Adjacency‐based, non‐intrusive reduced‐order modeling for fluid‐structure interactions

Abstract

Non‐intrusive model reduction is a promising solution to system dynamics prediction, especially in cases where data are collected from experimental campaigns or proprietary software simulations. In this work, we present a method for non‐intrusive model reduction applied to Fluid‐Structure Interaction (FSI) problems. The approach is based on the a priori known sparsity of the full‐order system operators, which is dictated by grid adjacency information. In order to enforce this type of sparsity, we solve a “local”, regularized least‐squares problem for each degree of freedom on a grid, considering only the training data from adjacent degrees of freedom (DoFs), thus making computation and storage of the inferred full‐order operators feasible. After constructing the non‐intrusive, sparse full‐order model (FOM), Proper Orthogonal Decomposition (POD) is used for its projection to a reduced dimension subspace and thus the construction of a reduced‐order model (ROM). The methodology is applied to the challenging Hron‐Turek benchmark FSI3, for Re = 200. A physics‐informed, non‐intrusive ROM is constructed to predict the two‐way coupled dynamics of a solid with a deformable, slender tail, subject to an incompressible, laminar flow. Results considering the accuracy and predictive capabilities of the inferred reduced models are discussed.