Optimal Clipping of Structural Subgrid Stress Closures for Large Eddy Simulation

TL;DR

This paper introduces an optimal clipping method for structural subgrid stress models in large eddy simulation, improving accuracy and stability by solving a constrained minimization problem to better match exact stresses.

Contribution

It proposes a novel, parameter-free optimal clipping strategy that enhances correlation with exact stresses over traditional methods, improving model predictions.

Findings

Optimal clipping yields higher correlation with exact stresses.

The method improves stability without sacrificing accuracy.

Significant performance gains in a priori and a posteriori tests.

Abstract

Structural subgrid stress models for large eddy simulation often allow for backscatter of energy from unresolved to resolved turbulent scales, but excessive model backscatter can eventually result in numerical instability. A commonly employed strategy to overcome this issue is to set predicted subgrid stresses to zero in regions of model backscatter. This clipping procedure improves the stability of structural models, however, at the cost of reduced correlation between the predicted subgrid stresses and the exact subgrid stresses. In this article, we propose an alternative strategy that removes model backscatter from model predictions through the solution of a constrained minimization problem. This procedure, which we refer to as optimal clipping, results in a parameter-free mixed model, and it yields predicted subgrid stresses in higher correlation with the exact subgrid stresses as compared with those attained with the traditional clipping procedure. We perform a series of a priori and a posteriori tests to investigate the impact of applying the traditional and optimal clipping procedures to Clark's gradient subgrid stress model, and we observe that optimal clipping leads to a significant improvement in model predictions as compared to the traditional clipping procedure.