半潜式浮式风机基础水动力特性及系泊系统张力特性预测

doi:10.11978/2024177

热带海洋学报 ›› 2025, Vol. 44 ›› Issue (3): 36-47.doi: 10.11978/2024177CSTR: 32234.14.2024177

半潜式浮式风机基础水动力特性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.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 出版日期:2025-05-10 发布日期:2025-06-04
通讯作者: 屈科
作者简介:
李天阔(2004—), 男, 本科生, 主要从事波浪水动力研究。email: 202204330112@stu.csust.edu.cn
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(S202410536097)

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

LI Tiankuo¹(), QU Ke¹^,²^,³(), LI Xiaohan¹, WANG Aoyu¹, WANG Chao¹

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

Received:2024-09-13 Revised:2024-11-19 Online:2025-05-10 Published:2025-06-04
Contact: QU Ke
Supported by:
National Key Research and Development Program of China(2022YFC3103601); The Provincial Undergraduate Training Program for Innovation and Entrepreneurship(S202410536097)

摘要/Abstract

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。为实现对系泊系统高效精准且低成本的安全预警, 提出了应用全神经网络根据波浪状况预测系泊缆张力、海上浮式风机基础的运动响应及载荷的方法。先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, 再应用全连接神经网络方法对数据进行学习及预测, 结果显示: 对系泊缆的张力、风机基础的运动响应和载荷预测精度分别达到99.57%、98.91%和99.79%, 证明该方法对系泊系统预测的可行性与可靠性, 为海上风机超前安全预警的实际应用提供参考。

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 wind turbine foundation at sea, leading to the slack-tension phenomenon of the mooring cables. This phenomenon greatly reduces the service life of the mooring system and poses a safety risk to the overall operation of the wind turbine platform. In order to achieve an efficient, accurate and low-cost safety warning for mooring systems, this paper proposes a method which uses a fully connected neural network to predict the load, dynamic response and tension characteristics of the mooring system based on wave conditions. Using numerical simulation, a database of wave heights and the load and motion response of the foundation of a semi-submersible floating wind turbine under extreme sea conditions was constructed, and then based on which a fully connected neural network method was used to learn and make predictions. The results showed that the prediction accuracy of the tension in the mooring cables, the motion response and the load of the wind turbine foundation reached 99.57%, 98.91% and 99.79%, respectively, which proved the feasibility and reliability of the method for predicting the safety of mooring systems and provided reference for the practical application of advanced safety warning for offshore wind turbines.

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

中图分类号:

李天阔, 屈科, 李晓涵, 王傲宇, 王超. 半潜式浮式风机基础水动力特性及系泊系统张力特性预测[J]. 热带海洋学报, 2025, 44(3): 36-47.

LI Tiankuo, QU Ke, LI Xiaohan, WANG Aoyu, WANG Chao. The hydrodynamic characteristics and the prediction of mooring system tensions for a semi-submersible floating wind turbine foundation[J]. Journal of Tropical Oceanography, 2025, 44(3): 36-47.

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工况	参数
工况	H_F/m	T/s	x_F/m	t_F/s
N1	0.0626	1.20	7.5	10.0
N2	0.1750	1.25	7.2	10.0

参数	设计参数
中心柱直径/m	6.5
侧柱直径/m	6.5
浮筒高度/m	6
浮筒宽度/m	9
侧柱圆心到中心柱圆心长度/m	41
浮筒长度/m	45.5
侧柱高度/m	34
中心柱高度/m	44
作业吃水/m	30
排水体积/m³	10555
浮体总重量/kg	9738000

参数	设计参数值
相邻系泊缆的夹角	120°
系泊点距离自由水面的距离/m	200
导缆孔距离自由水面的距离/m	18
系泊缆的原始长度/m	835.5
系泊缆的直径/m	0.0766
系泊缆的等效线密度/(kg·m^-1)	45.5

位置	x/m	y/m	z/m
导缆孔a	144.25	100.0	182.0
导缆孔b	77.875	138.322	182.0
导缆孔c	77.875	61.778	182.0
锚固点A	937.6	100.0	0.0
锚固点B	-318.8	825.4	0.0
锚固点C	-318.8	-625.4	0.0

模型	参数类型	参数配置
全连接神经网络	激活函数	Relu
	优化器	Adam
	损失函数	MSE
	丢弃层数量	2
	隐藏层数量	6
	隐藏层神经元数量	64
	迭代次数	10000
	批次大小	512

半潜式浮式风机基础水动力特性及系泊系统张力特性预测

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

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图/表 22

参考文献 23

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工况编号	有效波高H_S/m	谱峰周期T_P/s	工况编号	有效波高H_S/m	谱峰周期T_P/s
1	1	10	26	13.5	10
2	1.5	10	27	14	10
3	2	10	28	14.5	10
4	2.5	10	29	3	12
5	3	10	30	5	12
6	3.5	10	31	5.5	12
7	4	10	32	6	12
8	4.5	10	33	7	12
9	5	10	34	7.5	12
10	5.5	10	35	8	12
11	6	10	36	8.5	12
12	6.5	10	37	9	12
13	7	10	38	9.5	12
14	7.5	10	39	10	12
15	8	10	40	10.5	12
16	8.5	10	41	11	12
17	9	10	42	11.5	12
18	9.5	10	43	3	14
19	10	10	44	5	14
20	10.5	10	45	7	14
21	11	10	46	7.5	14
22	11.5	10	47	8	14
23	12	10	48	3	8
24	12.5	10	49	5	8
25	13	10	50	7.5	8

预测案例	RMSE	MAE	R²
图13a	5.7207384	4.4730477	0.99571687
图13b	6.565532	4.3595	0.9739415
图14a	0.29001784	0.22374631	0.9727198
图14b	0.0922789	0.068423904	0.98917085
图15a	2.670674	2.109916	0.9979997
图15b	2.9917114	2.5716374	0.9897062

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