National Library of Energy BETA

Sample records for model predictive control

  1. Optimal Control of Distributed Energy Resources using Model Predictive Control

    SciTech Connect (OSTI)

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

    2012-07-22

    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.

  2. Model Predictive Control of Integrated Gasification Combined Cycle Power Plants

    SciTech Connect (OSTI)

    B. Wayne Bequette; Priyadarshi Mahapatra

    2010-08-31

    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.

  3. Adaptive model predictive process control using neural networks

    DOE Patents [OSTI]

    Buescher, Kevin L.; Baum, Christopher C.; Jones, Roger D.

    1997-01-01

    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.

  4. Adaptive model predictive process control using neural networks

    DOE Patents [OSTI]

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

    1997-08-19

    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.

  5. Model Predictive Control for the Operation of Building Cooling Systems

    SciTech Connect (OSTI)

    Ma, Yudong; Borrelli, Francesco; Hencey, Brandon; Coffey, Brian; Bengea, Sorin; Haves, Philip

    2010-06-29

    A model-based predictive control (MPC) is designed for optimal thermal energy storage in building cooling systems. We focus on buildings equipped with a water tank used for actively storing cold water produced by a series of chillers. Typically the chillers are operated at night to recharge the storage tank in order to meet the building demands on the following day. In this paper, we build on our previous work, improve the building load model, and present experimental results. The experiments show that MPC can achieve reduction in the central plant electricity cost and improvement of its efficiency.

  6. Lithium-ion battery cell-level control using constrained model predictive control and equivalent circuit models

    SciTech Connect (OSTI)

    Xavier, MA; Trimboli, MS

    2015-07-01

    This paper introduces a novel application of model predictive control (MPC) to cell-level charging of a lithium-ion battery utilizing an equivalent circuit model of battery dynamics. The approach employs a modified form of the MPC algorithm that caters for direct feed-though signals in order to model near-instantaneous battery ohmic resistance. The implementation utilizes a 2nd-order equivalent circuit discrete-time state-space model based on actual cell parameters; the control methodology is used to compute a fast charging profile that respects input, output, and state constraints. Results show that MPC is well-suited to the dynamics of the battery control problem and further suggest significant performance improvements might be achieved by extending the result to electrochemical models. (C) 2015 Elsevier B.V. All rights reserved.

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

    SciTech Connect (OSTI)

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

    2014-02-01

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

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

    DOE Patents [OSTI]

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

    2013-04-09

    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.

  9. Controlling Bimetallic Nanostructures by the Microemulsion Method with Subnanometer Resolution Using a Prediction Model

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Buceta, D.; Tojo, C.; Vukmirovic, M.; Deepak, F. L.; Arturo Lopez-Quintela, M.

    2015-07-14

    We present a theoretical model to predict the atomic structure of Au/Pt nanoparticles synthesized in microemulsions. Excellent concordance with the experimental results shows that the structure of the nanoparticles can be controlled at sub-nanometer resolution simply by changing the reactants concentration. The results of this study not only offer a better understanding of the complex mechanisms governing reactions in microemulsions, but open up a simple new way to synthesize bimetallic nanoparticles with ad-hoc controlled nanostructures.

  10. Controlling bimetallic nanostructures by the microemulsion method with subnanometer resolution using a prediction model

    SciTech Connect (OSTI)

    Buceta, David; Tojo, Concha; Vukmirovic, Miomir B.; Deepak, F. Leonard; Lopez-Quintela, M. Arturo

    2015-06-02

    In this study, we present a theoretical model to predict the atomic structure of Au/Pt nanoparticles synthesized in microemulsions. Excellent concordance with the experimental results shows that the structure of the nanoparticles can be controlled at sub-nanometer resolution simply by changing the reactants concentration. The results of this study not only offer a better understanding of the complex mechanisms governing reactions in microemulsions, but open up a simple new way to synthesize bimetallic nanoparticles with ad-hoc controlled nanostructures.

  11. Simulation of complex glazing products; from optical data measurements to model based predictive controls

    SciTech Connect (OSTI)

    Kohler, Christian

    2012-08-01

    Complex glazing systems such as venetian blinds, fritted glass and woven shades require more detailed optical and thermal input data for their components than specular non light-redirecting glazing systems. Various methods for measuring these data sets are described in this paper. These data sets are used in multiple simulation tools to model the thermal and optical properties of complex glazing systems. The output from these tools can be used to generate simplified rating values or as an input to other simulation tools such as whole building annual energy programs, or lighting analysis tools. I also describe some of the challenges of creating a rating system for these products and which factors affect this rating. A potential future direction of simulation and building operations is model based predictive controls, where detailed computer models are run in real-time, receiving data for an actual building and providing control input to building elements such as shades.

  12. Model Predictive Control-based Optimal Coordination of Distributed Energy Resources

    SciTech Connect (OSTI)

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

    2013-01-07

    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.

  13. Model Predictive Control-based Optimal Coordination of Distributed Energy Resources

    SciTech Connect (OSTI)

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

    2013-04-03

    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.

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

    SciTech Connect (OSTI)

    Haves, Phillip; Hencey, Brandon; Borrell, Francesco; Elliot, John; Ma, Yudong; Coffey, Brian; Bengea, Sorin; Wetter, Michael

    2010-06-29

    A Model Predictive Control algorithm was developed for the UC Merced campus chilled water plant. Model predictive control (MPC) is an advanced control technology that has proven successful in the chemical process industry and other industries. The main goal of the research was to demonstrate the practical and commercial viability of MPC for optimization of building energy systems. The control algorithms were developed and implemented in MATLAB, allowing for rapid development, performance, and robustness assessment. The UC Merced chilled water 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. The control algorithms determined the optimal chilled water plant operation including chilled water supply (CHWS) temperature set-point, condenser water supply (CWS) temperature set-point and the charging start and stop times to minimize a cost function that includes energy consumption and peak electrical demand over a 3-day prediction horizon. A detailed model of the chilled water plant and simplified models of the buildings served by the plant 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 detailed dynamic model of the chilled water storage tank was also developed and calibrated. Simple, semi-empirical models were 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 model predictive control 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

  15. Battery Life Predictive Model

    Energy Science and Technology Software Center (OSTI)

    2009-12-31

    The Software consists of a model used to predict battery capacity fade and resistance growth for arbitrary cycling and temperature profiles. It allows the user to extrapolate from experimental data to predict actual life cycle.

  16. Reduced Order Modeling for Prediction and Control of Large-Scale Systems.

    SciTech Connect (OSTI)

    Kalashnikova, Irina; Arunajatesan, Srinivasan; Barone, Matthew Franklin; van Bloemen Waanders, Bart Gustaaf; Fike, Jeffrey A.

    2014-05-01

    This report describes work performed from June 2012 through May 2014 as a part of a Sandia Early Career Laboratory Directed Research and Development (LDRD) project led by the first author. The objective of the project is to investigate methods for building stable and efficient proper orthogonal decomposition (POD)/Galerkin reduced order models (ROMs): models derived from a sequence of high-fidelity simulations but having a much lower computational cost. Since they are, by construction, small and fast, ROMs can enable real-time simulations of complex systems for onthe- spot analysis, control and decision-making in the presence of uncertainty. Of particular interest to Sandia is the use of ROMs for the quantification of the compressible captive-carry environment, simulated for the design and qualification of nuclear weapons systems. It is an unfortunate reality that many ROM techniques are computationally intractable or lack an a priori stability guarantee for compressible flows. For this reason, this LDRD project focuses on the development of techniques for building provably stable projection-based ROMs. Model reduction approaches based on continuous as well as discrete projection are considered. In the first part of this report, an approach for building energy-stable Galerkin ROMs for linear hyperbolic or incompletely parabolic systems of partial differential equations (PDEs) using continuous projection is developed. The key idea is to apply a transformation induced by the Lyapunov function for the system, and to build the ROM in the transformed variables. It is shown that, for many PDE systems including the linearized compressible Euler and linearized compressible Navier-Stokes equations, the desired transformation is induced by a special inner product, termed the symmetry inner product. Attention is then turned to nonlinear conservation laws. A new transformation and corresponding energy-based inner product for the full nonlinear compressible Navier

  17. predictive modeling | National Nuclear Security Administration

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

    Apply for Our Jobs Our Jobs Working at NNSA Blog Home predictive modeling predictive modeling Fukushima: Five Years Later After the March 11, 2011, Japan earthquake, tsunami, and ...

  18. predictive-models | netl.doe.gov

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

    predictive-models DOE/BC-88/1/SP. EOR Predictive Models: Handbook for Personal Computer Versions of Enhanced Oil Recovery Predictive Models. BPO Staff. February 1988. 76 pp. NTIS Order No. DE89001204. FORTRAN source code and executable programs for the five EOR Predictive Models shown below are available. The five recovery processes modeled are Steamflood, In-Situ Combustion, Polymer, Chemical Flooding, and CO2 Miscible Flooding. The models are available individually. Min Req.: IBM PC/XT, PS-2,

  19. Towards feasible and effective predictive wavefront control for...

    Office of Scientific and Technical Information (OSTI)

    predictive wavefront control for adaptive optics Citation Details In-Document Search Title: Towards feasible and effective predictive wavefront control for adaptive optics We ...

  20. Towards feasible and effective predictive wavefront control for...

    Office of Scientific and Technical Information (OSTI)

    Service, Springfield, VA at www.ntis.gov. We have recently proposed Predictive Fourier Control, a computationally efficient and adaptive algorithm for predictive wavefront...

  1. Dynamic model predicts well bore surge and swab pressures

    SciTech Connect (OSTI)

    Bing, Z.; Kaiji, Z.

    1996-12-30

    A dynamic well control model predicts surge and swab pressures more accurately than a steady-state model, thereby providing better estimates of pressure fluctuations when pipe is tripped. Pressure fluctuations from tripping pipe into a well can contribute to lost circulation, kicks,and well control problems. This dynamic method of predicting surge and swab pressures was verified in a full-scale test well in the Zhong Yuan oil field in China. Both the dynamic model and steady state model were verified through the test data. The test data showed the dynamic model can correctly predict downhole pressures from running or pulling pipe in a well; steady state models may result in relatively large prediction errors, especially in deeper wells.

  2. Integrated Environmental Control Model

    Energy Science and Technology Software Center (OSTI)

    1999-09-03

    IECM is a powerful multimedia engineering software program for simulating an integrated coal-fired power plant. It provides a capability to model various conventional and advanced processes for controlling air pollutant emissions from coal-fired power plants before, during, or after combustion. The principal purpose of the model is to calculate the performance, emissions, and cost of power plant configurations employing alternative environmental control methods. The model consists of various control technology modules, which may be integratedmore » into a complete utility plant in any desired combination. In contrast to conventional deterministic models, the IECM offers the unique capability to assign probabilistic values to all model input parameters, and to obtain probabilistic outputs in the form of cumulative distribution functions indicating the likelihood of dofferent costs and performance results. A Graphical Use Interface (GUI) facilitates the configuration of the technologies, entry of data, and retrieval of results.« less

  3. Predictive Models of Li-ion Battery Lifetime

    SciTech Connect (OSTI)

    Smith, Kandler; Wood, Eric; Santhanagopalan, Shriram; Kim, Gi-heon; Shi, Ying; Pesaran, Ahmad

    2015-06-15

    It remains an open question how best to predict real-world battery lifetime based on accelerated calendar and cycle aging data from the laboratory. Multiple degradation mechanisms due to (electro)chemical, thermal, and mechanical coupled phenomena influence Li-ion battery lifetime, each with different dependence on time, cycling and thermal environment. The standardization of life predictive models would benefit the industry by reducing test time and streamlining development of system controls.

  4. Voltage control in pulsed system by predict-ahead control

    DOE Patents [OSTI]

    Payne, Anthony N.; Watson, James A.; Sampayan, Stephen E.

    1994-01-01

    A method and apparatus for predict-ahead pulse-to-pulse voltage control in a pulsed power supply system is disclosed. A DC power supply network is coupled to a resonant charging network via a first switch. The resonant charging network is coupled at a node to a storage capacitor. An output load is coupled to the storage capacitor via a second switch. A de-Q-ing network is coupled to the resonant charging network via a third switch. The trigger for the third switch is a derived function of the initial voltage of the power supply network, the initial voltage of the storage capacitor, and the present voltage of the storage capacitor. A first trigger closes the first switch and charges the capacitor. The third trigger is asserted according to the derived function to close the third switch. When the third switch is closed, the first switch opens and voltage on the node is regulated. The second trigger may be thereafter asserted to discharge the capacitor into the output load.

  5. Voltage control in pulsed system by predict-ahead control

    DOE Patents [OSTI]

    Payne, A.N.; Watson, J.A.; Sampayan, S.E.

    1994-09-13

    A method and apparatus for predict-ahead pulse-to-pulse voltage control in a pulsed power supply system is disclosed. A DC power supply network is coupled to a resonant charging network via a first switch. The resonant charging network is coupled at a node to a storage capacitor. An output load is coupled to the storage capacitor via a second switch. A de-Q-ing network is coupled to the resonant charging network via a third switch. The trigger for the third switch is a derived function of the initial voltage of the power supply network, the initial voltage of the storage capacitor, and the present voltage of the storage capacitor. A first trigger closes the first switch and charges the capacitor. The third trigger is asserted according to the derived function to close the third switch. When the third switch is closed, the first switch opens and voltage on the node is regulated. The second trigger may be thereafter asserted to discharge the capacitor into the output load. 4 figs.

  6. Predictive Models for Target Response During Penetration (Technical...

    Office of Scientific and Technical Information (OSTI)

    Predictive Models for Target Response During Penetration Citation Details In-Document Search Title: Predictive Models for Target Response During Penetration You are accessing a...

  7. Computational Tools for Predictive Modeling of Properties in...

    Office of Scientific and Technical Information (OSTI)

    Book: Computational Tools for Predictive Modeling of Properties in Complex Actinide Systems Citation Details In-Document Search Title: Computational Tools for Predictive Modeling ...

  8. Comparison of Uncertainty of Two Precipitation Prediction Models...

    Office of Scientific and Technical Information (OSTI)

    Prediction Models at Los Alamos National Lab Technical Area 54 Citation Details In-Document Search Title: Comparison of Uncertainty of Two Precipitation Prediction Models ...

  9. A predictive standard model for heavy electron systems (Conference...

    Office of Scientific and Technical Information (OSTI)

    A predictive standard model for heavy electron systems Citation Details In-Document Search Title: A predictive standard model for heavy electron systems You are accessing a ...

  10. Simplified Protein Models: Predicting Folding Pathways and Structure...

    Office of Scientific and Technical Information (OSTI)

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

  11. Testing model for predicting spillway cavitation damage

    SciTech Connect (OSTI)

    Lee, W.; Hoopes, J.A.

    1995-12-31

    Using fuzzy mathematics a comprehensive model has been developed to predict the time, location and level (intensity) of spillway cavitation damage. Five damage levels and four factors affecting damage are used. Membership functions express the degree that each factor effects damage, and weights express the relative importance of each factor. The model has been calibrated and tested with operating data and experience from the Glen Canyon Dam left tunnel spillway, which had major cavitation damage in 1983. An error analysis for the Glen Canyon Dam left tunnel spillway gave the best ranges for model weights. Prediction of damage at other spillways (4 tunnels, 3 chutes) with functions and parameters as for the Glen Canyon Dam left tunnel spillway gave reasonable predictions of damage intensity and location and poor estimates of occurrence time in the tunnels. Chute predictions were in poor agreement with observations, indicating need for different parameter values. Finally, two membership functions with constant or time varying parameters are compared with observed results from the Glen Canyon Dam left tunnel spillway.

  12. The selection of turbulence models for prediction of room airflow

    SciTech Connect (OSTI)

    Nielsen, P.V.

    1998-10-01

    The airflow in buildings involves a combination of many different flow elements. It is, therefore, difficult to find an adequate, all-round turbulence model covering all aspects. Consequently, it is appropriate and economical to choose turbulence models according to the situation that is to be predicted. This paper discusses the use of different turbulence models and their advantages in given situations. As an example, it is shown that a simple zero-equation model can be used for the prediction of special situations as flow with a low level of turbulence. A zero-equation model with compensation for room dimensions and velocity level also is discussed. A {kappa}-{epsilon} model expanded by damping functions is used to improve the prediction of the flow in a room ventilated by displacement ventilation. The damping functions especially take into account the turbulence level and the vertical temperature gradient. Low Reynolds number models (LNR models) are used to improve the prediction of evaporation-controlled emissions from building material, which is shown by an example. Finally, large eddy simulation (LES) of room airflow is discussed and demonstrated.

  13. New model accurately predicts reformate composition

    SciTech Connect (OSTI)

    Ancheyta-Juarez, J.; Aguilar-Rodriguez, E. )

    1994-01-31

    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.

  14. Progress towards a PETN Lifetime Prediction Model

    SciTech Connect (OSTI)

    Burnham, A K; Overturf III, G E; Gee, R; Lewis, P; Qiu, R; Phillips, D; Weeks, B; Pitchimani, R; Maiti, A; Zepeda-Ruiz, L; Hrousis, C

    2006-09-11

    Dinegar (1) showed that decreases in PETN surface area causes EBW detonator function times to increase. Thermal aging causes PETN to agglomerate, shrink, and densify indicating a ''sintering'' process. It has long been a concern that the formation of a gap between the PETN and the bridgewire may lead to EBW detonator failure. These concerns have led us to develop a model to predict the rate of coarsening that occurs with age for thermally driven PETN powder (50% TMD). To understand PETN contributions to detonator aging we need three things: (1) Curves describing function time dependence on specific surface area, density, and gap. (2) A measurement of the critical gap distance for no fire as a function of density and surface area for various wire configurations. (3) A model describing how specific surface area, density and gap change with time and temperature. We've had good success modeling high temperature surface area reduction and function time increase using a phenomenological deceleratory kinetic model based on a distribution of parallel nth-order reactions having evenly spaced activation energies where weighing factors of the reactions follows a Gaussian distribution about the reaction with the mean activation energy (Figure 1). Unfortunately, the mean activation energy derived from this approach is high (typically {approx}75 kcal/mol) so that negligible sintering is predicted for temperatures below 40 C. To make more reliable predictions, we've established a three-part effort to understand PETN mobility. First, we've measured the rates of step movement and pit nucleation as a function of temperature from 30 to 50 C for single crystals. Second, we've measured the evaporation rate from single crystals and powders from 105 to 135 C to obtain an activation energy for evaporation. Third, we've pursued mechanistic kinetic modeling of surface mobility, evaporation, and ripening.

  15. An Anisotropic Hardening Model for Springback Prediction

    SciTech Connect (OSTI)

    Zeng, Danielle; Xia, Z. Cedric

    2005-08-05

    As more Advanced High-Strength Steels (AHSS) are heavily used for automotive body structures and closures panels, accurate springback prediction for these components becomes more challenging because of their rapid hardening characteristics and ability to sustain even higher stresses. In this paper, a modified Mroz hardening model is proposed to capture realistic Bauschinger effect at reverse loading, such as when material passes through die radii or drawbead during sheet metal forming process. This model accounts for material anisotropic yield surface and nonlinear isotropic/kinematic hardening behavior. Material tension/compression test data are used to accurately represent Bauschinger effect. The effectiveness of the model is demonstrated by comparison of numerical and experimental springback results for a DP600 straight U-channel test.

  16. Predictive Capability Maturity Model for computational modeling and simulation.

    SciTech Connect (OSTI)

    Oberkampf, William Louis; Trucano, Timothy Guy; Pilch, Martin M.

    2007-10-01

    The Predictive Capability Maturity Model (PCMM) is a new model that can be used to assess the level of maturity of computational modeling and simulation (M&S) efforts. The development of the model is based on both the authors experience and their analysis of similar investigations in the past. The perspective taken in this report is one of judging the usefulness of a predictive capability that relies on the numerical solution to partial differential equations to better inform and improve decision making. The review of past investigations, such as the Software Engineering Institute's Capability Maturity Model Integration and the National Aeronautics and Space Administration and Department of Defense Technology Readiness Levels, indicates that a more restricted, more interpretable method is needed to assess the maturity of an M&S effort. The PCMM addresses six contributing elements to M&S: (1) representation and geometric fidelity, (2) physics and material model fidelity, (3) code verification, (4) solution verification, (5) model validation, and (6) uncertainty quantification and sensitivity analysis. For each of these elements, attributes are identified that characterize four increasing levels of maturity. Importantly, the PCMM is a structured method for assessing the maturity of an M&S effort that is directed toward an engineering application of interest. The PCMM does not assess whether the M&S effort, the accuracy of the predictions, or the performance of the engineering system satisfies or does not satisfy specified application requirements.

  17. Using micro saint to predict performance in a nuclear power plant control room

    SciTech Connect (OSTI)

    Lawless, M.T.; Laughery, K.R.; Persenky, J.J.

    1995-09-01

    The United States Nuclear Regulatory Commission (NRC) requires a technical basis for regulatory actions. In the area of human factors, one possible technical basis is human performance modeling technology including task network modeling. This study assessed the feasibility and validity of task network modeling to predict the performance of control room crews. Task network models were built that matched the experimental conditions of a study on computerized procedures that was conducted at North Carolina State University. The data from the {open_quotes}paper procedures{close_quotes} conditions were used to calibrate the task network models. Then, the models were manipulated to reflect expected changes when computerized procedures were used. These models` predictions were then compared to the experimental data from the {open_quotes}computerized conditions{close_quotes} of the North Carolina State University study. Analyses indicated that the models predicted some subsets of the data well, but not all. Implications for the use of task network modeling are discussed.

  18. Predictive Models of Li-ion Battery Lifetime (Presentation)

    SciTech Connect (OSTI)

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

    2014-09-01

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

  19. Stimulation Prediction Models | Open Energy Information

    Open Energy Info (EERE)

    Predictive Simulator for Enhanced Geothermal Systems California Science Applications International Corporation Recovery Act: Enhanced Geothermal Systems Component Research and...

  20. Predictable SCR co-benefits for mercury control

    SciTech Connect (OSTI)

    Pritchard, S.

    2009-01-15

    A test program, performed in cooperation with Dominion Power and the Babcock and Wilcox Co., was executed at Dominion Power's Mount Storm power plant in Grant County, W. Va. The program was focused on both the selective catalytic reduction (SCR) catalyst capability to oxide mercury as well as the scrubber's capability to capture and retain the oxidized mercury. This article focuses on the SCR catalyst performance aspects. The Mount Storm site consists of three units totaling approximately 1,660 MW. All units are equipped with SCR systems for NOx control. A full-scale test to evaluate the effect of the SCR was performed on Unit 2, a 550 MWT-fired boiler firing a medium sulfur bituminous coal. This test program demonstrated that the presence of an SCR catalyst can significantly affect the mercury speciation profile. Observation showed that in the absence of an SCR catalyst, the extent of oxidation of element a mercury at the inlet of the flue gas desulfurization system was about 64%. The presence of a Cornertech SCR catalyst improved this oxidation to levels greater than 95% almost all of which was captured by the downstream wet FGD system. Cornertech's proprietary SCR Hg oxidation model was used to accurately predict the field results. 1 ref., 2 figs., 1 tab.

  1. Predictive Models of Li-ion Battery Lifetime (Presentation) (Conference) |

    Office of Scientific and Technical Information (OSTI)

    SciTech Connect Predictive Models of Li-ion Battery Lifetime (Presentation) Citation Details In-Document Search Title: Predictive Models of Li-ion Battery Lifetime (Presentation) Predictive models of Li-ion battery reliability must consider a multiplicity of electrochemical, thermal and mechanical degradation modes experienced by batteries in application environments. Complicating matters, Li-ion batteries can experience several path dependent degradation trajectories dependent on storage

  2. Predictive Models of Li-ion Battery Lifetime (Presentation) Smith...

    Office of Scientific and Technical Information (OSTI)

    Predictive Models of Li-ion Battery Lifetime (Presentation) Smith, K.; Wood, E.; Santhanagopalan, S.; Kim, G.; Shi, Y.; Pesaran, A. 25 ENERGY STORAGE; 33 ADVANCED PROPULSION...

  3. Predictive modeling of reactive wetting and metal joining.

    SciTech Connect (OSTI)

    van Swol, Frank B.

    2013-09-01

    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.

  4. Project Profile: Predictive Physico-Chemical Modeling of Intrinsic

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

    Degradation Mechanisms for Advanced Reflector Materials | Department of Energy Predictive Physico-Chemical Modeling of Intrinsic Degradation Mechanisms for Advanced Reflector Materials Project Profile: Predictive Physico-Chemical Modeling of Intrinsic Degradation Mechanisms for Advanced Reflector Materials NREL logo NREL, under the Physics of Reliability: Evaluating Design Insights for Component Technologies in Solar (PREDICTS) Program will be developing a physics-based computational

  5. Demonstrating the improvement of predictive maturity of a computational model

    SciTech Connect (OSTI)

    Hemez, Francois M; Unal, Cetin; Atamturktur, Huriye S

    2010-01-01

    We demonstrate an improvement of predictive capability brought to a non-linear material model using a combination of test data, sensitivity analysis, uncertainty quantification, and calibration. A model that captures increasingly complicated phenomena, such as plasticity, temperature and strain rate effects, is analyzed. Predictive maturity is defined, here, as the accuracy of the model to predict multiple Hopkinson bar experiments. A statistical discrepancy quantifies the systematic disagreement (bias) between measurements and predictions. Our hypothesis is that improving the predictive capability of a model should translate into better agreement between measurements and predictions. This agreement, in turn, should lead to a smaller discrepancy. We have recently proposed to use discrepancy and coverage, that is, the extent to which the physical experiments used for calibration populate the regime of applicability of the model, as basis to define a Predictive Maturity Index (PMI). It was shown that predictive maturity could be improved when additional physical tests are made available to increase coverage of the regime of applicability. This contribution illustrates how the PMI changes as 'better' physics are implemented in the model. The application is the non-linear Preston-Tonks-Wallace (PTW) strength model applied to Beryllium metal. We demonstrate that our framework tracks the evolution of maturity of the PTW model. Robustness of the PMI with respect to the selection of coefficients needed in its definition is also studied.

  6. Degradation Mechanisms and Lifetime Prediction for Lithium-Ion Batteries -- A Control Perspective: Preprint

    SciTech Connect (OSTI)

    Smith, Kandler; Shi, Ying; Santhanagopalan, Shriram

    2015-07-29

    Predictive models of Li-ion battery lifetime must consider a multiplicity of electrochemical, thermal, and mechanical degradation modes experienced by batteries in application environments. To complicate matters, Li-ion batteries can experience different degradation trajectories that depend on storage and cycling history of the application environment. Rates of degradation are controlled by factors such as temperature history, electrochemical operating window, and charge/discharge rate. We present a generalized battery life prognostic model framework for battery systems design and control. The model framework consists of trial functions that are statistically regressed to Li-ion cell life datasets wherein the cells have been aged under different levels of stress. Degradation mechanisms and rate laws dependent on temperature, storage, and cycling condition are regressed to the data, with multiple model hypotheses evaluated and the best model down-selected based on statistics. The resulting life prognostic model, implemented in state variable form, is extensible to arbitrary real-world scenarios. The model is applicable in real-time control algorithms to maximize battery life and performance. We discuss efforts to reduce lifetime prediction error and accommodate its inevitable impact in controller design.

  7. CFD Modeling for Mercury Control Technology

    SciTech Connect (OSTI)

    Madsen, J.I.

    2006-12-01

    Compliance with the Clean Air Mercury Rule will require implementation of dedicated mercury control solutions at a significant portion of the U.S. coal-fired utility fleet. Activated Carbon Injection (ACI) upstream of a particulate control device (ESP or baghouse) remains one of the most promising near-term mercury control technologies. The DOE/NETL field testing program has advanced the understanding of mercury control by ACI, but a persistent need remains to develop predictive models that may improve the understanding and practical implementation of this technology. This presentation describes the development of an advanced model of in-flight mercury capture based on Computational Fluid Dynamics (CFD). The model makes detailed predictions of the induct spatial distribution and residence time of sorbent, as well as predictions of mercury capture efficiency for particular sorbent flow rates and injection grid configurations. Hence, CFD enables cost efficient optimization of sorbent injection systems for mercury control to a degree that would otherwise be impractical both for new and existing plants. In this way, modeling tools may directly address the main cost component of operating an ACI system the sorbent expense. A typical 300 MW system is expected to require between $1 and $2 million of sorbent per year, and so even modest reductions (say 10-20%) in necessary sorbent feed injection rates will quickly make any optimization effort very worthwhile. There are few existing models of mercury capture, and these typically make gross assumptions of plug gas flow, zero velocity slip between particle and gas phase, and uniform sorbent dispersion. All of these assumptions are overcome with the current model, which is based on first principles and includes mass transfer processes occurring at multiple scales, ranging from the large-scale transport in the duct to transport within the porous structure of a sorbent particle. In principle any single one of these processes

  8. Collaborative Research: Separating Forced and Unforced Decadal Predictability in Models and Observations

    SciTech Connect (OSTI)

    Tippett, Michael K.

    2014-04-09

    This report is a progress report of the accomplishments of the research grant “Collaborative Research: Separating Forced and Unforced Decadal Predictability in Models and Observa- tions” during the period 1 May 2011- 31 August 2013. This project is a collaborative one between Columbia University and George Mason University. George Mason University will submit a final technical report at the conclusion of their no-cost extension. The purpose of the proposed research is to identify unforced predictable components on decadal time scales, distinguish these components from forced predictable components, and to assess the reliability of model predictions of these components. Components of unforced decadal predictability will be isolated by maximizing the Average Predictability Time (APT) in long, multimodel control runs from state-of-the-art climate models. Components with decadal predictability have large APT, so maximizing APT ensures that components with decadal predictability will be detected. Optimal fingerprinting techniques, as used in detection and attribution analysis, will be used to separate variations due to natural and anthropogenic forcing from those due to unforced decadal predictability. This methodology will be applied to the decadal hindcasts generated by the CMIP5 project to assess the reliability of model projections. The question of whether anthropogenic forcing changes decadal predictability, or gives rise to new forms of decadal predictability, also will be investigated.

  9. LHC diphoton Higgs signal predicted by little Higgs models

    SciTech Connect (OSTI)

    Wang Lei; Yang Jinmin

    2011-10-01

    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 (< or approx. 10%) in the littlest Higgs model but can be quite severe ({approx_equal}90%) in other three models. This means that discovering the light 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.

  10. New model predicts once-mysterious chemical reactions

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

    New model predicts once-mysterious chemical reactions New model predicts once-mysterious chemical reactions Results will also be used to understand basic questions about nature such as the cooling mechanisms of the early universe and the formation of planets and stars. June 28, 2016 Mark Zammit, of Los Alamos' Physics and Chemistry of Materials group, is part of a team that developed a theoretical model to forecast the fundamental chemical reactions involving molecular hydrogen. Photo credit

  11. A predictive ocean oil spill model

    SciTech Connect (OSTI)

    Sanderson, J.; Barnette, D.; Papodopoulos, P.; Schaudt, K.; Szabo, D.

    1996-07-01

    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.

  12. Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling

    SciTech Connect (OSTI)

    Valerio, Luis G. . E-mail: luis.valerio@FDA.HHS.gov; Arvidson, Kirk B.; Chanderbhan, Ronald F.; Contrera, Joseph F.

    2007-07-01

    Consistent with the U.S. Food and Drug Administration (FDA) Critical Path Initiative, predictive toxicology software programs employing quantitative structure-activity relationship (QSAR) models are currently under evaluation for regulatory risk assessment and scientific decision support for highly sensitive endpoints such as carcinogenicity, mutagenicity and reproductive toxicity. At the FDA's Center for Food Safety and Applied Nutrition's Office of Food Additive Safety and the Center for Drug Evaluation and Research's Informatics and Computational Safety Analysis Staff (ICSAS), the use of computational SAR tools for both qualitative and quantitative risk assessment applications are being developed and evaluated. One tool of current interest is MDL-QSAR predictive discriminant analysis modeling of rodent carcinogenicity, which has been previously evaluated for pharmaceutical applications by the FDA ICSAS. The study described in this paper aims to evaluate the utility of this software to estimate the carcinogenic potential of small, organic, naturally occurring chemicals found in the human diet. In addition, a group of 19 known synthetic dietary constituents that were positive in rodent carcinogenicity studies served as a control group. In the test group of naturally occurring chemicals, 101 were found to be suitable for predictive modeling using this software's discriminant analysis modeling approach. Predictions performed on these compounds were compared to published experimental evidence of each compound's carcinogenic potential. Experimental evidence included relevant toxicological studies such as rodent cancer bioassays, rodent anti-carcinogenicity studies, genotoxic studies, and the presence of chemical structural alerts. Statistical indices of predictive performance were calculated to assess the utility of the predictive modeling method. Results revealed good predictive performance using this software's rodent carcinogenicity module of over 1200 chemicals

  13. SimTable helps firefighters model and predict fire direction

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

    SimTable models and predicts fire path SimTable helps firefighters model and predict fire direction In 2009, SimTable received $100,000 from the LANS Venture Acceleration Fund to improve the user interface and seed firefighting academies with customized set ups. April 3, 2012 Stephen Guerin (L) and Chip Garner (R) with SimTable Stephen Guerin (L), and Chip Garner (R), with SimTable, a Santa Fe company helping firefighters model and predict where a fire is most likely to spread, received support

  14. In silico modeling to predict drug-induced phospholipidosis

    SciTech Connect (OSTI)

    Choi, Sydney S.; Kim, Jae S.; Valerio, Luis G. Sadrieh, Nakissa

    2013-06-01

    Drug-induced phospholipidosis (DIPL) is a preclinical finding during pharmaceutical drug development that has implications on the course of drug development and regulatory safety review. A principal characteristic of drugs inducing DIPL is known to be a cationic amphiphilic structure. This provides evidence for a structure-based explanation and opportunity to analyze properties and structures of drugs with the histopathologic findings for DIPL. In previous work from the FDA, in silico quantitative structureactivity relationship (QSAR) modeling using machine learning approaches has shown promise with a large dataset of drugs but included unconfirmed data as well. In this study, we report the construction and validation of a battery of complementary in silico QSAR models using the FDA's updated database on phospholipidosis, new algorithms and predictive technologies, and in particular, we address high performance with a high-confidence dataset. The results of our modeling for DIPL include rigorous external validation tests showing 8081% concordance. Furthermore, the predictive performance characteristics include models with high sensitivity and specificity, in most cases above ? 80% leading to desired high negative and positive predictivity. These models are intended to be utilized for regulatory toxicology applied science needs in screening new drugs for DIPL. - Highlights: New in silico models for predicting drug-induced phospholipidosis (DIPL) are described. The training set data in the models is derived from the FDA's phospholipidosis database. We find excellent predictivity values of the models based on external validation. The models can support drug screening and regulatory decision-making on DIPL.

  15. Statistical thermodynamics model and empirical correlations for predicting

    Office of Scientific and Technical Information (OSTI)

    mixed hydrate phase equilibria (Journal Article) | SciTech Connect Statistical thermodynamics model and empirical correlations for predicting mixed hydrate phase equilibria Citation Details In-Document Search Title: Statistical thermodynamics model and empirical correlations for predicting mixed hydrate phase equilibria Authors: Garapati, Nagasree ; Anderson, Brian J Publication Date: 2014-07-01 OSTI Identifier: 1165558 Report Number(s): A-UNIV-PUB-100 Journal ID: ISSN 0378-3812 DOE Contract

  16. Prediction of cloud droplet number in a general circulation model

    SciTech Connect (OSTI)

    Ghan, S.J.; Leung, L.R.

    1996-04-01

    We have applied the Colorado State University Regional Atmospheric Modeling System (RAMS) bulk cloud microphysics parameterization to the treatment of stratiform clouds in the National Center for Atmospheric Research Community Climate Model (CCM2). The RAMS predicts mass concentrations of cloud water, cloud ice, rain and snow, and number concnetration of ice. We have introduced the droplet number conservation equation to predict droplet number and it`s dependence on aerosols.

  17. Using a Simple Binomial Model to Assess Improvement in Predictive

    Office of Scientific and Technical Information (OSTI)

    Capability: Sequential Bayesian Inference, Hypothesis Testing, and Power Analysis (Technical Report) | SciTech Connect Technical Report: Using a Simple Binomial Model to Assess Improvement in Predictive Capability: Sequential Bayesian Inference, Hypothesis Testing, and Power Analysis Citation Details In-Document Search Title: Using a Simple Binomial Model to Assess Improvement in Predictive Capability: Sequential Bayesian Inference, Hypothesis Testing, and Power Analysis We present a

  18. A thermodynamic model to predict the aqueous solubility of hydrocarbon

    Office of Scientific and Technical Information (OSTI)

    mixtures at two-phase hydrate-liquid water equilibrium (Journal Article) | SciTech Connect Journal Article: A thermodynamic model to predict the aqueous solubility of hydrocarbon mixtures at two-phase hydrate-liquid water equilibrium Citation Details In-Document Search This content will become publicly available on March 30, 2018 Title: A thermodynamic model to predict the aqueous solubility of hydrocarbon mixtures at two-phase hydrate-liquid water equilibrium Authors: Velaga, Srinath C.

  19. PV Module Intraconnect Thermomechanical Durability Damage Prediction Model

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

    | Department of Energy Module Intraconnect Thermomechanical Durability Damage Prediction Model PV Module Intraconnect Thermomechanical Durability Damage Prediction Model Presented at the PV Module Reliability Workshop, February 26 - 27 2013, Golden, Colorado pvmrw13_ps2_dow_gaston.pdf (1.26 MB) More Documents & Publications FY2015 Status Report: CIRFT Testing of High-Burnup Used Nuclear Fuel Rods from Pressurized Water Reactor and BWR Environments 2014 Propulsion Materials R&D Annual

  20. Predictive models of circulating fluidized bed combustors

    SciTech Connect (OSTI)

    Gidaspow, D.

    1992-07-01

    Steady flows influenced by walls cannot be described by inviscid models. Flows in circulating fluidized beds have significant wall effects. Particles in the form of clusters or layers can be seen to run down the walls. Hence modeling of circulating fluidized beds (CFB) without a viscosity is not possible. However, in interpreting Equations (8-1) and (8-2) it must be kept in mind that CFB or most other two phase flows are never in a true steady state. Then the viscosity in Equations (8-1) and (8-2) may not be the true fluid viscosity to be discussed next, but an Eddy type viscosity caused by two phase flow oscillations usually referred to as turbulence. In view of the transient nature of two-phase flow, the drag and the boundary layer thickness may not be proportional to the square root of the intrinsic viscosity but depend upon it to a much smaller extent. As another example, liquid-solid flow and settling of colloidal particles in a lamella electrosettler the settling process is only moderately affected by viscosity. Inviscid flow with settling is a good first approximation to this electric field driven process. The physical meaning of the particulate phase viscosity is described in detail in the chapter on kinetic theory. Here the conventional derivation resented in single phase fluid mechanics is generalized to multiphase flow.

  1. Predictive powertrain control using powertrain history and GPS data

    DOE Patents [OSTI]

    Weslati, Feisel; Krupadanam, Ashish A

    2015-03-03

    A method and powertrain apparatus that predicts a route of travel for a vehicle and uses historical powertrain loads and speeds for the predicted route of travel to optimize at least one powertrain operation for the vehicle.

  2. Sandia's ice sheet modeling of Greenland, Antarctica helps predict

    National Nuclear Security Administration (NNSA)

    sea-level rise | National Nuclear Security Administration | (NNSA) Sandia's ice sheet modeling of Greenland, Antarctica helps predict sea-level rise Wednesday, March 2, 2016 - 12:00am Sandia California researchers Irina Tezaur and Ray Tuminaro analyze a model of Antarctica. They are part of a Sandia team working to improve the reliability and efficiency of computational models that describe ice sheet behavior and dynamics. The Greenland and Antarctic ice sheets will make a dominant

  3. Lepton Flavor Violation in Predictive SUSY-GUT Models

    SciTech Connect (OSTI)

    Albright, Carl H.; Chen, Mu-Chun; /UC, Irvine

    2008-02-01

    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.

  4. Mathematical modeling to predict residential solid waste generation

    SciTech Connect (OSTI)

    Ojeda Benitez, Sara; Vega, Carolina Armijo de

    2008-07-01

    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.

  5. Product component genealogy modeling and field-failure prediction

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    King, Caleb; Hong, Yili; Meeker, William Q.

    2016-04-13

    Many industrial products consist of multiple components that are necessary for system operation. There is an abundance of literature on modeling the lifetime of such components through competing risks models. During the life-cycle of a product, it is common for there to be incremental design changes to improve reliability, to reduce costs, or due to changes in availability of certain part numbers. These changes can affect product reliability but are often ignored in system lifetime modeling. By incorporating this information about changes in part numbers over time (information that is readily available in most production databases), better accuracy can bemore » achieved in predicting time to failure, thus yielding more accurate field-failure predictions. This paper presents methods for estimating parameters and predictions for this generational model and a comparison with existing methods through the use of simulation. Our results indicate that the generational model has important practical advantages and outperforms the existing methods in predicting field failures.« less

  6. Predictive RANS simulations via Bayesian Model-Scenario Averaging

    SciTech Connect (OSTI)

    Edeling, W.N.; Cinnella, P.; Dwight, R.P.

    2014-10-15

    The turbulence closure model is the dominant source of error in most Reynolds-Averaged Navier–Stokes simulations, yet no reliable estimators for this error component currently exist. Here we develop a stochastic, a posteriori error estimate, calibrated to specific classes of flow. It is based on variability in model closure coefficients across multiple flow scenarios, for multiple closure models. The variability is estimated using Bayesian calibration against experimental data for each scenario, and Bayesian Model-Scenario Averaging (BMSA) is used to collate the resulting posteriors, to obtain a stochastic estimate of a Quantity of Interest (QoI) in an unmeasured (prediction) scenario. The scenario probabilities in BMSA are chosen using a sensor which automatically weights those scenarios in the calibration set which are similar to the prediction scenario. The methodology is applied to the class of turbulent boundary-layers subject to various pressure gradients. For all considered prediction scenarios the standard-deviation of the stochastic estimate is consistent with the measurement ground truth. Furthermore, the mean of the estimate is more consistently accurate than the individual model predictions.

  7. The origins of computer weather prediction and climate modeling

    SciTech Connect (OSTI)

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

    2008-03-20

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

  8. Numerical prediction of energy consumption in buildings with controlled interior temperature

    SciTech Connect (OSTI)

    Jarošová, P.; Št’astník, S.

    2015-03-10

    New European directives bring strong requirement to the energy consumption of building objects, supporting the renewable energy sources. Whereas in the case of family and similar houses this can lead up to absurd consequences, for building objects with controlled interior temperature the optimization of energy demand is really needed. The paper demonstrates the system approach to the modelling of thermal insulation and accumulation abilities of such objetcs, incorporating the significant influence of additional physical processes, as surface heat radiation and moisture-driven deterioration of insulation layers. An illustrative example shows the numerical prediction of energy consumption of a freezing plant in one Central European climatic year.

  9. Maximum likelihood Bayesian model averaging and its predictive analysis for groundwater reactive transport models

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Lu, Dan; Ye, Ming; Curtis, Gary P.

    2015-08-01

    While Bayesian model averaging (BMA) has been widely used in groundwater modeling, it is infrequently applied to groundwater reactive transport modeling because of multiple sources of uncertainty in the coupled hydrogeochemical processes and because of the long execution time of each model run. To resolve these problems, this study analyzed different levels of uncertainty in a hierarchical way, and used the maximum likelihood version of BMA, i.e., MLBMA, to improve the computational efficiency. Our study demonstrates the applicability of MLBMA to groundwater reactive transport modeling in a synthetic case in which twenty-seven reactive transport models were designed to predict themore » reactive transport of hexavalent uranium (U(VI)) based on observations at a former uranium mill site near Naturita, CO. Moreover, these reactive transport models contain three uncertain model components, i.e., parameterization of hydraulic conductivity, configuration of model boundary, and surface complexation reactions that simulate U(VI) adsorption. These uncertain model components were aggregated into the alternative models by integrating a hierarchical structure into MLBMA. The modeling results of the individual models and MLBMA were analyzed to investigate their predictive performance. The predictive logscore results show that MLBMA generally outperforms the best model, suggesting that using MLBMA is a sound strategy to achieve more robust model predictions relative to a single model. MLBMA works best when the alternative models are structurally distinct and have diverse model predictions. When correlation in model structure exists, two strategies were used to improve predictive performance by retaining structurally distinct models or assigning smaller prior model probabilities to correlated models. Since the synthetic models were designed using data from the Naturita site, the results of this study are expected to provide guidance for real-world modeling. Finally

  10. Maximum likelihood Bayesian model averaging and its predictive analysis for groundwater reactive transport models

    SciTech Connect (OSTI)

    Lu, Dan; Ye, Ming; Curtis, Gary P.

    2015-08-01

    While Bayesian model averaging (BMA) has been widely used in groundwater modeling, it is infrequently applied to groundwater reactive transport modeling because of multiple sources of uncertainty in the coupled hydrogeochemical processes and because of the long execution time of each model run. To resolve these problems, this study analyzed different levels of uncertainty in a hierarchical way, and used the maximum likelihood version of BMA, i.e., MLBMA, to improve the computational efficiency. Our study demonstrates the applicability of MLBMA to groundwater reactive transport modeling in a synthetic case in which twenty-seven reactive transport models were designed to predict the reactive transport of hexavalent uranium (U(VI)) based on observations at a former uranium mill site near Naturita, CO. Moreover, these reactive transport models contain three uncertain model components, i.e., parameterization of hydraulic conductivity, configuration of model boundary, and surface complexation reactions that simulate U(VI) adsorption. These uncertain model components were aggregated into the alternative models by integrating a hierarchical structure into MLBMA. The modeling results of the individual models and MLBMA were analyzed to investigate their predictive performance. The predictive logscore results show that MLBMA generally outperforms the best model, suggesting that using MLBMA is a sound strategy to achieve more robust model predictions relative to a single model. MLBMA works best when the alternative models are structurally distinct and have diverse model predictions. When correlation in model structure exists, two strategies were used to improve predictive performance by retaining structurally distinct models or assigning smaller prior model probabilities to correlated models. Since the synthetic models were designed using data from the Naturita site, the results of this study are expected to provide guidance for real-world modeling. Finally, limitations of

  11. Impact of heterogeneous chemistry on model predictions of ozone changes

    SciTech Connect (OSTI)

    Granier, C.; Brasseur, G. )

    1992-11-20

    A two-dimensional chemical/transport model of the middle atmosphere is used to assess the importance of chemical heterogeneous processes in the polar regions (on polar stratospheric clouds (PSCs)) and at other latitudes (on sulfate aerosols). When conversion on type I and type II PSCs of N[sub 2]O[sub 5] into HNO[sub 3] and of CIONO[sub 2] into reactive forms of chlorine is taken into account, enhanced CIO concentrations lead to the formation of a springtime ozone hole over the Antarctic continent; no such major reduction in the ozone column is found in the Arctic region. When conversion of nitrogen and chlorine compounds is assumed to occur on sulfate particles in the lower stratosphere, significant perturbations in the chemistry are also found. For background aerosol conditions, the concentration of nitric acid is enhanced and agrees with observed values, while that of nitrogen oxides is reduced and agrees less than if heterogeneous processes are ignored in the calculations. The concentration of the OH radical is significantly increased. Ozone number density appears to become larger between 16 and 30 km but smaller below 16 km, especially at high latitudes. The ozone column is only slightly modified, except at high latitudes where it is substantially reduced if the CIONO[sub 2] conversion into reactive chlorine is considered. After a large volcanic eruption these changes are further exacerbated. The ozone budget in the lower stratrosphere becomes less affected by nitrogen oxides but is largely controlled by the CIO[sub x] and HO[sub x] chemistries. A substantial decrease in the ozone column is predicted as a result of the Pinatubo volcanic eruption, mostly in winter at middle and high latitudes. 62 refs., 18 figs., 3 tabs.

  12. NREL: Transportation Research - NREL's Battery Life Predictive Model Helps

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

    Companies Take Charge NREL's Battery Life Predictive Model Helps Companies Take Charge October 26, 2015 A series of batteries hooked together next to a monitor. An example of a stationary, grid-connected battery is the NREL project from Erigo/EaglePicher Technologies, LLC Technologies. Inverters and nickel cadmium batteries inside of a utility scale 300 kW battery storage system will support Department of Defense micro-grids. Photo by Dennis Schroeder / NREL 32696 Companies that rely on

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

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

    Leadership Computing Facility electron density obtained from a density functional theory Shown here is the electron density obtained from a density functional theory (DFT) calculation of lithium oxide (Li2O) performed with the GPAW code. This visualization was the result of a simulation run on Intrepid, a supercomputer at the Argonne Leadership Computing Facility. Kah Chun Lau, Aaron Knoll and Larry A. Curtiss, Argonne National Laboratory Predictive Materials Modeling for Li-Air Battery

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

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

    Leadership Computing Facility Predictive Materials Modeling for Li-Air Battery Systems PI Name: Larry Curtiss PI Email: curtiss@anl.gov Institution: Argonne National Laboratory Allocation Program: INCITE Allocation Hours at ALCF: 50 Million Year: 2015 Research Domain: Materials Science A rechargeable lithium-air (Li-air) battery can potentially store five to ten times the energy of a lithium-ion (Li-ion) battery of the same weight. Realizing this enormous potential presents a challenging

  15. Prediction of interest rate using CKLS model with stochastic parameters

    SciTech Connect (OSTI)

    Ying, Khor Chia; Hin, Pooi Ah

    2014-06-19

    The Chan, Karolyi, Longstaff and Sanders (CKLS) model is a popular one-factor model for describing the spot interest rates. In this paper, the four parameters in the CKLS model are regarded as stochastic. The parameter vector φ{sup (j)} of four parameters at the (J+n)-th time point is estimated by the j-th window which is defined as the set consisting of the observed interest rates at the j′-th time point where j≤j′≤j+n. To model the variation of φ{sup (j)}, we assume that φ{sup (j)} depends on φ{sup (j−m)}, φ{sup (j−m+1)},…, φ{sup (j−1)} and the interest rate r{sub j+n} at the (j+n)-th time point via a four-dimensional conditional distribution which is derived from a [4(m+1)+1]-dimensional power-normal distribution. Treating the (j+n)-th time point as the present time point, we find a prediction interval for the future value r{sub j+n+1} of the interest rate at the next time point when the value r{sub j+n} of the interest rate is given. From the above four-dimensional conditional distribution, we also find a prediction interval for the future interest rate r{sub j+n+d} at the next d-th (d≥2) time point. The prediction intervals based on the CKLS model with stochastic parameters are found to have better ability of covering the observed future interest rates when compared with those based on the model with fixed parameters.

  16. QMC Simulations Database for Predictive Modeling and Theory | Argonne

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

    Leadership Computing Facility DMC spin densities for antiferromagnetic states AF1 (left) and AF3 (right) DMC spin densities for antiferromagnetic states AF1 (left) and AF3 (right). Shown only atoms with a non-null spin. The picture shows the localization of the spins in d-orbital. Blue is a spin down, while yellow is a spin up. Anouar Benali, Argonne National Laboratory QMC Simulations Database for Predictive Modeling and Theory PI Name: David Ceperley PI Email: ceperley@illinois.edu

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

    SciTech Connect (OSTI)

    Dowding, Kevin J.; Rutherford, Brian Milne

    2003-07-01

    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

  18. Predicting laser weld reliability with stochastic reduced-order models. Predicting laser weld reliability

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Emery, John M.; Field, Richard V.; Foulk, James W.; Karlson, Kyle N.; Grigoriu, Mircea D.

    2015-05-26

    Laser welds are prevalent in complex engineering systems and they frequently govern failure. The weld process often results in partial penetration of the base metals, leaving sharp crack-like features with a high degree of variability in the geometry and material properties of the welded structure. Furthermore, accurate finite element predictions of the structural reliability of components containing laser welds requires the analysis of a large number of finite element meshes with very fine spatial resolution, where each mesh has different geometry and/or material properties in the welded region to address variability. We found that traditional modeling approaches could not bemore » efficiently employed. Consequently, a method is presented for constructing a surrogate model, based on stochastic reduced-order models, and is proposed to represent the laser welds within the component. Here, the uncertainty in weld microstructure and geometry is captured by calibrating plasticity parameters to experimental observations of necking as, because of the ductility of the welds, necking – and thus peak load – plays the pivotal role in structural failure. The proposed method is exercised for a simplified verification problem and compared with the traditional Monte Carlo simulation with rather remarkable results.« less

  19. Predicting laser weld reliability with stochastic reduced-order models. Predicting laser weld reliability

    SciTech Connect (OSTI)

    Emery, John M.; Field, Richard V.; Foulk, James W.; Karlson, Kyle N.; Grigoriu, Mircea D.

    2015-05-26

    Laser welds are prevalent in complex engineering systems and they frequently govern failure. The weld process often results in partial penetration of the base metals, leaving sharp crack-like features with a high degree of variability in the geometry and material properties of the welded structure. Furthermore, accurate finite element predictions of the structural reliability of components containing laser welds requires the analysis of a large number of finite element meshes with very fine spatial resolution, where each mesh has different geometry and/or material properties in the welded region to address variability. We found that traditional modeling approaches could not be efficiently employed. Consequently, a method is presented for constructing a surrogate model, based on stochastic reduced-order models, and is proposed to represent the laser welds within the component. Here, the uncertainty in weld microstructure and geometry is captured by calibrating plasticity parameters to experimental observations of necking as, because of the ductility of the welds, necking – and thus peak load – plays the pivotal role in structural failure. The proposed method is exercised for a simplified verification problem and compared with the traditional Monte Carlo simulation with rather remarkable results.

  20. Predictive Optimal Control of Active and Passive Building Thermal Storage Inventory

    SciTech Connect (OSTI)

    Gregor P. Henze; Moncef Krarti

    2005-09-30

    Cooling of commercial buildings contributes significantly to the peak demand placed on an electrical utility grid. Time-of-use electricity rates encourage shifting of electrical loads to off-peak periods at night and weekends. Buildings can respond to these pricing signals by shifting cooling-related thermal loads either by precooling the building's massive structure or the use of active thermal energy storage systems such as ice storage. While these two thermal batteries have been engaged separately in the past, this project investigated the merits of harnessing both storage media concurrently in the context of predictive optimal control. To pursue the analysis, modeling, and simulation research of Phase 1, two separate simulation environments were developed. Based on the new dynamic building simulation program EnergyPlus, a utility rate module, two thermal energy storage models were added. Also, a sequential optimization approach to the cost minimization problem using direct search, gradient-based, and dynamic programming methods was incorporated. The objective function was the total utility bill including the cost of reheat and a time-of-use electricity rate either with or without demand charges. An alternative simulation environment based on TRNSYS and Matlab was developed to allow for comparison and cross-validation with EnergyPlus. The initial evaluation of the theoretical potential of the combined optimal control assumed perfect weather prediction and match between the building model and the actual building counterpart. The analysis showed that the combined utilization leads to cost savings that is significantly greater than either storage but less than the sum of the individual savings. The findings reveal that the cooling-related on-peak electrical demand of commercial buildings can be considerably reduced. A subsequent analysis of the impact of forecasting uncertainty in the required short-term weather forecasts determined that it takes only very simple

  1. Development of a fourth generation predictive capability maturity model.

    SciTech Connect (OSTI)

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

    2013-09-01

    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.

  2. Differential Angstrom model for predicting insolation from hours of sunshine

    SciTech Connect (OSTI)

    Yeboah-Amankwah, D.; Agyeman, K.

    1990-01-01

    The Angstrom model for predicting insolation is limited in scope because it gives equal weighting to sunshine hours recorded at any time of the day. The differential Angstrom model presented in this paper removes this limitation and relates insolation, q{sub j}, in the j{sup th} hour to the sunshine duration, n{sub j}, of the same period by the equation: q{sub j} = a{sub j} + b{sub j}. By regression analysis of monthly data, the set of constants a{sub j} and b{sub j} for each hour of each month of the year can be determined. Thus, using the appropriate set of a and b regression coefficients, any sunshine data can be transformed to insolation. The sum of the equation over a day gives the daily insolation from which monthly means can be calculated. The method has been applied to the 1986 and 1988 sunshine data recorded at the University of Papua New Guinea to predict the observed insolation to within 3.5%. The differential Angstrom method has applications in places which have much recorded data on hours of sunshine but have limited observed insolation data.

  3. Collaborative Research. Separating Forced and Unforced Decadal Predictability in Models and Observations

    SciTech Connect (OSTI)

    DelSole, Timothy

    2015-08-31

    The purpose of the proposed research was to identify unforced predictable components on decadal time scales, distinguish these components from forced predictable components, and to assess the reliability of model predictions of these components. The question of whether anthropogenic forcing changes decadal predictability, or gives rise to new forms of decadal predictability, also will be

  4. Advanced Sensors, Control, Platforms, and Modeling

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

    Advanced Sensors, Control, Platforms, and Modeling 1 for Manufacturing (Smart Manufacturing): 2 Technology Assessment 3 Contents 4 1. Introduction to the Technology/System ............................................................................................... 2 5 1.1 Overview ....................................................................................................................................... 2 6 1.2 Challenges and opportunities

  5. A comparison of simulation models for predicting soil water dynamics in bare and vegetated lysimeters

    SciTech Connect (OSTI)

    Link, S.O.; Kickert, R.N.; Fayer, M.J.; Gee, G.W.

    1993-06-01

    This report describes the results of simulation models used to predict soil water storage dynamics at the Field Lysimeter Test Facility (FLTF) weighing lysimeters. The objectives of this research is to develop the capability to predict soil water storage dynamics with plants in support of water infiltration control studies for the Hanford Permanent Isolation Barrier Development Program. It is important to gain confidence in one`s ability to simulate soil water dynamics over long time periods to assess the barrier`s ability to prevent drainage. Two models were compared for their ability to simulate soil water storage dynamics with and without plants in weighing lysimeters, the soil water infiltration and movement (SWIM) and the simulation of production and utilization of rangelands (SPUR-91) models. These models adequately simulated soil water storage dynamics for the weighing lysimeters. The range of root mean square error values for the two models was 7.0 to 19.8. This compares well with the range reported by Fayer et al. (1992) for the bare soil data sets of 8.1 to 22.1. Future research will test the predictive capability of these models for longer term lysimeter data sets and for historical data sets collected in various plant community types.

  6. Development of a land ice core for the Model for Prediction Across...

    Office of Scientific and Technical Information (OSTI)

    a land ice core for the Model for Prediction Across Scales (MPAS) Citation Details In-Document Search Title: Development of a land ice core for the Model for Prediction Across Scales ...

  7. Development of a land ice core for the Model for Prediction Across...

    Office of Scientific and Technical Information (OSTI)

    for the Model for Prediction Across Scales (MPAS) Citation Details In-Document Search Title: Development of a land ice core for the Model for Prediction Across Scales (MPAS) No ...

  8. Land-ice modeling for sea-level prediction (Technical Report...

    Office of Scientific and Technical Information (OSTI)

    Technical Report: Land-ice modeling for sea-level prediction Citation Details In-Document Search Title: Land-ice modeling for sea-level prediction Authors: Lipscomb, William H 1 ...

  9. RESIDUA UPGRADING EFFICIENCY IMPROVEMENT MODELS: COKE FORMATION PREDICTABILITY MAPS

    SciTech Connect (OSTI)

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

    2002-05-01

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

  10. Practical model for predicting pressure in gas-storage reservoirs

    SciTech Connect (OSTI)

    Mollnard, J.E.; Le Bitoux, P.; Pelce, V. ); Tek, M.R. )

    1990-11-01

    Optional planning, design, and operation of gas fields in production or storage critically depends on reliable models for predicting pressures that are often based on sophisticated numerical and mathematical concepts. The pressure that prevails at a given time is a direct function of production/injection schedules, past history, and surface equipment as related to reserves and reservoir characteristics. Maintaining the pressure within prescribed limits is particularly important in underground storage to avoid pressures above a maximum for reasons of safety and migration and below a minimum for surface-equipment and contracted-deliverability considerations. This paper presents a way to calculate the convolution integral on which the pressure variations depend. The classic methods were long and costly to run and seldom used. The author's method, which identifies this convolution integral with a finite sum of exponential terms, is much quicker and has been implemented in a program called PREPRE, usable on microcomputers. Based on a production/pressure schedule, the model is capable of forecasting the evolution of pressures in main zones of interest, such as wellbores, gathering systems, and surface equipment. The data required for the model-past production/pressure history-are matched by a special algorithm that automatically calculates the main reservoir parameters used as bases for future projections.

  11. Aquatic pathways model to predict the fate of phenolic compounds

    SciTech Connect (OSTI)

    Aaberg, R.L.; Peloquin, R.A.; Strenge, D.L.; Mellinger, P.J.

    1983-04-01

    Organic materials released from energy-related activities could affect human health and the environment. To better assess possible impacts, we developed a model to predict the fate of spills or discharges of pollutants into flowing or static bodies of fresh water. A computer code, Aquatic Pathways Model (APM), was written to implement the model. The computer programs use compartmental analysis to simulate aquatic ecosystems. The APM estimates the concentrations of chemicals in fish tissue, water and sediment, and is therefore useful for assessing exposure to humans through aquatic pathways. The APM will consider any aquatic pathway for which the user has transport data. Additionally, APM will estimate transport rates from physical and chemical properties of chemicals between several key compartments. The major pathways considered are biodegradation, fish and sediment uptake, photolysis, and evaporation. The model has been implemented with parameters for distribution of phenols, an important class of compounds found in the water-soluble fractions of coal liquids. Current modeling efforts show that, in comparison with many pesticides and polyaromatic hydrocarbons (PAH), the lighter phenolics (the cresols) are not persistent in the environment. The properties of heavier molecular weight phenolics (indanols, naphthols) are not well enough understood at this time to make similar judgements. For the twelve phenolics studied, biodegradation appears to be the major pathway for elimination from aquatic environments. A pond system simulation (using APM) of a spill of solvent refined coal (SRC-II) materials indicates that phenol, cresols, and other single cyclic phenolics are degraded to 16 to 25 percent of their original concentrations within 30 hours. Adsorption of these compounds into sediments and accumulation by fish was minor.

  12. LIFETIME PREDICTION FOR MODEL 9975 O-RINGS IN KAMS

    SciTech Connect (OSTI)

    Hoffman, E.; Skidmore, E.

    2009-11-24

    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

  13. Predictive modeling of synergistic effects in nanoscale ion track formation

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Zarkadoula, Eva; Pakarinen, Olli H.; Xue, Haizhou; Zhang, Yanwen; Weber, William J.

    2015-08-05

    Molecular dynamics techniques and the inelastic thermal spike model are used to study the coupled effects of inelastic energy loss due to 21 MeV Ni ion irradiation and pre-existing defects in SrTiO3. We determine the dependence on pre-existing defect concentration of nanoscale track formation occurring from the synergy between the inelastic energy loss and the pre-existing atomic defects. We show that the nanoscale ion tracks’ size can be controlled by the concentration of pre-existing disorder. This work identifies a major gap in fundamental understanding concerning the role played by defects in electronic energy dissipation and electron–lattice coupling.

  14. Estimating vehicle roadside encroachment frequency using accident prediction models

    SciTech Connect (OSTI)

    Miaou, S.-P.

    1996-07-01

    The existing data to support the development of roadside encroachment- based accident models are extremely limited and largely outdated. Under the sponsorship of the Federal Highway Administration and Transportation Research Board, several roadside safety projects have attempted to address this issue by providing rather comprehensive data collection plans and conducting pilot data collection efforts. It is clear from the results of these studies that the required field data collection efforts will be expensive. Furthermore, the validity of any field collected encroachment data may be questionable because of the technical difficulty to distinguish intentional from unintentional encroachments. This paper proposes an alternative method for estimating the basic roadside encroachment data without actually field collecting them. The method is developed by exploring the probabilistic relationships between a roadside encroachment event and a run-off-the-road event With some mild assumptions, the method is capable of providing a wide range of basic encroachment data from conventional accident prediction models. To illustrate the concept and use of such a method, some basic encroachment data are estimated for rural two-lane undivided roads. In addition, the estimated encroachment data are compared with the existing collected data. The illustration shows that the method described in this paper can be a viable approach to estimating basic encroachment data without actually collecting them which can be very costly.

  15. Model based control of a coke battery

    SciTech Connect (OSTI)

    Stone, P.M.; Srour, J.M.; Zulli, P.; Cunningham, R.; Hockings, K.

    1997-12-31

    This paper describes a model-based strategy for coke battery control at BHP Steel`s operations in Pt Kembla, Australia. The strategy uses several models describing the battery thermal and coking behavior. A prototype controller has been installed on the Pt Kembla No. 6 Battery (PK6CO). In trials, the new controller has been well accepted by operators and has resulted in a clear improvement in battery thermal stability, with a halving of the standard deviation of average battery temperature. Along with other improvements to that battery`s operations, this implementation has contributed to a 10% decrease in specific battery energy consumption. A number of enhancements to the low level control systems on that battery are currently being undertaken in order to realize further benefits.

  16. Model-Predictive Cascade Mitigation in Electric Power Systems With Storage and Renewables-Part I: Theory and Implementation

    SciTech Connect (OSTI)

    Almassalkhi, MR; Hiskens, IA

    2015-01-01

    A novel model predictive control (MPC) scheme is developed for mitigating the effects of severe line-overload disturbances in electrical power systems. A piece-wise linear convex approximation of line losses is employed to model the effect of transmission line power flow on conductor temperatures. Control is achieved through a receding-horizon model predictive control (MPC) strategy which alleviates line temperature overloads and thereby prevents the propagation of outages. The MPC strategy adjusts line flows by rescheduling generation, energy storage and controllable load, while taking into account ramp-rate limits and network limitations. In Part II of this paper, the MPC strategy is illustrated through simulation of the IEEE RTS-96 network, augmented to incorporate energy storage and renewable generation.

  17. Investigation of the effect of chemistry models on the numerical predictions of the supersonic combustion of hydrogen

    SciTech Connect (OSTI)

    Kumaran, K.; Babu, V.

    2009-04-15

    In this numerical study, the influence of chemistry models on the predictions of supersonic combustion in a model combustor is investigated. To this end, 3D, compressible, turbulent, reacting flow calculations with a detailed chemistry model (with 37 reactions and 9 species) and the Spalart-Allmaras turbulence model have been carried out. These results are compared with earlier results obtained using single step chemistry. Hydrogen is used as the fuel and three fuel injection schemes, namely, strut, staged (i.e., strut and wall) and wall injection, are considered to evaluate the impact of the chemistry models on the flow field predictions. Predictions of the mass fractions of major species, minor species, dimensionless stagnation temperature, dimensionless static pressure rise and thrust percentage along the combustor length are presented and discussed. Overall performance metrics such as mixing efficiency and combustion efficiency are used to draw inferences on the nature (whether mixing- or kinetic-controlled) and the completeness of the combustion process. The predicted values of the dimensionless wall static pressure are compared with experimental data reported in the literature. The calculations show that multi step chemistry predicts higher and more wide spread heat release than what is predicted by single step chemistry. In addition, it is also shown that multi step chemistry predicts intricate details of the combustion process such as the ignition distance and induction distance. (author)

  18. Modeling of Lean Exhaust Emissions Control Systems | Department...

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

    Lean Exhaust Emissions Control Systems Modeling of Lean Exhaust Emissions Control Systems 2002 DEER Conference Presentation: National Renewable Energy Laboratory ...

  19. Stability of Ensemble Models Predicts Productivity of Enzymatic Systems

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Theisen, Matthew K.; Lafontaine Rivera, Jimmy G.; Liao, James C.

    2016-03-10

    Stability in a metabolic system may not be obtained if incorrect amounts of enzymes are used. Without stability, some metabolites may accumulate or deplete leading to the irreversible loss of the desired operating point. Even if initial enzyme amounts achieve a stable steady state, changes in enzyme amount due to stochastic variations or environmental changes may move the system to the unstable region and lose the steady-state or quasi-steady-state flux. This situation is distinct from the phenomenon characterized by typical sensitivity analysis, which focuses on the smooth change before loss of stability. Here we show that metabolic networks differ significantlymore » in their intrinsic ability to attain stability due to the network structure and kinetic forms, and that after achieving stability, some enzymes are prone to cause instability upon changes in enzyme amounts. We use Ensemble Modelling for Robustness Analysis (EMRA) to analyze stability in four cell-free enzymatic systems when enzyme amounts are changed. Loss of stability in continuous systems can lead to lower production even when the system is tested experimentally in batch experiments. The predictions of instability by EMRA are supported by the lower productivity in batch experimental tests. Finally, the EMRA method incorporates properties of network structure, including stoichiometry and kinetic form, but does not require specific parameter values of the enzymes.« less

  20. Advanced LD Engine Systems and Emissions Control Modeling and...

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

    LD Engine Systems and Emissions Control Modeling and Analysis Advanced LD Engine Systems and Emissions Control Modeling and Analysis 2012 DOE Hydrogen and Fuel Cells Program and ...

  1. Advanced PHEV Engine Systems and Emissions Control Modeling and...

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

    PHEV Engine Systems and Emissions Control Modeling and Analysis Advanced PHEV Engine Systems and Emissions Control Modeling and Analysis 2011 DOE Hydrogen and Fuel Cells Program, ...

  2. Aggregated Modeling and Control of Air Conditioning Loads for...

    Office of Scientific and Technical Information (OSTI)

    Journal Article: Aggregated Modeling and Control of Air Conditioning Loads for Demand Response Citation Details In-Document Search Title: Aggregated Modeling and Control of Air...

  3. Project Profile: Predictive Physico-Chemical Modeling of Intrinsic...

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

    NREL logo NREL, under the Physics of Reliability: Evaluating Design Insights for Component Technologies in Solar (PREDICTS) Program will be developing a physics-based computational ...

  4. Eulerian CFD Models to Predict Thermophoretic Deposition of Soot...

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

    This paper describes an Eulerian axisymmetric method in Fluent(R) to predict the overall heat transfer reduction of a surrogate tube due to thermophoretic deposition of submicron ...

  5. New climate model predicts likelihood of Greenland ice melt, sea level rise

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

    and dangerous temperatures New climate model predicts likelihood of Greenland ice melt New climate model predicts likelihood of Greenland ice melt, sea level rise and dangerous temperatures A new computer model of accumulated carbon emissions predicts the likelihood of crossing several dangerous climate change thresholds. November 20, 2015 Greenland ice loss. Greenland ice loss. Contact Kevin Roark Communications Office (505) 665-9202 Email "The model is based on idealized

  6. Predictive modeling of synergistic effects in nanoscale ion track formation

    SciTech Connect (OSTI)

    Zarkadoula, Eva; Pakarinen, Olli H.; Xue, Haizhou; Zhang, Yanwen; Weber, William J.

    2015-08-05

    Molecular dynamics techniques and the inelastic thermal spike model are used to study the coupled effects of inelastic energy loss due to 21 MeV Ni ion irradiation and pre-existing defects in SrTiO3. We determine the dependence on pre-existing defect concentration of nanoscale track formation occurring from the synergy between the inelastic energy loss and the pre-existing atomic defects. We show that the nanoscale ion tracks’ size can be controlled by the concentration of pre-existing disorder. This work identifies a major gap in fundamental understanding concerning the role played by defects in electronic energy dissipation and electron–lattice coupling.

  7. Model Identification for Optimal Diesel Emissions Control

    SciTech Connect (OSTI)

    Stevens, Andrew J.; Sun, Yannan; Song, Xiaobo; Parker, Gordon

    2013-06-20

    In this paper we develop a model based con- troller for diesel emission reduction using system identification methods. Specifically, our method minimizes the downstream readings from a production NOx sensor while injecting a minimal amount of urea upstream. Based on the linear quadratic estimator we derive the closed form solution to a cost function that accounts for the case some of the system inputs are not controllable. Our cost function can also be tuned to trade-off between input usage and output optimization. Our approach performs better than a production controller in simulation. Our NOx conversion efficiency was 92.7% while the production controller achieved 92.4%. For NH3 conversion, our efficiency was 98.7% compared to 88.5% for the production controller.

  8. Characterization and validation of an in silico toxicology model to predict the mutagenic potential of drug impurities*

    SciTech Connect (OSTI)

    Valerio, Luis G.; Cross, Kevin P.

    2012-05-01

    Control and minimization of human exposure to potential genotoxic impurities found in drug substances and products is an important part of preclinical safety assessments of new drug products. The FDA's 2008 draft guidance on genotoxic and carcinogenic impurities in drug substances and products allows use of computational quantitative structureactivity relationships (QSAR) to identify structural alerts for known and expected impurities present at levels below qualified thresholds. This study provides the information necessary to establish the practical use of a new in silico toxicology model for predicting Salmonella t. mutagenicity (Ames assay outcome) of drug impurities and other chemicals. We describe the model's chemical content and toxicity fingerprint in terms of compound space, molecular and structural toxicophores, and have rigorously tested its predictive power using both cross-validation and external validation experiments, as well as case studies. Consistent with desired regulatory use, the model performs with high sensitivity (81%) and high negative predictivity (81%) based on external validation with 2368 compounds foreign to the model and having known mutagenicity. A database of drug impurities was created from proprietary FDA submissions and the public literature which found significant overlap between the structural features of drug impurities and training set chemicals in the QSAR model. Overall, the model's predictive performance was found to be acceptable for screening drug impurities for Salmonella mutagenicity. -- Highlights: ? We characterize a new in silico model to predict mutagenicity of drug impurities. ? The model predicts Salmonella mutagenicity and will be useful for safety assessment. ? We examine toxicity fingerprints and toxicophores of this Ames assay model. ? We compare these attributes to those found in drug impurities known to FDA/CDER. ? We validate the model and find it has a desired predictive performance.

  9. TH-A-9A-01: Active Optical Flow Model: Predicting Voxel-Level Dose Prediction in Spine SBRT

    SciTech Connect (OSTI)

    Liu, J; Wu, Q.J.; Yin, F; Kirkpatrick, J; Cabrera, A; Ge, Y

    2014-06-15

    Purpose: To predict voxel-level dose distribution and enable effective evaluation of cord dose sparing in spine SBRT. Methods: We present an active optical flow model (AOFM) to statistically describe cord dose variations and train a predictive model to represent correlations between AOFM and PTV contours. Thirty clinically accepted spine SBRT plans are evenly divided into training and testing datasets. The development of predictive model consists of 1) collecting a sequence of dose maps including PTV and OAR (spinal cord) as well as a set of associated PTV contours adjacent to OAR from the training dataset, 2) classifying data into five groups based on PTV's locations relative to OAR, two “Top”s, “Left”, “Right”, and “Bottom”, 3) randomly selecting a dose map as the reference in each group and applying rigid registration and optical flow deformation to match all other maps to the reference, 4) building AOFM by importing optical flow vectors and dose values into the principal component analysis (PCA), 5) applying another PCA to features of PTV and OAR contours to generate an active shape model (ASM), and 6) computing a linear regression model of correlations between AOFM and ASM.When predicting dose distribution of a new case in the testing dataset, the PTV is first assigned to a group based on its contour characteristics. Contour features are then transformed into ASM's principal coordinates of the selected group. Finally, voxel-level dose distribution is determined by mapping from the ASM space to the AOFM space using the predictive model. Results: The DVHs predicted by the AOFM-based model and those in clinical plans are comparable in training and testing datasets. At 2% volume the dose difference between predicted and clinical plans is 4.2±4.4% and 3.3±3.5% in the training and testing datasets, respectively. Conclusion: The AOFM is effective in predicting voxel-level dose distribution for spine SBRT. Partially supported by NIH/NCI under grant

  10. Lumped Parameter Modeling as a Predictive Tool for a Battery Status Monitor

    SciTech Connect (OSTI)

    Jon P. Christophersen; Chester G. Motloch; Chinh D. Ho; John L. Morrison; Ronald C. Fenton; Vincent S. Battaglia; Tien Q. Duong

    2003-10-01

    The Advanced Technology Development Program is currently evaluating the performance of the second generation of lithium-ion cells (i.e., Gen 2 cells). Both the Gen 2 Baseline and Variant C cells are tested in accordance with the cell-specific test plan, and are removed at roughly equal power fade increments and sent for destructive diagnostic analysis. The diagnostic laboratories did not need all test cells for analysis, and returned five spare cells to the Idaho National Engineering and Environmental Laboratory (INEEL). INEEL used these cells for special pulse testing at various duty cycles, amplitudes, and durations to investigate the usefulness of the lumped parameter model (LPM) as a predictive tool in a battery status monitor (BSM). The LPM is a simplified linear model that accurately predicts the voltage response during certain pulse conditions. A database of parameter trends should enable dynamic predictions of state-of-charge and state-of-health conditions during in-vehicle pulsing. This information could be used by the BSM to provide accurate information to the vehicle control system.

  11. Prediction of Regulation Reserve Requirements in California ISO Control Area based on BAAL Standard

    SciTech Connect (OSTI)

    Etingov, Pavel V.; Makarov, Yuri V.; Samaan, Nader A.; Ma, Jian; Loutan, Clyde

    2013-07-21

    This paper presents new methodologies developed at Pacific Northwest National Laboratory (PNNL) to estimate regulation capacity requirements in the California ISO control area. Two approaches have been developed: (1) an approach based on statistical analysis of actual historical area control error (ACE) and regulation data, and (2) an approach based on balancing authority ACE limit control performance standard. The approaches predict regulation reserve requirements on a day-ahead basis including upward and downward requirements, for each operating hour of a day. California ISO data has been used to test the performance of the proposed algorithms. Results show that software tool allows saving up to 30% on the regulation procurements cost .

  12. A Predictive Model of Fragmentation using Adaptive Mesh Refinement...

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

    A new arena for predictive simulations in high- powered laser systems "12 NIF" Or 96 ... Numerical simulations show how to mitigate damage from debris and shrapnel Damage prior to ...

  13. Global nuclear material flow/control model

    SciTech Connect (OSTI)

    Dreicer, J.S.; Rutherford, D.S.; Fasel, P.K.; Riese, J.M.

    1997-10-01

    This is the final report of a two-year, Laboratory Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). The nuclear danger can be reduced by a system for global management, protection, control, and accounting as part of an international regime for nuclear materials. The development of an international fissile material management and control regime requires conceptual research supported by an analytical and modeling tool which treats the nuclear fuel cycle as a complete system. The prototype model developed visually represents the fundamental data, information, and capabilities related to the nuclear fuel cycle in a framework supportive of national or an international perspective. This includes an assessment of the global distribution of military and civilian fissile material inventories, a representation of the proliferation pertinent physical processes, facility specific geographic identification, and the capability to estimate resource requirements for the management and control of nuclear material. The model establishes the foundation for evaluating the global production, disposition, and safeguards and security requirements for fissile nuclear material and supports the development of other pertinent algorithmic capabilities necessary to undertake further global nuclear material related studies.

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

    Broader source: Energy.gov [DOE]

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

  15. Statistical surrogate models for prediction of high-consequence...

    Office of Scientific and Technical Information (OSTI)

    existing coupled atmospheric models due to the high computational cost of each simulation. ... CLIMATES; DIAGNOSIS; DISTRIBUTION; FORECASTING; GENERAL CIRCULATION MODELS; METRICS; ...

  16. Reduced-Order Model Based Feedback Control For Modified Hasegawa...

    Office of Scientific and Technical Information (OSTI)

    Reduced-Order Model Based Feedback Control For Modified Hasegawa-Wakatani Model Citation Details In-Document Search Title: Reduced-Order Model Based Feedback Control For Modified ...

  17. QMC Simulations DataBase for Predictive Theory and Modeling | Argonne

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

    Leadership Computing Facility CO monoxide adsorbs on the Pt (111) surface CO monoxide adsorbs on the Pt (111) surface. One application of the QMC Simulations Database for the Predictive Modeling and Theory project is to model this important surface reaction which is poorly modeled by methods with static treatment of electron-electron interaction. Credit: William Parker, ALCF QMC Simulations DataBase for Predictive Theory and Modeling PI Name: David Ceperley PI Email: ceperley@illinois.ed

  18. A novel mathematical model for controllable near-field electrospinning

    SciTech Connect (OSTI)

    Ru, Changhai E-mail: luojun@shu.edu.cn; Robotics and Microsystems Center, Soochow University, Suzhou 215021 ; Chen, Jie; Shao, Zhushuai; Pang, Ming; Luo, Jun E-mail: luojun@shu.edu.cn

    2014-01-15

    Near-field electrospinning (NFES) had better controllability than conventional electrospinning. However, due to the lack of guidance of theoretical model, precise deposition of micro/nano fibers could only accomplished by experience. To analyze the behavior of charged jet in NFES using mathematical model, the momentum balance equation was simplified and a new expression between jet cross-sectional radius and axial position was derived. Using this new expression and mass conservation equation, expressions for jet cross-sectional radius and velocity were derived in terms of axial position and initial jet acceleration in the form of exponential functions. Based on Slender-body theory and Giesekus model, a quadratic equation for initial jet acceleration was acquired. With the proposed model, it was able to accurately predict the diameter and velocity of polymer fibers in NFES, and mathematical analysis rather than experimental methods could be applied to study the effects of the process parameters in NFES. Moreover, the movement velocity of the collector stage can be regulated by mathematical model rather than experience. Therefore, the model proposed in this paper had important guiding significance to precise deposition of polymer fibers.

  19. Modeling Combustion Control for High Power Diesel Mode Switching...

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

    Combustion Control for High Power Diesel Mode Switching Modeling Combustion Control for High Power Diesel Mode Switching Poster presentation given at the 16th Directions in ...

  20. SimTable helps firefighters model and predict fire direction

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

    for modeling lung cancer. In other news December, 1 2015 - Novel therapy for stomach cancer; grand opening of Manhattan Project National Historical Park; 2015 Northern New...

  1. Depositional sequence analysis and sedimentologic modeling for improved prediction of Pennsylvanian reservoirs (Annex 1)

    SciTech Connect (OSTI)

    Watney, W.L.

    1992-01-01

    Interdisciplinary studies of the Upper Pennsylvanian Lansing and Kansas City groups have been undertaken in order to improve the geologic characterization of petroleum reservoirs and to develop a quantitative understanding of the processes responsible for formation of associated depositional sequences. To this end, concepts and methods of sequence stratigraphy are being used to define and interpret the three-dimensional depositional framework of the Kansas City Group. The investigation includes characterization of reservoir rocks in oil fields in western Kansas, description of analog equivalents in near-surface and surface sites in southeastern Kansas, and construction of regional structural and stratigraphic framework to link the site specific studies. Geologic inverse and simulation models are being developed to integrate quantitative estimates of controls on sedimentation to produce reconstructions of reservoir-bearing strata in an attempt to enhance our ability to predict reservoir characteristics.

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

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

    Model-Based Controller for Fuel Efficient, Low Emissions Diesel Engines Fully model-based, practically-mapless engine control concept is viable PDF icon deer09allain.pdf...

  3. Validation of model based active control of combustion instability

    SciTech Connect (OSTI)

    Fleifil, M.; Ghoneim, Z.; Ghoniem, A.F.

    1998-07-01

    The demand for efficient, company and clean combustion systems have spurred research into the fundamental mechanisms governing their performance and means of interactively changing their performance characteristics. Thermoacoustic instability which is frequently observed in combustion systems with high power density, when burning close to the lean flammability limit, or using exhaust gas recirculation to meet more stringent emissions regulations, etc. Its occurrence and/or means to mitigate them passively lead to performance degradation such as reduced combustion efficiency, high local heat transfer rates, increase in the mixture equivalence ratio or system failure due to structural damage. This paper reports on their study of the origin of thermoacoustic instability, its dependence on system parameters and the means of actively controlling it. The authors have developed an analytical model of thermoacoustic instability in premixed combustors. The model combines a heat release dynamics model constructed using the kinematics of a premixed flame stabilized behind a perforated plate with the linearized conservation equations governing the system acoustics. This formulation allows model based controller design. In order to test the performance of the analytical model, a numerical solution of the partial differential equations governing the system has been carried out using the principle of harmonic separation and focusing on the dominant unstable mode. This leads to a system of ODEs governing the thermofluid variables. Analytical predictions of the frequency and growth ate of the unstable mode are shown to be in good agreement with the numerical simulations as well s with those obtained using experimental identification techniques when applied to a laboratory combustor. The authors use these results to confirm the validity of the assumptions used in formulating the analytical model. A controller based on the minimization of a cost function using the LQR technique has

  4. Modelling of edge localised modes and edge localised mode control [Modelling of ELMs and ELM control

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Huijsmans, G. T. A.; Chang, C. S.; Ferraro, N.; Sugiyama, L.; Waelbroeck, F.; Xu, X. Q.; Loarte, A.; Futatani, S.

    2015-02-07

    Edge Localised Modes (ELMs) in ITER Q = 10 H-mode plasmas are likely to lead to large transient heat loads to the divertor. In order to avoid an ELM induced reduction of the divertor lifetime, the large ELM energy losses need to be controlled. In ITER, ELM control is foreseen using magnetic field perturbations created by in-vessel coils and the injection of small D2 pellets. ITER plasmas are characterised by low collisionality at a high density (high fraction of the Greenwald density limit). These parameters cannot simultaneously be achieved in current experiments. Thus, the extrapolation of the ELM properties andmore » the requirements for ELM control in ITER relies on the development of validated physics models and numerical simulations. Here, we describe the modelling of ELMs and ELM control methods in ITER. The aim of this paper is not a complete review on the subject of ELM and ELM control modelling but rather to describe the current status and discuss open issues.« less

  5. Model-Predictive Cascade Mitigation in Electric Power Systems With Storage and Renewables-Part II: Case-Study

    SciTech Connect (OSTI)

    Almassalkhi, MR; Hiskens, IA

    2015-01-01

    The novel cascade-mitigation scheme developed in Part I of this paper is implemented within a receding-horizon model predictive control (MPC) scheme with a linear controller model. This present paper illustrates the MPC strategy with a case-study that is based on the IEEE RTS-96 network, though with energy storage and renewable generation added. It is shown that the MPC strategy alleviates temperature overloads on transmission lines by rescheduling generation, energy storage, and other network elements, while taking into account ramp-rate limits and network limitations. Resilient performance is achieved despite the use of a simplified linear controller model. The MPC scheme is compared against a base-case that seeks to emulate human operator behavior.

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

    SciTech Connect (OSTI)

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

    2013-12-18

    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.

  7. Predictive Modeling of Terrestrial Radiation Exposure from Geologic Materials

    SciTech Connect (OSTI)

    Malchow, Russell L.; Haber, Daniel University of Nevada, Las Vegas; Burnley, Pamela; Marsac, Kara; Hausrath, Elisabeth; Adcock, Christopher

    2015-01-01

    Aerial gamma ray surveys are important for those working in nuclear security and industry for determining locations of both anthropogenic radiological sources and natural occurrences of radionuclides. During an aerial gamma ray survey, a low flying aircraft, such as a helicopter, flies in a linear pattern across the survey area while measuring the gamma emissions with a sodium iodide (NaI) detector. Currently, if a gamma ray survey is being flown in an area, the only way to correct for geologic sources of gamma rays is to have flown the area previously. This is prohibitively expensive and would require complete national coverage. This project’s goal is to model the geologic contribution to radiological backgrounds using published geochemical data, GIS software, remote sensing, calculations, and modeling software. K, U and Th are the three major gamma emitters in geologic material. U and Th are assumed to be in secular equilibrium with their daughter isotopes. If K, U, and Th abundance values are known for a given geologic unit the expected gamma ray exposure rate can be calculated using the Grasty equation or by modeling software. Monte Carlo N-Particle Transport software (MCNP), developed by Los Alamos National Laboratory, is modeling software designed to simulate particles and their interactions with matter. Using this software, models have been created that represent various lithologies. These simulations randomly generate gamma ray photons at energy levels expected from natural radiologic sources. The photons take a random path through the simulated geologic media and deposit their energy at the end of their track. A series of nested spheres have been created and filled with simulated atmosphere to record energy deposition. Energies deposited are binned in the same manner as the NaI detectors used during an aerial survey. These models are used in place of the simplistic Grasty equation as they take into account absorption properties of the lithology which the

  8. Prediction of turbulent buoyant flow using an RNG {kappa}-{epsilon} model

    SciTech Connect (OSTI)

    Gan, G.

    1998-02-06

    Buoyant flows occur in various engineering practices such as heating, ventilation, and air-conditioning of buildings. This phenomenon is particularly important in rooms with displacement ventilation, where supply air velocities are generally very low (< 0.2 m/s) so that the predominant indoor airflow is largely due to thermal buoyancy created by internal heat sources such as occupants and equipment. This type of ventilation system has been shown to be an effective means to remove excess heat and achieve good indoor air quality. Here, numerical predictions were carried out for turbulent natural convection in two tall air cavities. The standard and RNG {kappa}-{epsilon} turbulence models were used for the predictions. The predicted results were compared with experimental data from the literature, and good agreement between prediction and measurement was obtained. Improved prediction was achieved using the RNG {kappa}-{epsilon} model in comparison with the standard {kappa}-{epsilon} model. The principal parameters for the improvement were investigated.

  9. Method for quantifying the prediction uncertainties associated with water quality models

    SciTech Connect (OSTI)

    Summers, J.K.; Wilson, H.T.; Kou, J.

    1993-01-01

    Many environmental regulatory agencies depend on models to organize, understand, and utilize the information for regulatory decision making. A general analytical protocol was developed to quantify prediction error associated with commonly used surface water quality models. Its application is demonstrated by comparing water quality models configured to represent different levels of spatial, temporal, and mechanistic complexity. This comparison can be accomplished by fitting the models to a benchmark data set. Once the models are successfully fitted to the benchmark data, the prediction errors associated with each application can be quantified using the Monte Carlo simulation techniques.

  10. Modelling hepatitis C therapy—predicting effects of treatment

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Perelson, Alan S.; Guedj, Jeremie

    2015-06-30

    Mathematically modelling changes in HCV RNA levels measured in patients who receive antiviral therapy has yielded many insights into the pathogenesis and effects of treatment on the virus. By determining how rapidly HCV is cleared when viral replication is interrupted by a therapy, one can deduce how rapidly the virus is produced in patients before treatment. This knowledge, coupled with estimates of the HCV mutation rate, enables one to estimate the frequency with which drug resistant variants arise. Modelling HCV also permits the deduction of the effectiveness of an antiviral agent at blocking HCV replication from the magnitude of themore » initial viral decline. One can also estimate the lifespan of an HCV-infected cell from the slope of the subsequent viral decline and determine the duration of therapy needed to cure infection. The original understanding of HCV RNA decline under interferon-based therapies obtained by modelling needed to be revised in order to interpret the HCV RNA decline kinetics seen when using direct-acting antiviral agents (DAAs). In addition, there also exist unresolved issues involving understanding therapies with combinations of DAAs, such as the presence of detectable HCV RNA at the end of therapy in patients who nonetheless have a sustained virologic response.« less

  11. Evaluation of cloud prediction and determination of critical relative humidity for a mesoscale numerical weather prediction model

    SciTech Connect (OSTI)

    Seaman, N.L.; Guo, Z.; Ackerman, T.P.

    1996-04-01

    Predictions of cloud occurrence and vertical location from the Pennsylvannia State University/National Center for Atmospheric Research nonhydrostatic mesoscale model (MM5) were evaluated statistically using cloud observations obtained at Coffeyville, Kansas, as part of the Second International satellite Cloud Climatology Project Regional Experiment campaign. Seventeen cases were selected for simulation during a November-December 1991 field study. MM5 was used to produce two sets of 36-km simulations, one with and one without four-dimensional data assimilation (FDDA), and a set of 12-km simulations without FDDA, but nested within the 36-km FDDA runs.

  12. Maintenance personnel performance simulation (MAPPS): a model for predicting maintenance performance reliability in nuclear power plants

    SciTech Connect (OSTI)

    Knee, H.E.; Krois, P.A.; Haas, P.M.; Siegel, A.I.; Ryan, T.G.

    1983-01-01

    The NRC has developed a structured, quantitative, predictive methodology in the form of a computerized simulation model for assessing maintainer task performance. Objective of the overall program is to develop, validate, and disseminate a practical, useful, and acceptable methodology for the quantitative assessment of NPP maintenance personnel reliability. The program was organized into four phases: (1) scoping study, (2) model development, (3) model evaluation, and (4) model dissemination. The program is currently nearing completion of Phase 2 - Model Development.

  13. Modeling predicts which counties could store more carbon in soil by growing

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

    bioenergy crops | Argonne National Laboratory Modeling predicts which counties could store more carbon in soil by growing bioenergy crops By Katie Elyce Jones * July 13, 2016 Tweet EmailPrint To help stakeholders in government and business make smart decisions about the best types of land and local climates for planting bioenergy crops, researchers at the U.S. Department of Energy's (DOE's) Argonne National Laboratory are using computational modeling to predict which counties could see

  14. Model based control of dynamic atomic force microscope

    SciTech Connect (OSTI)

    Lee, Chibum; Salapaka, Srinivasa M.

    2015-04-15

    A model-based robust control approach is proposed that significantly improves imaging bandwidth for the dynamic mode atomic force microscopy. A model for cantilever oscillation amplitude and phase dynamics is derived and used for the control design. In particular, the control design is based on a linearized model and robust H{sub ∞} control theory. This design yields a significant improvement when compared to the conventional proportional-integral designs and verified by experiments.

  15. Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches

    SciTech Connect (OSTI)

    Singh, Kunwar P.; Gupta, Shikha; Rai, Premanjali

    2013-10-15

    Robust global models capable of discriminating positive and non-positive carcinogens; and predicting carcinogenic potency of chemicals in rodents were developed. The dataset of 834 structurally diverse chemicals extracted from Carcinogenic Potency Database (CPDB) was used which contained 466 positive and 368 non-positive carcinogens. Twelve non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals and nonlinearity in the data were evaluated using Tanimoto similarity index and BrockDechertScheinkman statistics. Probabilistic neural network (PNN) and generalized regression neural network (GRNN) models were constructed for classification and function optimization problems using the carcinogenicity end point in rat. Validation of the models was performed using the internal and external procedures employing a wide series of statistical checks. PNN constructed using five descriptors rendered classification accuracy of 92.09% in complete rat data. The PNN model rendered classification accuracies of 91.77%, 80.70% and 92.08% in mouse, hamster and pesticide data, respectively. The GRNN constructed with nine descriptors yielded correlation coefficient of 0.896 between the measured and predicted carcinogenic potency with mean squared error (MSE) of 0.44 in complete rat data. The rat carcinogenicity model (GRNN) applied to the mouse and hamster data yielded correlation coefficient and MSE of 0.758, 0.71 and 0.760, 0.46, respectively. The results suggest for wide applicability of the inter-species models in predicting carcinogenic potency of chemicals. Both the PNN and GRNN (inter-species) models constructed here can be useful tools in predicting the carcinogenicity of new chemicals for regulatory purposes. - Graphical abstract: Figure (a) shows classification accuracies (positive and non-positive carcinogens) in rat, mouse, hamster, and pesticide data yielded by optimal PNN model. Figure (b) shows generalization and predictive abilities

  16. Solid phase evolution in the Biosphere 2 hillslope experiment as predicted by modeling of hydrologic and geochemical fluxes

    SciTech Connect (OSTI)

    Dontsova, K.; Steefel, C.I.; Desilets, S.; Thompson, A.; Chorover, J.

    2009-07-15

    A reactive transport geochemical modeling study was conducted to help predict the mineral transformations occurring over a ten year time-scale that are expected to impact soil hydraulic properties in the Biosphere 2 (B2) synthetic hillslope experiment. The modeling sought to predict the rate and extent of weathering of a granular basalt (selected for hillslope construction) as a function of climatic drivers, and to assess the feedback effects of such weathering processes on the hydraulic properties of the hillslope. Flow vectors were imported from HYDRUS into a reactive transport code, CrunchFlow2007, which was then used to model mineral weathering coupled to reactive solute transport. Associated particle size evolution was translated into changes in saturated hydraulic conductivity using Rosetta software. We found that flow characteristics, including velocity and saturation, strongly influenced the predicted extent of incongruent mineral weathering and neo-phase precipitation on the hillslope. Results were also highly sensitive to specific surface areas of the soil media, consistent with surface reaction controls on dissolution. Effects of fluid flow on weathering resulted in significant differences in the prediction of soil particle size distributions, which should feedback to alter hillslope hydraulic conductivities.

  17. Improving models to predict phenological responses to global change

    SciTech Connect (OSTI)

    Richardson, Andrew D.

    2015-11-25

    The term phenology describes both the seasonal rhythms of plants and animals, and the study of these rhythms. Plant phenological processes, including, for example, when leaves emerge in the spring and change color in the autumn, are highly responsive to variation in weather (e.g. a warm vs. cold spring) as well as longer-term changes in climate (e.g. warming trends and changes in the timing and amount of rainfall). We conducted a study to investigate the phenological response of northern peatland communities to global change. Field work was conducted at the SPRUCE experiment in northern Minnesota, where we installed 10 digital cameras. Imagery from the cameras is being used to track shifts in plant phenology driven by elevated carbon dioxide and elevated temperature in the different SPRUCE experimental treatments. Camera imagery and derived products (“greenness”) is being posted in near-real time on a publicly available web page (http://phenocam.sr.unh.edu/webcam/gallery/). The images will provide a permanent visual record of the progression of the experiment over the next 10 years. Integrated with other measurements collected as part of the SPRUCE program, this study is providing insight into the degree to which phenology may mediate future shifts in carbon uptake and storage by peatland ecosystems. In the future, these data will be used to develop improved models of vegetation phenology, which will be tested against ground observations collected by a local collaborator.

  18. Final Scientific Technical Report: INTEGRATED PREDICTIVE DEMAND RESPONSE CONTROLLER FOR COMMERCIAL BUILDINGS

    SciTech Connect (OSTI)

    Wenzel, Mike

    2013-10-14

    This project provides algorithms to perform demand response using the thermal mass of a building. Using the thermal mass of the building is an attractive method for performing demand response because there is no need for capital expenditure. The algorithms rely on the thermal capacitance inherent in the building?s construction materials. A near-optimal ?day ahead? predictive approach is developed that is meant to keep the building?s electrical demand constant during the high cost periods. This type of approach is appropriate for both time-of-use and critical peak pricing utility rate structures. The approach uses the past days data in order to determine the best temperature setpoints for the building during the high price periods on the next day. A second ?model predictive approach? (MPC) uses a thermal model of the building to determine the best temperature for the next sample period. The approach uses constant feedback from the building and is capable of appropriately handling real time pricing. Both approaches are capable of using weather forecasts to improve performance.

  19. Simplified predictive models for CO2 sequestration performance assessment

    SciTech Connect (OSTI)

    Mishra, Srikanta; Ganesh, Priya; Schuetter, Jared; He, Jincong; Jin, Zhaoyang; Durlofsky, Louis J.

    2015-09-30

    Latin Hypercube sampling (LHS) based design with a multidimensional kriging metamodel fit. For roughly the same number of simulations, the LHS-based metamodel yields a more robust predictive model, as verified by a k-fold cross-validation approach (with data split into training and test sets) as well by validation with an independent dataset. In the third category, a reduced-order modeling procedure is utilized that combines proper orthogonal decomposition (POD) for reducing problem dimensionality with trajectory-piecewise linearization (TPWL) in order to represent system response at new control settings from a limited number of training runs. Significant savings in computational time are observed with reasonable accuracy from the PODTPWL reduced-order model for both vertical and horizontal well problems – which could be important in the context of history matching, uncertainty quantification and optimization problems. The simplified physics and statistical learning based models are also validated using an uncertainty analysis framework. Reference cumulative distribution functions of key model outcomes (i.e., plume radius and reservoir pressure buildup) generated using a 97-run full-physics simulation are successfully validated against the CDF from 10,000 sample probabilistic simulations using the simplified models. The main contribution of this research project is the development and validation of a portfolio of simplified modeling approaches that will enable rapid feasibility and risk assessment for CO2 sequestration in deep saline formations.

  20. Experimental Studies for DPF and SCR Model, Control System, and...

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

    Using Diesel and Biodiesel Fuels Experimental Studies for DPF and SCR Model, Control System, and OBD Development for Engines Using Diesel and Biodiesel Fuels Combination and ...

  1. A simple Analytical Model to Study and Control Azimuthal Instabilities...

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

    A simple Analytical Model to Study and Control Azimuthal Instabilities in Annular Combustion Chambers Authors: Parmentier, J-F., Salas, P., Wolf, P., Staffelbach, G., Nicoud, F., ...

  2. Experimental Studies for DPF and SCR Model, Control System, and...

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

    Using Diesel and Biodiesel Fuels Experimental Studies for DPF and SCR Model, Control System, and OBD Development for Engines Using Diesel and Biodiesel Fuels Advanced Engine...

  3. Experimental Studies for DPF and SCR Model, Control System, and...

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

    Using Diesel and Biodiesel Fuels Experimental Studies for DPF and SCR Model, Control System, and OBD Development for Engines Using Diesel and Biodiesel Fuels Measuring PM ...

  4. REVIEW OF MECHANISTIC UNDERSTANDING AND MODELING AND UNCERTAINTY ANALYSIS METHODS FOR PREDICTING CEMENTITIOUS BARRIER PERFORMANCE

    SciTech Connect (OSTI)

    Langton, C.; Kosson, D.

    2009-11-30

    Cementitious barriers for nuclear applications are one of the primary controls for preventing or limiting radionuclide release into the environment. At the present time, performance and risk assessments do not fully incorporate the effectiveness of engineered barriers because the processes that influence performance are coupled and complicated. Better understanding the behavior of cementitious barriers is necessary to evaluate and improve the design of materials and structures used for radioactive waste containment, life extension of current nuclear facilities, and design of future nuclear facilities, including those needed for nuclear fuel storage and processing, nuclear power production and waste management. The focus of the Cementitious Barriers Partnership (CBP) literature review is to document the current level of knowledge with respect to: (1) mechanisms and processes that directly influence the performance of cementitious materials (2) methodologies for modeling the performance of these mechanisms and processes and (3) approaches to addressing and quantifying uncertainties associated with performance predictions. This will serve as an important reference document for the professional community responsible for the design and performance assessment of cementitious materials in nuclear applications. This review also provides a multi-disciplinary foundation for identification, research, development and demonstration of improvements in conceptual understanding, measurements and performance modeling that would be lead to significant reductions in the uncertainties and improved confidence in the estimating the long-term performance of cementitious materials in nuclear applications. This report identifies: (1) technology gaps that may be filled by the CBP project and also (2) information and computational methods that are in currently being applied in related fields but have not yet been incorporated into performance assessments of cementitious barriers. The various

  5. Statistical model selection for better prediction and discovering science mechanisms that affect reliability

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Anderson-Cook, Christine M.; Morzinski, Jerome; Blecker, Kenneth D.

    2015-08-19

    Understanding the impact of production, environmental exposure and age characteristics on the reliability of a population is frequently based on underlying science and empirical assessment. When there is incomplete science to prescribe which inputs should be included in a model of reliability to predict future trends, statistical model/variable selection techniques can be leveraged on a stockpile or population of units to improve reliability predictions as well as suggest new mechanisms affecting reliability to explore. We describe a five-step process for exploring relationships between available summaries of age, usage and environmental exposure and reliability. The process involves first identifying potential candidatemore » inputs, then second organizing data for the analysis. Third, a variety of models with different combinations of the inputs are estimated, and fourth, flexible metrics are used to compare them. As a result, plots of the predicted relationships are examined to distill leading model contenders into a prioritized list for subject matter experts to understand and compare. The complexity of the model, quality of prediction and cost of future data collection are all factors to be considered by the subject matter experts when selecting a final model.« less

  6. Mathematical approaches for complexity/predictivity trade-offs in complex system models : LDRD final report.

    SciTech Connect (OSTI)

    Goldsby, Michael E.; Mayo, Jackson R.; Bhattacharyya, Arnab; Armstrong, Robert C.; Vanderveen, Keith

    2008-09-01

    The goal of this research was to examine foundational methods, both computational and theoretical, that can improve the veracity of entity-based complex system models and increase confidence in their predictions for emergent behavior. The strategy was to seek insight and guidance from simplified yet realistic models, such as cellular automata and Boolean networks, whose properties can be generalized to production entity-based simulations. We have explored the usefulness of renormalization-group methods for finding reduced models of such idealized complex systems. We have prototyped representative models that are both tractable and relevant to Sandia mission applications, and quantified the effect of computational renormalization on the predictive accuracy of these models, finding good predictivity from renormalized versions of cellular automata and Boolean networks. Furthermore, we have theoretically analyzed the robustness properties of certain Boolean networks, relevant for characterizing organic behavior, and obtained precise mathematical constraints on systems that are robust to failures. In combination, our results provide important guidance for more rigorous construction of entity-based models, which currently are often devised in an ad-hoc manner. Our results can also help in designing complex systems with the goal of predictable behavior, e.g., for cybersecurity.

  7. Statistical model selection for better prediction and discovering science mechanisms that affect reliability

    SciTech Connect (OSTI)

    Anderson-Cook, Christine M.; Morzinski, Jerome; Blecker, Kenneth D.

    2015-08-19

    Understanding the impact of production, environmental exposure and age characteristics on the reliability of a population is frequently based on underlying science and empirical assessment. When there is incomplete science to prescribe which inputs should be included in a model of reliability to predict future trends, statistical model/variable selection techniques can be leveraged on a stockpile or population of units to improve reliability predictions as well as suggest new mechanisms affecting reliability to explore. We describe a five-step process for exploring relationships between available summaries of age, usage and environmental exposure and reliability. The process involves first identifying potential candidate inputs, then second organizing data for the analysis. Third, a variety of models with different combinations of the inputs are estimated, and fourth, flexible metrics are used to compare them. As a result, plots of the predicted relationships are examined to distill leading model contenders into a prioritized list for subject matter experts to understand and compare. The complexity of the model, quality of prediction and cost of future data collection are all factors to be considered by the subject matter experts when selecting a final model.

  8. Prediction of Lumen Output and Chromaticity Shift in LEDs Using Kalman Filter and Extended Kalman Filter Based Models

    SciTech Connect (OSTI)

    Lall, Pradeep; Wei, Junchao; Davis, J Lynn

    2014-06-24

    Abstract— Solid-state lighting (SSL) luminaires containing light emitting diodes (LEDs) have the potential of seeing excessive temperatures when being transported across country or being stored in non-climate controlled warehouses. They are also being used in outdoor applications in desert environments that see little or no humidity but will experience extremely high temperatures during the day. This makes it important to increase our understanding of what effects high temperature exposure for a prolonged period of time will have on the usability and survivability of these devices. Traditional light sources “burn out” at end-of-life. For an incandescent bulb, the lamp life is defined by B50 life. However, the LEDs have no filament to “burn”. The LEDs continually degrade and the light output decreases eventually below useful levels causing failure. Presently, the TM-21 test standard is used to predict the L70 life of LEDs from LM-80 test data. Several failure mechanisms may be active in a LED at a single time causing lumen depreciation. The underlying TM-21 Model may not capture the failure physics in presence of multiple failure mechanisms. Correlation of lumen maintenance with underlying physics of degradation at system-level is needed. In this paper, Kalman Filter (KF) and Extended Kalman Filters (EKF) have been used to develop a 70-percent Lumen Maintenance Life Prediction Model for LEDs used in SSL luminaires. Ten-thousand hour LM-80 test data for various LEDs have been used for model development. System state at each future time has been computed based on the state space at preceding time step, system dynamics matrix, control vector, control matrix, measurement matrix, measured vector, process noise and measurement noise. The future state of the lumen depreciation has been estimated based on a second order Kalman Filter model and a Bayesian Framework. Life prediction of L70 life for the LEDs used in SSL luminaires from KF and EKF based models have

  9. Control room habitability system review models

    SciTech Connect (OSTI)

    Gilpin, H. )

    1990-12-01

    This report provides a method of calculating control room operator doses from postulated reactor accidents and chemical spills as part of the resolution of TMI Action Plan III.D.3.4. The computer codes contained in this report use source concentrations calculated by either TACT5, FPFP, or EXTRAN, and transport them via user-defined flow rates to the control room envelope. The codes compute doses to six organs from up to 150 radionuclides (or 1 toxic chemical) for time steps as short as one second. Supporting codes written in Clipper assist in data entry and manipulation, and graphically display the results of the FORTRAN calculations. 7 refs., 22 figs.

  10. Development of a land ice core for the Model for Prediction Across Scales

    Office of Scientific and Technical Information (OSTI)

    (MPAS) (Conference) | SciTech Connect Development of a land ice core for the Model for Prediction Across Scales (MPAS) Citation Details In-Document Search Title: Development of a land ice core for the Model for Prediction Across Scales (MPAS) No abstract prepared. Authors: Hoffman, Matthew J [1] + Show Author Affiliations Los Alamos National Laboratory Publication Date: 2012-06-25 OSTI Identifier: 1044843 Report Number(s): LA-UR-12-22469 TRN: US201214%%525 DOE Contract Number: AC52-06NA25396

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

    SciTech Connect (OSTI)

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

    2011-12-05

    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.

  12. Mass-transport models to predict toxicity of inhaled gases in the upper respiratory tract

    SciTech Connect (OSTI)

    Hubal, E.A.C.; Fedkiw, P.S.; Kimbell, J.S. [North Carolina State Univ., Raleigh, NC (United States)

    1996-04-01

    Mass-transport (the movement of a chemical species) plays an important role in determining toxic responses of the upper respiratory tract (URT) to inhaled chemicals. Mathematical dosimetry models incorporate physical characteristics of mass transport and are used to predict quantitative uptake (absorption rate) and distribution of inhaled gases and vapors in the respiratory tract. Because knowledge of dose is an essential component of quantitative risk assessment, dosimetry modeling plays an important role in extrapolation of animal study results to humans. A survey of existing mathematical dosimetry models for the URT is presented, limitations of current models are discussed, and adaptations of existing models to produce a generally applicable model are suggested. Reviewed URT dosimetry models are categorized as early, lumped-parameter, and distributed-parameter models. Specific examples of other relevant modeling work are also presented. 35 refs., 11 figs., 1 tab.

  13. Integrated Sensing and Controls for Coal Gasification - Development of Model-Based Controls for GE's Gasifier and Syngas Cooler

    SciTech Connect (OSTI)

    Aditya Kumar

    2010-12-30

    This report summarizes the achievements and final results of this program. The objective of this program is to develop a comprehensive systems approach to integrated design of sensing and control systems for an Integrated Gasification Combined Cycle (IGCC) plant, using advanced model-based techniques. In particular, this program is focused on the model-based sensing and control system design for the core gasification section of an IGCC plant. The overall approach consists of (i) developing a first-principles physics-based dynamic model of the gasification section, (ii) performing model-reduction where needed to derive low-order models suitable for controls analysis and design, (iii) developing a sensing system solution combining online sensors with model-based estimation for important process variables not measured directly, and (iv) optimizing the steady-state and transient operation of the plant for normal operation as well as for startup using model predictive controls (MPC). Initially, available process unit models were implemented in a common platform using Matlab/Simulink{reg_sign}, and appropriate model reduction and model updates were performed to obtain the overall gasification section dynamic model. Also, a set of sensor packages were developed through extensive lab testing and implemented in the Tampa Electric Company IGCC plant at Polk power station in 2009, to measure temperature and strain in the radiant syngas cooler (RSC). Plant operation data was also used to validate the overall gasification section model. The overall dynamic model was then used to develop a sensing solution including a set of online sensors coupled with model-based estimation using nonlinear extended Kalman filter (EKF). Its performance in terms of estimating key unmeasured variables like gasifier temperature, carbon conversion, etc., was studied through extensive simulations in the presence sensing errors (noise and bias) and modeling errors (e.g. unknown gasifier kinetics, RSC

  14. Predicting ecological roles in the rhizosphere using metabolome and transportome modeling

    SciTech Connect (OSTI)

    Larsen, Peter E.; Collart, Frank R.; Dai, Yang; Blanchard, Jeffrey L.

    2015-09-02

    The ability to obtain complete genome sequences from bacteria in environmental samples, such as soil samples from the rhizosphere, has highlighted the microbial diversity and complexity of environmental communities. New algorithms to analyze genome sequence information in the context of community structure are needed to enhance our understanding of the specific ecological roles of these organisms in soil environments. We present a machine learning approach using sequenced Pseudomonad genomes coupled with outputs of metabolic and transportomic computational models for identifying the most predictive molecular mechanisms indicative of a Pseudomonad’s ecological role in the rhizosphere: a biofilm, biocontrol agent, promoter of plant growth, or plant pathogen. Computational predictions of ecological niche were highly accurate overall with models trained on transportomic model output being the most accurate (Leave One Out Validation F-scores between 0.82 and 0.89). The strongest predictive molecular mechanism features for rhizosphere ecological niche overlap with many previously reported analyses of Pseudomonad interactions in the rhizosphere, suggesting that this approach successfully informs a system-scale level understanding of how Pseudomonads sense and interact with their environments. The observation that an organism’s transportome is highly predictive of its ecological niche is a novel discovery and may have implications in our understanding microbial ecology. The framework developed here can be generalized to the analysis of any bacteria across a wide range of environments and ecological niches making this approach a powerful tool for providing insights into functional predictions from bacterial genomic data.

  15. Subtask 2.4 - Integration and Synthesis in Climate Change Predictive Modeling

    SciTech Connect (OSTI)

    Jaroslav Solc

    2009-06-01

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

  16. Long-Fiber Thermoplastic Injection Molded Composites: from Process Modeling to Property Prediction

    SciTech Connect (OSTI)

    Nguyen, Ba Nghiep; Holbery, Jim D.; Johnson, Kenneth I.; Smith, Mark T.

    2005-09-01

    Recently, long-fiber filled thermoplastics have become a great interest to the automotive industry since these materials offer much better property performance (e.g. elastic moduli, strength, durability) than their short-fiber analogues, and they can be processed through injection molding with some specific tool design. However, in order that long-fiber thermoplastic injection molded composites can be used efficiently for automotive applications, there is a tremendous need to develop process and constitutive models as well as computational tools to predict the microstructure of the as-formed composite, and its resulting properties and macroscopic responses from processing to the final product. The microstructure and properties of such a composite are governed by i) flow-induced fiber orientation, ii) fiber breakage during injection molding, and iii) processing conditions (e,g. pressure, mold and melt temperatures, mold geometries, injection speed, etc.). This paper highlights our efforts to address these challenging issues. The work is an integrated part of a research program supported by the US Department of Energy, which includes The development of process models for long-fiber filled thermoplastics, The construction of an interface between process modeling and property prediction as well as the development of new constitutive models to perform linear and nonlinear structural analyses, Experimental characterization of model parameters and verification of the model predictions.

  17. Reliability analysis and prediction of mixed mode load using Markov Chain Model

    SciTech Connect (OSTI)

    Nikabdullah, N.; Singh, S. S. K.; Alebrahim, R.; Azizi, M. A.; K, Elwaleed A.; Noorani, M. S. M.

    2014-06-19

    The aim of this paper is to present the reliability analysis and prediction of mixed mode loading by using a simple two state Markov Chain Model for an automotive crankshaft. The reliability analysis and prediction for any automotive component or structure is important for analyzing and measuring the failure to increase the design life, eliminate or reduce the likelihood of failures and safety risk. The mechanical failures of the crankshaft are due of high bending and torsion stress concentration from high cycle and low rotating bending and torsional stress. The Markov Chain was used to model the two states based on the probability of failure due to bending and torsion stress. In most investigations it revealed that bending stress is much serve than torsional stress, therefore the probability criteria for the bending state would be higher compared to the torsion state. A statistical comparison between the developed Markov Chain Model and field data was done to observe the percentage of error. The reliability analysis and prediction was derived and illustrated from the Markov Chain Model were shown in the Weibull probability and cumulative distribution function, hazard rate and reliability curve and the bathtub curve. It can be concluded that Markov Chain Model has the ability to generate near similar data with minimal percentage of error and for a practical application; the proposed model provides a good accuracy in determining the reliability for the crankshaft under mixed mode loading.

  18. Predicting adenocarcinoma recurrence using computational texture models of nodule components in lung CT

    SciTech Connect (OSTI)

    Depeursinge, Adrien; Yanagawa, Masahiro; Leung, Ann N.; Rubin, Daniel L.

    2015-04-15

    Purpose: To investigate the importance of presurgical computed tomography (CT) intensity and texture information from ground-glass opacities (GGO) and solid nodule components for the prediction of adenocarcinoma recurrence. Methods: For this study, 101 patients with surgically resected stage I adenocarcinoma were selected. During the follow-up period, 17 patients had disease recurrence with six associated cancer-related deaths. GGO and solid tumor components were delineated on presurgical CT scans by a radiologist. Computational texture models of GGO and solid regions were built using linear combinations of steerable Riesz wavelets learned with linear support vector machines (SVMs). Unlike other traditional texture attributes, the proposed texture models are designed to encode local image scales and directions that are specific to GGO and solid tissue. The responses of the locally steered models were used as texture attributes and compared to the responses of unaligned Riesz wavelets. The texture attributes were combined with CT intensities to predict tumor recurrence and patient hazard according to disease-free survival (DFS) time. Two families of predictive models were compared: LASSO and SVMs, and their survival counterparts: Cox-LASSO and survival SVMs. Results: The best-performing predictive model of patient hazard was associated with a concordance index (C-index) of 0.81 ± 0.02 and was based on the combination of the steered models and CT intensities with survival SVMs. The same feature group and the LASSO model yielded the highest area under the receiver operating characteristic curve (AUC) of 0.8 ± 0.01 for predicting tumor recurrence, although no statistically significant difference was found when compared to using intensity features solely. For all models, the performance was found to be significantly higher when image attributes were based on the solid components solely versus using the entire tumors (p < 3.08 × 10{sup −5}). Conclusions: This study

  19. Monte Carlo and analytical model predictions of leakage neutron exposures from passively scattered proton therapy

    SciTech Connect (OSTI)

    Prez-Andjar, Anglica [Department of Radiation Physics, Unit 1202, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030 (United States)] [Department of Radiation Physics, Unit 1202, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030 (United States); Zhang, Rui; Newhauser, Wayne [Department of Radiation Physics, Unit 1202, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030 and The University of Texas Graduate School of Biomedical Sciences at Houston, 6767 Bertner Avenue, Houston, Texas 77030 (United States)] [Department of Radiation Physics, Unit 1202, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030 and The University of Texas Graduate School of Biomedical Sciences at Houston, 6767 Bertner Avenue, Houston, Texas 77030 (United States)

    2013-12-15

    Purpose: Stray neutron radiation is of concern after radiation therapy, especially in children, because of the high risk it might carry for secondary cancers. Several previous studies predicted the stray neutron exposure from proton therapy, mostly using Monte Carlo simulations. Promising attempts to develop analytical models have also been reported, but these were limited to only a few proton beam energies. The purpose of this study was to develop an analytical model to predict leakage neutron equivalent dose from passively scattered proton beams in the 100-250-MeV interval.Methods: To develop and validate the analytical model, the authors used values of equivalent dose per therapeutic absorbed dose (H/D) predicted with Monte Carlo simulations. The authors also characterized the behavior of the mean neutron radiation-weighting factor, w{sub R}, as a function of depth in a water phantom and distance from the beam central axis.Results: The simulated and analytical predictions agreed well. On average, the percentage difference between the analytical model and the Monte Carlo simulations was 10% for the energies and positions studied. The authors found that w{sub R} was highest at the shallowest depth and decreased with depth until around 10 cm, where it started to increase slowly with depth. This was consistent among all energies.Conclusion: Simple analytical methods are promising alternatives to complex and slow Monte Carlo simulations to predict H/D values. The authors' results also provide improved understanding of the behavior of w{sub R} which strongly depends on depth, but is nearly independent of lateral distance from the beam central axis.

  20. Model-free adaptive control of advanced power plants

    SciTech Connect (OSTI)

    Cheng, George Shu-Xing; Mulkey, Steven L.; Wang, Qiang

    2015-08-18

    A novel 3-Input-3-Output (3.times.3) Model-Free Adaptive (MFA) controller with a set of artificial neural networks as part of the controller is introduced. A 3.times.3 MFA control system using the inventive 3.times.3 MFA controller is described to control key process variables including Power, Steam Throttle Pressure, and Steam Temperature of boiler-turbine-generator (BTG) units in conventional and advanced power plants. Those advanced power plants may comprise Once-Through Supercritical (OTSC) Boilers, Circulating Fluidized-Bed (CFB) Boilers, and Once-Through Supercritical Circulating Fluidized-Bed (OTSC CFB) Boilers.

  1. Reference Model for Control and Automation Systems in Electrical...

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

    Reference Model for Control and Automation Systems in Electrical Power (October 2005) (473.71 KB) More Documents & Publications DOE National SCADA Test Bed Program Multi-Year Plan ...

  2. Model Specification for Networked Outdoor Lighting Control Systems

    Broader source: Energy.gov [DOE]

    The DOE Municipal Solid-State Street Lighting Consortium's Model Specification for Networked Outdoor Lighting Control Systems is a tool designed to help cities, utilities, and other local agencies...

  3. Correlation of a hypoxia based tumor control model with observed local control rates in nasopharyngeal carcinoma treated with chemoradiotherapy

    SciTech Connect (OSTI)

    Avanzo, Michele; Stancanello, Joseph; Franchin, Giovanni; Sartor, Giovanna; Jena, Rajesh; Drigo, Annalisa; Dassie, Andrea; Gigante, Marco; Capra, Elvira

    2010-04-15

    Purpose: To extend the application of current radiation therapy (RT) based tumor control probability (TCP) models of nasopharyngeal carcinoma (NPC) to include the effects of hypoxia and chemoradiotherapy (CRT). Methods: A TCP model is described based on the linear-quadratic model modified to account for repopulation, chemotherapy, heterogeneity of dose to the tumor, and hypoxia. Sensitivity analysis was performed to determine which parameters exert the greatest influence on the uncertainty of modeled TCP. On the basis of the sensitivity analysis, the values of specific radiobiological parameters were set to nominal values reported in the literature for NPC or head and neck tumors. The remaining radiobiological parameters were determined by fitting TCP to clinical local control data from published randomized studies using both RT and CRT. Validation of the model was performed by comparison of estimated TCP and average overall local control rate (LCR) for 45 patients treated at the institution with conventional linear-accelerator-based or helical tomotherapy based intensity-modulated RT and neoadjuvant chemotherapy. Results: Sensitivity analysis demonstrates that the model is most sensitive to the radiosensitivity term {alpha} and the dose per fraction. The estimated values of {alpha} and OER from data fitting were 0.396 Gy{sup -1} and 1.417. The model estimate of TCP (average 90.9%, range 26.9%-99.2%) showed good correlation with the LCR (86.7%). Conclusions: The model implemented in this work provides clinicians with a useful tool to predict the success rate of treatment, optimize treatment plans, and compare the effects of multimodality therapy.

  4. Adsorption of selected pharmaceuticals and an endocrine disrupting compound by granular activated carbon. 2. Model prediction

    SciTech Connect (OSTI)

    Yu, Z.; Peldszus, S.; Huck, P.M.

    2009-03-01

    The adsorption of two representative pharmaceutically active compounds (PhACs) naproxen and carbamazepine and one endocrine disrupting compound (EDC) nonylphenol was studied in pilot-scale granular activated carbon (GAC) adsorbers using post-sedimentation (PS) water from a full-scale drinking water treatment plant. The GAC adsorbents were coal-based Calgon Filtrasorb 400 and coconut shell-based PICA CTIF TE. Acidic naproxen broke through fastest while nonylphenol was removed best, which was consistent with the degree to which fouling affected compound removals. Model predictions and experimental data were generally in good agreement for all three compounds, which demonstrated the effectiveness and robustness of the pore and surface diffusion model (PSDM) used in combination with the time-variable parameter approach for predicting removals at environmentally relevant concentrations (i.e., ng/L range). Sensitivity analyses suggested that accurate determination of film diffusion coefficients was critical for predicting breakthrough for naproxen and carbamazepine, in particular when high removals are targeted. Model simulations demonstrated that GAC carbon usage rates (CURs) for naproxen were substantially influenced by the empty bed contact time (EBCT) at the investigated conditions. Model-based comparisons between GAC CURs and minimum CURs for powdered activated carbon (PAC) applications suggested that PAC would be most appropriate for achieving 90% removal of naproxen, whereas GAC would be more suitable for nonylphenol. 25 refs., 4 figs., 1 tab.

  5. Modeling Combustion Control for High Power Diesel Mode Switching |

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

    Department of Energy Combustion Control for High Power Diesel Mode Switching Modeling Combustion Control for High Power Diesel Mode Switching Poster presentation given at the 16th Directions in Engine-Efficiency and Emissions Research (DEER) Conference in Detroit, MI, September 27-30, 2010. p-20_banerjee.pdf (160.26 KB) More Documents & Publications Diesel Injection Shear-Stress Advanced Nozzle (DISSAN) In-Cylinder Mechanisms of PCI Heat-Release Rate Control by Fuel Reactivity

  6. Predicting ecological roles in the rhizosphere using metabolome and transportome modeling

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Larsen, Peter E.; Collart, Frank R.; Dai, Yang; Blanchard, Jeffrey L.

    2015-09-02

    The ability to obtain complete genome sequences from bacteria in environmental samples, such as soil samples from the rhizosphere, has highlighted the microbial diversity and complexity of environmental communities. New algorithms to analyze genome sequence information in the context of community structure are needed to enhance our understanding of the specific ecological roles of these organisms in soil environments. We present a machine learning approach using sequenced Pseudomonad genomes coupled with outputs of metabolic and transportomic computational models for identifying the most predictive molecular mechanisms indicative of a Pseudomonad’s ecological role in the rhizosphere: a biofilm, biocontrol agent, promoter ofmore » plant growth, or plant pathogen. Computational predictions of ecological niche were highly accurate overall with models trained on transportomic model output being the most accurate (Leave One Out Validation F-scores between 0.82 and 0.89). The strongest predictive molecular mechanism features for rhizosphere ecological niche overlap with many previously reported analyses of Pseudomonad interactions in the rhizosphere, suggesting that this approach successfully informs a system-scale level understanding of how Pseudomonads sense and interact with their environments. The observation that an organism’s transportome is highly predictive of its ecological niche is a novel discovery and may have implications in our understanding microbial ecology. The framework developed here can be generalized to the analysis of any bacteria across a wide range of environments and ecological niches making this approach a powerful tool for providing insights into functional predictions from bacterial genomic data.« less

  7. An Elastic-Plastic and Strength Prediction Model for Injection-Molded Long-Fiber Thermoplastics

    SciTech Connect (OSTI)

    Nguyen, Ba Nghiep; Kunc, Vlastimil; Phelps, Jay; Tucker III, Charles L.; Bapanapalli, Satish K.

    2008-09-01

    This paper applies a recently developed model to predict the elastic-plastic stress/strain response and strength of injection-molded long-fiber thermoplastics (LFTs). The model combines a micro-macro constitutive modeling approach with experimental characterization and modeling of the composite microstructure to determine the composite stress/strain response and strength. Specifically, it accounts for elastic fibers embedded in a thermoplastic resin that exhibits the elastic-plastic behavior obeying the Ramberg-Osgood relation and J-2 deformation theory of plasticity. It also accounts for fiber length, orientation and volume fraction distributions in the composite formed by the injection-molding process. Injection-molded-long-glass-fiber/polypropylene (PP) specimens were prepared for mechanical characterization and testing. Fiber length, orientation, and volume fraction distributions were then measured at some selected locations for use in the computation. Fiber orientations in these specimens were also predicted using an anisotropic rotary diffusion model developed for LFTs. The stress-strain response of the as-formed composite was computed by an incremental procedure that uses the Eshelbys equivalent inclusion method, the Mori-Tanaka assumption and a fiber orientation averaging technique. The model has been validated against the experimental stress-strain results obtained for these long-glass-fiber/PP specimens.

  8. A comparison of general circulation model predictions to sand drift and dune orientations

    SciTech Connect (OSTI)

    Blumberg, D.G.; Greeley, R.

    1996-12-01

    The growing concern over climate change and decertification 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/Goddard Space Flight Center was used in conjunction with White`s aeolian sand flux model to produce a global potential aeolian transport map. Surface wind shear stress predictions were used from the output of a GCM simulation that was performed as part of the Atmospheric Model Intercomparison Project on 1979 climate conditions. The spatial resolution of this study (as driven by the GCM) is 4{degrees} X 5{degrees}; instantaneous 6-hourly wind stress data were saved by the GCM and used in this report. A global map showing potential sand transport was compared to drift potential directions as inferred from Landsat images from the 1980s for several sand seas and a coastal dune field. Generally, results show a good correlation between the simulated sand drift direction and the drift direction inferred for dune forms. Discrepancies between the drift potential and the drift inferred from images were found in the North American deserts and the Arabian peninsula. An attempt to predict the type of dune that would be formed in specific regions was not successful. The model could probably be further improved by incorporating soil moisture, surface roughness, and vegetation information for a better assessment of sand threshold conditions. The correlation may permit use of a GCM to analyze {open_quotes}fossil{close_quotes} dunes or to forecast aeolian processes. 48 refs., 8 figs.

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

    SciTech Connect (OSTI)

    Cairelli, J.E.; Geng, S.M.; Skupinski, R.C.

    1994-09-01

    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.

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

    Office of Science (SC) Website

    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 / Public Abstracts Additional Requirements and Guidance for Digital Data Management Peer Review Policies EFRCs FOA Applications from Universities and Other Research Institutions Construction Review EPSCoR DOE Office of Science Graduate Fellowship (DOE SCGF)

  11. Aquatic Pathways Model to predict the fate of phenolic compounds. Appendixes A through D

    SciTech Connect (OSTI)

    Aaberg, R.L.; Peloquin, R.A.; Strenge, D.L.; Mellinger, P.L.

    1983-04-01

    Organic materials released from energy-related activities could affect human health and the environment. We have developed a model to predict the fate of spills or discharges of pollutants into flowing or static bodies of fresh water. A computer code, Aquatic Pathways Model (APM), was written to implement the model. The APM estimates the concentrations of chemicals in fish tissue, water and sediment, and is therefore useful for assessing exposure to humans through aquatic pathways. The major pathways considered are biodegradation, fish and sediment uptake, photolysis, and evaporation. The model has been implemented with parameters for the distribution of phenols, an important class of compounds found in the water-soluble fractions of coal liquids. The model was developed to estimate the fate of liquids derived from coal. Current modeling efforts show that, in comparison with many pesticides and polyaromatic hydrocarbons (PAH), the lighter phenolics (the cresols) are not persistent in the environment. For the twelve phenolics studied, biodegradation appears to be the major pathway for elimination from aquatic environments. A pond system simulation of a spill of solvent-refined coal (SRC-II) materials indicates that phenol, cresols, and other single cyclic phenolics are degraded to 16 to 25 percent of their original concentrations within 30 hours. Adsorption of these compounds into sediments and accumulation by fish was minor. Results of a simulated spill of a coal liquid (SRC-II) into a pond show that APM predicted the allocation of 12 phenolic components among six compartments at 30 hours after a small spill. The simulation indicated that most of the introduced phenolic compounds were biodegraded. The phenolics remaining in the aquatic system partitioned according to their molecular weight and structure. A substantial amount was predicted to remain in the water, with less than 0.01% distributed in sediment or fish.

  12. Predicting individual action switching in covert and continuous interactive tasks using the fluid events model

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Radvansky, Gabriel A.; D’Mello, Sidney K.; Abbott, Robert G.; Bixler, Robert E.

    2016-01-27

    The Fluid Events Model is aimed at predicting changes in the actions people take on a moment-by-moment basis. In contrast with other research on action selection, this work does not investigate why some course of action was selected, but rather the likelihood of discontinuing the current course of action and selecting another in the near future. This is done using both task-based and experience-based factors. Prior work evaluated this model in the context of trial-by-trial, independent, interactive events, such as choosing how to copy a figure of a line drawing. In this paper, we extend this model to more covertmore » event experiences, such as reading narratives, as well as to continuous interactive events, such as playing a video game. To this end, the model was applied to existing data sets of reading time and event segmentation for written and picture stories. It was also applied to existing data sets of performance in a strategy board game, an aerial combat game, and a first person shooter game in which a participant’s current state was dependent on prior events. The results revealed that the model predicted behavior changes well, taking into account both the theoretically defined structure of the described events, as well as a person’s prior experience. Hence, theories of event cognition can benefit from efforts that take into account not only how events in the world are structured, but also how people experience those events.« less

  13. Stem thrust prediction model for W-K-M double wedge parallel expanding gate valves

    SciTech Connect (OSTI)

    Eldiwany, B.; Alvarez, P.D.; Wolfe, K.

    1996-12-01

    An analytical model for determining the required valve stem thrust during opening and closing strokes of W-K-M parallel expanding gate valves was developed as part of the EPRI Motor-Operated Valve Performance Prediction Methodology (EPRI MOV PPM) Program. The model was validated against measured stem thrust data obtained from in-situ testing of three W-K-M valves. Model predictions show favorable, bounding agreement with the measured data for valves with Stellite 6 hardfacing on the disks and seat rings for water flow in the preferred flow direction (gate downstream). The maximum required thrust to open and to close the valve (excluding wedging and unwedging forces) occurs at a slightly open position and not at the fully closed position. In the nonpreferred flow direction, the model shows that premature wedging can occur during {Delta}P closure strokes even when the coefficients of friction at different sliding surfaces are within the typical range. This paper summarizes the model description and comparison against test data.

  14. Application of a fuzzy neural network model in predicting polycyclic aromatic hydrocarbon-mediated perturbations of the Cyp1b1 transcriptional regulatory network in mouse skin

    SciTech Connect (OSTI)

    Larkin, Andrew; Siddens, Lisbeth K.; Krueger, Sharon K.; Tilton, Susan C.; Waters, Katrina M.; Williams, David E.; Baird, William M.

    2013-03-01

    Polycyclic aromatic hydrocarbons (PAHs) are present in the environment as complex mixtures with components that have diverse carcinogenic potencies and mostly unknown interactive effects. Non-additive PAH interactions have been observed in regulation of cytochrome P450 (CYP) gene expression in the CYP1 family. To better understand and predict biological effects of complex mixtures, such as environmental PAHs, an 11 gene input-1 gene output fuzzy neural network (FNN) was developed for predicting PAH-mediated perturbations of dermal Cyp1b1 transcription in mice. Input values were generalized using fuzzy logic into low, medium, and high fuzzy subsets, and sorted using k-means clustering to create Mamdani logic functions for predicting Cyp1b1 mRNA expression. Model testing was performed with data from microarray analysis of skin samples from FVB/N mice treated with toluene (vehicle control), dibenzo[def,p]chrysene (DBC), benzo[a]pyrene (BaP), or 1 of 3 combinations of diesel particulate extract (DPE), coal tar extract (CTE) and cigarette smoke condensate (CSC) using leave-one-out cross-validation. Predictions were within 1 log{sub 2} fold change unit of microarray data, with the exception of the DBC treatment group, where the unexpected down-regulation of Cyp1b1 expression was predicted but did not reach statistical significance on the microarrays. Adding CTE to DPE was predicted to increase Cyp1b1 expression, whereas adding CSC to CTE and DPE was predicted to have no effect, in agreement with microarray results. The aryl hydrocarbon receptor repressor (Ahrr) was determined to be the most significant input variable for model predictions using back-propagation and normalization of FNN weights. - Highlights: ? Tested a model to predict PAH mixture-mediated changes in Cyp1b1 expression ? Quantitative predictions in agreement with microarrays for Cyp1b1 induction ? Unexpected difference in expression between DBC and other treatments predicted ? Model predictions for

  15. Transient PVT measurements and model predictions for vessel heat transfer. Part II.

    SciTech Connect (OSTI)

    Felver, Todd G.; Paradiso, Nicholas Joseph; Winters, William S., Jr.; Evans, Gregory Herbert; Rice, Steven F.

    2010-07-01

    Part I of this report focused on the acquisition and presentation of transient PVT data sets that can be used to validate gas transfer models. Here in Part II we focus primarily on describing models and validating these models using the data sets. Our models are intended to describe the high speed transport of compressible gases in arbitrary arrangements of vessels, tubing, valving and flow branches. Our models fall into three categories: (1) network flow models in which flow paths are modeled as one-dimensional flow and vessels are modeled as single control volumes, (2) CFD (Computational Fluid Dynamics) models in which flow in and between vessels is modeled in three dimensions and (3) coupled network/CFD models in which vessels are modeled using CFD and flows between vessels are modeled using a network flow code. In our work we utilized NETFLOW as our network flow code and FUEGO for our CFD code. Since network flow models lack three-dimensional resolution, correlations for heat transfer and tube frictional pressure drop are required to resolve important physics not being captured by the model. Here we describe how vessel heat transfer correlations were improved using the data and present direct model-data comparisons for all tests documented in Part I. Our results show that our network flow models have been substantially improved. The CFD modeling presented here describes the complex nature of vessel heat transfer and for the first time demonstrates that flow and heat transfer in vessels can be modeled directly without the need for correlations.

  16. VALIDATION AND RESULTS OF A PSEUDO-MULTI-ZONE COMBUSTION TRAJECTORY PREDICTION MODEL FOR CAPTURING SOOT AND NOX FORMATION ON A MEDIUM DUTY DIESEL ENGINE

    SciTech Connect (OSTI)

    Bittle, Joshua A.; Gao, Zhiming; Jacobs, Timothy J.

    2013-01-01

    A pseudo-multi-zone phenomenological model has been created with the ultimate goal of supporting efforts to enable broader commercialization of low temperature combustion modes in diesel engines. The benefits of low temperature combustion are the simultaneous reduction in soot and nitric oxide emissions and increased engine efficiency if combustion is properly controlled. Determining what qualifies as low temperature combustion for any given engine can be difficult without expensive emissions analysis equipment. This determination can be made off-line using computer models or through factory calibration procedures. This process could potentially be simplified if a real-time prediction model could be implemented to run for any engine platform this is the motivation for this study. The major benefit of this model is the ability for it to predict the combustion trajectory, i.e. local temperature and equivalence ratio in the burning zones. The model successfully captures all the expected trends based on the experimental data and even highlights an opportunity for simply using the average reaction temperature and equivalence ratio as an indicator of emissions levels alone - without solving formation sub-models. This general type of modeling effort is not new, but a major effort was made to minimize the calculation duration to enable implementation as an input to real-time next-cycle engine controller Instead of simply using the predicted engine out soot and NOx levels, control decisions could be made based on the trajectory. This has the potential to save large amounts of calibration time because with minor tuning (the model has only one automatically determined constant) it is hoped that the control algorithm would be generally applicable.

  17. In-Service Design & Performance Prediction of Advanced Fusion Material Systems by Computational Modeling and Simulation

    SciTech Connect (OSTI)

    G. R. Odette; G. E. Lucas

    2005-11-15

    This final report on "In-Service Design & Performance Prediction of Advanced Fusion Material Systems by Computational Modeling and Simulation" (DE-FG03-01ER54632) consists of a series of summaries of work that has been published, or presented at meetings, or both. It briefly describes results on the following topics: 1) A Transport and Fate Model for Helium and Helium Management; 2) Atomistic Studies of Point Defect Energetics, Dynamics and Interactions; 3) Multiscale Modeling of Fracture consisting of: 3a) A Micromechanical Model of the Master Curve (MC) Universal Fracture Toughness-Temperature Curve Relation, KJc(T - To), 3b) An Embrittlement DTo Prediction Model for the Irradiation Hardening Dominated Regime, 3c) Non-hardening Irradiation Assisted Thermal and Helium Embrittlement of 8Cr Tempered Martensitic Steels: Compilation and Analysis of Existing Data, 3d) A Model for the KJc(T) of a High Strength NFA MA957, 3e) Cracked Body Size and Geometry Effects of Measured and Effective Fracture Toughness-Model Based MC and To Evaluations of F82H and Eurofer 97, 3-f) Size and Geometry Effects on the Effective Toughness of Cracked Fusion Structures; 4) Modeling the Multiscale Mechanics of Flow Localization-Ductility Loss in Irradiation Damaged BCC Alloys; and 5) A Universal Relation Between Indentation Hardness and True Stress-Strain Constitutive Behavior. Further details can be found in the cited references or presentations that generally can be accessed on the internet, or provided upon request to the authors. Finally, it is noted that this effort was integrated with our base program in fusion materials, also funded by the DOE OFES.

  18. A Screening Model to Predict Microalgae Biomass Growth in Photobioreactors and Raceway Ponds

    SciTech Connect (OSTI)

    Huesemann, Michael H.; Van Wagenen, Jonathan M.; Miller, Tyler W.; Chavis, Aaron R.; Hobbs, Watts B.; Crowe, Braden J.

    2013-06-01

    A microalgae biomass growth model was developed for screening novel strains for their potential to exhibit high biomass productivities under nutrient-replete conditions in photobioreactors or outdoor ponds. Growth is modeled by first estimating the light attenuation by biomass according to Beer-Lamberts law, and then calculating the specific growth rate in discretized culture volume slices that receive declining light intensities due to attenuation. The model requires only two physical and two species-specific biological input parameters, all of which are relatively easy to determine: incident light intensity, culture depth, as well as the biomass light absorption coefficient and the specific growth rate as a function of light intensity. Roux bottle culture experiments were performed with Nannochloropsis salina at constant temperature (23 C) at six different incident light intensities (5, 10, 25, 50, 100, 250, and 850 ?mol/m2? sec) to determine both the specific growth rate under non-shading conditions and the biomass light absorption coefficient as a function of light intensity. The model was successful in predicting the biomass growth rate in these Roux bottle cultures during the light-limited linear phase at different incident light intensities. Model predictions were moderately sensitive to minor variations in the values of input parameters. The model was also successful in predicting the growth performance of Chlorella sp. cultured in LED-lighted 800 L raceway ponds operated at constant temperature (30 C) and constant light intensity (1650 ?mol/m2? sec). Measurements of oxygen concentrations as a function of time demonstrated that following exposure to darkness, it takes at least 5 seconds for cells to initiate dark respiration. As a result, biomass loss due to dark respiration in the aphotic zone of a culture is unlikely to occur in highly mixed small-scale photobioreactors where cells move rapidly in and out of the light. By contrast, as supported also by

  19. Rolling Process Modeling Report: Finite-Element Prediction of Roll Separating Force and Rolling Defects

    SciTech Connect (OSTI)

    Soulami, Ayoub; Lavender, Curt A.; Paxton, Dean M.; Burkes, Douglas

    2014-04-23

    Pacific Northwest National Laboratory (PNNL) has been investigating manufacturing processes for the uranium-10% molybdenum (U-10Mo) alloy plate-type fuel for the U.S. high-performance research reactors. This work supports the Convert Program of the U.S. Department of Energy’s National Nuclear Security Administration (DOE/NNSA) Global Threat Reduction Initiative. This report documents modeling results of PNNL’s efforts to perform finite-element simulations to predict roll separating forces and rolling defects. Simulations were performed using a finite-element model developed using the commercial code LS-Dyna. Simulations of the hot rolling of U-10Mo coupons encapsulated in low-carbon steel have been conducted following two different schedules. Model predictions of the roll-separation force and roll-pack thicknesses at different stages of the rolling process were compared with experimental measurements. This report discusses various attributes of the rolled coupons revealed by the model (e.g., dog-boning and thickness non-uniformity).

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

    SciTech Connect (OSTI)

    Watney, W.L.

    1994-12-01

    Reservoirs in the Lansing-Kansas City limestone result from complex interactions among paleotopography (deposition, concurrent structural deformation), sea level, and diagenesis. Analysis of reservoirs and surface and near-surface analogs has led to developing a {open_quotes}strandline grainstone model{close_quotes} in which relative sea-level stabilized during regressions, resulting in accumulation of multiple grainstone buildups along depositional strike. Resulting stratigraphy in these carbonate units are generally predictable correlating to inferred topographic elevation along the shelf. This model is a valuable predictive tool for (1) locating favorable reservoirs for exploration, and (2) anticipating internal properties of the reservoir for field development. Reservoirs in the Lansing-Kansas City limestones are developed in both oolitic and bioclastic grainstones, however, re-analysis of oomoldic reservoirs provides the greatest opportunity for developing bypassed oil. A new technique, the {open_quotes}Super{close_quotes} Pickett crossplot (formation resistivity vs. porosity) and its use in an integrated petrophysical characterization, has been developed to evaluate extractable oil remaining in these reservoirs. The manual method in combination with 3-D visualization and modeling can help to target production limiting heterogeneities in these complex reservoirs and moreover compute critical parameters for the field such as bulk volume water. Application of this technique indicates that from 6-9 million barrels of Lansing-Kansas City oil remain behind pipe in the Victory-Northeast Lemon Fields. Petroleum geologists are challenged to quantify inferred processes to aid in developing rationale geologically consistent models of sedimentation so that acceptable levels of prediction can be obtained.

  1. Optimal control of CPR procedure using hemodynamic circulation model

    DOE Patents [OSTI]

    Lenhart, Suzanne M.; Protopopescu, Vladimir A.; Jung, Eunok

    2007-12-25

    A method for determining a chest pressure profile for cardiopulmonary resuscitation (CPR) includes the steps of representing a hemodynamic circulation model based on a plurality of difference equations for a patient, applying an optimal control (OC) algorithm to the circulation model, and determining a chest pressure profile. The chest pressure profile defines a timing pattern of externally applied pressure to a chest of the patient to maximize blood flow through the patient. A CPR device includes a chest compressor, a controller communicably connected to the chest compressor, and a computer communicably connected to the controller. The computer determines the chest pressure profile by applying an OC algorithm to a hemodynamic circulation model based on the plurality of difference equations.

  2. Predictive model for ionic liquid extraction solvents for rare earth elements

    SciTech Connect (OSTI)

    Grabda, Mariusz; Oleszek, Sylwia; Panigrahi, Mrutyunjay; Kozak, Dmytro; Shibata, Etsuro; Nakamura, Takashi; Eckert, Franck

    2015-12-31

    The purpose of our study was to select the most effective ionic liquid extraction solvents for dysprosium (III) fluoride using a theoretical approach. Conductor-like Screening Model for Real Solvents (COSMO-RS), based on quantum chemistry and the statistical thermodynamics of predefined DyF{sub 3}-ionic liquid systems, was applied to reach the target. Chemical potentials of the salt were predicted in 4,400 different ionic liquids. On the base of these predictions set of ionic liquids’ ions, manifesting significant decrease of the chemical potentials, were selected. Considering the calculated physicochemical properties (hydrophobicity, viscosity) of the ionic liquids containing these specific ions, the most effective extraction solvents for liquid-liquid extraction of DyF{sub 3} were proposed. The obtained results indicate that the COSMO-RS approach can be applied to quickly screen the affinity of any rare earth element for a large number of ionic liquid systems, before extensive experimental tests.

  3. Predicting reservoir facies distribution using high resolution forward stratigraphic modeling (upper Permian Zechstein 2 carbonate, North Germany)

    SciTech Connect (OSTI)

    Leyrer, K.; Strohmenger, C.; Rockenbauch, K.

    1995-08-01

    To improve the prediction of facies within the Upper Permian Zechstein 2 Carbonate (Ca2), high resolution forward stratigraphic modeling was performed. The results show differences in the sedimentary history of various parts of the Southern Permian Basin, permitting a better prediction of reservoir facies distribution. The Zechstein 2 Carbonate contains North Germany`s largest hydrocarbon accumulation. The reservoir overlies the anhydrites of the first Zechstein cycle (Werra Anhydrite, or A1) and is sealed by the anhydrites of the second Zechstein cycle (Basal Anhydrite, or A2). The Ca2 can be subdivided into platform, upper slope, middle slope, lower slope, and basina1 facies with a total of 26 subfacies types. It comprises the transgressive and highstand systems tract, of the third Zechstein sequence (ZS3) and the lowstand systems tract of the fourth Zechstein sequence (ZS4). Furthermore the Ca2 can be subdivided into seven parasequences indicating high-order fluctuations. Although the Ca2 in both Northwest and Northeast Germany share this geological framework, many differences concerning reservoir distribution exist between the two areas. A general stratigraphic, simulation program (PHIL{sup TM} 1.5) was used for two-dimensional modeling of the Ca2 throughout North Germany. Using well data along with published data and modifying the sedimentation-governing factors, it was possible to simulate the current sequence stratigraphic and facies model. Sedimentation during Ca2-time can be characterized as a highly complex system; thus, only slight variations of the input data result in vastly different facies and stratigraphic patterns. This sensitivity offers the possibility to test depositional models and to estimate the relative influences of the sediment-controlling parameters during Ca2-time in different paleotopographic settings.

  4. DEFINING THE PLAYERS IN HIGHER-ORDER NETWORKS: PREDICTIVE MODELING FOR REVERSE ENGINEERING FUNCTIONAL INFLUENCE NETWORKS

    SciTech Connect (OSTI)

    McDermott, Jason E.; Costa, Michelle N.; Stevens, S.L.; Stenzel-Poore, Mary; Sanfilippo, Antonio P.

    2011-01-20

    A difficult problem that is currently growing rapidly due to the sharp increase in the amount of high-throughput data available for many systems is that of determining useful and informative causative influence networks. These networks can be used to predict behavior given observation of a small number of components, predict behavior at a future time point, or identify components that are critical to the functioning of the system under particular conditions. In these endeavors incorporating observations of systems from a wide variety of viewpoints can be particularly beneficial, but has often been undertaken with the objective of inferring networks that are generally applicable. The focus of the current work is to integrate both general observations and measurements taken for a particular pathology, that of ischemic stroke, to provide improved ability to produce useful predictions of systems behavior. A number of hybrid approaches have recently been proposed for network generation in which the Gene Ontology is used to filter or enrich network links inferred from gene expression data through reverse engineering methods. These approaches have been shown to improve the biological plausibility of the inferred relationships determined, but still treat knowledge-based and machine-learning inferences as incommensurable inputs. In this paper, we explore how further improvements may be achieved through a full integration of network inference insights achieved through application of the Gene Ontology and reverse engineering methods with specific reference to the construction of dynamic models of transcriptional regulatory networks. We show that integrating two approaches to network construction, one based on reverse-engineering from conditional transcriptional data, one based on reverse-engineering from in situ hybridization data, and another based on functional associations derived from Gene Ontology, using probabilities can improve results of clustering as evaluated by a

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

    SciTech Connect (OSTI)

    Fok, Alex

    2013-10-30

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

  6. Validation of the thermal transport model used for ITER startup scenario predictions with DIII-D experimental data

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Casper, T. A.; Meyer, W. H.; Jackson, G. L.; Luce, T. C.; Hyatt, A. W.; Humphreys, D. A.; Turco, F.

    2010-12-08

    We are exploring characteristics of ITER startup scenarios in similarity experiments conducted on the DIII-D Tokamak. In these experiments, we have validated scenarios for the ITER current ramp up to full current and developed methods to control the plasma parameters to achieve stability. Predictive simulations of ITER startup using 2D free-boundary equilibrium and 1D transport codes rely on accurate estimates of the electron and ion temperature profiles that determine the electrical conductivity and pressure profiles during the current rise. Here we present results of validation studies that apply the transport model used by the ITER team to DIII-D discharge evolutionmore » and comparisons with data from our similarity experiments.« less

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

    SciTech Connect (OSTI)

    Strobel, Calvin J.

    1993-01-28

    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.

  8. Improving Thermal Model Prediction Through Statistical Analysis of Irradiation and Post-Irradiation Data from AGR Experiments

    SciTech Connect (OSTI)

    Binh T. Pham; Grant L. Hawkes; Jeffrey J. Einerson

    2014-05-01

    As part of the High Temperature Reactors (HTR) R&D program, a series of irradiation tests, designated as Advanced Gas-cooled Reactor (AGR), have been defined to support development and qualification of fuel design, fabrication process, and fuel performance under normal operation and accident conditions. The AGR tests employ fuel compacts placed in a graphite cylinder shrouded by a steel capsule and instrumented with thermocouples (TC) embedded in graphite blocks enabling temperature control. While not possible to obtain by direct measurements in the tests, crucial fuel conditions (e.g., temperature, neutron fast fluence, and burnup) are calculated using core physics and thermal modeling codes. This paper is focused on AGR test fuel temperature predicted by the ABAQUS code's finite element-based thermal models. The work follows up on a previous study, in which several statistical analysis methods were adapted, implemented in the NGNP Data Management and Analysis System (NDMAS), and applied for qualification of AGR-1 thermocouple data. Abnormal trends in measured data revealed by the statistical analysis are traced to either measuring instrument deterioration or physical mechanisms in capsules that may have shifted the system thermal response. The main thrust of this work is to exploit the variety of data obtained in irradiation and post-irradiation examination (PIE) for assessment of modeling assumptions. As an example, the uneven reduction of the control gas gap in Capsule 5 found in the capsule metrology measurements in PIE helps identify mechanisms other than TC drift causing the decrease in TC readings. This suggests a more physics-based modification of the thermal model that leads to a better fit with experimental data, thus reducing model uncertainty and increasing confidence in the calculated fuel temperatures of the AGR-1 test.

  9. Improving Thermal Model Prediction Through Statistical Analysis of Irradiation and Post-Irradiation Data from AGR Experiments

    SciTech Connect (OSTI)

    Dr. Binh T. Pham; Grant L. Hawkes; Jeffrey J. Einerson

    2012-10-01

    As part of the Research and Development program for Next Generation High Temperature Reactors (HTR), a series of irradiation tests, designated as Advanced Gas-cooled Reactor (AGR), have been defined to support development and qualification of fuel design, fabrication process, and fuel performance under normal operation and accident conditions. The AGR tests employ fuel compacts placed in a graphite cylinder shrouded by a steel capsule and instrumented with thermocouples (TC) embedded in graphite blocks enabling temperature control. The data representing the crucial test fuel conditions (e.g., temperature, neutron fast fluence, and burnup) while impossible to obtain from direct measurements are calculated by physics and thermal models. The irradiation and post-irradiation examination (PIE) experimental data are used in model calibration effort to reduce the inherent uncertainty of simulation results. This paper is focused on fuel temperature predicted by the ABAQUS code’s finite element-based thermal models. The work follows up on a previous study, in which several statistical analysis methods were adapted, implemented in the NGNP Data Management and Analysis System (NDMAS), and applied for improving qualification of AGR-1 thermocouple data. The present work exercises the idea that the abnormal trends of measured data observed from statistical analysis may be caused by either measuring instrument deterioration or physical mechanisms in capsules that may have shifted the system thermal response. As an example, the uneven reduction of the control gas gap in Capsule 5 revealed by the capsule metrology measurements in PIE helps justify the reduction in TC readings instead of TC drift. This in turn prompts modification of thermal model to better fit with experimental data, thus help increase confidence, and in other word reduce model uncertainties in thermal simulation results of the AGR-1 test.

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

    SciTech Connect (OSTI)

    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

    2009-03-03

    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.

  11. Neural Networks for Modeling and Control of Particle Accelerators

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Edelen, A. L.; Biedron, S. G.; Chase, B. E.; Edstrom, D.; Milton, S. V.; Stabile, P.

    2016-04-01

    Myriad nonlinear and complex physical phenomena are host to particle accelerators. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems,more » as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Moreover, many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. For the purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We also describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.« less

  12. Using Models to Provide Predicted Ranges for Building-Human Interfaces: Preprint

    SciTech Connect (OSTI)

    Long, N.; Scheib, J.; Pless, S.; Schott, M.

    2013-09-01

    Most building energy consumption dashboards provide only a snapshot of building performance; whereas some provide more detailed historic data with which to compare current usage. This paper will discuss the Building Agent(tm) platform, which has been developed and deployed in a campus setting at the National Renewable Energy Laboratory as part of an effort to maintain the aggressive energyperformance achieved in newly constructed office buildings and laboratories. The Building Agent(tm) provides aggregated and coherent access to building data, including electric energy, thermal energy, temperatures, humidity, and lighting levels, and occupant feedback, which are displayed in various manners for visitors, building occupants, facility managers, and researchers. This paper focuseson the development of visualizations for facility managers, or an energy performance assurance role, where metered data are used to generate models that provide live predicted ranges of building performance by end use. These predicted ranges provide simple, visual context for displayed performance data without requiring users to also assess historical information or trends. Several energymodelling techniques were explored including static lookup-based performance targets, reduced-order models derived from historical data using main effect variables such as solar radiance for lighting performance, and integrated energy models using a whole-building energy simulation program.

  13. Threshold Values for Identification of Contamination Predicted by Reduced-Order Models

    SciTech Connect (OSTI)

    Last, George V.; Murray, Christopher J.; Bott, Yi-Ju; Brown, Christopher F.

    2014-12-31

    The U.S. Department of Energys (DOEs) National Risk Assessment Partnership (NRAP) Project is developing reduced-order models to evaluate potential impacts on underground sources of drinking water (USDWs) if CO2 or brine leaks from deep CO2 storage reservoirs. Threshold values, below which there would be no predicted impacts, were determined for portions of two aquifer systems. These threshold values were calculated using an interwell approach for determining background groundwater concentrations that is an adaptation of methods described in the U.S. Environmental Protection Agencys Unified Guidance for Statistical Analysis of Groundwater Monitoring Data at RCRA Facilities.

  14. Threshold Values for Identification of Contamination Predicted by Reduced-Order Models

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Last, George V.; Murray, Christopher J.; Bott, Yi-Ju; Brown, Christopher F.

    2014-12-31

    The U.S. Department of Energy’s (DOE’s) National Risk Assessment Partnership (NRAP) Project is developing reduced-order models to evaluate potential impacts on underground sources of drinking water (USDWs) if CO2 or brine leaks from deep CO2 storage reservoirs. Threshold values, below which there would be no predicted impacts, were determined for portions of two aquifer systems. These threshold values were calculated using an interwell approach for determining background groundwater concentrations that is an adaptation of methods described in the U.S. Environmental Protection Agency’s Unified Guidance for Statistical Analysis of Groundwater Monitoring Data at RCRA Facilities.

  15. Modelling of edge localised modes and edge localised mode control

    SciTech Connect (OSTI)

    Huijsmans, G. T. A.; Loarte, A.; Chang, C. S.; Ferraro, N.; Sugiyama, L.; Waelbroeck, F.; Xu, X. Q.; Futatani, S.

    2015-02-15

    Edge Localised Modes (ELMs) in ITER Q = 10 H-mode plasmas are likely to lead to large transient heat loads to the divertor. To avoid an ELM induced reduction of the divertor lifetime, the large ELM energy losses need to be controlled. In ITER, ELM control is foreseen using magnetic field perturbations created by in-vessel coils and the injection of small D2 pellets. ITER plasmas are characterised by low collisionality at a high density (high fraction of the Greenwald density limit). These parameters cannot simultaneously be achieved in current experiments. Therefore, the extrapolation of the ELM properties and the requirements for ELM control in ITER relies on the development of validated physics models and numerical simulations. In this paper, we describe the modelling of ELMs and ELM control methods in ITER. The aim of this paper is not a complete review on the subject of ELM and ELM control modelling but rather to describe the current status and discuss open issues.

  16. NCAR Contribution to A U.S. National Multi-Model Ensemble (NMME) ISI Prediction System

    SciTech Connect (OSTI)

    Tribbia, Joseph

    2015-11-25

    NCAR brought the latest version of the Community Earth System Model (version 1, CESM1) into the mix of models in the NMME effort. This new version uses our newest atmospheric model CAM5 and produces a coupled climate and ENSO that are generally as good or better than those of the Community Climate System Model version 4 (CCSM4). Compared to CCSM4, the new coupled model has a superior climate response with respect to low clouds in both the subtropical stratus regimes and the Arctic. However, CESM1 has been run to date using a prognostic aerosol model that more than doubles its computational cost. We are currently evaluating a version of the new model using prescribed aerosols and expect it will be ready for integrations in summer 2012. Because of this NCAR has not been able to complete the hindcast integrations using the NCAR loosely-coupled ensemble Kalman filter assimilation method nor has it contributed to the current (Stage I) NMME operational utilization. The expectation is that this model will be included in the NMME in late 2012 or early 2013. The initialization method will utilize the Ensemble Kalman Filter Assimilation methods developed at NCAR using the Data Assimilation Research Testbed (DART) in conjunction with Jeff Anderson’s team in CISL. This methodology has been used in our decadal prediction contributions to CMIP5. During the course of this project, NCAR has setup and performed all the needed hindcast and forecast simulations and provide the requested fields to our collaborators. In addition, NCAR researchers have participated fully in research themes (i) and (ii). Specifically, i) we have begun to evaluate and optimize our system in hindcast mode, focusing on the optimal number of ensemble members, methodologies to recalibrate individual dynamical models, and accessing our forecasts across multiple time scales, i.e., beyond two weeks, and ii) we have begun investigation of the role of different ocean initial conditions in seasonal forecasts. The

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

    SciTech Connect (OSTI)

    Drover, Damion, Ryan

    2011-12-01

    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

  18. Electric Water Heater Modeling and Control Strategies for Demand Response

    SciTech Connect (OSTI)

    Diao, Ruisheng; Lu, Shuai; Elizondo, Marcelo A.; Mayhorn, Ebony T.; Zhang, Yu; Samaan, Nader A.

    2012-07-22

    Abstract Demand response (DR) has a great potential to provide balancing services at normal operating conditions and emergency support when a power system is subject to disturbances. Effective control strategies can significantly relieve the balancing burden of conventional generators and reduce investment on generation and transmission expansion. This paper is aimed at modeling electric water heaters (EWH) in households and tests their response to control strategies to implement DR. The open-loop response of EWH to a centralized signal is studied by adjusting temperature settings to provide regulation services; and two types of decentralized controllers are tested to provide frequency support following generator trips. EWH models are included in a simulation platform in DIgSILENT to perform electromechanical simulation, which contains 147 households in a distribution feeder. Simulation results show the dependence of EWH response on water heater usage . These results provide insight suggestions on the need of control strategies to achieve better performance for demand response implementation. Index Terms Centralized control, decentralized control, demand response, electrical water heater, smart grid

  19. Feasibility of High-Power Diode Laser Array Surrogate to Support Development of Predictive Laser Lethality Model

    SciTech Connect (OSTI)

    Lowdermilk, W H; Rubenchik, A M; Springer, H K

    2011-01-13

    Predictive modeling and simulation of high power laser-target interactions is sufficiently undeveloped that full-scale, field testing is required to assess lethality of military directed-energy (DE) systems. The cost and complexity of such testing programs severely limit the ability to vary and optimize parameters of the interaction. Thus development of advanced simulation tools, validated by experiments under well-controlled and diagnosed laboratory conditions that are able to provide detailed physics insight into the laser-target interaction and reduce requirements for full-scale testing will accelerate development of DE weapon systems. The ultimate goal is a comprehensive end-to-end simulation capability, from targeting and firing the laser system through laser-target interaction and dispersal of target debris; a 'Stockpile Science' - like capability for DE weapon systems. To support development of advanced modeling and simulation tools requires laboratory experiments to generate laser-target interaction data. Until now, to make relevant measurements required construction and operation of very high power and complex lasers, which are themselves costly and often unique devices, operating in dedicated facilities that don't permit experiments on targets containing energetic materials. High power diode laser arrays, pioneered by LLNL, provide a way to circumvent this limitation, as such arrays capable of delivering irradiances characteristic of De weapon requires are self-contained, compact, light weight and thus easily transportable to facilities, such as the High Explosives Applications Facility (HEAF) at Lawrence Livermore National Laboratory (LLNL) where testing with energetic materials can be performed. The purpose of this study was to establish the feasibility of using such arrays to support future development of advanced laser lethality and vulnerability simulation codes through providing data for materials characterization and laser-material interaction

  20. In silico prediction of toxicity of non-congeneric industrial chemicals using ensemble learning based modeling approaches

    SciTech Connect (OSTI)

    Singh, Kunwar P. Gupta, Shikha

    2014-03-15

    Ensemble learning approach based decision treeboost (DTB) and decision tree forest (DTF) models are introduced in order to establish quantitative structuretoxicity relationship (QSTR) for the prediction of toxicity of 1450 diverse chemicals. Eight non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals was evaluated using Tanimoto similarity index. Stochastic gradient boosting and bagging algorithms supplemented DTB and DTF models were constructed for classification and function optimization problems using the toxicity end-point in T. pyriformis. Special attention was drawn to prediction ability and robustness of the models, investigated both in external and 10-fold cross validation processes. In complete data, optimal DTB and DTF models rendered accuracies of 98.90%, 98.83% in two-category and 98.14%, 98.14% in four-category toxicity classifications. Both the models further yielded classification accuracies of 100% in external toxicity data of T. pyriformis. The constructed regression models (DTB and DTF) using five descriptors yielded correlation coefficients (R{sup 2}) of 0.945, 0.944 between the measured and predicted toxicities with mean squared errors (MSEs) of 0.059, and 0.064 in complete T. pyriformis data. The T. pyriformis regression models (DTB and DTF) applied to the external toxicity data sets yielded R{sup 2} and MSE values of 0.637, 0.655; 0.534, 0.507 (marine bacteria) and 0.741, 0.691; 0.155, 0.173 (algae). The results suggest for wide applicability of the inter-species models in predicting toxicity of new chemicals for regulatory purposes. These approaches provide useful strategy and robust tools in the screening of ecotoxicological risk or environmental hazard potential of chemicals. - Graphical abstract: Importance of input variables in DTB and DTF classification models for (a) two-category, and (b) four-category toxicity intervals in T. pyriformis data. Generalization and predictive abilities of the

  1. Control-Oriented Modeling for HCCI Combustion and Multi-Cylinder...

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

    Control-Oriented Modeling for HCCI Combustion and Multi-Cylinder HCCI Experimental Activities Control-Oriented Modeling for HCCI Combustion and Multi-Cylinder HCCI Experimental ...

  2. Physical control oriented model of large scale refrigerators to synthesize advanced control schemes. Design, validation, and first control results

    SciTech Connect (OSTI)

    Bonne, François; Bonnay, Patrick

    2014-01-29

    In this paper, a physical method to obtain control-oriented dynamical models of large scale cryogenic refrigerators is proposed, in order to synthesize model-based advanced control schemes. These schemes aim to replace classical user experience designed approaches usually based on many independent PI controllers. This is particularly useful in the case where cryoplants are submitted to large pulsed thermal loads, expected to take place in the cryogenic cooling systems of future fusion reactors such as the International Thermonuclear Experimental Reactor (ITER) or the Japan Torus-60 Super Advanced Fusion Experiment (JT-60SA). Advanced control schemes lead to a better perturbation immunity and rejection, to offer a safer utilization of cryoplants. The paper gives details on how basic components used in the field of large scale helium refrigeration (especially those present on the 400W @1.8K helium test facility at CEA-Grenoble) are modeled and assembled to obtain the complete dynamic description of controllable subsystems of the refrigerator (controllable subsystems are namely the Joule-Thompson Cycle, the Brayton Cycle, the Liquid Nitrogen Precooling Unit and the Warm Compression Station). The complete 400W @1.8K (in the 400W @4.4K configuration) helium test facility model is then validated against experimental data and the optimal control of both the Joule-Thompson valve and the turbine valve is proposed, to stabilize the plant under highly variable thermals loads. This work is partially supported through the European Fusion Development Agreement (EFDA) Goal Oriented Training Program, task agreement WP10-GOT-GIRO.

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

    SciTech Connect (OSTI)

    Yunovich, M.; Thompson, N.G.

    1998-12-31

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

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

    SciTech Connect (OSTI)

    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)

    2013-01-01

    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.

  5. Bayesian probabilistic model for life prediction and fault mode classification of solid state luminaires

    SciTech Connect (OSTI)

    Lall, Pradeep; Wei, Junchao; Sakalaukus, Peter

    2014-06-22

    A new method has been developed for assessment of the onset of degradation in solid state luminaires to classify failure mechanisms by using metrics beyond lumen degradation that are currently used for identification of failure. Luminous Flux output, Correlated Color Temperature Data on Philips LED Lamps has been gathered under 85°C/85%RH till lamp failure. Failure modes of the test population of the lamps have been studied to understand the failure mechanisms in 85°C/85%RH accelerated test. Results indicate that the dominant failure mechanism is the discoloration of the LED encapsulant inside the lamps which is the likely cause for the luminous flux degradation and the color shift. The acquired data has been used in conjunction with Bayesian Probabilistic Models to identify luminaires with onset of degradation much prior to failure through identification of decision boundaries between lamps with accrued damage and lamps beyond the failure threshold in the feature space. In addition luminaires with different failure modes have been classified separately from healthy pristine luminaires. The α-λ plots have been used to evaluate the robustness of the proposed methodology. Results show that the predicted degradation for the lamps tracks the true degradation observed during 85°C/85%RH during accelerated life test fairly closely within the ±20% confidence bounds. Correlation of model prediction with experimental results indicates that the presented methodology allows the early identification of the onset of failure much prior to development of complete failure distributions and can be used for assessing the damage state of SSLs in fairly large deployments. It is expected that, the new prediction technique will allow the development of failure distributions without testing till L70 life for the manifestation of failure.

  6. Comparison of limited measurements of the OTEC-1 plume with analytical-model predictions

    SciTech Connect (OSTI)

    Paddock, R.A.; Ditmars, J.D.

    1981-07-01

    Ocean Thermal Energy Conversion (OTEC) requires significant amounts of warm surface waters and cold deep waters for power production. Because these waters are returned to the ocean as effluents, their behavior may affect plant operation and impact the environment. The OTEC-1 facility tested 1-MWe heat exchangers aboard the vessel Ocean Energy Converter moored off the island of Hawaii. The warm and cold waters used by the OTEC-1 facility were combined prior to discharge from the vessel to create a mixed discharge condition. A limited field survey of the mixed discharge plume using fluorescent dye as a tracer was conducted on April 11, 1981, as part of the environmental studies at OTEC-1 coordinated by the Marine Sciences Group at Lawrence Berkeley Laboratory. Results of that survey were compared with analytical model predictions of plume behavior. Although the predictions were in general agreement with the results of the plume survey, inherent limitations in the field measurements precluded complete description of the plume or detailed evaluation of the models.

  7. Water and Heat Balance Model for Predicting Drainage Below the Plant Root Zone

    Energy Science and Technology Software Center (OSTI)

    1989-11-01

    UNSAT-H Version 2.0 is a one-dimensional model that simulates the dynamic processes of infiltration, drainage, redistribution, surface evaporation, and the uptake of water from soil by plants. The model was developed for assessing the water dynamics of arid sites used or proposed for near-surface waste disposal. In particular, the model is used for simulating the water balance of cover systems over buried waste and for estimating the recharge rate (i.e., the drainage rate beneath themore » plant root zone when a sizable vadose zone is present). The mathematical base of the model are Richards'' equation for water flow, Ficks'' law for vapor diffusion, and Fouriers law for heat flow. The simulated profile can be homogeneous or layered. The boundary conditions can be controlled as either constant (potential or temperature) or flux conditions to reflect actual conditions at a given site.« less

  8. Control of high level radioactive waste-glass melters. Part 5, Modelling of complex redox effects

    SciTech Connect (OSTI)

    Bickford, D.F.; Choi, A.S.

    1991-12-31

    Slurry Fed Melters (SFM) are being developed in the United States, Europe and Japan for the conversion of high-level radioactive waste to borosilicate glass for permanent disposal. The high transition metal, noble metal, nitrate, organic, and sulfate contents of these wastes lead to unique melter redox control requirements. Pilot waste-glass melter operations have indicated the possibility of nickel sulfide or noble-metal fission-product accumulation on melter floors, which can lead to distortion of electric heating patterns, and decrease melter life. Sulfide formation is prevented by control of the redox chemistry of the melter feed. The redox state of waste-glass melters is determined by balance between the reducing potential of organic compounds in the feed, and the oxidizing potential of gases above the melt, and nitrates and polyvalent elements in the waste. Semiquantitative models predicting limitations of organic content have been developed based on crucible testing. Computerized thermodynamic computations are being developed to predict the sequence and products of redox reactions and is assessing process variations. Continuous melter test results have been compared to improved computer staged-thermodynamic-models of redox behavior. Feed chemistry control to prevent sulfide and moderate noble metal accumulations are discussed. 17 refs., 3 figs.

  9. A Validated Prediction Model for Overall Survival From Stage III Non-Small Cell Lung Cancer: Toward Survival Prediction for Individual Patients

    SciTech Connect (OSTI)

    Oberije, Cary; De Ruysscher, Dirk; Houben, Ruud; Heuvel, Michel van de; Uyterlinde, Wilma; Deasy, Joseph O.; Belderbos, Jose; Dingemans, Anne-Marie C.; Rimner, Andreas; Din, Shaun; Lambin, Philippe

    2015-07-15

    Purpose: Although patients with stage III non-small cell lung cancer (NSCLC) are homogeneous according to the TNM staging system, they form a heterogeneous group, which is reflected in the survival outcome. The increasing amount of information for an individual patient and the growing number of treatment options facilitate personalized treatment, but they also complicate treatment decision making. Decision support systems (DSS), which provide individualized prognostic information, can overcome this but are currently lacking. A DSS for stage III NSCLC requires the development and integration of multiple models. The current study takes the first step in this process by developing and validating a model that can provide physicians with a survival probability for an individual NSCLC patient. Methods and Materials: Data from 548 patients with stage III NSCLC were available to enable the development of a prediction model, using stratified Cox regression. Variables were selected by using a bootstrap procedure. Performance of the model was expressed as the c statistic, assessed internally and on 2 external data sets (n=174 and n=130). Results: The final multivariate model, stratified for treatment, consisted of age, gender, World Health Organization performance status, overall treatment time, equivalent radiation dose, number of positive lymph node stations, and gross tumor volume. The bootstrapped c statistic was 0.62. The model could identify risk groups in external data sets. Nomograms were constructed to predict an individual patient's survival probability ( (www.predictcancer.org)). The data set can be downloaded at (https://www.cancerdata.org/10.1016/j.ijrobp.2015.02.048). Conclusions: The prediction model for overall survival of patients with stage III NSCLC highlights the importance of combining patient, clinical, and treatment variables. Nomograms were developed and validated. This tool could be used as a first building block for a decision support system.

  10. Aggregate Model for Heterogeneous Thermostatically Controlled Loads with Demand Response

    SciTech Connect (OSTI)

    Zhang, Wei; Kalsi, Karanjit; Fuller, Jason C.; Elizondo, Marcelo A.; Chassin, David P.

    2012-07-22

    Due to the potentially large number of Distributed Energy Resources (DERs) demand response, distributed generation, distributed storage - that are expected to be deployed, it is impractical to use detailed models of these resources when integrated with the transmission system. Being able to accurately estimate the fast transients caused by demand response is especially important to analyze the stability of the system under different demand response strategies. On the other hand, a less complex model is more amenable to design feedback control strategies for the population of devices to provide ancillary services. The main contribution of this paper is to develop aggregated models for a heterogeneous population of Thermostatic Controlled Loads (TCLs) to accurately capture their collective behavior under demand response and other time varying effects of the system. The aggregated model efficiently includes statistical information of the population and accounts for a second order effect necessary to accurately capture the collective dynamic behavior. The developed aggregated models are validated against simulations of thousands of detailed building models using GridLAB-D (an open source distribution simulation software) under both steady state and severe dynamic conditions caused due to temperature set point changes.

  11. Tectonic control of the sedimentary record: Constraints from quantitative basin modeling

    SciTech Connect (OSTI)

    Cloetingh, S.A.P.L.; Van Balen, R.T.; Zoetemeijer, B.P. )

    1993-09-01

    The incorporation of finite strength of the lithosphere during rifting in models for extensional basin formation in conjunction with temporal changes in tectonic stress levels leads to the prediction of rapid vertical motions in these basins with a rate and magnitude comparable to second- and third-order changes in relative sea level. We present results of modeling simulations, incorporating the interplay of flank uplift and erosion for rifted basins in the northern Atlantic/North Sea area. The incorporation of the mechanical properties of the lithosphere in forward stratigraphic modeling appears also to be of key importance for an accurate prediction of the record of vertical motions in foreland fold and thrust belts. Models invoking the mechanical coupling between plate flexure and near-surface brittle tectonics are capable of producing onlap/offlap patterns in syntectonic basins sometimes strikingly similar to the basin-fill signatures attributed to third-order glacio-eustatic signals. The full incorporation of structural geological constraints in forward modeling of basin stratigraphy proves to be a powerful instrument in linking different temporal and spatial scales in the sedimentary record. This approach also leads to a quantification of the tectonic control of the sedimentary record in frequency bands hitherto primarily attributed to external forcing functions.

  12. Wind Plant Power Optimization through Yaw Control using a Parametric Model for Wake Effects -- A CFD Simulation Study

    SciTech Connect (OSTI)

    Gebraad, P. M. O.; Teeuwisse, F. W.; van Wingerden, J. W.; Fleming, Paul A.; Ruben, S. D.; Marden, J. R.; Pao, L. Y.

    2016-01-01

    This article presents a wind plant control strategy that optimizes the yaw settings of wind turbines for improved energy production of the whole wind plant by taking into account wake effects. The optimization controller is based on a novel internal parametric model for wake effects, called the FLOw Redirection and Induction in Steady-state (FLORIS) model. The FLORIS model predicts the steady-state wake locations and the effective flow velocities at each turbine, and the resulting turbine electrical energy production levels, as a function of the axial induction and the yaw angle of the different rotors. The FLORIS model has a limited number of parameters that are estimated based on turbine electrical power production data. In high-fidelity computational fluid dynamics simulations of a small wind plant, we demonstrate that the optimization control based on the FLORIS model increases the energy production of the wind plant, with a reduction of loads on the turbines as an additional effect.

  13. Modeling of fluidized-bed combustion of coal: Phase II, final reports. Volume III. Model predictions and results

    SciTech Connect (OSTI)

    Louis, J.F.; Tung, S.E.

    1980-10-01

    This document is the third of a seven volume series of our Phase II Final Report. This volume deals with parametric studies carried out using the FBC model. A comparison with available pilot plant data is included where such data are available. This volume in essence documents model performance; describing predictions on bubble growth, combustion characteristics, sulfur capture, heat transfer and related parameters. The model has approximately forty input variables which are at the disposal of the user. The user has the option to change a few or all of these input variables. In the parametric studies reported here, a large number of input variables whose variation is less critical to the predicted results, were maintained constant at the default values. On the other hand, those parameters whose selection is very important in design and operation of the FBC's were varied in suitable operating regions. The chief among such parameters are: bed temperature, coal feed size distribution (2 parameters), average bed-sorbent size, calcium to sulfur molar ratio, superficial velocity, excess air fraction, and bed weight (or bed height). The computations for obtaining the parametric relationships are based upon selection of a geometrical design for the combustor. Bed cross-section is 6' x 6', bed height is 4', and the freeboard height is 16'. The heat transfer tubes have 2'' OD, a pitch of 10'', and are located on an equilateral triangle pattern. The air distributor is a perforated plate with 0.1'' diameter holes on a rectangular grid with 0.75'' center-to-center spacing.

  14. Predictive Maturity of Multi-Scale Simulation Models for Fuel Performance

    SciTech Connect (OSTI)

    Atamturktur, Sez; Unal, Cetin; Hemez, Francois; Williams, Brian; Tome, Carlos

    2015-03-16

    The project proposed to provide a Predictive Maturity Framework with its companion metrics that (1) introduce a formalized, quantitative means to communicate information between interested parties, (2) provide scientifically dependable means to claim completion of Validation and Uncertainty Quantification (VU) activities, and (3) guide the decision makers in the allocation of Nuclear Energy’s resources for code development and physical experiments. The project team proposed to develop this framework based on two complimentary criteria: (1) the extent of experimental evidence available for the calibration of simulation models and (2) the sophistication of the physics incorporated in simulation models. The proposed framework is capable of quantifying the interaction between the required number of physical experiments and degree of physics sophistication. The project team has developed this framework and implemented it with a multi-scale model for simulating creep of a core reactor cladding. The multi-scale model is composed of the viscoplastic self-consistent (VPSC) code at the meso-scale, which represents the visco-plastic behavior and changing properties of a highly anisotropic material and a Finite Element (FE) code at the macro-scale to represent the elastic behavior and apply the loading. The framework developed takes advantage of the transparency provided by partitioned analysis, where independent constituent codes are coupled in an iterative manner. This transparency allows model developers to better understand and remedy the source of biases and uncertainties, whether they stem from the constituents or the coupling interface by exploiting separate-effect experiments conducted within the constituent domain and integral-effect experiments conducted within the full-system domain. The project team has implemented this procedure with the multi- scale VPSC-FE model and demonstrated its ability to improve the predictive capability of the model. Within this

  15. Dynamic Modeling in Solid-Oxide Fuel Cells Controller Design

    SciTech Connect (OSTI)

    Lu, Ning; Li, Qinghe; Sun, Xin; Khaleel, Mohammad A.

    2007-06-28

    In this paper, a dynamic model of the solid-oxide fuel cell (SOFC) power unit is developed for the purpose of designing a controller to regulate fuel flow rate, fuel temperature, air flow rate, and air temperature to maintain the SOFC stack temperature, fuel utilization rate, and voltage within operation limits. A lumped model is used to consider the thermal dynamics and the electro-chemial dynamics inside an SOFC power unit. The fluid dynamics at the fuel and air inlets are considered by using the in-flow ramp-rates.

  16. Model and control of heat release in engines

    SciTech Connect (OSTI)

    Oppenheim, A.K.; Packard, A.K.; Hedrick, J.K.; Kuhl, A.L.; Johnson, W.P.

    1996-09-01

    The concept of the paper stems from the premise that the process of heat release in engines involves in essence the evolution and deposition of exothermic energy generated by combustion--events that can be governed promptly by a feedback, adaptive micro-electronic control system. The key to its realization is the principle of DISC (Direct Injection Stratified Charge) engine, implemented by a multi-jet system. The background and the salient features of such a system, referred to as a CCE (Controlled Combustion Engine), have been described in a companion paper (SAE 951961). Presented here are fundamental aspects of the model of the exothermic process and the intrinsic properties of its control system.

  17. Reference Model for Control and Automation Systems in Electrical Power

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

    Reference Model for Control and Automation Systems in Electrical Power Version 1.2 October 12, 2005 Prepared by: Sandia National Laboratories' Center for SCADA Security Jason Stamp, Technical Lead Michael Berg, Co-Technical Lead Michael Baca, Project Lead This work was conducted for the DOE Office of Electricity Delivery and Energy Reliability under Contract M64SCADSNL Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department

  18. Global warming and climate change - predictive models for temperate and tropical regions

    SciTech Connect (OSTI)

    Malini, B.H.

    1997-12-31

    Based on the assumption of 4{degree}C increase of global temperature by the turn of 21st century due to the accumulation of greenhouse gases an attempt is made to study the possible variations in different climatic regimes. The predictive climatic water balance model for Hokkaido island of Japan (a temperate zone) indicates the possible occurrence of water deficit for two to three months, which is a unknown phenomenon in this region at present. Similarly, India which represents tropical region also will experience much drier climates with increased water deficit conditions. As a consequence, the thermal region of Hokkaido which at present is mostly Tundra and Micro thermal will change into a Meso thermal category. Similarly, the moisture regime which at present supports per humid (A2, A3 and A4) and Humid (B4) climates can support A1, B4, B3, B2 and B1 climates indicating a shift towards drier side of the climatic spectrum. Further, the predictive modes of both the regions have indicated increased evapotranspiration rates. Although there is not much of change in the overall thermal characteristics of the Indian region the moisture regime indicates a clear shift towards the aridity in the country.

  19. Development and application of a statistical methodology to evaluate the predictive accuracy of building energy baseline models

    SciTech Connect (OSTI)

    Granderson, Jessica; Price, Phillip N.

    2014-03-01

    This paper documents the development and application of a general statistical methodology to assess the accuracy of baseline energy models, focusing on its application to Measurement and Verification (M&V) of whole-­building energy savings. The methodology complements the principles addressed in resources such as ASHRAE Guideline 14 and the International Performance Measurement and Verification Protocol. It requires fitting a baseline model to data from a ``training period’’ and using the model to predict total electricity consumption during a subsequent ``prediction period.’’ We illustrate the methodology by evaluating five baseline models using data from 29 buildings. The training period and prediction period were varied, and model predictions of daily, weekly, and monthly energy consumption were compared to meter data to determine model accuracy. Several metrics were used to characterize the accuracy of the predictions, and in some cases the best-­performing model as judged by one metric was not the best performer when judged by another metric.

  20. Development of a model for predicting transient hydrogen venting in 55-gallon drums

    SciTech Connect (OSTI)

    Apperson, Jason W; Clemmons, James S; Garcia, Michael D; Sur, John C; Zhang, Duan Z; Romero, Michael J

    2008-01-01

    Remote drum venting was performed on a population of unvented high activity drums (HAD) in the range of 63 to 435 plutonium equivalent Curies (PEC). These 55-gallon Transuranic (TRU) drums will eventually be shipped to the Waste Isolation Pilot Plant (WIPP). As a part of this process, the development of a calculational model was required to predict the transient hydrogen concentration response of the head space and polyethylene liner (if present) within the 55-gallon drum. The drum and liner were vented using a Remote Drum Venting System (RDVS) that provided a vent sampling path for measuring flammable hydrogen vapor concentrations and allow hydrogen to diffuse below lower flammability limit (LFL) concentrations. One key application of the model was to determine the transient behavior of hydrogen in the head space, within the liner, and the sensitivity to the number of holes made in the liner or number of filters. First-order differential mass transport equations were solved using Laplace transformations and numerically to verify the results. the Mathematica 6.0 computing tool was also used as a validation tool and for examining larger than two chamber systems. Results will be shown for a variety of configurations, including 85-gallon and 110-gallon overpack drums. The model was also validated against hydrogen vapor concentration assay measurements.

  1. Modeling Stress Strain Relationships and Predicting Failure Probabilities For Graphite Core Components

    SciTech Connect (OSTI)

    Duffy, Stephen

    2013-09-09

    This project will implement inelastic constitutive models that will yield the requisite stress-strain information necessary for graphite component design. Accurate knowledge of stress states (both elastic and inelastic) is required to assess how close a nuclear core component is to failure. Strain states are needed to assess deformations in order to ascertain serviceability issues relating to failure, e.g., whether too much shrinkage has taken place for the core to function properly. Failure probabilities, as opposed to safety factors, are required in order to capture the bariability in failure strength in tensile regimes. The current stress state is used to predict the probability of failure. Stochastic failure models will be developed that can accommodate possible material anisotropy. This work will also model material damage (i.e., degradation of mechanical properties) due to radiation exposure. The team will design tools for components fabricated from nuclear graphite. These tools must readily interact with finite element software--in particular, COMSOL, the software algorithm currently being utilized by the Idaho National Laboratory. For the eleastic response of graphite, the team will adopt anisotropic stress-strain relationships available in COMSO. Data from the literature will be utilized to characterize the appropriate elastic material constants.

  2. Models for prediction of temperature difference and ventilation effectiveness with displacement ventilation

    SciTech Connect (OSTI)

    Yuan, X.; Chen, Q.; Glicksman, L.R.

    1999-07-01

    Displacement ventilation may provide better indoor air quality than mixing ventilation. Proper design of displacement ventilation requires information concerning the air temperature difference between the head and foot level of a sedentary person and the ventilation effectiveness at the breathing level. This paper presents models to predict the air temperature difference and the ventilation effectiveness, based on a database of 56 cases with displacement ventilation. The database was generated by using a validated CFD program and covers four different types of US buildings: small offices, large offices with partitions, classrooms, and industrial workshops under different thermal and flow boundary conditions. Both the maximum cooling load that can be removed by displacement ventilation and the ventilation effectiveness are shown to depend on the heat source type and ventilation rate in a room.

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

    SciTech Connect (OSTI)

    Fisher, Jeffrey W. Twaddle, Nathan C.; Vanlandingham, Michelle; Doerge, Daniel R.

    2011-11-15

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

  4. Predictive Treatment Management: Incorporating a Predictive Tumor Response Model Into Robust Prospective Treatment Planning for Non-Small Cell Lung Cancer

    SciTech Connect (OSTI)

    Zhang, Pengpeng; Yorke, Ellen; Hu, Yu-Chi; Mageras, Gig; Rimner, Andreas; Deasy, Joseph O.

    2014-02-01

    Purpose: We hypothesized that a treatment planning technique that incorporates predicted lung tumor regression into optimization, predictive treatment planning (PTP), could allow dose escalation to the residual tumor while maintaining coverage of the initial target without increasing dose to surrounding organs at risk (OARs). Methods and Materials: We created a model to estimate the geometric presence of residual tumors after radiation therapy using planning computed tomography (CT) and weekly cone beam CT scans of 5 lung cancer patients. For planning purposes, we modeled the dynamic process of tumor shrinkage by morphing the original planning target volume (PTV{sub orig}) in 3 equispaced steps to the predicted residue (PTV{sub pred}). Patients were treated with a uniform prescription dose to PTV{sub orig}. By contrast, PTP optimization started with the same prescription dose to PTV{sub orig} but linearly increased the dose at each step, until reaching the highest dose achievable to PTV{sub pred} consistent with OAR limits. This method is compared with midcourse adaptive replanning. Results: Initial parenchymal gross tumor volume (GTV) ranged from 3.6 to 186.5 cm{sup 3}. On average, the primary GTV and PTV decreased by 39% and 27%, respectively, at the end of treatment. The PTP approach gave PTV{sub orig} at least the prescription dose, and it increased the mean dose of the true residual tumor by an average of 6.0 Gy above the adaptive approach. Conclusions: PTP, incorporating a tumor regression model from the start, represents a new approach to increase tumor dose without increasing toxicities, and reduce clinical workload compared with the adaptive approach, although model verification using per-patient midcourse imaging would be prudent.

  5. Development of a model for predicting intergranular stress corrosion cracking of Alloy 600 tubes in PWR primary water. Final report

    SciTech Connect (OSTI)

    Garud, Y.S.

    1985-01-01

    A preliminary mathematical model developed in this study may make it possible to predict stress corrosion cracking on the primary side of PWR steam generator tubing. The study outlines a comprehensive testing program that will provide the operational and experimental data to further develop and verify the model.

  6. Coupling a Mesoscale Numerical Weather Prediction Model with Large-Eddy Simulation for Realistic Wind Plant Aerodynamics Simulations (Poster)

    SciTech Connect (OSTI)

    Draxl, C.; Churchfield, M.; Mirocha, J.; Lee, S.; Lundquist, J.; Michalakes, J.; Moriarty, P.; Purkayastha, A.; Sprague, M.; Vanderwende, B.

    2014-06-01

    Wind plant aerodynamics are influenced by a combination of microscale and mesoscale phenomena. Incorporating mesoscale atmospheric forcing (e.g., diurnal cycles and frontal passages) into wind plant simulations can lead to a more accurate representation of microscale flows, aerodynamics, and wind turbine/plant performance. Our goal is to couple a numerical weather prediction model that can represent mesoscale flow [specifically the Weather Research and Forecasting model] with a microscale LES model (OpenFOAM) that can predict microscale turbulence and wake losses.

  7. A general unified non-equilibrium model for predicting saturated and subcooled critical two-phase flow rates through short and long tubes

    SciTech Connect (OSTI)

    Fraser, D.W.H.; Abdelmessih, A.H.

    1995-09-01

    A general unified model is developed to predict one-component critical two-phase pipe flow. Modelling of the two-phase flow is accomplished by describing the evolution of the flow between the location of flashing inception and the exit (critical) plane. The model approximates the nonequilibrium phase change process via thermodynamic equilibrium paths. Included are the relative effects of varying the location of flashing inception, pipe geometry, fluid properties and length to diameter ratio. The model predicts that a range of critical mass fluxes exist and is bound by a maximum and minimum value for a given thermodynamic state. This range is more pronounced at lower subcooled stagnation states and can be attributed to the variation in the location of flashing inception. The model is based on the results of an experimental study of the critical two-phase flow of saturated and subcooled water through long tubes. In that study, the location of flashing inception was accurately controlled and adjusted through the use of a new device. The data obtained revealed that for fixed stagnation conditions, the maximum critical mass flux occurred with flashing inception located near the pipe exit; while minimum critical mass fluxes occurred with the flashing front located further upstream. Available data since 1970 for both short and long tubes over a wide range of conditions are compared with the model predictions. This includes test section L/D ratios from 25 to 300 and covers a temperature and pressure range of 110 to 280{degrees}C and 0.16 to 6.9 MPa. respectively. The predicted maximum and minimum critical mass fluxes show an excellent agreement with the range observed in the experimental data.

  8. Impact of Pilot Light Modeling on the Predicted Annual Performance of Residential Gas Water Heaters: Preprint

    SciTech Connect (OSTI)

    Maguire, J.; Burch, J.

    2013-08-01

    Modeling residential water heaters with dynamic simulation models can provide accurate estimates of their annual energy consumption, if the units? characteristics and use conditions are known. Most gas storage water heaters (GSWHs) include a standing pilot light. It is generally assumed that the pilot light energy will help make up standby losses and have no impact on the predicted annual energy consumption. However, that is not always the case. The gas input rate and conversion efficiency of a pilot light for a GSWH were determined from laboratory data. The data were used in simulations of a typical GSWH with and without a pilot light, for two cases: 1) the GSWH is used alone; and 2) the GSWH is the second tank in a solar water heating (SWH) system. The sensitivity of wasted pilot light energy to annual hot water use, climate, and installation location was examined. The GSWH used alone in unconditioned space in a hot climate had a slight increase in energy consumption. The GSWH with a pilot light used as a backup to an SWH used up to 80% more auxiliary energy than one without in hot, sunny locations, from increased tank losses.

  9. A predictive analytic model for the solar modulation of cosmic rays

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Cholis, Ilias; Hooper, Dan; Linden, Tim

    2016-02-23

    An important factor limiting our ability to understand the production and propagation of cosmic rays pertains to the effects of heliospheric forces, commonly known as solar modulation. The solar wind is capable of generating time- and charge-dependent effects on the spectrum and intensity of low-energy (≲10 GeV) cosmic rays reaching Earth. Previous analytic treatments of solar modulation have utilized the force-field approximation, in which a simple potential is adopted whose amplitude is selected to best fit the cosmic-ray data taken over a given period of time. Making use of recently available cosmic-ray data from the Voyager 1 spacecraft, along withmore » measurements of the heliospheric magnetic field and solar wind, we construct a time-, charge- and rigidity-dependent model of solar modulation that can be directly compared to data from a variety of cosmic-ray experiments. Here, we provide a simple analytic formula that can be easily utilized in a variety of applications, allowing us to better predict the effects of solar modulation and reduce the number of free parameters involved in cosmic-ray propagation models.« less

  10. Optimal SCR Control Using Data-Driven Models

    SciTech Connect (OSTI)

    Stevens, Andrew J.; Sun, Yannan; Lian, Jianming; Devarakonda, Maruthi N.; Parker, Gordon

    2013-04-16

    We present an optimal control solution for the urea injection for a heavy-duty diesel (HDD) selective catalytic reduction (SCR). The approach taken here is useful beyond SCR and could be applied to any system where a control strategy is desired and input-output data is available. For example, the strategy could also be used for the diesel oxidation catalyst (DOC) system. In this paper, we identify and validate a one-step ahead Kalman state-space estimator for downstream NOx using the bench reactor data of an SCR core sample. The test data was acquired using a 2010 Cummins 6.7L ISB production engine with a 2010 Cummins production aftertreatment system. We used a surrogate HDD federal test procedure (FTP), developed at Michigan Technological University (MTU), which simulates the representative transients of the standard FTP cycle, but has less engine speed/load points. The identified state-space model is then used to develop a tunable cost function that simultaneously minimizes NOx emissions and urea usage. The cost function is quadratic and univariate, thus the minimum can be computed analytically. We show the performance of the closed-loop controller in using a reduced-order discrete SCR simulator developed at MTU. Our experiments with the surrogate HDD-FTP data show that the strategy developed in this paper can be used to identify performance bounds for urea dose controllers.

  11. Predictions of flow through an isothermal serpentine passage with linear eddy-viscosity Reynolds Averaged Navier Stokes models.

    SciTech Connect (OSTI)

    Laskowski, Gregory Michael

    2005-12-01

    Flows with strong curvature present a challenge for turbulence models, specifically eddy viscosity type models which assume isotropy and a linear and instantaneous equilibrium relation between stress and strain. Results obtained from three different codes and two different linear eddy viscosity turbulence models are compared to a DNS simulation in order to gain some perspective on the turbulence modeling capability of SIERRA/Fuego. The Fuego v2f results are superior to the more common two-layer k-e model results obtained with both a commercial and research code in terms of the concave near wall behavior predictions. However, near the convex wall, including the separated region, little improvement is gained using the v2f model and in general the turbulent kinetic energy prediction is fair at best.

  12. Model-reference adaptive control applied to load-following of a space-nuclear power system

    SciTech Connect (OSTI)

    Metzger, J.D.; El-Genk, M.S.; Parlos, A.G.; New Mexico Univ., Albuquerque, NM . Inst. for Space Nuclear Power Studies; Texas A and M Univ., College Station, TX . Dept. of Nuclear Engineering)

    1989-01-01

    Nuclear power systems are presently being investigated as an alternative for both commercial and military space power systems because of their projected longevity of 7 to 10 years, their mass advantage over other space power sources at powers above approximately 25 kW{sub e}, and their ability to operate without direct illumination from the sun. These space-nuclear power systems are being designed to supply from tens of kilowatts to multimegawatts of power for continuous operation of seven years and more. Space-nuclear power systems designs that meet these requirements will not be available for refueling or maintenance during their lifetime. To ensure that the space-nuclear power system will operate safely and will respond in a predictable and desired manner, the design of the system's controller must account for changes in the system parameters over its lifetime. This paper applies model-reference adaptive control to an increase in the power demand by the load. A model-reference adaptive controller will force the actual space-nuclear power system to follow the predictable and desired response of a reference model, despite changes in the actual system's operating parameters. Included in this paper are the model-reference adaptive control algorithm, the description of the computer simulation of a space-nuclear power system and the reference model, and results that demonstrate the application of model-reference adaptive control to a change in the load power demand. The results demonstrate that model-reference adaptive control can ensure the transient response of the system despite differences between the design of the system and the as-built system as well as for variations in the systems parameters. 5 refs., 3 figs.

  13. A coarse-grained model with implicit salt for RNAs: Predicting 3D structure, stability and salt effect

    SciTech Connect (OSTI)

    Shi, Ya-Zhou; Wang, Feng-Hua; Wu, Yuan-Yan; Tan, Zhi-Jie

    2014-09-14

    To bridge the gap between the sequences and 3-dimensional (3D) structures of RNAs, some computational models have been proposed for predicting RNA 3D structures. However, the existed models seldom consider the conditions departing from the room/body temperature and high salt (1M NaCl), and thus generally hardly predict the thermodynamics and salt effect. In this study, we propose a coarse-grained model with implicit salt for RNAs to predict 3D structures, stability, and salt effect. Combined with Monte Carlo simulated annealing algorithm and a coarse-grained force field, the model folds 46 tested RNAs (?45 nt) including pseudoknots into their native-like structures from their sequences, with an overall mean RMSD of 3.5 and an overall minimum RMSD of 1.9 from the experimental structures. For 30 RNA hairpins, the present model also gives the reliable predictions for the stability and salt effect with the mean deviation ? 1.0 C of melting temperatures, as compared with the extensive experimental data. In addition, the model could provide the ensemble of possible 3D structures for a short RNA at a given temperature/salt condition.

  14. Integrated Air Pollution Control System (IAPCS), Executable Model (Version 4. 0) (for microcomputers). Model-Simulation

    SciTech Connect (OSTI)

    Not Available

    1990-10-29

    The Integrated Air Pollution Control System (IAPCS) Cost Model is an IBM PC cost model that can be used to estimate the cost of installing SO2, NOx, and particulate matter control systems at coal-fired utility electric generating facilities. The model integrates various combinations of the following technologies: physical coal cleaning, coal switching, overfire air/low NOx burners, natural gas reburning, LIMB, ADVACATE, electrostatic precipitator, fabric filter, gas conditioning, wet lime or limestone FGD, lime spray drying/duct spray drying, dry sorbent injection, pressurized fluidized bed combustion, integrated gasification combined cycle, and pulverized coal burning boiler. The model generates capital, annualized, and unitized pollutant removal costs in either constant or current dollars for any year.

  15. Statistical circuit simulation with measurement-based active device models: Implications for process control and IC manufacturability

    SciTech Connect (OSTI)

    Root, D.E.; McGinty, D.; Hughes, B.

    1995-12-31

    This paper presents a new approach to statistical active circuit design which unifies device parametric-based process control and non-parametric circuit simulation. Predictions of circuit sensitivity to process variation and yield-loss of circuits fabricated in two different GaAs IC processes are described. The simulations make use of measurement-based active device models which are not formulated in terms of conventional parametric statistical variables. The technique is implemented in commercially available simulation software (HP MDS).

  16. Predicting oropharyngeal tumor volume throughout the course of radiation therapy from pretreatment computed tomography data using general linear models

    SciTech Connect (OSTI)

    Yock, Adam D. Kudchadker, Rajat J.; Rao, Arvind; Dong, Lei; Beadle, Beth M.; Garden, Adam S.; Court, Laurence E.

    2014-05-15

    Purpose: The purpose of this work was to develop and evaluate the accuracy of several predictive models of variation in tumor volume throughout the course of radiation therapy. Methods: Nineteen patients with oropharyngeal cancers were imaged daily with CT-on-rails for image-guided alignment per an institutional protocol. The daily volumes of 35 tumors in these 19 patients were determined and used to generate (1) a linear model in which tumor volume changed at a constant rate, (2) a general linear model that utilized the power fit relationship between the daily and initial tumor volumes, and (3) a functional general linear model that identified and exploited the primary modes of variation between time series describing the changing tumor volumes. Primary and nodal tumor volumes were examined separately. The accuracy of these models in predicting daily tumor volumes were compared with those of static and linear reference models using leave-one-out cross-validation. Results: In predicting the daily volume of primary tumors, the general linear model and the functional general linear model were more accurate than the static reference model by 9.9% (range: −11.6%–23.8%) and 14.6% (range: −7.3%–27.5%), respectively, and were more accurate than the linear reference model by 14.2% (range: −6.8%–40.3%) and 13.1% (range: −1.5%–52.5%), respectively. In predicting the daily volume of nodal tumors, only the 14.4% (range: −11.1%–20.5%) improvement in accuracy of the functional general linear model compared to the static reference model was statistically significant. Conclusions: A general linear model and a functional general linear model trained on data from a small population of patients can predict the primary tumor volume throughout the course of radiation therapy with greater accuracy than standard reference models. These more accurate models may increase the prognostic value of information about the tumor garnered from pretreatment computed tomography

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

    SciTech Connect (OSTI)

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

    2010-09-01

    , 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

  18. Modeling, Analysis, and Control of Demand Response Resources

    SciTech Connect (OSTI)

    Mathieu, Johanna L.

    2012-05-01

    While the traditional goal of an electric power system has been to control supply to fulfill demand, the demand-side can plan an active role in power systems via Demand Response (DR), defined by the Department of Energy (DOE) as “a tariff or program established to motivate changes in electric use by end-use customers in response to changes in the price of electricity over time, or to give incentive payments designed to induce lower electricity use at times of high market prices or when grid reliability is jeopardized” [29]. DR can provide a variety of benefits including reducing peak electric loads when the power system is stressed and fast timescale energy balancing. Therefore, DR can improve grid reliability and reduce wholesale energy prices and their volatility. This dissertation focuses on analyzing both recent and emerging DR paradigms. Recent DR programs have focused on peak load reduction in commercial buildings and industrial facilities (C&I facilities). We present methods for using 15-minute-interval electric load data, commonly available from C&I facilities, to help building managers understand building energy consumption and ‘ask the right questions’ to discover opportunities for DR. Additionally, we present a regression-based model of whole building electric load, i.e., a baseline model, which allows us to quantify DR performance. We use this baseline model to understand the performance of 38 C&I facilities participating in an automated dynamic pricing DR program in California. In this program, facilities are expected to exhibit the same response each DR event. We find that baseline model error makes it difficult to precisely quantify changes in electricity consumption and understand if C&I facilities exhibit event-to-event variability in their response to DR signals. Therefore, we present a method to compute baseline model error and a metric to determine how much observed DR variability results from baseline model error rather than real

  19. Using calibrated engineering models to predict energy savings in large-scale geothermal heat pump projects

    SciTech Connect (OSTI)

    Shonder, J.A.; Hughes, P.J.; Thornton, J.W.

    1998-10-01

    Energy savings performance contracting (ESPC) is now receiving greater attention as a means of implementing large-scale energy conservation projects in housing. Opportunities for such projects exist for military housing, federally subsidized low-income housing, and planned communities (condominiums, townhomes, senior centers), to name a few. Accurate prior (to construction) estimates of the energy savings in these projects reduce risk, decrease financing costs, and help avoid post-construction disputes over performance contract baseline adjustments. This paper demonstrates an improved method of estimating energy savings before construction takes place. Using an engineering model calibrated to pre-construction energy-use data collected in the field, this method is able to predict actual energy savings to a high degree of accuracy. This is verified with post-construction energy-use data from a geothermal heat pump ESPC at Fort Polk, Louisiana. This method also allows determination of the relative impact of the various energy conservation measures installed in a comprehensive energy conservation project. As an example, the breakout of savings at Fort Polk for the geothermal heat pumps, desuperheaters, lighting retrofits, and low-flow hot water outlets is provided.

  20. Using Calibrated Engineering Models To Predict Energy Savings In Large-Scale Geothermal Heat Pump Projects

    SciTech Connect (OSTI)

    Shonder, John A; Hughes, Patrick; Thornton, Jeff W.

    1998-01-01

    Energy savings performance contracting (ESPC) is now receiving greater attention as a means of implementing large-scale energy conservation projects in housing. Opportunities for such projects exist for military housing, federally subsidized low-income housing, and planned communities (condominiums, townhomes, senior centers), to name a few. Accurate prior (to construction) estimates of the energy savings in these projects reduce risk, decrease financing costs, and help avoid post-construction disputes over performance contract baseline adjustments. This paper demonstrates an improved method of estimating energy savings before construction takes place. Using an engineering model calibrated to pre-construction energy-use data collected in the field, this method is able to predict actual energy savings to a high degree of accuracy. This is verified with post-construction energy-use data from a geothermal heat pump ESPC at Fort Polk, Louisiana. This method also allows determination of the relative impact of the various energy conservation measures installed in a comprehensive energy conservation project. As an example, the breakout of savings at Fort Polk for the geothermal heat pumps, desuperheaters, lighting retrofits, and low-flow hot water outlets is provided.

  1. Model for the Prediction of the Hydriding Thermodynamics of Pd-Rh-Co Ternary Alloys

    SciTech Connect (OSTI)

    Teter, D.F.; Thoma, D.J.

    1999-03-01

    A dilute solution model (with respect to the substitutional alloying elements) has been developed, which accurately predicts the hydride formation and decomposition thermodynamics and the storage capacities of dilute ternary Pd-Rh-Co alloys. The effect of varying the rhodium and cobalt compositions on the thermodynamics of hydride formation and decomposition and hydrogen capacity of several palladium-rhodium-cobalt ternary alloys has been investigated using pressure-composition (PC) isotherms. Alloying in the dilute regime (<10 at.%) causes the enthalpy for hydride formation to linearly decrease with increasing alloying content. Cobalt has a stronger effect on the reduction in enthalpy than rhodium for equivalent alloying amounts. Also, cobalt reduces the hydrogen storage capacity with increasing alloying content. The plateau thermodynamics are strongly linked to the lattice parameters of the alloys. A near-linear dependence of the enthalpy of hydride formation on the lattice parameter was observed for both the binary Pd-Rh and Pd-Co alloys, as well as for the ternary Pd-Rh-Co alloys. The Pd-5Rh-3Co (at. %) alloy was found to have similar plateau thermodynamics as a Pd-10Rh alloy, however, this ternary alloy had a diminished hydrogen storage capacity relative to Pd-10Rh.

  2. Hydrodynamic modeling for corrosion control in the oil and gas industry

    SciTech Connect (OSTI)

    Palacios, C.A.; Morales, J.L.

    1995-10-01

    This article describes a methodology used to select and establish corrosion control programs. These include corrosion rate predictions using well known correlations for flowing systems, materials selection, optimization of inhibitors and corrosion monitoring techniques. The methodology characterizes internal corrosion phenomenon integrating the hydrodynamic conditions of the flow (flow velocities, flow pattern, liquid holdups, and where the condensation is taking place within a pipeline) with those that predict corrosion rates. It can be applied in the whole oil/gas production system, including subsurface and surface equipment. The methodology uses single and two phase flow modeling techniques to: (1) optimize the entire production system to obtain the most efficient objective flow rate, taking into consideration the corrosive/erosive nature of the produced fluids and (2) characterize the corrosion nature of oil and gas transmission lines. As an example of its use, a characterization of corrosion nature of a gas transmission line is described. The hydrodynamic simulation was performed using commercially available simulators, and the corrosion rates were determined using published correlations. Results using this methodology allowed for corrosion control strategies, protection and monitoring criteria, and inhibition optimization.

  3. Lattice and off-lattice side chain models of protein folding: Linear time structure prediction better than 86% of optimal

    SciTech Connect (OSTI)

    Hart, W.E.; Istrail, S. [Sandia National Labs., Albuquerque, NM (United States). Algorithms and Discrete Mathematics Dept.

    1996-08-09

    This paper considers the protein structure prediction problem for lattice and off-lattice protein folding models that explicitly represent side chains. Lattice models of proteins have proven extremely useful tools for reasoning about protein folding in unrestricted continuous space through analogy. This paper provides the first illustration of how rigorous algorithmic analyses of lattice models can lead to rigorous algorithmic analyses of off-lattice models. The authors consider two side chain models: a lattice model that generalizes the HP model (Dill 85) to explicitly represent side chains on the cubic lattice, and a new off-lattice model, the HP Tangent Spheres Side Chain model (HP-TSSC), that generalizes this model further by representing the backbone and side chains of proteins with tangent spheres. They describe algorithms for both of these models with mathematically guaranteed error bounds. In particular, the authors describe a linear time performance guaranteed approximation algorithm for the HP side chain model that constructs conformations whose energy is better than 865 of optimal in a face centered cubic lattice, and they demonstrate how this provides a 70% performance guarantee for the HP-TSSC model. This is the first algorithm in the literature for off-lattice protein structure prediction that has a rigorous performance guarantee. The analysis of the HP-TSSC model builds off of the work of Dancik and Hannenhalli who have developed a 16/30 approximation algorithm for the HP model on the hexagonal close packed lattice. Further, the analysis provides a mathematical methodology for transferring performance guarantees on lattices to off-lattice models. These results partially answer the open question of Karplus et al. concerning the complexity of protein folding models that include side chains.

  4. Modeling Species Inhibition of NO Oxidation in Urea-SCR Catalysts for Diesel Engine NOx Control

    SciTech Connect (OSTI)

    Devarakonda, Maruthi N.; Tonkyn, Russell G.; Tran, Diana N.; Lee, Jong H.; Herling, Darrell R.

    2011-04-20

    Urea-selective catalytic reduction (SCR) catalysts are regarded as the leading NOx aftertreatment technology to meet the 2010 NOx emission standards for on-highway vehicles running on heavy-duty diesel engines. However, issues such as low NOx conversion at low temperature conditions still exist due to various factors, including incomplete urea thermolysis, inhibition of SCR reactions by hydrocarbons and H2O. We have observed a noticeable reduction in the standard SCR reaction efficiency at low temperature with increasing water content. We observed a similar effect when hydrocarbons are present in the stream. This effect is absent under fast SCR conditions where NO ~ NO2 in the feed gas. As a first step in understanding the effects of such inhibition on SCR reaction steps, kinetic models that predict the inhibition behavior of H2O and hydrocarbons on NO oxidation are presented in the paper. A one-dimensional SCR model was developed based on conservation of species equations and was coded as a C-language S-function and implemented in Matlab/Simulink environment. NO oxidation and NO2 dissociation kinetics were defined as a function of the respective adsorbate’s storage in the Fe-zeolite SCR catalyst. The corresponding kinetic models were then validated on temperature ramp tests that showed good match with the test data. Such inhibition models will improve the accuracy of model based control design for integrated DPF-SCR aftertreatment systems.

  5. Predictions of bubbly flows in vertical pipes using two-fluid models in CFDS-FLOW3D code

    SciTech Connect (OSTI)

    Banas, A.O.; Carver, M.B.; Unrau, D.

    1995-09-01

    This paper reports the results of a preliminary study exploring the performance of two sets of two-fluid closure relationships applied to the simulation of turbulent air-water bubbly upflows through vertical pipes. Predictions obtained with the default CFDS-FLOW3D model for dispersed flows were compared with the predictions of a new model (based on the work of Lee), and with the experimental data of Liu. The new model, implemented in the CFDS-FLOW3D code, included additional source terms in the {open_quotes}standard{close_quotes} {kappa}-{epsilon} transport equations for the liquid phase, as well as modified model coefficients and wall functions. All simulations were carried out in a 2-D axisymmetric format, collapsing the general multifluid framework of CFDS-FLOW3D to the two-fluid (air-water) case. The newly implemented model consistently improved predictions of radial-velocity profiles of both phases, but failed to accurately reproduce the experimental phase-distribution data. This shortcoming was traced to the neglect of anisotropic effects in the modelling of liquid-phase turbulence. In this sense, the present investigation should be considered as the first step toward the ultimate goal of developing a theoretically sound and universal CFD-type two-fluid model for bubbly flows in channels.

  6. Spectral methods for global atmospheric flow applied to the modified AFIT (Air Force Institute of Technology) fallout prediction model. Master's thesis

    SciTech Connect (OSTI)

    Palmer, D.L.

    1986-09-01

    This thesis predicted the airborne spatial distribution of a high-explosive-generated dust cloud. A comparison of predicted cloud center positions to experimental data collected from an aircraft flying through the dust cloud center at various times and altitudes was also studied. The analysis was accomplished using a model called AFGL which produces global complex spectral coefficients. Spectral coefficients were applied as inp fallout prediction model (called REDRAM) to predict dust mass/cu. m.

  7. Combining Traditional Cyber Security Audit Data with Psychosocial Data: Towards Predictive Modeling for Insider Threat Mitigation

    SciTech Connect (OSTI)

    Greitzer, Frank L.; Frincke, Deborah A.

    2010-09-01

    The purpose of this chapter is to motivate the combination of traditional cyber security audit data with psychosocial data, so as to move from an insider threat detection stance to one that enables prediction of potential insider presence. Two distinctive aspects of the approach are the objective of predicting or anticipating potential risks and the use of organizational data in addition to cyber data to support the analysis. The chapter describes the challenges of this endeavor and progress in defining a usable set of predictive indicators, developing a framework for integrating the analysis of organizational and cyber security data to yield predictions about possible insider exploits, and developing the knowledge base and reasoning capability of the system. We also outline the types of errors that one expects in a predictive system versus a detection system and discuss how those errors can affect the usefulness of the results.

  8. A validated model to predict microalgae growth in outdoor pond cultures subjected to fluctuating light intensities and water temperatures

    SciTech Connect (OSTI)

    Huesemann, Michael H.; Crowe, Braden J.; Waller, Peter; Chavis, Aaron R.; Hobbs, Samuel J.; Edmundson, Scott J.; Wigmosta, Mark S.

    2015-12-11

    Here, a microalgae biomass growth model was developed for screening novel strains for their potential to exhibit high biomass productivities under nutrient-replete conditions in outdoor ponds subjected to fluctuating light intensities and water temperatures. Growth is modeled by first estimating the light attenuation by biomass according to a scatter-corrected Beer-Lambert Law, and then calculating the specific growth rate in discretized culture volume slices that receive declining light intensities due to attenuation. The model requires the following experimentally determined strain-specific input parameters: specific growth rate as a function of light intensity and temperature, biomass loss rate in the dark as a function of temperature and average light intensity during the preceding light period, and the scatter-corrected biomass light absorption coefficient. The model was successful in predicting the growth performance and biomass productivity of three different microalgae species (Chlorella sorokiniana, Nannochloropsis salina, and Picochlorum sp.) in raceway pond cultures (batch and semi-continuous) subjected to diurnal sunlight intensity and water temperature variations. Model predictions were moderately sensitive to minor deviations in input parameters. To increase the predictive power of this and other microalgae biomass growth models, a better understanding of the effects of mixing-induced rapid light dark cycles on photo-inhibition and short-term biomass losses due to dark respiration in the aphotic zone of the pond is needed.

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

    SciTech Connect (OSTI)

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

    2006-10-01

    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.

  10. A validated model to predict microalgae growth in outdoor pond cultures subjected to fluctuating light intensities and water temperatures

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Huesemann, Michael H.; Crowe, Braden J.; Waller, Peter; Chavis, Aaron R.; Hobbs, Samuel J.; Edmundson, Scott J.; Wigmosta, Mark S.

    2015-12-11

    Here, a microalgae biomass growth model was developed for screening novel strains for their potential to exhibit high biomass productivities under nutrient-replete conditions in outdoor ponds subjected to fluctuating light intensities and water temperatures. Growth is modeled by first estimating the light attenuation by biomass according to a scatter-corrected Beer-Lambert Law, and then calculating the specific growth rate in discretized culture volume slices that receive declining light intensities due to attenuation. The model requires the following experimentally determined strain-specific input parameters: specific growth rate as a function of light intensity and temperature, biomass loss rate in the dark as amore » function of temperature and average light intensity during the preceding light period, and the scatter-corrected biomass light absorption coefficient. The model was successful in predicting the growth performance and biomass productivity of three different microalgae species (Chlorella sorokiniana, Nannochloropsis salina, and Picochlorum sp.) in raceway pond cultures (batch and semi-continuous) subjected to diurnal sunlight intensity and water temperature variations. Model predictions were moderately sensitive to minor deviations in input parameters. To increase the predictive power of this and other microalgae biomass growth models, a better understanding of the effects of mixing-induced rapid light dark cycles on photo-inhibition and short-term biomass losses due to dark respiration in the aphotic zone of the pond is needed.« less