Lagrangian Refined Kolmogorov Similarity Hypothesis for Gradient Time-evolution in Turbulent Flows

TL;DR

This study investigates the time evolution of velocity and pressure gradients in isotropic turbulence using Lagrangian analysis, confirming that local Kolmogorov time-scales better describe turbulence decorrelation than global averages, supporting Kolmogorov's Refined Similarity Hypothesis.

Contribution

It introduces a Lagrangian approach to validate the Kolmogorov Refined Similarity Hypothesis using local dissipation rates in turbulent flows.

Findings

Correlation functions decay on timescales of the mean Kolmogorov time scale globally.

Local Kolmogorov time scales lead to better collapse of auto-correlation functions.

Results support the validity of Kolmogorov's Refined Similarity Hypothesis from a Lagrangian perspective.

Abstract

We study the time evolution of velocity and pressure gradients in isotropic turbulence, by quantifying their decorrelation time scales as one follows fluid particles in the flow. The Lagrangian analysis uses data in a public database generated using direct numerical simulation of the Naiver-Stokes equations, at a Reynolds number 430. It is confirmed that when averaging over the entire domain, correlation functions decay on timescales on the order of the mean Kolmogorov turnover time scale, computed from the globally averaged rate of dissipation and viscosity. However, when performing the analysis in different subregions of the flow, turbulence intermittency leads to large spatial variability in the decay time scales. Remarkably, excellent collapse of the auto-correlation functions is recovered when using the `local Kolmogorov time-scale' defined using the locally averaged, rather than the global, dissipation-rate. This provides new evidence for the validity of Kolmogorov's Refined Similarity Hypothesis, but from a Lagrangian viewpoint that provides a natural frame to describe the dynamical time evolution of turbulence.