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.

翻译：本文的主要目标是基于全阶模型（FOM）的高保真仿真结果数据，开发一种数据驱动的降阶模型（ROM）策略，以较低的计算成本预测流固耦合（FSI）问题解的时域演化。对于某些FSI应用，弹性固体FOM（通常选择准静态模型）的计算耗时可能远超流体FOM。在此背景下，为提升性能，可仅针对固体结构推导ROM，并尝试构建分区FOM流体求解器与ROM固体求解器的耦合系统。本文针对两个研究案例提出了数据驱动的分区ROM：（i）简化的1D-1D FSI问题，代表轴向对称弹性动脉血管模型与不可压缩流体流动的耦合；（ii）二维不可压缩圆柱绕流与双瓣弹性固体相互作用的尾流问题。我们评估了所提ROM-FOM策略在上述案例中的精度与性能，同时研究了模型超参数的影响，证明该策略能实现高预测精度和显著加速效果。