Is Turbulence as Simple as Tossing a Coin?

TL;DR

This paper explores the application of large deviation theory, specifically Cramer's rate function, to better understand intermittency in fluid turbulence, offering new insights beyond Gaussian approximations.

Contribution

It introduces the use of Cramer's rate function from large deviation theory as a novel approach to modeling turbulence intermittency.

Findings

Cramer's rate function provides a promising framework for turbulence analysis.

The approach offers a new perspective on the Jarzynski equality.

Gaussian assumptions are insufficient for describing turbulence intermittency.

Abstract

A large variety of problems in statistical physics use a Gaussian distribution as a starting point. For the problem of intermittency in fluid turbulence, the Gaussian approximation is not a useful beginning. We find that the Cramer's rate function in the theory of large deviations as used in a simple coin toss is a promising starting point for giving an account of intermittency. In addition, it offers another view of Jarzynski equality.