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Summary Recently, dynamic induction control is gaining the interest of the wind energy community as a promising strategy to increase the overall wind farm power production. Such a technique is based on a dynamic variation of the upstream rotor thrust, generated through a suitable blade pitch motion, to promote a faster wake recovery. Notwithstanding some promising results already published, the knowledge of the physical mechanism, connecting dynamic induction to the increased in‐wake velocity, was not yet exploited to enhance control effectiveness. This paper, through a computational fluid dynamics procedure based on large eddy simulations coupled with actuator line models, provides a description of the working principles of this control from a fluid dynamics standpoint. The analyses show that the faster recovery is strictly connected to the ability of the blade tip vortices to roll up and sucking energy from the outer flow. Exploiting such knowledge, a novel control strategy, which improves the vortex roll up mechanism, is proposed and analyzed. The new control proved more effective than standard techniques especially for very low turbine spacing.

In this study, the reduced-order multiscale-coupled (MSC) model, driven by realistic input statistics from a year-long mesoscale Weather Research and Forecasting (WRF) simulation, is applied to investigate gravity wave feedback effects on the efficiency of offshore wind farms in the North Sea. Results show that self-induced gravity waves raise cluster efficiency by about 3–4.5%, with the largest gains observed in high-density clusters, such as the Belgian–Dutch. Finally, using a wave diagram, atmospheric states are classified, and those supporting evanescent modes are found to be associated with the strongest gravity waves-induced flow perturbations.

In the context of large off-shore wind farms, power production is influenced greatly by the turbine array's interaction with the atmospheric boundary layer. One of the most influencing manifestations of such complex interaction is the increased level of shear stress observed within the farm. This leads to higher momentum fluxes that affect the wind speed at the turbine locations and in the cluster wake. At the wind farm entrance, an internal boundary layer (IBL) grows due to the change in effective roughness imposed by the wind turbines, and for large enough clusters, this can reach the unperturbed boundary layer height in what is referred to as the fully developed regime. Downwind, a second IBL starts growing, while the shear stress profile decays exponentially to its unperturbed state. In the present study, we propose a simple analytical model for the vertical profile of the horizontal shear stress components in the three regions identified above. The model builds upon the top-down model of Meneveau (J. Turbul., vol. 13, 2012, N7), and assumes that the flow develops in a conventionally neutral boundary layer. The proposed parametrization is verified successfully against large-eddy simulations, demonstrating its ability to capture the vertical profile of horizontal shear stress, and its evolution both inside and downwind of the wind farm. Our findings suggest that the developed model can prove extremely useful to enhance the physical grounds on which new classes of coupled wind farm engineering models are based, leading to a better estimation of meso-scale phenomena affecting the power production of large turbine arrays.

As offshore wind farms grow in size, the blockage effect associated with the atmospheric gravity waves they trigger is expected to become more important. To model this, recent research has produced an Atmospheric Perturbation Model (APM), which simulates the mesoscale flow in the atmospheric boundary layer at a low computational cost compared to traditional methods. However, as a simplified reduced-order model, it can not resolve individual turbine wakes, and has to be coupled to an engineering wake model to predict farm power output. Over the years, three coupling methods have been developed, and been combined into the open-source framework WAYVE. This paper compares them, discussing both their theoretical validity and their performance. For the latter, we validate the velocities and power outputs predicted by WAYVE against 27 LES simulations. We find that the velocity matching (VM) and the pressure-based (PB) methods perform the best. Of these two, the VM method is more consistent with the APM output, while the PB method has a significantly lower computational cost.

In this work, we calibrated FAST.Farm curled wake model against large-eddy simulation (LES) data for a single floating IEA 15-MW wind turbine. After calibration, the model performance was validated on a two-turbine array considering several environmental conditions and different turbine models, comparing wake dynamics and power output results with LES. The calibrated model showed an improved capability of reproducing the vertical deflection and, especially, the wake recovery behind the floating wind turbine in most of the considered cases. However, the calibrated model remains not universal, as its accuracy can decrease when environmental conditions or turbine type vary, leading to noticeable discrepancies with LES.

We assessed several coupled and uncoupled mesoscale atmospheric and wave models (WRF, HARMONIE, WRF-SWAN, MIKE 21) using multiple observation types (masts, airborne, and buoy measurements) for 24.-30.07.2021. Results indicate that wind farm parameterizations are essential for accurately representing atmospheric conditions near wind farms and their wakes. Wake effects on significant wave height are minor at buoy locations under the studied conditions, but high-resolution flight data reveal downstream wave impacts as the wake expands vertically. For these situations, models including wind farm effects better capture the sea state. Model performance varies depending on the measurement type, location, and metric. WRF+SWAN provides consistent atmosphere-wave coupling with acceptable accuracy, whereas HARMONIE excels at simulating background conditions.

The growing number and growing size of wind energy projects coupled with the rapid growth in high-performance computing technology are driving researchers toward conducting large-scale simulations of the flow field surrounding entire wind farms. This requires highly parallel-efficient tools, given the large number of degrees of freedom involved in such simulations, and yields valuable insights into farm-scale physical phenomena, such as gravity wave interaction with the wind farm and farm–farm wake interactions. In the current study, we introduce the open-source, finite-volume, large-eddy simulation (LES) code TOSCA (Toolbox fOr Stratified Convective Atmospheres) and demonstrate its capabilities by simulating the flow around a finite-size wind farm immersed in a shallow, conventionally neutral boundary layer (CNBL), ultimately assessing gravity-wave-induced blockage effects. Turbulent inflow conditions are generated using a new hybrid off-line–concurrent-precursor method. Velocity is forced with a novel pressure controller that allows us to prescribe a desired average hub-height wind speed while avoiding inertial oscillations above the atmospheric boundary layer (ABL) caused by the Coriolis force, a known problem in wind farm LES studies. Moreover, to eliminate the dependency of the potential-temperature profile evolution on the code architecture observed in previous studies, we introduce a method that allows us to maintain the mean potential-temperature profile constant throughout the precursor simulation. Furthermore, we highlight that different codes do not predict the same velocity inside the boundary layer under geostrophic forcing owing to their intrinsically different numerical dissipation. The proposed methodology allows us to reduce such spread by ensuring that inflow conditions produced from different codes feature the same hub wind and thermal stratification, regardless of the adopted precursor run time. Finally, validation of actuator line and disk models, CNBL evolution, and velocity profiles inside a periodic wind farm is also presented to assess TOSCA’s ability to model large-scale wind farm flows accurately and with high parallel efficiency.

This study introduces the actuator farm model (AFM), a novel parameterization for simulating wind turbines within large eddy simulations (LESs) of wind farms. Unlike conventional models like the actuator disk (AD) or actuator line (AL), the AFM utilizes a single actuator point at the rotor center and only requires two to three mesh cells across the rotor diameter. Turbine force is distributed to the surrounding cells using a new projection function characterized by an axisymmetric spatial support in the rotor plane and Gaussian decay in the streamwise direction. The spatial support's size is controlled by three parameters: the half-decay radius r1/2, smoothness s, and streamwise standard deviation σ. Numerical experiments on an isolated National Renewable Energy Laboratory (NREL) 5MW wind turbine demonstrate that selecting r1/2=R (where R is the turbine radius), s between 6 and 10, and σ≈Δx/1.6 (where Δx is the grid size in the streamwise direction) yields wake deficit profiles, turbine thrust, and power predictions similar to those obtained using the actuator disk model (ADM), irrespective of horizontal grid spacing down to the order of the rotor radius.Using these parameters, LESs of a small cluster of 25 turbines in both staggered and aligned layouts are conducted at different horizontal grid resolutions using the AFM. Results are compared against ADM simulations employing a spatial resolution that places at least 10 grid points across the rotor diameter. The wind farm is placed in a neutral atmospheric boundary layer (ABL) with turbulent inflow conditions interpolated from a previous simulation without turbines. At horizontal resolutions finer than or equal to R/2, the AFM yields similar velocity, shear stress, turbine thrust, and power as the ADM. Coarser resolutions reveal the AFM's ability to accurately capture power at the non-waked wind farm rows, although it underestimates the power of waked turbines. However, the far wake of the cluster can be predicted well even when the cell size is of the order of the turbine radius.Finally, combining the AFM with a domain nesting method allows us to conduct simulations of two aligned wind farms in a fully neutral ABL and of wind-farm-induced atmospheric gravity waves under a conventionally neutral ABL, obtaining excellent agreement with ADM simulations but with much lower computational cost. The simulations highlight the AFM's ability to investigate the mutual interactions between large turbine arrays and the thermally stratified atmosphere.

The growth in the number and size of wind energy projects in the last decade has revealed structural limitations in the current approach adopted by the wind industry to assess potential wind farm sites. These limitations are the result of neglecting the mutual interaction of large wind farms and the thermally stratified atmospheric boundary layer. While currently available analytical models are sufficiently accurate to conduct site assessments for isolated rotors or small wind turbine clusters, the wind farm's interaction with the atmosphere cannot be neglected for large-size arrays. Specifically, the wind farm displaces the boundary layer vertically, triggering atmospheric gravity waves that induce large-scale horizontal pressure gradients. These perturbations in pressure alter the velocity field at the turbine locations, ultimately affecting global wind farm power production. The implication of such dynamics can also produce an extended blockage region upstream of the first turbines and a favorable pressure gradient inside the wind farm. In this paper, we present the multi-scale coupled (MSC) model, a novel approach that allows the simultaneous prediction of micro-scale effects occurring at the wind turbine scale, such as individual wake interactions and rotor induction, and meso-scale phenomena occurring at the wind farm scale and larger, such as atmospheric gravity waves. This is achieved by evaluating wake models on a spatially heterogeneous background velocity field obtained from a reduced-order meso-scale model. Verification of the MSC model is performed against two large-eddy simulations (LESs) with similar average inflow velocity profiles and a different capping inversion strength, so that two distinct interfacial gravity wave regimes are produced, i.e. subcritical and supercritical. Interfacial waves can produce high blockage in the first case, as they are allowed to propagate upstream. On the other hand, in the supercritical regime their propagation speed is less than their advection velocity, and upstream blockage is only operated by internal waves. The MSC model not only proves to successfully capture both local induction and global blockage effects in the two analyzed regimes, but also captures the interaction between the wind farm and gravity waves, underestimating wind farm power by about only 2 % compared with the LES results. Conversely, wake models alone cannot distinguish between differences in thermal stratification, even if combined with a local induction model. Specifically, they are affected by a first-row overprediction bias that leads to an overestimation of the wind farm power by 13 % to 20 % for the modeled regimes.

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.