Accelerating deployment of nuclear fuels through reduced-order thermo- physical property models and machine learning

This project will develop a novel physics-based tool that combines 1) reduced-order models, 2) machine learning algorithms, 3) fuel performance methods, and 4) state-of- the-art thermal property characterization equipment and irradiated nuclear fuel data sets to accelerate nuclear fuel discovery, development, and deployment. The models will describe thermal conductivity, specific heat, thermal expansion, and self-diffusion coefficients as a function of temperature and irradiation.