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Lagrangian dynamic large-eddy simulation of wind turbine near wakes combined with an actuator line method

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  • Zhong, Hongmin
  • Du, Pingan
  • Tang, Fangning
  • Wang, Li

Abstract

Wind turbine wakes have significant effects on the production efficiency and fatigue loads, and these effects should be considered in the optimization of wind turbine structure and wind farm layouts. In this paper, a numerical model combining Lagrangian dynamic large-eddy models and actuator line methods (ALMs) is implemented to investigate the wind turbine near wakes at three representative tip speed ratios (TSRs). In the model, several model parameters that have been justified based on the existing literature and experiments are utilized to enhance the numerical stability and accuracy. These parameters are related to a physically meaningful length scale in the Gaussian smoothing function, a Prandtl tip/hub-loss factor and a 3D correction for airfoil data. The model is compared to the MEXICO measurements, in which a detailed stereo PIV measurement is carried out. According to the comparison of rotor power coefficients between the prediction and the measurements, there is a slight overestimate at TSRs of 6.67 and 10, while a slight underestimate at TSR of 4.17. Additionally, according to the comparison of streamwise traverses and spanwise distribution of axial velocities, good agreement is achieved at both TSRs of 4.17 and 6.67, while visible difference is found at TSR of 10. Moreover, the simulation result shows a helical behavior of wake tip vortices induced by the turbine rotor. This behavior gets more pronounced with a decreasing TSR. The tip vortices also give a reasonable explanation of why the maximum velocity deficit and turbulence intensity occur near the blade tip of wind turbines.

Suggested Citation

  • Zhong, Hongmin & Du, Pingan & Tang, Fangning & Wang, Li, 2015. "Lagrangian dynamic large-eddy simulation of wind turbine near wakes combined with an actuator line method," Applied Energy, Elsevier, vol. 144(C), pages 224-233.
    • Handle: RePEc:eee:appene:v:144:y:2015:i:c:p:224-233
    DOI: 10.1016/j.apenergy.2015.01.082
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    References listed on IDEAS

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    Cited by:

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    6. Ge, Mingwei & Wu, Ying & Liu, Yongqian & Li, Qi, 2019. "A two-dimensional model based on the expansion of physical wake boundary for wind-turbine wakes," Applied Energy, Elsevier, vol. 233, pages 975-984.
    7. Kuo, Jim Y.J. & Romero, David A. & Beck, J. Christopher & Amon, Cristina H., 2016. "Wind farm layout optimization on complex terrains – Integrating a CFD wake model with mixed-integer programming," Applied Energy, Elsevier, vol. 178(C), pages 404-414.
    8. Siddiqui, M. Salman & Rasheed, Adil & Tabib, Mandar & Kvamsdal, Trond, 2019. "Numerical investigation of modeling frameworks and geometric approximations on NREL 5 MW wind turbine," Renewable Energy, Elsevier, vol. 132(C), pages 1058-1075.
    9. Zhang, Xiaochun & Ma, Chun & Song, Xia & Zhou, Yuyu & Chen, Weiping, 2016. "The impacts of wind technology advancement on future global energy," Applied Energy, Elsevier, vol. 184(C), pages 1033-1037.
    10. Rafael V. Rodrigues & Corinne Lengsfeld, 2019. "Development of a Computational System to Improve Wind Farm Layout, Part I: Model Validation and Near Wake Analysis," Energies, MDPI, vol. 12(5), pages 1-24, March.
    11. El Fajri, Oumnia & Bowman, Joshua & Bhushan, Shanti & Thompson, David & O'Doherty, Tim, 2022. "Numerical study of the effect of tip-speed ratio on hydrokinetic turbine wake recovery," Renewable Energy, Elsevier, vol. 182(C), pages 725-750.
    12. Castellani, Francesco & Astolfi, Davide & Sdringola, Paolo & Proietti, Stefania & Terzi, Ludovico, 2017. "Analyzing wind turbine directional behavior: SCADA data mining techniques for efficiency and power assessment," Applied Energy, Elsevier, vol. 185(P2), pages 1076-1086.
    13. Gao, Xiaoxia & Li, Bingbing & Wang, Tengyuan & Sun, Haiying & Yang, Hongxing & Li, Yonghua & Wang, Yu & Zhao, Fei, 2020. "Investigation and validation of 3D wake model for horizontal-axis wind turbines based on filed measurements," Applied Energy, Elsevier, vol. 260(C).
    14. Huilai Ren & Xiaodong Zhang & Shun Kang & Sichao Liang, 2018. "Actuator Disc Approach of Wind Turbine Wake Simulation Considering Balance of Turbulence Kinetic Energy," Energies, MDPI, vol. 12(1), pages 1-19, December.
    15. Zhe Ma & Liping Lei & Earl Dowell & Pan Zeng, 2020. "An Experimental Study on the Actuator Line Method with Anisotropic Regularization Kernel," Energies, MDPI, vol. 13(4), pages 1-19, February.
    16. Wang, Zhenyu & Ozbay, Ahmet & Tian, Wei & Hu, Hui, 2018. "An experimental study on the aerodynamic performances and wake characteristics of an innovative dual-rotor wind turbine," Energy, Elsevier, vol. 147(C), pages 94-109.

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