Non-intrusive reduced order models for partitioned fluid–structure interactions

• Published in: Journal of fluids and structures 2024-08, Vol.128, p.104156, Article 104156
• Main Authors: Tiba, Azzeddine, Dairay, Thibault, De Vuyst, Florian, Mortazavi, Iraj, Berro Ramirez, Juan-Pedro
• Format: Article
• Language: English
• Subjects: Data-driven model; Fluid–structure interaction; Mathematics; Partitioned coupling; Physics; Reduced order model; ROM-FOM coupling
• ISSN: 0889-9746; 1095-8622
• DOI: 10.1016/j.jfluidstructs.2024.104156

The main goal of this work is to develop a data-driven Reduced Order Model (ROM) strategy from high-fidelity simulation result data of a Full Order Model (FOM). The goal is to predict at lower computational cost the time evolution of solutions of Fluid–Structure Interaction (FSI) problems. For some FSI applications, the elastic solid FOM (often chosen as quasi-static) can take far more computational time than the fluid one. In this context, for the sake of performance one could only derive a ROM for the structure and try to achieve a partitioned FOM fluid solver coupled with a ROM solid one. In this paper, we present a data-driven partitioned ROM on two study cases: (i) a simplified 1D-1D FSI problem representing an axisymmetric elastic model of an arterial vessel, coupled with an incompressible fluid flow; (ii) an incompressible 2D wake flow over a cylinder facing an elastic solid with two flaps. We evaluate the accuracy and performance of the proposed ROM-FOM strategy on these cases while investigating the effects of the model’s hyperparameters. We demonstrate a high prediction accuracy and significant speedup achievements using this strategy. •A data-driven reduced order model for partitioned fluid–structure interactions.•A new approach, coupling a reduced order solid model, and a full order fluid model.•Reduction of elastic quasi-static solid models coupled with less expensive fluid models.•Linear and nonlinear dimensionality reduction for forces and displacement fields.•Parsimonious regression models to learn the solid model in the latent space. [Display omitted]