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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Assessment of Deep Burnup HALEU Fuel Impact and Requirements

This report explores advancements in nuclear fuel technology, focusing on once-through high-assay low-enriched uranium (HALEU) fuels. It examines the potential for increased fuel residence time in reactors and the fuel cost implications. The study highlights the differences between fast and thermal reactors in terms of fuel enrichment and burnup, emphasizing the complex relationship in fast reactors in which increased core size and fuel density can lead to lower enrichment requirements. Various advances in fuel technologies for light-water reactors (LWR) and sodium-cooled fast reactors (SFR) are presented in this report to provide more comprehensive analysis.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Updates to the HTR-PROTEUS HALEU Benchmark Using Modern Analysis Methodologies

The HTR-PROTEUS IRPhEP Handbook benchmarks represent some of the highest quality benchmarks available for systems with TRISO-HALEU fuel, graphite pebbles, graphite reflector, and high neutron leakage. Nevertheless, significant variability in computed eigenvalue results was encountered in HTR-PROTEUS configurations that are relevant for transportation configurations. The team will investigate the source of these variabilities in the original benchmark. In addition, the team will investigate the HTR-PROTEUS subcritical measurements, kinetics data, and potential criticality effects from the introduction of hydrogen in the system (water ingress). The project will generate a benchmark evaluation of additional HTR-PROTEUS measurements that can significantly increase the value of these criticality benchmarks in testing nuclear codes and data to support transportation validation needs for industry and the U.S. NRC.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Chlorination of HALEU regulus casting dross

A scoping study was performed for chlorinating dross formed during uranium casting operations. Here, the purpose is to minimize the losses of uranium to dross wastes. The dross is primarily a mixture of uranium metal and uranium oxide with a minor fraction of crucible and crucible coating materials. The reaction chemistries were performed in a carrier salt of LiCl-KCl eutectic at 500 °C. The addition of FeCl 2 chlorinated the uranium metal to UCl 3 , by reducing the FeCl 2 to iron metal. After the uranium metal was chlorinated, zirconium metal was added to the salt. The residual FeCl 2 chlorinated the zirconium metal to ZrCl 4 , by reducing the FeCl 2 to iron metal. In turn, the ZrCl 4 chlorinated the uranium oxide to UCl 3 , by converting the ZrCl 4 to zirconium oxide. The effectiveness of the chlorination reactions was qualitatively verified by cyclic voltammograms that indicated the presence or absence of FeCl 2 and UCl 3 in the salt.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

HALEU Production in FCF

This is one more version of a PowerPoint presentation that has been given for many years.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Upsampling Monte Carlo reactor simulation tallies in depleted LWR assemblies fueled with LEU and HALEU using a convolutional neural network

Simulating nuclear reactor cores at the highest achievable spatial and energy resolution is critical in modeling these systems accurately. Increasing the resolution, however, can dramatically increase the memory and central processing unit time required to run simulations. A convolutional neural network was shown previously to accurately upsample tally results of simulated light water reactor assemblies fueled with fresh, low enriched uranium. Here, we show that a convolutional neural network can be used to upsample tally results in assemblies containing fresh and depleted fuel enriched from 1.6 to 19.9 atom percent. The network was trained using neutron flux tallies from simulations of light water reactor assemblies with a range of fuel and coolant temperatures and a diverse selection of geometries. Accurate predictions of flux tallies are possible even on test assemblies with geometries and burnup levels well outside the range of those present in the training and validation data. The network improves the data density by a factor of 8 over a broad range of light water reactor assemblies while incurring insignificant additional computational cost to a Monte Carlo simulation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗