A multi-objective design optimization approach for floating offshore wind turbine support structures - Karimi, Meysam; Hall, Matthew; Buckham, Brad; Crawford, Curran (Journal of ocean engineering and marine energy, 2017)
This paper presents a multi-objective design optimization approach for floating wind turbines with a design space that spans three stability classes of floating wind turbine support structures. A single design parameterization scheme was used to define the geometries of tension-leg, spar buoy, and semi-submersible candidate designs in terms of nine design variables. The seakeeping analysis of any particular platform configuration was completed using a simplified frequency-domain dynamic model applying linearized dynamics for the floating platform, mooring system, and a reference 5 MW wind turbine that were derived using existing functionality in FAST and WAMIT. Evaluation and comparison of different platforms was performed using a Pareto front pursuing multi-objective Genetic Algorithm (GA) optimization method to find the locus of platform cost minima and wind turbine performance maxima for a given environmental condition and sea state spectrum. Optimization results for the single-body platforms indicated a dominance of tension-leg platforms in this subset of the design space. Results for multi-body platforms showed that semi-submersible platforms with four floats demonstrated better stability and were more cost effective than other semi-submersible designs. In general, the full exploration of the design space demonstrated that four float semi-submersible platforms with angled taut mooring systems are a promising concept that can be used as a foundation for a detailed design and costing study. The results generated here are subject to the specifics of the targeted environmental conditions, cost model, linearized dynamics and choice of performance metric. As these elements evolve, the optimization framework presented here should be reapplied to track how the Pareto fronts for the different classes of platforms respond.