Full-scale crystal plasticity modeling and data-driven learning of microstructure effects in polycrystalline turbine blades
Abstract
• Full-scale crystal plasticity simulations of a turbine blade with up to 40,000 grains. • Interface-aware meshing strategy for embedding realistic microstructures into complex geometries. • Dislocation-density based crystal plasticity model including GNDs under finite thermomechanical loads. • Grain-wise parallel domain decomposition solved with Adaptive Multipreconditioned FETI (AMPFETI). • Graph neural networks trained to predict grain-averaged fields from morphological descriptors.
Fields of Study & Subjects
Machine Learning in Materials Science
Model Reduction and Neural Networks
Microstructure and mechanical properties
Publication details
- Journal / source
- Computer Methods in Applied Mechanics and Engineering
- Publisher
- Elsevier BV
- Volume
- 460
- Pages
- 119085–119085
- Publication date
- 29 May 2026
- Language
- EN