A-priori sparsification of Galerkin-based reduced order models

TL;DR

This paper introduces a method to create sparse Galerkin models for chaotic fluid flows by selecting basis functions that minimize triadic interactions, leading to efficient and interpretable reduced-order models.

Contribution

The paper proposes a novel subspace rotation technique to sparsify Galerkin models, ensuring stability and physical consistency in chaotic flow simulations.

Findings

Generated models with fewer active triadic interactions

Distributed interactions according to flow scale knowledge

Long-term stability is crucial for physical consistency

Abstract

A methodology to generate sparse Galerkin models of chaotic/unsteady fluid flows containing a minimal number of active triadic interactions is proposed. The key idea is to find an appropriate set of basis functions for the projection representing elementary flow structures that interact minimally one with the other and thus result in a triadic interaction coefficient tensor with sparse structure. Interpretable and computationally efficient Galerkin models can be thus obtained, since a reduced number of triadic interactions needs to be computed to evaluate the right hand side of the model. To find the basis functions, a subspace rotation technique is used, whereby a set of Proper Orthogonal Decomposition (POD) modes is rotated into a POD subspace of larger dimension using coordinates associated to low-energy dissipative scales to alter energy paths and the structure of the triadic interaction coefficient tensor. This rotation is obtained as the solution of a non-convex optimisation problem that maximises the energy captured by the new basis, promotes sparsity and ensures long-term temporal stability of the sparse Galerkin system. We demonstrate the approach on two-dimensional lid-driven cavity flow at $Re = 2 \times 10^4$ where the motion is chaotic. We show that the procedure generates Galerkin models with a reduced set of active triadic interactions, distributed in modal space according to established knowledge of scale interactions in two-dimensional flows. This property, however, is only observed if long-term temporal stability is explicitly included in the formulation, indicating that a dynamical constraint is necessary to obtain a physically consistent sparsification.