Physically Meaningful Samples in Randomized Local Model Order Reduction
Please login to view abstract download link
In this contribution, a methodology for fine scale modeling of large scale structures is proposed, which combines the variational multiscale method\cite{Hughes1998}, domain decomposition and model order reduction. The influence of the fine scale on the coarse scale is modelled by the use of an additive split of the displacement field, addressing applications without a clear scale separation. Based on the work of Buhr and Smetana\cite{BS2018}, 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.
complas_sec@cimne.upc.edu/ Telf. + 34 - 93 405 46 94
Copyright © 2022 CIMNE, All Rights Reserved. Terms of service
