Bivariate statistics of floating offshore wind turbine dynamic response under operational conditions

FOWT (Floating offshore wind turbines) belong to the modern offshore wind energy industry generating green renewable energy. Accurate extreme loads and response prediction during power generation is an important design concern. More accurate and reliable estimations of extreme responses are significant for the offshore wind industry as it advances the design, manufacturing and deployment of large FOWTs in the coming decade. In this study, the OpenFAST code was used to model offshore wind turbine mooring line tension force and blade bending moment due to environmental loads, acting on a site-specific FOWT under realistic local environmental conditions. This paper presents an efficient Monte Carlo based method to study bivariate extreme dynamic response statistics. The ACER2D (bivariate average conditional exceedance rate) method is briefly discussed. The ACER2D method enables robust estimation of bivariate statistics, efficiently utilizing available data. Large return period 2D (two-dimensional) probability contours were obtained using the ACER2D method. Based on the studied performance of the presented methodology, it was seen that ACER2D provides accurate and efficient predictions of extreme return period contours. The described approach may be utilized at the design stage, defining optimal FOWT design values to minimize potential structural damage due to extreme environmental loads. It should be noted that the bivariate design point is less conservative than the classic univariate one; therefore, this study advocates a design method leading to lower structural production costs.

Automated summary

This study presents an efficient Monte Carlo based method to study bivariate extreme dynamic response statistics in the context of FOWT design. The ACER2D method enables accurate and efficient predictions of extreme return period contours, which can help optimize FOWT design values and reduce structural production costs.