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热带海洋学报

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半潜式浮式风机基础复杂水动力及系泊系统张力特性预测

李天阔1 , 屈科1，2，3 , 李晓涵1 , 王傲宇1 , 王超1   

  1. 1. 长沙理工大学水利与环境工程学院，湖南 长沙 410114;

    2. 洞庭湖水环境治理与生态修复湖南省重点实验室，湖南 长沙 410114;

    3. 水沙科学与水灾害防治湖南省重点实验室，湖南 长沙 410114;

  • 收稿日期:2024-09-13 修回日期:2024-11-19 接受日期:2024-11-25
  • 通讯作者: 屈科
  • 基金资助:
    国家重点研发计划课题(2022YFC3103601); 省级大学生创新创业训练计划(S202310536108)

The complex hydrodynamic characteristics and the prediction of mooring system tensions for a semi-submersible floating fan foundation

LI Tiankuo1, QYU Ke1，2，3, LI Xiaohan1, WANG Aoyu1, WANG Chao1   

  1. 1. School of Hydraulic and Environmental Engineering, Changsha University of Science & Technology, Changsha 410114, China;

    2. Key Laboratory of Dongting Lake Aquatic Eco-Environmental Control and Restoration of Hunan Province, Changsha 410114, China;

    3. Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province, Changsha 410114, China;

    Disaster Prevention of Hunan Province 410000, China
  • Received:2024-09-13 Revised:2024-11-19 Accepted:2024-11-25
  • Contact: Qu ke
  • Supported by:
    National Key Research and Development Program of China(2022YFC3103601); The Provincial Undergraduate Training Program for Innovation and Entrepreneurship.(S202310536108)

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摘要： 全球气候变化导致我国沿海区域极端海况频发，引起海上半潜式浮式风机基础大幅度运动，造成系泊缆出现松弛－张紧现象，严重缩短系泊系统使用寿命，影响风机平台整体作业安全。通过数值模拟，构建了极端海况条件下波浪高度以及半潜式浮式风机基础载荷和运动响应的数据库，并应用全连接神经网络方法建立了半潜式浮式风机基础载荷和动力响应特性的预测模型，实现了在极端海况条件下对半潜式浮式风机基础载荷、动力响应特性以及系泊系统张力特性的预测。通过神经网络预测，可以根据波浪状况预测系泊缆的张力、海上浮式风机基础的运动响应及载荷，从而实现对系泊缆的张力及浮式风机基础的运动响应和载荷的评估。

关键词: 半潜浮式平台, 系泊系统, 全神经网络, 深度学习, 动态响应

Abstract: Climate change has caused frequent extreme sea conditions in China's coastal regions. This has resulted in significant dynamic movements of the semi-submersible floating fan foundation at sea, leading to the relaxation-tensioning phenomenon of the mooring lines. This phenomenon greatly reduces the useful life of the mooring system and poses a safety risk to the overall operation of the wind turbine platform. A database of wave height and semi-submersible floating fan foundation load and dynamic response is created using numerical simulation. The full connection neural network method is then applied to establish a prediction model for the characteristics of the semi-submersible floating fan foundation load and dynamic response. This model enables the prediction of the load, dynamic response characteristics, and tension characteristics of the mooring system under extreme sea state conditions. By utilizing neural network prediction, it is possible to forecast the tension of the mooring lines, the dynamic response, and the load of the floating wind turbine foundation based on the wave conditions. This enables the assessment of the tension of the mooring lines, as well as the dynamic response and load of the floating wind turbine foundation.

Key words: semi-submersible floating platform, mooring system, fully connected neural network, deep learning, dynamic response