| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Advanced Characterization and Modeling of Nuclear Fuels: Microstructure, Thermo-physical Properties
|
| Presentation Title |
Accelerating Nuclear Fuel Qualification through Multiscale Models |
| Author(s) |
Joshua T. White, Tammie Nelson, David Andersson |
| On-Site Speaker (Planned) |
Joshua T. White |
| Abstract Scope |
Qualification of nuclear fuels for use in modern nuclear reactors currently requires lengthy, and costly, irradiation test campaigns to provide assurance of the reactor performance under a variety of scenarios. The Nuclear Regulatory Commission is interested in advanced modeling and simulation methods to expedite the qualification of fuels by utilizing improved mechanistic models combined with separate effects experiments to decrease the number of required data sets to commission fuels for use. The use of innovative machine learning (ML) tools coupled with state-of-the-art experimental measurements will be used to benchmark the models as well as reduce the uncertainty, providing confidence in fuel performance in-pile. This talk will focus on UN and UC, two potential high-impact fuels for microreactors and gas-cooled reactors. The developed ML models will be integrated with multiphysics engineering-scale neutronics and reactor simulations that ultimately define safety margins for novel nuclear fuels within specific reactor conditions. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Characterization, Nuclear Materials |