Thermophysical characterization of UFe3B2 and USiNi: An experimental study

Abstract

In the development for advanced nuclear fuels for LWRs, the focus has traditionally centered on a limited array of materials like doped-UO2, UN, and U3Si2, leaving the vast majority of potential uranium compounds unexplored. To expand the search and expedite the discovery of advanced LWR fuels, we developed a machine learning (ML) classification model dedicated to identifying uranium compounds with excellent thermal conductivity. However, the model's predictive accuracy and the feasibility of such an ML-assisted approach to advanced nuclear fuel discovery have yet to be empirically validated. Therefore, this study examines the thermal properties of two uranium compounds, UFe3B2 and USiNi, that are predicted to outperform UO2 at high temperatures. Our findings confirm that the model precisely predicted the thermal conductivities of both compounds, thereby adding credibility to its reliability. However, the experimental process unveiled several challenges, such as the fabrication of single-phase USiNi and microcrack-free UFe3B2 samples, which our ML model, focused primarily on thermal conductivity, could not anticipate. These challenges underscore the need for a more comprehensive ML framework that considers the intricacies of sample fabrication. This advancement is crucial for the more efficient development of practical, accident-tolerant, advanced LWR fuels.