A Machine Learning Approach Toward Improving QA/QC of Coated Particle Fuels
Coated particle fuels, such as the tristructural isotropic (TRISO) fuel particle, have been studied and developed for decades for high-temperature gas reactor (HTGR) applications because of their efficiency and relative stability under off-normal conditions. Critical to this development is a strong understanding of the relationship between fuel fabrication, properties and performance, as is how the former can be adjusted and improved to optimize the latter. Accordingly, wide-scale implementation of coated nuclear particle fuels requires thorough and robust quality assurance/quality control (QA/QC) methods for fabrication, characterization, and deployment