IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v188y2022icp349-360.html
   My bibliography  Save this article

Large eddy simulations of curled wakes from tilted wind turbines

Author

Listed:
  • Johlas, Hannah M.
  • Schmidt, David P.
  • Lackner, Matthew A.

Abstract

One control strategy to increase power production in wind farms is angling wind turbine rotors, in order to steer wakes away from downwind turbines. Although rotor yaw is the most common approach to wake steering, tilting the rotor vertically to steer the wake downward can also increase total farm power. In this study, large eddy simulations of a 15 MW turbine are performed for rotor tilt angles of 0°, 15°, and 30° with below-rated turbulent inflow. Wake characteristics are analyzed, including using circulation to quantify the curled wake's counter-rotating vortex pair and quantifying wake shapes by fitting Legendre polynomials to wake edge polar coordinates. Tilting the rotor causes downward wake steering, wider and vertically compressed wake cross-sections, and stronger counter-rotating vortices. Although the wake velocity deficit recovers similarly for tilted and non-tilted wakes, the power available to a downwind rotor recovers faster because the tilted wake is steered away from the downwind rotor area and is replaced by high-speed air from above. This also causes higher effective wind shear across the downwind rotor. Additional simulations double the gap between the ground surface and the rotor bottom, which affects the wake geometry as well as the downwind power recovery and wind shear.

Suggested Citation

  • Johlas, Hannah M. & Schmidt, David P. & Lackner, Matthew A., 2022. "Large eddy simulations of curled wakes from tilted wind turbines," Renewable Energy, Elsevier, vol. 188(C), pages 349-360.
    • Handle: RePEc:eee:renene:v:188:y:2022:i:c:p:349-360
    DOI: 10.1016/j.renene.2022.02.018
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148122001574
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2022.02.018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gao, Zhiteng & Li, Ye & Wang, Tongguang & Shen, Wenzhong & Zheng, Xiaobo & Pröbsting, Stefan & Li, Deshun & Li, Rennian, 2021. "Modelling the nacelle wake of a horizontal-axis wind turbine under different yaw conditions," Renewable Energy, Elsevier, vol. 172(C), pages 263-275.
    2. Rebecca J. Barthelmie & Tristan J. Shepherd & Jeanie A. Aird & Sara C. Pryor, 2020. "Power and Wind Shear Implications of Large Wind Turbine Scenarios in the US Central Plains," Energies, MDPI, vol. 13(16), pages 1-21, August.
    3. Fleming, Paul A. & Gebraad, Pieter M.O. & Lee, Sang & van Wingerden, Jan-Willem & Johnson, Kathryn & Churchfield, Matt & Michalakes, John & Spalart, Philippe & Moriarty, Patrick, 2014. "Evaluating techniques for redirecting turbine wakes using SOWFA," Renewable Energy, Elsevier, vol. 70(C), pages 211-218.
    4. Adaramola, M.S. & Krogstad, P.-Å., 2011. "Experimental investigation of wake effects on wind turbine performance," Renewable Energy, Elsevier, vol. 36(8), pages 2078-2086.
    5. Sang Lee & Matthew Churchfield & Frederick Driscoll & Senu Sirnivas & Jason Jonkman & Patrick Moriarty & Bjόrn Skaare & Finn Gunnar Nielsen & Erik Byklum, 2018. "Load Estimation of Offshore Wind Turbines," Energies, MDPI, vol. 11(7), pages 1-15, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Guillem Armengol Barcos & Fernando Porté-Agel, 2023. "Enhancing Wind Farm Performance through Axial Induction and Tilt Control: Insights from Wind Tunnel Experiments," Energies, MDPI, vol. 17(1), pages 1-20, December.
    2. Rivera-Arreba, Irene & Li, Zhaobin & Yang, Xiaolei & Bachynski-Polić, Erin E., 2024. "Comparison of the dynamic wake meandering model against large eddy simulation for horizontal and vertical steering of wind turbine wakes," Renewable Energy, Elsevier, vol. 221(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Can Zhang & Jisheng Zhang & Athanasios Angeloudis & Yudi Zhou & Stephan C. Kramer & Matthew D. Piggott, 2023. "Physical Modelling of Tidal Stream Turbine Wake Structures under Yaw Conditions," Energies, MDPI, vol. 16(4), pages 1-21, February.
    2. Frederik, Joeri A. & van Wingerden, Jan-Willem, 2022. "On the load impact of dynamic wind farm wake mixing strategies," Renewable Energy, Elsevier, vol. 194(C), pages 582-595.
    3. Rivera-Arreba, Irene & Li, Zhaobin & Yang, Xiaolei & Bachynski-Polić, Erin E., 2024. "Comparison of the dynamic wake meandering model against large eddy simulation for horizontal and vertical steering of wind turbine wakes," Renewable Energy, Elsevier, vol. 221(C).
    4. Harsh S. Dhiman & Dipankar Deb & Vlad Muresan & Valentina E. Balas, 2019. "Wake Management in Wind Farms: An Adaptive Control Approach," Energies, MDPI, vol. 12(7), pages 1-18, April.
    5. Dou, Bingzheng & Guala, Michele & Lei, Liping & Zeng, Pan, 2019. "Wake model for horizontal-axis wind and hydrokinetic turbines in yawed conditions," Applied Energy, Elsevier, vol. 242(C), pages 1383-1395.
    6. Park, Jinkyoo & Law, Kincho H., 2016. "A data-driven, cooperative wind farm control to maximize the total power production," Applied Energy, Elsevier, vol. 165(C), pages 151-165.
    7. Guo-Wei Qian & Takeshi Ishihara, 2018. "A New Analytical Wake Model for Yawed Wind Turbines," Energies, MDPI, vol. 11(3), pages 1-24, March.
    8. Ma, Hongliang & Ge, Mingwei & Wu, Guangxing & Du, Bowen & Liu, Yongqian, 2021. "Formulas of the optimized yaw angles for cooperative control of wind farms with aligned turbines to maximize the power production," Applied Energy, Elsevier, vol. 303(C).
    9. Shen, Wen Zhong & Lin, Jian Wei & Jiang, Yu Hang & Feng, Ju & Cheng, Li & Zhu, Wei Jun, 2023. "A novel yaw wake model for wind farm control applications," Renewable Energy, Elsevier, vol. 218(C).
    10. Rubel C. Das & Yu-Lin Shen, 2023. "Analysis of Wind Farms under Different Yaw Angles and Wind Speeds," Energies, MDPI, vol. 16(13), pages 1-19, June.
    11. De-Zhi Wei & Ni-Na Wang & De-Cheng Wan, 2021. "Modelling Yawed Wind Turbine Wakes: Extension of a Gaussian-Based Wake Model," Energies, MDPI, vol. 14(15), pages 1-26, July.
    12. Jinkyoo Park & Soon-Duck Kwon & Kincho Law, 2017. "A Data-Driven, Cooperative Approach for Wind Farm Control: A Wind Tunnel Experimentation," Energies, MDPI, vol. 10(7), pages 1-17, June.
    13. Bottasso, C.L. & Cacciola, S. & Schreiber, J., 2018. "Local wind speed estimation, with application to wake impingement detection," Renewable Energy, Elsevier, vol. 116(PA), pages 155-168.
    14. Hayat, Imran & Chatterjee, Tanmoy & Liu, Huiwen & Peet, Yulia T. & Chamorro, Leonardo P., 2019. "Exploring wind farms with alternating two- and three-bladed wind turbines," Renewable Energy, Elsevier, vol. 138(C), pages 764-774.
    15. He, Ruiyang & Yang, Hongxing & Lu, Lin, 2023. "Optimal yaw strategy and fatigue analysis of wind turbines under the combined effects of wake and yaw control," Applied Energy, Elsevier, vol. 337(C).
    16. Li, Qing'an & Cai, Chang & Kamada, Yasunari & Maeda, Takao & Hiromori, Yuto & Zhou, Shuni & Xu, Jianzhong, 2021. "Prediction of power generation of two 30 kW Horizontal Axis Wind Turbines with Gaussian model," Energy, Elsevier, vol. 231(C).
    17. Arabgolarcheh, Alireza & Rouhollahi, Amirhossein & Benini, Ernesto, 2023. "Analysis of middle-to-far wake behind floating offshore wind turbines in the presence of multiple platform motions," Renewable Energy, Elsevier, vol. 208(C), pages 546-560.
    18. Guillem Armengol Barcos & Fernando Porté-Agel, 2023. "Enhancing Wind Farm Performance through Axial Induction and Tilt Control: Insights from Wind Tunnel Experiments," Energies, MDPI, vol. 17(1), pages 1-20, December.
    19. Su, Keye & Bliss, Donald, 2019. "A novel hybrid free-wake model for wind turbine performance and wake evolution," Renewable Energy, Elsevier, vol. 131(C), pages 977-992.
    20. Francesco Mazzeo & Derek Micheletto & Alessandro Talamelli & Antonio Segalini, 2022. "An Experimental Study on a Wind Turbine Rotor Affected by Pitch Imbalance," Energies, MDPI, vol. 15(22), pages 1-16, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:188:y:2022:i:c:p:349-360. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.