The selection of a suitable site for the installation of a wind turbine plays an important role in ensuring a safe operating lifetime of the structure. In this study, we show that mixture density networks can accelerate this process by inferring functions from data that can accurately map the environmental conditions to the loads but also propagate the uncertainty from the inflow to the response.

Floating wind turbines are subject to complex aerodynamics that are not yet fully understood. Lab-scale experiments are crucial for capturing these phenomena and validate numerical tools. This paper presents a new wind tunnel experimental setup able to study the response of a wind turbine rotor when subjected to prescribed motions in 6 degrees of freedom. The observed unsteady effects underscore the importance of pursuing research on the impact of floater motions on wind turbine performance.

Simulations of different fidelities made by the participants of the OC6 project Phase III are compared to wind tunnel wake measurements on a floating wind turbine. Results in the near wake confirm that simulations and experiments tend to diverge from the expected linearized quasi-steady behavior when the reduced frequency exceeds 0.5. In the far wake, the impact of platform motion is overestimated by simulations and even seems to be oriented to the generation of a wake less prone to dissipation.

Floating offshore wind turbines may experience large surge motions that, when faster than the local wind speed, cause rotor–wake interaction. We derive a model which is able to predict the wind speed at the wind turbine, even for large and fast motions and load variations in the wind turbine. The proposed dynamic inflow model includes an adaptation for highly loaded flow, and it is accurate and simple enough to be easily implemented in most blade element momentum design models.