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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

On-the-Fly Energy Condensation for Whole-Core Multiphysics Simulations

As the nuclear energy industry considers ways to achieve improved economics in the current fleet of light-water reactors (LWRs), one possible approach is to operate each cycle longer. This causes a greater portion of the fuel to be burned and reduces the frequency of outages, which ultimately reduces the cost to operate the reactor. However, this also leads to higher burnup fuels than have traditionally been allowed in these reactors. There are concerns about integrity of high-burnup (HBu) fuel, especially during accident conditions such as loss-of-coolant accidents (LOCAs), as shown by Capps et al. To investigate these concerns, advanced modeling and simulation capabilities are under development to determine the susceptibility of HBu fuel to fuel fragmentation, relocation, and dispersion (FFRD). Improvements have previously been made to fuel performance capabilities to model these phenomena more accurately; multiphysics simulations have also been conducted to determine the power and burnup histories of the HBu fuel, which are needed as inputs to the fuel performance calculations. Most recently, new statistical approaches have been developed to identify a subset of fuel rods that have greater FFRD susceptibility, reducing the total number of fuel performance simulations required.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRSDate not supplied

Energy of Fluid (EOF) Simulation of Large-Scale Densification and Solidification of Hydrogen

The current research develops a finite volume-based Computation Fluid Dynamic (CFD) model utilizing an Energy of Fluid (EOF) approach to generate a simulation of the densification of liquid hydrogen (LH2) in the Integrated Refrigeration and Storage (IRAS) experimental tank for the Ground Operations Demonstration Unit for Liquid Hydrogen Project (GODU-LH2) at the Kennedy Space Center (KSC). The computational code will integrate a commercial software pressure-based model with User Defined Functions (UDF) generated and added to the model. The modified model will solve the energy equation in terms of internal energy and provide temperature and pressure calculations for a given tank geometry. This method will decrease computational time when compared to the built-in commercially available Enthalpy solvers. Currently the research focused on utilizing User Defined Scalars (UDS) compared to the enthalpy model and a lumped node analysis to show the written UDS is as accurate as the built-in solvers, and the models will be verified using known solutions for solidification and validated with the experimental results from the Cryogenics Test Laboratory (CTL) at KSC for the IRAS densification process.

C. Mahony↗
DOE OSTI2021

The cosmic muon and detector simulation framework of the extreme energy events (EEE) experiment

Abstract This paper describes the simulation framework of the extreme energy events (EEE) experiment. EEE is a network of cosmic muon trackers, each made of three multi-gap resistive plate chambers (MRPC), able to precisely measure the absolute muon crossing time and the muon integrated angular flux at the ground level. The response of a single MRPC and the combination of three chambers have been implemented in a GEANT4-based framework (GEMC) to study the telescope response. The detector geometry, as well as details about the surrounding materials and the location of the telescopes have been included in the simulations in order to realistically reproduce the experimental set-up of each telescope. A model based on the latest parametrization of the cosmic muon flux has been used to generate single muon events. After validating the framework by comparing simulations to selected EEE telescope data, it has been used to determine detector parameters not accessible by analysing experimental data only, such as detection efficiency, angular and spatial resolution.

Abbrescia, M.↗
DOE OSTI2024

Simulation Studies of Electromagnetic Energy Absorption by Hydride Materials

This paper investigates the key parameters that contribute to electromagnetic (EM) energy absorption and heating of hydride materials which could be used to release hydrogen for use as a fuel. Our simulation study using COMSOL focuses on EM energy absorption and resulting temperature rise in a thin circular slab of hydride material using resonant dipole and loop antennas. Presented results delineate the effects of conductivity, relative permeability, antenna type and slab size on temperature rise when the distance and amount of RF power are kept fixed.

Brown, Michael A.↗
NASA NTRSDate not supplied

NASA’s Orion Crew Module Seakeeping Simulation and Test Comparisons in Ocean Wave Environments

The NASA Orion Crew Module (CM) post-landing seakeeping and uprighting capabilities are being evaluated across a variety of ocean wave conditions. This paper provides an overview of quarter-scale and full-scale test data and how it compares with the CM seakeeping dynamic simulation results evaluated using Wave Energy Convertor Simulator (WEC-Sim).This includes a discussion of the status of the dynamic modeling effort, correlation of model inputs with the wave field from the test, and a comparison of the results against the physical test data with emphasis on the full-scale results. Analysis in the frequency domain showed similar response characteristics between the model and the test and shows energy peaks corresponding to both the natural response and the forced response due to the wave environment. Time domain comparisons showed that the linear WEC-Sim model under predicted the amplitude of the pitch and roll motion of the test article, with improved matching for smaller, longer crested waves. An attempt was made to correlate the pitch/roll response to incident wave slope to account for a possible delay in the BTA’s response. The results did not show a strong relationship in the response characteristics to incident wave slope, but better correlation was seen by comparing responses in the frequency domain. Evaluation of the simulation’s ability to match test results aided in understanding the current fidelity of the CM model in WEC-Sim and identifying areas for model improvement

Abigail Lockard↗
DOE OSTI2025

Accelerating Innovative Energy Solutions Using Combustion Simulations

Combustion-based transportation, electricity generation, and industrial heating in manufacturing constitute the three largest sectors of energy demand. Some of the recent technology development in these sectors are: switching to low-carbon fuels for the transportation sector, increasing energy efficiency in the power sector, and capturing carbon emissions from conventional power generators. Several teams at the National Renewable Energy Laboratory have been actively advancing research in these areas by leveraging computational modeling of combustion processes across the heavy-duty land based transportation, aviation, and power generation sectors. This article summarizes some of these efforts, demonstrating the potential of advanced computational techniques to generate technological solutions that will transform the global energy system.

08 HYDROGEN↗
DOE OSTI2021

Model America: Data and Models for every U.S. Building

The 5-year goal of the “Model America” concept was to generate a model of every building in the United States. This data repository delivers on that goal with "Model America v1". 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). There were 125,715,609 buildings detected in the United States. Of this number, 122,146,671 (97.2%) buildings resulted in a successful generation and simulation of a building energy model. This dataset includes the full 125 million buildings. Future updates may include additional buildings, data improvements, or other algorithmic model enhancements in "Model America v2". This dataset contains OSM and IDF zip files for every U.S. county. Each zip file contains the generated buildings from that county. The .csv input data contains the following data fields: 1. ID - the Unique Building Identifier (UBID), generated using the Pacific Northwest National Laboratory (PNNL) BuildingID framework 2. Centroid - building center location in latitude/longitude (from Footprint2D) 3. Footprint2D - building polygon of 2D footprint (lat1/lon1_lat2/lon2_...) 4. State_abbr - state name 5. Area - estimate of total conditioned floor area (ft2) 6. Area2D - footprint area (ft2) 7. Height - building height (ft) 8. NumFloors - number of floors (above-grade) 9. WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings) 10. CZ - ASHRAE Climate Zone designation 11. BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards 12. Standard - building vintage This 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) Laboratory Directed Research and Development (LDRD), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), Biological and Environmental Research (BER), and National Nuclear Security Administration (NNSA). Update (September 23, 2025): We corrected the ID field in all state-level.csv input files to ensure one-to-one consistency with the corresponding .osm and .idf output files. The schema and file structure are unchanged; only the values in the ID column were modified. No files were added or removed, and the .zip bundles (containing .osm / .idf) are unchanged. The corrected .csv inputs were re-extracted in March 2025 from the original data generated ~ 2021 (Theta supercomputer runs), and published here to align input IDs with model outputs. Update (September 6, 2026): The Model America dataset was updated to replace the previous building ID field with the Unique Building Identifier (UBID), using the Pacific Northwest National Laboratory (PNNL) BuildingID framework. UBIDs provide standardized, location-based identifiers for individual building footprints and improve interoperability with other building and geospatial datasets. The data files containing the previous building identifiers were updated to include UBIDs. This update standardizes building identification; the underlying Model America building characteristics and energy simulation results were not recomputed as part of this update.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

Proton Straggling in Thick Silicon Detectors

Straggling functions for protons in thick silicon radiation detectors are computed by Monte Carlo simulation. Mean energy loss is constrained by the silicon stopping power, providing higher straggling at low energy and probabilities for stopping within the detector volume. By matching the first four moments of simulated energy-loss distributions, straggling functions are approximated by a log-normal distribution that is accurate for Vavilov k is greater than or equal to 0:3. They are verified by comparison to experimental proton data from a charged particle telescope.

Selesnick, R. S.↗
NASA NTRS1973

G-189A analytical simulation of the integrated waste management-water system using radioisotopes for thermal energy

An analytical simulation of the RITE-Integrated Waste Management and Water Recovery System using radioisotopes for thermal energy was prepared for the NASA-Manned Space Flight Center (MSFC). The RITE system is the most advanced concept water-waste management system currently under development and has undergone extended duration testing. It has the capability of disposing of nearly all spacecraft wastes including feces and trash and of recovering water from usual waste water sources: urine, condensate, wash water, etc. All of the process heat normally used in the system is produced from low penalty radioisotope heat sources. The analytical simulation was developed with the G189A computer program. The objective of the simulation was to obtain an analytical simulation which can be used to (1) evaluate the current RITE system steady state and transient performance during normal operating conditions, and also during off normal operating conditions including failure modes; and (2) evaluate the effects of variations in component design parameters and vehicle interface parameters on system performance.

Coggi, J. V.↗
DOE OSTI2020

Predicting Small Molecule Transfer Free Energies by Combining Molecular Dynamics Simulations and Deep Learning

Accurately predicting small molecule partitioning and hydrophobicity is critical in the drug discovery process. There are many heterogeneous chemical environments within a cell and entire human body. For example, drugs must be able to cross the hydrophobic cellular membrane to reach their intracellular targets, and hydrophobicity is an important driving force for drug–protein binding. Atomistic molecular dynamics (MD) simulations are routinely used to calculate free energies of small molecules binding to proteins, crossing lipid membranes, and solvation but are computationally expensive. Machine learning (ML) and empirical methods are also used throughout drug discovery but rely on experimental data, limiting the domain of applicability. We present atomistic MD simulations calculating 15,000 small molecule free energies of transfer from water to cyclohexane. This large data set is used to train ML models that predict the free energies of transfer. We show that a spatial graph neural network model achieves the highest accuracy, followed closely by a 3D-convolutional neural network, and shallow learning based on the chemical fingerprint is significantly less accurate. A mean absolute error of ~4 kJ/mol compared to the MD calculations was achieved for our best ML model. We also show that including data from the MD simulation improves the predictions, tests the transferability of each model to a diverse set of molecules, and show multitask learning improves the predictions. This work provides insight into the hydrophobicity of small molecules and ML cheminformatics modeling, and our data set will be useful for designing and testing future ML cheminformatics methods.

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

Physics guided machine learning for multi-material decomposition of tissues from dual-energy CT scans of simulated breast models with calcifications

We introduce a physics guided data-driven method for image-based multi-material decomposition for dual-energy computed tomography (CT) scans. The method is demonstrated for CT scans of virtual human phantoms containing more than two types of tissues. The method is a physics-driven supervised learning technique. We take advantage of the mass attenuation coefficient of dense materials compared to that of muscle tissues to perform a preliminary extraction of the dense material from the images using unsupervised methods. We then perform supervised deep learning on the images processed by the extracted dense material to obtain the final multi-material tissue map. The method is demonstrated on simulated breast models with calcifications as the dense material placed amongst the muscle tissues. The physics-guided machine learning method accurately decomposes the various tissues from input images, achieving a normalized root-mean-squared error of 2.75%.

Gopalakrishnan Meena, Murali↗
NASA NTRS2000

CLUST - EVAP Monte Carlo Simulation Applications for Determining Effective Energy Deposition in Silicon by High Energy Protons

The CLUST-EVAP is a Monte Carlo simulation of the interaction of high energy (25 - 400 MeV) protons with silicon nuclei. The initial nuclear cascade stage is modeled using the CLUST model developed by Indiana University over 30 years ago. The second stage, in which the excited nucleus evaporates particles in random directions, is modeled according to the evaporation algorithm provided by H. H. K. Tang of IBM. Using the CLUST-EVAP code to model fragment produ6tion and the Vavilov-Landau theory to model fluctuations in direct ionization in thin silicon layers, we have predicted energy deposition in silicon components for various geometrical configurations. We have compared actual measurements with model predictions for geometry's such as single, thin silicon particle detectors, telescopic particle detectors flown in space to measure the environment, and thin sensitive volumes of modern micro-electronic components. We have recently compared the model predictions with actual measurements made by the DOSTEL spectrometer flown in the Shuttle payload bay on STS-84. The model faithfully reproduces the features and aids in interpretation of flight results of this instrument. We have also applied the CLUST-EVAP model to determine energy deposition in the thin sensitive volumes of modern micro-electronic components. We have accessed the ability of high energy (200 MeV) protons to induce latch-up in certain devices that are known to latch up in heavy ion environments. However, some devices are not nearly as susceptible to proton induced latch-up as expected according to their measured heavy ion latch-up cross sections. The discrepancy is believed to be caused by the limited range of the proton-silicon interaction fragments. The CLUST-EV AP model was used to determine a distribution of these fragments and their range and this is compared to knowledge of the ranges required based on the known device structure. This information is especially useful in accessing the risk to on-orbit perfonnance in a heavy ion environment based on testing performed with only protons.

ONeill, Pat M.↗
DOE OSTI2023

Energy-Efficient and Resilient Infrastructure: Simulation, Validation, and Installation

Advanced, high-performance computing at the National Renewable Energy Laboratory (NREL) has enabled access to vast data resources with cutting-edge software techniques to understand, design, plan for, and maintain energy-efficient and resilient infrastructure. We have focused on cities and airports, but the technology we have developed will easily translate to seaports, inland ports, military installations, or other complex and large-scale energy-intensive systems. We can digitally simulate and explore current and future scenarios to make datadriven decisions for optimizing advanced energy systems, transportation and building operations, infrastructure planning and expansion, and battery storage to guide short- and long-term investments, electrification strategies, and integration of new technologies.

Athena↗
DOE OSTI2021

Transmission-and-Distribution Dynamic Co-simulation Framework for Distributed Energy Resource Frequency Response

The rapid deployment of distributed energy resources (DERs) in distribution networks has made it challenging to balance the transmission system and stabilize frequency. DERs have the ability to provide frequency regulation services; however, existing frequency dynamic simulation tools - which were developed mainly for the transmission system - lack the capability to simulate distribution network dynamics with high penetrations of DERs. Although electromagnetic transient simulation tools can simulate distribution network dynamics, the computation efficiency limits their use for large-scale transmission-and-distribution (T&D) co-simulation. This paper presents an efficient open-source T&D dynamic co-simulation framework for DER frequency response based on the HELICS platform and off-the-shelf T&D simulators. The challenge of synchronizing the simulation time between the transmission network and the DERs in the distribution network is solved through the detailed modeling of DERs in frequency dynamic models while DER power flow models are also preserved in the distribution networks, thereby respecting local voltage constraints when dispatching DER power for frequency response. DER frequency response (primary and secondary) is simulated in case studies to validate the proposed framework. Last, the accuracy of the proposed co-simulation model is benchmarked, and a large T&D system simulation (2k transmission and 1M distribution nodes) is presented to demonstrate the efficiency and effectiveness of the overall framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗