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

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Experimental Setup

_{hub}, where ω is the angular speed of the turbine rotor and u

_{hub}is the incoming velocity in hub height) for the maximum power output were prescribed, and were observed to be λ = 4 in both the UIF and the ABL conditions.

#### 2.1. UIF Wind Tunnel

_{hub}), was less than 1%.

#### 2.2. ABL Wind Tunnel

^{*}/0.4)ln(z/z

_{0}) with u

^{*}= 0.243 m/s and z

_{0}= 0.01 mm, and a power law with an exponent of 0.1 for u(z) = u

_{hub}(z/z

_{hub})

^{0.1}, where z is the vertical location from the tunnel floor. The incoming mean wind velocity and turbulence intensity profile were reported in Dou et al. [47].

## 3. Description of the Driving Modes

#### 3.1. Passive Driving Mode

#### 3.2. Active Driving Mode

## 4. Results and Discussions

#### 4.1. Basic Characteristics of the Wake Effect

_{p}= 0.342 and C

_{P}= 0.277 at about λ = 4 under the UIF and the ABL conditions, respectively.

#### 4.2. Linear Comparison

_{PDM}and u

_{ADM}are the mean wake velocities in PDM and ADM, respectively, I

_{PDM}and I

_{ADM}are the wake turbulence intensities in PDM and ADM, respectively, a and a’ are the fitting parameters to be determined.

^{2}hereafter denotes the coefficient of determination for the linear regression. It is important to stress that the fitting parameter a = 1 in Equation (1) represents the same measured mean wake velocities in the two driving modes. As it can be observed in Figure 12, the fitting parameter a is very close to 1, which means the measured wake velocities in ADM is approximately the same as that in PDM.

^{2}) of the fitting results is close to but less than 1. These coefficients of determination are related to the intrinsic variability in the turbine wake, potentially due to large-scale oscillations as wake meandering or, in the near-wake, to the skewness associated with the rotational direction of the blade, as also noticed by Howard et al. [10].

#### 4.3. Effect of the Tip Speed Ratio

^{2}) is observed to be relatively smaller under large tip speed ratio conditions as compared with that under small tip speed ratio conditions. Relatively large angular speeds and corresponding thrust coefficients (see Dou et al. [45]) are expected to enhance the velocity deficit, the shear layer around the rotor, and the instabilities governing the wake near the tips. In addition, any slight yaw misalignment would affect the coefficient of determination in the fitting results, even though it would only emerge as a deviation in the coefficients of Equations (1) and (2).

#### 4.4. Experimental Uncertainty under the UIF Condition

_{ADM,r}and I

_{ADM,r}are the mean wake velocity and turbulence intensity, respectively, during the repeated experiment. A small coefficient of determination R

^{2}is observed in both the linear fitting results in varying driving modes (Figure 18a,c) and in the baseline (Figure 18b,d), although a little smaller coefficient of determination is found in the wake turbulence intensities in the varying driving mode (Figure 18c), as compared with the baseline (Figure 18d). Therefore, it can be reasonably deduced that the coefficients of determination in the relationship fitting results (Equations (1) and (2)) for the mean wake velocity and turbulence intensity under the UIF condition are not predominantly caused by the change in the driving mode, but likely caused by the variability within the same mode, the experimental uncertainty, and the statistical convergence.

#### 4.5. Statistical Analysis

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Chamorro, L.P.; Porté-Agel, F. A Wind-Tunnel Investigation of Wind-Turbine Wakes: Boundary-Layer Turbulence Effects. Bound. Layer Meteorol.
**2009**, 132, 129–149. [Google Scholar] [CrossRef] [Green Version] - Adaramola, M.; Krogstad, P.Å. Experimental investigation of wake effects on wind turbine performance. Renew. Energy
**2011**, 36, 2078–2086. [Google Scholar] [CrossRef] - Hansen, K.S.; Barthelmie, R.; Jensen, L.E.; Sommer, A. The impact of turbulence intensity and atmospheric stability on power deficits due to wind turbine wakes at Horns Rev wind farm. Wind Energy
**2011**, 15, 183–196. [Google Scholar] [CrossRef] [Green Version] - Bossuyt, J.; Howland, M.F.; Meneveau, C.; Meyers, J. Measurement of unsteady loading and power output variability in a micro wind farm model in a wind tunnel. Exp. Fluids
**2016**, 58, 1. [Google Scholar] [CrossRef] [Green Version] - Tian, W.; Ozbay, A.; Hu, H. An experimental investigation on the wake interferences among wind turbines sited in aligned and staggered wind farms. Wind Energy
**2017**, 21, 100–114. [Google Scholar] [CrossRef] - Gil, M.D.P.; Gomis-Bellmunt, O.; Sumper, A.; Bergas-Jane, J. Power generation efficiency analysis of offshore wind farms connected to a SLPC (single large power converter) operated with variable frequencies considering wake effects. Energy
**2012**, 37, 455–468. [Google Scholar] - Hong, J.; Guala, M.; Chamorro, L.P.; Sotiropoulos, F. Probing wind-turbine/atmosphere interactions at utility scale: Novel insights from the EOLOS wind energy research station. J. Physics: Conf. Ser.
**2014**, 524, 012001. [Google Scholar] [CrossRef] [Green Version] - Nemes, A.; Dasari, T.; Hong, J.; Guala, M.; Coletti, F. Snowflakes in the atmospheric surface layer: Observation of particle–turbulence dynamics. J. Fluid Mech.
**2017**, 814, 592–613. [Google Scholar] [CrossRef] - Hu, H.; Yang, Z.; Sarkar, P. Dynamic wind loads and wake characteristics of a wind turbine model in an atmospheric boundary layer wind. Exp. Fluids
**2011**, 52, 1277–1294. [Google Scholar] [CrossRef] - Howard, K.B.; Singh, A.; Sotiropoulos, F.; Guala, M. On the statistics of wind turbine wake meandering: An experimental investigation. Phys. Fluids
**2015**, 27, 075103. [Google Scholar] [CrossRef] - Ali, N.; Kadum, H.F.; Cal, R.B. Focused-based multifractal analysis of the wake in a wind turbine array utilizing proper orthogonal decomposition. J. Renew. Sustain. Energy
**2016**, 8, 063306. [Google Scholar] [CrossRef] [Green Version] - Annoni, J.; Howard, K.; Seiler, P.; Guala, M. An experimental investigation on the effect of individual turbine control on wind farm dynamics. Wind Energy
**2015**, 19, 1453–1467. [Google Scholar] [CrossRef] - Stevens, R.J.A.M.; Meneveau, C. Flow Structure and Turbulence in Wind Farms. Annu. Rev. Fluid Mech.
**2017**, 49, 311–339. [Google Scholar] [CrossRef] - Göçmen, T.; Van Der Laan, P.; Réthoré, P.-E.; Peña, A.; Larsen, G.; Ott, S. Wind turbine wake models developed at the technical university of Denmark: A review. Renew. Sustain. Energy Rev.
**2016**, 60, 752–769. [Google Scholar] [CrossRef] [Green Version] - Dou, B.; Guala, M.; Lei, L.; Zeng, P. Wake model for horizontal-axis wind and hydrokinetic turbines in yawed conditions. Appl. Energy
**2019**, 242, 1383–1395. [Google Scholar] [CrossRef] - Yang, X.; Kang, S.; Sotiropoulos, F. Computational study and modeling of turbine spacing effects in infinite aligned wind farms. Phys. Fluids
**2012**, 24, 115107. [Google Scholar] [CrossRef] - Foti, D.; Yang, X.; Guala, M.; Sotiropoulos, F. Wake meandering statistics of a model wind turbine: Insights gained by large eddy simulations. Phys. Rev. Fluids
**2016**, 1, 4. [Google Scholar] [CrossRef] - Medici, D.; Alfredsson, P.H. Measurements on a wind turbine wake: 3D effects and bluff body vortex shedding. Wind Energy
**2006**, 9, 219–236. [Google Scholar] [CrossRef] - Cal, R.B.; Lebrón, J.; Castillo, L.; Kang, H.S.; Meneveau, C. Experimental study of the horizontally averaged flow structure in a model wind-turbine array boundary layer. J. Renew. Sustain. Energy
**2010**, 2, 013106. [Google Scholar] [CrossRef] - Iungo, G.V. Experimental characterization of wind turbine wakes: Wind tunnel tests and wind LiDAR measurements. J. Wind Eng. Ind. Aerodyn.
**2016**, 149, 35–39. [Google Scholar] [CrossRef] - Krogstad, P.Å.; Lund, J.A. An experimental and numerical study of the performance of a model turbine. Wind Energy
**2011**, 15, 443–457. [Google Scholar] [CrossRef] - Lignarolo, L.E.; Ragni, D.; Krishnaswami, C.; Chen, Q.; Ferreira, C.S.; Van Bussel, G. Experimental analysis of the wake of a horizontal-axis wind-turbine model. Renew. Energy
**2014**, 70, 31–46. [Google Scholar] [CrossRef] - Ryi, J.; Rhee, W.; Hwang, U.C.; Choi, J.-S. Blockage effect correction for a scaled wind turbine rotor by using wind tunnel test data. Renew. Energy
**2015**, 79, 227–235. [Google Scholar] [CrossRef] - Schumann, H.; Pierella, F.; Sætran, L. Experimental Investigation of Wind Turbine Wakes in the Wind Tunnel. Energy Procedia
**2013**, 35, 285–296. [Google Scholar] [CrossRef] [Green Version] - Pierella, F.; Saetran, L.; Sætran, L. Wind tunnel investigation on the effect of the turbine tower on wind turbines wake symmetry. Wind Energy
**2017**, 20, 1753–1769. [Google Scholar] [CrossRef] - Li, Q.; Murata, J.; Endo, M.; Maeda, T.; Kamada, Y. Experimental and numerical investigation of the effect of turbulent inflow on a Horizontal Axis Wind Turbine (part II: Wake characteristics). Energy
**2016**, 113, 1304–1315. [Google Scholar] [CrossRef] - Li, Q.; Murata, J.; Endo, M.; Maeda, T.; Kamada, Y. Experimental and numerical investigation of the effect of turbulent inflow on a Horizontal Axis Wind Turbine (Part I: Power performance). Energy
**2016**, 113, 713–722. [Google Scholar] [CrossRef] - Li, Q.; Kamada, Y.; Maeda, T.; Murata, J.; Yusuke, N. Effect of turbulence on power performance of a Horizontal Axis Wind Turbine in yawed and no-yawed flow conditions. Energy
**2016**, 109, 703–711. [Google Scholar] [CrossRef] - Xie, W.; Zeng, P.; Lei, L. Wind tunnel testing and improved blade element momentum method for umbrella-type rotor of horizontal axis wind turbine. Energy
**2017**, 119, 334–350. [Google Scholar] [CrossRef] - Xie, W.; Zeng, P.; Lei, L. Wind tunnel experiments for innovative pitch regulated blade of horizontal axis wind turbine. Energy
**2015**, 91, 1070–1080. [Google Scholar] [CrossRef] - Tian, W.; Ozbay, A.; Hu, H. Effects of incoming surface wind conditions on the wake characteristics and dynamic wind loads acting on a wind turbine model. Phys. Fluids
**2014**, 26, 125108. [Google Scholar] [CrossRef] [Green Version] - Wang, Z.; Özbay, A.; Tian, W.; Hu, H. An experimental study on the aerodynamic performances and wake characteristics of an innovative dual-rotor wind turbine. Energy
**2018**, 147, 94–109. [Google Scholar] [CrossRef] - Bastankhah, M.; Porté-Agel, F. Experimental and theoretical study of wind turbine wakes in yawed conditions. J. Fluid Mech.
**2016**, 806, 506–541. [Google Scholar] [CrossRef] - Odemark, Y.; Fransson, J. The stability and development of tip and root vortices behind a model wind turbine. Exp. Fluids
**2013**, 54, 9. [Google Scholar] [CrossRef] - Zhang, W.; Markfort, C.D.; Porté-Agel, F. Near-wake flow structure downwind of a wind turbine in a turbulent boundary layer. Exp. Fluids
**2011**, 52, 1219–1235. [Google Scholar] [CrossRef] [Green Version] - Talavera, M.; Shu, F. Experimental study of turbulence intensity influence on wind turbine performance and wake recovery in a low-speed wind tunnel. Renew. Energy
**2017**, 109, 363–371. [Google Scholar] [CrossRef] - Howard, K.B.; Chamorro, L.P.; Guala, M. A Comparative Analysis on the Response of a Wind-Turbine Model to Atmospheric and Terrain Effects. Bound. Layer Meteorol.
**2015**, 158, 229–255. [Google Scholar] [CrossRef] - Monteiro, J.; Silvestre, M.; Piggott, H.; André, J.C. Wind tunnel testing of a horizontal axis wind turbine rotor and comparison with simulations from two Blade Element Momentum codes. J. Wind Eng. Ind. Aerodyn.
**2013**, 123, 99–106. [Google Scholar] [CrossRef] - Chamorro, L.P.; Guala, M.; Arndt, R.; Sotiropoulos, F. On the evolution of turbulent scales in the wake of a wind turbine model. J. Turbul.
**2012**, 13, N27. [Google Scholar] [CrossRef] - Araya, D.B.; Dabiri, J.O. A comparison of wake measurements in motor-driven and flow-driven turbine experiments. Exp. Fluids
**2015**, 56, 7. [Google Scholar] [CrossRef] - Le, T.Q.; Lee, K.-S.; Park, J.-S.; Ko, J.H. Flow-driven rotor simulation of vertical axis tidal turbines: A comparison of helical and straight blades. Int. J. Nav. Arch. Ocean Eng.
**2014**, 6, 257–268. [Google Scholar] [CrossRef] [Green Version] - Mehta, D.; Van Zuijlen, A.; Koreň, B.; Holierhoek, J.; Bijl, H. Large Eddy Simulation of wind farm aerodynamics: A review. J. Wind Eng. Ind. Aerodyn.
**2014**, 133, 1–17. [Google Scholar] [CrossRef] - Wang, Z.; Tian, W.; Hu, H. A Comparative study on the aeromechanic performances of upwind and downwind horizontal-axis wind turbines. Energy Convers. Manag.
**2018**, 163, 100–110. [Google Scholar] [CrossRef] - Iungo, G.V.; Viola, F.; Camarri, S.; Porté-Agel, F.; Gallaire, F. Linear stability analysis of wind turbine wakes performed on wind tunnel measurements. J. Fluid Mech.
**2013**, 737, 499–526. [Google Scholar] [CrossRef] [Green Version] - Dou, B.; Guala, M.; Lei, L.; Zeng, P. Experimental investigation of the performance and wake effect of a small-scale wind turbine in a wind tunnel. Energy
**2019**, 166, 819–833. [Google Scholar] [CrossRef] - Guo, J.; Zeng, P.; Lei, L. Performance of a straight-bladed vertical axis wind turbine with inclined pitch axes by wind tunnel experiments. Energy
**2019**, 174, 553–561. [Google Scholar] [CrossRef] - Dou, B.; Guala, M.; Zeng, P.; Lei, L. Experimental investigation of the power performance of a minimal wind turbine array in an atmospheric boundary layer wind tunnel. Energy Convers. Manag.
**2019**, 196, 906–919. [Google Scholar] [CrossRef] - Mikkelsen, K. Effect of Free Stream Turbulence on Wind Turbine Performance. Master’s Thesis, Norwegian University of Science and Technology, Trondheim, Norway, 2013. [Google Scholar]
- She, Z.-S.; Jackson, E.; Orszag, S.A. Scale-dependent intermittency and coherence in turbulence. J. Sci. Comput.
**1988**, 3, 407–434. [Google Scholar] [CrossRef] - Aubrun, S.; Loyer, S.; Hancock, P.E.; Hayden, P. Wind turbine wake properties: Comparison between a non-rotating simplified wind turbine model and a rotating model. J. Wind Eng. Ind. Aerodyn.
**2013**, 120, 1–8. [Google Scholar] [CrossRef]

**Figure 2.**(

**a**) Photograph of the turbine model and hot-wire probe in the SAFL (St. Anthony Falls Laboratory) wind tunnel; (

**b**) corresponding schematic diagram of measurement locations at hub height, where D is the diameter of a turbine rotor.

**Figure 4.**Time sequence of the measured angular speed in PDM in the ABL (atmospheric boundary layer) wind tunnel. The red dash–dotted line denotes the mean angular speed.

**Figure 5.**Torque contributions acting on the turbine rotor, not accounting for the mechanical friction: (

**a**) PDM; ADM in conditions where the real-time angular speeds is lower (

**b**) or higher (

**c**) than the prescribed angular speed.

**Figure 7.**Time sequence of the measured angular speed in ADM in the ABL wind tunnel. The red dash–dotted line denotes the mean angular speed.

**Figure 12.**Relationship between the driving modes under the UIF condition in terms of the mean wake velocity.

**Figure 13.**Relationship between the driving modes under the ABL condition in terms of the mean wake velocity.

**Figure 14.**Relationship between the driving modes under the UIF condition in terms of the wake turbulence intensity.

**Figure 15.**Relationship between the driving modes under the ABL condition in terms of the wake turbulence intensity.

**Figure 16.**Relationship between the driving modes in terms of the mean wake velocity under the various tip speed ratios and the UIF condition.

**Figure 17.**Relationship between the driving modes in terms of the wake turbulence intensity under various tip speed ratios and the UIF condition.

**Figure 18.**Evaluation of the experimental uncertainty under the UIF condition. (

**a**) and (

**c**) represent the mean wake velocity and turbulence intensity, respectively, under different driving modes. (

**b**) and (

**d**) represent the experimental results under the same driving mode (ADM).

**Figure 19.**Relative deviation of the mean wake velocity and the turbulence intensity. The dashed line denotes the experimental error in the UIF condition.

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Dou, B.; Yang, Z.; Guala, M.; Qu, T.; Lei, L.; Zeng, P.
Comparison of Different Driving Modes for the Wind Turbine Wake in Wind Tunnels. *Energies* **2020**, *13*, 1915.
https://doi.org/10.3390/en13081915

**AMA Style**

Dou B, Yang Z, Guala M, Qu T, Lei L, Zeng P.
Comparison of Different Driving Modes for the Wind Turbine Wake in Wind Tunnels. *Energies*. 2020; 13(8):1915.
https://doi.org/10.3390/en13081915

**Chicago/Turabian Style**

Dou, Bingzheng, Zhanpei Yang, Michele Guala, Timing Qu, Liping Lei, and Pan Zeng.
2020. "Comparison of Different Driving Modes for the Wind Turbine Wake in Wind Tunnels" *Energies* 13, no. 8: 1915.
https://doi.org/10.3390/en13081915