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's 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 (Re=107).

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

Mola Andrea;
2025

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's 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 (Re=107).
2025
Finite volume method
Flow-induced vibration
Fluid-structure interaction
Galerkin projection
Long-short term memory
Mesh motion
Neural networks
Proper orthogonal decomposition
Radial basis network
Reduced-order model
Turbulence
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11771/37079
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