IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i14p3574-d383136.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/14/3574/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/14/3574/
    Download Restriction: no
    ---><---

    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. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Khan, Mehtab Ahmad & Javed, Adeel & Shakir, Sehar & Syed, Abdul Haseeb, 2021. "Optimization of a wind farm by coupled actuator disk and mesoscale models to mitigate neighboring wind farm wake interference from repowering perspective," Applied Energy, Elsevier, vol. 298(C).
    2. Giovanni Ferrara & Alessandro Bianchini, 2021. "Special Issue “Numerical Simulation of Wind Turbines”," Energies, MDPI, vol. 14(6), pages 1-2, March.
    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. 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.
    8. 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).
    9. 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).
    10. Zhaobin Li & Xiaohao Liu & Xiaolei Yang, 2022. "Review of Turbine Parameterization Models for Large-Eddy Simulation of Wind Turbine Wakes," Energies, MDPI, vol. 15(18), pages 1-28, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Feng, Dachuan & Li, Larry K.B. & Gupta, Vikrant & Wan, Minping, 2022. "Componentwise influence of upstream turbulence on the far-wake dynamics of wind turbines," Renewable Energy, Elsevier, vol. 200(C), pages 1081-1091.
    2. Tian, Linlin & Song, Yilei & Zhao, Ning & Shen, Wenzhong & Zhu, Chunling & Wang, Tongguang, 2020. "Effects of turbulence modelling in AD/RANS simulations of single wind & tidal turbine wakes and double wake interactions," Energy, Elsevier, vol. 208(C).
    3. Zhaobin Li & Xiaohao Liu & Xiaolei Yang, 2022. "Review of Turbine Parameterization Models for Large-Eddy Simulation of Wind Turbine Wakes," Energies, MDPI, vol. 15(18), pages 1-28, September.
    4. 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.
    5. Abutunis, A. & Taylor, G. & Fal, M. & Chandrashekhara, K., 2020. "Experimental evaluation of coaxial horizontal axis hydrokinetic composite turbine system," Renewable Energy, Elsevier, vol. 157(C), pages 232-245.
    6. Kale, Baris & Buckingham, Sophia & van Beeck, Jeroen & Cuerva-Tejero, Alvaro, 2023. "Comparison of the wake characteristics and aerodynamic response of a wind turbine under varying atmospheric conditions using WRF-LES-GAD and WRF-LES-GAL wind turbine models," Renewable Energy, Elsevier, vol. 216(C).
    7. 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.
    8. Gao, Xiaoxia & Chen, Yao & Xu, Shinai & Gao, Wei & Zhu, Xiaoxun & Sun, Haiying & Yang, Hongxing & Han, Zhonghe & Wang, Yu & Lu, Hao, 2022. "Comparative experimental investigation into wake characteristics of turbines in three wind farms areas with varying terrain complexity from LiDAR measurements," Applied Energy, Elsevier, vol. 307(C).
    9. Ge, Mingwei & Gayme, Dennice F. & Meneveau, Charles, 2021. "Large-eddy simulation of wind turbines immersed in the wake of a cube-shaped building," Renewable Energy, Elsevier, vol. 163(C), pages 1063-1077.
    10. Brown, S.A. & Ransley, E.J. & Greaves, D.M., 2020. "Developing a coupled turbine thrust methodology for floating tidal stream concepts: Verification under prescribed motion," Renewable Energy, Elsevier, vol. 147(P1), pages 529-540.
    11. Dou, Bingzheng & Guala, Michele & Lei, Liping & Zeng, Pan, 2019. "Experimental investigation of the performance and wake effect of a small-scale wind turbine in a wind tunnel," Energy, Elsevier, vol. 166(C), pages 819-833.
    12. Wu, Chutian & Yang, Xiaolei & Zhu, Yaxin, 2021. "On the design of potential turbine positions for physics-informed optimization of wind farm layout," Renewable Energy, Elsevier, vol. 164(C), pages 1108-1120.
    13. Navid Belvasi & Boris Conan & Benyamin Schliffke & Laurent Perret & Cian Desmond & Jimmy Murphy & Sandrine Aubrun, 2022. "Far-Wake Meandering of a Wind Turbine Model with Imposed Motions: An Experimental S-PIV Analysis," Energies, MDPI, vol. 15(20), pages 1-17, October.
    14. Hyebin Kim & Sang Lee, 2022. "Large Eddy Simulation of Yawed Wind Turbine Wake Deformation," Energies, MDPI, vol. 15(17), pages 1-12, August.
    15. Ingrid Neunaber & Michael Hölling & Martin Obligado, 2022. "Wind Tunnel Study on the Tip Speed Ratio’s Impact on a Wind Turbine Wake Development," Energies, MDPI, vol. 15(22), pages 1-15, November.
    16. Arabgolarcheh, Alireza & Rouhollahi, Amirhossein & Benini, Ernesto, 2023. "Analysis of middle-to-far wake behind floating offshore wind turbines in the presence of multiple platform motions," Renewable Energy, Elsevier, vol. 208(C), pages 546-560.
    17. Purohit, Shantanu & Ng, E.Y.K. & Syed Ahmed Kabir, Ijaz Fazil, 2022. "Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake," Renewable Energy, Elsevier, vol. 184(C), pages 405-420.
    18. Antonio Crespo, 2023. "Computational Fluid Dynamic Models of Wind Turbine Wakes," Energies, MDPI, vol. 16(4), pages 1-3, February.
    19. Miller, Aaron & Chang, Byungik & Issa, Roy & Chen, Gerald, 2013. "Review of computer-aided numerical simulation in wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 122-134.
    20. Victor P. Stein & Hans-Jakob Kaltenbach, 2022. "Validation of a Large-Eddy Simulation Approach for Prediction of the Ground Roughness Influence on Wind Turbine Wakes," Energies, MDPI, vol. 15(7), pages 1-25, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3574-:d:383136. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.