Routes to stratified turbulence and temporal intermittency revealed by a cluster-based network model of experimental data

TL;DR

This paper introduces a data-driven, cluster-based network model to analyze stratified turbulence in a shear flow, revealing multiple turbulence states, transition pathways, and intermittency patterns at high Reynolds numbers.

Contribution

It presents a novel combination of dimensionality reduction and clustering to identify turbulent states and transition pathways in stratified shear flow, enabling reduced-order modeling of complex turbulence.

Findings

Multiple turbulence types identified across parameter space

Intermittent cycling between turbulence states observed

Transition probabilities and residence times characterized

Abstract

Modelling fluid turbulence using a `skeleton' of coherent structures has traditionally progressed by focusing on a few canonical laboratory experiments such as pipe flow and Taylor-Couette flow. We here consider the stratified inclined duct, a sustained shear flow whose density stratification allows for the exploration of a wealth of new coherent and intermittent states at significantly higher Reynolds numbers than in unstratified flows. We automatically identify the underlying turbulent skeleton of this experiment with a data-driven method combining dimensionality reduction and unsupervised clustering of shadowgraph visualisations. We demonstrate the existence of multiple types of turbulence across parameter space and intermittent cycling between them, revealing distinct transition pathways. With a cluster-based network model of intermittency we uncover patterns in the transition probabilities and residence times under increasing levels of turbulent dissipation. Our method and results pave the way for new reduced-order models of multi-physics turbulence.