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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 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 OSTI2026

Computational Modeling of Atmospheric Processes at Texas Southern University

Texas Southern University (TSU) is strengthening its research program in atmospheric chemistry and physics with a climate science emphasis by leveraging partnerships with the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Facility, Brookhaven National Laboratory (BNL), and the Tracking Aerosol Convection Interactions ExpeRiment (TRACER). This RDPP-supported program focuses on secondary organic aerosols (SOAs) and reactive atmospheric species that influence cloud formation, precipitation processes, and radiative forcing. SOAs play a critical role in cloud microphysics and Earth’s energy balance, yet the chemical and physical mechanisms governing SOA–cloud interactions remain a significant source of uncertainty in predictive climate models. Through computational modeling, observational data analysis, and national laboratory collaboration, this program develops a skilled cohort of students trained in atmospheric science, environmental data analysis, and climate-relevant modeling. These research experiences build technical competencies that are transferable to careers in government laboratories, academia, and industry. By engaging students from historically underrepresented communities in high-impact climate research, TSU expands participation in the atmospheric sciences workforce while contributing meaningful scientific insights to DOE-supported ARM research activities. This partnership strengthens national capacity in climate science and supports the development of the next generation of atmospheric researchers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Overcoming the Challenges to Enhancing Experimental Plant Biology With Computational Modeling

The study of complex biological systems necessitates computational modeling approaches that are currently underutilized in plant biology. Many plant biologists have trouble identifying or adopting modeling methods to their research, particularly mechanistic mathematical modeling. Here we address challenges that limit the use of computational modeling methods, particularly mechanistic mathematical modeling. We divide computational modeling techniques into either pattern models (e.g., bioinformatics, machine learning, or morphology) or mechanistic mathematical models (e.g., biochemical reactions, biophysics, or population models), which both contribute to plant biology research at different scales to answer different research questions. We present arguments and recommendations for the increased adoption of modeling by plant biologists interested in incorporating more modeling into their research programs. As some researchers find math and quantitative methods to be an obstacle to modeling, we provide suggestions for easy-to-use tools for non-specialists and for collaboration with specialists. This may especially be the case for mechanistic mathematical modeling, and we spend some extra time discussing this. Through a more thorough appreciation and awareness of the power of different kinds of modeling in plant biology, we hope to facilitate interdisciplinary, transformative research.

58 GEOSCIENCES↗
DOE OSTI2022

Analyzing Stochastic Computer Models: A Review with Opportunities

In modern science, computer models are often used to understand complex phenomena and a thriving statistical community has grown around analyzing them. This review aims to bring a spotlight to the growing prevalence of stochastic computer models-providing a catalogue of statistical methods for practitioners, an introductory view for statisticians (whether familiar with deterministic computer models or not), and an emphasis on open questions of relevance to practitioners and statisticians. Gaussian process surrogate models take center stage in this review, and these, along with several extensions needed for stochastic settings, are explained. The basic issues of designing a stochastic computer experiment and calibrating a stochastic computer model are prominent in the discussion. Instructive examples, with data and code, are used to describe the implementation of, and results from, various methods.

agent based model↗
DOE OSTI2022

Computer Model Calibration with Time Series Data Using Deep Learning and Quantile Regression

Computer models play a key role in many scientific and engineering problems. One major source of uncertainty in computer model experiments is input parameter uncertainty. Computer model calibration is a formal statistical procedure to infer input parameters by combining information from model runs and observational data. The existing standard calibration framework suffers from inferential issues when the model output and observational data are high-dimensional dependent data, such as large time series, due to the difficulty in building an emulator and the nonidentifiability between effects from input parameters and data-model discrepancy. To overcome these challenges, we, in this work, propose a new calibration framework based on a deep neural network (DNN) with long short-term memory layers that directly emulates the inverse relationship between the model output and input parameters. Adopting the “learning with noise” idea, we train our DNN model to filter out the effects from data-model discrepancy on input parameter inference. We also formulate a new way to construct interval predictions for DNN using quantile regression to quantify the uncertainty in input parameter estimates. Through a simulation study and real data application with the Weather Research and Forecasting Model Hydrological modeling system (WRF-Hydro), we show our approach can yield accurate point estimates and well-calibrated interval estimates for input parameters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Comparing Emulation Methods for Computer Models with High Dimensional Output

This Master’s Capstone project will present a comparison of statistical models for computer simulation studies. The four models included in this comparison study were chosen for both their proven and diverse methodologies. We will present a case study on hurricane flood data in the Delaware bay which highlights the strengths and weaknesses of each model when applied to a very large spatial field. As computers have gotten faster, we have become interested in modeling increasingly large spatial fields in both size and resolution. Statistical algorithms that are able to efficiently handle these fields have never been more important. We therefore find this comparison to be extremely topical.

42 ENGINEERING↗
DOE OSTI2025

Including frameworks of public health ethics in computational modelling of infectious disease interventions

Decisions on public health interventions to control infectious diseases are often informed by computational models. Interpreting the predicted outcomes of a public health decision requires not only high-quality modelling but also an ethical framework for assessing the benefits and harms associated with different options. The design and specification of ethical frameworks matured independently of computational modelling, so many values recognized as important for ethical decision-making are missing from computational models. We demonstrate a proof-of-concept approach to incorporate multiple public health values into the evaluation of a simple computational model for vaccination against a pathogen such as SARS-CoV-2. By examining a bounded space of alternative prioritizations of three values relevant to public health ethics (aggregate clinical burden, equity in clinical burden, equity in adverse effects from vaccination), we identify value trade-offs, where the outcomes of optimal strategies differ depending on the ethical framework. This work demonstrates an approach to incorporating diverse values into decision criteria used to evaluate outcomes of models of infectious disease interventions.

"Mathematical Biology"↗
DOE OSTI2021

Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection

A key question in SARS-CoV-2 infection is why viral loads and patient outcomes vary dramatically across individuals. Because spatial-temporal dynamics of viral spread and immune response are challenging to study in vivo, we developed Spatial Immune Model of Coronavirus (SIMCoV), a scalable computational model that simulates hundreds of millions of lung cells, including respiratory epithelial cells and T cells. SIMCoV replicates viral growth dynamics observed in patients and shows how spatially dispersed infections can lead to increased viral loads. The model also shows how the timing and strength of the T cell response can affect viral persistence, oscillations, and control. By incorporating spatial interactions, SIMCoV provides a parsimonious explanation for the dramatically different viral load trajectories among patients by varying only the number of initial sites of infection and the magnitude and timing of the T cell immune response. When the branching airway structure of the lung is explicitly represented, we find that virus spreads faster than in a 2D layer of epithelial cells, but much more slowly than in an undifferentiated 3D grid or in a well-mixed differential equation model. These results illustrate how realistic, spatially explicit computational models can improve understanding of within-host dynamics of SARS-CoV-2 infection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Scaled Vecchia Approximation for Fast Computer-Model Emulation

Many scientific phenomena are studied using computer experiments consisting of multiple runs of a computer model while varying the input settings. Gaussian processes (GPs) are a popular tool for the analysis of computer experiments, enabling interpolation between input settings, but direct GP inference is computationally infeasible for large datasets. We adapt and extend a powerful class of GP methods from spatial statistics to enable the scalable analysis and emulation of large computer experiments. Specifically, we apply Vecchia’s ordered conditional approximation in a transformed input space, with each input scaled according to how strongly it relates to the computer-model response. The scaling is learned from the data by estimating parameters in the GP covariance function using Fisher scoring. Our methods are highly scalable, enabling estimation, joint prediction, and simulation in near-linear time in the number of model runs. In several numerical examples, our approach substantially outperformed existing methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

In-situ effect in cross-ply laminates under various loading conditions analyzed with hybrid macro/micro-scale computational models

In this article, multi-scale finite element analyses based on three-dimensional (3D) hybrid macro/micro-scale computational models subjected to various loading conditions are carried out to examine the in-situ effect imposed by the neighboring plies on the failure initiation and propagation of cross-ply laminates. A detailed comparative study on crack suppression mechanisms due to the effect of embedded laminar thickness and adjacent ply orientation is presented. Furthermore, we compare the results of in-situ transverse failure strain and strength between the computational models and analytical predictions. Good agreements are generally observed, indicating the constructed computational models are highly accurate to quantify the in-situ effect. Subsequently, empirical formulas for calculating the in-situ strengths as a function of embedded ply thickness and different ply angle between embedded and adjacent plies are developed, during which several material parameters are obtained using a reverse fitting method. Finally, a new set of failure criteria for σ 22 -τ 12 , σ 22 -τ 23 , and σ 11 -τ 12 accounting for the in-situ strengths are proposed to predict laminated composites failure under multi-axial stress states. This study demonstrates an effective and efficient computational technique towards the accurate prediction of the failure behaviors and strengths of cross-ply laminates by including the in-situ effects.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Generalized Bayesian MARS: Tools for Stochastic Computer Model Emulation

The multivariate adaptive regression spline (MARS) approach of Friedman and its Bayesian counterpart are effective approaches for the emulation of computer models. The traditional assumption of Gaussian errors limits the usefulness of MARS, and many popular alternatives, when dealing with stochastic computer models. Here, we propose a generalized Bayesian MARS (GBMARS) framework which admits the broad class of generalized hyperbolic distributions as the induced likelihood function. This allows us to develop tools for the emulation of stochastic simulators which are parsimonious, scalable, and interpretable and require minimal tuning, while providing powerful predictive and uncertainty quantification capabilities. GBMARS is capable of robust regression with t distributions, quantile regression with asymmetric Laplace distributions, and a general form of “Normal-Wald” regression in which the shape of the error distribution and the structure of the mean function are learned simultaneously. We demonstrate the effectiveness of GBMARS on various stochastic computer models, and we show that it compares favorably to several popular alternatives.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Nested active learning for efficient model contextualization and parameterization: pathway to generating simulated populations using multi-scale computational models

There is increasing interest in the use of mechanism-based multi-scale computational models (such as agent-based models (ABMs)) to generate simulated clinical populations in order to discover and evaluate potential diagnostic and therapeutic modalities. The description of the environment in which a biomedical simulation operates (model context) and parameterization of internal model rules (model content) requires the optimization of a large number of free parameters. In this work, we utilize a nested active learning (AL) workflow to efficiently parameterize and contextualize an ABM of systemic inflammation used to examine sepsis. Contextual parameter space was examined using four parameters external to the model’s rule set. The model’s internal parameterization, which represents gene expression and associated cellular behaviors, was explored through the augmentation or inhibition of signaling pathways for 12 signaling mediators associated with inflammation and wound healing. We have implemented a nested AL approach in which the clinically relevant (CR) model environment space for a given internal model parameterization is mapped using a small Artificial Neural Network (ANN). The outer AL level workflow is a larger ANN that uses AL to efficiently regress the volume and centroid location of the CR space given by a single internal parameterization. We have reduced the number of simulations required to efficiently map the CR parameter space of this model by approximately 99%. In addition, we have shown that more complex models with a larger number of variables may expect further improvements in efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

EQSIM—A multidisciplinary framework for fault-to-structure earthquake simulations on exascale computers part I: Computational models and workflow

Computational simulations have become central to the seismic analysis and design of major infrastructure over the past several decades. Most major structures are now “proof tested” virtually through representative simulations of earthquake-induced response. More recently, with the advancement of high-performance computing (HPC) platforms and the associated massively parallel computational ecosystems, simulation is beginning to play a role in increased understanding and prediction of ground motions for earthquake hazard assessments. However, the computational requirements for regional-scale geophysics-based ground motion simulations are extreme, which has restricted the frequency resolution of direct simulations and limited the ability to perform the large number of simulations required to numerically explore the problem parametric space. In this article, recent developments toward an integrated, multidisciplinary earth science-engineering computational framework for the regional-scale simulation of both ground motions and resulting structural response are described with a particular emphasis on advancing simulations to frequencies relevant to engineered systems. This multidisciplinary computational development is being carried out as part of the US Department of Energy (DOE) Exascale Computing Project with the goal of achieving a computational framework poised to exploit emerging DOE exaflop computer platforms scheduled for the 2022–2023 timeframe.

58 GEOSCIENCES↗
DOE OSTI2023

Development and Porting of Nuclear Reactor Computational Models for the NRIC Virtual Test Bed in FY23

The U.S. DOE’s National Reactor Innovation Center (NRIC) hosts an open-source website and associated GitHub repository called the Virtual Test Bed (VTB) on which computational models for advanced reactors are documented and shared with the reactor community. In FY23 under the NRIC program, computational models for nuclear reactor analysis were developed and contributed to the VTB Github repository during FY23 by contributors at Argonne National Laboratory. Other models developed outside of NRIC were also ported to the VTB as part of this work, serving a broader mission to make computational reactor analysis models more widely available to the reactor community. The model development activities focused on demonstration of gas-cooled microreactor models including computational fluid dynamics simulations of flow through an industry-inspired air jacket design, and simulation of multiphysics transients for a gas-cooled microreactor assembly. Additionally, development of a molten salt reactor (led at Idaho National Laboratory) was supported through Argonne’s expertise in multigroup cross sections generation. Models for these two reactor types were targeted due to their relevancy to NRIC’s Demonstration of Microreactor Experiments (DOME) and Laboratory for Operation and Testing in the U.S. (LOTUS) physical test beds, which are slated to host microreactor and molten salt reactor experiments. The model porting activities consisted of developing detailed documentation for several physics models which originate from the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. We include only contributions from Argonne National Laboratory in this report.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Co-Active Subspace Methods for the Joint Analysis of Adjacent Computer Models

Active subspace (AS) methods are a valuable tool for understanding the relationship between the inputs and outputs of a Physics simulation. In this article, an elegant generalization of the traditional ASM is developed to assess the co-activity of two computer models. This generalization, which we refer to as a Co-Active Subspace (Co-AS) Method, allows for the joint analysis of two or more computer models allowing for thorough exploration of the alignment (or non-alignment) of the respective gradient spaces. We define co-active directions, co-sensitivity indices, and a scalar “concordance” metric (and complementary “discordance” pseudo-metric) and we demonstrate that these are powerful tools for understanding the behavior of a class of computer models, especially when used to supplement traditional AS analysis. Details for efficient estimation of the Co-AS and an accompanying R package (concordance) are provided. Practical application is demonstrated through analyzing a set of simulated rate stick experiments for PBX 9501, a high explosive, offering insights into complex model dynamics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Uranium (VI) Adsorbate Structures on Portlandite [Ca(OH)2] Type Surfaces Determined by Computational Modelling and X-ray Absorption Spectroscopy

Portlandite [Ca(OH) 2 ] is a potentially dominant solid phase in the high pH fluids expected within the cementitious engineered barriers of Geological Disposal Facilities (GDF). This study combined X-ray Absorption Spectroscopy with computational modelling in order to provide atomic-scale data which improves our understanding of how a critically important radionuclide (U) will be adsorbed onto this phase under conditions relevant to a GDF environment. Such data are fundamental for predicting radionuclide mass transfer. Surface coordination chemistry and speciation of uranium with portlandite [Ca(OH) 2 ] under alkaline groundwater conditions (ca. pH 12) were determined by both in situ and ex situ grazing incidence extended X-ray absorption fine structure analysis (EXAFS) and by computational modelling at the atomic level. Free energies of sorption of aqueous uranyl hydroxides, [UO 2 (OH) n ] 2–n (n = 0–5) with the (001), (100) and (203) or (101) surfaces of portlandite are predicted from the potential of mean force using classical molecular umbrella sampling simulation methods and the structural interactions are further explored using fully periodic density functional theory computations. Although uranyl is predicted to only weakly adsorb to the (001) and (100) clean surfaces, there should be significantly stronger interactions with the (203/101) surface or at hydroxyl vacancies, both prevalent under groundwater conditions. The uranyl surface complex is typically found to include four equatorially coordinated hydroxyl ligands, forming an inner-sphere sorbate by direct interaction of a uranyl oxygen with surface calcium ions in both the (001) and (203/101) cases. In contrast, on the (100) surface, uranyl is sorbed with its axis more parallel to the surface plane. The EXAFS data are largely consistent with a surface structural layer or film similar to calcium uranate, but also show distinct uranyl characteristics, with the uranyl ion exhibiting the classic dioxygenyl oxygens at 1.8 Å and between four and five equatorial oxygen atoms at distances between 2.28 and 2.35 Å from the central U absorber. These experimental data are wholly consistent with the adsorbate configuration predicted by the computational models. These findings suggest that, under the strongly alkaline conditions of a cementitious backfill engineered barrier, there would be significant uptake of uranyl by portlandite to inhibit the mobility of U(VI) from the near field of a geological disposal facility.

36 MATERIALS SCIENCE↗