An optimisation-based domain-decomposition reduced order model for the incompressible Navier-Stokes equations

TL;DR

This paper introduces a novel reduced order modeling approach combining domain decomposition and optimal control for the stationary incompressible Navier-Stokes equations, significantly reducing computational costs in fluid dynamics simulations.

Contribution

It develops an optimization-based domain decomposition method integrated with reduced-order modeling using POD and Galerkin projection, enhancing efficiency for parameter-dependent Navier-Stokes problems.

Findings

Significant computational cost reduction in problem size.

Efficient domain decomposition via optimal control.

Successful application to fluid dynamics benchmarks.

Abstract

The aim of this work is to present a model reduction technique in the framework of optimal control problems for partial differential equations. We combine two approaches used for reducing the computational cost of the mathematical numerical models: domain-decomposition (DD) methods and reduced-order modelling (ROM). In particular, we consider an optimisation-based domain-decomposition algorithm for the parameter-dependent stationary incompressible Navier-Stokes equations. Firstly, the problem is described on the subdomains coupled at the interface and solved through an optimal control problem, which leads to the complete separation of the subdomain problems in the DD method. On top of that, a reduced model for the obtained optimal-control problem is built; the procedure is based on the Proper Orthogonal Decomposition technique and a further Galerkin projection. The presented methodology is tested on two fluid dynamics benchmarks: the stationary backward-facing step and lid-driven cavity flow. The numerical tests show a significant reduction of the computational costs in terms of both the problem dimensions and the number of optimisation iterations in the domain-decomposition algorithm.