IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i22p8607-d975230.html
   My bibliography  Save this article

Wind Tunnel Study on the Tip Speed Ratio’s Impact on a Wind Turbine Wake Development

Author

Listed:
  • Ingrid Neunaber

    (Department of Energy and Process Engineering, Norwegian University of Science and Technology, 7034 Trondheim, Norway
    LHEEA/CNRS, École Centrale de Nantes, 44300 Nantes, France)

  • Michael Hölling

    (Institute of Physics and for Wind, University of Oldenburg, 26129 Oldenburg, Germany)

  • Martin Obligado

    (CNRS, Grenoble INP, LEGI, Université Grenoble Alpes, 38000 Grenoble, France)

Abstract

We propose an experimental study on the influence of the tip speed ratio on the spatial development of a wind turbine wake. To accomplish this, a scaled wind turbine is tested in a wind tunnel, and its turbulent wake measured for streamwise distances between 1 and 30 diameters. Two different tip speed ratios (5.3 and 4.5) are tested by varying the pitch angle of the rotor blades between the optimal setting and one with an offset of + 6 ∘ . In addition, we test two Reynolds numbers for the optimal tip speed ratio, R e D = 1.9 × 10 5 and R e D = 2.9 × 10 5 (based on the turbine diameter and the freestream velocity). For all cases, the mean streamwise velocity deficit at the centerline evolves close to a power law in the far wake, and we check the validity of the Jensen and Bastankhah-Porté-Agel engineering wind turbine wake models and the Townsend-George wake model for free shear flows for this region. Lastly, we present radial profiles of the mean streamwise velocity and test different radial models. Our results show that the lateral profile of the wake is properly fitted by a super-Gaussian curve close to the rotor, while Gaussian-like profiles adapt better in the far wake.

Suggested Citation

  • Ingrid Neunaber & Michael Hölling & Martin Obligado, 2022. "Wind Tunnel Study on the Tip Speed Ratio’s Impact on a Wind Turbine Wake Development," Energies, MDPI, vol. 15(22), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8607-:d:975230
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/22/8607/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/22/8607/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lignarolo, L.E.M. & Ragni, D. & Krishnaswami, C. & Chen, Q. & Simão Ferreira, C.J. & van Bussel, G.J.W., 2014. "Experimental analysis of the wake of a horizontal-axis wind-turbine model," Renewable Energy, Elsevier, vol. 70(C), pages 31-46.
    2. Neunaber, Ingrid & Hölling, Michael & Whale, Jonathan & Peinke, Joachim, 2021. "Comparison of the turbulence in the wakes of an actuator disc and a model wind turbine by higher order statistics: A wind tunnel study," Renewable Energy, Elsevier, vol. 179(C), pages 1650-1662.
    3. Hegazy, Amr & Blondel, Frédéric & Cathelain, Marie & Aubrun, Sandrine, 2022. "LiDAR and SCADA data processing for interacting wind turbine wakes with comparison to analytical wake models," Renewable Energy, Elsevier, vol. 181(C), pages 457-471.
    4. Ingrid Neunaber & Michael Hölling & Richard J. A. M. Stevens & Gerard Schepers & Joachim Peinke, 2020. "Distinct Turbulent Regions in the Wake of a Wind Turbine and Their Inflow-Dependent Locations: The Creation of a Wake Map," Energies, MDPI, vol. 13(20), pages 1-20, October.
    5. Jeon, Sanghyeon & Kim, Bumsuk & Huh, Jongchul, 2015. "Comparison and verification of wake models in an onshore wind farm considering single wake condition of the 2 MW wind turbine," Energy, Elsevier, vol. 93(P2), pages 1769-1777.
    6. Bastankhah, Majid & Porté-Agel, Fernando, 2014. "A new analytical model for wind-turbine wakes," Renewable Energy, Elsevier, vol. 70(C), pages 116-123.
    7. Yu-Ting Wu & Chang-Yu Lin & Che-Ming Hsu, 2020. "An Experimental Investigation of Wake Characteristics and Power Generation Efficiency of a Small Wind Turbine under Different Tip Speed Ratios," Energies, MDPI, vol. 13(8), pages 1-19, April.
    8. Victor P. Stein & Hans-Jakob Kaltenbach, 2019. "Non-Equilibrium Scaling Applied to the Wake Evolution of a Model Scale Wind Turbine," Energies, MDPI, vol. 12(14), pages 1-24, July.
    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. 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).
    2. Hyungyu Kim & Kwansu Kim & Carlo Luigi Bottasso & Filippo Campagnolo & Insu Paek, 2018. "Wind Turbine Wake Characterization for Improvement of the Ainslie Eddy Viscosity Wake Model," Energies, MDPI, vol. 11(10), pages 1-19, October.
    3. 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).
    4. Sarlak, H. & Meneveau, C. & Sørensen, J.N., 2015. "Role of subgrid-scale modeling in large eddy simulation of wind turbine wake interactions," Renewable Energy, Elsevier, vol. 77(C), pages 386-399.
    5. Nicolas Tobin & Ali M. Hamed & Leonardo P. Chamorro, 2015. "An Experimental Study on the Effects ofWinglets on the Wake and Performance of a ModelWind Turbine," Energies, MDPI, vol. 8(10), pages 1-18, October.
    6. Wang, Yangwei & Lin, Jiahuan & Zhang, Jun, 2022. "Investigation of a new analytical wake prediction method for offshore floating wind turbines considering an accurate incoming wind flow," Renewable Energy, Elsevier, vol. 185(C), pages 827-849.
    7. Luo, Zhaohui & Wang, Longyan & Xu, Jian & Wang, Zilu & Yuan, Jianping & Tan, Andy C.C., 2024. "A reduced order modeling-based machine learning approach for wind turbine wake flow estimation from sparse sensor measurements," Energy, Elsevier, vol. 294(C).
    8. Feng, Dachuan & Li, Larry K.B. & Gupta, Vikrant & Wan, Minping, 2022. "Componentwise influence of upstream turbulence on the far-wake dynamics of wind turbines," Renewable Energy, Elsevier, vol. 200(C), pages 1081-1091.
    9. Li, Qing'an & Murata, Junsuke & Endo, Masayuki & Maeda, Takao & Kamada, Yasunari, 2016. "Experimental and numerical investigation of the effect of turbulent inflow on a Horizontal Axis Wind Turbine (part II: Wake characteristics)," Energy, Elsevier, vol. 113(C), pages 1304-1315.
    10. Wen, Jiahao & Zhou, Lei & Zhang, Hongfu, 2023. "Mode interpretation of blade number effects on wake dynamics of small-scale horizontal axis wind turbine," Energy, Elsevier, vol. 263(PA).
    11. Zhang, Yi & Li, Zhaobin & Liu, Xiaohao & Sotiropoulos, Fotis & Yang, Xiaolei, 2023. "Turbulence in waked wind turbine wakes: Similarity and empirical formulae," Renewable Energy, Elsevier, vol. 209(C), pages 27-41.
    12. Shakoor, Rabia & Hassan, Mohammad Yusri & Raheem, Abdur & Wu, Yuan-Kang, 2016. "Wake effect modeling: A review of wind farm layout optimization using Jensen׳s model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1048-1059.
    13. Yunliang Li & Zhaobin Li & Zhideng Zhou & Xiaolei Yang, 2023. "Large-Eddy Simulation of Wind Turbine Wakes in Forest Terrain," Sustainability, MDPI, vol. 15(6), pages 1-23, March.
    14. 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.
    15. Li, Qing’an & Maeda, Takao & Kamada, Yasunari & Mori, Naoya, 2017. "Investigation of wake effects on a Horizontal Axis Wind Turbine in field experiments (Part I: Horizontal axis direction)," Energy, Elsevier, vol. 134(C), pages 482-492.
    16. Lingkan, Elizabeth H. & Buxton, Oliver R.H., 2023. "An assessment of the scalings for the streamwise evolution of turbulent quantities in wakes produced by porous objects," Renewable Energy, Elsevier, vol. 209(C), pages 1-9.
    17. Zhou, Lei & Wen, Jiahao & Wang, Zhaokun & Deng, Pengru & Zhang, Hongfu, 2023. "High-fidelity wind turbine wake velocity prediction by surrogate model based on d-POD and LSTM," Energy, Elsevier, vol. 275(C).
    18. Syed Ahmed Kabir, Ijaz Fazil & Safiyullah, Ferozkhan & Ng, E.Y.K. & Tam, Vivian W.Y., 2020. "New analytical wake models based on artificial intelligence and rivalling the benchmark full-rotor CFD predictions under both uniform and ABL inflows," Energy, Elsevier, vol. 193(C).
    19. Cao, Lichao & Ge, Mingwei & Gao, Xiaoxia & Du, Bowen & Li, Baoliang & Huang, Zhi & Liu, Yongqian, 2022. "Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines," Applied Energy, Elsevier, vol. 323(C).
    20. Ziyu Zhang & Peng Huang & Haocheng Sun, 2020. "A Novel Analytical Wake Model with a Cosine-Shaped Velocity Deficit," Energies, MDPI, vol. 13(13), pages 1-20, June.

    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:gam:jeners:v:15:y:2022:i:22:p:8607-:d:975230. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.