基于人工神经网络的自航浮标测波方法可行性

doi:10.16183/j.cnki.jsjtu.2021.094

上海交通大学学报 ›› 2022, Vol. 56 ›› Issue (4): 498-505.doi: 10.16183/j.cnki.jsjtu.2021.094

基于人工神经网络的自航浮标测波方法可行性

秦艺超¹, 黄礼敏¹(), 王骁², 马学文¹, 段文洋¹, 郝伟¹

1.哈尔滨工程大学船舶工程学院,哈尔滨 150001
2.海军大连舰艇学院航海系,辽宁大连 116018

收稿日期:2021-03-23 出版日期:2022-04-28 发布日期:2022-05-07
通讯作者: 黄礼敏 E-mail:huanglimin@hrbeu.edu.cn
作者简介:秦艺超(1996-),男,山西省临汾市人,硕士生,从事波浪反演研究.
基金资助:
国家自然科学基金(51809066);装备发展部装备预先研究项目(JZX7Y20190247001001);上海交通大学海洋工程国家重点实验室开放课题(1902)

Feasibility of Wave Measurement by Using a Sailing Buoy and the Artificial Neural Network Technique

QIN Yichao¹, HUANG Limin¹(), WANG Xiao², MA Xuewen¹, DUAN Wenyang¹, HAO Wei¹

1. College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China
2. Department of Marine Navigation, Dalian Naval Academy, Dalian 116018, Liaoning, China

Received:2021-03-23 Online:2022-04-28 Published:2022-05-07
Contact: HUANG Limin E-mail:huanglimin@hrbeu.edu.cn

摘要/Abstract

摘要：

海洋波浪会对船舶营运和安全产生不利影响,实时准确的随船海浪监测对绿色智能船舶及航行安全至关重要.利用船舶运动反演遭遇海浪信息是一种重要的随船海浪监测手段,具有成本低、时空分辨率高等诸多优点,受到广泛关注.针对现有船舶运动反演海浪模型存在的不足,提出了一种基于人工神经网络(Artificial Neural Network, ANN)的反演模型.以船舶摇荡运动时历作为输入,以波浪时历作为输出,利用人工神经网络进行船舶运动特征提取,输入线性函数进行波面时历反演.为验证反演模型的可行性和精度,开展了船模水池试验.结果表明,提出的基于人工神经网络的测波方法能够很好地实现规则波和不规则波浪时历反演,规则波反演统计误差大多小于10%,不规则波反演误差在10%左右.提出的方法为船舶运动反演海浪时域信息提供了一种有效可行的手段.

关键词: 波浪反演, 人工神经网络, 自航波浪浮标, 水池试验

Abstract:

Ocean waves have adverse effects on the operation and safety of ships. Real-time and accurate onboard wave monitoring is essential for green smart ships and navigational safety. It is an important method to use ship motion to perform inversion of the information of encountering waves, which has many advantages such as low cost and high spatio-temporal resolution, and has attracted wide attention. An inversion model based on artificial neural network (ANN) is proposed to deal with the shortcomings of existing ship motion inversion ocean wave models. Taking the motion time history of the ship as the input and the wave elevation time history as the output, the artificial neural network is used to extract the motion features of the ship, and the linear function is input to perform wave surface time history inversion. To verify the feasibility and the accuracy of the inversion model, a ship model tank test is conducted. The results show that the proposed wave measurement method based on the artificial neural network can well achieve regular wave and irregular wave elevation time history inversion. Most of the statistical errors of regular wave inversion are less than 10% and the error of irregular wave inversion is about 10%. The method proposed provides an effective and feasible means for inversion of wave time information in ship motion.

Key words: wave inversion, artificial neural network, sailing wave buoy, tank testing

中图分类号:

U268.6

秦艺超, 黄礼敏, 王骁, 马学文, 段文洋, 郝伟. 基于人工神经网络的自航浮标测波方法可行性[J]. 上海交通大学学报, 2022, 56(4): 498-505.

QIN Yichao, HUANG Limin, WANG Xiao, MA Xuewen, DUAN Wenyang, HAO Wei. Feasibility of Wave Measurement by Using a Sailing Buoy and the Artificial Neural Network Technique[J]. Journal of Shanghai Jiao Tong University, 2022, 56(4): 498-505.

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参考文献 20

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参数	数值
船长/m	1.03
设计水线宽/mm	290.1
设计吃水/mm	300
排水量/kg	19.44
湿表面积/mm²	74571
方形系数	0.21
棱形系数	0.583
浮心纵向坐标/mm	51.06
船舶重心距离基线垂向高度/mm	210.4

指标	数值
长/m	108
宽/m	7
深/m	3.5
造波周期/s	0.4~4
规则波波高最大值/m	0.4
不规则波有义波高最大值/m	0.32

编号	波长船长比	波长/m	频率/ (rad·s^-1)	周期/s	波高/m	编号	波长船长比	波长/m	频率/ (rad·s^-1)	周期/s	波高/m
1	1.1	1.133	7.376	0.852	0.0227	7	2.0	2.060	5.470	1.149	0.0412
2	1.4	1.442	6.538	0.961	0.0288	8	2.1	2.163	5.338	1.177	0.0433
3	1.6	1.648	6.116	1.027	0.0330	9	2.2	2.266	5.215	1.205	0.0453
4	1.7	1.751	5.933	1.059	0.0350	10	2.5	2.575	4.893	1.284	0.0515
5	1.8	1.854	5.766	1.090	0.0371	11	3.0	3.090	4.466	1.407	0.0309
6	1.9	1.957	5.612	1.120	0.0391	12	3.5	3.605	4.135	1.520	0.0600

ω/(rad·s^-1)	RMSE/mm	e_r/%
7.372	2.629	11.60
6.535	1.501	5.21
6.113	1.486	4.51
5.930	1.277	3.65
5.763	2.740	7.39
5.609	3.001	7.67
5.467	6.717	10.34
5.335	4.117	9.52
5.213	3.182	7.02
4.890	2.228	5.39
4.464	5.552	3.60
4.133	3.592	2.99

基于人工神经网络的自航浮标测波方法可行性

Feasibility of Wave Measurement by Using a Sailing Buoy and the Artificial Neural Network Technique

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