Vortex-induced vibration (VIV) is a typical nonlinear fluid-structure interaction phenomenon, which widely exists in practical engineering (the flexible riser, the bridge and the aircraft wing, etc). The conventional finite element model (FEM)-based and data-driven approaches for VIV analysis often suffer from the challenges of the computational cost and acquisition of datasets. This paper proposed a transfer learning enhanced the physics-informed neural network (PINN) model to study the VIV (2D). The physics-informed neural network, when used in conjunction with the transfer learning method, enhances learning efficiency and keeps predictability in the target task by common characteristics knowledge from the source model without requiring a huge quantity of datasets. The datasets obtained from VIV experiment are divided evenly two parts (source domain and target domain), to evaluate the performance of the model. The results show that the proposed method match closely with the results available in the literature using conventional PINN algorithms even though the quantity of datasets acquired in training model gradually becomes smaller. The application of the model can break the limitation of monitoring equipment and methods in the practical projects, and promote the in-depth study of VIV.
翻译:Vortex诱发的振动(VIV)是一种典型的非线性流体结构互动现象,在实际工程中广泛存在(灵活的立方、桥梁和机翼等),基于常规有限要素模型(FEM)和数据驱动的VIV分析方法往往受到计算成本和获取数据集的挑战。本文建议进行转让学习,加强物理学-知情神经网络(PINN)模型,以研究VIV(2D) 。物理知情神经网络,在与转移学习方法同时使用时,通过来源模型的共同特性知识提高学习效率,并保持目标任务的可预测性,而不需要大量数据集。从VIV实验中获得的数据集平均分为两个部分(源域和目标域),以评价模型的性能。结果显示,拟议的方法与文献中使用常规的PINN算法获得的结果密切匹配,即使培训模型获得的数据集数量逐渐减少。模型的应用可以打破监测设备和方法在实际项目中的局限性,并促进VI的深入研究。