Initial Comparison of Single Cylinder Stirling Engine Computer Model Predictions with Test Results

NASA Technical Reports Server (NTRS)

Tew, R. C., Jr.; Thieme, L. G.; Miao, D.

1979-01-01

A Stirling engine digital computer model developed at NASA Lewis Research Center was configured to predict the performance of the GPU-3 single-cylinder rhombic drive engine. Revisions to the basic equations and assumptions are discussed. Model predictions with the early results of the Lewis Research Center GPU-3 tests are compared.

Model-Based and Model-Free Pavlovian Reward Learning: Revaluation, Revision and Revelation

PubMed Central

Dayan, Peter; Berridge, Kent C.

2014-01-01

Evidence supports at least two methods for learning about reward and punishment and making predictions for guiding actions. One method, called model-free, progressively acquires cached estimates of the long-run values of circumstances and actions from retrospective experience. The other method, called model-based, uses representations of the environment, expectations and prospective calculations to make cognitive predictions of future value. Extensive attention has been paid to both methods in computational analyses of instrumental learning. By contrast, although a full computational analysis has been lacking, Pavlovian learning and prediction has typically been presumed to be solely model-free. Here, we revise that presumption and review compelling evidence from Pavlovian revaluation experiments showing that Pavlovian predictions can involve their own form of model-based evaluation. In model-based Pavlovian evaluation, prevailing states of the body and brain influence value computations, and thereby produce powerful incentive motivations that can sometimes be quite new. We consider the consequences of this revised Pavlovian view for the computational landscape of prediction, response and choice. We also revisit differences between Pavlovian and instrumental learning in the control of incentive motivation. PMID:24647659

Model-based and model-free Pavlovian reward learning: revaluation, revision, and revelation.

PubMed

Dayan, Peter; Berridge, Kent C

2014-06-01

Evidence supports at least two methods for learning about reward and punishment and making predictions for guiding actions. One method, called model-free, progressively acquires cached estimates of the long-run values of circumstances and actions from retrospective experience. The other method, called model-based, uses representations of the environment, expectations, and prospective calculations to make cognitive predictions of future value. Extensive attention has been paid to both methods in computational analyses of instrumental learning. By contrast, although a full computational analysis has been lacking, Pavlovian learning and prediction has typically been presumed to be solely model-free. Here, we revise that presumption and review compelling evidence from Pavlovian revaluation experiments showing that Pavlovian predictions can involve their own form of model-based evaluation. In model-based Pavlovian evaluation, prevailing states of the body and brain influence value computations, and thereby produce powerful incentive motivations that can sometimes be quite new. We consider the consequences of this revised Pavlovian view for the computational landscape of prediction, response, and choice. We also revisit differences between Pavlovian and instrumental learning in the control of incentive motivation.

Using Predictability for Lexical Segmentation.

PubMed

Çöltekin, Çağrı

2017-09-01

This study investigates a strategy based on predictability of consecutive sub-lexical units in learning to segment a continuous speech stream into lexical units using computational modeling and simulations. Lexical segmentation is one of the early challenges during language acquisition, and it has been studied extensively through psycholinguistic experiments as well as computational methods. However, despite strong empirical evidence, the explicit use of predictability of basic sub-lexical units in models of segmentation is underexplored. This paper presents an incremental computational model of lexical segmentation for exploring the usefulness of predictability for lexical segmentation. We show that the predictability cue is a strong cue for segmentation. Contrary to earlier reports in the literature, the strategy yields state-of-the-art segmentation performance with an incremental computational model that uses only this particular cue in a cognitively plausible setting. The paper also reports an in-depth analysis of the model, investigating the conditions affecting the usefulness of the strategy. Copyright © 2016 Cognitive Science Society, Inc.

Computational modeling for prediction of the shear stress of three-dimensional isotropic and aligned fiber networks.

PubMed

Park, Seungman

2017-09-01

Interstitial flow (IF) is a creeping flow through the interstitial space of the extracellular matrix (ECM). IF plays a key role in diverse biological functions, such as tissue homeostasis, cell function and behavior. Currently, most studies that have characterized IF have focused on the permeability of ECM or shear stress distribution on the cells, but less is known about the prediction of shear stress on the individual fibers or fiber networks despite its significance in the alignment of matrix fibers and cells observed in fibrotic or wound tissues. In this study, I developed a computational model to predict shear stress for different structured fibrous networks. To generate isotropic models, a random growth algorithm and a second-order orientation tensor were employed. Then, a three-dimensional (3D) solid model was created using computer-aided design (CAD) software for the aligned models (i.e., parallel, perpendicular and cubic models). Subsequently, a tetrahedral unstructured mesh was generated and flow solutions were calculated by solving equations for mass and momentum conservation for all models. Through the flow solutions, I estimated permeability using Darcy's law. Average shear stress (ASS) on the fibers was calculated by averaging the wall shear stress of the fibers. By using nonlinear surface fitting of permeability, viscosity, velocity, porosity and ASS, I devised new computational models. Overall, the developed models showed that higher porosity induced higher permeability, as previous empirical and theoretical models have shown. For comparison of the permeability, the present computational models were matched well with previous models, which justify our computational approach. ASS tended to increase linearly with respect to inlet velocity and dynamic viscosity, whereas permeability was almost the same. Finally, the developed model nicely predicted the ASS values that had been directly estimated from computational fluid dynamics (CFD). The present computational models will provide new tools for predicting accurate functional properties and designing fibrous porous materials, thereby significantly advancing tissue engineering. Copyright © 2017 Elsevier B.V. All rights reserved.

Aerodynamic-structural model of offwind yacht sails

NASA Astrophysics Data System (ADS)

Mairs, Christopher M.

An aerodynamic-structural model of offwind yacht sails was created that is useful in predicting sail forces. Two sails were examined experimentally and computationally at several wind angles to explore a variety of flow regimes. The accuracy of the numerical solutions was measured by comparing to experimental results. The two sails examined were a Code 0 and a reaching asymmetric spinnaker. During experiment, balance, wake, and sail shape data were recorded for both sails in various configurations. Two computational steps were used to evaluate the computational model. First, an aerodynamic flow model that includes viscosity effects was used to examine the experimental flying shapes that were recorded. Second, the aerodynamic model was combined with a nonlinear, structural, finite element analysis (FEA) model. The aerodynamic and structural models were used iteratively to predict final flying shapes of offwind sails, starting with the design shapes. The Code 0 has relatively low camber and is used at small angles of attack. It was examined experimentally and computationally at a single angle of attack in two trim configurations, a baseline and overtrimmed setting. Experimentally, the Code 0 was stable and maintained large flow attachment regions. The digitized flying shapes from experiment were examined in the aerodynamic model. Force area predictions matched experimental results well. When the aerodynamic-structural tool was employed, the predictive capability was slightly worse. The reaching asymmetric spinnaker has higher camber and operates at higher angles of attack than the Code 0. Experimentally and computationally, it was examined at two angles of attack. Like the Code 0, at each wind angle, baseline and overtrimmed settings were examined. Experimentally, sail oscillations and large flow detachment regions were encountered. The computational analysis began by examining the experimental flying shapes in the aerodynamic model. In the baseline setting, the computational force predictions were fair at both wind angles examined. Force predictions were much improved in the overtrimmed setting when the sail was highly stalled and more stable. The same trends in force prediction were seen when employing the aerodynamic-structural model. Predictions were good to fair in the baseline setting but improved in the overtrimmed configuration.

Challenges facing developers of CAD/CAM models that seek to predict human working postures

NASA Astrophysics Data System (ADS)

Wiker, Steven F.

2005-11-01

This paper outlines the need for development of human posture prediction models for Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) design applications in product, facility and work design. Challenges facing developers of posture prediction algorithms are presented and discussed.

Model-based predictions for dopamine.

PubMed

Langdon, Angela J; Sharpe, Melissa J; Schoenbaum, Geoffrey; Niv, Yael

2018-04-01

Phasic dopamine responses are thought to encode a prediction-error signal consistent with model-free reinforcement learning theories. However, a number of recent findings highlight the influence of model-based computations on dopamine responses, and suggest that dopamine prediction errors reflect more dimensions of an expected outcome than scalar reward value. Here, we review a selection of these recent results and discuss the implications and complications of model-based predictions for computational theories of dopamine and learning. Copyright © 2017. Published by Elsevier Ltd.

Modelling milk production from feed intake in dairy cattle

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clarke, D.L.

1985-05-01

Predictive models were developed for both Holstein and Jersey cows. Since Holsteins comprised eighty-five percent of the data, the predictive models developed for Holsteins were used for the development of a user-friendly computer model. Predictive models included: milk production (squared multiple correlation .73), natural log (ln) of milk production (.73), four percent fat-corrected milk (.67), ln four percent fat-corrected milk (.68), fat-free milk (.73), ln fat-free milk (.73), dry matter intake (.61), ln dry matter intake (.60), milk fat (.52), and ln milk fat (.56). The predictive models for ln milk production, ln fat-free milk and ln dry matter intakemore » were incorporated into a computer model. The model was written in standard Fortran for use on mainframe or micro-computers. Daily milk production, fat-free milk production, and dry matter intake were predicted on a daily basis with the previous day's dry matter intake serving as an independent variable in the prediction of the daily milk and fat-free milk production. 21 refs.« less

Foundations for computer simulation of a low pressure oil flooded single screw air compressor

NASA Astrophysics Data System (ADS)

Bein, T. W.

1981-12-01

The necessary logic to construct a computer model to predict the performance of an oil flooded, single screw air compressor is developed. The geometric variables and relationships used to describe the general single screw mechanism are developed. The governing equations to describe the processes are developed from their primary relationships. The assumptions used in the development are also defined and justified. The computer model predicts the internal pressure, temperature, and flowrates through the leakage paths throughout the compression cycle of the single screw compressor. The model uses empirical external values as the basis for the internal predictions. The computer values are compared to the empirical values, and conclusions are drawn based on the results. Recommendations are made for future efforts to improve the computer model and to verify some of the conclusions that are drawn.

AHPCRC (Army High Performance Computing Rsearch Center) Bulletin. Volume 1, Issue 4

DTIC Science & Technology

2011-01-01

Computational and Mathematical Engineering, Stanford University esgs@stanford.edu (650) 723-3764 Molecular Dynamics Models of Antimicrobial ...simulations using low-fidelity Reynolds-av- eraged models illustrate the limited predictive capabili- ties of these schemes. The predictions for scalar and...driving force. The AHPCRC group has used their models to predict nonuniform concentra- tion profiles across small channels as a result of variations

A study of modelling simplifications in ground vibration predictions for railway traffic at grade

NASA Astrophysics Data System (ADS)

Germonpré, M.; Degrande, G.; Lombaert, G.

2017-10-01

Accurate computational models are required to predict ground-borne vibration due to railway traffic. Such models generally require a substantial computational effort. Therefore, much research has focused on developing computationally efficient methods, by either exploiting the regularity of the problem geometry in the direction along the track or assuming a simplified track structure. This paper investigates the modelling errors caused by commonly made simplifications of the track geometry. A case study is presented investigating a ballasted track in an excavation. The soil underneath the ballast is stiffened by a lime treatment. First, periodic track models with different cross sections are analyzed, revealing that a prediction of the rail receptance only requires an accurate representation of the soil layering directly underneath the ballast. A much more detailed representation of the cross sectional geometry is required, however, to calculate vibration transfer from track to free field. Second, simplifications in the longitudinal track direction are investigated by comparing 2.5D and periodic track models. This comparison shows that the 2.5D model slightly overestimates the track stiffness, while the transfer functions between track and free field are well predicted. Using a 2.5D model to predict the response during a train passage leads to an overestimation of both train-track interaction forces and free field vibrations. A combined periodic/2.5D approach is therefore proposed in this paper. First, the dynamic axle loads are computed by solving the train-track interaction problem with a periodic model. Next, the vibration transfer to the free field is computed with a 2.5D model. This combined periodic/2.5D approach only introduces small modelling errors compared to an approach in which a periodic model is used in both steps, while significantly reducing the computational cost.

Predicting knee replacement damage in a simulator machine using a computational model with a consistent wear factor.

PubMed

Zhao, Dong; Sakoda, Hideyuki; Sawyer, W Gregory; Banks, Scott A; Fregly, Benjamin J

2008-02-01

Wear of ultrahigh molecular weight polyethylene remains a primary factor limiting the longevity of total knee replacements (TKRs). However, wear testing on a simulator machine is time consuming and expensive, making it impractical for iterative design purposes. The objectives of this paper were first, to evaluate whether a computational model using a wear factor consistent with the TKR material pair can predict accurate TKR damage measured in a simulator machine, and second, to investigate how choice of surface evolution method (fixed or variable step) and material model (linear or nonlinear) affect the prediction. An iterative computational damage model was constructed for a commercial knee implant in an AMTI simulator machine. The damage model combined a dynamic contact model with a surface evolution model to predict how wear plus creep progressively alter tibial insert geometry over multiple simulations. The computational framework was validated by predicting wear in a cylinder-on-plate system for which an analytical solution was derived. The implant damage model was evaluated for 5 million cycles of simulated gait using damage measurements made on the same implant in an AMTI machine. Using a pin-on-plate wear factor for the same material pair as the implant, the model predicted tibial insert wear volume to within 2% error and damage depths and areas to within 18% and 10% error, respectively. Choice of material model had little influence, while inclusion of surface evolution affected damage depth and area but not wear volume predictions. Surface evolution method was important only during the initial cycles, where variable step was needed to capture rapid geometry changes due to the creep. Overall, our results indicate that accurate TKR damage predictions can be made with a computational model using a constant wear factor obtained from pin-on-plate tests for the same material pair, and furthermore, that surface evolution method matters only during the initial "break in" period of the simulation.

Assessment of traffic noise levels in urban areas using different soft computing techniques.

PubMed

Tomić, J; Bogojević, N; Pljakić, M; Šumarac-Pavlović, D

2016-10-01

Available traffic noise prediction models are usually based on regression analysis of experimental data, and this paper presents the application of soft computing techniques in traffic noise prediction. Two mathematical models are proposed and their predictions are compared to data collected by traffic noise monitoring in urban areas, as well as to predictions of commonly used traffic noise models. The results show that application of evolutionary algorithms and neural networks may improve process of development, as well as accuracy of traffic noise prediction.

gCUP: rapid GPU-based HIV-1 co-receptor usage prediction for next-generation sequencing.

PubMed

Olejnik, Michael; Steuwer, Michel; Gorlatch, Sergei; Heider, Dominik

2014-11-15

Next-generation sequencing (NGS) has a large potential in HIV diagnostics, and genotypic prediction models have been developed and successfully tested in the recent years. However, albeit being highly accurate, these computational models lack computational efficiency to reach their full potential. In this study, we demonstrate the use of graphics processing units (GPUs) in combination with a computational prediction model for HIV tropism. Our new model named gCUP, parallelized and optimized for GPU, is highly accurate and can classify >175 000 sequences per second on an NVIDIA GeForce GTX 460. The computational efficiency of our new model is the next step to enable NGS technologies to reach clinical significance in HIV diagnostics. Moreover, our approach is not limited to HIV tropism prediction, but can also be easily adapted to other settings, e.g. drug resistance prediction. The source code can be downloaded at http://www.heiderlab.de d.heider@wz-straubing.de. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A Computational Fluid Dynamics Study of Transitional Flows in Low-Pressure Turbines under a Wide Range of Operating Conditions

NASA Technical Reports Server (NTRS)

Suzen, Y. B.; Huang, P. G.; Ashpis, D. E.; Volino, R. J.; Corke, T. C.; Thomas, F. O.; Huang, J.; Lake, J. P.; King, P. I.

2007-01-01

A transport equation for the intermittency factor is employed to predict the transitional flows in low-pressure turbines. The intermittent behavior of the transitional flows is taken into account and incorporated into computations by modifying the eddy viscosity, mu(sub p) with the intermittency factor, gamma. Turbulent quantities are predicted using Menter's two-equation turbulence model (SST). The intermittency factor is obtained from a transport equation model which can produce both the experimentally observed streamwise variation of intermittency and a realistic profile in the cross stream direction. The model had been previously validated against low-pressure turbine experiments with success. In this paper, the model is applied to predictions of three sets of recent low-pressure turbine experiments on the Pack B blade to further validate its predicting capabilities under various flow conditions. Comparisons of computational results with experimental data are provided. Overall, good agreement between the experimental data and computational results is obtained. The new model has been shown to have the capability of accurately predicting transitional flows under a wide range of low-pressure turbine conditions.

Data Aggregation, Curation and Modeling Approaches to Deliver Prediction Models to Support Computational Toxicology at the EPA (ACS Fall meeting)

EPA Science Inventory

The U.S. Environmental Protection Agency (EPA) Computational Toxicology Program develops and utilizes QSAR modeling approaches across a broad range of applications. In terms of physical chemistry we have a particular interest in the prediction of basic physicochemical parameters ...

On the study of control effectiveness and computational efficiency of reduced Saint-Venant model in model predictive control of open channel flow

NASA Astrophysics Data System (ADS)

Xu, M.; van Overloop, P. J.; van de Giesen, N. C.

2011-02-01

Model predictive control (MPC) of open channel flow is becoming an important tool in water management. The complexity of the prediction model has a large influence on the MPC application in terms of control effectiveness and computational efficiency. The Saint-Venant equations, called SV model in this paper, and the Integrator Delay (ID) model are either accurate but computationally costly, or simple but restricted to allowed flow changes. In this paper, a reduced Saint-Venant (RSV) model is developed through a model reduction technique, Proper Orthogonal Decomposition (POD), on the SV equations. The RSV model keeps the main flow dynamics and functions over a large flow range but is easier to implement in MPC. In the test case of a modeled canal reach, the number of states and disturbances in the RSV model is about 45 and 16 times less than the SV model, respectively. The computational time of MPC with the RSV model is significantly reduced, while the controller remains effective. Thus, the RSV model is a promising means to balance the control effectiveness and computational efficiency.

Predictive computation of genomic logic processing functions in embryonic development

PubMed Central

Peter, Isabelle S.; Faure, Emmanuel; Davidson, Eric H.

2012-01-01

Gene regulatory networks (GRNs) control the dynamic spatial patterns of regulatory gene expression in development. Thus, in principle, GRN models may provide system-level, causal explanations of developmental process. To test this assertion, we have transformed a relatively well-established GRN model into a predictive, dynamic Boolean computational model. This Boolean model computes spatial and temporal gene expression according to the regulatory logic and gene interactions specified in a GRN model for embryonic development in the sea urchin. Additional information input into the model included the progressive embryonic geometry and gene expression kinetics. The resulting model predicted gene expression patterns for a large number of individual regulatory genes each hour up to gastrulation (30 h) in four different spatial domains of the embryo. Direct comparison with experimental observations showed that the model predictively computed these patterns with remarkable spatial and temporal accuracy. In addition, we used this model to carry out in silico perturbations of regulatory functions and of embryonic spatial organization. The model computationally reproduced the altered developmental functions observed experimentally. Two major conclusions are that the starting GRN model contains sufficiently complete regulatory information to permit explanation of a complex developmental process of gene expression solely in terms of genomic regulatory code, and that the Boolean model provides a tool with which to test in silico regulatory circuitry and developmental perturbations. PMID:22927416

Evaluation of a Computational Model of Situational Awareness

NASA Technical Reports Server (NTRS)

Burdick, Mark D.; Shively, R. Jay; Rutkewski, Michael (Technical Monitor)

2000-01-01

Although the use of the psychological construct of situational awareness (SA) assists researchers in creating a flight environment that is safer and more predictable, its true potential remains untapped until a valid means of predicting SA a priori becomes available. Previous work proposed a computational model of SA (CSA) that sought to Fill that void. The current line of research is aimed at validating that model. The results show that the model accurately predicted SA in a piloted simulation.

COSP - A computer model of cyclic oxidation

NASA Technical Reports Server (NTRS)

Lowell, Carl E.; Barrett, Charles A.; Palmer, Raymond W.; Auping, Judith V.; Probst, Hubert B.

1991-01-01

A computer model useful in predicting the cyclic oxidation behavior of alloys is presented. The model considers the oxygen uptake due to scale formation during the heating cycle and the loss of oxide due to spalling during the cooling cycle. The balance between scale formation and scale loss is modeled and used to predict weight change and metal loss kinetics. A simple uniform spalling model is compared to a more complex random spall site model. In nearly all cases, the simpler uniform spall model gave predictions as accurate as the more complex model. The model has been applied to several nickel-base alloys which, depending upon composition, form Al2O3 or Cr2O3 during oxidation. The model has been validated by several experimental approaches. Versions of the model that run on a personal computer are available.

CADRE-SS, an in Silico Tool for Predicting Skin Sensitization Potential Based on Modeling of Molecular Interactions.

PubMed

Kostal, Jakub; Voutchkova-Kostal, Adelina

2016-01-19

Using computer models to accurately predict toxicity outcomes is considered to be a major challenge. However, state-of-the-art computational chemistry techniques can now be incorporated in predictive models, supported by advances in mechanistic toxicology and the exponential growth of computing resources witnessed over the past decade. The CADRE (Computer-Aided Discovery and REdesign) platform relies on quantum-mechanical modeling of molecular interactions that represent key biochemical triggers in toxicity pathways. Here, we present an external validation exercise for CADRE-SS, a variant developed to predict the skin sensitization potential of commercial chemicals. CADRE-SS is a hybrid model that evaluates skin permeability using Monte Carlo simulations, assigns reactive centers in a molecule and possible biotransformations via expert rules, and determines reactivity with skin proteins via quantum-mechanical modeling. The results were promising with an overall very good concordance of 93% between experimental and predicted values. Comparison to performance metrics yielded by other tools available for this endpoint suggests that CADRE-SS offers distinct advantages for first-round screenings of chemicals and could be used as an in silico alternative to animal tests where permissible by legislative programs.

Improving Computational Efficiency of Prediction in Model-Based Prognostics Using the Unscented Transform

NASA Technical Reports Server (NTRS)

Daigle, Matthew John; Goebel, Kai Frank

2010-01-01

Model-based prognostics captures system knowledge in the form of physics-based models of components, and how they fail, in order to obtain accurate predictions of end of life (EOL). EOL is predicted based on the estimated current state distribution of a component and expected profiles of future usage. In general, this requires simulations of the component using the underlying models. In this paper, we develop a simulation-based prediction methodology that achieves computational efficiency by performing only the minimal number of simulations needed in order to accurately approximate the mean and variance of the complete EOL distribution. This is performed through the use of the unscented transform, which predicts the means and covariances of a distribution passed through a nonlinear transformation. In this case, the EOL simulation acts as that nonlinear transformation. In this paper, we review the unscented transform, and describe how this concept is applied to efficient EOL prediction. As a case study, we develop a physics-based model of a solenoid valve, and perform simulation experiments to demonstrate improved computational efficiency without sacrificing prediction accuracy.

Microarray-based cancer prediction using soft computing approach.

PubMed

Wang, Xiaosheng; Gotoh, Osamu

2009-05-26

One of the difficulties in using gene expression profiles to predict cancer is how to effectively select a few informative genes to construct accurate prediction models from thousands or ten thousands of genes. We screen highly discriminative genes and gene pairs to create simple prediction models involved in single genes or gene pairs on the basis of soft computing approach and rough set theory. Accurate cancerous prediction is obtained when we apply the simple prediction models for four cancerous gene expression datasets: CNS tumor, colon tumor, lung cancer and DLBCL. Some genes closely correlated with the pathogenesis of specific or general cancers are identified. In contrast with other models, our models are simple, effective and robust. Meanwhile, our models are interpretable for they are based on decision rules. Our results demonstrate that very simple models may perform well on cancerous molecular prediction and important gene markers of cancer can be detected if the gene selection approach is chosen reasonably.

Predicting Motivation: Computational Models of PFC Can Explain Neural Coding of Motivation and Effort-based Decision-making in Health and Disease.

PubMed

Vassena, Eliana; Deraeve, James; Alexander, William H

2017-10-01

Human behavior is strongly driven by the pursuit of rewards. In daily life, however, benefits mostly come at a cost, often requiring that effort be exerted to obtain potential benefits. Medial PFC (MPFC) and dorsolateral PFC (DLPFC) are frequently implicated in the expectation of effortful control, showing increased activity as a function of predicted task difficulty. Such activity partially overlaps with expectation of reward and has been observed both during decision-making and during task preparation. Recently, novel computational frameworks have been developed to explain activity in these regions during cognitive control, based on the principle of prediction and prediction error (predicted response-outcome [PRO] model [Alexander, W. H., & Brown, J. W. Medial prefrontal cortex as an action-outcome predictor. Nature Neuroscience, 14, 1338-1344, 2011], hierarchical error representation [HER] model [Alexander, W. H., & Brown, J. W. Hierarchical error representation: A computational model of anterior cingulate and dorsolateral prefrontal cortex. Neural Computation, 27, 2354-2410, 2015]). Despite the broad explanatory power of these models, it is not clear whether they can also accommodate effects related to the expectation of effort observed in MPFC and DLPFC. Here, we propose a translation of these computational frameworks to the domain of effort-based behavior. First, we discuss how the PRO model, based on prediction error, can explain effort-related activity in MPFC, by reframing effort-based behavior in a predictive context. We propose that MPFC activity reflects monitoring of motivationally relevant variables (such as effort and reward), by coding expectations and discrepancies from such expectations. Moreover, we derive behavioral and neural model-based predictions for healthy controls and clinical populations with impairments of motivation. Second, we illustrate the possible translation to effort-based behavior of the HER model, an extended version of PRO model based on hierarchical error prediction, developed to explain MPFC-DLPFC interactions. We derive behavioral predictions that describe how effort and reward information is coded in PFC and how changing the configuration of such environmental information might affect decision-making and task performance involving motivation.

Computer models for economic and silvicultural decisions

Treesearch

Rosalie J. Ingram

1989-01-01

Computer systems can help simplify decisionmaking to manage forest ecosystems. We now have computer models to help make forest management decisions by predicting changes associated with a particular management action. Models also help you evaluate alternatives. To be effective, the computer models must be reliable and appropriate for your situation.

Computational Modeling in Concert with Laboratory Studies: Application to B Cell Differentiation

EPA Science Inventory

Remediation is expensive, so accurate prediction of dose-response is important to help control costs. Dose response is a function of biological mechanisms. Computational models of these mechanisms improve the efficiency of research and provide the capability for prediction.

A Computational Workflow for the Automated Generation of Models of Genetic Designs.

PubMed

Misirli, Göksel; Nguyen, Tramy; McLaughlin, James Alastair; Vaidyanathan, Prashant; Jones, Timothy S; Densmore, Douglas; Myers, Chris; Wipat, Anil

2018-06-05

Computational models are essential to engineer predictable biological systems and to scale up this process for complex systems. Computational modeling often requires expert knowledge and data to build models. Clearly, manual creation of models is not scalable for large designs. Despite several automated model construction approaches, computational methodologies to bridge knowledge in design repositories and the process of creating computational models have still not been established. This paper describes a workflow for automatic generation of computational models of genetic circuits from data stored in design repositories using existing standards. This workflow leverages the software tool SBOLDesigner to build structural models that are then enriched by the Virtual Parts Repository API using Systems Biology Open Language (SBOL) data fetched from the SynBioHub design repository. The iBioSim software tool is then utilized to convert this SBOL description into a computational model encoded using the Systems Biology Markup Language (SBML). Finally, this SBML model can be simulated using a variety of methods. This workflow provides synthetic biologists with easy to use tools to create predictable biological systems, hiding away the complexity of building computational models. This approach can further be incorporated into other computational workflows for design automation.

The Effect of Nondeterministic Parameters on Shock-Associated Noise Prediction Modeling

NASA Technical Reports Server (NTRS)

Dahl, Milo D.; Khavaran, Abbas

2010-01-01

Engineering applications for aircraft noise prediction contain models for physical phenomenon that enable solutions to be computed quickly. These models contain parameters that have an uncertainty not accounted for in the solution. To include uncertainty in the solution, nondeterministic computational methods are applied. Using prediction models for supersonic jet broadband shock-associated noise, fixed model parameters are replaced by probability distributions to illustrate one of these methods. The results show the impact of using nondeterministic parameters both on estimating the model output uncertainty and on the model spectral level prediction. In addition, a global sensitivity analysis is used to determine the influence of the model parameters on the output, and to identify the parameters with the least influence on model output.

Paradigm of pretest risk stratification before coronary computed tomography.

PubMed

Jensen, Jesper Møller; Ovrehus, Kristian A; Nielsen, Lene H; Jensen, Jesper K; Larsen, Henrik M; Nørgaard, Bjarne L

2009-01-01

The optimal method of determining the pretest risk of coronary artery disease as a patient selection tool before coronary multidetector computed tomography (MDCT) is unknown. We investigated the ability of 3 different clinical risk scores to predict the outcome of coronary MDCT. This was a retrospective study of 551 patients consecutively referred for coronary MDCT on a suspicion of coronary artery disease. Diamond-Forrester, Duke, and Morise risk models were used to predict coronary artery stenosis (>50%) as assessed by coronary MDCT. The models were compared by receiver operating characteristic analysis. The distribution of low-, intermediate-, and high-risk persons, respectively, was established and compared for each of the 3 risk models. Overall, all risk prediction models performed equally well. However, the Duke risk model classified the low-risk patients more correctly than did the other models (P < 0.01). In patients without coronary artery calcification (CAC), the predictive value of the Duke risk model was superior to the other risk models (P < 0.05). Currently available risk prediction models seem to perform better in patients without CAC. Between the risk prediction models, there was a significant discrepancy in the distribution of patients at low, intermediate, or high risk (P < 0.01). The 3 risk prediction models perform equally well, although the Duke risk score may have advantages in subsets of patients. The choice of risk prediction model affects the referral pattern to MDCT. Copyright (c) 2009 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.

Multiplexed Predictive Control of a Large Commercial Turbofan Engine

NASA Technical Reports Server (NTRS)

Richter, hanz; Singaraju, Anil; Litt, Jonathan S.

2008-01-01

Model predictive control is a strategy well-suited to handle the highly complex, nonlinear, uncertain, and constrained dynamics involved in aircraft engine control problems. However, it has thus far been infeasible to implement model predictive control in engine control applications, because of the combination of model complexity and the time allotted for the control update calculation. In this paper, a multiplexed implementation is proposed that dramatically reduces the computational burden of the quadratic programming optimization that must be solved online as part of the model-predictive-control algorithm. Actuator updates are calculated sequentially and cyclically in a multiplexed implementation, as opposed to the simultaneous optimization taking place in conventional model predictive control. Theoretical aspects are discussed based on a nominal model, and actual computational savings are demonstrated using a realistic commercial engine model.

Computer Models of Personality: Implications for Measurement

ERIC Educational Resources Information Center

Cranton, P. A.

1976-01-01

Current research on computer models of personality is reviewed and categorized under five headings: (1) models of belief systems; (2) models of interpersonal behavior; (3) models of decision-making processes; (4) prediction models; and (5) theory-based simulations of specific processes. The use of computer models in personality measurement is…

Emerging approaches in predictive toxicology.

PubMed

Zhang, Luoping; McHale, Cliona M; Greene, Nigel; Snyder, Ronald D; Rich, Ivan N; Aardema, Marilyn J; Roy, Shambhu; Pfuhler, Stefan; Venkatactahalam, Sundaresan

2014-12-01

Predictive toxicology plays an important role in the assessment of toxicity of chemicals and the drug development process. While there are several well-established in vitro and in vivo assays that are suitable for predictive toxicology, recent advances in high-throughput analytical technologies and model systems are expected to have a major impact on the field of predictive toxicology. This commentary provides an overview of the state of the current science and a brief discussion on future perspectives for the field of predictive toxicology for human toxicity. Computational models for predictive toxicology, needs for further refinement and obstacles to expand computational models to include additional classes of chemical compounds are highlighted. Functional and comparative genomics approaches in predictive toxicology are discussed with an emphasis on successful utilization of recently developed model systems for high-throughput analysis. The advantages of three-dimensional model systems and stem cells and their use in predictive toxicology testing are also described. © 2014 Wiley Periodicals, Inc.

Emerging Approaches in Predictive Toxicology

PubMed Central

Zhang, Luoping; McHale, Cliona M.; Greene, Nigel; Snyder, Ronald D.; Rich, Ivan N.; Aardema, Marilyn J.; Roy, Shambhu; Pfuhler, Stefan; Venkatactahalam, Sundaresan

2016-01-01

Predictive toxicology plays an important role in the assessment of toxicity of chemicals and the drug development process. While there are several well-established in vitro and in vivo assays that are suitable for predictive toxicology, recent advances in high-throughput analytical technologies and model systems are expected to have a major impact on the field of predictive toxicology. This commentary provides an overview of the state of the current science and a brief discussion on future perspectives for the field of predictive toxicology for human toxicity. Computational models for predictive toxicology, needs for further refinement and obstacles to expand computational models to include additional classes of chemical compounds are highlighted. Functional and comparative genomics approaches in predictive toxicology are discussed with an emphasis on successful utilization of recently developed model systems for high-throughput analysis. The advantages of three-dimensional model systems and stem cells and their use in predictive toxicology testing are also described. PMID:25044351

How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology

PubMed Central

Aladjov, Hristo; Ankley, Gerald; Byrne, Hugh J.; de Knecht, Joop; Heinzle, Elmar; Klambauer, Günter; Landesmann, Brigitte; Luijten, Mirjam; MacKay, Cameron; Maxwell, Gavin; Meek, M. E. (Bette); Paini, Alicia; Perkins, Edward; Sobanski, Tomasz; Villeneuve, Dan; Waters, Katrina M.; Whelan, Maurice

2017-01-01

Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework provides a systematic approach for organizing knowledge that may support such inference. Likewise, computational models of biological systems at various scales provide another means and platform to integrate current biological understanding to facilitate inference and extrapolation. We argue that the systematic organization of knowledge into AOP frameworks can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. This concept was explored as part of a workshop on AOP-Informed Predictive Modeling Approaches for Regulatory Toxicology held September 24–25, 2015. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development is described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment. PMID:27994170

SALSA3D: A Tomographic Model of Compressional Wave Slowness in the Earth’s Mantle for Improved Travel-Time Prediction and Travel-Time Prediction Uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ballard, Sanford; Hipp, James R.; Begnaud, Michael L.

The task of monitoring the Earth for nuclear explosions relies heavily on seismic data to detect, locate, and characterize suspected nuclear tests. In this study, motivated by the need to locate suspected explosions as accurately and precisely as possible, we developed a tomographic model of the compressional wave slowness in the Earth’s mantle with primary focus on the accuracy and precision of travel-time predictions for P and Pn ray paths through the model. Path-dependent travel-time prediction uncertainties are obtained by computing the full 3D model covariance matrix and then integrating slowness variance and covariance along ray paths from source tomore » receiver. Path-dependent travel-time prediction uncertainties reflect the amount of seismic data that was used in tomography with very low values for paths represented by abundant data in the tomographic data set and very high values for paths through portions of the model that were poorly sampled by the tomography data set. The pattern of travel-time prediction uncertainty is a direct result of the off-diagonal terms of the model covariance matrix and underscores the importance of incorporating the full model covariance matrix in the determination of travel-time prediction uncertainty. In addition, the computed pattern of uncertainty differs significantly from that of 1D distance-dependent travel-time uncertainties computed using traditional methods, which are only appropriate for use with travel times computed through 1D velocity models.« less

SALSA3D: A Tomographic Model of Compressional Wave Slowness in the Earth’s Mantle for Improved Travel-Time Prediction and Travel-Time Prediction Uncertainty

DOE PAGES

Ballard, Sanford; Hipp, James R.; Begnaud, Michael L.; ...

2016-10-11

The task of monitoring the Earth for nuclear explosions relies heavily on seismic data to detect, locate, and characterize suspected nuclear tests. In this study, motivated by the need to locate suspected explosions as accurately and precisely as possible, we developed a tomographic model of the compressional wave slowness in the Earth’s mantle with primary focus on the accuracy and precision of travel-time predictions for P and Pn ray paths through the model. Path-dependent travel-time prediction uncertainties are obtained by computing the full 3D model covariance matrix and then integrating slowness variance and covariance along ray paths from source tomore » receiver. Path-dependent travel-time prediction uncertainties reflect the amount of seismic data that was used in tomography with very low values for paths represented by abundant data in the tomographic data set and very high values for paths through portions of the model that were poorly sampled by the tomography data set. The pattern of travel-time prediction uncertainty is a direct result of the off-diagonal terms of the model covariance matrix and underscores the importance of incorporating the full model covariance matrix in the determination of travel-time prediction uncertainty. In addition, the computed pattern of uncertainty differs significantly from that of 1D distance-dependent travel-time uncertainties computed using traditional methods, which are only appropriate for use with travel times computed through 1D velocity models.« less

A System Computational Model of Implicit Emotional Learning

PubMed Central

Puviani, Luca; Rama, Sidita

2016-01-01

Nowadays, the experimental study of emotional learning is commonly based on classical conditioning paradigms and models, which have been thoroughly investigated in the last century. Unluckily, models based on classical conditioning are unable to explain or predict important psychophysiological phenomena, such as the failure of the extinction of emotional responses in certain circumstances (for instance, those observed in evaluative conditioning, in post-traumatic stress disorders and in panic attacks). In this manuscript, starting from the experimental results available from the literature, a computational model of implicit emotional learning based both on prediction errors computation and on statistical inference is developed. The model quantitatively predicts (a) the occurrence of evaluative conditioning, (b) the dynamics and the resistance-to-extinction of the traumatic emotional responses, (c) the mathematical relation between classical conditioning and unconditioned stimulus revaluation. Moreover, we discuss how the derived computational model can lead to the development of new animal models for resistant-to-extinction emotional reactions and novel methodologies of emotions modulation. PMID:27378898

A System Computational Model of Implicit Emotional Learning.

PubMed

Puviani, Luca; Rama, Sidita

2016-01-01

Nowadays, the experimental study of emotional learning is commonly based on classical conditioning paradigms and models, which have been thoroughly investigated in the last century. Unluckily, models based on classical conditioning are unable to explain or predict important psychophysiological phenomena, such as the failure of the extinction of emotional responses in certain circumstances (for instance, those observed in evaluative conditioning, in post-traumatic stress disorders and in panic attacks). In this manuscript, starting from the experimental results available from the literature, a computational model of implicit emotional learning based both on prediction errors computation and on statistical inference is developed. The model quantitatively predicts (a) the occurrence of evaluative conditioning, (b) the dynamics and the resistance-to-extinction of the traumatic emotional responses, (c) the mathematical relation between classical conditioning and unconditioned stimulus revaluation. Moreover, we discuss how the derived computational model can lead to the development of new animal models for resistant-to-extinction emotional reactions and novel methodologies of emotions modulation.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, J. D.; Oberkampf, William Louis; Helton, Jon Craig

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 modelmore » 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.« less

Novel opportunities for computational biology and sociology in drug discovery☆

PubMed Central

Yao, Lixia; Evans, James A.; Rzhetsky, Andrey

2013-01-01

Current drug discovery is impossible without sophisticated modeling and computation. In this review we outline previous advances in computational biology and, by tracing the steps involved in pharmaceutical development, explore a range of novel, high-value opportunities for computational innovation in modeling the biological process of disease and the social process of drug discovery. These opportunities include text mining for new drug leads, modeling molecular pathways and predicting the efficacy of drug cocktails, analyzing genetic overlap between diseases and predicting alternative drug use. Computation can also be used to model research teams and innovative regions and to estimate the value of academy–industry links for scientific and human benefit. Attention to these opportunities could promise punctuated advance and will complement the well-established computational work on which drug discovery currently relies. PMID:20349528

Predicting neutron damage using TEM with in situ ion irradiation and computer modeling

NASA Astrophysics Data System (ADS)

Kirk, Marquis A.; Li, Meimei; Xu, Donghua; Wirth, Brian D.

2018-01-01

We have constructed a computer model of irradiation defect production closely coordinated with TEM and in situ ion irradiation of Molybdenum at 80 °C over a range of dose, dose rate and foil thickness. We have reexamined our previous ion irradiation data to assign appropriate error and uncertainty based on more recent work. The spatially dependent cascade cluster dynamics model is updated with recent Molecular Dynamics results for cascades in Mo. After a careful assignment of both ion and neutron irradiation dose values in dpa, TEM data are compared for both ion and neutron irradiated Mo from the same source material. Using the computer model of defect formation and evolution based on the in situ ion irradiation of thin foils, the defect microstructure, consisting of densities and sizes of dislocation loops, is predicted for neutron irradiation of bulk material at 80 °C and compared with experiment. Reasonable agreement between model prediction and experimental data demonstrates a promising direction in understanding and predicting neutron damage using a closely coordinated program of in situ ion irradiation experiment and computer simulation.

Integration of Gravitational Torques in Cerebellar Pathways Allows for the Dynamic Inverse Computation of Vertical Pointing Movements of a Robot Arm

PubMed Central

Gentili, Rodolphe J.; Papaxanthis, Charalambos; Ebadzadeh, Mehdi; Eskiizmirliler, Selim; Ouanezar, Sofiane; Darlot, Christian

2009-01-01

Background Several authors suggested that gravitational forces are centrally represented in the brain for planning, control and sensorimotor predictions of movements. Furthermore, some studies proposed that the cerebellum computes the inverse dynamics (internal inverse model) whereas others suggested that it computes sensorimotor predictions (internal forward model). Methodology/Principal Findings This study proposes a model of cerebellar pathways deduced from both biological and physical constraints. The model learns the dynamic inverse computation of the effect of gravitational torques from its sensorimotor predictions without calculating an explicit inverse computation. By using supervised learning, this model learns to control an anthropomorphic robot arm actuated by two antagonists McKibben artificial muscles. This was achieved by using internal parallel feedback loops containing neural networks which anticipate the sensorimotor consequences of the neural commands. The artificial neural networks architecture was similar to the large-scale connectivity of the cerebellar cortex. Movements in the sagittal plane were performed during three sessions combining different initial positions, amplitudes and directions of movements to vary the effects of the gravitational torques applied to the robotic arm. The results show that this model acquired an internal representation of the gravitational effects during vertical arm pointing movements. Conclusions/Significance This is consistent with the proposal that the cerebellar cortex contains an internal representation of gravitational torques which is encoded through a learning process. Furthermore, this model suggests that the cerebellum performs the inverse dynamics computation based on sensorimotor predictions. This highlights the importance of sensorimotor predictions of gravitational torques acting on upper limb movements performed in the gravitational field. PMID:19384420

A machine-learning approach for computation of fractional flow reserve from coronary computed tomography.

PubMed

Itu, Lucian; Rapaka, Saikiran; Passerini, Tiziano; Georgescu, Bogdan; Schwemmer, Chris; Schoebinger, Max; Flohr, Thomas; Sharma, Puneet; Comaniciu, Dorin

2016-07-01

Fractional flow reserve (FFR) is a functional index quantifying the severity of coronary artery lesions and is clinically obtained using an invasive, catheter-based measurement. Recently, physics-based models have shown great promise in being able to noninvasively estimate FFR from patient-specific anatomical information, e.g., obtained from computed tomography scans of the heart and the coronary arteries. However, these models have high computational demand, limiting their clinical adoption. In this paper, we present a machine-learning-based model for predicting FFR as an alternative to physics-based approaches. The model is trained on a large database of synthetically generated coronary anatomies, where the target values are computed using the physics-based model. The trained model predicts FFR at each point along the centerline of the coronary tree, and its performance was assessed by comparing the predictions against physics-based computations and against invasively measured FFR for 87 patients and 125 lesions in total. Correlation between machine-learning and physics-based predictions was excellent (0.9994, P < 0.001), and no systematic bias was found in Bland-Altman analysis: mean difference was -0.00081 ± 0.0039. Invasive FFR ≤ 0.80 was found in 38 lesions out of 125 and was predicted by the machine-learning algorithm with a sensitivity of 81.6%, a specificity of 83.9%, and an accuracy of 83.2%. The correlation was 0.729 (P < 0.001). Compared with the physics-based computation, average execution time was reduced by more than 80 times, leading to near real-time assessment of FFR. Average execution time went down from 196.3 ± 78.5 s for the CFD model to ∼2.4 ± 0.44 s for the machine-learning model on a workstation with 3.4-GHz Intel i7 8-core processor. Copyright © 2016 the American Physiological Society.

Application of linear regression analysis in accuracy assessment of rolling force calculations

NASA Astrophysics Data System (ADS)

Poliak, E. I.; Shim, M. K.; Kim, G. S.; Choo, W. Y.

1998-10-01

Efficient operation of the computational models employed in process control systems require periodical assessment of the accuracy of their predictions. Linear regression is proposed as a tool which allows separate systematic and random prediction errors from those related to measurements. A quantitative characteristic of the model predictive ability is introduced in addition to standard statistical tests for model adequacy. Rolling force calculations are considered as an example for the application. However, the outlined approach can be used to assess the performance of any computational model.

NASA Trapezoidal Wing Computations Including Transition and Advanced Turbulence Modeling

NASA Technical Reports Server (NTRS)

Rumsey, C. L.; Lee-Rausch, E. M.

2012-01-01

Flow about the NASA Trapezoidal Wing is computed with several turbulence models by using grids from the first High Lift Prediction Workshop in an effort to advance understanding of computational fluid dynamics modeling for this type of flowfield. Transition is accounted for in many of the computations. In particular, a recently-developed 4-equation transition model is utilized and works well overall. Accounting for transition tends to increase lift and decrease moment, which improves the agreement with experiment. Upper surface flap separation is reduced, and agreement with experimental surface pressures and velocity profiles is improved. The predicted shape of wakes from upstream elements is strongly influenced by grid resolution in regions above the main and flap elements. Turbulence model enhancements to account for rotation and curvature have the general effect of increasing lift and improving the resolution of the wing tip vortex as it convects downstream. However, none of the models improve the prediction of surface pressures near the wing tip, where more grid resolution is needed.

Computational modeling of GTA (gas tungsten arc) welding with emphasis on surface tension effects

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zacharia, T.; David, S.A.

1990-01-01

A computational study of the convective heat transfer in the weld pool during gas tungsten arch (GTA) welding of Type 304 stainless steel is presented. The solution of the transport equations is based on a control volume approach which utilized directly, the integral form of the governing equations. The computational model considers buoyancy and electromagnetic and surface tension forces in the solution of convective heat transfer in the weld pool. In addition, the model treats the weld pool surface as a deformable free surface. The computational model includes weld metal vaporization and temperature dependent thermophysical properties. The results indicate thatmore » consideration of weld pool vaporization effects and temperature dependent thermophysical properties significantly influence the weld model predictions. Theoretical predictions of the weld pool surface temperature distributions and the cross-sectional weld pool size and shape wee compared with corresponding experimental measurements. Comparison of the theoretically predicted and the experimentally obtained surface temperature profiles indicated agreement with {plus minus} 8%. The predicted weld cross-section profiles were found to agree very well with actual weld cross-sections for the best theoretical models. 26 refs., 8 figs.« less

Analyzing Log Files to Predict Students' Problem Solving Performance in a Computer-Based Physics Tutor

ERIC Educational Resources Information Center

Lee, Young-Jin

2015-01-01

This study investigates whether information saved in the log files of a computer-based tutor can be used to predict the problem solving performance of students. The log files of a computer-based physics tutoring environment called Andes Physics Tutor was analyzed to build a logistic regression model that predicted success and failure of students'…

COSIM: A Finite-Difference Computer Model to Predict Ternary Concentration Profiles Associated With Oxidation and Interdiffusion of Overlay-Coated Substrates

NASA Technical Reports Server (NTRS)

Nesbitt, James A.

2001-01-01

A finite-difference computer program (COSIM) has been written which models the one-dimensional, diffusional transport associated with high-temperature oxidation and interdiffusion of overlay-coated substrates. The program predicts concentration profiles for up to three elements in the coating and substrate after various oxidation exposures. Surface recession due to solute loss is also predicted. Ternary cross terms and concentration-dependent diffusion coefficients are taken into account. The program also incorporates a previously-developed oxide growth and spalling model to simulate either isothermal or cyclic oxidation exposures. In addition to predicting concentration profiles after various oxidation exposures, the program can also be used to predict coating life based on a concentration dependent failure criterion (e.g., surface solute content drops to 2%). The computer code is written in FORTRAN and employs numerous subroutines to make the program flexible and easily modifiable to other coating oxidation problems.

BEHAVE: fire behavior prediction and fuel modeling system-BURN Subsystem, part 1

Treesearch

Patricia L. Andrews

1986-01-01

Describes BURN Subsystem, Part 1, the operational fire behavior prediction subsystem of the BEHAVE fire behavior prediction and fuel modeling system. The manual covers operation of the computer program, assumptions of the mathematical models used in the calculations, and application of the predictions.

Computational discovery and in vivo validation of hnf4 as a regulatory gene in planarian regeneration.

PubMed

Lobo, Daniel; Morokuma, Junji; Levin, Michael

2016-09-01

Automated computational methods can infer dynamic regulatory network models directly from temporal and spatial experimental data, such as genetic perturbations and their resultant morphologies. Recently, a computational method was able to reverse-engineer the first mechanistic model of planarian regeneration that can recapitulate the main anterior-posterior patterning experiments published in the literature. Validating this comprehensive regulatory model via novel experiments that had not yet been performed would add in our understanding of the remarkable regeneration capacity of planarian worms and demonstrate the power of this automated methodology. Using the Michigan Molecular Interactions and STRING databases and the MoCha software tool, we characterized as hnf4 an unknown regulatory gene predicted to exist by the reverse-engineered dynamic model of planarian regeneration. Then, we used the dynamic model to predict the morphological outcomes under different single and multiple knock-downs (RNA interference) of hnf4 and its predicted gene pathway interactors β-catenin and hh Interestingly, the model predicted that RNAi of hnf4 would rescue the abnormal regenerated phenotype (tailless) of RNAi of hh in amputated trunk fragments. Finally, we validated these predictions in vivo by performing the same surgical and genetic experiments with planarian worms, obtaining the same phenotypic outcomes predicted by the reverse-engineered model. These results suggest that hnf4 is a regulatory gene in planarian regeneration, validate the computational predictions of the reverse-engineered dynamic model, and demonstrate the automated methodology for the discovery of novel genes, pathways and experimental phenotypes. michael.levin@tufts.edu. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology.

PubMed

Wittwehr, Clemens; Aladjov, Hristo; Ankley, Gerald; Byrne, Hugh J; de Knecht, Joop; Heinzle, Elmar; Klambauer, Günter; Landesmann, Brigitte; Luijten, Mirjam; MacKay, Cameron; Maxwell, Gavin; Meek, M E Bette; Paini, Alicia; Perkins, Edward; Sobanski, Tomasz; Villeneuve, Dan; Waters, Katrina M; Whelan, Maurice

2017-02-01

Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework provides a systematic approach for organizing knowledge that may support such inference. Likewise, computational models of biological systems at various scales provide another means and platform to integrate current biological understanding to facilitate inference and extrapolation. We argue that the systematic organization of knowledge into AOP frameworks can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. This concept was explored as part of a workshop on AOP-Informed Predictive Modeling Approaches for Regulatory Toxicology held September 24-25, 2015. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development is described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment. © The Author 2016. Published by Oxford University Press on behalf of the Society of Toxicology.

Novel opportunities for computational biology and sociology in drug discovery

PubMed Central

Yao, Lixia

2009-01-01

Drug discovery today is impossible without sophisticated modeling and computation. In this review we touch on previous advances in computational biology and by tracing the steps involved in pharmaceutical development, we explore a range of novel, high value opportunities for computational innovation in modeling the biological process of disease and the social process of drug discovery. These opportunities include text mining for new drug leads, modeling molecular pathways and predicting the efficacy of drug cocktails, analyzing genetic overlap between diseases and predicting alternative drug use. Computation can also be used to model research teams and innovative regions and to estimate the value of academy-industry ties for scientific and human benefit. Attention to these opportunities could promise punctuated advance, and will complement the well-established computational work on which drug discovery currently relies. PMID:19674801

Tandem internal models execute motor learning in the cerebellum.

PubMed

Honda, Takeru; Nagao, Soichi; Hashimoto, Yuji; Ishikawa, Kinya; Yokota, Takanori; Mizusawa, Hidehiro; Ito, Masao

2018-06-25

In performing skillful movement, humans use predictions from internal models formed by repetition learning. However, the computational organization of internal models in the brain remains unknown. Here, we demonstrate that a computational architecture employing a tandem configuration of forward and inverse internal models enables efficient motor learning in the cerebellum. The model predicted learning adaptations observed in hand-reaching experiments in humans wearing a prism lens and explained the kinetic components of these behavioral adaptations. The tandem system also predicted a form of subliminal motor learning that was experimentally validated after training intentional misses of hand targets. Patients with cerebellar degeneration disease showed behavioral impairments consistent with tandemly arranged internal models. These findings validate computational tandemization of internal models in motor control and its potential uses in more complex forms of learning and cognition. Copyright © 2018 the Author(s). Published by PNAS.

A computer program for predicting nonlinear uniaxial material responses using viscoplastic models

NASA Technical Reports Server (NTRS)

Chang, T. Y.; Thompson, R. L.

1984-01-01

A computer program was developed for predicting nonlinear uniaxial material responses using viscoplastic constitutive models. Four specific models, i.e., those due to Miller, Walker, Krieg-Swearengen-Rhode, and Robinson, are included. Any other unified model is easily implemented into the program in the form of subroutines. Analysis features include stress-strain cycling, creep response, stress relaxation, thermomechanical fatigue loop, or any combination of these responses. An outline is given on the theoretical background of uniaxial constitutive models, analysis procedure, and numerical integration methods for solving the nonlinear constitutive equations. In addition, a discussion on the computer program implementation is also given. Finally, seven numerical examples are included to demonstrate the versatility of the computer program developed.

Predicting uncertainty in future marine ice sheet volume using Bayesian statistical methods

NASA Astrophysics Data System (ADS)

Davis, A. D.

2015-12-01

The marine ice instability can trigger rapid retreat of marine ice streams. Recent observations suggest that marine ice systems in West Antarctica have begun retreating. However, unknown ice dynamics, computationally intensive mathematical models, and uncertain parameters in these models make predicting retreat rate and ice volume difficult. In this work, we fuse current observational data with ice stream/shelf models to develop probabilistic predictions of future grounded ice sheet volume. Given observational data (e.g., thickness, surface elevation, and velocity) and a forward model that relates uncertain parameters (e.g., basal friction and basal topography) to these observations, we use a Bayesian framework to define a posterior distribution over the parameters. A stochastic predictive model then propagates uncertainties in these parameters to uncertainty in a particular quantity of interest (QoI)---here, the volume of grounded ice at a specified future time. While the Bayesian approach can in principle characterize the posterior predictive distribution of the QoI, the computational cost of both the forward and predictive models makes this effort prohibitively expensive. To tackle this challenge, we introduce a new Markov chain Monte Carlo method that constructs convergent approximations of the QoI target density in an online fashion, yielding accurate characterizations of future ice sheet volume at significantly reduced computational cost.Our second goal is to attribute uncertainty in these Bayesian predictions to uncertainties in particular parameters. Doing so can help target data collection, for the purpose of constraining the parameters that contribute most strongly to uncertainty in the future volume of grounded ice. For instance, smaller uncertainties in parameters to which the QoI is highly sensitive may account for more variability in the prediction than larger uncertainties in parameters to which the QoI is less sensitive. We use global sensitivity analysis to help answer this question, and make the computation of sensitivity indices computationally tractable using a combination of polynomial chaos and Monte Carlo techniques.

Microbial burden prediction model for unmanned planetary spacecraft

NASA Technical Reports Server (NTRS)

Hoffman, A. R.; Winterburn, D. A.

1972-01-01

The technical development of a computer program for predicting microbial burden on unmanned planetary spacecraft is outlined. The discussion includes the derivation of the basic analytical equations, the selection of a method for handling several random variables, the macrologic of the computer programs and the validation and verification of the model. The prediction model was developed to (1) supplement the biological assays of a spacecraft by simulating the microbial accretion during periods when assays are not taken; (2) minimize the necessity for a large number of microbiological assays; and (3) predict the microbial loading on a lander immediately prior to sterilization and other non-lander equipment prior to launch. It is shown that these purposes not only were achieved but also that the prediction results compare favorably to the estimates derived from the direct assays. The computer program can be applied not only as a prediction instrument but also as a management and control tool. The basic logic of the model is shown to have possible applicability to other sequential flow processes, such as food processing.

Novel patch modelling method for efficient simulation and prediction uncertainty analysis of multi-scale groundwater flow and transport processes

NASA Astrophysics Data System (ADS)

Sreekanth, J.; Moore, Catherine

2018-04-01

The application of global sensitivity and uncertainty analysis techniques to groundwater models of deep sedimentary basins are typically challenged by large computational burdens combined with associated numerical stability issues. The highly parameterized approaches required for exploring the predictive uncertainty associated with the heterogeneous hydraulic characteristics of multiple aquifers and aquitards in these sedimentary basins exacerbate these issues. A novel Patch Modelling Methodology is proposed for improving the computational feasibility of stochastic modelling analysis of large-scale and complex groundwater models. The method incorporates a nested groundwater modelling framework that enables efficient simulation of groundwater flow and transport across multiple spatial and temporal scales. The method also allows different processes to be simulated within different model scales. Existing nested model methodologies are extended by employing 'joining predictions' for extrapolating prediction-salient information from one model scale to the next. This establishes a feedback mechanism supporting the transfer of information from child models to parent models as well as parent models to child models in a computationally efficient manner. This feedback mechanism is simple and flexible and ensures that while the salient small scale features influencing larger scale prediction are transferred back to the larger scale, this does not require the live coupling of models. This method allows the modelling of multiple groundwater flow and transport processes using separate groundwater models that are built for the appropriate spatial and temporal scales, within a stochastic framework, while also removing the computational burden associated with live model coupling. The utility of the method is demonstrated by application to an actual large scale aquifer injection scheme in Australia.

Data-Based Predictive Control with Multirate Prediction Step

NASA Technical Reports Server (NTRS)

Barlow, Jonathan S.

2010-01-01

Data-based predictive control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from input-output data. Thus, a controller can be designed from the outputs of complex simulation code or a physical system where no explicit model exists. If the output data happens to be corrupted by periodic disturbances, the designed controller will also have the built-in ability to reject these disturbances without the need to know them. When data-based predictive control is implemented online, it becomes a version of adaptive control. One challenge of MPC is computational requirements increasing with prediction horizon length. This paper develops a closed-loop dynamic output feedback controller that minimizes a multi-step-ahead receding-horizon cost function with multirate prediction step. One result is a reduced influence of prediction horizon and the number of system outputs on the computational requirements of the controller. Another result is an emphasis on portions of the prediction window that are sampled more frequently. A third result is the ability to include more outputs in the feedback path than in the cost function.

Representing, Running, and Revising Mental Models: A Computational Model

ERIC Educational Resources Information Center

Friedman, Scott; Forbus, Kenneth; Sherin, Bruce

2018-01-01

People use commonsense science knowledge to flexibly explain, predict, and manipulate the world around them, yet we lack computational models of how this commonsense science knowledge is represented, acquired, utilized, and revised. This is an important challenge for cognitive science: Building higher order computational models in this area will…

Predicting treatment effect from surrogate endpoints and historical trials: an extrapolation involving probabilities of a binary outcome or survival to a specific time

PubMed Central

Sargent, Daniel J.; Buyse, Marc; Burzykowski, Tomasz

2011-01-01

SUMMARY Using multiple historical trials with surrogate and true endpoints, we consider various models to predict the effect of treatment on a true endpoint in a target trial in which only a surrogate endpoint is observed. This predicted result is computed using (1) a prediction model (mixture, linear, or principal stratification) estimated from historical trials and the surrogate endpoint of the target trial and (2) a random extrapolation error estimated from successively leaving out each trial among the historical trials. The method applies to either binary outcomes or survival to a particular time that is computed from censored survival data. We compute a 95% confidence interval for the predicted result and validate its coverage using simulation. To summarize the additional uncertainty from using a predicted instead of true result for the estimated treatment effect, we compute its multiplier of standard error. Software is available for download. PMID:21838732

Structural behavior of composites with progressive fracture

NASA Technical Reports Server (NTRS)

Minnetyan, L.; Murthy, P. L. N.; Chamis, C. C.

1989-01-01

The objective of the study is to unify several computational tools developed for the prediction of progressive damage and fracture with efforts for the prediction of the overall response of damaged composite structures. In particular, a computational finite element model for the damaged structure is developed using a computer program as a byproduct of the analysis of progressive damage and fracture. Thus, a single computational investigation can predict progressive fracture and the resulting variation in structural properties of angleplied composites.

Analyses of ACPL thermal/fluid conditioning system

NASA Technical Reports Server (NTRS)

Stephen, L. A.; Usher, L. H.

1976-01-01

Results of engineering analyses are reported. Initial computations were made using a modified control transfer function where the systems performance was characterized parametrically using an analytical model. The analytical model was revised to represent the latest expansion chamber fluid manifold design, and systems performance predictions were made. Parameters which were independently varied in these computations are listed. Systems predictions which were used to characterize performance are primarily transient computer plots comparing the deviation between average chamber temperature and the chamber temperature requirement. Additional computer plots were prepared. Results of parametric computations with the latest fluid manifold design are included.

Computational Modeling of Hypothalamic-Pituitary-Gonadal Axis to Predict Adaptive Responses in Female Fathead Minnows Exposed to an Aromatase Inhibitor

EPA Science Inventory

Exposure to endocrine disrupting chemicals can affect reproduction and development in both humans and wildlife. We are developing a mechanistic computational model of the hypothalamic-pituitary-gonadal (HPG) axis in female fathead minnows to predict dose response and time-course...

ACToR A Aggregated Computational Toxicology Resource ...

EPA Pesticide Factsheets

We are developing the ACToR system (Aggregated Computational Toxicology Resource) to serve as a repository for a variety of types of chemical, biological and toxicological data that can be used for predictive modeling of chemical toxicology. We are developing the ACToR system (Aggregated Computational Toxicology Resource) to serve as a repository for a variety of types of chemical, biological and toxicological data that can be used for predictive modeling of chemical toxicology.

ACToR A Aggregated Computational Toxicology Resource (S) ...

EPA Pesticide Factsheets

We are developing the ACToR system (Aggregated Computational Toxicology Resource) to serve as a repository for a variety of types of chemical, biological and toxicological data that can be used for predictive modeling of chemical toxicology. We are developing the ACToR system (Aggregated Computational Toxicology Resource) to serve as a repository for a variety of types of chemical, biological and toxicological data that can be used for predictive modeling of chemical toxicology.

Modification of Hazen's equation in coarse grained soils by soft computing techniques

NASA Astrophysics Data System (ADS)

Kaynar, Oguz; Yilmaz, Isik; Marschalko, Marian; Bednarik, Martin; Fojtova, Lucie

2013-04-01

Hazen first proposed a Relationship between coefficient of permeability (k) and effective grain size (d10) was first proposed by Hazen, and it was then extended by some other researchers. However many attempts were done for estimation of k, correlation coefficients (R2) of the models were generally lower than ~0.80 and whole grain size distribution curves were not included in the assessments. Soft computing techniques such as; artificial neural networks, fuzzy inference systems, genetic algorithms, etc. and their hybrids are now being successfully used as an alternative tool. In this study, use of some soft computing techniques such as Artificial Neural Networks (ANNs) (MLP, RBF, etc.) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for prediction of permeability of coarse grained soils was described, and Hazen's equation was then modificated. It was found that the soft computing models exhibited high performance in prediction of permeability coefficient. However four different kinds of ANN algorithms showed similar prediction performance, results of MLP was found to be relatively more accurate than RBF models. The most reliable prediction was obtained from ANFIS model.

Principal component analysis in construction of 3D human knee joint models using a statistical shape model method.

PubMed

Tsai, Tsung-Yuan; Li, Jing-Sheng; Wang, Shaobai; Li, Pingyue; Kwon, Young-Min; Li, Guoan

2015-01-01

The statistical shape model (SSM) method that uses 2D images of the knee joint to predict the three-dimensional (3D) joint surface model has been reported in the literature. In this study, we constructed a SSM database using 152 human computed tomography (CT) knee joint models, including the femur, tibia and patella and analysed the characteristics of each principal component of the SSM. The surface models of two in vivo knees were predicted using the SSM and their 2D bi-plane fluoroscopic images. The predicted models were compared to their CT joint models. The differences between the predicted 3D knee joint surfaces and the CT image-based surfaces were 0.30 ± 0.81 mm, 0.34 ± 0.79 mm and 0.36 ± 0.59 mm for the femur, tibia and patella, respectively (average ± standard deviation). The computational time for each bone of the knee joint was within 30 s using a personal computer. The analysis of this study indicated that the SSM method could be a useful tool to construct 3D surface models of the knee with sub-millimeter accuracy in real time. Thus, it may have a broad application in computer-assisted knee surgeries that require 3D surface models of the knee.

Plans for Aeroelastic Prediction Workshop

NASA Technical Reports Server (NTRS)

Heeg, Jennifer; Ballmann, Josef; Bhatia, Kumar; Blades, Eric; Boucke, Alexander; Chwalowski, Pawel; Dietz, Guido; Dowell, Earl; Florance, Jennifer P.; Hansen, Thorsten;

Predicting Pilot Error in Nextgen: Pilot Performance Modeling and Validation Efforts

NASA Technical Reports Server (NTRS)

Wickens, Christopher; Sebok, Angelia; Gore, Brian; Hooey, Becky

2012-01-01

We review 25 articles presenting 5 general classes of computational models to predict pilot error. This more targeted review is placed within the context of the broader review of computational models of pilot cognition and performance, including such aspects as models of situation awareness or pilot-automation interaction. Particular emphasis is placed on the degree of validation of such models against empirical pilot data, and the relevance of the modeling and validation efforts to Next Gen technology and procedures.

Predictive codes of familiarity and context during the perceptual learning of facial identities

NASA Astrophysics Data System (ADS)

Apps, Matthew A. J.; Tsakiris, Manos

2013-11-01

Face recognition is a key component of successful social behaviour. However, the computational processes that underpin perceptual learning and recognition as faces transition from unfamiliar to familiar are poorly understood. In predictive coding, learning occurs through prediction errors that update stimulus familiarity, but recognition is a function of both stimulus and contextual familiarity. Here we show that behavioural responses on a two-option face recognition task can be predicted by the level of contextual and facial familiarity in a computational model derived from predictive-coding principles. Using fMRI, we show that activity in the superior temporal sulcus varies with the contextual familiarity in the model, whereas activity in the fusiform face area covaries with the prediction error parameter that updated facial familiarity. Our results characterize the key computations underpinning the perceptual learning of faces, highlighting that the functional properties of face-processing areas conform to the principles of predictive coding.

Simplified Models for Accelerated Structural Prediction of Conjugated Semiconducting Polymers

DOE PAGES

Henry, Michael M.; Jones, Matthew L.; Oosterhout, Stefan D.; ...

2017-11-08

We perform molecular dynamics simulations of poly(benzodithiophene-thienopyrrolodione) (BDT-TPD) oligomers in order to evaluate the accuracy with which unoptimized molecular models can predict experimentally characterized morphologies. The predicted morphologies are characterized using simulated grazing-incidence X-ray scattering (GIXS) and compared to the experimental scattering patterns. We find that approximating the aromatic rings in BDT-TPD with rigid bodies, rather than combinations of bond, angle, and dihedral constraints, results in 14% lower computational cost and provides nearly equivalent structural predictions compared to the flexible model case. The predicted glass transition temperature of BDT-TPD (410 +/- 32 K) is found to be in agreement withmore » experiments. Predicted morphologies demonstrate short-range structural order due to stacking of the chain backbones (p-p stacking around 3.9 A), and long-range spatial correlations due to the self-organization of backbone stacks into 'ribbons' (lamellar ordering around 20.9 A), representing the best-to-date computational predictions of structure of complex conjugated oligomers. We find that expensive simulated annealing schedules are not needed to predict experimental structures here, with instantaneous quenches providing nearly equivalent predictions at a fraction of the computational cost of annealing. We therefore suggest utilizing rigid bodies and fast cooling schedules for high-throughput screening studies of semiflexible polymers and oligomers to utilize their significant computational benefits where appropriate.« less

Simplified Models for Accelerated Structural Prediction of Conjugated Semiconducting Polymers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Henry, Michael M.; Jones, Matthew L.; Oosterhout, Stefan D.

We perform molecular dynamics simulations of poly(benzodithiophene-thienopyrrolodione) (BDT-TPD) oligomers in order to evaluate the accuracy with which unoptimized molecular models can predict experimentally characterized morphologies. The predicted morphologies are characterized using simulated grazing-incidence X-ray scattering (GIXS) and compared to the experimental scattering patterns. We find that approximating the aromatic rings in BDT-TPD with rigid bodies, rather than combinations of bond, angle, and dihedral constraints, results in 14% lower computational cost and provides nearly equivalent structural predictions compared to the flexible model case. The predicted glass transition temperature of BDT-TPD (410 +/- 32 K) is found to be in agreement withmore » experiments. Predicted morphologies demonstrate short-range structural order due to stacking of the chain backbones (p-p stacking around 3.9 A), and long-range spatial correlations due to the self-organization of backbone stacks into 'ribbons' (lamellar ordering around 20.9 A), representing the best-to-date computational predictions of structure of complex conjugated oligomers. We find that expensive simulated annealing schedules are not needed to predict experimental structures here, with instantaneous quenches providing nearly equivalent predictions at a fraction of the computational cost of annealing. We therefore suggest utilizing rigid bodies and fast cooling schedules for high-throughput screening studies of semiflexible polymers and oligomers to utilize their significant computational benefits where appropriate.« less

How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wittwehr, Clemens; Aladjov, Hristo; Ankley, Gerald

Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework has emerged as a systematic approach for organizing knowledge that supports such inference. We argue that this systematic organization of knowledge can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment.more » Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment.« less

Monte Carlo Computational Modeling of the Energy Dependence of Atomic Oxygen Undercutting of Protected Polymers

NASA Technical Reports Server (NTRS)

Banks, Bruce A.; Stueber, Thomas J.; Norris, Mary Jo

1998-01-01

A Monte Carlo computational model has been developed which simulates atomic oxygen attack of protected polymers at defect sites in the protective coatings. The parameters defining how atomic oxygen interacts with polymers and protective coatings as well as the scattering processes which occur have been optimized to replicate experimental results observed from protected polyimide Kapton on the Long Duration Exposure Facility (LDEF) mission. Computational prediction of atomic oxygen undercutting at defect sites in protective coatings for various arrival energies was investigated. The atomic oxygen undercutting energy dependence predictions enable one to predict mass loss that would occur in low Earth orbit, based on lower energy ground laboratory atomic oxygen beam systems. Results of computational model prediction of undercut cavity size as a function of energy and defect size will be presented to provide insight into expected in-space mass loss of protected polymers with protective coating defects based on lower energy ground laboratory testing.

Risk prediction and aversion by anterior cingulate cortex.

PubMed

Brown, Joshua W; Braver, Todd S

2007-12-01

The recently proposed error-likelihood hypothesis suggests that anterior cingulate cortex (ACC) and surrounding areas will become active in proportion to the perceived likelihood of an error. The hypothesis was originally derived from a computational model prediction. The same computational model now makes a further prediction that ACC will be sensitive not only to predicted error likelihood, but also to the predicted magnitude of the consequences, should an error occur. The product of error likelihood and predicted error consequence magnitude collectively defines the general "expected risk" of a given behavior in a manner analogous but orthogonal to subjective expected utility theory. New fMRI results from an incentivechange signal task now replicate the error-likelihood effect, validate the further predictions of the computational model, and suggest why some segments of the population may fail to show an error-likelihood effect. In particular, error-likelihood effects and expected risk effects in general indicate greater sensitivity to earlier predictors of errors and are seen in risk-averse but not risk-tolerant individuals. Taken together, the results are consistent with an expected risk model of ACC and suggest that ACC may generally contribute to cognitive control by recruiting brain activity to avoid risk.

Review of numerical models to predict cooling tower performance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, B.M.; Nomura, K.K.; Bartz, J.A.

1987-01-01

Four state-of-the-art computer models developed to predict the thermal performance of evaporative cooling towers are summarized. The formulation of these models, STAR and TEFERI (developed in Europe) and FACTS and VERA2D (developed in the U.S.), is summarized. A fifth code, based on Merkel analysis, is also discussed. Principal features of the codes, computation time and storage requirements are described. A discussion of model validation is also provided.

Prediction of resource volumes at untested locations using simple local prediction models

USGS Publications Warehouse

Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.

2006-01-01

This paper shows how local spatial nonparametric prediction models can be applied to estimate volumes of recoverable gas resources at individual undrilled sites, at multiple sites on a regional scale, and to compute confidence bounds for regional volumes based on the distribution of those estimates. An approach that combines cross-validation, the jackknife, and bootstrap procedures is used to accomplish this task. Simulation experiments show that cross-validation can be applied beneficially to select an appropriate prediction model. The cross-validation procedure worked well for a wide range of different states of nature and levels of information. Jackknife procedures are used to compute individual prediction estimation errors at undrilled locations. The jackknife replicates also are used with a bootstrap resampling procedure to compute confidence bounds for the total volume. The method was applied to data (partitioned into a training set and target set) from the Devonian Antrim Shale continuous-type gas play in the Michigan Basin in Otsego County, Michigan. The analysis showed that the model estimate of total recoverable volumes at prediction sites is within 4 percent of the total observed volume. The model predictions also provide frequency distributions of the cell volumes at the production unit scale. Such distributions are the basis for subsequent economic analyses. ?? Springer Science+Business Media, LLC 2007.

A computer model of the pediatric circulatory system for testing pediatric assist devices.

PubMed

Giridharan, Guruprasad A; Koenig, Steven C; Mitchell, Michael; Gartner, Mark; Pantalos, George M

2007-01-01

Lumped parameter computer models of the pediatric circulatory systems for 1- and 4-year-olds were developed to predict hemodynamic responses to mechanical circulatory support devices. Model parameters, including resistance, compliance and volume, were adjusted to match hemodynamic pressure and flow waveforms, pressure-volume loops, percent systole, and heart rate of pediatric patients (n = 6) with normal ventricles. Left ventricular failure was modeled by adjusting the time-varying compliance curve of the left heart to produce aortic pressures and cardiac outputs consistent with those observed clinically. Models of pediatric continuous flow (CF) and pulsatile flow (PF) ventricular assist devices (VAD) and intraaortic balloon pump (IABP) were developed and integrated into the heart failure pediatric circulatory system models. Computer simulations were conducted to predict acute hemodynamic responses to PF and CF VAD operating at 50%, 75% and 100% support and 2.5 and 5 ml IABP operating at 1:1 and 1:2 support modes. The computer model of the pediatric circulation matched the human pediatric hemodynamic waveform morphology to within 90% and cardiac function parameters with 95% accuracy. The computer model predicted PF VAD and IABP restore aortic pressure pulsatility and variation in end-systolic and end-diastolic volume, but diminish with increasing CF VAD support.

Ocean Modeling and Visualization on Massively Parallel Computer

NASA Technical Reports Server (NTRS)

Chao, Yi; Li, P. Peggy; Wang, Ping; Katz, Daniel S.; Cheng, Benny N.

1997-01-01

Climate modeling is one of the grand challenges of computational science, and ocean modeling plays an important role in both understanding the current climatic conditions and predicting future climate change.

Predictive information processing in music cognition. A critical review.

PubMed

Rohrmeier, Martin A; Koelsch, Stefan

2012-02-01

Expectation and prediction constitute central mechanisms in the perception and cognition of music, which have been explored in theoretical and empirical accounts. We review the scope and limits of theoretical accounts of musical prediction with respect to feature-based and temporal prediction. While the concept of prediction is unproblematic for basic single-stream features such as melody, it is not straight-forward for polyphonic structures or higher-order features such as formal predictions. Behavioural results based on explicit and implicit (priming) paradigms provide evidence of priming in various domains that may reflect predictive behaviour. Computational learning models, including symbolic (fragment-based), probabilistic/graphical, or connectionist approaches, provide well-specified predictive models of specific features and feature combinations. While models match some experimental results, full-fledged music prediction cannot yet be modelled. Neuroscientific results regarding the early right-anterior negativity (ERAN) and mismatch negativity (MMN) reflect expectancy violations on different levels of processing complexity, and provide some neural evidence for different predictive mechanisms. At present, the combinations of neural and computational modelling methodologies are at early stages and require further research. Copyright © 2012 Elsevier B.V. All rights reserved.

Computational modeling of human oral bioavailability: what will be next?

PubMed

Cabrera-Pérez, Miguel Ángel; Pham-The, Hai

2018-06-01

The oral route is the most convenient way of administrating drugs. Therefore, accurate determination of oral bioavailability is paramount during drug discovery and development. Quantitative structure-property relationship (QSPR), rule-of-thumb (RoT) and physiologically based-pharmacokinetic (PBPK) approaches are promising alternatives to the early oral bioavailability prediction. Areas covered: The authors give insight into the factors affecting bioavailability, the fundamental theoretical framework and the practical aspects of computational methods for predicting this property. They also give their perspectives on future computational models for estimating oral bioavailability. Expert opinion: Oral bioavailability is a multi-factorial pharmacokinetic property with its accurate prediction challenging. For RoT and QSPR modeling, the reliability of datasets, the significance of molecular descriptor families and the diversity of chemometric tools used are important factors that define model predictability and interpretability. Likewise, for PBPK modeling the integrity of the pharmacokinetic data, the number of input parameters, the complexity of statistical analysis and the software packages used are relevant factors in bioavailability prediction. Although these approaches have been utilized independently, the tendency to use hybrid QSPR-PBPK approaches together with the exploration of ensemble and deep-learning systems for QSPR modeling of oral bioavailability has opened new avenues for development promising tools for oral bioavailability prediction.

Application of the aeroacoustic analogy to a shrouded, subsonic, radial fan

NASA Astrophysics Data System (ADS)

Buccieri, Bryan M.; Richards, Christopher M.

2016-12-01

A study was conducted to investigate the predictive capability of computational aeroacoustics with respect to a shrouded, subsonic, radial fan. A three dimensional unsteady fluid dynamics simulation was conducted to produce aerodynamic data used as the acoustic source for an aeroacoustics simulation. Two acoustic models were developed: one modeling the forces on the rotating fan blades as a set of rotating dipoles located at the center of mass of each fan blade and one modeling the forces on the stationary fan shroud as a field of distributed stationary dipoles. Predicted acoustic response was compared to experimental data measured at two operating speeds using three different outlet restrictions. The blade source model predicted overall far field sound power levels within 5 dB averaged over the six different operating conditions while the shroud model predicted overall far field sound power levels within 7 dB averaged over the same conditions. Doubling the density of the computational fluids mesh and using a scale adaptive simulation turbulence model increased broadband noise accuracy. However, computation time doubled and the accuracy of the overall sound power level prediction improved by only 1 dB.

Dissertation Defense Computational Fluid Dynamics Uncertainty Analysis for Payload Fairing Spacecraft Environmental Control Systems

NASA Technical Reports Server (NTRS)

Groves, Curtis Edward

2014-01-01

Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This paper describes an approach to quantify the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft without the use of test data. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional "validation by test only" mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions. Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computational Fluid Dynamics can be used to verify these requirements; however, the model must be validated by test data. This research includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available and open source solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT, STARCCM+, and OPENFOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid Dynamics model using the methodology found in "Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations". This method requires three separate grids and solutions, which quantify the error bars around Computational Fluid Dynamics predictions. The method accounts for all uncertainty terms from both numerical and input variables. Objective three is to compile a table of uncertainty parameters that could be used to estimate the error in a Computational Fluid Dynamics model of the Environmental Control System /spacecraft system. Previous studies have looked at the uncertainty in a Computational Fluid Dynamics model for a single output variable at a single point, for example the re-attachment length of a backward facing step. For the flow regime being analyzed (turbulent, three-dimensional, incompressible), the error at a single point can propagate into the solution both via flow physics and numerical methods. Calculating the uncertainty in using Computational Fluid Dynamics to accurately predict airflow speeds around encapsulated spacecraft in is imperative to the success of future missions.

Dissertation Defense: Computational Fluid Dynamics Uncertainty Analysis for Payload Fairing Spacecraft Environmental Control Systems

NASA Technical Reports Server (NTRS)

Groves, Curtis Edward

2014-01-01

Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This paper describes an approach to quantify the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft without the use of test data. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional validation by test only mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions.Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computational Fluid Dynamics can be used to verify these requirements; however, the model must be validated by test data. This research includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available and open source solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT, STARCCM+, and OPENFOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid Dynamics model using the methodology found in Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations. This method requires three separate grids and solutions, which quantify the error bars around Computational Fluid Dynamics predictions. The method accounts for all uncertainty terms from both numerical and input variables. Objective three is to compile a table of uncertainty parameters that could be used to estimate the error in a Computational Fluid Dynamics model of the Environmental Control System spacecraft system.Previous studies have looked at the uncertainty in a Computational Fluid Dynamics model for a single output variable at a single point, for example the re-attachment length of a backward facing step. For the flow regime being analyzed (turbulent, three-dimensional, incompressible), the error at a single point can propagate into the solution both via flow physics and numerical methods. Calculating the uncertainty in using Computational Fluid Dynamics to accurately predict airflow speeds around encapsulated spacecraft in is imperative to the success of future missions.

Computational Fluid Dynamics Uncertainty Analysis for Payload Fairing Spacecraft Environmental Control Systems

NASA Technical Reports Server (NTRS)

Groves, Curtis E.

2013-01-01

Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This proposal describes an approach to validate the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft. The research described here is absolutely cutting edge. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional"validation by test only'' mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions. Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computationaf Fluid Dynamics can be used to veritY these requirements; however, the model must be validated by test data. The proposed research project includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT and OPEN FOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid . . . Dynamics model using the methodology found in "Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations". This method requires three separate grids and solutions, which quantify the error bars around Computational Fluid Dynamics predictions. The method accounts for all uncertainty terms from both numerical and input variables. Objective three is to compile a table of uncertainty parameters that could be used to estimate the error in a Computational Fluid Dynamics model of the Environmental Control System /spacecraft system. Previous studies have looked at the uncertainty in a Computational Fluid Dynamics model for a single output variable at a single point, for example the re-attachment length of a backward facing step. To date, the author is the only person to look at the uncertainty in the entire computational domain. For the flow regime being analyzed (turbulent, threedimensional, incompressible), the error at a single point can propagate into the solution both via flow physics and numerical methods. Calculating the uncertainty in using Computational Fluid Dynamics to accurately predict airflow speeds around encapsulated spacecraft in is imperative to the success of future missions.

A computational cognitive model of self-efficacy and daily adherence in mHealth.

PubMed

Pirolli, Peter

2016-12-01

Mobile health (mHealth) applications provide an excellent opportunity for collecting rich, fine-grained data necessary for understanding and predicting day-to-day health behavior change dynamics. A computational predictive model (ACT-R-DStress) is presented and fit to individual daily adherence in 28-day mHealth exercise programs. The ACT-R-DStress model refines the psychological construct of self-efficacy. To explain and predict the dynamics of self-efficacy and predict individual performance of targeted behaviors, the self-efficacy construct is implemented as a theory-based neurocognitive simulation of the interaction of behavioral goals, memories of past experiences, and behavioral performance.

Computational modeling to predict mechanical function of joints: application to the lower leg with simulation of two cadaver studies.

PubMed

Liacouras, Peter C; Wayne, Jennifer S

2007-12-01

Computational models of musculoskeletal joints and limbs can provide useful information about joint mechanics. Validated models can be used as predictive devices for understanding joint function and serve as clinical tools for predicting the outcome of surgical procedures. A new computational modeling approach was developed for simulating joint kinematics that are dictated by bone/joint anatomy, ligamentous constraints, and applied loading. Three-dimensional computational models of the lower leg were created to illustrate the application of this new approach. Model development began with generating three-dimensional surfaces of each bone from CT images and then importing into the three-dimensional solid modeling software SOLIDWORKS and motion simulation package COSMOSMOTION. Through SOLIDWORKS and COSMOSMOTION, each bone surface file was filled to create a solid object and positioned necessary components added, and simulations executed. Three-dimensional contacts were added to inhibit intersection of the bones during motion. Ligaments were represented as linear springs. Model predictions were then validated by comparison to two different cadaver studies, syndesmotic injury and repair and ankle inversion following ligament transection. The syndesmotic injury model was able to predict tibial rotation, fibular rotation, and anterior/posterior displacement. In the inversion simulation, calcaneofibular ligament extension and angles of inversion compared well. Some experimental data proved harder to simulate accurately, due to certain software limitations and lack of complete experimental data. Other parameters that could not be easily obtained experimentally can be predicted and analyzed by the computational simulations. In the syndesmotic injury study, the force generated in the tibionavicular and calcaneofibular ligaments reduced with the insertion of the staple, indicating how this repair technique changes joint function. After transection of the calcaneofibular ligament in the inversion stability study, a major increase in force was seen in several of the ligaments on the lateral aspect of the foot and ankle, indicating the recruitment of other structures to permit function after injury. Overall, the computational models were able to predict joint kinematics of the lower leg with particular focus on the ankle complex. This same approach can be taken to create models of other limb segments such as the elbow and wrist. Additional parameters can be calculated in the models that are not easily obtained experimentally such as ligament forces, force transmission across joints, and three-dimensional movement of all bones. Muscle activation can be incorporated in the model through the action of applied forces within the software for future studies.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smith, Kandler A; Santhanagopalan, Shriram; Yang, Chuanbo

Computer models are helping to accelerate the design and validation of next generation batteries and provide valuable insights not possible through experimental testing alone. Validated 3-D physics-based models exist for predicting electrochemical performance, thermal and mechanical response of cells and packs under normal and abuse scenarios. The talk describes present efforts to make the models better suited for engineering design, including improving their computation speed, developing faster processes for model parameter identification including under aging, and predicting the performance of a proposed electrode material recipe a priori using microstructure models.

Method for evaluation of predictive models of microwave ablation via post-procedural clinical imaging

NASA Astrophysics Data System (ADS)

Collins, Jarrod A.; Brown, Daniel; Kingham, T. Peter; Jarnagin, William R.; Miga, Michael I.; Clements, Logan W.

2015-03-01

Development of a clinically accurate predictive model of microwave ablation (MWA) procedures would represent a significant advancement and facilitate an implementation of patient-specific treatment planning to achieve optimal probe placement and ablation outcomes. While studies have been performed to evaluate predictive models of MWA, the ability to quantify the performance of predictive models via clinical data has been limited to comparing geometric measurements of the predicted and actual ablation zones. The accuracy of placement, as determined by the degree of spatial overlap between ablation zones, has not been achieved. In order to overcome this limitation, a method of evaluation is proposed where the actual location of the MWA antenna is tracked and recorded during the procedure via a surgical navigation system. Predictive models of the MWA are then computed using the known position of the antenna within the preoperative image space. Two different predictive MWA models were used for the preliminary evaluation of the proposed method: (1) a geometric model based on the labeling associated with the ablation antenna and (2) a 3-D finite element method based computational model of MWA using COMSOL. Given the follow-up tomographic images that are acquired at approximately 30 days after the procedure, a 3-D surface model of the necrotic zone was generated to represent the true ablation zone. A quantification of the overlap between the predicted ablation zones and the true ablation zone was performed after a rigid registration was computed between the pre- and post-procedural tomograms. While both model show significant overlap with the true ablation zone, these preliminary results suggest a slightly higher degree of overlap with the geometric model.

A comprehensive pipeline for multi-resolution modeling of the mitral valve: Validation, computational efficiency, and predictive capability.

PubMed

Drach, Andrew; Khalighi, Amir H; Sacks, Michael S

2018-02-01

Multiple studies have demonstrated that the pathological geometries unique to each patient can affect the durability of mitral valve (MV) repairs. While computational modeling of the MV is a promising approach to improve the surgical outcomes, the complex MV geometry precludes use of simplified models. Moreover, the lack of complete in vivo geometric information presents significant challenges in the development of patient-specific computational models. There is thus a need to determine the level of detail necessary for predictive MV models. To address this issue, we have developed a novel pipeline for building attribute-rich computational models of MV with varying fidelity directly from the in vitro imaging data. The approach combines high-resolution geometric information from loaded and unloaded states to achieve a high level of anatomic detail, followed by mapping and parametric embedding of tissue attributes to build a high-resolution, attribute-rich computational models. Subsequent lower resolution models were then developed and evaluated by comparing the displacements and surface strains to those extracted from the imaging data. We then identified the critical levels of fidelity for building predictive MV models in the dilated and repaired states. We demonstrated that a model with a feature size of about 5 mm and mesh size of about 1 mm was sufficient to predict the overall MV shape, stress, and strain distributions with high accuracy. However, we also noted that more detailed models were found to be needed to simulate microstructural events. We conclude that the developed pipeline enables sufficiently complex models for biomechanical simulations of MV in normal, dilated, repaired states. Copyright © 2017 John Wiley & Sons, Ltd.

Computer Model Predicts the Movement of Dust

NASA Technical Reports Server (NTRS)

2002-01-01

A new computer model of the atmosphere can now actually pinpoint where global dust events come from, and can project where they're going. The model may help scientists better evaluate the impact of dust on human health, climate, ocean carbon cycles, ecosystems, and atmospheric chemistry. Also, by seeing where dust originates and where it blows people with respiratory problems can get advanced warning of approaching dust clouds. 'The model is physically more realistic than previous ones,' said Mian Chin, a co-author of the study and an Earth and atmospheric scientist at Georgia Tech and the Goddard Space Flight Center (GSFC) in Greenbelt, Md. 'It is able to reproduce the short term day-to-day variations and long term inter-annual variations of dust concentrations and distributions that are measured from field experiments and observed from satellites.' The above images show both aerosols measured from space (left) and the movement of aerosols predicted by computer model for the same date (right). For more information, read New Computer Model Tracks and Predicts Paths Of Earth's Dust Images courtesy Paul Giroux, Georgia Tech/NASA Goddard Space Flight Center

CALCULATION OF NONLINEAR CONFIDENCE AND PREDICTION INTERVALS FOR GROUND-WATER FLOW MODELS.

USGS Publications Warehouse

Cooley, Richard L.; Vecchia, Aldo V.

1987-01-01

A method is derived to efficiently compute nonlinear confidence and prediction intervals on any function of parameters derived as output from a mathematical model of a physical system. The method is applied to the problem of obtaining confidence and prediction intervals for manually-calibrated ground-water flow models. To obtain confidence and prediction intervals resulting from uncertainties in parameters, the calibrated model and information on extreme ranges and ordering of the model parameters within one or more independent groups are required. If random errors in the dependent variable are present in addition to uncertainties in parameters, then calculation of prediction intervals also requires information on the extreme range of error expected. A simple Monte Carlo method is used to compute the quantiles necessary to establish probability levels for the confidence and prediction intervals. Application of the method to a hypothetical example showed that inclusion of random errors in the dependent variable in addition to uncertainties in parameters can considerably widen the prediction intervals.

Modeling the effect of shroud contact and friction dampers on the mistuned response of turbopumps

NASA Technical Reports Server (NTRS)

Griffin, Jerry H.; Yang, M.-T.

1994-01-01

The contract has been revised. Under the revised scope of work a reduced order model has been developed that can be used to predict the steady-state response of mistuned bladed disks. The approach has been implemented in a computer code, LMCC. It is concluded that: the reduced order model displays structural fidelity comparable to that of a finite element model of an entire bladed disk system with significantly improved computational efficiency; and, when the disk is stiff, both the finite element model and LMCC predict significantly more amplitude variation than was predicted by earlier models. This second result may have important practical ramifications, especially in the case of integrally bladed disks.

Sound transmission in the chest under surface excitation - An experimental and computational study with diagnostic applications

PubMed Central

Peng, Ying; Dai, Zoujun; Mansy, Hansen A.; Sandler, Richard H.; Balk, Robert A; Royston, Thomas. J

2014-01-01

Chest physical examination often includes performing chest percussion, which involves introducing sound stimulus to the chest wall and detecting an audible change. This approach relies on observations that underlying acoustic transmission, coupling, and resonance patterns can be altered by chest structure changes due to pathologies. More accurate detection and quantification of these acoustic alterations may provide further useful diagnostic information. To elucidate the physical processes involved, a realistic computer model of sound transmission in the chest is helpful. In the present study, a computational model was developed and validated by comparing its predictions with results from animal and human experiments which involved applying acoustic excitation to the anterior chest while detecting skin vibrations at the posterior chest. To investigate the effect of pathology on sound transmission, the computational model was used to simulate the effects of pneumothorax on sounds introduced at the anterior chest and detected at the posterior. Model predictions and experimental results showed similar trends. The model also predicted wave patterns inside the chest, which may be used to assess results of elastography measurements. Future animal and human tests may expand the predictive power of the model to include acoustic behavior for a wider range of pulmonary conditions. PMID:25001497

Climate Ocean Modeling on Parallel Computers

NASA Technical Reports Server (NTRS)

Wang, P.; Cheng, B. N.; Chao, Y.

1998-01-01

Ocean modeling plays an important role in both understanding the current climatic conditions and predicting future climate change. However, modeling the ocean circulation at various spatial and temporal scales is a very challenging computational task.

In-silico wear prediction for knee replacements--methodology and corroboration.

PubMed

Strickland, M A; Taylor, M

2009-07-22

The capability to predict in-vivo wear of knee replacements is a valuable pre-clinical analysis tool for implant designers. Traditionally, time-consuming experimental tests provided the principal means of investigating wear. Today, computational models offer an alternative. However, the validity of these models has not been demonstrated across a range of designs and test conditions, and several different formulas are in contention for estimating wear rates, limiting confidence in the predictive power of these in-silico models. This study collates and retrospectively simulates a wide range of experimental wear tests using fast rigid-body computational models with extant wear prediction algorithms, to assess the performance of current in-silico wear prediction tools. The number of tests corroborated gives a broader, more general assessment of the performance of these wear-prediction tools, and provides better estimates of the wear 'constants' used in computational models. High-speed rigid-body modelling allows a range of alternative algorithms to be evaluated. Whilst most cross-shear (CS)-based models perform comparably, the 'A/A+B' wear model appears to offer the best predictive power amongst existing wear algorithms. However, the range and variability of experimental data leaves considerable uncertainty in the results. More experimental data with reduced variability and more detailed reporting of studies will be necessary to corroborate these models with greater confidence. With simulation times reduced to only a few minutes, these models are ideally suited to large-volume 'design of experiment' or probabilistic studies (which are essential if pre-clinical assessment tools are to begin addressing the degree of variation observed clinically and in explanted components).

Orbital and maxillofacial computer aided surgery: patient-specific finite element models to predict surgical outcomes.

PubMed

Luboz, Vincent; Chabanas, Matthieu; Swider, Pascal; Payan, Yohan

2005-08-01

This paper addresses an important issue raised for the clinical relevance of Computer-Assisted Surgical applications, namely the methodology used to automatically build patient-specific finite element (FE) models of anatomical structures. From this perspective, a method is proposed, based on a technique called the mesh-matching method, followed by a process that corrects mesh irregularities. The mesh-matching algorithm generates patient-specific volume meshes from an existing generic model. The mesh regularization process is based on the Jacobian matrix transform related to the FE reference element and the current element. This method for generating patient-specific FE models is first applied to computer-assisted maxillofacial surgery, and more precisely, to the FE elastic modelling of patient facial soft tissues. For each patient, the planned bone osteotomies (mandible, maxilla, chin) are used as boundary conditions to deform the FE face model, in order to predict the aesthetic outcome of the surgery. Seven FE patient-specific models were successfully generated by our method. For one patient, the prediction of the FE model is qualitatively compared with the patient's post-operative appearance, measured from a computer tomography scan. Then, our methodology is applied to computer-assisted orbital surgery. It is, therefore, evaluated for the generation of 11 patient-specific FE poroelastic models of the orbital soft tissues. These models are used to predict the consequences of the surgical decompression of the orbit. More precisely, an average law is extrapolated from the simulations carried out for each patient model. This law links the size of the osteotomy (i.e. the surgical gesture) and the backward displacement of the eyeball (the consequence of the surgical gesture).

Surface temperature distribution of GTA weld pools on thin-plate 304 stainless steel

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zacharia, T.; David, S.A.; Vitek, J.M.

1995-11-01

A transient multidimensional computational model was utilized to study gas tungsten arc (GTA) welding of thin-plate 304 stainless steel (SS). The model eliminates several of the earlier restrictive assumptions including temperature-independent thermal-physical properties. Consequently, all important thermal-physical properties were considered as temperature dependent throughout the range of temperatures experienced by the weld metal. The computational model was used to predict surface temperature distribution of the GTA weld pools in 1.5-mm-thick AISI 304 SS. The welding parameters were chosen so as to correspond with an earlier experimental study that produced high-resolution surface temperature maps. One of the motivations of the presentmore » study was to verify the predictive capability of the computational model. Comparison of the numerical predictions and experimental observations indicate excellent agreement, thereby verifying the model.« less

Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles.

PubMed

Lampa, Samuel; Alvarsson, Jonathan; Spjuth, Ola

2016-01-01

Predictive modelling in drug discovery is challenging to automate as it often contains multiple analysis steps and might involve cross-validation and parameter tuning that create complex dependencies between tasks. With large-scale data or when using computationally demanding modelling methods, e-infrastructures such as high-performance or cloud computing are required, adding to the existing challenges of fault-tolerant automation. Workflow management systems can aid in many of these challenges, but the currently available systems are lacking in the functionality needed to enable agile and flexible predictive modelling. We here present an approach inspired by elements of the flow-based programming paradigm, implemented as an extension of the Luigi system which we name SciLuigi. We also discuss the experiences from using the approach when modelling a large set of biochemical interactions using a shared computer cluster.Graphical abstract.

Predicting phenotype from genotype: Improving accuracy through more robust experimental and computational modeling

PubMed Central

Gallion, Jonathan; Koire, Amanda; Katsonis, Panagiotis; Schoenegge, Anne‐Marie; Bouvier, Michel

2017-01-01

Abstract Computational prediction yields efficient and scalable initial assessments of how variants of unknown significance may affect human health. However, when discrepancies between these predictions and direct experimental measurements of functional impact arise, inaccurate computational predictions are frequently assumed as the source. Here, we present a methodological analysis indicating that shortcomings in both computational and biological data can contribute to these disagreements. We demonstrate that incomplete assaying of multifunctional proteins can affect the strength of correlations between prediction and experiments; a variant's full impact on function is better quantified by considering multiple assays that probe an ensemble of protein functions. Additionally, many variants predictions are sensitive to protein alignment construction and can be customized to maximize relevance of predictions to a specific experimental question. We conclude that inconsistencies between computation and experiment can often be attributed to the fact that they do not test identical hypotheses. Aligning the design of the computational input with the design of the experimental output will require cooperation between computational and biological scientists, but will also lead to improved estimations of computational prediction accuracy and a better understanding of the genotype–phenotype relationship. PMID:28230923

Predicting phenotype from genotype: Improving accuracy through more robust experimental and computational modeling.

PubMed

Gallion, Jonathan; Koire, Amanda; Katsonis, Panagiotis; Schoenegge, Anne-Marie; Bouvier, Michel; Lichtarge, Olivier

2017-05-01

Computational prediction yields efficient and scalable initial assessments of how variants of unknown significance may affect human health. However, when discrepancies between these predictions and direct experimental measurements of functional impact arise, inaccurate computational predictions are frequently assumed as the source. Here, we present a methodological analysis indicating that shortcomings in both computational and biological data can contribute to these disagreements. We demonstrate that incomplete assaying of multifunctional proteins can affect the strength of correlations between prediction and experiments; a variant's full impact on function is better quantified by considering multiple assays that probe an ensemble of protein functions. Additionally, many variants predictions are sensitive to protein alignment construction and can be customized to maximize relevance of predictions to a specific experimental question. We conclude that inconsistencies between computation and experiment can often be attributed to the fact that they do not test identical hypotheses. Aligning the design of the computational input with the design of the experimental output will require cooperation between computational and biological scientists, but will also lead to improved estimations of computational prediction accuracy and a better understanding of the genotype-phenotype relationship. © 2017 The Authors. **Human Mutation published by Wiley Periodicals, Inc.

The origins of computer weather prediction and climate modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lynch, Peter

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. Amore » 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.« less

The origins of computer weather prediction and climate modeling

NASA Astrophysics Data System (ADS)

Lynch, Peter

2008-03-01

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.

Predictive models in urology.

PubMed

Cestari, Andrea

2013-01-01

Predictive modeling is emerging as an important knowledge-based technology in healthcare. The interest in the use of predictive modeling reflects advances on different fronts such as the availability of health information from increasingly complex databases and electronic health records, a better understanding of causal or statistical predictors of health, disease processes and multifactorial models of ill-health and developments in nonlinear computer models using artificial intelligence or neural networks. These new computer-based forms of modeling are increasingly able to establish technical credibility in clinical contexts. The current state of knowledge is still quite young in understanding the likely future direction of how this so-called 'machine intelligence' will evolve and therefore how current relatively sophisticated predictive models will evolve in response to improvements in technology, which is advancing along a wide front. Predictive models in urology are gaining progressive popularity not only for academic and scientific purposes but also into the clinical practice with the introduction of several nomograms dealing with the main fields of onco-urology.

Methodology for Uncertainty Analysis of Dynamic Computational Toxicology Models

EPA Science Inventory

The task of quantifying the uncertainty in both parameter estimates and model predictions has become more important with the increased use of dynamic computational toxicology models by the EPA. Dynamic toxicological models include physiologically-based pharmacokinetic (PBPK) mode...

Airport Noise Prediction Model -- MOD 7

DOT National Transportation Integrated Search

1978-07-01

The MOD 7 Airport Noise Prediction Model is fully operational. The language used is Fortran, and it has been run on several different computer systems. Its capabilities include prediction of noise levels for single parameter changes, for multiple cha...

Model-free and model-based reward prediction errors in EEG.

PubMed

Sambrook, Thomas D; Hardwick, Ben; Wills, Andy J; Goslin, Jeremy

2018-05-24

Learning theorists posit two reinforcement learning systems: model-free and model-based. Model-based learning incorporates knowledge about structure and contingencies in the world to assign candidate actions with an expected value. Model-free learning is ignorant of the world's structure; instead, actions hold a value based on prior reinforcement, with this value updated by expectancy violation in the form of a reward prediction error. Because they use such different learning mechanisms, it has been previously assumed that model-based and model-free learning are computationally dissociated in the brain. However, recent fMRI evidence suggests that the brain may compute reward prediction errors to both model-free and model-based estimates of value, signalling the possibility that these systems interact. Because of its poor temporal resolution, fMRI risks confounding reward prediction errors with other feedback-related neural activity. In the present study, EEG was used to show the presence of both model-based and model-free reward prediction errors and their place in a temporal sequence of events including state prediction errors and action value updates. This demonstration of model-based prediction errors questions a long-held assumption that model-free and model-based learning are dissociated in the brain. Copyright © 2018 Elsevier Inc. All rights reserved.

A computational substrate for incentive salience.

PubMed

McClure, Samuel M; Daw, Nathaniel D; Montague, P Read

2003-08-01

Theories of dopamine function are at a crossroads. Computational models derived from single-unit recordings capture changes in dopaminergic neuron firing rate as a prediction error signal. These models employ the prediction error signal in two roles: learning to predict future rewarding events and biasing action choice. Conversely, pharmacological inhibition or lesion of dopaminergic neuron function diminishes the ability of an animal to motivate behaviors directed at acquiring rewards. These lesion experiments have raised the possibility that dopamine release encodes a measure of the incentive value of a contemplated behavioral act. The most complete psychological idea that captures this notion frames the dopamine signal as carrying 'incentive salience'. On the surface, these two competing accounts of dopamine function seem incommensurate. To the contrary, we demonstrate that both of these functions can be captured in a single computational model of the involvement of dopamine in reward prediction for the purpose of reward seeking.

COSIM: A Finite-Difference Computer Model to Predict Ternary Concentration Profiles Associated with Oxidation and Interdiffusion of Overlay-Coated Substrates

NASA Technical Reports Server (NTRS)

Nesbitt, James A.

2000-01-01

A finite-difference computer program (COSIM) has been written which models the one-dimensional, diffusional transport associated with high-temperature oxidation and interdiffusion of overlay-coated substrates. The program predicts concentration profiles for up to three elements in the coating and substrate after various oxidation exposures. Surface recession due to solute loss is also predicted. Ternary cross terms and concentration-dependent diffusion coefficients are taken into account. The program also incorporates a previously-developed oxide growth and spalling model to simulate either isothermal or cyclic oxidation exposures. In addition to predicting concentration profiles after various oxidation exposures, the program can also be used to predict coating fife based on a concentration dependent failure criterion (e.g., surface solute content drops to two percent). The computer code, written in an extension of FORTRAN 77, employs numerous subroutines to make the program flexible and easily modifiable to other coating oxidation problems.

Agent-Based Multicellular Modeling for Predictive Toxicology

EPA Science Inventory

Biological modeling is a rapidly growing field that has benefited significantly from recent technological advances, expanding traditional methods with greater computing power, parameter-determination algorithms, and the development of novel computational approaches to modeling bi...

Development of a computer model for prediction of collision response of a railroad passenger car

DOT National Transportation Integrated Search

2002-04-23

The paper describes the development of a detailed finite element model that is capable of predicting the response of a rail passenger car to collision conditions. This model was developed to predict the car crush, the three-dimensional gross motions ...

Care 3 model overview and user's guide, first revision

NASA Technical Reports Server (NTRS)

Bavuso, S. J.; Petersen, P. L.

1985-01-01

A manual was written to introduce the CARE III (Computer-Aided Reliability Estimation) capability to reliability and design engineers who are interested in predicting the reliability of highly reliable fault-tolerant systems. It was also structured to serve as a quick-look reference manual for more experienced users. The guide covers CARE III modeling and reliability predictions for execution in the CDC CYber 170 series computers, DEC VAX-11/700 series computer, and most machines that compile ANSI Standard FORTRAN 77.

Image Discrimination Predictions of a Single Channel Model with Contrast Gain Control

NASA Technical Reports Server (NTRS)

Ahumada, Albert J., Jr.; Null, Cynthia H.

1995-01-01

Image discrimination models predict the number of just-noticeable-differences between two images. We report the predictions of a single channel model with contrast masking for a range of standard discrimination experiments. Despite its computational simplicity, this model has performed as well as a multiple channel model in an object detection task.

Aeroacoustic Prediction Codes

NASA Technical Reports Server (NTRS)

Gliebe, P; Mani, R.; Shin, H.; Mitchell, B.; Ashford, G.; Salamah, S.; Connell, S.; Huff, Dennis (Technical Monitor)

2000-01-01

This report describes work performed on Contract NAS3-27720AoI 13 as part of the NASA Advanced Subsonic Transport (AST) Noise Reduction Technology effort. Computer codes were developed to provide quantitative prediction, design, and analysis capability for several aircraft engine noise sources. The objective was to provide improved, physics-based tools for exploration of noise-reduction concepts and understanding of experimental results. Methods and codes focused on fan broadband and 'buzz saw' noise and on low-emissions combustor noise and compliment work done by other contractors under the NASA AST program to develop methods and codes for fan harmonic tone noise and jet noise. The methods and codes developed and reported herein employ a wide range of approaches, from the strictly empirical to the completely computational, with some being semiempirical analytical, and/or analytical/computational. Emphasis was on capturing the essential physics while still considering method or code utility as a practical design and analysis tool for everyday engineering use. Codes and prediction models were developed for: (1) an improved empirical correlation model for fan rotor exit flow mean and turbulence properties, for use in predicting broadband noise generated by rotor exit flow turbulence interaction with downstream stator vanes: (2) fan broadband noise models for rotor and stator/turbulence interaction sources including 3D effects, noncompact-source effects. directivity modeling, and extensions to the rotor supersonic tip-speed regime; (3) fan multiple-pure-tone in-duct sound pressure prediction methodology based on computational fluid dynamics (CFD) analysis; and (4) low-emissions combustor prediction methodology and computer code based on CFD and actuator disk theory. In addition. the relative importance of dipole and quadrupole source mechanisms was studied using direct CFD source computation for a simple cascadeigust interaction problem, and an empirical combustor-noise correlation model was developed from engine acoustic test results. This work provided several insights on potential approaches to reducing aircraft engine noise. Code development is described in this report, and those insights are discussed.

Reynolds-Averaged Navier-Stokes Analysis of Zero Efflux Flow Control over a Hump Model

NASA Technical Reports Server (NTRS)

Rumsey, Christopher L.

2006-01-01

The unsteady flow over a hump model with zero efflux oscillatory flow control is modeled computationally using the unsteady Reynolds-averaged Navier-Stokes equations. Three different turbulence models produce similar results, and do a reasonably good job predicting the general character of the unsteady surface pressure coefficients during the forced cycle. However, the turbulent shear stresses are underpredicted in magnitude inside the separation bubble, and the computed results predict too large a (mean) separation bubble compared with experiment. These missed predictions are consistent with earlier steady-state results using no-flow-control and steady suction, from a 2004 CFD validation workshop for synthetic jets.

Reynolds-Averaged Navier-Stokes Analysis of Zero Efflux Flow Control Over a Hump Model

NASA Technical Reports Server (NTRS)

Rumsey, Christopher L.

2006-01-01

The unsteady flow over a hump model with zero efflux oscillatory flow control is modeled computationally using the unsteady Reynolds-averaged Navier-Stokes equations. Three different turbulence models produce similar results, and do a reasonably good job predicting the general character of the unsteady surface pressure coefficients during the forced cycle. However, the turbulent shear stresses are underpredicted in magnitude inside the separation bubble, and the computed results predict too large a (mean) separation bubble compared with experiment. These missed predictions are consistent with earlier steady-state results using no-flow-control and steady suction, from a 2004 CFD validation workshop for synthetic jets.

Predicting Bone Mechanical State During Recovery After Long-Duration Skeletal Unloading Using QCT and Finite Element Modeling

NASA Technical Reports Server (NTRS)

Chang, Katarina L.; Pennline, James A.

2013-01-01

During long-duration missions at the International Space Station, astronauts experience weightlessness leading to skeletal unloading. Unloading causes a lack of a mechanical stimulus that triggers bone cellular units to remove mass from the skeleton. A mathematical system of the cellular dynamics predicts theoretical changes to volume fractions and ash fraction in response to temporal variations in skeletal loading. No current model uses image technology to gather information about a skeletal site s initial properties to calculate bone remodeling changes and then to compare predicted bone strengths with the initial strength. The goal of this study is to use quantitative computed tomography (QCT) in conjunction with a computational model of the bone remodeling process to establish initial bone properties to predict changes in bone mechanics during bone loss and recovery with finite element (FE) modeling. Input parameters for the remodeling model include bone volume fraction and ash fraction, which are both computed from the QCT images. A non-destructive approach to measure ash fraction is also derived. Voxel-based finite element models (FEM) created from QCTs provide initial evaluation of bone strength. Bone volume fraction and ash fraction outputs from the computational model predict changes to the elastic modulus of bone via a two-parameter equation. The modulus captures the effect of bone remodeling and functions as the key to evaluate of changes in strength. Application of this time-dependent modulus to FEMs and composite beam theory enables an assessment of bone mechanics during recovery. Prediction of bone strength is not only important for astronauts, but is also pertinent to millions of patients with osteoporosis and low bone density.

A CFD Study on the Prediction of Cyclone Collection Efficiency

NASA Astrophysics Data System (ADS)

Gimbun, Jolius; Chuah, T. G.; Choong, Thomas S. Y.; Fakhru'L-Razi, A.

2005-09-01

This work presents a Computational Fluid Dynamics calculation to predict and to evaluate the effects of temperature, operating pressure and inlet velocity on the collection efficiency of gas cyclones. The numerical solutions were carried out using spreadsheet and commercial CFD code FLUENT 6.0. This paper also reviews four empirical models for the prediction of cyclone collection efficiency, namely Lapple [1], Koch and Licht [2], Li and Wang [3], and Iozia and Leith [4]. All the predictions proved to be satisfactory when compared with the presented experimental data. The CFD simulations predict the cyclone cut-off size for all operating conditions with a deviation of 3.7% from the experimental data. Specifically, results obtained from the computer modelling exercise have demonstrated that CFD model is the best method of modelling the cyclones collection efficiency.

Prediction of the Thermal Conductivity of Refrigerants by Computational Methods and Artificial Neural Network.

PubMed

Ghaderi, Forouzan; Ghaderi, Amir H; Ghaderi, Noushin; Najafi, Bijan

2017-01-01

Background: The thermal conductivity of fluids can be calculated by several computational methods. However, these methods are reliable only at the confined levels of density, and there is no specific computational method for calculating thermal conductivity in the wide ranges of density. Methods: In this paper, two methods, an Artificial Neural Network (ANN) approach and a computational method established upon the Rainwater-Friend theory, were used to predict the value of thermal conductivity in all ranges of density. The thermal conductivity of six refrigerants, R12, R14, R32, R115, R143, and R152 was predicted by these methods and the effectiveness of models was specified and compared. Results: The results show that the computational method is a usable method for predicting thermal conductivity at low levels of density. However, the efficiency of this model is considerably reduced in the mid-range of density. It means that this model cannot be used at density levels which are higher than 6. On the other hand, the ANN approach is a reliable method for thermal conductivity prediction in all ranges of density. The best accuracy of ANN is achieved when the number of units is increased in the hidden layer. Conclusion: The results of the computational method indicate that the regular dependence between thermal conductivity and density at higher densities is eliminated. It can develop a nonlinear problem. Therefore, analytical approaches are not able to predict thermal conductivity in wide ranges of density. Instead, a nonlinear approach such as, ANN is a valuable method for this purpose.

Prediction of the Thermal Conductivity of Refrigerants by Computational Methods and Artificial Neural Network

PubMed Central

Ghaderi, Forouzan; Ghaderi, Amir H.; Ghaderi, Noushin; Najafi, Bijan

2017-01-01

Background: The thermal conductivity of fluids can be calculated by several computational methods. However, these methods are reliable only at the confined levels of density, and there is no specific computational method for calculating thermal conductivity in the wide ranges of density. Methods: In this paper, two methods, an Artificial Neural Network (ANN) approach and a computational method established upon the Rainwater-Friend theory, were used to predict the value of thermal conductivity in all ranges of density. The thermal conductivity of six refrigerants, R12, R14, R32, R115, R143, and R152 was predicted by these methods and the effectiveness of models was specified and compared. Results: The results show that the computational method is a usable method for predicting thermal conductivity at low levels of density. However, the efficiency of this model is considerably reduced in the mid-range of density. It means that this model cannot be used at density levels which are higher than 6. On the other hand, the ANN approach is a reliable method for thermal conductivity prediction in all ranges of density. The best accuracy of ANN is achieved when the number of units is increased in the hidden layer. Conclusion: The results of the computational method indicate that the regular dependence between thermal conductivity and density at higher densities is eliminated. It can develop a nonlinear problem. Therefore, analytical approaches are not able to predict thermal conductivity in wide ranges of density. Instead, a nonlinear approach such as, ANN is a valuable method for this purpose. PMID:29188217

Predicting solar radiation based on available weather indicators

NASA Astrophysics Data System (ADS)

Sauer, Frank Joseph

Solar radiation prediction models are complex and require software that is not available for the household investor. The processing power within a normal desktop or laptop computer is sufficient to calculate similar models. This barrier to entry for the average consumer can be fixed by a model simple enough to be calculated by hand if necessary. Solar radiation modeling has been historically difficult to predict and accurate models have significant assumptions and restrictions on their use. Previous methods have been limited to linear relationships, location restrictions, or input data limits to one atmospheric condition. This research takes a novel approach by combining two techniques within the computational limits of a household computer; Clustering and Hidden Markov Models (HMMs). Clustering helps limit the large observation space which restricts the use of HMMs. Instead of using continuous data, and requiring significantly increased computations, the cluster can be used as a qualitative descriptor of each observation. HMMs incorporate a level of uncertainty and take into account the indirect relationship between meteorological indicators and solar radiation. This reduces the complexity of the model enough to be simply understood and accessible to the average household investor. The solar radiation is considered to be an unobservable state that each household will be unable to measure. The high temperature and the sky coverage are already available through the local or preferred source of weather information. By using the next day's prediction for high temperature and sky coverage, the model groups the data and then predicts the most likely range of radiation. This model uses simple techniques and calculations to give a broad estimate for the solar radiation when no other universal model exists for the average household.

Computer prediction of insecticide efficacy for western spruce budworm and Douglas-fir tussock moth

Treesearch

Jacqueline L. Robertson; Molly W. Stock

1986-01-01

A generalized interactive computer model that simulates and predicts insecticide efficacy, over seasonal development of western spruce budworm and Douglas-fir tussock moth, is described. This model can be used for any insecticide for which the user has laboratory-based concentration-response data. The program has four options, is written in BASIC, and can be operated...

Pinatubo global cooling on target

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kerr, R.A.

1993-01-29

When Pinatubo blasted millions of tons of debris into the stratosphere in June 1991, Hansen of NASA's Goddard Institute for Space Studies used his computer climate model to predict that the shade cost by the debris would cool the globe by about half a degree C. Year end temperature reports for 1992 are now showing that the prediction was on target-confirming the tentative belief that volcanos can temporarily cool the climate and validating at least one component of the computer models predicting a greenhouse warming.

External validation of a prediction model for surgical site infection after thoracolumbar spine surgery in a Western European cohort.

PubMed

Janssen, Daniël M C; van Kuijk, Sander M J; d'Aumerie, Boudewijn B; Willems, Paul C

2018-05-16

A prediction model for surgical site infection (SSI) after spine surgery was developed in 2014 by Lee et al. This model was developed to compute an individual estimate of the probability of SSI after spine surgery based on the patient's comorbidity profile and invasiveness of surgery. Before any prediction model can be validly implemented in daily medical practice, it should be externally validated to assess how the prediction model performs in patients sampled independently from the derivation cohort. We included 898 consecutive patients who underwent instrumented thoracolumbar spine surgery. To quantify overall performance using Nagelkerke's R 2 statistic, the discriminative ability was quantified as the area under the receiver operating characteristic curve (AUC). We computed the calibration slope of the calibration plot, to judge prediction accuracy. Sixty patients developed an SSI. The overall performance of the prediction model in our population was poor: Nagelkerke's R 2 was 0.01. The AUC was 0.61 (95% confidence interval (CI) 0.54-0.68). The estimated slope of the calibration plot was 0.52. The previously published prediction model showed poor performance in our academic external validation cohort. To predict SSI after instrumented thoracolumbar spine surgery for the present population, a better fitting prediction model should be developed.

Impact of implementation choices on quantitative predictions of cell-based computational models

NASA Astrophysics Data System (ADS)

Kursawe, Jochen; Baker, Ruth E.; Fletcher, Alexander G.

2017-09-01

'Cell-based' models provide a powerful computational tool for studying the mechanisms underlying the growth and dynamics of biological tissues in health and disease. An increasing amount of quantitative data with cellular resolution has paved the way for the quantitative parameterisation and validation of such models. However, the numerical implementation of cell-based models remains challenging, and little work has been done to understand to what extent implementation choices may influence model predictions. Here, we consider the numerical implementation of a popular class of cell-based models called vertex models, which are often used to study epithelial tissues. In two-dimensional vertex models, a tissue is approximated as a tessellation of polygons and the vertices of these polygons move due to mechanical forces originating from the cells. Such models have been used extensively to study the mechanical regulation of tissue topology in the literature. Here, we analyse how the model predictions may be affected by numerical parameters, such as the size of the time step, and non-physical model parameters, such as length thresholds for cell rearrangement. We find that vertex positions and summary statistics are sensitive to several of these implementation parameters. For example, the predicted tissue size decreases with decreasing cell cycle durations, and cell rearrangement may be suppressed by large time steps. These findings are counter-intuitive and illustrate that model predictions need to be thoroughly analysed and implementation details carefully considered when applying cell-based computational models in a quantitative setting.

Evaluation of MM5 model resolution when applied to prediction of national fire danger rating indexes

Treesearch

Jeanne L. Hoadley; Miriam L. Rorig; Larry Bradshaw; Sue A. Ferguson; Kenneth J. Westrick; Scott L. Goodrick; Paul Werth

2006-01-01

Weather predictions from the MM5 mesoscale model were used to compute gridded predictions of National Fire Danger Rating System (NFDRS) indexes. The model output was applied to a case study of the 2000 fire season in Northern Idaho and Western Montana to simulate an extreme event. To determine the preferred resolution for automating NFD RS predictions, model...

Comparison of measured ozone in southeastern Virginia with computer predictions from a photochemical model

NASA Technical Reports Server (NTRS)

Wakelyn, N. T.; Gregory, G. L.

1980-01-01

Data for one day of the 1977 southeastern Virginia urban plume study are compared with computer predictions from a traveling air parcel model using a contemporary photochemical mechanism with a minimal description of nonmethane hydrocarbon (NMHC) constitution and chemistry. With measured initial NOx and O3 concentrations and a current separate estimate of urban source loading input to the model, and for a variation of initial NMHC over a reasonable range, an ozone increase over the day is predicted from the photochemical simulation which is consistent with the flight path averaged airborne data.

Predicting visual semantic descriptive terms from radiological image data: preliminary results with liver lesions in CT.

PubMed

Depeursinge, Adrien; Kurtz, Camille; Beaulieu, Christopher; Napel, Sandy; Rubin, Daniel

2014-08-01

We describe a framework to model visual semantics of liver lesions in CT images in order to predict the visual semantic terms (VST) reported by radiologists in describing these lesions. Computational models of VST are learned from image data using linear combinations of high-order steerable Riesz wavelets and support vector machines (SVM). In a first step, these models are used to predict the presence of each semantic term that describes liver lesions. In a second step, the distances between all VST models are calculated to establish a nonhierarchical computationally-derived ontology of VST containing inter-term synonymy and complementarity. A preliminary evaluation of the proposed framework was carried out using 74 liver lesions annotated with a set of 18 VSTs from the RadLex ontology. A leave-one-patient-out cross-validation resulted in an average area under the ROC curve of 0.853 for predicting the presence of each VST. The proposed framework is expected to foster human-computer synergies for the interpretation of radiological images while using rotation-covariant computational models of VSTs to 1) quantify their local likelihood and 2) explicitly link them with pixel-based image content in the context of a given imaging domain.

A human ether-á-go-go-related (hERG) ion channel atomistic model generated by long supercomputer molecular dynamics simulations and its use in predicting drug cardiotoxicity.

PubMed

Anwar-Mohamed, Anwar; Barakat, Khaled H; Bhat, Rakesh; Noskov, Sergei Y; Tyrrell, D Lorne; Tuszynski, Jack A; Houghton, Michael

2014-11-04

Acquired cardiac long QT syndrome (LQTS) is a frequent drug-induced toxic event that is often caused through blocking of the human ether-á-go-go-related (hERG) K(+) ion channel. This has led to the removal of several major drugs post-approval and is a frequent cause of termination of clinical trials. We report here a computational atomistic model derived using long molecular dynamics that allows sensitive prediction of hERG blockage. It identified drug-mediated hERG blocking activity of a test panel of 18 compounds with high sensitivity and specificity and was experimentally validated using hERG binding assays and patch clamp electrophysiological assays. The model discriminates between potent, weak, and non-hERG blockers and is superior to previous computational methods. This computational model serves as a powerful new tool to predict hERG blocking thus rendering drug development safer and more efficient. As an example, we show that a drug that was halted recently in clinical development because of severe cardiotoxicity is a potent inhibitor of hERG in two different biological assays which could have been predicted using our new computational model. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Reinforcement learning in depression: A review of computational research.

PubMed

Chen, Chong; Takahashi, Taiki; Nakagawa, Shin; Inoue, Takeshi; Kusumi, Ichiro

2015-08-01

Despite being considered primarily a mood disorder, major depressive disorder (MDD) is characterized by cognitive and decision making deficits. Recent research has employed computational models of reinforcement learning (RL) to address these deficits. The computational approach has the advantage in making explicit predictions about learning and behavior, specifying the process parameters of RL, differentiating between model-free and model-based RL, and the computational model-based functional magnetic resonance imaging and electroencephalography. With these merits there has been an emerging field of computational psychiatry and here we review specific studies that focused on MDD. Considerable evidence suggests that MDD is associated with impaired brain signals of reward prediction error and expected value ('wanting'), decreased reward sensitivity ('liking') and/or learning (be it model-free or model-based), etc., although the causality remains unclear. These parameters may serve as valuable intermediate phenotypes of MDD, linking general clinical symptoms to underlying molecular dysfunctions. We believe future computational research at clinical, systems, and cellular/molecular/genetic levels will propel us toward a better understanding of the disease. Copyright © 2015 Elsevier Ltd. All rights reserved.

Status of Computational Aerodynamic Modeling Tools for Aircraft Loss-of-Control

NASA Technical Reports Server (NTRS)

Frink, Neal T.; Murphy, Patrick C.; Atkins, Harold L.; Viken, Sally A.; Petrilli, Justin L.; Gopalarathnam, Ashok; Paul, Ryan C.

2016-01-01

A concerted effort has been underway over the past several years to evolve computational capabilities for modeling aircraft loss-of-control under the NASA Aviation Safety Program. A principal goal has been to develop reliable computational tools for predicting and analyzing the non-linear stability & control characteristics of aircraft near stall boundaries affecting safe flight, and for utilizing those predictions for creating augmented flight simulation models that improve pilot training. Pursuing such an ambitious task with limited resources required the forging of close collaborative relationships with a diverse body of computational aerodynamicists and flight simulation experts to leverage their respective research efforts into the creation of NASA tools to meet this goal. Considerable progress has been made and work remains to be done. This paper summarizes the status of the NASA effort to establish computational capabilities for modeling aircraft loss-of-control and offers recommendations for future work.

Plans and Example Results for the 2nd AIAA Aeroelastic Prediction Workshop

NASA Technical Reports Server (NTRS)

Heeg, Jennifer; Chwalowski, Pawel; Schuster, David M.; Raveh, Daniella; Jirasek, Adam; Dalenbring, Mats

2015-01-01

This paper summarizes the plans for the second AIAA Aeroelastic Prediction Workshop. The workshop is designed to assess the state-of-the-art of computational methods for predicting unsteady flow fields and aeroelastic response. The goals are to provide an impartial forum to evaluate the effectiveness of existing computer codes and modeling techniques, and to identify computational and experimental areas needing additional research and development. This paper provides guidelines and instructions for participants including the computational aerodynamic model, the structural dynamic properties, the experimental comparison data and the expected output data from simulations. The Benchmark Supercritical Wing (BSCW) has been chosen as the configuration for this workshop. The analyses to be performed will include aeroelastic flutter solutions of the wing mounted on a pitch-and-plunge apparatus.

An improved shuffled frog leaping algorithm based evolutionary framework for currency exchange rate prediction

NASA Astrophysics Data System (ADS)

Dash, Rajashree

2017-11-01

Forecasting purchasing power of one currency with respect to another currency is always an interesting topic in the field of financial time series prediction. Despite the existence of several traditional and computational models for currency exchange rate forecasting, there is always a need for developing simpler and more efficient model, which will produce better prediction capability. In this paper, an evolutionary framework is proposed by using an improved shuffled frog leaping (ISFL) algorithm with a computationally efficient functional link artificial neural network (CEFLANN) for prediction of currency exchange rate. The model is validated by observing the monthly prediction measures obtained for three currency exchange data sets such as USD/CAD, USD/CHF, and USD/JPY accumulated within same period of time. The model performance is also compared with two other evolutionary learning techniques such as Shuffled frog leaping algorithm and Particle Swarm optimization algorithm. Practical analysis of results suggest that, the proposed model developed using the ISFL algorithm with CEFLANN network is a promising predictor model for currency exchange rate prediction compared to other models included in the study.

Principal Component Analysis in Construction of 3D Human Knee Joint Models Using a Statistical Shape Model Method

PubMed Central

Tsai, Tsung-Yuan; Li, Jing-Sheng; Wang, Shaobai; Li, Pingyue; Kwon, Young-Min; Li, Guoan

2013-01-01

The statistical shape model (SSM) method that uses 2D images of the knee joint to predict the 3D joint surface model has been reported in literature. In this study, we constructed a SSM database using 152 human CT knee joint models, including the femur, tibia and patella and analyzed the characteristics of each principal component of the SSM. The surface models of two in vivo knees were predicted using the SSM and their 2D bi-plane fluoroscopic images. The predicted models were compared to their CT joint models. The differences between the predicted 3D knee joint surfaces and the CT image-based surfaces were 0.30 ± 0.81 mm, 0.34 ± 0.79 mm and 0.36 ± 0.59 mm for the femur, tibia and patella, respectively (average ± standard deviation). The computational time for each bone of the knee joint was within 30 seconds using a personal computer. The analysis of this study indicated that the SSM method could be a useful tool to construct 3D surface models of the knee with sub-millimeter accuracy in real time. Thus it may have a broad application in computer assisted knee surgeries that require 3D surface models of the knee. PMID:24156375

Predicting operator workload during system design

NASA Technical Reports Server (NTRS)

Aldrich, Theodore B.; Szabo, Sandra M.

1988-01-01

A workload prediction methodology was developed in response to the need to measure workloads associated with operation of advanced aircraft. The application of the methodology will involve: (1) conducting mission/task analyses of critical mission segments and assigning estimates of workload for the sensory, cognitive, and psychomotor workload components of each task identified; (2) developing computer-based workload prediction models using the task analysis data; and (3) exercising the computer models to produce predictions of crew workload under varying automation and/or crew configurations. Critical issues include reliability and validity of workload predictors and selection of appropriate criterion measures.

Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates.

PubMed

LeDell, Erin; Petersen, Maya; van der Laan, Mark

In binary classification problems, the area under the ROC curve (AUC) is commonly used to evaluate the performance of a prediction model. Often, it is combined with cross-validation in order to assess how the results will generalize to an independent data set. In order to evaluate the quality of an estimate for cross-validated AUC, we obtain an estimate of its variance. For massive data sets, the process of generating a single performance estimate can be computationally expensive. Additionally, when using a complex prediction method, the process of cross-validating a predictive model on even a relatively small data set can still require a large amount of computation time. Thus, in many practical settings, the bootstrap is a computationally intractable approach to variance estimation. As an alternative to the bootstrap, we demonstrate a computationally efficient influence curve based approach to obtaining a variance estimate for cross-validated AUC.

Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates

PubMed Central

Petersen, Maya; van der Laan, Mark

2015-01-01

In binary classification problems, the area under the ROC curve (AUC) is commonly used to evaluate the performance of a prediction model. Often, it is combined with cross-validation in order to assess how the results will generalize to an independent data set. In order to evaluate the quality of an estimate for cross-validated AUC, we obtain an estimate of its variance. For massive data sets, the process of generating a single performance estimate can be computationally expensive. Additionally, when using a complex prediction method, the process of cross-validating a predictive model on even a relatively small data set can still require a large amount of computation time. Thus, in many practical settings, the bootstrap is a computationally intractable approach to variance estimation. As an alternative to the bootstrap, we demonstrate a computationally efficient influence curve based approach to obtaining a variance estimate for cross-validated AUC. PMID:26279737

Computer models for predicting the probability of violating CO air quality standards : the model SIMCO.

DOT National Transportation Integrated Search

1982-01-01

This report presents the user instructions and data requirements for SIMCO, a combined simulation and probability computer model developed to quantify and evaluate carbon monoxide in roadside environments. The model permits direct determinations of t...

Computer program for Stirling engine performance calculations

NASA Technical Reports Server (NTRS)

Tew, R. C., Jr.

1983-01-01

The thermodynamic characteristics of the Stirling engine were analyzed and modeled on a computer to support its development as a possible alternative to the automobile spark ignition engine. The computer model is documented. The documentation includes a user's manual, symbols list, a test case, comparison of model predictions with test results, and a description of the analytical equations used in the model.

Prediction of Patient-Controlled Analgesic Consumption: A Multimodel Regression Tree Approach.

PubMed

Hu, Yuh-Jyh; Ku, Tien-Hsiung; Yang, Yu-Hung; Shen, Jia-Ying

2018-01-01

Several factors contribute to individual variability in postoperative pain, therefore, individuals consume postoperative analgesics at different rates. Although many statistical studies have analyzed postoperative pain and analgesic consumption, most have identified only the correlation and have not subjected the statistical model to further tests in order to evaluate its predictive accuracy. In this study involving 3052 patients, a multistrategy computational approach was developed for analgesic consumption prediction. This approach uses data on patient-controlled analgesia demand behavior over time and combines clustering, classification, and regression to mitigate the limitations of current statistical models. Cross-validation results indicated that the proposed approach significantly outperforms various existing regression methods. Moreover, a comparison between the predictions by anesthesiologists and medical specialists and those of the computational approach for an independent test data set of 60 patients further evidenced the superiority of the computational approach in predicting analgesic consumption because it produced markedly lower root mean squared errors.

Agricultural soil moisture experiment, Colby, Kansas 1978: Measured and predicted hydrological properties of the soil

NASA Technical Reports Server (NTRS)

Arya, L. M. (Principal Investigator)

1980-01-01

Predictive procedures for developing soil hydrologic properties (i.e., relationships of soil water pressure and hydraulic conductivity to soil water content) are presented. Three models of the soil water pressure-water content relationship and one model of the hydraulic conductivity-water content relationship are discussed. Input requirements for the models are indicated, and computational procedures are outlined. Computed hydrologic properties for Keith silt loam, a soil typer near Colby, Kansas, on which the 1978 Agricultural Soil Moisture Experiment was conducted, are presented. A comparison of computed results with experimental data in the dry range shows that analytical models utilizing a few basic hydrophysical parameters can produce satisfactory data for large-scale applications.

Applicability Analysis of Validation Evidence for Biomedical Computational Models

DOE PAGES

Pathmanathan, Pras; Gray, Richard A.; Romero, Vicente J.; ...

2017-09-07

Computational modeling has the potential to revolutionize medicine the way it transformed engineering. However, despite decades of work, there has only been limited progress to successfully translate modeling research to patient care. One major difficulty which often occurs with biomedical computational models is an inability to perform validation in a setting that closely resembles how the model will be used. For example, for a biomedical model that makes in vivo clinically relevant predictions, direct validation of predictions may be impossible for ethical, technological, or financial reasons. Unavoidable limitations inherent to the validation process lead to challenges in evaluating the credibilitymore » of biomedical model predictions. Therefore, when evaluating biomedical models, it is critical to rigorously assess applicability, that is, the relevance of the computational model, and its validation evidence to the proposed context of use (COU). However, there are no well-established methods for assessing applicability. In this paper, we present a novel framework for performing applicability analysis and demonstrate its use with a medical device computational model. The framework provides a systematic, step-by-step method for breaking down the broad question of applicability into a series of focused questions, which may be addressed using supporting evidence and subject matter expertise. The framework can be used for model justification, model assessment, and validation planning. While motivated by biomedical models, it is relevant to a broad range of disciplines and underlying physics. Finally, the proposed applicability framework could help overcome some of the barriers inherent to validation of, and aid clinical implementation of, biomedical models.« less

Applicability Analysis of Validation Evidence for Biomedical Computational Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pathmanathan, Pras; Gray, Richard A.; Romero, Vicente J.

Computational modeling has the potential to revolutionize medicine the way it transformed engineering. However, despite decades of work, there has only been limited progress to successfully translate modeling research to patient care. One major difficulty which often occurs with biomedical computational models is an inability to perform validation in a setting that closely resembles how the model will be used. For example, for a biomedical model that makes in vivo clinically relevant predictions, direct validation of predictions may be impossible for ethical, technological, or financial reasons. Unavoidable limitations inherent to the validation process lead to challenges in evaluating the credibilitymore » of biomedical model predictions. Therefore, when evaluating biomedical models, it is critical to rigorously assess applicability, that is, the relevance of the computational model, and its validation evidence to the proposed context of use (COU). However, there are no well-established methods for assessing applicability. In this paper, we present a novel framework for performing applicability analysis and demonstrate its use with a medical device computational model. The framework provides a systematic, step-by-step method for breaking down the broad question of applicability into a series of focused questions, which may be addressed using supporting evidence and subject matter expertise. The framework can be used for model justification, model assessment, and validation planning. While motivated by biomedical models, it is relevant to a broad range of disciplines and underlying physics. Finally, the proposed applicability framework could help overcome some of the barriers inherent to validation of, and aid clinical implementation of, biomedical models.« less

A-Priori Tuning of Modified Magnussen Combustion Model

NASA Technical Reports Server (NTRS)

Norris, A. T.

2016-01-01

In the application of CFD to turbulent reacting flows, one of the main limitations to predictive accuracy is the chemistry model. Using a full or skeletal kinetics model may provide good predictive ability, however, at considerable computational cost. Adding the ability to account for the interaction between turbulence and chemistry improves the overall fidelity of a simulation but adds to this cost. An alternative is the use of simple models, such as the Magnussen model, which has negligible computational overhead, but lacks general predictive ability except for cases that can be tuned to the flow being solved. In this paper, a technique will be described that allows the tuning of the Magnussen model for an arbitrary fuel and flow geometry without the need to have experimental data for that particular case. The tuning is based on comparing the results of the Magnussen model and full finite-rate chemistry when applied to perfectly and partially stirred reactor simulations. In addition, a modification to the Magnussen model is proposed that allows the upper kinetic limit for the reaction rate to be set, giving better physical agreement with full kinetic mechanisms. This procedure allows a simple reacting model to be used in a predictive manner, and affords significant savings in computational costs for simulations.

Prediction of pressure drop in fluid tuned mounts using analytical and computational techniques

NASA Technical Reports Server (NTRS)

Lasher, William C.; Khalilollahi, Amir; Mischler, John; Uhric, Tom

1993-01-01

A simplified model for predicting pressure drop in fluid tuned isolator mounts was developed. The model is based on an exact solution to the Navier-Stokes equations and was made more general through the use of empirical coefficients. The values of these coefficients were determined by numerical simulation of the flow using the commercial computational fluid dynamics (CFD) package FIDAP.

Navier-Stokes and Comprehensive Analysis Performance Predictions of the NREL Phase VI Experiment

NASA Technical Reports Server (NTRS)

Duque, Earl P. N.; Burklund, Michael D.; Johnson, Wayne

2003-01-01

A vortex lattice code, CAMRAD II, and a Reynolds-Averaged Navier-Stoke code, OVERFLOW-D2, were used to predict the aerodynamic performance of a two-bladed horizontal axis wind turbine. All computations were compared with experimental data that was collected at the NASA Ames Research Center 80- by 120-Foot Wind Tunnel. Computations were performed for both axial as well as yawed operating conditions. Various stall delay models and dynamics stall models were used by the CAMRAD II code. Comparisons between the experimental data and computed aerodynamic loads show that the OVERFLOW-D2 code can accurately predict the power and spanwise loading of a wind turbine rotor.

Bayesian Mapping Reveals That Attention Boosts Neural Responses to Predicted and Unpredicted Stimuli.

PubMed

Garrido, Marta I; Rowe, Elise G; Halász, Veronika; Mattingley, Jason B

2018-05-01

Predictive coding posits that the human brain continually monitors the environment for regularities and detects inconsistencies. It is unclear, however, what effect attention has on expectation processes, as there have been relatively few studies and the results of these have yielded contradictory findings. Here, we employed Bayesian model comparison to adjudicate between 2 alternative computational models. The "Opposition" model states that attention boosts neural responses equally to predicted and unpredicted stimuli, whereas the "Interaction" model assumes that attentional boosting of neural signals depends on the level of predictability. We designed a novel, audiospatial attention task that orthogonally manipulated attention and prediction by playing oddball sequences in either the attended or unattended ear. We observed sensory prediction error responses, with electroencephalography, across all attentional manipulations. Crucially, posterior probability maps revealed that, overall, the Opposition model better explained scalp and source data, suggesting that attention boosts responses to predicted and unpredicted stimuli equally. Furthermore, Dynamic Causal Modeling showed that these Opposition effects were expressed in plastic changes within the mismatch negativity network. Our findings provide empirical evidence for a computational model of the opposing interplay of attention and expectation in the brain.

Identifying influential data points in hydrological model calibration and their impact on streamflow predictions

NASA Astrophysics Data System (ADS)

Wright, David; Thyer, Mark; Westra, Seth

2015-04-01

Highly influential data points are those that have a disproportionately large impact on model performance, parameters and predictions. However, in current hydrological modelling practice the relative influence of individual data points on hydrological model calibration is not commonly evaluated. This presentation illustrates and evaluates several influence diagnostics tools that hydrological modellers can use to assess the relative influence of data. The feasibility and importance of including influence detection diagnostics as a standard tool in hydrological model calibration is discussed. Two classes of influence diagnostics are evaluated: (1) computationally demanding numerical "case deletion" diagnostics; and (2) computationally efficient analytical diagnostics, based on Cook's distance. These diagnostics are compared against hydrologically orientated diagnostics that describe changes in the model parameters (measured through the Mahalanobis distance), performance (objective function displacement) and predictions (mean and maximum streamflow). These influence diagnostics are applied to two case studies: a stage/discharge rating curve model, and a conceptual rainfall-runoff model (GR4J). Removing a single data point from the calibration resulted in differences to mean flow predictions of up to 6% for the rating curve model, and differences to mean and maximum flow predictions of up to 10% and 17%, respectively, for the hydrological model. When using the Nash-Sutcliffe efficiency in calibration, the computationally cheaper Cook's distance metrics produce similar results to the case-deletion metrics at a fraction of the computational cost. However, Cooks distance is adapted from linear regression with inherit assumptions on the data and is therefore less flexible than case deletion. Influential point detection diagnostics show great potential to improve current hydrological modelling practices by identifying highly influential data points. The findings of this study establish the feasibility and importance of including influential point detection diagnostics as a standard tool in hydrological model calibration. They provide the hydrologist with important information on whether model calibration is susceptible to a small number of highly influent data points. This enables the hydrologist to make a more informed decision of whether to (1) remove/retain the calibration data; (2) adjust the calibration strategy and/or hydrological model to reduce the susceptibility of model predictions to a small number of influential observations.

Computing organic stereoselectivity - from concepts to quantitative calculations and predictions.

PubMed

Peng, Qian; Duarte, Fernanda; Paton, Robert S

2016-11-07

Advances in theory and processing power have established computation as a valuable interpretative and predictive tool in the discovery of new asymmetric catalysts. This tutorial review outlines the theory and practice of modeling stereoselective reactions. Recent examples illustrate how an understanding of the fundamental principles and the application of state-of-the-art computational methods may be used to gain mechanistic insight into organic and organometallic reactions. We highlight the emerging potential of this computational tool-box in providing meaningful predictions for the rational design of asymmetric catalysts. We present an accessible account of the field to encourage future synergy between computation and experiment.

A thermal NO(x) prediction model - Scalar computation module for CFD codes with fluid and kinetic effects

NASA Technical Reports Server (NTRS)

Mcbeath, Giorgio; Ghorashi, Bahman; Chun, Kue

1993-01-01

A thermal NO(x) prediction model is developed to interface with a CFD, k-epsilon based code. A converged solution from the CFD code is the input to the postprocessing model for prediction of thermal NO(x). The model uses a decoupled analysis to estimate the equilibrium level of (NO(x))e which is the constant rate limit. This value is used to estimate the flame (NO(x)) and in turn predict the rate of formation at each node using a two-step Zeldovich mechanism. The rate is fixed on the NO(x) production rate plot by estimating the time to reach equilibrium by a differential analysis based on the reaction: O + N2 = NO + N. The rate is integrated in the nonequilibrium time space based on the residence time at each node in the computational domain. The sum of all nodal predictions yields the total NO(x) level.

Computations of Flow over a Hump Model Using Higher Order Method with Turbulence Modeling

NASA Technical Reports Server (NTRS)

Balakumar, P.

2005-01-01

Turbulent separated flow over a two-dimensional hump is computed by solving the RANS equations with k - omega (SST) turbulence model for the baseline, steady suction and oscillatory blowing/suction flow control cases. The flow equations and the turbulent model equations are solved using a fifth-order accurate weighted essentially. nonoscillatory (WENO) scheme for space discretization and a third order, total variation diminishing (TVD) Runge-Kutta scheme for time integration. Qualitatively the computed pressure distributions exhibit the same behavior as those observed in the experiments. The computed separation regions are much longer than those observed experimentally. However, the percentage reduction in the separation region in the steady suction case is closer to what was measured in the experiment. The computations did not predict the expected reduction in the separation length in the oscillatory case. The predicted turbulent quantities are two to three times smaller than the measured values pointing towards the deficiencies in the existing turbulent models when they are applied to strong steady/unsteady separated flows.

An experimental and numerical investigation of shock-wave induced turbulent boundary-layer separation at hypersonic speeds

NASA Technical Reports Server (NTRS)

Marvin, J. G.; Horstman, C. C.; Rubesin, M. W.; Coakley, T. J.; Kussoy, M. I.

1975-01-01

An experiment designed to test and guide computations of the interaction of an impinging shock wave with a turbulent boundary layer is described. Detailed mean flow-field and surface data are presented for two shock strengths which resulted in attached and separated flows, respectively. Numerical computations, employing the complete time-averaged Navier-Stokes equations along with algebraic eddy-viscosity and turbulent Prandtl number models to describe shear stress and heat flux, are used to illustrate the dependence of the computations on the particulars of the turbulence models. Models appropriate for zero-pressure-gradient flows predicted the overall features of the flow fields, but were deficient in predicting many of the details of the interaction regions. Improvements to the turbulence model parameters were sought through a combination of detailed data analysis and computer simulations which tested the sensitivity of the solutions to model parameter changes. Computer simulations using these improvements are presented and discussed.

Prediction of 5-year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods.

PubMed

Obrzut, Bogdan; Kusy, Maciej; Semczuk, Andrzej; Obrzut, Marzanna; Kluska, Jacek

2017-12-12

Computational intelligence methods, including non-linear classification algorithms, can be used in medical research and practice as a decision making tool. This study aimed to evaluate the usefulness of artificial intelligence models for 5-year overall survival prediction in patients with cervical cancer treated by radical hysterectomy. The data set was collected from 102 patients with cervical cancer FIGO stage IA2-IIB, that underwent primary surgical treatment. Twenty-three demographic, tumor-related parameters and selected perioperative data of each patient were collected. The simulations involved six computational intelligence methods: the probabilistic neural network (PNN), multilayer perceptron network, gene expression programming classifier, support vector machines algorithm, radial basis function neural network and k-Means algorithm. The prediction ability of the models was determined based on the accuracy, sensitivity, specificity, as well as the area under the receiver operating characteristic curve. The results of the computational intelligence methods were compared with the results of linear regression analysis as a reference model. The best results were obtained by the PNN model. This neural network provided very high prediction ability with an accuracy of 0.892 and sensitivity of 0.975. The area under the receiver operating characteristics curve of PNN was also high, 0.818. The outcomes obtained by other classifiers were markedly worse. The PNN model is an effective tool for predicting 5-year overall survival in cervical cancer patients treated with radical hysterectomy.

ASME V\\&V challenge problem: Surrogate-based V&V

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beghini, Lauren L.; Hough, Patricia D.

2015-12-18

The process of verification and validation can be resource intensive. From the computational model perspective, the resource demand typically arises from long simulation run times on multiple cores coupled with the need to characterize and propagate uncertainties. In addition, predictive computations performed for safety and reliability analyses have similar resource requirements. For this reason, there is a tradeoff between the time required to complete the requisite studies and the fidelity or accuracy of the results that can be obtained. At a high level, our approach is cast within a validation hierarchy that provides a framework in which we perform sensitivitymore » analysis, model calibration, model validation, and prediction. The evidence gathered as part of these activities is mapped into the Predictive Capability Maturity Model to assess credibility of the model used for the reliability predictions. With regard to specific technical aspects of our analysis, we employ surrogate-based methods, primarily based on polynomial chaos expansions and Gaussian processes, for model calibration, sensitivity analysis, and uncertainty quantification in order to reduce the number of simulations that must be done. The goal is to tip the tradeoff balance to improving accuracy without increasing the computational demands.« less

Computational Modeling of Space Physiology for Informing Spaceflight Countermeasure Design and Predictions of Efficacy

NASA Technical Reports Server (NTRS)

Lewandowski, B. E.; DeWitt, J. K.; Gallo, C. A.; Gilkey, K. M.; Godfrey, A. P.; Humphreys, B. T.; Jagodnik, K. M.; Kassemi, M.; Myers, J. G.; Nelson, E. S.;

A Bayesian approach for parameter estimation and prediction using a computationally intensive model

DOE PAGES

Higdon, Dave; McDonnell, Jordan D.; Schunck, Nicolas; ...

2015-02-05

Bayesian methods have been successful in quantifying uncertainty in physics-based problems in parameter estimation and prediction. In these cases, physical measurements y are modeled as the best fit of a physics-based modelmore » $$\\eta (\\theta )$$, where θ denotes the uncertain, best input setting. Hence the statistical model is of the form $$y=\\eta (\\theta )+\\epsilon ,$$ where $$\\epsilon $$ accounts for measurement, and possibly other, error sources. When nonlinearity is present in $$\\eta (\\cdot )$$, the resulting posterior distribution for the unknown parameters in the Bayesian formulation is typically complex and nonstandard, requiring computationally demanding computational approaches such as Markov chain Monte Carlo (MCMC) to produce multivariate draws from the posterior. Although generally applicable, MCMC requires thousands (or even millions) of evaluations of the physics model $$\\eta (\\cdot )$$. This requirement is problematic if the model takes hours or days to evaluate. To overcome this computational bottleneck, we present an approach adapted from Bayesian model calibration. This approach combines output from an ensemble of computational model runs with physical measurements, within a statistical formulation, to carry out inference. A key component of this approach is a statistical response surface, or emulator, estimated from the ensemble of model runs. We demonstrate this approach with a case study in estimating parameters for a density functional theory model, using experimental mass/binding energy measurements from a collection of atomic nuclei. Lastly, we also demonstrate how this approach produces uncertainties in predictions for recent mass measurements obtained at Argonne National Laboratory.« less

Using Computational Simulations to Confront Students' Mental Models

ERIC Educational Resources Information Center

Rodrigues, R.; Carvalho, P. Simeão

2014-01-01

In this paper we show an example of how to use a computational simulation to obtain visual feedback for students' mental models, and compare their predictions with the simulated system's behaviour. Additionally, we use the computational simulation to incrementally modify the students' mental models in order to accommodate new data,…

First Order Fire Effects Model: FOFEM 4.0, user's guide

Treesearch

Elizabeth D. Reinhardt; Robert E. Keane; James K. Brown

1997-01-01

A First Order Fire Effects Model (FOFEM) was developed to predict the direct consequences of prescribed fire and wildfire. FOFEM computes duff and woody fuel consumption, smoke production, and fire-caused tree mortality for most forest and rangeland types in the United States. The model is available as a computer program for PC or Data General computer.

A computationally efficient modelling of laminar separation bubbles

NASA Technical Reports Server (NTRS)

Dini, Paolo; Maughmer, Mark D.

1989-01-01

The goal is to accurately predict the characteristics of the laminar separation bubble and its effects on airfoil performance. Toward this end, a computational model of the separation bubble was developed and incorporated into the Eppler and Somers airfoil design and analysis program. Thus far, the focus of the research was limited to the development of a model which can accurately predict situations in which the interaction between the bubble and the inviscid velocity distribution is weak, the so-called short bubble. A summary of the research performed in the past nine months is presented. The bubble model in its present form is then described. Lastly, the performance of this model in predicting bubble characteristics is shown for a few cases.

Biomechanical Model for Computing Deformations for Whole-Body Image Registration: A Meshless Approach

PubMed Central

Li, Mao; Miller, Karol; Joldes, Grand Roman; Kikinis, Ron; Wittek, Adam

2016-01-01

Patient-specific biomechanical models have been advocated as a tool for predicting deformations of soft body organs/tissue for medical image registration (aligning two sets of images) when differences between the images are large. However, complex and irregular geometry of the body organs makes generation of patient-specific biomechanical models very time consuming. Meshless discretisation has been proposed to solve this challenge. However, applications so far have been limited to 2-D models and computing single organ deformations. In this study, 3-D comprehensive patient-specific non-linear biomechanical models implemented using Meshless Total Lagrangian Explicit Dynamics (MTLED) algorithms are applied to predict a 3-D deformation field for whole-body image registration. Unlike a conventional approach which requires dividing (segmenting) the image into non-overlapping constituents representing different organs/tissues, the mechanical properties are assigned using the Fuzzy C-Means (FCM) algorithm without the image segmentation. Verification indicates that the deformations predicted using the proposed meshless approach are for practical purposes the same as those obtained using the previously validated finite element models. To quantitatively evaluate the accuracy of the predicted deformations, we determined the spatial misalignment between the registered (i.e. source images warped using the predicted deformations) and target images by computing the edge-based Hausdorff distance. The Hausdorff distance-based evaluation determines that our meshless models led to successful registration of the vast majority of the image features. PMID:26791945

Novel prediction model of renal function after nephrectomy from automated renal volumetry with preoperative multidetector computed tomography (MDCT).

PubMed

Isotani, Shuji; Shimoyama, Hirofumi; Yokota, Isao; Noma, Yasuhiro; Kitamura, Kousuke; China, Toshiyuki; Saito, Keisuke; Hisasue, Shin-ichi; Ide, Hisamitsu; Muto, Satoru; Yamaguchi, Raizo; Ukimura, Osamu; Gill, Inderbir S; Horie, Shigeo

2015-10-01

The predictive model of postoperative renal function may impact on planning nephrectomy. To develop the novel predictive model using combination of clinical indices with computer volumetry to measure the preserved renal cortex volume (RCV) using multidetector computed tomography (MDCT), and to prospectively validate performance of the model. Total 60 patients undergoing radical nephrectomy from 2011 to 2013 participated, including a development cohort of 39 patients and an external validation cohort of 21 patients. RCV was calculated by voxel count using software (Vincent, FUJIFILM). Renal function before and after radical nephrectomy was assessed via the estimated glomerular filtration rate (eGFR). Factors affecting postoperative eGFR were examined by regression analysis to develop the novel model for predicting postoperative eGFR with a backward elimination method. The predictive model was externally validated and the performance of the model was compared with that of the previously reported models. The postoperative eGFR value was associated with age, preoperative eGFR, preserved renal parenchymal volume (RPV), preserved RCV, % of RPV alteration, and % of RCV alteration (p < 0.01). The significant correlated variables for %eGFR alteration were %RCV preservation (r = 0.58, p < 0.01) and %RPV preservation (r = 0.54, p < 0.01). We developed our regression model as follows: postoperative eGFR = 57.87 - 0.55(age) - 15.01(body surface area) + 0.30(preoperative eGFR) + 52.92(%RCV preservation). Strong correlation was seen between postoperative eGFR and the calculated estimation model (r = 0.83; p < 0.001). The external validation cohort (n = 21) showed our model outperformed previously reported models. Combining MDCT renal volumetry and clinical indices might yield an important tool for predicting postoperative renal function.

Special Issue: Big data and predictive computational modeling

NASA Astrophysics Data System (ADS)

Koutsourelakis, P. S.; Zabaras, N.; Girolami, M.

2016-09-01

The motivation for this special issue stems from the symposium on "Big Data and Predictive Computational Modeling" that took place at the Institute for Advanced Study, Technical University of Munich, during May 18-21, 2015. With a mindset firmly grounded in computational discovery, but a polychromatic set of viewpoints, several leading scientists, from physics and chemistry, biology, engineering, applied mathematics, scientific computing, neuroscience, statistics and machine learning, engaged in discussions and exchanged ideas for four days. This special issue contains a subset of the presentations. Video and slides of all the presentations are available on the TUM-IAS website http://www.tum-ias.de/bigdata2015/.

Predicting Flow Reversals in a Computational Fluid Dynamics Simulated Thermosyphon Using Data Assimilation.

PubMed

Reagan, Andrew J; Dubief, Yves; Dodds, Peter Sheridan; Danforth, Christopher M

2016-01-01

A thermal convection loop is a annular chamber filled with water, heated on the bottom half and cooled on the top half. With sufficiently large forcing of heat, the direction of fluid flow in the loop oscillates chaotically, dynamics analogous to the Earth's weather. As is the case for state-of-the-art weather models, we only observe the statistics over a small region of state space, making prediction difficult. To overcome this challenge, data assimilation (DA) methods, and specifically ensemble methods, use the computational model itself to estimate the uncertainty of the model to optimally combine these observations into an initial condition for predicting the future state. Here, we build and verify four distinct DA methods, and then, we perform a twin model experiment with the computational fluid dynamics simulation of the loop using the Ensemble Transform Kalman Filter (ETKF) to assimilate observations and predict flow reversals. We show that using adaptively shaped localized covariance outperforms static localized covariance with the ETKF, and allows for the use of less observations in predicting flow reversals. We also show that a Dynamic Mode Decomposition (DMD) of the temperature and velocity fields recovers the low dimensional system underlying reversals, finding specific modes which together are predictive of reversal direction.

Predicting Flow Reversals in a Computational Fluid Dynamics Simulated Thermosyphon Using Data Assimilation

PubMed Central

Reagan, Andrew J.; Dubief, Yves; Dodds, Peter Sheridan; Danforth, Christopher M.

2016-01-01

A thermal convection loop is a annular chamber filled with water, heated on the bottom half and cooled on the top half. With sufficiently large forcing of heat, the direction of fluid flow in the loop oscillates chaotically, dynamics analogous to the Earth’s weather. As is the case for state-of-the-art weather models, we only observe the statistics over a small region of state space, making prediction difficult. To overcome this challenge, data assimilation (DA) methods, and specifically ensemble methods, use the computational model itself to estimate the uncertainty of the model to optimally combine these observations into an initial condition for predicting the future state. Here, we build and verify four distinct DA methods, and then, we perform a twin model experiment with the computational fluid dynamics simulation of the loop using the Ensemble Transform Kalman Filter (ETKF) to assimilate observations and predict flow reversals. We show that using adaptively shaped localized covariance outperforms static localized covariance with the ETKF, and allows for the use of less observations in predicting flow reversals. We also show that a Dynamic Mode Decomposition (DMD) of the temperature and velocity fields recovers the low dimensional system underlying reversals, finding specific modes which together are predictive of reversal direction. PMID:26849061

Real-time stylistic prediction for whole-body human motions.

PubMed

Matsubara, Takamitsu; Hyon, Sang-Ho; Morimoto, Jun

2012-01-01

The ability to predict human motion is crucial in several contexts such as human tracking by computer vision and the synthesis of human-like computer graphics. Previous work has focused on off-line processes with well-segmented data; however, many applications such as robotics require real-time control with efficient computation. In this paper, we propose a novel approach called real-time stylistic prediction for whole-body human motions to satisfy these requirements. This approach uses a novel generative model to represent a whole-body human motion including rhythmic motion (e.g., walking) and discrete motion (e.g., jumping). The generative model is composed of a low-dimensional state (phase) dynamics and a two-factor observation model, allowing it to capture the diversity of motion styles in humans. A real-time adaptation algorithm was derived to estimate both state variables and style parameter of the model from non-stationary unlabeled sequential observations. Moreover, with a simple modification, the algorithm allows real-time adaptation even from incomplete (partial) observations. Based on the estimated state and style, a future motion sequence can be accurately predicted. In our implementation, it takes less than 15 ms for both adaptation and prediction at each observation. Our real-time stylistic prediction was evaluated for human walking, running, and jumping behaviors. Copyright © 2011 Elsevier Ltd. All rights reserved.

Improve SSME power balance model

NASA Technical Reports Server (NTRS)

Karr, Gerald R.

1992-01-01

Effort was dedicated to development and testing of a formal strategy for reconciling uncertain test data with physically limited computational prediction. Specific weaknesses in the logical structure of the current Power Balance Model (PBM) version are described with emphasis given to the main routing subroutines BAL and DATRED. Selected results from a variational analysis of PBM predictions are compared to Technology Test Bed (TTB) variational study results to assess PBM predictive capability. The motivation for systematic integration of uncertain test data with computational predictions based on limited physical models is provided. The theoretical foundation for the reconciliation strategy developed in this effort is presented, and results of a reconciliation analysis of the Space Shuttle Main Engine (SSME) high pressure fuel side turbopump subsystem are examined.

Towards feasible and effective predictive wavefront control for adaptive optics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Poyneer, L A; Veran, J

We have recently proposed Predictive Fourier Control, a computationally efficient and adaptive algorithm for predictive wavefront control that assumes frozen flow turbulence. We summarize refinements to the state-space model that allow operation with arbitrary computational delays and reduce the computational cost of solving for new control. We present initial atmospheric characterization using observations with Gemini North's Altair AO system. These observations, taken over 1 year, indicate that frozen flow is exists, contains substantial power, and is strongly detected 94% of the time.

Quantifying the predictive consequences of model error with linear subspace analysis

USGS Publications Warehouse

White, Jeremy T.; Doherty, John E.; Hughes, Joseph D.

2014-01-01

All computer models are simplified and imperfect simulators of complex natural systems. The discrepancy arising from simplification induces bias in model predictions, which may be amplified by the process of model calibration. This paper presents a new method to identify and quantify the predictive consequences of calibrating a simplified computer model. The method is based on linear theory, and it scales efficiently to the large numbers of parameters and observations characteristic of groundwater and petroleum reservoir models. The method is applied to a range of predictions made with a synthetic integrated surface-water/groundwater model with thousands of parameters. Several different observation processing strategies and parameterization/regularization approaches are examined in detail, including use of the Karhunen-Loève parameter transformation. Predictive bias arising from model error is shown to be prediction specific and often invisible to the modeler. The amount of calibration-induced bias is influenced by several factors, including how expert knowledge is applied in the design of parameterization schemes, the number of parameters adjusted during calibration, how observations and model-generated counterparts are processed, and the level of fit with observations achieved through calibration. Failure to properly implement any of these factors in a prediction-specific manner may increase the potential for predictive bias in ways that are not visible to the calibration and uncertainty analysis process.

Extracting falsifiable predictions from sloppy models.

PubMed

Gutenkunst, Ryan N; Casey, Fergal P; Waterfall, Joshua J; Myers, Christopher R; Sethna, James P

2007-12-01

Successful predictions are among the most compelling validations of any model. Extracting falsifiable predictions from nonlinear multiparameter models is complicated by the fact that such models are commonly sloppy, possessing sensitivities to different parameter combinations that range over many decades. Here we discuss how sloppiness affects the sorts of data that best constrain model predictions, makes linear uncertainty approximations dangerous, and introduces computational difficulties in Monte-Carlo uncertainty analysis. We also present a useful test problem and suggest refinements to the standards by which models are communicated.

Using 3D infrared imaging to calibrate and refine computational fluid dynamic modeling for large computer and data centers

NASA Astrophysics Data System (ADS)

Stockton, Gregory R.

2011-05-01

Over the last 10 years, very large government, military, and commercial computer and data center operators have spent millions of dollars trying to optimally cool data centers as each rack has begun to consume as much as 10 times more power than just a few years ago. In fact, the maximum amount of data computation in a computer center is becoming limited by the amount of available power, space and cooling capacity at some data centers. Tens of millions of dollars and megawatts of power are being annually spent to keep data centers cool. The cooling and air flows dynamically change away from any predicted 3-D computational fluid dynamic modeling during construction and as time goes by, and the efficiency and effectiveness of the actual cooling rapidly departs even farther from predicted models. By using 3-D infrared (IR) thermal mapping and other techniques to calibrate and refine the computational fluid dynamic modeling and make appropriate corrections and repairs, the required power for data centers can be dramatically reduced which reduces costs and also improves reliability.

QSAR Methods.

PubMed

Gini, Giuseppina

2016-01-01

In this chapter, we introduce the basis of computational chemistry and discuss how computational methods have been extended to some biological properties and toxicology, in particular. Since about 20 years, chemical experimentation is more and more replaced by modeling and virtual experimentation, using a large core of mathematics, chemistry, physics, and algorithms. Then we see how animal experiments, aimed at providing a standardized result about a biological property, can be mimicked by new in silico methods. Our emphasis here is on toxicology and on predicting properties through chemical structures. Two main streams of such models are available: models that consider the whole molecular structure to predict a value, namely QSAR (Quantitative Structure Activity Relationships), and models that find relevant substructures to predict a class, namely SAR. The term in silico discovery is applied to chemical design, to computational toxicology, and to drug discovery. We discuss how the experimental practice in biological science is moving more and more toward modeling and simulation. Such virtual experiments confirm hypotheses, provide data for regulation, and help in designing new chemicals.

Computational substrates of social value in interpersonal collaboration.

PubMed

Fareri, Dominic S; Chang, Luke J; Delgado, Mauricio R

2015-05-27

Decisions to engage in collaborative interactions require enduring considerable risk, yet provide the foundation for building and maintaining relationships. Here, we investigate the mechanisms underlying this process and test a computational model of social value to predict collaborative decision making. Twenty-six participants played an iterated trust game and chose to invest more frequently with their friends compared with a confederate or computer despite equal reinforcement rates. This behavior was predicted by our model, which posits that people receive a social value reward signal from reciprocation of collaborative decisions conditional on the closeness of the relationship. This social value signal was associated with increased activity in the ventral striatum and medial prefrontal cortex, which significantly predicted the reward parameters from the social value model. Therefore, we demonstrate that the computation of social value drives collaborative behavior in repeated interactions and provide a mechanistic account of reward circuit function instantiating this process. Copyright © 2015 the authors 0270-6474/15/358170-11$15.00/0.

A Geometric Model for Specularity Prediction on Planar Surfaces with Multiple Light Sources.

PubMed

Morgand, Alexandre; Tamaazousti, Mohamed; Bartoli, Adrien

2018-05-01

Specularities are often problematic in computer vision since they impact the dynamic range of the image intensity. A natural approach would be to predict and discard them using computer graphics models. However, these models depend on parameters which are difficult to estimate (light sources, objects' material properties and camera). We present a geometric model called JOLIMAS: JOint LIght-MAterial Specularity, which predicts the shape of specularities. JOLIMAS is reconstructed from images of specularities observed on a planar surface. It implicitly includes light and material properties, which are intrinsic to specularities. This model was motivated by the observation that specularities have a conic shape on planar surfaces. The conic shape is obtained by projecting a fixed quadric on the planar surface. JOLIMAS thus predicts the specularity using a simple geometric approach with static parameters (object material and light source shape). It is adapted to indoor light sources such as light bulbs and fluorescent lamps. The prediction has been tested on synthetic and real sequences. It works in a multi-light context by reconstructing a quadric for each light source with special cases such as lights being switched on or off. We also used specularity prediction for dynamic retexturing and obtained convincing rendering results. Further results are presented as supplementary video material, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TVCG.2017.2677445.

Perspective - Systematic study of Reynolds stress closure models in the computations of plane channel flows

NASA Technical Reports Server (NTRS)

Demuren, A. O.; Sarkar, S.

1993-01-01

The roles of pressure-strain and turbulent diffusion models in the numerical calculation of turbulent plane channel flows with second-moment closure models are investigated. Three turbulent diffusion and five pressure-strain models are utilized in the computations. The main characteristics of the mean flow and the turbulent fields are compared against experimental data. All the features of the mean flow are correctly predicted by all but one of the Reynolds stress closure models. The Reynolds stress anisotropies in the log layer are predicted to varying degrees of accuracy (good to fair) by the models. None of the models could predict correctly the extent of relaxation towards isotropy in the wake region near the center of the channel. Results from the directional numerical simulation are used to further clarify this behavior of the models.

Systematic study of Reynolds stress closure models in the computations of plane channel flows

NASA Technical Reports Server (NTRS)

Demuren, A. O.; Sarkar, S.

1992-01-01

The roles of pressure-strain and turbulent diffusion models in the numerical calculation of turbulent plane channel flows with second-moment closure models are investigated. Three turbulent diffusion and five pressure-strain models are utilized in the computations. The main characteristics of the mean flow and the turbulent fields are compared against experimental data. All the features of the mean flow are correctly predicted by all but one of the Reynolds stress closure models. The Reynolds stress anisotropies in the log layer are predicted to varying degrees of accuracy (good to fair) by the models. None of the models could predict correctly the extent of relaxation towards isotropy in the wake region near the center of the channel. Results from the directional numerical simulation are used to further clarify this behavior of the models.

Electrochemical carbon dioxide concentrator subsystem math model. [for manned space station

NASA Technical Reports Server (NTRS)

Marshall, R. D.; Carlson, J. N.; Schubert, F. H.

1974-01-01

A steady state computer simulation model has been developed to describe the performance of a total six man, self-contained electrochemical carbon dioxide concentrator subsystem built for the space station prototype. The math model combines expressions describing the performance of the electrochemical depolarized carbon dioxide concentrator cells and modules previously developed with expressions describing the performance of the other major CS-6 components. The model is capable of accurately predicting CS-6 performance over EDC operating ranges and the computer simulation results agree with experimental data obtained over the prediction range.

Boundary-layer computational model for predicting the flow and heat transfer in sudden expansions

NASA Technical Reports Server (NTRS)

Lewis, J. P.; Pletcher, R. H.

1986-01-01

Fully developed turbulent and laminar flows through symmetric planar and axisymmetric expansions with heat transfer were modeled using a finite-difference discretization of the boundary-layer equations. By using the boundary-layer equations to model separated flow in place of the Navier-Stokes equations, computational effort was reduced permitting turbulence modelling studies to be economically carried out. For laminar flow, the reattachment length was well predicted for Reynolds numbers as low as 20 and the details of the trapped eddy were well predicted for Reynolds numbers above 200. For turbulent flows, the Boussinesq assumption was used to express the Reynolds stresses in terms of a turbulent viscosity. Near-wall algebraic turbulence models based on Prandtl's-mixing-length model and the maximum Reynolds shear stress were compared.

Cloud-Based Numerical Weather Prediction for Near Real-Time Forecasting and Disaster Response

NASA Technical Reports Server (NTRS)

Molthan, Andrew; Case, Jonathan; Venners, Jason; Schroeder, Richard; Checchi, Milton; Zavodsky, Bradley; Limaye, Ashutosh; O'Brien, Raymond

2015-01-01

The use of cloud computing resources continues to grow within the public and private sector components of the weather enterprise as users become more familiar with cloud-computing concepts, and competition among service providers continues to reduce costs and other barriers to entry. Cloud resources can also provide capabilities similar to high-performance computing environments, supporting multi-node systems required for near real-time, regional weather predictions. Referred to as "Infrastructure as a Service", or IaaS, the use of cloud-based computing hardware in an on-demand payment system allows for rapid deployment of a modeling system in environments lacking access to a large, supercomputing infrastructure. Use of IaaS capabilities to support regional weather prediction may be of particular interest to developing countries that have not yet established large supercomputing resources, but would otherwise benefit from a regional weather forecasting capability. Recently, collaborators from NASA Marshall Space Flight Center and Ames Research Center have developed a scripted, on-demand capability for launching the NOAA/NWS Science and Training Resource Center (STRC) Environmental Modeling System (EMS), which includes pre-compiled binaries of the latest version of the Weather Research and Forecasting (WRF) model. The WRF-EMS provides scripting for downloading appropriate initial and boundary conditions from global models, along with higher-resolution vegetation, land surface, and sea surface temperature data sets provided by the NASA Short-term Prediction Research and Transition (SPoRT) Center. This presentation will provide an overview of the modeling system capabilities and benchmarks performed on the Amazon Elastic Compute Cloud (EC2) environment. In addition, the presentation will discuss future opportunities to deploy the system in support of weather prediction in developing countries supported by NASA's SERVIR Project, which provides capacity building activities in environmental monitoring and prediction across a growing number of regional hubs throughout the world. Capacity-building applications that extend numerical weather prediction to developing countries are intended to provide near real-time applications to benefit public health, safety, and economic interests, but may have a greater impact during disaster events by providing a source for local predictions of weather-related hazards, or impacts that local weather events may have during the recovery phase.

Reducing usage of the computational resources by event driven approach to model predictive control

NASA Astrophysics Data System (ADS)

Misik, Stefan; Bradac, Zdenek; Cela, Arben

2017-08-01

This paper deals with a real-time and optimal control of dynamic systems while also considers the constraints which these systems might be subject to. Main objective of this work is to propose a simple modification of the existing Model Predictive Control approach to better suit needs of computational resource-constrained real-time systems. An example using model of a mechanical system is presented and the performance of the proposed method is evaluated in a simulated environment.

Cell-model prediction of the melting of a Lennard-Jones solid

DOE Office of Scientific and Technical Information (OSTI.GOV)

Holian, B.L.

The classical free energy of the Lennard-Jones 6-12 solid is computed from a single-particle anharmonic cell model with a correction to the entropy given by the classical correlational entropy of quasiharmonic lattice dynamics. The free energy of the fluid is obtained from the Hansen-Ree analytic fit to Monte Carlo equation-of-state calculations. The resulting predictions of the solid-fluid coexistence curves by this corrected cell model of the solid are in excellent agreement with the computer experiments.

The Coastal Ocean Prediction Systems program: Understanding and managing our coastal ocean

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eden, H.F.; Mooers, C.N.K.

1990-06-01

The goal of COPS is to couple a program of regular observations to numerical models, through techniques of data assimilation, in order to provide a predictive capability for the US coastal ocean including the Great Lakes, estuaries, and the entire Exclusive Economic Zone (EEZ). The objectives of the program include: determining the predictability of the coastal ocean and the processes that govern the predictability; developing efficient prediction systems for the coastal ocean based on the assimilation of real-time observations into numerical models; and coupling the predictive systems for the physical behavior of the coastal ocean to predictive systems for biological,more » chemical, and geological processes to achieve an interdisciplinary capability. COPS will provide the basis for effective monitoring and prediction of coastal ocean conditions by optimizing the use of increased scientific understanding, improved observations, advanced computer models, and computer graphics to make the best possible estimates of sea level, currents, temperatures, salinities, and other properties of entire coastal regions.« less

Structure Prediction of the Second Extracellular Loop in G-Protein-Coupled Receptors

PubMed Central

Kmiecik, Sebastian; Jamroz, Michal; Kolinski, Michal

2014-01-01

G-protein-coupled receptors (GPCRs) play key roles in living organisms. Therefore, it is important to determine their functional structures. The second extracellular loop (ECL2) is a functionally important region of GPCRs, which poses significant challenge for computational structure prediction methods. In this work, we evaluated CABS, a well-established protein modeling tool for predicting ECL2 structure in 13 GPCRs. The ECL2s (with between 13 and 34 residues) are predicted in an environment of other extracellular loops being fully flexible and the transmembrane domain fixed in its x-ray conformation. The modeling procedure used theoretical predictions of ECL2 secondary structure and experimental constraints on disulfide bridges. Our approach yielded ensembles of low-energy conformers and the most populated conformers that contained models close to the available x-ray structures. The level of similarity between the predicted models and x-ray structures is comparable to that of other state-of-the-art computational methods. Our results extend other studies by including newly crystallized GPCRs. PMID:24896119

Preoperative computer simulation for planning of vascular access surgery in hemodialysis patients.

PubMed

Zonnebeld, Niek; Huberts, Wouter; van Loon, Magda M; Delhaas, Tammo; Tordoir, Jan H M

2017-03-06

The arteriovenous fistula (AVF) is the preferred vascular access for hemodialysis patients. Unfortunately, 20-40% of all constructed AVFs fail to mature (FTM), and are therefore not usable for hemodialysis. AVF maturation importantly depends on postoperative blood volume flow. Predicting patient-specific immediate postoperative flow could therefore support surgical planning. A computational model predicting blood volume flow is available, but the effect of blood flow predictions on the clinical endpoint of maturation (at least 500 mL/min blood volume flow, diameter of the venous cannulation segment ≥4 mm) remains undetermined. A multicenter randomized clinical trial will be conducted in which 372 patients will be randomized (1:1 allocation ratio) between conventional healthcare and computational model-aided decision making. All patients are extensively examined using duplex ultrasonography (DUS) during preoperative assessment (12 venous and 11 arterial diameter measurements; 3 arterial volume flow measurements). The computational model will predict patient-specific immediate postoperative blood volume flows based on this DUS examination. Using these predictions, the preferred AVF configuration is recommended for the individual patient (radiocephalic, brachiocephalic, or brachiobasilic). The primary endpoint is FTM rate at six weeks in both groups, secondary endpoints include AVF functionality and patency rates at 6 and 12 months postoperatively. ClinicalTrials.gov (NCT02453412), and ToetsingOnline.nl (NL51610.068.14).

Interior Noise Predictions in the Preliminary Design of the Large Civil Tiltrotor (LCTR2)

NASA Technical Reports Server (NTRS)

Grosveld, Ferdinand W.; Cabell, Randolph H.; Boyd, David D.

2013-01-01

A prediction scheme was established to compute sound pressure levels in the interior of a simplified cabin model of the second generation Large Civil Tiltrotor (LCTR2) during cruise conditions, while being excited by turbulent boundary layer flow over the fuselage, or by tiltrotor blade loading and thickness noise. Finite element models of the cabin structure, interior acoustic space, and acoustically absorbent (poro-elastic) materials in the fuselage were generated and combined into a coupled structural-acoustic model. Fluctuating power spectral densities were computed according to the Efimtsov turbulent boundary layer excitation model. Noise associated with the tiltrotor blades was predicted in the time domain as fluctuating surface pressures and converted to power spectral densities at the fuselage skin finite element nodes. A hybrid finite element (FE) approach was used to compute the low frequency acoustic cabin response over the frequency range 6-141 Hz with a 1 Hz bandwidth, and the Statistical Energy Analysis (SEA) approach was used to predict the interior noise for the 125-8000 Hz one-third octave bands.

Computational methods for a three-dimensional model of the petroleum-discovery process

USGS Publications Warehouse

Schuenemeyer, J.H.; Bawiec, W.J.; Drew, L.J.

1980-01-01

A discovery-process model devised by Drew, Schuenemeyer, and Root can be used to predict the amount of petroleum to be discovered in a basin from some future level of exploratory effort: the predictions are based on historical drilling and discovery data. Because marginal costs of discovery and production are a function of field size, the model can be used to make estimates of future discoveries within deposit size classes. The modeling approach is a geometric one in which the area searched is a function of the size and shape of the targets being sought. A high correlation is assumed between the surface-projection area of the fields and the volume of petroleum. To predict how much oil remains to be found, the area searched must be computed, and the basin size and discovery efficiency must be estimated. The basin is assumed to be explored randomly rather than by pattern drilling. The model may be used to compute independent estimates of future oil at different depth intervals for a play involving multiple producing horizons. We have written FORTRAN computer programs that are used with Drew, Schuenemeyer, and Root's model to merge the discovery and drilling information and perform the necessary computations to estimate undiscovered petroleum. These program may be modified easily for the estimation of remaining quantities of commodities other than petroleum. ?? 1980.

Computational prediction of chemical reactions: current status and outlook.

PubMed

Engkvist, Ola; Norrby, Per-Ola; Selmi, Nidhal; Lam, Yu-Hong; Peng, Zhengwei; Sherer, Edward C; Amberg, Willi; Erhard, Thomas; Smyth, Lynette A

2018-06-01

Over the past few decades, various computational methods have become increasingly important for discovering and developing novel drugs. Computational prediction of chemical reactions is a key part of an efficient drug discovery process. In this review, we discuss important parts of this field, with a focus on utilizing reaction data to build predictive models, the existing programs for synthesis prediction, and usage of quantum mechanics and molecular mechanics (QM/MM) to explore chemical reactions. We also outline potential future developments with an emphasis on pre-competitive collaboration opportunities. Copyright © 2018 Elsevier Ltd. All rights reserved.

Computer program to minimize prediction error in models from experiments with 16 hypercube points and 0 to 6 center points

NASA Technical Reports Server (NTRS)

Holms, A. G.

1982-01-01

A previous report described a backward deletion procedure of model selection that was optimized for minimum prediction error and which used a multiparameter combination of the F - distribution and an order statistics distribution of Cochran's. A computer program is described that applies the previously optimized procedure to real data. The use of the program is illustrated by examples.

Correlation tracking study for meter-class solar telescope on space shuttle. [solar granulation

NASA Technical Reports Server (NTRS)

Smithson, R. C.; Tarbell, T. D.

1977-01-01

The theory and expected performance level of correlation trackers used to control the pointing of a solar telescope in space using white light granulation as a target were studied. Three specific trackers were modeled and their performance levels predicted for telescopes of various apertures. The performance of the computer model trackers on computer enhanced granulation photographs was evaluated. Parametric equations for predicting tracker performance are presented.

Experimental investigations, modeling, and analyses of high-temperature devices for space applications: Part 1. Final report, June 1996--December 1998

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tournier, J.; El-Genk, M.S.; Huang, L.

1999-01-01

The Institute of Space and Nuclear Power Studies at the University of New Mexico has developed a computer simulation of cylindrical geometry alkali metal thermal-to-electric converter cells using a standard Fortran 77 computer code. The objective and use of this code was to compare the experimental measurements with computer simulations, upgrade the model as appropriate, and conduct investigations of various methods to improve the design and performance of the devices for improved efficiency, durability, and longer operational lifetime. The Institute of Space and Nuclear Power Studies participated in vacuum testing of PX series alkali metal thermal-to-electric converter cells and developedmore » the alkali metal thermal-to-electric converter Performance Evaluation and Analysis Model. This computer model consisted of a sodium pressure loss model, a cell electrochemical and electric model, and a radiation/conduction heat transfer model. The code closely predicted the operation and performance of a wide variety of PX series cells which led to suggestions for improvements to both lifetime and performance. The code provides valuable insight into the operation of the cell, predicts parameters of components within the cell, and is a useful tool for predicting both the transient and steady state performance of systems of cells.« less

Experimental investigations, modeling, and analyses of high-temperature devices for space applications: Part 2. Final report, June 1996--December 1998

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tournier, J.; El-Genk, M.S.; Huang, L.

1999-01-01

The Institute of Space and Nuclear Power Studies at the University of New Mexico has developed a computer simulation of cylindrical geometry alkali metal thermal-to-electric converter cells using a standard Fortran 77 computer code. The objective and use of this code was to compare the experimental measurements with computer simulations, upgrade the model as appropriate, and conduct investigations of various methods to improve the design and performance of the devices for improved efficiency, durability, and longer operational lifetime. The Institute of Space and Nuclear Power Studies participated in vacuum testing of PX series alkali metal thermal-to-electric converter cells and developedmore » the alkali metal thermal-to-electric converter Performance Evaluation and Analysis Model. This computer model consisted of a sodium pressure loss model, a cell electrochemical and electric model, and a radiation/conduction heat transfer model. The code closely predicted the operation and performance of a wide variety of PX series cells which led to suggestions for improvements to both lifetime and performance. The code provides valuable insight into the operation of the cell, predicts parameters of components within the cell, and is a useful tool for predicting both the transient and steady state performance of systems of cells.« less

Toward a computational framework for cognitive biology: Unifying approaches from cognitive neuroscience and comparative cognition

NASA Astrophysics Data System (ADS)

Fitch, W. Tecumseh

2014-09-01

Progress in understanding cognition requires a quantitative, theoretical framework, grounded in the other natural sciences and able to bridge between implementational, algorithmic and computational levels of explanation. I review recent results in neuroscience and cognitive biology that, when combined, provide key components of such an improved conceptual framework for contemporary cognitive science. Starting at the neuronal level, I first discuss the contemporary realization that single neurons are powerful tree-shaped computers, which implies a reorientation of computational models of learning and plasticity to a lower, cellular, level. I then turn to predictive systems theory (predictive coding and prediction-based learning) which provides a powerful formal framework for understanding brain function at a more global level. Although most formal models concerning predictive coding are framed in associationist terms, I argue that modern data necessitate a reinterpretation of such models in cognitive terms: as model-based predictive systems. Finally, I review the role of the theory of computation and formal language theory in the recent explosion of comparative biological research attempting to isolate and explore how different species differ in their cognitive capacities. Experiments to date strongly suggest that there is an important difference between humans and most other species, best characterized cognitively as a propensity by our species to infer tree structures from sequential data. Computationally, this capacity entails generative capacities above the regular (finite-state) level; implementationally, it requires some neural equivalent of a push-down stack. I dub this unusual human propensity "dendrophilia", and make a number of concrete suggestions about how such a system may be implemented in the human brain, about how and why it evolved, and what this implies for models of language acquisition. I conclude that, although much remains to be done, a neurally-grounded framework for theoretical cognitive science is within reach that can move beyond polarized debates and provide a more adequate theoretical future for cognitive biology.

Toward a computational framework for cognitive biology: unifying approaches from cognitive neuroscience and comparative cognition.

PubMed

Fitch, W Tecumseh

2014-09-01

Progress in understanding cognition requires a quantitative, theoretical framework, grounded in the other natural sciences and able to bridge between implementational, algorithmic and computational levels of explanation. I review recent results in neuroscience and cognitive biology that, when combined, provide key components of such an improved conceptual framework for contemporary cognitive science. Starting at the neuronal level, I first discuss the contemporary realization that single neurons are powerful tree-shaped computers, which implies a reorientation of computational models of learning and plasticity to a lower, cellular, level. I then turn to predictive systems theory (predictive coding and prediction-based learning) which provides a powerful formal framework for understanding brain function at a more global level. Although most formal models concerning predictive coding are framed in associationist terms, I argue that modern data necessitate a reinterpretation of such models in cognitive terms: as model-based predictive systems. Finally, I review the role of the theory of computation and formal language theory in the recent explosion of comparative biological research attempting to isolate and explore how different species differ in their cognitive capacities. Experiments to date strongly suggest that there is an important difference between humans and most other species, best characterized cognitively as a propensity by our species to infer tree structures from sequential data. Computationally, this capacity entails generative capacities above the regular (finite-state) level; implementationally, it requires some neural equivalent of a push-down stack. I dub this unusual human propensity "dendrophilia", and make a number of concrete suggestions about how such a system may be implemented in the human brain, about how and why it evolved, and what this implies for models of language acquisition. I conclude that, although much remains to be done, a neurally-grounded framework for theoretical cognitive science is within reach that can move beyond polarized debates and provide a more adequate theoretical future for cognitive biology. Copyright © 2014. Published by Elsevier B.V.

Probabilistic Fatigue Damage Prognosis Using a Surrogate Model Trained Via 3D Finite Element Analysis

NASA Technical Reports Server (NTRS)

Leser, Patrick E.; Hochhalter, Jacob D.; Newman, John A.; Leser, William P.; Warner, James E.; Wawrzynek, Paul A.; Yuan, Fuh-Gwo

2015-01-01

Utilizing inverse uncertainty quantification techniques, structural health monitoring can be integrated with damage progression models to form probabilistic predictions of a structure's remaining useful life. However, damage evolution in realistic structures is physically complex. Accurately representing this behavior requires high-fidelity models which are typically computationally prohibitive. In the present work, a high-fidelity finite element model is represented by a surrogate model, reducing computation times. The new approach is used with damage diagnosis data to form a probabilistic prediction of remaining useful life for a test specimen under mixed-mode conditions.

Crowd computing: using competitive dynamics to develop and refine highly predictive models.

PubMed

Bentzien, Jörg; Muegge, Ingo; Hamner, Ben; Thompson, David C

2013-05-01

A recent application of a crowd computing platform to develop highly predictive in silico models for use in the drug discovery process is described. The platform, Kaggle™, exploits a competitive dynamic that results in model optimization as the competition unfolds. Here, this dynamic is described in detail and compared with more-conventional modeling strategies. The complete and full structure of the underlying dataset is disclosed and some thoughts as to the broader utility of such 'gamification' approaches to the field of modeling are offered. Copyright © 2013 Elsevier Ltd. All rights reserved.

Multiscale modeling and distributed computing to predict cosmesis outcome after a lumpectomy

NASA Astrophysics Data System (ADS)

Garbey, M.; Salmon, R.; Thanoon, D.; Bass, B. L.

2013-07-01

Surgery for early stage breast carcinoma is either total mastectomy (complete breast removal) or surgical lumpectomy (only tumor removal). The lumpectomy or partial mastectomy is intended to preserve a breast that satisfies the woman's cosmetic, emotional and physical needs. But in a fairly large number of cases the cosmetic outcome is not satisfactory. Today, predicting that surgery outcome is essentially based on heuristic. Modeling such a complex process must encompass multiple scales, in space from cells to tissue, as well as in time, from minutes for the tissue mechanics to months for healing. The goal of this paper is to present a first step in multiscale modeling of the long time scale prediction of breast shape after tumor resection. This task requires coupling very different mechanical and biological models with very different computing needs. We provide a simple illustration of the application of heterogeneous distributed computing and modular software design to speed up the model development. Our computational framework serves currently to test hypothesis on breast tissue healing in a pilot study with women who have been elected to undergo BCT and are being treated at the Methodist Hospital in Houston, TX.

A theoretical and computational study of lithium-ion battery thermal management for electric vehicles using heat pipes

NASA Astrophysics Data System (ADS)

Greco, Angelo; Cao, Dongpu; Jiang, Xi; Yang, Hong

2014-07-01

A simplified one-dimensional transient computational model of a prismatic lithium-ion battery cell is developed using thermal circuit approach in conjunction with the thermal model of the heat pipe. The proposed model is compared to an analytical solution based on variable separation as well as three-dimensional (3D) computational fluid dynamics (CFD) simulations. The three approaches, i.e. the 1D computational model, analytical solution, and 3D CFD simulations, yielded nearly identical results for the thermal behaviours. Therefore the 1D model is considered to be sufficient to predict the temperature distribution of lithium-ion battery thermal management using heat pipes. Moreover, a maximum temperature of 27.6 °C was predicted for the design of the heat pipe setup in a distributed configuration, while a maximum temperature of 51.5 °C was predicted when forced convection was applied to the same configuration. The higher surface contact of the heat pipes allows a better cooling management compared to forced convection cooling. Accordingly, heat pipes can be used to achieve effective thermal management of a battery pack with confined surface areas.

Intrinsic dimensionality predicts the saliency of natural dynamic scenes.

PubMed

Vig, Eleonora; Dorr, Michael; Martinetz, Thomas; Barth, Erhardt

2012-06-01

Since visual attention-based computer vision applications have gained popularity, ever more complex, biologically inspired models seem to be needed to predict salient locations (or interest points) in naturalistic scenes. In this paper, we explore how far one can go in predicting eye movements by using only basic signal processing, such as image representations derived from efficient coding principles, and machine learning. To this end, we gradually increase the complexity of a model from simple single-scale saliency maps computed on grayscale videos to spatiotemporal multiscale and multispectral representations. Using a large collection of eye movements on high-resolution videos, supervised learning techniques fine-tune the free parameters whose addition is inevitable with increasing complexity. The proposed model, although very simple, demonstrates significant improvement in predicting salient locations in naturalistic videos over four selected baseline models and two distinct data labeling scenarios.

Exploratory modeling of forest disturbance scenarios in central Oregon using computational experiments in GIS

Treesearch

Deana D. Pennington

2007-01-01

Exploratory modeling is an approach used when process and/or parameter uncertainties are such that modeling attempts at realistic prediction are not appropriate. Exploratory modeling makes use of computational experimentation to test how varying model scenarios drive model outcome. The goal of exploratory modeling is to better understand the system of interest through...

A new predictive multi-zone model for HCCI engine combustion

DOE PAGES

Bissoli, Mattia; Frassoldati, Alessio; Cuoci, Alberto; ...

2016-06-30

Here, this work introduces a new predictive multi-zone model for the description of combustion in Homogeneous Charge Compression Ignition (HCCI) engines. The model exploits the existing OpenSMOKE++ computational suite to handle detailed kinetic mechanisms, providing reliable predictions of the in-cylinder auto-ignition processes. All the elements with a significant impact on the combustion performances and emissions, like turbulence, heat and mass exchanges, crevices, residual burned gases, thermal and feed stratification are taken into account. Compared to other computational approaches, this model improves the description of mixture stratification phenomena by coupling a wall heat transfer model derived from CFD application with amore » proper turbulence model. Furthermore, the calibration of this multi-zone model requires only three parameters, which can be derived from a non-reactive CFD simulation: these adaptive variables depend only on the engine geometry and remain fixed across a wide range of operating conditions, allowing the prediction of auto-ignition, pressure traces and pollutants. This computational framework enables the use of detail kinetic mechanisms, as well as Rate of Production Analysis (RoPA) and Sensitivity Analysis (SA) to investigate the complex chemistry involved in the auto-ignition and the pollutants formation processes. In the final sections of the paper, these capabilities are demonstrated through the comparison with experimental data.« less

Deriving Points of Departure and Performance Baselines for Predictive Modeling of Systemic Toxicity using ToxRefDB (SOT)

EPA Science Inventory

A primary goal of computational toxicology is to generate predictive models of toxicity. An elusive target of alternative test methods and models has been the accurate prediction of systemic toxicity points of departure (PoD). We aim not only to provide a large and valuable resou...

IVUS-Based Computational Modeling and Planar Biaxial Artery Material Properties for Human Coronary Plaque Vulnerability Assessment

PubMed Central

Liu, Haofei; Cai, Mingchao; Yang, Chun; Zheng, Jie; Bach, Richard; Kural, Mehmet H.; Billiar, Kristen L.; Muccigrosso, David; Lu, Dongsi; Tang, Dalin

2012-01-01

Image-based computational modeling has been introduced for vulnerable atherosclerotic plaques to identify critical mechanical conditions which may be used for better plaque assessment and rupture predictions. In vivo patient-specific coronary plaque models are lagging due to limitations on non-invasive image resolution, flow data, and vessel material properties. A framework is proposed to combine intravascular ultrasound (IVUS) imaging, biaxial mechanical testing and computational modeling with fluid-structure interactions and anisotropic material properties to acquire better and more complete plaque data and make more accurate plaque vulnerability assessment and predictions. Impact of pre-shrink-stretch process, vessel curvature and high blood pressure on stress, strain, flow velocity and flow maximum principal shear stress was investigated. PMID:22428362

Predicting field weed emergence with empirical models and soft computing techniques

USDA-ARS?s Scientific Manuscript database

Seedling emergence is the most important phenological process that influences the success of weed species; therefore, predicting weed emergence timing plays a critical role in scheduling weed management measures. Important efforts have been made in the attempt to develop models to predict seedling e...

Experimental and computational prediction of glass transition temperature of drugs.

PubMed

Alzghoul, Ahmad; Alhalaweh, Amjad; Mahlin, Denny; Bergström, Christel A S

2014-12-22

Glass transition temperature (Tg) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between Tg and melting temperature (Tm) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of Tg were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on Tm predicted Tg with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict Tg of drug-like molecules with high accuracy were developed. If Tm is available, a simple linear regression can be used to predict Tg. However, the results also suggest that support vector regression and calculated molecular descriptors can predict Tg with equal accuracy, already before compound synthesis.

Comparison of FDNS liquid rocket engine plume computations with SPF/2

NASA Technical Reports Server (NTRS)

Kumar, G. N.; Griffith, D. O., II; Warsi, S. A.; Seaford, C. M.

1993-01-01

Prediction of a plume's shape and structure is essential to the evaluation of base region environments. The JANNAF standard plume flowfield analysis code SPF/2 predicts plumes well, but cannot analyze base regions. Full Navier-Stokes CFD codes can calculate both zones; however, before they can be used, they must be validated. The CFD code FDNS3D (Finite Difference Navier-Stokes Solver) was used to analyze the single plume of a Space Transportation Main Engine (STME) and comparisons were made with SPF/2 computations. Both frozen and finite rate chemistry models were employed as well as two turbulence models in SPF/2. The results indicate that FDNS3D plume computations agree well with SPF/2 predictions for liquid rocket engine plumes.

A novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modelling

PubMed Central

Borghi, Alessandro; Ruggiero, Federica; Badiali, Giovanni; Bianchi, Alberto; Marchetti, Claudio; Rodriguez-Florez, Naiara; Breakey, Richard W. F.; Jeelani, Owase; Dunaway, David J.; Schievano, Silvia

2018-01-01

Repositioning of the maxilla in orthognathic surgery is carried out for functional and aesthetic purposes. Pre-surgical planning tools can predict 3D facial appearance by computing the response of the soft tissue to the changes to the underlying skeleton. The clinical use of commercial prediction software remains controversial, likely due to the deterministic nature of these computational predictions. A novel probabilistic finite element model (FEM) for the prediction of postoperative facial soft tissues is proposed in this paper. A probabilistic FEM was developed and validated on a cohort of eight patients who underwent maxillary repositioning and had pre- and postoperative cone beam computed tomography (CBCT) scans taken. Firstly, a variables correlation assessed various modelling parameters. Secondly, a design of experiments (DOE) provided a range of potential outcomes based on uniformly distributed input parameters, followed by an optimisation. Lastly, the second DOE iteration provided optimised predictions with a probability range. A range of 3D predictions was obtained using the probabilistic FEM and validated using reconstructed soft tissue surfaces from the postoperative CBCT data. The predictions in the nose and upper lip areas accurately include the true postoperative position, whereas the prediction under-estimates the position of the cheeks and lower lip. A probabilistic FEM has been developed and validated for the prediction of the facial appearance following orthognathic surgery. This method shows how inaccuracies in the modelling and uncertainties in executing surgical planning influence the soft tissue prediction and it provides a range of predictions including a minimum and maximum, which may be helpful for patients in understanding the impact of surgery on the face. PMID:29742139

A novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modelling.

PubMed

Knoops, Paul G M; Borghi, Alessandro; Ruggiero, Federica; Badiali, Giovanni; Bianchi, Alberto; Marchetti, Claudio; Rodriguez-Florez, Naiara; Breakey, Richard W F; Jeelani, Owase; Dunaway, David J; Schievano, Silvia

2018-01-01

Repositioning of the maxilla in orthognathic surgery is carried out for functional and aesthetic purposes. Pre-surgical planning tools can predict 3D facial appearance by computing the response of the soft tissue to the changes to the underlying skeleton. The clinical use of commercial prediction software remains controversial, likely due to the deterministic nature of these computational predictions. A novel probabilistic finite element model (FEM) for the prediction of postoperative facial soft tissues is proposed in this paper. A probabilistic FEM was developed and validated on a cohort of eight patients who underwent maxillary repositioning and had pre- and postoperative cone beam computed tomography (CBCT) scans taken. Firstly, a variables correlation assessed various modelling parameters. Secondly, a design of experiments (DOE) provided a range of potential outcomes based on uniformly distributed input parameters, followed by an optimisation. Lastly, the second DOE iteration provided optimised predictions with a probability range. A range of 3D predictions was obtained using the probabilistic FEM and validated using reconstructed soft tissue surfaces from the postoperative CBCT data. The predictions in the nose and upper lip areas accurately include the true postoperative position, whereas the prediction under-estimates the position of the cheeks and lower lip. A probabilistic FEM has been developed and validated for the prediction of the facial appearance following orthognathic surgery. This method shows how inaccuracies in the modelling and uncertainties in executing surgical planning influence the soft tissue prediction and it provides a range of predictions including a minimum and maximum, which may be helpful for patients in understanding the impact of surgery on the face.

Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms

PubMed Central

Jian, Jhih-Wei; Elumalai, Pavadai; Pitti, Thejkiran; Wu, Chih Yuan; Tsai, Keng-Chang; Chang, Jeng-Yih; Peng, Hung-Pin; Yang, An-Suei

2016-01-01

Predicting ligand binding sites (LBSs) on protein structures, which are obtained either from experimental or computational methods, is a useful first step in functional annotation or structure-based drug design for the protein structures. In this work, the structure-based machine learning algorithm ISMBLab-LIG was developed to predict LBSs on protein surfaces with input attributes derived from the three-dimensional probability density maps of interacting atoms, which were reconstructed on the query protein surfaces and were relatively insensitive to local conformational variations of the tentative ligand binding sites. The prediction accuracy of the ISMBLab-LIG predictors is comparable to that of the best LBS predictors benchmarked on several well-established testing datasets. More importantly, the ISMBLab-LIG algorithm has substantial tolerance to the prediction uncertainties of computationally derived protein structure models. As such, the method is particularly useful for predicting LBSs not only on experimental protein structures without known LBS templates in the database but also on computationally predicted model protein structures with structural uncertainties in the tentative ligand binding sites. PMID:27513851

Use of a Machine-learning Method for Predicting Highly Cited Articles Within General Radiology Journals.

PubMed

Rosenkrantz, Andrew B; Doshi, Ankur M; Ginocchio, Luke A; Aphinyanaphongs, Yindalon

2016-12-01

This study aimed to assess the performance of a text classification machine-learning model in predicting highly cited articles within the recent radiological literature and to identify the model's most influential article features. We downloaded from PubMed the title, abstract, and medical subject heading terms for 10,065 articles published in 25 general radiology journals in 2012 and 2013. Three machine-learning models were applied to predict the top 10% of included articles in terms of the number of citations to the article in 2014 (reflecting the 2-year time window in conventional impact factor calculations). The model having the highest area under the curve was selected to derive a list of article features (words) predicting high citation volume, which was iteratively reduced to identify the smallest possible core feature list maintaining predictive power. Overall themes were qualitatively assigned to the core features. The regularized logistic regression (Bayesian binary regression) model had highest performance, achieving an area under the curve of 0.814 in predicting articles in the top 10% of citation volume. We reduced the initial 14,083 features to 210 features that maintain predictivity. These features corresponded with topics relating to various imaging techniques (eg, diffusion-weighted magnetic resonance imaging, hyperpolarized magnetic resonance imaging, dual-energy computed tomography, computed tomography reconstruction algorithms, tomosynthesis, elastography, and computer-aided diagnosis), particular pathologies (prostate cancer; thyroid nodules; hepatic adenoma, hepatocellular carcinoma, non-alcoholic fatty liver disease), and other topics (radiation dose, electroporation, education, general oncology, gadolinium, statistics). Machine learning can be successfully applied to create specific feature-based models for predicting articles likely to achieve high influence within the radiological literature. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Two-Level Weld-Material Homogenization for Efficient Computational Analysis of Welded Structure Blast-Survivability

NASA Astrophysics Data System (ADS)

Grujicic, M.; Arakere, G.; Hariharan, A.; Pandurangan, B.

2012-06-01

The introduction of newer joining technologies like the so-called friction-stir welding (FSW) into automotive engineering entails the knowledge of the joint-material microstructure and properties. Since, the development of vehicles (including military vehicles capable of surviving blast and ballistic impacts) nowadays involves extensive use of the computational engineering analyses (CEA), robust high-fidelity material models are needed for the FSW joints. A two-level material-homogenization procedure is proposed and utilized in this study to help manage computational cost and computer storage requirements for such CEAs. The method utilizes experimental (microstructure, microhardness, tensile testing, and x-ray diffraction) data to construct: (a) the material model for each weld zone and (b) the material model for the entire weld. The procedure is validated by comparing its predictions with the predictions of more detailed but more costly computational analyses.

Pressure Loss Predictions of the Reactor Simulator Subsystem at NASA Glenn Research Center

NASA Technical Reports Server (NTRS)

Reid, Terry V.

2016-01-01

Testing of the Fission Power System (FPS) Technology Demonstration Unit (TDU) is being conducted at NASA Glenn Research Center. The TDU consists of three subsystems: the reactor simulator (RxSim), the Stirling Power Conversion Unit (PCU), and the heat exchanger manifold (HXM). An annular linear induction pump (ALIP) is used to drive the working fluid. A preliminary version of the TDU system (which excludes the PCU for now) is referred to as the "RxSim subsystem" and was used to conduct flow tests in Vacuum Facility 6 (VF 6). In parallel, a computational model of the RxSim subsystem was created based on the computer-aided-design (CAD) model and was used to predict loop pressure losses over a range of mass flows. This was done to assess the ability of the pump to meet the design intent mass flow demand. Measured data indicates that the pump can produce 2.333 kg/sec of flow, which is enough to supply the RxSim subsystem with a nominal flow of 1.75 kg/sec. Computational predictions indicated that the pump could provide 2.157 kg/sec (using the Spalart-Allmaras (S?A) turbulence model) and 2.223 kg/sec (using the k- turbulence model). The computational error of the predictions for the available mass flow is ?0.176 kg/sec (with the S-A turbulence model) and -0.110 kg/sec (with the k- turbulence model) when compared to measured data.

COMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer's disease.

PubMed

Zhu, Fan; Panwar, Bharat; Dodge, Hiroko H; Li, Hongdong; Hampstead, Benjamin M; Albin, Roger L; Paulson, Henry L; Guan, Yuanfang

2016-10-05

We present COMPASS, a COmputational Model to Predict the development of Alzheimer's diSease Spectrum, to model Alzheimer's disease (AD) progression. This was the best-performing method in recent crowdsourcing benchmark study, DREAM Alzheimer's Disease Big Data challenge to predict changes in Mini-Mental State Examination (MMSE) scores over 24-months using standardized data. In the present study, we conducted three additional analyses beyond the DREAM challenge question to improve the clinical contribution of our approach, including: (1) adding pre-validated baseline cognitive composite scores of ADNI-MEM and ADNI-EF, (2) identifying subjects with significant declines in MMSE scores, and (3) incorporating SNPs of top 10 genes connected to APOE identified from functional-relationship network. For (1) above, we significantly improved predictive accuracy, especially for the Mild Cognitive Impairment (MCI) group. For (2), we achieved an area under ROC of 0.814 in predicting significant MMSE decline: our model has 100% precision at 5% recall, and 91% accuracy at 10% recall. For (3), "genetic only" model has Pearson's correlation of 0.15 to predict progression in the MCI group. Even though addition of this limited genetic model to COMPASS did not improve prediction of progression of MCI group, the predictive ability of SNP information extended beyond well-known APOE allele.

Development and application of computational aerothermodynamics flowfield computer codes

NASA Technical Reports Server (NTRS)

Venkatapathy, Ethiraj

1994-01-01

Research was performed in the area of computational modeling and application of hypersonic, high-enthalpy, thermo-chemical nonequilibrium flow (Aerothermodynamics) problems. A number of computational fluid dynamic (CFD) codes were developed and applied to simulate high altitude rocket-plume, the Aeroassist Flight Experiment (AFE), hypersonic base flow for planetary probes, the single expansion ramp model (SERN) connected with the National Aerospace Plane, hypersonic drag devices, hypersonic ramp flows, ballistic range models, shock tunnel facility nozzles, transient and steady flows in the shock tunnel facility, arc-jet flows, thermochemical nonequilibrium flows around simple and complex bodies, axisymmetric ionized flows of interest to re-entry, unsteady shock induced combustion phenomena, high enthalpy pulsed facility simulations, and unsteady shock boundary layer interactions in shock tunnels. Computational modeling involved developing appropriate numerical schemes for the flows on interest and developing, applying, and validating appropriate thermochemical processes. As part of improving the accuracy of the numerical predictions, adaptive grid algorithms were explored, and a user-friendly, self-adaptive code (SAGE) was developed. Aerothermodynamic flows of interest included energy transfer due to strong radiation, and a significant level of effort was spent in developing computational codes for calculating radiation and radiation modeling. In addition, computational tools were developed and applied to predict the radiative heat flux and spectra that reach the model surface.

Estimation of Model's Marginal likelihood Using Adaptive Sparse Grid Surrogates in Bayesian Model Averaging

NASA Astrophysics Data System (ADS)

Zeng, X.

2015-12-01

A large number of model executions are required to obtain alternative conceptual models' predictions and their posterior probabilities in Bayesian model averaging (BMA). The posterior model probability is estimated through models' marginal likelihood and prior probability. The heavy computation burden hinders the implementation of BMA prediction, especially for the elaborated marginal likelihood estimator. For overcoming the computation burden of BMA, an adaptive sparse grid (SG) stochastic collocation method is used to build surrogates for alternative conceptual models through the numerical experiment of a synthetical groundwater model. BMA predictions depend on model posterior weights (or marginal likelihoods), and this study also evaluated four marginal likelihood estimators, including arithmetic mean estimator (AME), harmonic mean estimator (HME), stabilized harmonic mean estimator (SHME), and thermodynamic integration estimator (TIE). The results demonstrate that TIE is accurate in estimating conceptual models' marginal likelihoods. The BMA-TIE has better predictive performance than other BMA predictions. TIE has high stability for estimating conceptual model's marginal likelihood. The repeated estimated conceptual model's marginal likelihoods by TIE have significant less variability than that estimated by other estimators. In addition, the SG surrogates are efficient to facilitate BMA predictions, especially for BMA-TIE. The number of model executions needed for building surrogates is 4.13%, 6.89%, 3.44%, and 0.43% of the required model executions of BMA-AME, BMA-HME, BMA-SHME, and BMA-TIE, respectively.

Computationally modeling interpersonal trust.

PubMed

Lee, Jin Joo; Knox, W Bradley; Wormwood, Jolie B; Breazeal, Cynthia; Desteno, David

2013-01-01

We present a computational model capable of predicting-above human accuracy-the degree of trust a person has toward their novel partner by observing the trust-related nonverbal cues expressed in their social interaction. We summarize our prior work, in which we identify nonverbal cues that signal untrustworthy behavior and also demonstrate the human mind's readiness to interpret those cues to assess the trustworthiness of a social robot. We demonstrate that domain knowledge gained from our prior work using human-subjects experiments, when incorporated into the feature engineering process, permits a computational model to outperform both human predictions and a baseline model built in naiveté of this domain knowledge. We then present the construction of hidden Markov models to investigate temporal relationships among the trust-related nonverbal cues. By interpreting the resulting learned structure, we observe that models built to emulate different levels of trust exhibit different sequences of nonverbal cues. From this observation, we derived sequence-based temporal features that further improve the accuracy of our computational model. Our multi-step research process presented in this paper combines the strength of experimental manipulation and machine learning to not only design a computational trust model but also to further our understanding of the dynamics of interpersonal trust.

Computational Electrocardiography: Revisiting Holter ECG Monitoring.

PubMed

Deserno, Thomas M; Marx, Nikolaus

2016-08-05

Since 1942, when Goldberger introduced the 12-lead electrocardiography (ECG), this diagnostic method has not been changed. After 70 years of technologic developments, we revisit Holter ECG from recording to understanding. A fundamental change is fore-seen towards "computational ECG" (CECG), where continuous monitoring is producing big data volumes that are impossible to be inspected conventionally but require efficient computational methods. We draw parallels between CECG and computational biology, in particular with respect to computed tomography, computed radiology, and computed photography. From that, we identify technology and methodology needed for CECG. Real-time transfer of raw data into meaningful parameters that are tracked over time will allow prediction of serious events, such as sudden cardiac death. Evolved from Holter's technology, portable smartphones with Bluetooth-connected textile-embedded sensors will capture noisy raw data (recording), process meaningful parameters over time (analysis), and transfer them to cloud services for sharing (handling), predicting serious events, and alarming (understanding). To make this happen, the following fields need more research: i) signal processing, ii) cycle decomposition; iii) cycle normalization, iv) cycle modeling, v) clinical parameter computation, vi) physiological modeling, and vii) event prediction. We shall start immediately developing methodology for CECG analysis and understanding.

Flood predictions using the parallel version of distributed numerical physical rainfall-runoff model TOPKAPI

NASA Astrophysics Data System (ADS)

Boyko, Oleksiy; Zheleznyak, Mark

2015-04-01

The original numerical code TOPKAPI-IMMS of the distributed rainfall-runoff model TOPKAPI ( Todini et al, 1996-2014) is developed and implemented in Ukraine. The parallel version of the code has been developed recently to be used on multiprocessors systems - multicore/processors PC and clusters. Algorithm is based on binary-tree decomposition of the watershed for the balancing of the amount of computation for all processors/cores. Message passing interface (MPI) protocol is used as a parallel computing framework. The numerical efficiency of the parallelization algorithms is demonstrated for the case studies for the flood predictions of the mountain watersheds of the Ukrainian Carpathian regions. The modeling results is compared with the predictions based on the lumped parameters models.

Lower- and higher-order aberrations predicted by an optomechanical model of arcuate keratotomy for astigmatism.

PubMed

Navarro, Rafael; Palos, Fernando; Lanchares, Elena; Calvo, Begoña; Cristóbal, José A

2009-01-01

To develop a realistic model of the optomechanical behavior of the cornea after curved relaxing incisions to simulate the induced astigmatic change and predict the optical aberrations produced by the incisions. ICMA Consejo Superior de Investigaciones Científicas and Universidad de Zaragoza, Zaragoza, Spain. A 3-dimensional finite element model of the anterior hemisphere of the ocular surface was used. The corneal tissue was modeled as a quasi-incompressible, anisotropic hyperelastic constitutive behavior strongly dependent on the physiological collagen fibril distribution. Similar behaviors were assigned to the limbus and sclera. With this model, some corneal incisions were computer simulated after the Lindstrom nomogram. The resulting geometry of the biomechanical simulation was analyzed in the optical zone, and finite ray tracing was performed to compute refractive power and higher-order aberrations (HOAs). The finite-element simulation provided new geometry of the corneal surfaces, from which elevation topographies were obtained. The surgically induced astigmatism (SIA) of the simulated incisions according to the Lindstrom nomogram was computed by finite ray tracing. However, paraxial computations would yield slightly different results (undercorrection of astigmatism). In addition, arcuate incisions would induce significant amounts of HOAs. Finite-element models, together with finite ray-tracing computations, yielded realistic simulations of the biomechanical and optical changes induced by relaxing incisions. The model reproduced the SIA indicated by the Lindstrom nomogram for the simulated incisions and predicted a significant increase in optical aberrations induced by arcuate keratotomy.

Base Flow Model Validation

NASA Technical Reports Server (NTRS)

Sinha, Neeraj; Brinckman, Kevin; Jansen, Bernard; Seiner, John

2011-01-01

A method was developed of obtaining propulsive base flow data in both hot and cold jet environments, at Mach numbers and altitude of relevance to NASA launcher designs. The base flow data was used to perform computational fluid dynamics (CFD) turbulence model assessments of base flow predictive capabilities in order to provide increased confidence in base thermal and pressure load predictions obtained from computational modeling efforts. Predictive CFD analyses were used in the design of the experiments, available propulsive models were used to reduce program costs and increase success, and a wind tunnel facility was used. The data obtained allowed assessment of CFD/turbulence models in a complex flow environment, working within a building-block procedure to validation, where cold, non-reacting test data was first used for validation, followed by more complex reacting base flow validation.

Molecular Modeling of Environmentally Important Processes: Reduction Potentials

ERIC Educational Resources Information Center

Lewis, Anne; Bumpus, John A.; Truhlar, Donald G.; Cramer, Christopher J.

2004-01-01

The increasing use of computational quantum chemistry in the modeling of environmentally important processes is described. The employment of computational quantum mechanics for the prediction of oxidation-reduction potential for solutes in an aqueous medium is discussed.

Computer models and output, Spartan REM: Appendix B

NASA Technical Reports Server (NTRS)

Marlowe, D. S.; West, E. J.

1984-01-01

A computer model of the Spartan Release Engagement Mechanism (REM) is presented in a series of numerical charts and engineering drawings. A crack growth analysis code is used to predict the fracture mechanics of critical components.

Predictive Models and Computational Embryology

EPA Science Inventory

EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

Computational Approaches to Chemical Hazard Assessment

PubMed Central

Luechtefeld, Thomas; Hartung, Thomas

2018-01-01

Summary Computational prediction of toxicity has reached new heights as a result of decades of growth in the magnitude and diversity of biological data. Public packages for statistics and machine learning make model creation faster. New theory in machine learning and cheminformatics enables integration of chemical structure, toxicogenomics, simulated and physical data in the prediction of chemical health hazards, and other toxicological information. Our earlier publications have characterized a toxicological dataset of unprecedented scale resulting from the European REACH legislation (Registration Evaluation Authorisation and Restriction of Chemicals). These publications dove into potential use cases for regulatory data and some models for exploiting this data. This article analyzes the options for the identification and categorization of chemicals, moves on to the derivation of descriptive features for chemicals, discusses different kinds of targets modeled in computational toxicology, and ends with a high-level perspective of the algorithms used to create computational toxicology models. PMID:29101769

Support System Effects on the NASA Common Research Model

NASA Technical Reports Server (NTRS)

Rivers, S. Melissa B.; Hunter, Craig A.

2012-01-01

An experimental investigation of the NASA Common Research Model was conducted in the NASA Langley National Transonic Facility and NASA Ames 11-Foot Transonic Wind Tunnel Facility for use in the Drag Prediction Workshop. As data from the experimental investigations was collected, a large difference in moment values was seen between the experimental and the computational data from the 4th Drag Prediction Workshop. This difference led to the present work. In this study, a computational assessment has been undertaken to investigate model support system interference effects on the Common Research Model. The configurations computed during this investigation were the wing/body/tail=0deg without the support system and the wing/body/tail=0deg with the support system. The results from this investigation confirm that the addition of the support system to the computational cases does shift the pitching moment in the direction of the experimental results.

Development and validation of a computational model of the knee joint for the evaluation of surgical treatments for osteoarthritis

PubMed Central

Mootanah, R.; Imhauser, C.W.; Reisse, F.; Carpanen, D.; Walker, R.W.; Koff, M.F.; Lenhoff, M.W.; Rozbruch, S.R.; Fragomen, A.T.; Dewan, Z.; Kirane, Y.M.; Cheah, Pamela A.; Dowell, J.K.; Hillstrom, H.J.

2014-01-01

A three-dimensional (3D) knee joint computational model was developed and validated to predict knee joint contact forces and pressures for different degrees of malalignment. A 3D computational knee model was created from high-resolution radiological images to emulate passive sagittal rotation (full-extension to 65°-flexion) and weight acceptance. A cadaveric knee mounted on a six-degree-of-freedom robot was subjected to matching boundary and loading conditions. A ligament-tuning process minimised kinematic differences between the robotically loaded cadaver specimen and the finite element (FE) model. The model was validated by measured intra-articular force and pressure measurements. Percent full scale error between EE-predicted and in vitro-measured values in the medial and lateral compartments were 6.67% and 5.94%, respectively, for normalised peak pressure values, and 7.56% and 4.48%, respectively, for normalised force values. The knee model can accurately predict normalised intra-articular pressure and forces for different loading conditions and could be further developed for subject-specific surgical planning. PMID:24786914

Development and validation of a computational model of the knee joint for the evaluation of surgical treatments for osteoarthritis.

PubMed

Mootanah, R; Imhauser, C W; Reisse, F; Carpanen, D; Walker, R W; Koff, M F; Lenhoff, M W; Rozbruch, S R; Fragomen, A T; Dewan, Z; Kirane, Y M; Cheah, K; Dowell, J K; Hillstrom, H J

2014-01-01

A three-dimensional (3D) knee joint computational model was developed and validated to predict knee joint contact forces and pressures for different degrees of malalignment. A 3D computational knee model was created from high-resolution radiological images to emulate passive sagittal rotation (full-extension to 65°-flexion) and weight acceptance. A cadaveric knee mounted on a six-degree-of-freedom robot was subjected to matching boundary and loading conditions. A ligament-tuning process minimised kinematic differences between the robotically loaded cadaver specimen and the finite element (FE) model. The model was validated by measured intra-articular force and pressure measurements. Percent full scale error between FE-predicted and in vitro-measured values in the medial and lateral compartments were 6.67% and 5.94%, respectively, for normalised peak pressure values, and 7.56% and 4.48%, respectively, for normalised force values. The knee model can accurately predict normalised intra-articular pressure and forces for different loading conditions and could be further developed for subject-specific surgical planning.

A new method for enhancer prediction based on deep belief network.

PubMed

Bu, Hongda; Gan, Yanglan; Wang, Yang; Zhou, Shuigeng; Guan, Jihong

2017-10-16

Studies have shown that enhancers are significant regulatory elements to play crucial roles in gene expression regulation. Since enhancers are unrelated to the orientation and distance to their target genes, it is a challenging mission for scholars and researchers to accurately predicting distal enhancers. In the past years, with the high-throughout ChiP-seq technologies development, several computational techniques emerge to predict enhancers using epigenetic or genomic features. Nevertheless, the inconsistency of computational models across different cell-lines and the unsatisfactory prediction performance call for further research in this area. Here, we propose a new Deep Belief Network (DBN) based computational method for enhancer prediction, which is called EnhancerDBN. This method combines diverse features, composed of DNA sequence compositional features, DNA methylation and histone modifications. Our computational results indicate that 1) EnhancerDBN outperforms 13 existing methods in prediction, and 2) GC content and DNA methylation can serve as relevant features for enhancer prediction. Deep learning is effective in boosting the performance of enhancer prediction.

Zero side force volute development

NASA Technical Reports Server (NTRS)

Anderson, P. G.; Franz, R. J.; Farmer, R. C.; Chen, Y. S.

1995-01-01

Collector scrolls on high performance centrifugal pumps are currently designed with methods which are based on very approximate flowfield models. Such design practices result in some volute configurations causing excessive side loads even at design flowrates. The purpose of this study was to develop and verify computational design tools which may be used to optimize volute configurations with respect to avoiding excessive loads on the bearings. The new design methodology consisted of a volute grid generation module and a computational fluid dynamics (CFD) module to describe the volute geometry and predict the radial forces for a given flow condition, respectively. Initially, the CFD module was used to predict the impeller and the volute flowfields simultaneously; however, the required computation time was found to be excessive for parametric design studies. A second computational procedure was developed which utilized an analytical impeller flowfield model and an ordinary differential equation to describe the impeller/volute coupling obtained from the literature, Adkins & Brennen (1988). The second procedure resulted in 20 to 30 fold increase in computational speed for an analysis. The volute design analysis was validated by postulating a volute geometry, constructing a volute to this configuration, and measuring the steady radial forces over a range of flow coefficients. Excellent agreement between model predictions and observed pump operation prove the computational impeller/volute pump model to be a valuable design tool. Further applications are recommended to fully establish the benefits of this new methodology.

Computational models for predicting interactions with membrane transporters.

PubMed

Xu, Y; Shen, Q; Liu, X; Lu, J; Li, S; Luo, C; Gong, L; Luo, X; Zheng, M; Jiang, H

2013-01-01

Membrane transporters, including two members: ATP-binding cassette (ABC) transporters and solute carrier (SLC) transporters are proteins that play important roles to facilitate molecules into and out of cells. Consequently, these transporters can be major determinants of the therapeutic efficacy, toxicity and pharmacokinetics of a variety of drugs. Considering the time and expense of bio-experiments taking, research should be driven by evaluation of efficacy and safety. Computational methods arise to be a complementary choice. In this article, we provide an overview of the contribution that computational methods made in transporters field in the past decades. At the beginning, we present a brief introduction about the structure and function of major members of two families in transporters. In the second part, we focus on widely used computational methods in different aspects of transporters research. In the absence of a high-resolution structure of most of transporters, homology modeling is a useful tool to interpret experimental data and potentially guide experimental studies. We summarize reported homology modeling in this review. Researches in computational methods cover major members of transporters and a variety of topics including the classification of substrates and/or inhibitors, prediction of protein-ligand interactions, constitution of binding pocket, phenotype of non-synonymous single-nucleotide polymorphisms, and the conformation analysis that try to explain the mechanism of action. As an example, one of the most important transporters P-gp is elaborated to explain the differences and advantages of various computational models. In the third part, the challenges of developing computational methods to get reliable prediction, as well as the potential future directions in transporter related modeling are discussed.

Demonstration of the Water Erosion Prediction Project (WEPP) internet interface and services

USDA-ARS?s Scientific Manuscript database

The Water Erosion Prediction Project (WEPP) model is a process-based FORTRAN computer simulation program for prediction of runoff and soil erosion by water at hillslope profile, field, and small watershed scales. To effectively run the WEPP model and interpret results additional software has been de...

Predictive uncertainty analysis of plume distribution for geological carbon sequestration using sparse-grid Bayesian method

NASA Astrophysics Data System (ADS)

Shi, X.; Zhang, G.

2013-12-01

Because of the extensive computational burden, parametric uncertainty analyses are rarely conducted for geological carbon sequestration (GCS) process based multi-phase models. The difficulty of predictive uncertainty analysis for the CO2 plume migration in realistic GCS models is not only due to the spatial distribution of the caprock and reservoir (i.e. heterogeneous model parameters), but also because the GCS optimization estimation problem has multiple local minima due to the complex nonlinear multi-phase (gas and aqueous), and multi-component (water, CO2, salt) transport equations. The geological model built by Doughty and Pruess (2004) for the Frio pilot site (Texas) was selected and assumed to represent the 'true' system, which was composed of seven different facies (geological units) distributed among 10 layers. We chose to calibrate the permeabilities of these facies. Pressure and gas saturation values from this true model were then extracted and used as observations for subsequent model calibration. Random noise was added to the observations to approximate realistic field conditions. Each simulation of the model lasts about 2 hours. In this study, we develop a new approach that improves computational efficiency of Bayesian inference by constructing a surrogate system based on an adaptive sparse-grid stochastic collocation method. This surrogate response surface global optimization algorithm is firstly used to calibrate the model parameters, then prediction uncertainty of the CO2 plume position is quantified due to the propagation from parametric uncertainty in the numerical experiments, which is also compared to the actual plume from the 'true' model. Results prove that the approach is computationally efficient for multi-modal optimization and prediction uncertainty quantification for computationally expensive simulation models. Both our inverse methodology and findings can be broadly applicable to GCS in heterogeneous storage formations.

Investigation of computational aeroacoustic tools for noise predictions of wind turbine aerofoils

NASA Astrophysics Data System (ADS)

Humpf, A.; Ferrer, E.; Munduate, X.

2007-07-01

In this work trailing edge noise levels of a research aerofoil have been computed and compared to aeroacoustic measurements using two different approaches. On the other hand, aerodynamic and aeroacoustic calculations were performed with the full Navier-Stokes CFD code Fluent [Fluent Inc 2005 Fluent 6.2 Users Guide, Lebanon, NH, USA] on the basis of a steady RANS simulation. Aerodynamic characteristics were computed by the aid of various turbulence models. By the combined usage of implemented broadband noise source models, it was tried to isolate and determine the trailing edge noise level. Throughout this work two methods of different computational cost have been tested and quantitative and qualitative results obtained. On the one hand, the semi-empirical noise prediction tool NAFNoise [Moriarty P 2005 NAFNoise User's Guide. Golden, Colorado, July. http://wind.nrel.gov/designcodes/ simulators/NAFNoise] was used to directly predict trailing edge noise by taking into consideration the nature of the experiments.

Progress in Earth System Modeling since the ENIAC Calculation

NASA Astrophysics Data System (ADS)

Fung, I.

2009-05-01

The success of the first numerical weather prediction experiment on the ENIAC computer in 1950 was hinged on the expansion of the meteorological observing network, which led to theoretical advances in atmospheric dynamics and subsequently the implementation of the simplified equations on the computer. This paper briefly reviews the progress in Earth System Modeling and climate observations, and suggests a strategy to sustain and expand the observations needed to advance climate science and prediction.

Pre-launch Optical Characteristics of the Oculus-ASR Nanosatellite for Attitude and Shape Recognition Experiments

DTIC Science & Technology

2011-12-02

construction and validation of predictive computer models such as those used in Time-domain Analysis Simulation for Advanced Tracking (TASAT), a...characterization data, successful construction and validation of predictive computer models was accomplished. And an investigation in pose determination from...currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE (DD-MM-YYYY) 2. REPORT TYPE 3. DATES

Prediction of drug transport processes using simple parameters and PLS statistics. The use of ACD/logP and ACD/ChemSketch descriptors.

PubMed

Osterberg, T; Norinder, U

2001-01-01

A method of modelling and predicting biopharmaceutical properties using simple theoretically computed molecular descriptors and multivariate statistics has been investigated for several data sets related to solubility, IAM chromatography, permeability across Caco-2 cell monolayers, human intestinal perfusion, brain-blood partitioning, and P-glycoprotein ATPase activity. The molecular descriptors (e.g. molar refractivity, molar volume, index of refraction, surface tension and density) and logP were computed with ACD/ChemSketch and ACD/logP, respectively. Good statistical models were derived that permit simple computational prediction of biopharmaceutical properties. All final models derived had R(2) values ranging from 0.73 to 0.95 and Q(2) values ranging from 0.69 to 0.86. The RMSEP values for the external test sets ranged from 0.24 to 0.85 (log scale).

Thermodynamic model effects on the design and optimization of natural gas plants

DOE Office of Scientific and Technical Information (OSTI.GOV)

Diaz, S.; Zabaloy, M.; Brignole, E.A.

1999-07-01

The design and optimization of natural gas plants is carried out on the basis of process simulators. The physical property package is generally based on cubic equations of state. By rigorous thermodynamics phase equilibrium conditions, thermodynamic functions, equilibrium phase separations, work and heat are computed. The aim of this work is to analyze the NGL turboexpansion process and identify possible process computations that are more sensitive to model predictions accuracy. Three equations of state, PR, SRK and Peneloux modification, are used to study the effect of property predictions on process calculations and plant optimization. It is shown that turboexpander plantsmore » have moderate sensitivity with respect to phase equilibrium computations, but higher accuracy is required for the prediction of enthalpy and turboexpansion work. The effect of modeling CO{sub 2} solubility is also critical in mixtures with high CO{sub 2} content in the feed.« less

Investigation of Particle Deposition in Internal Cooling Cavities of a Nozzle Guide Vane

NASA Astrophysics Data System (ADS)

Casaday, Brian Patrick

Experimental and computational studies were conducted regarding particle deposition in the internal film cooling cavities of nozzle guide vanes. An experimental facility was fabricated to simulate particle deposition on an impingement liner and upstream surface of a nozzle guide vane wall. The facility supplied particle-laden flow at temperatures up to 1000°F (540°C) to a simplified impingement cooling test section. The heated flow passed through a perforated impingement plate and impacted on a heated flat wall. The particle-laden impingement jets resulted in the buildup of deposit cones associated with individual impingement jets. The deposit growth rate increased with increasing temperature and decreasing impinging velocities. For some low flow rates or high flow temperatures, the deposit cones heights spanned the entire gap between the impingement plate and wall, and grew through the impingement holes. For high flow rates, deposit structures were removed by shear forces from the flow. At low temperatures, deposit formed not only as individual cones, but as ridges located at the mid-planes between impinging jets. A computational model was developed to predict the deposit buildup seen in the experiments. The test section geometry and fluid flow from the experiment were replicated computationally and an Eulerian-Lagrangian particle tracking technique was employed. Several particle sticking models were employed and tested for adequacy. Sticking models that accurately predicted locations and rates in external deposition experiments failed to predict certain structures or rates seen in internal applications. A geometry adaptation technique was employed and the effect on deposition prediction was discussed. A new computational sticking model was developed that predicts deposition rates based on the local wall shear. The growth patterns were compared to experiments under different operating conditions. Of all the sticking models employed, the model based on wall shear, in conjunction with geometry adaptation, proved to be the most accurate in predicting the forms of deposit growth. It was the only model that predicted the changing deposition trends based on flow temperature or Reynolds number, and is recommended for further investigation and application in the modeling of deposition in internal cooling cavities.

Computer-Related Success and Failure: A Longitudinal Field Study of the Factors Influencing Computer-Related Performance.

ERIC Educational Resources Information Center

Rozell, E. J.; Gardner, W. L., III

1999-01-01

A model of the intrapersonal processes impacting computer-related performance was tested using data from 75 manufacturing employees in a computer training course. Gender, computer experience, and attributional style were predictive of computer attitudes, which were in turn related to computer efficacy, task-specific performance expectations, and…

Analysis of rocket engine injection combustion processes

NASA Technical Reports Server (NTRS)

Salmon, J. W.; Saltzman, D. H.

1977-01-01

Mixing methodology improvement for the JANNAF DER and CICM injection/combustion analysis computer programs was accomplished. ZOM plane prediction model development was improved for installation into the new standardized DER computer program. An intra-element mixing model developing approach was recommended for gas/liquid coaxial injection elements for possible future incorporation into the CICM computer program.

Vehicular traffic noise prediction using soft computing approach.

PubMed

Singh, Daljeet; Nigam, S P; Agrawal, V P; Kumar, Maneek

2016-12-01

A new approach for the development of vehicular traffic noise prediction models is presented. Four different soft computing methods, namely, Generalized Linear Model, Decision Trees, Random Forests and Neural Networks, have been used to develop models to predict the hourly equivalent continuous sound pressure level, Leq, at different locations in the Patiala city in India. The input variables include the traffic volume per hour, percentage of heavy vehicles and average speed of vehicles. The performance of the four models is compared on the basis of performance criteria of coefficient of determination, mean square error and accuracy. 10-fold cross validation is done to check the stability of the Random Forest model, which gave the best results. A t-test is performed to check the fit of the model with the field data. Copyright © 2016 Elsevier Ltd. All rights reserved.

Development of a computer model to predict platform station keeping requirements in the Gulf of Mexico using remote sensing data

NASA Technical Reports Server (NTRS)

Barber, Bryan; Kahn, Laura; Wong, David

1990-01-01

Offshore operations such as oil drilling and radar monitoring require semisubmersible platforms to remain stationary at specific locations in the Gulf of Mexico. Ocean currents, wind, and waves in the Gulf of Mexico tend to move platforms away from their desired locations. A computer model was created to predict the station keeping requirements of a platform. The computer simulation uses remote sensing data from satellites and buoys as input. A background of the project, alternate approaches to the project, and the details of the simulation are presented.

Computational biology for cardiovascular biomarker discovery.

PubMed

Azuaje, Francisco; Devaux, Yvan; Wagner, Daniel

2009-07-01

Computational biology is essential in the process of translating biological knowledge into clinical practice, as well as in the understanding of biological phenomena based on the resources and technologies originating from the clinical environment. One such key contribution of computational biology is the discovery of biomarkers for predicting clinical outcomes using 'omic' information. This process involves the predictive modelling and integration of different types of data and knowledge for screening, diagnostic or prognostic purposes. Moreover, this requires the design and combination of different methodologies based on statistical analysis and machine learning. This article introduces key computational approaches and applications to biomarker discovery based on different types of 'omic' data. Although we emphasize applications in cardiovascular research, the computational requirements and advances discussed here are also relevant to other domains. We will start by introducing some of the contributions of computational biology to translational research, followed by an overview of methods and technologies used for the identification of biomarkers with predictive or classification value. The main types of 'omic' approaches to biomarker discovery will be presented with specific examples from cardiovascular research. This will include a review of computational methodologies for single-source and integrative data applications. Major computational methods for model evaluation will be described together with recommendations for reporting models and results. We will present recent advances in cardiovascular biomarker discovery based on the combination of gene expression and functional network analyses. The review will conclude with a discussion of key challenges for computational biology, including perspectives from the biosciences and clinical areas.

Operation of the computer model for microenvironment solar exposure

NASA Technical Reports Server (NTRS)

Gillis, J. R.; Bourassa, R. J.; Gruenbaum, P. E.

1995-01-01

A computer model for microenvironmental solar exposure was developed to predict solar exposure to satellite surfaces which may shadow or reflect on one another. This document describes the technical features of the model as well as instructions for the installation and use of the program.

On Non-Linear Sensitivity of Marine Biological Models to Parameter Variations

DTIC Science & Technology

2007-01-01

M.B., 2002. Understanding uncertain enviromental systems. In: Grasman, J., van Straten, G. (Eds.), Predictability and Nonlinear Modelling in Natural...model evaluations to compute sensitivity indices. Comput. Phys. Commun. 145, 280–297. Saltelli, A., Andres, T.H., Homma, T., 1993. Some new techniques

Numerical formulation for the prediction of solid/liquid change of a binary alloy

NASA Technical Reports Server (NTRS)

Schneider, G. E.; Tiwari, S. N.

1990-01-01

A computational model is presented for the prediction of solid/liquid phase change energy transport including the influence of free convection fluid flow in the liquid phase region. The computational model considers the velocity components of all non-liquid phase change material control volumes to be zero but fully solves the coupled mass-momentum problem within the liquid region. The thermal energy model includes the entire domain and uses an enthalpy like model and a recently developed method for handling the phase change interface nonlinearity. Convergence studies are performed and comparisons made with experimental data for two different problem specifications. The convergence studies indicate that grid independence was achieved and the comparison with experimental data indicates excellent quantitative prediction of the melt fraction evolution. Qualitative data is also provided in the form of velocity vector diagrams and isotherm plots for selected times in the evolution of both problems. The computational costs incurred are quite low by comparison with previous efforts on solving these problems.

On a turbulent wall model to predict hemolysis numerically in medical devices

NASA Astrophysics Data System (ADS)

Lee, Seunghun; Chang, Minwook; Kang, Seongwon; Hur, Nahmkeon; Kim, Wonjung

2017-11-01

Analyzing degradation of red blood cells is very important for medical devices with blood flows. The blood shear stress has been recognized as the most dominant factor for hemolysis in medical devices. Compared to laminar flows, turbulent flows have higher shear stress values in the regions near the wall. In case of predicting hemolysis numerically, this phenomenon can require a very fine mesh and large computational resources. In order to resolve this issue, the purpose of this study is to develop a turbulent wall model to predict the hemolysis more efficiently. In order to decrease the numerical error of hemolysis prediction in a coarse grid resolution, we divided the computational domain into two regions and applied different approaches to each region. In the near-wall region with a steep velocity gradient, an analytic approach using modeled velocity profile is applied to reduce a numerical error to allow a coarse grid resolution. We adopt the Van Driest law as a model for the mean velocity profile. In a region far from the wall, a regular numerical discretization is applied. The proposed turbulent wall model is evaluated for a few turbulent flows inside a cannula and centrifugal pumps. The results present that the proposed turbulent wall model for hemolysis improves the computational efficiency significantly for engineering applications. Corresponding author.

Biomechanical model for computing deformations for whole-body image registration: A meshless approach.

PubMed

Li, Mao; Miller, Karol; Joldes, Grand Roman; Kikinis, Ron; Wittek, Adam

2016-12-01

Patient-specific biomechanical models have been advocated as a tool for predicting deformations of soft body organs/tissue for medical image registration (aligning two sets of images) when differences between the images are large. However, complex and irregular geometry of the body organs makes generation of patient-specific biomechanical models very time-consuming. Meshless discretisation has been proposed to solve this challenge. However, applications so far have been limited to 2D models and computing single organ deformations. In this study, 3D comprehensive patient-specific nonlinear biomechanical models implemented using meshless Total Lagrangian explicit dynamics algorithms are applied to predict a 3D deformation field for whole-body image registration. Unlike a conventional approach that requires dividing (segmenting) the image into non-overlapping constituents representing different organs/tissues, the mechanical properties are assigned using the fuzzy c-means algorithm without the image segmentation. Verification indicates that the deformations predicted using the proposed meshless approach are for practical purposes the same as those obtained using the previously validated finite element models. To quantitatively evaluate the accuracy of the predicted deformations, we determined the spatial misalignment between the registered (i.e. source images warped using the predicted deformations) and target images by computing the edge-based Hausdorff distance. The Hausdorff distance-based evaluation determines that our meshless models led to successful registration of the vast majority of the image features. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

A Computer Simulation Approach to Assessing Therapeutic Intervention Points for the Prevention of Cytokine-Induced Cartilage Breakdown

PubMed Central

Proctor, CJ; Macdonald, C; Milner, JM; Rowan, AD; Cawston, TE

2014-01-01

Objective To use a novel computational approach to examine the molecular pathways involved in cartilage breakdown and to use computer simulation to test possible interventions for reducing collagen release. Methods We constructed a computational model of the relevant molecular pathways using the Systems Biology Markup Language, a computer-readable format of a biochemical network. The model was constructed using our experimental data showing that interleukin-1 (IL-1) and oncostatin M (OSM) act synergistically to up-regulate collagenase protein levels and activity and initiate cartilage collagen breakdown. Simulations were performed using the COPASI software package. Results The model predicted that simulated inhibition of JNK or p38 MAPK, and overexpression of tissue inhibitor of metalloproteinases 3 (TIMP-3) led to a reduction in collagen release. Overexpression of TIMP-1 was much less effective than that of TIMP-3 and led to a delay, rather than a reduction, in collagen release. Simulated interventions of receptor antagonists and inhibition of JAK-1, the first kinase in the OSM pathway, were ineffective. So, importantly, the model predicts that it is more effective to intervene at targets that are downstream, such as the JNK pathway, rather than those that are close to the cytokine signal. In vitro experiments confirmed the effectiveness of JNK inhibition. Conclusion Our study shows the value of computer modeling as a tool for examining possible interventions by which to reduce cartilage collagen breakdown. The model predicts that interventions that either prevent transcription or inhibit the activity of collagenases are promising strategies and should be investigated further in an experimental setting. PMID:24757149

Genomic Prediction Accounting for Residual Heteroskedasticity

PubMed Central

Ou, Zhining; Tempelman, Robert J.; Steibel, Juan P.; Ernst, Catherine W.; Bates, Ronald O.; Bello, Nora M.

2015-01-01

Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit. PMID:26564950

The prediction of satellite ephemeris errors as they result from surveillance system measurement errors

NASA Astrophysics Data System (ADS)

Simmons, B. E.

1981-08-01

This report derives equations predicting satellite ephemeris error as a function of measurement errors of space-surveillance sensors. These equations lend themselves to rapid computation with modest computer resources. They are applicable over prediction times such that measurement errors, rather than uncertainties of atmospheric drag and of Earth shape, dominate in producing ephemeris error. This report describes the specialization of these equations underlying the ANSER computer program, SEEM (Satellite Ephemeris Error Model). The intent is that this report be of utility to users of SEEM for interpretive purposes, and to computer programmers who may need a mathematical point of departure for limited generalization of SEEM.

Advances and trends in computational structural mechanics

NASA Technical Reports Server (NTRS)

Noor, A. K.

1986-01-01

Recent developments in computational structural mechanics are reviewed with reference to computational needs for future structures technology, advances in computational models for material behavior, discrete element technology, assessment and control of numerical simulations of structural response, hybrid analysis, and techniques for large-scale optimization. Research areas in computational structural mechanics which have high potential for meeting future technological needs are identified. These include prediction and analysis of the failure of structural components made of new materials, development of computational strategies and solution methodologies for large-scale structural calculations, and assessment of reliability and adaptive improvement of response predictions.

Three-dimensional digital-computer model of the Ferron sandstone aquifer near Emery, Utah

USGS Publications Warehouse

Morrissey, Daniel J.; Lines, Gregory C.; Bartholoma, Scott D.

1980-01-01

A three-dimensional finite-difference computer model of the Ferron sandstone aquifer was used to simulate groundwater flow in the Emery coal field in east-central Utah. The model also was used to predict the effects of proposed surface mining and the resulting mine dewatering on potentiometric surfaces of the aquifer. The model was calibrated in a steady-state simulation using water levels and manmade discharges from the aquifer that were observed during 1979. Too few data were available to verify the calibrated model in a transient-state simulation with historical aquifer response to manmade discharges. Predictions made with the model are considered to be semiquantitative. Discharge from the proposed surface mine was predicted to average 0.3 cubic foot per second through 15 years of operation. Drawdowns of 5 feet in the potentiometric surface of the aquifer were predicted to extend as much as 3 miles from the proposed mine after 15 years of operation. (USGS)

A multidimensional stability model for predicting shallow landslide size and shape across landscapes

Treesearch

David G. Milledge; Dino Bellugi; Jim A. McKean; Alexander L. Densmore; William E. Dietrich

2014-01-01

The size of a shallow landslide is a fundamental control on both its hazard and geomorphic importance. Existing models are either unable to predict landslide size or are computationally intensive such that they cannot practically be applied across landscapes. We derive a model appropriate for natural slopes that is capable of predicting shallow landslide size but...

Evaluating the Theoretic Adequacy and Applied Potential of Computational Models of the Spacing Effect.

PubMed

Walsh, Matthew M; Gluck, Kevin A; Gunzelmann, Glenn; Jastrzembski, Tiffany; Krusmark, Michael

2018-06-01

The spacing effect is among the most widely replicated empirical phenomena in the learning sciences, and its relevance to education and training is readily apparent. Yet successful applications of spacing effect research to education and training is rare. Computational modeling can provide the crucial link between a century of accumulated experimental data on the spacing effect and the emerging interest in using that research to enable adaptive instruction. In this paper, we review relevant literature and identify 10 criteria for rigorously evaluating computational models of the spacing effect. Five relate to evaluating the theoretic adequacy of a model, and five relate to evaluating its application potential. We use these criteria to evaluate a novel computational model of the spacing effect called the Predictive Performance Equation (PPE). Predictive Performance Equation combines elements of earlier models of learning and memory including the General Performance Equation, Adaptive Control of Thought-Rational, and the New Theory of Disuse, giving rise to a novel computational account of the spacing effect that performs favorably across the complete sets of theoretic and applied criteria. We implemented two other previously published computational models of the spacing effect and compare them to PPE using the theoretic and applied criteria as guides. Copyright © 2018 Cognitive Science Society, Inc.

Prediction of Chemical Function: Model Development and Application

EPA Science Inventory

The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (...

Predictive Models and Computational Toxicology (II IBAMTOX)

EPA Science Inventory

EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

Verification of a VRF Heat Pump Computer Model in EnergyPlus

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nigusse, Bereket; Raustad, Richard

2013-06-15

This paper provides verification results of the EnergyPlus variable refrigerant flow (VRF) heat pump computer model using manufacturer's performance data. The paper provides an overview of the VRF model, presents the verification methodology, and discusses the results. The verification provides quantitative comparison of full and part-load performance to manufacturer's data in cooling-only and heating-only modes of operation. The VRF heat pump computer model uses dual range bi-quadratic performance curves to represent capacity and Energy Input Ratio (EIR) as a function of indoor and outdoor air temperatures, and dual range quadratic performance curves as a function of part-load-ratio for modeling part-loadmore » performance. These performance curves are generated directly from manufacturer's published performance data. The verification compared the simulation output directly to manufacturer's performance data, and found that the dual range equation fit VRF heat pump computer model predicts the manufacturer's performance data very well over a wide range of indoor and outdoor temperatures and part-load conditions. The predicted capacity and electric power deviations are comparbale to equation-fit HVAC computer models commonly used for packaged and split unitary HVAC equipment.« less

Self-consistent clustering analysis: an efficient multiscale scheme for inelastic heterogeneous materials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Z.; Bessa, M. A.; Liu, W.K.

A predictive computational theory is shown for modeling complex, hierarchical materials ranging from metal alloys to polymer nanocomposites. The theory can capture complex mechanisms such as plasticity and failure that span across multiple length scales. This general multiscale material modeling theory relies on sound principles of mathematics and mechanics, and a cutting-edge reduced order modeling method named self-consistent clustering analysis (SCA) [Zeliang Liu, M.A. Bessa, Wing Kam Liu, “Self-consistent clustering analysis: An efficient multi-scale scheme for inelastic heterogeneous materials,” Comput. Methods Appl. Mech. Engrg. 306 (2016) 319–341]. SCA reduces by several orders of magnitude the computational cost of micromechanical andmore » concurrent multiscale simulations, while retaining the microstructure information. This remarkable increase in efficiency is achieved with a data-driven clustering method. Computationally expensive operations are performed in the so-called offline stage, where degrees of freedom (DOFs) are agglomerated into clusters. The interaction tensor of these clusters is computed. In the online or predictive stage, the Lippmann-Schwinger integral equation is solved cluster-wise using a self-consistent scheme to ensure solution accuracy and avoid path dependence. To construct a concurrent multiscale model, this scheme is applied at each material point in a macroscale structure, replacing a conventional constitutive model with the average response computed from the microscale model using just the SCA online stage. A regularized damage theory is incorporated in the microscale that avoids the mesh and RVE size dependence that commonly plagues microscale damage calculations. The SCA method is illustrated with two cases: a carbon fiber reinforced polymer (CFRP) structure with the concurrent multiscale model and an application to fatigue prediction for additively manufactured metals. For the CFRP problem, a speed up estimated to be about 43,000 is achieved by using the SCA method, as opposed to FE2, enabling the solution of an otherwise computationally intractable problem. The second example uses a crystal plasticity constitutive law and computes the fatigue potency of extrinsic microscale features such as voids. This shows that local stress and strain are capture sufficiently well by SCA. This model has been incorporated in a process-structure-properties prediction framework for process design in additive manufacturing.« less

Rapid high performance liquid chromatography method development with high prediction accuracy, using 5cm long narrow bore columns packed with sub-2microm particles and Design Space computer modeling.

PubMed

Fekete, Szabolcs; Fekete, Jeno; Molnár, Imre; Ganzler, Katalin

2009-11-06

Many different strategies of reversed phase high performance liquid chromatographic (RP-HPLC) method development are used today. This paper describes a strategy for the systematic development of ultrahigh-pressure liquid chromatographic (UHPLC or UPLC) methods using 5cmx2.1mm columns packed with sub-2microm particles and computer simulation (DryLab((R)) package). Data for the accuracy of computer modeling in the Design Space under ultrahigh-pressure conditions are reported. An acceptable accuracy for these predictions of the computer models is presented. This work illustrates a method development strategy, focusing on time reduction up to a factor 3-5, compared to the conventional HPLC method development and exhibits parts of the Design Space elaboration as requested by the FDA and ICH Q8R1. Furthermore this paper demonstrates the accuracy of retention time prediction at elevated pressure (enhanced flow-rate) and shows that the computer-assisted simulation can be applied with sufficient precision for UHPLC applications (p>400bar). Examples of fast and effective method development in pharmaceutical analysis, both for gradient and isocratic separations are presented.

20170312 - In Silico Dynamics: computer simulation in a ...

EPA Pesticide Factsheets

Abstract: Utilizing cell biological information to predict higher order biological processes is a significant challenge in predictive toxicology. This is especially true for highly dynamical systems such as the embryo where morphogenesis, growth and differentiation require precisely orchestrated interactions between diverse cell populations. In patterning the embryo, genetic signals setup spatial information that cells then translate into a coordinated biological response. This can be modeled as ‘biowiring diagrams’ representing genetic signals and responses. Because the hallmark of multicellular organization resides in the ability of cells to interact with one another via well-conserved signaling pathways, multiscale computational (in silico) models that enable these interactions provide a platform to translate cellular-molecular lesions perturbations into higher order predictions. Just as ‘the Cell’ is the fundamental unit of biology so too should it be the computational unit (‘Agent’) for modeling embryogenesis. As such, we constructed multicellular agent-based models (ABM) with ‘CompuCell3D’ (www.compucell3d.org) to simulate kinematics of complex cell signaling networks and enable critical tissue events for use in predictive toxicology. Seeding the ABMs with HTS/HCS data from ToxCast demonstrated the potential to predict, quantitatively, the higher order impacts of chemical disruption at the cellular or bioche

In Silico Dynamics: computer simulation in a Virtual Embryo ...

EPA Pesticide Factsheets

Abstract: Utilizing cell biological information to predict higher order biological processes is a significant challenge in predictive toxicology. This is especially true for highly dynamical systems such as the embryo where morphogenesis, growth and differentiation require precisely orchestrated interactions between diverse cell populations. In patterning the embryo, genetic signals setup spatial information that cells then translate into a coordinated biological response. This can be modeled as ‘biowiring diagrams’ representing genetic signals and responses. Because the hallmark of multicellular organization resides in the ability of cells to interact with one another via well-conserved signaling pathways, multiscale computational (in silico) models that enable these interactions provide a platform to translate cellular-molecular lesions perturbations into higher order predictions. Just as ‘the Cell’ is the fundamental unit of biology so too should it be the computational unit (‘Agent’) for modeling embryogenesis. As such, we constructed multicellular agent-based models (ABM) with ‘CompuCell3D’ (www.compucell3d.org) to simulate kinematics of complex cell signaling networks and enable critical tissue events for use in predictive toxicology. Seeding the ABMs with HTS/HCS data from ToxCast demonstrated the potential to predict, quantitatively, the higher order impacts of chemical disruption at the cellular or biochemical level. This is demonstrate

Wall Modeled Large Eddy Simulation of Airfoil Trailing Edge Noise

NASA Astrophysics Data System (ADS)

Kocheemoolayil, Joseph; Lele, Sanjiva

2014-11-01

Large eddy simulation (LES) of airfoil trailing edge noise has largely been restricted to low Reynolds numbers due to prohibitive computational cost. Wall modeled LES (WMLES) is a computationally cheaper alternative that makes full-scale Reynolds numbers relevant to large wind turbines accessible. A systematic investigation of trailing edge noise prediction using WMLES is conducted. Detailed comparisons are made with experimental data. The stress boundary condition from a wall model does not constrain the fluctuating velocity to vanish at the wall. This limitation has profound implications for trailing edge noise prediction. The simulation over-predicts the intensity of fluctuating wall pressure and far-field noise. An improved wall model formulation that minimizes the over-prediction of fluctuating wall pressure is proposed and carefully validated. The flow configurations chosen for the study are from the workshop on benchmark problems for airframe noise computations. The large eddy simulation database is used to examine the adequacy of scaling laws that quantify the dependence of trailing edge noise on Mach number, Reynolds number and angle of attack. Simplifying assumptions invoked in engineering approaches towards predicting trailing edge noise are critically evaluated. We gratefully acknowledge financial support from GE Global Research and thank Cascade Technologies Inc. for providing access to their massively-parallel large eddy simulation framework.

Initial comparison of single cylinder Stirling engine computer model predictions with test results

NASA Technical Reports Server (NTRS)

Tew, R. C., Jr.; Thieme, L. G.; Miao, D.

1979-01-01

A NASA developed digital computer code for a Stirling engine, modelling the performance of a single cylinder rhombic drive ground performance unit (GPU), is presented and its predictions are compared to test results. The GPU engine incorporates eight regenerator/cooler units and the engine working space is modelled by thirteen control volumes. The model calculates indicated power and efficiency for a given engine speed, mean pressure, heater and expansion space metal temperatures and cooler water inlet temperature and flow rate. Comparison of predicted and observed powers implies that the reference pressure drop calculations underestimate actual pressure drop, possibly due to oil contamination in the regenerator/cooler units, methane contamination in the working gas or the underestimation of mechanical loss. For a working gas of hydrogen, the predicted values of brake power are from 0 to 6% higher than experimental values, and brake efficiency is 6 to 16% higher, while for helium the predicted brake power and efficiency are 2 to 15% higher than the experimental.

Analysis of seismograms from a downhole array in sediments near San Francisco Bay

USGS Publications Warehouse

Joyner, William B.; Warrick, Richard E.; Oliver, Adolph A.

1976-01-01

A four-level downhole array of three-component instruments was established on the southwest shore of San Francisco Bay to monitor the effect of the sediments on low-amplitude seismic ground motion. The deepest instrument is at a depth of 186 meters, two meters below the top of the Franciscan bedrock. Earthquake data from regional distances (29 km ≤ Δ ≤ 485 km) over a wide range of azimuths are compared with the predictions of a simple plane-layered model with material properties independently determined. Spectral ratios between the surface and bedrock computed for the one horizontal component of motion that was analyzed agree rather well with the model predictions; the model predicts the frequencies of the first three peaks within 10 percent in most cases and the height of the peaks within 50 percent in most cases. Surface time histories computed from the theoretical model predict the time variations of amplitude and frequency content reasonably well, but correlations of individual cycles cannot be made between observed and predicted traces.

Prediction of pork loin quality using online computer vision system and artificial intelligence model.

PubMed

Sun, Xin; Young, Jennifer; Liu, Jeng-Hung; Newman, David

2018-06-01

The objective of this project was to develop a computer vision system (CVS) for objective measurement of pork loin under industry speed requirement. Color images of pork loin samples were acquired using a CVS. Subjective color and marbling scores were determined according to the National Pork Board standards by a trained evaluator. Instrument color measurement and crude fat percentage were used as control measurements. Image features (18 color features; 1 marbling feature; 88 texture features) were extracted from whole pork loin color images. Artificial intelligence prediction model (support vector machine) was established for pork color and marbling quality grades. The results showed that CVS with support vector machine modeling reached the highest prediction accuracy of 92.5% for measured pork color score and 75.0% for measured pork marbling score. This research shows that the proposed artificial intelligence prediction model with CVS can provide an effective tool for predicting color and marbling in the pork industry at online speeds. Copyright © 2018 Elsevier Ltd. All rights reserved.

Several examples where turbulence models fail in inlet flow field analysis

NASA Technical Reports Server (NTRS)

Anderson, Bernhard H.

1993-01-01

Computational uncertainties in turbulence modeling for three dimensional inlet flow fields include flows approaching separation, strength of secondary flow field, three dimensional flow predictions of vortex liftoff, and influence of vortex-boundary layer interactions; computational uncertainties in vortex generator modeling include representation of generator vorticity field and the relationship between generator and vorticity field. The objectives of the inlet flow field studies presented in this document are to advance the understanding, prediction, and control of intake distortion and to study the basic interactions that influence this design problem.

LAMPS software

NASA Technical Reports Server (NTRS)

Perkey, D. J.; Kreitzberg, C. W.

1984-01-01

The dynamic prediction model along with its macro-processor capability and data flow system from the Drexel Limited-Area and Mesoscale Prediction System (LAMPS) were converted and recorded for the Perkin-Elmer 3220. The previous version of this model was written for Control Data Corporation 7600 and CRAY-1a computer environment which existed until recently at the National Center for Atmospheric Research. The purpose of this conversion was to prepare LAMPS for porting to computer environments other than that encountered at NCAR. The emphasis was shifted from programming tasks to model simulation and evaluation tests.

Computational Materials Research

NASA Technical Reports Server (NTRS)

Hinkley, Jeffrey A. (Editor); Gates, Thomas S. (Editor)

1996-01-01

Computational Materials aims to model and predict thermodynamic, mechanical, and transport properties of polymer matrix composites. This workshop, the second coordinated by NASA Langley, reports progress in measurements and modeling at a number of length scales: atomic, molecular, nano, and continuum. Assembled here are presentations on quantum calculations for force field development, molecular mechanics of interfaces, molecular weight effects on mechanical properties, molecular dynamics applied to poling of polymers for electrets, Monte Carlo simulation of aromatic thermoplastics, thermal pressure coefficients of liquids, ultrasonic elastic constants, group additivity predictions, bulk constitutive models, and viscoplasticity characterization.

Bayesian inference based on dual generalized order statistics from the exponentiated Weibull model

NASA Astrophysics Data System (ADS)

Al Sobhi, Mashail M.

2015-02-01

Bayesian estimation for the two parameters and the reliability function of the exponentiated Weibull model are obtained based on dual generalized order statistics (DGOS). Also, Bayesian prediction bounds for future DGOS from exponentiated Weibull model are obtained. The symmetric and asymmetric loss functions are considered for Bayesian computations. The Markov chain Monte Carlo (MCMC) methods are used for computing the Bayes estimates and prediction bounds. The results have been specialized to the lower record values. Comparisons are made between Bayesian and maximum likelihood estimators via Monte Carlo simulation.

Computational Fluid Dynamics of Whole-Body Aircraft

NASA Astrophysics Data System (ADS)

Agarwal, Ramesh

1999-01-01

The current state of the art in computational aerodynamics for whole-body aircraft flowfield simulations is described. Recent advances in geometry modeling, surface and volume grid generation, and flow simulation algorithms have led to accurate flowfield predictions for increasingly complex and realistic configurations. As a result, computational aerodynamics has emerged as a crucial enabling technology for the design and development of flight vehicles. Examples illustrating the current capability for the prediction of transport and fighter aircraft flowfields are presented. Unfortunately, accurate modeling of turbulence remains a major difficulty in the analysis of viscosity-dominated flows. In the future, inverse design methods, multidisciplinary design optimization methods, artificial intelligence technology, and massively parallel computer technology will be incorporated into computational aerodynamics, opening up greater opportunities for improved product design at substantially reduced costs.

Analysis of Test Case Computations and Experiments for the First Aeroelastic Prediction Workshop

NASA Technical Reports Server (NTRS)

Schuster, David M.; Heeg, Jennifer; Wieseman, Carol D.; Chwalowski, Pawel

2013-01-01

This paper compares computational and experimental data from the Aeroelastic Prediction Workshop (AePW) held in April 2012. This workshop was designed as a series of technical interchange meetings to assess the state of the art of computational methods for predicting unsteady flowfields and static and dynamic aeroelastic response. The goals are to provide an impartial forum to evaluate the effectiveness of existing computer codes and modeling techniques to simulate aeroelastic problems and to identify computational and experimental areas needing additional research and development. Three subject configurations were chosen from existing wind-tunnel data sets where there is pertinent experimental data available for comparison. Participant researchers analyzed one or more of the subject configurations, and results from all of these computations were compared at the workshop.

Application of a time-magnitude prediction model for earthquakes

NASA Astrophysics Data System (ADS)

An, Weiping; Jin, Xueshen; Yang, Jialiang; Dong, Peng; Zhao, Jun; Zhang, He

2007-06-01

In this paper we discuss the physical meaning of the magnitude-time model parameters for earthquake prediction. The gestation process for strong earthquake in all eleven seismic zones in China can be described by the magnitude-time prediction model using the computations of the parameters of the model. The average model parameter values for China are: b = 0.383, c=0.154, d = 0.035, B = 0.844, C = -0.209, and D = 0.188. The robustness of the model parameters is estimated from the variation in the minimum magnitude of the transformed data, the spatial extent, and the temporal period. Analysis of the spatial and temporal suitability of the model indicates that the computation unit size should be at least 4° × 4° for seismic zones in North China, at least 3° × 3° in Southwest and Northwest China, and the time period should be as long as possible.

An experimental and theoretical investigation of deposition patterns from an agricultural airplane

NASA Technical Reports Server (NTRS)

Morris, D. J.; Croom, C. C.; Vandam, C. P.; Holmes, B. J.

1984-01-01

A flight test program has been conducted with a representative agricultural airplane to provide data for validating a computer program model which predicts aerially applied particle deposition. Test procedures and the data from this test are presented and discussed. The computer program features are summarized, and comparisons of predicted and measured particle deposition are presented. Applications of the computer program for spray pattern improvement are illustrated.

Orbital maneuvering engine feed system coupled stability investigation

NASA Technical Reports Server (NTRS)

Kahn, D. R.; Schuman, M. D.; Hunting, J. K.; Fertig, K. W.

1975-01-01

A digital computer model used to analyze and predict engine feed system coupled instabilities over a frequency range of 10 to 1000 Hz was developed and verified. The analytical approach to modeling the feed system hydrodynamics, combustion dynamics, chamber dynamics, and overall engineering model structure is described and the governing equations in each of the technical areas are presented. This is followed by a description of the generalized computer model, including formulation of the discrete subprograms and their integration into an overall engineering model structure. The operation and capabilities of the engineering model were verified by comparing the model's theoretical predictions with experimental data from an OMS-type engine with a known feed system/engine chugging history.

Development of a fourth generation predictive capability maturity model.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hills, Richard Guy; Witkowski, Walter R.; Urbina, Angel

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 SNLs mission, themore » 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.« less

Optimization of monitoring networks based on uncertainty quantification of model predictions of contaminant transport

NASA Astrophysics Data System (ADS)

Vesselinov, V. V.; Harp, D.

2010-12-01

The process of decision making to protect groundwater resources requires a detailed estimation of uncertainties in model predictions. Various uncertainties associated with modeling a natural system, such as: (1) measurement and computational errors; (2) uncertainties in the conceptual model and model-parameter estimates; (3) simplifications in model setup and numerical representation of governing processes, contribute to the uncertainties in the model predictions. Due to this combination of factors, the sources of predictive uncertainties are generally difficult to quantify individually. Decision support related to optimal design of monitoring networks requires (1) detailed analyses of existing uncertainties related to model predictions of groundwater flow and contaminant transport, (2) optimization of the proposed monitoring network locations in terms of their efficiency to detect contaminants and provide early warning. We apply existing and newly-proposed methods to quantify predictive uncertainties and to optimize well locations. An important aspect of the analysis is the application of newly-developed optimization technique based on coupling of Particle Swarm and Levenberg-Marquardt optimization methods which proved to be robust and computationally efficient. These techniques and algorithms are bundled in a software package called MADS. MADS (Model Analyses for Decision Support) is an object-oriented code that is capable of performing various types of model analyses and supporting model-based decision making. The code can be executed under different computational modes, which include (1) sensitivity analyses (global and local), (2) Monte Carlo analysis, (3) model calibration, (4) parameter estimation, (5) uncertainty quantification, and (6) model selection. The code can be externally coupled with any existing model simulator through integrated modules that read/write input and output files using a set of template and instruction files (consistent with the PEST I/O protocol). MADS can also be internally coupled with a series of built-in analytical simulators. MADS provides functionality to work directly with existing control files developed for the code PEST (Doherty 2009). To perform the computational modes mentioned above, the code utilizes (1) advanced Latin-Hypercube sampling techniques (including Improved Distributed Sampling), (2) various gradient-based Levenberg-Marquardt optimization methods, (3) advanced global optimization methods (including Particle Swarm Optimization), and (4) a selection of alternative objective functions. The code has been successfully applied to perform various model analyses related to environmental management of real contamination sites. Examples include source identification problems, quantification of uncertainty, model calibration, and optimization of monitoring networks. The methodology and software codes are demonstrated using synthetic and real case studies where monitoring networks are optimized taking into account the uncertainty in model predictions of contaminant transport.

Prediction of miRNA targets.

PubMed

Oulas, Anastasis; Karathanasis, Nestoras; Louloupi, Annita; Pavlopoulos, Georgios A; Poirazi, Panayiota; Kalantidis, Kriton; Iliopoulos, Ioannis

2015-01-01

Computational methods for miRNA target prediction are currently undergoing extensive review and evaluation. There is still a great need for improvement of these tools and bioinformatics approaches are looking towards high-throughput experiments in order to validate predictions. The combination of large-scale techniques with computational tools will not only provide greater credence to computational predictions but also lead to the better understanding of specific biological questions. Current miRNA target prediction tools utilize probabilistic learning algorithms, machine learning methods and even empirical biologically defined rules in order to build models based on experimentally verified miRNA targets. Large-scale protein downregulation assays and next-generation sequencing (NGS) are now being used to validate methodologies and compare the performance of existing tools. Tools that exhibit greater correlation between computational predictions and protein downregulation or RNA downregulation are considered the state of the art. Moreover, efficiency in prediction of miRNA targets that are concurrently verified experimentally provides additional validity to computational predictions and further highlights the competitive advantage of specific tools and their efficacy in extracting biologically significant results. In this review paper, we discuss the computational methods for miRNA target prediction and provide a detailed comparison of methodologies and features utilized by each specific tool. Moreover, we provide an overview of current state-of-the-art high-throughput methods used in miRNA target prediction.

Near-wall k-epsilon turbulence modeling

NASA Technical Reports Server (NTRS)

Mansour, N. N.; Kim, J.; Moin, P.

1987-01-01

The flow fields from a turbulent channel simulation are used to compute the budgets for the turbulent kinetic energy (k) and its dissipation rate (epsilon). Data from boundary layer simulations are used to analyze the dependence of the eddy-viscosity damping-function on the Reynolds number and the distance from the wall. The computed budgets are used to test existing near-wall turbulence models of the k-epsilon type. It was found that the turbulent transport models should be modified in the vicinity of the wall. It was also found that existing models for the different terms in the epsilon-budget are adequate in the region from the wall, but need modification near the wall. The channel flow is computed using a k-epsilon model with an eddy-viscosity damping function from the data and no damping functions in the epsilon-equation. These computations show that the k-profile can be adequately predicted, but to correctly predict the epsilon-profile, damping functions in the epsilon-equation are needed.

A computer model for predicting grapevine cold hardiness

USDA-ARS?s Scientific Manuscript database

We developed a robust computer model of grapevine bud cold hardiness that will aid in the anticipation of and response to potential injury from fluctuations in winter temperature and from extreme cold events. The model uses time steps of 1 day along with the measured daily mean air temperature to ca...

First Steps in Computational Systems Biology: A Practical Session in Metabolic Modeling and Simulation

ERIC Educational Resources Information Center

Reyes-Palomares, Armando; Sanchez-Jimenez, Francisca; Medina, Miguel Angel

2009-01-01

A comprehensive understanding of biological functions requires new systemic perspectives, such as those provided by systems biology. Systems biology approaches are hypothesis-driven and involve iterative rounds of model building, prediction, experimentation, model refinement, and development. Developments in computer science are allowing for ever…

A Pulsatile Cardiovascular Computer Model for Teaching Heart-Blood Vessel Interaction.

ERIC Educational Resources Information Center

Campbell, Kenneth; And Others

1982-01-01

Describes a model which gives realistic predictions of pulsatile pressure, flow, and volume events in the cardiovascular system. Includes computer oriented laboratory exercises for veterinary and graduate students; equations of the dynamic and algebraic models; and a flow chart for the cardiovascular teaching program. (JN)

High skill in low-frequency climate response through fluctuation dissipation theorems despite structural instability.

PubMed

Majda, Andrew J; Abramov, Rafail; Gershgorin, Boris

2010-01-12

Climate change science focuses on predicting the coarse-grained, planetary-scale, longtime changes in the climate system due to either changes in external forcing or internal variability, such as the impact of increased carbon dioxide. The predictions of climate change science are carried out through comprehensive, computational atmospheric, and oceanic simulation models, which necessarily parameterize physical features such as clouds, sea ice cover, etc. Recently, it has been suggested that there is irreducible imprecision in such climate models that manifests itself as structural instability in climate statistics and which can significantly hamper the skill of computer models for climate change. A systematic approach to deal with this irreducible imprecision is advocated through algorithms based on the Fluctuation Dissipation Theorem (FDT). There are important practical and computational advantages for climate change science when a skillful FDT algorithm is established. The FDT response operator can be utilized directly for multiple climate change scenarios, multiple changes in forcing, and other parameters, such as damping and inverse modelling directly without the need of running the complex climate model in each individual case. The high skill of FDT in predicting climate change, despite structural instability, is developed in an unambiguous fashion using mathematical theory as guidelines in three different test models: a generic class of analytical models mimicking the dynamical core of the computer climate models, reduced stochastic models for low-frequency variability, and models with a significant new type of irreducible imprecision involving many fast, unstable modes.

Computational Modeling and Simulation of Developmental ...

EPA Pesticide Factsheets

Standard practice for assessing developmental toxicity is the observation of apical endpoints (intrauterine death, fetal growth retardation, structural malformations) in pregnant rats/rabbits following exposure during organogenesis. EPA’s computational toxicology research program (ToxCast) generated vast in vitro cellular and molecular effects data on >1858 chemicals in >600 high-throughput screening (HTS) assays. The diversity of assays has been increased for developmental toxicity with several HTS platforms, including the devTOX-quickPredict assay from Stemina Biomarker Discovery utilizing the human embryonic stem cell line (H9). Translating these HTS data into higher order-predictions of developmental toxicity is a significant challenge. Here, we address the application of computational systems models that recapitulate the kinematics of dynamical cell signaling networks (e.g., SHH, FGF, BMP, retinoids) in a CompuCell3D.org modeling environment. Examples include angiogenesis (angiodysplasia) and dysmorphogenesis. Being numerically responsive to perturbation, these models are amenable to data integration for systems Toxicology and Adverse Outcome Pathways (AOPs). The AOP simulation outputs predict potential phenotypes based on the in vitro HTS data ToxCast. A heuristic computational intelligence framework that recapitulates the kinematics of dynamical cell signaling networks in the embryo, together with the in vitro profiling data, produce quantitative predic

Direct Methods for Predicting Movement Biomechanics Based Upon Optimal Control Theory with Implementation in OpenSim.

PubMed

Porsa, Sina; Lin, Yi-Chung; Pandy, Marcus G

2016-08-01

The aim of this study was to compare the computational performances of two direct methods for solving large-scale, nonlinear, optimal control problems in human movement. Direct shooting and direct collocation were implemented on an 8-segment, 48-muscle model of the body (24 muscles on each side) to compute the optimal control solution for maximum-height jumping. Both algorithms were executed on a freely-available musculoskeletal modeling platform called OpenSim. Direct collocation converged to essentially the same optimal solution up to 249 times faster than direct shooting when the same initial guess was assumed (3.4 h of CPU time for direct collocation vs. 35.3 days for direct shooting). The model predictions were in good agreement with the time histories of joint angles, ground reaction forces and muscle activation patterns measured for subjects jumping to their maximum achievable heights. Both methods converged to essentially the same solution when started from the same initial guess, but computation time was sensitive to the initial guess assumed. Direct collocation demonstrates exceptional computational performance and is well suited to performing predictive simulations of movement using large-scale musculoskeletal models.

Modeling methods for merging computational and experimental aerodynamic pressure data

NASA Astrophysics Data System (ADS)

Haderlie, Jacob C.

This research describes a process to model surface pressure data sets as a function of wing geometry from computational and wind tunnel sources and then merge them into a single predicted value. The described merging process will enable engineers to integrate these data sets with the goal of utilizing the advantages of each data source while overcoming the limitations of both; this provides a single, combined data set to support analysis and design. The main challenge with this process is accurately representing each data source everywhere on the wing. Additionally, this effort demonstrates methods to model wind tunnel pressure data as a function of angle of attack as an initial step towards a merging process that uses both location on the wing and flow conditions (e.g., angle of attack, flow velocity or Reynold's number) as independent variables. This surrogate model of pressure as a function of angle of attack can be useful for engineers that need to predict the location of zero-order discontinuities, e.g., flow separation or normal shocks. Because, to the author's best knowledge, there is no published, well-established merging method for aerodynamic pressure data (here, the coefficient of pressure Cp), this work identifies promising modeling and merging methods, and then makes a critical comparison of these methods. Surrogate models represent the pressure data for both data sets. Cubic B-spline surrogate models represent the computational simulation results. Machine learning and multi-fidelity surrogate models represent the experimental data. This research compares three surrogates for the experimental data (sequential--a.k.a. online--Gaussian processes, batch Gaussian processes, and multi-fidelity additive corrector) on the merits of accuracy and computational cost. The Gaussian process (GP) methods employ cubic B-spline CFD surrogates as a model basis function to build a surrogate model of the WT data, and this usage of the CFD surrogate in building the WT data could serve as a "merging" because the resulting WT pressure prediction uses information from both sources. In the GP approach, this model basis function concept seems to place more "weight" on the Cp values from the wind tunnel (WT) because the GP surrogate uses the CFD to approximate the WT data values. Conversely, the computationally inexpensive additive corrector method uses the CFD B-spline surrogate to define the shape of the spanwise distribution of the Cp while minimizing prediction error at all spanwise locations for a given arc length position; this, too, combines information from both sources to make a prediction of the 2-D WT-based Cp distribution, but the additive corrector approach gives more weight to the CFD prediction than to the WT data. Three surrogate models of the experimental data as a function of angle of attack are also compared for accuracy and computational cost. These surrogates are a single Gaussian process model (a single "expert"), product of experts, and generalized product of experts. The merging approach provides a single pressure distribution that combines experimental and computational data. The batch Gaussian process method provides a relatively accurate surrogate that is computationally acceptable, and can receive wind tunnel data from port locations that are not necessarily parallel to a variable direction. On the other hand, the sequential Gaussian process and additive corrector methods must receive a sufficient number of data points aligned with one direction, e.g., from pressure port bands (tap rows) aligned with the freestream. The generalized product of experts best represents wind tunnel pressure as a function of angle of attack, but at higher computational cost than the single expert approach. The format of the application data from computational and experimental sources in this work precluded the merging process from including flow condition variables (e.g., angle of attack) in the independent variables, so the merging process is only conducted in the wing geometry variables of arc length and span. The merging process of Cp data allows a more "hands-off" approach to aircraft design and analysis, (i.e., not as many engineers needed to debate the Cp distribution shape) and generates Cp predictions at any location on the wing. However, the cost with these benefits are engineer time (learning how to build surrogates), computational time in constructing the surrogates, and surrogate accuracy (surrogates introduce error into data predictions). This dissertation effort used the Trap Wing / First AIAA CFD High-Lift Prediction Workshop as a relevant transonic wing with a multi-element high-lift system, and this work identified that the batch GP model for the WT data and the B-spline surrogate for the CFD might best be combined using expert belief weights to describe Cp as a function of location on the wing element surface. (Abstract shortened by ProQuest.).

Strategy Plan A Methodology to Predict the Uniformity of Double-Shell Tank Waste Slurries Based on Mixing Pump Operation

DOE Office of Scientific and Technical Information (OSTI.GOV)

J.A. Bamberger; L.M. Liljegren; P.S. Lowery

This document presents an analysis of the mechanisms influencing mixing within double-shell slurry tanks. A research program to characterize mixing of slurries within tanks has been proposed. The research program presents a combined experimental and computational approach to produce correlations describing the tank slurry concentration profile (and therefore uniformity) as a function of mixer pump operating conditions. The TEMPEST computer code was used to simulate both a full-scale (prototype) and scaled (model) double-shell waste tank to predict flow patterns resulting from a stationary jet centered in the tank. The simulation results were used to evaluate flow patterns in the tankmore » and to determine whether flow patterns are similar between the full-scale prototype and an existing 1/12-scale model tank. The flow patterns were sufficiently similar to recommend conducting scoping experiments at 1/12-scale. Also, TEMPEST modeled velocity profiles of the near-floor jet were compared to experimental measurements of the near-floor jet with good agreement. Reported values of physical properties of double-shell tank slurries were analyzed to evaluate the range of properties appropriate for conducting scaled experiments. One-twelfth scale scoping experiments are recommended to confirm the prioritization of the dimensionless groups (gravitational settling, Froude, and Reynolds numbers) that affect slurry suspension in the tank. Two of the proposed 1/12-scale test conditions were modeled using the TEMPEST computer code to observe the anticipated flow fields. This information will be used to guide selection of sampling probe locations. Additional computer modeling is being conducted to model a particulate laden, rotating jet centered in the tank. The results of this modeling effort will be compared to the scaled experimental data to quantify the agreement between the code and the 1/12-scale experiment. The scoping experiment results will guide selection of parameters to be varied in the follow-on experiments. Data from the follow-on experiments will be used to develop correlations to describe slurry concentration profile as a function of mixing pump operating conditions. This data will also be used to further evaluate the computer model applications. If the agreement between the experimental data and the code predictions is good, the computer code will be recommended for use to predict slurry uniformity in the tanks under various operating conditions. If the agreement between the code predictions and experimental results is not good, the experimental data correlations will be used to predict slurry uniformity in the tanks within the range of correlation applicability.« less

Computational neurorehabilitation: modeling plasticity and learning to predict recovery.

PubMed

Reinkensmeyer, David J; Burdet, Etienne; Casadio, Maura; Krakauer, John W; Kwakkel, Gert; Lang, Catherine E; Swinnen, Stephan P; Ward, Nick S; Schweighofer, Nicolas

2016-04-30

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling - regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity.

Verification of predicted specimen-specific natural and implanted patellofemoral kinematics during simulated deep knee bend.

PubMed

Baldwin, Mark A; Clary, Chadd; Maletsky, Lorin P; Rullkoetter, Paul J

2009-10-16

Verified computational models represent an efficient method for studying the relationship between articular geometry, soft-tissue constraint, and patellofemoral (PF) mechanics. The current study was performed to evaluate an explicit finite element (FE) modeling approach for predicting PF kinematics in the natural and implanted knee. Experimental three-dimensional kinematic data were collected on four healthy cadaver specimens in their natural state and after total knee replacement in the Kansas knee simulator during a simulated deep knee bend activity. Specimen-specific FE models were created from medical images and CAD implant geometry, and included soft-tissue structures representing medial-lateral PF ligaments and the quadriceps tendon. Measured quadriceps loads and prescribed tibiofemoral kinematics were used to predict dynamic kinematics of an isolated PF joint between 10 degrees and 110 degrees femoral flexion. Model sensitivity analyses were performed to determine the effect of rigid or deformable patellar representations and perturbed PF ligament mechanical properties (pre-tension and stiffness) on model predictions and computational efficiency. Predicted PF kinematics from the deformable analyses showed average root mean square (RMS) differences for the natural and implanted states of less than 3.1 degrees and 1.7 mm for all rotations and translations. Kinematic predictions with rigid bodies increased average RMS values slightly to 3.7 degrees and 1.9 mm with a five-fold decrease in computational time. Two-fold increases and decreases in PF ligament initial strain and linear stiffness were found to most adversely affect kinematic predictions for flexion, internal-external tilt and inferior-superior translation in both natural and implanted states. The verified models could be used to further investigate the effects of component alignment or soft-tissue variability on natural and implant PF mechanics.

AGDRIFT: A MODEL FOR ESTIMATING NEAR-FIELD SPRAY DRIFT FROM AERIAL APPLICATIONS

EPA Science Inventory

The aerial spray prediction model AgDRIFT(R) embodies the computational engine found in the near-wake Lagrangian model AGricultural DISPersal (AGDISP) but with several important features added that improve the speed and accuracy of its predictions. This article summarizes those c...

Evaluation of Industry Standard Turbulence Models on an Axisymmetric Supersonic Compression Corner

NASA Technical Reports Server (NTRS)

DeBonis, James R.

2015-01-01

Reynolds-averaged Navier-Stokes computations of a shock-wave/boundary-layer interaction (SWBLI) created by a Mach 2.85 flow over an axisymmetric 30-degree compression corner were carried out. The objectives were to evaluate four turbulence models commonly used in industry, for SWBLIs, and to evaluate the suitability of this test case for use in further turbulence model benchmarking. The Spalart-Allmaras model, Menter's Baseline and Shear Stress Transport models, and a low-Reynolds number k- model were evaluated. Results indicate that the models do not accurately predict the separation location; with the SST model predicting the separation onset too early and the other models predicting the onset too late. Overall the Spalart-Allmaras model did the best job in matching the experimental data. However there is significant room for improvement, most notably in the prediction of the turbulent shear stress. Density data showed that the simulations did not accurately predict the thermal boundary layer upstream of the SWBLI. The effect of turbulent Prandtl number and wall temperature were studied in an attempt to improve this prediction and understand their effects on the interaction. The data showed that both parameters can significantly affect the separation size and location, but did not improve the agreement with the experiment. This case proved challenging to compute and should provide a good test for future turbulence modeling work.

A computational model for epidural electrical stimulation of spinal sensorimotor circuits.

PubMed

Capogrosso, Marco; Wenger, Nikolaus; Raspopovic, Stanisa; Musienko, Pavel; Beauparlant, Janine; Bassi Luciani, Lorenzo; Courtine, Grégoire; Micera, Silvestro

2013-12-04

Epidural electrical stimulation (EES) of lumbosacral segments can restore a range of movements after spinal cord injury. However, the mechanisms and neural structures through which EES facilitates movement execution remain unclear. Here, we designed a computational model and performed in vivo experiments to investigate the type of fibers, neurons, and circuits recruited in response to EES. We first developed a realistic finite element computer model of rat lumbosacral segments to identify the currents generated by EES. To evaluate the impact of these currents on sensorimotor circuits, we coupled this model with an anatomically realistic axon-cable model of motoneurons, interneurons, and myelinated afferent fibers for antagonistic ankle muscles. Comparisons between computer simulations and experiments revealed the ability of the model to predict EES-evoked motor responses over multiple intensities and locations. Analysis of the recruited neural structures revealed the lack of direct influence of EES on motoneurons and interneurons. Simulations and pharmacological experiments demonstrated that EES engages spinal circuits trans-synaptically through the recruitment of myelinated afferent fibers. The model also predicted the capacity of spatially distinct EES to modulate side-specific limb movements and, to a lesser extent, extension versus flexion. These predictions were confirmed during standing and walking enabled by EES in spinal rats. These combined results provide a mechanistic framework for the design of spinal neuroprosthetic systems to improve standing and walking after neurological disorders.

NAPR: a Cloud-Based Framework for Neuroanatomical Age Prediction.

PubMed

Pardoe, Heath R; Kuzniecky, Ruben

2018-01-01

The availability of cloud computing services has enabled the widespread adoption of the "software as a service" (SaaS) approach for software distribution, which utilizes network-based access to applications running on centralized servers. In this paper we apply the SaaS approach to neuroimaging-based age prediction. Our system, named "NAPR" (Neuroanatomical Age Prediction using R), provides access to predictive modeling software running on a persistent cloud-based Amazon Web Services (AWS) compute instance. The NAPR framework allows external users to estimate the age of individual subjects using cortical thickness maps derived from their own locally processed T1-weighted whole brain MRI scans. As a demonstration of the NAPR approach, we have developed two age prediction models that were trained using healthy control data from the ABIDE, CoRR, DLBS and NKI Rockland neuroimaging datasets (total N = 2367, age range 6-89 years). The provided age prediction models were trained using (i) relevance vector machines and (ii) Gaussian processes machine learning methods applied to cortical thickness surfaces obtained using Freesurfer v5.3. We believe that this transparent approach to out-of-sample evaluation and comparison of neuroimaging age prediction models will facilitate the development of improved age prediction models and allow for robust evaluation of the clinical utility of these methods.

Patient-specific non-linear finite element modelling for predicting soft organ deformation in real-time: application to non-rigid neuroimage registration.

PubMed

Wittek, Adam; Joldes, Grand; Couton, Mathieu; Warfield, Simon K; Miller, Karol

2010-12-01

Long computation times of non-linear (i.e. accounting for geometric and material non-linearity) biomechanical models have been regarded as one of the key factors preventing application of such models in predicting organ deformation for image-guided surgery. This contribution presents real-time patient-specific computation of the deformation field within the brain for six cases of brain shift induced by craniotomy (i.e. surgical opening of the skull) using specialised non-linear finite element procedures implemented on a graphics processing unit (GPU). In contrast to commercial finite element codes that rely on an updated Lagrangian formulation and implicit integration in time domain for steady state solutions, our procedures utilise the total Lagrangian formulation with explicit time stepping and dynamic relaxation. We used patient-specific finite element meshes consisting of hexahedral and non-locking tetrahedral elements, together with realistic material properties for the brain tissue and appropriate contact conditions at the boundaries. The loading was defined by prescribing deformations on the brain surface under the craniotomy. Application of the computed deformation fields to register (i.e. align) the preoperative and intraoperative images indicated that the models very accurately predict the intraoperative deformations within the brain. For each case, computing the brain deformation field took less than 4 s using an NVIDIA Tesla C870 GPU, which is two orders of magnitude reduction in computation time in comparison to our previous study in which the brain deformation was predicted using a commercial finite element solver executed on a personal computer. Copyright © 2010 Elsevier Ltd. All rights reserved.

Consensus models to predict endocrine disruption for all ...

EPA Pesticide Factsheets

Humans are potentially exposed to tens of thousands of man-made chemicals in the environment. It is well known that some environmental chemicals mimic natural hormones and thus have the potential to be endocrine disruptors. Most of these environmental chemicals have never been tested for their ability to disrupt the endocrine system, in particular, their ability to interact with the estrogen receptor. EPA needs tools to prioritize thousands of chemicals, for instance in the Endocrine Disruptor Screening Program (EDSP). Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) was intended to be a demonstration of the use of predictive computational models on HTS data including ToxCast and Tox21 assays to prioritize a large chemical universe of 32464 unique structures for one specific molecular target – the estrogen receptor. CERAPP combined multiple computational models for prediction of estrogen receptor activity, and used the predicted results to build a unique consensus model. Models were developed in collaboration between 17 groups in the U.S. and Europe and applied to predict the common set of chemicals. Structure-based techniques such as docking and several QSAR modeling approaches were employed, mostly using a common training set of 1677 compounds provided by U.S. EPA, to build a total of 42 classification models and 8 regression models for binding, agonist and antagonist activity. All predictions were evaluated on ToxCast data and on an exte

Comparison of Experimental Surface and Flow Field Measurements to Computational Results of the Juncture Flow Model

NASA Technical Reports Server (NTRS)

Roozeboom, Nettie H.; Lee, Henry C.; Simurda, Laura J.; Zilliac, Gregory G.; Pulliam, Thomas H.

2016-01-01

Wing-body juncture flow fields on commercial aircraft configurations are challenging to compute accurately. The NASA Advanced Air Vehicle Program's juncture flow committee is designing an experiment to provide data to improve Computational Fluid Dynamics (CFD) modeling in the juncture flow region. Preliminary design of the model was done using CFD, yet CFD tends to over-predict the separation in the juncture flow region. Risk reduction wind tunnel tests were requisitioned by the committee to obtain a better understanding of the flow characteristics of the designed models. NASA Ames Research Center's Fluid Mechanics Lab performed one of the risk reduction tests. The results of one case, accompanied by CFD simulations, are presented in this paper. Experimental results suggest the wall mounted wind tunnel model produces a thicker boundary layer on the fuselage than the CFD predictions, resulting in a larger wing horseshoe vortex suppressing the side of body separation in the juncture flow region. Compared to experimental results, CFD predicts a thinner boundary layer on the fuselage generates a weaker wing horseshoe vortex resulting in a larger side of body separation.

Uncertainty Quantification and Certification Prediction of Low-Boom Supersonic Aircraft Configurations

NASA Technical Reports Server (NTRS)

West, Thomas K., IV; Reuter, Bryan W.; Walker, Eric L.; Kleb, Bil; Park, Michael A.

2014-01-01

The primary objective of this work was to develop and demonstrate a process for accurate and efficient uncertainty quantification and certification prediction of low-boom, supersonic, transport aircraft. High-fidelity computational fluid dynamics models of multiple low-boom configurations were investigated including the Lockheed Martin SEEB-ALR body of revolution, the NASA 69 Delta Wing, and the Lockheed Martin 1021-01 configuration. A nonintrusive polynomial chaos surrogate modeling approach was used for reduced computational cost of propagating mixed, inherent (aleatory) and model-form (epistemic) uncertainty from both the computation fluid dynamics model and the near-field to ground level propagation model. A methodology has also been introduced to quantify the plausibility of a design to pass a certification under uncertainty. Results of this study include the analysis of each of the three configurations of interest under inviscid and fully turbulent flow assumptions. A comparison of the uncertainty outputs and sensitivity analyses between the configurations is also given. The results of this study illustrate the flexibility and robustness of the developed framework as a tool for uncertainty quantification and certification prediction of low-boom, supersonic aircraft.

ASSESSING A COMPUTER MODEL FOR PREDICTING HUMAN EXPOSURE TO PM2.5

EPA Science Inventory

This paper compares outputs of a model for predicting PM2.5 exposure with experimental data obtained from exposure studies of selected subpopulations. The exposure model is built on a WWW platform called pCNEM, "A PC Version of pNEM." Exposure models created by pCNEM are sim...

Evaluation of the AnnAGNPS model for predicting runoff and sediment yield in a small Mediterranean agricultural watershed in Navarre (Spain)

USDA-ARS?s Scientific Manuscript database

AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model) is a system of computer models developed to predict non-point source pollutant loadings within agricultural watersheds. It contains a daily time step distributed parameter continuous simulation surface runoff model designed to assis...

Subtypes of Developmental Dyslexia: Testing the Predictions of the Dual-Route and Connectionist Frameworks

ERIC Educational Resources Information Center

Peterson, Robin L.; Pennington, Bruce F.; Olson, Richard K.

2013-01-01

We investigated the phonological and surface subtypes of developmental dyslexia in light of competing predictions made by two computational models of single word reading, the Dual-Route Cascaded Model (DRC; Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001) and Harm and Seidenberg's connectionist model (HS model; Harm & Seidenberg, 1999). The…

Public Databases Supporting Computational Toxicology

EPA Science Inventory

A major goal of the emerging field of computational toxicology is the development of screening-level models that predict potential toxicity of chemicals from a combination of mechanistic in vitro assay data and chemical structure descriptors. In order to build these models, resea...

Computational Embryology and Predictive Toxicology of Cleft Palate

EPA Science Inventory

Capacity to model and simulate key events in developmental toxicity using computational systems biology and biological knowledge steps closer to hazard identification across the vast landscape of untested environmental chemicals. In this context, we chose cleft palate as a model ...

Source Term Model for Vortex Generator Vanes in a Navier-Stokes Computer Code

NASA Technical Reports Server (NTRS)

Waithe, Kenrick A.

2004-01-01

A source term model for an array of vortex generators was implemented into a non-proprietary Navier-Stokes computer code, OVERFLOW. The source term models the side force created by a vortex generator vane. The model is obtained by introducing a side force to the momentum and energy equations that can adjust its strength automatically based on the local flow. The model was tested and calibrated by comparing data from numerical simulations and experiments of a single low profile vortex generator vane on a flat plate. In addition, the model was compared to experimental data of an S-duct with 22 co-rotating, low profile vortex generators. The source term model allowed a grid reduction of about seventy percent when compared with the numerical simulations performed on a fully gridded vortex generator on a flat plate without adversely affecting the development and capture of the vortex created. The source term model was able to predict the shape and size of the stream-wise vorticity and velocity contours very well when compared with both numerical simulations and experimental data. The peak vorticity and its location were also predicted very well when compared to numerical simulations and experimental data. The circulation predicted by the source term model matches the prediction of the numerical simulation. The source term model predicted the engine fan face distortion and total pressure recovery of the S-duct with 22 co-rotating vortex generators very well. The source term model allows a researcher to quickly investigate different locations of individual or a row of vortex generators. The researcher is able to conduct a preliminary investigation with minimal grid generation and computational time.

Structure prediction of the second extracellular loop in G-protein-coupled receptors.

PubMed

Kmiecik, Sebastian; Jamroz, Michal; Kolinski, Michal

2014-06-03

G-protein-coupled receptors (GPCRs) play key roles in living organisms. Therefore, it is important to determine their functional structures. The second extracellular loop (ECL2) is a functionally important region of GPCRs, which poses significant challenge for computational structure prediction methods. In this work, we evaluated CABS, a well-established protein modeling tool for predicting ECL2 structure in 13 GPCRs. The ECL2s (with between 13 and 34 residues) are predicted in an environment of other extracellular loops being fully flexible and the transmembrane domain fixed in its x-ray conformation. The modeling procedure used theoretical predictions of ECL2 secondary structure and experimental constraints on disulfide bridges. Our approach yielded ensembles of low-energy conformers and the most populated conformers that contained models close to the available x-ray structures. The level of similarity between the predicted models and x-ray structures is comparable to that of other state-of-the-art computational methods. Our results extend other studies by including newly crystallized GPCRs. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Final Report, DOE Early Career Award: Predictive modeling of complex physical systems: new tools for statistical inference, uncertainty quantification, and experimental design

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marzouk, Youssef

Predictive simulation of complex physical systems increasingly rests on the interplay of experimental observations with computational models. Key inputs, parameters, or structural aspects of models may be incomplete or unknown, and must be developed from indirect and limited observations. At the same time, quantified uncertainties are needed to qualify computational predictions in the support of design and decision-making. In this context, Bayesian statistics provides a foundation for inference from noisy and limited data, but at prohibitive computional expense. This project intends to make rigorous predictive modeling *feasible* in complex physical systems, via accelerated and scalable tools for uncertainty quantification, Bayesianmore » inference, and experimental design. Specific objectives are as follows: 1. Develop adaptive posterior approximations and dimensionality reduction approaches for Bayesian inference in high-dimensional nonlinear systems. 2. Extend accelerated Bayesian methodologies to large-scale {\\em sequential} data assimilation, fully treating nonlinear models and non-Gaussian state and parameter distributions. 3. Devise efficient surrogate-based methods for Bayesian model selection and the learning of model structure. 4. Develop scalable simulation/optimization approaches to nonlinear Bayesian experimental design, for both parameter inference and model selection. 5. Demonstrate these inferential tools on chemical kinetic models in reacting flow, constructing and refining thermochemical and electrochemical models from limited data. Demonstrate Bayesian filtering on canonical stochastic PDEs and in the dynamic estimation of inhomogeneous subsurface properties and flow fields.« less

Capturing anharmonicity in a lattice thermal conductivity model for high-throughput predictions

DOE PAGES

Miller, Samuel A.; Gorai, Prashun; Ortiz, Brenden R.; ...

2017-01-06

High-throughput, low-cost, and accurate predictions of thermal properties of new materials would be beneficial in fields ranging from thermal barrier coatings and thermoelectrics to integrated circuits. To date, computational efforts for predicting lattice thermal conductivity (κ L) have been hampered by the complexity associated with computing multiple phonon interactions. In this work, we develop and validate a semiempirical model for κ L by fitting density functional theory calculations to experimental data. Experimental values for κ L come from new measurements on SrIn 2O 4, Ba 2SnO 4, Cu 2ZnSiTe 4, MoTe 2, Ba 3In 2O 6, Cu 3TaTe 4, SnO,more » and InI as well as 55 compounds from across the published literature. Here, to capture the anharmonicity in phonon interactions, we incorporate a structural parameter that allows the model to predict κ L within a factor of 1.5 of the experimental value across 4 orders of magnitude in κ L values and over a diverse chemical and structural phase space, with accuracy similar to or better than that of computationally more expensive models.« less

Prediction of beta-turns in proteins using the first-order Markov models.

PubMed

Lin, Thy-Hou; Wang, Ging-Ming; Wang, Yen-Tseng

2002-01-01

We present a method based on the first-order Markov models for predicting simple beta-turns and loops containing multiple turns in proteins. Sequences of 338 proteins in a database are divided using the published turn criteria into the following three regions, namely, the turn, the boundary, and the nonturn ones. A transition probability matrix is constructed for either the turn or the nonturn region using the weighted transition probabilities computed for dipeptides identified from each region. There are two such matrices constructed for the boundary region since the transition probabilities for dipeptides immediately preceding or following a turn are different. The window used for scanning a protein sequence from amino (N-) to carboxyl (C-) terminal is a hexapeptide since the transition probability computed for a turn tetrapeptide is capped at both the N- and C- termini with a boundary transition probability indexed respectively from the two boundary transition matrices. A sum of the averaged product of the transition probabilities of all the hexapeptides involving each residue is computed. This is then weighted with a probability computed from assuming that all the hexapeptides are from the nonturn region to give the final prediction quantity. Both simple beta-turns and loops containing multiple turns in a protein are then identified by the rising of the prediction quantity computed. The performance of the prediction scheme or the percentage (%) of correct prediction is evaluated through computation of Matthews correlation coefficients for each protein predicted. It is found that the prediction method is capable of giving prediction results with better correlation between the percent of correct prediction and the Matthews correlation coefficients for a group of test proteins as compared with those predicted using some secondary structural prediction methods. The prediction accuracy for about 40% of proteins in the database or 50% of proteins in the test set is better than 70%. Such a percentage for the test set is reduced to 30 if the structures of all the proteins in the set are treated as unknown.

Prediction of Solution Properties of Flexible-Chain Polymers: A Computer Simulation Undergraduate Experiment

ERIC Educational Resources Information Center

de la Torre, Jose Garcia; Cifre, Jose G. Hernandez; Martinez, M. Carmen Lopez

2008-01-01

This paper describes a computational exercise at undergraduate level that demonstrates the employment of Monte Carlo simulation to study the conformational statistics of flexible polymer chains, and to predict solution properties. Three simple chain models, including excluded volume interactions, have been implemented in a public-domain computer…

Computational modelling of biomaterial surface interactions with blood platelets and osteoblastic cells for the prediction of contact osteogenesis.

PubMed

Amor, N; Geris, L; Vander Sloten, J; Van Oosterwyck, H

2011-02-01

Surface microroughness can induce contact osteogenesis (bone formation initiated at the implant surface) around oral implants, which may result from different mechanisms, such as blood platelet-biomaterial interactions and/or interaction with (pre-)osteoblast cells. We have developed a computational model of implant endosseous healing that takes into account these interactions. We hypothesized that the initial attachment and growth factor release from activated platelets is crucial in achieving contact osteogenesis. In order to investigate this, a computational model was applied to an animal experiment [7] that looked at the effect of surface microroughness on endosseous healing. Surface-specific model parameters were implemented based on in vitro data (Lincks et al. Biomaterials 1998;19:2219-32). The predicted spatio-temporal patterns of bone formation correlated with the histological data. It was found that contact osteogenesis could not be predicted if only the osteogenic response of cells was up-regulated by surface microroughness. This could only be achieved if platelet-biomaterial interactions were sufficiently up-regulated as well. These results confirmed our hypothesis and demonstrate the added value of the computational model to study the importance of surface-mediated events for peri-implant endosseous healing. Copyright © 2010 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Patient-Specific Computational Modeling of Upper Extremity Arteriovenous Fistula Creation: Its Feasibility to Support Clinical Decision-Making

PubMed Central

Bosboom, E. Marielle H.; Kroon, Wilco; van der Linden, Wim P. M.; Planken, R. Nils; van de Vosse, Frans N.; Tordoir, Jan H. M.

2012-01-01

Introduction Inadequate flow enhancement on the one hand, and excessive flow enhancement on the other hand, remain frequent complications of arteriovenous fistula (AVF) creation, and hamper hemodialysis therapy in patients with end-stage renal disease. In an effort to reduce these, a patient-specific computational model, capable of predicting postoperative flow, has been developed. The purpose of this study was to determine the accuracy of the patient-specific model and to investigate its feasibility to support decision-making in AVF surgery. Methods Patient-specific pulse wave propagation models were created for 25 patients awaiting AVF creation. Model input parameters were obtained from clinical measurements and literature. For every patient, a radiocephalic AVF, a brachiocephalic AVF, and a brachiobasilic AVF configuration were simulated and analyzed for their postoperative flow. The most distal configuration with a predicted flow between 400 and 1500 ml/min was considered the preferred location for AVF surgery. The suggestion of the model was compared to the choice of an experienced vascular surgeon. Furthermore, predicted flows were compared to measured postoperative flows. Results Taken into account the confidence interval (25th and 75th percentile interval), overlap between predicted and measured postoperative flows was observed in 70% of the patients. Differentiation between upper and lower arm configuration was similar in 76% of the patients, whereas discrimination between two upper arm AVF configurations was more difficult. In 3 patients the surgeon created an upper arm AVF, while model based predictions allowed for lower arm AVF creation, thereby preserving proximal vessels. In one patient early thrombosis in a radiocephalic AVF was observed which might have been indicated by the low predicted postoperative flow. Conclusions Postoperative flow can be predicted relatively accurately for multiple AVF configurations by using computational modeling. This model may therefore be considered a valuable additional tool in the preoperative work-up of patients awaiting AVF creation. PMID:22496816

Variable selection models for genomic selection using whole-genome sequence data and singular value decomposition.

PubMed

Meuwissen, Theo H E; Indahl, Ulf G; Ødegård, Jørgen

2017-12-27

Non-linear Bayesian genomic prediction models such as BayesA/B/C/R involve iteration and mostly Markov chain Monte Carlo (MCMC) algorithms, which are computationally expensive, especially when whole-genome sequence (WGS) data are analyzed. Singular value decomposition (SVD) of the genotype matrix can facilitate genomic prediction in large datasets, and can be used to estimate marker effects and their prediction error variances (PEV) in a computationally efficient manner. Here, we developed, implemented, and evaluated a direct, non-iterative method for the estimation of marker effects for the BayesC genomic prediction model. The BayesC model assumes a priori that markers have normally distributed effects with probability [Formula: see text] and no effect with probability (1 - [Formula: see text]). Marker effects and their PEV are estimated by using SVD and the posterior probability of the marker having a non-zero effect is calculated. These posterior probabilities are used to obtain marker-specific effect variances, which are subsequently used to approximate BayesC estimates of marker effects in a linear model. A computer simulation study was conducted to compare alternative genomic prediction methods, where a single reference generation was used to estimate marker effects, which were subsequently used for 10 generations of forward prediction, for which accuracies were evaluated. SVD-based posterior probabilities of markers having non-zero effects were generally lower than MCMC-based posterior probabilities, but for some regions the opposite occurred, resulting in clear signals for QTL-rich regions. The accuracies of breeding values estimated using SVD- and MCMC-based BayesC analyses were similar across the 10 generations of forward prediction. For an intermediate number of generations (2 to 5) of forward prediction, accuracies obtained with the BayesC model tended to be slightly higher than accuracies obtained using the best linear unbiased prediction of SNP effects (SNP-BLUP model). When reducing marker density from WGS data to 30 K, SNP-BLUP tended to yield the highest accuracies, at least in the short term. Based on SVD of the genotype matrix, we developed a direct method for the calculation of BayesC estimates of marker effects. Although SVD- and MCMC-based marker effects differed slightly, their prediction accuracies were similar. Assuming that the SVD of the marker genotype matrix is already performed for other reasons (e.g. for SNP-BLUP), computation times for the BayesC predictions were comparable to those of SNP-BLUP.

A comparison between theoretical prediction and experimental measurement of the dynamic behavior of spur gears

NASA Technical Reports Server (NTRS)

Rebbechi, Brian; Forrester, B. David; Oswald, Fred B.; Townsend, Dennis P.

1992-01-01

A comparison was made between computer model predictions of gear dynamics behavior and experimental results. The experimental data were derived from the NASA gear noise rig, which was used to record dynamic tooth loads and vibration. The experimental results were compared with predictions from the DSTO Aeronautical Research Laboratory's gear dynamics code for a matrix of 28 load speed points. At high torque the peak dynamic load predictions agree with the experimental results with an average error of 5 percent in the speed range 800 to 6000 rpm. Tooth separation (or bounce), which was observed in the experimental data for light torque, high speed conditions, was simulated by the computer model. The model was also successful in simulating the degree of load sharing between gear teeth in the multiple tooth contact region.

Regression Analysis of Top of Descent Location for Idle-thrust Descents

NASA Technical Reports Server (NTRS)

Stell, Laurel; Bronsvoort, Jesper; McDonald, Greg

2013-01-01

In this paper, multiple regression analysis is used to model the top of descent (TOD) location of user-preferred descent trajectories computed by the flight management system (FMS) on over 1000 commercial flights into Melbourne, Australia. The independent variables cruise altitude, final altitude, cruise Mach, descent speed, wind, and engine type were also recorded or computed post-operations. Both first-order and second-order models are considered, where cross-validation, hypothesis testing, and additional analysis are used to compare models. This identifies the models that should give the smallest errors if used to predict TOD location for new data in the future. A model that is linear in TOD altitude, final altitude, descent speed, and wind gives an estimated standard deviation of 3.9 nmi for TOD location given the trajec- tory parameters, which means about 80% of predictions would have error less than 5 nmi in absolute value. This accuracy is better than demonstrated by other ground automation predictions using kinetic models. Furthermore, this approach would enable online learning of the model. Additional data or further knowl- edge of algorithms is necessary to conclude definitively that no second-order terms are appropriate. Possible applications of the linear model are described, including enabling arriving aircraft to fly optimized descents computed by the FMS even in congested airspace. In particular, a model for TOD location that is linear in the independent variables would enable decision support tool human-machine interfaces for which a kinetic approach would be computationally too slow.

Modeling resident error-making patterns in detection of mammographic masses using computer-extracted image features: preliminary experiments

NASA Astrophysics Data System (ADS)

Mazurowski, Maciej A.; Zhang, Jing; Lo, Joseph Y.; Kuzmiak, Cherie M.; Ghate, Sujata V.; Yoon, Sora

2014-03-01

Providing high quality mammography education to radiology trainees is essential, as good interpretation skills potentially ensure the highest benefit of screening mammography for patients. We have previously proposed a computer-aided education system that utilizes trainee models, which relate human-assessed image characteristics to interpretation error. We proposed that these models be used to identify the most difficult and therefore the most educationally useful cases for each trainee. In this study, as a next step in our research, we propose to build trainee models that utilize features that are automatically extracted from images using computer vision algorithms. To predict error, we used a logistic regression which accepts imaging features as input and returns error as output. Reader data from 3 experts and 3 trainees were used. Receiver operating characteristic analysis was applied to evaluate the proposed trainee models. Our experiments showed that, for three trainees, our models were able to predict error better than chance. This is an important step in the development of adaptive computer-aided education systems since computer-extracted features will allow for faster and more extensive search of imaging databases in order to identify the most educationally beneficial cases.

Performance Prediction Toolkit

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chennupati, Gopinath; Santhi, Nanadakishore; Eidenbenz, Stephen

The Performance Prediction Toolkit (PPT), is a scalable co-design tool that contains the hardware and middle-ware models, which accept proxy applications as input in runtime prediction. PPT relies on Simian, a parallel discrete event simulation engine in Python or Lua, that uses the process concept, where each computing unit (host, node, core) is a Simian entity. Processes perform their task through message exchanges to remain active, sleep, wake-up, begin and end. The PPT hardware model of a compute core (such as a Haswell core) consists of a set of parameters, such as clock speed, memory hierarchy levels, their respective sizes,more » cache-lines, access times for different cache levels, average cycle counts of ALU operations, etc. These parameters are ideally read off a spec sheet or are learned using regression models learned from hardware counters (PAPI) data. The compute core model offers an API to the software model, a function called time_compute(), which takes as input a tasklist. A tasklist is an unordered set of ALU, and other CPU-type operations (in particular virtual memory loads and stores). The PPT application model mimics the loop structure of the application and replaces the computational kernels with a call to the hardware model's time_compute() function giving tasklists as input that model the compute kernel. A PPT application model thus consists of tasklists representing kernels and the high-er level loop structure that we like to think of as pseudo code. The key challenge for the hardware model's time_compute-function is to translate virtual memory accesses into actual cache hierarchy level hits and misses.PPT also contains another CPU core level hardware model, Analytical Memory Model (AMM). The AMM solves this challenge soundly, where our previous alternatives explicitly include the L1,L2,L3 hit-rates as inputs to the tasklists. Explicit hit-rates inevitably only reflect the application modeler's best guess, perhaps informed by a few small test problems using hardware counters; also, hard-coded hit-rates make the hardware model insensitive to changes in cache sizes. Alternatively, we use reuse distance distributions in the tasklists. In general, reuse profiles require the application modeler to run a very expensive trace analysis on the real code that realistically can be done at best for small examples.« less

Predictive representations can link model-based reinforcement learning to model-free mechanisms.

PubMed

Russek, Evan M; Momennejad, Ida; Botvinick, Matthew M; Gershman, Samuel J; Daw, Nathaniel D

2017-09-01

Humans and animals are capable of evaluating actions by considering their long-run future rewards through a process described using model-based reinforcement learning (RL) algorithms. The mechanisms by which neural circuits perform the computations prescribed by model-based RL remain largely unknown; however, multiple lines of evidence suggest that neural circuits supporting model-based behavior are structurally homologous to and overlapping with those thought to carry out model-free temporal difference (TD) learning. Here, we lay out a family of approaches by which model-based computation may be built upon a core of TD learning. The foundation of this framework is the successor representation, a predictive state representation that, when combined with TD learning of value predictions, can produce a subset of the behaviors associated with model-based learning, while requiring less decision-time computation than dynamic programming. Using simulations, we delineate the precise behavioral capabilities enabled by evaluating actions using this approach, and compare them to those demonstrated by biological organisms. We then introduce two new algorithms that build upon the successor representation while progressively mitigating its limitations. Because this framework can account for the full range of observed putatively model-based behaviors while still utilizing a core TD framework, we suggest that it represents a neurally plausible family of mechanisms for model-based evaluation.

Predictive representations can link model-based reinforcement learning to model-free mechanisms

PubMed Central

Botvinick, Matthew M.

2017-01-01

Humans and animals are capable of evaluating actions by considering their long-run future rewards through a process described using model-based reinforcement learning (RL) algorithms. The mechanisms by which neural circuits perform the computations prescribed by model-based RL remain largely unknown; however, multiple lines of evidence suggest that neural circuits supporting model-based behavior are structurally homologous to and overlapping with those thought to carry out model-free temporal difference (TD) learning. Here, we lay out a family of approaches by which model-based computation may be built upon a core of TD learning. The foundation of this framework is the successor representation, a predictive state representation that, when combined with TD learning of value predictions, can produce a subset of the behaviors associated with model-based learning, while requiring less decision-time computation than dynamic programming. Using simulations, we delineate the precise behavioral capabilities enabled by evaluating actions using this approach, and compare them to those demonstrated by biological organisms. We then introduce two new algorithms that build upon the successor representation while progressively mitigating its limitations. Because this framework can account for the full range of observed putatively model-based behaviors while still utilizing a core TD framework, we suggest that it represents a neurally plausible family of mechanisms for model-based evaluation. PMID:28945743

Modulation of the error-related negativity by response conflict.

PubMed

Danielmeier, Claudia; Wessel, Jan R; Steinhauser, Marco; Ullsperger, Markus

2009-11-01

An arrow version of the Eriksen flanker task was employed to investigate the influence of conflict on the error-related negativity (ERN). The degree of conflict was modulated by varying the distance between flankers and the target arrow (CLOSE and FAR conditions). Error rates and reaction time data from a behavioral experiment were used to adapt a connectionist model of this task. This model was based on the conflict monitoring theory and simulated behavioral and event-related potential data. The computational model predicted an increased ERN amplitude in FAR incompatible (the low-conflict condition) compared to CLOSE incompatible errors (the high-conflict condition). A subsequent ERP experiment confirmed the model predictions. The computational model explains this finding with larger post-response conflict in far trials. In addition, data and model predictions of the N2 and the LRP support the conflict interpretation of the ERN.

Development of a recursion RNG-based turbulence model

NASA Technical Reports Server (NTRS)

Zhou, YE; Vahala, George; Thangam, S.

1993-01-01

Reynolds stress closure models based on the recursion renormalization group theory are developed for the prediction of turbulent separated flows. The proposed model uses a finite wavenumber truncation scheme to account for the spectral distribution of energy. In particular, the model incorporates effects of both local and nonlocal interactions. The nonlocal interactions are shown to yield a contribution identical to that from the epsilon-renormalization group (RNG), while the local interactions introduce higher order dispersive effects. A formal analysis of the model is presented and its ability to accurately predict separated flows is analyzed from a combined theoretical and computational stand point. Turbulent flow past a backward facing step is chosen as a test case and the results obtained based on detailed computations demonstrate that the proposed recursion -RNG model with finite cut-off wavenumber can yield very good predictions for the backstep problem.

Unsteady Aerodynamic Validation Experiences From the Aeroelastic Prediction Workshop

NASA Technical Reports Server (NTRS)

Heeg, Jennifer; Chawlowski, Pawel

2014-01-01

The AIAA Aeroelastic Prediction Workshop (AePW) was held in April 2012, bringing together communities of aeroelasticians, computational fluid dynamicists and experimentalists. The extended objective was to assess the state of the art in computational aeroelastic methods as practical tools for the prediction of static and dynamic aeroelastic phenomena. As a step in this process, workshop participants analyzed unsteady aerodynamic and weakly-coupled aeroelastic cases. Forced oscillation and unforced system experiments and computations have been compared for three configurations. This paper emphasizes interpretation of the experimental data, computational results and their comparisons from the perspective of validation of unsteady system predictions. The issues examined in detail are variability introduced by input choices for the computations, post-processing, and static aeroelastic modeling. The final issue addressed is interpreting unsteady information that is present in experimental data that is assumed to be steady, and the resulting consequences on the comparison data sets.

Laminar fMRI and computational theories of brain function.

PubMed

Stephan, K E; Petzschner, F H; Kasper, L; Bayer, J; Wellstein, K V; Stefanics, G; Pruessmann, K P; Heinzle, J

2017-11-02

Recently developed methods for functional MRI at the resolution of cortical layers (laminar fMRI) offer a novel window into neurophysiological mechanisms of cortical activity. Beyond physiology, laminar fMRI also offers an unprecedented opportunity to test influential theories of brain function. Specifically, hierarchical Bayesian theories of brain function, such as predictive coding, assign specific computational roles to different cortical layers. Combined with computational models, laminar fMRI offers a unique opportunity to test these proposals noninvasively in humans. This review provides a brief overview of predictive coding and related hierarchical Bayesian theories, summarises their predictions with regard to layered cortical computations, examines how these predictions could be tested by laminar fMRI, and considers methodological challenges. We conclude by discussing the potential of laminar fMRI for clinically useful computational assays of layer-specific information processing. Copyright © 2017 Elsevier Inc. All rights reserved.

Predictive modeling of dynamic fracture growth in brittle materials with machine learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moore, Bryan A.; Rougier, Esteban; O’Malley, Daniel

We use simulation data from a high delity Finite-Discrete Element Model to build an e cient Machine Learning (ML) approach to predict fracture growth and coalescence. Our goal is for the ML approach to be used as an emulator in place of the computationally intensive high delity models in an uncertainty quanti cation framework where thousands of forward runs are required. The failure of materials with various fracture con gurations (size, orientation and the number of initial cracks) are explored and used as data to train our ML model. This novel approach has shown promise in predicting spatial (path tomore » failure) and temporal (time to failure) aspects of brittle material failure. Predictions of where dominant fracture paths formed within a material were ~85% accurate and the time of material failure deviated from the actual failure time by an average of ~16%. Additionally, the ML model achieves a reduction in computational cost by multiple orders of magnitude.« less

Predictive modeling of dynamic fracture growth in brittle materials with machine learning

DOE PAGES

Moore, Bryan A.; Rougier, Esteban; O’Malley, Daniel; ...

2018-02-22

We use simulation data from a high delity Finite-Discrete Element Model to build an e cient Machine Learning (ML) approach to predict fracture growth and coalescence. Our goal is for the ML approach to be used as an emulator in place of the computationally intensive high delity models in an uncertainty quanti cation framework where thousands of forward runs are required. The failure of materials with various fracture con gurations (size, orientation and the number of initial cracks) are explored and used as data to train our ML model. This novel approach has shown promise in predicting spatial (path tomore » failure) and temporal (time to failure) aspects of brittle material failure. Predictions of where dominant fracture paths formed within a material were ~85% accurate and the time of material failure deviated from the actual failure time by an average of ~16%. Additionally, the ML model achieves a reduction in computational cost by multiple orders of magnitude.« less

Computer-Assisted Decision Support for Student Admissions Based on Their Predicted Academic Performance.

PubMed

Muratov, Eugene; Lewis, Margaret; Fourches, Denis; Tropsha, Alexander; Cox, Wendy C

2017-04-01

Objective. To develop predictive computational models forecasting the academic performance of students in the didactic-rich portion of a doctor of pharmacy (PharmD) curriculum as admission-assisting tools. Methods. All PharmD candidates over three admission cycles were divided into two groups: those who completed the PharmD program with a GPA ≥ 3; and the remaining candidates. Random Forest machine learning technique was used to develop a binary classification model based on 11 pre-admission parameters. Results. Robust and externally predictive models were developed that had particularly high overall accuracy of 77% for candidates with high or low academic performance. These multivariate models were highly accurate in predicting these groups to those obtained using undergraduate GPA and composite PCAT scores only. Conclusion. The models developed in this study can be used to improve the admission process as preliminary filters and thus quickly identify candidates who are likely to be successful in the PharmD curriculum.

A Performance Prediction Model for a Fault-Tolerant Computer During Recovery and Restoration. Ph.D. Thesis Report, 1 Jan. - 31 Dec. 1992

NASA Technical Reports Server (NTRS)

Stoughton, John W.; Obando, Rodrigo A.

1993-01-01

The modeling and design of a fault-tolerant multiprocessor system is addressed. In particular, the behavior of the system during recovery and restoration after a fault has occurred is investigated. Given that a multicomputer system is designed using the Algorithm to Architecture to Mapping Model (ATAMM), and that a fault (death of a computing resource) occurs during its normal steady-state operation, a model is presented as a viable research tool for predicting the performance bounds of the system during its recovery and restoration phases. Furthermore, the bounds of the performance behavior of the system during this transient mode can be assessed. These bounds include: time to recover from the fault (t(sub rec)), time to restore the system (t(sub rec)) and whether there is a permanent delay in the system's Time Between Input and Output (TBIO) after the system has reached a steady state. An implementation of an ATAMM based computer was developed with the Generic VHSIC Spaceborne Computer (GVSC) as the target system. A simulation of the GVSC was also written based on the code used in ATAMM Multicomputer Operating System (AMOS). The simulation is in turn used to validate the new model in the usefulness and accuracy in tracking the propagation of the delay through the system and predicting the behavior in the transient state of recovery and restoration. The model is validated as an accurate method to predict the transient behavior of an ATAMM based multicomputer during recovery and restoration.

Predicting Document Retrieval System Performance: An Expected Precision Measure.

ERIC Educational Resources Information Center

Losee, Robert M., Jr.

1987-01-01

Describes an expected precision (EP) measure designed to predict document retrieval performance. Highlights include decision theoretic models; precision and recall as measures of system performance; EP graphs; relevance feedback; and computing the retrieval status value of a document for two models, the Binary Independent Model and the Two Poisson…

Acoustic environmental accuracy requirements for response determination

NASA Technical Reports Server (NTRS)

Pettitt, M. R.

1983-01-01

A general purpose computer program was developed for the prediction of vehicle interior noise. This program, named VIN, has both modal and statistical energy analysis capabilities for structural/acoustic interaction analysis. The analytic models and their computer implementation were verified through simple test cases with well-defined experimental results. The model was also applied in a space shuttle payload bay launch acoustics prediction study. The computer program processes large and small problems with equal efficiency because all arrays are dynamically sized by program input variables at run time. A data base is built and easily accessed for design studies. The data base significantly reduces the computational costs of such studies by allowing the reuse of the still-valid calculated parameters of previous iterations.

Student Use of Physics to Make Sense of Incomplete but Functional VPython Programs in a Lab Setting

NASA Astrophysics Data System (ADS)

Weatherford, Shawn A.

2011-12-01

Computational activities in Matter & Interactions, an introductory calculus-based physics course, have the instructional goal of providing students with the experience of applying the same set of a small number of fundamental principles to model a wide range of physical systems. However there are significant instructional challenges for students to build computer programs under limited time constraints, especially for students who are unfamiliar with programming languages and concepts. Prior attempts at designing effective computational activities were successful at having students ultimately build working VPython programs under the tutelage of experienced teaching assistants in a studio lab setting. A pilot study revealed that students who completed these computational activities had significant difficultly repeating the exact same tasks and further, had difficulty predicting the animation that would be produced by the example program after interpreting the program code. This study explores the interpretation and prediction tasks as part of an instructional sequence where students are asked to read and comprehend a functional, but incomplete program. Rather than asking students to begin their computational tasks with modifying program code, we explicitly ask students to interpret an existing program that is missing key lines of code. The missing lines of code correspond to the algebraic form of fundamental physics principles or the calculation of forces which would exist between analogous physical objects in the natural world. Students are then asked to draw a prediction of what they would see in the simulation produced by the VPython program and ultimately run the program to evaluate the students' prediction. This study specifically looks at how the participants use physics while interpreting the program code and creating a whiteboard prediction. This study also examines how students evaluate their understanding of the program and modification goals at the beginning of the modification task. While working in groups over the course of a semester, study participants were recorded while they completed three activities using these incomplete programs. Analysis of the video data showed that study participants had little difficulty interpreting physics quantities, generating a prediction, or determining how to modify the incomplete program. Participants did not base their prediction solely from the information from the incomplete program. When participants tried to predict the motion of the objects in the simulation, many turned to their knowledge of how the system would evolve if it represented an analogous real-world physical system. For example, participants attributed the real-world behavior of springs to helix objects even though the program did not include calculations for the spring to exert a force when stretched. Participants rarely interpreted lines of code in the computational loop during the first computational activity, but this changed during latter computational activities with most participants using their physics knowledge to interpret the computational loop. Computational activities in the Matter & Interactions curriculum were revised in light of these findings to include an instructional sequence of tasks to build a comprehension of the example program. The modified activities also ask students to create an additional whiteboard prediction for the time-evolution of the real-world phenomena which the example program will eventually model. This thesis shows how comprehension tasks identified by Palinscar and Brown (1984) as effective in improving reading comprehension are also effective in helping students apply their physics knowledge to interpret a computer program which attempts to model a real-world phenomena and identify errors in their understanding of the use, or omission, of fundamental physics principles in a computational model.

Scapular notching in reverse shoulder arthroplasty: validation of a computer impingement model.

PubMed

Roche, Christopher P; Marczuk, Yann; Wright, Thomas W; Flurin, Pierre-Henri; Grey, Sean G; Jones, Richard B; Routman, Howard D; Gilot, Gregory J; Zuckerman, Joseph D

2013-01-01

The purpose of this study is to validate a reverse shoulder computer impingement model and quantify the impact of implant position on scapular impingement by comparing it to that of a radiographic analysis of 256 patients who received the same prosthesis and were followed postoperatively for an average of 22.2 months. A geometric computer analysis quantified anterior and posterior scapular impingement as the humerus was internally and externally rotated at varying levels of abduction and adduction relative to a fixed scapula at defined glenoid implant positions. These impingement results were compared to radiographic study of 256 patients who were analyzed for notching, glenoid baseplate position, and glenosphere overhang. The computer model predicted no impingement at 0° humeral abduction in the scapular plane for the 38 mm, 42 mm, and 46 mm devices when the glenoid baseplate cage peg is positioned 18.6 mm, 20.4 mm, and 22.7 mm from the inferior glenoid rim (of the reamed glenoid) or when glenosphere overhang of 4.6 mm, 4.7 mm, and 4.5 mm was obtained with each size glenosphere, respectively. When compared to the radiographic analysis, the computer model correctly predicted impingement based upon glenoid base- plate position in 18 of 26 patients with scapular notching and based upon glenosphere overhang in 15 of 26 patients with scapular notching. Reverse shoulder implant positioning plays an important role in scapular notching. The results of this study demonstrate that the computer impingement model can effectively predict impingement based upon implant positioning in a majority of patients who developed scapular notching clinically. This computer analysis provides guidance to surgeons on implant positions that reduce scapular notching, a well-documented complication of reverse shoulder arthroplasty.

Uncertainty quantification for environmental models

USGS Publications Warehouse

Hill, Mary C.; Lu, Dan; Kavetski, Dmitri; Clark, Martyn P.; Ye, Ming

2012-01-01

Environmental models are used to evaluate the fate of fertilizers in agricultural settings (including soil denitrification), the degradation of hydrocarbons at spill sites, and water supply for people and ecosystems in small to large basins and cities—to mention but a few applications of these models. They also play a role in understanding and diagnosing potential environmental impacts of global climate change. The models are typically mildly to extremely nonlinear. The persistent demand for enhanced dynamics and resolution to improve model realism [17] means that lengthy individual model execution times will remain common, notwithstanding continued enhancements in computer power. In addition, high-dimensional parameter spaces are often defined, which increases the number of model runs required to quantify uncertainty [2]. Some environmental modeling projects have access to extensive funding and computational resources; many do not. The many recent studies of uncertainty quantification in environmental model predictions have focused on uncertainties related to data error and sparsity of data, expert judgment expressed mathematically through prior information, poorly known parameter values, and model structure (see, for example, [1,7,9,10,13,18]). Approaches for quantifying uncertainty include frequentist (potentially with prior information [7,9]), Bayesian [13,18,19], and likelihood-based. A few of the numerous methods, including some sensitivity and inverse methods with consequences for understanding and quantifying uncertainty, are as follows: Bayesian hierarchical modeling and Bayesian model averaging; single-objective optimization with error-based weighting [7] and multi-objective optimization [3]; methods based on local derivatives [2,7,10]; screening methods like OAT (one at a time) and the method of Morris [14]; FAST (Fourier amplitude sensitivity testing) [14]; the Sobol' method [14]; randomized maximum likelihood [10]; Markov chain Monte Carlo (MCMC) [10]. There are also bootstrapping and cross-validation approaches.Sometimes analyses are conducted using surrogate models [12]. The availability of so many options can be confusing. Categorizing methods based on fundamental questions assists in communicating the essential results of uncertainty analyses to stakeholders. Such questions can focus on model adequacy (e.g., How well does the model reproduce observed system characteristics and dynamics?) and sensitivity analysis (e.g., What parameters can be estimated with available data? What observations are important to parameters and predictions? What parameters are important to predictions?), as well as on the uncertainty quantification (e.g., How accurate and precise are the predictions?). The methods can also be classified by the number of model runs required: few (10s to 1000s) or many (10,000s to 1,000,000s). Of the methods listed above, the most computationally frugal are generally those based on local derivatives; MCMC methods tend to be among the most computationally demanding. Surrogate models (emulators)do not necessarily produce computational frugality because many runs of the full model are generally needed to create a meaningful surrogate model. With this categorization, we can, in general, address all the fundamental questions mentioned above using either computationally frugal or demanding methods. Model development and analysis can thus be conducted consistently using either computation-ally frugal or demanding methods; alternatively, different fundamental questions can be addressed using methods that require different levels of effort. Based on this perspective, we pose the question: Can computationally frugal methods be useful companions to computationally demanding meth-ods? The reliability of computationally frugal methods generally depends on the model being reasonably linear, which usually means smooth nonlin-earities and the assumption of Gaussian errors; both tend to be more valid with more linear

Annual Rainfall Forecasting by Using Mamdani Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Fallah-Ghalhary, G.-A.; Habibi Nokhandan, M.; Mousavi Baygi, M.

2009-04-01

Long-term rainfall prediction is very important to countries thriving on agro-based economy. In general, climate and rainfall are highly non-linear phenomena in nature giving rise to what is known as "butterfly effect". The parameters that are required to predict the rainfall are enormous even for a short period. Soft computing is an innovative approach to construct computationally intelligent systems that are supposed to possess humanlike expertise within a specific domain, adapt themselves and learn to do better in changing environments, and explain how they make decisions. Unlike conventional artificial intelligence techniques the guiding principle of soft computing is to exploit tolerance for imprecision, uncertainty, robustness, partial truth to achieve tractability, and better rapport with reality. In this paper, 33 years of rainfall data analyzed in khorasan state, the northeastern part of Iran situated at latitude-longitude pairs (31°-38°N, 74°- 80°E). this research attempted to train Fuzzy Inference System (FIS) based prediction models with 33 years of rainfall data. For performance evaluation, the model predicted outputs were compared with the actual rainfall data. Simulation results reveal that soft computing techniques are promising and efficient. The test results using by FIS model showed that the RMSE was obtained 52 millimeter.

Hydrologic modeling strategy for the Islamic Republic of Mauritania, Africa

USGS Publications Warehouse

Friedel, Michael J.

2008-01-01

The government of Mauritania is interested in how to maintain hydrologic balance to ensure a long-term stable water supply for minerals-related, domestic, and other purposes. Because of the many complicating and competing natural and anthropogenic factors, hydrologists will perform quantitative analysis with specific objectives and relevant computer models in mind. Whereas various computer models are available for studying water-resource priorities, the success of these models to provide reliable predictions largely depends on adequacy of the model-calibration process. Predictive analysis helps us evaluate the accuracy and uncertainty associated with simulated dependent variables of our calibrated model. In this report, the hydrologic modeling process is reviewed and a strategy summarized for future Mauritanian hydrologic modeling studies.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deline, C.

Computer modeling is able to predict the performance of distributed power electronics (microinverters, power optimizers) in PV systems. However, details about partial shade and other mismatch must be known in order to give the model accurate information to go on. This talk will describe recent updates in NREL’s System Advisor Model program to model partial shading losses with and without distributed power electronics, along with experimental validation results. Computer modeling is able to predict the performance of distributed power electronics (microinverters, power optimizers) in PV systems. However, details about partial shade and other mismatch must be known in order tomore » give the model accurate information to go on. This talk will describe recent updates in NREL’s System Advisor Model program to model partial shading losses.« less

Computation of turbulent high speed mixing layers using a two-equation turbulence model

NASA Technical Reports Server (NTRS)

Narayan, J. R.; Sekar, B.

1991-01-01

A two-equation turbulence model was extended to be applicable for compressible flows. A compressibility correction based on modelling the dilational terms in the Reynolds stress equations were included in the model. The model is used in conjunction with the SPARK code for the computation of high speed mixing layers. The observed trend of decreasing growth rate with increasing convective Mach number in compressible mixing layers is well predicted by the model. The predictions agree well with the experimental data and the results from a compressible Reynolds stress model. The present model appears to be well suited for the study of compressible free shear flows. Preliminary results obtained for the reacting mixing layers are included.

Identification of informative features for predicting proinflammatory potentials of engine exhausts.

PubMed

Wang, Chia-Chi; Lin, Ying-Chi; Lin, Yuan-Chung; Jhang, Syu-Ruei; Tung, Chun-Wei

2017-08-18

The immunotoxicity of engine exhausts is of high concern to human health due to the increasing prevalence of immune-related diseases. However, the evaluation of immunotoxicity of engine exhausts is currently based on expensive and time-consuming experiments. It is desirable to develop efficient methods for immunotoxicity assessment. To accelerate the development of safe alternative fuels, this study proposed a computational method for identifying informative features for predicting proinflammatory potentials of engine exhausts. A principal component regression (PCR) algorithm was applied to develop prediction models. The informative features were identified by a sequential backward feature elimination (SBFE) algorithm. A total of 19 informative chemical and biological features were successfully identified by SBFE algorithm. The informative features were utilized to develop a computational method named FS-CBM for predicting proinflammatory potentials of engine exhausts. FS-CBM model achieved a high performance with correlation coefficient values of 0.997 and 0.943 obtained from training and independent test sets, respectively. The FS-CBM model was developed for predicting proinflammatory potentials of engine exhausts with a large improvement on prediction performance compared with our previous CBM model. The proposed method could be further applied to construct models for bioactivities of mixtures.

SIM_ADJUST -- A computer code that adjusts simulated equivalents for observations or predictions

USGS Publications Warehouse

Poeter, Eileen P.; Hill, Mary C.

2008-01-01

This report documents the SIM_ADJUST computer code. SIM_ADJUST surmounts an obstacle that is sometimes encountered when using universal model analysis computer codes such as UCODE_2005 (Poeter and others, 2005), PEST (Doherty, 2004), and OSTRICH (Matott, 2005; Fredrick and others (2007). These codes often read simulated equivalents from a list in a file produced by a process model such as MODFLOW that represents a system of interest. At times values needed by the universal code are missing or assigned default values because the process model could not produce a useful solution. SIM_ADJUST can be used to (1) read a file that lists expected observation or prediction names and possible alternatives for the simulated values; (2) read a file produced by a process model that contains space or tab delimited columns, including a column of simulated values and a column of related observation or prediction names; (3) identify observations or predictions that have been omitted or assigned a default value by the process model; and (4) produce an adjusted file that contains a column of simulated values and a column of associated observation or prediction names. The user may provide alternatives that are constant values or that are alternative simulated values. The user may also provide a sequence of alternatives. For example, the heads from a series of cells may be specified to ensure that a meaningful value is available to compare with an observation located in a cell that may become dry. SIM_ADJUST is constructed using modules from the JUPITER API, and is intended for use on any computer operating system. SIM_ADJUST consists of algorithms programmed in Fortran90, which efficiently performs numerical calculations.

Predictive Software Cost Model Study. Volume I. Final Technical Report.

DTIC Science & Technology

1980-06-01

development phase to identify computer resources necessary to support computer programs after transfer of program manangement responsibility and system... classical model development with refinements specifically applicable to avionics systems. The refinements are the result of the Phase I literature search

A novel one-class SVM based negative data sampling method for reconstructing proteome-wide HTLV-human protein interaction networks.

PubMed

Mei, Suyu; Zhu, Hao

2015-01-26

Protein-protein interaction (PPI) prediction is generally treated as a problem of binary classification wherein negative data sampling is still an open problem to be addressed. The commonly used random sampling is prone to yield less representative negative data with considerable false negatives. Meanwhile rational constraints are seldom exerted on model selection to reduce the risk of false positive predictions for most of the existing computational methods. In this work, we propose a novel negative data sampling method based on one-class SVM (support vector machine, SVM) to predict proteome-wide protein interactions between HTLV retrovirus and Homo sapiens, wherein one-class SVM is used to choose reliable and representative negative data, and two-class SVM is used to yield proteome-wide outcomes as predictive feedback for rational model selection. Computational results suggest that one-class SVM is more suited to be used as negative data sampling method than two-class PPI predictor, and the predictive feedback constrained model selection helps to yield a rational predictive model that reduces the risk of false positive predictions. Some predictions have been validated by the recent literature. Lastly, gene ontology based clustering of the predicted PPI networks is conducted to provide valuable cues for the pathogenesis of HTLV retrovirus.

Bridging the Gap between Human Judgment and Automated Reasoning in Predictive Analytics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sanfilippo, Antonio P.; Riensche, Roderick M.; Unwin, Stephen D.

2010-06-07

Events occur daily that impact the health, security and sustainable growth of our society. If we are to address the challenges that emerge from these events, anticipatory reasoning has to become an everyday activity. Strong advances have been made in using integrated modeling for analysis and decision making. However, a wider impact of predictive analytics is currently hindered by the lack of systematic methods for integrating predictive inferences from computer models with human judgment. In this paper, we present a predictive analytics approach that supports anticipatory analysis and decision-making through a concerted reasoning effort that interleaves human judgment and automatedmore » inferences. We describe a systematic methodology for integrating modeling algorithms within a serious gaming environment in which role-playing by human agents provides updates to model nodes and the ensuing model outcomes in turn influence the behavior of the human players. The approach ensures a strong functional partnership between human players and computer models while maintaining a high degree of independence and greatly facilitating the connection between model and game structures.« less

Development of a prototype automatic controller for liquid cooling garment inlet temperature

NASA Technical Reports Server (NTRS)

Weaver, C. S.; Webbon, B. W.; Montgomery, L. D.

1982-01-01

The development of a computer control of a liquid cooled garment (LCG) inlet temperature is descirbed. An adaptive model of the LCG is used to predict the heat-removal rates for various inlet temperatures. An experimental system that contains a microcomputer was constructed. The LCG inlet and outlet temperatures and the heat exchanger outlet temperature form the inputs to the computer. The adaptive model prediction method of control is successful during tests where the inlet temperature is automatically chosen by the computer. It is concluded that the program can be implemented in a microprocessor of a size that is practical for a life support back-pack.

Advancing Predictive Hepatotoxicity at the Intersection of Experimental, in Silico, and Artificial Intelligence Technologies.

PubMed

Fraser, Keith; Bruckner, Dylan M; Dordick, Jonathan S

2018-06-18

Adverse drug reactions, particularly those that result in drug-induced liver injury (DILI), are a major cause of drug failure in clinical trials and drug withdrawals. Hepatotoxicity-mediated drug attrition occurs despite substantial investments of time and money in developing cellular assays, animal models, and computational models to predict its occurrence in humans. Underperformance in predicting hepatotoxicity associated with drugs and drug candidates has been attributed to existing gaps in our understanding of the mechanisms involved in driving hepatic injury after these compounds perfuse and are metabolized by the liver. Herein we assess in vitro, in vivo (animal), and in silico strategies used to develop predictive DILI models. We address the effectiveness of several two- and three-dimensional in vitro cellular methods that are frequently employed in hepatotoxicity screens and how they can be used to predict DILI in humans. We also explore how humanized animal models can recapitulate human drug metabolic profiles and associated liver injury. Finally, we highlight the maturation of computational methods for predicting hepatotoxicity, the untapped potential of artificial intelligence for improving in silico DILI screens, and how knowledge acquired from these predictions can shape the refinement of experimental methods.

From QSAR to QSIIR: Searching for Enhanced Computational Toxicology Models

PubMed Central

Zhu, Hao

2017-01-01

Quantitative Structure Activity Relationship (QSAR) is the most frequently used modeling approach to explore the dependency of biological, toxicological, or other types of activities/properties of chemicals on their molecular features. In the past two decades, QSAR modeling has been used extensively in drug discovery process. However, the predictive models resulted from QSAR studies have limited use for chemical risk assessment, especially for animal and human toxicity evaluations, due to the low predictivity of new compounds. To develop enhanced toxicity models with independently validated external prediction power, novel modeling protocols were pursued by computational toxicologists based on rapidly increasing toxicity testing data in recent years. This chapter reviews the recent effort in our laboratory to incorporate the biological testing results as descriptors in the toxicity modeling process. This effort extended the concept of QSAR to Quantitative Structure In vitro-In vivo Relationship (QSIIR). The QSIIR study examples provided in this chapter indicate that the QSIIR models that based on the hybrid (biological and chemical) descriptors are indeed superior to the conventional QSAR models that only based on chemical descriptors for several animal toxicity endpoints. We believe that the applications introduced in this review will be of interest and value to researchers working in the field of computational drug discovery and environmental chemical risk assessment. PMID:23086837

Genomic Prediction Accounting for Residual Heteroskedasticity.

PubMed

Ou, Zhining; Tempelman, Robert J; Steibel, Juan P; Ernst, Catherine W; Bates, Ronald O; Bello, Nora M

2015-11-12

Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit. Copyright © 2016 Ou et al.

Correlation of predicted and measured thermal stresses on a truss-type aircraft structure

NASA Technical Reports Server (NTRS)

Jenkins, J. M.; Schuster, L. S.; Carter, A. L.

1978-01-01

A test structure representing a portion of a hypersonic vehicle was instrumented with strain gages and thermocouples. This test structure was then subjected to laboratory heating representative of supersonic and hypersonic flight conditions. A finite element computer model of this structure was developed using several types of elements with the NASA structural analysis (NASTRAN) computer program. Temperature inputs from the test were used to generate predicted model thermal stresses and these were correlated with the test measurements.

Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets

USGS Publications Warehouse

Finley, Andrew O.; Banerjee, Sudipto; Cook, Bruce D.; Bradford, John B.

2013-01-01

In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.

MicroRNAs and complex diseases: from experimental results to computational models.

PubMed

Chen, Xing; Xie, Di; Zhao, Qi; You, Zhu-Hong

2017-10-17

Plenty of microRNAs (miRNAs) were discovered at a rapid pace in plants, green algae, viruses and animals. As one of the most important components in the cell, miRNAs play a growing important role in various essential and important biological processes. For the recent few decades, amounts of experimental methods and computational models have been designed and implemented to identify novel miRNA-disease associations. In this review, the functions of miRNAs, miRNA-target interactions, miRNA-disease associations and some important publicly available miRNA-related databases were discussed in detail. Specially, considering the important fact that an increasing number of miRNA-disease associations have been experimentally confirmed, we selected five important miRNA-related human diseases and five crucial disease-related miRNAs and provided corresponding introductions. Identifying disease-related miRNAs has become an important goal of biomedical research, which will accelerate the understanding of disease pathogenesis at the molecular level and molecular tools design for disease diagnosis, treatment and prevention. Computational models have become an important means for novel miRNA-disease association identification, which could select the most promising miRNA-disease pairs for experimental validation and significantly reduce the time and cost of the biological experiments. Here, we reviewed 20 state-of-the-art computational models of predicting miRNA-disease associations from different perspectives. Finally, we summarized four important factors for the difficulties of predicting potential disease-related miRNAs, the framework of constructing powerful computational models to predict potential miRNA-disease associations including five feasible and important research schemas, and future directions for further development of computational models. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Automatic prediction of tongue muscle activations using a finite element model.

PubMed

Stavness, Ian; Lloyd, John E; Fels, Sidney

2012-11-15

Computational modeling has improved our understanding of how muscle forces are coordinated to generate movement in musculoskeletal systems. Muscular-hydrostat systems, such as the human tongue, involve very different biomechanics than musculoskeletal systems, and modeling efforts to date have been limited by the high computational complexity of representing continuum-mechanics. In this study, we developed a computationally efficient tracking-based algorithm for prediction of muscle activations during dynamic 3D finite element simulations. The formulation uses a local quadratic-programming problem at each simulation time-step to find a set of muscle activations that generated target deformations and movements in finite element muscular-hydrostat models. We applied the technique to a 3D finite element tongue model for protrusive and bending movements. Predicted muscle activations were consistent with experimental recordings of tongue strain and electromyography. Upward tongue bending was achieved by recruitment of the superior longitudinal sheath muscle, which is consistent with muscular-hydrostat theory. Lateral tongue bending, however, required recruitment of contralateral transverse and vertical muscles in addition to the ipsilateral margins of the superior longitudinal muscle, which is a new proposition for tongue muscle coordination. Our simulation framework provides a new computational tool for systematic analysis of muscle forces in continuum-mechanics models that is complementary to experimental data and shows promise for eliciting a deeper understanding of human tongue function. Copyright © 2012 Elsevier Ltd. All rights reserved.

Modeling Students' Problem Solving Performance in the Computer-Based Mathematics Learning Environment

ERIC Educational Resources Information Center

Lee, Young-Jin

2017-01-01

Purpose: The purpose of this paper is to develop a quantitative model of problem solving performance of students in the computer-based mathematics learning environment. Design/methodology/approach: Regularized logistic regression was used to create a quantitative model of problem solving performance of students that predicts whether students can…

Computational prediction of formulation strategies for beyond-rule-of-5 compounds.

PubMed

Bergström, Christel A S; Charman, William N; Porter, Christopher J H

2016-06-01

The physicochemical properties of some contemporary drug candidates are moving towards higher molecular weight, and coincidentally also higher lipophilicity in the quest for biological selectivity and specificity. These physicochemical properties move the compounds towards beyond rule-of-5 (B-r-o-5) chemical space and often result in lower water solubility. For such B-r-o-5 compounds non-traditional delivery strategies (i.e. those other than conventional tablet and capsule formulations) typically are required to achieve adequate exposure after oral administration. In this review, we present the current status of computational tools for prediction of intestinal drug absorption, models for prediction of the most suitable formulation strategies for B-r-o-5 compounds and models to obtain an enhanced understanding of the interplay between drug, formulation and physiological environment. In silico models are able to identify the likely molecular basis for low solubility in physiologically relevant fluids such as gastric and intestinal fluids. With this baseline information, a formulation scientist can, at an early stage, evaluate different orally administered, enabling formulation strategies. Recent computational models have emerged that predict glass-forming ability and crystallisation tendency and therefore the potential utility of amorphous solid dispersion formulations. Further, computational models of loading capacity in lipids, and therefore the potential for formulation as a lipid-based formulation, are now available. Whilst such tools are useful for rapid identification of suitable formulation strategies, they do not reveal drug localisation and molecular interaction patterns between drug and excipients. For the latter, Molecular Dynamics simulations provide an insight into the interplay between drug, formulation and intestinal fluid. These different computational approaches are reviewed. Additionally, we analyse the molecular requirements of different targets, since these can provide an early signal that enabling formulation strategies will be required. Based on the analysis we conclude that computational biopharmaceutical profiling can be used to identify where non-conventional gateways, such as prediction of 'formulate-ability' during lead optimisation and early development stages, are important and may ultimately increase the number of orally tractable contemporary targets. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Computer Model Inversion and Uncertainty Quantification in the Geosciences

NASA Astrophysics Data System (ADS)

White, Jeremy T.

The subject of this dissertation is use of computer models as data analysis tools in several different geoscience settings, including integrated surface water/groundwater modeling, tephra fallout modeling, geophysical inversion, and hydrothermal groundwater modeling. The dissertation is organized into three chapters, which correspond to three individual publication manuscripts. In the first chapter, a linear framework is developed to identify and estimate the potential predictive consequences of using a simple computer model as a data analysis tool. The framework is applied to a complex integrated surface-water/groundwater numerical model with thousands of parameters. Several types of predictions are evaluated, including particle travel time and surface-water/groundwater exchange volume. The analysis suggests that model simplifications have the potential to corrupt many types of predictions. The implementation of the inversion, including how the objective function is formulated, what minimum of the objective function value is acceptable, and how expert knowledge is enforced on parameters, can greatly influence the manifestation of model simplification. Depending on the prediction, failure to specifically address each of these important issues during inversion is shown to degrade the reliability of some predictions. In some instances, inversion is shown to increase, rather than decrease, the uncertainty of a prediction, which defeats the purpose of using a model as a data analysis tool. In the second chapter, an efficient inversion and uncertainty quantification approach is applied to a computer model of volcanic tephra transport and deposition. The computer model simulates many physical processes related to tephra transport and fallout. The utility of the approach is demonstrated for two eruption events. In both cases, the importance of uncertainty quantification is highlighted by exposing the variability in the conditioning provided by the observations used for inversion. The worth of different types of tephra data to reduce parameter uncertainty is evaluated, as is the importance of different observation error models. The analyses reveal the importance using tephra granulometry data for inversion, which results in reduced uncertainty for most eruption parameters. In the third chapter, geophysical inversion is combined with hydrothermal modeling to evaluate the enthalpy of an undeveloped geothermal resource in a pull-apart basin located in southeastern Armenia. A high-dimensional gravity inversion is used to define the depth to the contact between the lower-density valley fill sediments and the higher-density surrounding host rock. The inverted basin depth distribution was used to define the hydrostratigraphy for the coupled groundwater-flow and heat-transport model that simulates the circulation of hydrothermal fluids in the system. Evaluation of several different geothermal system configurations indicates that the most likely system configuration is a low-enthalpy, liquid-dominated geothermal system.

Assessment of flat rolling theories for the use in a model-based controller for high-precision rolling applications

NASA Astrophysics Data System (ADS)

Stockert, Sven; Wehr, Matthias; Lohmar, Johannes; Abel, Dirk; Hirt, Gerhard

2017-10-01

In the electrical and medical industries the trend towards further miniaturization of devices is accompanied by the demand for smaller manufacturing tolerances. Such industries use a plentitude of small and narrow cold rolled metal strips with high thickness accuracy. Conventional rolling mills can hardly achieve further improvement of these tolerances. However, a model-based controller in combination with an additional piezoelectric actuator for high dynamic roll adjustment is expected to enable the production of the required metal strips with a thickness tolerance of +/-1 µm. The model-based controller has to be based on a rolling theory which can describe the rolling process very accurately. Additionally, the required computing time has to be low in order to predict the rolling process in real-time. In this work, four rolling theories from literature with different levels of complexity are tested for their suitability for the predictive controller. Rolling theories of von Kármán, Siebel, Bland & Ford and Alexander are implemented in Matlab and afterwards transferred to the real-time computer used for the controller. The prediction accuracy of these theories is validated using rolling trials with different thickness reduction and a comparison to the calculated results. Furthermore, the required computing time on the real-time computer is measured. Adequate results according the prediction accuracy can be achieved with the rolling theories developed by Bland & Ford and Alexander. A comparison of the computing time of those two theories reveals that Alexander's theory exceeds the sample rate of 1 kHz of the real-time computer.

A strategy for improved computational efficiency of the method of anchored distributions

NASA Astrophysics Data System (ADS)

Over, Matthew William; Yang, Yarong; Chen, Xingyuan; Rubin, Yoram

2013-06-01

This paper proposes a strategy for improving the computational efficiency of model inversion using the method of anchored distributions (MAD) by "bundling" similar model parametrizations in the likelihood function. Inferring the likelihood function typically requires a large number of forward model (FM) simulations for each possible model parametrization; as a result, the process is quite expensive. To ease this prohibitive cost, we present an approximation for the likelihood function called bundling that relaxes the requirement for high quantities of FM simulations. This approximation redefines the conditional statement of the likelihood function as the probability of a set of similar model parametrizations "bundle" replicating field measurements, which we show is neither a model reduction nor a sampling approach to improving the computational efficiency of model inversion. To evaluate the effectiveness of these modifications, we compare the quality of predictions and computational cost of bundling relative to a baseline MAD inversion of 3-D flow and transport model parameters. Additionally, to aid understanding of the implementation we provide a tutorial for bundling in the form of a sample data set and script for the R statistical computing language. For our synthetic experiment, bundling achieved a 35% reduction in overall computational cost and had a limited negative impact on predicted probability distributions of the model parameters. Strategies for minimizing error in the bundling approximation, for enforcing similarity among the sets of model parametrizations, and for identifying convergence of the likelihood function are also presented.

Assessment of computational prediction of tail buffeting

NASA Technical Reports Server (NTRS)

Edwards, John W.

1990-01-01

Assessments of the viability of computational methods and the computer resource requirements for the prediction of tail buffeting are made. Issues involved in the use of Euler and Navier-Stokes equations in modeling vortex-dominated and buffet flows are discussed and the requirement for sufficient grid density to allow accurate, converged calculations is stressed. Areas in need of basic fluid dynamics research are highlighted: vorticity convection, vortex breakdown, dynamic turbulence modeling for free shear layers, unsteady flow separation for moderately swept, rounded leading-edge wings, vortex flows about wings at high subsonic speeds. An estimate of the computer run time for a buffeting response calculation for a full span F-15 aircraft indicates that an improvement in computer and/or algorithm efficiency of three orders of magnitude is needed to enable routine use of such methods. Attention is also drawn to significant uncertainties in the estimates, in particular with regard to nonlinearities contained within the modeling and the question of the repeatability or randomness of buffeting response.

Iterative Refinement of a Binding Pocket Model: Active Computational Steering of Lead Optimization

PubMed Central

2012-01-01

Computational approaches for binding affinity prediction are most frequently demonstrated through cross-validation within a series of molecules or through performance shown on a blinded test set. Here, we show how such a system performs in an iterative, temporal lead optimization exercise. A series of gyrase inhibitors with known synthetic order formed the set of molecules that could be selected for “synthesis.” Beginning with a small number of molecules, based only on structures and activities, a model was constructed. Compound selection was done computationally, each time making five selections based on confident predictions of high activity and five selections based on a quantitative measure of three-dimensional structural novelty. Compound selection was followed by model refinement using the new data. Iterative computational candidate selection produced rapid improvements in selected compound activity, and incorporation of explicitly novel compounds uncovered much more diverse active inhibitors than strategies lacking active novelty selection. PMID:23046104

Unsteady flow model for circulation-control airfoils

NASA Technical Reports Server (NTRS)

Rao, B. M.

1979-01-01

An analysis and a numerical lifting surface method are developed for predicting the unsteady airloads on two-dimensional circulation control airfoils in incompressible flow. The analysis and the computer program are validated by correlating the computed unsteady airloads with test data and also with other theoretical solutions. Additionally, a mathematical model for predicting the bending-torsion flutter of a two-dimensional airfoil (a reference section of a wing or rotor blade) and a computer program using an iterative scheme are developed. The flutter program has a provision for using the CC airfoil airloads program or the Theodorsen hard flap solution to compute the unsteady lift and moment used in the flutter equations. The adopted mathematical model and the iterative scheme are used to perform a flutter analysis of a typical CC rotor blade reference section. The program seems to work well within the basic assumption of the incompressible flow.

Parameterized reduced-order models using hyper-dual numbers.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fike, Jeffrey A.; Brake, Matthew Robert

2013-10-01

The goal of most computational simulations is to accurately predict the behavior of a real, physical system. Accurate predictions often require very computationally expensive analyses and so reduced order models (ROMs) are commonly used. ROMs aim to reduce the computational cost of the simulations while still providing accurate results by including all of the salient physics of the real system in the ROM. However, real, physical systems often deviate from the idealized models used in simulations due to variations in manufacturing or other factors. One approach to this issue is to create a parameterized model in order to characterize themore » effect of perturbations from the nominal model on the behavior of the system. This report presents a methodology for developing parameterized ROMs, which is based on Craig-Bampton component mode synthesis and the use of hyper-dual numbers to calculate the derivatives necessary for the parameterization.« less

Evaluation of Inelastic Constitutive Models for Nonlinear Structural Analysis

NASA Technical Reports Server (NTRS)

Kaufman, A.

1983-01-01

The influence of inelastic material models on computed stress-strain states, and therefore predicted lives, was studied for thermomechanically loaded structures. Nonlinear structural analyses were performed on a fatigue specimen which was subjected to thermal cycling in fluidized beds and on a mechanically load cycled benchmark notch specimen. Four incremental plasticity creep models (isotropic, kinematic, combined isotropic-kinematic, combined plus transient creep) were exercised. Of the plasticity models, kinematic hardening gave results most consistent with experimental observations. Life predictions using the computed strain histories at the critical location with a Strainrange Partitioning approach considerably overpredicted the crack initiation life of the thermal fatigue specimen.

Computational Fluid Dynamics Simulation of Flows in an Oxidation Ditch Driven by a New Surface Aerator.

PubMed

Huang, Weidong; Li, Kun; Wang, Gan; Wang, Yingzhe

2013-11-01

In this article, we present a newly designed inverse umbrella surface aerator, and tested its performance in driving flow of an oxidation ditch. Results show that it has a better performance in driving the oxidation ditch than the original one with higher average velocity and more uniform flow field. We also present a computational fluid dynamics model for predicting the flow field in an oxidation ditch driven by a surface aerator. The improved momentum source term approach to simulate the flow field of the oxidation ditch driven by an inverse umbrella surface aerator was developed and validated through experiments. Four kinds of turbulent models were investigated with the approach, including the standard k - ɛ model, RNG k - ɛ model, realizable k - ɛ model, and Reynolds stress model, and the predicted data were compared with those calculated with the multiple rotating reference frame approach (MRF) and sliding mesh approach (SM). Results of the momentum source term approach are in good agreement with the experimental data, and its prediction accuracy is better than MRF, close to SM. It is also found that the momentum source term approach has lower computational expenses, is simpler to preprocess, and is easier to use.

A Performance Prediction Model for a Fault-Tolerant Computer During Recovery and Restoration

NASA Technical Reports Server (NTRS)

Obando, Rodrigo A.; Stoughton, John W.

1995-01-01

The modeling and design of a fault-tolerant multiprocessor system is addressed. Of interest is the behavior of the system during recovery and restoration after a fault has occurred. The multiprocessor systems are based on the Algorithm to Architecture Mapping Model (ATAMM) and the fault considered is the death of a processor. The developed model is useful in the determination of performance bounds of the system during recovery and restoration. The performance bounds include time to recover from the fault, time to restore the system, and determination of any permanent delay in the input to output latency after the system has regained steady state. Implementation of an ATAMM based computer was developed for a four-processor generic VHSIC spaceborne computer (GVSC) as the target system. A simulation of the GVSC was also written on the code used in the ATAMM Multicomputer Operating System (AMOS). The simulation is used to verify the new model for tracking the propagation of the delay through the system and predicting the behavior of the transient state of recovery and restoration. The model is shown to accurately predict the transient behavior of an ATAMM based multicomputer during recovery and restoration.

Influence of 2D Finite Element Modeling Assumptions on Debonding Prediction for Composite Skin-stiffener Specimens Subjected to Tension and Bending

NASA Technical Reports Server (NTRS)

Krueger, Ronald; Minguet, Pierre J.; Bushnell, Dennis M. (Technical Monitor)

2002-01-01

The influence of two-dimensional finite element modeling assumptions on the debonding prediction for skin-stiffener specimens was investigated. Geometrically nonlinear finite element analyses using two-dimensional plane-stress and plane strain elements as well as three different generalized plane strain type approaches were performed. The computed deflections, skin and flange strains, transverse tensile stresses and energy release rates were compared to results obtained from three-dimensional simulations. The study showed that for strains and energy release rate computations the generalized plane strain assumptions yielded results closest to the full three-dimensional analysis. For computed transverse tensile stresses the plane stress assumption gave the best agreement. Based on this study it is recommended that results from plane stress and plane strain models be used as upper and lower bounds. The results from generalized plane strain models fall between the results obtained from plane stress and plane strain models. Two-dimensional models may also be used to qualitatively evaluate the stress distribution in a ply and the variation of energy release rates and mixed mode ratios with lamination length. For more accurate predictions, however, a three-dimensional analysis is required.

The computational modeling of supercritical carbon dioxide flow in solid wood material

NASA Astrophysics Data System (ADS)

Gething, Brad Allen

The use of supercritical carbon dioxide (SC CO2) as a solvent to deliver chemicals to porous media has shown promise in various industries. Recently, efforts by the wood treating industry have been made to use SC CO 2 as a replacement to more traditional methods of chemical preservative delivery. Previous studies have shown that the SC CO2 pressure treatment process is capable of impregnating solid wood materials with chemical preservatives, but concentration gradients of preservative often develop during treatment. Widespread application of the treatment process is unlikely unless the treatment inconsistencies can be improved for greater overall treating homogeneity. The development of a computational flow model to accurately predict the internal pressure of CO2 during treatment is integral to a more consistent treatment process. While similar models that attempt to describe the flow process have been proposed by Ward (1989) and Sahle-Demessie (1994), neither have been evaluated for accuracy. The present study was an evaluation of those models. More specifically, the present study evaluated the performance of a computational flow model, which was based on the viscous flow of compressible CO2 as a single phase through a porous medium at the macroscopic scale. Flow model performance was evaluated through comparisons between predicted pressures that corresponded to internal pressure development measured with inserted sensor probes during treatment of specimens. Pressure measurements were applied through a technique developed by Schneider (2000), which utilizes epoxy-sealed stainless steel tubes that are inserted into the wood as pressure probes. Two different wood species were investigated as treating specimens, Douglas-fir and shortleaf pine. Evaluations of the computational flow model revealed that it is sensitive to input parameters that relate to both processing conditions and material properties, particularly treating temperature and wood permeability, respectively. This sensitivity requires that the input parameters, principally permeability, be relatively accurate to evaluate the appropriateness of the phenomenological relationships of the computational flow model. Providing this stipulation, it was observed that below the region of transition from CO2 gas to supercritical fluid, the computational flow model has the potential to predict flow accurately. However, above the transition region, the model does not fully account for the physics of the flow process, resulting in prediction inaccuracy. One potential cause for the loss of prediction accuracy in the supercritical region was attributed to a dynamic change in permeability that is likely caused by an interaction between the flowing SC CO2 and the wood material. Furthermore, a hysteresis was observed between the pressurization and depressurization stages of treatment, which cannot be explained by the current flow model. If greater accuracy in the computational flow model is desired, a more complex approach to the model is necessary, which would include non-constant input parameters of temperature and permeability. Furthermore, the implications of a multi-scale methodology for the flow model were explored from a qualitative standpoint.

Clinical use of computational modeling for surgical planning of arteriovenous fistula for hemodialysis.

PubMed

Bozzetto, Michela; Rota, Stefano; Vigo, Valentina; Casucci, Francesco; Lomonte, Carlo; Morale, Walter; Senatore, Massimo; Tazza, Luigi; Lodi, Massimo; Remuzzi, Giuseppe; Remuzzi, Andrea

2017-03-14

Autogenous arteriovenous fistula (AVF) is the best vascular access (VA) for hemodialysis, but its creation is still a critical procedure. Physical examination, vascular mapping and doppler ultrasound (DUS) evaluation are recommended for AVF planning, but they can not provide direct indication on AVF outcome. We recently developed and validated in a clinical trial a patient-specific computational model to predict pre-operatively the blood flow volume (BFV) in AVF for different surgical configuration on the basis of demographic, clinical and DUS data. In the present investigation we tested power of prediction and usability of the computational model in routine clinical setting. We developed a web-based system (AVF.SIM) that integrates the computational model in a single procedure, including data collection and transfer, simulation management and data storage. A usability test on observational data was designed to compare predicted vs. measured BFV and evaluate the acceptance of the system in the clinical setting. Six Italian nephrology units were involved in the evaluation for a 6-month period that included all incident dialysis patients with indication for AVF surgery. Out of the 74 patients, complete data from 60 patients were included in the final dataset. Predicted brachial BFV at 40 days after surgery showed a good correlation with measured values (in average 787 ± 306 vs. 751 ± 267 mL/min, R = 0.81, p < 0.001). For distal AVFs the mean difference (±SD) between predicted vs. measured BFV was -2.0 ± 20.9%, with 50% of predicted values in the range of 86-121% of measured BFV. Feedbacks provided by clinicians indicate that AVF.SIM is easy to use and well accepted in clinical routine, with limited additional workload. Clinical use of computational modeling for AVF surgical planning can help the surgeon to select the best surgical strategy, reducing AVF early failures and complications. This approach allows individualization of VA care, with the aim to reduce the costs associated with VA dysfunction, and to improve AVF clinical outcome.

Computational optimization and biological evolution.

PubMed

Goryanin, Igor

2010-10-01

Modelling and optimization principles become a key concept in many biological areas, especially in biochemistry. Definitions of objective function, fitness and co-evolution, although they differ between biology and mathematics, are similar in a general sense. Although successful in fitting models to experimental data, and some biochemical predictions, optimization and evolutionary computations should be developed further to make more accurate real-life predictions, and deal not only with one organism in isolation, but also with communities of symbiotic and competing organisms. One of the future goals will be to explain and predict evolution not only for organisms in shake flasks or fermenters, but for real competitive multispecies environments.

Modeling "Throughput Capacity": Using Computational Thinking to Envision More Graduates without Investing More Resources

ERIC Educational Resources Information Center

Wick, Michael R.; Kleine, Patricia A.; Nelson, Andrew J.

2011-01-01

This article presents the development, testing, and application of an enrollment model. The model incorporates incoming freshman enrollment class size and historical persistence, transfer, and graduation rates to predict a six-year enrollment window and associated annual graduate production. The model predicts six-year enrollment to within 0.67…

Bellows flow-induced vibrations

NASA Technical Reports Server (NTRS)

Tygielski, P. J.; Smyly, H. M.; Gerlach, C. R.

1983-01-01

The bellows flow excitation mechanism and results of comprehensive test program are summarized. The analytical model for predicting bellows flow induced stress is refined. The model includes the effects of an upstream elbow, arbitrary geometry, and multiple piles. A refined computer code for predicting flow induced stress is described which allows life prediction if a material S-N diagram is available.

Evidence of common and separate eye and hand accumulators underlying flexible eye-hand coordination

PubMed Central

Jana, Sumitash; Gopal, Atul

2016-01-01

Eye and hand movements are initiated by anatomically separate regions in the brain, and yet these movements can be flexibly coupled and decoupled, depending on the need. The computational architecture that enables this flexible coupling of independent effectors is not understood. Here, we studied the computational architecture that enables flexible eye-hand coordination using a drift diffusion framework, which predicts that the variability of the reaction time (RT) distribution scales with its mean. We show that a common stochastic accumulator to threshold, followed by a noisy effector-dependent delay, explains eye-hand RT distributions and their correlation in a visual search task that required decision-making, while an interactive eye and hand accumulator model did not. In contrast, in an eye-hand dual task, an interactive model better predicted the observed correlations and RT distributions than a common accumulator model. Notably, these two models could only be distinguished on the basis of the variability and not the means of the predicted RT distributions. Additionally, signatures of separate initiation signals were also observed in a small fraction of trials in the visual search task, implying that these distinct computational architectures were not a manifestation of the task design per se. Taken together, our results suggest two unique computational architectures for eye-hand coordination, with task context biasing the brain toward instantiating one of the two architectures. NEW & NOTEWORTHY Previous studies on eye-hand coordination have considered mainly the means of eye and hand reaction time (RT) distributions. Here, we leverage the approximately linear relationship between the mean and standard deviation of RT distributions, as predicted by the drift-diffusion model, to propose the existence of two distinct computational architectures underlying coordinated eye-hand movements. These architectures, for the first time, provide a computational basis for the flexible coupling between eye and hand movements. PMID:27784809

Efficient Computation of Info-Gap Robustness for Finite Element Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stull, Christopher J.; Hemez, Francois M.; Williams, Brian J.

2012-07-05

A recent research effort at LANL proposed info-gap decision theory as a framework by which to measure the predictive maturity of numerical models. Info-gap theory explores the trade-offs between accuracy, that is, the extent to which predictions reproduce the physical measurements, and robustness, that is, the extent to which predictions are insensitive to modeling assumptions. Both accuracy and robustness are necessary to demonstrate predictive maturity. However, conducting an info-gap analysis can present a formidable challenge, from the standpoint of the required computational resources. This is because a robustness function requires the resolution of multiple optimization problems. This report offers anmore » alternative, adjoint methodology to assess the info-gap robustness of Ax = b-like numerical models solved for a solution x. Two situations that can arise in structural analysis and design are briefly described and contextualized within the info-gap decision theory framework. The treatments of the info-gap problems, using the adjoint methodology are outlined in detail, and the latter problem is solved for four separate finite element models. As compared to statistical sampling, the proposed methodology offers highly accurate approximations of info-gap robustness functions for the finite element models considered in the report, at a small fraction of the computational cost. It is noted that this report considers only linear systems; a natural follow-on study would extend the methodologies described herein to include nonlinear systems.« less

Pressure Loss Predictions of the Reactor Simulator Subsystem at NASA GRC

NASA Technical Reports Server (NTRS)

Reid, Terry V.

2015-01-01

Testing of the Fission Power System (FPS) Technology Demonstration Unit (TDU) is being conducted at NASA GRC. The TDU consists of three subsystems: the Reactor Simulator (RxSim), the Stirling Power Conversion Unit (PCU), and the Heat Exchanger Manifold (HXM). An Annular Linear Induction Pump (ALIP) is used to drive the working fluid. A preliminary version of the TDU system (which excludes the PCU for now), is referred to as the RxSim subsystem and was used to conduct flow tests in Vacuum Facility 6 (VF 6). In parallel, a computational model of the RxSim subsystem was created based on the CAD model and was used to predict loop pressure losses over a range of mass flows. This was done to assess the ability of the pump to meet the design intent mass flow demand. Measured data indicates that the pump can produce 2.333 kg/sec of flow, which is enough to supply the RxSim subsystem with a nominal flow of 1.75 kg/sec. Computational predictions indicated that the pump could provide 2.157 kg/sec (using the Spalart-Allmaras turbulence model), and 2.223 kg/sec (using the k-? turbulence model). The computational error of the predictions for the available mass flow is -0.176 kg/sec (with the S-A turbulence model) and -0.110 kg/sec (with the k-epsilon turbulence model) when compared to measured data.

Computer simulation to predict energy use, greenhouse gas emissions and costs for production of fluid milk using alternative processing methods

USDA-ARS?s Scientific Manuscript database

Computer simulation is a useful tool for benchmarking the electrical and fuel energy consumption and water use in a fluid milk plant. In this study, a computer simulation model of the fluid milk process based on high temperature short time (HTST) pasteurization was extended to include models for pr...

Three-dimensional turbopump flowfield analysis

NASA Technical Reports Server (NTRS)

Sharma, O. P.; Belford, K. A.; Ni, R. H.

1992-01-01

A program was conducted to develop a flow prediction method applicable to rocket turbopumps. The complex nature of a flowfield in turbopumps is described and examples of flowfields are discussed to illustrate that physics based models and analytical calculation procedures based on computational fluid dynamics (CFD) are needed to develop reliable design procedures for turbopumps. A CFD code developed at NASA ARC was used as the base code. The turbulence model and boundary conditions in the base code were modified, respectively, to: (1) compute transitional flows and account for extra rates of strain, e.g., rotation; and (2) compute surface heat transfer coefficients and allow computation through multistage turbomachines. Benchmark quality data from two and three-dimensional cascades were used to verify the code. The predictive capabilities of the present CFD code were demonstrated by computing the flow through a radial impeller and a multistage axial flow turbine. Results of the program indicate that the present code operated in a two-dimensional mode is a cost effective alternative to full three-dimensional calculations, and that it permits realistic predictions of unsteady loadings and losses for multistage machines.

Assessment of Turbulent Shock-Boundary Layer Interaction Computations Using the OVERFLOW Code

NASA Technical Reports Server (NTRS)

Oliver, A. B.; Lillard, R. P.; Schwing, A. M.; Blaisdell, G> A.; Lyrintzis, A. S.

2007-01-01

The performance of two popular turbulence models, the Spalart-Allmaras model and Menter s SST model, and one relatively new model, Olsen & Coakley s Lag model, are evaluated using the OVERFLOWcode. Turbulent shock-boundary layer interaction predictions are evaluated with three different experimental datasets: a series of 2D compression ramps at Mach 2.87, a series of 2D compression ramps at Mach 2.94, and an axisymmetric coneflare at Mach 11. The experimental datasets include flows with no separation, moderate separation, and significant separation, and use several different experimental measurement techniques (including laser doppler velocimetry (LDV), pitot-probe measurement, inclined hot-wire probe measurement, preston tube skin friction measurement, and surface pressure measurement). Additionally, the OVERFLOW solutions are compared to the solutions of a second CFD code, DPLR. The predictions for weak shock-boundary layer interactions are in reasonable agreement with the experimental data. For strong shock-boundary layer interactions, all of the turbulence models overpredict the separation size and fail to predict the correct skin friction recovery distribution. In most cases, surface pressure predictions show too much upstream influence, however including the tunnel side-wall boundary layers in the computation improves the separation predictions.

REVIEW: Widespread access to predictive models in the motor system: a short review

NASA Astrophysics Data System (ADS)

Davidson, Paul R.; Wolpert, Daniel M.

2005-09-01

Recent behavioural and computational studies suggest that access to internal predictive models of arm and object dynamics is widespread in the sensorimotor system. Several systems, including those responsible for oculomotor and skeletomotor control, perceptual processing, postural control and mental imagery, are able to access predictions of the motion of the arm. A capacity to make and use predictions of object dynamics is similarly widespread. Here, we review recent studies looking at the predictive capacity of the central nervous system which reveal pervasive access to forward models of the environment.

CFD Modeling of Launch Vehicle Aerodynamic Heating

NASA Technical Reports Server (NTRS)

Tashakkor, Scott B.; Canabal, Francisco; Mishtawy, Jason E.

2011-01-01

The Loci-CHEM 3.2 Computational Fluid Dynamics (CFD) code is being used to predict Ares-I launch vehicle aerodynamic heating. CFD has been used to predict both ascent and stage reentry environments and has been validated against wind tunnel tests and the Ares I-X developmental flight test. Most of the CFD predictions agreed with measurements. On regions where mismatches occurred, the CFD predictions tended to be higher than measured data. These higher predictions usually occurred in complex regions, where the CFD models (mainly turbulence) contain less accurate approximations. In some instances, the errors causing the over-predictions would cause locations downstream to be affected even though the physics were still being modeled properly by CHEM. This is easily seen when comparing to the 103-AH data. In the areas where predictions were low, higher grid resolution often brought the results closer to the data. Other disagreements are attributed to Ares I-X hardware not being present in the grid, as a result of computational resources limitations. The satisfactory predictions from CHEM provide confidence that future designs and predictions from the CFD code will provide an accurate approximation of the correct values for use in design and other applications

Sustained sensorimotor control as intermittent decisions about prediction errors: computational framework and application to ground vehicle steering.

PubMed

Markkula, Gustav; Boer, Erwin; Romano, Richard; Merat, Natasha

2018-06-01

A conceptual and computational framework is proposed for modelling of human sensorimotor control and is exemplified for the sensorimotor task of steering a car. The framework emphasises control intermittency and extends on existing models by suggesting that the nervous system implements intermittent control using a combination of (1) motor primitives, (2) prediction of sensory outcomes of motor actions, and (3) evidence accumulation of prediction errors. It is shown that approximate but useful sensory predictions in the intermittent control context can be constructed without detailed forward models, as a superposition of simple prediction primitives, resembling neurobiologically observed corollary discharges. The proposed mathematical framework allows straightforward extension to intermittent behaviour from existing one-dimensional continuous models in the linear control and ecological psychology traditions. Empirical data from a driving simulator are used in model-fitting analyses to test some of the framework's main theoretical predictions: it is shown that human steering control, in routine lane-keeping and in a demanding near-limit task, is better described as a sequence of discrete stepwise control adjustments, than as continuous control. Results on the possible roles of sensory prediction in control adjustment amplitudes, and of evidence accumulation mechanisms in control onset timing, show trends that match the theoretical predictions; these warrant further investigation. The results for the accumulation-based model align with other recent literature, in a possibly converging case against the type of threshold mechanisms that are often assumed in existing models of intermittent control.

Computational Model of Human and System Dynamics in Free Flight: Studies in Distributed Control Technologies

NASA Technical Reports Server (NTRS)

Corker, Kevin M.; Pisanich, Gregory; Lebacqz, J. Victor (Technical Monitor)

1998-01-01

This paper presents a set of studies in full mission simulation and the development of a predictive computational model of human performance in control of complex airspace operations. NASA and the FAA have initiated programs of research and development to provide flight crew, airline operations and air traffic managers with automation aids to increase capacity in en route and terminal area to support the goals of safe, flexible, predictable and efficient operations. In support of these developments, we present a computational model to aid design that includes representation of multiple cognitive agents (both human operators and intelligent aiding systems). The demands of air traffic management require representation of many intelligent agents sharing world-models, coordinating action/intention, and scheduling goals and actions in a potentially unpredictable world of operations. The operator-model structure includes attention functions, action priority, and situation assessment. The cognitive model has been expanded to include working memory operations including retrieval from long-term store, and interference. The operator's activity structures have been developed to provide for anticipation (knowledge of the intention and action of remote operators), and to respond to failures of the system and other operators in the system in situation-specific paradigms. System stability and operator actions can be predicted by using the model. The model's predictive accuracy was verified using the full-mission simulation data of commercial flight deck operations with advanced air traffic management techniques.

10 CFR 431.445 - Determination of small electric motor efficiency.

Code of Federal Regulations, 2012 CFR

2012-01-01

... statistical analysis, computer simulation or modeling, or other analytic evaluation of performance data. (3... statistical analysis, computer simulation or modeling, and other analytic evaluation of performance data on.... (ii) If requested by the Department, the manufacturer shall conduct simulations to predict the...

Distinctive Features Hold a Privileged Status in the Computation of Word Meaning: Implications for Theories of Semantic Memory

ERIC Educational Resources Information Center

Cree, George S.; McNorgan, Chris; McRae, Ken

2006-01-01

The authors present data from 2 feature verification experiments designed to determine whether distinctive features have a privileged status in the computation of word meaning. They use an attractor-based connectionist model of semantic memory to derive predictions for the experiments. Contrary to central predictions of the conceptual structure…

A Hybrid Approach To Tandem Cylinder Noise

NASA Technical Reports Server (NTRS)

Lockard, David P.

2004-01-01

Aeolian tone generation from tandem cylinders is predicted using a hybrid approach. A standard computational fluid dynamics (CFD) code is used to compute the unsteady flow around the cylinders, and the acoustics are calculated using the acoustic analogy. The CFD code is nominally second order in space and time and includes several turbulence models, but the SST k - omega model is used for most of the calculations. Significant variation is observed between laminar and turbulent cases, and with changes in the turbulence model. A two-dimensional implementation of the Ffowcs Williams-Hawkings (FW-H) equation is used to predict the far-field noise.

Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness

PubMed Central

Li, Jin; Tran, Maggie; Siwabessy, Justy

2016-01-01

Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia’s marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage at limited locations. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e., hard90 and hard70). We developed optimal predictive models to predict seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), knowledge informed AVI (KIAVI), Boruta and regularized RF (RRF) were tested based on predictive accuracy. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were examined. Finally, spatial predictions generated using the most accurate models were visually examined and analysed. This study confirmed that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness of four classes can be predicted with a high degree of accuracy; 3) the typical approach used to pre-select predictive variables by excluding highly correlated variables needs to be re-examined; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving predictive models; 5) FS methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to ‘small p and large n’ problems in environmental sciences. Additionally, automated computational programs for AVI need to be developed to increase its computational efficiency and caution should be taken when applying filter FS methods in selecting predictive models. PMID:26890307

Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness.

PubMed

Li, Jin; Tran, Maggie; Siwabessy, Justy

2016-01-01

Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia's marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage at limited locations. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e., hard90 and hard70). We developed optimal predictive models to predict seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), knowledge informed AVI (KIAVI), Boruta and regularized RF (RRF) were tested based on predictive accuracy. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were examined. Finally, spatial predictions generated using the most accurate models were visually examined and analysed. This study confirmed that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness of four classes can be predicted with a high degree of accuracy; 3) the typical approach used to pre-select predictive variables by excluding highly correlated variables needs to be re-examined; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving predictive models; 5) FS methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to 'small p and large n' problems in environmental sciences. Additionally, automated computational programs for AVI need to be developed to increase its computational efficiency and caution should be taken when applying filter FS methods in selecting predictive models.

Does Cation Size Affect Occupancy and Electrostatic Screening of the Nucleic Acid Ion Atmosphere?

PubMed Central

2016-01-01

Electrostatics are central to all aspects of nucleic acid behavior, including their folding, condensation, and binding to other molecules, and the energetics of these processes are profoundly influenced by the ion atmosphere that surrounds nucleic acids. Given the highly complex and dynamic nature of the ion atmosphere, understanding its properties and effects will require synergy between computational modeling and experiment. Prior computational models and experiments suggest that cation occupancy in the ion atmosphere depends on the size of the cation. However, the computational models have not been independently tested, and the experimentally observed effects were small. Here, we evaluate a computational model of ion size effects by experimentally testing a blind prediction made from that model, and we present additional experimental results that extend our understanding of the ion atmosphere. Giambasu et al. developed and implemented a three-dimensional reference interaction site (3D-RISM) model for monovalent cations surrounding DNA and RNA helices, and this model predicts that Na+ would outcompete Cs+ by 1.8–2.1-fold; i.e., with Cs+ in 2-fold excess of Na+ the ion atmosphere would contain an equal number of each cation (Nucleic Acids Res.2015, 43, 8405). However, our ion counting experiments indicate that there is no significant preference for Na+ over Cs+. There is an ∼25% preferential occupancy of Li+ over larger cations in the ion atmosphere but, counter to general expectations from existing models, no size dependence for the other alkali metal ions. Further, we followed the folding of the P4–P6 RNA and showed that differences in folding with different alkali metal ions observed at high concentration arise from cation–anion interactions and not cation size effects. Overall, our results provide a critical test of a computational prediction, fundamental information about ion atmosphere properties, and parameters that will aid in the development of next-generation nucleic acid computational models. PMID:27479701

Computational and human observer image quality evaluation of low dose, knowledge-based CT iterative reconstruction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eck, Brendan L.; Fahmi, Rachid; Miao, Jun

2015-10-15

Purpose: Aims in this study are to (1) develop a computational model observer which reliably tracks the detectability of human observers in low dose computed tomography (CT) images reconstructed with knowledge-based iterative reconstruction (IMR™, Philips Healthcare) and filtered back projection (FBP) across a range of independent variables, (2) use the model to evaluate detectability trends across reconstructions and make predictions of human observer detectability, and (3) perform human observer studies based on model predictions to demonstrate applications of the model in CT imaging. Methods: Detectability (d′) was evaluated in phantom studies across a range of conditions. Images were generated usingmore » a numerical CT simulator. Trained observers performed 4-alternative forced choice (4-AFC) experiments across dose (1.3, 2.7, 4.0 mGy), pin size (4, 6, 8 mm), contrast (0.3%, 0.5%, 1.0%), and reconstruction (FBP, IMR), at fixed display window. A five-channel Laguerre–Gauss channelized Hotelling observer (CHO) was developed with internal noise added to the decision variable and/or to channel outputs, creating six different internal noise models. Semianalytic internal noise computation was tested against Monte Carlo and used to accelerate internal noise parameter optimization. Model parameters were estimated from all experiments at once using maximum likelihood on the probability correct, P{sub C}. Akaike information criterion (AIC) was used to compare models of different orders. The best model was selected according to AIC and used to predict detectability in blended FBP-IMR images, analyze trends in IMR detectability improvements, and predict dose savings with IMR. Predicted dose savings were compared against 4-AFC study results using physical CT phantom images. Results: Detection in IMR was greater than FBP in all tested conditions. The CHO with internal noise proportional to channel output standard deviations, Model-k4, showed the best trade-off between fit and model complexity according to AIC{sub c}. With parameters fixed, the model reasonably predicted detectability of human observers in blended FBP-IMR images. Semianalytic internal noise computation gave results equivalent to Monte Carlo, greatly speeding parameter estimation. Using Model-k4, the authors found an average detectability improvement of 2.7 ± 0.4 times that of FBP. IMR showed greater improvements in detectability with larger signals and relatively consistent improvements across signal contrast and x-ray dose. In the phantom tested, Model-k4 predicted an 82% dose reduction compared to FBP, verified with physical CT scans at 80% reduced dose. Conclusions: IMR improves detectability over FBP and may enable significant dose reductions. A channelized Hotelling observer with internal noise proportional to channel output standard deviation agreed well with human observers across a wide range of variables, even across reconstructions with drastically different image characteristics. Utility of the model observer was demonstrated by predicting the effect of image processing (blending), analyzing detectability improvements with IMR across dose, size, and contrast, and in guiding real CT scan dose reduction experiments. Such a model observer can be applied in optimizing parameters in advanced iterative reconstruction algorithms as well as guiding dose reduction protocols in physical CT experiments.« less

Modeling the distribution of white spruce (Picea glauca) for Alaska with high accuracy: an open access role-model for predicting tree species in last remaining wilderness areas

Treesearch

Bettina Ohse; Falk Huettmann; Stefanie M. Ickert-Bond; Glenn P. Juday

2009-01-01

Most wilderness areas still lack accurate distribution information on tree species. We met this need with a predictive GIS modeling approach, using freely available digital data and computer programs to efficiently obtain high-quality species distribution maps. Here we present a digital map with the predicted distribution of white spruce (Picea glauca...

Unsteady Fast Random Particle Mesh method for efficient prediction of tonal and broadband noises of a centrifugal fan unit

NASA Astrophysics Data System (ADS)

Heo, Seung; Cheong, Cheolung; Kim, Taehoon

2015-09-01

In this study, efficient numerical method is proposed for predicting tonal and broadband noises of a centrifugal fan unit. The proposed method is based on Hybrid Computational Aero-Acoustic (H-CAA) techniques combined with Unsteady Fast Random Particle Mesh (U-FRPM) method. The U-FRPM method is developed by extending the FRPM method proposed by Ewert et al. and is utilized to synthesize turbulence flow field from unsteady RANS solutions. The H-CAA technique combined with U-FRPM method is applied to predict broadband as well as tonal noises of a centrifugal fan unit in a household refrigerator. Firstly, unsteady flow field driven by a rotating fan is computed by solving the RANS equations with Computational Fluid Dynamic (CFD) techniques. Main source regions around the rotating fan are identified by examining the computed flow fields. Then, turbulence flow fields in the main source regions are synthesized by applying the U-FRPM method. The acoustic analogy is applied to model acoustic sources in the main source regions. Finally, the centrifugal fan noise is predicted by feeding the modeled acoustic sources into an acoustic solver based on the Boundary Element Method (BEM). The sound spectral levels predicted using the current numerical method show good agreements with the measured spectra at the Blade Pass Frequencies (BPFs) as well as in the high frequency range. On the more, the present method enables quantitative assessment of relative contributions of identified source regions to the sound field by comparing predicted sound pressure spectrum due to modeled sources.

High Throughput pharmacokinetic modeling using computationally predicted parameter values: dissociation constants (TDS)

EPA Science Inventory

Estimates of the ionization association and dissociation constant (pKa) are vital to modeling the pharmacokinetic behavior of chemicals in vivo. Methodologies for the prediction of compound sequestration in specific tissues using partition coefficients require a parameter that ch...

PREDICTIVE MODEL OF CONJUGATIVE PLASMID TRANSFER IN THE RHIZOSPHERE AND PHYLLOSPHERE

EPA Science Inventory

A computer simulation model was used to predict the dynamics of survival and conjugation of Pseudomonas cepacia (carrying the transmissible recombinant plasmid R388:Tn1721) with a nonrecombinant recipient strain in simple rhizosphere and phyllosphere microcosms. lasmid transfer r...

Micro Finite Element models of the vertebral body: Validation of local displacement predictions.

PubMed

Costa, Maria Cristiana; Tozzi, Gianluca; Cristofolini, Luca; Danesi, Valentina; Viceconti, Marco; Dall'Ara, Enrico

2017-01-01

The estimation of local and structural mechanical properties of bones with micro Finite Element (microFE) models based on Micro Computed Tomography images depends on the quality bone geometry is captured, reconstructed and modelled. The aim of this study was to validate microFE models predictions of local displacements for vertebral bodies and to evaluate the effect of the elastic tissue modulus on model's predictions of axial forces. Four porcine thoracic vertebrae were axially compressed in situ, in a step-wise fashion and scanned at approximately 39μm resolution in preloaded and loaded conditions. A global digital volume correlation (DVC) approach was used to compute the full-field displacements. Homogeneous, isotropic and linear elastic microFE models were generated with boundary conditions assigned from the interpolated displacement field measured from the DVC. Measured and predicted local displacements were compared for the cortical and trabecular compartments in the middle of the specimens. Models were run with two different tissue moduli defined from microindentation data (12.0GPa) and a back-calculation procedure (4.6GPa). The predicted sum of axial reaction forces was compared to the experimental values for each specimen. MicroFE models predicted more than 87% of the variation in the displacement measurements (R2 = 0.87-0.99). However, model predictions of axial forces were largely overestimated (80-369%) for a tissue modulus of 12.0GPa, whereas differences in the range 10-80% were found for a back-calculated tissue modulus. The specimen with the lowest density showed a large number of elements strained beyond yield and the highest predictive errors. This study shows that the simplest microFE models can accurately predict quantitatively the local displacements and qualitatively the strain distribution within the vertebral body, independently from the considered bone types.

GPU-based RFA simulation for minimally invasive cancer treatment of liver tumours.

PubMed

Mariappan, Panchatcharam; Weir, Phil; Flanagan, Ronan; Voglreiter, Philip; Alhonnoro, Tuomas; Pollari, Mika; Moche, Michael; Busse, Harald; Futterer, Jurgen; Portugaller, Horst Rupert; Sequeiros, Roberto Blanco; Kolesnik, Marina

2017-01-01

Radiofrequency ablation (RFA) is one of the most popular and well-standardized minimally invasive cancer treatments (MICT) for liver tumours, employed where surgical resection has been contraindicated. Less-experienced interventional radiologists (IRs) require an appropriate planning tool for the treatment to help avoid incomplete treatment and so reduce the tumour recurrence risk. Although a few tools are available to predict the ablation lesion geometry, the process is computationally expensive. Also, in our implementation, a few patient-specific parameters are used to improve the accuracy of the lesion prediction. Advanced heterogeneous computing using personal computers, incorporating the graphics processing unit (GPU) and the central processing unit (CPU), is proposed to predict the ablation lesion geometry. The most recent GPU technology is used to accelerate the finite element approximation of Penne's bioheat equation and a three state cell model. Patient-specific input parameters are used in the bioheat model to improve accuracy of the predicted lesion. A fast GPU-based RFA solver is developed to predict the lesion by doing most of the computational tasks in the GPU, while reserving the CPU for concurrent tasks such as lesion extraction based on the heat deposition at each finite element node. The solver takes less than 3 min for a treatment duration of 26 min. When the model receives patient-specific input parameters, the deviation between real and predicted lesion is below 3 mm. A multi-centre retrospective study indicates that the fast RFA solver is capable of providing the IR with the predicted lesion in the short time period before the intervention begins when the patient has been clinically prepared for the treatment.

A Modeling Framework for Optimal Computational Resource Allocation Estimation: Considering the Trade-offs between Physical Resolutions, Uncertainty and Computational Costs

NASA Astrophysics Data System (ADS)

Moslehi, M.; de Barros, F.; Rajagopal, R.

2014-12-01

Hydrogeological models that represent flow and transport in subsurface domains are usually large-scale with excessive computational complexity and uncertain characteristics. Uncertainty quantification for predicting flow and transport in heterogeneous formations often entails utilizing a numerical Monte Carlo framework, which repeatedly simulates the model according to a random field representing hydrogeological characteristics of the field. The physical resolution (e.g. grid resolution associated with the physical space) for the simulation is customarily chosen based on recommendations in the literature, independent of the number of Monte Carlo realizations. This practice may lead to either excessive computational burden or inaccurate solutions. We propose an optimization-based methodology that considers the trade-off between the following conflicting objectives: time associated with computational costs, statistical convergence of the model predictions and physical errors corresponding to numerical grid resolution. In this research, we optimally allocate computational resources by developing a modeling framework for the overall error based on a joint statistical and numerical analysis and optimizing the error model subject to a given computational constraint. The derived expression for the overall error explicitly takes into account the joint dependence between the discretization error of the physical space and the statistical error associated with Monte Carlo realizations. The accuracy of the proposed framework is verified in this study by applying it to several computationally extensive examples. Having this framework at hand aims hydrogeologists to achieve the optimum physical and statistical resolutions to minimize the error with a given computational budget. Moreover, the influence of the available computational resources and the geometric properties of the contaminant source zone on the optimum resolutions are investigated. We conclude that the computational cost associated with optimal allocation can be substantially reduced compared with prevalent recommendations in the literature.

The Use of High Performance Computing (HPC) to Strengthen the Development of Army Systems

DTIC Science & Technology

2011-11-01

accurately predicting the supersonic magus effect about spinning cones, ogive- cylinders , and boat-tailed afterbodies. This work led to the successful...successful computer model of the proposed product or system, one can then build prototypes on the computer and study the effects on the performance of...needed. The NRC report discusses the requirements for effective use of such computing power. One needs “models, algorithms, software, hardware

First principles nickel-cadmium and nickel hydrogen spacecraft battery models

NASA Technical Reports Server (NTRS)

Timmerman, P.; Ratnakumar, B. V.; Distefano, S.

1996-01-01

The principles of Nickel-Cadmium and Nickel-Hydrogen spacecraft battery models are discussed. The Ni-Cd battery model includes two phase positive electrode and its predictions are very close to actual data. But the Ni-H2 battery model predictions (without the two phase positive electrode) are unacceptable even though the model is operational. Both models run on UNIX and Macintosh computers.

Computer-aided roll pass design in rolling of airfoil shapes

NASA Technical Reports Server (NTRS)

Akgerman, N.; Lahoti, G. D.; Altan, T.

1980-01-01

This paper describes two computer-aided design (CAD) programs developed for modeling the shape rolling process for airfoil sections. The first program, SHPROL, uses a modular upper-bound method of analysis and predicts the lateral spread, elongation, and roll torque. The second program, ROLPAS, predicts the stresses, roll separating force, the roll torque and the details of metal flow by simulating the rolling process, using the slab method of analysis. ROLPAS is an interactive program; it offers graphic display capabilities and allows the user to interact with the computer via a keyboard, CRT, and a light pen. The accuracy of the computerized models was evaluated by (a) rolling a selected airfoil shape at room temperature from 1018 steel and isothermally at high temperature from Ti-6Al-4V, and (b) comparing the experimental results with computer predictions. The comparisons indicated that the CAD systems, described here, are useful for practical engineering purposes and can be utilized in roll pass design and analysis for airfoil and similar shapes.

A unified thermal and vertical trajectory model for the prediction of high altitude balloon performance

NASA Technical Reports Server (NTRS)

Carlson, L. A.; Horn, W. J.

1981-01-01

A computer model for the prediction of the trajectory and thermal behavior of zero-pressure high altitude balloon was developed. In accord with flight data, the model permits radiative emission and absorption of the lifting gas and daytime gas temperatures above that of the balloon film. It also includes ballasting, venting, and valving. Predictions obtained with the model are compared with flight data from several flights and newly discovered features are discussed.

Combining Modeling and Gaming for Predictive Analytics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Riensche, Roderick M.; Whitney, Paul D.

2012-08-22

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

Free surface profiles in river flows: Can standard energy-based gradually-varied flow computations be pursued?

NASA Astrophysics Data System (ADS)

Cantero, Francisco; Castro-Orgaz, Oscar; Garcia-Marín, Amanda; Ayuso, José Luis; Dey, Subhasish

2015-10-01

Is the energy equation for gradually-varied flow the best approximation for the free surface profile computations in river flows? Determination of flood inundation in rivers and natural waterways is based on the hydraulic computation of flow profiles. This is usually done using energy-based gradually-varied flow models, like HEC-RAS, that adopts a vertical division method for discharge prediction in compound channel sections. However, this discharge prediction method is not so accurate in the context of advancements over the last three decades. This paper firstly presents a study of the impact of discharge prediction on the gradually-varied flow computations by comparing thirteen different methods for compound channels, where both energy and momentum equations are applied. The discharge, velocity distribution coefficients, specific energy, momentum and flow profiles are determined. After the study of gradually-varied flow predictions, a new theory is developed to produce higher-order energy and momentum equations for rapidly-varied flow in compound channels. These generalized equations enable to describe the flow profiles with more generality than the gradually-varied flow computations. As an outcome, results of gradually-varied flow provide realistic conclusions for computations of flow in compound channels, showing that momentum-based models are in general more accurate; whereas the new theory developed for rapidly-varied flow opens a new research direction, so far not investigated in flows through compound channels.

Low Boom Configuration Analysis with FUN3D Adjoint Simulation Framework

NASA Technical Reports Server (NTRS)

Park, Michael A.

2011-01-01

Off-body pressure, forces, and moments for the Gulfstream Low Boom Model are computed with a Reynolds Averaged Navier Stokes solver coupled with the Spalart-Allmaras (SA) turbulence model. This is the first application of viscous output-based adaptation to reduce estimated discretization errors in off-body pressure for a wing body configuration. The output adaptation approach is compared to an a priori grid adaptation technique designed to resolve the signature on the centerline by stretching and aligning the grid to the freestream Mach angle. The output-based approach produced good predictions of centerline and off-centerline measurements. Eddy viscosity predicted by the SA turbulence model increased significantly with grid adaptation. Computed lift as a function of drag compares well with wind tunnel measurements for positive lift, but predicted lift, drag, and pitching moment as a function of angle of attack has significant differences from the measured data. The sensitivity of longitudinal forces and moment to grid refinement is much smaller than the differences between the computed and measured data.

Assessment and prediction of urban air pollution caused by motor transport exhaust gases using computer simulation methods

NASA Astrophysics Data System (ADS)

Boyarshinov, Michael G.; Vaismana, Yakov I.

2016-10-01

The following methods were used in order to identify the pollution fields of urban air caused by the motor transport exhaust gases: the mathematical model, which enables to consider the influence of the main factors that determine pollution fields formation in the complex spatial domain; the authoring software designed for computational modeling of the gas flow, generated by numerous mobile point sources; the results of computing experiments on pollutant spread analysis and evolution of their concentration fields. The computational model of exhaust gas distribution and dispersion in a spatial domain, which includes urban buildings, structures and main traffic arteries, takes into account a stochastic character of cars apparition on the borders of the examined territory and uses a Poisson process. The model also considers the traffic lights switching and permits to define the fields of velocity, pressure and temperature of the discharge gases in urban air. The verification of mathematical model and software used confirmed their satisfactory fit to the in-situ measurements data and the possibility to use the obtained computing results for assessment and prediction of urban air pollution caused by motor transport exhaust gases.

Modeling the evolution of channel shape: Balancing computational efficiency with hydraulic fidelity

USGS Publications Warehouse

Wobus, C.W.; Kean, J.W.; Tucker, G.E.; Anderson, R. Scott

2008-01-01

The cross-sectional shape of a natural river channel controls the capacity of the system to carry water off a landscape, to convey sediment derived from hillslopes, and to erode its bed and banks. Numerical models that describe the response of a landscape to changes in climate or tectonics therefore require formulations that can accommodate evolution of channel cross-sectional geometry. However, fully two-dimensional (2-D) flow models are too computationally expensive to implement in large-scale landscape evolution models, while available simple empirical relationships between width and discharge do not adequately capture the dynamics of channel adjustment. We have developed a simplified 2-D numerical model of channel evolution in a cohesive, detachment-limited substrate subject to steady, unidirectional flow. Erosion is assumed to be proportional to boundary shear stress, which is calculated using an approximation of the flow field in which log-velocity profiles are assumed to apply along vectors that are perpendicular to the local channel bed. Model predictions of the velocity structure, peak boundary shear stress, and equilibrium channel shape compare well with predictions of a more sophisticated but more computationally demanding ray-isovel model. For example, the mean velocities computed by the two models are consistent to within ???3%, and the predicted peak shear stress is consistent to within ???7%. Furthermore, the shear stress distributions predicted by our model compare favorably with available laboratory measurements for prescribed channel shapes. A modification to our simplified code in which the flow includes a high-velocity core allows the model to be extended to estimate shear stress distributions in channels with large width-to-depth ratios. Our model is efficient enough to incorporate into large-scale landscape evolution codes and can be used to examine how channels adjust both cross-sectional shape and slope in response to tectonic and climatic forcing. Copyright 2008 by the American Geophysical Union.

Computer-based personality judgments are more accurate than those made by humans

PubMed Central

Youyou, Wu; Kosinski, Michal; Stillwell, David

2015-01-01

Judging others’ personalities is an essential skill in successful social living, as personality is a key driver behind people’s interactions, behaviors, and emotions. Although accurate personality judgments stem from social-cognitive skills, developments in machine learning show that computer models can also make valid judgments. This study compares the accuracy of human and computer-based personality judgments, using a sample of 86,220 volunteers who completed a 100-item personality questionnaire. We show that (i) computer predictions based on a generic digital footprint (Facebook Likes) are more accurate (r = 0.56) than those made by the participants’ Facebook friends using a personality questionnaire (r = 0.49); (ii) computer models show higher interjudge agreement; and (iii) computer personality judgments have higher external validity when predicting life outcomes such as substance use, political attitudes, and physical health; for some outcomes, they even outperform the self-rated personality scores. Computers outpacing humans in personality judgment presents significant opportunities and challenges in the areas of psychological assessment, marketing, and privacy. PMID:25583507

Computer-based personality judgments are more accurate than those made by humans.

PubMed

Youyou, Wu; Kosinski, Michal; Stillwell, David

2015-01-27

Judging others' personalities is an essential skill in successful social living, as personality is a key driver behind people's interactions, behaviors, and emotions. Although accurate personality judgments stem from social-cognitive skills, developments in machine learning show that computer models can also make valid judgments. This study compares the accuracy of human and computer-based personality judgments, using a sample of 86,220 volunteers who completed a 100-item personality questionnaire. We show that (i) computer predictions based on a generic digital footprint (Facebook Likes) are more accurate (r = 0.56) than those made by the participants' Facebook friends using a personality questionnaire (r = 0.49); (ii) computer models show higher interjudge agreement; and (iii) computer personality judgments have higher external validity when predicting life outcomes such as substance use, political attitudes, and physical health; for some outcomes, they even outperform the self-rated personality scores. Computers outpacing humans in personality judgment presents significant opportunities and challenges in the areas of psychological assessment, marketing, and privacy.

Comparison of Hydrodynamic Load Predictions Between Engineering Models and Computational Fluid Dynamics for the OC4-DeepCwind Semi-Submersible: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Benitz, M. A.; Schmidt, D. P.; Lackner, M. A.

Hydrodynamic loads on the platforms of floating offshore wind turbines are often predicted with computer-aided engineering tools that employ Morison's equation and/or potential-flow theory. This work compares results from one such tool, FAST, NREL's wind turbine computer-aided engineering tool, and the computational fluid dynamics package, OpenFOAM, for the OC4-DeepCwind semi-submersible analyzed in the International Energy Agency Wind Task 30 project. Load predictions from HydroDyn, the offshore hydrodynamics module of FAST, are compared with high-fidelity results from OpenFOAM. HydroDyn uses a combination of Morison's equations and potential flow to predict the hydrodynamic forces on the structure. The implications of the assumptionsmore » in HydroDyn are evaluated based on this code-to-code comparison.« less

Bayesian molecular design with a chemical language model

NASA Astrophysics Data System (ADS)

Ikebata, Hisaki; Hongo, Kenta; Isomura, Tetsu; Maezono, Ryo; Yoshida, Ryo

2017-04-01

The aim of computational molecular design is the identification of promising hypothetical molecules with a predefined set of desired properties. We address the issue of accelerating the material discovery with state-of-the-art machine learning techniques. The method involves two different types of prediction; the forward and backward predictions. The objective of the forward prediction is to create a set of machine learning models on various properties of a given molecule. Inverting the trained forward models through Bayes' law, we derive a posterior distribution for the backward prediction, which is conditioned by a desired property requirement. Exploring high-probability regions of the posterior with a sequential Monte Carlo technique, molecules that exhibit the desired properties can computationally be created. One major difficulty in the computational creation of molecules is the exclusion of the occurrence of chemically unfavorable structures. To circumvent this issue, we derive a chemical language model that acquires commonly occurring patterns of chemical fragments through natural language processing of ASCII strings of existing compounds, which follow the SMILES chemical language notation. In the backward prediction, the trained language model is used to refine chemical strings such that the properties of the resulting structures fall within the desired property region while chemically unfavorable structures are successfully removed. The present method is demonstrated through the design of small organic molecules with the property requirements on HOMO-LUMO gap and internal energy. The R package iqspr is available at the CRAN repository.

Bayesian molecular design with a chemical language model.

PubMed

Ikebata, Hisaki; Hongo, Kenta; Isomura, Tetsu; Maezono, Ryo; Yoshida, Ryo

2017-04-01

The aim of computational molecular design is the identification of promising hypothetical molecules with a predefined set of desired properties. We address the issue of accelerating the material discovery with state-of-the-art machine learning techniques. The method involves two different types of prediction; the forward and backward predictions. The objective of the forward prediction is to create a set of machine learning models on various properties of a given molecule. Inverting the trained forward models through Bayes' law, we derive a posterior distribution for the backward prediction, which is conditioned by a desired property requirement. Exploring high-probability regions of the posterior with a sequential Monte Carlo technique, molecules that exhibit the desired properties can computationally be created. One major difficulty in the computational creation of molecules is the exclusion of the occurrence of chemically unfavorable structures. To circumvent this issue, we derive a chemical language model that acquires commonly occurring patterns of chemical fragments through natural language processing of ASCII strings of existing compounds, which follow the SMILES chemical language notation. In the backward prediction, the trained language model is used to refine chemical strings such that the properties of the resulting structures fall within the desired property region while chemically unfavorable structures are successfully removed. The present method is demonstrated through the design of small organic molecules with the property requirements on HOMO-LUMO gap and internal energy. The R package iqspr is available at the CRAN repository.

Overview of the Aeroelastic Prediction Workshop

NASA Technical Reports Server (NTRS)

Heeg, Jennifer; Chwalowski, Pawel; Schuster, David M.; Dalenbring, Mats

2013-01-01

The AIAA Aeroelastic Prediction Workshop (AePW) was held in April, 2012, bringing together communities of aeroelasticians and computational fluid dynamicists. The objective in conducting this workshop on aeroelastic prediction was to assess state-of-the-art computational aeroelasticity methods as practical tools for the prediction of static and dynamic aeroelastic phenomena. No comprehensive aeroelastic benchmarking validation standard currently exists, greatly hindering validation and state-of-the-art assessment objectives. The workshop was a step towards assessing the state of the art in computational aeroelasticity. This was an opportunity to discuss and evaluate the effectiveness of existing computer codes and modeling techniques for unsteady flow, and to identify computational and experimental areas needing additional research and development. Three configurations served as the basis for the workshop, providing different levels of geometric and flow field complexity. All cases considered involved supercritical airfoils at transonic conditions. The flow fields contained oscillating shocks and in some cases, regions of separation. The computational tools principally employed Reynolds-Averaged Navier Stokes solutions. The successes and failures of the computations and the experiments are examined in this paper.

Prospective estimation of organ dose in CT under tube current modulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tian, Xiaoyu, E-mail: xt3@duke.edu; Li, Xiang; Segars, W. Paul

Purpose: Computed tomography (CT) has been widely used worldwide as a tool for medical diagnosis and imaging. However, despite its significant clinical benefits, CT radiation dose at the population level has become a subject of public attention and concern. In this light, optimizing radiation dose has become a core responsibility for the CT community. As a fundamental step to manage and optimize dose, it may be beneficial to have accurate and prospective knowledge about the radiation dose for an individual patient. In this study, the authors developed a framework to prospectively estimate organ dose for chest and abdominopelvic CT examsmore » under tube current modulation (TCM). Methods: The organ dose is mainly dependent on two key factors: patient anatomy and irradiation field. A prediction process was developed to accurately model both factors. To model the anatomical diversity and complexity in the patient population, the authors used a previously developed library of computational phantoms with broad distributions of sizes, ages, and genders. A selected clinical patient, represented by a computational phantom in the study, was optimally matched with another computational phantom in the library to obtain a representation of the patient’s anatomy. To model the irradiation field, a previously validated Monte Carlo program was used to model CT scanner systems. The tube current profiles were modeled using a ray-tracing program as previously reported that theoretically emulated the variability of modulation profiles from major CT machine manufacturers Li et al., [Phys. Med. Biol. 59, 4525–4548 (2014)]. The prediction of organ dose was achieved using the following process: (1) CTDI{sub vol}-normalized-organ dose coefficients (h{sub organ}) for fixed tube current were first estimated as the prediction basis for the computational phantoms; (2) each computation phantom, regarded as a clinical patient, was optimally matched with one computational phantom in the library; (3) to account for the effect of the TCM scheme, a weighted organ-specific CTDI{sub vol} [denoted as (CTDI{sub vol}){sub organ,weighted}] was computed for each organ based on the TCM profile and the anatomy of the “matched” phantom; (4) the organ dose was predicted by multiplying the weighted organ-specific CTDI{sub vol} with the organ dose coefficients (h{sub organ}). To quantify the prediction accuracy, each predicted organ dose was compared with the corresponding organ dose simulated from the Monte Carlo program with the TCM profile explicitly modeled. Results: The predicted organ dose showed good agreements with the simulated organ dose across all organs and modulation profiles. The average percentage error in organ dose estimation was generally within 20% across all organs and modulation profiles, except for organs located in the pelvic and shoulder regions. For an average CTDI{sub vol} of a CT exam of 10 mGy, the average error at full modulation strength (α = 1) across all organs was 0.91 mGy for chest exams, and 0.82 mGy for abdominopelvic exams. Conclusions: This study developed a quantitative model to predict organ dose for clinical chest and abdominopelvic scans. Such information may aid in the design of optimized CT protocols in relation to a targeted level of image quality.« less

Feasibility of MHD submarine propulsion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Doss, E.D.; Sikes, W.C.

1992-09-01

This report describes the work performed during Phase 1 and Phase 2 of the collaborative research program established between Argonne National Laboratory (ANL) and Newport News Shipbuilding and Dry Dock Company (NNS). Phase I of the program focused on the development of computer models for Magnetohydrodynamic (MHD) propulsion. Phase 2 focused on the experimental validation of the thruster performance models and the identification, through testing, of any phenomena which may impact the attractiveness of this propulsion system for shipboard applications. The report discusses in detail the work performed in Phase 2 of the program. In Phase 2, a two Teslamore » test facility was designed, built, and operated. The facility test loop, its components, and their design are presented. The test matrix and its rationale are discussed. Representative experimental results of the test program are presented, and are compared to computer model predictions. In general, the results of the tests and their comparison with the predictions indicate that thephenomena affecting the performance of MHD seawater thrusters are well understood and can be accurately predicted with the developed thruster computer models.« less

Data Assimilation and Propagation of Uncertainty in Multiscale Cardiovascular Simulation

NASA Astrophysics Data System (ADS)

Schiavazzi, Daniele; Marsden, Alison

2015-11-01

Cardiovascular modeling is the application of computational tools to predict hemodynamics. State-of-the-art techniques couple a 3D incompressible Navier-Stokes solver with a boundary circulation model and can predict local and peripheral hemodynamics, analyze the post-operative performance of surgical designs and complement clinical data collection minimizing invasive and risky measurement practices. The ability of these tools to make useful predictions is directly related to their accuracy in representing measured physiologies. Tuning of model parameters is therefore a topic of paramount importance and should include clinical data uncertainty, revealing how this uncertainty will affect the predictions. We propose a fully Bayesian, multi-level approach to data assimilation of uncertain clinical data in multiscale circulation models. To reduce the computational cost, we use a stable, condensed approximation of the 3D model build by linear sparse regression of the pressure/flow rate relationship at the outlets. Finally, we consider the problem of non-invasively propagating the uncertainty in model parameters to the resulting hemodynamics and compare Monte Carlo simulation with Stochastic Collocation approaches based on Polynomial or Multi-resolution Chaos expansions.

A simplified approach to quasi-linear viscoelastic modeling

PubMed Central

Nekouzadeh, Ali; Pryse, Kenneth M.; Elson, Elliot L.; Genin, Guy M.

2007-01-01

The fitting of quasi-linear viscoelastic (QLV) constitutive models to material data often involves somewhat cumbersome numerical convolution. A new approach to treating quasi-linearity in one dimension is described and applied to characterize the behavior of reconstituted collagen. This approach is based on a new principle for including nonlinearity and requires considerably less computation than other comparable models for both model calibration and response prediction, especially for smoothly applied stretching. Additionally, the approach allows relaxation to adapt with the strain history. The modeling approach is demonstrated through tests on pure reconstituted collagen. Sequences of “ramp-and-hold” stretching tests were applied to rectangular collagen specimens. The relaxation force data from the “hold” was used to calibrate a new “adaptive QLV model” and several models from literature, and the force data from the “ramp” was used to check the accuracy of model predictions. Additionally, the ability of the models to predict the force response on a reloading of the specimen was assessed. The “adaptive QLV model” based on this new approach predicts collagen behavior comparably to or better than existing models, with much less computation. PMID:17499254

Building a three-dimensional model of CYP2C9 inhibition using the Autocorrelator: an autonomous model generator.

PubMed

Lardy, Matthew A; Lebrun, Laurie; Bullard, Drew; Kissinger, Charles; Gobbi, Alberto

2012-05-25

In modern day drug discovery campaigns, computational chemists have to be concerned not only about improving the potency of molecules but also reducing any off-target ADMET activity. There are a plethora of antitargets that computational chemists may have to consider. Fortunately many antitargets have crystal structures deposited in the PDB. These structures are immediately useful to our Autocorrelator: an automated model generator that optimizes variables for building computational models. This paper describes the use of the Autocorrelator to construct high quality docking models for cytochrome P450 2C9 (CYP2C9) from two publicly available crystal structures. Both models result in strong correlation coefficients (R² > 0.66) between the predicted and experimental determined log(IC₅₀) values. Results from the two models overlap well with each other, converging on the same scoring function, deprotonated charge state, and predicted the binding orientation for our collection of molecules.

Computer-aided drug design at Boehringer Ingelheim

NASA Astrophysics Data System (ADS)

Muegge, Ingo; Bergner, Andreas; Kriegl, Jan M.

2017-03-01

Computer-Aided Drug Design (CADD) is an integral part of the drug discovery endeavor at Boehringer Ingelheim (BI). CADD contributes to the evaluation of new therapeutic concepts, identifies small molecule starting points for drug discovery, and develops strategies for optimizing hit and lead compounds. The CADD scientists at BI benefit from the global use and development of both software platforms and computational services. A number of computational techniques developed in-house have significantly changed the way early drug discovery is carried out at BI. In particular, virtual screening in vast chemical spaces, which can be accessed by combinatorial chemistry, has added a new option for the identification of hits in many projects. Recently, a new framework has been implemented allowing fast, interactive predictions of relevant on and off target endpoints and other optimization parameters. In addition to the introduction of this new framework at BI, CADD has been focusing on the enablement of medicinal chemists to independently perform an increasing amount of molecular modeling and design work. This is made possible through the deployment of MOE as a global modeling platform, allowing computational and medicinal chemists to freely share ideas and modeling results. Furthermore, a central communication layer called the computational chemistry framework provides broad access to predictive models and other computational services.

RNA secondary structure prediction with pseudoknots: Contribution of algorithm versus energy model.

PubMed

Jabbari, Hosna; Wark, Ian; Montemagno, Carlo

2018-01-01

RNA is a biopolymer with various applications inside the cell and in biotechnology. Structure of an RNA molecule mainly determines its function and is essential to guide nanostructure design. Since experimental structure determination is time-consuming and expensive, accurate computational prediction of RNA structure is of great importance. Prediction of RNA secondary structure is relatively simpler than its tertiary structure and provides information about its tertiary structure, therefore, RNA secondary structure prediction has received attention in the past decades. Numerous methods with different folding approaches have been developed for RNA secondary structure prediction. While methods for prediction of RNA pseudoknot-free structure (structures with no crossing base pairs) have greatly improved in terms of their accuracy, methods for prediction of RNA pseudoknotted secondary structure (structures with crossing base pairs) still have room for improvement. A long-standing question for improving the prediction accuracy of RNA pseudoknotted secondary structure is whether to focus on the prediction algorithm or the underlying energy model, as there is a trade-off on computational cost of the prediction algorithm versus the generality of the method. The aim of this work is to argue when comparing different methods for RNA pseudoknotted structure prediction, the combination of algorithm and energy model should be considered and a method should not be considered superior or inferior to others if they do not use the same scoring model. We demonstrate that while the folding approach is important in structure prediction, it is not the only important factor in prediction accuracy of a given method as the underlying energy model is also as of great value. Therefore we encourage researchers to pay particular attention in comparing methods with different energy models.

A geostatistical approach to the change-of-support problem and variable-support data fusion in spatial analysis

NASA Astrophysics Data System (ADS)

Wang, Jun; Wang, Yang; Zeng, Hui

2016-01-01

A key issue to address in synthesizing spatial data with variable-support in spatial analysis and modeling is the change-of-support problem. We present an approach for solving the change-of-support and variable-support data fusion problems. This approach is based on geostatistical inverse modeling that explicitly accounts for differences in spatial support. The inverse model is applied here to produce both the best predictions of a target support and prediction uncertainties, based on one or more measurements, while honoring measurements. Spatial data covering large geographic areas often exhibit spatial nonstationarity and can lead to computational challenge due to the large data size. We developed a local-window geostatistical inverse modeling approach to accommodate these issues of spatial nonstationarity and alleviate computational burden. We conducted experiments using synthetic and real-world raster data. Synthetic data were generated and aggregated to multiple supports and downscaled back to the original support to analyze the accuracy of spatial predictions and the correctness of prediction uncertainties. Similar experiments were conducted for real-world raster data. Real-world data with variable-support were statistically fused to produce single-support predictions and associated uncertainties. The modeling results demonstrate that geostatistical inverse modeling can produce accurate predictions and associated prediction uncertainties. It is shown that the local-window geostatistical inverse modeling approach suggested offers a practical way to solve the well-known change-of-support problem and variable-support data fusion problem in spatial analysis and modeling.

Predicting pork loin intramuscular fat using computer vision system.

PubMed

Liu, J-H; Sun, X; Young, J M; Bachmeier, L A; Newman, D J

2018-09-01

The objective of this study was to investigate the ability of computer vision system to predict pork intramuscular fat percentage (IMF%). Center-cut loin samples (n = 85) were trimmed of subcutaneous fat and connective tissue. Images were acquired and pixels were segregated to estimate image IMF% and 18 image color features for each image. Subjective IMF% was determined by a trained grader. Ether extract IMF% was calculated using ether extract method. Image color features and image IMF% were used as predictors for stepwise regression and support vector machine models. Results showed that subjective IMF% had a correlation of 0.81 with ether extract IMF% while the image IMF% had a 0.66 correlation with ether extract IMF%. Accuracy rates for regression models were 0.63 for stepwise and 0.75 for support vector machine. Although subjective IMF% has shown to have better prediction, results from computer vision system demonstrates the potential of being used as a tool in predicting pork IMF% in the future. Copyright © 2018 Elsevier Ltd. All rights reserved.

Comprehensive computational model for combining fluid hydrodynamics, light transport and biomass growth in a Taylor vortex algal photobioreactor: Lagrangian approach.

PubMed

Gao, Xi; Kong, Bo; Vigil, R Dennis

2017-01-01

A comprehensive quantitative model incorporating the effects of fluid flow patterns, light distribution, and algal growth kinetics on biomass growth rate is developed in order to predict the performance of a Taylor vortex algal photobioreactor for culturing Chlorella vulgaris. A commonly used Lagrangian strategy for coupling the various factors influencing algal growth was employed whereby results from computational fluid dynamics and radiation transport simulations were used to compute numerous microorganism light exposure histories, and this information in turn was used to estimate the global biomass specific growth rate. The simulations provide good quantitative agreement with experimental data and correctly predict the trend in reactor performance as a key reactor operating parameter is varied (inner cylinder rotation speed). However, biomass growth curves are consistently over-predicted and potential causes for these over-predictions and drawbacks of the Lagrangian approach are addressed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Satellite freeze forecast system: Executive summary

NASA Technical Reports Server (NTRS)

Martsolf, J. D. (Principal Investigator)

1983-01-01

A satellite-based temperature monitoring and prediction system consisting of a computer controlled acquisition, processing, and display system and the ten automated weather stations called by that computer was developed and transferred to the national weather service. This satellite freeze forecasting system (SFFS) acquires satellite data from either one of two sources, surface data from 10 sites, displays the observed data in the form of color-coded thermal maps and in tables of automated weather station temperatures, computes predicted thermal maps when requested and displays such maps either automatically or manually, archives the data acquired, and makes comparisons with historical data. Except for the last function, SFFS handles these tasks in a highly automated fashion if the user so directs. The predicted thermal maps are the result of two models, one a physical energy budget of the soil and atmosphere interface and the other a statistical relationship between the sites at which the physical model predicts temperatures and each of the pixels of the satellite thermal map.

Computational chemistry in 25 years

NASA Astrophysics Data System (ADS)

Abagyan, Ruben

2012-01-01

Here we are making some predictions based on three methods: a straightforward extrapolations of the existing trends; a self-fulfilling prophecy; and picking some current grievances and predicting that they will be addressed or solved. We predict the growth of multicore computing and dramatic growth of data, as well as the improvements in force fields and sampling methods. We also predict that effects of therapeutic and environmental molecules on human body, as well as complex natural chemical signalling will be understood in terms of three dimensional models of their binding to specific pockets.

Characterization of Aerodynamic Interactions with the Mars Science Laboratory Reaction Control System Using Computation and Experiment

NASA Technical Reports Server (NTRS)

Schoenenberger, Mark; VanNorman, John; Rhode, Matthew; Paulson, John

2013-01-01

On August 5 , 2012, the Mars Science Laboratory (MSL) entry capsule successfully entered Mars' atmosphere and landed the Curiosity rover in Gale Crater. The capsule used a reaction control system (RCS) consisting of four pairs of hydrazine thrusters to fly a guided entry. The RCS provided bank control to fly along a flight path commanded by an onboard computer and also damped unwanted rates due to atmospheric disturbances and any dynamic instabilities of the capsule. A preliminary assessment of the MSL's flight data from entry showed that the capsule flew much as predicted. This paper will describe how the MSL aerodynamics team used engineering analyses, computational codes and wind tunnel testing in concert to develop the RCS system and certify it for flight. Over the course of MSL's development, the RCS configuration underwent a number of design iterations to accommodate mechanical constraints, aeroheating concerns and excessive aero/RCS interactions. A brief overview of the MSL RCS configuration design evolution is provided. Then, a brief description is presented of how the computational predictions of RCS jet interactions were validated. The primary work to certify that the RCS interactions were acceptable for flight was centered on validating computational predictions at hypersonic speeds. A comparison of computational fluid dynamics (CFD) predictions to wind tunnel force and moment data gathered in the NASA Langley 31-Inch Mach 10 Tunnel was the lynch pin to validating the CFD codes used to predict aero/RCS interactions. Using the CFD predictions and experimental data, an interaction model was developed for Monte Carlo analyses using 6-degree-of-freedom trajectory simulation. The interaction model used in the flight simulation is presented.

Explaining neural signals in human visual cortex with an associative learning model.

PubMed

Jiang, Jiefeng; Schmajuk, Nestor; Egner, Tobias

2012-08-01

"Predictive coding" models posit a key role for associative learning in visual cognition, viewing perceptual inference as a process of matching (learned) top-down predictions (or expectations) against bottom-up sensory evidence. At the neural level, these models propose that each region along the visual processing hierarchy entails one set of processing units encoding predictions of bottom-up input, and another set computing mismatches (prediction error or surprise) between predictions and evidence. This contrasts with traditional views of visual neurons operating purely as bottom-up feature detectors. In support of the predictive coding hypothesis, a recent human neuroimaging study (Egner, Monti, & Summerfield, 2010) showed that neural population responses to expected and unexpected face and house stimuli in the "fusiform face area" (FFA) could be well-described as a summation of hypothetical face-expectation and -surprise signals, but not by feature detector responses. Here, we used computer simulations to test whether these imaging data could be formally explained within the broader framework of a mathematical neural network model of associative learning (Schmajuk, Gray, & Lam, 1996). Results show that FFA responses could be fit very closely by model variables coding for conditional predictions (and their violations) of stimuli that unconditionally activate the FFA. These data document that neural population signals in the ventral visual stream that deviate from classic feature detection responses can formally be explained by associative prediction and surprise signals.

Biophysically inspired model for functionalized nanocarrier adhesion to cell surface: roles of protein expression and mechanical factors

NASA Astrophysics Data System (ADS)

Ramakrishnan, N.; Tourdot, Richard W.; Eckmann, David M.; Ayyaswamy, Portonovo S.; Muzykantov, Vladimir R.; Radhakrishnan, Ravi

2016-06-01

In order to achieve selective targeting of affinity-ligand coated nanoparticles to the target tissue, it is essential to understand the key mechanisms that govern their capture by the target cell. Next-generation pharmacokinetic (PK) models that systematically account for proteomic and mechanical factors can accelerate the design, validation and translation of targeted nanocarriers (NCs) in the clinic. Towards this objective, we have developed a computational model to delineate the roles played by target protein expression and mechanical factors of the target cell membrane in determining the avidity of functionalized NCs to live cells. Model results show quantitative agreement with in vivo experiments when specific and non-specific contributions to NC binding are taken into account. The specific contributions are accounted for through extensive simulations of multivalent receptor-ligand interactions, membrane mechanics and entropic factors such as membrane undulations and receptor translation. The computed NC avidity is strongly dependent on ligand density, receptor expression, bending mechanics of the target cell membrane, as well as entropic factors associated with the membrane and the receptor motion. Our computational model can predict the in vivo targeting levels of the intracellular adhesion molecule-1 (ICAM1)-coated NCs targeted to the lung, heart, kidney, liver and spleen of mouse, when the contributions due to endothelial capture are accounted for. The effect of other cells (such as monocytes, etc.) do not improve the model predictions at steady state. We demonstrate the predictive utility of our model by predicting partitioning coefficients of functionalized NCs in mice and human tissues and report the statistical accuracy of our model predictions under different scenarios.

Molecular factor computing for predictive spectroscopy.

PubMed

Dai, Bin; Urbas, Aaron; Douglas, Craig C; Lodder, Robert A

2007-08-01

The concept of molecular factor computing (MFC)-based predictive spectroscopy was demonstrated here with quantitative analysis of ethanol-in-water mixtures in a MFC-based prototype instrument. Molecular computing of vectors for transformation matrices enabled spectra to be represented in a desired coordinate system. New coordinate systems were selected to reduce the dimensionality of the spectral hyperspace and simplify the mechanical/electrical/computational construction of a new MFC spectrometer employing transmission MFC filters. A library search algorithm was developed to calculate the chemical constituents of the MFC filters. The prototype instrument was used to collect data from 39 ethanol-in-water mixtures (range 0-14%). For each sample, four different voltage outputs from the detector (forming two factor scores) were measured by using four different MFC filters. Twenty samples were used to calibrate the instrument and build a multivariate linear regression prediction model, and the remaining samples were used to validate the predictive ability of the model. In engineering simulations, four MFC filters gave an adequate calibration model (r2 = 0.995, RMSEC = 0.229%, RMSECV = 0.339%, p = 0.05 by f test). This result is slightly better than a corresponding PCR calibration model based on corrected transmission spectra (r2 = 0.993, RMSEC = 0.359%, RMSECV = 0.551%, p = 0.05 by f test). The first actual MFC prototype gave an RMSECV = 0.735%. MFC was a viable alternative to conventional spectrometry with the potential to be more simply implemented and more rapid and accurate.

Analysis of rocket engine injection combustion processes

NASA Technical Reports Server (NTRS)

Salmon, J. W.

1976-01-01

A critique is given of the JANNAF sub-critical propellant injection/combustion process analysis computer models and application of the models to correlation of well documented hot fire engine data bases. These programs are the distributed energy release (DER) model for conventional liquid propellants injectors and the coaxial injection combustion model (CICM) for gaseous annulus/liquid core coaxial injectors. The critique identifies model inconsistencies while the computer analyses provide quantitative data on predictive accuracy. The program is comprised of three tasks: (1) computer program review and operations; (2) analysis and data correlations; and (3) documentation.

Prediction of monthly regional groundwater levels through hybrid soft-computing techniques

NASA Astrophysics Data System (ADS)

Chang, Fi-John; Chang, Li-Chiu; Huang, Chien-Wei; Kao, I.-Feng

2016-10-01

Groundwater systems are intrinsically heterogeneous with dynamic temporal-spatial patterns, which cause great difficulty in quantifying their complex processes, while reliable predictions of regional groundwater levels are commonly needed for managing water resources to ensure proper service of water demands within a region. In this study, we proposed a novel and flexible soft-computing technique that could effectively extract the complex high-dimensional input-output patterns of basin-wide groundwater-aquifer systems in an adaptive manner. The soft-computing models combined the Self Organized Map (SOM) and the Nonlinear Autoregressive with Exogenous Inputs (NARX) network for predicting monthly regional groundwater levels based on hydrologic forcing data. The SOM could effectively classify the temporal-spatial patterns of regional groundwater levels, the NARX could accurately predict the mean of regional groundwater levels for adjusting the selected SOM, the Kriging was used to interpolate the predictions of the adjusted SOM into finer grids of locations, and consequently the prediction of a monthly regional groundwater level map could be obtained. The Zhuoshui River basin in Taiwan was the study case, and its monthly data sets collected from 203 groundwater stations, 32 rainfall stations and 6 flow stations during 2000 and 2013 were used for modelling purpose. The results demonstrated that the hybrid SOM-NARX model could reliably and suitably predict monthly basin-wide groundwater levels with high correlations (R2 > 0.9 in both training and testing cases). The proposed methodology presents a milestone in modelling regional environmental issues and offers an insightful and promising way to predict monthly basin-wide groundwater levels, which is beneficial to authorities for sustainable water resources management.

CRCP-9: Improved Computer Program for Mechanistic Analysis of Continuously Reinforced Concrete Pavements

DOT National Transportation Integrated Search

2001-02-01

A new version of the CRCP computer program, CRCP-9, has been developed in this study. The numerical model of the CRC pavements was developed using finite element theories, the crack spacing prediction model was developed using the Monte Carlo method,...

Computation of flows in a turn-around duct and a turbine cascade using advanced turbulence models

NASA Technical Reports Server (NTRS)

Lakshminarayana, B.; Luo, J.

1993-01-01

Numerical investigation has been carried out to evaluate the capability of the Algebraic Reynolds Stress Model (ARSM) and the Nonlinear Stress Model (NLSM) to predict strongly curved turbulent flow in a turn-around duct (TAD). The ARSM includes the near-wall damping term of pressure-strain correlation phi(sub ij,w), which enables accurate prediction of individual Reynolds stress components in wall flows. The TAD mean flow quantities are reasonably well predicted by various turbulence models. The ARSM yields better predictions for both the mean flow and the turbulence quantities than the NLSM and the k-epsilon (k = turbulent kinetic energy, epsilon = dissipation rate of k) model. The NLSM also shows slight improvement over the k-epsilon model. However, all the models fail to capture the recovery of the flow from strong curvature effects. The formulation for phi(sub ij,w) appears to be incorrect near the concave surface. The hybrid k-epsilon/ARSM, Chien's k-epsilon model, and Coakley's q-omega (q = the square root of k, omega = epsilon/k) model have also been employed to compute the aerodynamics and heat transfer of a transonic turbine cascade. The surface pressure distributions and the wake profiles are predicted well by all the models. The k-epsilon model and the k-epsilon/ARSM model provide better predictions of heat transfer than the q-omega model. The k-epsilon/ARSM solutions show significant differences in the predicted skin friction coefficients, heat transfer rates and the cascade performance parameters, as compared to the k-epsilon model. The k-epsilon/ARSM model appears to capture, qualitatively, the anisotropy associated with by-pass transition.

Short-term Temperature Prediction Using Adaptive Computing on Dynamic Scales

NASA Astrophysics Data System (ADS)

Hu, W.; Cervone, G.; Jha, S.; Balasubramanian, V.; Turilli, M.

2017-12-01

When predicting temperature, there are specific places and times when high accuracy predictions are harder. For example, not all the sub-regions in the domain require the same amount of computing resources to generate an accurate prediction. Plateau areas might require less computing resources than mountainous areas because of the steeper gradient of temperature change in the latter. However, it is difficult to estimate beforehand the optimal allocation of computational resources because several parameters play a role in determining the accuracy of the forecasts, in addition to orography. The allocation of resources to perform simulations can become a bottleneck because it requires human intervention to stop jobs or start new ones. The goal of this project is to design and develop a dynamic approach to generate short-term temperature predictions that can automatically determines the required computing resources and the geographic scales of the predictions based on the spatial and temporal uncertainties. The predictions and the prediction quality metrics are computed using a numeric weather prediction model, Analog Ensemble (AnEn), and the parallelization on high performance computing systems is accomplished using Ensemble Toolkit, one component of the RADICAL-Cybertools family of tools. RADICAL-Cybertools decouple the science needs from the computational capabilities by building an intermediate layer to run general ensemble patterns, regardless of the science. In this research, we show how the ensemble toolkit allows generating high resolution temperature forecasts at different spatial and temporal resolution. The AnEn algorithm is run using NAM analysis and forecasts data for the continental United States for a period of 2 years. AnEn results show that temperature forecasts perform well according to different probabilistic and deterministic statistical tests.

Tertiary structure-based analysis of microRNA–target interactions

PubMed Central

Gan, Hin Hark; Gunsalus, Kristin C.

2013-01-01

Current computational analysis of microRNA interactions is based largely on primary and secondary structure analysis. Computationally efficient tertiary structure-based methods are needed to enable more realistic modeling of the molecular interactions underlying miRNA-mediated translational repression. We incorporate algorithms for predicting duplex RNA structures, ionic strength effects, duplex entropy and free energy, and docking of duplex–Argonaute protein complexes into a pipeline to model and predict miRNA–target duplex binding energies. To ensure modeling accuracy and computational efficiency, we use an all-atom description of RNA and a continuum description of ionic interactions using the Poisson–Boltzmann equation. Our method predicts the conformations of two constructs of Caenorhabditis elegans let-7 miRNA–target duplexes to an accuracy of ∼3.8 Å root mean square distance of their NMR structures. We also show that the computed duplex formation enthalpies, entropies, and free energies for eight miRNA–target duplexes agree with titration calorimetry data. Analysis of duplex–Argonaute docking shows that structural distortions arising from single-base-pair mismatches in the seed region influence the activity of the complex by destabilizing both duplex hybridization and its association with Argonaute. Collectively, these results demonstrate that tertiary structure-based modeling of miRNA interactions can reveal structural mechanisms not accessible with current secondary structure-based methods. PMID:23417009

Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling

NASA Astrophysics Data System (ADS)

Galelli, S.; Castelletti, A.

2013-02-01

Combining randomization methods with ensemble prediction is emerging as an effective option to balance accuracy and computational efficiency in data-driven modeling. In this paper we investigate the prediction capability of extremely randomized trees (Extra-Trees), in terms of accuracy, explanation ability and computational efficiency, in a streamflow modeling exercise. Extra-Trees are a totally randomized tree-based ensemble method that (i) alleviates the poor generalization property and tendency to overfitting of traditional standalone decision trees (e.g. CART); (ii) is computationally very efficient; and, (iii) allows to infer the relative importance of the input variables, which might help in the ex-post physical interpretation of the model. The Extra-Trees potential is analyzed on two real-world case studies (Marina catchment (Singapore) and Canning River (Western Australia)) representing two different morphoclimatic contexts comparatively with other tree-based methods (CART and M5) and parametric data-driven approaches (ANNs and multiple linear regression). Results show that Extra-Trees perform comparatively well to the best of the benchmarks (i.e. M5) in both the watersheds, while outperforming the other approaches in terms of computational requirement when adopted on large datasets. In addition, the ranking of the input variable provided can be given a physically meaningful interpretation.

Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling

NASA Astrophysics Data System (ADS)

Galelli, S.; Castelletti, A.

2013-07-01

Combining randomization methods with ensemble prediction is emerging as an effective option to balance accuracy and computational efficiency in data-driven modelling. In this paper, we investigate the prediction capability of extremely randomized trees (Extra-Trees), in terms of accuracy, explanation ability and computational efficiency, in a streamflow modelling exercise. Extra-Trees are a totally randomized tree-based ensemble method that (i) alleviates the poor generalisation property and tendency to overfitting of traditional standalone decision trees (e.g. CART); (ii) is computationally efficient; and, (iii) allows to infer the relative importance of the input variables, which might help in the ex-post physical interpretation of the model. The Extra-Trees potential is analysed on two real-world case studies - Marina catchment (Singapore) and Canning River (Western Australia) - representing two different morphoclimatic contexts. The evaluation is performed against other tree-based methods (CART and M5) and parametric data-driven approaches (ANNs and multiple linear regression). Results show that Extra-Trees perform comparatively well to the best of the benchmarks (i.e. M5) in both the watersheds, while outperforming the other approaches in terms of computational requirement when adopted on large datasets. In addition, the ranking of the input variable provided can be given a physically meaningful interpretation.

Overview of the status of predictive computer models for skin sensitization (JRC Expert meeting on pre- and pro-haptens )

EPA Science Inventory

No abstract was prepared or requested. This is a short presentation aiming to present a status of what in silico models and approaches exists in the prediction of skin sensitization potential and/or potency.

FUN3D and CFL3D Computations for the First High Lift Prediction Workshop

NASA Technical Reports Server (NTRS)

Park, Michael A.; Lee-Rausch, Elizabeth M.; Rumsey, Christopher L.

2011-01-01

Two Reynolds-averaged Navier-Stokes codes were used to compute flow over the NASA Trapezoidal Wing at high lift conditions for the 1st AIAA CFD High Lift Prediction Workshop, held in Chicago in June 2010. The unstructured-grid code FUN3D and the structured-grid code CFL3D were applied to several different grid systems. The effects of code, grid system, turbulence model, viscous term treatment, and brackets were studied. The SST model on this configuration predicted lower lift than the Spalart-Allmaras model at high angles of attack; the Spalart-Allmaras model agreed better with experiment. Neglecting viscous cross-derivative terms caused poorer prediction in the wing tip vortex region. Output-based grid adaptation was applied to the unstructured-grid solutions. The adapted grids better resolved wake structures and reduced flap flow separation, which was also observed in uniform grid refinement studies. Limitations of the adaptation method as well as areas for future improvement were identified.

Sharply curved turn around duct flow predictions using spectral partitioning of the turbulent kinetic energy and a pressure modified wall law

NASA Technical Reports Server (NTRS)

Santi, L. Michael

1986-01-01

Computational predictions of turbulent flow in sharply curved 180 degree turn around ducts are presented. The CNS2D computer code is used to solve the equations of motion for two-dimensional incompressible flows transformed to a nonorthogonal body-fitted coordinate system. This procedure incorporates the pressure velocity correction algorithm SIMPLE-C to iteratively solve a discretized form of the transformed equations. A multiple scale turbulence model based on simplified spectral partitioning is employed to obtain closure. Flow field predictions utilizing the multiple scale model are compared to features predicted by the traditional single scale k-epsilon model. Tuning parameter sensitivities of the multiple scale model applied to turn around duct flows are also determined. In addition, a wall function approach based on a wall law suitable for incompressible turbulent boundary layers under strong adverse pressure gradients is tested. Turn around duct flow characteristics utilizing this modified wall law are presented and compared to results based on a standard wall treatment.

A Feasibility Study of Synthesizing Subsurfaces Modeled with Computational Neural Networks

NASA Technical Reports Server (NTRS)

Wang, John T.; Housner, Jerrold M.; Szewczyk, Z. Peter

1998-01-01

This paper investigates the feasibility of synthesizing substructures modeled with computational neural networks. Substructures are modeled individually with computational neural networks and the response of the assembled structure is predicted by synthesizing the neural networks. A superposition approach is applied to synthesize models for statically determinate substructures while an interface displacement collocation approach is used to synthesize statically indeterminate substructure models. Beam and plate substructures along with components of a complicated Next Generation Space Telescope (NGST) model are used in this feasibility study. In this paper, the limitations and difficulties of synthesizing substructures modeled with neural networks are also discussed.

Exploring the Integration of Computational Modeling in the ASU Modeling Curriculum

NASA Astrophysics Data System (ADS)

Schatz, Michael; Aiken, John; Burk, John; Caballero, Marcos; Douglas, Scott; Thoms, Brian

2012-03-01

We describe the implementation of computational modeling in a ninth grade classroom in the context of the Arizona Modeling Instruction physics curriculum. Using a high-level programming environment (VPython), students develop computational models to predict the motion of objects under a variety of physical situations (e.g., constant net force), to simulate real world phenomenon (e.g., car crash), and to visualize abstract quantities (e.g., acceleration). We discuss how VPython allows students to utilize all four structures that describe a model as given by the ASU Modeling Instruction curriculum. Implications for future work will also be discussed.

Dealing with Diversity in Computational Cancer Modeling

PubMed Central

Johnson, David; McKeever, Steve; Stamatakos, Georgios; Dionysiou, Dimitra; Graf, Norbert; Sakkalis, Vangelis; Marias, Konstantinos; Wang, Zhihui; Deisboeck, Thomas S.

2013-01-01

This paper discusses the need for interconnecting computational cancer models from different sources and scales within clinically relevant scenarios to increase the accuracy of the models and speed up their clinical adaptation, validation, and eventual translation. We briefly review current interoperability efforts drawing upon our experiences with the development of in silico models for predictive oncology within a number of European Commission Virtual Physiological Human initiative projects on cancer. A clinically relevant scenario, addressing brain tumor modeling that illustrates the need for coupling models from different sources and levels of complexity, is described. General approaches to enabling interoperability using XML-based markup languages for biological modeling are reviewed, concluding with a discussion on efforts towards developing cancer-specific XML markup to couple multiple component models for predictive in silico oncology. PMID:23700360

High-resolution Modeling Assisted Design of Customized and Individualized Transcranial Direct Current Stimulation Protocols

PubMed Central

Bikson, Marom; Rahman, Asif; Datta, Abhishek; Fregni, Felipe; Merabet, Lotfi

2012-01-01

Objectives Transcranial direct current stimulation (tDCS) is a neuromodulatory technique that delivers low-intensity currents facilitating or inhibiting spontaneous neuronal activity. tDCS is attractive since dose is readily adjustable by simply changing electrode number, position, size, shape, and current. In the recent past, computational models have been developed with increased precision with the goal to help customize tDCS dose. The aim of this review is to discuss the incorporation of high-resolution patient-specific computer modeling to guide and optimize tDCS. Methods In this review, we discuss the following topics: (i) The clinical motivation and rationale for models of transcranial stimulation is considered pivotal in order to leverage the flexibility of neuromodulation; (ii) The protocols and the workflow for developing high-resolution models; (iii) The technical challenges and limitations of interpreting modeling predictions, and (iv) Real cases merging modeling and clinical data illustrating the impact of computational models on the rational design of rehabilitative electrotherapy. Conclusions Though modeling for non-invasive brain stimulation is still in its development phase, it is predicted that with increased validation, dissemination, simplification and democratization of modeling tools, computational forward models of neuromodulation will become useful tools to guide the optimization of clinical electrotherapy. PMID:22780230

The effects of geometric uncertainties on computational modelling of knee biomechanics

NASA Astrophysics Data System (ADS)

Meng, Qingen; Fisher, John; Wilcox, Ruth

2017-08-01

The geometry of the articular components of the knee is an important factor in predicting joint mechanics in computational models. There are a number of uncertainties in the definition of the geometry of cartilage and meniscus, and evaluating the effects of these uncertainties is fundamental to understanding the level of reliability of the models. In this study, the sensitivity of knee mechanics to geometric uncertainties was investigated by comparing polynomial-based and image-based knee models and varying the size of meniscus. The results suggested that the geometric uncertainties in cartilage and meniscus resulting from the resolution of MRI and the accuracy of segmentation caused considerable effects on the predicted knee mechanics. Moreover, even if the mathematical geometric descriptors can be very close to the imaged-based articular surfaces, the detailed contact pressure distribution produced by the mathematical geometric descriptors was not the same as that of the image-based model. However, the trends predicted by the models based on mathematical geometric descriptors were similar to those of the imaged-based models.

LDEF microenvironments, observed and predicted

NASA Astrophysics Data System (ADS)

Bourassa, R. J.; Pippin, H. G.; Gillis, J. R.

1993-04-01

A computer model for prediction of atomic oxygen exposure of spacecraft in low earth orbit, referred to as the primary atomic oxygen model, was originally described at the First Long Duration Exposure Facility (LDEF) Post-Retrieval Symposium. The primary atomic oxygen model accounts for variations in orbit parameters, the condition of the atmosphere, and for the orientation of exposed surfaces relative to the direction of spacecraft motion. The use of the primary atomic oxygen model to define average atomic oxygen exposure conditions for a spacecraft is discussed and a second microenvironments computer model is described that accounts for shadowing and scattering of atomic oxygen by complex surface protrusions and indentations. Comparisons of observed and predicted erosion of fluorinated ethylene propylene (FEP) thermal control blankets using the models are presented. Experimental and theoretical results are in excellent agreement. Work is in progress to expand modeling capability to include ultraviolet radiation exposure and to obtain more detailed information on reflecting and scattering characteristics of material surfaces.

LDEF microenvironments, observed and predicted

NASA Technical Reports Server (NTRS)

Bourassa, R. J.; Pippin, H. G.; Gillis, J. R.

1993-01-01

A computer model for prediction of atomic oxygen exposure of spacecraft in low earth orbit, referred to as the primary atomic oxygen model, was originally described at the First Long Duration Exposure Facility (LDEF) Post-Retrieval Symposium. The primary atomic oxygen model accounts for variations in orbit parameters, the condition of the atmosphere, and for the orientation of exposed surfaces relative to the direction of spacecraft motion. The use of the primary atomic oxygen model to define average atomic oxygen exposure conditions for a spacecraft is discussed and a second microenvironments computer model is described that accounts for shadowing and scattering of atomic oxygen by complex surface protrusions and indentations. Comparisons of observed and predicted erosion of fluorinated ethylene propylene (FEP) thermal control blankets using the models are presented. Experimental and theoretical results are in excellent agreement. Work is in progress to expand modeling capability to include ultraviolet radiation exposure and to obtain more detailed information on reflecting and scattering characteristics of material surfaces.

Thermomechanical Modeling of Sintered Silver - A Fracture Mechanics-based Approach: Extended Abstract: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Paret, Paul P; DeVoto, Douglas J; Narumanchi, Sreekant V

Sintered silver has proven to be a promising candidate for use as a die-attach and substrate-attach material in automotive power electronics components. It holds promise of greater reliability than lead-based and lead-free solders, especially at higher temperatures (less than 200 degrees Celcius). Accurate predictive lifetime models of sintered silver need to be developed and its failure mechanisms thoroughly characterized before it can be deployed as a die-attach or substrate-attach material in wide-bandgap device-based packages. We present a finite element method (FEM) modeling methodology that can offer greater accuracy in predicting the failure of sintered silver under accelerated thermal cycling. Amore » fracture mechanics-based approach is adopted in the FEM model, and J-integral/thermal cycle values are computed. In this paper, we outline the procedures for obtaining the J-integral/thermal cycle values in a computational model and report on the possible advantage of using these values as modeling parameters in a predictive lifetime model.« less

Machine Learning for Flood Prediction in Google Earth Engine

NASA Astrophysics Data System (ADS)

Kuhn, C.; Tellman, B.; Max, S. A.; Schwarz, B.

2015-12-01

With the increasing availability of high-resolution satellite imagery, dynamic flood mapping in near real time is becoming a reachable goal for decision-makers. This talk describes a newly developed framework for predicting biophysical flood vulnerability using public data, cloud computing and machine learning. Our objective is to define an approach to flood inundation modeling using statistical learning methods deployed in a cloud-based computing platform. Traditionally, static flood extent maps grounded in physically based hydrologic models can require hours of human expertise to construct at significant financial cost. In addition, desktop modeling software and limited local server storage can impose restraints on the size and resolution of input datasets. Data-driven, cloud-based processing holds promise for predictive watershed modeling at a wide range of spatio-temporal scales. However, these benefits come with constraints. In particular, parallel computing limits a modeler's ability to simulate the flow of water across a landscape, rendering traditional routing algorithms unusable in this platform. Our project pushes these limits by testing the performance of two machine learning algorithms, Support Vector Machine (SVM) and Random Forests, at predicting flood extent. Constructed in Google Earth Engine, the model mines a suite of publicly available satellite imagery layers to use as algorithm inputs. Results are cross-validated using MODIS-based flood maps created using the Dartmouth Flood Observatory detection algorithm. Model uncertainty highlights the difficulty of deploying unbalanced training data sets based on rare extreme events.

Further Investigation of the Support System Effects and Wing Twist on the NASA Common Research Model

NASA Technical Reports Server (NTRS)

Rivers, Melissa B.; Hunter, Craig A.; Campbell, Richard L.

2012-01-01

An experimental investigation of the NASA Common Research Model was conducted in the NASA Langley National Transonic Facility and NASA Ames 11-foot Transonic Wind Tunnel Facility for use in the Drag Prediction Workshop. As data from the experimental investigations was collected, a large difference in moment values was seen between the experiment and computational data from the 4th Drag Prediction Workshop. This difference led to a computational assessment to investigate model support system interference effects on the Common Research Model. The results from this investigation showed that the addition of the support system to the computational cases did increase the pitching moment so that it more closely matched the experimental results, but there was still a large discrepancy in pitching moment. This large discrepancy led to an investigation into the shape of the as-built model, which in turn led to a change in the computational grids and re-running of all the previous support system cases. The results of these cases are the focus of this paper.

The Canadian Hydrological Model (CHM): A multi-scale, variable-complexity hydrological model for cold regions

NASA Astrophysics Data System (ADS)

Marsh, C.; Pomeroy, J. W.; Wheater, H. S.

2016-12-01

There is a need for hydrological land surface schemes that can link to atmospheric models, provide hydrological prediction at multiple scales and guide the development of multiple objective water predictive systems. Distributed raster-based models suffer from an overrepresentation of topography, leading to wasted computational effort that increases uncertainty due to greater numbers of parameters and initial conditions. The Canadian Hydrological Model (CHM) is a modular, multiphysics, spatially distributed modelling framework designed for representing hydrological processes, including those that operate in cold-regions. Unstructured meshes permit variable spatial resolution, allowing coarse resolutions at low spatial variability and fine resolutions as required. Model uncertainty is reduced by lessening the necessary computational elements relative to high-resolution rasters. CHM uses a novel multi-objective approach for unstructured triangular mesh generation that fulfills hydrologically important constraints (e.g., basin boundaries, water bodies, soil classification, land cover, elevation, and slope/aspect). This provides an efficient spatial representation of parameters and initial conditions, as well as well-formed and well-graded triangles that are suitable for numerical discretization. CHM uses high-quality open source libraries and high performance computing paradigms to provide a framework that allows for integrating current state-of-the-art process algorithms. The impact of changes to model structure, including individual algorithms, parameters, initial conditions, driving meteorology, and spatial/temporal discretization can be easily tested. Initial testing of CHM compared spatial scales and model complexity for a spring melt period at a sub-arctic mountain basin. The meshing algorithm reduced the total number of computational elements and preserved the spatial heterogeneity of predictions.

Petascale Computing: Impact on Future NASA Missions

NASA Technical Reports Server (NTRS)

Brooks, Walter

2006-01-01

This slide presentation reviews NASA's use of a new super computer, called Columbia, capable of operating at 62 Tera Flops. This computer is the 4th fastest computer in the world. This computer will serve all mission directorates. The applications that it would serve are: aerospace analysis and design, propulsion subsystem analysis, climate modeling, hurricane prediction and astrophysics and cosmology.

Multi-scale Modeling of the Evolution of a Large-Scale Nourishment

NASA Astrophysics Data System (ADS)

Luijendijk, A.; Hoonhout, B.

2016-12-01

Morphological predictions are often computed using a single morphological model commonly forced with schematized boundary conditions representing the time scale of the prediction. Recent model developments are now allowing us to think and act differently. This study presents some recent developments in coastal morphological modeling focusing on flexible meshes, flexible coupling between models operating at different time scales, and a recently developed morphodynamic model for the intertidal and dry beach. This integrated modeling approach is applied to the Sand Engine mega nourishment in The Netherlands to illustrate the added-values of this integrated approach both in accuracy and computational efficiency. The state-of-the-art Delft3D Flexible Mesh (FM) model is applied at the study site under moderate wave conditions. One of the advantages is that the flexibility of the mesh structure allows a better representation of the water exchange with the lagoon and corresponding morphological behavior than with the curvilinear grid used in the previous version of Delft3D. The XBeach model is applied to compute the morphodynamic response to storm events in detail incorporating the long wave effects on bed level changes. The recently developed aeolian transport and bed change model AeoLiS is used to compute the bed changes in the intertidal and dry beach area. In order to enable flexible couplings between the three abovementioned models, a component-based environment has been developed using the BMI method. This allows a serial coupling of Delft3D FM and XBeach steered by a control module that uses a hydrodynamic time series as input (see figure). In addition, a parallel online coupling, with information exchange in each timestep will be made with the AeoLiS model that predicts the bed level changes at the intertidal and dry beach area. This study presents the first years of evolution of the Sand Engine computed with the integrated modelling approach. Detailed comparisons are made between the observed and computed morphological behaviour for the Sand Engine on an aggregated as well as sub-system level.

A Planar Quasi-Static Constraint Mode Tire Model

DTIC Science & Technology

2015-07-10

strikes a balance between simple tire models that lack the fidelity to make accurate chassis load predictions and computationally intensive models that...strikes a balance between heuristic tire models (such as a linear point-follower) that lack the fidelity to make accurate chassis load predictions...UNCLASSIFIED: Distribution Statement A. Cleared for public release A PLANAR QUASI-STATIC CONSTRAINT MODE TIRE MODEL Rui Maa John B. Ferris

Maximum likelihood convolutional decoding (MCD) performance due to system losses

NASA Technical Reports Server (NTRS)

Webster, L.

1976-01-01

A model for predicting the computational performance of a maximum likelihood convolutional decoder (MCD) operating in a noisy carrier reference environment is described. This model is used to develop a subroutine that will be utilized by the Telemetry Analysis Program to compute the MCD bit error rate. When this computational model is averaged over noisy reference phase errors using a high-rate interpolation scheme, the results are found to agree quite favorably with experimental measurements.

Predictive and mechanistic multivariate linear regression models for reaction development

PubMed Central

Santiago, Celine B.; Guo, Jing-Yao

2018-01-01

Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis. PMID:29719711

CFD validation experiments at McDonnell Aircraft Company

NASA Technical Reports Server (NTRS)

Verhoff, August

1987-01-01

Information is given in viewgraph form on computational fluid dynamics (CFD) validation experiments at McDonnell Aircraft Company. Topics covered include a high speed research model, a supersonic persistence fighter model, a generic fighter wing model, surface grids, force and moment predictions, surface pressure predictions, forebody models with 65 degree clipped delta wings, and the low aspect ratio wing/body experiment.

Modeling streamflow in a snow-dominated forest watershed using the Water Erosion Prediction Project (WEPP) model

Treesearch

A. Srivastava; J. Q. Wu; W. J. Elliot; E. S. Brooks; D. C. Flanagan

2017-01-01

The Water Erosion Prediction Project (WEPP) model was originally developed for hillslope and small watershed applications. Recent improvements to WEPP have led to enhanced computations for deep percolation, subsurface lateral flow, and frozen soil. In addition, the incorporation of channel routing has made the WEPP model well suited for large watersheds with perennial...

Interfacing comprehensive rotorcraft analysis with advanced aeromechanics and vortex wake models

NASA Astrophysics Data System (ADS)

Liu, Haiying

This dissertation describes three aspects of the comprehensive rotorcraft analysis. First, a physics-based methodology for the modeling of hydraulic devices within multibody-based comprehensive models of rotorcraft systems is developed. This newly proposed approach can predict the fully nonlinear behavior of hydraulic devices, and pressure levels in the hydraulic chambers are coupled with the dynamic response of the system. The proposed hydraulic device models are implemented in a multibody code and calibrated by comparing their predictions with test bench measurements for the UH-60 helicopter lead-lag damper. Predicted peak damping forces were found to be in good agreement with measurements, while the model did not predict the entire time history of damper force to the same level of accuracy. The proposed model evaluates relevant hydraulic quantities such as chamber pressures, orifice flow rates, and pressure relief valve displacements. This model could be used to design lead-lag dampers with desirable force and damping characteristics. The second part of this research is in the area of computational aeroelasticity, in which an interface between computational fluid dynamics (CFD) and computational structural dynamics (CSD) is established. This interface enables data exchange between CFD and CSD with the goal of achieving accurate airloads predictions. In this work, a loose coupling approach based on the delta-airloads method is developed in a finite-element method based multibody dynamics formulation, DYMORE. To validate this aerodynamic interface, a CFD code, OVERFLOW-2, is loosely coupled with a CSD program, DYMORE, to compute the airloads of different flight conditions for Sikorsky UH-60 aircraft. This loose coupling approach has good convergence characteristics. The predicted airloads are found to be in good agreement with the experimental data, although not for all flight conditions. In addition, the tight coupling interface between the CFD program, OVERFLOW-2, and the CSD program, DYMORE, is also established. The ability to accurately capture the wake structure around a helicopter rotor is crucial for rotorcraft performance analysis. In the third part of this thesis, a new representation of the wake vortex structure based on Non-Uniform Rational B-Spline (NURBS) curves and surfaces is proposed to develop an efficient model for prescribed and free wakes. NURBS curves and surfaces are able to represent complex shapes with remarkably little data. The proposed formulation has the potential to reduce the computational cost associated with the use of Helmholtz's law and the Biot-Savart law when calculating the induced flow field around the rotor. An efficient free-wake analysis will considerably decrease the computational cost of comprehensive rotorcraft analysis, making the approach more attractive to routine use in industrial settings.

Corrosion Prediction with Parallel Finite Element Modeling for Coupled Hygro-Chemo Transport into Concrete under Chloride-Rich Environment

PubMed Central

Na, Okpin; Cai, Xiao-Chuan; Xi, Yunping

2017-01-01

The prediction of the chloride-induced corrosion is very important because of the durable life of concrete structure. To simulate more realistic durability performance of concrete structures, complex scientific methods and more accurate material models are needed. In order to predict the robust results of corrosion initiation time and to describe the thin layer from concrete surface to reinforcement, a large number of fine meshes are also used. The purpose of this study is to suggest more realistic physical model regarding coupled hygro-chemo transport and to implement the model with parallel finite element algorithm. Furthermore, microclimate model with environmental humidity and seasonal temperature is adopted. As a result, the prediction model of chloride diffusion under unsaturated condition was developed with parallel algorithms and was applied to the existing bridge to validate the model with multi-boundary condition. As the number of processors increased, the computational time decreased until the number of processors became optimized. Then, the computational time increased because the communication time between the processors increased. The framework of present model can be extended to simulate the multi-species de-icing salts ingress into non-saturated concrete structures in future work. PMID:28772714

The Use of Finite Element Analyses to Design and Fabricate Three-Dimensional Scaffolds for Skeletal Tissue Engineering

PubMed Central

Hendrikson, Wim. J.; van Blitterswijk, Clemens. A.; Rouwkema, Jeroen; Moroni, Lorenzo

2017-01-01

Computational modeling has been increasingly applied to the field of tissue engineering and regenerative medicine. Where in early days computational models were used to better understand the biomechanical requirements of targeted tissues to be regenerated, recently, more and more models are formulated to combine such biomechanical requirements with cell fate predictions to aid in the design of functional three-dimensional scaffolds. In this review, we highlight how computational modeling has been used to understand the mechanisms behind tissue formation and can be used for more rational and biomimetic scaffold-based tissue regeneration strategies. With a particular focus on musculoskeletal tissues, we discuss recent models attempting to predict cell activity in relation to specific mechanical and physical stimuli that can be applied to them through porous three-dimensional scaffolds. In doing so, we review the most common scaffold fabrication methods, with a critical view on those technologies that offer better properties to be more easily combined with computational modeling. Finally, we discuss how modeling, and in particular finite element analysis, can be used to optimize the design of scaffolds for skeletal tissue regeneration. PMID:28567371

Enhanced Fan Noise Modeling for Turbofan Engines

NASA Technical Reports Server (NTRS)

Krejsa, Eugene A.; Stone, James R.

2014-01-01

This report describes work by consultants to Diversitech Inc. for the NASA Glenn Research Center (GRC) to revise the fan noise prediction procedure based on fan noise data obtained in the 9- by 15 Foot Low-Speed Wind Tunnel at GRC. The purpose of this task is to begin development of an enhanced, analytical, more physics-based, fan noise prediction method applicable to commercial turbofan propulsion systems. The method is to be suitable for programming into a computational model for eventual incorporation into NASA's current aircraft system noise prediction computer codes. The scope of this task is in alignment with the mission of the Propulsion 21 research effort conducted by the coalition of NASA, state government, industry, and academia to develop aeropropulsion technologies. A model for fan noise prediction was developed based on measured noise levels for the R4 rotor with several outlet guide vane variations and three fan exhaust areas. The model predicts the complete fan noise spectrum, including broadband noise, tones, and for supersonic tip speeds, combination tones. Both spectra and directivity are predicted. Good agreement with data was achieved for all fan geometries. Comparisons with data from a second fan, the ADP fan, also showed good agreement.

Grid-based Meteorological and Crisis Applications

NASA Astrophysics Data System (ADS)

Hluchy, Ladislav; Bartok, Juraj; Tran, Viet; Lucny, Andrej; Gazak, Martin

2010-05-01

We present several applications from domain of meteorology and crisis management we developed and/or plan to develop. Particularly, we present IMS Model Suite - a complex software system designed to address the needs of accurate forecast of weather and hazardous weather phenomena, environmental pollution assessment, prediction of consequences of nuclear accident and radiological emergency. We discuss requirements on computational means and our experiences how to meet them by grid computing. The process of a pollution assessment and prediction of the consequences in case of radiological emergence results in complex data-flows and work-flows among databases, models and simulation tools (geographical databases, meteorological and dispersion models, etc.). A pollution assessment and prediction requires running of 3D meteorological model (4 nests with resolution from 50 km to 1.8 km centered on nuclear power plant site, 38 vertical levels) as well as running of the dispersion model performing the simulation of the release transport and deposition of the pollutant with respect to the numeric weather prediction data, released material description, topography, land use description and user defined simulation scenario. Several post-processing options can be selected according to particular situation (e.g. doses calculation). Another example is a forecasting of fog as one of the meteorological phenomena hazardous to the aviation as well as road traffic. It requires complicated physical model and high resolution meteorological modeling due to its dependence on local conditions (precise topography, shorelines and land use classes). An installed fog modeling system requires a 4 time nested parallelized 3D meteorological model with 1.8 km horizontal resolution and 42 levels vertically (approx. 1 million points in 3D space) to be run four times daily. The 3D model outputs and multitude of local measurements are utilized by SPMD-parallelized 1D fog model run every hour. The fog forecast model is a subject of the parameterization and parameter optimization before its real deployment. The parameter optimization requires tens of evaluations of the parameterized model accuracy and each evaluation of the model parameters requires re-running of the hundreds of meteorological situations collected over the years and comparison of the model output with the observed data. The architecture and inherent heterogeneity of both examples and their computational complexity and their interfaces to other systems and services make them well suited for decomposition into a set of web and grid services. Such decomposition has been performed within several projects we participated or participate in cooperation with academic sphere, namely int.eu.grid (dispersion model deployed as a pilot application to an interactive grid), SEMCO-WS (semantic composition of the web and grid services), DMM (development of a significant meteorological phenomena prediction system based on the data mining), VEGA 2009-2011 and EGEE III. We present useful and practical applications of technologies of high performance computing. The use of grid technology provides access to much higher computation power not only for modeling and simulation, but also for the model parameterization and validation. This results in the model parameters optimization and more accurate simulation outputs. Having taken into account that the simulations are used for the aviation, road traffic and crisis management, even small improvement in accuracy of predictions may result in significant improvement of safety as well as cost reduction. We found grid computing useful for our applications. We are satisfied with this technology and our experience encourages us to extend its use. Within an ongoing project (DMM) we plan to include processing of satellite images which extends our requirement on computation very rapidly. We believe that thanks to grid computing we are able to handle the job almost in real time.

Nonlinear Visco-Elastic Response of Composites via Micro-Mechanical Models

NASA Technical Reports Server (NTRS)

Gates, Thomas S.; Sridharan, Srinivasan

2005-01-01

Micro-mechanical models for a study of nonlinear visco-elastic response of composite laminae are developed and their performance compared. A single integral constitutive law proposed by Schapery and subsequently generalized to multi-axial states of stress is utilized in the study for the matrix material. This is used in conjunction with a computationally facile scheme in which hereditary strains are computed using a recursive relation suggested by Henriksen. Composite response is studied using two competing micro-models, viz. a simplified Square Cell Model (SSCM) and a Finite Element based self-consistent Cylindrical Model (FECM). The algorithm is developed assuming that the material response computations are carried out in a module attached to a general purpose finite element program used for composite structural analysis. It is shown that the SSCM as used in investigations of material nonlinearity can involve significant errors in the prediction of transverse Young's modulus and shear modulus. The errors in the elastic strains thus predicted are of the same order of magnitude as the creep strains accruing due to visco-elasticity. The FECM on the other hand does appear to perform better both in the prediction of elastic constants and the study of creep response.

MRI-Based Computational Fluid Dynamics in Experimental Vascular Models: Toward the Development of an Approach for Prediction of Cardiovascular Changes During Prolonged Space Missions

NASA Technical Reports Server (NTRS)

Spirka, T. A.; Myers, J. G.; Setser, R. M.; Halliburton, S. S.; White, R. D.; Chatzimavroudis, G. P.

2005-01-01

A priority of NASA is to identify and study possible risks to astronauts health during prolonged space missions [l]. The goal is to develop a procedure for a preflight evaluation of the cardiovascular system of an astronaut and to forecast how it will be affected during the mission. To predict these changes, a computational cardiovascular model must be constructed. Although physiology data can be used to make a general model, a more desirable subject-specific model requires anatomical, functional, and flow data from the specific astronaut. MRI has the unique advantage of providing images with all of the above information, including three-directional velocity data which can be used as boundary conditions in a computational fluid dynamics (CFD) program [2,3]. MRI-based CFD is very promising for reproduction of the flow patterns of a specific subject and prediction of changes in the absence of gravity. The aim of this study was to test the feasibility of this approach by reconstructing the geometry of MRI-scanned arterial models and reproducing the MRI-measured velocities using CFD simulations on these geometries.

Computer Simulation of Embryonic Systems: What can a ...

EPA Pesticide Factsheets

(1) Standard practice for assessing developmental toxicity is the observation of apical endpoints (intrauterine death, fetal growth retardation, structural malformations) in pregnant rats/rabbits following exposure during organogenesis. EPA’s computational toxicology research program (ToxCast) generated vast in vitro cellular and molecular effects data on >1858 chemicals in >600 high-throughput screening (HTS) assays. The diversity of assays has been increased for developmental toxicity with several HTS platforms, including the devTOX-quickPredict assay from Stemina Biomarker Discovery utilizing the human embryonic stem cell line (H9). Translating these HTS data into higher order-predictions of developmental toxicity is a significant challenge. Here, we address the application of computational systems models that recapitulate the kinematics of dynamical cell signaling networks (e.g., SHH, FGF, BMP, retinoids) in a CompuCell3D.org modeling environment. Examples include angiogenesis (angiodysplasia) and dysmorphogenesis. Being numerically responsive to perturbation, these models are amenable to data integration for systems Toxicology and Adverse Outcome Pathways (AOPs). The AOP simulation outputs predict potential phenotypes based on the in vitro HTS data ToxCast. A heuristic computational intelligence framework that recapitulates the kinematics of dynamical cell signaling networks in the embryo, together with the in vitro profiling data, produce quantitative pr

Vibro-acoustic propagation of gear dynamics in a gear-bearing-housing system

NASA Astrophysics Data System (ADS)

Guo, Yi; Eritenel, Tugan; Ericson, Tristan M.; Parker, Robert G.

2014-10-01

This work developed a computational process to predict noise radiation from gearboxes. It developed a system-level vibro-acoustic model of an actual gearbox, including gears, bearings, shafts, and housing structure, and compared the results to experiments. The meshing action of gear teeth causes vibrations to propagate through shafts and bearings to the housing radiating noise. The vibration excitation from the gear mesh and the system response were predicted using finite element and lumped-parameter models. From these results, the radiated noise was calculated using a boundary element model of the housing. Experimental vibration and noise measurements from the gearbox confirmed the computational predictions. The developed tool was used to investigate the influence of standard rolling element and modified journal bearings on gearbox radiated noise.

Computational toxicology using the OpenTox application programming interface and Bioclipse

PubMed Central

2011-01-01

Background Toxicity is a complex phenomenon involving the potential adverse effect on a range of biological functions. Predicting toxicity involves using a combination of experimental data (endpoints) and computational methods to generate a set of predictive models. Such models rely strongly on being able to integrate information from many sources. The required integration of biological and chemical information sources requires, however, a common language to express our knowledge ontologically, and interoperating services to build reliable predictive toxicology applications. Findings This article describes progress in extending the integrative bio- and cheminformatics platform Bioclipse to interoperate with OpenTox, a semantic web framework which supports open data exchange and toxicology model building. The Bioclipse workbench environment enables functionality from OpenTox web services and easy access to OpenTox resources for evaluating toxicity properties of query molecules. Relevant cases and interfaces based on ten neurotoxins are described to demonstrate the capabilities provided to the user. The integration takes advantage of semantic web technologies, thereby providing an open and simplifying communication standard. Additionally, the use of ontologies ensures proper interoperation and reliable integration of toxicity information from both experimental and computational sources. Conclusions A novel computational toxicity assessment platform was generated from integration of two open science platforms related to toxicology: Bioclipse, that combines a rich scriptable and graphical workbench environment for integration of diverse sets of information sources, and OpenTox, a platform for interoperable toxicology data and computational services. The combination provides improved reliability and operability for handling large data sets by the use of the Open Standards from the OpenTox Application Programming Interface. This enables simultaneous access to a variety of distributed predictive toxicology databases, and algorithm and model resources, taking advantage of the Bioclipse workbench handling the technical layers. PMID:22075173

Characterizing and modeling organic binder burnout from green ceramic compacts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ewsuk, K.G.; Cesarano, J. III; Cochran, R.J.

New characterization and computational techniques have been developed to evaluate and simulate binder burnout from pressed powder compacts. Using engineering data and a control volume finite element method (CVFEM) thermal model, a nominally one dimensional (1-D) furnace has been designed to test, refine, and validate computer models that simulate binder burnout assuming a 1-D thermal gradient across the ceramic body during heating. Experimentally, 1-D radial heat flow was achieved using a rod-shaped heater that directly heats the inside surface of a stack of ceramic annuli surrounded by thermal insulation. The computational modeling effort focused on producing a macroscopic model formore » binder burnout based on continuum approaches to heat and mass conservation for porous media. Two increasingly complex models have been developed that predict the temperature and mass of a porous powder compact as a function of time during binder burnout. The more complex model also predicts the pressure within a powder compact during binder burnout. Model predictions are in reasonably good agreement with experimental data on binder burnout from a 57--65% relative density pressed powder compact of a 94 wt% alumina body containing {approximately}3 wt% binder. In conjunction with the detailed experimental data from the prototype binder burnout furnace, the models have also proven useful for conducting parametric studies to elucidate critical i-material property data required to support model development.« less

Effect of Turbulence Models on Two Massively-Separated Benchmark Flow Cases

NASA Technical Reports Server (NTRS)

Rumsey, Christopher L.

2003-01-01

Two massively-separated flow cases (the 2-D hill and the 3-D Ahmed body) were computed with several different turbulence models in the Reynolds-averaged Navier-Stokes code CFL3D as part of participation in a turbulence modeling workshop held in Poitiers, France in October, 2002. Overall, results were disappointing, but were consistent with results from other RANS codes and other turbulence models at the workshop. For the 2-D hill case, those turbulence models that predicted separation location accurately ended up yielding a too-long separation extent downstream. The one model that predicted a shorter separation extent in better agreement with LES data did so only by coincidence: its prediction of earlier reattachment was due to a too-late prediction of the separation location. For the Ahmed body, two slant angles were computed, and CFD performed fairly well for one of the cases (the larger slant angle). Both turbulence models tested in this case were very similar to each other. For the smaller slant angle, CFD predicted massive separation, whereas the experiment showed reattachment about half-way down the center of the face. These test cases serve as reminders that state- of-the-art CFD is currently not a reliable predictor of massively-separated flow physics, and that further validation studies in this area would be beneficial.

Optimizing heat shock protein expression induced by prostate cancer laser therapy through predictive computational models

NASA Astrophysics Data System (ADS)

Rylander, Marissa N.; Feng, Yusheng; Zhang, Yongjie; Bass, Jon; Stafford, Roger J.; Hazle, John D.; Diller, Kenneth R.

2006-07-01

Thermal therapy efficacy can be diminished due to heat shock protein (HSP) induction in regions of a tumor where temperatures are insufficient to coagulate proteins. HSP expression enhances tumor cell viability and imparts resistance to chemotherapy and radiation treatments, which are generally employed in conjunction with hyperthermia. Therefore, an understanding of the thermally induced HSP expression within the targeted tumor must be incorporated into the treatment plan to optimize the thermal dose delivery and permit prediction of the overall tissue response. A treatment planning computational model capable of predicting the temperature, HSP27 and HSP70 expression, and damage fraction distributions associated with laser heating in healthy prostate tissue and tumors is presented. Measured thermally induced HSP27 and HSP70 expression kinetics and injury data for normal and cancerous prostate cells and prostate tumors are employed to create the first HSP expression predictive model and formulate an Arrhenius damage model. The correlation coefficients between measured and model predicted temperature, HSP27, and HSP70 were 0.98, 0.99, and 0.99, respectively, confirming the accuracy of the model. Utilization of the treatment planning model in the design of prostate cancer thermal therapies can enable optimization of the treatment outcome by controlling HSP expression and injury.

Coupling of rainfall-induced landslide triggering model with predictions of debris flow runout distances

NASA Astrophysics Data System (ADS)

Lehmann, Peter; von Ruette, Jonas; Fan, Linfeng; Or, Dani

2014-05-01

Rapid debris flows initiated by rainfall induced shallow landslides present a highly destructive natural hazard in steep terrain. The impact and run-out paths of debris flows depend on the volume, composition and initiation zone of released material and are requirements to make accurate debris flow predictions and hazard maps. For that purpose we couple the mechanistic 'Catchment-scale Hydro-mechanical Landslide Triggering (CHLT)' model to compute timing, location, and landslide volume with simple approaches to estimate debris flow runout distances. The runout models were tested using two landslide inventories obtained in the Swiss Alps following prolonged rainfall events. The predicted runout distances were in good agreement with observations, confirming the utility of such simple models for landscape scale estimates. In a next step debris flow paths were computed for landslides predicted with the CHLT model for a certain range of soil properties to explore its effect on runout distances. This combined approach offers a more complete spatial picture of shallow landslide and subsequent debris flow hazards. The additional information provided by CHLT model concerning location, shape, soil type and water content of the released mass may also be incorporated into more advanced models of runout to improve predictability and impact of such abruptly-released mass.

Hyper-resolution hydrological modeling: Completeness of Formulation, Appropriateness of Descritization, and Physical LImits of Predictability

NASA Astrophysics Data System (ADS)

Ogden, F. L.

2017-12-01

HIgh performance computing and the widespread availabilities of geospatial physiographic and forcing datasets have enabled consideration of flood impact predictions with longer lead times and more detailed spatial descriptions. We are now considering multi-hour flash flood forecast lead times at the subdivision level in so-called hydroblind regions away from the National Hydrography network. However, the computational demands of such models are high, necessitating a nested simulation approach. Research on hyper-resolution hydrologic modeling over the past three decades have illustrated some fundamental limits on predictability that are simultaneously related to runoff generation mechanism(s), antecedent conditions, rates and total amounts of precipitation, discretization of the model domain, and complexity or completeness of the model formulation. This latter point is an acknowledgement that in some ways hydrologic understanding in key areas related to land use, land cover, tillage practices, seasonality, and biological effects has some glaring deficiencies. This presentation represents a review of what is known related to the interacting effects of precipitation amount, model spatial discretization, antecedent conditions, physiographic characteristics and model formulation completeness for runoff predictions. These interactions define a region in multidimensional forcing, parameter and process space where there are in some cases clear limits on predictability, and in other cases diminished uncertainty.

Modeling of temperature-induced near-infrared and low-field time-domain nuclear magnetic resonance spectral variation: chemometric prediction of limonene and water content in spray-dried delivery systems.

PubMed

Andrade, Letícia; Farhat, Imad A; Aeberhardt, Kasia; Bro, Rasmus; Engelsen, Søren Balling

2009-02-01

The influence of temperature on near-infrared (NIR) and nuclear magnetic resonance (NMR) spectroscopy complicates the industrial applications of both spectroscopic methods. The focus of this study is to analyze and model the effect of temperature variation on NIR spectra and NMR relaxation data. Different multivariate methods were tested for constructing robust prediction models based on NIR and NMR data acquired at various temperatures. Data were acquired on model spray-dried limonene systems at five temperatures in the range from 20 degrees C to 60 degrees C and partial least squares (PLS) regression models were computed for limonene and water predictions. The predictive ability of the models computed on the NIR spectra (acquired at various temperatures) improved significantly when data were preprocessed using extended inverted signal correction (EISC). The average PLS regression prediction error was reduced to 0.2%, corresponding to 1.9% and 3.4% of the full range of limonene and water reference values, respectively. The removal of variation induced by temperature prior to calibration, by direct orthogonalization (DO), slightly enhanced the predictive ability of the models based on NMR data. Bilinear PLS models, with implicit inclusion of the temperature, enabled limonene and water predictions by NMR with an error of 0.3% (corresponding to 2.8% and 7.0% of the full range of limonene and water). For NMR, and in contrast to the NIR results, modeling the data using multi-way N-PLS improved the models' performance. N-PLS models, in which temperature was included as an extra variable, enabled more accurate prediction, especially for limonene (prediction error was reduced to 0.2%). Overall, this study proved that it is possible to develop models for limonene and water content prediction based on NIR and NMR data, independent of the measurement temperature.

A Worst-Case Approach for On-Line Flutter Prediction

NASA Technical Reports Server (NTRS)

Lind, Rick C.; Brenner, Martin J.

1998-01-01

Worst-case flutter margins may be computed for a linear model with respect to a set of uncertainty operators using the structured singular value. This paper considers an on-line implementation to compute these robust margins in a flight test program. Uncertainty descriptions are updated at test points to account for unmodeled time-varying dynamics of the airplane by ensuring the robust model is not invalidated by measured flight data. Robust margins computed with respect to this uncertainty remain conservative to the changing dynamics throughout the flight. A simulation clearly demonstrates this method can improve the efficiency of flight testing by accurately predicting the flutter margin to improve safety while reducing the necessary flight time.

A statistical model including age to predict passenger postures in the rear seats of automobiles.

PubMed

Park, Jangwoon; Ebert, Sheila M; Reed, Matthew P; Hallman, Jason J

2016-06-01

Few statistical models of rear seat passenger posture have been published, and none has taken into account the effects of occupant age. This study developed new statistical models for predicting passenger postures in the rear seats of automobiles. Postures of 89 adults with a wide range of age and body size were measured in a laboratory mock-up in seven seat configurations. Posture-prediction models for female and male passengers were separately developed by stepwise regression using age, body dimensions, seat configurations and two-way interactions as potential predictors. Passenger posture was significantly associated with age and the effects of other two-way interaction variables depended on age. A set of posture-prediction models are presented for women and men, and the prediction results are compared with previously published models. This study is the first study of passenger posture to include a large cohort of older passengers and the first to report a significant effect of age for adults. The presented models can be used to position computational and physical human models for vehicle design and assessment. Practitioner Summary: The significant effects of age, body dimensions and seat configuration on rear seat passenger posture were identified. The models can be used to accurately position computational human models or crash test dummies for older passengers in known rear seat configurations.

User's manual for the Noise 1 area computer program for transportation noise prediction : report under project entitled "area computer model for transportation noise prediction : phase 1 : adaptation of MICNOISE".

DOT National Transportation Integrated Search

1975-01-01

It was found that the coordinates of the highways required for Noise 1 could be supplied on punched cards by the Photogrammetry Section of the Department. In preparing data for contour plotting, it was found advisable to divide the area into sectors,...

Prediction of Software Reliability using Bio Inspired Soft Computing Techniques.

PubMed

Diwaker, Chander; Tomar, Pradeep; Poonia, Ramesh C; Singh, Vijander

2018-04-10

A lot of models have been made for predicting software reliability. The reliability models are restricted to using particular types of methodologies and restricted number of parameters. There are a number of techniques and methodologies that may be used for reliability prediction. There is need to focus on parameters consideration while estimating reliability. The reliability of a system may increase or decreases depending on the selection of different parameters used. Thus there is need to identify factors that heavily affecting the reliability of the system. In present days, reusability is mostly used in the various area of research. Reusability is the basis of Component-Based System (CBS). The cost, time and human skill can be saved using Component-Based Software Engineering (CBSE) concepts. CBSE metrics may be used to assess those techniques which are more suitable for estimating system reliability. Soft computing is used for small as well as large-scale problems where it is difficult to find accurate results due to uncertainty or randomness. Several possibilities are available to apply soft computing techniques in medicine related problems. Clinical science of medicine using fuzzy-logic, neural network methodology significantly while basic science of medicine using neural-networks-genetic algorithm most frequently and preferably. There is unavoidable interest shown by medical scientists to use the various soft computing methodologies in genetics, physiology, radiology, cardiology and neurology discipline. CBSE boost users to reuse the past and existing software for making new products to provide quality with a saving of time, memory space, and money. This paper focused on assessment of commonly used soft computing technique like Genetic Algorithm (GA), Neural-Network (NN), Fuzzy Logic, Support Vector Machine (SVM), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). This paper presents working of soft computing techniques and assessment of soft computing techniques to predict reliability. The parameter considered while estimating and prediction of reliability are also discussed. This study can be used in estimation and prediction of the reliability of various instruments used in the medical system, software engineering, computer engineering and mechanical engineering also. These concepts can be applied to both software and hardware, to predict the reliability using CBSE.

An integral equation formulation for predicting radiation patterns of a space shuttle annular slot antenna

NASA Technical Reports Server (NTRS)

Jones, J. E.; Richmond, J. H.

1974-01-01

An integral equation formulation is applied to predict pitch- and roll-plane radiation patterns of a thin VHF/UHF (very high frequency/ultra high frequency) annular slot communications antenna operating at several locations in the nose region of the space shuttle orbiter. Digital computer programs used to compute radiation patterns are given and the use of the programs is illustrated. Experimental verification of computed patterns is given from measurements made on 1/35-scale models of the orbiter.

Prediction of overall and blade-element performance for axial-flow pump configurations

NASA Technical Reports Server (NTRS)

Serovy, G. K.; Kavanagh, P.; Okiishi, T. H.; Miller, M. J.

1973-01-01

A method and a digital computer program for prediction of the distributions of fluid velocity and properties in axial flow pump configurations are described and evaluated. The method uses the blade-element flow model and an iterative numerical solution of the radial equilbrium and continuity conditions. Correlated experimental results are used to generate alternative methods for estimating blade-element turning and loss characteristics. Detailed descriptions of the computer program are included, with example input and typical computed results.

Comparison of rigorous and simple vibrational models for the CO2 gasdynamic laser

NASA Technical Reports Server (NTRS)

Monson, D. J.

1977-01-01

The accuracy of a simple vibrational model for computing the gain in a CO2 gasdynamic laser is assessed by comparing results computed from it with results computed from a rigorous vibrational model. The simple model is that of Anderson et al. (1971), in which the vibrational kinetics are modeled by grouping the nonequilibrium vibrational degrees of freedom into two modes, to each of which there corresponds an equation describing vibrational relaxation. The two models agree fairly well in the computed gain at low temperatures, but the simple model predicts too high a gain at the higher temperatures of current interest. The sources of error contributing to the overestimation given by the simple model are determined by examining the simplified relaxation equations.

Multifidelity, Multidisciplinary Design Under Uncertainty with Non-Intrusive Polynomial Chaos

NASA Technical Reports Server (NTRS)

West, Thomas K., IV; Gumbert, Clyde

2017-01-01

The primary objective of this work is to develop an approach for multifidelity uncertainty quantification and to lay the framework for future design under uncertainty efforts. In this study, multifidelity is used to describe both the fidelity of the modeling of the physical systems, as well as the difference in the uncertainty in each of the models. For computational efficiency, a multifidelity surrogate modeling approach based on non-intrusive polynomial chaos using the point-collocation technique is developed for the treatment of both multifidelity modeling and multifidelity uncertainty modeling. Two stochastic model problems are used to demonstrate the developed methodologies: a transonic airfoil model and multidisciplinary aircraft analysis model. The results of both showed the multifidelity modeling approach was able to predict the output uncertainty predicted by the high-fidelity model as a significant reduction in computational cost.

galario: Gpu Accelerated Library for Analyzing Radio Interferometer Observations

NASA Astrophysics Data System (ADS)

Tazzari, Marco; Beaujean, Frederik; Testi, Leonardo

2017-10-01

The galario library exploits the computing power of modern graphic cards (GPUs) to accelerate the comparison of model predictions to radio interferometer observations. It speeds up the computation of the synthetic visibilities given a model image (or an axisymmetric brightness profile) and their comparison to the observations.

Computational modeling in the optimization of corrosion control to reduce lead in drinking water

EPA Science Inventory

An international “proof-of-concept” research project (UK, US, CA) will present its findings during this presentation. An established computational modeling system developed in the UK is being calibrated and validated in U.S. and Canadian case studies. It predicts LCR survey resul...

Computational Systems Toxicology: recapitulating the logistical dynamics of cellular response networks in virtual tissue models (Eurotox_2017)

EPA Science Inventory

Translating in vitro data and biological information into a predictive model for human toxicity poses a significant challenge. This is especially true for complex adaptive systems such as the embryo where cellular dynamics are precisely orchestrated in space and time. Computer ce...

A PHYSIOLOGICALLY BASED COMPUTATIONAL MODEL OF THE BPG AXIS IN FATHEAD MINNOWS: PREDICTING EFFECTS OF ENDOCRINE DISRUPTING CHEMICAL EXPOSURE ON REPRODUCTIVE ENDPOINTS

EPA Science Inventory

This presentation describes development and application of a physiologically-based computational model that simulates the brain-pituitary-gonadal (BPG) axis and other endpoints important in reproduction such as concentrations of sex steroid hormones, 17-estradiol, testosterone, a...

Adaptive Response in Female Fathead Minnows Exposed to an Aromatase Inhibitor: Computational Modeling of the Hypothalamic-Pituitary-Gonadal Axis

EPA Science Inventory

Exposure to endocrine disrupting chemicals can affect reproduction and development in both humans and wildlife. We are developing a mechanistic computational model of the hypothalamic-pituitary-gonadal (HPG) axis in female fathead minnows to predict dose-response and time-course ...

Recent advances, and unresolved issues, in the application of computational modelling to the prediction of the biological effects of nanomaterials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Winkler, David A., E-mail: dave.winkler@csiro.au

2016-05-15

Nanomaterials research is one of the fastest growing contemporary research areas. The unprecedented properties of these materials have meant that they are being incorporated into products very quickly. Regulatory agencies are concerned they cannot assess the potential hazards of these materials adequately, as data on the biological properties of nanomaterials are still relatively limited and expensive to acquire. Computational modelling methods have much to offer in helping understand the mechanisms by which toxicity may occur, and in predicting the likelihood of adverse biological impacts of materials not yet tested experimentally. This paper reviews the progress these methods, particularly those QSAR-based,more » have made in understanding and predicting potentially adverse biological effects of nanomaterials, and also the limitations and pitfalls of these methods. - Highlights: • Nanomaterials regulators need good information to make good decisions. • Nanomaterials and their interactions with biology are very complex. • Computational methods use existing data to predict properties of new nanomaterials. • Statistical, data driven modelling methods have been successfully applied to this task. • Much more must be learnt before robust toolkits will be widely usable by regulators.« less

COMPARISON OF DATA FROM AN IAQ TEST HOUSE WITH PREDICTIONS OF AN IAQ COMPUTER MODEL

EPA Science Inventory

The paper describes several experiments to evaluate the impact of indoor air pollutant sources on indoor air quality (IAQ). Measured pollutant concentrations are compared with concentrations predicted by an IAQ model. The measured concentrations are in excellent agreement with th...

Predicting Adaptive Response to Fadrozole Exposure:Computational Model of the Fathead MinnowsHypothalamic-Pituitary-Gonadal Axis

EPA Science Inventory

Exposure to endocrine disrupting chemicals can affect reproduction and development in both humans and wildlife. We are developing a mechanistic mathematical model of the hypothalamic-pituitary-gonadal (HPG) axis in female fathead minnows to predict doseresponse and time-course (...