Fluctuations around Turbulence Models

TL;DR

This paper investigates the impact of small-scale fluctuations on turbulence modeling, highlighting the importance of stochastic approaches to capture the non-Gaussian, fat-tailed distributions of energy flux deviations in subgrid scale models.

Contribution

It introduces a detailed analysis of subgrid scale fluctuations using DNS and Gaussian filtering, emphasizing the need for stochastic modeling in turbulence simulations.

Findings

Clark model captures mean energy flux well

Energy flux fluctuations exhibit fat-tailed, non-Gaussian distributions

Fluctuations are significant and should be incorporated into models

Abstract

Numerical simulations of turbulent flows at realistic Reynolds numbers generally rely on filtering out small scales from the Navier Stokes equations and modeling their impact through the Reynolds stress tensor ${\tau}_{ij}$. Traditional models approximate ${\tau}_{ij}$ solely as a function of the filtered velocity gradient, leading to deterministic subgrid scale closures. However, small scale fluctuations can locally exhibit instantaneous values whose deviation from the mean can have a significant influence on flow dynamics. In this work, we investigate these effects by employing direct numerical simulations combined with Gaussian filtering to quantify subgrid scale effects and evaluating the local energy flux in both space and time. The mean performance of the canonical Clark model is assessed by conditioning the energy flux distributions on the invariants of the filtered velocity gradient tensor, $Q$ and $R$. The Clark model captures to a good degree the mean energy flux. However, the fluctuations around these mean values for given ($Q,R$) are of the order of the mean displaying fat tailed distributions. To become more precise, we examine the joint distributions of true energy flux and the predictions from both the Clark and the Smagorinsky models. This approach mirrors the strategy adopted in early stochastic subgrid scale models. Clear non Gaussian characteristics emerge from the obtained distributions, particularly through the appearance of heavy tails. The mean, the variance, the skewness and flatness of these distributions are quantified. Our results emphasize that fluctuations are an integral component of the small scale feedback onto large scale dynamics and should be incorporated into subgrid scale modeling through an appropriate stochastic framework.