scholarly journals ESCOAMENTO NA ESTEIRA DE TURBINAS EÓLICAS: ANÁLISE ESPECTRAL DA TURBULÊNCIA MEDIANTE TESTES EM TÚNEL DE VENTO

2016 ◽  
Vol 38 ◽  
pp. 46
Author(s):  
Adrián Roberto Wittwer ◽  
Rodrigo Dorado ◽  
Gervásio Annes Degrazia ◽  
Acir Mércio Loredo-Souza ◽  
Bardo Ernst Josef Bodmann
Keyword(s):  
Boundary Layer ◽  
Wind Tunnel ◽  
Wind Farm ◽  
Wake Flow ◽  
Mean Velocity ◽  
Velocity Fluctuations ◽  
Velocity Deficit ◽  
Scale Models ◽  
Spectral Evaluation ◽  
Turbine Wake

The interaction between the incident wind and wind turbines in a wind farm causes mean velocity deficit and increased levels of turbulence in the wake. The turbulent flow is characterized by the superposition of the wind turbine wakes. In this work, the technique of turbulence spectral evaluation to reduce scale models in a boundary layer wind tunnel is presented, and different measurements of velocity fluctuations are analyzed. The results allow evaluating the spectrum configuration at different frequency ranges and the differences of the spectral behavior between the incident wind and the turbine wake flow.  

Wakes behind two-dimensional surface obstacles in turbulent boundary layers

1974 ◽  
Vol 64 (3) ◽  
pp. 529-564 ◽  
Author(s):  
J. Counihan ◽  
J. C. R. Hunt ◽  
P. S. Jackson
Keyword(s):  
Boundary Layer ◽  
Wind Tunnel ◽  
Turbulent Boundary Layer ◽  
The Body ◽  
Wake Flow ◽  
Two Dimensional ◽  
Mean Velocity ◽  
Velocity Deficit ◽  
Wind Tunnel Measurements ◽  
The Mean

By making simple assumptions, an analytical theory is deduced for the mean velocity behind a two-dimensional obstacle (of heighth) placed on a rigid plane over which flows a turbulent boundary layer (of thickness δ). It is assumed thath[Gt ] δ, and that the wake can be divided into three regions. The velocity deficit −uis greatest in the two regions in which the change in shear stress is important, a wall region (W) close to the wall and a mixing region (M) spreading from the top of the obstacle. Above these is the external region (E) in which the velocity field is an inviscid perturbation on the incident boundary-layer velocity, which is taken to have a power-law profileU(y) =U∞(y−y1)n/δn, wheren[Gt ] 1. In (M), assuming that an eddy viscosity (=KhU(h)) can be defined for the perturbed flow in terms of the incident boundary-layer flow and that the velocity is self-preserving, it is found thatu(x,y) has the form$\frac{u}{U(h)} = \frac{ C }{Kh^2U^2(h)} \frac{f(n)}{x/h},\;\;\;\; {\rm where}\;\;\;\; \eta = (y/h)/[Kx/h]^{1/(n+2)}$, and the constant which defines the strength of the wake is$C = \int^\infty_0 y^U(y)(u-u_E)dy$, whereu=uE(x, y) asy→ 0 in region (E).In region (W),u(y) is proportional to Iny.By considering a large control surface enclosing the obstacle it is shown that the constant of the wake flow is not simply related to the drag of the obstacle, but is equal to the sum of the couple on the obstacle and an integral of the pressure field on the surface near the body.New wind-tunnel measurements of mean and turbulent velocities and Reynolds stresses in the wake behind a two-dimensional rectangular block on a roughened surface are presented. The turbulent boundary layer is artificially developed by well-established methods (Counihan 1969) in such a way that δ = 8h. These measurements are compared with the theory, with other wind-tunnel measurements and also with full-scale measurements of the wind behind windbreaks.It is found that the theory describes the distribution of mean velocity reasonably well, in particular the (x/h)−1decay law is well confirmed. The theory gives the correct self-preserving form for the distribution of Reynolds stress and the maximum increase of the mean-square turbulent velocity is found to decay downstream approximately as$ (\frac{x}{h})^{- \frac{3}{2}} $in accordance with the theory. The theory also suggests that the velocity deficit is affected by the roughness of the terrain (as measured by the roughness lengthy0) in proportion to In (h/y0), and there seems to be some experimental support for this hypothesis.

Effect of wind turbine nacelle on turbine wake dynamics in large wind farms

2019 ◽  
Vol 869 ◽  
pp. 1-26 ◽  
Author(s):  
Daniel Foti ◽  
Xiaolei Yang ◽  
Lian Shen ◽  
Fotis Sotiropoulos
Keyword(s):  
Wind Turbine ◽  
Coherent Structures ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Practical Significance ◽  
Mean Velocity ◽  
Velocity Deficit ◽  
Farm Scale ◽  
Turbine Wake

Wake meandering, a phenomenon of large-scale lateral oscillation of the wake, has significant effects on the velocity deficit and turbulence intensities in wind turbine wakes. Previous studies of a single turbine (Kang et al., J. Fluid. Mech., vol. 774, 2014, pp. 374–403; Foti et al., Phys. Rev. Fluids, vol. 1 (4), 2016, 044407) have shown that the turbine nacelle induces large-scale coherent structures in the near field that can have a significant effect on wake meandering. However, whether nacelle-induced coherent structures at the turbine scale impact the emergent turbine wake dynamics at the wind farm scale is still an open question of both fundamental and practical significance. We take on this question by carrying out large-eddy simulation of atmospheric turbulent flow over the Horns Rev wind farm using actuator surface parameterisations of the turbines without and with the turbine nacelle taken into account. While the computed mean turbine power output and the mean velocity field away from the nacelle wake are similar for both cases, considerable differences are found in the turbine power fluctuations and turbulence intensities. Furthermore, wake meandering amplitude and area defined by wake meanders, which indicates the turbine wake unsteadiness, are larger for the simulations with the turbine nacelle. The wake influenced area computed from the velocity deficit profiles, which describes the spanwise extent of the turbine wakes, and the spanwise growth rate, on the other hand, are smaller for some rows in the simulation with the nacelle model. Our work shows that incorporating the nacelle model in wind farm scale simulations is critical for accurate predictions of quantities that affect the wind farm levelised cost of energy, such as the dynamics of wake meandering and the dynamic loads on downwind turbines.

scholarly journals Blind test comparison of the performance and wake flow between two in-line wind turbines exposed to different atmospheric inflow conditions

2016 ◽  
Author(s):  
Jan Bartl ◽  
Lars Sætran
Keyword(s):  
Wind Tunnel ◽  
Wind Turbines ◽  
Turbulence Intensity ◽  
Wake Flow ◽  
Detached Eddy Simulation ◽  
Mean Velocity ◽  
Blind Test ◽  
The Mean ◽  
Inlet Conditions ◽  
Inflow Conditions

Abstract. This is a summary of the results of the fourth Blind test workshop which was held in Trondheim in October 2015. Herein, computational predictions on the performance of two in-line model wind turbines as well as the mean and turbulent wake flow are compared to experimental data measured at NTNU's wind tunnel. A detailed description of the model geometry, the wind tunnel boundary conditions and the test case specifications was published before the workshop. Expert groups within Computational Fluid Dynamics (CFD) were invited to submit predictions on wind turbine performance and wake flow without knowing the experimental results at the outset. The focus of this blind test comparison is to examine the model turbines' performance and wake development up until 9 rotor diameters downstream at three different atmospheric inflow conditions. Besides a spatially uniform inflow field of very low turbulence intensity (TI = 0.23 %) as well as high turbulence intensity (TI = 10.0 %), the turbines are exposed to a grid-generated atmospheric shear flow (TI = 10.1 %). Five different research groups contributed with their predictions using a variety of simulation models, ranging from fully resolved Reynolds Averaged Navier Stokes (RANS) models to Large Eddy Simulations (LES). For the three inlet conditions the power and the thrust force of the upstream turbine is predicted fairly well by most models, while the predictions of the downstream turbine's performance show a significantly higher scatter. Comparing the mean velocity profiles in the wake, most models approximate the mean velocity deficit level sufficiently well. However, larger variations between the models for higher downstream positions are observed. The prediction of the turbulence kinetic energy in the wake is observed to be very challenging. Both the LES model and the IDDES (Improved Delayed Detached Eddy Simulation) model, however, are consistently managing to provide fairly accurate predictions of the wake turbulence.

scholarly journals Utility-Scale Wind Turbine Wake Characterization Using Nacelle-Based Long-Range Scanning Lidar

2014 ◽  
Vol 31 (7) ◽  
pp. 1529-1539 ◽  
Author(s):  
Matthew L. Aitken ◽  
Julie K. Lundquist
Keyword(s):  
Wind Turbine ◽  
Long Range ◽  
Wind Farm ◽  
Thrust Coefficient ◽  
Velocity Deficit ◽  
Scanning Lidar ◽  
Range Scanning ◽  
Utility Scale ◽  
Turbine Wake ◽  
Wind Turbine Wake

Abstract To facilitate the optimization of turbine spacing at modern wind farms, computational simulations of wake effects must be validated through comparison with full-scale field measurements of wakes from utility-scale turbines operating in the real atmosphere. Scanning remote sensors are particularly well suited for this objective, as they can sample wind fields over large areas at high temporal and spatial resolutions. Although ground-based systems are useful, the vantage point from the nacelle is favorable in that scans can more consistently transect the central part of the wake. To the best of the authors’ knowledge, the work described here represents the first analysis in the published literature of a utility-scale wind turbine wake using nacelle-based long-range scanning lidar. The results presented are of a field experiment conducted in the fall of 2011 at a wind farm in the western United States, quantifying wake attributes such as the velocity deficit, centerline location, and wake width. Notable findings include a high average velocity deficit, decreasing from 60% at a downwind distance x of 1.8 rotor diameters (D) to 40% at x = 6D, resulting from a low average wind speed and therefore a high average turbine thrust coefficient. Moreover, the wake width was measured to expand from 1.5D at x = 1.8D to 2.5D at x = 6D. Both the wake growth rate and the amplitude of wake meandering were observed to be greater for high ambient turbulence intensity and daytime conditions as compared to low turbulence and nocturnal conditions.

scholarly journals Comparison of Different Driving Modes for the Wind Turbine Wake in Wind Tunnels

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1915
Author(s):  
Bingzheng Dou ◽  
Zhanpei Yang ◽  
Michele Guala ◽  
Timing Qu ◽  
Liping Lei ◽  
...  
Keyword(s):  
Wind Tunnel ◽  
Wind Turbine ◽  
Wind Farm ◽  
Relevant Information ◽  
Operating Conditions ◽  
Wind Tunnels ◽  
Driving Mode ◽  
Significant Difference ◽  
The Mean ◽  
Turbine Wake

The wake of upstream wind turbine is known to affect the operation of downstream turbines and the overall efficiency of the wind farm. Wind tunnel experiments provide relevant information for understanding and modeling the wake and its dependency on the turbine operating conditions. There are always two main driving modes to operate turbines in a wake experiment: (1) the turbine rotor is driven and controlled by a motor, defined active driving mode; (2) the rotor is driven by the incoming wind and subject to a drag torque, defined passive driving mode. The effect of the varying driving mode on the turbine wake is explored in this study. The mean wake velocities, turbulence intensities, skewness and kurtosis of the velocity time-series estimated from hot-wire anemometry data, were obtained at various downstream locations, in a uniform incoming flow wind tunnel and in an atmospheric boundary layer wind tunnel. The results show that there is not a significant difference in the mean wake velocity between these two driving modes. An acceptable agreement is observed in the comparison of wake turbulence intensity and higher-order statistics in the two wind tunnels.

scholarly journals An Analytical Model for the Effect of Vertical Wind Veer on Wind Turbine Wakes

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1838 ◽  
Author(s):  
Mahdi Abkar ◽  
Jens Sørensen ◽  
Fernando Porté-Agel
Keyword(s):  
Wind Turbine ◽  
Input Parameter ◽  
Mass Conservation ◽  
Wake Flow ◽  
Mean Velocity ◽  
Gaussian Shape ◽  
Velocity Deficit ◽  
Proposed Model ◽  
The Mean ◽  
Wake Model

In this study, an analytical wake model for predicting the mean velocity field downstream of a wind turbine under veering incoming wind is systematically derived and validated. The new model, which is an extended version of the one introduced by Bastankhah and Porté-Agel, is based upon the application of mass conservation and momentum theorem and considering a skewed Gaussian distribution for the wake velocity deficit. Particularly, using a skewed (instead of axisymmetric) Gaussian shape allows accounting for the lateral shear in the incoming wind induced by the Coriolis force. This analytical wake model requires only the wake expansion rate as an input parameter to predict the mean wake flow downstream. The performance of the proposed model is assessed using the large-eddy simulation (LES) data of a full-scale wind turbine wake under the stably stratified condition. The results show that the proposed model is capable of predicting the skewed structure of the wake downwind of the turbine, and its prediction for the wake velocity deficit is in good agreement with the high-fidelity simulation data.

scholarly journals FLOWSTAR-Energy: a high resolution wind farm wake model

2016 ◽  
Author(s):  
Amy Stidworthy ◽  
David Carruthers
Keyword(s):  
Boundary Layer ◽  
Wind Turbine ◽  
Complex Terrain ◽  
Wind Farm ◽  
Wind Farms ◽  
Measured Data ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Wake Model ◽  
Turbine Wake

Abstract. A new model, FLOWSTAR-Energy, has been developed for the practical calculation of wind farm energy production. It includes a semi-analytic model for airflow over complex surfaces (FLOWSTAR) and a wind turbine wake model that simulates wake-wake interaction by exploiting some similarities between the decay of a wind turbine wake and the dispersion of plume of passive gas emitted from an elevated source. Additional turbulence due to the wind shear at the wake edge is included and the assumption is made that wind turbines are only affected by wakes from upstream wind turbines. The model takes account of the structure of the atmospheric boundary layer, which means that the effect of atmospheric stability is included. A marine boundary layer scheme is also included to enable offshore as well as onshore sites to be modelled. FLOWSTAR-Energy has been used to model three different wind farms and the predicted energy output compared with measured data. Maps of wind speed and turbulence have also been calculated for two of the wind farms. The Tjaæreborg wind farm is an onshore site consisting of a single 2 MW wind turbine, the NoordZee offshore wind farm consists of 36 V90 VESTAS 3 MW turbines and the Nysted offshore wind farm consists of 72 Bonus 2.3 MW turbines. The NoordZee and Nysted measurement datasets include stability distribution data, which was included in the modelling. Of the two offshore wind farm datasets, the Noordzee dataset focuses on a single 5-degree wind direction sector and therefore only represents a limited number of measurements (1,284); whereas the Nysted dataset captures data for seven 5-degree wind direction sectors and represents a larger number of measurements (84,363). The best agreement between modelled and measured data was obtained with the Nysted dataset, with high correlation (0.98 or above) and low normalised mean square error (0.007 or below) for all three flow cases. The results from Tjæreborg show that the model replicates the Gaussian shape of the wake deficit two turbine diameters downstream of the turbine, but the lack of stability information in this dataset makes it difficult to draw conclusions about model performance. One of the key strengths of FLOWSTAR-Energy is its ability to model the effects of complex terrain on the airflow. However, although the airflow model has been previously compared extensively with flow data, it has so far not been used in detail to predict energy yields from wind farms in complex terrain. This will be the subject of a further validation study for FLOWSTAR-Energy.

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