Data-Driven POD-Galerkin Reduced Order Model for Turbulent Flows

TL;DR

This paper introduces a novel data-driven POD-Galerkin reduced order model tailored for turbulent flows, combining projection techniques with data-driven strategies to improve accuracy and efficiency in finite volume simulations.

Contribution

It presents a mixed reduction approach that integrates data-driven eddy viscosity approximation with classical POD-Galerkin projection for turbulent flow modeling.

Findings

Validated on steady and unsteady benchmark cases

Effective for Reynolds numbers up to 10^5

Improves computational efficiency in turbulent flow simulations

Abstract

In this work we present a Reduced Order Model which is specifically designed to deal with turbulent flows in a finite volume setting. The method used to build the reduced order model is based on the idea of merging/combining projection-based techniques with data-driven reduction strategies. In particular, the work presents a mixed strategy that exploits a data-driven reduction method to approximate the eddy viscosity solution manifold and a classical POD-Galerkin projection approach for the velocity and the pressure fields, respectively. The newly proposed reduced order model has been validated on benchmark test cases in both steady and unsteady settings with Reynolds up to Re=O(10^5).