IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i7p1639-d340355.html
   My bibliography  Save this article

Wind Turbine Blade Optimal Design Considering Multi-Parameters and Response Surface Method

Author

Listed:
  • Sang-Lae Lee

    (Korean Register of Shipping, 36, Myeongji Ocean City 9-ro, Gangseo-gu, Busan 46762, Korea)

  • SangJoon Shin

    (Institute of Advanced Aerospace Technology, Department of Mechanical and Aerospace Engineering, Seoul National University, Gwanak-ro, Gwanak-gu, Seoul 08826, Korea)

Abstract

Within the framework of blade aerodynamic design, the maximum aerodynamic efficiency, power production, and minimum thrust force are the targets to obtain. This paper describes an improved optimization framework for blade aerodynamic design under realistic conditions, while considering multiple design parameters. The relationship between the objective function and the design parameters, such as the chord length, maximum chord, and twist angle, were obtained by using the second-order response surface methodology (RSM). Moreover, the identified parameters were organized to optimize the aerodynamic design of the blades. Furthermore, the initial and optimized blade geometries were compared and showed that the performance of the optimized blade improved significantly. In fact, the efficiency was increased by approximately 10%, although its thrust was not varied. In addition, to demonstrate the improvement in the resulting optimized blades, the annual energy production (AEP) was estimated when installed in a specific regional location. The result showed a significant improvement when compared to the baseline blades. This result will be extended to a new perspective approach for a more robust optimal design of a wind turbine blade.

Suggested Citation

  • Sang-Lae Lee & SangJoon Shin, 2020. "Wind Turbine Blade Optimal Design Considering Multi-Parameters and Response Surface Method," Energies, MDPI, vol. 13(7), pages 1-23, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1639-:d:340355
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/7/1639/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/7/1639/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kim, Bumsuk & Kim, Woojune & Lee, Sanglae & Bae, Sungyoul & Lee, Youngho, 2013. "Developement and verification of a performance based optimal design software for wind turbine blades," Renewable Energy, Elsevier, vol. 54(C), pages 166-172.
    2. Fatehi, Mostafa & Nili-Ahmadabadi, Mahdi & Nematollahi, Omid & Minaiean, Ali & Kim, Kyung Chun, 2019. "Aerodynamic performance improvement of wind turbine blade by cavity shape optimization," Renewable Energy, Elsevier, vol. 132(C), pages 773-785.
    3. Sessarego, Matias & Feng, Ju & Ramos-García, Néstor & Horcas, Sergio González, 2020. "Design optimization of a curved wind turbine blade using neural networks and an aero-elastic vortex method under turbulent inflow," Renewable Energy, Elsevier, vol. 146(C), pages 1524-1535.
    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. Galih Bangga, 2022. "Progress and Outlook in Wind Energy Research," Energies, MDPI, vol. 15(18), pages 1-5, September.
    2. Wenbin Su & Hongbo Wei & Penghua Guo & Qiao Hu & Mengyuan Guo & Yuanjie Zhou & Dayu Zhang & Zhufeng Lei & Chaohui Wang, 2021. "Research on Hydraulic Conversion Technology of Small Ocean Current Turbines for Low-Flow Current Energy Generation," Energies, MDPI, vol. 14(20), pages 1-19, October.
    3. Jia, Liangyue & Hao, Jia & Hall, John & Nejadkhaki, Hamid Khakpour & Wang, Guoxin & Yan, Yan & Sun, Mengyuan, 2021. "A reinforcement learning based blade twist angle distribution searching method for optimizing wind turbine energy power," Energy, Elsevier, vol. 215(PA).
    4. Mohammed Debbache & Messaoud Hazmoune & Semcheddine Derfouf & Dana-Alexandra Ciupageanu & Gheorghe Lazaroiu, 2021. "Wind Blade Twist Correction for Enhanced Annual Energy Production of Wind Turbines," Sustainability, MDPI, vol. 13(12), pages 1-17, June.
    5. Gisela Pujol-Vazquez & Leonardo Acho & José Gibergans-Báguena, 2020. "Fault Detection Algorithm for Wind Turbines’ Pitch Actuator Systems," Energies, MDPI, vol. 13(11), pages 1-14, June.

    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. Alex Mendonça Bimbato & Luiz Antonio Alcântara Pereira & Miguel Hiroo Hirata, 2020. "Study of Surface Roughness Effect on a Bluff Body—The Formation of Asymmetric Separation Bubbles," Energies, MDPI, vol. 13(22), pages 1-20, November.
    2. Li, B. & Zhou, D.L. & Wang, Y. & Shuai, Y. & Liu, Q.Z. & Cai, W.H., 2020. "The design of a small lab-scale wind turbine model with high performance similarity to its utility-scale prototype," Renewable Energy, Elsevier, vol. 149(C), pages 435-444.
    3. Jordi, Zavala J. & Erasmo, Cadenas & Rafael, Campos-Amezcua, 2024. "Multi-criteria optimal design of small wind turbine blades based on deep learning methods," Energy, Elsevier, vol. 293(C).
    4. Elahi, Ehsan & Zhang, Zhixin & Khalid, Zainab & Xu, Haiyun, 2022. "Application of an artificial neural network to optimise energy inputs: An energy- and cost-saving strategy for commercial poultry farms," Energy, Elsevier, vol. 244(PB).
    5. Liu, Qingsong & Miao, Weipao & Ye, Qi & Li, Chun, 2022. "Performance assessment of an innovative Gurney flap for straight-bladed vertical axis wind turbine," Renewable Energy, Elsevier, vol. 185(C), pages 1124-1138.
    6. Acarer, Sercan & Uyulan, Çağlar & Karadeniz, Ziya Haktan, 2020. "Optimization of radial inflow wind turbines for urban wind energy harvesting," Energy, Elsevier, vol. 202(C).
    7. Wang, Yuqi & Du, Qiuwan & Li, Yunzhu & Zhang, Di & Xie, Yonghui, 2022. "Field reconstruction and off-design performance prediction of turbomachinery in energy systems based on deep learning techniques," Energy, Elsevier, vol. 238(PB).
    8. Miller, Aaron & Chang, Byungik & Issa, Roy & Chen, Gerald, 2013. "Review of computer-aided numerical simulation in wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 122-134.
    9. Dominik Błoński & Katarzyna Strzelecka & Henryk Kudela, 2021. "Vortex Trapping Cavity on Airfoil: High-Order Penalized Vortex Method Numerical Simulation and Water Tunnel Experimental Investigation," Energies, MDPI, vol. 14(24), pages 1-32, December.
    10. Tahani, Mojtaba & Kavari, Ghazale & Masdari, Mehran & Mirhosseini, Mojtaba, 2017. "Aerodynamic design of horizontal axis wind turbine with innovative local linearization of chord and twist distributions," Energy, Elsevier, vol. 131(C), pages 78-91.
    11. Tjiu, Willy & Marnoto, Tjukup & Mat, Sohif & Ruslan, Mohd Hafidz & Sopian, Kamaruzzaman, 2015. "Darrieus vertical axis wind turbine for power generation II: Challenges in HAWT and the opportunity of multi-megawatt Darrieus VAWT development," Renewable Energy, Elsevier, vol. 75(C), pages 560-571.
    12. Jia, Liangyue & Hao, Jia & Hall, John & Nejadkhaki, Hamid Khakpour & Wang, Guoxin & Yan, Yan & Sun, Mengyuan, 2021. "A reinforcement learning based blade twist angle distribution searching method for optimizing wind turbine energy power," Energy, Elsevier, vol. 215(PA).
    13. Bai, Chi-Jeng & Wang, Wei-Cheng, 2016. "Review of computational and experimental approaches to analysis of aerodynamic performance in horizontal-axis wind turbines (HAWTs)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 506-519.
    14. 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.
    15. Nikolić, Vlastimir & Sajjadi, Shahin & Petković, Dalibor & Shamshirband, Shahaboddin & Ćojbašić, Žarko & Por, Lip Yee, 2016. "Design and state of art of innovative wind turbine systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 258-265.
    16. Acarer, Sercan, 2020. "Peak lift-to-drag ratio enhancement of the DU12W262 airfoil by passive flow control and its impact on horizontal and vertical axis wind turbines," Energy, Elsevier, vol. 201(C).
    17. Du, Weikang & Zhao, Yongsheng & He, Yanping & Liu, Yadong, 2016. "Design, analysis and test of a model turbine blade for a wave basin test of floating wind turbines," Renewable Energy, Elsevier, vol. 97(C), pages 414-421.
    18. Cemil Yigit, 2020. "Effect of Air-Ducted Blade Design on Horizontal Axis Wind Turbine Performance," Energies, MDPI, vol. 13(14), pages 1-15, July.
    19. Yan, Jie & Nuertayi, Akejiang & Yan, Yamin & Liu, Shan & Liu, Yongqian, 2023. "Hybrid physical and data driven modeling for dynamic operation characteristic simulation of wind turbine," Renewable Energy, Elsevier, vol. 215(C).
    20. Li, Jichao & Dao, My Ha & Le, Quang Tuyen, 2024. "Data-driven modal parameterization for robust aerodynamic shape optimization of wind turbine blades," Renewable Energy, Elsevier, vol. 224(C).

    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:13:y:2020:i:7:p:1639-:d:340355. 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.