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：本研究的主要目标是开发一种数据驱动的降阶模型（ROM）策略，该策略基于全阶模型（FOM）的高保真模拟结果数据，旨在以较低的计算成本预测流固耦合（FSI）问题解的时域演化。对于轮胎与水相互作用等FSI应用，FOM固体模型（通常选择准静态模型）的计算时间可能远超高保真流体模型。在此背景下，为提升性能，可仅对结构部分推导降阶模型，并尝试实现分区高保真流体求解器与ROM固体求解器的耦合。本文以一维-一维简化FSI问题（代表轴对称弹性血管模型与不可压缩流体耦合）作为研究案例，提出一种数据驱动的分区ROM方法。我们推导出纯数据驱动的固体ROM用于FOM流体-ROM结构分区耦合，并展示了初步结果。