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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A state-of-the-art review of experimental and computational studies of granular materials: Properties, advances, challenges, and future directions

Modeling of heterogeneous materials and media is a problem of fundamental importance to a wide class of phenomena and systems, ranging from condensed matter physics, soft materials, and composite media to porous media, biological systems, geosystems, ceramic engineering, pharmaceutical science and even in space discoveries. Among the most important materials are granular systems, which have received intense interest from the engineering, physics, and mathematics communities. In this review paper, the recent developments and new advances in experimental, and computational methods on a variety of scales and physics that extend understanding to a wide range of materials and phenomena are reviewed. Experimental advances include computed neutron and nanometer-scale tomography, magnetic resonance imaging, refractive index matching, digital image correlation, acoustic emission analysis, and the most recent 4D techniques. Furthermore, a tremendous shift has occurred from the continuum scale to micro-scale and developing multiscale approaches. As such, various computational methods, including, constitutive modeling, discrete modeling, and multiscale approaches, have been developed. In conclusion, aside from all these evolutions, more complicated modeling called coupled, or multiphysics, systems representing a simultaneous presence of heat, fluid, chemical variation, and mechanical effect are also explored.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Development of multi-scale computational frameworks to solve fusion materials science challenges

Over the past two decades, the US-DOE has funded multiple projects that rely on high-performance computing and exascale computing platforms to accelerate scientific discoveries and address grand scientific challenges, such as harnessing fusion energy. In this article, we review in detail one of these efforts aimed at enhancing our capability to model plasma-facing materials subject to plasma and high-energy ion/neutron irradiation. The plasma surface interactions project has built a multi-scale modeling framework where many of the plasma- and high-energy ion/neutron irradiation-induced effects occurring in tungsten are explored. Here, this knowledge is used to develop atomistically-informed, high-fidelity continuum and meso-scale models that can be validated against experiments. We review the developments within this project, with attention to experimental validation efforts, and specifically highlight activities associated with: helium bubble bursting and equation of state, and hydrogen-helium interactions in tungsten; atomistically-informed model development for beryllium-tungsten material mixing; coupling of scrape-of-layer plasma, sheath and material models; and coupling of stochastic cluster-dynamics and crystal plasticity models to address radiation effects in tungsten under stress. Finally, we present how the project is preparing for future computational architectures, for instance through efforts to adapt atomistic methods to exascale computing.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Impact of Tropical and Extratropical Cyclones on Future U.S. Offshore Wind Energy

Over 60 participants, including government officials, regulators, certification bodies, national laboratory researchers, academia, and industry representatives, gathered in person twice for a comprehensive discussion on the impacts of extreme weather on large-scale deployment of offshore wind energy for the U.S. The dialogue focused on addressing modeling challenges, the need for detailed observational data, refining risk assessment methodologies, and understanding the implications of climate change.

14 SOLAR ENERGY↗
DOE OSTI2024

Study of impacts of two types of cellular aging on the yeast bud morphogenesis

Understanding the mechanisms of the cellular aging processes is crucial for attempting to extend organismal lifespan and for studying age-related degenerative diseases. Yeast cells divide through budding, providing a classical biological model for studying cellular aging. With their powerful genetics, relatively short cell cycle, and well-established signaling pathways also found in animals, yeast cells offer valuable insights into the aging process. Recent experiments suggested the existence of two aging modes in yeast characterized by nucleolar and mitochondrial declines, respectively. By analyzing experimental data, this study shows that cells evolving into those two aging modes behave differently when they are young. While buds grow linearly in both modes, cells that consistently generate spherical buds throughout their lifespan demonstrate greater efficacy in controlling bud size and growth rate at young ages. A three-dimensional multiscale chemical-mechanical model was developed and used to suggest and test hypothesized impacts of aging on bud morphogenesis. Experimentally calibrated model simulations showed that during the early stage of budding, tubular bud shape in one aging mode could be generated by locally inserting new materials at the bud tip, a process guided by the polarized Cdc42 signal. Furthermore, the aspect ratio of the tubular bud could be stabilized during the late stage as observed in experiments in this work. The model simulation results suggest that the localization of new cell surface material insertion, regulated by chemical signal polarization, could be weakened due to cellular aging in yeast and other cell types, leading to the change and stabilization of the bud aspect ratio.

Tsai, Kevin↗
DOE OSTI2025

Simulating Pyrocumulonimbus Clouds Using a Multiscale Wildfire Simulation Framework

Pyrocumulonimbus (pyroCb) clouds, driven by extreme fires under favorable meteorological conditions, can inject smoke into the stratosphere at magnitudes comparable to those of moderate volcanic eruptions, potentially altering the global radiative balance and atmospheric composition. However, simulating pyroCb is particularly challenging in Earth system models. Using the Energy Exascale Earth System Model (E3SM), we developed a novel global multiscale framework to model pyroCb events in California, which includes a high‐resolution fire radiative power time series, a one‐dimensional plume‐rise parameterization, a fire‐induced vertical water vapor transport scheme, and a surface wildfire sensible heat flux representation. Our simulation successfully reproduces many pyroCb features, including cloud height, spatiotemporal evolution, and convective intensity in comparison with satellite and ground‐based observations. Sensitivity experiments show that realistic pyroCb simulation depends on vertical water vapor transport. These advances provide a basis for future exploration of pyroCb impacts at regional and global scales within climate models.

E3SM↗
DOE OSTI2023

From Femtoseconds to Gigaseconds: The SolDeg Project to Analyze and Mitigate the Performance Degradation of a-Si:H/c-Si Heterojunction Solar Cells

We pursued two major projects. In the first, we studied the degradation of a-Si/c-Si Heterojunction cells. We substantially expanded our previously developed SolDeg platform, and incorporated hydrogen into it. We also expanded the capabilities of SolDeg by developing a three channel model for a very efficient way to determine long term junction dynamics. The first main result was that we identified hydrogen drifting away from the interface as a key degradation channel. The second was that we developed and proposed the concept of a “reverse silicon density gradient” in the a-Si layer that had the promise of reducing degradation by up to 80%. In the second project, we studied the degradation of TOPCon solar cells. We again identified a key degradation channel, which in these cells was the formation of pinholes across the oxide layer. We then again were able to develop and to propose a way to dramatically reduce degradation by discovering that tuning the hydrogen concentration forms a sharp and deep minimum of the recombination current.

14 SOLAR ENERGY↗
DOE OSTI2022

Woven ceramic matrix composite surrogate model based on physics-informed recurrent neural network

A recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear constitutive behavior of woven ceramic matrix composites (CMCs) driven by matrix damage at multiple length scales. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the multiscale generalized method of cells (MSGMC) approach coupled with a matrix damage model. This coupling permits simulating the nonlinear behavior of woven CMCs based on constituent response at the micro-, meso-, and macroscales. The multiscale repeating unit cell is loaded under non-monotonic conditions including multiple load / unload cycles and tension / compression. The fiber volume fraction as well as the intra- and intertow void volume fractions are also varied in the generation of training data. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input strain sequence and fiber and void volume fractions, the resulting stress versus strain response while satisfying physical constraints such as positive semi-definiteness of the tangent stiffness matrix and linear elastic unloading. Further, the trained surrogate model effectively matches the stress versus strain response and successfully predicts the tangent modulus throughout the loading regime. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex architectures, nonlinear multiaxial material response, and under non-monotonic loading conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Impact of wildfires on regional ozone and PM 2.5 : Considering the light absorption of Brown carbon

The influence of wildfire brown carbon (BrC) in moderating the formation of O 3 and fine particulate matter (PM 2.5 ) due to its absorption of ultraviolet (UV) radiation is, for the first time, investigated using a modified community multiscale air quality (CMAQ) model. The wavelength-dependent imaginary refractive index of the organic carbon from wildfires, which are needed by the CMAQ model for its inline photolysis rate calculation, are generated experimentally from wood burning aerosols from a combustion chamber. On high emission days of the Bastrop County Complex fire in Texas in early September 2011, BrC UV absorption reduces the daytime average NO 2 photolysis rate by up to 38% in the core region of the wildfire plume compared to the base scenario that does not consider BrC absorption. Consequently, O 3 production in the smoke plume is suppressed compared to the base scenario due to reduced HO x radical concentrations. Further, in the core region, the predicted O 3 increase due to wildfire reaches as high as 47–123 ppb without considering BrC absorption, the predicted increase of O 3 is 5–15% lower when BrC absorption is considered. Similarly, considering the BrC UV absorption leads to approximately 1% (or ~2–3 µgm -3 ) lower estimation of the wildfire emissions’ impact on total PM 2.5 . This change is small because secondary aerosols, which are the components affected by BrC absorption, only account for a small fraction of the total PM 2.5 in wildfire impacted regions in this study. In addition, our study shows that assumptions about aerosol mixing state (core-shell vs. homogeneous) in the inline photolysis rate calculation would not significantly affect out assessment of the impact of wildfire BrC light absorption on O 3 and PM 2.5 .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Persistent high PM 2.5 pollution driven by unfavorable meteorological conditions during the COVID-19 lockdown period in the Beijing-Tianjin-Hebei region, China

Lockdown measures to curtail the COVID-19 pandemic in China halted most nonessential activities on January 23, 2020. Despite significant reductions in anthropogenic emissions, the Beijing-Tianjin-Hebei (BTH) region still experienced high air pollution concentrations. In this work, employing two emissions reduction scenarios, the Community Multiscale Air Quality (CMAQ) model was used to investigate the PM 2.5 concentrations change in this region. The model using the scenario (C3) with greater traffic reductions performed better compared to the observed PM 2.5 . Compared with the no reductions base-case (scenario C1), PM 2.5 reductions with scenario C3 were 2.70, 2.53, 2.90, 2.98, 3.30, 2.81, 2.82, 2.98, 2.68, and 2.83 µg/m 3 in Beijing, Tianjin, Shijiazhuang, Baoding, Cangzhou, Chengde, Handan, Hengshui, Tangshan, and Xingtai, respectively. During high-pollution days in scenario C3, the percentage reductions in PM 2.5 concentrations in Beijing, Tianjin, Shijiazhuang, Baoding, Cangzhou, Chengde, Handan, Hengshui, Tangshan, and Xingtai were 3.76, 3.54, 3.28, 3.22, 3.57, 3.56, 3.47, 6.10, 3.61, and 3.67%, respectively. However, significant increases caused by unfavorable meteorological conditions counteracted the emissions reduction effects resulting in high air pollution in BTH region during the lockdown period. This study shows that effective air pollution control strategies incorporating these results are urgently required in BTH to avoid severe pollution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Extraction of Furfural and Furfural/5-Hydroxymethylfurfural from Mixed Lignocellulosic Biomass-Derived Feedstocks

We present a combined in silico and experimental study on the extraction of furfural and 5-hydroxymethylfurfural (HMF) in aqueous–organic biphasic systems. We predict the liquid–liquid equilibria and furfural/HMF partition coefficients of over 2200 water-organic biphasic systems using the multiscale COSMO-RS model and measure experimentally single-component (furfural) and mixture (furfural and HMF) partition coefficients at room and dehydration reaction-relevant temperatures in 28 solvents. We find the experimental data to be within a factor of 2 from the COSMO-RS predictions. Even though furfural and HMF have chemical similarity, the slight differences in molecular structure render the separation of furfural easier by the supply of more solvents of higher partition coefficient for extraction. We leverage this molecular difference and experimentally demonstrate that with an additional extraction step, using dichloromethane or toluene, we can selectively extract furfural from furfural-HMF mixtures, which can coexist in lignocellulosic biomass dehydration products, despite their partition coefficients being generally correlated. We complement solvent selection criteria for biphasic lignocellulosic biomass processes with a simple mass balance extraction model for determining volume ratios in multistage extraction. Lastly, the molecular nature of the preferential furfural extraction is rationalized using COSMO-RS σ-profile analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Morphodynamics of dendrite growth in alumina based all solid-state sodium metal batteries

All solid-state batteries (ASSBs) with ceramic electrolytes and alkali metal anodes are a potential future energy storage technology for vehicle electrification and smart grids. However, uncontrollable dendrite growth toward ultimate short circuiting in solid electrolytes (SEs) has become a serious concern in the design of long-cycle, safe ASSBs, and the underlying mechanism has remained unclear. Here through multiscale imaging and morphodynamic tracking we show that Na dendrites grow in β''-Al 2 O 3 SEs through an alternating sequence of Na deposition and crack propagation. Atomic-scale imaging evidenced that electrochemical cycling causes massive delamination cracking along the Na + conduction planes, accompanied by the closure of neighboring conduction channels. In situ SEM observations revealed a dynamic interplay between Na deposition and crack propagation: Na deposition accumulates mechanical stress that induces cracking; cracking releases the local stress, which promotes further Na deposition. Thus, Na deposition and cracking alternatingly proceed until short circuits take place. A multiscale phase-field model is developed to recapitulate the morphodynamics of Na dendrite growth, predicting the tree-like fractal morphology of the growing dendrites. Importantly, our findings suggest that decoupling between Na deposition and cracking represents an important route to mitigate uncontrollable dendrite growth in ASSBs.

25 ENERGY STORAGE↗
DOE OSTI2022

Theoretical and numerical studies of inverse source problem for the linear parabolic equation with sparse boundary measurements

We consider the inverse source problem in the parabolic equation, where the unknown source possesses the semi-discrete formulation. Theoretically, we prove that the flux data from any nonempty open subset of the boundary can uniquely determine the semi-discrete source. This means the observed area can be extremely small, and that is the reason we call it sparse boundary data. For the numerical reconstruction, we formulate the problem from the Bayesian sequential prediction perspective and conduct the numerical examples which estimate the space-time-dependent source state by state. To better demonstrate the method’s performance, we solve two common multiscale problems from two models with a long source sequence. The numerical results illustrate that the inversion is accurate and efficient.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Enhanced Planar Antenna Efficiency Through Magnetic Thin-Films

This work proposes to use magnetic material as the substrate of planar antennas to overcome the platform effect caused by the conducting ground plane. The upper bound of the radiation efficiency of an electric-current-driven low-profile antenna is theoretically derived, which is inversely proportional to the Gilbert damping factor of the magnetic material. Meanwhile, the improvement of radiation due to the use of magnetic material is demonstrated by a three-dimensional (3D) multiphysics and multiscale time-domain model. The simulation results match the theoretical derivation, showing 25% radiation efficiency from a planar antenna backed by a FeGaB thin film with 2.56 m thickness. Furthermore, for conductive ferromagnetic materials, it is shown that the eddy current loss can be well suppressed by laminating the thin film into multiple layers. Here, the radiation efficiency of the modeled antenna with a conductive ferromagnetic substrate is improved from 2.2% to 11.8% by dividing the substrate into 10 layers, with a ferromagnetic material fill factor of 93%.

42 ENGINEERING↗
DOE OSTI2022

components for the MuMMI software release

The Department of Energy and the National Cancer Institute have developed new software for conducting multi-scale simulations of complex systems. This software, called the Multiscale Machine-Learned Modeling Infrastructure (MuMMI), couples simulations on three spatial scales to study slow, large-scale reorganizations of biomolecular systems with the speed of continuum and coarse-grained models while revealing selected interactions at full atomic precision. In these simulations, coarse-to-fine model conversions are used to spawn relevant fine-scale simulations along chosen order parameters, and fine-to-coarse feedback is used to iteratively improve the accuracy and multi-scale consistency of coarse-scale and continuum simulations. The complete MuMMI framework will be reviewed and released as open-source software by Lawrence Livermore National Laboratory. This review covers a subset of the MuMMI components that were developed exclusively at the Los Alamos National Laboratory.

Neale, Christopher↗
DOE OSTI2026

Multitask graph neural networks for elastoplastic response prediction in dual-phase polycrystals

Microstructure-sensitive prediction of elastoplastic response remains a recurring bottleneck in multiscale damage and fatigue modeling, where large ensembles of statistically distinct polycrystals are required to quantify variability and extreme-value behavior. In this work, we develop a multitask graph neural network (GNN) surrogate that maps dual-phase ferrite–martensite polycrystal microstructures to Statistical Volume Element (SVE)-level elastoplastic Quantities of Interest (QoIs). Each SVE is represented as a grain-adjacency graph, with node features encoding phase, geometry, and crystallographic orientation, and edge features encoding relative misorientation. A message-passing graph convolution generates node embeddings, which are pooled into a graph representation and passed to a multitask regression head that jointly predicts 10 scalar QoIs and vector-valued stress–strain responses in orthogonal loading directions across multiple martensite volume fractions and SVE sizes. Results show high accuracy for scalar QoIs and strong agreement for full stress–strain trajectories, with population envelopes reproducing both median behavior and finite-SVE variability across compositions and partition scales. A unified model trained on pooled volume-fraction data preserves most within-regime accuracy relative to regime-specific models while also capturing the broader cross-regime variation reflected in the pooled test set. Distributional comparisons further demonstrate that the surrogate preserves heterogeneity under SVE partitioning, enabling statistically consistent block-wise random-field construction for mesoscale analyses. Overall, the proposed grain-graph surrogate provides a practical pathway to accelerate ensemble-based studies of SVE-level constitutive variability in dual-phase polycrystals.

Crystal plasticity↗
DOE OSTI2024

Multi-Scale Integrated Monitoring System for Enhancing Methane Emission Detection, Quantification & Prediction

This report details the progress and findings of a comprehensive study on reviewing existing solutions, identifying technology gaps, and formulating an “all-in-one” integrated strategy for developing the next-generation multiscale methane monitoring and modeling platform, conducted under grant number DE-FE0032292. Co-led by Dr. David Ebert, Dr. Binbin Weng, and Dr. Chenghao Wang at the University of Oklahoma, the project’s goal was to develop an integrated approach for building this engineering platform to detect, quantify, and mitigate methane emissions across various temporal scale, spatial scales, and sectors. The planning grant study began with an extensive review of various methane sensing and monitoring technologies and systems, surveying over 100 technology providers globally. This review revealed the prevalence of optical methods over chemical methods in commercially available sensors, with Non-Dispersive Infrared (NDIR), Tunable Diode Laser Absorption Spectroscopy (TDLAS), and Optical Gas Imaging (OGI) cameras being the most prevalent options. A trend towards more advanced optical techniques was observed, driven by increased regulatory focus and technological advancements. The technical evaluation of these sensing technologies provided crucial insights into their capabilities and limitations. The study examined emerging technologies such as Differential Absorption LiDAR (DIAL), which show promise for high-precision and long-range detection. The team then investigated the features and application bandwidth of various sensing platforms, including handheld, fixed/stationary, mobile, aerials, and spaceborne monitors. Pilot field studies were conducted to assess the capabilities of solutions for different emission scenarios. Field work with sensor deployments was conducted at three distinct site types: an oil & gas industry site, a cattle ranching operation, and a waste processing facility. The team also conducted a thorough review of methane flux inverse modeling approaches, focused on physically based methods. These approaches were categorized into simple, intermediate, and advanced methods. A realtime WRF-GHG (Weather Research and Forecasting-Greenhouse Gas) modeling system was developed and applied, incorporating multiple data sources to guide field experiments and inform methane plume detection. The project identified and analyzed numerous categories of methane data sources, including satellite measurements, ground-based sensors, and inventory databases. Key platforms examined include EDGAR, EPA GHGI, NASA TROPOMI, Carbon Mapper, and Climate TRACE, among others. The team proposed an architecture for a comprehensive methane monitoring platform. This system incorporates multi-source data acquisition, advanced data processing and assimilation, interactive visualization tools, and analytical capabilities for emissions forecasting and scenario analysis. The proposed platform aims to provide a user-friendly interface catering to various stakeholders, from researchers to policymakers. The architecture includes sophisticated data ingestion methods, a centralized data warehouse, and advanced analytical tools for data fusion and interpretation. To ensure the relevance and effectiveness of the proposed system, a comprehensive survey was conducted to gather stakeholder input on system requirements. Key findings include a strong need for integrating various data types and formats, a preference for real-time data updates and advanced visualization tools, and a demand for user-friendly interfaces catering to different expertise levels.

03 NATURAL GAS↗
DOE OSTI2025

Beyond Magic Barrels: Digital manufacturing for crystallization, process development and optimization of explosive materials: Part II Resveratrol Exemplar

This SAND report summarizes work supported by an Engineering Sciences Research Foundation (ESRF) Lab Directed Research and Development (LDRD) project entitled “Beyond Magic Barrels: Digital manufacturing for crystallization, process development and optimization of explosive materials.” This SAND report is written in two parts with Part 1 discusses recrystallization of our explosive exemplar and Part 2 summarizing our work with recrystallization of resveratrol. We have studied resveratrol recrystallization with a multiscale approach combining experiments, modeling and simulation. At the single crystal scale, microscopy experiments illuminate crystal time-dependent growth rates using advanced image analysis. Bench scale experiments were carried out to look at growth of multiple particles in a small reactor creating thousands of particles and analyzing the results with microscopy and μCT. For the modeling we combine kinetic Monte Carlo (kMC) models with subscale information from density functional theory (DFT) or molecular dynamics. This work is discussed in Part 1 and can also be found in a paper from the project discussing a coarse-grained kMC model specifically developed for resveratrol. For well-mixed systems, we have population balance equations (PBE) linked with species mass conservation forming a set of ordinary differential equations that can be solved quickly. For more complicated geometries, such as the vat crystallization used throughout the complex, a coupled computational fluid dynamic (CFD)/PBE method was developed to account for gradients in temperature and concentration and differences in crystallization rates throughout the domain. These simulations are more complex and require high performance computing. We present results for two cases: 5% seed fast cool with parameters fit to the well-mixed case and 5% seed slow cool using the same parameters. We show reasonable agreement with experiments though are particles are significantly larger than the experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Acceleration of Thermochemistry Solves in MOOSE and Pronghorn

This work focuses on the development and implementation of strategies to accelerate thermochemical calculations within MOOSE-based multiphysics simulations, particularly for applications in MSRs. We highlight the inherent complexity of nuclear materials, which require a multiscale approach to accurately model their behavior across various physical domains, including mechanical, chemical, and thermal phenomena. Thermochemical equilibrium calculations are crucial for predicting material properties and enhancing the fidelity of these simulations. The integration of Thermochimica, a Gibbs energy minimizer, into MOOSE allows for the direct minimization of Gibbs energy at every point on the mesh. However, the computational cost of such integration is significant. To address this, we explored acceleration strategies such as multi-threading support and the use of a thermodynamic ValueCache to reduce redundant calculations. Additionally, we investigated modifications to Thermochimica to enable phase constraints and improve its coupling with phase-field models, which are essential for simulating microstructural evolution and corrosion in MSR. These efforts aim to optimize the computational efficiency and accuracy of multiphysics simulations, thereby supporting the development of reliable and efficient nuclear materials for next-generation reactor technologies.

36 - MATERIALS SCIENCE↗