A hybrid reduced-order model for segregated fluid-structure interaction solvers in an ALE approach at high Reynolds number

TL;DR

This paper develops a hybrid reduced-order model for high Reynolds number fluid-structure interaction in an ALE framework, combining POD with data-driven techniques to accurately simulate flow-induced vibrations.

Contribution

It introduces a novel hybrid ROM approach that integrates Galerkin projection with neural networks and radial basis functions for segregated FSI in ALE at high Reynolds numbers.

Findings

ROM accurately captures FSI physics

Validated on flow-induced vibration of an airfoil

Effective at Reynolds number 10 million

Abstract

This study introduces a first step for constructing a hybrid reduced-order models (ROMs) for segregated fluid-structure interaction in an Arbitrary Lagrangian-Eulerian (ALE) approach at a high Reynolds number using the Finite Volume Method (FVM). The ROM is driven by proper orthogonal decomposition (POD) with hybrid techniques that combines the classical Galerkin projection and two data-driven methods (radial basis networks , and neural networks/ long short term memory). Results demonstrate the ROM ability to accurately capture the physics of fluid-structure interaction phenomena. This approach is validated through a case study focusing on flow-induced vibration (FIV) of a pitch-plunge airfoil at a high Reynolds number 10000000.