Bivariate statistics of wind farm support vessel motions while docking. (7th February 2021)

• Record Type: Journal Article
• Authors: Gaidai, Oleg; Xu, Xiaosen; Naess, Arvid; Cheng, Yong; Ye, Renchuan; Wang, Junlei
• Is Part Of: Ships and offshore structures. Volume 16:Number 2(2021)
• Page Start: 135
• Page End: 143
• Publication Date: 2021-02-07
• Subjects: Wind farm support vessel (WFSV) -- docking -- Monte Carlo -- bivariate distribution -- offshore wind turbine -- wind energy; Ships -- Periodicals; Offshore structures -- Periodicals; Marine engineering -- Periodicals; Marine engineering -- Technological innovations -- Periodicals; Ocean engineering -- Periodicals; Ocean engineering -- Technological innovations -- Periodicals; 623.8
• DOI: 10.1080/17445302.2019.1710936
• Languages: English
• ISSNs: 1744-5302

Abstract

ABSTRACT: 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.