Machine-aided turbulence theory

TL;DR

This paper explores using unsupervised machine methods to identify significant sub-volumes in turbulent flows, focusing on vortices and strain effects in two-dimensional decaying turbulence.

Contribution

It introduces a novel machine-aided approach to detect influential flow regions without prior assumptions, based on the Navier-Stokes equations.

Findings

Significance varies intermittently in the flow.

Vortices are most significant features.

Strain-dominated regions are least significant.

Abstract

The question of whether significant sub-volumes of a turbulent flow can be identified by automatic means, independently of a-priori assumptions, is addressed using the example of two-dimensional decaying turbulence. Significance is defined as influence on the future evolution of the flow, and the problem is cast as an unsupervised machine `game' in which the rules are the Navier--Stokes equations. It is shown that significance is an intermittent quantity in this particular flow, and that, in accordance with previous intuition, its most significant features are vortices, while the least significant ones are dominated by strain. Subject to cost considerations, the method should be applicable to more general turbulent flows.