A hybrid FEM-DNN-based vortex-induced Vibration Prediction Method for Flexible Pipes under oscillatory flow in the time domain. (15th February 2022)

• Record Type: Journal Article
• Authors: Zhang, Mengmeng; Fu, Shixiao; Ren, Haojie; Ma, Leixin; Xu, Yuwang
• Is Part Of: Ocean engineering. Volume 246(2022)
• Publication Date: 2022-02-15
• Subjects: Vortex-induced vibration prediction -- Flexible pipes -- Deep neural network -- Vortex-induced force coefficient model -- Oscillatory flow; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2021.110488
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: In this paper, a hybrid FEM-DNN-based vortex-induced vibration (VIV) prediction method for flexible pipes under an oscillatory flow in the time domain is proposed. In this method, a vortex-induced force coefficient model is regressed by a deep neural network (DNN) from experimental data. The model takes into account the effects of flow velocity variation, VIV responses and their coupling features on vortex-induced forces. Then, it is combined with finite element method (FEM) to predict the VIV responses of flexible pipes in time domain. In addition, a phase modulation model is developed to ensure that synchronization between forces and responses can be achieved. The proposed prediction method is used to predict the VIV responses of the flexible pipe used in DNN regression training under oscillatory flows. Comparisons between the predicted results and the experimental results are conducted to verify the feasibility and accuracy of the proposed method. Then, the generalizability of the proposed method is further verified via comparisons between the predicted VIV results and the experimental results of another flexible pipe whose structural parameters are different from the DNN training pipe. Highlights: A hybrid FEM-DNN-based VIV prediction method for flexible pipes under an oscillatory flow is proposed. The effects of flow velocity variation, KC number, β number and other flow field features are considered in the DNN-based VIF model. A phase modulation model is developed to ensure the synchronization between VIF and VIV velocities. The feasibility and accuracy of the proposed method are statistically verified by experimental results.