Multiscale modeling of linear elastic heterogeneous structures via localized model order reduction

In this article, a methodology for fine scale modeling of large scale linear elastic structures is proposed, which combines the variational multiscale method, domain decomposition and model order reduction. The influence of the fine scale on the coarse scale is modeled by the use of an additive split of the displacement field, addressing applications without a clear scale separation. Local reduced spaces are constructed by solving an oversampling problem with random boundary conditions. Herein, we inform the boundary conditions by a global reduced problem and compare our approach using physically meaningful correlated samples with existing approaches using uncorrelated samples. The local spaces are designed such that the local contribution of each subdomain can be coupled in a conforming way, which also preserves the sparsity pattern of standard finite element assembly procedures. Several numerical experiments show the accuracy and efficiency of the method, as well as its potential to reduce the size of the local spaces and the number of training samples compared to the uncorrelated sampling.

Automated summary

This article proposes a methodology for fine scale modeling of large scale linear elastic structures by combining the variational multiscale method, domain decomposition, and model order reduction. The influence of the fine scale on the coarse scale is addressed by using an additive split of the displacement field, suitable for applications without clear scale separation. Local reduced spaces are constructed by solving an oversampling problem with random boundary conditions. The method is compared to existing approaches using physically meaningful correlated samples, showing its accuracy and efficiency in reducing the size of local spaces and training samples.