About this Abstract |
Meeting |
2020 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Materials Science and Engineering of Materials in Nuclear Reactors
|
Presentation Title |
A Machine Learning Approach to Thermal Conductivity Modelling of Irradiated Nuclear Fuels |
Author(s) |
Elizabeth Jane Kautz, Alexander Hagen, Jesse Johns, Douglas Burkes |
On-Site Speaker (Planned) |
Elizabeth Jane Kautz |
Abstract Scope |
A deep neural network was developed for the purpose of predicting thermal conductivity with a case study performed on neutron irradiated nuclear fuel. Traditional thermal conductivity modeling approaches rely on existing theoretical frameworks that describe known, relevant phenomena that govern the microstructural evolution processes during irradiation (e.g. recrystallization, and pore size, distribution and morphology). Current empirical modeling approaches, however, do not represent all irradiation test data well. Here, we develop a machine learning approach to thermal conductivity modeling that does not require a priori knowledge of a specific material microstructure and system of interest. Our approach allows researchers to probe dependency of thermal conductivity on a variety of reactor operating and material conditions. Results indicate our model generalizes well to never before seen data, and thus use of deep learning methods for material property predictions from limited, historic irradiation test data is a viable approach. |
Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |