Journal of Marine Sciences ›› 2024, Vol. 42 ›› Issue (3): 131-141.DOI: 10.3969/j.issn.1001-909X.2024.03.011

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Application of convolutional neural network method in evolution of tidal bore hydrodynamic characteristics

WANG Zhihong1(), QU Ke1,3,4,*(), YANG Yuanping2, WANG Xu1,3, GAO Rongze1   

  1. 1. School of Hydraulic and Environmental Engineering, Changsha University of Science & Technology, Changsha 410114, China
    2. Zhejiang Water Conservancy and Estuary Research Institute, Hangzhou 310017, China
    3. Key Laboratory of Dongting Lake Aquatic Eco-Environmental Control and Restoration of Hunan Province, Changsha 410114, China
    4. Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province, Changsha 410114, China
  • Received:2023-10-12 Revised:2024-02-26 Online:2024-09-15 Published:2024-11-25

Abstract:

The open source software OpenFOAM was used to solve the Reynolds-averaged Navier-Stokes equations, numerical simulations of wave surges (Fr=1.2~1.3) was implemented, and the results were compared with physical experimental data to verify the accuracy of the numerical simulations. The data was studied based on the CONV1D convolutional neural network model to predict the dynamic development process of tidal bore on terrain with slope. Comparison of the model prediction and numerical simulation results for the arrival time and the maximum water level of the tidal bore at x=30.0 m shows that the average relative error in the arrival time is 2.28%, and the average relative error in the maximum water level is 3.73%. The smaller relative error proves the accuracy of the CONV1D model. This result has certain research significance for the simulation of the hydrodynamic process of tidal bore, early warning of tidal bore disasters, and the study of future development trends of primary tidal bore.

Key words: tidal bore, Qiantang River, OpenFOAM, hydrodynamic process, slope, convolutional neural network, CONV1D, numerical simulation

CLC Number: