Abstract Summary
Floating Offshore Wind Turbines (FOWTs) are ground-breaking systems in the renewable sector, capable to exploit wind energy in deep-water areas, where the resource is stronger and abundant with respect to onshore and near-cost sites. Their dynamic behavior results from the complex interaction of flexible and rigid structural elements, such as the rotor blades, the turbine tower, the nacelle, and the platform, with wind and waves. In this regard, the development of efficient and reliable numerical tools is fundamental for the design and the optimization of such structures. In this contribution, a site-specific optimization procedure aimed at finding the optimal substructure configuration for a 10MW FOWT is presented. An in-house developed Frequency Domain (FD) model is adopted for the simulation of the coupled system. The tool is based on first order floating platform hydrodynamics. Viscous drag forces are modelled with Morison’s equation and linearized with Borgman linearization. Mooring lines are modelled with a quasi-static approach. A linearized simulation, performed with the well-known code FAST, allows to estimate the rotor blades and moorings contributions to the equation of motion, leading to a fully coupled FD model. The presented formulation is implemented in an optimization procedure, which is proposed to find platform and mooring system configuration which most effectively reduces the manufacturing costs. An installation site near to the Italia coastline is selected. The joint probability distribution of wind speed, significant wave height and peak spectral period is calculated based on a metocean database of wind and wave records lasting 20 years. The optimization, based on a Genetic Algorithm (GA), is constrained on the turbine and moorings structural responses under a 50-year return period extreme event. Moreover, constraints on the admissible platform displacements, cables geometry, and anchor loads are considered. Results show that the optimized solution significantly reduces the cost of the system with a controlled increase of stresses, opening interesting perspectives for the reduction of the Levelized Cost of Energy (LCOE) in sites characterized by mild sea states and low wind resource.
