Dynamical Origins for Non-Gaussian Vorticity Distributions in Turbulent Flows

TL;DR

This paper investigates how dynamical processes like vortex stretching influence the non-Gaussian distribution of vorticity in turbulent flows, using conditional averages to connect flow dynamics with statistical properties.

Contribution

It introduces a statistical framework based on conditional averages to link dynamical effects with the shape of vorticity PDFs in turbulence.

Findings

Conditional averages reveal the role of vortex stretching in non-Gaussian vorticity statistics

Numerical evaluation provides insights into the dynamical origins of vorticity distribution shapes

The approach clarifies the connection between flow dynamics and statistical properties in turbulence

Abstract

We present results on the connection between the vorticity equation and the shape of the single-point vorticity PDF. The statistical framework for these observations is cast in form of conditional averages. The numerical evaluation of these conditional averages provides insights into the intimate relation of dynamical effects like vortex stretching and vorticity diffusion and non-Gaussian vorticity statistics.