Predicting Flow-Induced Vibration in Isolated and Tandem Cylinders Using Hypergraph Neural Networks

TL;DR

This paper introduces a hypergraph neural network framework inspired by finite element methods to accurately predict flow-induced vibrations and fluid-structure interactions in cylinders, capturing complex wake effects and nonlinear dynamics.

Contribution

The novel hypergraph neural network architecture encodes higher-order spatial relationships and models fluid-structure interactions for flow-induced vibrations, advancing surrogate modeling techniques.

Findings

Accurately predicts oscillation amplitudes across Reynolds numbers and velocities.

Resolves complex wake-body interactions in tandem cylinders.

Reproduces force statistics and flow dynamics with high fidelity.

Abstract

We present a finite element-inspired hypergraph neural network framework for predicting flow-induced vibrations in freely oscillating cylinders. The surrogate architecture transforms unstructured computational meshes into node-element hypergraphs that encode higher-order spatial relationships through element-based connectivity, preserving the geometric and topological structure of the underlying finite-element discretization. The temporal evolution of the fluid-structure interaction is modeled via a modular partitioned architecture: a complex-valued, proper orthogonal decomposition-based sub-network predicts mesh deformation using a low-rank representation of Arbitrary Lagrangian-Eulerian (ALE) grid displacements, while a hypergraph-based message-passing network predicts the unsteady flow field using geometry-aware node, element, and hybrid edge features. High-fidelity ALE-based simulations provide training and evaluation data across a range of Reynolds numbers and reduced velocities for isolated and tandem cylinder configurations. The framework demonstrates stable roll-outs and accurately captures the nonlinear variation of oscillation amplitudes with respect to reduced velocity, a key challenge in surrogate modeling of flow-induced vibrations. In the tandem configuration, the model successfully resolves complex wake-body interactions and multi-scale coupling effects, enabling accurate prediction of pressure and velocity fields under strong wake interference conditions. Our results show high fidelity in reproducing force statistics, dominant frequencies, and flow-field dynamics, supporting the framework's potential as a robust surrogate model for digital twin applications.