Analysis of Kolmogorov Flow and Rayleigh-B\'enard Convection using Persistent Homology

TL;DR

This paper applies persistent homology to analyze complex fluid flow patterns from simulations, providing a quantitative and multiscale understanding of their dynamics and symmetries.

Contribution

It introduces a novel application of persistent homology to characterize and analyze flow field patterns and their dynamics in fluid systems.

Findings

Persistent homology effectively captures flow pattern features.

The method identifies symmetries and recurrent dynamics.

Applicable to a broad range of complex systems.

Abstract

We use persistent homology to build a quantitative understanding of large complex systems that are driven far-from-equilibrium; in particular, we analyze image time series of flow field patterns from numerical simulations of two important problems in fluid dynamics: Kolmogorov flow and Rayleigh-B\'enard convection. For each image we compute a persistence diagram to yield a reduced description of the flow field; by applying different metrics to the space of persistence diagrams, we relate characteristic features in persistence diagrams to the geometry of the corresponding flow patterns. We also examine the dynamics of the flow patterns by a second application of persistent homology to the time series of persistence diagrams. We demonstrate that persistent homology provides an effective method both for quotienting out symmetries in families of solutions and for identifying multiscale recurrent dynamics. Our approach is quite general and it is anticipated to be applicable to a broad range of open problems exhibiting complex spatio-temporal behavior.