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Mechanisms of dynamic near-wake modulation of a utility-scale wind turbine

Published online by Cambridge University Press:  13 September 2021

Aliza Abraham Open the ORCID record for Aliza Abraham [Opens in a new window] ,
Luis A. Martínez-Tossas  and
Jiarong Hong Open the ORCID record for Jiarong Hong [Opens in a new window]
Aliza Abraham
Affiliation:
St. Anthony Falls Laboratory, University of Minnesota, Minneapolis, MN 55455, USA Department of Mechanical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
Luis A. Martínez-Tossas
Affiliation:
National Renewable Energy Laboratory, Golden, CO 80401, USA
Jiarong Hong*
Affiliation:
St. Anthony Falls Laboratory, University of Minnesota, Minneapolis, MN 55455, USA Department of Mechanical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
*
†Email address for correspondence: [email protected]
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Abstract

The current study uses large eddy simulations to investigate the transient response of a utility-scale wind turbine wake to dynamic changes in atmospheric and operational conditions, as observed in previous field-scale measurements. Most wind turbine wake investigations assume quasi-steady conditions, but real wind turbines operate in a highly stochastic atmosphere, and their operation (e.g. blade pitch, yaw angle) changes constantly in response. Furthermore, dynamic control strategies have been recently proposed to optimize wind farm power generation and longevity. Therefore, improved understanding of dynamic wake behaviours is essential. First, changes in blade pitch are investigated and the wake expansion response is found to display hysteresis as a result of flow inertia. The time scales of the wake response to different pitch rates are quantified. Next, changes in wind direction with different time scales are explored. Under short time scales, the wake deflection is in the opposite direction of that observed under quasi-steady conditions. Finally, yaw changes are implemented at different rates, and the maximum inverse wake deflection and time scale are quantified, showing a clear dependence on yaw rate. To gain further physical understanding of the mechanism behind the inverse wake deflection, the streamwise vorticity in different parts of the wake is quantified. The results of this study provide guidance for the design of advanced wake flow control algorithms. The lag in wake response observed for both blade pitch and yaw changes shows that proposed dynamic control strategies must implement turbine operational changes with a time scale of the order of the rotor time scale or slower.

JFM classification

Aerodynamics: Flow-structure interactions Vortex Flows: Vortex dynamics Wakes/Jets: Wakes
Type
JFM Papers
Information
Journal of Fluid Mechanics , Volume 926 , 10 November 2021 , A29
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Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

REFERENCES

Abraham, A., Dasari, T. & Hong, J. 2019 Effect of turbine nacelle and tower on the near wake of a utility-scale wind turbine. J. Wind Engng Ind. Aerodyn. 193, 103981.CrossRefGoogle Scholar
Abraham, A. & Hong, J. 2020 Dynamic wake modulation induced by utility-scale wind turbine operation. Appl. Energy 257, 114003.CrossRefGoogle Scholar
Abraham, A., Martínez-Tossas, L.A. & Hong, J. 2020 The effect of dynamic near-wake modulation on utility-scale wind turbine wake development. J. Phys.: Conf. Ser. 1618, 062063.Google Scholar
Aitken, M.L., Banta, R.M., Pichugina, Y.L. & Lundquist, J.K. 2014 Quantifying wind turbine wake characteristics from scanning remote sensor data. J. Atmos. Ocean. Technol. 31, 765–787.CrossRefGoogle Scholar
Andersen, S.J. & Sørensen, J.N. 2018 Instantaneous response and mutual interaction between wind turbine and flow. J. Phys.: Conf. Ser. 1037, 072011.Google Scholar
Annoni, J., Scholbrock, A., Churchfield, M. & Fleming, P. 2017 Evaluating tilt for wind plants. In Proceedings of the American Control Conference, pp. 717–722. IEEE.CrossRefGoogle Scholar
Bastankhah, M. & Porté-Agel, F. 2014 A new analytical model for wind-turbine wakes. Renew. Energy 70, 116–123.CrossRefGoogle Scholar
Bastankhah, M. & Porté-Agel, F. 2016 Experimental and theoretical study of wind turbine wakes in yawed conditions. J. Fluid Mech. 806, 506–541.CrossRefGoogle Scholar
Berger, F. & Kühn, M. 2018 Experimental investigation of dynamic inflow effects with a scaled wind turbine in a controlled wind tunnel environment. J. Phys.: Conf. Ser. 1037, 052017.Google Scholar
Blaylock, M.L., Houchens, B.C., Maniaci, D.C., Herges, T., Hsieh, A., Knaus, R.C. & Sakievich, P. 2019 Comparison of field measurements and large eddy simulations of the scaled wind farm technology (SWiFT) site. In Proceedings of the ASME-JSME-KSME 2019 Joint Fluids Engineering Conference 59070, V004T04A012. ASME.CrossRefGoogle Scholar
Bossuyt, J., Scott, R., Ali, N. & Cal, R.B. 2021 Quantification of wake shape modulation and deflection for tilt and yaw misaligned wind turbines. J. Fluid Mech. 917, A3.CrossRefGoogle Scholar
Brown, K., Houck, D., Maniaci, D. & Westergaard, C. 2021 Rapidly recovering wind turbine wakes with dynamic pitch and rotor speed control. In AIAA Scitech 2021 Forum, p. 1182. AIAA.CrossRefGoogle Scholar
Cathelain, M., Blondel, F., Joulin, P.A. & Bozonnet, P. 2020 Calibration of a super-Gaussian wake model with a focus on near-wake characteristics. J. Phys.: Conf. Ser. 1618, 062008.Google Scholar
Chamorro, L.P., Lee, S.J., Olsen, D., Milliren, C., Marr, J., Arndt, R.E.A. & Sotiropoulos, F. 2015 Turbulence effects on a full-scale 2.5 MW horizontal-axis wind turbine under neutrally stratified conditions. Wind Energy 18, 339–349.CrossRefGoogle Scholar
Dasari, T., Wu, Y., Liu, Y. & Hong, J. 2019 Near-wake behaviour of a utility-scale wind turbine. J. Fluid Mech. 859, 204–246.CrossRefGoogle Scholar
Doubrawa, P., Barthelmie, R.J., Wang, H. & Churchfield, M.J. 2017 A stochastic wind turbine wake model based on new metrics for wake characterization. Wind Energy 20, 449–463.CrossRefGoogle Scholar
Ebrahimi, A. & Sekandari, M. 2018 Transient response of the flexible blade of horizontal-axis wind turbines in wind gusts and rapid yaw changes. Energy 145, 261–275.CrossRefGoogle Scholar
Felli, M., Camussi, R. & Di Felice, F. 2011 Mechanisms of evolution of the propeller wake in the transition and far fields. J. Fluid Mech. 682, 5–53.CrossRefGoogle Scholar
Fleming, P., King, J., Dykes, K. & Simley, E. et al. 2019 Initial results from a field campaign of wake steering applied at a commercial wind farm – part 1. Wind Energy Sci. 4, 273–285.CrossRefGoogle Scholar
Frederik, J., Weber, R., Cacciola, S., Campagnolo, F., Croce, A., Bottasso, C. & van Wingerden, J.-W. 2020 Periodic dynamic induction control of wind farms: proving the potential in simulations and wind tunnel experiments. Wind Energy Sci. 5, 245–257.CrossRefGoogle Scholar
Gebraad, P.M.O., Fleming, P.A. & van Wingerden, J.W. 2015 Wind turbine wake estimation and control using FLORIDyn, a control-oriented dynamic wind plant model. In Proceedings of the American Control Conference, pp. 1702–1708. IEEE.CrossRefGoogle 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.CrossRefGoogle Scholar
Goit, J.P. & Meyers, J. 2015 Optimal control of energy extraction in wind-farm boundary layers. J. Fluid Mech. 768, 5–50.CrossRefGoogle 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, 4216.CrossRefGoogle ScholarPubMed
Howland, M.F., Bossuyt, J., Martínez-Tossas, L.A., Meyers, J. & Meneveau, C. 2016 Wake structure in actuator disk models of wind turbines in yaw under uniform inflow conditions. J. Renew. Sustain. Energy 8, 043301.CrossRefGoogle Scholar
Howland, M.F., Ghate, A.S., Lele, S.K. & Dabiri, J.O. 2020 Optimal closed-loop wake steering - Part 1: conventionally neutral atmospheric boundary layer conditions. Wind Energy Sci. 5, 1315–1338.CrossRefGoogle Scholar
Ishihara, T. & Qian, G.W. 2018 A new Gaussian-based analytical wake model for wind turbines considering ambient turbulence intensities and thrust coefficient effects. J. Wind Engng Ind. Aerodyn. 177, 275–292.CrossRefGoogle Scholar
Ivanell, S., Mikkelsen, R., Sørensen, J.N. & Henningson, D. 2010 Stability analysis of the tip vortices of a wind turbine. Wind Energy 13, 705–715.CrossRefGoogle Scholar
Jiménez, Á., Crespo, A. & Migoya, E. 2010 Application of a LES technique to characterize the wake deflection of a wind turbine in yaw. Wind Energy 13, 559–572.CrossRefGoogle Scholar
Kanev, S.K., Savenije, F.J. & Engels, W.P. 2018 Active wake control: an approach to optimize the lifetime operation of wind farms. Wind Energy 21, 488–501.CrossRefGoogle Scholar
Kang, S., Yang, X. & 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.CrossRefGoogle Scholar
Keane, A., Aguirre, P.E.O., Ferchland, H., Clive, P. & Gallacher, D. 2016 An analytical model for a full wind turbine wake. J. Phys.: Conf. Ser. 753 (3), 032039.Google Scholar
Kim, Y., Jost, E., Bangga, G., Weihing, P. & Lutz, T. 2016 Effects of ambient turbulence on the near wake of a wind turbine. J. Phys.: Conf. Ser. 753 (3), 032047.Google Scholar
Kleusberg, E., Schlatter, P. & Henningson, D.S. 2020 Parametric dependencies of the yawed wind-turbine wake development. Wind Energy 23 (6), 1367–1380.CrossRefGoogle Scholar
Leishman, J.G. 2002 Challenges in modelling the unsteady aerodynamics of wind turbines. Wind Energy 5, 85–132.CrossRefGoogle Scholar
Leweke, T., Le Dizès, S. & Williamson, C.H.K. 2016 Dynamics and instabilities of vortex pairs. Annu. Rev. Fluid Mech. 48, 507–541.CrossRefGoogle Scholar
Li, C., Abraham, A., Li, B. & Hong, J. 2020 Incoming flow measurements of a utility-scale wind turbine using super-large-scale particle image velocimetry. J. Wind Engng Ind. Aerodyn. 197, 104074.CrossRefGoogle Scholar
Lignarolo, L.E.M., Ragni, D., Scarano, F., Simão Ferreira, C.J. & Van Bussel, G.J.W. 2015 Tip-vortex instability and turbulent mixing in wind-turbine wakes. J. Fluid Mech. 781, 467–493.CrossRefGoogle Scholar
Lu, H. & Porté-Agel, F. 2011 Large-eddy simulation of a very large wind farm in a stable atmospheric boundary layer. Phys. Fluids 23, 065101.CrossRefGoogle Scholar
Macrí, S., Aubrun, S., Leroy, A. & Girard, N. 2021 Experimental investigation of wind turbine wake and load dynamics during yaw maneuvers. Wind Energy Sci. 6, 585–599.CrossRefGoogle Scholar
Martínez-Tossas, L.A. 2017 Large eddy simulations and theoretical analysis of wind turbine aerodynamics using an actuator line model. PhD thesis, Johns Hopkins University.Google Scholar
Martínez-Tossas, L.A., Annoni, J., Fleming, P.A. & Churchfield, M.J. 2019 The aerodynamics of the curled wake: a simplified model in view of flow control. Wind Energy Sci. 4, 127–138.CrossRefGoogle Scholar
Martínez-Tossas, L.A., Churchfield, M.J. & Leonardi, S. 2014 Large eddy simulations of the flow past wind turbines: actuator line and disk modeling. Wind Energy 18, 1047–1060.CrossRefGoogle Scholar
Martínez-Tossas, L.A., Churchfield, M.J., Yilmaz, A.E., Sarlak, H., Johnson, P.L., Sørensen, J.N., Meyers, J. & Meneveau, C. 2018 Comparison of four large-eddy simulation research codes and effects of model coefficient and inflow turbulence in actuator-line-based wind turbine modeling. J. Renew. Sustain. Energy 10 (3), 033301.CrossRefGoogle Scholar
Munters, W. & Meyers, J. 2017 An optimal control framework for dynamic induction control of wind farms and their interaction with the atmospheric boundary layer. Phil. Trans. R. Soc. A 375, 20160100.CrossRefGoogle ScholarPubMed
Munters, W. & Meyers, J. 2018 a Dynamic strategies for yaw and induction control of wind farms based on large-eddy simulation and optimization. Energies 11, 177.CrossRefGoogle Scholar
Munters, W. & Meyers, J. 2018 b Towards practical dynamic induction control of wind farms: analysis of optimally controlled wind-farm boundary layers and sinusoidal induction control of first-row turbines. Wind Energy Sci. 3 (1), 409–425.CrossRefGoogle Scholar
Nielson, J. & Bhaganagar, K. 2019 Using field data–based large eddy simulation to understand role of atmospheric stability on energy production of wind turbines. Wind Energy 43 (6), 625–638.Google Scholar
Nilsson, K., Shen, W.Z., Sørensen, J.N., Breton, S.-P. & Ivanell, S. 2015 Validation of the actuator line method using near wake measurements of the MEXICO rotor. Wind Energy 18, 499–514.CrossRefGoogle Scholar
Ortega, J.M., Bristol, R.L. & Savaş, Ö 2003 Experimental study of the instability of unequal-strength counter-rotating vortex pairs. J. Fluid Mech. 474, 35–84.CrossRefGoogle Scholar
Porté-Agel, F., Bastankhah, M. & Shamsoddin, S. 2018 Wind-turbine and wind-farm flows: a review. Boundary-Layer Meteorol. 174, 1–59.CrossRefGoogle Scholar
Quon, E.W., Doubrawa, P. & Debnath, M. 2020 Comparison of rotor wake identification and characterization methods for the analysis of wake dynamics and evolution. J. Phys.: Conf. Ser. 1452, 012070.Google Scholar
Raach, S., Boersma, S., Doekemeijer, B., van Wingerden, J.-W. & Cheng, P.W. 2018 Lidar-based closed-loop wake redirection in high-fidelity simulation. J. Phys.: Conf. Ser. 1037, 032016.Google Scholar
Raach, S., van Wingerden, J.-W., Boersma, S., Schlipf, D. & Cheng, P.W. 2017 $\mathscr {H}\infty$ controller design for closed-loop wake redirection. In Proceedings of the American Control Conference, pp. 703–708. IEEE.CrossRefGoogle Scholar
Rahimi, H., Schepers, J.G., Shen, W.Z., Ramos García, N., Schneider, M.S., Micallef, D., Simao Ferreira, C.J., Jost, E., Klein, L. & Herráez, I. 2018 Evaluation of different methods for determining the angle of attack on wind turbine blades with CFD results under axial inflow conditions. Renew. Energy 125, 866–876.CrossRefGoogle Scholar
Rockel, S., Camp, E., Schmidt, J., Peinke, J., Cal, R.B. & Hölling, M. 2014 Experimental study on influence of pitch motion on the wake of a floating wind turbine model. Energies 7 (4), 1954–1985.CrossRefGoogle Scholar
Sanderse, B., van der Pijl, S.P. & Koren, B. 2011 Review of computational fluid dynamics for wind turbine wake aerodynamics. Wind Energy 14, 799–819.CrossRefGoogle Scholar
Santhanagopalan, V, Rotea, M.A. & Iungo, G.V. 2018 Performance optimization of a wind turbine column for different incoming wind turbulence. Renew. Energy 116, 232–243.CrossRefGoogle Scholar
Sarlak, H., Nishino, T., Martínez-Tossas, L.A., Meneveau, C. & Sørensen, J.N. 2016 Assessment of blockage effects on the wake characteristics and power of wind turbines. Renew. Energy 93, 340–352.CrossRefGoogle 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.CrossRefGoogle Scholar
Schepers, J.G. 2007 IEA Annex XX: dynamic inflow effects at fast pitching steps on a wind turbine placed in the NASA-Ames wind tunnel. Tech. Rep. ECN, Petten.Google Scholar
Shapiro, C.R., Gayme, D.F. & Meneveau, C. 2018 Modelling yawed wind turbine wakes: a lifting line approach. J. Fluid Mech. 841, R1.CrossRefGoogle Scholar
Shapiro, C.R., Meyers, J., Meneveau, C. & Gayme, D.F. 2017 Dynamic wake modeling and state estimation for improved model-based receding horizon control of wind farms. In Proceedings of the American Control Conference, pp. 709–716. IEEE.CrossRefGoogle Scholar
Sørensen, J.N. 2011 Instability of helical tip vortices in rotor wakes. J. Fluid Mech. 682, 1–4.CrossRefGoogle Scholar
Sørensen, J.N. & Shen, W.Z. 2002 Numerical modeling of wind turbine wakes. J. Fluids Engng 124, 393–399.CrossRefGoogle Scholar
Sprague, M.A., Ananthan, S., Vijayakumar, G. & Robinson, M. 2020 ExaWind: a multifidelity modeling and simulation environment for wind energy. J. Phys.: Conf. Ser. 1452, 012071.Google Scholar
Stevens, R.J.A.M. & Meneveau, C. 2017 Flow structure and turbulence in wind farms. Annu. Rev. Fluid Mech. 49, 311–339.CrossRefGoogle Scholar
Su, K. & Bliss, D. 2020 A numerical study of tilt-based wake steering using a hybrid free-wake method. Wind Energy 23 (2), 258–273.CrossRefGoogle Scholar
Troldborg, N., Zahle, F., Rethore, P.-E. & Sørensen, N.N. 2015 Comparison of wind turbine wake properties in non-sheared inflow predicted by different computational fluid dynamics rotor models. Wind Energy 18, 1239–1250.CrossRefGoogle Scholar
Tsalicoglou, C., Jafari, S., Chokani, N. & Abhari, R.S. 2014 RANS computations of MEXICO rotor in uniform and yawed inflow. Trans. ASME J. Engng Gas Turbines Power 136, 011202.CrossRefGoogle Scholar
Veers, P., et al. 2019 Grand challenges in the science of wind energy. Science 366, eaau2027.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Yang, X. & Sotiropoulos, F. 2019 A review on the meandering of wind turbine wakes. Energies 12 (24), 4725.CrossRefGoogle Scholar
Yılmaz, A.E. & Meyers, J. 2018 Optimal dynamic induction control of a pair of inline wind turbines. Phys. Fluids 30, 085106.CrossRefGoogle Scholar
Yu, W., Hong, V.W., Ferreira, C. & van Kuik, G.A.M. 2017 Experimental analysis on the dynamic wake of an actuator disc undergoing transient loads. Exp. Fluids 58, 149.CrossRefGoogle Scholar
Zhan, L., Letizia, S. & Iungo, G.V. 2020 LiDAR measurements for an onshore wind farm: wake variability for different incoming wind speeds and atmospheric stability regimes. Wind Energy 23, 501–527.CrossRefGoogle Scholar
Zong, H. & Porté-Agel, F. 2020 A point vortex transportation model for yawed wind turbine wakes. J. Fluid Mech. 890, A8.CrossRefGoogle Scholar

Abraham et al. supplementary movie 1

Spanwise velocity contours (v) on the x-y plane at hub height showing the wake response to sinusoidal fluctuations in wind direction with a period of 20 s.
Download Abraham et al. supplementary movie 1(Video)
Video 52.7 MB

Abraham et al. supplementary movie 2

Spanwise velocity contours (v) on the x-y plane at hub height showing the wake response to sinusoidal fluctuations in wind direction with a period of 50 s.
Download Abraham et al. supplementary movie 2(Video)
Video 45.9 MB

Abraham et al. supplementary movie 3

Isocontours of streamwise vorticity in the near-wake during a change in rotor yaw angle. The values of the isocontours are ωx=0.1 s-1 and ωx=-0.1 s-1 for the positive (red) and negative (blue) isocontours, respectively.
Download Abraham et al. supplementary movie 3(Video)
Video 28.5 MB