About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|
Presentation Title |
Annular Metallic Nuclear Fuel Informatics at 50-nm Resolution |
Author(s) |
Fei Xu, Lu Cai, Daniele Salvato, Fidelma Dilemma, Michael Benson, Daniel Murray, Cynthia Adkins, Joshua Kane, Luca Capriotti, Tiankai Yao |
On-Site Speaker (Planned) |
Lu Cai |
Abstract Scope |
U-10wt.% Zr (U-10Zr) based metallic fuel is the leading candidate for next-generation sodium cooled fast reactor in United States. Advanced post-irradiation characterization (from sub-nanometer to micrometer) helps to understand fuel microstructure and property change during irradiation, benefiting fuel qualification for commercial application. With high velocity image data generating method, an automatic way to extract the microstructural information quantitively can better serve the needs from post irradiation characterization. A trained machine learning model, named Decision Tree, is employed to categorize pores caused by fission gas release and to aid phase identification. This work presents a showcase of this approach on different irradiated U-10Zr metallic fuels. This quantitative data offers insights into the fission product migration and potentially thermal conductivity degradation. This information from machine learning will be fed into fuel design code for better prediction of fuel performance. |
Proceedings Inclusion? |
Planned: |
Keywords |
Nuclear Materials, |