Large Parallel Adaptive Multiscale Modeling of Interfaces

The computationally demanding nature of multiscale modeling (e.g., Computational Homogenization (CH)) necessitates the development of parallel and adaptive strategies for industrial-scale applications. Accordingly, this dissertation introduces an adaptive and parallel multiscale strategy for interface modeling. It adopts an adaptive approach, selecting between two microscale models through an offline database. This database utilizes nonlinear classifiers based on Support Vector Regression (SVR) constructed from microscale sampling data to serve as a preprocessing step for multiscale simulations. A co-designed parallel network library, facilitating seamless model selection, integrates tailored communication layers to ensure scalability, which is essential in parallel computing environments. This work presents a novel multiscale solver capable of executing high-fidelity, large-scale engineering simulations. The implementation of the solver is verified and validated through the application, demonstrating its ability to capture the physics observed in experimental data at both macro and micro scales. This is illustrated through the analysis of the failure of a large wind turbine blade.