Abstract. The expansion of offshore wind energy creates increasing potential for the use of scanning lidars for wind resource assessment, power curve verification, and wake monitoring. These applications require accurate measurements, which require precise alignment calibration. However, performing such calibration offshore is challenging due to the absence of fixed hard targets. The sea surface levelling (SSL) method, which uses the sea surface as a reference, has emerged as a promising alternative but, so far, has not been able to determine the static elevation offset and lacks a rigorous uncertainty evaluation. This study presents a generalisation of the SSL method that enables simultaneous determination of pitch, roll, and elevation offset by incorporating scans at multiple elevation and azimuth angles, either with range height indicator (RHI) or plan position indicator (PPI) scans. An analysis of uncertainties is performed, taking into account incorrect distance measurements of the lidar's point of entry into the water, as well as the influence of waves and statistical noise. The extended SSL method is applied to data from a scanning lidar (Vaisala WindCube WLS400S) installed on the transition piece of an offshore wind turbine in the German North Sea. Results show that pitch and roll can be determined with high confidence and reproducibility, even with incomplete azimuth coverage. For the first time, we demonstrate that elevation offset can be derived directly from SSL, though its accuracy depends strongly on distance determination. Further, correcting the lidar probe length improves the agreement with an independent validation. Wave effects were negligible under calm conditions but are expected to increase in rougher seas. Overall, the extended SSL method enables the alignment calibration with typical uncertainties of 0.03 to 0.04° at suitable elevation angles, in this example, from −1.5 to −0.3° at a lidar height of around 20 m. The extended SSL method provides a robust, transferable alternative to hard-target calibration for scanning lidars offshore.

Abstract. A dual-lidar system comprising two WindCube v2.1 Doppler lidars – one oriented horizontally at a 45° angle relative to the other – was deployed to estimate atmospheric turbulence using the variance method. This approach derives second-order velocity statistics directly from line-of-sight (LOS) velocity variances, enabling the reconstruction of the three-dimensional velocity variance components without retrieving instantaneous wind vectors. Its performance is evaluated against the traditional method commonly used in the wind energy sector, which reconstructs instantaneous velocity components from single-lidar LOS measurements prior to computing turbulence statistics. Both methods are assessed at a single measurement height using a 30 d collocated dataset, with sonic anemometer measurements as reference and classification by atmospheric stability. The analysis focuses on turbulence intensity (TI), and performance is quantified using the mean relative bias error (MRBE) and relative root mean square error (RRMSE), following Det Norske Veritas (DNV) load-validation acceptance criteria (MRBE within ±5 % and RRMSE ≤ 15 %). Results show that the variance method demonstrates improved agreement with the reference measurements across nearly all wind-speed bins. For along-wind TI, MRBE values range between 0 % and −5 %, with RRMSE remaining below 15 % over the full wind-speed range, fully satisfying the DNV criteria. For cross-wind TI, the MRBE criterion is met, while the RRMSE threshold is exceeded. In contrast, the traditional method meets both DNV criteria for along-wind TI only at wind speeds above approximately 7 m s−1 and fails to satisfy either criterion for cross-wind TI across the investigated range. Overall, the results demonstrate that the variance method provides more robust and DNV-compliant TI estimates than the traditional reconstruction approach, particularly for along-wind turbulence relevant to wind turbine load validation.

Abstract. Wind energy harvesting from the atmosphere takes place in the atmospheric boundary layer. The boundary layer shear and buoyancy create three-dimensional turbulent eddies spanning a range of scales that form a continuous forward cascade of kinetic energy to the smallest scales of motion where energy is dissipated. Large-scale atmospheric circulations modulate the boundary layer turbulence, characterized by coherence and intermittency. As wind turbines grow in size and the integrated control of both turbines and wind farms spans greater distances, the relationship between the scales of atmospheric turbulence and the design and operation of wind energy facilities has entered new territory. The boundary layer turbulence impacts both wind turbine power production and turbine loads. Optimizing wind turbine and wind farm performance requires an understanding of how turbulence affects both wind turbine efficiency and reliability. While the characteristics of atmospheric boundary layer turbulence have been observed and studied in detail over the last few decades, there are still significant gaps in our understanding of the impact of turbulence on wind power resources and wind farm operations. This paper outlines the current state of turbulence research relevant to wind energy applications and points to gaps in our knowledge that need to be addressed to effectively utilize wind resources.

Abstract. This study examines the role of turbulence distortion in predicting inflow turbulence (IT) noise generation from large wind turbines via Amiet's theory. Two subsequent distortion mechanisms are investigated: (i) the streamtube expansion in the rotor induction zone and (ii) the interaction with the surface of thick-blade profiles. Large-eddy simulations reveal that the turbulence spectra, which reflect distortion effects, remain largely unaffected by rotor induction within the frequency range relevant for noise generation. As for the other mechanism, the distortion of the turbulence approaching a blade leading edge is modeled with a simplified closed-form solution of Goldstein's rapid distortion theory. This model, based on vorticity deflection, is extended here beyond the high-frequency approximation and integrated into an analytical Amiet-based IT noise tool. Applications to representative test cases show that while distortion effects are minimal for current turbine sizes, they become relevant for future configurations featuring larger rotor sizes and thicker airfoils. The developed model reveals that IT noise levels do not necessarily scale with rotor size but are shaped by spectral changes induced by the blade geometry, operational parameters, and inflow conditions. This model offers a physically consistent, computationally efficient framework for the aeroacoustic assessment of next-generation wind turbine design.

Abstract. Large rolling bearings with complex interfaces need reliable finite-element (FE) models to determine the load distribution and deformation behavior. To ensure the accuracy of the results, it is important to validate the models against experimental data. Several works on models with different approaches are published, but rarely is this validated. The present work firstly validates a finite-element model of an original size three-row roller wind turbine blade bearing. For the validation, strain gauges and laser sensors are used to compare the deformation behavior and radial displacements of the bearing rings against experimental results. A characteristic of three-row roller bearings is the segmentation of one of the rings for manufacturing purposes. In this work, the authors investigate the influence of different coefficients of friction between the segmented outer ring and different bolt preloads on the occurring strain on the bearing rings. Two different sets of bolt preloads were considered: one to represent operational behavior with no relative movement between the segments of the split ring and one with gap opening and sliding to investigate nonlinear behavior of the bearing. The result of this work is a validated finite-element bearing and test rig model for different parameter sets and loads.

Abstract. Two mechanisms cause wind speed recovery in the wake of a wind farm: momentum mixing and the Coriolis force. To study these mechanisms, we use a steady linearised two-layer fast Fourier transform (FFT) model so that both analytical expressions and full flow fields can be derived. The model represents the complex vertical mixing processes by a simple Rayleigh friction. Rayleigh friction decays the wind disturbance at a rate proportional to its local value. Pressure gradient forces are computed using a two-part vertical wave number formulation in the upper layer. The Coriolis force recovery occurs by deflecting flow leftward (in the Northern Hemisphere). The Coriolis force, acting on this cross-flow, re-accelerates the flow in the downwind direction. The relative importance of Rayleigh versus Coriolis wake recovery depends on their two coefficients: C and f respectively, each with units of inverse time. When the coefficient ratio is large, i.e. C/f≫1, Rayleigh friction restores the wake before Coriolis can act. Farm size and atmospheric static stability are also important in wake recovery. The wakes of small- and medium-size farms will quickly approach geostrophic balance. Once balance is established, the ratio of farm size “a” to the Rossby radius of deformation (RRD) determines the amount of Coriolis recovery. For a small farm in a stable atmosphere (a< RRD), Coriolis acts by adjusting the pressure field to obtain geostrophic balance rather than accelerating the wind. When this occurs, only momentum mixing can restore the “inner” wake. For large farms in less stable conditions (a> RRD), the Coriolis force significantly contributes to wake recovery. In this case, the leftward deflected flow creates “edge jets” on either side of the wake. Including the Coriolis force when modelling wind farm wakes is demonstrated to be increasingly important for larger wind farms or farm clusters.

Abstract. Vertically propagating mountain waves, accompanied by strong downslope winds, occur frequently along the coast of Norway and cause accelerated surface winds on the lee side and downstream of the mountain. Mountain waves form when stably stratified air flows over a mountain and can potentially impact the power production in wind parks located in complex terrains. Although mountain waves and downslope windstorms have received significant attention within the meteorology community, they have received less focus within the wind energy industry. Taking advantage of wind and power production data from a grid of 67 wind turbines spread across two nearby mountains, this study documents accelerated wind speeds and enhanced power production on the lee side of the mountains compared to the mountain crest. The result of this study suggests that considering mountain waves in the planning phase of future wind parks may allow for an optimal layout of the wind turbines and improve profitability. The non-dimensional mountain height, H^, is a key parameter for describing the development of mountain waves, and this study finds a strong relationship between H^ and the accelerated downslope winds. The results of this study suggest that mountain-wave-induced accelerated downslope winds tend to occur in the wind park when H^<3; above this value, the lee side wind tends to be weaker than at the mountain crest. Finally, the Weather Research and Forecasting model reproduces the spatial variations in the wind speeds within the two wind parks relatively well during periods of strong downslope winds. However, the differences in the wind speeds at the windward side, the mountain top, and the lee side are not as pronounced as in the observations.

Abstract. In this study, a seven-member ensemble of mesoscale-to-microscale simulations with varying sea surface temperature (SST) is conducted for a case in which an offshore low-level jet was observed via floating lidar. The performance of each SST setup in reproducing the physical characteristics of the observed low-level jet is compared across the mesoscale and microscale domains. It is shown that the representation of low-level shear, jet-nose height, and hub-height wind speed are generally improved when moving from mesoscale to microscale. Specifically, low-level shear is improved in the microscale by reducing near-surface wind speeds and lowering the jet-nose height to be closer to that observed. Counterintuitively, the sensible heat flux on the mesoscale domains is more negative than on the microscale domains, which would indicate a more stable boundary layer with higher shear; however, the low-level shear in the mesoscale is weaker than that of the microscale domains. This indicates over-mixing of the (planetary boundary layer) PBL scheme in the mesoscale domains and/or the overprediction of surface drag in the microscale domain. We analyze performance considering a real-world scenario in which the computational burden of running an ensemble of large-eddy simulations (LESs) limits a study to performing a mesoscale ensemble to select the best model setup that will drive a single LES run. In the context of this study, the best model setup is subjective and weighs model performance in the physical representation of the low-level jet as well as the model surface forcing through the temperature gradient between air and sea. The expectation of this approach is that the best-performing setup of the mesoscale simulations will produce the best result for the microscale simulations. It is shown that there are large fundamental changes in the characteristics of the low-level jet as well as in the surface forcing conditions between the mesoscale and microscale domains. This results in a non-linear ranking of performance between the mesoscale domains and the microscale domains. While the best-performing mesoscale setup is also deemed to produce the best results on the microscale, the second-best-performing mesoscale setup produces the worst results on the microscale.

Abstract. This work investigates the influence of aeroelastic modeling fidelity on design optimization of floating offshore wind turbines. To this end, the QBlade simulation environment was coupled to the Wind Energy with Integrated Servo-control wind turbine design and optimization framework. QBlade offers aerodynamic and structural models with varying levels of aeroelastic fidelity within a computationally efficient implementation. This enables time-domain optimization studies with levels of aeroelastic fidelity that are currently often deemed unfeasible for such purposes due to the computational expense involved. Five fidelity combinations are considered, ranging from blade element momentum aerodynamics with torsion-constrained Euler–Bernoulli beams to lifting-line free vortex wake aerodynamics with fully populated Timoshenko beams. To assess how aerodynamic and structural modeling fidelity influences optimization outcomes, the parameters of the floating wind turbine controller are co-designed together with the floating substructure, a system typically considered less sensitive to aeroelastic fidelity. The results show that final design outcomes are affected by the chosen fidelity level. In this case study, higher-fidelity models modified and broadened the feasible design space and thereby led to improved design outcomes, albeit at the expense of higher computational cost.

Abstract. For the largest wind turbines currently being designed, operation close to cut-out conditions can lead to the tip airfoil experiencing transonic flow conditions. To date, this phenomenon has been explored primarily through numerical simulations, but modelling uncertainties limit the reliability of these predictions. In response to this challenge, our study marks the first experimental investigation of a wind turbine airfoil under transonic conditions, for which we selected the FFA-W3-211 airfoil. Measurements were carried out in the high-subsonic range (Mach 0.5 and 0.6), utilizing schlieren visualization and particle image velocimetry (PIV) to characterize the airfoil across a range of angles of attack (AoAs) expected to be close to the boundary of transonic flow occurrence. Unsteady shock wave formation was observed for the higher Mach number, with the shock oscillation range increasing with steeper angles of attack. In addition, it was confirmed that the presence of a local supersonic flow region does not necessarily result in a shock wave. For cases with shock waves and trailing-edge separation, a buffet cycle was identified that is similar to, but distinct from, those seen in aviation applications. Our findings highlight the need for unsteady analyses even in steady operating conditions and call for dedicated research on wind turbine tip airfoils in transonic flow.