Adaptive and Parallel Multiscale Framework for Modeling Cohesive Failure in Engineering Scale Systems

TL;DR

This paper presents an adaptive, parallel multiscale modeling framework that efficiently simulates cohesive failure in engineering systems, combining machine learning-based microscale models with scalable parallel computing techniques.

Contribution

It introduces a novel adaptive method using SVM-based microscale models and a new parallel network library for scalable multiscale simulations of cohesive failure.

Findings

Verified on crack propagation in adhesive layers

Predicted failure of wind turbine blade

Demonstrated scalability in large-scale problems

Abstract

The high computational demands of multiscale modeling necessitate advanced parallel and adaptive strategies. To address this challenge, we introduce an adaptive method that utilizes two microscale models based on an offline database for multiscale modeling of curved interfaces (e.g., adhesive layers). This database employs nonlinear classifiers, developed using Support Vector Machines from microscale sampling data, as a preprocessing step for multiscale simulations. Next, we develop a new parallel network library that enables seamless model selection with customized communication layers, ensuring scalability in parallel computing environments. The correctness and effectiveness of the hierarchically parallel solver are verified on a crack propagation problem within the curved adhesive layer. Finally, we predict the ultimate bending moment and adhesive layer failure of a wind turbine blade and validate the solver on a difficult large-scale engineering problem.