Causally coherent structures in turbulent dynamical systems

TL;DR

This paper introduces a novel method using adaptive Shannon transfer entropy to identify causally coherent structures in turbulent flows, revealing information transfer patterns across different boundary layer regions.

Contribution

It presents an adaptive tuning approach for transfer entropy hyperparameters and introduces causally coherent structures as a new way to interpret spatio-temporal causality in turbulence.

Findings

Identified dominant top-down causality between inner and outer boundary layer regions.

Characterized different boundary layer layers by their information fluxes.

Extended transfer entropy techniques to complex turbulent systems.

Abstract

The extraction of spatio-temporal coherence in high-dimensional, chaotic, non-linear dynamical systems, such as turbulent flows, remains a fundamental challenge in physics, mathematics and engineering. In this work, we employ Shannon transfer entropy (TE) to identify causally coherent motions in a zero-pressure-gradient turbulent boundary layer (TBL). This causality metric, rooted in information theory, enables the identification of sources and targets in dynamical systems using the corresponding time series. However, TE requires sophisticated tuning of various hyperparameters, such as the Markovian order of the source ($m$), which can spatially vary in wall-bounded turbulent flow. Here, we present an adaptive tuning and discuss the influence of $m$ across different TBLs. We introduce the concept of causally coherent structures (CCS), i.e. coherent structures interpreted as spatio-temporal patterns of causality. Moreover, the net transfer entropy flux is also utilised to identify boundary layer locations acting either as sources or targets. The standard viscous, logarithmic, and outer layers are characterised by information fluxes, highlighting, for example, dominant top-down interactions between the inner and outer layers, analogously to the classical energy cascade. This work extends techniques previously employed in the literature, such as correlation and spectral analysis, and presents an approach that is inherently general and applicable to a wide range of chaotic dynamical systems, with applications in cognitive sciences, systems biology and finance.