| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Advanced Characterization and Modeling of Nuclear Fuels: Microstructure, Thermo-physical Properties
|
| Presentation Title |
Thermal Conductivity Degradation by Solid Fission Products: Machine Learning Coupled with First Principles Model |
| Author(s) |
Elina Charatsidou, Kyle Johnson, Marcus Hedberg, Pär Olsson, Denise Adorno Lopes |
| On-Site Speaker (Planned) |
Elina Charatsidou |
| Abstract Scope |
Characterization of thermophysical properties of nuclear fuel is an essential step in understanding its behavior and predicting its performance under irradiation. For thermal conductivity, κ limited data is available in the literature for most advanced fuel concepts. This work provides an innovative approach by coupling machine learning and first-principles electronic structure theory to predict κ as a function of solid fission product concentrations. A machine learning model trained on (XO2) compounds was created using attributes parameters extracted from density functional theory (DFT). The model developed was cross-validated with experiments using the hot bridge transient method on SIMFUEL samples. The methodology developed here for UO2 can be extended to advanced fuels, where there is no available data of thermal conductivity degradation during irradiation. This effort is a step in an Accelerated Fuel Qualification methodology where separate tests and modeling can reduce the time needed to develop and qualify new fuel systems. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Nuclear Materials, Modeling and Simulation |