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The wake flow characteristics downstream from the rotor blade of small Horizontal Axis Wind Turbine (HAWT) have been investigated using Particle Image Velocimetry and an Actuator Disk Method developed in OpenFOAM at low Reynolds number of Re ≈ 47 000 for four TSR values of 6, 5.5, 5 and 4.5. The wind tunnel measured mean velocity distributions along the rotor radius, for two TSRs of 5.5 and 4.5 corresponding respectively to the optimum operating point of the HAWT and the rotational augmentation effect appearance, have been compared quantitatively at five downstream planes of 0.225D, 0.45D, 0.9D, 1.8D and 2.4D in order to investigate the near wake evolution and the nacelle effect on the flow field characteristics. At a distance of 0.225D downstream from the rotor mostly affected by the rotor blade predicted performances, the 3D corrections for stall delay phenomenon consideration have provided noticeable improvements for the predicted velocity field near the root of the rotor blade especially the axial and tangential velocity components. However, by getting further from the rotor i.e. at a distance of 2.4D, the flow field is mostly affected by the nacelle and the velocity field has loosed its structured distribution which has been explained as the wake expansion occurrence. The near wake upper limit has been located at a distance of 1.8 Z D ≤ 2.4 downstream from the rotor. Also, the turbulent kinetic energy and the specific turbulence dissipation fields have been investigated qualitatively where it has been demonstrated that the rotor blade tip is the principal element causing the turbulence production in contrary to the nacelle which causes the turbulence dissipation.

This paper aims at investigating the effect of the inflow turbulence on the aerodynamics and the wake instabilities of the wind turbine. The improved delayed detached eddy simulation (IDDES) combined with the overset grid method is performed to model the NREL S826 airfoil wind turbine at design operating condition ( λ = 6.0 ). The mean loads of power coefficient ( C P ) and thrust coefficient ( C T ) show good agreement with existing experiments data. The normalized mean velocity U / U r e f profile and normalized turbulent kinetic energy k / U 2 r e f profile within the wake is also consistent with the experiments data, indicating a fairly accurate predictions of the wake turbulence. The detailed analysis of wake vortex structure, mean Reynold's stress distribution and PSD spectrum of turbulent kinetic energy are utilized to explore the effect of incoming turbulence on the wind turbine performance and the mechanism of the wake instability. The incoming turbulence and wind shear may promote the tip vortex instability and accelerate wake recovery, thus more attention should be paid to the design procedure and layout of wind farms.

A control-oriented large eddy simulation (LES) code is developed to predict wind turbine wake and is validated by laboratory-scale and utility-scale wind turbines. Firstly, the wind turbine control algorithms including torque, pitch and yaw controls are implemented in LES with Actuator Line Model (ALM). Two sets of numerical simulations under uniform inflow with time-varying wind speeds and wind directions are performed. The simulated thrust and torque forces agree well with those calculated by the aeroelastic code, FAST. The predicted mean velocity and turbulence intensity in the wake of a laboratory-scale wind turbine show favorable agreement with those measured in wind tunnel experiments. The blade rotation induced dynamic effects on wake flows and rotor loading are well reproduced by ALM. Finally, numerical simulations for a utility-scale wind turbine are conducted, in which the ambient flow filed with time-varying wind speeds and wind directions are generated based on the wind condition measured by the Met-mast and the Coriolis force effect is incorporated as well. The predicted time series of control signals and power production agree well with the wind turbine SCADA data and the predicted mean wind speed in the wake region show favorable agreement with those measured by Doppler scanning LiDAR.

The aerodynamic performance and wake dynamics of wind turbines are known to be affected by platform surge motion. This study utilized an improved delayed detached eddy simulation combined with the overset grid method to investigate the effect of surge motion amplitudes and periods (frequencies) on the aerodynamics and wake instabilities of wind turbines. The effect of the Reynolds number and azimuthal increment sensitivity analysis (time step) on the wind turbine loads and flow fields were investigated in detail. A comparison of the mean loads of the power coefficient and thrust coefficient between the numerical simulation and experiment with a consistent Reynolds number shows that the grid and simulation parameters used in this study can accurately capture the flow field and aerodynamic performance of the wind turbine. A detailed analysis of the flow fields, wake vortex structure, and mean Reynolds stress distribution was utilized to explore the effect of surge motion amplitudes and periods on the wind turbine performance, as well as the mechanism of the wake instability. Our results indicate that the surge motion can improve the efficiency of power generation and promote wake instabilities and vortex interactions. Meanwhile, surge motion amplitudes and frequencies have a positive effect on the wake recovery.

This paper investigates the diagonal inflow effect on the wake characteristics of a HAWT (Horizontal Axis Wind Turbine) in a wind farm. In this study, a HAWT with the generator capacity of 30 kW and the rotor diameter of 10.0 m is used. Firstly, the effect of pitch angle on the power and thrust performances of wind turbine are investigated through field experiment. On this basis, the wake characteristics of the HAWT are examined with different wind directions and pitch angles. The velocity field is measured by the ultrasonic anemometers, the three-cup type anemometer and the wind vane. Finally, the annual power generation of the downstream wind turbine is predicted by different Gaussian models. As a result, the maximum power coefficients Cp are 0.31, 0.33 and 0.27, corresponding to the tip speed ratios λ = 7.5, 7.4 and 6.8 and the pitch angles β = 0°, 2° and 4°, respectively. The standard deviations of wind direction are divided into four regions between σθ = 5° and 20°. The maximum deficit values of the wake velocity decreases from 0.27 to 0.16, and the full wake widths at half maximum Dr increases from 1.35 to 1.71 with σθ increasing from 5°∼9°–14°∼20°. The amount of annual energy production predicted by the wind tunnel experiment model is about 51 % of the field experiment model. This study provides a better understanding of the wake characteristics and a guiding significance for the layout optimization of wind farms.

The physical characteristics of a wind field significantly influence the energy produced by a wind farm because they are related to the available kinetic energy in the wake region created by wind turbine generators; they also affect wake recovery. To understand the correlation between the external environment and the amount of energy produced, a study based on the interaction between the power characteristics of wind turbines and atmospheric factors is required. In this study, the distribution characteristics of the turbulence dissipation rate (e) with the regime were analyzed after classifying atmospheric stability by regimes. In addition, the normalized power of wind turbines was analyzed, and the difference in annual energy production (AEP) with the regime was presented. The AEP showed the best value in the slightly unstable regime and the lowest value in the stable regime. Finally, considering the annual mean wind speed at the study site, a difference of approximately 5%–7% was observed between the AEP under the neutral atmospheric regime and other atmospheric regimes. Further, this study highlights the impact of atmospheric stability regimes and the turbulence dissipation rate on energy production, thereby indicating the necessity of integrating these factors to predict the AEP of wind farms.

Using the random forest (RF) algorithm, this study presented a key parameter to characterize the mean wake of H-rotor VAWTs while modelling the wake. First, the RF algorithm was used to establish the regression relationship between the average wake velocity distribution and the rotor features. Next, the feature crosses method was combined with the RF algorithm to analyze the interaction and importance of the inputs. It was found that the normalized importance of a synthetic feature in wake modelling occupied a considerable significance, reaching 0.884 out of 1. The RF wake model with this parameter as the only input feature could successfully reconstruct the wake. It was found that this feature may reflect the ability of incident wind passing through the operating rotor and played a decisive role in the wake velocity distribution, including initial velocity deficit and wake recovery rate. The universality of this parameter was proved through cases analysis of wind turbines under different sizes and operating conditions. The study of the wake field is important for the modelling of the H-rotor VAWT wake field, and hence affects the optimal configuration of the wind farm.

Offshore wind turbines (OWTs) are used to harvest wind energy on the seas. Their foundations must bear wind and wave loads simultaneously, resulting in greater cost than onshore wind turbines. To harvest more energy and reduce the total cost, a wind-wave hybrid energy system is proposed in this study. Oscillating wave surge converters (OWSCs) are used to harvest wave energy and protect OWTs from waves. The reduced wave impact on the wind turbine foundation reduces the total cost. Wind turbines and converters are independent; even existing wind farms can convert to hybrid farms. An analytical wave wake model for the converter is presented to optimize the layout of the devices. The wave wake of the wind turbine foundations is also examined. An innovative GPU-accelerated multi-objective differential evolution algorithm is used to globally optimize the device layout in actual environmental conditions. The optimized hybrid farm has 37.75% greater energy output and 43.65% less wave loads on the wind turbine foundations than wind-only farms. The wave wake model for the converter has an accuracy of 94.73%. The GPU-accelerated codes can run 1136 times faster than the CPU-based codes, and are available to other researchers.

In this study, the combined impact of atmospheric stability, speed-up and wake effect on the production of pairs of wind turbines in a wind farm located on complex terrain was evaluated considering the full wake effect wind direction. Prior analysis of the climate and atmospheric stability at the wind farm and the power production was carried out. It was found that the downwind turbine performs better than the upwind turbine at dawn and the atmospheric stability does not affect the ratio of power production of the wind turbine pairs. When the wind farm data were compared with the speed-up and wake effects models, it was observed that the effects are reversed, that is, when the speed-up model had small errors the wake effects model errors were large. This behavior was observed in the early hours of the morning while in the daytime the wake effects model had small errors and the speed-up model errors were large, indicating that the two phenomena are dominant at different times of the day.

A challenging topic arising in dynamic wind turbine wake is modeling, especially the low-order approximation. The central problem is the fact that it has high-dimensional and nonlinear wake characteristics. In this paper, a Koopman-linear flow estimator is designed according to the Koopman operator theory. Different from the conventional flow reconstruction with the linear stochastic estimation method, a dynamic state-space model with physical states is constructed. The wake dynamics are approximated using a limited number of measurable physical parameters by the dynamic part; then, the full wake flow is reconstructed from the low-order states by the estimation part. The flow estimator is designed into three different forms following Extended Dynamic Mode Decomposition (EDMD) method. Each form has its unique advantages. Precisely, probe sensors are placed in the studied space and provide direct information of the wake, and a few in-directly physical parameters are also included. Nonlinear integer programming is further adopted using a heuristic optimization algorithm, by which the sensor configurations are optimized. Comparisons with the standard Dynamic Mode Decomposition (DMD)-based wake model are adopted in time domain and frequency domain to verify the effectiveness of the proposed flow estimators. The results show acceptable accuracy in typical modeling cases and maintain good estimation accuracy when the measurement noises are involved. Finally, the proposed Koopman-linear flow estimator is compared with related stochastic estimation methods, in which the connections of the proposed estimator with stochastic ones are also discussed.

Near wake flow has a great effect on the technology of horizontal-axis wind turbines. In this paper, the effect of the nacelle and tower on the near wake of a small-scale horizontal-axis wind turbine is studied by a numerical method. A large eddy simulation is used to obtain the time-varying wake flow. There are tip vortices, hub vortices, and blade and tower shedding vortices active in the near wake. Dynamic mode decomposition (DMD) is employed to analyse the features of these unsteady vorticities in both an absolute frame and a frame static relative to the rotor. The DMD results in the absolute frame illustrate the downstream motion of the tip vortices. In the relative frame, the results indicate that the disturbances distort the helical vortex filament and finally lead to a breakdown of the tip vortices in the case without a nacelle and tower. However, considering the effect of the nacelle and tower, the shedding vortices of the nacelle and tower directly break the tip vortices. The interaction between the tip vortices and the tower shedding vortices not only dominates the breakdown of the tip vortices but also promotes the instability of the tip vortices. This condition leads to an earlier wake recovery and influences the energy extraction and fatigue loads of the downstream turbines.

In this work, CFD simulation along with the site complete wind rose are used for the first time to estimate a key magnitude for the project development of a wind farm, the capacity factor, which is directly related with the energy yield. The large computational cost that restricted CFD simulation for such a task is drastically reduced by means of a novel interpolation-extrapolation methodology, requiring the simulation of only three inlet velocities for each wind rose sector. This methodology is based on the velocity at met mast and results of the reference velocity for each wind turbine from pre-simulated cases, which are then used to compute a wide range of cases not explicitly simulated. A comparative study is carried out, in which the measured capacity factor of an onshore wind farm in the Argentinean Patagonia is compared against different solution approaches. In the particular case of this wind farm, it is found that the separate effects of wakes and terrain produce errors in the opposite sense, and results very close to the measured value are achieved when both are considered. Also, the increase in the number of simulated direction sectors from 16 to 32 does not significantly change the results.

Vertical axis wind turbines (VAWTs) have distinctive attributes, for example, exceptional performance in built environments and high packing density in wind farms. This study reviews recent research in the wake aerodynamics of H-rotor VAWTs. The general mathematical expressions for the analysis of the rotor aerodynamics of VAWTs are presented, followed by a classic aerodynamic model. Velocity deficit and turbulence increase are common features of wind turbine wakes, and the deep dynamic stall of operational VAWTs further complicates their wakes. This study systematically compiles the investigations of the wake flow fields and vortical structures of VAWTs. In these investigations, a wide range of research methods have been used, for instance, wind tunnel testing, particle image velocimetry (PIV) tests, field tests, computational fluid dynamics (CFD) simulations, and theoretical development. The results of these studies suggest that the wakes of VAWTs are characterized by distinctive features, such as strong asymmetry and counter-rotating vortical motion. Based on these characteristics, researchers have developed various wake models. The proposed wake models can be used to design standalone VAWTs or layouts for multiple VAWTs. Finally, this paper provides discussions and suggestions pertaining to the investigations of the wakes of VAWTs.

As a continuation of authors’ previous work, this work extends and hackles the numerical method for wind turbine wakes based on the vortex method, and proposes the Lagrangian actuator model (LAM) which is used for the representation of the wind turbine rotor under the Lagarangian framework. This paper provides two examples of the LAM, the Lagrangian actuator line (LAL) model and the Lagrangian actuator disc (LAD) model, and constructs matching numerical methods for wake predictions respectively. Those methods have high computation efficiency, and the results coincide with the wind tunnel test data well. Moreover, based on that, a vorticity description framework centered on vortex geometric structures is established to illustrate wind turbine wake phenomena and explore the wake evolution mechanism.

In order to improve the total efficiency of power generation in a wind farm, this paper investigated the effect of different operating conditions of the upstream wind turbine on the power of the downstream wind turbine by Gaussian wake model. A three-bladed Horizontal Axis Wind Turbine with the generator capacity of 30 kW was used. Firstly, Gaussian model for evaluating the wake flow was developed, which fit well with experiment data. Then, the influence of the operating condition of the upstream turbine on the downstream turbine performance was estimated by calculating the energy amount resulted from the velocity deficit of the wake. Finally, the effect of the installation position of the downstream wind turbine was analyzed. In consequence, the total power coefficient Ptotal of the wind turbines was relatively higher when the upstream turbine was at tip speed ratio λ = 6.4 and pitch angle β = 2° among different test conditions. Meanwhile, the Ptotal showed the maximum value when the downstream turbine was installed at the streamwise location x/D = 2.0 and lateral offset location y/R = 1.5 when the inflow turbulence intensity is 0.22≤TIref ≤ 0.35. This paper provided a better guidance for optimizing the wind turbine layout and improving the total power output of the entire wind farm.

A novel bio-inspired V-shaped layout is proposed to enhance energy conversion performance for VIV hydrokinetic energy converters. The layout method is inspired by wild geese flying in a V-formation. In this paper, 2-dimensional RANS equations with the SST k- ω turbulence model are used to simulate the VIV responses at various distances. The numerical method is verified by comparing with experiments. Two-cylinder and five-cylinder V-shaped layout are investigated separately to analyze multi-cylinder interactions. The main conclusions are: (1) The VIV response of the upstream cylinder is suppressed due to the blockage effect when at small distance. (2) The energy recovery area of downstream cylinders is identified and located on the edge of the wake. (3) In the energy recovery area, the enhancement mechanism of this novel layout is the upstream and downstream vortices coalesce, resulting in a significant increase in lift force on the downstream cylinder. (4) The converted power of downstream cylinders in the energy recovery area is increased by up to 6.3%–8.4%. Future work is directed to optimize the distance among the cylinders for optimal energy conversion.