Adjacency-based, Non-intrusive Reduced-order Modeling for Fluid-Structure Interactions

TL;DR

This paper introduces a non-intrusive, sparse operator-based model reduction method for fluid-structure interaction problems, leveraging adjacency information to efficiently predict coupled dynamics with improved computational feasibility.

Contribution

It presents a novel adjacency-based sparsity enforcement technique for non-intrusive FSI model reduction, combining local least-squares fitting with POD for effective reduced-order modeling.

Findings

Accurate prediction of FSI dynamics at Re=200

Efficient sparse operator inference reduces computational costs

Validated on Hron-Turek benchmark FSI3 problem

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, thus making computation and storage of the inferred full-order operators feasible. After constructing the non-intrusive, sparse full-order model, 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.