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Near-wake behaviour of a utility-scale wind turbine

Published online by Cambridge University Press:  16 November 2018

Teja Dasari ,
Yue Wu ,
Yun Liu  and
Jiarong Hong Open the ORCID record for Jiarong Hong [Opens in a new window]
Teja Dasari
Affiliation:
Department of Mechanical Engineering, University of Minnesota, Minneapolis, MN 55455, USA St. Anthony Falls Laboratory, University of Minnesota, Minneapolis, MN 55414, USA
Yue Wu
Affiliation:
St. Anthony Falls Laboratory, University of Minnesota, Minneapolis, MN 55414, USA
Yun Liu
Affiliation:
St. Anthony Falls Laboratory, University of Minnesota, Minneapolis, MN 55414, USA Department of Mechanical and Civil Engineering, Purdue University Northwest, Westville, IN 46391, USA
Jiarong Hong*
Affiliation:
Department of Mechanical Engineering, University of Minnesota, Minneapolis, MN 55455, USA St. Anthony Falls Laboratory, University of Minnesota, Minneapolis, MN 55414, USA
*
†Email address for correspondence: [email protected]
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Abstract

Super-large-scale particle image velocimetry (SLPIV) and the associated flow visualization technique using natural snowfall have been shown to be effective tools to probe the turbulent velocity field and coherent structures around utility-scale wind turbines (Hong et al.Nat. Commun., vol. 5, 2014, article 4216). Here, we present a follow-up study using the data collected during multiple deployments from 2014 to 2016 around the 2.5 MW turbine at the EOLOS field station. These data include SLPIV measurements in the near wake of the turbine in a field of view of 115 m (vertical) $\times$ 66 m (streamwise), and the visualization of tip vortex behaviour near the elevation corresponding to the bottom blade tip over a broad range of turbine operational conditions. The SLPIV measurements provide velocity deficit and turbulent kinetic energy assessments over the entire rotor span. The instantaneous velocity fields from SLPIV indicate the presence of intermittent wake contraction states which are in clear contrast with the expansion states typically associated with wind turbine wakes. These contraction states feature a pronounced upsurge of velocity in the central portion of the wake. The wake velocity ratio $R_{w}$, defined as the ratio of the spatially averaged velocity of the inner wake to that of the outer wake, is introduced to categorize the instantaneous near wake into expansion ($R_{w}<1$) and contraction states ($R_{w}>1$). Based on the $R_{w}$ criterion, the wake contraction occurs 25 % of the time during a 30 min time duration of SLPIV measurements. The contraction states are found to be correlated with the rate of change of blade pitch by examining the distribution and samples of time sequences of wake states with different turbine operation parameters. Moreover, blade pitch change is shown to be strongly correlated to the tower and blade strains measured on the turbine, and the result suggests that the flexing of the turbine tower and the blades could indeed lead to the interaction of the rotor with the turbine wake, causing wake contraction. The visualization of tip vortex behaviour demonstrates the presence of a state of consistent vortex formation as well as various types of disturbed vortex states. The histograms corresponding to the consistent and disturbed states are examined over a number of turbine operation/response parameters, including turbine power and tower strain as well as the fluctuation of these quantities, with different conditional sampling restrictions. This analysis establishes a clear statistical correspondence between these turbine parameters and tip vortex behaviours under different turbine operation conditions, which is further substantiated by examining samples of time series of these turbine parameters and tip vortex patterns. This study not only offers benchmark datasets for comparison with the-state-of-the-art numerical simulation, laboratory and field measurements, but also sheds light on understanding wake characteristics and the downstream development of the wake, turbine performance and regulation, as well as developing novel turbine or wind farm control strategies.

JFM classification

Geophysical and Geological Flows: Atmospheric flows Geophysical and Geological Flows: Geophysical and Geological Flows Wakes/Jets: Wakes
Type
JFM Papers
Information
Journal of Fluid Mechanics , Volume 859 , 25 January 2019 , pp. 204 - 246
Copyright
© 2018 Cambridge University Press 

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References

Ainslie, J. F. 1988 Calculating the flowfield in the wake of wind turbines. J. Wind Engng Ind. Aerodyn. 27 (6), 213–224.Google Scholar
Asay-Davis, X. S., Marcus, P. S., Wong, M. H. & de Pater, I. 2009 Jupiter’s shrinking Great Red Spot and steady Oval BA: velocity measurements with the ‘Advection Corrected Correlation Image Velocimetry’ automated cloud-tracking method. Icarus 203 (1), 164–188.Google Scholar
Aya, S., Fujita, I. & Yagyu, M. 1995 Field-observation of flood in a river by video image analysis. Proc. Hydraul. Engng 39, 447–452.Google Scholar
Bang, H. J., Kim, H. I. & Lee, K. S. 2012 Measurement of strain and bending deflection of a wind turbine tower using arrayed FBG sensors. Intl J. Precis. Engng Manuf. 13 (12), 2121–2126.Google Scholar
Barthelmie, R. J., Frandsen, S. T., Nielsen, M. N., Pryor, S. C., Rethore, P. E. & Jørgensen, H. E. 2007 Modelling and measurements of power losses and turbulence intensity in wind turbine wakes at middelgrunden offshore wind farm. Wind Energy 10 (6), 517–528.Google Scholar
Barthelmie, R. J., Hansen, K., Frandsen, S. T., Rathmann, O., Schepers, J. G., Schlez, W., Phillips, J., Rados, K., Zervos, A., Politis, E. S. & Chaviaropoulos, P. K. 2009 Modelling and measuring flow and wind turbine wakes in large wind farms offshore. Wind Energy 12 (5), 431–444.Google Scholar
Bastankhah, M. & Porté-Agel, F. 2014 A new analytical model for wind-turbine wakes. Renew. Energy 70, 116–123.Google Scholar
Bazilevs, Y., Hsu, M. C., Kiendl, J., Wüchner, R. & Bletzinger, K. U. 2011 3D simulation of wind turbine rotors at full scale. Part II. Fluid–structure interaction modeling with composite blades. Intl J. Numer. Meth. Fluids 65, 236–253.Google Scholar
Bazilevs, Y., Takizawa, K., Tezduyar, T. E., Hsu, M. C., Kostov, N. & McIntyre, S. 2014 Aerodynamic and FSI analysis of wind turbines with the ALE-VMS and ST-VMS methods. Arch. Comput. Meth. Engng 21 (4), 359–398.Google Scholar
Choi, D. S., Banfield, D., Gierasch, P. & Showman, A. P. 2007 Velocity and vorticity measurements of Jupiter’s Great Red Spot using automated cloud feature tracking. Icarus 188 (1), 35–46.Google Scholar
Crespo, A., Hernandez, J., Fraga, E. & Andreu, C. 1988 Experimental validation of the UPM computer code to calculate wind turbine wakes and comparison with other models. J. Wind Engng Ind. Aerodyn. 27 (1–3), 77–88.Google Scholar
Eggleston, D. M. & Stoddard, F. S. 1987 Wind Turbine Engineering Design. Van Nostrand Reinhold Company.Google Scholar
El-kafafy, M., Devriendt, C., Weijtjens, W. & Sitter, G. De 2014 Evaluating different automated operational modal analysis techniques for the continuous monitoring of offshore wind turbines. In Dynamics of Civil Structures (ed. Necati, F. C.), vol. 4, pp. 313–329. Springer.Google Scholar
Foti, D., Yang, X., Campagnolo, F., Maniaci, D. & Sotiropoulos, F. 2018 Wake meandering of a model wind turbine operating in two different regimes. Phys. Rev. Fluids 3 (5), 1–34.Google Scholar
Frandsen, S.2007 Turbulence and Turbulence-Generated Structural Loading in Wind Turbine Clusters. Riso National Laboratory for Sustainable Energy, Riso-R-1188(EN).Google Scholar
Frandsen, S., Barthelmie, R., Pryor, S., Rathmann, O., Larsen, S. & Højstrup, J. 2006 Analytical modeling deficit in large offshore wind farms. Wind Energy 9 (January), 39–53.Google Scholar
Fujita, I., Muste, M. & Kruger, A. 1998 Large-scale particle image velocimetry for flow analysis in hydraulic engineering applications. J. Hydraul. Res. 36 (3), 397–414.Google Scholar
Göçmen, T., Van Der Laan, P., Réthoré, P. E., Diaz, A. P., Larsen, G. C. & Ott, S. 2016 Wind turbine wake models developed at the technical university of Denmark: a review. Renew. Sustain. Energy Rev. 60, 752–769.Google Scholar
Guala, M., Liberzon, A., Hoyer, K., Tsinober, A. & Kinzelbach, W. 2008 Experimental study on clustering of large particles in homogeneous turbulent flow. J. Turbul. 9 (34), 1–20.Google Scholar
Gupta, B. P. & Loewy, R. G. 1974 Theoretical analysis of the aerodynamic stability of multiple, interdigitated helical vortices. AIAA J. 12 (10), 1381–1387.Google Scholar
Hancock, P. E. & Pascheke, F. 2014 Wind-tunnel simulation of the wake of a large wind turbine in a stable boundary layer. Part 2. The wake flow. Boundary-Layer Meteorol. 151 (1), 23–37.Google Scholar
Hirth, B. D., Schroeder, J. L., Gunter, W. S. & Guynes, J. G. 2015 Coupling doppler radar-derived wind maps with operational turbine data to document wind farm complex flows. Wind Energy 18 (3), 529–540.Google Scholar
Hong, J., Toloui, M., Chamorro, L. P., Guala, M., Howard, K., Riley, S., Tucker, J. & Sotiropoulos, F. 2014 Natural snowfall reveals large-scale flow structures in the wake of a 2.5-MW wind turbine. Nat. Commun. 5 (May), 4216.Google Scholar
Hu, H., Yang, Z. & Sarkar, P. 2012 Dynamic wind loads and wake characteristics of a wind turbine model in an atmospheric boundary layer wind. Exp. Fluids 52 (5), 1277–1294.Google Scholar
Iungo, G. V., Wu, Y. T. & Porté-Agel, F. 2012 Field measurements of wind turbine wakes with lidars. J. Atmos. Ocean. Technol. 30 (2), 274–287.Google Scholar
Ivanell, S., Leweke, T., Sarmast, S., Quaranta, H. U., Mikkelsen, R. F. & Sørensen, J. N. 2015 Comparison between experiments and Large-Eddy simulations of tip spiral structure and geometry. J. Phys.: Conf. Ser. 625, 012018.Google Scholar
Ivanell, S., Mikkelsen, R., Sørensen, J. N. & Henningson, D. 2010 Stability analysis of the tip vortices of a wind turbine. Wind Energy 13 (8), 705–715.Google Scholar
Jensen, N. O.1983 A note on wind generator interaction. Tech. Rep. Risø-M-2411, Risø Natl. Lab. Roskilde, Denmark.Google Scholar
Jonkman, J., Butterfield, S., Musial, W. & Scott, G. 2009 Definition of a 5-MW Reference Wind Turbine for Offshore System Development. National Renewable Energy Laboratory.Google Scholar
Kang, S., Yang, X. L. & Sotiropoulos, F. 2014 On the onset of wake meandering for an axial flow turbine in a turbulent open channel flow. J. Fluid Mech. 744, 376–403.Google Scholar
Katic, I., Højstrup, J. & Jensen, N. O. 1986 A simple model for cluster efficiency. In European Wind Energy Conference and Exhibition, Rome, Italy, pp. 407–410.Google Scholar
Kumer, V. M., Reuder, J., Dorninger, M., Zauner, R. & Grubisic, V. 2016 Turbulent kinetic energy estimates from profiling wind LiDAR measurements and their potential for wind energy applications. Renew. Energy 99, 898–910.Google Scholar
Kunkel, G. J. & Marusic, I. 2006 Study of the near-wall-turbulent region of the high-Reynolds-number boundary layer using an atmospheric flow. J. Fluid Mech. 548, 375–402.Google Scholar
Leishman, J. G. 2002 Challenges in modeling the unsteady aerodynamics of wind turbines. Wind Energy 5 (February), 85–132.Google Scholar
Leung, D. Y. C. & Yang, Y. 2012 Wind energy development and its environmental impact: a review. Renew. Sustain. Energy Rev. 16 (1), 1031–1039.Google Scholar
Liu, T. & Shen, L. 2008 Fluid flow and optical flow. J. Fluid Mech. 614, 253–291.Google Scholar
Liu, T., Wang, B. & Choi, D. S. 2012 Flow structures of Jupiter’s Great Red Spot extracted by using optical flow method. Phys. Fluids 24 (9), 096601.Google Scholar
Magnusson, M. 1999 Near-wake behaviour of wind turbines. J. Wind Engng Ind. Aerodyn. 80 (1–2), 147–167.Google Scholar
Musial, W., Butterfield, S. & McNiff, B. 2007 Improving wind turbine gearbox reliability. In European Wind Energy Conference, Milan, May 7–10, 2007.Google Scholar
Nasrabad, V. S. 2016 Data-Driven Modeling of Wind Turbine Structural Dynamics and Its Application to Wind Speed Estimation. University of Calgary.Google Scholar
Nemes, A., Dasari, T., Hong, J., Guala, M. & Coletti, F. 2017 Snowflakes in the atmospheric surface layer: observation of particle–turbulence dynamics. J. Fluid Mech. 814, 592–613.Google Scholar
Nemes, A., Lo Jacono, D., Blackburn, H. M. & Sheridan, J. 2015 Mutual inductance of two helical vortices. J. Fluid Mech. 774, 298–310.Google Scholar
Okulov, V. L. 2004 On the stability of multiple helical vortices. J. Fluid Mech. 521, 319–342.Google Scholar
Okulov, V. L. & Sørensen, J. N. 2007 Stability of helical tip vortices in a rotor far wake. J. Fluid Mech. 576, 1–25.Google Scholar
Patsaeva, M. V., Khatuntsev, I. V., Patsaev, D. V., Titov, D. V., Ignatiev, N. I., Markiewicz, W. J. & Rodin, A. V. 2015 The relationship between mesoscale circulation and cloud morphology at the upper cloud level of Venus from VMC/Venus Express. Planet. Space Sci. 113, 100–108.Google Scholar
Raffel, M., Willert, C. E., Wereley, S. T. & Kompenhans, J. 2007 Particle Image Velocimetry: A Practical Guide, 2nd edn. Springer.Google Scholar
Sanderse, B., van der Pijl, S. P. & Koren, B. 2011 Review of computational fluid dynamics for wind turbine wake aerodynamics. Wind Energy 14 (7), 799–819.Google Scholar
Santoni, C., Carrasquillo, K., Arenas-Navarro, I. & Leonardi, S. 2017 Effect of tower and nacelle on the flow past a wind turbine. Wind Energy 20 (12), 1927–1939.Google Scholar
Sarmast, S., Dadfar, R., Mikkelsen, R. F., Schlatter, P., Ivanell, S., Sørensen, J. N. & Henningson, D. S. 2014 Mutual inductance instability of the tip vortices behind a wind turbine. J. Fluid Mech. 755, 705–731.Google Scholar
Sayanagi, K. M., Dyudina, U. A., Ewald, S. P., Fischer, G., Ingersoll, A. P., Kurth, W. S., Muro, G. D., Porco, C. C. & West, R. A. 2013 Dynamics of Saturn’s great storm of 2010-2011 from Cassini ISS and RPWS. Icarus 223 (1), 460–478.Google Scholar
Scarano, F. 2002 Iterative image deformation methods in PIV. Meas. Sci. Technol. 13, 1–19.Google Scholar
Schulz, C., Letzgus, P., Lutz, T. & Krämer, E. 2017 CFD study on the impact of yawed inflow on loads, power and near wake of a generic wind turbine. Wind Energy 20 (2), 253–268.Google Scholar
Sebastian, T. & Lackner, M. 2012 Analysis of the induction and wake evolution of an offshore floating wind turbine. Energies 5 (12), 968–1000.Google Scholar
Sebastian, T. & Lackner, M. A. 2011 Offshore floating wind turbines – an aerodynamic perspective. In 49th AIAA Aerospace Sciences Meeting, Orlando, Florida, 720.Google Scholar
Sheng, S. & Veers, P. 2011 Wind turbine drivetrain condition monitoring – an overview. In Machinery Failure Prevention Technology (MFPT): Applied Systems Health Management Conference 2011, Virginia Beach, Virginia.Google Scholar
Sherry, M., Nemes, A., Lo Jacono, D., Blackburn, H. M. & Sheridan, J. 2013a The interaction of helical tip and root vortices in a wind turbine wake. Phys. Fluids 25 (11), 117102.Google Scholar
Sherry, M., Sheridan, J. & Lo Jacono, D. 2013b Characterisation of a horizontal axis wind turbine’s tip and root vortices. Exp. Fluids 54 (3), 1417.Google Scholar
Snel, H. 2003 Review of aerodynamics for wind turbines. Wind Energy 6 (3), 203–211.Google Scholar
Sørensen, J. N. 2011a Aerodynamic aspects of wind energy conversion. Annu. Rev. Fluid Mech. 43 (1), 427–448.Google Scholar
Sørensen, J. N. 2011b Instability of helical tip vortices in rotor wakes. J. Fluid Mech. 682, 1–4.Google Scholar
Sørensen, J. N., Shen, W. Z. & Munduate, X. 1998 Analysis of wake states by a full-field actuator disc model. Wind Energy 1 (2), 73–88.Google Scholar
Toloui, M., Chamorro, L. P. & Hong, J. 2015 Detection of tip-vortex signatures behind a 2.5 MW wind turbine. J. Wind Engng Ind. Aerodyn. 143, 105–112.Google Scholar
Toloui, M., Riley, S., Hong, J., Howard, K., Chamorro, L. P., Guala, M. & Tucker, J. 2014 Measurement of atmospheric boundary layer based on super-large-scale particle image velocimetry using natural snowfall. Exp. Fluids 55 (5), 1737.Google Scholar
Vermeer, L. J., Sorensen, J. N. & Crespo, A. 2003 Wind turbine wake aerodynamics. Prog. Aerosp. Sci. 39 (6–7), 467–510.Google Scholar
Whale, J., Papadopoulos, K. H., Anderson, C. G., Helmis, C. G. & Skyner, D. J. 1996 A study of the near wake structure of a wind turbine comparing measurements from laboratory and full-scale experiments. Sol. Energy 56 (6), 621–633.Google Scholar
Widnall, S. E. 1972 The stability of a helical vortex filament. J. Fluid Mech. 54 (4), 641–663.Google Scholar
Wu, J., Ma, H. & Zhou, M. 2005 Vorticity and Vortex Dynamics. Springer.Google Scholar
Yang, X., Hong, J., Barone, M. & Sotiropoulos, F. 2016 Coherent dynamics in the rotor tip shear layer of utility-scale wind turbines. J. Fluid Mech. 804, 90–115.Google Scholar
Zendehbad, M., Chokani, N. & Abhari, R. S. 2017 Measurements of tower deflections on full-scale wind turbines using an opto-mechanical platform. J. Wind Engng Ind. Aerodyn. 168, 72–80.Google Scholar
Zheng, C. W., Li, C. Y., Pan, J., Liu, M. Y. & Xia, L. L. 2016 An overview of global ocean wind energy resource evaluations. Renew. Sustain. Energy Rev. 53 (667), 1240–1251.Google Scholar

Dasari et al, supplementary movie 1

Sample video showcasing the temporal correspondence between the wake velocity field with the blade pitch β, corresponding rate of change of pitch dβ/dt, and the Rw for the case of a strong contraction phenomena

Download Dasari et al, supplementary movie 1(Video)
Video 7.3 MB

Dasari et al. supplementary movie 2

Sample video showcasing the temporal correspondence between the wake velocity field with the blade pitch β, corresponding rate of change of pitch dβ/dt, and the Rw for the case of a weak contraction phenomena

Download Dasari et al. supplementary movie 2(Video)
Video 7.3 MB