| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Phase Transformations and Microstructural Evolution
|
| Presentation Title |
Surrogate Model to Predict Microstructure and Mechanical Properties in Stainless Steel Cladding under Reactor Operating Conditions |
| Author(s) |
William E. Frazier, Yucheng Fu, Lei Li, Ram Devanathan |
| On-Site Speaker (Planned) |
William E. Frazier |
| Abstract Scope |
A machine-learning surrogate model was developed to provide rapid predictions of microstructural evolution and service lifetime for reactor materials under conditions of varying temperature and irradiation dose rate. To acquire high-fidelity training data, a Kinetic Monte Carlo (KMC) model was developed to simulate M23C6, γ’, and G phase precipitation kinetics in a 316 series stainless steel cladding. Experimentally reported behaviors of 316 SS in literature were linked to the kinetic parameters of the simulated precipitation in our model. Temperature and irradiation dose rate histories were generated synthetically for periods of up to 10,000 hours for the simulations. Precipitation progress parameters, including volume fraction, number density, and particle size were correlated using statistical methods to develop the surrogate model. Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based Finite Element Method (FEM) analysis. The fidelity of our surrogate modeling to the predictions of these simulations is discussed. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
ICME, Nuclear Materials, Machine Learning |