The upscaling of wind turbines has extended rotor-swept areas into the atmospheric boundary layer, several hundred meters above the surface layer. Traditional wind field models have relied on neutral surface-layer characteristics. Recent research emphasizes incorporating atmospheric stability and dynamics at higher altitudes, as larger turbines experience greater load variations due to eddies comparable in size to the rotor, causing non-uniform inflow. This study evaluates a measurement-validated coherence model for aeroelastic analysis of a 5 MW and a 22 MW turbine. The model characterizes atmospheric stability using tuned coefficients and is applied alongside stability-dependent spectral wind field models. Six seeds of 30 minute simulations were conducted for each wind turbine model ranging from near cut-in to near cut-out wind speed, in intervals of 1 m/s, using on one hand, the proposed coherence model with stability-dependent spectra and on the other hand, the IEC Kaimal coherence model. Aeroelastic results reveal distinct turbine responses to atmospheric stability. The trends of higher tower fore–aft bending moments in unstable conditions and lower moments in stable conditions, together with increased blade-root flapwise moments at above-rated wind speeds under stable, high-shear conditions, are captured. Wavelet analysis confirms that larger turbines face greater load variations because eddies are smaller than the rotor, whereas smaller turbines experience more uniform frequency-time correlations with hub-height velocity. Simulations with flexible and rigid blades and towers indicate that flexibility effects are secondary to eddy size and coherence. Overall, it is demonstrated that simple stability-dependent empirical coherence and spectral models can effectively replicate the commonly observed impact of atmospheric stability on wind turbine loads.

Accurate modeling of atmospheric turbulence is critical for the design and operation of next-generation large-scale wind turbines, particularly those exceeding 15 MW rated capacity and spanning well above the atmospheric surface layer (typically 10 − 20% of the atmospheric boundary layer (ABL)). In this study, Large Eddy Simulations (LES) were performed to investigate turbulence characteristics at high altitudes, up to 300 m above ground level — a region increasingly relevant for large turbine rotors. Turbulence coherence was analyzed and compared with field measurements to assess the fidelity of numerical predictions. Coherence estimates from LES were validated against lidar-based measurements obtained under stable, neutral, and unstable atmospheric conditions. Results show good agreement in the coherence decay rates and cross-spectral characteristics, with notable discrepancies only at very low frequencies (on the order of several 10 ⁻⁴ Hz) and large spatial separations (on the order of several 10 ² m). Consequently, a LES-tuned empirical lateral coherence model is proposed, featuring distinct coherence decay rates for each atmospheric stability regime (stable, neutral, and unstable ABL), offering improved representation of turbulence structures across a range of operating conditions. These findings provide a valuable reference for refining turbulence models for improving load estimation methodologies for next-generation wind turbines operating at hub heights above 200 m.

In this work, an experimental campaign was carried out to determine both the static and the dynamic aerodynamic properties of the FFA-W3-211 airfoil. This airfoil is widely used in the wind energy community as part of IEA reference wind turbine designs but is lacking experimental data for design, simulation tool validation and dynamic stall modeling purposes. The airfoil model was designed and manufactured for testing in the low-speed, low-turbulence wind tunnel at TU Delft. The airfoil was tested statically for Reynolds numbers ranging from R _ec=5× ¹⁰⁵ to R _ec=3.5× ¹⁰⁶ and dynamically for up to R _ec=2× ¹⁰⁶, encompassing steady, unsteady and highly unsteady aerodynamic behavior. Data were acquired through pressure measurements at the surface of the airfoil and in the wake, as well as by using thermal cameras. The static results highlighted a strong dependence of the lift and drag polars on the Reynolds number and a change in trends around R _ec=2× ¹⁰⁶. The suspected presence of laminar separation bubbles for the lower Reynolds numbers could explain this fundamental change in flow behavior. The dynamic behavior was studied at high positive angles of attack, high negative angles of attack and within the linear region of the polar around the zero-lift angle. The positive region is governed by the lack of a leading-edge vortex. This is in contrast to the negative region of the polars where the effects of a vortex appearing close to the leading edge dominate. The sensitivity of the results to reduced frequency, amplitude and Reynolds number is discussed. Overall, for the FFA-W3-211 airfoil, it is recommended to use experimental data of R _ec=2× ¹⁰⁶ or above to capture the correct physical (static and dynamic) trends relevant for larger wind turbine blades. For dynamic stall model tuning, it may also be important to consider the significant change in behavior between positive and negative stall angles.

Floating wind energy is a relatively new area that consists of harnessing wind energy from wind turbines that are supported by a floating foundation. This enables the installation of offshore wind turbines in deep seas, which means tapping into offshore wind resources that are unreachable with bottom-fixed wind turbines. Up to now, the feasibility of floating wind turbine technology has been demonstrated in small pilot farms. However, floating wind turbines are still subject to unexpected failures. Therefore, a better fundamental understanding of these turbines is needed to improve the technology to accelerate its deployment and reduce the cost of energy. Furthermore, the dynamics of floating wind turbines is different from those of their bottom-fixed counterparts. This presents challenges and opportunities across the different phases of their development and operation. This position paper addresses the fluid mechanics community and presents key challenges and research needs in the field of floating wind energy. Building on the grand challenges identified in the wind energy community, the manuscript addresses three focus areas and their interactions: the met-ocean conditions, the wind turbine, and the wind farm. Five groups of fluid mechanics driven challenges are highlighted: unsteady aerodynamics, high-speed flows, non-linear hydrodynamics, flow-induced vibrations, and wake dynamics. In addition, the kind of research methods and infrastructure needed to address these challenges are discussed, including cross-cutting themes such as digitalisation and co-creation across stakeholders and disciplines. Finally, the conclusions provide overarching recommendations to solve the upcoming challenges in floating wind energy and highlight the role that the fluid mechanics community could play.

Modern large wind turbine rotors can encounter airflow at inflow Mach numbers of around 0.3 and Reynolds numbers of the order of 10 million at the blade tip. Our previous study showed that for these operational conditions, the incompressibility assumption is violated, and supersonic flow can occur locally. The present follow-up study reports on a numerical investigation of the dynamic behavior of the FFA-W3-211 wind turbine tip airfoil in transonic flow using unsteady Reynolds-averaged Navier–Stokes (URANS) simulations. The computations are performed for a highly unsteady aerodynamic regime by imposing a dynamic sinusoidal pitching motion across the transonic threshold determined in our previous study. This way, the airfoil is forced to enter and leave the supersonic flow regime. The simulations are conducted by varying the reduced frequency and the inflow Mach number, while keeping the Reynolds number constant at nine million. The choice of non-negligible inflow Mach numbers combined with high Reynolds numbers results in a realistic combination for full-scale wind turbines, but it is still challenging to achieve experimentally with the test facilities available nowadays. The dynamic pitching motion is found to lead to the formation of a hysteresis loop with an extent, depending on both reduced frequency and inflow Mach number. In particular, it is observed that an increase in one of these two parameters induces an expansion of the hysteresis loop with the consequences of (1) an increase in the magnitude and variability of loads experienced by the airfoil, (2) a delay in the beginning and ending of the transonic flow regime, and (3) the onset of shock waves occurring at inflow Mach numbers lower than those estimated under static conditions. Moreover, since the formation of a hysteresis loop implies a range of conditions in which transonic flow can occur, this needs to be better understood and considered when defining any safety margin in the definition of the transonic threshold for turbine design and operation purposes. In general, this study suggests the need to take into account dynamic effects when predicting aerodynamic loads and performance for next-generation wind turbine rotors.

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.

Airborne wind energy (AWE) is an innovative technology that differs from the operating principles of horizontal axis wind turbines (HAWTs). It uses tethered flying devices, denoted as kites, to harvest higher-altitude wind resources. Kites eliminate the need for a tower but introduce a penalty in power generation since the kite has to spend part of its aerodynamic force to counter its weight. The differences between the two technologies lead to different scaling behaviours, and understanding these as well as the design drivers of AWE systems is essential for developing this technology further. To this end, we developed a multidisciplinary design, analysis, and optimisation (MDAO) framework which employs models evaluating the wind resource, power curve, energy production, overall component and operation costs, and various economic metrics. This framework was used to design fixed-wing ground-generation (GG) AWE systems based on the objective of minimising the levelised cost of energy (LCoE). The variables used to define the system were the wing area, aspect ratio, tether diameter, and rated power of the generator. The framework was employed to find optimal system designs for rated power ranging from 100 to 2000 kW. The results show that kite mass, energy storage, and tether replacements are the key LCoE driving factors. Moreover, in contradistinction to HAWTs, the total lifetime operational costs are equal to or higher than the initial investment costs. This distribution of costs over the project's lifetime, rather than as a large upfront investment, could make it easier to secure project financing. The scaling results show that the LCoE-driven optimum lies within the 100 to 1000 kW system size. The reason for this is that the kite mass penalty increases the cut-in and rated wind speeds, reducing the capacity factor of the larger systems. Sensitivity analyses with respect to extreme scenarios considering technological advancements, financial uncertainties, and environmental conditions show that this optimum is robust within our modelling assumptions.

For the largest wind turbines currently designed, when operating at rated power and at high wind speeds, the tip airfoils can experience large negative angles of attack. For these conditions and in combination with turbulence, the airfoils are at risk of reaching locally supersonic flow, even at low free-stream Mach numbers. The possibility of shock wave formation and its consequences endangers the lifetime of these largest rotating machines ever built. So far only numerical analyses of this challenge have been attempted with significant modelling uncertainty. Here, for the first time, a wind turbine airfoil (the FFA-W3-211, used at the blade tip of the IEA 15MW reference wind turbine) is studied under transonic conditions using experimental techniques. Schlieren visualization and Particle Image Velocimetry were employed for free-stream Mach numbers of 0.5 and 0.6 and various angles of attack. It was shown that calculations based on isentropic flow theory and compressibility corrections were able to predict the situations where supersonic flow occurred. However, they could not predict the frequency of occurrence and whether shock waves were formed. In conclusion, an unsteady characterization of such airfoil behavior in transonic flow seems to be warranted.

Two setups are used to investigate differences between modeling a wind turbine nacelle by means of an actuator-line model (ALM) and a wall-model (WM) using large-eddy simulations. One advantage of the ALM is that it requires a lower mesh refinement, making it less computationally costly. In the first setup, the nacelle is in standalone configuration and the ALM results show a much lower turbulence intensity and a significantly slower wake recovery when compared to the WM cases. In the second setup, the nacelle is in a rotor-nacelle assembly configuration and many variations of the ALM are tested in order to match the results from the experiment addressed in the OC6 task phase III. Contrary to previous findings that the nacelle might affect the turbine loads, this study shows that the improved match with the experiment stems from the increased mesh refinement in the nacelle region rather than the actual presence of the nacelle. Nevertheless, the wake profiles in the near-wake show a very good agreement between the ALM and WM, regardless of the refinement in the nacelle region. These cases also show a higher wake deficit than not using any nacelle at all.

The erosion-safe mode (ESM) is a novel mitigation strategy that reduces rainfall-induced erosion damage by lowering the tip-speed of the turbine during precipitation events. The ESM requires accurate information about future expected rainfall for its control. In current research, it is debated what method or source should be used to this end. This study explores the effectiveness of driving the ESM using a state-of-the-art weather-radar-based probabilistic rainfall nowcast provided by the Royal Netherlands Meteorological Institute (KNMI). The performance of the nowcast is assessed for various lead times with an impingement-based damage model for three sample sites in the Netherlands and for two distinct ESM strategies. The results show that the quality of the nowcast degrades with increasing lead times, where the 5- and 15-minute lead times exhibit sufficiently good accuracy and response time for adjusting turbine speeds. Overall, the results highlight that the probabilistic information in the nowcast can be employed to improve the efficiency and viability of the ESM.

The future of wind turbines will be characterised by long, slender blades subject to dynamic inflow and aeroelastic deflections. This makes the next generation of blades more prone to encounter dynamic stall effects, in which significant forces and loads fluctuations can be expected. Dynamic stall models can be tailored to suit the aerodynamics of different airfoils. Although different dynamic stall models exist, the impact of the choice of model, its implementation and calibration on the overall wind turbine performance remains to be assessed. In this work, we gathered an experimental dynamic dataset for a representative airfoil, the FFA-W3-211, to define the semi-empirical time constants for the Beddoes-Leishman dynamic stall model. An important differentiation is made between stall regions for positive and negative angles of attack, and the impact of tailored coefficients is assessed at airfoil scale. The difference between the tailored and untailored model is quantified for power performance and loads of the IEA 15 MW reference wind turbine. The results highlight a significant load over-prediction from the untailored Beddoes-Leishman model, whereas changes in power performance are negligible.

Traditionally, wind turbine and wind farm designs have been optimized to minimize the cost of energy. Such a design would make sense when bidding in price-based auctions. However, in a future with a high share of renewables and zero subsidies, the wind farm developer is exposed to the volatility of market prices, where the price paid per kilowatt-hour of energy would not be constant anymore. The developer might then have to maximize the revenue earned by participating in different energy, capacity, or ancillary services markets. In such a scenario, a turbine designed for maximizing its market value could be more profitable for the developer compared to a turbine designed for minimizing the levelized cost of electricity (LCoE). This study is in line with this paradigm shift in the field of turbine and farm design. It is a continuation of a previous study conducted by the same authors , which explicitly focused on the drivers of turbine sizing with respect to LCoE. The goal of this study is to optimize the design for a new set of objective functions and analyze how various day-Ahead market conditions and objectives drive turbine design. A simplified market model that can generate hourly day-Ahead market prices is developed and coupled with a wind-farm-level multidisciplinary design analysis and optimization (MDAO) framework to evaluate key economic indicators of the wind farm. The results show how the optimum turbine design is driven by both the choice of the economic metric and the market scenario. However, an LCoE-optimized design is found to perform well with respect to profitability-based economic metrics like modified internal rate of return (MIRR) or profitability index (PI), indicating a limited need to redesign turbines for a specific day-Ahead market scenario.