About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
|
Algorithm Development in Materials Science and Engineering
|
Presentation Title |
Statistical Predictions of Failure in Hydrided Zirconium Materials |
Author(s) |
Tamir Hasan, Laurent Capolungo , Mohammed A. Zikry |
On-Site Speaker (Planned) |
Tamir Hasan |
Abstract Scope |
A dislocation-density based multiple slip crystalline plasticity formulation and a microstructurally-based fracture methodology have been used in conjunction with a new statistical framework to develop a representation of failure probabilities for hydrided Zircalloy-4 materials. Using a genetic learning algorithm and Bayes’ rule, an extreme value theory (EVT) of fracture was obtained for a stochastic representation of crack nucleation and propagation. This resulting validated model of crack probability can be applied either topographically to generate contours of crack likelihood on a representative material or applied directly to the input parameters to provide a library of fracture probabilities for a broad range of microstructural characteristics and mechanisms. The proposed framework can provide a framework for understanding material failure, and how fundamental material mechanisms can be used to inform predictions at the microstructural scale. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Computational Materials Science & Engineering, Modeling and Simulation |