IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i16p4113-d1459101.html
   My bibliography  Save this article

A Method to Design an Efficient Airfoil for Small Wind Turbines in Low Wind Speed Conditions Using XFLR5 and CFD Simulations

Author

Listed:
  • Le Quang Sang

    (Institute of Science and Technology for Energy and Environment, Vietnam Academy of Science and Technology, Hanoi 10072, Vietnam
    Faculty of Materials Science and Energy, Graduate University of Science and Technology, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet, Cau Giay, Hanoi 10072, Vietnam)

  • Tinnapob Phengpom

    (Institute for Innovative Learning, Mahidol University, 999 Phuttamonthon 4 Road, Salaya, Phutthamonthon, Nakhon Pathom 73170, Thailand)

  • Dinh Van Thin

    (Faculty of Energy Technology, Electric Power University, 235 Hoang Quoc Viet, Hanoi 11355, Vietnam)

  • Nguyen Huu Duc

    (Faculty of Energy Technology, Electric Power University, 235 Hoang Quoc Viet, Hanoi 11355, Vietnam)

  • Le Thi Thuy Hang

    (Institute of Science and Technology for Energy and Environment, Vietnam Academy of Science and Technology, Hanoi 10072, Vietnam)

  • Cu Thi Thanh Huyen

    (Institute of Science and Technology for Energy and Environment, Vietnam Academy of Science and Technology, Hanoi 10072, Vietnam)

  • Nguyen Thi Thu Huong

    (Institute of Science and Technology for Energy and Environment, Vietnam Academy of Science and Technology, Hanoi 10072, Vietnam)

  • Quynh T. Tran

    (Institute of Science and Technology for Energy and Environment, Vietnam Academy of Science and Technology, Hanoi 10072, Vietnam
    Hawaii Natural Energy Institute, University of Hawaii at Manoa, Honolulu, HI 96822, USA)

Abstract

Small wind turbines operating in low wind speed regions have not had any significant success. In addition, small wind speed regions occupy a large area of the world, so they represent a potential area for installing small wind turbines in the future. In this paper, a method to design an efficient airfoil for small wind turbines in low wind speed conditions using XFLR5 and CFD simulations is implemented. Because the impact of the airflow on the blade surface under low Re number conditions can change suddenly for small geometries, designing the airfoil shape to optimize the aerodynamic performance is essential. The tuning of the key geometric parameters using inversion techniques for better aerodynamic performance is presented in this study. A two-dimensional model was used to consider the airflow on the airfoil surface with differences in the angle of attack. The original S1010 airfoil was used to design a new airfoil for increasing the aerodynamic efficiency by using V6.57 XFLR5 software. Subsequently, the new VAST-EPU-S1010 airfoil model was adjusted to the maximum thickness and the maximum thickness position. It was simulated in low wind speed conditions of 4–6 m/s by a computational fluid dynamics simulation. The lift coefficient, drag coefficient, and C L / C D coefficient ratio were evaluated under the effect of the angle of attack and the maximum thickness by using the k-ε model. The simulation results show that the VAST-EPU-S1010 airfoil achieved the greatest aerodynamic efficiency at an angle of attack of 3°, a maximum thickness of 8%, and a maximum thickness position of 20.32%. The maximum value of C L / C D of the new airfoil at 6 m/s was higher than at 4 m/s by about 6.25%.

Suggested Citation

  • Le Quang Sang & Tinnapob Phengpom & Dinh Van Thin & Nguyen Huu Duc & Le Thi Thuy Hang & Cu Thi Thanh Huyen & Nguyen Thi Thu Huong & Quynh T. Tran, 2024. "A Method to Design an Efficient Airfoil for Small Wind Turbines in Low Wind Speed Conditions Using XFLR5 and CFD Simulations," Energies, MDPI, vol. 17(16), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:4113-:d:1459101
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/16/4113/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/16/4113/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liao, C.C. & Zhao, X.L. & Xu, J.Z., 2012. "Blade layers optimization of wind turbines using FAST and improved PSO Algorithm," Renewable Energy, Elsevier, vol. 42(C), pages 227-233.
    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 Lu & Minyan Zhu & Haijun Wu & Jianzhong Wu, 2022. "A Review and Case Analysis on Biaxial Synchronous Loading Technology and Fast Moment-Matching Methods for Fatigue Tests of Wind Turbine Blades," Energies, MDPI, vol. 15(13), pages 1-34, July.
    2. Shen, Xin & Chen, Jin-Ge & Zhu, Xiao-Cheng & Liu, Peng-Yin & Du, Zhao-Hui, 2015. "Multi-objective optimization of wind turbine blades using lifting surface method," Energy, Elsevier, vol. 90(P1), pages 1111-1121.
    3. Jie Zhu & Xin Cai & Pan Pan & Rongrong Gu, 2014. "Multi-Objective Structural Optimization Design of Horizontal-Axis Wind Turbine Blades Using the Non-Dominated Sorting Genetic Algorithm II and Finite Element Method," Energies, MDPI, vol. 7(2), pages 1-15, February.
    4. Lu, Liang & Wu, Haijun & Wu, Jianzhong, 2021. "A case study for the optimization of moment-matching in wind turbine blade fatigue tests with a resonant type exciting approach," Renewable Energy, Elsevier, vol. 174(C), pages 769-785.
    5. Wang, Long & Wang, Tongguang & Wu, Jianghai & Chen, Guoping, 2017. "Multi-objective differential evolution optimization based on uniform decomposition for wind turbine blade design," Energy, Elsevier, vol. 120(C), pages 346-361.
    6. Jie Zhu & Xin Cai & Rongrong Gu, 2016. "Aerodynamic and Structural Integrated Optimization Design of Horizontal-Axis Wind Turbine Blades," Energies, MDPI, vol. 9(2), pages 1-18, January.
    7. Yong Ma & Aiming Zhang & Lele Yang & Chao Hu & Yue Bai, 2019. "Investigation on Optimization Design of Offshore Wind Turbine Blades based on Particle Swarm Optimization," Energies, MDPI, vol. 12(10), pages 1-18, May.
    8. Long Wang & Ran Han & Tongguang Wang & Shitang Ke, 2018. "Uniform Decomposition and Positive-Gradient Differential Evolution for Multi-Objective Design of Wind Turbine Blade," Energies, MDPI, vol. 11(5), pages 1-19, May.
    9. Iqbal, M. & Azam, M. & Naeem, M. & Khwaja, A.S. & Anpalagan, A., 2014. "Optimization classification, algorithms and tools for renewable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 640-654.
    10. Xin Cai & Jie Zhu & Pan Pan & Rongrong Gu, 2012. "Structural Optimization Design of Horizontal-Axis Wind Turbine Blades Using a Particle Swarm Optimization Algorithm and Finite Element Method," Energies, MDPI, vol. 5(11), pages 1-14, November.
    11. Song, Dongran & Fan, Xinyu & Yang, Jian & Liu, Anfeng & Chen, Sifan & Joo, Young Hoon, 2018. "Power extraction efficiency optimization of horizontal-axis wind turbines through optimizing control parameters of yaw control systems using an intelligent method," Applied Energy, Elsevier, vol. 224(C), pages 267-279.
    12. Chehouri, Adam & Younes, Rafic & Ilinca, Adrian & Perron, Jean, 2015. "Review of performance optimization techniques applied to wind turbines," Applied Energy, Elsevier, vol. 142(C), pages 361-388.

    More about this item

    Keywords

    airfoil; CFD; XFLR5; low wind speed; lift and drag coefficients;
    All these keywords.

    JEL classification:

    Statistics

    Access and download statistics

    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:17:y:2024:i:16:p:4113-:d:1459101. 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.