Floating Offshore Wind Turbines (FOWTs) have low natural frequencies of motion, typically around 0.01−0.001 Hz. If external wind fluctuations match these frequencies, they can excite the motion, leading to increased loads and higher fatigue at specific locations of the wind turbine. Until now, wind turbine design has only considered high-frequency, microscale turbulence, modelled using the Mann (1994) model, as recommended by the IEC. However, this model does not capture the full energy content of the wind fluctuations around the FOWT motions natural frequencies. Syed and Mann (2024) proposed a model for these low-frequency, mesoscale fluctuations. In this thesis, the two turbulence models were implemented, combined, and input in the Siemens Gamesa Renewable Energy (SGRE) in-house servo-hydro-aeroelastic wind turbine solver for a SGRE FOWT loads simulation. The goal was to assess whether adding low-frequency turbulence increases damage on the tower bottom and mooring lines compared to using high-frequency turbulence alone. It was found that indeed for the simplified load simulations done here, the lifetime of a FOWT could be impacted by mesoscale turbulence and thus could be considered in the FOWT design.

The transition to renewable energy sources is essential to mitigate climate change, and floating wind turbines (FOWTs) present a promising solution to harness offshore wind resources. Light Detection and Ranging (LiDAR) systems mounted on nacelles provide a cost-effective and efficient means to measure wind fields, critical for turbine performance, control and load simulation. However, FOWT's motion introduces complexities in LiDAR measurements due to velocity and positional changes. This thesis focusses on developing a correction method for LiDAR measurements on FOWTs, addressing the influence of motion on wind velocity, position, and direction. The accuracy and uncertainty of these corrected measurements are quantified.

Simulated six degrees of freedom (6DOF) motion and a power law wind field are inserted in a numerical LiDAR model, in which corrected and uncorrected measurement position, direction and line of sight velocity are constructed. The corrected outputs are validated through reconstructed wind fields and the uncertainty of the correction is quantified. In this study, significant motion-induced bias is identified in the reconstructed wind fields. The dominant motion affecting measurement accuracy was identified as pitch motion, especially when it exhibits a non-zero mean. The relative error of the reconstructed power law wind field parameters is reduced by 3 orders of magnitude. Despite an increase in uncertainties associated with the correction method applied, the correction remains effective in reducing the error in LiDAR measurements induced by FOWT motions. The findings highlight the necessity and feasibility of motion correction for LiDAR measurements, offering substantial improvements in the accuracy and reliability of reconstructed wind fields for floating wind turbine applications.