About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Interrelated Extremes in Materials Degradation for Fission and Fusion Environments
|
| Presentation Title |
Predicting Fracture Toughness Degradation in Irradiated Duplex Structure Stainless Steels Using Data-Driven Methods |
| Author(s) |
Yong Yang |
| On-Site Speaker (Planned) |
Yong Yang |
| Abstract Scope |
Accurate prediction of fracture toughness degradation in duplex structure stainless steels including cast and welds is essential for extending the life of nuclear reactors beyond 60 years. This study combines mechanical testing, microstructural characterization, finite element modeling (FEM), and Bayesian neural networks (BNNs) to assess embrittlement in CF-3, CF-8, and 308L welds irradiated up to 40 dpa. J-R curves from thermally aged samples and in-situ micro-tensile testing of irradiated TEM discs provide critical input for FEM simulations using GTN damage models. The resulting dataset informs a BNN model to predict JIC with uncertainty quantification. This integrated approach addresses data gaps at high doses and supports predictive models for safe, long-term reactor operation. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Nuclear Materials, Machine Learning, Iron and Steel |