This study presents results from a long-term measurement campaign on a research wind turbine in the field. Pressure measurements are conducted at 25% blade radius over several months. Together with inflow measurements provided by a LiDAR system, they form an extensive dataset, which is used in the validation of numerical aerodynamic models. The model validation is conducted based on both ten-minute average data as well as time-resolved unsteady data. Initially, it is investigated how representative ten-minute average pressure measurements are of the underlying unsteady aerodynamics. Binned ten-minute average pressure distributions are then analysed together with their numerical counterpart, consisting of a combination of rotor and airfoil level aerodynamic/aeroelastic simulation results using average environmental and operating conditions as input. Finally, time-resolved measurements and simulation results are compared, validating the aeroelastic tools' capability to reproduce unsteady aerodynamics. Overall, reasonable agreement is found between numerical simulations and field experiment data showcasing two aspects: Numerical tools based on blade element momentum theory and panel methods with viscous-inviscid interaction remain relevant for simulating modern multi-megawatt wind turbines, and long-term pressure measurements provide invaluable means for validating such tools.

Different Design Driving Load constraints (DDLs), are explored in this work to determine under which constraints and conditions a winglet can have an added value to the wind turbine blade design. Multi-objective Bayesian optimization is used to maximize the rotor's power production while minimizing the flapwise DDLs. Surrogate models, created using machine learning techniques such as Gaussian Processes and Bayesian Neural Networks, are used in combination with an acquisition function, to determine what designs should be evaluated by the lifting line model AWSM, with the goal to obtain designs that lie on the Pareto front of two or more objectives. The recent Bayesian Neural Networks as surrogate model were able to find the Pareto-front most effectively in this work. Furthermore, the results show that different DDL constraints led to different winglet designs, with noticeable differences between upwind and downwind winglet designs. Winglet designs were found to be able to increase power without increasing the thrust, root flapwise bending moment and flapwise bending moment at radial locations on the blade. A noticeable increase in power was found when introducing sweep to the winglet design.

Rain-induced leading-edge erosion of wind turbine blades is associated with high repair and maintenance costs. For efficient operation and maintenance, erosion models are required that provide estimates of blade coating lifetime at a real scale. In this study, a statistical rainfall model is established that describes probabilistic distributions of rain parameters that are critical for site-specific leading-edge erosion assessment. A new droplet size distribution (DSD) is determined based on two years’ onshore rainfall data of an inland site in the Netherlands and the obtained DSD is compared with those from the literature. Joint probability distribution functions of rain intensities and droplet sizes are also established for this site as well as for a coastal site in the Netherlands. Then, the application of the proposed model is presented for a 5 MW wind turbine, where the model is combined with wind statistics along with an analytical surface fatigue model that describes lab-scale coating degradation. The expected lifetime of the blade coating is found three to four times less for the wind turbine operating at the coastal site than for the inland site - primarily due to rainfall at higher wind speeds. Further, the robustness of the proposed model is found consistent with varying data periods used for the analyses.

Rain-induced leading-edge erosion (LEE) of wind turbine blades (WTBs) is associated with high repair and maintenance costs. The effects of LEE can be triggered in less than 1 to 2 years for some wind turbine sites, whereas it may take several years for others. In addition, the growth of erosion may also differ for different blades and turbines operating at the same site. Hence, LEE is a site- and turbine-specific problem. In this paper, we propose a probabilistic long-term framework for assessing site-specific lifetime of a WTB coating system. Case studies are presented for 1.5 and 10 MW wind turbines, where geographic bubble charts for the leading-edge lifetime and number of repairs expected over the blade's service life are established for 31 sites in the Netherlands. The proposed framework efficiently captures the effects of spatial and orographic features of the sites and wind turbine specifications on LEE calculations. For instance, the erosion is highest at the coastal sites and for sites located at higher altitudes. In addition, erosion is faster for turbines associated with higher tip speeds, and the effects are critical for such sites where the exceedance probability for rated wind conditions are high. The study will aid in the development of efficient operation and maintenance strategies for wind farms.