Within a wind farm, each wind turbine extracts kinetic energy from the flow to convert it into electric energy. Unavoidably, this reduces the downstream availability of kinetic energy, diminishing the power generation of turbines operating in the waked region. These wake-induced power losses cumulate throughout the wind farm, posing a risk to its economic feasibility. One method that mitigates these power losses is helix active wake control. By leveraging individual blade pitch control, it induces an uneven thrust distribution over the rotor plane, which rotates either in clockwise (CW) or counterclockwise (CCW) direction around the rotor center. The wake deforms into a helical shape that recovers faster than the wake of a conventionally controlled turbine and thereby increases the total generated power. Notably, the CCW helix consistently outperforms the CW helix across all available studies. This work investigates the physical principles underlying these wake recovery enhancements using large eddy simulations (LES) of a wind turbine exposed to laminar, uniform flow. We observe a spatially coherent helical vortex structure in the wake boundary, which actively transports mean kinetic energy into the wake and, therefore, poses a fundamental contributor to the wake recovery enhancement. The opposing rotational directions of CW and CCW helixes result in distinct interactions of the helical vortex with the hub vortex, leading to different wake recovery mechanisms. In the investigated laminar inflow, the CCW helix has transported 44.8% more mean kinetic energy into the wake than the CW helix up to a streamwise position of 5D, explaining their differing efficacies observed in previous studies.

The actuator line method for modeling wind turbine blades in wind farm flow simulations often offers a good compromise between accuracy and computational cost. A variety of methods have been proposed to correct the force prediction by the actuator line near the tip and root due to smearing the force in the flow domain. This article compares the two most commonly used methods (the filtered lifting line and the vortex-based smearing correction) in terms of accuracy, applicability and computational performance. Both corrections perform well for a single turbine, significantly reducing the force and power overprediction. The effect on power is larger than on thrust. Applying the corrections leads to more accurate results even at lower resolutions. The application of the corrections in an ALM simulation of a wind farm with 30 turbines reduces power by up to 10% compared to a case without correction. Different turbines in the farm are affected differently. The improvements in accuracy due to the corrections far outweigh the additional computational cost.

The actuator line model (ALM) is an approach commonly used to represent lifting and dragging devices like wings and blades in large-eddy simulations (LES). The crux of the ALM is the projection of the actuator point forces onto the LES grid by means of a Gaussian regularisation kernel. The minimum width of the kernel is constrained by the grid size; however, for most practical applications like LES of wind turbines, this value is an order of magnitude larger than the optimal value that maximises accuracy. This discrepancy motivated the development of corrections for the actuator line, which, however, neglect the effect of unsteady spanwise shed vorticity. In this work we develop a model for the impact of spanwise shed vorticity on the unsteady loading of an aerofoil modelled as a Gaussian body force distribution, where the model is applicable within the regime of unsteady attached flow. The model solution is derived both in the time and frequency domain and features an explicit dependence on the Gaussian kernel width. We verify the model with ALM-LES for both pitch steps and periodic pitching. The model solution is compared withTheodorsen theory and validated with both computational fluid dynamics using body fitted grids and experiment. It is concluded that the optimal kernel width for unsteady aerodynamics is approximately 40 % of the chord. The ALM is able to predict the magnitude of the unsteady loading up to a reduced frequency of k ≈ 0.2.

In recent years, the relevance of the interaction between neighboring wind farms has grown steadily. As one farm extracts energy from the wind, a downstream one can systematically experience lower wind speeds which threatens the economic viability of the farm. Significant progress has been made in understanding these farm-farm wake interactions, but we still lack methodologies to mitigate their undesired effects. In this study, we introduce Active Cluster Wake Mixing (ACWM). This novel method aims to accelerate the recovery of the cluster wake using dynamic control actions: By exciting the thrust of the individual turbines depending on their relative location, we generate non-uniform patterns of energy extraction. Phase offsets between the individual excitation signals propagate these regions through the wind farm. This results in large-scale velocity gradients inside the farm, which also affect the flow in the cluster wake region. An in-depth exploration and optimization of ACWM requires significant computational effort. Therefore, we compare three different wind farm modeling approaches in Large Eddy Simulations (LES) that differ in their computational costs regarding their suitability for further exploration of ACWM. For this purpose, we use an unoptimized ACWM scheme with two different excitation frequencies. For the first time ever we successfully show that ACWM manipulates the flow inside the wind farm with favorable effects on the wake velocity. We also demonstrate that the modeling of cluster wakes is challenging and has a significant effect on the potential gain.

A variety of wind farm control strategies exist in order to reduce unfavorable wake effects in large wind farms. While strategies like wake steering already reached a high maturity level, it is interesting to compare them to more recently proposed strategies. Such a comparison can form the basis for the development of a symbiotic wind farm control toolbox, from which a control strategy is chosen and activated depending on the operating conditions. The present study compares wake steering with helix control across a wide range of turbine spacings and wind directions using large-eddy simulation (LES). The size of the search space is made computationally tractable for LES by adopting a setup based on one physical upstream turbine and a distribution of virtual downstream turbines which do not exert any thrust force. It is found that helix control is beneficial for full wake overlap and turbine spacing of less than six rotor diameters whereas wake steering proves to be optimal further downstream and for partial wake overlap. Furthermore, the results show that the helix control setpoint in the proximity of full wake overlap scenarios is less susceptible to wind direction variations. This finding indicates that the combination of wake steering and helix control has potential for the design of a wind farm controller which is more robust in full wake overlap scenarios and can reduce the need for large yaw offset adjustments.

The performance of wind farms can substantially increase when their individual turbines deviate from their own greedy control strategy and instead also take into account downstream turbines operating in the wake. The helix approach is a recently introduced dynamic wind farm control strategy that tackles this issue by leveraging individual pitch control to accelerate wake recovery. Its effective implementation requires detailed knowledge about the scaling between control input and the resulting power gain and turbine loading across the farm. In the present work this scaling is explored by means of large-eddy simulation of a two-turbine farm in the conventionally neutral atmospheric boundary layer. A parameter sweep for the amplitude of the helix is performed showing monotonous increase of the farm's power output with increasing pitch amplitude within the considered range of zero to six degrees. The scaling of the power gain suggests that a threshold amplitude should be exceeded for effective speed-up of the wake recovery, whereas the damage equivalent loads computed for the turbines indicate an upper limit for the amplitude despite increasing power gains.

The development of new wind farm control strategies can benefit from combined analysis of flow dynamics in the farm and the behavior of individual turbines within one simulation environment. In this work, we present such an environment by developing a new coupling between the large-eddy simulation (LES) code GRASP and the multiphysics wind turbine simulation tool OpenFAST via an actuator line model (ALM). In addition, the implementation of the recently proposed filtered actuator line model (FALM) within the coupling is described. The new ALM implementation is cross-verified with results from four other commonly used research LES codes. The results for the blade loads and the near wake obtained with the new coupling are consistent with the other codes. Deviations are observed in the far wake. The results further indicate that the FALM is able to reduce the lift and power overprediction from which the traditional ALM suffers on coarse LES grids. This new simulation environment paves the way for future wind farm simulations under realistic weather conditions by leveraging GRASP's ability to impose data from large-scale meteorological models as boundary conditions.