About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Ceramics and Ceramic-Based Composites for Nuclear Applications III
|
| Presentation Title |
Inferring the Local, Temperature-Dependent, Anisotropic Thermal Conductivity of TRISO fuel Constituents from Modulated Photothermal Phase Data |
| Author(s) |
Kyle Joshua Kelley, Christopher Nyborg, Thomas Andrews, Troy R Munro, Oliver K Johnson |
| On-Site Speaker (Planned) |
Kyle Joshua Kelley |
| Abstract Scope |
Tristructural isotropic (TRISO) fuels are strong candidates for use in Generation IV nuclear reactors. The stability of TRISO fuels under extreme operating conditions has already been demonstrated through experiments. However, uncertainty persists in predicting TRISO fuel performance via simulations due, in part, to the lack of reliable thermal conductivity data with rigorous uncertainty quantification (UQ). To address this gap, we present a Bayesian framework for inferring the local, temperature-dependent, thermal conductivity of an anisotropic sample and validate this method against computational (FEA) thermal transport simulations in a simplified, synthetic, two-phase TRISO-particle/graphite-matrix composite. Our approach generates predictive models for temperature-dependent thermal conductivity of the constituent phases of the TRISO composite, with UQ derived from Bayesian statistics and Monte Carlo approximations. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Characterization, Nuclear Materials, Machine Learning |