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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Refinery Integration Analysis: Pathways, Challenges, and Opportunities

Integrating biomass-derived intermediates into traditional petroleum refineries presents unique challenges and opportunities, requiring innovative analysis approaches that account for biofuel producers and refiners. Consequently, teams within the National Renewable Energy Laboratory (NREL) have developed a comprehensive refinery integration analysis framework that combines experimental data, detailed techno-economic analyses of bio-conversion pathways, and economic projections within refinery linear programming (LP) optimization models. These models enable the identification of promising bio-integration strategies tailored to specific refinery configurations, economic conditions, and production goals. They also capture upstream and downstream impacts, highlighting critical bottlenecks and research opportunities for increasing the share of biogenic feedstocks in traditional refining operations. Refinery models are also packaged into a broader bio-economy optimization framework which enables biofuel supply chain optimization with standalone biofuel production and refinery co-processing/repurposing options. This presentation discusses promising refinery bio-integration strategies along with key challenges and opportunities identified using NREL's refinery and bio-economy optimization frameworks.

09 BIOMASS FUELS↗
DOE OSTI2025

A LOCA Analysis Tool: Coupling RELAP5-3D to BISON

Experimental evidence illustrates that at burnups slightly above the current regulatory limit of a rod-averaged burnup of 62 MWd/kgU, the ceramic UO 2 inside light-water reactor fuel rods becomes susceptible to a phenomenon known as fuel fragmentation, relocation, and dispersal (FFRD) during a loss of coolant accident (LOCA) transient. The severity of FFRD is strongly influenced by the zirconium-based (Zircaloy) cladding behavior during the LOCA transient. A Technology Commercialization Fund (TCF) project was awarded to an Electric Power Research Institute (EPRI)/Idaho National Laboratory team to create a LOCA analysis tool that couples BISON to the systems/thermal-hydraulics code RELAP5-3D [1] for analysis of LOCA scenarios. In addition, further refinements to existing BISON models were identified as necessary to more accurately represent more recent experimental evidence from the Studsvik Cladding Integrity Project (SCIP) and other experimental programs.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

An Integrated ML/AI Framework for Digitizing, Structuring and Searching DOE U-TRU-Fuels Data with Gap Analysis of Non-DOE Records

The U.S. Department of Energy (DOE) Advanced Fuels Campaign (AFC) is advancing transmutation fuel technologies to reduce long-lived radioactive waste by converting minor actinides into shorter-lived or stable elements through irradiation in sodium-cooled fast reactors. Key experiments such as AFC-1, AFC-2, FUels for the transmutation of Trans-URanium elements In phéniX (FUTURIX)-Fortes Teneurs en Actinides (FTA), and Experimental Breeder Reactor-II (EBR-II) X501 have provided fuel fabrication, irradiation, and performance data on various transuranic-bearing fuel forms. This report documents the creation of an artificial-intelligence assisted database, which has consolidated all DOE-owned data related to Transuranic (TRU)-bearing fuel experiments and stored across it across both the Idaho National Laboratory (INL) Nuclear Data Management and Analysis System and the INL high performance computing (HPC) infrastructure. A dedicated webpage, hosted on the INL HPC system, has been developed to support role-based access and data interaction. The database architecture allows researchers to navigate large, heterogeneous archives with far greater speed and accuracy than manual search and lays the foundation for future expansion into multimodal nuclear materials analysis environments. The database represents a major step towards a nationally integrated fuels database utilizing artificial intelligence tools.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Enabling Dynamic Probabilistic Risk Assessment of Physical Security Using EMRALD and MAAP (Presentation)

The optimization of physical security in nuclear power plants requires sophisticated methodologies that integrate operator actions and plant behavior through advanced simulation tools. Idaho National Laboratory has developed the Modeling and Analysis for Safety and Security using the Dynamic EMRALD Framework (MASS-DEF) methodology, an approach that integrates force-on-force simulations, dynamic probabilistic risk assessment, and thermal hydraulics modeling to enhance security planning while reducing costs. A reduced order model for thermal hydraulic simulations performed by the Modular Accident Analysis Program (MAAP) was developed to evaluate reactor core behavior during attack scenarios. MAAP simulations are computationally intensive and must be run in a secure environment, complicating analysis and validation. By pre-computed scenario outcomes for a small number of modified parameters, the reduced order model significantly decreases the computational cost and enables offsite review of the results.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

LDES-Sizing-and-siting-model (Power grid test cases for long-duration energy storage (LDES) siting) [SWR-25-75]

This repository contains code and data for performing a siting analysis of long-duration energy storage (LDES) in the 5-bus and RTS systems using the Sienna suite developed by the National Renewable Energy Laboratory for production cost modeling (PCM). This analysis involves moving the LDES component to different buses in the system, running a simulation, and considering the production cost of the simulation. Different system configurations are also analyzed with these scripts (such as moving load or renewable dispatch generators to different buses) to observe the impacts the system configuration has on optimal siting. This repository contains three sub directories discussed below. Both the 5-bus and RTS systems have two different initial configurations for the renewable energy components in them, one that is predominantly PV-driven and one that is predominantly wind-driven. Sienna suite can be found here: https://github.com/NREL-Sienna

Cole, David [University of Wisconsin]↗
DOE OSTI2025

The Elastic Analysis Facility's (EAF's) Contribution to the Future of Analysis at Multi-Experiment Institutions and Future Colliders

The Elastic Analysis Facility (EAF) hosted at Fermi National Accelerator Laboratory (Fermilab) is a platform being developed with the goal of providing a fast and efficient facility for physics analysis. As high-energy physics moves towards collecting larger datasets, such as those from the High-Luminosity LHC, the EAF strives to provide a powerful and adaptable framework for future colliders and multi-experiment institutions. Currently, the EAF supports several experiments including CMS, NOvA, and DUNE as well as serving accelerator physicists and beam line operations through integrated software and secure connections to Fermilab's computing resources. In addition, the EAF was designed with a user-friendly interface, intended to be more intuitive for emerging generations of physicists, that is still accessible for established styles of analysis. The EAF can also achieve better analysis efficiency due to the modernization of software and tools that can better utilize Fermilab's computing power. Furthermore, its design incorporates industry standards whenever possible, enhancing its sustainability and making it a possible template for other national or international laboratories and research facilities. Overall, the EAF is a forward-looking solution that will meet the evolving needs of particle physics, ensuring readiness for future colliders and multi-experiment research institutions.

Chavez, Elise [Wisconsin U., Madison]↗
DOE OSTI2025

Using Calibrated Sodium Data for Preliminary Validation of the SRT Code for Advanced Reactors

Various types of non-light water reactors are currently engaged in the U.S. licensing process. Because of inherent differences compared with well-established large light water reactors, appropriate assessment tools are needed. Specifically, source term analysis, which determines environmental dose impacts from potential accident scenarios, is a crucial part of design and licensing. The U.S. Nuclear Regulatory Commission has emphasized the importance of mechanistic source term analysis for advanced reactor deployments. To align with these needs, Argonne National Laboratory has developed the Simplified Radionuclide Transport (SRT) source term analysis code for metal fuel Sodium-cooled Fast Reactors (SFRs) and microreactors. SRT conducts time-dependent radionuclide transport and retention in SFRs for core and ex-core radionuclide source accident sequences. The main objective of SRT is to provide rapid sensitivity and uncertainty analyses, incorporating parametric uncertainties and summarizing probabilistic results. As part of the code validation process, a study focused on the bubble scrubbing module was performed using an experiment recently carried out by the University of Wisconsin-Madison. Based on the analysis, the modeling approach in SRT provides accurate results for small and large aerosols, while slight underprediction of radionuclide aerosol removal are observed for medium sized aerosols. However, the deviation is minor, considering the highly uncertain phenomenon and range of results, and is in the conservative direction. In addition, uncertainty information derived from the experiments is further implemented, reflecting the actual span of parameters, which leads to enhanced agreement with code predictions. The results demonstrate that SRT provides reasonable predictions for the bubble scrubbing process in sodium pool.

Kam, Dong Hoon↗
DOE OSTI2025

Evaluation of CMIP6 Streamflow in the Arctic

Earth system models (ESMs) serve as the primary basis for projecting future streamflow changes, but they are biased in terms of the their ability to reproduce historical observations of streamflow. Rigorous downscaling and bias-correction procedures, which are time-consuming and introduce uncertainties, are commonly used to address these biases in streamflow or runoff. However, the applicable limits of streamflow projections remain largely untested through direct comparisons with observations. In this work, we compare historical streamflow time series observations with a suite of Coupled Model Intercomparison Project phase 6 (CMIP6) ESMs to assess the space and time scales at which each ESM represents historic streamflow observations. We focus our analysis on streamflow across the Arctic given the higher degree of warming experienced over this region, underscoring its importance for understanding the future changes. A series of metrics—volume, seasonality, extreme capturing streamflow variability, drought, and flooding is tested to assess ESM simulation skill for total events and overall distributions. Our results indicate that although improvements are necessary in ESM runoff and streamflow projections, the use of temporal averaging and targeting the best-fit model for comparison to observations when a suite of ESMs is considered can provide robust streamflow projections. Moreover, this work provides a basis for contextualizing the limits of ESM applicability for studies aiming to assess streamflow in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

ExaFEL: extreme-scale real-time data processing for X-ray free electron laser science

ExaFEL is an HPC-capable X-ray Free Electron Laser (XFEL) data analysis software suite for both Serial Femtosecond Crystallography (SFX) and Single Particle Imaging (SPI) developed in collaboration with the Linac Coherent Lightsource (LCLS), Lawrence Berkeley National Laboratory (LBNL) and Los Alamos National Laboratory. ExaFEL supports real-time data analysis via a cross-facility workflow spanning LCLS and HPC centers such as NERSC and OLCF. Our work therefore constitutes initial path-finding for the US Department of Energy's (DOE) Integrated Research Infrastructure (IRI) program. We present the ExaFEL team's 7 years of experience in developing real-time XFEL data analysis software for the DOE's exascale supercomputers. We present our experiences and lessons learned with the Perlmutter and Frontier supercomputers. Furthermore we outline essential data center services (and the implications for institutional policy) required for real-time data analysis. Finally we summarize our software and performance engineering approaches and our experiences with NERSC's Perlmutter and OLCF's Frontier systems. This work is intended to be a practical blueprint for similar efforts in integrating exascale compute resources into other cross-facility workflows.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Herbaceous Feedstock 2022 State of Technology Report

The U.S. Department of Energy promotes production of advanced liquid transportation fuels from lignocellulosic biomass by funding fundamental and applied research that advances the state of technology (SOT). As part of its involvement in this mission, Idaho National Laboratory completes an annual SOT report for nth-plant and 1st-plant herbaceous biomass feedstock logistics. The purpose of the SOT is to provide the status of feedstock supply system technology development for herbaceous biomass to biofuels relative to technical targets and cost goals from specific design cases, based on data and experimental results. Although conventional feedstock supply systems form the backbone of the emerging biofuels industry, they have limitations that restrict widespread implementation on a national scale. To meet the demands of the future industry, the feedstock supply system must shift from the conventional system to what has been termed “advanced” supply systems. In advanced designs, a distributed network of aggregation and processing centers, termed “depots,” are employed near the points of biomass production (i.e., the field or forest) to reduce feedstock variability and produce feedstocks of a uniform format, moving toward biomass commoditization. The 2022 Herbaceous SOT is part of a vision of achieving an implemented advanced feedstock supply system, which produces a stable, tradable commodity at the decentralized distributed depot. It utilizes feedstock fractionation by incorporating technologies that can separate the biomass into its anatomical fractions (leaves, husks, stems and cobs) to reduce impurities and produce fractions that satisfy downstream quality considerations. By using a series of air classification steps, this strategy can reduce the extrinsic ash in corn stover and produce enriched tissue fractions that can be blended to a conversion specification or converted individually in optimized biochemical conversion campaigns. Additionally, a majority of the leaves (which do not meet the quality specification) are separated out early and can be supplied to alternate markets. The 2022 Herbaceous SOT incorporates an advanced biomass fractionation and processing system to produce pellets enriched tissues from three-pass corn stover. The resulting enriched pellets are delivered to the biorefinery individually where they can be blended to a specification or converted in campaigns where the conditions are optimized for each tissue. Unused fractions can be sent to a a midstream market or to a different conversion process that is better suited to their properties to offset the cost of the delivered feedstock. The main benefits from the proposed system can be summarized as: (1) $6.86/dry ton (2016$) lower cost for the air classification due to elimination of the requirement to discard the high ash lights fraction; (2) $1.56/dry ton lower delivered cost by selling the unsuitable leaf fraction into the feed market as a midstream co-product (assuming a selling price that is 11% higher than their cost of production); (3) 0.98% increase in carbohydrate content (from 60.16% to 61.14%); and (4) 0.97% decrease in ash content (from 6.00% to 5.03%) compared to the 2021 Herbaceous SOT. Overall, the 2022 nth-plant Herbaceous SOT predicts a modeled delivered feedstock cost of $78.64/dry ton (2016$) if it is assumed that the enriched leaf fraction is sold at its production cost; this is a slight increase of $0.43/dry ton increase from the 2021 Herbaceous SOT nth-Supply case cost. The increased cost derived from a $0.38/dry ton increase in transportation and handling cost to procure more biomass (to replace the enriched leaf fraction that was not delivered to the biorefinery. The total preprocessing cost was $0.27/dry ton higher than the 2021 result because of updates to energy consumption, purchasing price and dry matter loss data for the rotary shear ($3.00/dry ton increase) and the pelleting mill ($4.52/dry ton increase). The data utilized were generated in pilot-scale tests in the Biomass Feedstock National User Facility (BFNUF) at INL and at Forest Concepts, including tests for rotary shear and pelleting of the air classified fractions. A greenhouse gas emissions analysis was performed by Argonne National Laboratory using the most up to date version of the Greenhouse Gases, Regulated Emissions, and Energy use in Transportation model (GREET®). The analysis showed an increase of 17.34 kg CO2e/dry ton from the 2021 SOT (67.71 kg CO2e/ton in the 2021 Herbaceous SOT to 85.05 kg CO2e/ton in the 2022 Herbaceous SOT). The net increase is primarily attributed to increased energy consumption in pelleting mill.

09 BIOMASS FUELS↗
DOE OSTI2026

Sharing the Sun: Community Solar Deployment and Subscriptions (As of January 2026)

The community solar market analysis presented here is based primarily on data collected through Sharing the Sun, an initiative of the National Community Solar Partnership+ (NCSP+). Sharing the Sun data collection and analysis are conducted by the National Laboratory of the Rockies (NLR) as part of its support for implementation of NCSP+. NLR first released a dataset of community solar projects in 2018 and updates it biannually. The January 2026 dataset, data collection methodology, and all the previous datasets are available from NLR's Data Catalog: https://data.nlr.gov/submissions/244. The dataset presents project-level information including location, capacity, operating utility, and year of interconnection. The dataset is created from multiple data sources such as utility data, public utility commissions, project developer websites, media releases, primary data collection by NLR, and data provided by developers under nondisclosure agreements. This presentation builds on a previous analysis of the community solar project dataset, Sharing the Sun: Community Solar Deployment and Subscriptions (as of June 2024). Dr. Gabriel Chan and his team at the University of Minnesota contribute to this effort. NCSP+ is led and funded by U.S. Department of Energy's Integrated Energy Systems Office (IESO).

14 SOLAR ENERGY↗
DOE OSTI2025

SAM Two-Phase Flow Model Development and Applications for Operational Transients in Advanced Reactors

As advanced nuclear technologies continue to develop, the need for the flexible operation and generation of these advanced reactors becomes necessary to maximize economic potential. As large-scale experiments are not always feasible, modeling and simulations of advanced reactors play a crucial role in design optimization and analysis. The SAM (System Analysis Module) code developed at Argonne National Laboratory is a state-of-the-art system-level thermal-hydraulic code aimed at simulating advanced reactor systems. Recent code developments have implemented two-phase flow modeling using the homogeneous equilibrium model, and a new steam generator component has been developed to utilize the two-phase flow implementation. In addition to verification tests, a load-following simulation was performed to model a realistic load-following transient in a proposed integrated system consisting of a conceptual advanced reactor known as the Advanced Burner Test Reactor (ABTR) and thermal energy storage (TES) tanks. The integrated system model uses two large TES tanks designed for sodium and a model helical coil steam generator to simulate the operational load-following transient. The flow rates of the feedwater and secondary loops are regulated to meet a prescribed steam generator load consistent with the electricity demand over a 24-h period. In conclusion, the results found the ABTR system was able to maintain stable reactor conditions and primary- and secondary-side characteristics over the course of the load-following transient.

Advanced Burner Test Reactor (ABTR)↗
DOE OSTI2024

Imaging and Analysis of Insoluble Electrorefiner Material

Throughout pyroprocessing efforts at the Hot Fuel Examination Facility (HFEF), insoluble material has accumulated in the Electrorefiner (ER) vessel. The objective of this effort was to analyze the accumulated material to determine its origin. Material was removed from the ER salt bath and distilled to remove excess salt prior to performing analysis. Three samples were sent for chemical and isotopic analysis and underwent an ethyl acetate-bromine dissolution to segregate the oxide fraction from the metal fraction. Actinide concentrations were determined using a quadrupole–inductively coupled plasma–mass spectrometer (Q-ICP-MS). Three additional samples were sent for morphologic and elemental analysis by scanning electron microscopy (SEM) with Energy Dispersive X-ray (EDX) analysis at the Irradiated Materials Characterization Laboratory (IMCL). Analyses suggest that the material is primarily composed of UO2. with a small fraction of metal. This study draws no single conclusion as to the origin of the insoluble material in the ER. The particle sizes and morphologies observed in SEM micrographs indicate that the larger particles observed may be a result of introducing material to the ER that has not been completely reduced in Oxide Reduction (OR) operations, which precede electrorefining. Smaller particles may be due to reactions with oxygen and moisture present in HFEF. This research suggests that microscopic analysis of fuel particles before and after OR operations (including after distillation) as well as the uranium product collected on the cathode in the ER would increase understanding about particle morphology within the pyrochemical process.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Legacy Metallic Fuel U(Pu)Zr Data Qualification

The Metallic Fuels Irradiation and Physics Database (FIPD) is an organized collection of legacy metallic fuel U(Pu)Zr data, measurements, and reactor conditions from the Experimental Breeder Reactor II (EBR-II) and the Fast Flux Test Facility (FFTF). The database provides a wealth of information and is easily accessed and utilized by the U.S. nuclear industry and the Nuclear Regulatory Commission (NRC). The database contains three categories of data. The first category covers fuel pin fabrication specifications, including fuel slug diameter and length, cladding diameter, smeared density, etc. The second consists of operational conditions, which include axial distributions of power, temperature, fluence, burnup, isotopic density, etc. The third category contains post-irradiation examination (PIE) results, largely collected at facilities now part of the Idaho National Laboratory (INL), formerly part of Argonne National Laboratory -West (ANL-W) and at the Alpha-Gamma Hot Cell Facility (AGHCF) at Argonne National Laboratory, comprising fission gas release and chemistry analysis, profilometry measurements, neutron radiography data, etc. All these data have been organized and preserved in FIPD.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Spread spectrum time domain reflectometry (SSTDR) and frequency domain reflectometry (FDR) cable inspection using machine learning

Cables are initially qualified for nuclear power plant use for 40 years. As plants extend their operating license to 60 and 80 years, justification for continued cable use must shift to a condition-based approach since it is cost prohibitive to completely replace cables that are likely still capable of performing their design function. The Pacific Northwest National Laboratory (PNNL) Accelerated and Real Time Experimental Nodal Analysis (ARENA) cable motor test bed was used to test the response of a commercial spread spectrum time domain reflectometry (SSTDR) system, a laboratory instrument software-controlled SSTDR, and a vector network analyzer-based frequency domain reflectometry (FDR) system to various cable anomalies. The three instrument systems were able to interrogate cables over a range of frequency bandwidths that can be helpful for human data analysis. Data were subjected to supervised and unsupervised machine learning (ML) analyses to distinguish normal undamaged cable responses from anomalous cable responses. Both supervised and unsupervised ML approaches produced encouraging results with an undamaged/anomalous prediction accuracy from 0.69% to 0.87%. Recommendations for further development and field implementation include increased and more balanced sample sets particularly including more training data.

SSTDR, FDR, Reflectometry, Machine Learning, ARENA↗