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A Simplified Numerical Model for the Prediction of Wake Interaction in Multiple Wind Turbines

Author

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  • Jong-Hyeon Shin

    (Department of Mechanical Engineering, Kunsan National University, Gunsan 54150, Korea)

  • Jong-Hwi Lee

    (Department of Mechanical Engineering, Kunsan National University, Gunsan 54150, Korea)

  • Se-Myong Chang

    (Department of Mechanical Engineering, Kunsan National University, Gunsan 54150, Korea)

Abstract

In the design of wind energy farms, the loss of power should be seriously considered for the second wind turbine located inside the wake region of the first one. The rotation of the first wind-front rotor generates a high-vorticity wake with turbulence, and a suitable model is required in computational fluid dynamics (CFD) to predict the deficit of energy of the second turbine for the given configuration. A simplified numerical model based on the classical momentum theory is proposed in this study for multiple wind turbines, which is proposed with a couple of tuning parameters applied to Reynolds-averaged Navier-Stokes (RANS) analysis, resulting in a remarkable reduction of computational load compared with advanced methods, such as large eddy simulation (LES) where two parameters reflect on axial and rotational wake motion, simply tuned with the wind-tunnel test and its corresponding LES result. As a lumped parameter for the figure of merit, we regard the normalized efficiency on the kinetic power output of computational domain, which should be directed to maximize for the optimization of wind farms. The parameter surface is plotted in a dimensionless form versus intervals between turbines, and a simple correlation is obtained for a given hub height of 70% diameter and a fixed rotational speed tuned from the experimental data in a wide range.

Suggested Citation

  • Jong-Hyeon Shin & Jong-Hwi Lee & Se-Myong Chang, 2019. "A Simplified Numerical Model for the Prediction of Wake Interaction in Multiple Wind Turbines," Energies, MDPI, vol. 12(21), pages 1-14, October.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:21:p:4122-:d:281363
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    References listed on IDEAS

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    1. Charlotte Bay Hasager & Pauline Vincent & Jake Badger & Merete Badger & Alessandro Di Bella & Alfredo Peña & Romain Husson & Patrick J. H. Volker, 2015. "Using Satellite SAR to Characterize the Wind Flow around Offshore Wind Farms," Energies, MDPI, vol. 8(6), pages 1-27, June.
    2. Bastankhah, Majid & Porté-Agel, Fernando, 2014. "A new analytical model for wind-turbine wakes," Renewable Energy, Elsevier, vol. 70(C), pages 116-123.
    3. González-Longatt, F. & Wall, P. & Terzija, V., 2012. "Wake effect in wind farm performance: Steady-state and dynamic behavior," Renewable Energy, Elsevier, vol. 39(1), pages 329-338.
    4. Shuting Wan & Lifeng Cheng & Xiaoling Sheng, 2015. "Effects of Yaw Error on Wind Turbine Running Characteristics Based on the Equivalent Wind Speed Model," Energies, MDPI, vol. 8(7), pages 1-16, June.
    5. Deepu Dilip & Fernando Porté-Agel, 2017. "Wind Turbine Wake Mitigation through Blade Pitch Offset," Energies, MDPI, vol. 10(6), pages 1-17, May.
    6. Husien, Walid & El-Osta, Wedad & Dekam, Elhadi, 2013. "Effect of the wake behind wind rotor on optimum energy output of wind farms," Renewable Energy, Elsevier, vol. 49(C), pages 128-132.
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