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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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DOE OSTI2024

WarpX: Delivering Unprecedented Particle-in-Cell Simulation Capability

Since 1939, more than 30 percent of all Nobel prizes in physics and four of the past 14 prizes in chemistry have been awarded for work with particle accelerators. These tools are now used to treat cancer and produce medical supplies, support research in applied fields such as pharmaceuticals, create microcircuits, and sterilize food—and the applications list continues to grow. However, the size of the facilities needed to house these machines—which are often dozens of kilometers long—has limited their future potential. Thus, researchers are motivated to create novel particle accelerator designs that provide new imaging capability but have a much smaller footprint. The Exascale Computing Project’s WarpX application captures the full complexity of the acceleration processes within plasma-based design, supporting the rapid and cost-efficient development of tens of thousands of new particle accelerators for various applications from improving human health to nuclear fusion reactors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows

The 2024 Workflows Community Summit report presents the outcomes of a three-day international gathering that brought together 109 experts from 18 countries to discuss future trends and challenges in scientific workflows. The summit focused on six key areas: time-sensitive workflows, convergence of AI and HPC workflows, multi-facility workflows, heterogeneous HPC environments, user experience and interfaces, and FAIR computational workflows. Discussions highlighted emerging challenges such as integrating AI with traditional HPC, managing workflows across diverse facilities, addressing heterogeneity in computing environments, and ensuring workflows are findable, accessible, interoperable, and reusable (FAIR). The report outlines recent advances, ongoing challenges, and provides recommendations for each topic area, emphasizing the need for standardization, improved interoperability, and the development of more sophisticated tools and frameworks to support the evolving landscape of scientific workflows in the era of exascale computing and AI integration.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Improving North American Wildfire Prediction by Integrating a Machine-Learning Fire Model in a Land Surface Model

Wildfires have shown increasing trends in both frequency and severity across the Contiguous United States (CONUS). However, process-based fire models have difficulties in accurately simulating the burned area over the CONUS due to a simplification of the physical process and cannot capture the interplay among fire, ignition, climate, and human activities. The deficiency of burned area simulation deteriorates the description of fire impact on energy balance, water budget, and carbon fluxes in the Earth System Models (ESMs). Alternatively, machine learning (ML) based fire models, which capture statistical relationships between the burned area and environmental factors, have shown promising burned area predictions and corresponding fire impact simulation. We develop a hybrid framework (ML4Fire-XGB) that integrates a pretrained eXtreme Gradient Boosting (XGBoost) wildfire model with the Energy Exascale Earth System Model (E3SM) land model (ELM) version 2.1. A Fortran-C-Python deep learning bridge is adapted to support online communication between ELM and the ML fire model. Specifically, the burned area predicted by the ML-based wildfire model is directly passed to ELM to adjust the carbon pool and vegetation dynamics after disturbance, which are then used as predictors in the ML-based fire model in the next time step. Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001-2020, the ML4Fire-XGB model outperforms process-based fire models in terms of spatial distribution and seasonal variations. Sensitivity analysis confirms that the ML4Fire-XGB well captures the responses of the burned area to rising temperatures. The ML4Fire-XGB model has proved to be a new tool for studying vegetation-fire interactions, and more importantly, enables seamless exploration of climate-fire feedback, working as an active component in E3SM.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Brochure on the 2024 ASCR Workshop on Energy-Efficient Computing for Science

Large-scale computing has enabled numerous scientific discoveries, including ground-breaking achievements facilitated by the US Department of Energy (DOE) supercomputers and advances in applied mathematics and computer science. While important advances were made in energy efficiency to enable exascale computing, continued efforts are needed to dramatically improve the energy efficiency of the next generation of high-performance computing (HPC) systems and, more broadly, AI data centers. Without substantial improvements in energy efficiency, the energy consumption associated with computing could become a limiting factor for future scientific discovery, national security, and technological advancement.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Modeling and simulation of multiphase flows

This presentation provides an overview of the National Energy Technology Laboratory’s (NETL) multiphase computational fluid dynamics codes. The highly successful Multiphase Flows with Interphase eXchanges (MFIX) suite has been used to model a wide range of applications including post-combustion carbon capture, bioreactor optimization, and bio-FCC regeneration. MFIX-Exa, a state-of-the-art CFD code, developed under DOE’s Exascale Computing Project, is built on the AMReX software framework (https://amrex-codes.github.io/) and is designed to leverage modern accelerator-based compute architectures. This presentation further reviews the underlying physical models of both MFIX and MFIX-Exa and contrasts their similarities and differences. Examples of past and present CFD simulations will illustrate how scientific computing at NETL is being used not only for scientific exploration but also for design, optimization and scale-up of multiphase flow devices.

Musser, Jordan [NETL]↗
DOE OSTI2024

Influence of Antarctic and Greenland Continental Shelf Circulation on High‐Latitude Oceans in E3SM

The science objectives of this project are to simulate and understand the impacts of both deep-basin warm-water intrusions and land-ice melt on the continental shelf circulations and sea-ice distributions around the margins of Greenland and Antarctica. As well, the role of subsurface ocean heat from the Atlantic on declining sea-ice cover in the Arctic is explored. Mesoscale processes and fine bathymetry are implicated in cross-shelf property transports around both Greenland and Antarctica. Therefore, we configured and ran an atmospheric reanalysis-forced global ocean/sea-ice simulation on a grid that reduces from 8 km at the Equator to 2 km at the poles (UH8to2) with 60 vertical levels. It was produced using the Energy Exascale Earth System Model ‘‘HiLAT’’ code (E3SMv0-HiLAT) that uses the Parallel Ocean Program (POP) and CICE5 as its ocean and sea-ice components, respectively. Two main UH8to2 simulations were carried out: one for 1975-2009 and the other for July 2016-2020 after it was initialized from a 1/25° data-assimilative ocean/sea-ice prediction system ocean/sea-ice state. The UH8to2 is not coupled to an active land-ice model. Rather, land-ice melt is represented by observationally informed freshwater fluxes (FWFs). Short (multi-year) UH8to2 simulations were conducted to understand sensitivities when Greenland ice sheet (GrIS) melt is released only at the ocean surface or when it is distributed over the upper water column in accordance with fjord melt plume behavior; these cases were compared with a no GrIS melt case. West Greenland continental shelf currents were fastest in the vertical distribution case and an increase in baroclinic conversion at the shelf break associated with increased eddy kinetic energy was found relative to the surface release case. Further, salinity is lower and meltwater volume greater in the eastern Labrador Sea in the vertical distribution case. For the Arctic, the veracity of the UH8to2 was evaluated for 2017-2020 using available observations. Simulated seasonal sea-ice thickness and concentration are realistic, but the ice is unrealistically thin in the central and eastern Arctic in the fall. Comparisons of vertical sections of ocean temperature, salinity, and buoyancy collected from Ice-Tethered Profilers (ITPs) in the eastern Arctic in the fall and winter of 2019/2020 and co-located/concurrent UH8to2 fields show the stratification over the top 100 m of the water column is too low in the model, the simulated mixed layer too deep, and the simulated subsurface Atlantic Water (AW) too warm; these biases may contribute to the sea-ice biases. A model intercomparison study using the UH8to2 and a forced 1/25° regional Arctic ocean/sea-ice (uses the HYbrid Coordinate Ocean Model and CICE5) simulation further investigates the relationship between AW and sea-ice in the eastern Arctic. The models show a mesoscale-rich pulse of Atlantic Water extending into the eastern basin that reaches maximum intensity in late winter of 2018, after which it decreases in strength. Concurrent and co-located sea-ice melt or the inhibition of sea-ice growth is seen and is attributed to halocline mesoscale eddies doming into the mixed layer with convection bringing this heat into the vicinity of the sea-ice.

58 GEOSCIENCES↗
DOE OSTI2024

R-Adaptivity to Enable Compression of Elementary Computations in Extreme-Scale Finite Element Simulators

Modern computing systems are capable of exascale calculations, which are revolutionizing the development and application of high-fidelity numerical models in computational science and engineering. While these systems continue to grow in processing power, the available system memory has not increased commensurately, and electrical power consumption continues to grow. A predominant approach to limit the memory usage in large-scale applications is to exploit the abundant processing power and continually recompute many low-level simulation quantities, rather than storing them. However, this approach can adversely impact the throughput of the simulation and diminish the benefits of modern computing architectures. We present three novel contributions to reduce the memory burden while maintaining, and sometimes improving, performance in simulations based on finite element discretizations. The first contribution develops dictionary-based data compression schemes that detect and exploit the structure of the discretization, due to redundancies across the finite element mesh. While these schemes are shown to reduce memory requirements by more than 99% on meshes with large numbers of identical mesh cells, there are applications where this structure does not exist. The second contribution leverages a recently developed augmented Lagrangian optimization algorithm to enable r-adaptivity for meshes with the goal of enhancing the redundancies in the mesh. The third contribution extends these methods to patch-based linear solvers and preconditioners by compressing local matrices. Numerical results demonstrate the effectiveness of the proposed methods to detect, enhance and exploit mesh structure on a suite of examples inspired by large-scale applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

2019 Budget Request for the DOE Computational Science Graduate Fellowship (CSGF) Grant

The Department of Energy Computational Science Graduate Fellowship (DOE CSGF) is necessary to meet the continual challenging national workforce needs that arise as computational science and engineering problems continue to grow in scope and complexity. Computational science and engineering (CSE) is a multidisciplinary approach that uses scientific computing to solve practical problems methods and to supply technical tools across the scientific discovery spectrum. In particular, the DOE CSGF emphasizes high-performance computing (HPC) that enables CSE that advances science and engineering in directions important to the DOE and the economy in general. Over the past half-century, HPC has been an essential tool for DOE’s success. During this period, important missions, such as nuclear stockpile stewardship, have turned to HPC as an essential technology. Entire science disciplines, such as biology and cosmology, have been transformed through the augmentation of scientific observation via HPC. At government laboratories and in industry, DOE CSGF alumni are helping push traditional HPC boundaries while contributing to discoveries in high-energy physics, renewable energy, fusion-reactor design, additive manufacturing, nanomaterials for next-generation batteries and transistors, and turbine and advanced nuclear reactor modeling. In addition, HPC is used to address national health needs that will eventually point to cures both by helping cancer researchers manage and analyze huge troves of data, by simulating biological mechanisms, and by accelerating drug development — including continuing to rise to the challenge of pandemic-related research. A 2023 report from the ASCAC Subcommittee on American Competitiveness and Innovation to the ASCR office, “Can the United States Maintain Its Leadership in High-Performance Computing?” says of the Program, “The CSGF program provides a barometer for disciplines that will be of interest to future DOE computing.” An explosion in scientific and technological data has driven the need for increasingly sophisticated HPC to transform those data into scientific understanding. With access to more and more data and the proliferation of HPC, Machine Learning and Artificial Intelligence are experiencing a renaissance, complementing the now well-established use of computational simulation. Indeed, in its September 2020 subcommittee report on “AI/ML, Data Intensive Science and High-Performance Computing”, the DOE Advanced Scientific Computing Advisory Committee (ASCAC) explicitly called for a fellowship program to train computational and data scientists to tackle exascale and data-intensive computing challenges. This collaboration of empirical and theory-based modeling will increasingly inform federal policymakers whose decisions affect American society and future generations, and it requires highly skilled and intellectually agile computational scientists who can support the fast-moving DOE National Laboratory research environment. In fact, the DOE CSGF program has explicitly and consistently addressed this need.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Improving the Parameterization of Cloud and Rain Microphysics in E3SM using Novel Observationally-Constrained Bayesian Approach (Final Technical Report)

In this project, we sought to develop new cloud and rain microphysics frameworks within the Energy Exascale Earth System Model (E3SM). This work encompassed two primary avenues of research: 1) Further development of a Bayesian-based scheme called BOSS (Bayesian Observationally-constrained Statistical-physical Scheme) to represent cloud and rain microphysics, testing it in realistic high-resolution cloud models, and implementing it in E3SM; 2) Development of a methodology utilizing machine learning to enable computationally tractable use of tractable use of Markov chain Monte Carlo sampling for Bayesian parameter estimation in Earth system and cloud models. In this project, we adapted the BOSS microphysics scheme, originally formulated for rain-only, to include all liquid-phase microphysical processes for cloud and rain, in particular the processes that mediate between these two categories, for example the conversion from cloud to rain through collision and coalescence of drops. We constrained the scheme via comparison and testing against a detailed model that explicitly represents the evolution of cloud and rain particles, called a bin microphysics scheme.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Support for the Core Research Activities and Studies of the Computer Science and Telecommunications Board (DE-SC0020446 Final Technical Report)

Supported the core operations of the National Academies' Computer Science and Telecommunications Board (CSTB). Helped support planning and conducting of board meetings, identification of priority topics in computer science and other areas of computing and communications technologies, and oversight for CSTB's portfolio of studies and convenings. Activities shaped and overseen included: a workshop on Al for scientific discovery; collaborative work with other Academies units on a study on foundational research gaps and future directions for digital twins; a study on current capabilities, future prospects, and governance of facial recognition technologies, a study on post- exascale computing; a study on fostering responsible computing research, collaborative work with other Academies units on automated research workflows for accelerated scientific discovery; a study on meeting federal cybersecurity workforce needs, and a study of the ecosystem driving information technology innovation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Multiscale Nuclear-Electronic Orbital Quantum Dynamics in Complex Environments

Many renewable energy conversion processes rely on the movement of protons as well as electrons through either electrocatalysis or photoexcitation. The simulation of such processes requires a quantum mechanical description of coupled nuclear-electronic dynamics in a solvent or heterogeneous chemical environment. The overall objective of this project is the development of theoretical and computational capabilities for simulating nuclear-electronic quantum dynamics in complex environments and the creation of high-performance, open-source software. This multiscale framework will enable simulations of the real-time dynamics of nonequilibrium excited state proton-coupled electron transfer, quantum decoherence, vibronic energy transfer, and ultrafast radiolysis, as well as their associated time-resolved multidimensional spectroscopies. An important outcome of this project will be a sustainable, reusable, and interoperable open-source software ecosystem. This software will be designed for emerging exascale and future national leadership computers. Another key outcome will be a multiscale quantum dynamics method and software enabling simulations of nonequilibrium nuclear-electronic quantum dynamics in complex environments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

High-Resolution Modeling of the Gulf of Mexico using E3SM

Coastal ocean modeling is a high priority in the DOE‘s Energy Exascale Earth System Model (E3SM). The goal is to accurately predict the risk of damage to coastal resources and infrastructure due to a changing climate in the coming decades. North American coastal communities are areas of particular interest, as this fits under the topic of US national security and planning assessments in a changing climate. LANL Institutional Computing time for the Tier 1 allocation ”Coastal Ocean and Sea Ice Modeling” have been used for development and testing of numerical methods needed for E3SM coastal applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Brochure for the DOE Office of Science Workshop on Envisioning Frontiers in AI and Computing for Biological Research

In February of 2025 a joint ASCR/BER workshop was held to identify key transformational research directions for understanding biology using artificial intelligence (AI), digital twins and high-performance (HPC) computational methods to facilitate scientific discovery and innovation in support of the Department of Energy mission. AI technologies offer exciting new groundbreaking methods to analyze large volumes of complex biological data, thereby greatly accelerating the ability to understand, predict, and design biological processes for beneficial purposes. In the laboratory, the bridging of AI-enabled automated experimental technologies, HPC and digital twins will provide potent tools for researchers to explore the fundamental nature of biology and harness its inherent metabolic potential for a variety of beneficial purposes. The focus of this workshop was on how high-performance computational methods can impact this objective by exploring digital twins, foundational models, and data-driven approaches with applications to advance automated laboratory experiments, modeling of complex living systems and engineering new functions into plants and microbial systems relevant to DOE mission. Workshop attendees with expertise in plant science, microbiology, mathematics, computer science, and AI assessed the current state of the science, trends, and AI challenges at the interface of plant and microbial systems biology and computational science to identify opportunities for high-impact research. This collaborative effort capitalized on ASCR's advancements in applied mathematics, computer science, and Exascale systems, and BER's expertise in basic genomics-enabled research on DOE relevant plant and microbial systems. The workshop culminated in four key priority research directions to guide future research and development within DOE Office of Science programs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Advancing the Understanding of Cloud Microphysical Processes and Aerosol Indirect Effects in High-Latitude Mixed-Phase Clouds by Linking ARM Measurements with Climate Model Simulations (Final Report)

The key objectives of this project were to advance our understanding of cloud microphysical characteristics and aerosol indirect effects on mixed-phase clouds in high latitudes. To improve the representation of ice and mixed-phase clouds in Earth System Models (ESMs), we propose an integrated observation and modeling study of cloud macro- and microphysical properties, including spatial heterogeneities, mass partitioning between ice crystals and supercooled liquid water, effects of ice nucleating particles (INPs), and efficiency of secondary ice production (SIP), etc. Specifically, we took four main approaches in this project: (1) examining macro- and microphysical properties of ice and mixed-phase clouds based on in-situ and ground-based observations from multiple field campaigns funded by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program, including the Mixed-Phase Arctic Cloud Experiment (M-PACE), Indirect and Semi-Direct Aerosol Campaign (ISDAC), Ice Nucleating Particle Sources at Oliktok Point (INPOP), ARM West Antarctic Radiation Experiment (AWARE), Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS), and Macquarie Island Cloud and Radiation Experiment (MICRE); (2) evaluating the DOE Energy Exascale Earth System Model (E3SM) simulations based on observations, particularly for ice and mixed-phase cloud microphysical properties; (3) examining the impacts of INPs on ice and mixed-phase clouds. Specifically, a series of comparisons were conducted using observations over the Arctic, Southern Ocean, and Antarctica, including comparisons between the lower and higher southern latitudes as well as comparisons between the northern and southern hemispheres. In addition, aerosol indirect effects from distinct sources of dust particles were examined; and (4) investigating the impacts of SIP. Ultimately, these results helped to improve cloud microphysics and aerosol-cloud interaction parameterizations in the E3SM model. Overall, the project provided improved understanding regarding various factors, including thermodynamic, dynamic, and aerosol conditions, on the micro- and macrophysical properties of ice and mixed-phase clouds in the high latitudes. Resulting analysis helped to provide an improved physical basis for refining the current cloud microphysics parameterizations related to ice and mixed-phase clouds in E3SM.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Building collaboration to advance our understanding of regional climate impacts of dust in California's San Joaquin Valley

This project successfully achieved its central objective of building collaborative research capabilities at UC Merced, a Hispanic-Serving Institution, to advance understanding of the regional climate impacts of dust in California's San Joaquin Valley. Through strategic partnerships with three DOE national laboratories (PNNL, LLNL, and LBNL), we developed critical expertise in the Energy Exascale Earth System Model (E3SM) and Atmospheric Radiation Measurement (ARM) facilities. Among the project's scientific contributions, one key publication includes demonstrating that fallowed agricultural lands are the primary source of anthropogenic dust in California's Central Valley, with dust activities increasing substantially between 2008 and 2022, in correlation with drought severity and expanded fallowed land coverage. This finding suggests that current climate models, including E3SM, likely underestimate the dust burden due to inadequate representation of agricultural land-use changes. Beyond the scientific contributions, the project successfully trained a PhD student, established ongoing collaborations resulting in multiple manuscripts in preparation, and positioned UC Merced to participate in the DUSTIEAIM campaign for 2026-2027, thereby building sustainable research capacity while addressing climate science questions directly relevant to the California Central Valley.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Basic Research Needs for Inverse Methods for Complex Systems under Uncertainty

Inverse problems, which aim to infer unknown properties of a system using experimental and observational data, are central to addressing many of the U.S. Department of Energy’s (DOE) most critical scientific and engineering challenges. Accurate, computationally efficient, and data-efficient solutions to inverse problems are essential for advancing DOE mission-critical science drivers, including analyzing data from large-scale experimental facilities, optimizing fusion reactor performance, accelerating materials discovery, enhancing geophysical imaging, improving wildfire predictions, and enabling autonomous systems and digital twins. However, these problems are becoming increasingly complex, often involving nonlinear, highdimensional, and interconnected systems and models that span multiple physics and scales, while relying on data with varying quantity, quality, and information content. Compounding these challenges is the uncertainty inherent in DOE-relevant systems, where errors in inputs, noise in data, incompleteness of data, and discrepancies between models and reality constrain the accuracy and precision of solutions. At the same time, the convergence of recent scientific computing trends—scientific machine learning, artificial intelligence, and computing advances such as exascale computing—is creating unprecedented opportunities for tackling these challenges. The cross-cutting nature of inverse problems, combined with their growing complexity and rapidly evolving data and algorithmic demands, strongly motivates the formulation of a prioritized research agenda to maximize their capabilities and impact. In response to this need, DOE’s Advanced Scientific Computing Research (ASCR) program in the Office of Science convened the Workshop on Basic Research Needs for Inverse Problems for Complex Systems Under Uncertainty in June 2025. This workshop brought together experts across disciplines to identify grand challenges and major opportunities in the field. Through collaborative discussions, the workshop defined transformative research directions aimed at addressing the mathematical, statistical, and computational challenges posed by inverse problems under uncertainty. As a result of these efforts, four priority research directions (PRDs) were identified to guide future research and development in this area. These PRDs, summarized below, represent a roadmap for advancing the foundational science and mathematics of inverse problems, enabling robust, scalable, and uncertainty-aware solutions that are critical for DOE applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Characterization of Root Zone Soil Moisture and Herpetofaunal Biodiversity in the Southern Great Plains

(1) Goal: The scientific objective to the proposed research is to develop a proto-type downscaled (<1 km) version of the root-zone soil moisture product called SoilMERGE or SMERGE. This objective is linked as root zone soil moisture is a critical climate variable that has a more direct influence on plant growth than precipitation. Therefore, the developed downscaled version of SMERGE can be used as input into biodiversity informatic techniques (i.e., maximum entropy modeling) Development of downscaled SMERGE will be facilitated by Department of Energy (DOE) resources specifically data from the DOE Atmospheric Radiation Measurement (ARM) facility and modeling platforms such as the Energy Exascale Earth System Model (E3SM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Developing Partnership between San Jose State University and DOE Lawrence Livermore National Laboratory to Enhance Climate Research Equity and Inclusion

One of the key objectives of this project was to develop partnership between San Jose State University (SJSU) and the Lawrence Livermore National Laboratory (LLNL), a US Department of Energy (DOE) funded national laboratory. Both institutions are closely located within the San Francisco Bay Area in California and their researchers share overlapping research interests and expertise related to Earth system sciences. By leveraging their connections with LLNL, faculty and students from SJSU gained exposure to state-of-the-art observations and simulations related to Earth system sciences, including but not limited to the usage of the facility data provided by the DOE Atmospheric Radiation Measurement (ARM) program and data analysis techniques for interpreting and analyzing the DOE Energy Exascale Earth System Model (E3SM) simulations.

54 ENVIRONMENTAL SCIENCES↗