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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 OSTI2024

Fuel Performance Evaluation of THOR-C Experiments

The Temperature Heatsink Overpower Response Commissioning (THOR-C) and THOR-Metal (THOR-M) experiments will be performed as part of an ongoing project for testing sodium fast reactor fuels with the Japan Atomic Energy Agency (JAEA). The THOR-C experiments consist of fresh metallic fuel pins and have been analyzed using the ABAQUS, Ansys codes and the BISON fuel performance code. THOR-M-Loss of Flow-1 (THOR-M-LOF-1) is designed to test an EBR-II irradiated fuel pin under LOF conditions. Simulation of the THOR-MLOF-1 experiment required first simulating the base irradiation of the fuel pin in EBR-II. MFUEL module of SAS4A/SASSYS-1 [1] is a physics-based metallic fuel performance model applicable to the normal operation, transient scenarios and fuel failure modeling including scenarios with bulk fuel melting. The model has been validated using EBR-II normal operation, separate effect transient tests as well as TREAT M-Series transient tests [2]. In this study, MFUEL models has been utilized together with a new capsule heat transfer model developed in this project. The new heat transfer model was necessary due to (1) significant amount of heat losses that required 2D heat transfer, (2) the presence of a titanium heat sink, rejecting a significant amount of heat, and (3) stagnant coolant conditions, which are inconsistent with SAS4A/SASSYS-1 (SAS) heat transfer model. Updates to SAS4A/SASSYS-1 and MFUEL has been described below, followed by a preliminary validation effort using the results from THOR-C-2 fresh fuel capsule experiment. A previous study for THOR-C-2 analysis using BISON code is also utilized in this study to model this test [3]. [1] D. O’Grady, A. J. Brunett, L. Ibarra, A. Karahan, T. Kim, T. S. Sumner, R. Thomas, T. H. Fanning, “The SAS4A/SASSYS-2 Version 5.7 Safety Analysis Code System,” Argonne National Laboratory,ANL/NSE-SAS/5.7, (2023). [2] A. Karahan, T. Kim, T. Fanning, D. O’Grady, “Validation of MFUEL Metal Fuel Performance Models of SAS4A/SASSYS-1,” Argonne National Laboratory, ANL/NSE-23/11, (2023). [3] M. Mihelish, A. Zabriskie, K. Paaren, P. Medvedev, C. Jensen, “Fuel Performance Predictions for the TREAT THOR-C Experiments,” Idaho National Laboratory, INL/RPT-23-73397, Revision 0, (2023)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

GDSA Framework Development and Process Model Integration FY2024

The Disposal Research & Development (Disposal R&D) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Spent Fuel & High-Level Waste Disposition is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). A high priority for Disposal R&D is disposal system modeling (Sassani et al. 2023). The Geologic Disposal Safety Assessment (GDSA) work package is charged with developing a disposal system modeling and analysis capability for evaluating generic disposal system performance for nuclear waste in geologic media.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Updating and Refining BISON TRISO Assessment

Several advanced reactor concepts consider tristructural isotropic (TRISO) fuel in their designs. To support the advanced reactor industry, the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program within the U.S. Department of Energy (DOE) is developing modeling and simulation capabilities within the BISON fuel performance code. Assessment of these capabilities against available experi- mental data, such as the Advanced Gas Reactor (AGR) program, is essential in instilling confidence in the code predictions. Associated uncertainties in model inadequacy, experimental measurements, and manufacturing are also of importance. This report discusses the updating and standardization of the existing assessment suite in BISON for TRISO fuel performance analysis. The cases considered the AGR-1, AGR-2, and AGR-3/4 experimental programs. The report also includes a preliminary investigation into the sources of model inadequacy associated with silver (Ag) release fractions using the AGR-2 Compact 2-1-2 as a case study. The results indicate that uncertainties in the operating temperatures supplied to the model and the form of those temperature conditions (spatially averaged versus spatially resolved) both affect Ag release predictions. This suggests that previously observed model inadequacy can be attributed to both physical models and the operating conditions supplied to them. These findings will be leveraged to guide development of assessment cases for additional experiments and refinement of physical models for behaviors that potentially influence Ag release, such as palladium penetration.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

STRUCTURAL MODELING TO SUPPORT POST-YIELD ACCEPTANCE CRITERIA FOR SPENT NUCLEAR FUEL CLADDING

Spent nuclear fuel (SNF) is evaluated for structural failure during storage and transportation scenarios. The U.S. Department of Energy’s Spent Fuel and Waste Science and Technology (SFWST) program has sponsored significant research in quantifying mechanical loads on SNF during storage and transportation scenarios using experimental and modeling methods. The SFWST program has also performed significant research on measuring the mechanical behavior of irradiated SNF as defueled cladding segments and cladding with fuel pellets to measure composite behavior. This paper considers some of the key material data from the Sibling Pin testing and uses structural modeling and analysis methods that have been informed by testing to consider post-yield acceptance criteria for SNF cladding structural analysis. Test data published by Oak Ridge National Laboratory (ORNL) and Pacific Northwest National Laboratory (PNNL) are the foundation for informing the material behavior of the models developed in this study. In particular, four-point bend (4PB) tests of fueled and defueled cladding segments provide significant information about the bending failure mode of SNF. ORNL’s 4PB test data is on fueled cladding segments, so the composite behavior of SNF is demonstrated. This paper describes PNNL’s coincident beam model that was developed to approximate the composite behavior of SNF. This paper also presents PNNL’s structural dynamic finite element models of a cask tip-over scenario, which is predicted to cause the strongest mechanical loads on SNF of all postulated storage and transportation scenarios. SNF bending loads predicted in the cask tip-over scenario and cladding acceptance criteria beyond yield are considered, with justification based on the Sibling Pin test data. ASME Boiler and Pressure Vessel code stress intensity limits are also considered. The ultimate goal of this work is to aid in the justification of structural acceptance criteria for SNF cladding beyond the cladding’s irradiated yield strength for use in structural analysis of all storage and transportation scenarios.

Klymyshyn, Nicholas A.↗
DOE OSTI2024

Advances on CHP District Energy and Microgrids Deployment: Simplified Tool for Rapidly Deploying Feasibility Analytics for the Non-Technical User (Final Technical Report)

Community energy systems have proven to have the potential to improve cost efficiency, resilience, and decarbonize. However, investing in community energy systems such as community microgrids or district energy systems is a complex decision due to the high initial investment and the uncertainties associated with the long development time and lifecycle of the project. Tools that make feasibility assessments accessible to non-technical users like investors, policymakers, and other stakeholders will result in more feasibility analyses completed, more candidate projects identified, and more community energy systems deployed. The pilot tool developed under this award is named Energy Fellow. Energy Fellow allows technical and non-technical users to complete feasibility analyses for district energy systems and community microgrids. This is the first software tool of its kind designed for non-technical users and available at no cost. Its scope was adjusted to a 25x25-mile region within the Houston area in Texas to make its development compatible with the funding available. However, the findings and models developed make this pilot tool easily scalable to the US. The lessons learned during the design, implementation, and testing stages have helped find trade-off solutions to software and hardware challenges related to implementing 3D models in online tools. Green software strategies has been successfully applied to the design and operations of the tool, and the team has researched the aspects of the (non-technical) user experience that will make commercial developments of this tool even more impactful.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Predicting Fuel Properties and Emissions for Advanced Biofuels for Diesel Engines (CRADA Final Report)

The project will investigate variations in biofuel composition and optimize performance in combustion for conventional and future compression ignition engines. It will evaluate a variety of bio-derived molecules in the diesel range that can be produced using technology in ExxonMobil’s portfolio as well as fuels that cover the range of potential molecular structures for robust model development. Changes in fuel/air premixing and stratification in advanced engines could alter the relationship between fuel properties and performance in comparison to current generation spray combustion approaches. NREL experience in fuel and combustion modeling will enable development of general rules for predicting performance of a wide range of biofuel options.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Machine learning for the redox potential prediction of molecules in organic redox flow battery

Here, organic redox flow batteries (ORFB) are recognized as an innovative technology for the large-scale storage of renewable energy. The redox potential of organic redox-active molecules plays a vital role in their performance. Advanced screening techniques like high-throughput experiment and machine learning (ML) have significantly enhanced organic material performance and transformed the field of ORFB. However, the scarcity of experimental data poses a considerable challenge for ML model development in this domain. In our study, we developed lightweight graph-based Gaussian process regression (GPR) models with GPU-accelerated marginalized graph kernel and hybrid kernel to predict the redox potentials of organic redox-active molecules for ORFBs, specifically focusing on small datasets. To evaluate model accuracy, we created a new experimental database of organic redox-active molecules by the data from hundreds of published papers and assembled previous computational datasets. We also considered some key parameters, such as pH conditions and solvent type, to assess their impact on redox potential prediction. Our GPR model predicted redox potentials with high accuracy across all datasets using minimal training data. The study provides powerful tools for molecule screening and design and delivers valuable guidance on designing training datasets for costly experiments.

25 ENERGY STORAGE↗
DOE OSTI2024

Macroscopic modeling of gas permeability in hierarchical micro/nanoporous media: A unified characterization of rarefaction using Klinkenberg theory and equivalent diameter

Estimating gas transport through a hierarchical micro/nanoporous system is challenging due to non-equilibrium gas dynamics. The primary difficulty lies in determining the rarefaction level, because identifying a representative flow dimension in a complex porous system with multiple pore scales is not straightforward. Our study performed a pore-level analysis for gas permeability in dual-scale porous media with varying porosity, throat size, and secondary pore size under different rarefaction conditions. We found that secondary porosity negatively affects permeability due to increased friction forces, with this influence growing as the secondary pore size and porosity increase until the secondary pore becomes comparable to the throat. However, rarefaction reduces the effects of secondary pores due to boundary slip. Traditional Knudsen number (Kn) calculations based on Darcy-defined height failed to accurately describe the rarefaction effects on gas permeability. Instead, we introduced an equivalent diameter to calculate the Kn, which provided an accurate normalization of apparent gas permeability independent of pore geometry. Furthermore, the extended Kozeny–Carman–Klinkenberg model developed in our previous study successfully yielded a macroscopic model for apparent gas permeability in hierarchical micro/nanoporous systems as a function of the traditional Darcy height and porosity.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2026

Cyote-attack Chain Estimator

Attack Chain Estimator (ACE) Application Overview The Attack Chain Estimator (ACE) Application is a sophisticated tool designed for the ingestion, classification, sequencing, and enrichment of cybersecurity threat reports. This application leverages advanced machine learning models and extensive historical data to provide comprehensive insights into cyber threats, specifically targeting Industrial Control Systems (ICS). Purpose The primary functions of the ACE Application include: Ingestion of Cybersecurity Threat Reporting: Capable of ingesting text-based threat reports in markdown or text file format. Supports ingestion of structured data from other sources in STIX/JSON format. Classification of Report’s Text-Based Events: Utilizes a DeBERTa classifier, specifically trained on cybersecurity data, to map the events to MITRE ATT&CK for ICS Tactics and Techniques. Classification is performed using multiple Jupyter notebooks and machine learning workflows hosted as FastAPI microservices: regex_data deberta_base_35_train_hft_classifier_mlflow.ipynb hft_regex_classifier_mlflow.ipynb param_train_hft_classifier_mlflow.ipynb regex_tactic_tech.ipynb Ordering of Tactics, Techniques, and Observable Events: Sequences the identified tactics, techniques, and events to form a coherent attack chain. Enrichment with Historical Attack Chain Details: Enhances the attack chain with details from historical attacks using a Markov model developed from CyOTE Precursor Analysis Report data. The Markov model is available as a FastAPI endpoint for seamless integration. Enrichment with Adversary Emulation Capabilities Data: Integrates adversary emulation capabilities data using MITRE Caldera for OT adversary abilities UUIDs. Export of Output Files: Provides options to export the enriched attack chain in JSON or CSV formats. Routing of Output to Other Applications: Facilitates routing of output to various platforms and applications, including: Threat Intelligence Platforms COREII Scout for Threat Intelligence Analysis COREII Modeling and Simulation for Adversary Emulation Technical Description The ACE Application is an advanced cybersecurity tool designed to provide detailed threat analysis and sequence generation. It is built on a robust architecture that integrates natural language processing, machine learning, and historical data modeling. Key Components: Data Ingestion Module: Handles the input of threat reports and data from various formats, ensuring flexibility in data sources. Classification Engine: Employs DeBERTa-based classifiers hosted as FastAPI microservices to analyze and classify threat report events in accordance with the MITRE ATT&CK framework for ICS. Sequence Generator: Orders the classified events into a logical attack chain, providing clear insight into the sequence of tactics and techniques used in the threat. Enrichment Engine: Integrates historical data and adversary emulation capabilities to enhance the attack chain with valuable context and additional details. The historical data enrichment is powered by a Markov model, which is available as a FastAPI endpoint. Export and Routing Module: Facilitates the export of the enriched attack chain in multiple formats and routes the output to designated applications for further analysis or emulation.

Paul, Tony [Idaho National Laboratory (INL), Idaho↗
DOE OSTI2024

Global Geo-processed Data of Aquifer Properties by 0.5° Grid, Country and Water Basins

This repository of global hydrogeologic datasets contains aquifer properties on 0.5° scale, including depth to groundwater (Fan et al., 2013), aquifer thickness (de Graaf et al., 2015), WHYMap aquifer classes (Richts et al., 2011), recharge (Döll and Fiedler, 2008; Gleeson et al., 2016), lakes (Messager et al., 2016), porosity and permeability (Gleeson et al., 2014), digitized and geo-processed from their respective sources. Globally gridded aquifer properties could be used independently to estimate global groundwater availability or used as critical inputs to the superwell model to simulate groundwater extraction and provide estimates of pumped volumes and unit costs under user-specific scenarios. Key resources related to this data are: Niazi, H., Ferencz, S. B., Graham, N. T., Yoon, J., Wild, T. B., Hejazi, M., Watson, D. J., & Vernon, C. R. (2025). Long-term hydro-economic analysis tool for evaluating global groundwater cost and supply: Superwell v1.1. Geoscientific Model Development, 18(5), 1737-1767. https://doi.org/10.5194/gmd-18-1737-2025 superwell model repository which uses this data to simulate groundwater extraction and provides estimates of the global extractable volumes and unit-costs ($/km3) of accessible groundwater production under user-specified extraction scenarios. Repository Overview Main output: aquifer_properties_rec.csv contains all processed outputs, including aquifer properties like porosity, permeability, recharge, lake areas, aquifer thickness, and depth to groundwater. shapefiles.zip: contains all digitized GIS databases and shapefile for all aquifer properties prep_inputs.R and prep_inputs_recharge_lakes.R: R scripts that process the shapefiles to produce the aquifer_properties_rec.csv file plot_inputs.R: R script for plotting the maps and conducting preliminary analysis on the available groundwater volume basin_to_country_mapping.csv, basin_country_region_mapping.csv and continent_county_mapping.csv provide the mapping between continents, 32 energy-economic macro regions, countries, and water basins for post-processing aquifer_properties_rec.csv Maps: Each map visualizes the spatial distribution of one of the aquifer properties across the globe map_in_Porosity.png map_in_Permeability.png map_in_Aquifer_thickness.png map_in_Depth_to_water.png map_in_Recharge.png map_in_Grid_area_km.png map_in_Lake_area_km.png map_in_WHYClass.png Sample inputs sample_inputs.py: this script samples inputs from the aquifer_properties_rec dataset, ensuring the sampled and original inputs maintain the same distributions sampled_data_100.csv contains 100 sampled data points and sampled_data_100.png compares their distributions Dataset Overview The main outputs are consolidated in a comprehensive aquifer_properties_rec.csv file and include the following fields: GridCellID: Unique identifier for each (roughly 0.5°) grid cell Continent: Continent name Country: Country name GCAM_basin_ID: Identifier for GCAM hydrologic basin Basin_long_name: Full name of the basin WHYClass: Hydrogeologic classification based on WHYMap aquifer classes (Richts et al., 2011) Porosity: Soil porosity (%) (Gleeson et al., 2014) Permeability: Soil permeability (in square meters; Gleeson et al., 2014) Aquifer_thickness: Thickness of the aquifer (in meters; de Graaf et al., 2015) Depth_to_water: Depth to groundwater (in meters; Fan et al., 2013) Recharge: long-term annual averaged recharge rates (in m/yr; Döll and Fiedler, 2008; Gleeson et al., 2016) Grid_area: Area of the grid cell (in square meters) Lakes_area: Area of inland lakes (in square meters; Messager et al., 2016) Key References The datasets are digitized versions of global hydrogeologic properties from the following key literature sources: Depth to Groundwater: Fan, Y., Li, H., & Miguez-Macho, G. (2013). Global Patterns of Groundwater Table Depth. Science, 339(6122), 940-943. https://doi.org/10.1126/science.1229881 Aquifer Thickness: de Graaf, I. E. M., Sutanudjaja, E. H., van Beek, L. P. H., & Bierkens, M. F. P. (2015). A high-resolution global-scale groundwater model. Hydrol. Earth Syst. Sci., 19(2), 823-837. https://doi.org/10.5194/hess-19-823-2015 Porosity and Permeability: Gleeson, T., Moosdorf, N., Hartmann, J., & van Beek, L. P. H. (2014). A glimpse beneath earth's surface: GLobal HYdrogeology MaPS (GLHYMPS) of permeability and porosity. Geophysical Research Letters, 41(11), 3891-3898. https://doi.org/10.1002/2014GL059856 Aquifer classes: Richts, A., Struckmeier, W. F., & Zaepke, M. (2011). WHYMAP and the Groundwater Resources Map of the World 1:25,000,000. In J. A. A. Jones (Ed.), Sustaining Groundwater Resources: A Critical Element in the Global Water Crisis (pp. 159-173). Springer Netherlands. https://doi.org/10.1007/978-90-481-3426-7_10 Recharge: Döll, P., & Fiedler, K. (2008). Global-scale modeling of groundwater recharge. Hydrol. Earth Syst. Sci., 12(3), 863-885. https://doi.org/10.5194/hess-12-863-2008; Gleeson, T., Befus, K. M., Jasechko, S., Luijendijk, E., & Cardenas, M. B. (2016). The global volume and distribution of modern groundwater. Nature Geoscience, 9(2), 161-167. https://doi.org/10.1038/ngeo2590 Inland Lakes: Messager, M. L., Lehner, B., Grill, G., Nedeva, I., & Schmitt, O. (2016). Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nature Communications, 7(1), 13603. https://doi.org/10.1038/ncomms13603 Cite as Niazi, H., Watson, D., Hejazi, M., Yonkofski, C., Ferencz, S., Vernon, C., Graham, N., Wild, T., & Yoon, J. (2024). Global Geo-processed Data of Aquifer Properties by 0.5° Grid, Country and Water Basins. MultiSector Dynamics-Living, Intuitive, Value-adding, Environment. https://doi.org/10.57931/2484226 Contact Reach out to Hassan Niazi or open an issue in the superwell repository for questions or suggestions.

aquifer thickness↗
DOE OSTI2025

Model Calibration with Markov Chain Monte Carlo Tutorial

The purpose of this tutorial is to demonstrate how to use Markov chain Monte Carlo (MCMC) to calibrate a model. By calibration, we mean the selection of model parameters (and, when relevant, structures). A common goal in model development and diagnostics is calibration, or the identification of model structures and parameters which are consistent with data. While models can be calibrated through hand-tuning parameters or minimizing simple error metrics such as root-mean-square-error (RMSE), these approaches can underrepresent the probabilistic nature of the data-generating process, as well as the potential for multiple model configurations to be consistent with the data. Probabilistic uncertainty quantification, which is the topic of this notebook, can address these concerns. This tutorial is presented as an appendix to the e-book: Addressing Uncertainty in MultiSector Dynamics Research.

Markov chain Monte Carlo↗
DOE OSTI2026

Informing Robust Functional Relationship Benchmarks: An Evaluation of the Temperature Sensitivity of Ecosystem Respiration Across the Arctic-Boreal Region

During land model development, simulated carbon dynamics are often benchmarked against observational data sets to evaluate model performance. Functional relationship benchmarks are the relationship between a driving variable (e.g., temperature) and a response variable (e.g., ecosystem respiration) and are a promising tool for assessing model performance by evaluating modeled sensitivities to changing environmental conditions. However, observed functional relationships can be influenced by choices made during data collection and throughout the benchmarking process, impacting the inferred skill of land models. To avoid misrepresenting a model's true performance, it is necessary to systematically evaluate best practices when constructing functional relationship benchmarks. We developed a set of guidelines for constructing functional relationship benchmarks, considering the choice of data set, number of daily observations, temporal extent, and temporal resolution across Alaska and Canada over a 20-year period from 2001 to 2020. The temperature sensitivity of ecosystem respiration from observations, evaluated through an apparent Q 10 , is highly variable both spatially and as a result of the data processing approach applied in the benchmark formation. When benchmarking 13 models from the Warming Permafrost Model Intercomparison Project (WrPMIP), the range in inferred model skill is substantially impacted by the choices applied in constructing functional relationship benchmarks. The inferred performance of a given model is most sensitive to the number of daily observations and temporal extent, followed by choice of benchmark data set and temporal averaging. Results from this analysis can guide the development of consistent and robust functional relationships for future model evaluation studies.

Poe, Jeralyn [Northern Arizona University, Flagsta↗
DOE OSTI2025

CFD Simulations of Lower Plenum Mixing

Review of model development and validation performed in the Advanced Reactor Technologies (ART) program for thermal mixing at the outlet of High Temperature Gas Reactors (HTGRs). Understanding the mixing that occurs in the lower plenum in an HTGR is necessary to facilitate design improvements and to perform reactor safety analysis. Numerical models are one possible approach to gain a better understanding of mixing in the lower plenum. Given the complexity of the geometry and the intense mixing present, it is important to perform validation of numerical models. Three models have been developed during FY2025: a porous media with Pronghorn, a Reynolds Averaged Navier Stokes (RANS) with STAR-CCM+, and a Large Eddy Simulation (LES) with NekRS. The reference facility is a scaled-down version of the lower plenum of the High Temperature Gas-Cooled Reactor - Pebble-bed Module (HTR-PM) demonstration reactor. Preliminary results of the porous media and the RANS shows general good agreement against experimental benchmark data. Future work will leverage high-fidelity results obtained through LES to guide model selection and improvements to the lower-fidelity models, with particular attention to the Pronghorn porous media.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Surrogate construction via weight parameterization of residual neural networks

Surrogate model development is a critical step for uncertainty quantification or other sample-intensive tasks for complex computational models. Here, in this work, we develop a multi-output surrogate form using a class of neural networks (NNs) that employ shortcut connections, namely Residual NNs (ResNets). ResNets are known to regularize the surrogate learning problem and improve the efficiency and accuracy of the resulting surrogate. Inspired by the continuous, Neural ODE analogy, we augment ResNets with weight parameterization strategy with respect to ResNet depth. Weight-parameterized ResNets regularize the NN surrogate learning problem and allow better generalization with a drastically reduced number of learnable parameters. We demonstrate that weight-parameterized ResNets are more accurate and efficient than conventional feed-forward multi-layer perceptron networks. We also compare various options for parameterization of the weights as functions of ResNet depth. We demonstrate the results on both synthetic examples and a large scale earth system model of interest.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Aluminum Ultra-conductors for Energy-Efficient Aerospace Busbar Applications (Abstract)

In this project, we will develop aluminum ultra-conductors with graphene additives demonstrating enhanced electrical conductivity at 90 °C compared to electric grade aluminum alloy AA1100 (43% IACS at 90 °C). While ultra-conductivity has been developed in copper and copper alloys, it is yet to be reported extensively in aluminum-based materials. This project will scale initial work done at PNNL on aluminum ultra-conductors using shear-assisted processing and extrusion (ShAPETM), a novel solid phase processing technique. Ultra-conductors are an emerging class of composites, comprised of a metal substrate with small quantities of nanocrystalline additives such as graphene or carbon nanotubes that demonstrate enhanced conductivity at relevant operating temperatures. Aluminum ultra-conductors can improve efficiency and power density while reducing the demand for copper in a wide range of applications, such as power transmission cables and electric motors. Busbars are an important component in aerospace systems that require lightweight and high-current power distribution including both future electric vertical take-off and landing (eVTOL) aircrafts and current aircraft electrical systems. We will accelerate aluminum ultra-conductor composite formulation development using combinatorial synthesis and testing methods aided by process/microstructure modeling, developed previously at PNNL. Eaton will test the properties of the ShAPE aluminum ultra-conductor feedstock (used to make the busbars) in relevant operating conditions (20 – 90 °C), predict the improvement in busbar performance when manufactured with ultra-conductors over commercial conductors (such as AA1100), and perform technoeconomic analysis to evaluate the potential for commercialization of ShAPE aluminum ultra-conductors. The project is expected to have a budget of $\$375$K, with $\$300$K in federal funding and $\$75$K in-kind cost-share contribution from Eaton over a period of performance of 24 months. Of the $\$300$K of federal funds, $\$140$K is allocated for CRADA activities that generate intellectual property (IP), and the remaining $\$160$K is reserved for modeling, material testing, characterization, travel, and reporting-related activities.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Defect Thermodynamics and Transport Properties of Proton Conducting Perovskite Electrode and Electrolyte Materials Evaluated Based on Density Functional Theory Modeling

Both electron-rich and electron-poor perovskite oxides have been used in solid oxide cell applications as electrode and electrolyte materials. The rich oxygen defect chemistry and its coupling to temperature, hydrogen-steam or oxygen-steam gas pressure, or to the applied potentials creates enormous complexities for modeling performance and degradation of the materials. Herein, density functional theory-based thermodynamic modeling was carried out to describe the defect chemistry and transport properties of the proton-conducting electrolyte BaZr1-xYxO3-δ (x≤0.1) and of the triple-conducting perovskite (La,Ba)(Fe,M)O3-δ (M=Y and Zr). The defect thermodynamics of intrinsic point defects and the hydrogen-related defect reactions were solved in integrated defect models and further used to predict the Brouwer diagram and the transport properties of the functional perovskites. For the electron-poor electrolytes BaZr0.9Y0.1O3-δ, the developed model has been used to describe the experimental transport properties in the SOC operating conditions. Specifically, the roles played by the acceptor-bound holes and the intrinsic and hydrogen point defects upon the conductivities of holes, protons, and oxygen vacancies under the hydrogen-rich and oxygen-rich conditions at various humidity levels were demonstrated. A defect modeling tool was also developed for the triple-conducting perovskite (La,Ba)(Fe,M)O3-δ (M=Y and Zr) to examine magnetic effects and hydride defects in defect equilibria.

defect thermodynamics↗
DOE OSTI2026

Editorial: Predicting near-earth space environment: new perspective and capabilities in the AI age

Editorial on the Research Topic Predicting near-earth space environment: new perspective and capabilities in the AI age The near-Earth space environment is not only an operational hazard for space missions, but also a scientific laboratory for advancing our understanding and prediction of space plasma populations. This Research Topic is organized around three interconnected themes: observational datasets, machine-learning (ML) model development, and the discovery of new physical insights through those models. Its primary goal is to highlight the emerging capabilities in space environment prediction that are enabled, or will be enabled, by integrating advanced techniques—including AI/ML methods—with long-term curated datasets.

58 GEOSCIENCES↗
DOE OSTI2024

Solidification and crystallographic texture modeling of laser powder bed fusion Ti-6Al-4V using finite difference-monte carlo method

Laser powder bed fusion (LPBF) additive manufacturing makes near-net-shaped parts with reduced material cost and time, rising as a promising technology to fabricate Ti-6Al-4V, a widely used titanium alloy in aerospace and medical industries. However, LPBF Ti-6Al-4V parts produced with 67° rotation between layers, a scan strategy commonly used to reduce microstructure and property inhomogeneity, have varying grain morphologies and weak crystallographic textures that change depending on processing parameters. Here, this study predicts LPBF Ti-6Al-4V solidification at three energy levels using a finite difference-Monte Carlo method and validates the simulations with large-area electron backscatter diffraction (EBSD) scans. The developed model accurately shows that a <001> texture forms at low energy and a <111> texture occurs at higher energies parallel to the build direction but with a lower strength than the textures observed from EBSD. A validated and well-established method of combining spatial correlation and general spherical harmonics representation of texture is developed to calculate a difference score between simulations and experiments. The quantitative comparison enables effective fine-tuning of nucleation density (N 0 ) input, which shows a nonlinear relationship with increasing energy level. Future improvements in texture prediction code and a more comprehensive study of N 0 with different energy levels will further advance the optimization of LPBF Ti-6Al-4V components. These developments contribute a novel understanding of crystallographic texture formation in LPBF Ti-6Al-4V, the development of robust model validation and calibration pipeline methodologies, and provide a platform for mechanical property prediction and process parameter optimization.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗