IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v149y2020icp735-748.html
   My bibliography  Save this article

The near-field of a lab-scale wind turbine in tailored turbulent shear flows

Author

Listed:
  • Li, L.
  • Hearst, R.J.
  • Ferreira, M.A.
  • Ganapathisubramani, B.

Abstract

Real wind turbines experience a wide range of turbulent shear flows that naturally occur within the atmospheric boundary layer, however, these are often difficult to simulate in experiments. An active grid was used to expand the testable parameter space compared to conventional methods. Specific focus was placed on decoupling the shear from the turbulence intensity. Particle image velocimetry was used to capture the mean velocity and velocity fluctuation fields in the near-field wake of a model wind turbine subjected to seven different combinations of shear and turbulence intensity. It was found that if the incoming mean profile was removed, the velocity deficit is approximately symmetric about the hub, even for highly sheared cases. The absolute wake velocity deficit profiles are asymmetric for the sheared cases, and the combination of the wake and shear flow results in a local increase in shear on the high-velocity side of the wake immediately downstream of the turbine. This in turn leads to higher turbulence production within that region, leading to larger velocity fluctuations. It is also demonstrated that the mean power of the model turbine is not particularly sensitive to the incoming shear, but the power fluctuations scale linearly with the incoming turbulence intensity.

Suggested Citation

  • Li, L. & Hearst, R.J. & Ferreira, M.A. & Ganapathisubramani, B., 2020. "The near-field of a lab-scale wind turbine in tailored turbulent shear flows," Renewable Energy, Elsevier, vol. 149(C), pages 735-748.
  • Handle: RePEc:eee:renene:v:149:y:2020:i:c:p:735-748
    DOI: 10.1016/j.renene.2019.12.049
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148119319214
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2019.12.049?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. Rockel, Stanislav & Peinke, Joachim & Hölling, Michael & Cal, Raúl Bayoán, 2017. "Dynamic wake development of a floating wind turbine in free pitch motion subjected to turbulent inflow generated with an active grid," Renewable Energy, Elsevier, vol. 112(C), pages 1-16.
    2. Yaqing Jin & Huiwen Liu & Rajan Aggarwal & Arvind Singh & Leonardo P. Chamorro, 2016. "Effects of Freestream Turbulence in a Model Wind Turbine Wake," Energies, MDPI, vol. 9(10), pages 1-12, October.
    3. Talavera, Miguel & Shu, Fangjun, 2017. "Experimental study of turbulence intensity influence on wind turbine performance and wake recovery in a low-speed wind tunnel," Renewable Energy, Elsevier, vol. 109(C), pages 363-371.
    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.
    Full references (including those not matched with items on IDEAS)

    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. Liang, Xiaoling & Fu, Shifeng & Cai, Fulin & Han, Xingxing & Zhu, Weijun & Yang, Hua & Shen, Wenzhong, 2023. "Experimental investigation on wake characteristics of wind turbine and a new two-dimensional wake model," Renewable Energy, Elsevier, vol. 203(C), pages 373-381.
    2. Kaldellis, John K. & Triantafyllou, Panagiotis & Stinis, Panagiotis, 2021. "Critical evaluation of Wind Turbines’ analytical wake models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    3. 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.
    4. 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).
    5. Öztürk, Buğrahan & Hassanein, Abdelrahman & Akpolat, M Tuğrul & Abdulrahim, Anas & Perçin, Mustafa & Uzol, Oğuz, 2023. "On the wake characteristics of a model wind turbine and a porous disc: Effects of freestream turbulence intensity," Renewable Energy, Elsevier, vol. 212(C), pages 238-250.
    6. Bingzheng Dou & Zhanpei Yang & Michele Guala & Timing Qu & Liping Lei & Pan Zeng, 2020. "Comparison of Different Driving Modes for the Wind Turbine Wake in Wind Tunnels," Energies, MDPI, vol. 13(8), pages 1-17, April.
    7. Bayron, Paul & Kelso, Richard & Chin, Rey, 2024. "Experimental investigation of tip-speed-ratio influence on horizontal-axis wind turbine wake dynamics," Renewable Energy, Elsevier, vol. 225(C).
    8. He, Ruiyang & Sun, Haiying & Gao, Xiaoxia & Yang, Hongxing, 2022. "Wind tunnel tests for wind turbines: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    9. Piotr Wiklak & Michal Kulak & Michal Lipian & Damian Obidowski, 2022. "Experimental Investigation of the Cooperation of Wind Turbines," Energies, MDPI, vol. 15(11), pages 1-20, May.
    10. Zheng, Yidan & Liu, Huiwen & Chamorro, Leonardo P. & Zhao, Zhenzhou & Li, Ye & Zheng, Yuan & Tang, Kexin, 2023. "Impact of turbulence level on intermittent-like events in the wake of a model wind turbine," Renewable Energy, Elsevier, vol. 203(C), pages 45-55.
    11. Haojun Tang & Kit-Ming Lam & Kei-Man Shum & Yongle Li, 2019. "Wake Effect of a Horizontal Axis Wind Turbine on the Performance of a Downstream Turbine," Energies, MDPI, vol. 12(12), pages 1-18, June.
    12. Amiri, Mojtaba Maali & Shadman, Milad & Estefen, Segen F., 2024. "A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    13. Fei Zhao & Yihan Gao & Tengyuan Wang & Jinsha Yuan & Xiaoxia Gao, 2020. "Experimental Study on Wake Evolution of a 1.5 MW Wind Turbine in a Complex Terrain Wind Farm Based on LiDAR Measurements," Sustainability, MDPI, vol. 12(6), pages 1-14, March.
    14. 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).
    15. 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).
    16. 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.
    17. Dou, Bingzheng & Guala, Michele & Lei, Liping & Zeng, Pan, 2019. "Experimental investigation of the performance and wake effect of a small-scale wind turbine in a wind tunnel," Energy, Elsevier, vol. 166(C), pages 819-833.
    18. Martin Robinius & Alexander Otto & Konstantinos Syranidis & David S. Ryberg & Philipp Heuser & Lara Welder & Thomas Grube & Peter Markewitz & Vanessa Tietze & Detlef Stolten, 2017. "Linking the Power and Transport Sectors—Part 2: Modelling a Sector Coupling Scenario for Germany," Energies, MDPI, vol. 10(7), pages 1-23, July.
    19. Khadijah Barashid & Amr Munshi & Ahmad Alhindi, 2023. "Wind Farm Power Prediction Considering Layout and Wake Effect: Case Study of Saudi Arabia," Energies, MDPI, vol. 16(2), pages 1-22, January.
    20. 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.

    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:149:y:2020:i:c:p:735-748. 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.