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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Modeling and Simulation Development Pathways to Accelerating KP-FHR Licensing (Final Report)

This project assembles a strong U.S. industry and national laboratory team to complete scope of work. Kairos Power (KP), headquartered in Alameda, CA, is the leader of this effort and has built an internal team of highly competent engineers and managers with extensive combined experience in nuclear power, conventional power, product development, and licensing. INL, ANL, and LANL bring unique capabilities in advanced reactor R&D and licensing. The project funding source is the result of FOA No. 0001817, U. S. Industry Opportunities for Advanced Nuclear Technology Development. There has been on-going work and this Access Cooperative Research and Development Agreement (CRADA) will cover the remaining work scope of FOA 0001817. KP is implementing innovative strategies that can reduce the cost and accelerate the initial demonstration of the Kairos Power Fluoride-salt-cooled, High-temperature Reactor (KP-FHR) to meet the needs of the U.S. electricity market by 2030. Licensing of the KP-FHR could be significantly accelerated using advanced computing methods with sufficient predictive capabilities to be able to extrapolate potential response of the structure in different scenarios. However, currently used computational methods heavily rely on empirical fits and cannot be used for extrapolation. The scope focuses on improving the modeling capability in the NEAMS Grizzly structural mechanics code and the NEAMS BISON fuel performance code and the NEAMS SAM systems analysis code. This work leverages the expertise and know-how gathered in three DOE National Laboratories – INL, ANL, and LANL.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Advanced CO 2 Capture Solvent Systems for Dynamic Power Generation

RTI International, in collaboration with Pacific Northwest National Laboratory (PNNL), Carbon Capture Simulation for Industry Impact (CCSI 2 ), Electricity Power Research Institute (EPRI), and West Virginia University (WVU), successfully completed a joint research effort in developing a cost-effective, resilient, load-following advanced CO 2 capture technology for natural gas power plants. The project’s objective was to develop a CO 2 capture process that maximizes the net present value (NPV) of the electricity sale by minimizing the levelized cost of electricity (LCOE) under dynamic plant loads and high renewable penetration environments. The two key innovations developed in this project were the use of (i) advanced water-lean solvents (WLSs) and (ii) process intensification equipment, such as a rotating packed bed (RPB) absorber and dual-stage flash regeneration. The process’s low CO 2 capture cost is realized through WLSs’ low energy required for solvent regeneration, which lowers the operating cost while RPBs intensify the absorption process and reduces the power plant capital cost. A suite of advanced computational and simulation packages was implemented to guide the process design, validate the dynamic response of the capture plant, evaluate system-wide performance, and maximize the power plant’s profit. The project also engaged with power producers and other stakeholders to ensure its technical relevance and techno-economic viability. The development of this highly disruptive CO 2 capture technology could accelerate the industry adoption and thereby lower the greenhouse gas emissions of the U.S. power sector. Deployment of this technology can increase the reliability and decrease the cost of electricity generation in the U.S. by enabling the use of low-carbon fossil fuels to balance fluctuations of renewable energy availability.

03 NATURAL GAS↗
DOE OSTI2024

The SciDAC QuantOm Framework: A composable Workflow

As part of the Scientific Discovery through Advanced Computing (SciDAC) program, the Quantum Chromodynamics Nuclear Tomography (QuantOM) project aims to analyze data from Deep Inelastic Scattering (DIS) experiments conducted at Jefferson Lab and the upcoming Electron Ion Collider. The DIS data analysis is performed on an event-level by leveraging nuclear theory models and accounting for experimental conditions. In order to efficiently run multiple analyses under varying conditions, a composable workflow was designed where each section (theory, experiment, objective minimization, etc.) has its own dedicated module. The optimization, i.e. the fit of theory to experimental data is carried out by deep learning techniques, such as Generative Adversarial Networks (GANs) or Reinforcement Learning (RL). This presentation gives an overview of the current status of the workflow, highlights present and future challenges, and highlights possible extensions to other projects with similar requirements.

Lersch, Daniel↗
DOE OSTI2024

Laboratory Directed Research and Development Program: FY 2023 Completed Projects Report

Oak Ridge National Laboratory (ORNL) is the US Department of Energy’s (DOE’s) largest multiprogram science, technology, and energy laboratory. It possesses distinctive capabilities in a variety of fields, such as neutron science, computing, advanced materials, and nuclear science and technology. Using these capabilities, ORNL conducts basic and applied research and development (R&D) to support DOE’s overarching mission “to ensure America’s security and prosperity by addressing its energy, environmental and nuclear challenges through transformative science and technology solutions.” As a national resource, ORNL also applies its capabilities and skills to the specific needs of other federal agencies and customers through the DOE Strategic Partnership Projects (SPP) Program. Information about the laboratory and its programs is available on the ORNL website. The Laboratory Directed Research and Development (LDRD) Program at ORNL operates under the authority of the DOE Order 413.2C, Laboratory Directed Research and Development, which establishes DOE’s requirements for the program while providing the laboratory director broad flexibility for program implementation. The LDRD Program funds are obtained through a charge to all laboratory programs. Although it represents a relatively small portion of the overall research budget, the LDRD Program plays an essential role in maintaining the laboratory’s ability to respond to national needs. The program allows ORNL to improve its distinctive capabilities and to enhance its ability to conduct cutting-edge R&D. This report provides an overview of the LDRD Program at ORNL in FY 2023 and contains summaries of all the LDRD research projects that concluded between October 1, 2022, and September 30, 2023.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

BISON: A Flexible Code for Advanced Simulation of the Performance of Multiple Nuclear Fuel Forms [Slides]

As fuel vendors and designers pursue the development of advanced reactors or the increase in burnup limits for existing reactors, advanced computational tools are necessary to understand the fuel performance. BISON, a fuel performance code developed primarily at Idaho National Laboratory, which is built upon the Multiphysics Object-Oriented Simulation Environment (MOOSE), provides capabilities to analyze multiple nuclear fuel forms in a wide variety of dimensions. Since its inception, BISON has been used to investigate the performance of light-water reactor fuel rods, accident tolerant fuel concepts, metallic and mixed-oxide (MOX) fuels for fast reactors, plate fuels for research reactors, and tri-structural isotropic (TRISO) fuel particles. This talk will provide a history of BISON, highlights of major development milestones, tributes to key contributors, and applications of its use to various fuel forms in one-, two-, and three-dimensions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Electron Cloud Simulations in the Fermilab Booster

As part of Fermilab's Proton Improvement Plan-II (PIP-II), the Fermilab Booster synchrotron will operate at a higher intensity, increasing from 4.5×1012 to 6.7×1012 protons per pulse [ppp]. A potential challenge for achieving high-intensity performance arises from rapid transverse instabilities induced by electron cloud (EC). This research presents EC simulations using PyECLOUD, which is an advanced computational tool that incorporates measurements of the secondary electron yield (SEY) from the Booster's combined function magnet material. By systematically varying beam parameters in PyECLOUD, such as bunch structure, bunch length, and intensity, the EC effects on beam stability and overall performance of Booster can be predicted.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Collaborative Research: Parametric Instabilities of Alfven Waves in Low‐beta Plasmas

Alfven waves are of fundamental importance in magnetized plasmas. This project aims to advance our understanding of the basic physics of the parametric instabilities of Alfven waves using advanced computer simulations. The results will have implications for important processes in the heliosphere involving Alfven waves, such as coronal heating and solar wind heating.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Scaling the SciDAC QuantOm Workflow

As part of the Scientific Discovery through Advanced Computing (SciDAC) program, the Quantum Chromodynamics Nuclear Tomography (QuantOM) project aims to analyze data from Deep Inelastic Scattering (DIS) experiments conducted at Jefferson Lab and the upcoming Electron Ion Collider. The DIS data analysis is performed on an event-level by combining the input from theoretical and experimental nuclear physics into a single, composable workflow. The optimization itself (I.e. fitting the experimental data with theoretical predictions) is carried out by a machine / deep learning algorithm. The size of the acquired DIS data as well as the complexity of the workflow itself require that the analysis is performed across multiple GPUs on high performance computing systems, such as Polaris at Argonne National Laboratory. This presentation discusses the novelties and challenges that came along with parallelizing this workflow. Recent results are compared to common distributed training techniques.

Lersch, Daniel↗
DOE OSTI2024

DOE Final Report on Virginia Tech’s contribution to “Tokamak Disruption Simulation”

This work was performed by Virginia Tech in collaboration with multiple institutions led by Los Alamos National Laboratory as a part of the Tokamak Disruption Simulation SciDAC (Scientific Discovery through Advanced Computing) project supported jointly by the Department of Energy Office of Science and Ad- vanced Scientific Computing Research. This report summarizes Virginia Tech’s contributions to the Sci- DAC project. Virginia Tech researchers (presently University of Washington researchers) focused on the fundamental role of plasma-material interaction on transport, which could then have macroscopic effects on simulations of tokamak disruptions. The plasma sheath, which regulates plasma particle and energy fluxes to the wall, is an essential component in the study of plasma-material interaction (PMI). Understanding sheath theory by accounting for finite sheath thickness and transport in the vicinity of the sheath entrance can sig- nificantly modify typical assumptions that are made in the Bohm speed analysis, where the Bohm speed provides the lower bound of the plasma exit flow speed. Our work provides a modified Bohm speed formu- lation that accounts for the critical role of transport and for applications that are away from the asymptotic limits that are typically assumed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Tokamak Disruption Simulation (Final Technical Report)

The Tokamak Disruption Simulation collaboration was a SciDAC (Scientific Discovery through Advanced Computing) program led by Xianzhu Tang of the Los Alamos National Laboratory but with separate grants to each institution. I was the PI for Columbia University and the only person at Columbia directly funded by this SciDAC. Disruptions are a sudden loss of confinement in tokamaks, and a reliable method of preventing disruptions must be developed before tokamak fusion power plants become feasible. A related plasma confinement concept, the stellarator, is the only way known way of preventing disruptions with the required reliability. The largest devices, both existing and under construction, for confining plasmas for fusion using magnetic fields are tokamaks, so it is important to solve their disruption issues. The sudden loss of confinement in tokamaks can be from a bit of material falling into the plasma, which causes the plasma energy to be quickly lost by radiation, or by an instability causing a breakup of the confining surfaces formed by the magnetic field. This breakup can occur more than a million times faster than one would naively think possible. The sudden breakup of surfaces is called a fast magnetic reconnection and also results in a sudden cooling of the plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

AI Driven Optimization of Public Transit

This project explores the application of AI-driven methods to optimize public transit operations for the Chattanooga Area Regional Transportation Authority (CARTA). By leveraging data analytics, machine learning, and predictive modeling, the initiative seeks to enhance system efficiency, improve rider experience, and support sustainability goals. This research, supported by the National Science Foundation and the U.S. Department of Energy, integrates real-time transit data with advanced computational tools to inform decision-making, optimize routes, and balance operational demands. The work exemplifies a forward-looking model for mid-sized cities aiming to modernize mobility systems through intelligent technology integration.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Laboratory Directed Research and Development Program: FY 2024 Completed Projects Report

Oak Ridge National Laboratory (ORNL) is the US Department of Energy’s (DOE’s) largest multiprogram science, technology, and energy laboratory. It possesses distinctive capabilities in a variety of fields, such as neutron science, computing, advanced materials, and nuclear science and technology. Using these capabilities, ORNL conducts basic and applied research and development (R&D) to support DOE’s overarching mission “to ensure America’s security and prosperity by addressing its energy, environmental and nuclear challenges through transformative science and technology solutions.” As a national resource, ORNL also applies its capabilities and skills to the specific needs of other federal agencies and customers through the DOE Strategic Partnership Projects (SPP) Program. Information about the laboratory and its programs is available on the ORNL website. The Laboratory Directed Research and Development (LDRD) Program at ORNL operates under the authority of DOE Order 413.2C, Laboratory Directed Research and Development,3 which establishes DOE’s requirements for the program while providing the laboratory director broad flexibility for program implementation. The LDRD Program funds are obtained through a charge to all laboratory programs. Although it represents a relatively small portion of the overall research budget, the LDRD Program plays an essential role in maintaining the laboratory’s ability to respond to national needs. The program allows ORNL to improve its distinctive capabilities and to enhance its ability to conduct cutting-edge R&D. In accordance with the DOE order, R&D projects funded through the LDRD Program at ORNL support the goals of • maintaining the scientific and technical vitality of the laboratory; • enhancing the laboratory’s ability to address future DOE missions; • fostering creativity and stimulating exploration of forefront areas of science and technology; • serving as a proving ground for new concepts in R&D; and • supporting high-risk, potentially high-value R&D. This report provides an overview of the LDRD Program at ORNL in FY 2024 and contains summaries of all the LDRD research projects that concluded between October 1, 2023, and September 30, 2024.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Utilization of Carbon Supply Chain Wastes and Byproducts to Manufacture Graphite for Energy Storage Applications

This project explored how coal and waste coal can be transformed into high-value graphite used in batteries for electric vehicles and power grids. A manufacturing technique that converts coal into carbon foam and subsequently graphite was developed and refined to improve the thermal and mechanical properties of the resulting materials. Cost-effectiveness and environmental impacts were also evaluated. Coal-derived graphite, especially when processed at high temperatures, was shown to perform comparably to commercial graphite in battery tests. Advanced computer simulations were used to model battery behavior and predict long-term performance, demonstrating that coal-based graphite has the potential to support electric vehicle deployment while reducing reliance on imported materials. This work offers a promising pathway for cleaner energy storage solutions and creates economic opportunities for coal-reliant communities by generating new uses for mining byproducts.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

SAGIPS: A scalable Framework for scidac quantom

As part of the Scientific Discovery through Advanced Computing (SciDAC) program, the Quantum Chromodynamics Nuclear Tomography (QuantOM) project aims to analyze data from Deep Inelastic Scattering (DIS) experiments conducted at Thomas Jefferson National Accelerator Facility and the upcoming Electron Ion Collider. The DIS data analysis is performed on an event level by taking into leveraging nuclear theory models and accounting for experimental conditions. In order to efficiently run multiple analyses under varying conditions, a composable workflow was designed where each section (theory, experiment, objective minimization, etc.) has its own dedicated module. This presentation gives an overview over of the current status of this workflow, highlights present and future challenges, and highlights possible extensions to other projects with similar requirements.

Lersch, Daniel [Thomas Jefferson National Accelera↗
DOE OSTI2025

Web-Based Tools for Data-Informed Remedy Optimization: Software Theory and User Guide

This report documents the development and application of two web-based decision-support tools for pump-and-treat (P&T) groundwater remediation systems: PTOLEMY (Pump-and-Treat Optimized Location Evaluation to Maximize Yields) and OPTIMA (Optimization for Pump-and-Treat Implementation, Management, & Assessment). These tools enhance remedy design and management by leveraging advanced computational methods – specifically deep learning and multi-objective optimization – within a user-friendly platform. By integrating data-driven models with established hydrogeological knowledge, PTOLEMY and OPTIMA enable more efficient evaluation of well placement and operational strategies, helping site managers balance multiple remediation objectives under complex conditions. Both tools are implemented as modules within the SOCRATES (Suite Of Comprehensive Rapid Analysis Tools for Environmental Sites) web platform, which provides data access, visualization, and analytics to support remedy optimization across sites in the U.S. Department of Energy Office of Environmental Management complex. PTOLEMY is a rapid screening module designed to identify promising locations for new extraction wells. It employs a multi-channel three-dimensional convolutional neural network (MC3D-CNN) trained on high-fidelity simulation data to predict the relative performance (in terms of contaminant mass recovery) of potential well sites. Through an interactive web interface, PTOLEMY visualizes the probability of high performance across a site, highlighting areas where an extraction well is likely to yield above-threshold contaminant removal over a multi-year period. PTOLEMY’s map-based displays and exportable results support transparent communication of screening analyses. By focusing attention on the most favorable candidate locations, the tool augments traditional engineering judgment and physics-based modeling, providing a data informed basis for subsequent detailed evaluations. OPTIMA is a multi objective optimization module designed to find wellfield layouts and operating schedules that meet various cleanup goals. It quickly evaluates thousands of candidate setups – combinations of well locations, timing, and rates – and returns a small set of best trade-off options for comparison. At its core, OPTIMA uses a U-Net-based surrogate model – a deep-learning emulator of a groundwater flow and transport simulator – to dramatically accelerate scenario evaluations. Coupling this fast surrogate with the NSGA-II (Non-dominated Sorting Genetic Algorithm II) evolutionary algorithm, OPTIMA explores a wide decision space of well locations and schedules to identify Pareto-optimal solutions that trade off key objectives (e.g., minimizing cleanup time, maximizing contaminant mass removal, and minimizing plume extent). The tool outputs a family of optimal configurations and visualizes their trade-offs (Pareto frontiers of cleanup metrics and maps of optimized well placements). Site managers can use these results to understand the range of viable strategies and to select candidate designs for more detailed verification. OPTIMA is currently under active development and not yet fully released; this guide provides early documentation to support planning and gather user feedback.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Examples of Mission-driven Data Science from Jefferson Lab and ACES

This presentation details mission-driven data science initiatives at Jefferson Lab and the Joint Institute for Advanced Computing on Environmental Studies (ACES). JLab, a U.S. Department of Energy Office of Science national laboratory, operates the Continuous Electron Beam Accelerator Facility (CEBAF), and is the lead institute for the new High Performance Data Facility (HPDF) Hub. The Joint Institute for ACES brings together interdisciplinary teams in health informatics, climate modeling, computer science, and physics to address environmental challenges, including flood modeling. The Hampton Roads region, particularly Norfolk and Virginia Beach, faces increasing flood risks, motivating the need for rapid, reliable, and risk-aware decision support. ACES’s flooding work has a focus on uncertainty quantification (UQ) and machine learning (ML) for coastal flood management. The work is motivated by the increasing vulnerability of communities such as Norfolk and Virginia Beach, Virginia, to frequent coastal flooding events, and the need for rapid, reliable decision support. The research develops computationally efficient ML surrogate models to forecast water levels and flooding risk. A central theme is the quantification and calibration of predictive uncertainty, especially for out-of-distribution (OOD) scenarios, using techniques such as Monte Carlo Dropout, Deep Ensembles, Gaussian Processes, and Deep Quantile Regression (DQR). The study demonstrates that distance-aware UQ is critical for reliable scientific AI, particularly in high-dimensional, safety-critical, and real-time applications.

McSpadden, Diana [Thomas Jefferson National Accele↗
DOE OSTI2026

Summary Report from the 2025 Interfaces for Energy and the Environment Conference

The inaugural Interfaces for Energy and the Environment Conference (IEEC) took place on May 19-23, 2025, at Pacific Northwest National Laboratory (PNNL), Richland, Washington (USA). The aim of this first-of-its-kind interdisciplinary meeting was to provide a forum for participants to share the latest cutting edge experimental and computational advances in interfacial science across energy and environmental applications. The sessions below (elaborated further in the report summaries) highlighted fundamental and applied collaborative research aimed at understanding the interactions occurring at interfaces in aqueous environments, including, but not limited to, the fields of geochemistry, atmospheric chemistry, agriculture, environmental management, and catalysis. They were organized to stimulate and provide opportunities to create, renew, and deepen collaborations. The conference included activities such as oral and poster presentations, honoree mentoring session, and a team building exercise to support all career stages (detailed summaries of these activities are in the Appendices).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

IC Workflow Project (Final Report)

The Workflow Project is a joint research task between the CASL Advanced Modeling Applications (AMA) and the Virtual Reactor Integration (VRI) focus areas, and the Industry Council. The purpose of the project is to solicit the expertise of the IC member organizations to inform CASL staff and provide feedback for the development of CASL products. The goal is for CASL staff to understand existing workflows at end-user organizations in order to achieve compatibility of CASL products with current needs and future plans. CASL is committed to developing and delivering advanced capabilities and solutions that will allow the nuclear power industry to solve complex problems and improve the operating performance and efficiency of light water reactors. Through the R&D being performed by CASL, advanced computational methods and software is being developed and embodied in the Virtual Environment for Reactor Applications (VERA). The information collected through this project will provide additional considerations that must be included in the development of VERA to ensure that it meets the needs of the nuclear industry. As will be discussed below, key additional areas that must be considered is the usability of the system in an engineering environment including the user input, quality assurance, output of results, computational runtime, and integration into existing engineering analysis processes. This document contains a summary of information gathered through a series of interviews and meetings with IC organizations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗