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Design studies of swept wind turbine blades

Author

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  • Larwood, Scott
  • van Dam, C.P.
  • Schow, Daniel

Abstract

The growth of wind energy is sustained by innovation that lowers the cost of energy. One recent innovation is the swept blade, which deflects in operation and lowers loads. With sweep, a design rotor diameter can increase, capturing more power, with the loads remaining within limits. This concept has been demonstrated in a U.S. program and is in commercial production. This paper describes a parametric study of swept blade design parameters for a 750 kW machine. The amount of tip sweep had the largest effect on the energy production and blade loads; other parameters had less impact. The authors then conducted a design study to implement a swept design on 1.5 MW, 3 MW, and 5 MW turbines. An aeroelastic code, previously described, was developed to model the behavior and determine the loads of the swept blade. The design goal was to increase annual energy production 5% over the straight blade, without increasing blade loads. Successful designs were developed for the 1.5 MW and 3.0 MW turbines. The swept 5 MW turbine exhibited a twist instability at high wind speeds. Further study is required to determine if sweep can be implemented for larger turbines, which are approaching flutter boundaries in unswept designs.

Suggested Citation

  • Larwood, Scott & van Dam, C.P. & Schow, Daniel, 2014. "Design studies of swept wind turbine blades," Renewable Energy, Elsevier, vol. 71(C), pages 563-571.
    • Handle: RePEc:eee:renene:v:71:y:2014:i:c:p:563-571
    DOI: 10.1016/j.renene.2014.05.050
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    Cited by:

    1. Miriam L. A. Gemaque & Jerson R. P. Vaz & Osvaldo R. Saavedra, 2022. "Optimization of Hydrokinetic Swept Blades," Sustainability, MDPI, vol. 14(21), pages 1-13, October.
    2. Momeni, Farhang & Sabzpoushan, Seyedali & Valizadeh, Reza & Morad, Mohammad Reza & Liu, Xun & Ni, Jun, 2019. "Plant leaf-mimetic smart wind turbine blades by 4D printing," Renewable Energy, Elsevier, vol. 130(C), pages 329-351.
    3. Scott, Samuel & Capuzzi, Marco & Langston, David & Bossanyi, Ervin & McCann, Graeme & Weaver, Paul M. & Pirrera, Alberto, 2017. "Effects of aeroelastic tailoring on performance characteristics of wind turbine systems," Renewable Energy, Elsevier, vol. 114(PB), pages 887-903.
    4. Silvia C. de P. Andrade & Déborah A. T. D. do Rio Vaz & Jerson R. P. Vaz, 2023. "A Simplified Optimization Model for Hydrokinetic Blades with Diffuser and Swept Rotor," Sustainability, MDPI, vol. 16(1), pages 1-15, December.
    5. Li, Jinxing & Liu, Tianyuan & Wang, Yuqi & Xie, Yonghui, 2022. "Integrated graph deep learning framework for flow field reconstruction and performance prediction of turbomachinery," Energy, Elsevier, vol. 254(PC).
    6. Ikeda, Teruaki & Tanaka, Hiroto & Yoshimura, Ryosuke & Noda, Ryusuke & Fujii, Takeo & Liu, Hao, 2018. "A robust biomimetic blade design for micro wind turbines," Renewable Energy, Elsevier, vol. 125(C), pages 155-165.
    7. Yangyang Zheng & Wenxian Yang & Kexiang Wei & Yanling Chen & Hongxiang Zou, 2024. "Enhancing Efficiency and Reliability of Tidal Stream Energy Conversion through Swept-Blade Design," Energies, MDPI, vol. 17(2), pages 1-26, January.
    8. 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.
    9. Zhang, Xiaoling & Zhang, Kejia & Yang, Xiao & Fazeres-Ferradosa, Tiago & Zhu, Shun-Peng, 2023. "Transfer learning and direct probability integral method based reliability analysis for offshore wind turbine blades under multi-physics coupling," Renewable Energy, Elsevier, vol. 206(C), pages 552-565.
    10. Michael K. McWilliam & Antariksh C. Dicholkar & Frederik Zahle & Taeseong Kim, 2022. "Post-Optimum Sensitivity Analysis with Automatically Tuned Numerical Gradients Applied to Swept Wind Turbine Blades," Energies, MDPI, vol. 15(9), pages 1-19, April.

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