The main goal of this research 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 like tire/water interaction, the FOM solid model (often chosen as quasistatic) can take far more computational time than the HF fluid one. In this context, for the sake of performance one could only derive a reduced-order model for the structure and try to achieve a partitioned HF fluid solver coupled with a ROM solid one. In this paper, we present a datadriven partitioned ROM on a study case involving a simplified 1D-1D FSI problem representing an axisymmetric elastic model of an arterial vessel, coupled with an incompressible fluid flow. We derive a purely data-driven solid ROM for FOM fluid-ROM structure partitioned coupling and present early results.

翻译：暂无翻译