IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i17p6165-d897030.html
   My bibliography  Save this article

Wind Tunnel Tests of Wake Characteristics for a Scaled Wind Turbine Model Based on Dynamic Similarity

Author

Listed:
  • Wei Yang

    (Chongqing Key Laboratory of Wind Engineering and Wind Energy Utilization, School of Civil Engineering, Chongqing University, Chongqing 400045, China
    College of Automation, Chongqing University, Chongqing 400045, China
    CSSC Haizhuang Windpower Co., Ltd., Chongqing 401122, China)

  • Meng Yu

    (Chongqing Key Laboratory of Wind Engineering and Wind Energy Utilization, School of Civil Engineering, Chongqing University, Chongqing 400045, China)

  • Bowen Yan

    (Chongqing Key Laboratory of Wind Engineering and Wind Energy Utilization, School of Civil Engineering, Chongqing University, Chongqing 400045, China)

  • Guoqing Huang

    (Chongqing Key Laboratory of Wind Engineering and Wind Energy Utilization, School of Civil Engineering, Chongqing University, Chongqing 400045, China)

  • Qingshan Yang

    (Chongqing Key Laboratory of Wind Engineering and Wind Energy Utilization, School of Civil Engineering, Chongqing University, Chongqing 400045, China)

  • Senqin Zhang

    (Chongqing Key Laboratory of Wind Engineering and Wind Energy Utilization, School of Civil Engineering, Chongqing University, Chongqing 400045, China
    Energy Research Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China)

  • Tianhao Hong

    (Chongqing Key Laboratory of Wind Engineering and Wind Energy Utilization, School of Civil Engineering, Chongqing University, Chongqing 400045, China)

  • Xu Zhou

    (Chongqing Key Laboratory of Wind Engineering and Wind Energy Utilization, School of Civil Engineering, Chongqing University, Chongqing 400045, China)

  • Xiaowei Deng

    (Department of Civil Engineering, The University of Hong Kong, Pokfulam, Hong Kong)

Abstract

This wind tunnel study was conducted to investigate the similarity laws involved in the reasonable simulation of the wake characteristics of a full-scale wind turbine. A 5 MW scaled wind turbine model was designed using an optimization method based on the blade element momentum (BEM) theory. Subsequently, wind tunnel tests were carried out on the geometrically similar model and the thrust-optimized model, with different yaw angles and under various upstream flow conditions. The results indicated that the wake development of the wind turbine model was closely related to the thrust forces of the wind turbine, and both kinematic and dynamic similarity laws should be observed to achieve wake characteristics that are reasonably similar to those of a full-scale wind turbine. This study investigated the aerodynamic similarity principles of small-scale wind turbine models to develop a more effective method for simulating full-scale turbine wake characteristics in wind tunnel tests. The outcomes of this study revealed the limitations of the anomalously low thrust coefficients in geometrically similar wind turbine models and present reasonable model design methodologies for small-scale wind turbine models in wind tunnel tests.

Suggested Citation

  • Wei Yang & Meng Yu & Bowen Yan & Guoqing Huang & Qingshan Yang & Senqin Zhang & Tianhao Hong & Xu Zhou & Xiaowei Deng, 2022. "Wind Tunnel Tests of Wake Characteristics for a Scaled Wind Turbine Model Based on Dynamic Similarity," Energies, MDPI, vol. 15(17), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6165-:d:897030
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/17/6165/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/17/6165/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Zong, Haohua & Porté-Agel, Fernando, 2021. "Experimental investigation and analytical modelling of active yaw control for wind farm power optimization," Renewable Energy, Elsevier, vol. 170(C), pages 1228-1244.
    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. 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).
    2. Wei Li & Shinai Xu & Baiyun Qian & Xiaoxia Gao & Xiaoxun Zhu & Zeqi Shi & Wei Liu & Qiaoliang Hu, 2022. "Large-Scale Wind Turbine’s Load Characteristics Excited by the Wind and Grid in Complex Terrain: A Review," Sustainability, MDPI, vol. 14(24), pages 1-29, December.
    3. Kim, Taewan & Kim, Changwook & Song, Jeonghwan & You, Donghyun, 2024. "Optimal control of a wind farm in time-varying wind using deep reinforcement learning," Energy, Elsevier, vol. 303(C).
    4. 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.
    5. 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).
    6. He, Ruiyang & Yang, Hongxing & Lu, Lin & Gao, Xiaoxia, 2024. "Site-specific wake steering strategy for combined power enhancement and fatigue mitigation within wind farms," Renewable Energy, Elsevier, vol. 225(C).
    7. 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).
    8. 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).
    9. 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.
    10. 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.
    11. 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.
    12. 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).
    13. Chen, Kaixuan & Lin, Jin & Qiu, Yiwei & Liu, Feng & Song, Yonghua, 2022. "Joint optimization of wind farm layout considering optimal control," Renewable Energy, Elsevier, vol. 182(C), pages 787-796.
    14. 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.
    15. 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.
    16. 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.
    17. Deepu Dilip & Fernando Porté-Agel, 2017. "Wind Turbine Wake Mitigation through Blade Pitch Offset," Energies, MDPI, vol. 10(6), pages 1-17, May.
    18. 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.
    19. Aju, Emmanuvel Joseph & Kumar, Devesh & Leffingwell, Melissa & Rotea, Mario A. & Jin, Yaqing, 2023. "The influence of yaw misalignment on turbine power output fluctuations and unsteady aerodynamic loads within wind farms," Renewable Energy, Elsevier, vol. 215(C).
    20. Ciprian Sorandaru & Sorin Musuroi & Flaviu Mihai Frigura-Iliasa & Doru Vatau & Marian Dordescu, 2019. "Analysis of the Wind System Operation in the Optimal Energetic Area at Variable Wind Speed over Time," Sustainability, MDPI, vol. 11(5), pages 1-16, February.

    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:17:p:6165-:d:897030. 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.