National Library of Energy BETA

Sample records for model based predictive

  1. 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

  2. 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

  3. 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.

  4. 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.

  5. 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 structure–toxicity 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

  6. 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.

  7. 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.

  8. 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.

  9. 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

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

    SciTech Connect (OSTI)

    Harrison, T.D.

    1981-05-01

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

  11. SIMPLIFIED PREDICTIVE MODELS FOR CO₂ SEQUESTRATION PERFORMANCE ASSESSMENT RESEARCH TOPICAL REPORT ON TASK #3 STATISTICAL LEARNING BASED MODELS

    SciTech Connect (OSTI)

    Mishra, Srikanta; Schuetter, Jared

    2014-11-01

    We compare two approaches for building a statistical proxy model (metamodel) for CO₂ geologic sequestration from the results of full-physics compositional simulations. The first approach involves a classical Box-Behnken or Augmented Pairs experimental design with a quadratic polynomial response surface. The second approach used a space-filling maxmin Latin Hypercube sampling or maximum entropy design with the choice of five different meta-modeling techniques: quadratic polynomial, kriging with constant and quadratic trend terms, multivariate adaptive regression spline (MARS) and additivity and variance stabilization (AVAS). Simulations results for CO₂ injection into a reservoir-caprock system with 9 design variables (and 97 samples) were used to generate the data for developing the proxy models. The fitted models were validated with using an independent data set and a cross-validation approach for three different performance metrics: total storage efficiency, CO₂ plume radius and average reservoir pressure. The Box-Behnken–quadratic polynomial metamodel performed the best, followed closely by the maximin LHS–kriging metamodel.

  12. Elevated carbon dioxide is predicted to promote coexistence among competing species in a trait-based model

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

    Ali, Ashehad A.; Medlyn, Belinda E.; Aubier, Thomas G.; Crous, Kristine Y.; Reich, Peter B.

    2015-10-06

    Differential species responses to atmospheric CO2 concentration (Ca) could lead to quantitative changes in competition among species and community composition, with flow-on effects for ecosystem function. However, there has been little theoretical analysis of how elevated Ca (eCa) will affect plant competition, or how composition of plant communities might change. Such theoretical analysis is needed for developing testable hypotheses to frame experimental research. Here, we investigated theoretically how plant competition might change under eCa by implementing two alternative competition theories, resource use theory and resource capture theory, in a plant carbon and nitrogen cycling model. The model makes several novelmore » predictions for the impact of eCa on plant community composition. Using resource use theory, the model predicts that eCa is unlikely to change species dominance in competition, but is likely to increase coexistence among species. Using resource capture theory, the model predicts that eCa may increase community evenness. Collectively, both theories suggest that eCa will favor coexistence and hence that species diversity should increase with eCa. Our theoretical analysis leads to a novel hypothesis for the impact of eCa on plant community composition. In this study, the hypothesis has potential to help guide the design and interpretation of eCa experiments.« less

  13. 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 ...

  14. Elevated carbon dioxide is predicted to promote coexistence among competing species in a trait-based model

    SciTech Connect (OSTI)

    Ali, Ashehad A.; Medlyn, Belinda E.; Aubier, Thomas G.; Crous, Kristine Y.; Reich, Peter B.

    2015-10-06

    Differential species responses to atmospheric CO2 concentration (Ca) could lead to quantitative changes in competition among species and community composition, with flow-on effects for ecosystem function. However, there has been little theoretical analysis of how elevated Ca (eCa) will affect plant competition, or how composition of plant communities might change. Such theoretical analysis is needed for developing testable hypotheses to frame experimental research. Here, we investigated theoretically how plant competition might change under eCa by implementing two alternative competition theories, resource use theory and resource capture theory, in a plant carbon and nitrogen cycling model. The model makes several novel predictions for the impact of eCa on plant community composition. Using resource use theory, the model predicts that eCa is unlikely to change species dominance in competition, but is likely to increase coexistence among species. Using resource capture theory, the model predicts that eCa may increase community evenness. Collectively, both theories suggest that eCa will favor coexistence and hence that species diversity should increase with eCa. Our theoretical analysis leads to a novel hypothesis for the impact of eCa on plant community composition. In this study, the hypothesis has potential to help guide the design and interpretation of eCa experiments.

  15. 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,

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

    SciTech Connect (OSTI)

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

    1995-02-22

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

  17. TH-E-BRF-05: Comparison of Survival-Time Prediction Models After Radiotherapy for High-Grade Glioma Patients Based On Clinical and DVH Features

    SciTech Connect (OSTI)

    Magome, T; Haga, A; Igaki, H; Sekiya, N; Masutani, Y; Sakumi, A; Mukasa, A; Nakagawa, K

    2014-06-15

    Purpose: Although many outcome prediction models based on dose-volume information have been proposed, it is well known that the prognosis may be affected also by multiple clinical factors. The purpose of this study is to predict the survival time after radiotherapy for high-grade glioma patients based on features including clinical and dose-volume histogram (DVH) information. Methods: A total of 35 patients with high-grade glioma (oligodendroglioma: 2, anaplastic astrocytoma: 3, glioblastoma: 30) were selected in this study. All patients were treated with prescribed dose of 30–80 Gy after surgical resection or biopsy from 2006 to 2013 at The University of Tokyo Hospital. All cases were randomly separated into training dataset (30 cases) and test dataset (5 cases). The survival time after radiotherapy was predicted based on a multiple linear regression analysis and artificial neural network (ANN) by using 204 candidate features. The candidate features included the 12 clinical features (tumor location, extent of surgical resection, treatment duration of radiotherapy, etc.), and the 192 DVH features (maximum dose, minimum dose, D95, V60, etc.). The effective features for the prediction were selected according to a step-wise method by using 30 training cases. The prediction accuracy was evaluated by a coefficient of determination (R{sup 2}) between the predicted and actual survival time for the training and test dataset. Results: In the multiple regression analysis, the value of R{sup 2} between the predicted and actual survival time was 0.460 for the training dataset and 0.375 for the test dataset. On the other hand, in the ANN analysis, the value of R{sup 2} was 0.806 for the training dataset and 0.811 for the test dataset. Conclusion: Although a large number of patients would be needed for more accurate and robust prediction, our preliminary Result showed the potential to predict the outcome in the patients with high-grade glioma. This work was partly supported by

  18. WE-E-17A-02: Predictive Modeling of Outcome Following SABR for NSCLC Based On Radiomics of FDG-PET Images

    SciTech Connect (OSTI)

    Li, R; Aguilera, T; Shultz, D; Rubin, D; Diehn, M; Loo, B

    2014-06-15

    Purpose: This study aims to develop predictive models of patient outcome by extracting advanced imaging features (i.e., Radiomics) from FDG-PET images. Methods: We acquired pre-treatment PET scans for 51 stage I NSCLC patients treated with SABR. We calculated 139 quantitative features from each patient PET image, including 5 morphological features, 8 statistical features, 27 texture features, and 100 features from the intensity-volume histogram. Based on the imaging features, we aim to distinguish between 2 risk groups of patients: those with regional failure or distant metastasis versus those without. We investigated 3 pattern classification algorithms: linear discriminant analysis (LDA), naive Bayes (NB), and logistic regression (LR). To avoid the curse of dimensionality, we performed feature selection by first removing redundant features and then applying sequential forward selection using the wrapper approach. To evaluate the predictive performance, we performed 10-fold cross validation with 1000 random splits of the data and calculated the area under the ROC curve (AUC). Results: Feature selection identified 2 texture features (homogeneity and/or wavelet decompositions) for NB and LR, while for LDA SUVmax and one texture feature (correlation) were identified. All 3 classifiers achieved statistically significant improvements over conventional PET imaging metrics such as tumor volume (AUC = 0.668) and SUVmax (AUC = 0.737). Overall, NB achieved the best predictive performance (AUC = 0.806). This also compares favorably with MTV using the best threshold at an SUV of 11.6 (AUC = 0.746). At a sensitivity of 80%, NB achieved 69% specificity, while SUVmax and tumor volume only had 36% and 47% specificity. Conclusion: Through a systematic analysis of advanced PET imaging features, we are able to build models with improved predictive value over conventional imaging metrics. If validated in a large independent cohort, the proposed techniques could potentially aid in

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

    SciTech Connect (OSTI)

    Harrison, T.D.

    1981-04-01

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

  20. 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.

  1. 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.

  2. 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

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

    SciTech Connect (OSTI)

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

    2013-03-19

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

  4. Simplified Predictive Models for CO2 Sequestration Performance Assessment: Research Topical Report on Task #4 - Reduced-Order Method (ROM) Based Models

    SciTech Connect (OSTI)

    Mishra, Srikanta; Jin, Larry; He, Jincong; Durlofsky, Louis

    2015-06-30

    Reduced-order models provide a means for greatly accelerating the detailed simulations that will be required to manage CO2 storage operations. In this work, we investigate the use of one such method, POD-TPWL, which has previously been shown to be effective in oil reservoir simulation problems. This method combines trajectory piecewise linearization (TPWL), in which the solution to a new (test) problem is represented through a linearization around the solution to a previously-simulated (training) problem, with proper orthogonal decomposition (POD), which enables solution states to be expressed in terms of a relatively small number of parameters. We describe the application of POD-TPWL for CO2-water systems simulated using a compositional procedure. Stanford’s Automatic Differentiation-based General Purpose Research Simulator (AD-GPRS) performs the full-order training simulations and provides the output (derivative matrices and system states) required by the POD-TPWL method. A new POD-TPWL capability introduced in this work is the use of horizontal injection wells that operate under rate (rather than bottom-hole pressure) control. Simulation results are presented for CO2 injection into a synthetic aquifer and into a simplified model of the Mount Simon formation. Test cases involve the use of time-varying well controls that differ from those used in training runs. Results of reasonable accuracy are consistently achieved for relevant well quantities. Runtime speedups of around a factor of 370 relative to full- order AD-GPRS simulations are achieved, though the preprocessing needed for POD-TPWL model construction corresponds to the computational requirements for about 2.3 full-order simulation runs. A preliminary treatment for POD-TPWL modeling in which test cases differ from training runs in terms of geological parameters (rather than well controls) is also presented. Results in this case involve only small differences between

  5. 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 structure–activity relationship (QSAR) modeling using machine learning approaches has shown promise with a large dataset of drugs but included unconfirmed data as well. In this study, we report the construction and validation of a battery of complementary in silico QSAR models using the FDA's updated database on phospholipidosis, new algorithms and predictive technologies, and in particular, we address high performance with a high-confidence dataset. The results of our modeling for DIPL include rigorous external validation tests showing 80–81% concordance. Furthermore, the predictive performance characteristics include models with high sensitivity and specificity, in most cases above ? 80% leading to desired high negative and positive predictivity. These models are intended to be utilized for regulatory toxicology applied science needs in screening new drugs for DIPL. - Highlights: • New in silico models for predicting drug-induced phospholipidosis (DIPL) are described. • The training set data in the models is derived from the FDA's phospholipidosis database. • We find excellent predictivity values of the models based on external validation. • The models can support drug screening and regulatory decision-making on DIPL.

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

    SciTech Connect (OSTI)

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

    2010-09-06

    Satellite collision debris poses risks to existing space assets and future space missions. Predictive models of debris generated from these hypervelocity collisions are critical for developing accurate space situational awareness tools and effective mitigation strategies. Hypervelocity collisions involve complex phenomenon that spans several time- and length-scales. We have developed a satellite collision debris modeling approach consisting of a Lagrangian hydrocode enriched with smooth particle hydrodynamics (SPH), advanced material failure models, detailed satellite mesh models, and massively parallel computers. These computational studies enable us to investigate the influence of satellite center-of-mass (CM) overlap and orientation, relative velocity, and material composition on the size, velocity, and material type distributions of collision debris. We have applied our debris modeling capability to the recent Iridium 33-Cosmos 2251 collision event. While the relative velocity was well understood in this event, the degree of satellite CM overlap and orientation was ill-defined. In our simulations, we varied the collision CM overlap and orientation of the satellites from nearly maximum overlap to partial overlap on the outermost extents of the satellites (i.e, solar panels and gravity boom). As expected, we found that with increased satellite overlap, the overall debris cloud mass and momentum (transfer) increases, the average debris size decreases, and the debris velocity increases. The largest predicted debris can also provide insight into which satellite components were further removed from the impact location. A significant fraction of the momentum transfer is imparted to the smallest debris (< 1-5mm, dependent on mesh resolution), especially in large CM overlap simulations. While the inclusion of the smallest debris is critical to enforcing mass and momentum conservation in hydrocode simulations, there seems to be relatively little interest in their

  7. 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...

  8. 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 ...

  9. 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 ...

  10. 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 ...

  11. 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 ...

  12. 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.

  13. 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.

  14. Predictive Models for Regional Hepatic Function Based on 99mTc-IDA SPECT and Local Radiation Dose for Physiologic Adaptive Radiation Therapy

    SciTech Connect (OSTI)

    Wang, Hesheng; Feng, Mary; Frey, Kirk A.; Ten Haken, Randall K.; Lawrence, Theodore S.; Cao, Yue

    2013-08-01

    Purpose: High-dose radiation therapy (RT) for intrahepatic cancer is limited by the development of liver injury. This study investigated whether regional hepatic function assessed before and during the course of RT using 99mTc-labeled iminodiacetic acid (IDA) single photon emission computed tomography (SPECT) could predict regional liver function reserve after RT. Methods and Materials: Fourteen patients treated with RT for intrahepatic cancers underwent dynamic 99mTc-IDA SPECT scans before RT, during, and 1 month after completion of RT. Indocyanine green (ICG) tests, a measure of overall liver function, were performed within 1 day of each scan. Three-dimensional volumetric hepatic extraction fraction (HEF) images of the liver were estimated by deconvolution analysis. After coregistration of the CT/SPECT and the treatment planning CT, HEF dose–response functions during and after RT were generated. The volumetric mean of the HEFs in the whole liver was correlated with ICG clearance time. Three models, dose, priori, and adaptive models, were developed using multivariate linear regression to assess whether the regional HEFs measured before and during RT helped predict regional hepatic function after RT. Results: The mean of the volumetric liver HEFs was significantly correlated with ICG clearance half-life time (r=−0.80, P<.0001), for all time points. Linear correlations between local doses and regional HEFs 1 month after RT were significant in 12 patients. In the priori model, regional HEF after RT was predicted by the planned dose and regional HEF assessed before RT (R=0.71, P<.0001). In the adaptive model, regional HEF after RT was predicted by regional HEF reassessed during RT and the remaining planned local dose (R=0.83, P<.0001). Conclusions: 99mTc-IDA SPECT obtained during RT could be used to assess regional hepatic function and helped predict post-RT regional liver function reserve. This could support individualized adaptive radiation treatment strategies

  15. Streamflow effects on spawning, rearing, and outmigration of fall-run chinook salmon (Oncorhynchus tshawytscha) predicted by a spatial and individual-based model

    SciTech Connect (OSTI)

    Jager, H.I.; Sale, M.J.; Cardwell, H.E.; Deangelis, D.L.; Bevelhimer, M.J.; Coutant, C.C. )

    1994-06-01

    The thread posed to Pacific salmon by competing water demands is a great concern to regulators of the hydropower industry. Finding the balance between fish resource and economic objectives depends on our ability to quantify flow effects on salmon production. Because field experiments are impractical, simulation models are needed to predict the effects of minimum flows on chinook salmon during their freshwater residence. We have developed a model to simulate the survival and development of eggs and alevins in redds and the growth, survival, and movement of juvenile chinook in response to local stream conditions (flow, temperature, chinook and predator density). Model results suggest that smolt production during dry years can be increased by raising spring minimum flows.

  16. GIS-BASED PREDICTION OF HURRICANE FLOOD INUNDATION

    SciTech Connect (OSTI)

    JUDI, DAVID; KALYANAPU, ALFRED; MCPHERSON, TIMOTHY; BERSCHEID, ALAN

    2007-01-17

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

  17. Illustrating the future prediction of performance based on computer...

    Office of Scientific and Technical Information (OSTI)

    Illustrating the future prediction of performance based on computer code, physical ... Citation Details In-Document Search Title: Illustrating the future prediction of ...

  18. 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.

  19. 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

  20. 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

  1. 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.

  2. 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.

  3. 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.

  4. 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

  5. SU-E-J-257: A PCA Model to Predict Adaptive Changes for Head&neck Patients Based On Extraction of Geometric Features From Daily CBCT Datasets

    SciTech Connect (OSTI)

    Chetvertkov, M; Siddiqui, F; Chetty, I; Kim, J; Kumarasiri, A; Liu, C; Gordon, J

    2015-06-15

    Purpose: Using daily cone beam CTs (CBCTs) to develop principal component analysis (PCA) models of anatomical changes in head and neck (H&N) patients and to assess the possibility of using these prospectively in adaptive radiation therapy (ART). Methods: Planning CT (pCT) images of 4 H&N patients were deformed to model several different systematic changes in patient anatomy during the course of the radiation therapy (RT). A Pinnacle plugin was used to linearly interpolate the systematic change in patient for the 35 fraction RT course and to generate a set of 35 synthetic CBCTs. Each synthetic CBCT represents the systematic change in patient anatomy for each fraction. Deformation vector fields (DVFs) were acquired between the pCT and synthetic CBCTs with random fraction-to-fraction changes were superimposed on the DVFs. A patient-specific PCA model was built using these DVFs containing systematic plus random changes. It was hypothesized that resulting eigenDVFs (EDVFs) with largest eigenvalues represent the major anatomical deformations during the course of treatment. Results: For all 4 patients, the PCA model provided different results depending on the type and size of systematic change in patient’s body. PCA was more successful in capturing the systematic changes early in the treatment course when these were of a larger scale with respect to the random fraction-to-fraction changes in patient’s anatomy. For smaller scale systematic changes, random changes in patient could completely “hide” the systematic change. Conclusion: The leading EDVF from the patientspecific PCA models could tentatively be identified as a major systematic change during treatment if the systematic change is large enough with respect to random fraction-to-fraction changes. Otherwise, leading EDVF could not represent systematic changes reliably. This work is expected to facilitate development of population-based PCA models that can be used to prospectively identify significant

  6. 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...

  7. 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.

  8. 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 jâ€Č-th time point where j≀jâ€Č≀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.

  9. Roadmap Toward a Predictive Performance-based Commercial Energy Code

    SciTech Connect (OSTI)

    Rosenberg, Michael I.; Hart, Philip R.

    2014-10-01

    Energy codes have provided significant increases in building efficiency over the last 38 years, since the first national energy model code was published in late 1975. The most commonly used path in energy codes, the prescriptive path, appears to be reaching a point of diminishing returns. The current focus on prescriptive codes has limitations including significant variation in actual energy performance depending on which prescriptive options are chosen, a lack of flexibility for designers and developers, and the inability to handle control optimization that is specific to building type and use. This paper provides a high level review of different options for energy codes, including prescriptive, prescriptive packages, EUI Target, outcome-based, and predictive performance approaches. This paper also explores a next generation commercial energy code approach that places a greater emphasis on performance-based criteria. A vision is outlined to serve as a roadmap for future commercial code development. That vision is based on code development being led by a specific approach to predictive energy performance combined with building specific prescriptive packages that are designed to be both cost-effective and to achieve a desired level of performance. Compliance with this new approach can be achieved by either meeting the performance target as demonstrated by whole building energy modeling, or by choosing one of the prescriptive packages.

  10. Illustrating the future prediction of performance based on computer...

    Office of Scientific and Technical Information (OSTI)

    Illustrating the future prediction of performance based on computer code, physical experiments, and critical performance parameter samples Citation Details In-Document Search ...

  11. 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

  12. 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...

  13. 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 ...

  14. Model-based tomographic reconstruction

    DOE Patents [OSTI]

    Chambers, David H.; Lehman, Sean K.; Goodman, Dennis M.

    2012-06-26

    A model-based approach to estimating wall positions for a building is developed and tested using simulated data. It borrows two techniques from geophysical inversion problems, layer stripping and stacking, and combines them with a model-based estimation algorithm that minimizes the mean-square error between the predicted signal and the data. The technique is designed to process multiple looks from an ultra wideband radar array. The processed signal is time-gated and each section processed to detect the presence of a wall and estimate its position, thickness, and material parameters. The floor plan of a building is determined by moving the array around the outside of the building. In this paper we describe how the stacking and layer stripping algorithms are combined and show the results from a simple numerical example of three parallel walls.

  15. 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.

  16. 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

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  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. 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

  3. 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.

  4. 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

  5. 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

  6. 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

  7. 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.

  8. 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

  9. 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.

  10. 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

  11. 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.

  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. 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.

  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. 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

  16. A comparison of Unified creep-plasticity and conventional creep models for rock salt based on predictions of creep behavior measured in several in situ and bench-scale experiments

    SciTech Connect (OSTI)

    Morgan, H.S.; Krieg, R.D.

    1988-04-01

    A unified creep-plasticity (UCP) model, a conventional elastic-secondary creep (ESC) model, and an elastic-secondary creep model with greatly reduced elastic moduli (RESC model) are used to compute creep responses for five experimental configurations in which rock salt is subjected to several different complex loadings. The UCP model is exercised with three sets of model parameters. Two sets are for salt from the site of the Waste Isolation Pilot Plant (WIPP) in southeastern New Mexico, and the third is for salt from Avery Island, Louisiana. The WIPP reference secondary creep parameters are used in both the EC and RESC models. The WIPP reference values for the elastic moduli are also used in the ESC model. These moduli are divided by 12.5 in the RESC model. The geometrical configurations include the South Drift at the WIPP site, a hypothetical shaft in rock salt, a large hollow cylinder of rock salt subjected to external pressure while still in the floor of a drift at Avery Island, Louisiana, a laboratory-scale hollow cylinder subjected to external pressure, and a model pillar of salt subjected to axial load. Measured creep responses are available for all of these experiments except the hypothetical shaft. In all cases, deformations computed with the UCP model are much larger than the ESC predictions and are in better agreement with the data. The RESC model also produces larger deformations than the ESC model, and for the South Drift, the RESC predictions agree well with measured closures. 46 refs., 19 figs., 2 tabs.

  17. 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.

  18. Microsoft Word - NRAP-TRS-I-005-2014_Use of Science-Based Prediction...

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

    Use of Science-Based Prediction to Characterize Reservoir Behavior as a Function of ... H.; Zhang, Y.; Guthrie, G. Use of Science-Based Prediction to Characterize ...

  19. 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

  20. 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.

  1. Principles of models based engineering

    SciTech Connect (OSTI)

    Dolin, R.M.; Hefele, J.

    1996-11-01

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

  2. 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.

  3. 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.

  4. 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

  5. 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

  6. 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

  7. Predictive based monitoring of nuclear plant component degradation using support vector regression

    SciTech Connect (OSTI)

    Agarwal, Vivek; Alamaniotis, Miltiadis; Tsoukalas, Lefteri H.

    2015-02-01

    Nuclear power plants (NPPs) are large installations comprised of many active and passive assets. Degradation monitoring of all these assets is expensive (labor cost) and highly demanding task. In this paper a framework based on Support Vector Regression (SVR) for online surveillance of critical parameter degradation of NPP components is proposed. In this case, on time replacement or maintenance of components will prevent potential plant malfunctions, and reduce the overall operational cost. In the current work, we apply SVR equipped with a Gaussian kernel function to monitor components. Monitoring includes the one-step-ahead prediction of the component’s respective operational quantity using the SVR model, while the SVR model is trained using a set of previous recorded degradation histories of similar components. Predictive capability of the model is evaluated upon arrival of a sensor measurement, which is compared to the component failure threshold. A maintenance decision is based on a fuzzy inference system that utilizes three parameters: (i) prediction evaluation in the previous steps, (ii) predicted value of the current step, (iii) and difference of current predicted value with components failure thresholds. The proposed framework will be tested on turbine blade degradation data.

  8. Knowledge-based prediction of plan quality metrics in intracranial stereotactic radiosurgery

    SciTech Connect (OSTI)

    Shiraishi, Satomi; Moore, Kevin L.; Tan, Jun; Olsen, Lindsey A.

    2015-02-15

    Purpose: The objective of this work was to develop a comprehensive knowledge-based methodology for predicting achievable dose–volume histograms (DVHs) and highly precise DVH-based quality metrics (QMs) in stereotactic radiosurgery/radiotherapy (SRS/SRT) plans. Accurate QM estimation can identify suboptimal treatment plans and provide target optimization objectives to standardize and improve treatment planning. Methods: Correlating observed dose as it relates to the geometric relationship of organs-at-risk (OARs) to planning target volumes (PTVs) yields mathematical models to predict achievable DVHs. In SRS, DVH-based QMs such as brain V{sub 10Gy} (volume receiving 10 Gy or more), gradient measure (GM), and conformity index (CI) are used to evaluate plan quality. This study encompasses 223 linear accelerator-based SRS/SRT treatment plans (SRS plans) using volumetric-modulated arc therapy (VMAT), representing 95% of the institution’s VMAT radiosurgery load from the past four and a half years. Unfiltered models that use all available plans for the model training were built for each category with a stratification scheme based on target and OAR characteristics determined emergently through initial modeling process. Model predictive accuracy is measured by the mean and standard deviation of the difference between clinical and predicted QMs, ήQM = QM{sub clin} − QM{sub pred}, and a coefficient of determination, R{sup 2}. For categories with a large number of plans, refined models are constructed by automatic elimination of suspected suboptimal plans from the training set. Using the refined model as a presumed achievable standard, potentially suboptimal plans are identified. Predictions of QM improvement are validated via standardized replanning of 20 suspected suboptimal plans based on dosimetric predictions. The significance of the QM improvement is evaluated using the Wilcoxon signed rank test. Results: The most accurate predictions are obtained when plans are

  9. 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.

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

    SciTech Connect (OSTI)

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

    2011-06-01

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

  11. 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

  12. 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

  13. 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.

  14. GIS-Based Infrastructure Modeling

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

    GIS-Based Infrastructure Modeling Hydrogen Scenario Meeting August 9-10, 2006 Keith Parks, NREL GIS-Based Infrastructure Modeling * Station Analysis - Selection Criteria - Los Angeles * By 2015 (10 & 20 Station Layouts) * By 2025 (100 & 600 Station Layouts) - NYC * Early Infrastructure (20 stations) Comparison * Station Layouts (SMR, Liquid, Pipeline) * Delivery Discussion Station Selection Criteria * Consumer strategy attributes rated good and above * Proximal to major civic airports *

  15. 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.

  16. Mining Behavior Based Safety Data to Predict Safety Performance

    SciTech Connect (OSTI)

    Jeffrey C. Joe

    2010-06-01

    The Idaho National Laboratory (INL) operates a behavior based safety program called Safety Observations Achieve Results (SOAR). This peer-to-peer observation program encourages employees to perform in-field observations of each other's work practices and habits (i.e., behaviors). The underlying premise of conducting these observations is that more serious accidents are prevented from occurring because lower level “at risk” behaviors are identified and corrected before they can propagate into culturally accepted “unsafe” behaviors that result in injuries or fatalities. Although the approach increases employee involvement in safety, the premise of the program has not been subject to sufficient empirical evaluation. The INL now has a significant amount of SOAR data on these lower level “at risk” behaviors. This paper describes the use of data mining techniques to analyze these data to determine whether they can predict if and when a more serious accident will occur.

  17. 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.

  18. 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

  19. 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.

  20. 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 ...

  1. 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 ...

  2. 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 ...

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. SIFTER search: a web server for accurate phylogeny-based protein function prediction

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

    Sahraeian, Sayed M.; Luo, Kevin R.; Brenner, Steven E.

    2015-05-15

    We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access tomore » precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. Lastly, the SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.« less

  12. SIFTER search: a web server for accurate phylogeny-based protein function prediction

    SciTech Connect (OSTI)

    Sahraeian, Sayed M.; Luo, Kevin R.; Brenner, Steven E.

    2015-05-15

    We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access to precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. Lastly, the SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.

  13. 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.

  14. 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.

  15. Model-based safety assessments

    SciTech Connect (OSTI)

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

    1998-04-01

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

  16. 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

  17. 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

  18. 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 ...

  19. 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

  20. 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

  1. Physics-based models of the plasmasphere

    SciTech Connect (OSTI)

    Jordanova, Vania K; Pierrard, Vivane; Goldstein, Jerry; Andr'e, Nicolas; Lemaire, Joseph F; Liemohn, Mike W; Matsui, H

    2008-01-01

    We describe recent progress in physics-based models of the plasmasphere using the Auid and the kinetic approaches. Global modeling of the dynamics and inAuence of the plasmasphere is presented. Results from global plasmasphere simulations are used to understand and quantify (i) the electric potential pattern and evolution during geomagnetic storms, and (ii) the inAuence of the plasmasphere on the excitation of electromagnetic ion cyclotron (ElvIIC) waves a.nd precipitation of energetic ions in the inner magnetosphere. The interactions of the plasmasphere with the ionosphere a.nd the other regions of the magnetosphere are pointed out. We show the results of simulations for the formation of the plasmapause and discuss the inAuence of plasmaspheric wind and of ultra low frequency (ULF) waves for transport of plasmaspheric material. Theoretical formulations used to model the electric field and plasma distribution in the plasmasphere are given. Model predictions are compared to recent CLUSTER and MAGE observations, but also to results of earlier models and satellite observations.

  2. 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

  3. 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.

  4. 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.

  5. 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 ...

  6. Mechanism-based classification of PAH mixtures to predict carcinogenic potential

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

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

    2015-04-22

    We have previously shown that relative potency factors and DNA adduct measurements are inadequate for predicting carcinogenicity of certain polycyclic aromatic hydrocarbons (PAHs) and PAH mixtures, particularly those that function through alternate pathways or exhibit greater promotional activity compared to benzo[a]pyrene (BaP). Therefore, we developed a pathway based approach for classification of tumor outcome after dermal exposure to PAH/mixtures. FVB/N mice were exposed to dibenzo[def,p]chrysene (DBC), BaP or environmental PAH mixtures (Mix 1-3) following a two-stage initiation/promotion skin tumor protocol. Resulting tumor incidence could be categorized by carcinogenic potency as DBC>>BaP=Mix2=Mix3>Mix1=Control, based on statistical significance. Gene expression profiles measured inmore » skin of mice collected 12 h post-initiation were compared to tumor outcome for identification of short-term bioactivity profiles. A Bayesian integration model was utilized to identify biological pathways predictive of PAH carcinogenic potential during initiation. Integration of probability matrices from four enriched pathways (p<0.05) for DNA damage, apoptosis, response to chemical stimulus and interferon gamma signaling resulted in the highest classification accuracy with leave-one-out cross validation. This pathway-driven approach was successfully utilized to distinguish early regulatory events during initiation prognostic for tumor outcome and provides proof-of-concept for using short-term initiation studies to classify carcinogenic potential of environmental PAH mixtures. As a result, these data further provide a ‘source-to outcome’ model that could be used to predict PAH interactions during tumorigenesis and provide an example of how mode-of-action based risk assessment could be employed for environmental PAH mixtures.« less

  7. Mechanism-based classification of PAH mixtures to predict carcinogenic potential

    SciTech Connect (OSTI)

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

    2015-04-22

    We have previously shown that relative potency factors and DNA adduct measurements are inadequate for predicting carcinogenicity of certain polycyclic aromatic hydrocarbons (PAHs) and PAH mixtures, particularly those that function through alternate pathways or exhibit greater promotional activity compared to benzo[a]pyrene (BaP). Therefore, we developed a pathway based approach for classification of tumor outcome after dermal exposure to PAH/mixtures. FVB/N mice were exposed to dibenzo[def,p]chrysene (DBC), BaP or environmental PAH mixtures (Mix 1-3) following a two-stage initiation/promotion skin tumor protocol. Resulting tumor incidence could be categorized by carcinogenic potency as DBC>>BaP=Mix2=Mix3>Mix1=Control, based on statistical significance. Gene expression profiles measured in skin of mice collected 12 h post-initiation were compared to tumor outcome for identification of short-term bioactivity profiles. A Bayesian integration model was utilized to identify biological pathways predictive of PAH carcinogenic potential during initiation. Integration of probability matrices from four enriched pathways (p<0.05) for DNA damage, apoptosis, response to chemical stimulus and interferon gamma signaling resulted in the highest classification accuracy with leave-one-out cross validation. This pathway-driven approach was successfully utilized to distinguish early regulatory events during initiation prognostic for tumor outcome and provides proof-of-concept for using short-term initiation studies to classify carcinogenic potential of environmental PAH mixtures. As a result, these data further provide a ‘source-to outcome’ model that could be used to predict PAH interactions during tumorigenesis and provide an example of how mode-of-action based risk assessment could be employed for environmental PAH mixtures.

  8. Mechanism-based classification of PAH mixtures to predict carcinogenic potential

    SciTech Connect (OSTI)

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

    2015-04-22

    We have previously shown that relative potency factors and DNA adduct measurements are inadequate for predicting carcinogenicity of certain polycyclic aromatic hydrocarbons (PAHs) and PAH mixtures, particularly those that function through alternate pathways or exhibit greater promotional activity compared to benzo[a]pyrene (BaP). Therefore, we developed a pathway based approach for classification of tumor outcome after dermal exposure to PAH/mixtures. FVB/N mice were exposed to dibenzo[def,p]chrysene (DBC), BaP or environmental PAH mixtures (Mix 1-3) following a two-stage initiation/promotion skin tumor protocol. Resulting tumor incidence could be categorized by carcinogenic potency as DBC>>BaP=Mix2=Mix3>Mix1=Control, based on statistical significance. Gene expression profiles measured in skin of mice collected 12 h post-initiation were compared to tumor outcome for identification of short-term bioactivity profiles. A Bayesian integration model was utilized to identify biological pathways predictive of PAH carcinogenic potential during initiation. Integration of probability matrices from four enriched pathways (p<0.05) for DNA damage, apoptosis, response to chemical stimulus and interferon gamma signaling resulted in the highest classification accuracy with leave-one-out cross validation. This pathway-driven approach was successfully utilized to distinguish early regulatory events during initiation prognostic for tumor outcome and provides proof-of-concept for using short-term initiation studies to classify carcinogenic potential of environmental PAH mixtures. As a result, these data further provide a ‘source-to outcome’ model that could be used to predict PAH interactions during tumorigenesis and provide an example of how mode-of-action based risk assessment could be employed for environmental PAH mixtures.

  9. 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.

  10. 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 structure–activity 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.

  11. 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

  12. 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; ...

  13. 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.

  14. 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.

  15. Prediction of global solar irradiance based on time series analysis: Application to solar thermal power plants energy production planning

    SciTech Connect (OSTI)

    Martin, Luis; Marchante, Ruth; Cony, Marco; Zarzalejo, Luis F.; Polo, Jesus; Navarro, Ana

    2010-10-15

    Due to strong increase of solar power generation, the predictions of incoming solar energy are acquiring more importance. Photovoltaic and solar thermal are the main sources of electricity generation from solar energy. In the case of solar thermal energy plants with storage energy system, its management and operation need reliable predictions of solar irradiance with the same temporal resolution as the temporal capacity of the back-up system. These plants can work like a conventional power plant and compete in the energy stock market avoiding intermittence in electricity production. This work presents a comparisons of statistical models based on time series applied to predict half daily values of global solar irradiance with a temporal horizon of 3 days. Half daily values consist of accumulated hourly global solar irradiance from solar raise to solar noon and from noon until dawn for each day. The dataset of ground solar radiation used belongs to stations of Spanish National Weather Service (AEMet). The models tested are autoregressive, neural networks and fuzzy logic models. Due to the fact that half daily solar irradiance time series is non-stationary, it has been necessary to transform it to two new stationary variables (clearness index and lost component) which are used as input of the predictive models. Improvement in terms of RMSD of the models essayed is compared against the model based on persistence. The validation process shows that all models essayed improve persistence. The best approach to forecast half daily values of solar irradiance is neural network models with lost component as input, except Lerida station where models based on clearness index have less uncertainty because this magnitude has a linear behaviour and it is easier to simulate by models. (author)

  16. 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.

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

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

    and Vehicle Technologies Program Annual Merit Review and Peer Evaluation Meeting lm057sun2012o.pdf (3.7 MB) More Documents & Publications PNNL: Mechanistic-Based Ductility ...

  18. Agent-based Infrastructure Interdependency Model

    Energy Science and Technology Software Center (OSTI)

    2003-10-01

    The software is used to analyze infrastructure interdependencies. Agent-based modeling is used for the analysis.

  19. 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.

  20. 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).

  1. 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...

  2. Kitaev models based on unitary quantum groupoids

    SciTech Connect (OSTI)

    Chang, Liang

    2014-04-15

    We establish a generalization of Kitaev models based on unitary quantum groupoids. In particular, when inputting a Kitaev-Kong quantum groupoid H{sub C}, we show that the ground state manifold of the generalized model is canonically isomorphic to that of the Levin-Wen model based on a unitary fusion category C. Therefore, the generalized Kitaev models provide realizations of the target space of the Turaev-Viro topological quantum field theory based on C.

  3. 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.

  4. 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 Autodeskź Simulation Moldflowź 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 ABAQUSź 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.

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

    Broader source: Energy.gov [DOE]

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

  6. Prediction of hydrocarbon-bearing structures based on remote sensing

    SciTech Connect (OSTI)

    Smirnova, I.; Gololobov, Yu.; Rusanova, A. )

    1993-09-01

    The technology we developed is based on the use of remotely sensed data and has proved to be effective for identification of structures that appear promising for oil and gas, in particular, reefs in the hydrocarbon-bearing basin of central Asia (Turkmenistan and Uzbekistan). It implements the [open quotes]geoindication[close quotes] concept, the main idea being that landscape components (geoindicators) and subsurface geological features are correlated and depend on each other. Subsurface features (uplifts, depressions, faults, reefs, and other lithological and structural heterogeneities) cause physical and chemical alterations in overlying rocks up to the land surface; thus, they are reflected in distribution of landscape components and observed on airborne and satellite images as specific patterns. The following identified geoindicators are related to different subsurface geological features: definite formations, anticlines, and reefs (barrier, atoll, and bioherm). The geoindicators are extracted from images either visually or by using computer systems. Specially developed software is applied to analyze geoindicator distribution and calculate their characteristics. In the course of processing, it is possible to distinguish folds from reefs. Distribution of geoindicator characteristics is examined on the well studied reefs, and from the regularities, established promising areas with reefs are revealed. When applying the technology in central Asia, the results were successfully verified by field works, seismic methods, and drilling.

  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. 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

  10. Project Profile: Physics-Based Reliability Models for Supercritical-CO2

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

    Turbomachinery Components | Department of Energy Physics-Based Reliability Models for Supercritical-CO2 Turbomachinery Components Project Profile: Physics-Based Reliability Models for Supercritical-CO2 Turbomachinery Components Abengoa logo GE, under the Physics of Reliability: Evaluating Design Insights for Component Technologies in Solar (PREDICTS) Program will be leveraging internally developed models to predict the reliability of hybrid gas bearing (HGB) and dry gas seal (DGS) components

  11. 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.

  12. 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

  13. 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.

  14. 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.

  15. A Geometric Rendezvous-Based Domain Model

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

    University of Wisconsin - Madison 1500 Engineering Dr. Madison, WI 53716 sslattery@wisc.edu March 20, 2013 1 A Geometric Rendezvous-Based Domain Model for Data Transfer...

  16. 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.

  17. 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.

  18. Rate-based degradation modeling of lithium-ion cells

    SciTech Connect (OSTI)

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

    2012-05-01

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

  19. 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

  20. 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.

  1. 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.

  2. A fugacity-based indoor residential pesticide fate model

    SciTech Connect (OSTI)

    Bennett, Deborah H.; Furtaw, Edward J.; McKone, Thomas E.

    2002-06-01

    Dermal and non-dietary pathways are potentially significant exposure pathways to pesticides used in residences. Exposure pathways include dermal contact with residues on surfaces, ingestion from hand- and object-to-mouth activities, and absorption of pesticides into food. A limited amount of data has been collected on pesticide concentrations in various residential compartments following an application. But models are needed to interpret this data and make predictions about other pesticides based on chemical properties. In this paper, we propose a mass-balance compartment model based on fugacity principles. We include air (both gas phase and aerosols), carpet, smooth flooring, and walls as model compartments. Pesticide concentrations on furniture and toys, and in food, are being added to the model as data becomes available. We determine the compartmental fugacity capacity and mass transfer-rate coefficient for wallboard as an example. We also present the framework and equations needed for a dynamic mass-balance model.

  3. Fragment-based {sup 13}C nuclear magnetic resonance chemical shift predictions in molecular crystals: An alternative to planewave methods

    SciTech Connect (OSTI)

    Hartman, Joshua D.; Beran, Gregory J. O.; Monaco, Stephen; Schatschneider, Bohdan

    2015-09-14

    We assess the quality of fragment-based ab initio isotropic {sup 13}C chemical shift predictions for a collection of 25 molecular crystals with eight different density functionals. We explore the relative performance of cluster, two-body fragment, combined cluster/fragment, and the planewave gauge-including projector augmented wave (GIPAW) models relative to experiment. When electrostatic embedding is employed to capture many-body polarization effects, the simple and computationally inexpensive two-body fragment model predicts both isotropic {sup 13}C chemical shifts and the chemical shielding tensors as well as both cluster models and the GIPAW approach. Unlike the GIPAW approach, hybrid density functionals can be used readily in a fragment model, and all four hybrid functionals tested here (PBE0, B3LYP, B3PW91, and B97-2) predict chemical shifts in noticeably better agreement with experiment than the four generalized gradient approximation (GGA) functionals considered (PBE, OPBE, BLYP, and BP86). A set of recommended linear regression parameters for mapping between calculated chemical shieldings and observed chemical shifts are provided based on these benchmark calculations. Statistical cross-validation procedures are used to demonstrate the robustness of these fits.

  4. 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

  5. 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 Brock–Dechert–Scheinkman 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

  6. 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.

  7. 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.

  8. Modeling uranium transport in acidic contaminated groundwater with base addition

    SciTech Connect (OSTI)

    Zhang, Fan; Luo, Wensui; Parker, Jack C.; Brooks, Scott C; Watson, David B; Jardine, Philip; Gu, Baohua

    2011-01-01

    This study investigates reactive transport modeling in a column of uranium(VI)-contaminated sediments with base additions in the circulating influent. The groundwater and sediment exhibit oxic conditions with low pH, high concentrations of NO{sub 3}{sup -}, SO{sub 4}{sup 2-}, U and various metal cations. Preliminary batch experiments indicate that additions of strong base induce rapid immobilization of U for this material. In the column experiment that is the focus of the present study, effluent groundwater was titrated with NaOH solution in an inflow reservoir before reinjection to gradually increase the solution pH in the column. An equilibrium hydrolysis, precipitation and ion exchange reaction model developed through simulation of the preliminary batch titration experiments predicted faster reduction of aqueous Al than observed in the column experiment. The model was therefore modified to consider reaction kinetics for the precipitation and dissolution processes which are the major mechanism for Al immobilization. The combined kinetic and equilibrium reaction model adequately described variations in pH, aqueous concentrations of metal cations (Al, Ca, Mg, Sr, Mn, Ni, Co), sulfate and U(VI). The experimental and modeling results indicate that U(VI) can be effectively sequestered with controlled base addition due to sorption by slowly precipitated Al with pH-dependent surface charge. The model may prove useful to predict field-scale U(VI) sequestration and remediation effectiveness.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. Viscoplastic characterization and fatigue modeling of titanium based metal matrix composites. Master`s thesis

    SciTech Connect (OSTI)

    Foringer, M.A.

    1994-12-01

    Viscoplastic characterization and fatigue modeling of titanium-based metal matrix composites was accomplished by combining a unified viscoplastic theory, a nonlinear micromechanics model, and a linear damage accumulation model. First. Ti 15-3 was characterized using the Bodner-Partom viscoplastic theory. A micromechanics model was then employed in a linear damage accumulation fatigue model to predict the fatigue behavior of titanium based composites. The viscoplastic behavior predictions of the micromechanics model were used to eliminate separately defined time dependent terms in the fatigue model. Also, a new linear damage accumulation model was developed from the fatigue behavior of the composite constituents. Finally, it was found that the microstresses in the 0 degree ply could be used to accurately predict the fatigue behavior of a laminate containing these plies.

  15. 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.

  16. Model-Based Design and Integration of Large Li-ion Battery Systems

    SciTech Connect (OSTI)

    Smith, Kandler; Kim, Gi-Heon; Santhanagopalan, Shriram; Shi, Ying; Pesaran, Ahmad; Mukherjee, Partha; Barai, Pallab; Maute, Kurt; Behrou, Reza; Patil, Chinmaya

    2015-11-17

    This presentation introduces physics-based models of batteries and software toolsets, including those developed by the U.S. Department of Energy's (DOE) Computer-Aided Engineering for Electric-Drive Vehicle Batteries Program (CAEBAT). The presentation highlights achievements and gaps in model-based tools for materials-to-systems design, lifetime prediction and control.

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

    SciTech Connect (OSTI)

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

    2013-02-01

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

  18. the-schedule-based-transit-model

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

    The Schedule-Based transit model of the Chicago Metropolitan Area Vadim Sokolov Transportation Research and Analysis Computing Center Argonne National Laboratory List of Authors ================ Vadim Sokolov Transportation Research and Analysis Computing Center Argonne National Laboratory 277 International Drive West Chicago, IL 60185 Abstract ========= Usually public transit systems are modeled using so called frequency based approach. In this case transit route times are defined in terms of

  19. Information system of rice planting calendar based on ten-day (Dasarian) rainfall prediction

    SciTech Connect (OSTI)

    Susandi, Armi; Tamamadin, Mamad; Djamal, Erizal; Las, Irsal

    2015-09-30

    This paper describes information system of rice planting calendar to help farmers in determining the time for rice planting. The information includes rainfall prediction in ten days (dasarian) scale overlaid to map of rice field to produce map of rice planting in village level. The rainfall prediction was produced by stochastic modeling using Fast Fourier Transform (FFT) and Non-Linier Least Squares methods to fit the curve of function to the rainfall data. In this research, the Fourier series has been modified become non-linear function to follow the recent characteristics of rainfall that is non stationary. The results have been also validated in 4 steps, including R-Square, RMSE, R-Skill, and comparison with field data. The development of information system (cyber extension) provides information such as rainfall prediction, prediction of the planting time, and interactive space for farmers to respond to the information submitted. Interfaces for interactive response will be critical to the improvement of prediction accuracy of information, both rainfall and planting time. The method used to get this information system includes mapping on rice planting prediction, converting the format file, developing database system, developing website, and posting website. Because of this map was overlaid with the Google map, the map files must be converted to the .kml file format.

  20. 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

  1. 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.

  2. 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.

  3. 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.

  4. Microstructure-based approach for predicting crack initiation and early growth in metals.

    SciTech Connect (OSTI)

    Cox, James V.; Emery, John M.; Brewer, Luke N.; Reedy, Earl David, Jr.; Puskar, Joseph David; Bartel, Timothy James; Dingreville, Remi P. M.; Foulk, James W., III; Battaile, Corbett Chandler; Boyce, Brad Lee

    2009-09-01

    Fatigue cracking in metals has been and is an area of great importance to the science and technology of structural materials for quite some time. The earliest stages of fatigue crack nucleation and growth are dominated by the microstructure and yet few models are able to predict the fatigue behavior during these stages because of a lack of microstructural physics in the models. This program has developed several new simulation tools to increase the microstructural physics available for fatigue prediction. In addition, this program has extended and developed microscale experimental methods to allow the validation of new microstructural models for deformation in metals. We have applied these developments to fatigue experiments in metals where the microstructure has been intentionally varied.

  5. 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.

  6. 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.

  7. NEAR FIELD MODELING OF SPE1 EXPERIMENT AND PREDICTION OF THE...

    Office of Scientific and Technical Information (OSTI)

    provide a validated computation capability for the nuclear explosion monitoring community. ... needed for a science-based modeling capability for nuclearmore explosion monitoring. ...

  8. 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.

  9. 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.

  10. Prediction of Liver Function by Using Magnetic Resonance-based Portal Venous Perfusion Imaging

    SciTech Connect (OSTI)

    Cao Yue; Wang Hesheng; Johnson, Timothy D.; Pan, Charlie; Hussain, Hero; Balter, James M.; Normolle, Daniel; Ben-Josef, Edgar; Ten Haken, Randall K.; Lawrence, Theodore S.; Feng, Mary

    2013-01-01

    Purpose: To evaluate whether liver function can be assessed globally and spatially by using volumetric dynamic contrast-enhanced magnetic resonance imaging MRI (DCE-MRI) to potentially aid in adaptive treatment planning. Methods and Materials: Seventeen patients with intrahepatic cancer undergoing focal radiation therapy (RT) were enrolled in institution review board-approved prospective studies to obtain DCE-MRI (to measure regional perfusion) and indocyanine green (ICG) clearance rates (to measure overall liver function) prior to, during, and at 1 and 2 months after treatment. The volumetric distribution of portal venous perfusion in the whole liver was estimated for each scan. We assessed the correlation between mean portal venous perfusion in the nontumor volume of the liver and overall liver function measured by ICG before, during, and after RT. The dose response for regional portal venous perfusion to RT was determined using a linear mixed effects model. Results: There was a significant correlation between the ICG clearance rate and mean portal venous perfusion in the functioning liver parenchyma, suggesting that portal venous perfusion could be used as a surrogate for function. Reduction in regional venous perfusion 1 month after RT was predicted by the locally accumulated biologically corrected dose at the end of RT (P<.0007). Regional portal venous perfusion measured during RT was a significant predictor for regional venous perfusion assessed 1 month after RT (P<.00001). Global hypovenous perfusion pre-RT was observed in 4 patients (3 patients with hepatocellular carcinoma and cirrhosis), 3 of whom had recovered from hypoperfusion, except in the highest dose regions, post-RT. In addition, 3 patients who had normal perfusion pre-RT had marked hypervenous perfusion or reperfusion in low-dose regions post-RT. Conclusions: This study suggests that MR-based volumetric hepatic perfusion imaging may be a biomarker for spatial distribution of liver function, which

  11. Rate-based process modeling study of CO{sub 2} capture with aqueous monoethanolamine solution

    SciTech Connect (OSTI)

    Zhang, Y.; Chen, H.; Chen, C.C.; Plaza, J.M.; Dugas, R.; Rochelle, G.T.

    2009-10-15

    Rate-based process modeling technology has matured and is increasingly gaining acceptance over traditional equilibrium-stage modeling approaches. Recently comprehensive pilot plant data for carbon dioxide (CO{sub 2}) capture with aqueous monoethanolamine (MEA) solution have become available from the University of Texas at Austin. The pilot plant data cover key process variables including CO{sub 2} concentration in the gas stream, CO{sub 2} loading in lean MEA solution, liquid to gas ratio, and packing type. In this study, we model the pilot plant operation with Aspen RateSep, a second generation rate-based multistage separation unit operation model in Aspen Plus. After a brief review of rate-based modeling, thermodynamic and kinetic models for CO{sub 2} absorption with the MEA solution, and transport property models, we show excellent match of the rate-based model predictions against the comprehensive pilot plant data and we validate the superiority of the rate-based models over the traditional equilibrium-stage models. We further examine the impacts of key rate-based modeling options, i.e., film discretization options and flow model options. The rate-based model provides excellent predictive capability, and it should be very useful for design and scale-up of CO{sub 2} capture processes.

  12. Model-Based Sampling and Inference

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

    Model-Based Sampling, Inference and Imputation James R. Knaub, Jr., Energy Information Administration, EI-53.1 James.Knaub@eia.doe.gov Key Words: Survey statistics, Randomization, Conditionality, Random sampling, Cutoff sampling Abstract: Picking a sample through some randomization mechanism, such as random sampling within groups (stratified random sampling), or, say, sampling every fifth item (systematic random sampling), may be familiar to a lot of people. These are design-based samples.

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

    SciTech Connect (OSTI)

    Pérez-Andújar, Angélica [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.

  14. 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

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

  15. A MULTISCALE, CELL-BASED FRAMEWORK FOR MODELING CANCER DEVELOPMENT

    SciTech Connect (OSTI)

    JIANG, YI

    2007-01-16

    Cancer remains to be one of the leading causes of death due to diseases. We use a systems approach that combines mathematical modeling, numerical simulation, in vivo and in vitro experiments, to develop a predictive model that medical researchers can use to study and treat cancerous tumors. The multiscale, cell-based model includes intracellular regulations, cellular level dynamics and intercellular interactions, and extracellular level chemical dynamics. The intracellular level protein regulations and signaling pathways are described by Boolean networks. The cellular level growth and division dynamics, cellular adhesion and interaction with the extracellular matrix is described by a lattice Monte Carlo model (the Cellular Potts Model). The extracellular dynamics of the signaling molecules and metabolites are described by a system of reaction-diffusion equations. All three levels of the model are integrated through a hybrid parallel scheme into a high-performance simulation tool. The simulation results reproduce experimental data in both avasular tumors and tumor angiogenesis. By combining the model with experimental data to construct biologically accurate simulations of tumors and their vascular systems, this model will enable medical researchers to gain a deeper understanding of the cellular and molecular interactions associated with cancer progression and treatment.

  16. A DISLOCATION-BASED CLEAVAGE INITIATION MODEL FOR PRESSURE VESSEL

    SciTech Connect (OSTI)

    Cochran, Kristine B; Erickson, Marjorie A; Williams, Paul T; Klasky, Hilda B; Bass, Bennett Richard

    2012-01-01

    Efforts are under way to develop a theoretical, multi-scale model for the prediction of fracture toughness of ferritic steels in the ductile-to-brittle transition temperature (DBTT) region that accounts for temperature, irradiation, strain rate, and material condition (chemistry and heat treatment) effects. This new model is intended to address difficulties associated with existing empirically-derived models of the DBTT region that cannot be extrapolated to conditions for which data are unavailable. Dislocation distribution equations, derived from the theories of Yokobori et al., are incorporated to account for the local stress state prior to and following initiation of a microcrack from a second-phase particle. The new model is the basis for the DISlocation-based FRACture (DISFRAC) computer code being developed at the Oak Ridge National Laboratory (ORNL). The purpose of this code is to permit fracture safety assessments of ferritic structures with only tensile properties required as input. The primary motivation for the code is to assist in the prediction of radiation effects on nuclear reactor pressure vessels, in parallel with the EURATOM PERFORM 60 project.

  17. 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 .

  18. Comparison of a Recurrent Neural Network PV System Model with a Traditional Component-Based PV System Model

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

    a Recurrent Neural Network PV System Model with a Traditional Component-Based PV System Model Daniel Riley, Sandia National Laboratories, Albuquerque, New Mexico, USA | Ganesh K. Venayagamoorthy, Missouri University of Science and Technology, Rolla, Missouri, USA Abstract Traditional PV system modeling approaches require system components to be tested in order to determine performance parameters. In some cases, system owners may wish to predict system performance, but lack the parameters

  19. 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

  20. 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 Eshelby’s 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.

  1. Project Profile: Physics-Based Reliability Models for Supercritical...

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

    Physics-Based Reliability Models for Supercritical-CO2 Turbomachinery Components Project Profile: Physics-Based Reliability Models for Supercritical-CO2 Turbomachinery Components ...

  2. Physics-based statistical learning approach to mesoscopic model...

    Office of Scientific and Technical Information (OSTI)

    Physics-based statistical learning approach to mesoscopic model selection Citation Details ... Title: Physics-based statistical learning approach to mesoscopic model selection Authors: ...

  3. 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.

  4. 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)

  5. 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.

  6. 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.

  7. Model-based Tomographic Reconstruction Literature Search

    SciTech Connect (OSTI)

    Chambers, D H; Lehman, S K

    2005-11-30

    In the process of preparing a proposal for internal research funding, a literature search was conducted on the subject of model-based tomographic reconstruction (MBTR). The purpose of the search was to ensure that the proposed research would not replicate any previous work. We found that the overwhelming majority of work on MBTR which used parameterized models of the object was theoretical in nature. Only three researchers had applied the technique to actual data. In this note, we summarize the findings of the literature search.

  8. Ductile Tearing of Thin Aluminum Plates Under Blast Loading. Predictions with Fully Coupled Models and Biaxial Material Response Characterization

    SciTech Connect (OSTI)

    Corona, Edmundo; Gullerud, Arne S.; Haulenbeek, Kimberly K.; Reu, Phillip L.

    2015-06-01

    The work presented in this report concerns the response and failure of thin 2024- T3 aluminum alloy circular plates to a blast load produced by the detonation of a nearby spherical charge. The plates were fully clamped around the circumference and the explosive charge was located centrally with respect to the plate. The principal objective was to conduct a numerical model validation study by comparing the results of predictions to experimental measurements of plate deformation and failure for charges with masses in the vicinity of the threshold between no tearing and tearing of the plates. Stereo digital image correlation data was acquired for all tests to measure the deflection and strains in the plates. The size of the virtual strain gage in the measurements, however, was relatively large, so the strain measurements have to be interpreted accordingly as lower bounds of the actual strains in the plate and of the severity of the strain gradients. A fully coupled interaction model between the blast and the deflection of the structure was considered. The results of the validation exercise indicated that the model predicted the deflection of the plates reasonably accurately as well as the distribution of strain on the plate. The estimation of the threshold charge based on a critical value of equivalent plastic strain measured in a bulge test, however, was not accurate. This in spite of efforts to determine the failure strain of the aluminum sheet under biaxial stress conditions. Further work is needed to be able to predict plate tearing with some degree of confidence. Given the current technology, at least one test under the actual blast conditions where the plate tears is needed to calibrate the value of equivalent plastic strain when failure occurs in the numerical model. Once that has been determined, the question of the explosive mass value at the threshold could be addressed with more confidence.

  9. 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.

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

    SciTech Connect (OSTI)

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

    2006-01-01

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

  11. 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-Lambert’s 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

  12. 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.

  13. 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).

  14. 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.

  15. 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 ...

  16. MM-Estimator and Adjusted Super Smoother based Simultaneous Prediction Confedenc

    Energy Science and Technology Software Center (OSTI)

    2002-07-19

    A Novel Application of Regression Analysis (MM-Estimator) with Simultaneous Prediction Confidence Intervals are proposed to detect up- or down-regulated genes, which are outliers in scatter plots based on log-transformed red (Cy5 fluorescent dye) versus green (Cy3 fluorescent Dye) intensities. Advantages of the application: 1) Robust and Resistant MM-Estimator is a Reliable Method to Build Linear Regression In the presence of Outliers, 2) Exploratory Data Analysis Tools (Boxplots, Averaged Shifted Histograms, Quantile-Quantile Normal Plots and Scattermore » Plots) are Unsed to Test Visually underlying assumptions of linearity and Contaminated Normality in Microarray data), 3) Simultaneous prediction confidence intervals (SPCIs) Guarantee a desired confidence level across the whole range of the data points used for the scatter plots. Results of the outlier detection procedure is a set of significantly differentially expressed genes extracted from the employed microarray data set. A scatter plot smoother (super smoother or locally weighted regression) is used to quantify heteroscendasticity is residual variance (Commonly takes place in lower and higher intensity areas). The set of differentially expressed genes is quantified using interval estimates for P-values as a probabilistic measure of being outlier by chance. Monte Carlo simultations are used to adjust super smoother-based SPCIs.her.« less

  17. 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.

  18. 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 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.

  19. 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.

  20. 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.

  1. Biologically based multistage modeling of radiation effects

    SciTech Connect (OSTI)

    William Hazelton; Suresh Moolgavkar; E. Georg Luebeck

    2005-08-30

    This past year we have made substantial progress in modeling the contribution of homeostatic regulation to low-dose radiation effects and carcinogenesis. We have worked to refine and apply our multistage carcinogenesis models to explicitly incorporate cell cycle states, simple and complex damage, checkpoint delay, slow and fast repair, differentiation, and apoptosis to study the effects of low-dose ionizing radiation in mouse intestinal crypts, as well as in other tissues. We have one paper accepted for publication in ''Advances in Space Research'', and another manuscript in preparation describing this work. I also wrote a chapter describing our combined cell-cycle and multistage carcinogenesis model that will be published in a book on stochastic carcinogenesis models edited by Wei-Yuan Tan. In addition, we organized and held a workshop on ''Biologically Based Modeling of Human Health Effects of Low dose Ionizing Radiation'', July 28-29, 2005 at Fred Hutchinson Cancer Research Center in Seattle, Washington. We had over 20 participants, including Mary Helen Barcellos-Hoff as keynote speaker, talks by most of the low-dose modelers in the DOE low-dose program, experimentalists including Les Redpath (and Mary Helen), Noelle Metting from DOE, and Tony Brooks. It appears that homeostatic regulation may be central to understanding low-dose radiation phenomena. The primary effects of ionizing radiation (IR) are cell killing, delayed cell cycling, and induction of mutations. However, homeostatic regulation causes cells that are killed or damaged by IR to eventually be replaced. Cells with an initiating mutation may have a replacement advantage, leading to clonal expansion of these initiated cells. Thus we have focused particularly on modeling effects that disturb homeostatic regulation as early steps in the carcinogenic process. There are two primary considerations that support our focus on homeostatic regulation. First, a number of epidemiologic studies using multistage

  2. 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

  3. 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.

  4. 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.

  5. Mathematical model for predicting the probability of acute mortality in a human population exposed to accidentally released airborne radionuclides. Final report for Phase I

    SciTech Connect (OSTI)

    Filipy, R.E.; Borst, F.J.; Cross, F.T.; Park, J.F.; Moss, O.R.; Roswell, R.L.; Stevens, D.L.

    1980-05-01

    A mathematical model was constructed for the purpose of predicting the fraction of human population which would die within 1 year of an accidental exposure to airborne radionuclides. The model is based on data from laboratory experiments with rats, dogs and baboons, and from human epidemiological data. Doses from external, whole-body irradiation and from inhaled, alpha- and beta-emitting radionuclides are calculated for several organs. The probabilities of death from radiation pneumonitis and from bone marrow irradiation are predicted from doses accumulated within 30 days of exposure to the radioactive aerosol. The model is compared with existing similar models under hypothetical exposure conditions. Suggestions for further experiments with inhaled radionuclides are included. 25 refs., 16 figs., 13 tabs.

  6. 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)

  7. 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.

  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. Prediction of dissolved actinide concentrations in concentrated electrolyte solutions: a conceptual model and model results for the Waste Isolation Pilot Plant (WIPP)

    SciTech Connect (OSTI)

    Novak, C.F.; Moore, R.C.; Bynum, R.V.

    1996-10-25

    The conceptual model for WIPP dissolved concentrations is a description of the complex natural and artificial chemical conditions expected to influence dissolved actinide concentrations in the repository. By a set of physical and chemical assumptions regarding chemical kinetics, sorption substrates, and waste-brine interactions, the system was simplified to be amenable to mathematical description. The analysis indicated that an equilibrium thermodynamic model for describing actinide solubilities in brines would be tractable and scientifically supportable. This paper summarizes the conceptualization and modeling approach and the computational results as used in the WIPP application for certification of compliance with relevant regulations for nuclear waste repositories. The WIPP site contains complex natural brines ranging from sea water to 10x more concentrated than sea water. Data bases for predicting solubility of Am(III) (as well as Pu(III) and Nd(III)), Th(IV), and Np(V) in these brines under potential repository conditions have been developed, focusing on chemical interactions with Na, K, Mg, Cl, SO{sub 4}, and CO{sub 3} ions, and the organic acid anions acetate, citrate, EDTA, and oxalate. The laboratory and modeling effort augmented the Harvie et al. parameterization of the Pitzer activity coefficient model so that it could be applied to the actinides and oxidation states important to the WIPP system.

  10. Modeling of stagnation-line nonequilibrium flows by means of quantum based collisional models

    SciTech Connect (OSTI)

    MunafĂČ, A. Magin, T. E.

    2014-09-15

    The stagnation-line flow over re-entry bodies is analyzed by means of a quantum based collisional model which accounts for dissociation and energy transfer in N{sub 2}-N interactions. The physical model is based on a kinetic database developed at NASA Ames Research Center. The reduction of the kinetic mechanism is achieved by lumping the rovibrational energy levels of the N{sub 2} molecule in energy bins. The energy bins are treated as separate species, thus allowing for non-Boltzmann distributions of their populations. The governing equations are discretized in space by means of the Finite Volume method. A fully implicit time-integration is used to obtain steady-state solutions. The results show that the population of the energy bins strongly deviate from a Boltzmann distribution close to the shock wave and across the boundary layer. The sensitivity analysis to the number of energy bins reveals that accurate estimation of flow quantities (such as chemical composition and wall heat flux) can be obtained by using only 10 energy bins. A comparison with the predictions obtained by means of conventional multi-temperature models indicates that the former can lead to an overestimation of the wall heat flux, due to an inaccurate modeling of recombination in the boundary layer.

  11. Model-Based Transient Calibration Optimization for Next Generation...

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

    Based Transient Calibration Optimization for Next Generation Diesel Engines Model-Based Transient Calibration Optimization for Next Generation Diesel Engines 2005 Diesel Engine...

  12. Commercial Buildings Sector Agent-Based Model | Open Energy Informatio...

    Open Energy Info (EERE)

    OpenEI Keyword(s): EERE tool, Commercial Buildings Sector Agent-Based Model Language: English References: Building Efficiency: Development of an Agent-based Model of the US...

  13. 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...

  14. Evaluation and prediction of color-tunable organic light-emitting diodes based on carrier/exciton adjusting interlayer

    SciTech Connect (OSTI)

    Liu, Shengqiang; Li, Jie; Yu, Junsheng; Du, Chunlei

    2015-07-27

    A color tuning index (I{sub CT}) parameter for evaluating the color change capability of color-tunable organic light-emitting diodes (CT-OLEDs) was proposed and formulated. And a series of CT-OLEDs, consisting of five different carrier/exciton adjusting interlayers (C/EALs) inserted between two complementary emitting layers, were fabricated and applied to disclose the relationship between I{sub CT} and C/EALs. The result showed that the trend of electroluminescence spectra behavior in CT-OLEDs has good accordance with I{sub CT} values, indicating that the I{sub CT} parameter is feasible for the evaluation of color variation. Meanwhile, by changing energy level and C/EAL thickness, the optimized device with the widest color tuning range was based on N,Nâ€Č-dicarbazolyl-3,5-benzene C/EAL, exhibiting the highest I{sub CT} value of 41.2%. Based on carrier quadratic hopping theory and exciton transfer model, two fitting I{sub CT} formulas derived from the highest occupied molecular orbital (HOMO) energy level and triplet energy level were simulated. Finally, a color tuning prediction (CTP) model was developed to deduce the I{sub CT} via C/EAL HOMO and triplet energy levels, and verified by the fabricated OLEDs with five different C/EALs. We believe that the CTP model assisted with I{sub CT} parameter will be helpful for fabricating high performance CT-OLEDs with a broad range of color tuning.

  15. 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.

  16. Parametric Adaptive Model Based Diagnostics | Department of Energy

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

    Parametric Adaptive Model Based Diagnostics Parametric Adaptive Model Based Diagnostics A model-based adaptive, robust technology is presented for on-board diagnostics of failure of diesel engine emission control devices and ethanol estimation of flex-fuel vehicles. p-06_franchek.pdf (256.08 KB) More Documents & Publications Model-Based Transient Calibration Optimization for Next Generation Diesel Engines Evaluation of 2010 Urea-SCR Technology for Hybrid Vehicles using PSAT System

  17. Emulating a System Dynamics Model with Agent-Based Models: A Methodological Case Study in Simulation of Diabetes Progression

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

    Schryver, Jack; Nutaro, James; Shankar, Mallikarjun

    2015-10-30

    An agent-based simulation model hierarchy emulating disease states and behaviors critical to progression of diabetes type 2 was designed and implemented in the DEVS framework. The models are translations of basic elements of an established system dynamics model of diabetes. In this model hierarchy, which mimics diabetes progression over an aggregated U.S. population, was dis-aggregated and reconstructed bottom-up at the individual (agent) level. Four levels of model complexity were defined in order to systematically evaluate which parameters are needed to mimic outputs of the system dynamics model. Moreover, the four estimated models attempted to replicate stock counts representing disease statesmore » in the system dynamics model, while estimating impacts of an elderliness factor, obesity factor and health-related behavioral parameters. Health-related behavior was modeled as a simple realization of the Theory of Planned Behavior, a joint function of individual attitude and diffusion of social norms that spread over each agent s social network. Although the most complex agent-based simulation model contained 31 adjustable parameters, all models were considerably less complex than the system dynamics model which required numerous time series inputs to make its predictions. In all three elaborations of the baseline model provided significantly improved fits to the output of the system dynamics model. The performances of the baseline agent-based model and its extensions illustrate a promising approach to translate complex system dynamics models into agent-based model alternatives that are both conceptually simpler and capable of capturing main effects of complex local agent-agent interactions.« less

  18. 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

  19. 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.

  20. 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.

  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. Physics-based statistical model and simulation method of RF propagation in urban environments

    DOE Patents [OSTI]

    Pao, Hsueh-Yuan; Dvorak, Steven L.

    2010-09-14

    A physics-based statistical model and simulation/modeling method and system of electromagnetic wave propagation (wireless communication) in urban environments. In particular, the model is a computationally efficient close-formed parametric model of RF propagation in an urban environment which is extracted from a physics-based statistical wireless channel simulation method and system. The simulation divides the complex urban environment into a network of interconnected urban canyon waveguides which can be analyzed individually; calculates spectral coefficients of modal fields in the waveguides excited by the propagation using a database of statistical impedance boundary conditions which incorporates the complexity of building walls in the propagation model; determines statistical parameters of the calculated modal fields; and determines a parametric propagation model based on the statistical parameters of the calculated modal fields from which predictions of communications capability may be made.

  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. A physiologically based biodynamic (PBBD) model for estragole DNA binding in rat liver based on in vitro kinetic data and estragole DNA adduct formation in primary hepatocytes

    SciTech Connect (OSTI)

    Paini, Alicia; Punt, Ans; Viton, Florian; Scholz, Gabriele; Delatour, Thierry; Marin-Kuan, Maricel; Schilter, Benoit; Bladeren, Peter J. van; Rietjens, Ivonne M.C.M.

    2010-05-15

    Estragole has been shown to be hepatocarcinogenic in rodent species at high-dose levels. Translation of these results into the likelihood of formation of DNA adducts, mutation, and ultimately cancer upon more realistic low-dose exposures remains a challenge. Recently we have developed physiologically based biokinetic (PBBK) models for rat and human predicting bioactivation of estragole. These PBBK models, however, predict only kinetic characteristics. The present study describes the extension of the PBBK model to a so-called physiologically based biodynamic (PBBD) model predicting in vivo DNA adduct formation of estragole in rat liver. This PBBD model was developed using in vitro data on DNA adduct formation in rat primary hepatocytes exposed to 1'-hydroxyestragole. The model was extended by linking the area under the curve for 1'-hydroxyestragole formation predicted by the PBBK model to the area under the curve for 1'-hydroxyestragole in the in vitro experiments. The outcome of the PBBD model revealed a linear increase in DNA adduct formation with increasing estragole doses up to 100 mg/kg bw. Although DNA adduct formation of genotoxic carcinogens is generally seen as a biomarker of exposure rather than a biomarker of response, the PBBD model now developed is one step closer to the ultimate toxic effect of estragole than the PBBK model described previously. Comparison of the PBBD model outcome to available data showed that the model adequately predicts the dose-dependent level of DNA adduct formation. The PBBD model predicts DNA adduct formation at low levels of exposure up to a dose level showing to cause cancer in rodent bioassays, providing a proof of principle for modeling a toxicodynamic in vivo endpoint on the basis of solely in vitro experimental data.

  5. Radionuclide migration in groundwater at a low-level waste disposal site: a comparison of predictive performance modeling versus field observations

    SciTech Connect (OSTI)

    Robertson, D.E. Myers, D.A.; Bergeron, M.P.; Champ, D.R.; Killey, R.W.D.; Moltyaner, G.L.; Young, J.L.

    1985-08-01

    This paper describes a project which is structured to test the concept of modeling a shallow land low-level waste burial site. The project involves a comparison of the actual observed radionuclide migration in groundwaters at a 30-year-old well-monitored field site with the results of predictive transport modeling. The comparison is being conducted as a cooperative program with the Atomic Energy of Canada Ltd. (AECL) at the low-level waste management area at the Chalk River Nuclear Laboratories, Ontario, Canada. A joint PNL-AECL field inviestigation was conducted in 1983 and 1984 to complement the existing extensive data base on actual radionuclide migration. Predictive transport modeling is currently being conducted for this site; first, as if it were a new location being considered for a low-level waste shallow-land burial site and only minimal information about the site were available, and second, utilizing the extensive data base available for the site. The modeling results will then be compared with the empirical observations to provide insight into the level of effort needed to reasonably predict the spacial and temporal movement of radionuclides in the groundwater enviroment. 8 refs., 5 figs.,

  6. 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.

  7. 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.

  8. 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.

  9. 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.

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

    Broader source: Energy.gov [DOE]

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