Hyperbox method: a design-oriented selection of fatigue load cases for offshore wind turbines

Abstract In offshore wind engineering, efficient and well-targeted environmental condition sampling is key to strike the best compromise between accuracy in load assessment and number of simulations. This is even more challenging for floating offshore wind, because of the increased sensitivity to environmental directionality and wind-wave misalignments compared to fixed-bottom. For the fatigue analysis, the selection of the Design Load Cases (DLCs) for a mastered design process should both capture the synthetic information from large amount of data and remain representative of the environment, considering insights from system responses. Since standards do not provide a detailed approach to achieve the representativity goal, a fatigue DLC selection method answering the above challenges is thus proposed, based on direct exploitation of environmental wind and wave long-term time series. It proceeds through a sequence of iterative binning steps to define an appropriate set of boxes in hyperspace, here called hyperboxes , i.e. discrete multidimensional bins in the environment’s descriptive parametric space. Representative metrics for each variable within the boxes can be either defined from conventional statistical indicators or determined as a function of the expected system fatigue responses. It ultimately produces a compact and representative fatigue DLC list for the integrated load analysis (ILA) task, accounting for system response sensitivities where relevant. Selection performance is assessed against the original distribution for a set of discretization choices, depending on the offshore wind turbine design characteristics. Project-wise, this method is flexible and adjustable for all design phases, allowing to make the most of a few tens up to several thousands of DLCs. Floating and fixed-bottom offshore wind system design iterations benefit from the presented approach from concept to detailed phases, increasing the efficiency of the engineering team and the chances of project success.