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Abstract. Offshore wind turbine (OWT) support structures are exposed to the risk of fatigue damage and scour, and this risk can be effectively mitigated by installing structural control devices such as tuned mass dampers (TMDs). However, time-varying scour altering OWTs' dynamic characteristics has an impact on the TMD design and fatigue life, which has rarely been studied before. In this paper, a simplified modal model is used to investigate the influence of scour and a TMD on the fatigue life evaluation of a 5 MW OWT's support structure, and a traditional method and a newly developed optimization technique are both presented to obtain TMD parameters. This optimization technique aims at finding optimal parameters of the TMD which maximize the fatigue life of a hotspot at the mudline, and the effect of time-varying scour can be considered. This study assumes that the TMD operates in the fore–aft (FA) direction, while the vibration in the side–side (SS) direction is uncontrolled. Results show that scour can decrease the fatigue life by about 24.1 % and that the TMD can effectively suppress vibration and increase the fatigue life. When the scour depth reaches 1.3 times the pile diameter, the TMD with a mass ratio of 1 % can increase the fatigue life of an OWT's support structure by about 64.6 %. Further, it is found that the fatigue life can be extended by 25 % with the TMD optimized by the proposed optimization technique rather than using a traditional design method which does not take the change in dynamic characteristics into account.

Abstract. Offshore wind turbine (OWT) support structures are exposed to the risk of fatigue damage and scour, and this risk can be effectively mitigated by installing structural control devices such as tuned mass dampers (TMDs). However, time-varying scour altering OWTs' dynamic characteristics has an impact on the TMD design and fatigue life, which has rarely been studied before. In this paper, a simplified modal model is used to investigate the influence of scour and a TMD on the fatigue life evaluation of a 5 MW OWT's support structure, and a traditional method and a newly developed optimization technique are both presented to obtain TMD parameters. This optimization technique aims at finding optimal parameters of the TMD which maximize the fatigue life of a hotspot at the mudline, and the effect of time-varying scour can be considered. This study assumes that the TMD operates in the fore–aft (FA) direction, while the vibration in the side–side (SS) direction is uncontrolled. Results show that scour can decrease the fatigue life by about 24.1 % and that the TMD can effectively suppress vibration and increase the fatigue life. When the scour depth reaches 1.3 times the pile diameter, the TMD with a mass ratio of 1 % can increase the fatigue life of an OWT's support structure by about 64.6 %. Further, it is found that the fatigue life can be extended by 25 % with the TMD optimized by the proposed optimization technique rather than using a traditional design method which does not take the change in dynamic characteristics into account.

Abstract. Establishing a clear correlation between blade leading-edge erosion (LEE) and the performance of operational wind turbines is challenging due to the complex interaction of various factors. This study aims to improve the understanding and analysis of real wind turbine measurements by employing aeroelastic simulations to investigate the combined effects of LEE, turbulence intensity (TI), and time averaging as a data processing technique and to show how they obscure the effects of erosion. The study does not aim to investigate each contributing factor in detail but seeks to provide insights through selected examples, thereby illustrating how these conditions hinder the detection of blade erosion’s effects on power loss. An aeroelastic model provided by an offshore original-equipment manufacturer (OEM) was used to simulate various scenarios. Turbulence intensity was varied for a range of wind speeds, and the aerofoil characteristics for the blade were modified to simulate different degrees of erosion, represented by varying levels of roughness. For a given site, findings reveal that even mild simulated erosion can reduce the annual energy production (AEP) by 0.82 % at 6 % TI, while more severe erosion leads to a 1.46 % decrease. Furthermore, increasing TI exacerbates these losses, with 15 % TI causing up to 2.14 % AEP reduction for eroded blades, making it increasingly difficult to distinguish between the effects of blade erosion and turbulence intensity on turbine performance. These effects are most pronounced at sites with lower average wind speeds. Moreover, the interaction between TI levels and longer time-averaging periods, which vary with wind speed, can obscure the true magnitude of LEE's impact on short-term power fluctuations. This study suggests that 10 min time-averaging periods can mask performance and that analysing unsteady-rotor data with shorter time periods, such as 1 s periods, is preferable. The work emphasises the importance of considering the blade condition's impact in the context of various influencing factors for accurate AEP assessments, performance monitoring, and improved wind turbine design for operational wind turbines.

Abstract. Establishing a clear correlation between blade leading-edge erosion (LEE) and the performance of operational wind turbines is challenging due to the complex interaction of various factors. This study aims to improve the understanding and analysis of real wind turbine measurements by employing aeroelastic simulations to investigate the combined effects of LEE, turbulence intensity (TI), and time averaging as a data processing technique and to show how they obscure the effects of erosion. The study does not aim to investigate each contributing factor in detail but seeks to provide insights through selected examples, thereby illustrating how these conditions hinder the detection of blade erosion’s effects on power loss. An aeroelastic model provided by an offshore original-equipment manufacturer (OEM) was used to simulate various scenarios. Turbulence intensity was varied for a range of wind speeds, and the aerofoil characteristics for the blade were modified to simulate different degrees of erosion, represented by varying levels of roughness. For a given site, findings reveal that even mild simulated erosion can reduce the annual energy production (AEP) by 0.82 % at 6 % TI, while more severe erosion leads to a 1.46 % decrease. Furthermore, increasing TI exacerbates these losses, with 15 % TI causing up to 2.14 % AEP reduction for eroded blades, making it increasingly difficult to distinguish between the effects of blade erosion and turbulence intensity on turbine performance. These effects are most pronounced at sites with lower average wind speeds. Moreover, the interaction between TI levels and longer time-averaging periods, which vary with wind speed, can obscure the true magnitude of LEE's impact on short-term power fluctuations. This study suggests that 10 min time-averaging periods can mask performance and that analysing unsteady-rotor data with shorter time periods, such as 1 s periods, is preferable. The work emphasises the importance of considering the blade condition's impact in the context of various influencing factors for accurate AEP assessments, performance monitoring, and improved wind turbine design for operational wind turbines.

Abstract. Uncertainty quantification (UQ) is a well-established category of methods to estimate the effect of parameter variations on a quantity of interest, based on a solid mathematical fundament. In the wind energy field most UQ studies were focused on the sensitivity of turbine loads. This article presents a framework, wrapped around a modern Python UQ library, to analyze the impact of uncertain turbine properties on aeroelastic stability. The UQ methodology applies a polynomial chaos expansion surrogate model to increase the numerical efficiency. A comparison is made between different wind turbine simulation tools on the engineering model level (alaska/Wind, Bladed, HAWC2/HAWCStab2 and Simpack). Two case studies are used to demonstrate the effectiveness of the method to analyze the sensitivity of the aeroelastic damping of an unstable turbine mode to variations of structural blade cross section parameters. The code-to-code comparison shows a good agreement between the simulation tools for the reference model, but also significant differences in the sensitivities.

Abstract. Uncertainty quantification (UQ) is a well-established category of methods to estimate the effect of parameter variations on a quantity of interest, based on a solid mathematical fundament. In the wind energy field most UQ studies were focused on the sensitivity of turbine loads. This article presents a framework, wrapped around a modern Python UQ library, to analyze the impact of uncertain turbine properties on aeroelastic stability. The UQ methodology applies a polynomial chaos expansion surrogate model to increase the numerical efficiency. A comparison is made between different wind turbine simulation tools on the engineering model level (alaska/Wind, Bladed, HAWC2/HAWCStab2 and Simpack). Two case studies are used to demonstrate the effectiveness of the method to analyze the sensitivity of the aeroelastic damping of an unstable turbine mode to variations of structural blade cross section parameters. The code-to-code comparison shows a good agreement between the simulation tools for the reference model, but also significant differences in the sensitivities.

Abstract. Rapid growth in the offshore wind energy sector means more offshore wind farms are placed closer to each other and in the lee of large land masses. Synthetic aperture radar (SAR) offers maps of the wind speed offshore with high resolution over large areas. These can be used to detect horizontal wind speed gradients close to shore and wind farm wake effects. SAR observations have become much more available with the free and open-access data from European satellite missions through Copernicus. Examples of applications and tools for using large archives of SAR wind maps to aid offshore site assessment are few. The Anholt wind farm operated by the utility company Ørsted is located in coastal waters and experiences strong spatial variations in the mean wind speed. Wind speeds derived from the Supervisory Control And Data Acquisition (SCADA) system are available at the turbine locations for comparison with winds retrieved from SAR. The correlation is good, both for free-stream and waked conditions. Spatial wind speed variations along the rows of wind turbines derived from SAR wind maps prior to the wind farm construction agree well with information gathered by the SCADA system and a numerical weather prediction model. Wind farm wakes are detected by comparisons between images before and after the wind farm construction. SAR wind maps clearly show wakes for long and constant fetches but the wake effect is less pronounced for short and varying fetches. Our results suggest that SAR wind maps can support offshore wind energy site assessment by introducing observations in the early phases of wind farm projects.