As the wind industry develops larger turbines for offshore deployment, the problems with station keeping systems are exacerbated. As turbines increase in size, so do the loads on the turbine. Meanwhile, many offshore sites available for leasing occur in intermediate water depths (55–85 m), which will appear ever smaller relative to the increasing platform size of floating offshore wind turbines. This complicates the process of designing mooring systems for these larger systems and emphasizes the importance of having a good methodology for automating this process. In this paper, a routine is developed that will map objectives for a multi-objective genetic algorithm to obtain mooring radius-lowest cost designs over a range of radii simultaneously. This work will implement and expand on a tiered-constraint evaluation scheme that was developed in the previous work by West et al. [Modelling 2, 728–752 (2021)]. New components and constraints are added to the optimization problem to allow the optimizer to find realistically deployable designs with reasonably accurate cost estimates. These techniques will then be used to find the most economic mooring designs for a 15-MW floating offshore wind turbine with a hybrid mooring system.

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