船舶结构海冰载荷的实船测量及反演方法研究

摘要
图/表
参考文献(33)
相关文章 (15)

全文: PDF (1808 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要极地船舶通航中，海冰载荷是影响船舶安全航行的重要环境载荷，可造成船舶结构的严重损毁和疲劳破坏。由于海冰条件的差异和船体结构的复杂性，海冰荷载的实船测量是确定海冰载荷分布的关键途径。然而，船舶水线位置处往往因水密隔舱等密闭结构而不易安装传感器，这需要采用远场海冰载荷识别方法以克服传感器位置的限制。智能算法中的支持向量机方法具有小样本学习和强泛化性等算法特征，可适用于载荷识别过程中非线性映射关系的反演。为此，本文采用基于支持向量机方法的参数识别模型对船体结构上的海冰载荷进行远场反演分析。本文对船舶的海冰载荷反演结果在时域上很好地反映了船-冰碰撞特征，且本文反演的海冰载荷与其他现场测量的冰载荷数值和离散性也比较接近，进而也验证了本文海冰载荷识别方法的可靠性。基于支持向量机方法远场载荷识别方法是对传统船舶结构海冰载荷识别方法的扩展，有效地增加了海冰载荷的监测范围。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	孔帅
	崔洪宇
	季顺迎

关键词 ：船体结构, 海冰载荷, 实船测量, 载荷识别, 支持向量机方法

Abstract：

As a major environmental load during the navigation in a polar area, sea ice load can cause structural damage and fatigue damage to a ship structure. Due to the difference of sea ice conditions and the complexity of the ship structure, filed measurement is a key approach to determine the distribution of sea ice load on the ship hull. However, the installation of sensors around the waterline is not easy to operate due to waterproofed cabins. In this situation, a far-field ice load identification method is required to overcome the limitation of location of sensors. As an intelligent algorithm, the Support Vector Machine method has the small sample learning and the strong generalization characteristics in ice load identification which can be applied to determine the non-linear relationship between the sea ice load and the responses of ship hull. In this work, a far-field load identification model based on the Support Vector Machine method was established to determine ice load on the ship hull. The identified sea ice load of the ship hull in the present work can reflect the colliding characteristics between the ship hull and the ice in the time domain. Furthermore, the magnitude and the dispersion of identified ice load in this study are close to the results of other field tests which could verify the reliability of the identification model. The far-field sea ice load identification model based on the Support Vector Machine method is the extension of the traditional load identification model and it can widen the measurement field to an extent.

Key words： ship structure sea ice load field measurement onboard ship load identification Support Vector Machine

收稿日期: 2019-04-28 出版日期: 2020-10-15

引用本文:

孔帅，崔洪宇，季顺迎. 船舶结构海冰载荷的实船测量及反演方法研究[J]. 振动与冲击, 2020, 39(20): 8-16.
KONG Shuai，CUI Hongyu，JI Shunying. State Key Laboratory of Structure Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116024, China. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(20): 8-16.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2020/V39/I20/8

[1] 李振福. 北极航线的中国战略分析[J]. 中国软科学, 2009, 1:1-7.
LI Zhenfu. Analysis of China’s Strategy on Arctic Route[J].China Soft Science, 2009, 1:1-7.
[2]Ebinger C K, Zambetakis E. The geopolitics of Arctic melt[J]. International Affairs, 2009, 85(6): 1215-1232.
[3]任奕舟, 邹早建. 破冰船在冰层中连续破冰过程的数值模拟[J]. 振动与冲击, 2016, 35(18):210-213.
REN Yizhou, ZOU Zaojian. Numerical simulation of the continuous icebreaking by an icebreaker in level ice[J]. Journal of Vibration and Shock, 2016, 35(18):210-213.
[4]黄焱, 李伟, 王迎晖, 等. 大型运输船极地浮冰区航行阻力的模型试验[J]. 中国造船, 2016, 57(3):26-35.
HUANG Yan, LI Wei, WANG Yinghui, et al. Model tests on the resistance of a large transport ship in arctic region with pack ice[J]. Shipbuilding of China, 2016, 57(3): 26-35.
[5]李宇辰, 刘巨斌, 丁志勇，等. 基于Rankine源法的气垫船破冰数值模拟[J]. 振动与冲击, 2017，36(23):37-31
LI Yuchen, LIU Jubin, DING Zhiyong et al. Numerical simulation of ACVs’ ice-breaking based on Rankine source method. Journal of Vibration and Shock, 2017, 36(23):27-31
[6]徐双东, 胡志强, 陈刚. 冰区加强LNG船舷侧抗撞性能仿真研究[J]. 船舶工程, 2016, 38(6):1-5.
XU Shuangdong, HU Zhiqiang, CHEN Gang. Numerical Simulation of Strengthening Side Crashworthiness of LNG Ships in Ice Zone[J]. Ship Engineering, 2016, 38(6):1-5.
[7]杨红军, 车驰东, 张维竞, 等. 冰载荷冲击下的船舶推进轴系瞬态扭转振动响应分析[J]. 船舶力学, 2015, 19(1-2):176-181.
YANG Hongjun, CHE Chidong, ZHANG Weijing, et al. Transient torsional vibration analysis for ice impact of ship propulsion shaft[J]. Journal of Ship Mechanics, 2015, 19(1-2):176-181.
[8]季顺迎, 雷瑞波, 李春花, 等. “雪龙”号科考船在冰区航行的船体振动测量研究[J]. 极地研究，2017, 29(4): 427-435.
JI Shunying, LEI Ruibo, LI Chunhua, et al. Measurement of ice-induced local vibration of R/V XueLong icebreaker during its navigation in ice-covered fields[J]. Chinese Journal of Polar Research, 2017, 29(4):427-435.
[9]沈权, 赵炎平. 破冰船技术及几种破冰方法[J]. 航海技术, 2010, (1):5-7.
SHEN Quan, ZHAO Yanping. Icebreaker technology and several ice-breaking methods[J].Marine Technology, 2010, (1):5-7.
[10]Suominen M, Kujala P. Variation in short-term ice-induced load amplitudes on a ship's hull and related probability distributions[J]. Cold Regions Science and Technology, 2014, 106-107: 131-140.
[11]Suyuthi A, Leira B J, Riska K. Short term extreme statistics of local ice loads on ship hulls[J]. Cold Regions Science and Technology, 2012, 82: 130-143.
[12]Johnston M, Ritch R, Gagnon R. Comparison of impact forces measured by different instrumentation systems on the CCGS Terry Fox during the Bergy Bit Trials[J]. Cold Regions Science and Technology, 2008, 52: 83-97.
[13]Jeon M, Choi K, Min J K, et al. Estimation of local ice load by analyzing shear strain data from the IBRV ARAON's 2016 Arctic voyage[J]. International Journal of Naval Architecture and Ocean Engineering, 2018, 10(3): 421-425.
[14]Suominen M, Kujala P, Romanoff J, et al. Influence of load length on short-term ice load statistics in full-scale[J]. Marine Structures, 2017, 52: 153-172.
[15]Kujala P. Semi-empirical evaluation of long term ice loads on a ship hull[J]. Marine structures, 1996, 9: 849-871.
[16]Kotilainen M, Vanhatalo J, Suominen M, et al. Predicting ice-induced load amplitudes on ship bow conditional on ice thickness and ship speed in the Baltic Sea[J]. Cold Regions Science and Technology, 2017, 135: 116-126.
[17]Ritch R, Frederking R, Johnston M, et al. Local ice pressures measured on a strain gauge panel during the CCGS Terry Fox bergy bit impact study[J]. Cold Regions Science and Technology, 2008, 52: 29-49.
[18]Heyn H M, Skjetne R. Time-frequency analysis of acceleration data from ship-ice interaction events[J]. Cold Regions Science and Technology, 2018, 156: 61-74.
[19]Uhl T. The inverse identification problem and its technical application[J]. Archive of Applied Mechanics, 2007, 77: 325-337.
[20]Wang C. Load Identification of Acoustic and Vibration Sources Following Linear Regression and Least-squares of Generalized Matrix Inverse Method[J]. Journal of Information and Computational Science, 2014, 11(9): 3229-3239.
[21]Ikonen T, Peltokorpi O, Karhune J. Inverse ice-induced moment determination on the propeller of an ice-going vessel[J]. Cold Regions Science and Technology, 2015, 112: 1-13.
[22]许传华, 刁虎, 任青文, 等. 紫金山金铜矿初始地应力场反演分析[J]. 岩土力学, 2009, 30(2):425-428.
XU Chuanhua, DIAO Hu, REN Qingwen, et al. Back analysis of initial geostress field of Zijinshan gold and copper mine[J]. Rock and Soil Mechanics, 2009, 30(2):425-428.
[23]王军祥, 姜谙男. 大连地铁隧道监测数据分析及参数智能反演[J]. 土木工程学报, 2011, 44:135-138.
WANG Junxiang, JIANG Annan. The analysis of monitoring data and intelligent inversion of parameters of Dalian subway tunnel[J]. China Civil Engineering Journal, 2011, 44:135-138.
[24]Vapnik V. The nature of statistical learning theory[M]. Springer, 1995.
[25]Yin Y M, Cui H Y, Hong M, et al. Prediction of the vertical vibration of ship hull based on grey relational analysis and SVM method[J]. Journal of Marine Science and Technology, 2015, 20: 467-474.
[26]Zhao H, Yin S. Inverse analysis of geomechanical parameters by the artificial bee colony algorithm and multi-output support vector machine[J]. Inverse Problems in Science and Engineering, 2016, 24(7): 1266-1281.
[27]Smola A J, Schölkopf B. A tutorial on support vector regression[J]. Statistics and computing, 14: 199-222.
[28]Worby A P, Geiger C A, Paget M J, et al. Thickness distribution of Antarctic sea ice[J]. Journal of Geophysical Research: Oceans, 2008, 113(C5):C05S92.
[29]秦斌, 易怀洋, 王欣. 基于极限学习机的风电机组叶根载荷辨识建模[J]. 振动与冲击, 2018, 37(4):257-262.
QIN Bin, YI Huaiyang, WANG Xin. A model of wind turbine blade root loads based on the extreme learning machine[J]. Journal of Vibration and Shock, 2018, 37(4):257-262.
[30]张学工. 关于统计学习理论与支持向量机[J]. 自动化学报, 2000, 26(1):32-42.
ZHANG Xuegong. Introduction of statistical learning theory and support vector machines[J]. ACTA AUTOMATICA SINICA, 2000, 26(1):32-42.
[31]Weissling B, Ackley S, Wagner P, et al. EISCAM—Digital image acquisition and processing for sea ice parameters from ships[J]. Cold Regions Science and Technology, 2009, 57: 49-60.
[32]McHugh M L. The chi-square test of independence[J]. Biochemia medica: Biochemia medica, 2013, 23(2): 143-149.
[33]Timco G W, Weeks W F. A review of the engineering properties of sea ice[J]. Cold regions science and technology, 2010, 60(2): 107-129.