Network community-based model reduction for vortical flows

TL;DR

This paper introduces a network community-based reduced-order model that captures key vortical interactions in high-dimensional unsteady flows, enabling accurate prediction of aerodynamic forces and flow dynamics.

Contribution

It develops a novel data-inspired, network-theoretic approach to identify vortical communities and reduce system complexity for flow modeling and force prediction.

Findings

Accurately captures macroscopic vortex dynamics.

Predicts lift and drag forces effectively.

Robust against noise and turbulence.

Abstract

A network community-based reduced-order model is developed to capture key interactions amongst coherent structures in high-dimensional unsteady vortical flows. The present approach is data-inspired and founded on network-theoretic techniques to identify important vortical communities that are comprised of vortical elements that share similar dynamical behavior. The overall interaction-based physics of the high-dimensional flow field is distilled into the vortical community centroids, considerably reducing the system dimension. Taking advantage of these vortical interactions, the proposed methodology is applied to formulate reduced-order models for the inter-community dynamics of vortical flows, and predict lift and drag forces on bodies in wake flows. We demonstrate the capabilities of these models by accurately capturing the macroscopic dynamics of a collection of discrete point vortices, and the complex unsteady aerodynamic forces on a circular cylinder and an airfoil with a Gurney flap. The present formulation is found to be robust against simulated experimental noise and turbulence due to its integrating nature of the system reduction.