|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||Advanced Characterization and Modeling of Nuclear Fuels: Microstructure, Thermo-physical Properties
||An Integrated Approach for Coupling Experimental Data, Physics-based Models, and Machine Learning Algorithms for Predicting the Effective Thermal Conductivity of U-based Fuels
||Fergany Badry, Monika Singh, Timothy Coffman, Mohammed Gomaa Abdoelatef, Sean McDeavitt, Karim Ahmed
|On-Site Speaker (Planned)
We present a new approach for coupling experimental data, physical models, and machine learning algorithms to predict the effective thermal conductivity of UO2-BeO, UO2-Mo, and U-10Zr nuclear fuels. First, a physics-based mesoscale model that takes into account the effect of underlying microstructure, temperature, and interface thermal resistance is developed. Then the model is verified and validated against available experimental data. The validated model is then utilized to generate more data at conditions different from experiments. Existing experimental data and the additional model results are then used to train and test non-linear regression models in open-source machine learning software. The final outcome of this framework is validated, physics-based reduced order models of the effective conductivity of these fuels. This novel approach reduces the required number of experiments and at the same time minimizes the computational cost necessary for qualifying new materials.
||Computational Materials Science & Engineering, Nuclear Materials, Machine Learning