Optimal Nonlinear Eddy Viscosity in Galerkin Models of Turbulent Flows

TL;DR

This paper introduces a variational method to identify an optimal nonlinear eddy viscosity function for POD models of turbulent flows, improving model accuracy and physical insights.

Contribution

It presents a novel variational optimization approach to determine nonlinear eddy viscosities, enhancing reduced-order turbulence modeling.

Findings

Optimal eddy viscosities improve transient dynamics.

Enhanced energy distribution accuracy in POD models.

Negative eddy-viscosity regimes identified at low fluctuation levels.

Abstract

We propose a variational approach to identification of an optimal nonlinear eddy viscosity as a subscale turbulence representation for POD models. The ansatz for the eddy viscosity is given in terms of an arbitrary function of the resolved fluctuation energy. This function is found as a minimizer of a cost functional measuring the difference between the target data coming from a resolved direct or large-eddy simulation of the flow and its reconstruction based on the POD model. The optimization is performed with a data-assimilation approach generalizing the 4D-VAR method. POD models with optimal eddy viscosities are presented for a 2D incompressible mixing layer at $Re=500$ (based on the initial vorticity thickness and the velocity of the high-speed stream) and a 3D Ahmed body wake at $Re=300,000$ (based on the body height and the free-stream velocity). The variational optimization formulation elucidates a number of interesting physical insights concerning the eddy-viscosity ansatz used. The 20-dimensional model of the mixing-layer reveals a negative eddy-viscosity regime at low fluctuation levels which improves the transient times towards the attractor. The 100-dimensional wake model yields more accurate energy distributions as compared to the nonlinear modal eddy-viscosity benchmark {proposed recently} by \"Osth et al. (2014). Our methodology can be applied to construct quite arbitrary closure relations and, more generally, constitutive relations optimizing statistical properties of a broad class of reduced-order models.