About this Abstract |
Meeting |
2020 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Materials Science and Engineering of Materials in Nuclear Reactors
|
Presentation Title |
E-37: Machine Learning-assisted Risk-informed Sensitivity Analysis for ATF Under SBO |
Author(s) |
Jianguo Yu, Cole Blakely, Hongbin Zhang |
On-Site Speaker (Planned) |
Jianguo Yu |
Abstract Scope |
ATF fuel rods have been designed to have similar or improved behavior in normal operation and provide increased coping time during design basis accidents (DBA) and beyond DBA. A key aspect in the evaluation of the ATF designs is the determination of potential increases in coping time with respect to advancements in fuel and cladding materials. After Fukushima Daiichi nuclear power plant accident, station blackout (SBO) has been widely recognized as one of the most severe postulated events and the fuel rod behavior should be effectively evaluated in the operations of nuclear power plants. However, the studies on fuel rod performance such as the cladding failure under SBO are still scarce. In this work, we will present our recent progress on machine learning-assisted risk-informed sensitivity analysis for ATF under SBO. We will demonstrate that it is feasible to estimate the coping time as ATF fuel and cladding are chosen. |
Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |