文献类型： 外文期刊
作者： Zhao, Xudong 1 ; Ma, Qingfen 1 ; Li, Jingru 1 ; Wu, Zhongye 1 ; Lu, Hui 2 ; Xiong, Yang 1 ;
作者机构： 1.Hainan Univ, Coll Mech & Elect Engn, Haikou 570228, Peoples R China
2.Chinese Acad Trop Agr Sci, Inst Environm & Plant Protect, Haikou 571101, Peoples R China
关键词： dynamic submarine cable; BP neural network; optimization algorithm; tornado optimization (TOC); offshore wind power; lazy wave
期刊名称：JOURNAL OF MARINE SCIENCE AND ENGINEERING （ 影响因子：2.8； 五年影响因子：2.8 ）
ISSN：
年卷期： 2025 年 13 卷 5 期
页码：
收录情况： SCI
摘要： The safe and efficient design of dynamic submarine cables is critical for the reliability of floating offshore wind turbines, yet traditional time-domain simulation-based optimization approaches are computationally intensive and time consuming. To address this challenge, this study proposes a closed-loop optimization framework that couples machine learning with intelligent optimization algorithms for a dynamic cable configuration design. A high-fidelity surrogate model based on a backpropagation (BP) neural network was trained to accurately predict cable dynamic responses. Three optimization algorithms-Particle Swarm Optimization (PSO), Ivy Optimization (IVY), and Tornado Optimization (TOC)-were evaluated for their effectiveness in optimizing the arrangement of buoyancy and weight blocks. The TOC algorithm exhibited superior accuracy and convergence stability. Optimization results show an 18.3% reduction in maximum curvature while maintaining allowable effective tension limits. This approach significantly enhances optimization efficiency and provides a viable strategy for the intelligent design of dynamic cable systems. Future work will incorporate platform motions induced by wind turbine operation and explore multi-objective optimization schemes to further improve cable performance.
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