IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i16p5198-d619708.html
   My bibliography  Save this article

On the Accuracy of uRANS and LES-Based CFD Modeling Approaches for Rotor and Wake Aerodynamics of the (New) MEXICO Wind Turbine Rotor Phase-III

Author

Listed:
  • Shantanu Purohit

    (School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Block N3, Singapore 639798, Singapore)

  • Ijaz Fazil Syed Ahmed Kabir

    (School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Block N3, Singapore 639798, Singapore)

  • E. Y. K. Ng

    (School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Block N3, Singapore 639798, Singapore)

Abstract

This work presents a comparison study of the CFD modeling with two different turbulence modeling approaches viz. unsteady RANS and LES, on a full-scale model of the (New) MEXICO rotor wind turbine. The main emphasis of the paper is on the rotor and wake aerodynamics. Simulations are carried out for the three wind speeds considered in the MEXICO experiment (10, 15, and 24 ms −1 ). The results of uRANS and LES are compared against the (New) MEXICO experimental measurements of pressure distributions, axial, radial, and azimuth traverse of three velocity components. The near wake characteristics and vorticity are also analyzed. The pressure distribution results show that the LES can predict the onset of flow separation more accurately than uRANS when the turbine operates in the stall condition. The LES can compute the flow structures in wake significantly better than the uRANS for the stall condition of the blade. For the design condition, the mean absolute error in axial and radial velocity components along radial traverse is less than 10% for both the modeling approaches, whereas tangential component error is less than 2% from the LES approach. The results also reveal that wake recovers faster in the uRANS approach, requiring further research of the far wake region using both CFD modeling approaches.

Suggested Citation

  • Shantanu Purohit & Ijaz Fazil Syed Ahmed Kabir & E. Y. K. Ng, 2021. "On the Accuracy of uRANS and LES-Based CFD Modeling Approaches for Rotor and Wake Aerodynamics of the (New) MEXICO Wind Turbine Rotor Phase-III," Energies, MDPI, vol. 14(16), pages 1-26, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:5198-:d:619708
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/16/5198/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/16/5198/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yaoru Qian & Zhenyu Zhang & Tongguang Wang, 2018. "Comparative Study of the Aerodynamic Performance of the New MEXICO Rotor under Yaw Conditions," Energies, MDPI, vol. 11(4), pages 1-18, April.
    2. Syed Ahmed Kabir, Ijaz Fazil & Ng, E.Y.K., 2019. "Effect of different atmospheric boundary layers on the wake characteristics of NREL phase VI wind turbine," Renewable Energy, Elsevier, vol. 130(C), pages 1185-1197.
    3. Zhang, Ye & Deng, Shuanghou & Wang, Xiaofang, 2019. "RANS and DDES simulations of a horizontal-axis wind turbine under stalled flow condition using OpenFOAM," Energy, Elsevier, vol. 167(C), pages 1155-1163.
    4. 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.
    5. Thé, Jesse & Yu, Hesheng, 2017. "A critical review on the simulations of wind turbine aerodynamics focusing on hybrid RANS-LES methods," Energy, Elsevier, vol. 138(C), pages 257-289.
    6. Regodeseves, P. García & Morros, C. Santolaria, 2020. "Unsteady numerical investigation of the full geometry of a horizontal axis wind turbine: Flow through the rotor and wake," Energy, Elsevier, vol. 202(C).
    7. 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.
    8. Iván Herráez & Bernhard Stoevesandt & Joachim Peinke, 2014. "Insight into Rotational Effects on a Wind Turbine Blade Using Navier–Stokes Computations," Energies, MDPI, vol. 7(10), pages 1-25, October.
    9. Amiri, Mojtaba Maali & Shadman, Milad & Estefen, Segen F., 2020. "URANS simulations of a horizontal axis wind turbine under stall condition using Reynolds stress turbulence models," Energy, Elsevier, vol. 213(C).
    10. Syed Ahmed Kabir, Ijaz Fazil & Ng, E.Y.K., 2017. "Insight into stall delay and computation of 3D sectional aerofoil characteristics of NREL phase VI wind turbine using inverse BEM and improvement in BEM analysis accounting for stall delay effect," Energy, Elsevier, vol. 120(C), pages 518-536.
    11. Rafael V. Rodrigues & Corinne Lengsfeld, 2019. "Development of a Computational System to Improve Wind Farm Layout, Part II: Wind Turbine Wakes Interaction," Energies, MDPI, vol. 12(7), pages 1-27, April.
    12. Li, Yuwei & Paik, Kwang-Jun & Xing, Tao & Carrica, Pablo M., 2012. "Dynamic overset CFD simulations of wind turbine aerodynamics," Renewable Energy, Elsevier, vol. 37(1), pages 285-298.
    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. 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.
    2. Monjardín-Gámez, José de Jesús & Campos-Amezcua, Rafael & Gómez-Martínez, Roberto & Sánchez-García, Raúl & Campos-Amezcua, Alfonso & Trujillo-Franco, Luis G. & Abundis-Fong, Hugo F., 2023. "Large eddy simulation and experimental study of the turbulence on wind turbines," Energy, Elsevier, vol. 273(C).
    3. Shaokai Liao & Yan Zhang & Xi Chen & Pengcheng Cao, 2022. "Research on Aerodynamic Characteristics of Crescent Iced Conductor Based on S-A Finite Element Turbulence Model," Energies, MDPI, vol. 15(20), pages 1-16, October.

    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. Zhong, Junwei & Li, Jingyin, 2020. "Aerodynamic performance prediction of NREL phase VI blade adopting biplane airfoil," Energy, Elsevier, vol. 206(C).
    2. Regodeseves, P. García & Morros, C. Santolaria, 2020. "Unsteady numerical investigation of the full geometry of a horizontal axis wind turbine: Flow through the rotor and wake," Energy, Elsevier, vol. 202(C).
    3. Zhang, Dongqin & Liu, Zhenqing & Li, Weipeng & Hu, Gang, 2023. "LES simulation study of wind turbine aerodynamic characteristics with fluid-structure interaction analysis considering blade and tower flexibility," Energy, Elsevier, vol. 282(C).
    4. 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.
    5. Zhao, Shuang & Wang, Jianwen & Han, Yuxia & Liu, Zhen, 2022. "Research on the rotor speed and aerodynamic characteristics of a dynamic yawing wind turbine with a short-time uniform wind direction variation," Energy, Elsevier, vol. 249(C).
    6. Mauro, S. & Lanzafame, R. & Messina, M. & Brusca, S., 2023. "On the importance of the root-to-hub adapter effects on HAWT performance: A CFD-BEM numerical investigation," Energy, Elsevier, vol. 275(C).
    7. Kaldellis, John K. & Triantafyllou, Panagiotis & Stinis, Panagiotis, 2021. "Critical evaluation of Wind Turbines’ analytical wake models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    8. Thé, Jesse & Yu, Hesheng, 2017. "A critical review on the simulations of wind turbine aerodynamics focusing on hybrid RANS-LES methods," Energy, Elsevier, vol. 138(C), pages 257-289.
    9. Takanori Uchida & Yoshihiro Taniyama & Yuki Fukatani & Michiko Nakano & Zhiren Bai & Tadasuke Yoshida & Masaki Inui, 2020. "A New Wind Turbine CFD Modeling Method Based on a Porous Disk Approach for Practical Wind Farm Design," Energies, MDPI, vol. 13(12), pages 1-27, June.
    10. Amiri, Mojtaba Maali & Shadman, Milad & Estefen, Segen F., 2020. "URANS simulations of a horizontal axis wind turbine under stall condition using Reynolds stress turbulence models," Energy, Elsevier, vol. 213(C).
    11. Ye, Maokun & Chen, Hamn-Ching & Koop, Arjen, 2023. "High-fidelity CFD simulations for the wake characteristics of the NTNU BT1 wind turbine," Energy, Elsevier, vol. 265(C).
    12. Syed Ahmed Kabir, Ijaz Fazil & Safiyullah, Ferozkhan & Ng, E.Y.K. & Tam, Vivian W.Y., 2020. "New analytical wake models based on artificial intelligence and rivalling the benchmark full-rotor CFD predictions under both uniform and ABL inflows," Energy, Elsevier, vol. 193(C).
    13. Ali, Qazi Shahzad & Kim, Man-Hoe, 2022. "Power conversion performance of airborne wind turbine under unsteady loads," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    14. Li, Qing’an & Xu, Jianzhong & Kamada, Yasunari & Takao, Maeda & Nishimura, Shogo & Wu, Guangxing & Cai, Chang, 2020. "Experimental investigations of airfoil surface flow of a horizontal axis wind turbine with LDV measurements," Energy, Elsevier, vol. 191(C).
    15. Syed Ahmed Kabir, Ijaz Fazil & Ng, E.Y.K., 2019. "Effect of different atmospheric boundary layers on the wake characteristics of NREL phase VI wind turbine," Renewable Energy, Elsevier, vol. 130(C), pages 1185-1197.
    16. de Oliveira, M. & Puraca, R.C. & Carmo, B.S., 2022. "Blade-resolved numerical simulations of the NREL offshore 5 MW baseline wind turbine in full scale: A study of proper solver configuration and discretization strategies," Energy, Elsevier, vol. 254(PB).
    17. Takanori Uchida & Susumu Takakuwa, 2019. "A Large-Eddy Simulation-Based Assessment of the Risk of Wind Turbine Failures Due to Terrain-Induced Turbulence over a Wind Farm in Complex Terrain," Energies, MDPI, vol. 12(10), pages 1-19, May.
    18. Tian, Linlin & Song, Yilei & Xiao, Pengcheng & Zhao, Ning & Shen, Wenzhong & Zhu, Chunling, 2022. "A new three-dimensional analytical model for wind turbine wake turbulence intensity predictions," Renewable Energy, Elsevier, vol. 189(C), pages 762-776.
    19. Cao, Lichao & Ge, Mingwei & Gao, Xiaoxia & Du, Bowen & Li, Baoliang & Huang, Zhi & Liu, Yongqian, 2022. "Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines," Applied Energy, Elsevier, vol. 323(C).
    20. Yang, Mao & Wang, Da & Xu, Chuanyu & Dai, Bozhi & Ma, Miaomiao & Su, Xin, 2023. "Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting," Renewable Energy, Elsevier, vol. 211(C), pages 582-594.

    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:14:y:2021:i:16:p:5198-:d:619708. 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.