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Performance improvement of horizontal axis wind turbines by aerodynamic shape optimization including aeroealstic deformation

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  • Lee, Hak Min
  • Kwon, Oh Joon

Abstract

Aerodynamic shape optimization of horizontal axis wind turbines was performed to minimize the cost of energy. The optimum blade was obtained by modifying the blade section contours at selected blade radial stations. For the optimization, the design variables were defined as the coordinates of the blade section contours perpendicular to the chord. An aerodynamic performance database was constructed for those blade configurations defined by random combinations of design variables selected within a given range by Latin-Hypercube sampling. An artificial neural network was applied to derive the approximate functions between the design variables and the aerodynamic performance of the database. By utilizing the approximate functions, a genetic algorithm was adopted to search for an optimized blade. To construct the database, a CFD flow solver based on unstructured meshes was utilized. To consider the effects of aeroelastic deformation of the structurally flexible blades, a coupled CFD-CSD method was also adopted. The applications were made for the NREL phase VI and NREL 5 MW reference wind turbine rotor blades. After optimization, the cost of energy was reduced by 0.82% for the NREL phase VI rotor blade and one percent for the NREL 5 MW reference wind turbine blade, respectively.

Suggested Citation

  • Lee, Hak Min & Kwon, Oh Joon, 2020. "Performance improvement of horizontal axis wind turbines by aerodynamic shape optimization including aeroealstic deformation," Renewable Energy, Elsevier, vol. 147(P1), pages 2128-2140.
    • Handle: RePEc:eee:renene:v:147:y:2020:i:p1:p:2128-2140
    DOI: 10.1016/j.renene.2019.09.125
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    Cited by:

    1. Win Naung, Shine & Nakhchi, Mahdi Erfanian & Rahmati, Mohammad, 2021. "High-fidelity CFD simulations of two wind turbines in arrays using nonlinear frequency domain solution method," Renewable Energy, Elsevier, vol. 174(C), pages 984-1005.
    2. Du, Qiuwan & Li, Yunzhu & Yang, Like & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2022. "Performance prediction and design optimization of turbine blade profile with deep learning method," Energy, Elsevier, vol. 254(PA).
    3. Zhang, Dongqin & Liu, Zhenqing & Li, Weipeng & Hu, Gang, 2023. "LES simulation study of wind turbine aerodynamic characteristics with fluid-structure interaction analysis considering blade and tower flexibility," Energy, Elsevier, vol. 282(C).
    4. Win Naung, Shine & Rahmati, Mohammad & Farokhi, Hamed, 2021. "Nonlinear frequency domain solution method for aerodynamic and aeromechanical analysis of wind turbines," Renewable Energy, Elsevier, vol. 167(C), pages 66-81.
    5. Nakhchi, M.E. & Naung, S. Win & Dala, L. & Rahmati, M., 2022. "Direct numerical simulations of aerodynamic performance of wind turbine aerofoil by considering the blades active vibrations," Renewable Energy, Elsevier, vol. 191(C), pages 669-684.
    6. Rasool, Safdar & Muttaqi, Kashem M. & Sutanto, Danny & Hemer, Mark, 2022. "Quantifying the reduction in power variability of co-located offshore wind-wave farms," Renewable Energy, Elsevier, vol. 185(C), pages 1018-1033.

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