基于人工神经网络的极地船舶冰阻力预报方法

doi:10.16183/j.cnki.jsjtu.2022.316

上海交通大学学报 ›› 2024, Vol. 58 ›› Issue (2): 156-165.doi: 10.16183/j.cnki.jsjtu.2022.316

基于人工神经网络的极地船舶冰阻力预报方法

孙乾洋¹, 周利²(), 丁仕风¹, 刘仁伟¹, 丁一¹

1.江苏科技大学船舶与海洋工程学院,江苏镇江 212100
2.上海交通大学船舶海洋与建筑工程学院,上海 200240

收稿日期:2022-08-19 修回日期:2023-02-17 接受日期:2023-02-20 出版日期:2024-02-28 发布日期:2024-03-04
通讯作者: 周利,教授,博士生导师;E-mail:zhouli209@hotmail.com.
作者简介:孙乾洋 (1995-),博士生,主要从事冰载荷方面的研究.
基金资助:
国家重点研发计划(2022YFE0107000);国家自然科学基金面上项目(52171259);工信部高技术船舶科研项目(工信部重装函[2021]342号)

An Artificial Neural Network-Based Method for Prediction of Ice Resistance of Polar Ships

SUN Qianyang¹, ZHOU Li²(), DING Shifeng¹, LIU Renwei¹, DING Yi¹

1. School of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, Jiangsu, China
2. School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received:2022-08-19 Revised:2023-02-17 Accepted:2023-02-20 Online:2024-02-28 Published:2024-03-04

摘要/Abstract

摘要：

极地船舶冰区航行时,冰阻力的准确预报在保障船舶航行安全方面起着重要作用.近年来,机器学习在船舶方面的应用越来越广泛,其中,人工神经网络(ANN)是机器学习领域中一种常用的方法.本文的重点是设计一个用于预报极地船舶冰阻力的ANN模型.参考传统的经验和半经验公式,选择合适的输入特征参数,通过大量的船舶模型试验数据来训练神经网络,搭建径向基(RBF)神经网络模型,并选用遗传算法(GA)进行模型优化.研究表明,基于7个特征参数输入的遗传算法优化径向基(RBF-GA)神经网络模型具有良好的泛化效果,与模型试验和实船试验数据对比,平均误差在8%左右,具有较高的精度,可作为冰阻力预报工具.

关键词: 冰阻力, 机器学习, 径向基函数神经网络, 遗传算法, 船舶试验

Abstract:

Accurate prediction of ice resistance plays an important role in ensuring the safety of ship sailing in polar navigation in ice areas. In recent years, machine learning has been widely used in the field of ships, among which artificial neural network (ANN) is a common method. The focus of this paper is to design an ANN model for predicting the ice resistance of polar ships. According to the traditional empirical and semi-empirical formula, appropriate input characteristic parameters are selected. The radial basis function (RBF) neural network model is built based on a large number of ship model test data, and the genetic algorithm (GA) is used to optimize the model. The research shows that the radial basis function neural network model optimized by genetic algorithm (RBF-GA) based on seven characteristic parameters input has good generalization effect. Compared with the model test and full-scale test data, the average error is about 8%, which shows that the RBF-GA model has a high accuracy, and can be used as a tool for ice resistance prediction.

Key words: ice resistance, machine learning, radial basis function (RBF) neural network, genetic algorithm (GA), ship test

中图分类号:

U661

孙乾洋, 周利, 丁仕风, 刘仁伟, 丁一. 基于人工神经网络的极地船舶冰阻力预报方法[J]. 上海交通大学学报, 2024, 58(2): 156-165.

SUN Qianyang, ZHOU Li, DING Shifeng, LIU Renwei, DING Yi. An Artificial Neural Network-Based Method for Prediction of Ice Resistance of Polar Ships[J]. Journal of Shanghai Jiao Tong University, 2024, 58(2): 156-165.

图/表 12

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参考文献 40

[1]	ZHOU L, SU B, RISKA K, et al. Numerical simulation of moored structure station keeping in level ice[J]. Cold Regions Science and Technology, 2012, 71: 54-66. doi: 10.1016/j.coldregions.2011.10.008 URL
[2]	HU J, ZHOU L. Further study on level ice resistance and channel resistance for an icebreaking vessel[J]. International Journal of Naval Architecture and Ocean Engineering, 2016, 8: 169-176. doi: 10.1016/j.ijnaoe.2016.01.004 URL
[3]	MARKOPOULOS A P, GEORGIOPOULOS S, MANOLAKOS D E. On the use of back propagation and radial basis function neural networks in surface roughness prediction[J]. Journal of Industrial Engineering International, 2016, 12: 389-400. doi: 10.1007/s40092-016-0146-x URL
[4]	KOUSHAN K. Empirical prediction of ship resistance and wetted surface area using artificial neural networks[J]. Practical Design of Ships & Other Floating Structures, 2001, 1: 501-507.
[5]	COUSER P, MASON A, MASON G, et al. Artificial neural networks for hull resistance prediction[C]// 3rd International Conference on Computer and IT Applications in the Maritime Industries. Siguenza, Spain:Compit, 2004: 391-402.
[6]	CUI H, TURAN O, SAYER P. Learning-based ship design optimization approach[J]. Computer-Aided Design, 2012, 44: 186-195. doi: 10.1016/j.cad.2011.06.011 URL
[7]	YANG Y, TU H, SONG L, et al. Research on accurate prediction of the container ship resistance by RBFNN and other machine learning algorithms[J]. Journal of Marine Science and Engineering, 2021, 9(4): 376-393. doi: 10.3390/jmse9040376 URL
[8]	陈爱国, 叶家玮. 基于神经网络的船舶阻力计算数值实验研究[J]. 中国造船, 2010, 51(2): 21-27.
	CHEN Aiguo, YE Jiawei. Numerical experimental study of ship resistance calculation based on neural network[J]. Shipbuilding of China, 2010, 51(2): 21-27.
[9]	张乔宇, 黄国富, 金建海. 基于机器学习的船舶阻力预报模型研究[J]. 舰船科学技术, 2019, 41(23): 6-10.
	ZHANG Qiaoyu, HUANG Guofu, JIN Jianhai. Research on ship resistance prediction model based on machine learning[J]. Ship Science and Technology, 2019, 41(23): 6-10.
[10]	KIM J H, KIM Y, LU W. Prediction of ice resistance for ice-going ships in level ice using artificial neural network technique[J]. Ocean Engineering, 2020, 9(6): 613-623.
[11]	ISLAM M. Machine learning techniques for ship performance predictions in open water and ice[C]// The 31st International Ocean and Polar Engineering Conference. Rhodes, Greece: OnePetro, 2021: ISOPE-I-21-1296.
[12]	ZHANG M, SUN Q, GARME K, et al. Analysis of inland waterway ship performance in ice: Operation Time Window[J]. Ocean Engineering, 2022, 263: 112409. doi: 10.1016/j.oceaneng.2022.112409 URL
[13]	BROOMHEAD D, LOWE D. Multivariable functional interpolation and adaptive networks[J]. Complex Systems, 1988, 2: 321-355.
[14]	GAN M, PENG H, DONG X P. A hybrid algorithm to optimize RBF network architecture and parameters for nonlinear time series prediction[J]. Applied Mathematical Modelling, 2012, 36: 2911-2919. doi: 10.1016/j.apm.2011.09.066 URL
[15]	LEWIS J W, EDWARDS R Y. Methods for predicting icebreaking and ice resistance characteristics of icebreakers[J]. SNAME Transactions, 1970, 78: 213-249.
[16]	VANCE G. Analysis of the performance of a 140-foot great lakes icebreaker:USCGC Katmai Bay. Report No.80-8[R]. Hanover, USA: U.S. ACRREL, 1980.
[17]	ZAHN P, PHILLIPS L. Towed resistance trials in ice of the USCGC Mobile Bay (WTGB103)[M]. Montreal, Canada: Transportation Development Centre, 1987.
[18]	LINDQVIST G. A straightforward method for calculation of ice resistance of ships[C]// Proceedings of 10th International Conference on Port and Ocean Engineering under Arctic Conditions. Lulea, Sweden: POAC, 1989: 722-735.
[19]	RISKA K, WILHELMSON M, ENGLUND K, et al. Performance of merchant vessels in ice in the Baltic. Research Report No. 52[R]. Helsinki, Finland: Winter Navigation Research Board, 1998.
[20]	IONOV B. Ice resistance and its composition[R]. Moscow, Russia: Arctic and Antarctic Research Institute, 1988.
[21]	KEINONEN A, BROWNE R P, REVILL C R, et al. Icebreaker performance prediction[J]. Transactions-Society of Naval Architects and Marine Engineers, 1991, 99: 221-248.
[22]	ZHANG M, GARME K, BURMAN M, et al. A numerical ice load prediction model based on ice-hull collision mechanism[J]. Applied Sciences, 2020, 10: 692-713. doi: 10.3390/app10020692 URL
[23]	CHO S R, LEE S. A prediction method of ice breaking resistance using a multiple regression analysis[J]. International Journal of Naval Architecture and Ocean Engineering, 2015, 7: 708-719. doi: 10.1515/ijnaoe-2015-0050 URL
[24]	YUM J G, KANG K J, JANG J H, et al. A Study on the Hull form design and ice resistance & propulsion performance of a platform support vessel (PSV) operated in the Arctic Ocean[J]. Journal of the Society of Naval Architects of Korea, 2018, 55: 497-504. doi: 10.3744/SNAK.2018.55.6.497 URL
[25]	JEONG S Y, CHOI K. A review on ice resistance prediction formula for icebreaking vessels[C]// Proceedings of the International Offshore and Polar Engineering Conference. Osaka, Japan: ISOPE, 2009: 513-522.
[26]	JEONG S Y, JANG J, KIM C H, et al. A study of ship resistance characteristics for ice-strengthened vessel by broken ice channel width and size of broken ice pieces[J]. Journal of the Society of Naval Architects of Korea, 2018, 55: 22-27. doi: 10.3744/SNAK.2018.55.1.22 URL
[27]	SONG Y Y, KIM M C, CHUN H H. A study on resistance test of icebreaker with synthetic ice[J]. Journal of the Society of Naval Architects of Korea, 2007, 44: 389-397. doi: 10.3744/SNAK.2007.44.4.389 URL
[28]	ZHOU L, DIAO F, SONG M, et al. Calculation methods of icebreaking capability for a double-acting polar ship[J]. Journal of Marine Science and Engineering, 2020, 8(3): 179-200. doi: 10.3390/jmse8030179 URL
[29]	KIM M C, LEE W J, SHIN Y J. Comparative study on the resistance performance of an icebreaking cargo vessel according to the variation of waterline angles in pack ice conditions[J]. International Journal of Naval Architecture and Ocean Engineering, 2014, 6: 876-893. doi: 10.2478/IJNAOE-2013-0219 URL
[30]	CHO S R, JEONG S Y, LEE S. Development of effective model test in pack ice conditions of square-type ice model basin[J]. Ocean Engineering, 2013, 67: 35-44. doi: 10.1016/j.oceaneng.2013.04.011 URL
[31]	HU J, ZHOU L. Experimental and numerical study on ice resistance for icebreaking vessels[J]. International Journal of Naval Architecture and Ocean Engineering, 2015, 7: 626-639. doi: 10.1515/ijnaoe-2015-0044 URL
[32]	JEONG S. Ice resistance prediction method based on icebreaking pattern and ice-hull contact conditions[D]. Republic of Korea: Korea Maritime and Ocean University, 2016.
[33]	KIM M C, LEE S K, LEE W J, et al. Numerical and experimental investigation of the resistance performance of an icebreaking cargo vessel in pack ice conditions[J]. International Journal of Naval Architecture and Ocean Engineering, 2013, 5: 116-131. doi: 10.2478/IJNAOE-2013-0121 URL
[34]	HUANG Y, HUANG S, SUN J. Experiments on navigating resistance of an icebreaker in snow covered level ice[J]. Cold Regions Science and Technology, 2018, 152: 1-14. doi: 10.1016/j.coldregions.2018.04.007 URL
[35]	CHAKRABARTI S K. Offshore structure modeling[M]. Chicago, USA: World Scientific, 1994.
[36]	SCHWARZ J. Some latest developments in icebreaker technology[J]. Journal of Energy Resources Technology, 1986, 108: 161-167. doi: 10.1115/1.3231256 URL
[37]	BENESTY J, CHEN J, HUANG Y, et al. Pearson correlation coefficient[M]. Berlin & Heidelberg, Germany: Springer-Verlag, 2009.
[38]	RUDER S. An overview of gradient descent optimization algorithms[DB/OL]. (2016-09-15) [2022-08-18]. https://doi.org/10.48550/arXiv.1609.04747.
[39]	于晨芳, 吕烈彪, 柳卫东. 破冰船冰阻力估算方法研究[J]. 船舶标准化工程师, 2018, 51(4): 74-81.
	YU Chenfang, LV Liebiao, LIU Weidong. Research on ice resistance estimation method for icebreakers[J]. Ship Standardization Engineer, 2018, 51(4): 74-81.
[40]	RISKA K, LEIVISKÄ T, NYMAN T, et al. Ice performance of the Swedish multi-purpose icebreaker Tor Viking II[C]// Proceedings of the International Conference on Port and Ocean Engineering under Arctic Conditions. Ottawa, Canada: POAC, 2001: 849-866.

类别	参数	Lewis等^[15]	Vance^[16]	Zahn等^[17]	Lindqvist^[18]	Riska等^[19]	Keinonen等^[21]
船舶参数	船长/m	√		√	√	√	√
	船宽/m	√	√	√	√	√	√
	吃水/m				√	√	√
	首柱倾角/(°)				√	√
	水线角/(°)				√
	船速/(m·s^-1)	√	√	√	√	√	√
冰参数	冰厚/m	√	√	√	√	√	√
	弯曲强度/kPa	√	√		√	√	√
	弹性模量/kPa				√
	雪厚/m						√

船名	来源	冰况
Ferry NB550	文献[22]	平整冰
CCGS (Terry Fox)	文献[23]	平整冰
USCGC (Healy)		平整冰
Korean RV (Araon)		平整冰
ICE BREAKER PSV(KS1654)	文献[24]	平整冰
MV Arctic	文献[25]	平整冰
Polar Star		平整冰
Japanese Model Ship		平整冰
Terry Fox		平整冰
PM Teshio		平整冰
R-Class		平整冰
Healy		平整冰
SA-15		平整冰
Korean RV (Araon)	文献[26]	碎冰
Terry Fox	文献[27]	碎冰
An artic tanker	文献[28]	平整冰
Icebreaking cargo vessel	文献[29]	碎冰
Korean RV (Araon)	文献[30]	碎冰
MT Uikku	文献[31]	平整冰
MT Uikku	文献[1]	平整冰
MT Uikku	文献[2]	平整冰
Korean RV Araon	文献[32]	平整冰
Arctic PSV		平整冰
Icebreaking cargo vessel	文献[33]	碎冰
A new icebreaker of China	文献[34]	平整冰

参数	比例系数
船长	λ
船宽	λ
吃水	λ
首柱倾角	1
船速	λ¹^/²
水线角	1
冰厚	λ
弯曲强度	λ
冰密度	1
摩擦系数	1
冰阻力	λ

类别	特征	取值	ρ_x_,_y
船舶相关参数	船速,v/kn	0~11	0.056
	船长,L/m	26.155~259.2	0.630
	船宽,B/m	7.5~44	0.774
	船舶吃水,T/m	2.3~15	0.645
	首柱倾角,ϕ/(°)	19~35	-0.178
冰相关参数	冰厚,h_i/m	0.2~1.6	0.628
	冰弯曲强度,σ_f/kPa	392~1 200	0.122

名称	数值
激活函数	Sigmoid
初始化粒子数	100
最大迭代次数	100
种群规模	50
交叉概率	0.1
变异概率	0.2
个体长度	10
最佳个体适应度	0.7
学习率	0.01
损失函数	MSE

基于人工神经网络的极地船舶冰阻力预报方法

An Artificial Neural Network-Based Method for Prediction of Ice Resistance of Polar Ships

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