The design and evaluation of floating horizontal-axis tidal turbines involves a vast design space, especially in the early conceptual stages. Both the turbine and the floating platform must be carefully considered, as the rotor's performance is directly influenced by the dynamic motion of the platform, which responds to the environmental conditions at the deployment site. This study investigates the relationship between platform design features and turbine performance under wave conditions informed by real world data. An automated workflow has been developed to assess performance metrics for parametric platform designs. This workflow begins with generating hull geometries based on specified design parameters. Using the boundary element method, the radiation and diffraction problems are solved, and the added mass and inertia matrix is obtained for each hull. The Added mass and inertia matrices are converted to WAMIT format for coupled aero-hydro-elastic model analysis within OpenFAST. Rotor performance is evaluated using blade element momentum theory, with the addition of a Beddoes-Leishman correction model for unsteady flows, capturing the effects of platform motion. The workflow is validated against experimental data for the rotor, and computational simulation for the combined platform and rotor. By analysing platform-turbine combinations across real world environmental scenarios, sensitivity to design changes such as the beam of the float, and the tension in the mooring system can be evaluated. Key metrics include blade loads, the presence of flow separation, turbine thrust, and overall power output. Response amplitude operators (RAOs) (Figure 1) quantify the excitation of platform motion by wave direction and frequency, and its subsequent impact on turbine dynamics. By selecting platform-turbine combinations, a parameter space of limiting operating conditions will defined. This workflow offers a cost-effective tool for exploring design impacts on system performance early in the development process, complementing higher-fidelity models that are typically constrained by computational expense and time.
Clarke et al. (Mon,) studied this question.