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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Calibration of RAFM Micromechanical Model for Creep Using Bayesian Optimization for Functional Output

A Bayesian optimization procedure is presented for calibrating a multimechanism micromechanical model for creep to experimental data of F82H steel. Reduced activation ferritic martensitic (RAFM) steels based on Fe(8–9)%Cr are the most promising candidates for some fusion reactor structures. Although there are indications that RAFM steel could be viable for fusion applications at temperatures up to 600°C, the maximum operating temperature will be determined by the creep properties of the structural material and the breeder material compatibility with the structural material. Due to the relative paucity of available creep data on F82H steel compared to other alloys such as Grade 91 steel, micromechanical models are sought for simulating creep based on relevant deformation mechanisms. As a point of departure, this work recalibrates a model form that was previously proposed for Grade 91 steel to match creep curves for F82H steel. Due to the large number of parameters (9) and cost of the nonlinear simulations, an automated approach for tuning the parameters is pursued using a recently developed Bayesian optimization for functional output (BOFO) framework (Huang et al., 2021, “Bayesian optimization of functional output in inverse problems,” Optim. Eng., 22, pp. 2553–2574). Incorporating extensions such as batch sequencing and weighted experimental load cases into BOFO, a reasonably small error between experimental and simulated creep curves at two load levels is achieved in a reasonable number of iterations. In conclusion, validation with an additional creep curve provides confidence in the fitted parameters obtained from the automated calibration procedure to describe the creep behavior of F82H steel.

42 ENGINEERING↗
DOE OSTI2025

Multiscale electro-chemo-mechanical model of high-capacity amorphous silicon anode material in sodium-ion batteries

The growing interest in sodium-ion batteries (SIBs) is fueled by their abundant resources and environmentally friendly nature, with amorphous silicon (a-Si) emerging as a promising anode material for enhancing capacity. However, the key challenge lies in sustaining reversible capacity during cycling. Here, in this work, we developed a multiscale electrochemical model incorporating an a-Si anode to elucidate the performance parameters of SIBs. Additionally, we integrated an electro-chemo-mechanical model at the particle level to capture stress generation, an essential factor in the degradation of high-capacity electrodes. Unlike existing models, our approach accounts for large-deformation chemo-mechanics at the particle scale and includes simulations under varying charge rates to explore multiscale behavior. The results reveal that coupled sodiation significantly prolongs complete cycling times and influences discharge dynamics, indicating that neglecting this coupling leads to an underestimation of actual capacity. Furthermore, we observed pronounced polarization effects at higher charge rates, resulting in heterogeneous stress distributions across the anode. With the identification of critical failure parameters for both active particles and binder materials, offering novel insights for mitigating degradation in high-capacity electrode systems.

Amorphous silicon anode↗
DOE OSTI2025

Machine-Learning-Based Multiscale Methods for 3D Modelling of Granular Materials by Incorporating History-Dependent State Variables

Over the past decades, the prevalence of machine learning (ML) methods has made the development of ML-based constitutive models for granular materials undoubtedly a popular subject. Numerous studies have been made to feature the loading path or history-dependent stress-strain response of granular media using neural networks. In this work, a novel finite element method (FEM)–ML multiscale approach was developed by incorporating internal variables to improve the simulation accuracy of 3D history-dependent granular materials for the first time. To this end, a surrogate constitutive model based on the single-step-based multi-layer perceptron (MLP) neural network was used to replace representative volume element (RVE) simulations conducted by the discrete element method (DEM) in the multiscale FEM–DEM approach. Although the prediction principle of the MLP aligns with the FEM algorithm, artificially added internal variables are required to differentiate the loading history. To address this issue, history variables associated with the Frobenius norm are proposed to be fed into the MLP coupled with the strain tensor to extract the history-dependent behaviour of granular assemblies. The developed FEM–ML approach was demonstrated in 3D conventional triaxial compression (CTC) simulations. Compared to the multiscale FEM–DEM approach, the proposed FEM–ML method exhibits a significantly improved computational efficiency.

granular materials↗
NASA NTRS2016

Enhanced Representation of Soil NO Emissions in the Community Multiscale Air Quality (CMAQ) Model Version 5.0.2

Modeling of soil nitric oxide (NO) emissions is highly uncertain and may misrepresent its spatial and temporal distribution. This study builds upon a recently introduced parameterization to improve the timing and spatial distribution of soil NO emission estimates in the Community Multiscale Air Quality (CMAQ) model. The parameterization considers soil parameters, meteorology, land use, and mineral nitrogen (N) availability to estimate NO emissions. We incorporate daily year-specific fertilizer data from the Environmental Policy Integrated Climate (EPIC) agricultural model to replace the annual generic data of the initial parameterization, and use a 12km resolution soil biome map over the continental USA. CMAQ modeling for July 2011 shows slight differences in model performance in simulating fine particulate matter and ozone from Interagency Monitoring of Protected Visual Environments (IMPROVE) and Clean Air Status and Trends Network (CASTNET) sites and NO2 columns from Ozone Monitoring Instrument (OMI) satellite retrievals. We also simulate how the change in soil NO emissions scheme affects the expected O3 response to projected emissions reductions.

remote sensing↗
DOE OSTI2025

Advancing Organized Convection Representation in the Unified Model: Implementing and Enhancing Multiscale Coherent Structure Parameterization

To address the effect of stratiform latent heating on meso- to large-scale circulations, an enhanced implementation of the Multiscale Coherent Structure Parameterization (MCSP) is developed for the Met Office Unified Model. MCSP represents the top-heavy stratiform latent heating from under-resolved organized convection in general circulation models. We couple the MCSP with a mass-flux convection scheme (CoMorph-A) to improve storm lifecycle continuity. The improved MCSP trigger is specifically designed for mixed-phase deep convective cloud, combined with a background vertical wind shear, both known to be crucial for stratiform development. We also test a cloud top temperature dependent convective-stratiform heating partitioning, in contrast to the earlier fixed partitioning. Assessments from ensemble weather forecasts and decadal simulations demonstrate that MCSP directly reduces cloud deepening and precipitation areas by moderating mesoscale circulations. Indirectly, it amends tropical precipitation biases, notably correcting dry and wet biases over India and the Indian Ocean, respectively. Remarkably, the scheme outperforms a climate model ensemble by improving seasonal precipitation cycle predictions in these regions. The scheme also improves Madden-Julian Oscillation (MJO) spectra, achieving better alignment with observational and reanalysis data by intensifying the simulated MJO over the Indian Ocean during phases 4 to 5. However, the scheme increases precipitation overestimation over the Western Pacific. Shifting from fixed to temperature-dependent convective-stratiform partitioning reduces the Pacific precipitation overestimation and further improves the seasonal cycle in India. Spatially correlated biases highlight the necessity for advances beyond deterministic approaches to align MCSP with environmental conditions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

TPS Certification by Analysis: Model-Driven Characterization of Properties and Failure in Woven Thermal Protection Systems

Woven, ablative thermal protection system (TPS) materials provide a robust option for aggressive (re)entries and thus have been baselined for the upcoming Mars Sample Return (MSR) mission’s Earth Entry System (EES). The reliability requirements for MSR-EES necessitate understanding of material property variability, which could be significant given the complex structure and anisotropic nature of properties in TPS weaves, as well as the response to potential impact with micrometeoroids or orbital debris during the EES re-entry. The TPS Certification by Analysis effort within the Entry Systems Modeling project seeks to provide computational models and analyses that support the certification against such material-based risks. For the present talk, focus will be given to the characterization of baseline woven TPS material properties and mechanical failure limits, which entails (1) use of computational techniques (e.g., machine learning) to interpret computed tomography images of the weave to generate representative structural models and (2) application of multiscale material modeling approaches to characterize thermomechanical and failure properties.

Justin B. Haskins↗
DOE OSTI2021

A review of microwave-assisted process intensified multiphase reactors

Microwaves provide alternative heating and allow process intensification due to their rapid, volumetric, and selective nature. Recognizing the central role of multiphase reactors in chemical industry, a recent surge in employing microwaves is observed. We review the recent experimental and modeling investigations of microwave heating of multiphase reactors with emphasis on chemical engineering applications. Here we demonstrate that there is accumulated evidence for improved performance via microwave heating and a clear opportunity for further process intensification. In most of the cases, this improved performance stems from a temperature gradient between two phases. We discuss the ongoing debate on the mechanism by which microwaves affect chemical processes exacerbated by the inability of measuring the temperature distribution in a microwave cavity. We outline recent progress in this direction in monolith reactors and needs for future work. We underscore the lack of detailed modeling and simulation tools even for single-phase systems and emphasize the imperative for multiscale predictive modeling to bridge the experimental-modeling gap. Promising results are shown by a few recently published modeling studies that can predict the experimental measurements in complex multiphase reactors. A combination of experimental and modeling tools can provide a comprehensive picture of the microwave multiphase reactors as well as a means toward scale-up and optimization.

42 ENGINEERING↗
NASA NTRS2009

Hydrogen Cyanide in the Upper Troposphere: GEM-AQ Simulation and Comparison with ACE-FTS Observations

We investigate the spatial and temporal distribution of hydrogen cyanide (HCN) in the upper troposphere through numerical simulations and comparison with observations from a space-based instrument. To perform the simulations, we used the Global Environmental Multiscale Air Quality model (GEM-AQ), which is based on the threedimensional Gobal multiscale model developed by the Meteorological Service of Canada for operational weather forecasting. The model was run for the period 2004-2006 on a 1.5deg x 1.5deg global grid with 28 hybrid vertical levels from the surface up to 10 hPa. Objective analysis data from the Canadian Meteorological Centre were used to update the meteorological fields every 24 h. Fire emission fluxes of gas species were generated by using year-specific inventories of carbon emissions with 8-day temporal resolution from the Global Fire Emission Database (GFED) version 2. The model output is compared with HCN profiles measured by the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) instrument onboard the Canadian SCISAT-1 satellite. High values of up to a few ppbv are observed in the tropics in the Southern Hemisphere; the enhancement in HCN volume mixing ratios in the upper troposphere is most prominent in October. Low upper-tropospheric mixing ratios of less than 100 pptv are mostly recorded at middle and high latitudes in the Southern Hemisphere in May-July. Mixing ratios in Northern Hemisphere peak in the boreal summer. The amplitude of the seasonal variation is less pronounced than in the Southern Hemisphere. The comparison with the satellite data shows that in the upper troposphere GEM-AQ perform7s well globally for all seasons, except at northern hi gh and middle latitudes in surnmer, where the model has a large negative bias, and in the tropics in winter and spring, where it exhibits large positive bias. This may reflect inaccurate emissions or possible inaccuracies in the emission profile. The model is able to explain most of the observed variability in the upper troposphere HCN field, includin g the interannual variations in the observed mixing ratio. A complementary comparison with daily total columns of HCN from two middle latitude ground-based stations in Northern Japan for the same simulation period shows that the model captures the observed seasonal variation and also points to an underestimation of model emissions in the Northern Hemisphere in the summer. The estimated average global emission equals 1.3 Tg N/yr. The average atmospheric burden is 0.53 Tg N, and the corresponding lifetime is 4.9 months.

Lupu, A.↗
DOE OSTI2026

Simulations of grain growth in tungsten armor materials under ARC plasma edge operation conditions using an integrated plasma-edge/materials model

An integrated model of grain growth deuterium-exposed tungsten polycrystals, consisting of a two-dimensional vertex dynamics model fitted to atomistic data, has been developed to assess the grain growth kinetics of deuterium-exposed polycrystalline tungsten (W). The model tracks the motion of grain boundaries under the effect of driving forces stemming from grain boundary curvature and differential deuterium concentration accumulation. Here, we apply the model to experimentally synthesized tungsten polycrystals under deuterium-saturated conditions relevant to the ARC concept design. The results indicate rapid grain growth kinetics in the near-surface region adjacent to the plasma, where the temperature reaches 1400 K, whereas the microstructure remains stable deeper in the material with the lower temperature of 1000 K. The combined modeling and analysis further reveal that monolithic tungsten produced via conventional fabrication routes is highly susceptible to grain coarsening at temperatures exceeding 1000 K, largely independent of the magnitude of the applied driving force. Moreover, the accumulation of deuterium near grain boundaries has a pronounced inhibitory effect on grain boundary migration. High-angle grain boundaries ( > 50°) contribute more significantly to the overall grain growth process.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Seamlessly joining length scales: From atomistic thermal graphs to anisotropic continuum conductivity

Thermal transport in complex solids is governed by local structure, defects, and anisotropy, yet most continuum models still rely on oversimplified and homogenized conductivities. Here, we bridge atomistic and continuum descriptions by building finite element (FE) models directly from the site-projected thermal conductivity (SPTC), an atomic-level decomposition of the Green–Kubo thermal conductivity. We introduce a toolkit, the “Simulator Collection for Atomic-to-Continuum Scales (SCACS)”, which uses a graph neural network to predict SPTC on large atomic structures, coarse-grains these fields into anisotropic conductivity tensors, and embeds them into the heat-flow FE equation with a customized, anisotropy-aware adaptive mesh refinement scheme. Applied to silicon nanostructures, the resulting FE models act as representative volume elements, reproduce bulk conductivities, and capture interfacial and defect-driven anisotropy while maintaining thermodynamic consistency. Additionally, SCACS predicts experimental conductance trends and fields. This work demonstrates a general route for transferring atomistic transport information into device-scale thermal simulations with physics-based approximations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

A Stochastic Reduced-Order Model for Statistical Microstructure Descriptors Evolution

Integrated computational materials engineering (ICME) models have been a crucial building block for modern materials development, relieving heavy reliance on experiments and significantly accelerating the materials design process. However, ICME models are also computationally expensive, particularly with respect to time integration for dynamics, which hinders the ability to study statistical ensembles and thermodynamic properties of large systems for long time scales. To alleviate the computational bottleneck, we propose to model the evolution of statistical microstructure descriptors as a continuous-time stochastic process using a non-linear Langevin equation, where the probability density function (PDF) of the statistical microstructure descriptors, which are also the quantities of interests (QoIs), is modeled by the Fokker–Planck equation. In this work, we discuss how to calibrate the drift and diffusion terms of the Fokker–Planck equation from the theoretical and computational perspectives. The calibrated Fokker–Planck equation can be used as a stochastic reduced-order model to simulate the microstructure evolution of statistical microstructure descriptors PDF. Considering statistical microstructure descriptors in the microstructure evolution as QoIs, we demonstrate our proposed methodology in three integrated computational materials engineering (ICME) models: kinetic Monte Carlo, phase field, and molecular dynamics simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Model America - Arizona extract from ORNL's AutoBEM v1.1

Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM).Two sets of sample data are provided for 2,555,152 buildings located within the boundary of Arizona in the United States:Data (846.3MB *.csv) - minimalist list of each building (rows) for the following fields (columns) • ID - unique building ID • Centroid - building center location in latitude/longitude (from Footprint2D) • Footprint2D - building polygon of 2D footprint (lat1/lon1_lat2/lon2_...) • State_abbr - state name • Area - estimate of total conditioned floor area (ft2) • Area2D - footprint area (ft2) • Height - building height (ft) • NumFloors - number of floors (above-grade) • WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings) • CZ - ASHRAE Climate Zone designation • BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards • Standard - building vintage • Sample Models (114GB*.zip by county) - OpenStudio and EnergyPlus building energy models named according to IDThis data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), and Biological and Environmental Research (BER).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Use of Remote Sensing and In-situ Observations to Develop and Evaluate Improved Representations of Convection and Clouds for the Accelerated Climate Model for Energy. Final report

Representation of unresolved cloud processes has been one of the most challenging problems in numerical modeling of the atmosphere. During the last decades, considerable efforts have been spent to better represent the collective effects of those processes in terms of resolved scales (see Arakawa 2004 for a review). Still, the progress on this problem has been unacceptably slow as pointed out by Randall et al. (2003). The large part of the difficulty is inherent in the artificial separation of atmospheric processes into the explicitly simulated resolved scales and the highly parameterized subgrid scales. Arakawa (2004) and Arakawa et al. (2011) suggested two plausible approaches to eliminate the problem of artificial separation of atmospheric processes: “parameterize everything” and “resolve everything”.

58 GEOSCIENCES↗
NASA NTRS2021

Mineral Dust Cycle in the Multiscale Online Nonhydrostatic AtmospheRe CHemistry Model (Monarch) Version 2.0

We present the dust module in the Multiscale Online Non-hydrostatic AtmospheRe CHemistry model (MONARCH) version 2.0, a chemical weather prediction system that can be used for regional and global modeling at a range of resolutions. The representations of dust processes in MONARCH were upgraded with a focus on dust emission (emission parameterizations, entrainment thresholds, considerations of soil moisture and surface cover), lower boundary conditions (roughness, potential dust sources), and dust–radiation interactions. MONARCH now allows modeling of global and regional mineral dust cycles using fundamentally different paradigms, ranging from strongly simplified to physics-based parameterizations. We present a detailed description of these updates along with four global benchmark simulations, which use conceptually different dust emission parameterizations, and we evaluate the simulations against observations of dust optical depth. We determine key dust parameters, such as global annual emission/deposition flux, dust loading, dust optical depth, mass-extinction efficiency, single-scattering albedo, and direct radiative effects. For dust-particle diameters up to 20 µm, the total annual dust emission and deposition fluxes obtained with our four experiments range between about 3500 and 6000 Tg, which largely depend upon differences in the emitted size distribution. Considering ellipsoidal particle shapes and dust refractive indices that account for size-resolved mineralogy, we estimate the global total (longwave and shortwave) dust direct radiative effect (DRE) at the surface to range between about −0.90 and −0.63 W m−2 and at the top of the atmosphere between −0.20 and −0.28 W m−2. Our evaluation demonstrates that MONARCH is able to reproduce key features of the spatiotemporal variability of the global dust cycle with important and insightful differences between the different configurations.

Dust cycle↗
DOE OSTI2025

Integrating Ultra-Coarse-Grained Protein Models into Accessible Workflows for Multiscale Molecular Dynamics

To capture protein conformational transitions using molecular dynamics (MD), several simulation resolutions covering different spatial and temporal scales are typically needed. All-atom (AA) simulations provide fine resolution, but are computationally infeasible for large systems over longer durations. Coarse-grained (CG) and ultra-coarse-grained (UCG) models have a lower resolution and computational cost while still being able to conserve essential protein features. Prior work on a Multiscale Machinelearned Modeling Infrastructure (MuMMI) combined both AA and CG simulations to study RAS-RAF protein interactions, leveraging CG models for longer time scales and using AA to investigate unusual conformations in greater detail. However, MuMMI is still resource-intensive, and this study aims to maximize exploration of the protein conformational space while reducing computational cost. In this paper, we build on prior work that integrates UCG models based on heterogeneous elastic network modeling (hENM) into the MuMMI workflow. We demonstrate that UCG models enable accurate sampling of protein conformations, focusing on simulating RAS-RAF protein interactions. Using higher-resolution CG Martini simulation data, we can automatically refine intramolecular interactions in UCG models. We present a scalable Python package that uses fluctuations observed in higher-resolution CG Martini simulations to estimate bond coefficients of the UCG model. We built novel machine learning-based backmapping methods to recover more detailed CG Martini structures from UCG structures, using diffusion models to learn the mapping between scales. Finally, we present UCG-mini-MuMMI, an accessible and less compute-intensive version of MuMMI as a resource for the scientific community. Incorporating UCG models into MD studies is applicable to a broad range of systems and proteins, and our study offers insights into the advantages and limitations of these methods.

Chemical structure↗
DOE OSTI2021

A Novel Multiphysics Multiscale Multiporosity Shale Gas Transport Model for Geomechanics/Flow Coupling in Steady and Transient States

Summary A novel multiphysics multiscale multiporosity shale gas transport (M3ST) model was developed to investigate shale gas transport in both transient and steady states. The microscale model component contains a kerogen domain and an inorganic matrix domain, and each domain has its own geomechanical and gas transport properties. Permeabilities of various shale cores were measured in the laboratory using a pulse decay permeameter (PDP) with different pore pressure and confining stress combinations. The PDP-measured apparent permeability as a function of pore pressure under two effective stresses was fitted using the microscale M3ST model component based on nonlinear least squares fitting (NLSF), and the fitted model parameters were able to provide accurate model predictions for another effective stress. The parameters and petrophysical properties determined in the steady state were then used in the transient-state, continuum-scale M3ST model component, which performed history matching of the evolutions of the upstream and downstream gas pressures. In addition, a double-exponential empirical model was developed as a powerful alternative to the M3ST model to fit laboratory-measured apparent permeability under various effective stresses and pore pressures. The developed M3ST model and the research findings in this study provided critical insights into the role of the multiphysics mechanisms, including geomechanics, fluid dynamics and transport, and the Klinkenberg effect on shale gas transport across different spatial scales in both steady and transient states.

Engineering↗
DOE OSTI2026

Bemnifosbuvir: An HCV NS5B Inhibitor With Multiple Modes of Action

Bemnifosbuvir (BEM) is a potent, pan-genotypic inhibitor targeting the hepatitis C virus (HCV) NS5B polymerase. Its antiviral activity was evaluated in an ascending dose phase I clinical trial involving 30 patients treated once a day for 7 days. After treatment initiation, plasma HCV RNA declined in a biphasic manner with a mean reduction of 2.3 log 10 IU/mL after 24 hours and 4.4 log10 IU/mL by day 7 for the highest dose. Alanine aminotransferase (ALT) also normalized in most patients. We employed a multiscale mathematical model fitted to the HCV RNA and ALT dynamics to quantify the antiviral activity and evaluate the modes of action of BEM. We found that models in which BEM only acted as a typical HCV RNA polymerase inhibitor and reduced the intracellular production of HCV RNA did not fit the data as well as models in which BEM had multiple modes of action, including suppressing viral assembly and secretion and enhancing intracellular HCV RNA degradation. BEM's effectiveness in inhibiting intracellular HCV RNA production increased with dose (150 mg/day: 88.2%, 300 mg/day: 98.8%, 600 mg/day: 99.5%), while inhibition of viral assembly and release was ~95% effective regardless of dose. We observed a dose-dependent enhancement in the degradation of intracellular HCV RNA, with degradation rates 1.5-fold higher in patients receiving 300 mg/day and 2.7-fold higher in those receiving 600 mg/day than in patients receiving 150 mg/day. No significant differences in antiviral activity were detected between HCV genotypes 1b and 3 or between patients with and without compensated cirrhosis.

59 BASIC BIOLOGICAL SCIENCES↗