About this Abstract |
Meeting |
2021 TMS Annual Meeting & Exhibition
|
Symposium
|
Accelerated Discovery and Qualification of Nuclear Materials for Energy Applications
|
Presentation Title |
An Integrated Approach for Coupling Experimental Data, Physics-based Models, and Machine Learning Algorithms for Predicting the Effective Thermal Conductivity of U-based Fuels |
Author(s) |
Karim Ahmed, Fergany Badry |
On-Site Speaker (Planned) |
Karim Ahmed |
Abstract Scope |
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. |
Proceedings Inclusion? |
Planned: |
Keywords |
Nuclear Materials, Modeling and Simulation, |