A novel design approach for estimation of extreme load responses of a 10-MW floating semi-submersible type wind turbine. (1st October 2022)

• Record Type: Journal Article
• Authors: Balakrishna, Rajiv; Gaidai, Oleg; Wang, Fang; Xing, Yihan; Wang, Shuaishuai
• Is Part Of: Ocean engineering. Volume 261(2022)
• Publication Date: 2022-10-01
• Subjects: Floating wind turbine -- FAST -- ACER2D method -- Extreme responses -- Bivariate probability distribution; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2022.112007
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: Offshore structures are constructed to withstand extreme wind and wave-induced loads, so studying these extreme loads is vital as it allows offshore structures, e.g., wind turbines, to be designed and operated with minimal disruption. A novel statistical model that is precise and meticulous will facilitate these extreme load values to be estimated accurately. Bivariate average conditional exceedance rate (ACER2D) method was utilized in this paper. This multivariate statistical analysis is more appropriate than a univariate statistical analysis for complete structures, e.g., wind turbines, since it can extrapolate the extreme values with better accuracy. This paper uses this ACER2D method to explore a novel approach to estimating the extreme load responses of a 10-MW semi-submersible type floating wind turbine (FWT). Two cases are considered to understand the feasibility of the ACER2D on the extreme load responses. The first case analyses the blade root flap wise bending moment, while the second one analyses the tower bottom fore-aft bending moment. Based on the performance of the proposed novel method, the ACER2D method can offer better robust and precise bivariate predictions of the bending moments of the FWT. Highlights: 10-MW semi-submersible type floating wind turbine (FWT) has been accurately modelled. Correlated bivariate response statistics has been studied under realistic environmental conditions. Novel bivariate design approach has been presented, as opposed to a traditional univariate approach.