Bivariate statistics of wind farm support vessel motions while docking

被引:42
作者
Gaidai, Oleg [1 ]
Xu, Xiaosen [2 ]
Naess, Arvid [3 ]
Cheng, Yong [1 ]
Ye, Renchuan [1 ]
Wang, Junlei [4 ]
机构
[1] Jiangsu Univ Sci & Technol, Zhenjiang, Jiangsu, Peoples R China
[2] Florida Inst Technol, Melbourne, FL 32901 USA
[3] Norwegian Univ Sci & Technol, Trondheim, Norway
[4] Zhengzhou Univ, Sch Mech & Power Engn, Zhengzhou, Peoples R China
关键词
Wind farm support vessel (WFSV); docking; Monte Carlo; bivariate distribution; offshore wind turbine; wind energy; WAVE CHARACTERISTICS; SPEED PREDICTION; EXTREMES;
D O I
10.1080/17445302.2019.1710936
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
070403 [天体物理学]; 082301 [道路与铁道工程];
摘要
Robust prediction of extreme motions during wind farm support vessel (WFSV) operation is an important safety concern. In particular, it is important to study safety of operation in random sea conditions during WFSV docking against the wind tower, while workers are able to get on to the tower. Docking is performed by thrusting the vessel fender against the wind tower (the alternative docking maneuver by hinging is not studied here). In this paper, the finite element software AQWA has been used to analyse the vessel response due to hydrodynamic wave loads, acting on a specific maintenance ship under actual sea conditions. Excessive motions may occur during certain sea conditions, posing a risk to the crew transfer operation. This paper presents a novel method for estimating bivariate statistics, based on Monte Carlo simulations (or measurements if available). The bivariate average conditional exceedance rate (ACER2D) method is briefly outlined.
引用
收藏
页码:135 / 143
页数:9
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