiphysics Representations  

Science Conference Proceedings (OSTI)

A multiphysics and a stochastic kinetic-energy backscatter scheme are employed to represent model uncertainty in a mesoscale ensemble prediction system using the Weather Research and Forecasting model. Both model-error schemes lead to significant ...

J. Berner; S.-Y. Ha; J. P. Hacker; A. Fournier; C. Snyder

2011-06-01T23:59:59.000Z

244

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.

245

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.

246

An Efficient Stochastic Bayesian Approach to Optimal Parameter and Uncertainty Estimation for Climate Model Predictions  

Science Conference Proceedings (OSTI)

One source of uncertainty for climate model predictions arises from the fact that climate models have been optimized to reproduce observational means. To quantify the uncertainty resulting from a realistic range of model configurations, it is ...

Charles Jackson; Mrinal K. Sen; Paul L. Stoffa

2004-07-01T23:59:59.000Z

247

Integrated model-based control and diagnostic monitoring for automotive catalyst systems  

Science Conference Proceedings (OSTI)

An integrated model-based automotive catalyst control and diagnostic monitoring system is presented. This system incorporates a simplified dynamic catalyst model that describes oxygen storage and release in the catalyst and predicts the post-catalyst ... Keywords: automotive catalyst, model predictive control, on-board diagnostic monitoring

Kenneth R. Muske; James C. Peyton Jones

2007-11-01T23:59:59.000Z

248

Application of the U.S. high cycle fatigue data base to wind turbine blade lifetime predictions  

DOE Green Energy (OSTI)

This paper demonstrates a methodology for predicting the service lifetime of wind turbine blades using the high-cycle fatigue data base for typical U.S. blade materials developed by Mandell, et al. (1995). The first step in the analysis is to normalize the data base (composed primarily of data obtained from specialized, relatively small coupons) with fatigue data from typical industrial laminates to obtain a Goodman Diagram that is suitable for analyzing wind turbine blades. The LIFE2 fatigue analysis code for wind turbines is then used for the fatigue analysis of a typical turbine blade with a known load spectrum. In the analysis, a linear damage model, Miner`s Rule, is used to demonstrate the prediction of the service lifetime for a typical wind turbine blade under assumed operating strain ranges and stress concentration factors. In contrast to typical European data, the asymmetry in this data base predicts failures under typical loads to be compressive.

Sutherland, H.J. [Sandia National Labs., Albuquerque, NM (United States); Mandell, J.F. [Montana State Univ., Bozeman, MT (United States)

1995-12-01T23:59:59.000Z

249

An Analytical Model for Predicting the Remaining Battery Capacity of Lithium-Ion Batteries  

E-Print Network (OSTI)

An Analytical Model for Predicting the Remaining Battery Capacity of Lithium-Ion Batteries Peng cycle-life tends to shrink significantly. The capacities of commercial lithium-ion batteries fade by 10 prediction model to estimate the remaining capacity of a Lithium-Ion battery. The proposed analytical model

Pedram, Massoud

250

Continuous-time nonlinear model predictive control of time-delayed Wiener-type systems  

Science Conference Proceedings (OSTI)

This paper deals with a novel method of continuous-time model predictive control for nonlinear time-delayed systems. The problems regarding time delays are solved by incorporating delayed and undelayed model outputs in the control-law derivation. Nonlinear-mapping ... Keywords: Wiener-type model, continuous system, nonlinear predictive control, time-delayed system

Simon Oblak; Igor Škrjanc

2006-02-01T23:59:59.000Z

251

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

Science Conference Proceedings (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

252

Defining and applying prediction performance metrics on a recurrent NARX time series model  

Science Conference Proceedings (OSTI)

Nonlinear autoregressive moving average with exogenous inputs (NARMAX) models have been successfully demonstrated for modeling the input-output behavior of many complex systems. This paper deals with the proposition of a scheme to provide time series ... Keywords: NARX models, Prediction performance metrics, Recurrent radial basis function network, Time series prediction

Ryad Zemouri; Rafael Gouriveau; Noureddine Zerhouni

2010-08-01T23:59:59.000Z

253

Prediction and ranking algorithms for event-based network data  

Science Conference Proceedings (OSTI)

Event-based network data consists of sets of events over time, each of which may involve multiple entities. Examples include email traffic, telephone calls, and research publications (interpreted as co-authorship events). Traditional network analysis ...

Joshua O'Madadhain; Jon Hutchins; Padhraic Smyth

2005-12-01T23:59:59.000Z

254

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

E-Print Network (OSTI)

Model Predictive Control of HVAC Systems:    Implementation and  air  conditioning  (HVAC)  account  for  27%  of  the reduction potential of HVAC systems with  active thermal 

Haves, Phillip

2010-01-01T23:59:59.000Z

255

A Climatic Model for the Prediction of Percentile Statistics for Ambient Temperature  

Science Conference Proceedings (OSTI)

The probability density function (pdf) for ambient temperature is predicted from daily maximum and daily minimum temperature and sunshine, data by means of a climatic model.

Aleck J. Hunter

1981-04-01T23:59:59.000Z

256

Modeling Metal Fatigue As a Key Step in PV Module Life Time Prediction (Presentation)  

DOE Green Energy (OSTI)

This presentation covers modeling metal fatigue as a key step in photovoltaic (PV) module lifetime predictions. Described are time-dependent and time-independent case studies.

Bosco, N.

2012-02-01T23:59:59.000Z

257

Influence of two dynamic predictive clothing insulation models on building energy performance  

E-Print Network (OSTI)

Predictive Clothing Insulation Models on Building Energyunnecessarily higher clothing insulation and lower heatingthat the constant clothing insulation assumption lead to the

Lee, Kwang Ho; Schiavon, Stefano

2013-01-01T23:59:59.000Z

258

Quantifying the Predictive Skill in Long-Range Forecasting. Part I: Coarse-Grained Predictions in a Simple Ocean Model  

Science Conference Proceedings (OSTI)

An information-theoretic framework is developed to assess the long-range coarse-grained predictive skill in a perfect-model environment. Central to the scheme is the notion that long-range forecasting involves regimes; specifically, that the ...

Dimitrios Giannakis; Andrew J. Majda

2012-03-01T23:59:59.000Z

259

Model Based Enterprise / Technical Data Package Summit ...  

Science Conference Proceedings (OSTI)

Page 1. NIST Technical Note 1753 Model Based Enterprise / Technical Data Package Summit Report Joshua Lubell Kenway ...

2012-10-22T23:59:59.000Z

260

Assessing phylogenetic motif models for predicting transcription factor binding sites  

Science Conference Proceedings (OSTI)

Motivation: A variety of algorithms have been developed to predict transcription factor binding sites (TFBSs) within the genome by exploiting the evolutionary information implicit in multiple alignments of the genomes of related species. One such ...

John Hawkins; Charles Grant; William Stafford Noble; Timothy L. Bailey

2009-06-01T23:59:59.000Z

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.


261

Application of a computer model for predicting remote noise levels  

Science Conference Proceedings (OSTI)

The prediction of noise levels at selected remote locations is an integral part of estimating the environmental impact of new stationary sources or of noise reduction for existing facilities. A three?dimensional computermodel

S. H. Judd; S. L. Dryden

1975-01-01T23:59:59.000Z

262

Implicit Versus Explicit Convective Heating in Numerical Weather Prediction Models  

Science Conference Proceedings (OSTI)

The ability of several explicit formulations of convective heating to predict the precipitation associated with a mesoscale convective complex was compared to that of a cumulus parameterization on a ½ deg latitude-longitude mesh. In the explicit ...

John Molinari; Michael Dudek

1986-10-01T23:59:59.000Z

263

Predictions of Saturation Ratio for Cloud Microphysical Models  

Science Conference Proceedings (OSTI)

The saturation development equation is solved analytically to give a solution that is more general than the existing analytical solution. This analytical solution provides accurate predictions of the saturation ratio and allows the use of ...

Jen-Ping Chen

1994-05-01T23:59:59.000Z

264

An engineering model for prediction of in situ oil shale retort blasting  

SciTech Connect

The in situ extraction of oil from most oil shale beds is highly dependent upon explosive fracturing and rubbling of rock in a controlled and predictable manner. In blasting, it is necessary not only to fracture the rock, but also to move the broken rubble in a predictable manner. Most in situ extraction techniques require rubblization to take place in a confined region where rock motion is a predominate factor in creating a permeable broken bed. In this paper, an engineering model is presented which describes the large rubble motion during blasting. In this model the rock medium is represented by a discrete series of circular regions of fractured material. These regions are set in motion by pressure loads from the explosive. The motion of the regions is calculated using a step-wise, explicit, numerical time integration method. Interaction of adjacent regions is based on inelastic impact of spherical bodies. The derivation of this model is presented along with the background for selecting loading pressure based on explosive behavior.

Quong, R.

1983-04-01T23:59:59.000Z

265

AGENT-BASED MODELING AND SIMULATION: ABMS EXAMPLES  

E-Print Network (OSTI)

Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous, interacting agents. ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use electronic laboratories to support their research. Some have gone so far as to contend that ABMS “is a third way of doing science,” in addition to traditional deductive and inductive reasoning (Axelrod 1997). Computational advances have made possible a growing number of agent-based models across a variety of application domains. Applications range from modeling agent behavior in the stock market, supply chains, and consumer markets, to predicting the spread of epidemics, the threat of bio-warfare, and the factors responsible for the fall of ancient civilizations. This tutorial describes the theoretical and practical foundations of ABMS, identifies toolkits and methods for developing agent models, and illustrates the development of a simple agent-based model.

Charles M. Macal; Michael J. North

2008-01-01T23:59:59.000Z

266

Crude Oil Price Prediction Based On Multi-scale Decomposition  

Science Conference Proceedings (OSTI)

A synergetic model (DWT-LSSVM) is presented in this paper. First of all, the raw data is decomposed into approximate coefficients and the detail coefficients at different scales by discrete wavelet transforms (DWT). These coefficients obtained by previous ... Keywords: crude oil price, least squares vector machines, wavelet transform

Yejing Bao; Xun Zhang; Lean Yu; Shouyang Wang

2007-05-01T23:59:59.000Z

267

Bulalo field, Philippines: Reservoir modeling for prediction of limits to sustainable generation  

DOE Green Energy (OSTI)

The Bulalo geothermal field, located in Laguna province, Philippines, supplies 12% of the electricity on the island of Luzon. The first 110 MWe power plant was on line May 1979; current 330 MWe (gross) installed capacity was reached in 1984. Since then, the field has operated at an average plant factor of 76%. The National Power Corporation plans to add 40 MWe base load and 40 MWe standby in 1995. A numerical simulation model for the Bulalo field has been created that matches historic pressure changes, enthalpy and steam flash trends and cumulative steam production. Gravity modeling provided independent verification of mass balances and time rate of change of liquid desaturation in the rock matrix. Gravity modeling, in conjunction with reservoir simulation provides a means of predicting matrix dry out and the time to limiting conditions for sustainable levelized steam deliverability and power generation.

Strobel, Calvin J.

1993-01-28T23:59:59.000Z

268

A Mathematical Model for Predicting the Life of PEM Fuel Cell Membranes Subjected to Hydration Cycling  

E-Print Network (OSTI)

Under typical PEM fuel cell operating conditions, part of membrane electrode assembly is subjected to humidity cycling due to variation of inlet gas RH and/or flow rate. Cyclic membrane hydration/dehydration would cause cyclic swelling/shrinking of the unconstrained membrane. In a constrained membrane, it causes cyclic stress resulting in mechanical failure in the area adjacent to the gas inlet. A mathematical modeling framework for prediction of the lifetime of a PEM FC membrane subjected to hydration cycling is developed in this paper. The model predicts membrane lifetime as a function of RH cycling amplitude and membrane mechanical properties. The modeling framework consists of three model components: a fuel cell RH distribution model, a hydration/dehydration induced stress model that predicts stress distribution in the membrane, and a damage accrual model that predicts membrane life-time. Short descriptions of the model components along with overall framework are presented in the paper. The model was used...

Burlatsky, S F; O'Neill, J; Atrazhev, V V; Varyukhin, A N; Dmitriev, D V; Erikhman, N S

2013-01-01T23:59:59.000Z

269

Research on Fish Intelligence for Fish Trajectory Prediction Based on Neural Network  

Science Conference Proceedings (OSTI)

This paper researches the behavior modes of some intelligent creature in some environment. The gained modes are used as movement models to construct NN to predict the moving trajectory and then catch it. Firstly the behavior patterns of fish that kept ... Keywords: Genetic algorithm, Intelligent robot, Neural network, Predicting trajectory, Visual servo

Yanmin Xue; Hongzhao Liu; Xiaohui Zhang; Mamoru Minami

2008-09-01T23:59:59.000Z

270

The Study on Corn Production Prediction in Heilongjiang Province Based on Support Vector Machine  

Science Conference Proceedings (OSTI)

This paper uses the support vector machine (SVM) algorithm to study the prediction of corn production in Heilongjiang province, forms the sample set with the 1991-2008 data in Heilongjiang province, and set up the SVM model between factors and corn production. ... Keywords: corn production, support vector machine, prediction

Zhu Jing; Fan Yadong

2012-01-01T23:59:59.000Z

271

Snow Model Verification Using Ensemble Prediction and Operational Benchmarks  

Science Conference Proceedings (OSTI)

Hydrologic model evaluations have traditionally focused on measuring how closely the model can simulate various characteristics of historical observations. Although advancing hydrologic forecasting is an often-stated goal of numerous modeling ...

Kristie J. Franz; Terri S. Hogue; Soroosh Sorooshian

2008-12-01T23:59:59.000Z

272

Prediction of early heat of hydration of plain and blended cements using neuro-fuzzy modelling techniques  

Science Conference Proceedings (OSTI)

In this study, a new approach based on an adaptive neuro-fuzzy inference system (ANFIS) was presented for the prediction of early heat of hydration of plain and blended cements. Two different type of model is trained and tested using these data. The ... Keywords: ANFIS, Cement, Fuzzy logic, Hydration heat, Neural networks

Abdulhamit Subasi; Ahmet Serdar Yilmaz; Hanifi Binici

2009-04-01T23:59:59.000Z

273

Uncertainties in Predicted Ozone Concentrations Due to Input Uncertainties for the UAM-V Photochemical Grid Model  

Science Conference Proceedings (OSTI)

Based on studies of ozone episodes in the eastern United States using the photochemical grid model, UAM-V, regulatory agencies have made decisions concerning emissions controls. This project analyzes effects of uncertainties in UAM-V input variables (emissions, initial and boundary conditions, meteorological variables, and chemical reactions) on uncertainties in UAM-V ozone predictions for the July 1995 episode.

2000-11-06T23:59:59.000Z

274

Stellar Imager (SI): developing and testing a predictive dynamo model for the Sun by imaging other stars  

E-Print Network (OSTI)

The Stellar Imager mission concept is a space-based UV/Optical interferometer designed to resolve surface magnetic activity and subsurface structure and flows of a population of Sun-like stars, in order to accelerate the development and validation of a predictive dynamo model for the Sun and enable accurate long-term forecasting of solar/stellar magnetic activity.

Carpenter, Kenneth G; Karovska, Margarita; Kraemer, Steve; Lyon, Richard; Mozurkewich, David; Airapetian, Vladimir; Adams, John C; Allen, Ronald J; Brown, Alex; Bruhweiler, Fred; Conti, Alberto; Christensen-Dalsgaard, Joergen; Cranmer, Steve; Cuntz, Manfred; Danchi, William; Dupree, Andrea; Elvis, Martin; Evans, Nancy; Giampapa, Mark; Harper, Graham; Hartman, Kathy; Labeyrie, Antoine; Leitner, Jesse; Lillie, Chuck; Linsky, Jeffrey L; Lo, Amy; Mighell, Ken; Miller, David; Noecker, Charlie; Parrish, Joe; Phillips, Jim; Rimmele, Thomas; Saar, Steve; Sasselov, Dimitar; Stahl, H Philip; Stoneking, Eric; Strassmeier, Klaus; Walter, Frederick; Windhorst, Rogier; Woodgate, Bruce; Woodruff, Robert

2010-01-01T23:59:59.000Z

275

Development and verification of simplified prediction models for enhanced-oil-recovery application. Monthly technical progress report for the period May 1981  

Science Conference Proceedings (OSTI)

The following tasks and sub-tasks have been defined and all reporting which follows will be identifiable according to these categories until January 1982: (1) Reservoir Data Collection - all processes; (2) Steamflood Predictive Performance Model Development which includes literature analysis, algorithm development, computer coding of algorithm, process data base utilizing algorithm; validate algorithm; numerical simulation analysis; and final report; (3) Carbon Dioxide Predictive Performance Model Development which includes literature analysis, algorithm development, computer coding of algorithm, process data base utilizing algorithm, validate algorithm, numerical simulation analysis, and final report; and (4) Polymer Flooding Predictive Performance Model Development which includes literature analysis. The current status of the literature analysis, algorithm development, computer coding of algorithm, and numerical simulation analysis of steamflood predictive performance model, and the literature analysis and numerical simulation analysis of carbon dioxide predictive performance model are reported.

McElhiney, J.E.

1981-06-02T23:59:59.000Z

276

Predictive geochemical modeling of interactions between uranium-mill-tailings solutions and sediments in a flow-through system: model formulations and preliminary results  

Science Conference Proceedings (OSTI)

An equilibrium thermodynamic conceptual model consisting of minerals and solid phases was developed to represent a soil column. A computer program was used as a tool to solve the system of mathematical equations imposed by the conceptual chemical model. The combined conceptual model and computer program were used to predict aqueous phase compositions of effluent solutions from permeability cells packed with geologic materials and percolated with uranium mill tailings solutions. Initial calculations of ion speciation and mineral solubility and our understanding of the chemical processes occurring in the modeled system were used to select solid phases for inclusion in the conceptual model. The modeling predictions were compared to the analytically determined column effluent concentrations. Hypotheses were formed, based on modeling predictions and laboratory evaluations, as to the probable mechanisms controlling the migration of selected contaminants. An assemblage of minerals and other solid phases could be used to predict the concentrations of several of the macro constituents (e.g., Ca, SO/sub 4/, Al, Fe, and Mn) but could not be used to predict trace element concentrations. These modeling conclusions are applicable to situations where uranium mill tailings solutions of low pH and high total dissolved solids encounter either clay liners or natural geologic materials that contain inherent acid neutralizing capacities. 116 references, 22 figures, 6 tables.

Peterson, S.R.; Felmy, A.R.; Serne, R.J.; Gee, G.W.

1983-08-01T23:59:59.000Z

277

A Fast Hybrid Decision Algorithm for H.264/AVC Intra Prediction Based on Entropy Theory  

Science Conference Proceedings (OSTI)

Rate Distortion Optimization based spatial intra coding is a new feature of H.264/AVC standard. It efficiently improves the video coding performance by brutally utilizing variable block sizes and multiple prediction modes. Thus, extremely high computation ... Keywords: Block Size Decision, Entropy Theory, H.264/AVC, Intra Prediction, Mode Decision

Guifen Tian; Tianruo Zhang; Takeshi Ikenaga; Satoshi Goto

2009-01-01T23:59:59.000Z

278

A Regional Ensemble Prediction System Based on Moist Targeted Singular Vectors and Stochastic Parameter Perturbations  

Science Conference Proceedings (OSTI)

A regional ensemble prediction system (REPS) with the limited-area version of the Canadian Global Environmental Multiscale (GEM) model at 15-km horizontal resolution is developed and tested. The total energy norm singular vectors (SVs) targeted ...

Xiaoli Li; Martin Charron; Lubos Spacek; Guillem Candille

2008-02-01T23:59:59.000Z

279

Ensemble-Based Estimates of the Predictability of Wind-Driven Coastal Ocean Flow over Topography  

Science Conference Proceedings (OSTI)

The predictability of coastal ocean circulation over the central Oregon shelf, a region of strong wind-driven currents and variable topography, is studied using ensembles of 50-day primitive equation ocean model simulations with realistic ...

Sangil Kim; R. M. Samelson; Chris Snyder

2009-08-01T23:59:59.000Z

280

Operational Convective-Scale Numerical Weather Prediction with the COSMO Model: Description and Sensitivities  

Science Conference Proceedings (OSTI)

Since April 2007, the numerical weather prediction model, COSMO (Consortium for Small Scale Modelling), has been used operationally in a convection-permitting configuration, named COSMO-DE, at the Deutscher Wetterdienst (DWD; German weather ...

Michael Baldauf; Axel Seifert; Jochen Förstner; Detlev Majewski; Matthias Raschendorfer; Thorsten Reinhardt

2011-12-01T23:59:59.000Z

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.


281

Sea Surface Height Predictions from the Global Navy Coastal Ocean Model during 1998–2001  

Science Conference Proceedings (OSTI)

A ?° global version of the Navy Coastal Ocean Model (NCOM), operational at the Naval Oceanographic Office (NAVOCEANO), is used for prediction of sea surface height (SSH) on daily and monthly time scales during 1998–2001. Model simulations that ...

Charlie N. Barron; A. Birol Kara; Harley E. Hurlburt; C. Rowley; Lucy F. Smedstad

2004-12-01T23:59:59.000Z

282

Two Tales of Initializing Decadal Climate Prediction Experiments with the ECHAM5/MPI-OM Model  

Science Conference Proceedings (OSTI)

This paper investigates the impact of different ocean initialization strategies on the forecast skill of decadal prediction experiments performed with the ECHAM5/Max Planck Institute Ocean Model (MPI-OM) coupled model. The ocean initializations ...

Daniela Matei; Holger Pohlmann; Johann Jungclaus; Wolfgang Müller; Helmuth Haak; Jochem Marotzke

2012-12-01T23:59:59.000Z

283

Ability of a Regional-Scale Model to Predict the Genesis of Intense Mesoscale Convective Systems  

Science Conference Proceedings (OSTI)

This paper presents the mesoscale part of a two-part evaluation of thirty forecasts produced by a mesoscalenumerical weather prediction model (MASS 2.0). The general approach taken to evaluate the mesoscale predictivecapabilities of the model is ...

Steven E. Koch

1985-10-01T23:59:59.000Z

284

Empirical Probability Models to Predict Precipitation Levels over Puerto Rico Stations  

Science Conference Proceedings (OSTI)

A new algorithm is proposed to predict the level of rainfall (above normal, normal, and below normal) in Puerto Rico that relies on probability and empirical models. The algorithm includes a theoretical probability model in which parameters are ...

Nazario D. Ramirez-Beltran; William K. M. Lau; Amos Winter; Joan M. Castro; Nazario Ramirez Escalante

2007-03-01T23:59:59.000Z

285

Numerical Prediction of Convectively Driven Mesoscale Pressure Systems. Part II. Mesoscale Model  

Science Conference Proceedings (OSTI)

A 20-level, three-dimensional, primitive equation model with 20 km horizontal resolution is used to predict the development of convectively driven mesoscale pressure systems. Systems produced by the model have life histories and structural ...

J. M. Fritsch; C. F. Chappell

1980-08-01T23:59:59.000Z

286

Localized Precipitation Forecasts from a Numerical Weather Prediction Model Using Artificial Neural Networks  

Science Conference Proceedings (OSTI)

Although the resolution of numerical weather prediction models continues to improve, many of the processes that influence precipitation are still not captured adequately by the scales of present operational models, and consequently precipitation ...

Robert J. Kuligowski; Ana P. Barros

1998-12-01T23:59:59.000Z

287

The National Meteorological Center's Quasi-Lagrangian Model for Hurricane Prediction  

Science Conference Proceedings (OSTI)

A description is presented of the National Meteorological Center's Quasi-Lagrangian Model (QLM), which is used for operational hurricane prediction. The model uses the primitive equations with high horizontal and vertical resolution, and includes ...

Mukut B. Mathur

1991-06-01T23:59:59.000Z

288

A Minimal Model for the Short-Term Prediction of Rainfall in the Tropics  

Science Conference Proceedings (OSTI)

A “minimal” model is proposed here for the short-term prediction (up to 12 h ahead) of precipitation occurrence in the tropics. The model is purely statistical, consisting of an optimally weighted linear combination of a Markov chain and ...

K. Fraedrich; L. M. Leslie

1988-09-01T23:59:59.000Z

289

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

Science Conference Proceedings (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

290

QNH: Design and Test of a Quasi-Nonhydrostatic Model for Mesoscale Weather Prediction  

Science Conference Proceedings (OSTI)

A new mesoscale weather prediction model, called QNH, is described. It is characterized by a parameter that multiplies the hydrostatic terms in the vertical equation of motion. Models of this type are referred to generically as “quasi-...

A. E. MacDonald; J. L. Lee; S. Sun

2000-04-01T23:59:59.000Z

291

Models for the Prediction of Tropical Cyclone Motion over the North Atlantic: An Operational Evaluation  

Science Conference Proceedings (OSTI)

This study provides an operational evaluation of the seven prediction models-five statistical and two dynamical-used at the National Hurricane Center. Following a brief description of the rationale for each model, various performance ...

Charles J. Neumann; Joseph M. Pelissier

1981-03-01T23:59:59.000Z

292

Comparison of Structure Parameter Scaling Expressions with Turbulence Closure Model Predictions  

Science Conference Proceedings (OSTI)

The convective boundary-layer scaling expressions presented by Wyngaard and LeMone (1980) are compared with predictions from a turbulence closure model. We first examine a model experiment involving a clear-air, convectively driven boundary layer ...

Stephen D. Burk

1981-04-01T23:59:59.000Z

293

Initialization of Soil-Water Content in Regional-Scale Atmospheric Prediction Models  

Science Conference Proceedings (OSTI)

The purpose of this study is to demonstrate the feasibility of determining the soil-water content fields required as initial conditions for land surface components within atmospheric prediction models. This is done using a model of the hydrologic ...

Christopher B. Smith; Mercedes N. Lakhtakia; William J. Capehart; Toby N. Carlson

1994-04-01T23:59:59.000Z

294

Potential Predictability and AMIP Implications of Midlatitude Climate Variability in Two General Circulation Models  

Science Conference Proceedings (OSTI)

Ensembles of extended Atmospheric Model Intercomparison Project (AMIP) runs from the general circulation models of the National Centers for Environmental Prediction (formerly the National Meteorological Center) and the Max-Planck Institute (...

T. P. Barnett; K. Arpe; L. Bengtsson; M. Ji; A. Kumar

1997-09-01T23:59:59.000Z

295

A Vertically Nested Regional Numerical Weather Prediction Model with Second-Order Closure Physics  

Science Conference Proceedings (OSTI)

The model we describe involves a unique strategy in which a high vertical resolution grid is nested within the coarse vertical resolution grid of a regional numerical weather prediction (NWP) model. Physics computations performed on the high ...

Stephen D. Burk; William T. Thompson

1989-11-01T23:59:59.000Z

296

Modeling and prediction of nonlinear environmental system using Bayesian methods  

Science Conference Proceedings (OSTI)

An environmental dynamic system is usually modeled as a nonlinear system described by a set of nonlinear ODEs. A central challenge in computational modeling of environmental systems is the determination of the model parameters. In these cases, estimating ... Keywords: Extended Kalman filter, Leaf area index and soil moisture model, Nonlinear environmental system, Particle filter, State and parameter estimation, Variational filter

Majdi Mansouri; Benjamin Dumont; Marie-France Destain

2013-03-01T23:59:59.000Z

297

Comparison of the prediction accuracy of daily and monthly regression models for energy consumption in commercial buildings  

E-Print Network (OSTI)

The measured energy savings from retrofits in commercial buildings are generally determined as the difference between the energy consumption predicted using a baseline model and the measured energy consumption during the post retrofit period. Most baseline models are developed by regressing the daily energy consumption versus the daily average temperature (daily models) or by regressing the monthly energy consumption versus the monthly average temperature (monthly models). Since the post-retrofit weather is generally different from the weather used for model development, the prediction error of the baseline model may be different from the fitting error. Daily and monthly baseline models were developed for a midsize commercial building with (i) dual-duct CAV and VAV systems, (ii) office and university occupancy schedules, and (iii) different operating practices using the weather of a mild weather year. The prediction errors were identified as the difference between the energy use predicted by the regression models and the values simulated by a calibrated simulation program when both models use weather from a year very different from the weather year used to develop the regression model. The major results are summarized below: 1. When the AHUs operate 24 hours per day, annual energy prediction errors of daily regression models were found to be less than 1.4%. The errors of monthly regression models were found to be in the same range as the error of the daily models. 2. When the AHUs were shut down during unoccupied periods, annual prediction errors for both daily and monthly regression models were as high as 15%. However, the prediction error of daily regression models can be decreased to a range of 2% to 3% if the daily average energy consumption is regressed versus the average temperature during the operation period. Based on these findings, we suggest use of daily or monthly regression models when the AHUs are operated 24 hours per day. When shut-down is performed during unoccupied hours, daily energy consumption should be regressed versus the average ambient temperature during operating hours to develop the baseline model.

Wang, Jinrong

1996-01-01T23:59:59.000Z

298

Weight optimisation for iterative distributed model predictive control applied to power networks  

Science Conference Proceedings (OSTI)

This paper presents a weight tuning technique for iterative distributed Model Predictive Control (MPC). Particle Swarm Optimisation (PSO) is used to optimise both the weights associated with disturbance rejection and those associated with achieving consensus ... Keywords: Distributed model predictive control, Multi-agent, Particle swarm optimisation, Power networks, Smart grids, Weight tuning

Paul Mc Namara; Rudy R. Negenborn; Bart De Schutter; Gordon Lightbody

2013-01-01T23:59:59.000Z

299

Estimation and regularization techniques for regression models with multidimensional prediction functions  

Science Conference Proceedings (OSTI)

Boosting is one of the most important methods for fitting regression models and building prediction rules. A notable feature of boosting is that the technique can be modified such that it includes a built-in mechanism for shrinking coefficient estimates ... Keywords: Count data model, Gradient boosting, Multidimensional prediction function, Scale parameter estimation, Variable selection

Matthias Schmid; Sergej Potapov; Annette Pfahlberg; Torsten Hothorn

2010-04-01T23:59:59.000Z

300

Statistical validation of event predictors: A comparative study based on the field of seizure prediction  

Science Conference Proceedings (OSTI)

The prediction of events is of substantial interest in many research areas. To evaluate the performance of prediction methods, the statistical validation of these methods is of utmost importance. Here, we compare an analytical validation method to numerical approaches that are based on Monte Carlo simulations. The comparison is performed in the field of the prediction of epileptic seizures. In contrast to the analytical validation method, we found that for numerical validation methods insufficient but realistic sample sizes can lead to invalid high rates of false positive conclusions. Hence we outline necessary preconditions for sound statistical tests on above chance predictions.

Feldwisch-Drentrup, Hinnerk [Bernstein Center Freiburg (BCF), University of Freiburg, Freiburg (Germany); Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg (Germany); Department of Neurobiology and Biophysics, Faculty of Biology, University of Freiburg, Freiburg (Germany); Freiburg Institute for Advanced Studies, University of Freiburg, Freiburg (Germany); Department of Physics, University of Freiburg, Freiburg (Germany); Schulze-Bonhage, Andreas [Bernstein Center Freiburg (BCF), University of Freiburg, Freiburg (Germany); Epilepsy Center, University Hospital of Freiburg, Freiburg (Germany); Timmer, Jens [Bernstein Center Freiburg (BCF), University of Freiburg, Freiburg (Germany); Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg (Germany); Freiburg Institute for Advanced Studies, University of Freiburg, Freiburg (Germany); Department of Physics, University of Freiburg, Freiburg (Germany); Department of Clinical and Experimental Medicine, Linkoeping University, Linkoeping (Sweden); Schelter, Bjoern [Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg (Germany); Department of Physics, University of Freiburg, Freiburg (Germany); Institute for Complex Systems and Mathematical Biology, SUPA, University of Aberdeen, Aberdeen (United Kingdom)

2011-06-15T23:59:59.000Z

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

Prediction of Spring Elbe Discharge Based on Stable Teleconnections with Winter Global Temperature and Precipitation  

Science Conference Proceedings (OSTI)

The predictability of Elbe streamflow anomalies during spring is examined using previous winter sea surface temperature (SST), temperature over land (TT), and precipitation (PP) anomalies. Based on running correlation analysis, the authors ...

Monica Ionita; Gerrit Lohmann; Norel Rimbu

2008-12-01T23:59:59.000Z

302

Local vs. global models for effort estimation and defect prediction  

Science Conference Proceedings (OSTI)

Data miners can infer rules showing how to improve either (a) the effort estimates of a project or (b) the defect predictions of a software module. Such studies often exhibit conclusion instability regarding what is the most effective action for different ...

Tim Menzies; Andrew Butcher; Andrian Marcus; Thomas Zimmermann; David Cok

2011-11-01T23:59:59.000Z

303

A Business Intelligence Model to Predict Bankruptcy using Financial Domain Ontology with Association Rule Mining Algorithm  

E-Print Network (OSTI)

Today in every organization financial analysis provides the basis for understanding and evaluating the results of business operations and delivering how well a business is doing. This means that the organizations can control the operational activities primarily related to corporate finance. One way that doing this is by analysis of bankruptcy prediction. This paper develops an ontological model from financial information of an organization by analyzing the Semantics of the financial statement of a business. One of the best bankruptcy prediction models is Altman Z-score model. Altman Z-score method uses financial rations to predict bankruptcy. From the financial ontological model the relation between financial data is discovered by using data mining algorithm. By combining financial domain ontological model with association rule mining algorithm and Zscore model a new business intelligence model is developed to predict the bankruptcy.

Martin, A; Venkatesan, Dr V Prasanna

2011-01-01T23:59:59.000Z

304

Integrated Experimental and Modeling Studies to Predict the Impact Response of Explosives and Propellants  

DOE Green Energy (OSTI)

Understanding and predicting the impact response of explosives and propellants remains a challenging area in the energetic materials field. Efforts are underway at LLNL (and other laboratories) to apply modern diagnostic tools and computational analysis to move beyond the current level of imprecise approximations towards a predictive approach more closely based on fundamental understanding of the relevant mechanisms. In this paper we will discuss a set of underlying mechanisms that govern the impact response of explosives and propellants: (a) mechanical insult (impact) leading to material damage and/or direct ignition; (b) ignition leading to flame spreading; (c) combustion being driven by flame spreading, perhaps in damaged materials; (d) combustion causing further material damage; (e) combustion leading to pressure build-up or relief; (f) pressure changes driving the rates of combustion and flame spread; (g) pressure buildup leading to structural response and damage, which causes many of the physical hazards. We will briefly discuss our approach to modeling up these mechanistic steps using ALE 3D, the LLNL hydrodynamic code with fully coupled chemistry, heat flow, mass transfer, and slow mechanical motion as well as hydrodynamic processes. We will identify the necessary material properties needed for our models, and will discuss our experimental efforts to characterize these properties and the overall mechanistic steps, in order to develop and parameterize the models in ALE 3D and to develop a qualitative understanding of impact response.

Maienschein, J L; Nichols III, A L; Reaugh, J E; McClelland, M E; Hsu, P C

2005-05-25T23:59:59.000Z

305

Gridpoint Predictions of High Temperature from a Mesoscale Model  

Science Conference Proceedings (OSTI)

Mesoscale model gridpoint temperature data from simulations in the southwestern United States during the summer of 1990 are compared with both observations and statistical guidance from large-scale models over a 32-day period. Although the raw ...

David J. Stensrud; Jon A. Skindlov

1996-03-01T23:59:59.000Z

306

Human walking model predicts joint mechanics, electromyography and mechanical economy  

E-Print Network (OSTI)

In this paper, we present an under-actuated model of human walking, comprising only a soleus muscle and flexion/extension monoarticular hip muscles. The remaining muscle groups of the human leg are modeled using quasi-passive, ...

Endo, Ken

307

ENSO Prediction with Markov Models: The Impact of Sea Level  

Science Conference Proceedings (OSTI)

A series of seasonally varying linear Markov models are constructed in a reduced multivariate empirical orthogonal function (MEOF) space of observed sea surface temperature, surface wind stress, and sea level analysis. The Markov models are ...

Yan Xue; Ants Leetmaa; Ming Ji

2000-02-01T23:59:59.000Z

308

Multi-Scale Modeling to Predict Properties of Thermoplastic ...  

Science Conference Proceedings (OSTI)

A Continuum General Noise Brownian Thermostat with Applications to Film Morphology · A Multiscale, Nonlinear, Modeling Framework Enabling the Design and ...

309

Experimental measurements and modeling prediction of flammability limits of binary hydrocarbon mixtures  

E-Print Network (OSTI)

Flammability limit is a significant safety issue for industrial processes. A certain amount of flammability limit data for pure hydrocarbons are available in the literature, but for industrial applications, there are conditions including different combinations of fuels at standard and non-standard conditions, in which the flammability limit data are scarce and sometimes unavailable. This research is two-fold: (i) Performing experimental measurements to estimate the lower flammability limits and upper flammability limits of binary hydrocarbon mixtures, conducting experimental data numerical analysis to quantitatively characterize the flammability limits of these mixtures with parameters, such as component compositions, flammability properties of pure hydrocarbons, and thermo-kinetic values; (ii) Estimating flammability limits of binary hydrocarbon mixtures through CFT-V modeling prediction (calculated flame temperature at constant volume), which is based on a comprehensive consideration of energy conservation. For the experimental part, thermal detection was used in this experiment. The experimental results indicate that the experimental results fit Le Chatelier’s Law within experimental uncertainty at the lower flammability limit condition. At the upper flammability limit condition, Le Chatelier’s Law roughly fits the saturated hydrocarbon mixture data, while with mixtures that contain one or more unsaturated components, a modification of Le Chatelier’s is preferred to fit the experimental data. The easy and efficient way to modify Le Chatelier’s Law is to power the molar percentage concentrations of hydrocarbon components. For modeling prediction part, the CFT-V modeling is an extended modification of CAFT modeling at constant volume and is significantly related to the reaction vessel configuration. This modeling prediction is consistent with experimental observation and Le Chatelier’s Law at the concentrations of lower flammability limits. When the quenching effect is negligible, this model can be simplified by ignoring heat loss from the reaction vessel to the external surroundings. Specifically, when the total mole changes in chemical reactions can be neglected and the quenching effect is small, CFTV modeling can be simplified to CAFT modeling.

Zhao, Fuman

2008-05-01T23:59:59.000Z

310

Web-based simulation 2: performance prediction of dynamic component substitutions  

Science Conference Proceedings (OSTI)

The Web-based Environment for Systems Engineering (wese) is a web-based modeling and simulation environment in which the level of abstraction of a model can be configured statically (prior to simulation) or dynamically (during ...

Dhananjai M. Rao; Philip A. Wilsey

2002-12-01T23:59:59.000Z

311

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

312

Predicting the potential habitat of oaks with data mining models and the R system  

Science Conference Proceedings (OSTI)

Oak forests are essential for the ecosystems of many countries, particularly when they are used in vegetal restoration. Therefore, models for predicting the potential habitat of oaks can be a valuable tool for work in the environment. In accordance with ... Keywords: Classification trees, Data mining models, Ensemble models, Habitat modelling, Neural networks, Oaks, R system, Supervised classification, Support vector machines

Rafael Pino-Mejías; María Dolores Cubiles-de-la-Vega; María Anaya-Romero; Antonio Pascual-Acosta; Antonio Jordán-López; Nicolás Bellinfante-Crocci

2010-07-01T23:59:59.000Z

313

The Ellie Caulkins Opera House: A study in simplified predictive modeling  

Science Conference Proceedings (OSTI)

Recent advances in predictive modeling and experience in understanding and interpreting the significance of the data provided by such models have given rise to a spate of wonderful new performance spaces. However the models ? whether computermodels or physical scale models ? demand an investment in time

Robert Mahoney

2008-01-01T23:59:59.000Z

314

BIRD FATALITY ASSOCIATIONS AND PREDICTIVE MODELS FOR THE APWRA  

E-Print Network (OSTI)

at those models in our sample in the APWRA Micon 65 Bonus Danwin Flowind Windmatic Enertech KCS-56 KVS-33 Enertech KCS-56 KVS-33 Howden Nordtank W.E.G. 25002000150010005000 N Effort Turbine model Sum proportion Predictor Variable df GOEA RTHA AMKE BUOW BAOW GHOW Turbine model 10 17.98t 20.70* 78.59** 44.59** 7.23 5

315

CFD Model for Prediction of Liquid Steel Temperature in Ladle ...  

Science Conference Proceedings (OSTI)

2D and 3D Numerical Modeling of Solidification Benchmark of Sn-3% Pb Wt. Alloy under ... 3D CAFE Simulation of a Macrosegregation Benchmark Experiment.

316

The Neural Network Model using for Predictions Mechanical ...  

Science Conference Proceedings (OSTI)

Study of Composite Materials Application for Horizontal Axis Wind Turbine Blades ... Using the Computational Modelling to Improve Durability of Diesel Engine ...

317

Web based integrated models for participatory planning  

Science Conference Proceedings (OSTI)

The present paper focuses on the development of an integrated assessment model that embeds the web dimension and aims at increasing awareness in society, especially on environmental issues. The model incorporates features that make it capable of promoting ... Keywords: greenhouse gas emissions, increasing awareness, integrated assessment models, web based participatory integrated assessment models

Grammatikogiannis Elias; Maria Giaoutzi

2011-06-01T23:59:59.000Z

318

Development of Model to Predict Stress Corrosion Cracking and Corrosion Fatigue of Low Pressure Turbine Components  

Science Conference Proceedings (OSTI)

Most outage hours for steam turbines are the result of corrosion of low pressure (LP) blades and disks in the phase transition zone (PTZ). Developing an effective localized corrosion damage prediction model is essential to successfully avoid unscheduled outages of steam turbines. This report provides the latest analytical model for predicting failure and includes the electrochemical data for a blade material (17-4PH) that will be used in the model.

2007-02-26T23:59:59.000Z

319

Temperature Control of Continued Hyperthermic Celiac Perfusion Based on Generalized Predictive Self-Tuning Control  

Science Conference Proceedings (OSTI)

On the basis of the characteristic and requirement of the continued hyperthermic celiac perfusion (CHCP) temperature process. A model is set up, the generalized predictive self-tuning control (GPSC) algorithm is used to control the whole system to get ... Keywords: CHCP, GPSC, Modeling

Xing-hui Zhang; Hui-min Jiang; Zhao-lin Gu; Zeng-qiang Chen

2008-10-01T23:59:59.000Z

320

Rate-based degradation modeling of lithium-ion cells  

DOE Green Energy (OSTI)

Accelerated degradation testing is commonly used as the basis to characterize battery cell performance over a range of stress conditions (e.g., temperatures). Performance is measured by some response that is assumed to be related to the state of health of the cell (e.g., discharge resistance). Often, the ultimate goal of such testing is to predict cell life at some reference stress condition, where cell life is defined to be the point in time where performance has degraded to some critical level. These predictions are based on a degradation model that expresses the expected performance level versus the time and conditions under which a cell has been aged. Usually, the degradation model relates the accumulated degradation to the time at a constant stress level. The purpose of this article is to present an alternative framework for constructing a degradation model that focuses on the degradation rate rather than the accumulated degradation. One benefit of this alternative approach is that prediction of cell life is greatly facilitated in situations where the temperature exposure is not isothermal. This alternative modeling framework is illustrated via a family of rate-based models and experimental data acquired during calendar-life testing of high-power lithium-ion cells.

E.V. Thomas; I. Bloom; J.P. Christophersen; V.S. Battaglia

2012-05-01T23:59:59.000Z

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.


321

Comparison of Predictive Models for Photovoltaic Module Performance: Preprint  

DOE Green Energy (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

322

Scale Interaction and Predictability in a Mesoscale Model  

Science Conference Proceedings (OSTI)

Scale interaction is examined in the limited-area PSU/NCAR mesoscale model, with emphasis on the forcing of small scales by the small-scale fields themselves. Output data from the model are filtered by expanding fields at each level in two-...

Andrew H. Van Tuyl; Ronald M. Errico

1989-03-01T23:59:59.000Z

323

Impact of Rotor Surface Velocity, Leakage Models and Real Gas Properties on Rotordynamic Force Predictions of Gas Labyrinth Seals  

E-Print Network (OSTI)

Rotordynamic coefficients of a gas labyrinth seal are assumed to be frequency independent. However, this assumption loses its validity as rotor surface velocity approaches Mach 1. The solution procedure of 1CV model by Childs and Scharrer which assumes frequency independent force coefficients is modified to allow for calculating frequency dependent force coefficients. A comparative study of the impact of using frequency-dependent model and the original frequency-independent model on stability analysis is made. The results indicate that frequency dependency of force coefficients should be accounted for in stability analysis as rotor surface velocity approaches a significant fraction of Mach number. The bulk flow rotordynamic analysis model by Childs and Scharrer is modified to investigate the impact of leakage-flow models on predictions. A number of leakage models are incorporated in the one-control volume model, and a comparative study is made. Kinetic energy carryover factor of a leakage equation is one of the dominant factors in seal cross-force generation. A leakage equation based on a model proposed by Gamal which uses Hodkinson?s kinetic energy carryover factor is found to improve predictions of direct damping and cross-coupled stiffness. A test case is implemented to study the impact of variation of seal axial radial clearance on stability characteristics. The 1CV model by Childs and Scharrer and subsequent bulk flow models are based on the assumption of isothermal flow across the labyrinth seal. The 1CV model by Childs and Scharrer is modified to include energy equation, and the flow process is assumed to be adiabatic. However, predicted cross-coupled stiffness and direct damping coefficients using the new model do not compare well with the experimental results by Picardo as compared to the isothermal model. The impact of using real gas properties on static and rotordynamic characteristics of the seal is studied.

Thorat, Manish R.

2010-05-01T23:59:59.000Z

324

Injection-Molded Long-Fiber Thermoplastic Composites: From Process Modeling to Prediction of Mechanical Properties  

SciTech Connect

This article illustrates the predictive capabilities for long-fiber thermoplastic (LFT) composites that first simulate the injection molding of LFT structures by Autodesk® Simulation Moldflow® Insight (ASMI) to accurately predict fiber orientation and length distributions in these structures. After validating fiber orientation and length predictions against the experimental data, the predicted results are used by ASMI to compute distributions of elastic properties in the molded structures. In addition, local stress-strain responses and damage accumulation under tensile loading are predicted by an elastic-plastic damage model of EMTA-NLA, a nonlinear analysis tool implemented in ABAQUS® via user-subroutines using an incremental Eshelby-Mori-Tanaka approach. Predicted stress-strain responses up to failure and damage accumulations are compared to the experimental results to validate the model.

Nguyen, Ba Nghiep; Kunc, Vlastimil; Jin, Xiaoshi; Tucker III, Charles L.; Costa, Franco

2013-12-18T23:59:59.000Z

325

An Examination of the MOS Objective Temperature Prediction Model  

Science Conference Proceedings (OSTI)

In this study, the performance of the Model Output Statistics (MOS) objective temperature forecasting for Albany, NY, during the period 1975–81 is examined by using various statistical technique. Both paired and unpaired statistical analysis ...

Eli Jacks; S. Trivikrama Rao

1985-01-01T23:59:59.000Z

326

A transient model for data center thermal prediction  

Science Conference Proceedings (OSTI)

Fast thermal maps are a crucial component for many green data center design techniques. However, most state of the art work on thermal mapping ignores critical temporal aspects of thermal behavior and relies on modeling assumptions, such as the steady ...

Michael Jonas; Rose Robin Gilbert; Joshua Ferguson; Georgios Varsamopoulos; Sandeep K. S. Gupta

2012-06-01T23:59:59.000Z

327

A Study of Barotropic Model Flows: Intermittency, Waves and Predictability  

Science Conference Proceedings (OSTI)

The régime flows corresponding to the barotropic nondivergent equation with forcing, drag and subgrid-scale dissipation are studied using spectral model on the plane and on the sphere. The flow régimes obtained exhibit clear evidence of the ...

C. Basdevant; B. Legras; R. Sadourny; M. Béland

1981-11-01T23:59:59.000Z

328

A Global Numerical Weather Prediction Model with Variable Resolution  

Science Conference Proceedings (OSTI)

A conformal transformation suggested by F. Schmidt is followed to implement a global spectral model with variable resolution. A conformal mapping is defined from a physical sphere (like the earth) to a transformed (computational) sphere. The ...

Vivek Hardiker

1997-01-01T23:59:59.000Z

329

Two-Time-Step Oscillations in Numerical Weather Prediction Models  

Science Conference Proceedings (OSTI)

Spurious, nonamplifying, two-time-step oscillations are present in several numerical models of the atmosphere where the vertical diffusion is parameterized using a nonlinear diffusion equation. The problems become particularly pronounced when the ...

Ulla Hammarstrand

1997-12-01T23:59:59.000Z

330

Prediction of Landfalling Hurricanes with the Advanced Hurricane WRF Model  

Science Conference Proceedings (OSTI)

Real-time forecasts of five landfalling Atlantic hurricanes during 2005 using the Advanced Research Weather Research and Forecasting (WRF) (ARW) Model at grid spacings of 12 and 4 km revealed performance generally competitive with, and ...

Christopher Davis; Wei Wang; Shuyi S. Chen; Yongsheng Chen; Kristen Corbosiero; Mark DeMaria; Jimy Dudhia; Greg Holland; Joe Klemp; John Michalakes; Heather Reeves; Richard Rotunno; Chris Snyder; Qingnong Xiao

2008-06-01T23:59:59.000Z

331

A Simple Model for Coastal Sea Level Prediction  

Science Conference Proceedings (OSTI)

Reliable forecasting of wind-forced coastal sea level on the synoptic scale is available for most of the coastal areas of the United States through the National Weather Service Extratropical Storm Surge Model (ESSM). However, in many coastal ...

Charles E. Tilburg; Richard W. Garvine

2004-06-01T23:59:59.000Z

332

History-based, online, battery lifetime prediction for embedded and mobile devices  

E-Print Network (OSTI)

This paper presents a novel, history-based, statistical technique for online battery lifetime prediction. The approach first takes a one-time, full cycle, voltage measurement of a constant load, and uses it to transform the partial voltage curve of the current workload into a form with robust predictability. Based on the transformed history curve, we apply a statistical method to make a lifetime prediction. We investigate the performance of the implementation of our approach on a widely used mobile device (HP iPAQ) running Linux, and compare it to two similar battery prediction technologies: ACPI and Smart Battery. We employ twenty-two constant and variable workloads to verify the efficacy of our approach. Our results show that this approach is efficient, accurate, and able to adapt to different systems and batteries easily. 1

Ye Wen; Rich Wolski; Chandra Krintz

2003-01-01T23:59:59.000Z

333

Model-Generated Predictions of Dry Thunderstorm Potential  

Science Conference Proceedings (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

334

The effects of digital elevation model resolution on the calculation and predictions of topographic wetness indices.  

DOE Green Energy (OSTI)

One of the largest exports in the Southeast U.S. is forest products. Interest in biofuels using forest biomass has increased recently, leading to more research into better forest management BMPs. The USDA Forest Service, along with the Oak Ridge National Laboratory, University of Georgia and Oregon State University are researching the impacts of intensive forest management for biofuels on water quality and quantity at the Savannah River Site in South Carolina. Surface runoff of saturated areas, transporting excess nutrients and contaminants, is a potential water quality issue under investigation. Detailed maps of variable source areas and soil characteristics would therefore be helpful prior to treatment. The availability of remotely sensed and computed digital elevation models (DEMs) and spatial analysis tools make it easy to calculate terrain attributes. These terrain attributes can be used in models to predict saturated areas or other attributes in the landscape. With laser altimetry, an area can be flown to produce very high resolution data, and the resulting data can be resampled into any resolution of DEM desired. Additionally, there exist many maps that are in various resolutions of DEM, such as those acquired from the U.S. Geological Survey. Problems arise when using maps derived from different resolution DEMs. For example, saturated areas can be under or overestimated depending on the resolution used. The purpose of this study was to examine the effects of DEM resolution on the calculation of topographic wetness indices used to predict variable source areas of saturation, and to find the best resolutions to produce prediction maps of soil attributes like nitrogen, carbon, bulk density and soil texture for low-relief, humid-temperate forested hillslopes. Topographic wetness indices were calculated based on the derived terrain attributes, slope and specific catchment area, from five different DEM resolutions. The DEMs were resampled from LiDAR, which is a laser altimetry remote sensing method, obtained from the USDA Forest Service at Savannah River Site. The specific DEM resolutions were chosen because they are common grid cell sizes (10m, 30m, and 50m) used in mapping for management applications and in research. The finer resolutions (2m and 5m) were chosen for the purpose of determining how finer resolutions performed compared with coarser resolutions at predicting wetness and related soil attributes. The wetness indices were compared across DEMs and with each other in terms of quantile and distribution differences, then in terms of how well they each correlated with measured soil attributes. Spatial and non-spatial analyses were performed, and predictions using regression and geostatistics were examined for efficacy relative to each DEM resolution. Trends in the raw data and analysis results were also revealed.

Drover, Damion, Ryan

2011-12-01T23:59:59.000Z

335

Incorporating Single-nucleotide Polymorphisms Into the Lyman Model to Improve Prediction of Radiation Pneumonitis  

SciTech Connect

Purpose: To determine whether single-nucleotide polymorphisms (SNPs) in genes associated with DNA repair, cell cycle, transforming growth factor-{beta}, tumor necrosis factor and receptor, folic acid metabolism, and angiogenesis can significantly improve the fit of the Lyman-Kutcher-Burman (LKB) normal-tissue complication probability (NTCP) model of radiation pneumonitis (RP) risk among patients with non-small cell lung cancer (NSCLC). Methods and Materials: Sixteen SNPs from 10 different genes (XRCC1, XRCC3, APEX1, MDM2, TGF{beta}, TNF{alpha}, TNFR, MTHFR, MTRR, and VEGF) were genotyped in 141 NSCLC patients treated with definitive radiation therapy, with or without chemotherapy. The LKB model was used to estimate the risk of severe (grade {>=}3) RP as a function of mean lung dose (MLD), with SNPs and patient smoking status incorporated into the model as dose-modifying factors. Multivariate analyses were performed by adding significant factors to the MLD model in a forward stepwise procedure, with significance assessed using the likelihood-ratio test. Bootstrap analyses were used to assess the reproducibility of results under variations in the data. Results: Five SNPs were selected for inclusion in the multivariate NTCP model based on MLD alone. SNPs associated with an increased risk of severe RP were in genes for TGF{beta}, VEGF, TNF{alpha}, XRCC1 and APEX1. With smoking status included in the multivariate model, the SNPs significantly associated with increased risk of RP were in genes for TGF{beta}, VEGF, and XRCC3. Bootstrap analyses selected a median of 4 SNPs per model fit, with the 6 genes listed above selected most often. Conclusions: This study provides evidence that SNPs can significantly improve the predictive ability of the Lyman MLD model. With a small number of SNPs, it was possible to distinguish cohorts with >50% risk vs <10% risk of RP when they were exposed to high MLDs.

Tucker, Susan L., E-mail: sltucker@mdanderson.org [Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Li Minghuan [Department of Radiation Oncology, Shandong Cancer Hospital, Jinan, Shandong (China)] [Department of Radiation Oncology, Shandong Cancer Hospital, Jinan, Shandong (China); Xu Ting; Gomez, Daniel [Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas (United States)] [Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Yuan Xianglin [Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan (China)] [Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan (China); Yu Jinming [Department of Radiation Oncology, Shandong Cancer Hospital, Jinan, Shandong (China)] [Department of Radiation Oncology, Shandong Cancer Hospital, Jinan, Shandong (China); Liu Zhensheng; Yin Ming; Guan Xiaoxiang; Wang Lie; Wei Qingyi [Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, Texas (United States)] [Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Mohan, Radhe [Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas (United States)] [Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Vinogradskiy, Yevgeniy [University of Colorado School of Medicine, Aurora, Colorado (United States)] [University of Colorado School of Medicine, Aurora, Colorado (United States); Martel, Mary [Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas (United States)] [Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Liao Zhongxing [Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas (United States)] [Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas (United States)

2013-01-01T23:59:59.000Z

336

The LHC di-photon Higgs signal predicted by little Higgs models  

E-Print Network (OSTI)

Little Higgs theory naturally predicts a light Higgs boson whose most important discovery channel at the LHC is the di-photon signal $pp\\to h\\to \\gamma\\gamma$. In this work we perform a comparative study for this signal in some typical little Higgs models, namely the littlest Higgs model (LH), two littlest Higgs models with T-parity (named LHT-I and LHT-II) and the simplest little Higgs modes (SLH). We find that compared with the Standard Model prediction, the di-photon signal rate is always suppressed and the suppression extent can be quite different for different models. The suppression is mild ($\\lsim 10%$) in the LH model but can be quite severe ($\\simeq 90%$) in other three models. This means that discovering the light Higgs boson predicted by the little Higgs theory through the di-photon channel at the LHC will be more difficult than discovering the SM Higgs boson.

Lei Wang; Jin Min Yang

2011-06-20T23:59:59.000Z

337

Propagation prediction model and performance analysis of RFID system under metallic container production circumstance  

Science Conference Proceedings (OSTI)

Radio-wave attenuation negatively affects RF communications in a RFID system. In order to ensure the reliability of RFID system, one must predict the radio-wave path-loss accurately before detailed design. However, it is infeasible to estimate the path-loss ... Keywords: Metallic container, Propagation prediction model, RFID

Cao Xiaohua; Xiao Hanbin

2011-02-01T23:59:59.000Z

338

`TVLSI-00029-2003.R1 An Analytical Model for Predicting the Remaining Battery  

E-Print Network (OSTI)

`TVLSI-00029-2003.R1 1 An Analytical Model for Predicting the Remaining Battery Capacity of Lithium-Ion Batteries Peng Rong, Student Member, IEEE and Massoud Pedram, Fellow, IEEE Abstract -- Predicting the residual energy of the battery source that powers a portable electronic device is imperative in designing

Pedram, Massoud

339

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

NLE Websites -- All DOE Office Websites (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.

340

STRUCTURE-BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION  

SciTech Connect

Significant progress continued to be made during the past reporting quarter on both major technical tasks. During the reporting period at OSU, computational investigations were conducted of addition vs. abstraction reactions of H, O(3 P), and OH with monocyclic aromatic hydrocarbons. The potential energy surface for more than 80 unique reactions of H, O ( 3 P), and OH with aromatic hydrocarbons were determined at the B3LYP/6-31G(d) level of theory. The calculated transition state barriers and reaction free energies indicate that the addition channel is preferred at 298K, but that the abstraction channel becomes dominant at high temperatures. The thermodynamic preference for reactivity with aromatic hydrocarbons increases in the order O(3 P) < H < OH. Abstraction from six-membered aromatic rings is more facile than abstraction from five-membered aromatic rings. However, addition to five-membered rings is thermodynamically more favorable than addition to six-membered rings. The free energies for the abstraction and addition reactions of H, O, and OH with aromatic hydrocarbons and the characteristics of the respective transition states can be used to calculate the reaction rate constants for these important combustion reactions. Experimental work at Brown University on the effect of reaction on the structural evolution of different chars (i.e., phenolic resin char and chars produced from three different coals) have been investigated in a TGA/TPD-MS system. It has been found that samples of different age of these chars appeared to lose their "memory" concerning their initial structures at high burn-offs. During the reporting period, thermal desorption experiments of selected samples were conducted. These spectra show that the population of low temperature oxygen surface complexes, which are primarily responsible for reactivity, are more similar for the high burn-off than for the low burn-off samples of different ages; i.e., the population of active sites are more similar for the ?younger? and ?older? chars at high burn-offs. Progress continued on experimental work at OSU. Another furnace run was conducted with a Pittsburgh seam coal. Temperature profiles were obtained, as well as char samples from three sampling ports. Nonisothermal TGA reactivities were also obtained for these samples. Work is continuing on final ?fine-tuning? of the gas analysis section.

CHRISTOPHER M. HADAD; JOSEPH M. CALO; ROBERT H. ESSENHIGH; ROBERT H. HURT

1999-01-13T23:59:59.000Z

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341

Structure-Based Predictive model for Coal Char Combustion.  

SciTech Connect

The first quarter of this project was used to carry out a detailed planning process to coordinate the various aspects of this collaborative effort. A workshop was held at Brown University on December 4, 1996, attended by all project participants and key visitors, in which presentations were given by the principal investigators on their respective subtasks. The planning process culminated in the completion of a comprehensive document submitted to DOE / FETC under separate cover. Following the planning exercise, research work was initiated and will be continued in the second project quarter.

Hurt, R.; Calo, J. [Brown Univ., Providence, RI (United States). Div. of Engineering; Essenhigh, R.; Hadad, C [Ohio State Univ., Columbus, OH (United States). Dept. of Chemistry; Stanley, E. [Boston Univ., MA (United States). Dept. of Physics

1997-03-28T23:59:59.000Z

342

Development of a predictive kinetic model for homogeneous Hg oxidation data  

Science Conference Proceedings (OSTI)

Several researchers have developed kinetic models to predict the effects of various flue gas components on homogeneous mercury (Hg) oxidation. Most of these models make use of over 50 reversible reactions that involve radicals in a combustion or post-combustion ... Keywords: Chlorine gas, Homogeneous mercury oxidation, Kinetic model, Simulated flue gas

Hans Agarwal; Harvey G. Stenger

2007-01-01T23:59:59.000Z

343

Hybridization of intelligent techniques and ARIMA models for time series prediction  

Science Conference Proceedings (OSTI)

Traditionally, the autoregressive moving average (ARMA) model has been one of the most widely used linear models in time series prediction. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising ... Keywords: ARIMA models, Fuzzy systems, Hybrid system, Neural networks, Time series

O. Valenzuela; I. Rojas; F. Rojas; H. Pomares; L. J. Herrera; A. Guillen; L. Marquez; M. Pasadas

2008-04-01T23:59:59.000Z

344

Evaluation of a simulation model in predicting the drying parameters for deep-bed paddy drying  

Science Conference Proceedings (OSTI)

A simulation model for deep-bed batch drying of paddy was developed to predict the profiles of grain moisture content, grain temperature, air temperature and air humidity during the drying process. In order to evaluate the validity of this model, a laboratory-scale ... Keywords: Deep-bed, Energy optimization, Paddy, Simulation model

Dariush Zare; Guangnan Chen

2009-08-01T23:59:59.000Z

345

Thermoregulatory model to predict physiological status from ambient environment and heart rate  

Science Conference Proceedings (OSTI)

A real-time thermoregulatory model was developed for predicting real-time physiological responses of workers engaged in various tasks for prolonged time. The unique feature of the present model is primarily on metabolic activity inputs derived from minimum ... Keywords: Air temperature, Core temperature, Heart rate, Heat stress, Real-time modeling

Miyo Yokota; Larry Berglund; Samuel Cheuvront; William Santee; William Latzka; Scott Montain; Margaret Kolka; Daniel Moran

2008-11-01T23:59:59.000Z

346

Bootstrapping to Assess and Improve Atmospheric Prediction Models  

Science Conference Proceedings (OSTI)

Bootstrapping is a simple technique typically used to assess accuracy of estimates of model parameters by using simple plug-in principles and replacing sometimes unwieldy theory by computer simulation. Common uses include variance estimation and confidence ... Keywords: CART, bootstrap, classification, hurricanes, instability, supervised learning, weather data

J. Sunil Rao

2000-04-01T23:59:59.000Z

347

Energy Consumption Models and Predictions for Large-Scale Systems  

Science Conference Proceedings (OSTI)

Responsible, efficient and well-planned power consumption is becoming a necessity for monetary returns and scalability of computing infrastructures. While there are numerous sources from which power data can be obtained, analyzing this data is an intrinsically ... Keywords: Energy model, Grid'5000, distrbuted systems

Taghrid Samak, Christine Morin, David Bailey

2013-05-01T23:59:59.000Z

348

Development of a Prediction Model for Skid Resistance of Asphalt Pavements  

E-Print Network (OSTI)

The skid resistance of asphalt pavement is a major characteristic that determines the driving safety on a road, especially under wet surface conditions. Skid resistance is primarily a function of the microtexture and macrotexture of a pavement surface. Microtexture is influenced by aggregate surface characteristics and is required to disrupt the continuity of surface water film and attain frictional resistance between the tire and the pavement surface. Macrotexture is affected mostly by mixture design or aggregate gradation and contributes to skid resistance by providing drainage paths of water that can be otherwise trapped between a tire and a pavement surface. The increase in macrotexture contributes to preventing hydroplaning and improving wet frictional resistance, particularly at high speeds. While much research has been conducted in the past to identify material factors that affect skid resistance, there is still a need to develop a model for predicting asphalt pavement skid resistance as a function of mixture characteristics and traffic level. The purpose of this study was to develop such a model based on extensive laboratory experiments and field measurements involving different mixture types and aggregate sources. The model incorporates functions that describe the resistance of aggregates to polishing and aggregate size distribution. The aggregate resistance to polishing was quantified by measuring aggregate texture using the Aggregate Imaging System (AIMS) before and after polishing in the Micro-Deval device. The analysis in this dissertation demonstrates how this model can be used to design mixtures and classify aggregates that provide desirable skid resistance levels.

Rezaei, Arash

2010-12-01T23:59:59.000Z

349

A New Visibility Parameterization for Warm-Fog Applications in Numerical Weather Prediction Models  

Science Conference Proceedings (OSTI)

The objective of this work is to suggest a new warm-fog visibility parameterization scheme for numerical weather prediction (NWP) models. In situ observations collected during the Radiation and Aerosol Cloud Experiment, representing boundary ...

I. Gultepe; M. D. Müller; Z. Boybeyi

2006-11-01T23:59:59.000Z

350

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

Science Conference Proceedings (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

351

A Consolidated CLIPER Model for Improved August–September ENSO Prediction Skill  

Science Conference Proceedings (OSTI)

A prime challenge for ENSO seasonal forecast models is to predict boreal summer ENSO conditions at lead. August–September ENSO has a strong influence on Atlantic hurricane activity, Northwest Pacific typhoon activity, and tropical precipitation. ...

Benjamin Lloyd-Hughes; Mark A. Saunders; Paul Rockett

2004-12-01T23:59:59.000Z

352

Application and Validation of a Seasonal Ensemble Prediction System Using a Dynamic Malaria Model  

Science Conference Proceedings (OSTI)

Seasonal multimodel forecasts from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) project are used to drive a malaria model and create reforecasts of malaria incidence for Botswana, in ...

Anne E. Jones; Andrew P. Morse

2010-08-01T23:59:59.000Z

353

Use of Medium-Range Numerical Weather Prediction Model Output to Produce Forecasts of Streamflow  

Science Conference Proceedings (OSTI)

This paper examines an archive containing over 40 years of 8-day atmospheric forecasts over the contiguous United States from the NCEP reanalysis project to assess the possibilities for using medium-range numerical weather prediction model output ...

Martyn P. Clark; Lauren E. Hay

2004-02-01T23:59:59.000Z

354

Performance of the United Kingdom Meteorological Office Global Model in Predicting the Movement of Tropical Cyclones  

Science Conference Proceedings (OSTI)

A detailed evaluation of the performance of the United Kingdom Meteorological Office Global Model (UKMO) in predicting the movement of 15 tropical cyclones (TCs) that occurred over the western North Pacific during 1987 is presented. The ...

Johnny C. L. Chan; Wai-Kau Kay

1993-09-01T23:59:59.000Z

355

Predicting Cloud-to-Ground and Intracloud Lightning in Weather Forecast Models  

Science Conference Proceedings (OSTI)

A new prognostic, spatially and temporally dependent variable is introduced to the Weather Research and Forecasting Model (WRF). This variable is called the potential electrical energy (Ep). It was used to predict the dynamic contribution of the ...

Barry H. Lynn; Yoav Yair; Colin Price; Guy Kelman; Adam J. Clark

2012-12-01T23:59:59.000Z

356

Estimates of Cn2 from Numerical Weather Prediction Model Output and Comparison with Thermosonde Data  

Science Conference Proceedings (OSTI)

Area-averaged estimates of Cn2 from high-resolution numerical weather prediction (NWP) model output are produced from local estimates of the spatial structure functions of refractive index with corrections for the inherent smoothing and filtering ...

Rod Frehlich; Robert Sharman; Francois Vandenberghe; Wei Yu; Yubao Liu; Jason Knievel; George Jumper

2010-08-01T23:59:59.000Z

357

Modeling the Distribution of Precipitation Forecasts from the Canadian Ensemble Prediction System Using Kernel Density Estimation  

Science Conference Proceedings (OSTI)

Kernel density estimation is employed to fit smooth probabilistic models to precipitation forecasts of the Canadian ensemble prediction system. An intuitive nonparametric technique, kernel density estimation has become a powerful tool widely used ...

Syd Peel; Laurence J. Wilson

2008-08-01T23:59:59.000Z

358

A Potential Predictability Study Conducted with an Atmospheric General Circulation Model  

Science Conference Proceedings (OSTI)

This paper describes a potential predictability study on the results of a 20.5 year simulation conducted with the Canadian Climate Centre (CCC) General Circulation Model (GCM). The CCC GCM is an atmosphere GCM with surface hydrology, soil ...

F. W. Zwiers

1987-12-01T23:59:59.000Z

359

Tropical Cyclone Prediction Using a Barotropic Model Initialized by a Generalized Inverse Method  

Science Conference Proceedings (OSTI)

A nested, nondivergent barotropic numerical weather prediction model for forecasting tropical cyclone motion out to 48 h is initialized at time t = 0 by assimilating data from the preceding 24 h. The assimilation scheme finds the generalized ...

A. F. Bennett; L. M. Leslie; C. R. Hagelberg; P. E. Powers

1993-06-01T23:59:59.000Z

360

Coupled Variability and Predictability in a Stochastic Climate Model of the Tropical Atlantic  

Science Conference Proceedings (OSTI)

The coupled variability and predictability of the tropical Atlantic ocean–atmosphere system were analyzed within the framework of a linear stochastic climate model. Despite the existence of a meridional dipole as the leading mode, tropical ...

Faming Wang; Ping Chang

2008-12-01T23:59:59.000Z

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.


361

A Quasi-Lagrangian Regional Model Designed for Operational Weather Prediction  

Science Conference Proceedings (OSTI)

A regional numerical weather prediction model is designed using the quasi-Lagrangian method for operational forecasting of synoptic and mesoscale disturbances. The nonlinear advective terms and the total forcing experienced by a fluid parcel are ...

Mukut B. Mathur

1983-10-01T23:59:59.000Z

362

The Impact of Satellite Sounding Data on the Systematic Error of a Numerical Weather Prediction Model  

Science Conference Proceedings (OSTI)

The impact of satellite sounding data on the systematic errors of the numerical weather prediction model of the Israel Meteorological Service has been investigated. In general, satellite data have been shown to reduce systematic error, and in ...

Noah Wolfson; Albert Thomasell; Arnold Gruber; George Ohring

1985-06-01T23:59:59.000Z

363

Interannual to Decadal Predictability in a Coupled Ocean–Atmosphere General Circulation Model  

Science Conference Proceedings (OSTI)

The predictability of the coupled ocean–atmosphere climate system on interannual to decadal timescales has been studied by means of ensemble forecast experiments with a global coupled ocean–atmosphere general circulation model. Over most parts of ...

A. Grötzner; M. Latif; A. Timmermann; R. Voss

1999-08-01T23:59:59.000Z

364

Microscale Numerical Prediction over Montreal with the Canadian External Urban Modeling System  

Science Conference Proceedings (OSTI)

The Canadian urban and land surface external modeling system (known as urban GEM-SURF) has been developed to provide surface and near-surface meteorological variables to improve numerical weather prediction and to become a tool for environmental ...

Sylvie Leroyer; Stéphane Bélair; Jocelyn Mailhot; Ian B. Strachan

2011-12-01T23:59:59.000Z

365

Calibrated Surface Temperature Forecasts from the Canadian Ensemble Prediction System Using Bayesian Model Averaging  

Science Conference Proceedings (OSTI)

Bayesian model averaging (BMA) has recently been proposed as a way of correcting underdispersion in ensemble forecasts. BMA is a standard statistical procedure for combining predictive distributions from different sources. The output of BMA is a ...

Laurence J. Wilson; Stephane Beauregard; Adrian E. Raftery; Richard Verret

2007-04-01T23:59:59.000Z

366

Comparison of a Two-Dimensional Wave Prediction Model with Synoptic Measurements in Lake Michigan  

Science Conference Proceedings (OSTI)

We compare results from a simple parametric, dynamical, deep-water wave prediction model with two sets of measured wave height maps of Lake Michigan. The measurements were made with an airborne laser altimeter under two distinctly different wind ...

Paul C. Liu; David J. Schwab; John R. Bennett

1984-09-01T23:59:59.000Z

367

Evolving Multisensor Precipitation Estimation Methods: Their Impacts on Flow Prediction Using a Distributed Hydrologic Model  

Science Conference Proceedings (OSTI)

This study investigates evolving methodologies for radar and merged gauge–radar quantitative precipitation estimation (QPE) to determine their influence on the flow predictions of a distributed hydrologic model. These methods include the National ...

David Kitzmiller; Suzanne Van Cooten; Feng Ding; Kenneth Howard; Carrie Langston; Jian Zhang; Heather Moser; Yu Zhang; Jonathan J. Gourley; Dongsoo Kim; David Riley

2011-12-01T23:59:59.000Z

368

Skill of Operational Dynamical Models in Cyclone Prediction Out to Five-Days Range during ERICA  

Science Conference Proceedings (OSTI)

Investigating the skill of prediction of surface cyclones by operational models to ranges of five days, we studied the central and western North Atlantic region for the December 1988 through February 1989 period of the Experiment on Rapidly ...

Frederick Sanders

1992-03-01T23:59:59.000Z

369

The Use of Quasi-Nonhydrostatic Models for Mesoscale Weather Prediction  

Science Conference Proceedings (OSTI)

In recent years, there has been extensive study of the mathematical basis of weather prediction leading to a new system of continuous equations that are well posed, and a set of conditions that make discrete atmospheric and other models stable ...

A. E. MacDonald; J. L. Lee; Y. Xie

2000-08-01T23:59:59.000Z

370

The Canadian Seasonal to Interannual Prediction System. Part I: Models and Initialization  

Science Conference Proceedings (OSTI)

The Canadian Seasonal to Interannual Prediction System (CanSIPS) became operational at Environment Canada's Canadian Meteorological Centre (CMC) in December 2011, replacing CMC's previous two-tier system. CanSIPS is a two-model forecasting system ...

William J. Merryfield; Woo-Sung Lee; George J. Boer; Viatcheslav V. Kharin; John F. Scinocca; Gregory M. Flato; R. S. Ajayamohan; John C. Fyfe; Youmin Tang; Saroja Polavarapu

2013-08-01T23:59:59.000Z

371

Prediction Experiments of Hurricane Gloria (1985) Using a Multiply Nested Movable Mesh Model  

Science Conference Proceedings (OSTI)

The prediction capability of the GFDL triply nested, movable mesh model, with finest grid resolution of degree, was investigated using several case studies of Hurricane Gloria ( 1985) during the period that the storm approached and moved up the ...

Yoshio Kurihara; Morris A. Bender; Robert E. Tuleya; Rebecca J. Ross

1990-10-01T23:59:59.000Z

372

Spatial Predictions of Extreme Wind Speeds over Switzerland Using Generalized Additive Models  

Science Conference Proceedings (OSTI)

The purpose of this work is to present a methodology aimed at predicting extreme wind speeds over Switzerland. Generalized additive models are used to regionalize wind statistics for Swiss weather stations using a number of variables that ...

Christophe Etienne; Anthony Lehmann; Stéphane Goyette; Juan-Ignacio Lopez-Moreno; Martin Beniston

2010-09-01T23:59:59.000Z

373

Numerical Prediction of an Antarctic Severe Wind Event with the Weather Research and Forecasting (WRF) Model  

Science Conference Proceedings (OSTI)

This study initiates the application of the maturing Weather Research and Forecasting (WRF) model to the polar regions in the context of the real-time Antarctic Mesoscale Prediction System (AMPS). The behavior of the Advanced Research WRF (ARW) ...

Jordan G. Powers

2007-09-01T23:59:59.000Z

374

A novel 2-D model approach for the prediction of hourly solar radiation  

Science Conference Proceedings (OSTI)

In this work, a two-dimensional (2-D) representation of the hourly solar radiation data is proposed. The model enables accurate forecasting using image prediction methods. One year solar radiation data that is acquired and collected between August 1, ...

F. Onur Hocaoglu; Ö Nezih Gerek; Mehmet Kurban

2007-06-01T23:59:59.000Z

375

Model Estimates of Land-Driven Predictability in a Changing Climate from CCSM4  

Science Conference Proceedings (OSTI)

The climate system model of the National Center for Atmospheric Research is used to examine the predictability arising from the land surface initialization of seasonal climate ensemble forecasts in current, preindustrial, and projected future ...

Paul A. Dirmeyer; Sanjiv Kumar; Michael J. Fennessy; Eric L. Altshuler; Timothy DelSole; Zhichang Guo; Benjamin A. Cash; David Straus

2013-11-01T23:59:59.000Z

376

Using Temporal Modes of Rainfall to Evaluate the Performance of a Numerical Weather Prediction Model  

Science Conference Proceedings (OSTI)

The authors demonstrate that much can be learned about the performance of a numerical weather prediction (NWP) model by examining the temporal modes of its simulated rainfall. Observations from the Weather Surveillance Radar-1988 Doppler (WSR-88D)...

Jason C. Knievel; David A. Ahijevych; Kevin W. Manning

2004-12-01T23:59:59.000Z

377

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

Science Conference Proceedings (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

378

Dynamics of Prediction Errors under the Combined Effect of Initial Condition and Model Errors  

Science Conference Proceedings (OSTI)

The transient evolution of prediction errors in the short to intermediate time regime is considered under the combined effect of initial condition and model errors. Some generic features are brought out and connected with intrinsic properties. ...

C. Nicolis; Rui A. P. Perdigao; S. Vannitsem

2009-03-01T23:59:59.000Z

379

A Mathematical Model for Predicting the Life of PEM Fuel Cell Membranes Subjected to Hydration Cycling  

E-Print Network (OSTI)

Under typical PEM fuel cell operating conditions, part of membrane electrode assembly is subjected to humidity cycling due to variation of inlet gas RH and/or flow rate. Cyclic membrane hydration/dehydration would cause cyclic swelling/shrinking of the unconstrained membrane. In a constrained membrane, it causes cyclic stress resulting in mechanical failure in the area adjacent to the gas inlet. A mathematical modeling framework for prediction of the lifetime of a PEM FC membrane subjected to hydration cycling is developed in this paper. The model predicts membrane lifetime as a function of RH cycling amplitude and membrane mechanical properties. The modeling framework consists of three model components: a fuel cell RH distribution model, a hydration/dehydration induced stress model that predicts stress distribution in the membrane, and a damage accrual model that predicts membrane life-time. Short descriptions of the model components along with overall framework are presented in the paper. The model was used for lifetime prediction of a GORE-SELECT membrane.

S. F. Burlatsky; M. Gummalla; J. O'Neill; V. V. Atrazhev; A. N. Varyukhin; D. V. Dmitriev; N. S. Erikhman

2013-06-19T23:59:59.000Z

380

Online modelling based on Genetic Programming  

Science Conference Proceedings (OSTI)

Genetic Programming (GP), a heuristic optimisation technique based on the theory of Genetic Algorithms (GAs), is a method successfully used to identify non-linear model structures by analysing a system's measured signals. Mostly, ... Keywords: GP, automatic learning, data driven model identification, fault diagnosis, genetic programming, machine learning, online modelling, real time, self-adaption

Stephan Winkler; Hajrudin Efendic; Luigi Del Re; Michael Affenzeller; Stefan Wagner

2007-06-01T23:59:59.000Z

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

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

DOE Patents (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

382

Model predictive control of a wet limestone flue gas desulfurization pilot plant  

SciTech Connect

A model predictive control (MPC) strategy based on a dynamic matrix (DMC) is designed and applied to a wet limestone flue gas desulfurization (WLFGD) pilot plant to evaluate what enhancement in control performance can be achieved with respect to a conventional decentralized feedback control strategy. The results reveal that MPC can significantly improve both reference tracking and disturbance rejection. For disturbance rejection, the main control objective in WLFGD plants, selection of tuning parameters and sample time, is of paramount importance due to the fast effect of the main disturbance (inlet SO{sub 2} load to the absorber) on the most important controlled variable (outlet flue gas SO{sub 2} concentration). The proposed MPC strategy can be easily applied to full-scale WLFGD plants.

Perales, A.L.V.; Ollero, P.; Ortiz, F.J.G.; Gomez-Barea, A. [University of Seville, Seville (Spain). Dept. of Chemical & Environmental Engineering

2009-06-15T23:59:59.000Z

383

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

NLE Websites -- All DOE Office Websites (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

384

Air Leakage of U.S. Homes: Model Prediction  

Science Conference Proceedings (OSTI)

Air tightness is an important property of building envelopes. It is a key factor in determining infiltration and related wall-performance properties such as indoor air quality, maintainability and moisture balance. Air leakage in U.S. houses consumes roughly 1/3 of the HVAC energy but provides most of the ventilation used to control IAQ. The Lawrence Berkeley National Laboratory has been gathering residential air leakage data from many sources and now has a database of more than 100,000 raw measurements. This paper uses a model developed from that database in conjunction with US Census Bureau data for estimating air leakage as a function of location throughout the US.

Sherman, Max H.; McWilliams, Jennifer A.

2007-01-01T23:59:59.000Z

385

Monitoring-based temporal prediction of power entities in smart grid cities  

Science Conference Proceedings (OSTI)

This paper develops prediction models for groundwater and wind speed in smart grid cities, where the gradual penetration of electric vehicles keeps increasing load in its power system. As a major consumption source and a renewable energy source, water ... Keywords: artificial neural network, electric vehicle, groundwater, smart grid city, wind power

Junghoon Lee; Seong Baeg Kim; Gyung-Leen Park; Chan Jung Park

2012-10-01T23:59:59.000Z

386

Artificial neural network based prediction of drill flank wear from motor current signals  

Science Conference Proceedings (OSTI)

In this work, a multilayer neural network with back propagation algorithm (BPNN) has been applied to predict the average flank wear of a high speed steel (HSS) drill bit for drilling on a mild steel work piece. Root mean square (RMS) value of the spindle ... Keywords: Artificial neural network, Current sensors, Drilling, Flank wear, Regression model

Karali Patra; Surjya K. Pal; Kingshook Bhattacharyya

2007-06-01T23:59:59.000Z

387

Based on Two Swarm Optimized Algorithm of Neural Network to Prediction the Switch's Traffic of Coal  

Science Conference Proceedings (OSTI)

Coal accurately predict multi-channel network traffic monitoring network for transmission to enhance and improve the QoS is very important, the characteristics of coalmine monitoring network, the first neural network model was constructed, followed by ... Keywords: Coal, network traffic, ant colony algorithm, Particle swarm optimization

Xiao-qiang Shao

2011-07-01T23:59:59.000Z

388

One-Month Ahead Prediction of Wind Speed and Output Power Based on EMD and LSSVM  

Science Conference Proceedings (OSTI)

Wind speed is a kind of non-stationary time series, it is difficult to construct the model for accurate forecast. The way improving accuracy of the model for predicting wind speed up to one-month ahead has been investigated using measured data recorded ... Keywords: wind speed forecasting, empirical mode decomposition(EMD), least square support vector machine (LSSVM), intrinsic mode function(IFM), wind power

Wang Xiaolan; Li Hui

2009-10-01T23:59:59.000Z

389

Agent-based modelling of social organisations  

E-Print Network (OSTI)

In the paper, the model of the society represented by a social network and the model of a multi-agent system built on the basis of this, is presented. The particular aim of the system is to predict the evolution of a society and an analysis of the communities that appear, their characteristic features and reasons for coming into being. As an example of application, an analysis was made of a social portal which makes it possible to o?er and reserve places in rooms for travelling tourists

Ko?lak, Jaros?aw

2013-01-01T23:59:59.000Z

390

Human error modeling predictions: increasing occupational safety using human performance modeling tools  

E-Print Network (OSTI)

Abstract: The use of computer-aided job analysis tools has been increasing in the recent past as a result of decreases in computational costs, augmentations in the reality of the computer-aided job analysis tools, and usefulness of the output generated from these tools. One tool set known as integrated Human Performance Modeling (HPM) is a humanout-of-the-loop (HOOTL) computational methodology used to generate predictions of complex human-automation integration and system flow patterns. These tools provide computational representations of humans incorporating physical, cognitive, perceptual, and environmental characteristics. Increasingly complex automation leads to a new class of errors and error vulnerabilities. Hollnagel’s (1993) Contextual Control Model (CoCoM) will be used as the human error theory behind a HOOTL simulation using Air Man-machine Integration Design and Analysis System (Air MIDAS) to evaluate complex humanautomation integration considerations currently underway at NASA Ames Research Center. This paper will highlight the importance of the physical and cognitive link of a specific task and will outline attempts being made to understand the factors underlying human error, a critical consideration of human-complex system performance.

Edited B. Das; Brian F. Gore; Kevin M. Corker

2001-01-01T23:59:59.000Z

391

PREDICTION OF REMAINING LIFE OF POWER TRANSFORMERS BASED ON LEFT TRUNCATED AND RIGHT  

E-Print Network (OSTI)

, for the overall fleet of transformers. 1. Introduction. 1.1. Background. Electrical transmission is an important. The prediction of the remaining life can be based on historical lifetime information about the transformer the remaining life of the healthy individual transformers in their fleet and the rate at which

392

Adaptive prediction based approach for congestion estimation (APACE) in active queue management  

Science Conference Proceedings (OSTI)

Active Queue Management (AQM) policies provide an early indication of incipient congestion to the sources. In this paper, we propose a new AQM policy called APACE. APACE stands for Adaptive Prediction based Approach for Congestion Estimation in AQM that ... Keywords: Active queue management (AQM), Congestion control, Random early detection (RED), TCP

Abhishek Jain; Abhay Karandikar; Rahul Verma

2004-10-01T23:59:59.000Z

393

A prediction method for time series based on wavelet neural networks  

Science Conference Proceedings (OSTI)

This paper introduces a prediction method for time series that is based on the multi-resolution analysis of wavelets (MRA). The MRA is better able to decompose the non-stationary time series of nonlinear systems into different components, allowing a ...

Xiaobing Gan; Ying Liu; Francis R. Austin

2005-12-01T23:59:59.000Z

394

Folksonomy link prediction based on a tripartite graph for tag recommendation  

Science Conference Proceedings (OSTI)

Nowadays social tagging has become a popular way to annotate, search, navigate and discover online resources, in turn leading to the sheer amount of user-generated metadata. This paper addresses the problem of recommending suitable tags during folksonomy ... Keywords: Folksonomy, Graph-based ranking, Link prediction, Social tagging, Tag recommendation, Tripartite graph

Majdi Rawashdeh; Heung-Nam Kim; Jihad Mohamad Alja'Am; Abdulmotaleb Saddik

2013-04-01T23:59:59.000Z

395

Dealing with world-model-based programs  

Science Conference Proceedings (OSTI)

We introduce POINTY, an interactive system for constructing world-model-based programs for robots. POINTY combines an interactive programming environment with the teaching-by-guiding methodology that has been successful in industrial robotics. Owing ...

Giuseppina C. Gini; M. L. Gini

1985-04-01T23:59:59.000Z

396

Simple predictive model for performance of desiccant beds for solar dehumidification  

DOE Green Energy (OSTI)

A computer model is outlined for the absorption/desorption process that can be used to predict the performance of desiccant beds for solar regenerated dehumidification of passively cooled buildings. Instead of solving a set of coupled differential equations, the model uses simple algebraic equations for steady-state heat and mass exchangers. A comparison of computer predictions and experimental data demonstrate the validity and accuracy of the model. The physics of the adsorption process is discussed in terms of two psychrometric process lines, and planned research efforts at SERI are described.

Barlow, R.S.

1981-08-01T23:59:59.000Z

397

A hybrid neural network and ARIMA model for water quality time series prediction  

Science Conference Proceedings (OSTI)

Accurate predictions of time series data have motivated the researchers to develop innovative models for water resources management. Time series data often contain both linear and nonlinear patterns. Therefore, neither ARIMA nor neural networks can be ... Keywords: ARIMA, Backpropagation, Hybrid model, Neural networks, Time series

Durdu Ömer Faruk

2010-06-01T23:59:59.000Z

398

Neural network prediction model for the methane fraction in biogas from field-scale landfill bioreactors  

Science Conference Proceedings (OSTI)

In this study we present a neural network model for predicting the methane fraction in landfill gas originating from field-scale landfill bioreactors. Landfill bioreactors were constructed at the Odayeri Sanitary Landfill, Istanbul, Turkey, and operated ... Keywords: Anaerobic digestion, Landfill gas, Leachate, Methane fraction, Modeling, Neural network

Bestamin Ozkaya; Ahmet Demir; M. Sinan Bilgili

2007-06-01T23:59:59.000Z

399

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

NLE Websites -- All DOE Office Websites (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.

400

Quantifying the Predictive Skill in Long-Range Forecasting. Part II: Model Error in Coarse-Grained Markov Models with Application to Ocean-Circulation Regimes  

Science Conference Proceedings (OSTI)

An information-theoretic framework is developed to assess the predictive skill and model error in imperfect climate models for long-range forecasting. Here, of key importance is a climate equilibrium consistency test for detecting false predictive ...

Dimitrios Giannakis; Andrew J. Majda

2012-03-01T23:59:59.000Z

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

A physically-based night sky model  

Science Conference Proceedings (OSTI)

This paper presents a physically-based model of the night sky for realistic image synthesis. We model both the direct appearance of the night sky and the illumination coming from the Moon, the stars, the zodiacal light, and the atmosphere. To accurately ...

Henrik Wann Jensen; Frédo Durand; Julie Dorsey; Michael M. Stark; Peter Shirley; Simon Premože

2001-08-01T23:59:59.000Z

402

Load Modeling Using a Measurement Based Approach  

Science Conference Proceedings (OSTI)

This report summarizes the work performed in the second phase of a multi-year collaborative load modeling research program that was initiated in 2004. The measurement based approach described in this report will help utilities to develop representative load models using suitable measurement data.

2007-12-17T23:59:59.000Z

403

Impact of Nighttime Shut Down on the Prediction Accuracy of Monthly Regression Models for Energy Consumption in Commercial Buildings  

E-Print Network (OSTI)

Regression models of measured energy use in buildings are widely used as baseline models to determine retrofit savings from measured energy consumption. It is less expensive to determine savings from monthly utility bills when they are available than to install hourly metering equipment. However, little is known about the impact of nighttime shut off on the accuracy of savings determined from monthly data. This paper reports a preliminary investigation of this question by comparing the heating and cooling energy use predicted by regression models based on monthly data against the predictions of calibrated hourly simulation models when applied to a medium-sized university building in Texas with (i) DDCAV system operating 24 hours per day, (ii) DDCAV system with nighttime shut down, (iii) DDVAV system operating 24 hours per day, and (iv) DDVAV system with nighttime shut down. The results of the four cases studied indicate : 1) when the AHUs are operated 24 hours/day, the annual prediction error of the cooling regression models is less than 0.5% of the annual cooling energy consumption; however, 2) when the AHUs are operated with nighttime shut down, the annual prediction error of the cooling models becomes as high as 6% of annual energy consumption. It should be noted that the cases considered here include only single end-uses of energy and have not investigated energy-use data which includes multiple end-uses. Modified regression models are therefore recommended when AHUs are not operated 24 hours per day and the temperature pattern is significantly different between pre and post retrofit years.

Wang, J.; Claridge, D. E.

1998-01-01T23:59:59.000Z

404

Regional Weather Prediction with a Model Combining Terrain-following and Isentropic Coordinates. Part I: Model Description  

Science Conference Proceedings (OSTI)

A short-range numerical prediction model, which is part of a real-time 3-h data assimilation and forecast system, is described. The distinguishing feature of the model is the use of terrain-following (?) coordinate surfaces in the lower ...

Rainer Bleck; Stanley G. Benjamin

1993-06-01T23:59:59.000Z

405

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. Prediction of regional precipitation is important for a multitude of reasons. 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 surface air temperature (GSAT) increasing over the last several decades. 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 GSAT is accommodated using special runs of a global climate model to build an initial set of predictive models, while ground data is used to train, combine, and calibrate them. In examples I show seasonal prediction of precipitation in a few geographical regions with high statistical significance. In one region were precipitation rose near the 4 sigma level above the mean, the correlation was above 0.8. Also, I examine one region over longer time and tentatively identifying a tight coupling between GSAT and patterns of climate anomalies, with implications for attribution. This demonstration of invertability of the climate patterns to identify parameters of the climate system holds promise for allowing statistical evaluation of parameterizations of climate models. I expect these methods may be applicable both to a number of other measures of climate and weather as well as other high dimensional chaotic systems.

M. LuValle

2013-10-09T23:59:59.000Z

406

An analytical model for predicting the remaining battery capacity of lithium-ion batteries  

E-Print Network (OSTI)

Abstract—Predicting the residual energy of the battery source that powers a portable electronic device is imperative in designing and applying an effective dynamic power management policy for the device. This paper starts up by showing that a 30 % error in predicting the battery capacity of a lithium-ion battery can result in up to 20 % performance degradation for a dynamic voltage and frequency scaling algorithm. Next, this paper presents a closed form analytical expression for predicting the remaining capacity of a lithium-ion battery. The proposed high-level model, which relies on online current and voltage measurements, correctly accounts for the temperature and cycle aging effects. The accuracy of the highlevel model is validated by comparing it with DUALFOIL simulation results, demonstrating a maximum of 5 % error between simulated and predicted data. Index Terms—Accelerated rate capacity, cycle aging and dynamic voltage scaling, remaining battery capacity, temperature. I.

Peng Rong; Student Member; Massoud Pedram

2003-01-01T23:59:59.000Z

407

Predictive models for emission of hydrogen powered car using various artificial intelligent tools  

Science Conference Proceedings (OSTI)

This paper investigates the use of artificial intelligent models as virtual sensors to predict relevant emissions such as carbon dioxide, carbon monoxide, unburnt hydrocarbons and oxides of nitrogen for a hydrogen powered car. The virtual sensors are ... Keywords: Adaptive neuro-fuzzy inference systems, Artificial intelligent techniques, Back-propagation neural networks with Levenberg–Marquardt algorithm, Hydrogen emission prediction, Hydrogen powered car, UTAS artificial neural networks

Vishy Karri; Tien Nhut Ho

2009-06-01T23:59:59.000Z

408

Agent-Based vs. Equation-based Epidemiological Models:A Model Selection Case Study  

SciTech Connect

This paper is motivated by the need to design model validation strategies for epidemiological disease-spread models. We consider both agent-based and equation-based models of pandemic disease spread and study the nuances and complexities one has to consider from the perspective of model validation. For this purpose, we instantiate an equation based model and an agent based model of the 1918 Spanish flu and we leverage data published in the literature for our case- study. We present our observations from the perspective of each implementation and discuss the application of model-selection criteria to compare the risk in choosing one modeling paradigm to another. We conclude with a discussion of our experience and document future ideas for a model validation framework.

Sukumar, Sreenivas R [ORNL; Nutaro, James J [ORNL

2012-01-01T23:59:59.000Z

409

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

410

Agent-Based Modeling of Intracellular Transport  

E-Print Network (OSTI)

We develop an agent-based model of the motion and pattern formation of vesicles. These intracellular particles can be found in four different modes of (undirected and directed) motion and can fuse with other vesicles. While the size of vesicles follows a log-normal distribution that changes over time due to fusion processes, their spatial distribution gives rise to distinct patterns. Their occurrence depends on the concentration of proteins which are synthesized based on the transcriptional activities of some genes. Hence, differences in these spatio-temporal vesicle patterns allow indirect conclusions about the (unknown) impact of these genes. By means of agent-based computer simulations we are able to reproduce such patterns on real temporal and spatial scales. Our modeling approach is based on Brownian agents with an internal degree of freedom, $\\theta$, that represents the different modes of motion. Conditions inside the cell are modeled by an effective potential that differs for agents dependent on their...

Birbaumer, Mirko; 10.1140/epjb/e2011-20283-x

2011-01-01T23:59:59.000Z

411

A multi-component partitioning model to predict organic leaching from stabilized/solidified oily wastes  

E-Print Network (OSTI)

Stabilization/Solidification (S/S) is an established remediation process in hazardous waste management. Recently this process has been applied to hazardous organic wastes with mixed results. These results have prompted further studies to examine the effectiveness of this process in containing organic contaminants. The primary goal of S/S is to contain the contaminants in a solidified form, removing them from the environment. This is accomplished by decreasing the contaminant surface area and chemically converting the waste by reducing the contaminant solubility. The most common S/S processes utilize the chemical reactions achieved in cement-based and pozzolanic mixes. The effectiveness of this process is determined by the degree to which contaminants will leach from the waste end-product. Leach models, therefore, are an effective way to predict the leaching of contaminants and to describe the immobilization and binding mechanisms that take place. The multi-component nature of oily wastes requires that a multi-component approach be taken to describe the partitioning between the aqueous and non-aqueous phases. The heterogeneous nature of these wastes precludes analysis of partitioning of all chemical species. Thus a pseudo-component model has been developed that describes the partitioning of TOC as caused by the partitioning of a small number of pseudo-components. A pseudo-component is used to represent a group of chemical species that have similar tendencies to partition between the aqueous and non-aqueous phases. A linear partitioning relationship is used to develop the partitioning model, with the values of the partitioning coefficients chosen to represent strongly sorbed, moderately sorbed, and weakly sorbed components. The partitioning characteristics of the waste were determined in a series of sequential experiments in which different amounts of water were added. After each addition, the system was allowed to equilibrate, the added water removed by centrifugation and its TOC measured. The model predicts that the measured concentrations of TOC are due to the sum of all pseudo-components in the aqueous or mobile phase.

O'Cleirigh, Declan Ronan

1997-01-01T23:59:59.000Z

412

Intelligent predictive control of micro heat exchanger  

Science Conference Proceedings (OSTI)

An intelligent predictive control to temperature control of a micro heat exchanger is addressed. First, the dynamics of the micro heat exchanger is identified using a locally linear model. Then, the predictive control strategy based on this model of ...

Mehdi Galily; Farzad Habibipour Roudsari; Masoum Fardis; Ali Yazdian

2005-06-01T23:59:59.000Z

413

Predictions of monthly energy consumption and annual patterns of energy usage for convenience stores by using multiple and nonlinear regression models  

E-Print Network (OSTI)

Thirty convenience stores in College Station, Texas, have been selected as the samples for an energy consumption prediction. The predicted models assist facility energy managers for making decisions of energy demand/supply plans. The models are applied to historical data for two years: 2001 and 2002. The approaches are (1) to analyze nonlinear regression models for long term forecasting of annual patterns compared with outdoor temperature, and (2) to analyze multiple regression models for the building type regardless of outdoor temperature. In the first approach, twenty four buildings are categorized as base load group and no base group. Average temperature, cooling efficiencies, and cooling knot temperature are estimated by nonlinear regression models: segment and parabola models. The adjusted r-square results in good performance up to ninety percent accuracy. In the second approach, the other selected six buildings are categorized as no trend group. This group does not respond to outdoor temperature. As the result, multiple a regression model is formed by combination of variables from the nonlinear models and physical building variables of cooling efficiency, cooling temperature, light bulbs, area, outdoor temperature, and orientation of fronts. This model explains up to sixty percent of all convenience stores' data. In conclusion, the accuracy of prediction models is measured by the adjusted r-square results. Among these three models, the multiple regression model shows the highest adjusted r-square (0.597) over the parabola (0.5419) and segment models (0.4806). When the three models come to the application, the multiple regression model is best fit for no trend data type. However, when it is used to predict the energy consumption with the buildings that relate to outdoor temperature, segment and parabola model provide a better prediction result.

Muendej, Krisanee

2004-08-01T23:59:59.000Z

414

Field-based tests of geochemical modeling codes using New Zealand hydrothermal systems  

DOE Green Energy (OSTI)

Hydrothermal systems in the Taupo Volcanic Zone, North Island, New Zealand are being used as field-based modeling exercises for the EQ3/6 geochemical modeling code package. Comparisons of the observed state and evolution of the hydrothermal systems with predictions of fluid-solid equilibria made using geochemical modeling codes will determine how the codes can be used to predict the chemical and mineralogical response of the environment to nuclear waste emplacement. Field-based exercises allow us to test the models on time scales unattainable in the laboratory. Preliminary predictions of mineral assemblages in equilibrium with fluids sampled from wells in the Wairakei and Kawerau geothermal field suggest that affinity-temperature diagrams must be used in conjunction with EQ6 to minimize the effect of uncertainties in thermodynamic and kinetic data on code predictions.

Bruton, C.J.; Glassley, W.E.; Bourcier, W.L.

1994-06-01T23:59:59.000Z

415

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

416

Quantitative Vapor-phase IR Intensities and DFT Computations to Predict Absolute IR Spectra based on Molecular Structure: I. Alkanes  

SciTech Connect

Recently recorded quantitative IR spectra of a variety of gas-phase alkanes are shown to have integrated intensities in both the C-H stretching and C-H bending regions that depend linearly on the molecular size, i.e. the number of C-H bonds. This result is well predicted from CH4 to C15H32 by DFT computations of IR spectra at the B3LYP/6-31+G(d,p) level of DFT theory. A simple model predicting the absolute IR band intensities of alkanes based only on structural formula is proposed: For the C-H stretching band near 2930 cm-1 this is given by (in km/mol): CH¬_str = (34±3)*CH – (41±60) where CH is number of C-H bonds in the alkane. The linearity is explained in terms of coordinated motion of methylene groups rather than the summed intensities of autonomous -CH2- units. The effect of alkyl chain length on the intensity of a C-H bending mode is explored and interpreted in terms of conformer distribution. The relative intensity contribution of a methyl mode compared to the total C-H stretch intensity is shown to be linear in the number of terminal methyl groups in the alkane, and can be used to predict quantitative spectra a priori based on structure alone.

Williams, Stephen D.; Johnson, Timothy J.; Sharpe, Steven W.; Yavelak, Veronica; Oats, R. P.; Brauer, Carolyn S.

2013-11-13T23:59:59.000Z

417

The Golden Ratio Prediction for the Solar Angle from a Natural Model with A5 Flavour Symmetry  

E-Print Network (OSTI)

We formulate a consistent model predicting, in the leading order approximation, maximal atmospheric mixing angle, vanishing reactor angle and tan {\\theta}_12 = 1/{\\phi} where {\\phi} is the Golden Ratio. The model is based on the flavour symmetry A5 \\tiems Z5 \\times Z3, spontaneously broken by a set of flavon fields. By minimizing the scalar potential of the theory up to the next-to-leading order in the symmetry breaking parameter, we demonstrate that this mixing pattern is naturally achieved in a finite portion of the parameter space, through the vacuum alignment of the flavon fields. The leading order approximation is stable against higher-order corrections. We also compare our construction to other models based on discrete symmetry groups.

Feruglio, Ferruccio

2011-01-01T23:59:59.000Z

418

The Golden Ratio Prediction for the Solar Angle from a Natural Model with A5 Flavour Symmetry  

E-Print Network (OSTI)

We formulate a consistent model predicting, in the leading order approximation, maximal atmospheric mixing angle, vanishing reactor angle and tan {\\theta}_12 = 1/{\\phi} where {\\phi} is the Golden Ratio. The model is based on the flavour symmetry A5 \\times Z5 \\times Z3, spontaneously broken by a set of flavon fields. By minimizing the scalar potential of the theory up to the next-to-leading order in the symmetry breaking parameter, we demonstrate that this mixing pattern is naturally achieved in a finite portion of the parameter space, through the vacuum alignment of the flavon fields. The leading order approximation is stable against higher-order corrections. We also compare our construction to other models based on discrete symmetry groups.

Ferruccio Feruglio; Alessio Paris

2011-01-02T23:59:59.000Z

419

A Predictive Model of Fragmentation using Adaptive Mesh Refinement and a Hierarchical Material Model  

Science Conference Proceedings (OSTI)

Fragmentation is a fundamental material process that naturally spans spatial scales from microscopic to macroscopic. We developed a mathematical framework using an innovative combination of hierarchical material modeling (HMM) and adaptive mesh refinement (AMR) to connect the continuum to microstructural regimes. This framework has been implemented in a new multi-physics, multi-scale, 3D simulation code, NIF ALE-AMR. New multi-material volume fraction and interface reconstruction algorithms were developed for this new code, which is leading the world effort in hydrodynamic simulations that combine AMR with ALE (Arbitrary Lagrangian-Eulerian) techniques. The interface reconstruction algorithm is also used to produce fragments following material failure. In general, the material strength and failure models have history vector components that must be advected along with other properties of the mesh during remap stage of the ALE hydrodynamics. The fragmentation models are validated against an electromagnetically driven expanding ring experiment and dedicated laser-based fragmentation experiments conducted at the Jupiter Laser Facility. As part of the exit plan, the NIF ALE-AMR code was applied to a number of fragmentation problems of interest to the National Ignition Facility (NIF). One example shows the added benefit of multi-material ALE-AMR that relaxes the requirement that material boundaries must be along mesh boundaries.

Koniges, A E; Masters, N D; Fisher, A C; Anderson, R W; Eder, D C; Benson, D; Kaiser, T B; Gunney, B T; Wang, P; Maddox, B R; Hansen, J F; Kalantar, D H; Dixit, P; Jarmakani, H; Meyers, M A

2009-03-03T23:59:59.000Z

420

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

SciTech Connect

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

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

A Nonlinear Model Based (NMPC) Control Strategy for the ...  

Science Conference Proceedings (OSTI)

New advanced process control systems imply utilizing state of the art process control systems as e.g. Nonlinear Model Predictive Control (NMPC). Although the

422

Meteorological Patterns Associated with Maximum 3-Hour Average Concentrations Predicted by the CRSTER Model for a Tall Stack Source  

Science Conference Proceedings (OSTI)

Regional meteorological patterns associated with maximum 3-hour average concentrations predicted by the U.S. EPA CRSTER model for emissions from a tall stack were examined for a limited sample. Causes of predicted peaks were the movements of weak ...

Paul N. Derezotes

1984-11-01T23:59:59.000Z

423

Warehousing and inventory management: integrating simulation modeling and equipment condition diagnostics for predictive maintenance strategies -a case study  

Science Conference Proceedings (OSTI)

This paper presents results from a case study in predictive maintenance at a distribution warehouse. A simulation model was built with ARENATM 5.0 for integrating predictive maintenance strategies with production planning strategies, ...

Luis Rene Contreras; Chirag Modi; Arunkumar Pennathur

2002-12-01T23:59:59.000Z

424

Predictability of SST in an Idealized, One-Dimensional, Coupled Atmosphere–Ocean Climate Model with Stochastic Forcing and Advection  

Science Conference Proceedings (OSTI)

The predictability of sea surface temperature (SST) is examined through analysis of an idealized, one-dimensional, stochastically forced climate model. The influence on SST predictability of including advection by a constant mean current is ...

Robert B. Scott

2003-01-01T23:59:59.000Z

425

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

Science Conference Proceedings (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

426

SOPHIA: a Modeling Language for Model-Based Safety Engineering  

E-Print Network (OSTI)

Development of increasingly more sophisticated safety-critical embedded systems requires new paradigms, since manual approaches are reaching their limits. Experiences have shown that model-driven engineering is an approach that can overcome many of these limitations. Using model-based approaches however lead to new challenges regarding the cohesive integration of both safety engineering and system design along the system development process. In this paper, we present SOPHIA, a modelling language that formalizes safety-related concepts and their relations with system modelling constructs. We particularly focus on accident models and on how to achieve confidence that the frequency of possible accidents will be tolerable. In addition, we explore some strategies to implement SOPHIA as a complementary modelling language to SysML and reuse some useful constructs form the UML MARTE profile.

Daniela Cancila; Francois Terrier; Fabien Belmonte; Hubert Dubois; Huascar Espinoza; Sébastien Gérard; Arnaud Cuccuru

2009-01-01T23:59:59.000Z

427

Dynamic Modeling of Aerobic Growth of Shewanella oneidensis. Predicting Triauxic Growth, Flux Distributions and Energy Requirement for Growth  

SciTech Connect

A model-based analysis is conducted to investigate metabolism of Shewanella oneidensis MR-1 strain in aerobic batch culture, which exhibits an intriguing growth pattern by sequentially consuming substrate (i.e., lactate) and by-products (i.e., pyruvate and acetate). A general protocol is presented for developing a detailed network-based dynamic model for S. oneidensis based on the Lumped Hybrid Cybernetic Model (LHCM) framework. The L-HCM, although developed from only limited data, is shown to accurately reproduce exacting dynamic metabolic shifts, and provide reasonable estimates of energy requirement for growth. Flux distributions in S. oneidensis predicted by the L-HCM compare very favorably with 13C-metabolic flux analysis results reported in the literature. Predictive accuracy is enhanced by incorporating measurements of only a few intracellular fluxes, in addition to extracellular metabolites. The L-HCM developed here for S. oneidensis is consequently a promising tool for the analysis of intracellular flux distribution and metabolic engineering.

Song, Hyun-Seob; Ramkrishna, Doraiswami; Pinchuk, Grigoriy E.; Beliaev, Alex S.; Konopka, Allan; Fredrickson, Jim K.

2013-01-01T23:59:59.000Z

428

An Empirical Model for Predicting the Decay of Tropical Cyclone Wind Speed after Landfall over the Indian Region  

Science Conference Proceedings (OSTI)

An empirical model for predicting the maximum surface wind speed associated with a tropical cyclone after crossing the east coast of India is described. The model parameters are determined from the database of 19 recent cyclones. The model is ...

S. K. Roy Bhowmik; S. D. Kotal; S. R. Kalsi

2005-01-01T23:59:59.000Z

429

Decadal Predictability of the Atlantic Ocean in a Coupled GCM: Forecast Skill and Optimal Perturbations Using Linear Inverse Modeling  

Science Conference Proceedings (OSTI)

The decadal predictability of three-dimensional Atlantic Ocean anomalies is examined in a coupled global climate model [the third climate configuration of the Met Office Unified Model (HadCM3)] using a linear inverse modeling (LIM) approach. It ...

Ed Hawkins; Rowan Sutton

2009-07-01T23:59:59.000Z

430

Accurate Electrical Battery Model Capable of Predicting Runtime and I-V Performance  

E-Print Network (OSTI)

Abstract—Low power dissipation and maximum battery runtime are crucial in portable electronics. With accurate and efficient circuit and battery models in hand, circuit designers can predict and optimize battery runtime and circuit performance. In this paper, an accurate, intuitive, and comprehensive electrical battery model is proposed and implemented in a Cadence environment. This model accounts for all dynamic characteristics of the battery, from nonlinear open-circuit voltage, current-, temperature-, cycle number-, and storage time-dependent capacity to transient response. A simplified model neglecting the effects of self-discharge, cycle number, and temperature, which are nonconsequential in low-power Li-ion-supplied applications, is validated with experimental data on NiMH and polymer Li-ion batteries. Less than 0.4 % runtime error and 30-mV maximum error voltage show that the proposed model predicts both the battery runtime and I–V performance accurately. The model can also be easily extended to other battery and power sourcing technologies. Index Terms—Batteries, cadence simulation, electrical model, I–V performance, nickel-metal hydride battery, polymer lithiumion battery, runtime prediction, test system. I.

Min Chen; Student Member; Gabriel A. Rincón-mora; Senior Member

2006-01-01T23:59:59.000Z

431

Improving the Fanger model's thermal comfort predictions for naturally ventilated spaces  

E-Print Network (OSTI)

The Fanger model is the official thermal comfort model in U.S. and international standards and is based on the heat balance of the human body with the environment. This investigation focuses on re-specifying the parameters ...

Truong, Phan Hue

2010-01-01T23:59:59.000Z

432

Three-dimensional Fast Flux Test Facility plenum model turbulent flow prediction and data comparison  

Science Conference Proceedings (OSTI)

Two- and three-dimensional numerical simulations of turbulent flow in a scaled Fast Flux Test Facility (FFTF) upper plenum model were performed using the TEMPEST hydrothermal code. A standard k-element of model was used to describe turbulence through an effective viscosity. Comparisons with previously reported mean velocity and turbulence field data measured in the plenum model and two-dimensional numerical simulations using the TEACH code were made. Predicted horizontal and vertical mean velocities and turbulent kinetic energy are shown to be in good agreement with available experimental data when inlet conditions of the dissipation of turbulent kinetic energy are appropriately prescribed. The three-dimensional quarter-symmetry simulation predicts the turbulent kinetic energy field significantly better than the two-dimensional centerplane simulations. These results lead to conclusions concerning deficiencies in the experimental data and the turbulence model.

Eyler, L.L.; Sawdye, R.W.

1981-01-01T23:59:59.000Z

433

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

NLE Websites -- All DOE Office Websites (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

434

Glass viscosity calculation based on a global statistical modelling approach  

SciTech Connect

A global statistical glass viscosity model was developed for predicting the complete viscosity curve, based on more than 2200 composition-property data of silicate glasses from the scientific literature, including soda-lime-silica container and float glasses, TV panel glasses, borosilicate fiber wool and E type glasses, low expansion borosilicate glasses, glasses for nuclear waste vitrification, lead crystal glasses, binary alkali silicates, and various further compositions from over half a century. It is shown that within a measurement series from a specific laboratory the reported viscosity values are often over-estimated at higher temperatures due to alkali and boron oxide evaporation during the measurement and glass preparation, including data by Lakatos et al. (1972) and the recently published High temperature glass melt property database for process modeling by Seward et al. (2005). Similarly, in the glass transition range many experimental data of borosilicate glasses are reported too high due to phase separation effects. The developed global model corrects those errors. The model standard error was 9-17°C, with R^2 = 0.985-0.989. The prediction 95% confidence interval for glass in mass production largely depends on the glass composition of interest, the composition uncertainty, and the viscosity level. New insights in the mixed-alkali effect are provided.

Fluegel, Alex

2007-02-01T23:59:59.000Z

435

Postprocessing Model-Predicted Rainfall Fields in the Spectral Domain Using Phase Information from Radar Observations  

Science Conference Proceedings (OSTI)

In an attempt to combine the short-term skill of radar nowcasting and the long-term skill of numerical models, successive 15-min rainfall accumulations obtained from the U.S. national radar composites and predicted by the Weather Research and ...

Basivi Radhakrishna; Isztar Zawadzki; Frédéric Fabry

2013-04-01T23:59:59.000Z

436

Development of a linear predictive model for carbon dioxide sequestration in deep saline carbonate aquifers  

Science Conference Proceedings (OSTI)

CO"2 injection into deep saline aquifers is a preferred method for mitigating CO"2 emission. Although deep saline aquifers are found in many sedimentary basins and provide very large storage capacities, several numerical simulations are needed before ... Keywords: CO2 sequestration, Deep saline carbonate aquifer, Latin hypercube space filling design, Predictive model

Sultan Anbar; Serhat Akin

2011-11-01T23:59:59.000Z

437

Technical Report -DTU -Informatics and Mathematical Modeling (May 31, 2007) Temperature Prediction in District  

E-Print Network (OSTI)

Prediction in District Heating Systems with cFIR models Pierre Pinson , Torben S. Nielsen, Henrik Aa. Nielsen, Lyngby, Denmark Abstract Current methodologies for the optimal operation of district heating systems regularization. Results are given for the test case of the Roskilde district heating system, over a period

438

Prediction of spontaneous heating susceptibility of Indian coals using fuzzy logic and artificial neural network models  

Science Conference Proceedings (OSTI)

Coal mine fires due to spontaneous heating are a major concern worldwide. Most of these fires could be averted if suitable preventive measures are taken. Since the spontaneous heating potential of all types of coals are not the same, its accurate prediction ... Keywords: Artificial neural network, Coal, Crossing point temperature, Fuzzy expert system, Spontaneous heating, Sugeno model

H. B. Sahu; S. Padhee; S. S. Mahapatra

2011-03-01T23:59:59.000Z

439

Accurate Modeling and Prediction of Energy Availability in Energy Harvesting Real-Time Embedded Systems  

E-Print Network (OSTI)

on the type of energy harvesting technology, ECM1 can be either DC/DC or AC/DC converter. ECM2 is usually a DC/DCAccurate Modeling and Prediction of Energy Availability in Energy Harvesting Real-Time Embedded}@binghamton.edu Abstract -- Energy availability is the primary subject that drives the research innovations in energy

Qiu, Qinru

440

A logit model for predicting wetland location using ASTER and GIS  

Science Conference Proceedings (OSTI)

Data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were used to develop a logistic regression model to predict the location of wetlands in the Coastal Plain of Virginia. We used the first five bands from two ASTER scenes ...

E. Pantaleoni; R. H. Wynne; J. M. Galbraith; J. B. Campbell

2009-01-01T23:59:59.000Z

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.


441

Formant tracking linear prediction model using HMMs and Kalman filters for noisy speech processing  

Science Conference Proceedings (OSTI)

This paper presents a formant tracking linear prediction (LP) model for speech processing in noise. The main focus of this work is on the utilization of the correlation of the energy contours of speech, along the formant tracks, for improved formant ...

Qin Yan; Saeed Vaseghi; Esfandiar Zavarehei; Ben Milner; Jonathan Darch; Paul White; Ioannis Andrianakis

2007-07-01T23:59:59.000Z

442

Enhancing product performance in machining processes: statistical analysis and development of predictive models  

Science Conference Proceedings (OSTI)

Process parameters, tool geometry and operating conditions considerably influence the quality and the functional performance, including the service-life, of machined components. Surface characteristics of the machined products such as hardness and roughness ... Keywords: lubrication systems, predictive models, statistical analysis, sustainable machining

G. Rotella, S. Rizzuti, D. Umbrello

2013-07-01T23:59:59.000Z

443

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

444

LINSOL: a model for predicting the optical performance of parabolic trough solar thermal systems  

DOE Green Energy (OSTI)

A detailed model has been developed to predict the optical performance of parabolic trough solar energy systems. The model is one to two orders of magnitude faster than previous, less complete calculations and makes tractable investigation of a wide range of design and application alternatives for trough systems. Representative results are presented that show the dependence of the trough optical performance on field orientation and site latitude.

Dellin, T.A.

1981-01-01T23:59:59.000Z

445

Application of RBF Network Based on Immune Algorithm to Predicting of Wastewater Treatment  

Science Conference Proceedings (OSTI)

Wastewater treatment is a nonlinear, time-varing and time- delay process. It is difficult to establish exact mathematic model. A novel radial basis function (RBF) neural network model based on immune algorithm (IA) is presented in this paper. It combines ...

Hongtao Ye; Fei Luo; Yuge Xu

2009-05-01T23:59:59.000Z

446

Depositional sequence analysis and sedimentologic modeling for improved prediction of Pennsylvanian reservoirs  

SciTech Connect

The objectives of this research are to: (1) assist producers in locating and producing petroleum not currently produced because of technological problems or the inability to identify details of reservoir compartmentalization, (2) to decrease risk in field development, and (3) accelerate the retrieval and analysis of baseline geoscience information for initial reservoir description. The interdisciplinary data sought in this research will be used to resolve specific problems in correlation of strata and to establish the mechanisms responsible for the Upper Pennsylvanian stratigraphic architecture in the Midcontinent. The data will better constrain ancillary problems related to the validation of depositional sequence and subsequence correlation, subsidence patterns, sedimentation rates, sea-level changes, and the relationship of sedimentary sequences to basement terrains. The geoscientific information, including data from field studies, surface and near-surface reservoir analogues, and regional database development, will also be used for development of geologic computer process-based simulation models tailored to specific depositional sequences for use in improving prediction of reservoir characteristics. 4 refs.

Watney, W.L.

1991-01-01T23:59:59.000Z

447

Physically based modeling and animation of fire  

Science Conference Proceedings (OSTI)

We present a physically based method for modeling and animating fire. Our method is suitable for both smooth (laminar) and turbulent flames, and it can be used to animate the burning of either solid or gas fuels. We use the incompressible Navier-Stokes ... Keywords: blackbody radiation, chemical reaction, fire, flames, implicit surface, incompressible flow, smoke, stable fluids, vorticity confinement

Duc Quang Nguyen; Ronald Fedkiw; Henrik Wann Jensen

2002-07-01T23:59:59.000Z

448

A coherence model based on syntactic patterns  

Science Conference Proceedings (OSTI)

We introduce a model of coherence which captures the intentional discourse structure in text. Our work is based on the hypothesis that syntax provides a proxy for the communicative goal of a sentence and therefore the sequence of sentences in a coherent ...

Annie Louis; Ani Nenkova

2012-07-01T23:59:59.000Z

449

Modelling Office Energy Consumption: An Agent Based  

E-Print Network (OSTI)

Modelling Office Energy Consumption: An Agent Based Approach Tao Zhang, Peer-Olaf Siebers, Uwe · Overall Project Background · Office Energy Consumption · Case Study · Simulation Experiments · Conclusions #12;Overall Project Background · EPSRC funded City Energy Future Project ­ Under Energy & Complexity

Aickelin, Uwe

450

Atoms-to-Grains Corrosion Modeling for Predictive Design of Mg ...  

Science Conference Proceedings (OSTI)

... will be presented to demonstrate the power of first-principle theories in predicting ... High-Capacity Hydrogen-Based Green-Energy Storage Solutions for the Grid Balancing ... K-22: Insights into the Nucleation of Extension Twins in Mg Alloys.

451

A Hierarchy of Data-Based ENSO Models  

Science Conference Proceedings (OSTI)

Global sea surface temperature (SST) evolution is analyzed by constructing predictive models that best describe the dataset’s statistics. These inverse models assume that the system’s variability is driven by spatially coherent, additive noise ...

D. Kondrashov; S. Kravtsov; A. W. Robertson; M. Ghil

2005-11-01T23:59:59.000Z

452

Supersonic combustion studies using a multivariate quadrature based method for combustion modeling  

E-Print Network (OSTI)

Supersonic combustion studies using a multivariate quadrature based method for combustion modeling function (PDF) of thermochemical variables can be used for accurately computing the combustion source term of predictive models for supersonic combustion is a critical step in design and development of scramjet engines

Raman, Venkat

453

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

SciTech Connect

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

454

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

NLE Websites -- All DOE Office Websites (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

455

Evaluating the applicability of current models of workload to peer-based human-robot teams  

Science Conference Proceedings (OSTI)

Human-Robot peer-based teams are evolving from a far-off possibility into a reality. Human Performance Moderator Functions (HPMFs) can be used to predict human behavior by incorporating the effects of internal and external influences such as fatigue ... Keywords: human performance modeling, human-robot peer-based teams

Caroline E. Harriott; Tao Zhang; Julie A. Adams

2011-03-01T23:59:59.000Z

456

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

457

PREDICTIVE GEOSPATIAL MODELING FOR ARCHAEOLOGICAL RESEARCH AND CONSERVATION: CASE STUDIES FROM THE GALISTEO BASIN, VERMONT AND CHACO CANYON.  

E-Print Network (OSTI)

??Geospatial modeling of ancient landscapes for predictive scientific research and hypothesis testing is an important emerging approach in contemporary archaeology. This doctoral dissertation is comprised… (more)

Dorshow, Wetherbee Bryan

2012-01-01T23:59:59.000Z

458

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

459

Handbook for personal computer versions enhanced oil recovery predictive models: Supporting technology for enhanced oil recovery  

SciTech Connect

The personal computer (PC) programs described in this handbook were adapted from the Tertiary Oil Recovery Information System (TORIS) enhanced oil recovery (EOR) predictive models. The models, both those developed for the Department of Energy and those developed for the National Petroleum Council (NPC), were designed by Scientific Software-Intercomp and were used in the 1984 NPC study on the national potential for enhanced oil recovery. The Department of Energy, Bartlesville Project Office, supported the NPC study and has maintained the models since the study was completed. 10 refs.

Allison, E.; Waldrop, R.; Ray, R.M.

1988-02-01T23:59:59.000Z

460

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

Science Conference Proceedings (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

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

Interim Models Developed to Predict Key Hanford Waste Glass Properties Using Composition  

Science Conference Proceedings (OSTI)

Over the past several years the amount of waste glass property data available in the open literature has increased markedly. We have compiled the data from over 2000 glass compositions, evaluated the data for consistency, and fit glass property models to portions of this database.[1] The properties modeled include normalized releases of boron (rB), sodium (rNa), and lithium (rLi) from glass exposed to the product consistency test (PCT), liquidus temperature (TL) of glasses in the spinel and zircon primary phase field, viscosity (?) at 1150°C (?1150) and as a function of temperature (?T), and molar volume (V). These models were compared to some of the previously available models and were found to predict the properties of glasses not used in model fitting better and covered broader glass composition regions than the previous ones. This paper summarizes the data collected and the models that resulted from this effort.

Vienna, John D.; Kim, Dong-Sang; Hrma, Pavel R.

2003-08-08T23:59:59.000Z

462

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

Science Conference Proceedings (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

463

Development of a Multicomponent Prediction Model for Acute Esophagitis in Lung Cancer Patients Receiving Chemoradiotherapy  

SciTech Connect

Purpose: To construct a model for the prediction of acute esophagitis in lung cancer patients receiving chemoradiotherapy by combining clinical data, treatment parameters, and genotyping profile. Patients and Methods: Data were available for 273 lung cancer patients treated with curative chemoradiotherapy. Clinical data included gender, age, World Health Organization performance score, nicotine use, diabetes, chronic disease, tumor type, tumor stage, lymph node stage, tumor location, and medical center. Treatment parameters included chemotherapy, surgery, radiotherapy technique, tumor dose, mean fractionation size, mean and maximal esophageal dose, and overall treatment time. A total of 332 genetic polymorphisms were considered in 112 candidate genes. The predicting model was achieved by lasso logistic regression for predictor selection, followed by classic logistic regression for unbiased estimation of the coefficients. Performance of the model was expressed as the area under the curve of the receiver operating characteristic and as the false-negative rate in the optimal point on the receiver operating characteristic curve. Results: A total of 110 patients (40%) developed acute esophagitis Grade {>=}2 (Common Terminology Criteria for Adverse Events v3.0). The final model contained chemotherapy treatment, lymph node stage, mean esophageal dose, gender, overall treatment time, radiotherapy technique, rs2302535 (EGFR), rs16930129 (ENG), rs1131877 (TRAF3), and rs2230528 (ITGB2). The area under the curve was 0.87, and the false-negative rate was 16%. Conclusion: Prediction of acute esophagitis can be improved by combining clinical, treatment, and genetic factors. A multicomponent prediction model for acute esophagitis with a sensitivity of 84% was constructed with two clinical parameters, four treatment parameters, and four genetic polymorphisms.

De Ruyck, Kim, E-mail: kim.deruyck@UGent.be [Department of Basic Medical Sciences, Ghent University, Ghent (Belgium); Sabbe, Nick [Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Ghent (Belgium); Oberije, Cary [Department of Radiation Oncology (MAASTRO Clinic), Research Institute of Growth and Development, Maastricht University Medical Center, Maastricht (Netherlands); Vandecasteele, Katrien [Department of Radiation Oncology, Ghent University Hospital, Ghent (Belgium); Thas, Olivier [Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Ghent (Belgium); De Ruysscher, Dirk; Lambin, Phillipe [Department of Radiation Oncology (MAASTRO Clinic), Research Institute of Growth and Development, Maastricht University Medical Center, Maastricht (Netherlands); Van Meerbeeck, Jan [Department of Respiratory Medicine, Ghent University Hospital, Ghent (Belgium); De Neve, Wilfried [Department of Radiation Oncology, Ghent University Hospital, Ghent (Belgium); Thierens, Hubert [Department of Basic Medical Sciences, Ghent University, Ghent (Belgium)

2011-10-01T23:59:59.000Z

464

Active-Learning-Based Surrogate Models  

NLE Websites -- All DOE Office Websites (Extended Search)

the dynaTree algorithm (dT) with 10 repetitions, as recommended by the package authors, taking the prediction at each x as the mean of the 10 predictions. We compare this...

465

Fixing Data Anomalies with Prediction Based Algorithm in Wireless Sensor Networks  

E-Print Network (OSTI)

Data inconsistencies are present in the data collected over a large wireless sensor network (WSN), usually deployed for any kind of monitoring applications. Before passing this data to some WSN applications for decision making, it is necessary to ensure that the data received are clean and accurate. In this paper, we have used a statistical tool to examine the past data to fit in a highly sophisticated prediction model i.e., ARIMA for a given sensor node and with this, the model corrects the data using forecast value if any data anomaly exists there. Another scheme is also proposed for detecting data anomaly at sink among the aggregated data in the data are received from a particular sensor node. The effectiveness of our methods are validated by data collected over a real WSN application consisting of Crossbow IRIS Motes \\cite{Crossbow:2009}.

Singh, Abhishek Kr; Mandal, Partha Sarathi

2011-01-01T23:59:59.000Z

466

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.

467

A Storm Surge Prediction Model for the Northern Bay of Bengal with Application to the Cyclone Disaster in April 1991  

Science Conference Proceedings (OSTI)

A numerical model for simulating and predicting tides and storm surges in regions that include areas of open sea combined with estuarine channels and intertidal banks is described. The model makes use of modified depth-averaged equations with a ...

Roger A. Flather

1994-01-01T23:59:59.000Z

468

An Analysis of the Accuracy of 120-h Predictions by the National Meteorological Center's Medium-Range Forecast Model  

Science Conference Proceedings (OSTI)

An assessment was made of the 120-h predictions by the medium range forecast (MRF) run of the National Meteorological Center's (NMC's) global spectral model. The ability of the model to forecast surface cyclones and anticyclones was evaluated and ...

Mary A. Bedrick; Anthony J. Cristaldi III; Stephen J. Colucci; Daniel S. Wilks

1994-03-01T23:59:59.000Z

469

Predictability of Linear Coupled Systems. Part II: An Application to a Simple Model of Tropical Atlantic Variability  

Science Conference Proceedings (OSTI)

A predictability analysis developed within a general framework of linear stochastic dynamics in a companion paper is applied to a simple coupled climate model of tropical Atlantic variability (TAV). The simple model extends the univariate ...

Ping Chang; R. Saravanan; Faming Wang; Link Ji

2004-04-01T23:59:59.000Z

470

Multi-sensor Information Processing using Prediction Market-based Belief Aggregation  

E-Print Network (OSTI)

We consider the problem of information fusion from multiple sensors of different types with the objective of improving the confidence of inference tasks, such as object classification, performed from the data collected by the sensors. We propose a novel technique based on distributed belief aggregation using a multi-agent prediction market to solve this information fusion problem. To monitor the improvement in the confidence of the object classification as well as to dis-incentivize agents from misreporting information, we have introduced a market maker that rewards the agents instantaneously as well as at the end of the inference task, based on the quality of the submitted reports. We have implemented the market maker's reward calculation in the form of a scoring rule and have shown analytically that it incentivizes truthful revelation or accurate reporting by each agent. We have experimentally verified our technique for multi-sensor information fusion for an automated landmine detection scenario. Our experi...

Jumadinova, Janyl

2012-01-01T23:59:59.000Z

471

Quark-mass dependence of the three-flavor QCD phase diagram at zero and imaginary chemical potential: Model prediction  

Science Conference Proceedings (OSTI)

We draw the three-flavor phase diagram as a function of light- and strange-quark masses for both zero and imaginary quark-number chemical potential, using the Polyakov-loop extended Nambu-Jona-Lasinio model with an effective four-quark vertex depending on the Polyakov loop. The model prediction is qualitatively consistent with 2+1 flavor lattice QCD prediction at zero chemical potential and with degenerate three-flavor lattice QCD prediction at imaginary chemical potential.

Sasaki, Takahiro; Sakai, Yuji; Yahiro, Masanobu [Department of Physics, Graduate School of Sciences, Kyushu University, Fukuoka 812-8581 (Japan); Kouno, Hiroaki [Department of Physics, Saga University, Saga 840-8502 (Japan)

2011-11-01T23:59:59.000Z

472

Forest dynamics at regional scales: predictive models constrained with inventory data  

E-Print Network (OSTI)

infrequent but have extreme impacts. Models of the relationship between wind and fire events and vegetation have revealed impacts on species composition and stand biomass (Hickler et al., 2004), size structure (Liedloff and Cook, 2007; Uriarte et al., 2009... limitation, that resource use and supply are equal, birth and death rates are equal and that stems' allometry and demographic rates are as defined above, allowing predictions such as stand size distribution, canopy structure and resource use (West et al...

Lines, Emily

2012-06-12T23:59:59.000Z

473

Progress on building a predictive model of indoor concentrations of outdoor PM-2.5 in homes  

Science Conference Proceedings (OSTI)

The goal of this project is to develop a physically-based, semi-empirical model that describes the concentration of indoor concentration of PM-2.5 (particle mass that is less than 2.5 microns in diameter) and its sulfate, nitrate, organic and black carbon constituents, derived from outdoor sources. We have established the methodology and experimental plan for building the model. Experimental measurements in residential style houses, in Richmond and Fresno, California, are being conducted to provide parameters for and evaluation of this model. The model will be used to improve estimates of human exposures to PM-2.5 of outdoor origin. The objectives of this study are to perform measurement and modeling tasks that produce a tested, semi-mechanistic description of chemical species-specific and residential PM-2.5 arising from the combination of outdoor PM and gas phase sources (HNO{sub 3} and NH{sub 3}), and indoor gas phase (e.g. NH{sub 3}) sources. We specifically address how indoor PM is affected by differences between indoor and outdoor temperature and relative humidity. In addition, we are interested in losses of particles within the building and as they migrate through the building shell. The resulting model will be general enough to predict probability distributions for species-specific indoor concentrations of PM-2.5 based on outdoor PM, and gas phase species concentrations, meteorological conditions, building construction characteristics, and HVAC operating conditions. Controlled intensive experiments were conducted at a suburban research house located in Clovis, California. The experiments utilized a large suite of instruments including conventional aerosol, meteorological and house characterization devices. In addition, two new instruments were developed providing high time resolution for the important particulate species of nitrate, sulfate, and carbon as well as important gaseous species including ammonia and nitric acid. Important initial observations include the result that, with rare exceptions, there is virtually no nitrate found inside the house. This nitrate appears to dissociate into ammonia and nitric acid with the nitric acid quickly depositing out. Initial model development has included work on characterizing penetration and deposition rates, the dynamic behavior of the indoor/outdoor ratio, and predicting infiltration rates. Results from the exploration of the indoor/outdoor ratio show that the traditional assumption of steady state conditions does not hold in general. Many values of the indoor/outdoor ratio exist for any single value of the infiltration rate. Successful prediction of the infiltration rate from measured driving variables is important for extending the results from the Clovis house to the larger housing stock.

Lunden, Melissa M.; Thatcher, Tracy L.; Littlejohn, David; Fischer, Marc L.; Kirchstetter, Thomas W.; Brown, Nancy J.; Hering, Susanne

2001-09-01T23:59:59.000Z

474