Articles | Volume 10, issue 7
https://doi.org/10.5194/wes-10-1403-2025
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the Creative Commons Attribution 4.0 License.
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https://doi.org/10.5194/wes-10-1403-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Jul 2025
Research article |
| 18 Jul 2025
| 18 Jul 2025
Modeling the effects of active wake mixing on wake behavior through large-scale coherent structures
Sandia National Laboratories, Livermore, CA, USA
Gopal Yalla
Sandia National Laboratories, Albuquerque, NM, USA
Prakash Mohan
National Renewable Energy Laboratory, Golden, CO, USA
Alan Hsieh
Sandia National Laboratories, Albuquerque, NM, USA
Kenneth Brown
Sandia National Laboratories, Albuquerque, NM, USA
Nathaniel deVelder
Sandia National Laboratories, Albuquerque, NM, USA
Daniel Houck
Sandia National Laboratories, Albuquerque, NM, USA
Marc T. Henry de Frahan
National Renewable Energy Laboratory, Golden, CO, USA
National Renewable Energy Laboratory, Golden, CO, USA
Michael Sprague
National Renewable Energy Laboratory, Golden, CO, USA
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This paper presents one half of a companion paper series that studies strategies to reduce negative aerodynamic interference (i.e., wake effects) between nearby wind turbines in a wind farm. The approach leverages high-fidelity flow simulations of an open-source design for a wind turbine. Complimenting the companion paper’s analysis of the power and loading effects of the wake-control strategies, this article uncovers the underlying fluid-dynamic causes for these effects.
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Cited articles
Ainslie, J. F.: Calculating the flowfield in the wake of wind turbines, J. Wind Eng. Ind. Aerod., 27, 213–224, 1988. a
Ali, N., Cortina, G., Hamilton, N., Calaf, M., and Cal, R.: Turbulence characteristics of a thermally stratified wind turbine array boundary layer via proper orthogonal decomposition, J. Fluid Mech., 828, 175–195, 2017. a
Bastankhah, M. and Porté-Agel, F.: A new analytical model for wind-turbine wakes, Renew. Energ., 70, 116–123, 2014. a
Bastine, D., Witha, B., Wächter, M., and Peinke, J.: Towards a simplified dynamic wake model using POD analysis, Energies, 8, 895–920, 2015. a
Brown, K., Yalla, G., Cheung, L., Frederik, J., Houck, D., deVelder, N., Simley, E., and Fleming, P.: Comparison of wind-farm control strategies under realistic offshore wind conditions: wake quantities of interest, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2024-191, accepted, 2025. a, b
Byrd, R. H., Lu, P., Nocedal, J., and Zhu, C.: A limited memory algorithm for bound constrained optimization, SIAM J. Sci. Comput., 16, 1190–1208, 1995. a
Cheung, L., Yalla, G., Brown, K., deVelder, N., Houck, D., Herges, T., Maniaci, D., Sakievich, P., and Abraham, A.: Modification of wind turbine wakes by large-scale convective atmospheric boundary layer structures, J. Renew. Sustain. Ener., 16, 063304, https://doi.org/10.1063/5.0211722, 2024b. a, b
Cheung, L. C. and Zaki, T. A.: A nonlinear PSE method for two-fluid shear flows with complex interfacial topology, J. Comput. Phys., 230, 6756–6777, 2011. a
Crow, S. C. and Champagne, F.: Orderly structure in jet turbulence, J. Fluid Mech., 48, 547–591, 1971. a
DNV: NYSERDA Floating LiDAR Buoy Data, Mason, J. Energy Assessment Report. Tech. Rep. 10124962, DNV, 2022. a
Fedeli, L., Huebl, A., Boillod-Cerneux, F., Clark, T., Gott, K., Hillairet, C., Jaure, S., Leblanc, A., Lehe, R., Myers, A., Piechurski, C., Sato, M., Zaim, N., Zhang, W., Vay, J.-L., and Vincenti, H.: Pushing the frontier in the design of laser-based electron accelerators with groundbreaking mesh-refined particle-in-cell simulations on exascale-class supercomputers, in: SC22: International Conference for High Performance Computing, Networking, Storage and Analysis, IEEE Computer Society, 13–18 November 2022, Dallas, TX, USA, Los Alamitos, CA, USA, 1–12, https://doi.org/10.1109/SC41404.2022.00008, 2022. a
Frederik, J. A., Doekemeijer, B. M., Mulders, S. P., and van Wingerden, J.-W.: The helix approach: Using dynamic individual pitch control to enhance wake mixing in wind farms, Wind Energy, 23, 1739–1751, 2020a. a
Frederik, J. A., Weber, R., Cacciola, S., Campagnolo, F., Croce, A., Bottasso, C., and van Wingerden, J.-W.: Periodic dynamic induction control of wind farms: proving the potential in simulations and wind tunnel experiments, Wind Energ. Sci., 5, 245–257, https://doi.org/10.5194/wes-5-245-2020, 2020b. a
Fuchs, H. V., Mercker, E., and Michel, U.: Large-scale coherent structures in the wake of axisymmetric bodies, J. Fluid Mech., 93, 185–207, 1979. a
Gaertner, E., Rinker, J., Sethuraman, L., Zahle, F., Anderson, B., Barter, G., Abbas, N., Meng, F., Bortolotti, P., Skrzypinski, W., Scott, G., Feil, R., Bredmose, H., Dykes, K., Shields, M., Allen, C., and Visell, A.: IEA wind TCP task 37: definition of the IEA 15-megawatt offshore reference wind turbine, Tech. rep., National Renewable Energy Laboratory (NREL), Golden, CO (United States), https://docs.nrel.gov/docs/fy20osti/75698.pdf (last access: 16 July 2025), 2020. a
Goit, J. P. and Meyers, J.: Optimal control of energy extraction in wind-farm boundary layers, J. Fluid Mech., 768, 5–50, 2015. a
Gutknecht, J., Becker, M., Muscari, C., Lutz, T., and van Wingerden, J.-W.: Scaling DMD modes for modeling Dynamic Induction Control wakes in various wind speeds, in: 2023 IEEE Conference on Control Technology and Applications (CCTA), IEEE, 574–580, 16–18 August 2023, Bridgetown, Barbados, https://doi.org/10.1109/CCTA54093.2023.10252400, 2023. a
Hack, M. P. and Zaki, T. A.: Modal and non-modal stability of boundary layers forced by spanwise wall oscillations, J. Fluid Mech., 778, 389–427, 2015. a
Hamilton, N., Viggiano, B., Calaf, M., Tutkun, M., and Cal, R. B.: A generalized framework for reduced-order modeling of a wind turbine wake, Wind Energy, 21, 373–390, 2018. a
Henry de Frahan, M. T., Rood, J. S., Day, M. S., Sitaraman, H., Yellapantula, S., Perry, B. A., Grout, R. W., Almgren, A., Zhang, W., Bell, J. B., and Chen, J. H.: PeleC: An adaptive mesh refinement solver for compressible reacting flows, Int. J. High Perform. C., 2022, 109434202211211, https://doi.org/10.1177/10943420221121151, 2022. a
Henry de Frahan, M. T., Esclapez, L., Rood, J., Wimer, N. T., Mullowney, P., Perry, B. A., Owen, L., Sitaraman, H., Yellapantula, S., Hassanaly, M., Rahimi, M. J., Martin, M. J., Doronina, O. A., Sreejith, N. A., Rieth, M., Ge, W., Sankaran, R., Almgren, A. S., Zhang, W., Bell, J. B., Grout, R., Day, M. S., and Chen, J. H.: The Pele simulation suite for reacting flows at exascale, in: Proceedings of the 2024 SIAM Conference on Parallel Processing for Scientific Computing, 5–8 March 2024, Baltimore, MD, USA, 13–25, https://doi.org/10.1137/1.9781611977967.2, 2024. a
Ho, C.-M. and Huerre, P.: Perturbed free shear layers, Annu. Rev. Fluid Mech., 16, 365–424, 1984. a
Hsieh, A. S., Cheung, L. C., Blaylock, M. L., Brown, K. A., Houck, D. R., Herges, T. G., deVelder, N. B., Maniaci, D. C., Yalla, G. R., Sakievich, P. J., Radunz, W. C., Carmo, B. S.: Model intercomparison of the ABL, turbines, and wakes within the AWAKEN wind farms under neutral stability conditions, J. Renew. Sustain. Energ., 17, 023301, https://doi.org/10.1063/5.0211729, 2025. a
Hussain, A. F.: Coherent structures and turbulence, J. Fluid Mech., 173, 303–356, 1986. a
Iqbal, M. and Thomas, F.: Coherent structure in a turbulent jet via a vector implementation of the proper orthogonal decomposition, J. Fluid Mech., 571, 281–326, 2007. a
Jensen, N. O.: A note on wind generator interaction, Risø National Laboratory, Risø-M No. 2411, https://orbit.dtu.dk/files/55857682/ris_m_2411.pdf (last access: July 2025), 1983. a
Jonkman, J. M., Wright, A. D., Hayman, G. J., and Robertson, A. N.: Full-system linearization for floating offshore wind turbines in OpenFAST, in: International Conference on Offshore Mechanics and Arctic Engineering, vol. 51975, American Society of Mechanical Engineers, 17–22 June 2018, Madrid, Spain, https://doi.org/10.1115/IOWTC2018-1025, V001T01A028, 2018. a
Korb, H., Asmuth, H., and Ivanell, S.: The characteristics of helically deflected wind turbine wakes, J. Fluid Mech., 965, A2, https://doi.org/10.1017/jfm.2023.390, 2023. a
Kuhn, M. B., Henry de Frahan, M. T., Mohan, P., Deskos, G., Churchfield, M., Cheung, L., Sharma, A., Almgren, A., Ananthan, S., Brazell, M. J., Martínez-Tossas, L., Thedin, R., Rood, J., Sakievich, P., Vijayakumar, G., Zhang, W., and Sprague, M. A.: AMR-Wind: A performance-portable, high-fidelity flow solver for wind farm simulations, Wind Energy, 28, e70010, https://doi.org/10.1002/we.70010, 2025. a
Kwon, Y., Hutchins, N., and Monty, J.: On the use of the Reynolds decomposition in the intermittent region of turbulent boundary layers, J. Fluid Mech., 794, 5–16, 2016. a
Larsen, G. C., Aagaard Madsen, H., and Bingöl, F.: Dynamic wake meandering modeling, Risoe National Lab., DTU, Roskilde (Denmark), Technical report RISO-R-1607, https://www.osti.gov/etdeweb/servlets/purl/20941220 (last access: 16 July 2025), 2007. a
Li, Z. and Yang, X.: Resolvent-based motion-to-wake modelling of wind turbine wakes under dynamic rotor motion, J. Fluid Mech., 980, A48, https://doi.org/10.1017/jfm.2023.1097, 2024. a
Lumley, J. L.: The structure of inhomogeneous turbulent flows, Atmospheric turbulence and radio wave propagation, Defense Technical Information Center, 166–178, https://cir.nii.ac.jp/crid/1574231874542771712 (last access: 16 July 2025), 1967. a
Madsen, H. A., Larsen, G. C., Larsen, T. J., Troldborg, N., and Mikkelsen, R.: Calibration and validation of the dynamic wake meandering model for implementation in an aeroelastic code, ASME J. Sol. Energy Eng., 132, 041014, https://doi.org/10.1115/1.4002555, 2010. a
Mason, J.: Energy Assessment Report, Tech. Rep. 10124962, DNV, 2022. a
Meyers, J., Bottasso, C., Dykes, K., Fleming, P., Gebraad, P., Giebel, G., Göçmen, T., and van Wingerden, J.-W.: Wind farm flow control: prospects and challenges, Wind Energ. Sci., 7, 2271–2306, https://doi.org/10.5194/wes-7-2271-2022, 2022. a
Moeng, C.-H.: A large-eddy-simulation model for the study of planetary boundary-layer turbulence, J. Atmos. Sci., 41, 2052–2062, 1984. a
Munters, W. and Meyers, J.: 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 Energ. Sci., 3, 409–425, https://doi.org/10.5194/wes-3-409-2018, 2018. a
NREL: OpenFAST Documentation, https://openfast.readthedocs.io (last access: : July 2025), 2023. a
Robinson, S.: Coherent motions in the turbulent boundary layer, Annu. Rev. Fluid Mech., 23, 601–639, 1991. a
Schmid, P. J.: Nonmodal stability theory, Annu. Rev. Fluid Mech., 39, 129–162, 2007. a
Sharma, A., Brazell, M. J., Vijayakumar, G., Ananthan, S., Cheung, L., deVelder, N., Henry de Frahan, M. T., Matula, N., Mullowney, P., Rood, J., Sakievich, P., Almgren, A., Crozier, P. S., and Sprague, M.: ExaWind: Open-source CFD for hybrid-RANS/LES geometry-resolved wind turbine simulations in atmospheric flows, Wind Energy, 27, 225–257, https://doi.org/10.1002/we.2886, 2024. a
Sinner, M. and Fleming, P.: Robust wind farm layout optimization, J. Phys. Conf. Ser., 2767, 032036, https://doi.org/10.1088/1742-6596/2767/3/032036, 2024. a
Sprague, M. A., Ananthan, S., Vijayakumar, G., and Robinson, M.: ExaWind: A multifidelity modeling and simulation environment for wind energy, J. Phys. Conf. Ser., 1452, 012071, https://doi.org/10.1088/1742-6596/1452/1/012071, 2020. a
Towne, A., Schmidt, O. T., and Colonius, T.: Spectral proper orthogonal decomposition and its relationship to dynamic mode decomposition and resolvent analysis, J. Fluid Mech., 847, 821–867, 2018. a
Yalla, G. R., Brown, K., Cheung, L., Houck, D., deVelder, N., and Hamilton, N.: Spectral proper orthogonal decomposition of active wake mixing dynamics in a stable atmospheric boundary layer, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2025-14, accepted, 2025. a, b, c
Zhang, W., Almgren, A., Beckner, V., Bell, J., Blaschke, J., Chan, C., Day, M., Friesen, B., Gott, K., Graves, D., Katz, M., Myers, A., Nguyen, T., Nonaka, A., Rosso, M., Williams, S., and Zingale, M.: AMReX: a framework for block-structured adaptive mesh refinement, Journal of Open Source Software, 4, 1370, https://doi.org/10.21105/joss.01370, 2019. a
Zhu, C., Byrd, R. H., Lu, P., and Nocedal, J.: Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization, ACM T. Math. Software, 23, 550–560, 1997. a
Short summary
Mitigating turbine wakes is an important aspect to maximizing wind farm energy production but is a challenge to model. We demonstrate a new approach to modeling active wake mixing, which re-energizes turbine wake through periodic blade pitching. The new model divides the wake into separate steady, unsteady, and turbulent components and solves for each in a computationally efficient manner. Our results show that the model can reasonably predict the faster wake recovery due to mixing.
Mitigating turbine wakes is an important aspect to maximizing wind farm energy production but is...
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