TY - CONF A1 - Diercks, Philipp A1 - Veroy, K. A1 - Robens-Radermacher, Annika A1 - Unger, Jörg F. T1 - Multiscale modeling of heterogeneous structures based on a localized model order reduction approach N2 - Many of today’s problems in engineering demand reliable and accurate prediction of failure mechanisms of mechanical structures. Thus, it is necessary to take into account the heterogeneous structure on the smaller scale, to capture the underlying physical phenomena. However, this poses a great challenge to the numerical solution since the computational cost is significantly increased by resolving the smaller scale in the model. Moreover, in applications where scale separation as the basis of classical homogenization schemes does not hold, the influence of the smaller scale on the larger scale has to be modelled directly. This work aims to develop an efficient concurrent methodology to model heterogeneous structures combining the variational multiscale method (VMM) [1] and model order reduction techniques (e. g. [2]). First, the influence of the smaller scale on the larger scale can be taken into account following the additive split of the displacement field as in the VMM. Here, also a decomposition of the global domain into subdomains, each containing a fine grid discretization of the smaller scale, is introduced. Second, local reduced approximation spaces for the smaller scale solution are constructed by exploring possible solutions for each subdomain based on the concept of oversampling [3]. The associated transfer operator is approximated by random sampling [4]. Herein, we propose to incorporate the actual physical behaviour of the structure of interest in the training data by drawing random samples from a multivariate normal distribution with the solution of a reduced global problem as mean. The local reduced spaces are designed such that local contributions of each subdomain can be coupled in a conforming way. Thus, the resulting global system is sparse and reduced in size compared to the direct numerical simulation, leading to a faster solution of the problem. T2 - ECCOMAS YIC CY - Porto, Portugal DA - 19.06.2023 KW - Multiscale Method KW - Variational Multiscale Method KW - Domain Decomposition KW - Model Order Reduction PY - 2023 AN - OPUS4-58251 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -