Open Access

ARTICLE

Vortex-Induced Vibration Prediction in Floating Structures via Unstructured CFD and Attention-Based Convolutional Modeling

Yan Li^1,2,*, Yibin Wu^1,2, Bo Zhang^1,2

1 School of Marine Engineering, Jimei University, Xiamen, 361021, China
2 Fujian Provincial Key Laboratory for Naval Architecture and Ocean Engineering, Xiamen, 361021, China

* Corresponding Author: Yan Li. Email:

Fluid Dynamics & Materials Processing 2025, 21(12), 2905-2925. https://doi.org/10.32604/fdmp.2025.072979

Received 08 September 2025; Accepted 12 December 2025; Issue published 31 December 2025

Abstract

Traditional Computational Fluid Dynamics (CFD) simulations are computationally expensive when applied to complex fluid–structure interaction problems and often struggle to capture the essential flow features governing vortex-induced vibrations (VIV) of floating structures. To overcome these limitations, this study develops a hybrid framework that integrates high-fidelity CFD modeling with deep learning techniques to enhance the accuracy and efficiency of VIV response prediction. First, an unstructured finite-volume fluid–structure coupling model is established to generate high-resolution flow field data and extract multi-component time-series feature tensors. These tensors serve as inputs to a Squeeze-and-Excitation Convolutional Neural Network (SE-CNN), which models the nonlinear coupling between flow disturbances and structural responses. The SE-CNN architecture incorporates an attention-based weighting mechanism through an embedded Squeeze-and-Excitation module, dynamically optimizing channel feature importance and improving sensitivity to critical flow characteristics. During training, multidimensional inputs, including pressure, velocity gradient, and displacement sequences, are used to capture the full complexity of fluid–structure interactions. Results demonstrate that the proposed method achieves a maximum amplitude prediction error of only 2.9% and a main frequency deviation below 0.03 Hz, outperforming conventional CNN models by reducing amplitude prediction error from 3.2% to 1.9%. The approach is validated using a representative semi-submersible platform, confirming its robustness across varying damping conditions and flow velocities.

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Keywords

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