  UZr based metallic nuclear fuel is the leading candidate for next-generation
sodium-cooled fast reactors in the United States. US research reactors have
been using and testing this fuel type since the 1960s and accumulated
considerable experience and knowledge about the fuel performance. However, most
of knowledge remains empirical. The lack of mechanistic understanding of fuel
performance is preventing the qualification of UZr fuel for commercial use.
This paper proposes a data-driven approach, coupled with advanced post
irradiation examination, powered by machine learning algorithms, to facilitate
the development of such understandings by providing unpreceded quantified new
insights into fission gas bubbles. Specifically, based on the advanced
postirradiation examination data collected on a neutron-irradiated U-10Zr
annular fuel, we developed a method to automatically detect, classify ~19,000
fission gas bubbles into different categories, and quantitatively link the data
to lanthanide transpiration along the radial temperature gradient. The approach
is versatile and can be modified to study different coupled irradiation
effects, such as secondary phase redistribution and degradation of thermal
conductivity, in irradiated nuclear fuel.
