Network-theoretic modeling of fluid-structure interactions

TL;DR

This paper introduces two network-based models to analyze fluid-structure interactions in laminar flow over a compliant flat plate, enabling better understanding and prediction of complex multi-physics systems.

Contribution

It proposes novel multi-layer network approaches for modeling FSI, combining energy transfer analysis with community detection for reduced-order modeling.

Findings

Network measures reveal temporal behavior of FSI components.

Community detection simplifies complex network interactions.

Predictive models demonstrate potential for system analysis.

Abstract

The coupling interactions between deformable structures and unsteady fluid flows occur across a wide range of spatial and temporal scales in many engineering applications. These fluid-structure interactions (FSI) pose significant challenges in accurately predicting flow physics. In the present work, two multi-layer network approaches are proposed that characterize the interactions between the fluid and structural layers for an incompressible laminar flow over a two-dimensional compliant flat plate at a 35-degrees angle of attack. In the first approach, the network nodes are formed by wake vortices and bound vortexlets, and the edges of the network are defined by the induced velocity between these elements. In the second approach, coherent structures (fluid modes), contributing to the kinetic energy of the flow and structural modes, contributing to the kinetic energy of the compliant structure constitute the network nodes. The energy transfers between the modes are extracted using a perturbation approach. Furthermore, the network structure of the FSI system is simplified using the community detection algorithm in the vortical approach and by selecting dominant modes in the modal approach. Network measures are used to reveal the temporal behavior of the individual nodes within the simplified FSI system. Predictive models are then built using both data-driven and physics-based methods. Overall, this work sets the foundation for network-theoretic reduced-order modeling of fluid-structure interactions, generalizable to other multi-physics systems.