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A Numerical Study on the Development of Self-Similarity in a Wind Turbine Wake Using an Improved Pseudo-Spectral Large-Eddy Simulation Solver

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  • Pin Lyu

    (Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin 150090, China
    Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin 150090, China
    Department of Mechanical Engineering and St. Anthony Falls Laboratory, University of Minnesota, Minneapolis, MN 55455, USA)

  • Wen-Li Chen

    (Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin 150090, China
    Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin 150090, China)

  • Hui Li

    (Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin 150090, China
    Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin 150090, China)

  • Lian Shen

    (Department of Mechanical Engineering and St. Anthony Falls Laboratory, University of Minnesota, Minneapolis, MN 55455, USA)

Abstract

Large-eddy simulation (LES) is performed to investigate self-similarity in a wind turbine wake flow. The turbine is represented using an actuator line model in a pseudo-spectral method-based solver. A new hybrid approach of smoothed pseudo-spectral method and finite-difference method (sPSMFDM) is proposed to alleviate the Gibbs phenomenon caused by the jump of velocity and pressure around the turbine. The LES is validated with the mean velocity and turbulence statistics obtained from wind-tunnel measurement reported in the literature. Through an appropriate choice of characteristic scales of velocity and length, self-similarity is elucidated in the normalized mean velocity and Reynolds stress profiles at various distances. The development of self-similarity is categorized into three stages based on the variation in the characteristic scales and the spanwise distribution of normalized velocity deficit. The mechanisms responsible for the transition of self-similarity stages are analyzed in detail. The findings of the flow physics obtained in this study will be useful for the modeling and fast prediction of wind turbine wake flows.

Suggested Citation

  • Pin Lyu & Wen-Li Chen & Hui Li & Lian Shen, 2019. "A Numerical Study on the Development of Self-Similarity in a Wind Turbine Wake Using an Improved Pseudo-Spectral Large-Eddy Simulation Solver," Energies, MDPI, vol. 12(4), pages 1-24, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:643-:d:206640
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    References listed on IDEAS

    as
    1. Stevens, Richard J.A.M. & Martínez-Tossas, Luis A. & Meneveau, Charles, 2018. "Comparison of wind farm large eddy simulations using actuator disk and actuator line models with wind tunnel experiments," Renewable Energy, Elsevier, vol. 116(PA), pages 470-478.
    2. Wu, Yu-Ting & Porté-Agel, Fernando, 2015. "Modeling turbine wakes and power losses within a wind farm using LES: An application to the Horns Rev offshore wind farm," Renewable Energy, Elsevier, vol. 75(C), pages 945-955.
    3. Yu-Ting Wu & Fernando Porté-Agel, 2012. "Atmospheric Turbulence Effects on Wind-Turbine Wakes: An LES Study," Energies, MDPI, vol. 5(12), pages 1-23, December.
    4. Bastankhah, Majid & Porté-Agel, Fernando, 2014. "A new analytical model for wind-turbine wakes," Renewable Energy, Elsevier, vol. 70(C), pages 116-123.
    5. Yang, Di & Meneveau, Charles & Shen, Lian, 2014. "Effect of downwind swells on offshore wind energy harvesting – A large-eddy simulation study," Renewable Energy, Elsevier, vol. 70(C), pages 11-23.
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    1. Xiong, Xue-Lu & Lyu, Pin & Chen, Wen-Li & Li, Hui, 2020. "Self-similarity in the wake of a semi-submersible offshore wind turbine considering the interaction with the wake of supporting platform," Renewable Energy, Elsevier, vol. 156(C), pages 328-341.
    2. Zhaobin Li & Xiaolei Yang, 2020. "Evaluation of Actuator Disk Model Relative to Actuator Surface Model for Predicting Utility-Scale Wind Turbine Wakes," Energies, MDPI, vol. 13(14), pages 1-18, July.

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