海上风电导管架基础波浪荷载特性研究及敏感性分析

吴豪, 张赛, 高上松, 黎刘杨, 高祎凡, 何方

太阳能学报 ›› 2026, Vol. 47 ›› Issue (7) : 191-196.

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (7) : 191-196. DOI: 10.19912/j.0254-0096.tynxb.2025-0407

海上风电导管架基础波浪荷载特性研究及敏感性分析

  • 吴豪1, 张赛1, 高上松2, 黎刘杨2, 高祎凡3, 何方2
作者信息 +

WAVE LOAD CHARACTERISTICS AND SENSITIVITY ANALYSIS OF JACKET FOUNDATIONS FOR OFFSHORE WIND POWER

  • Wu Hao1, Zhang Sai1, Gao Shangsong2, Li Liuyang2, Gao Yifan3, He Fang2
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文章历史 +

摘要

该文开展水动力物理模型实验,明确导管架基础的水平波浪荷载特性,探究导管架基础在不同波浪要素工况下的水平波浪荷载的变化规律。并基于支持向量机(SVM)使用水动力物理模型实验数据集进行训练,获取可靠、吻合良好的预测模型。结合SVM预测模型对水深、波浪周期和入射波高使用Sobol法开展全局敏感性分析,进一步明确了各波浪要素对导管架基础水平波浪荷载的影响。发现在各波浪要素中入射波高对水平波浪荷载的影响最为显著。

Abstract

This study carried out physical model experiments to identify the characteristics of horizontal wave loads on jacket foundations and investigate the variation patterns of wave loads under different wave conditions. A reliable and well-fitted prediction model was obtained by training a support vector machine (SVM) based on the physical model experimental dataset. Combined with the SVM prediction model, a global sensitivity analysis was performed using the Sobol method with respect to water depth, wave period and incident wave height, further clarifying the influence of each parameter on the horizontal wave loads of jacket foundations. It was found that among the parameters, incident wave height exerts the most significant effect on horizontal wave loads.

关键词

海上风电 / 导管架基础 / 波浪荷载 / 敏感性分析 / 物理模型试验

Key words

offshore wind power / jacket foundation / wave load / sensitivity analysis / physical model test

引用本文

导出引用
吴豪, 张赛, 高上松, 黎刘杨, 高祎凡, 何方. 海上风电导管架基础波浪荷载特性研究及敏感性分析[J]. 太阳能学报. 2026, 47(7): 191-196 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0407
Wu Hao, Zhang Sai, Gao Shangsong, Li Liuyang, Gao Yifan, He Fang. WAVE LOAD CHARACTERISTICS AND SENSITIVITY ANALYSIS OF JACKET FOUNDATIONS FOR OFFSHORE WIND POWER[J]. Acta Energiae Solaris Sinica. 2026, 47(7): 191-196 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0407
中图分类号: TM614   

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基金

国家自然科学基金(52022092); 浙江省“领雁”研发攻关计划(2023C03122); 浙江省万人计划科技创新领军人才项目(2021R52050)

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