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Results for “Computational Modeling”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Applicability Analysis of Validation Evidence for Biomedical Computational Models

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 credibility 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.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Computer Modeling VRF Heat Pumps in Commercial Buildings using EnergyPlus

Variable Refrigerant Flow (VRF) heat pumps are increasingly used in commercial buildings in the United States. Monitored energy use of field installations have shown, in some cases, savings exceeding 30% compared to conventional heating, ventilating, and air-conditioning (HVAC) systems. A simulation study was conducted to identify the installation or operational characteristics that lead to energy savings for VRF systems. The study used the Department of Energy EnergyPlus? building simulation software and four reference building models. Computer simulations were performed in eight U.S. climate zones. The baseline reference HVAC system incorporated packaged single-zone direct-expansion cooling with gas heating (PSZ-AC) or variable-air-volume systems (VAV with reheat). An alternate baseline HVAC system using a heat pump (PSZ-HP) was included for some buildings to directly compare gas and electric heating results. These baseline systems were compared to a VRF heat pump model to identify differences in energy use. VRF systems combine multiple indoor units with one or more outdoor unit(s). These systems move refrigerant between the outdoor and indoor units which eliminates the need for duct work in most cases. Since many applications install duct work in unconditioned spaces, this leads to installation differences between VRF systems and conventional HVAC systems. To characterize installation differences, a duct heat gain model was included to identify the energy impacts of installing ducts in unconditioned spaces. The configuration of variable refrigerant flow heat pumps will ultimately eliminate or significantly reduce energy use due to duct heat transfer. Fan energy is also studied to identify savings associated with non-ducted VRF terminal units. VRF systems incorporate a variable-speed compressor which may lead to operational differences compared to single-speed compression systems. To characterize operational differences, the computer model performance curves used to simulate cooling operation are also evaluated. The information in this paper is intended to provide a relative difference in system energy use and compare various installation practices that can impact performance. Comparative results of VRF versus conventional HVAC systems include energy use differences due to duct location, differences in fan energy when ducts are eliminated, and differences associated with electric versus fossil fuel type heating systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Efficient calibration for imperfect computer models

Many computer models contain unknown parameters which need to be estimated using physical observations. Furthermore, the calibration method based on Gaussian process models may lead to unreasonable estimate for imperfect computer models. In this work, we extend their study to calibration problems with stochastic physical data. We propose a novel method, called the L 2 calibration, and show its semiparametric efficiency. The conventional method of the ordinary least squares is also studied. Theoretical analysis shows that it is consistent but not efficient. Here, numerical examples show that the proposed method outperforms the existing ones.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

25 Mg NMR and Computational Modeling Studies of the Solvation Structures and Molecular Dynamics in Magnesium Based Liquid Electrolytes

There is increasing evidence that the solvation structure of the active components in a liquid electrolyte solution strongly impacts the performance in electrochemical applications. In this work, the nanoscale solvation structures and dynamics of Mg(BH 4 ) 2 and Mg(TFSI) 2 dissolved in diglyme (DGM) at various concentrations and ratios of Mg(BH 4 ) 2 /Mg(TFSI) 2 were investigated using a combination of natural abundance 25 Mg NMR, quantum chemistry calculations of 25 Mg NMR chemical shifts, classical molecular dynamics (MD) calculations, and electrochemical performance tests. By mixing two competing Mg salts, we were able to reduce the strong covalent interactions between Mg 2+ and BH 4 – anions. A small increase is observed in the coordination number of Mg-TFSI and a significant increase in the interaction of Mg 2+ ions with glymes. Through a combination of NMR, DFT and MD simulations, various stable species around 1 nm in size were detected in the mixed salt solution, which play key roles in the enhanced electrochemical performance of the mixed electrolyte. It is established that for the neat Mg(TFSI) 2 in DGM electrolyte at dilute concentrations the TFSI - is fully dissociated from Mg 2+ . At higher concentrations, Mg 2+ and TFSI - are only partially dissociated as contact ion pairs are formed. In contrast, at 0.01 M Mg(BH 4 ) 2 (saturated concentration) in DGM, the first solvation shell of a Mg 2+ ion contains two BH 4 - anions and one DGM molecule, while the second solvation shell consists of five to six DGM molecules. An exchange mechanism between the solvation structures in the combined electrolyte containing both Mg(BH 4 ) 2 and Mg(TFSI) 2 in DGM was found to result in the observation of a single 25 Mg NMR peak. This exchange is responsible for an increase in uncoordinated anions, as well as improved stability and ionic conductivity as compared to single anion solution. Solvent molecule rearrangement and direct Mg-ion exchange between the basic solvation structures are hypothesized as likely reasons for the exchange. We elucidate that the solvent rearrangement is energetically much more favorable than direct Mg-ion hopping and is thus suggested as the dominant exchange mechanism.

Magnesium battery, electrolytes, Mg(BH4)2, Mg(TFSI↗
DOE OSTI2023

Sequential Bayesian Methods for Analyzing Computer Models

Efficient analysis of computer models is essential for the validation and uncertainty quantification of those models. Surrogate models, and Gaussian processes in particular, are a common and powerful approach to analyzing computer models that treat computer models as a black-box function. Gaussian processes form a Bayesian model over a space of functions that gives a measure of uncertainty about the computer model output at unobserved locations and a framework for sequential sampling. This tutorial will show how to fit Gaussian processes on a series of test functions and apply sequential design techniques to estimate extrema, level sets, and reliabilities of those test functions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Computational modeling of electrostatic charge and fields produced by hypervelocity impact

Following prior experimental evidence of electrostatic charge separation, electric and magnetic fields produced by hypervelocity impact, we have developed a model of electrostatic charge separation based on plasma sheath theory and implemented it into the CTH shock physics code. Preliminary assessment of the model shows good qualitative and quantitative agreement between the model and prior experiments at least in the hypervelocity regime for the porous carbonate material tested. The model agrees with the scaling analysis of experimental data performed in the prior work, suggesting that electric charge separation and the resulting electric and magnetic fields can be a substantial effect at larger scales, higher impact velocities, or both.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

On the Usage of Ultrasound Computational Models for Decision Making Under Ambiguity

Computer modeling and simulation is becoming pervasive within the non-destructive evaluation (NDE) industry as a convenient tool for designing and assessing inspection techniques. This raises a pressing need for developing quantitative techniques for demonstrating the validity and applicability of the computational models. Computational models provide deterministic results based on deterministic and well-defined input, or stochastic results based on inputs defined by probability distributions. However, computational models cannot account for the effects of personnel, procedures, and equipment, resulting in ambiguity about the efficacy of inspections based on guidance from computational models only. In addition ambiguity arises when model inputs, such as the representation of realistic cracks, cannot be defined deterministically, probabilistically, or by intervals. In this work, Pacific Northwest National Laboratory demonstrates the ability of computational models to represent field measurements under known variabilities, and quantify the differences using maximum amplitude and power spectrum density metrics. Sensitivity studies are also conducted to quantify the effects of different input parameters on the simulation results.

Dib, Gerges↗
DOE OSTI2015

Computational modeling of Krypton gas puffs with tailored mass density profiles on Z

Large diameter multi-shell gas puffs rapidly imploded by high current (~20 MA, ~100 ns) on the Z generator of Sandia National Laboratories are able to produce high-intensity Krypton K-shell emission at ~13 keV. Efficiently radiating at these high photon energies is a significant challenge which requires the careful design and optimization of the gas distribution. To facilitate this, we hydrodynamically model the gas flow out of the nozzle and then model its implosion using a 3-dimensional resistive, radiative MHD code (GORGON). This approach enables us to iterate between modeling the implosion and gas flow from the nozzle to optimize radiative output from this combined system. Furthermore, guided by our implosion calculations, we have designed gas profiles that help mitigate disruption from Magneto-Rayleigh–Taylor implosion instabilities, while preserving sufficient kinetic energy to thermalize to the high temperatures required for K-shell emission.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

In silico environmental chemical science: properties and processes from statistical and computational modelling

Quantitative structure–activity relationships (QSARs) have long been used in the environmental sciences. More recently, molecular modeling and chemoinformatic methods have become widespread. These methods have the potential to expand and accelerate advances in environmental chemistry because they complement observational and experimental data with “in silico” results and analysis. The opportunities and challenges that arise at the intersection between statistical and theoretical in silico methods are most apparent in the context of properties that determine the environmental fate and effects of chemical contaminants (degradation rate constants, partition coefficients, toxicities, etc.). The main example of this is the calibration of QSARs using descriptor variable data calculated from molecular modeling, which can make QSARs more useful for predicting property data that are unavailable, but also can make them more powerful tools for diagnosis of fate determining pathways and mechanisms. Emerging opportunities for “in silico environmental chemical science” are to move beyond the calculation of specific chemical properties using statistical models and toward more fully in silico models, prediction of transformation pathways and products, incorporation of environmental factors into model predictions, integration of databases and predictive models into more comprehensive and efficient tools for exposure assessment, and extending the applicability of all the above from chemicals to biologicals and materials.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

On the Bayesian calibration of computer model mixtures through experimental data, and the design of predictive models

For many real systems, several computer models may exist with different physics and predictive abilities. To achieve more accurate simulations/predictions, it is desirable for these models to be properly combined and calibrated. Here, we propose the Bayesian calibration of computer model mixture method which relies on the idea of representing the real system output as a mixture of the available computer model outputs with unknown input dependent weight functions. The method builds a fully Bayesian predictive model as an emulator for the real system output by combining, weighting, and calibrating the available models in the Bayesian framework. Moreover, it fits a mixture of calibrated computer models that can be used by the domain scientist as a mean to combine the available computer models, in a flexible and principled manner, and perform reliable simulations. It can address realistic cases where one model may be more accurate than the others at different input values because the mixture weights, indicating the contribution of each model, are functions of the input. Inference on the calibration parameters can consider multiple computer models associated with different physics. The method does not require knowledge of the fidelity order of the models. We provide a technique able to mitigate the computational overhead due to the consideration of multiple computer models that is suitable to the mixture model framework. We implement the proposed method in a real-world application involving the Weather Research and Forecasting large-scale climate model.

97 MATHEMATICS AND COMPUTING↗