No abstract

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.

This paper describes the SPOWTT project. The intention of this project was to understand how sailing by crew transfer vessel (CTVs) to offshore wind farms affects the mental and physical wellbeing of individuals on board. The focus was on quantifying this impact, understanding the key drivers, with an aim to ensuring personnel can arrive to the wind turbines in a fit state to work safely and effectively. Impacts looked at subjective state beyond simply vomiting. Key results include the ability now to predict vessel motions from given Metocean conditions and vessel designs. We also discovered that the impact of vessel motions on seasickness is different for different symptoms and is driven not only by vertical z-axis accelerations but also by certain frequencies of motion in the y-axis. Frequencies other than 0.16 Hz were found to be impactful, and x-axis movements appeared to have a longer-lasting effect on the day's work. Through the formulation of a new, evidence-based understanding of seasickness, we have created an operational planning tool, designed to have a direct benefit on the safety and productivity of offshore wind farm operations.

Abstract. Leading edge erosion (LEE) is one of the most critical degradation mechanisms that occur with wind turbine blades (WTBs), generally starting from the tip section of the blade. A detailed understanding of the LEE process and the impact on aerodynamic performance due to the damaged leading edge (LE) is required to select the most appropriate leading edge protection (LEP) system and optimize blade maintenance. Providing accurate modeling tools is therefore essential. This paper presents a two-part study investigating computational fluid dynamics (CFD) modeling approaches for different orders of magnitudes in erosion damage. The first part details the flow transition modeling for eroded surfaces with roughness on the order of 0.1–0.2 mm, while the second part focuses on a novel study modeling high-resolution scanned LE surfaces from an actual blade with LEE damage on the order of 10–20 mm (approx. 1 % chord); 2D and 3D surface-resolved Reynolds-averaged Navier–Stokes (RANS) CFD models have been applied to investigate wind turbine blade sections in the Reynolds number (Re) range of 3–6 million. From the first part, the calibrated CFD model for modeling flow transition accounting for roughness shows good agreement of the aerodynamic forces for airfoils with leading-edge roughness heights on the order of 140–200 µm while showing poor agreement for smaller roughness heights on the order of 100 µm. Results from the second part of the study indicate that up to a 3.3 % reduction in annual energy production (AEP) can be expected when the LE shape is degraded by 0.8 % of the chord, based on the NREL5MW turbine. The results also suggest that under fully turbulent conditions, the degree of eroded LE shapes studied in this work show the minimal effect on the aerodynamic performances, which results in a negligible difference to AEP.

Abstract. Leading edge erosion (LEE) is one of the most critical degradation mechanisms that occur with wind turbine blades (WTBs), generally starting from the tip section of the blade. A detailed understanding of the LEE process and the impact on aerodynamic performance due to the damaged leading edge (LE) is required to select the most appropriate Leading Edge Protection (LEP) system and optimize blade maintenance. Providing accurate modeling tools is therefore essential. This paper presents a two-part study investigating Computational Fluid Dynamics (CFD) modeling approaches for different orders of magnitudes in erosion damage. The first part details the flow transition modeling for eroded surfaces with roughness in the order of 0.1–0.2 mm, while the second part focuses on a novel study modeling high-resolution scanned LE surfaces from an actual blade with LEE damage in the order of 10–20 mm (approx. 1 % chord). 2D and 3D surface resolved Reynolds Average Navier Stokes (RANS) CFD models have been applied to investigate wind turbine blade section in the Reynolds number range of 3–6 million. From the first part, the calibrated CFD model for modeling flow transition accounting roughness shows good agreement of the aerodynamic forces for airfoils with leading-edge roughness heights in the order of 140–200 μm, while showing poor agreement for smaller roughness heights in the order of 100 μm. Results from the second part of the study indicate that up to 3.3 % reduction in AEP can be expected when the LE shape is degraded by 0.8 % of the chord, based on the NREL 5MW turbine. The results also suggest that under fully turbulent condition the eroded LE shapes show the least amount of influence on the aerodynamic performances and results in negligible difference to AEP.