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Unsteady aerodynamic prediction for dynamic stall of wind turbine airfoils with the reduced order modeling

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  • Liu, Pengyin
  • Yu, Guohua
  • Zhu, Xiaocheng
  • Du, Zhaohui

Abstract

A surrogate based reduced order method is developed for the prediction of the unsteady dynamic loading of wind turbine airfoils under dynamic stall situation. S809 airfoil undergoing sinusoidal pitch oscillation at different reduced frequencies, mean angles of attack and pitch oscillation amplitudes at Re = 106 are simulated by the CFD method. Then, the Kriging function is selected to establish the unsteady and nonlinear aerodynamic model with training cases generated by the CFD method. The results reveal an encouraging agreement between the CFD simulations and the reduced order model predictions. Furthermore, Ohio State University measurement data and Leishman–Beddoes model predictions for test cases are also included. Because CFD has good accuracy simulation for flow field around the airfoil under dynamic stall condition, the reduced order model can more effectively predict the airfoil nonlinear hysteretic behavior than L–B model. In addition, the reduced order method has relatively less computational cost compared with the CFD method. Therefore, it would be useful in engineering conditioning for aero-elastic analysis and design optimization of wind turbines.

Suggested Citation

  • Liu, Pengyin & Yu, Guohua & Zhu, Xiaocheng & Du, Zhaohui, 2014. "Unsteady aerodynamic prediction for dynamic stall of wind turbine airfoils with the reduced order modeling," Renewable Energy, Elsevier, vol. 69(C), pages 402-409.
    • Handle: RePEc:eee:renene:v:69:y:2014:i:c:p:402-409
    DOI: 10.1016/j.renene.2014.03.066
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    References listed on IDEAS

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    1. Lanzafame, R. & Mauro, S. & Messina, M., 2013. "Wind turbine CFD modeling using a correlation-based transitional model," Renewable Energy, Elsevier, vol. 52(C), pages 31-39.
    2. Li, Yuwei & Paik, Kwang-Jun & Xing, Tao & Carrica, Pablo M., 2012. "Dynamic overset CFD simulations of wind turbine aerodynamics," Renewable Energy, Elsevier, vol. 37(1), pages 285-298.
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    Cited by:

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    2. Liu, Xiong & Liang, Shi & Li, Gangqiang & Godbole, Ajit & Lu, Cheng, 2020. "An improved dynamic stall model and its effect on wind turbine fatigue load prediction," Renewable Energy, Elsevier, vol. 156(C), pages 117-130.
    3. Gharali, Kobra & Gharaei, Eshagh & Soltani, M. & Raahemifar, Kaamran, 2018. "Reduced frequency effects on combined oscillations, angle of attack and free stream oscillations, for a wind turbine blade element," Renewable Energy, Elsevier, vol. 115(C), pages 252-259.
    4. Ge, Mingwei & Sun, Haitao & Meng, Hang & Li, Xintao, 2024. "An improved B-L model for dynamic stall prediction of rough-surface airfoils," Renewable Energy, Elsevier, vol. 226(C).
    5. Wenyan Li & Yuxuan Xiong & Guoliang Su & Zuyang Ye & Guowu Wang & Zhao Chen, 2023. "The Aerodynamic Performance of Horizontal Axis Wind Turbines under Rotation Condition," Sustainability, MDPI, vol. 15(16), pages 1-15, August.
    6. Li, Zhiguo & Gao, Zhiying & Chen, Yongyan & Zhang, Liru & Wang, Jianwen, 2022. "A novel dynamic stall model based on Theodorsen theory and its application," Renewable Energy, Elsevier, vol. 193(C), pages 344-356.

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