摘要
适用于远海作业的超大型浮式液化天然气生产储卸装置FLNG(Floating Liquefied Natural Gas)因风浪流等作用会引起储舱内液体剧烈晃荡产生砰击荷载。室内晃荡模型试验是研究晃荡荷载的有效手段,但由于试验条件的局限性,不能开展长时间的晃荡模型试验,因此需要合理有效的统计分析方法。文章通过开展1/20比尺横荡及横摇晃荡模型试验,采用三种晃荡荷载常用拟合方法对晃荡砰击荷载进行了统计预测。结果表明,晃荡荷载峰值结果服从广义极值分布、三参Weibull分布和两参Weibull分布,其中广义极值分布拟合晃荡荷载峰值数据更加精确。研究结果可为液舱结构晃荡荷载设计提供有益参考。
The giant Floating Liquefied Natural Gas facility(FLNG) with several functions such as production, storage and offloading, is a viable approach for development of offshore natural gas resources. However,sloshing-induced slamming phenomenon exists in the cargo containment system due to wind, wave and current. Indoor sloshing model experiment is an effective methodology to study sloshing load. Because of limitation of test conditions, sloshing model test could not be carried out for a long period, therefore, a reasonable and effective statistical method should be applied to find out load regularity. In this paper, a 1/20-scale sloshing model test is performed under sway and roll excitation, then, three common fitting methods will be used for the statistical prediction of sloshing load. The results illustrate that the peaks of sloshing load obey twoparameter Weibull distribution, three-parameter Weibull distribution and generalized extreme value distribution. Moreover, the generalized extreme value distribution is more precise for fitting of sloshing peak load,which can serve as estimation for sloshing loads during tank design.
作者
杨志勋
骆松
徐潜岳
张文首
岳前进
YANG Zhi-xun;LUO Song;XU Qian-yue;ZHANG Wen-shou;YUE Qian-jin(College of Mechanical and Electrical Engineering,Harbin Engineering University,Harbin 150001,China;State Key Laboratory of Structural Analysis for Industrial Equipment,Dalian University of Technology,Dalian 116023,China;Department of Ocean Science and Technology,Dalian University of Technology,Panjin 124221,China)
出处
《船舶力学》
EI
CSCD
北大核心
2020年第3期294-300,共7页
Journal of Ship Mechanics
基金
国家科技重大专项资助项目(2011ZX05026-006-06)
中央高校基本科研业务费项目(3072019CFJ0702)。
关键词
FLNG
晃荡荷载
模型试验
统计分析
拟合分布
FLNG
sloshing load
sloshing model test
statistical analysis
fitting distribution