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Evaluation of Actuator Disk Model Relative to Actuator Surface Model for Predicting Utility-Scale Wind Turbine Wakes

Author

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  • Zhaobin Li

    (The State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China)

  • Xiaolei Yang

    (The State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China
    School of Engineering Sciences, University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

The Actuator Disk (AD) model is widely used in Large-Eddy Simulations (LES) to simulate wind turbine wakes because of its computing efficiency. The capability of the AD model in predicting time-average quantities of wind tunnel-scale turbines has been assessed extensively in the literature. However, its capability in predicting wakes of utility-scale wind turbines especially for the coherent flow structures is not clear yet. In this work, we take the time-averaged statistics and Dynamic Mode Decomposition (DMD) modes computed from a well-validated Actuator Surface (AS) model as references to evaluate the capability of the AD model in predicting the wake of a 2.5 MW utility-scale wind turbine for uniform inflow and fully developed turbulent inflow conditions. For the uniform inflow cases, the predictions from the AD model are significantly different from those from the AS model for the time-averaged velocity, and the turbulence kinetic energy until nine rotor diameters ( D ) downstream of the turbine. For the turbulent inflow cases, on the other hand, the differences in the time-averaged quantities predicted by the AS and AD models are not significant especially at far wake locations. As for DMD modes, significant differences are observed in terms of dominant frequencies and DMD patterns for both inflows. Moreover, the effects of incoming large eddies, bluff body shear layer instability, and hub vortexes on the coherent flow structures are discussed in this paper.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3574-:d:383136
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    References listed on IDEAS

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    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. 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.
    3. Xiaolei Yang & Fotis Sotiropoulos, 2019. "A Review on the Meandering of Wind Turbine Wakes," Energies, MDPI, vol. 12(24), pages 1-20, December.
    4. Castellani, Francesco & Vignaroli, Andrea, 2013. "An application of the actuator disc model for wind turbine wakes calculations," Applied Energy, Elsevier, vol. 101(C), pages 432-440.
    5. Yu-Ting Wu & Chang-Yu Lin & Che-Ming Hsu, 2020. "An Experimental Investigation of Wake Characteristics and Power Generation Efficiency of a Small Wind Turbine under Different Tip Speed Ratios," Energies, MDPI, vol. 13(8), pages 1-19, April.
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    Cited by:

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    3. Diogo Menezes & Mateus Mendes & Jorge Alexandre Almeida & Torres Farinha, 2020. "Wind Farm and Resource Datasets: A Comprehensive Survey and Overview," Energies, MDPI, vol. 13(18), pages 1-24, September.
    4. Galih Bangga, 2022. "Progress and Outlook in Wind Energy Research," Energies, MDPI, vol. 15(18), pages 1-5, September.
    5. Zhang, Shuaibin & Du, Bowen & Ge, Mingwei & Zuo, Yingtao, 2022. "Study on the operation of small rooftop wind turbines and its effect on the wind environment in blocks," Renewable Energy, Elsevier, vol. 183(C), pages 708-718.
    6. Yunliang Li & Zhaobin Li & Zhideng Zhou & Xiaolei Yang, 2023. "Large-Eddy Simulation of Wind Turbine Wakes in Forest Terrain," Sustainability, MDPI, vol. 15(6), pages 1-23, March.
    7. Jia, Rui & Ge, Mingwei & Zhang, Ziliang & Li, Xintao & Du, Bowen, 2024. "A numerical simulation framework for wakes downstream of large wind farms based on equivalent roughness model," Energy, Elsevier, vol. 307(C).
    8. Dong, Guodan & Li, Zhaobin & Qin, Jianhua & Yang, Xiaolei, 2022. "Predictive capability of actuator disk models for wakes of different wind turbine designs," Renewable Energy, Elsevier, vol. 188(C), pages 269-281.
    9. Yang, Xiaolei & Milliren, Christopher & Kistner, Matt & Hogg, Christopher & Marr, Jeff & Shen, Lian & Sotiropoulos, Fotis, 2021. "High-fidelity simulations and field measurements for characterizing wind fields in a utility-scale wind farm," Applied Energy, Elsevier, vol. 281(C).
    10. Ma, Hongliang & Ge, Mingwei & Wu, Guangxing & Du, Bowen & Liu, Yongqian, 2021. "Formulas of the optimized yaw angles for cooperative control of wind farms with aligned turbines to maximize the power production," Applied Energy, Elsevier, vol. 303(C).
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