Understanding fission gas bubble distribution, lanthanide transportation, and thermal conductivity degradation in neutron-irradiated α-U using machine learning

Abstract

U 10Zr based metallic nuclear fuel is the leading candidate for next-generation sodium-cooled fast reactors in the United States. US research reactors have used and tested this fuel type since the 1960s and accumulated considerable experience and knowledge about the fuel performance. Most of the knowledge, however, remains empirical. The lack of mechanistic understanding of fuel performance puts a large burden on proof through experimental verification for the qualification of U 10Zr fuel for commercial use. This paper proposes an image data-driven machine learning approach, coupled with domain knowledge provided by advanced post irradiation examination, to provide unprecedented quantified insights into the morphology, size, density and the connectivity of fission gas bubbles and their effects on the fission product transportation and thermal conductivity. Specifically, we developed a method to automatically detect, extract statistics, and classify ~19,000 fission gas bubbles into different categories, and quantitatively link the data to lanthanide transportation through connected bubbles and degradation of thermal conductivity along the radial temperature gradient in a neutron irradiated U 10Zr annular fuel. Results indicate the approach can be modified to study other irradiation effects, such as secondary phase redistribution and gaseous fuel swelling in other irradiated nuclear fuels. • This study showcases a novel method utilizing an image data-driven machine learning approach for advanced post irradiation examination. • The features and statistics of 19,000 fission gas bubbles are derived by the decision tree algorithm. • The bubble statistics provide quantified insights into microstructural features of irradiated fuel and how they relate to fuel performance.