|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Understanding Fission Gas Bubble Distribution, Lanthanide Transportation, and Thermal Conductivity Degradation in Neutron-irradiated α-U Using Machine Learning
||Tiankai Yao, Lu Cai, Fei Xu, Fidelma Dilemma, Michael Benson, Daniel J. Murray, Cynthia A Adkins, Joshua J Kane, Min Xian, Luca Capriotti
|On-Site Speaker (Planned)
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 fuel performance. Most of the knowledge, however, remains empirical. 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 effect on the fission product transportation and thermal conductivity. The approach can be modified to study other irradiation effects, such as secondary phase redistribution and gaseous fuel swelling in other irradiated nuclear fuels.
||Nuclear Materials, Machine Learning, Other