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

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.

References in corpus (4)

• Network Structure of Two-Dimensional Decaying Isotropic Turbulence
• Coherent structure coloring: identification of coherent structures from sparse data using graph theory
• Network-theoretic approach to sparsified discrete vortex dynamics
• Optimal Nonlinear Eddy Viscosity in Galerkin Models of Turbulent Flows

Cited by in corpus (5)

• From snapshots to manifolds - A tale of shear flows
• Trajectory-optimized cluster-based network model for the sphere wake
• Machine-learned control-oriented flow estimation for multiactuator multi-sensor systems exemplified for the fluidic pinball
• Self-tuning model predictive control for wake flows
• Learning the Latent dynamics of Fluid flows from High-Fidelity Numerical Simulations using Parsimonious Diffusion Maps