• Energy Research

ABSTRACTThe aim of this study is to assess the load predicting capability of a classical Beddoes–Leishman dynamic stall model in a horizontal axis wind turbine environment, in the presence of yaw misalignment. The dynamic stall model was tailored to the horizontal axis wind turbine environment and validated against unsteady thick airfoil data. Subsequently, the dynamic stall model was implemented in a blade element‐momentum code for yawed flow, and the results were compared with aerodynamic measurements obtained in the MEXICO (Model Rotor Experiments under Controlled Conditions) project on a wind turbine rotor placed in a large scale wind tunnel. In general, reasonable to good agreement was found between the blade element‐momentum model and MEXICO data. When large yaw misalignments were imposed, poor agreement was found in the downstroke of the movement between the model and the experiment. Still, over a revolution, the maximum normal force coefficient predicted was always within 8% of experimental data at the inboard stations, which is encouraging especially when blade fatigue calculations are being considered. Copyright © 2012 John Wiley & Sons, Ltd.

We describe a probabilistic approach to design airfoils for wind energy applications. An analytical expression is derived for the probability of perturbations to the operational blade-section angle of attack. It includes the combined influence of wind shear, yaw-misalignment, and turbulence intensity. The theoretical fluctuations in angle of attack are validated against an aero-structural simulation of a 10 MW horizontal axis wind turbine, operating under different inflow conditions. Finally we incorporate the probabilistic approach into a multi-objective airfoil optimization problem, which is solved with a genetic algorithm. The results illustrate the compromise between airfoil performance for a specific angle of attack and robustness of airfoil performance over a large range of angle of attack fluctuations

AbstractThis study describes a methodology for designing airfoils suitable to employ actuation in a wind energy environment. The novel airfoil sections are baptized wind energy actuated profiles (WAP). A genetic algorithm‐based multi‐objective airfoil optimizer is formulated by setting two cost functions: one cost function for wind energy performance and the other representing actuation suitability. The wind energy cost function compares the candidate airfoils' performance with ‘reference’ wind energy airfoils, considering a probabilistic approach to include the effects of turbulence and wind shear. The actuation suitability cost function is developed considering horizontal axis wind turbines active stall control, including two different control strategies designated by ‘enhanced’ and ‘decreased’ performance. Two different actuation types are considered, namely, boundary layer transpiration and dielectric barrier discharge plasma. Results show that using WAP airfoils provides much higher control efficiency than adding actuation on reference wind energy airfoils, without detrimental effects in non‐actuated operation. The WAP sections yield an actuator employment efficiency that is two to four times larger than those obtained with reference wind energy airfoils, at equivalent wind energy performance. Regarding geometry, and compared with typical wind energy airfoils, WAP sections for decreased performance display an upper surface concave aft region, while for increased performance, a convex upper surface aft region is obtained. The present study emphasizes that there is much to gain in designing airfoils from the beginning to include actuation effects, especially compared with employing actuation on already existing airfoils. The results demonstrate the potential of including actuation effects in the airfoil design process, thus enabling novel horizontal axis wind turbines control strategies. Copyright © 2017 John Wiley & Sons, Ltd.

Unsteady loads are a major limiting factor for further upscaling of HAWTs considering the high costs associated to strict structural requirements. Alleviation of these unsteady loads on HAWT blades, e.g. using active flow control (AFC), is of high importance. In order to devise effective AFC methods, the unsteady loading sources need to be identified and their relative contribution to the load fluctuations experienced by blades needs to be quantified. The current study investigates the effects of various atmospheric and operational parameters on the fluctuations of α and CL for a large HAWT. The investigated parameters include turbulence, wind shear, yawed inflow, tower shadow, gravity and rotational imbalances. The study uses the DTU's aeroelastic software HAWC2. The study identifies the individual and the aggregate effect of each source on the aforementioned fluctuations in order to distinguish the major contributing factors to unsteady loading. The quantification of contribution of each source on the total fatigue loads reveals >65% of flapwise fatigue loads is a result of turbulence while gravity results in >80% of edgewise fatigue loads. The extensive parametric study shows that the standard deviation of CL is 0.25. The results support to design active load control systems by highlighting the magnitude of CL and α variations experienced by HAWTs, and thus the dCL that needs to be delivered by an AFC system.

Wind energy is paramount to the European Union’s decarbonization and electrification goals. As wind farms expand with larger turbines and more powerful generators, conventional ‘greedy’ control strategies become insufficient. Coordinated control approaches are increasingly needed to optimize not only power output but also structural loads, supporting longer asset lifetimes and enhanced profitability. Despite recent progress, the effective implementation of multi-objective wind farm control strategies—especially those involving yaw-based wake steering—remains limited and fragmented. This study addresses this gap through a structured review of recent developments that consider both power maximization and fatigue load mitigation. Key concepts are introduced to support interdisciplinary understanding. A comparative analysis of recent studies is conducted, highlighting optimization strategies, modelling approaches, and fidelity levels. The review identifies a shift towards surrogate-based optimization frameworks that balance computational cost and physical realism. The reported benefits include power gains of up to 12.5% and blade root fatigue load reductions exceeding 30% under specific scenarios. However, challenges in model validation, generalizability, and real-world deployment remain. AI emerges as a key enabler in strategy optimization and fatigue damage prediction. The findings underscore the need for integrated approaches that combine physics-based models, AI techniques, and instrumentation to fully leverage the potential of wind farm control.