Cluster-based hierarchical network model of the fluidic pinball -- Cartographing transient and post-transient, multi-frequency, multi-attractor behaviour

TL;DR

This paper introduces a hierarchical clustering-based reduced-order model for analyzing complex, multi-scale flow dynamics in the fluidic pinball, capturing transient, multi-frequency, and multi-attractor behaviors across different Reynolds number regimes.

Contribution

The authors develop a self-supervised hierarchical clustering methodology that models and visualizes complex flow transitions and invariant sets in fluid dynamics, advancing reduced-order modeling techniques.

Findings

Successfully models flow dynamics at different Reynolds numbers.

Identifies multiple invariant sets and transition pathways.

Provides a visual, interpretable representation of complex flow behavior.

Abstract

We propose a self-supervised cluster-based hierarchical reduced-order modelling methodology to model and analyse the complex dynamics arising from a sequence of bifurcations for a two-dimensional incompressible flow of the unforced fluidic pinball. The hierarchy is guided by a triple decomposition separating a slowly varying base flow, dominant shedding and secondary flow structures. All these flow components are kinematically resolved by a hierarchy of clusters, starting with the base flow in the first layer, resolving the vortex shedding in the second layer and distilling the secondary flow structures in the third layer. The transition dynamics between these clusters is described by a directed network, called the cluster-based hierarchical network model (HiCNM) in the sequel. Three consecutive Reynolds number regimes for different dynamics are considered: (i) periodic shedding at $Re=80$, (ii) quasi-periodic shedding at $Re=105$, and (iii) chaotic shedding at $Re=130$, involving three unstable fixed points, three limit cycles, two quasi-periodic attractors and a chaotic attractor. The HiCNM enables identifying the transient and post-transient dynamics between multiple invariant sets in a self-supervised manner. Both the global trends and the local structures during the transition are well resolved by a moderate number of hierarchical clusters. The proposed reduced-order modelling provides a visual representation of transient and post-transient, multi-frequency, multi-attractor behaviour and may automate the identification and analysis of complex dynamics with multiple scales and multiple invariant sets.