IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i12p3197-d373938.html
   My bibliography  Save this article

A New Wind Turbine CFD Modeling Method Based on a Porous Disk Approach for Practical Wind Farm Design

Author

Listed:
  • Takanori Uchida

    (Research Institute for Applied Mechanics (RIAM), Kyushu University, 6-1 Kasuga-kouen, Kasuga, Fukuoka 816-8580, Japan)

  • Yoshihiro Taniyama

    (Mechanical Engineering R&D Department, Energy Systems Research and Development Center Toshiba Energy Systems & Solutions Corporation, 2-4 Suehiro-cho, Tsurumi-ku Yokohama-shi, Kanagawa 230-0045, Japan)

  • Yuki Fukatani

    (Mechanical Engineering R&D Department, Energy Systems Research and Development Center Toshiba Energy Systems & Solutions Corporation, 2-4 Suehiro-cho, Tsurumi-ku Yokohama-shi, Kanagawa 230-0045, Japan)

  • Michiko Nakano

    (Technology Planning & Quality Control Department, Grid Aggregation Division Toshiba Energy Systems & Solutions Corporation, 72-34 Horikawa-cho, Saiwai-ku Kawasaki-shi, Kanagawa 212-8585, Japan)

  • Zhiren Bai

    (Mechanical Engineering R&D Department, Energy Systems Research and Development Center Toshiba Energy Systems & Solutions Corporation, 2-4 Suehiro-cho, Tsurumi-ku Yokohama-shi, Kanagawa 230-0045, Japan)

  • Tadasuke Yoshida

    (Deputy Manager Engineering And Technology Development Department Wind Power Business Unit, Hitachi Zosen Corporation, 7-89 Nanko-Kita 1-chome, Suminoe-ku, Osaka, 559-8559, Japan)

  • Masaki Inui

    (Technical Research Institute, Business Planning & Technology Development Headquarters, Hitachi Zosen Corporation, 2-11 Funamachi 2-chome, Taisho-ku, Osaka, 551-0022, Japan)

Abstract

In this study, the new computational fluid dynamics (CFD) porous disk (PD) wake model was proposed in order to accurately predict the time-averaged wind speed deficits in the wind turbine wake region formed on the downstream side by the 2-MW wind turbine operating at a wind speed of 10 m/s. We use the concept of forest canopy model as a new CFD PD wake model, which has many research results in the meteorological field. In the forest canopy model, an aerodynamic resistance is added as an external force term to all governing equations (Navier–Stokes equations) in the streamwise, spanwise, and vertical directions. Therefore, like the forest model, the aerodynamic resistance is added to the governing equations in the three directions as an external force term in the CFD PD wake model. In addition, we have positioned the newly proposed the LES using the CFD PD wake model approach as an intermediate method between the engineering wake model (empirical/analytical wake model) and the LES combined with actuator disk (AD) or actuator line (AL) models. The newly proposed model is intended for use in large-scale offshore wind farms (WFs) consisting of multiple wind turbines. In order to verify the validity of the new method, the optimal model parameter C RC was estimated by comparison with the time-averaged wind speed database in the wind turbine wake region with fully resolved geometries, combined with unsteady Reynolds-averaged Navier–Stokes (RANS) equations, implemented using the ANSYS(R) CFX(R) software. Here, product names (mentioned herein) may be trademarks of their respective companies. As a result, in the range from x = 5D of the near wake region to x = 10D of the far wake region, by selecting model parameter C RC , it was clarified that it is possible to accurately evaluate the time-averaged wind speed deficits at those separation distances. We also examined the effect of the spatial grid resolution using the CFD PD wake model that is proposed in the present study, clarifying that the spatial grid resolution has little effect on the simulation results shown here.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3197-:d:373938
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/12/3197/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/12/3197/
    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. 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.
    3. 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.
    4. Mou Lin & Fernando Porté-Agel, 2019. "Large-Eddy Simulation of Yawed Wind-Turbine Wakes: Comparisons with Wind Tunnel Measurements and Analytical Wake Models," Energies, MDPI, vol. 12(23), pages 1-18, November.
    5. 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.
    6. Takanori Uchida, 2019. "Numerical Investigation of Terrain-Induced Turbulence in Complex Terrain Using High-Resolution Elevation Data and Surface Roughness Data Constructed with a Drone," Energies, MDPI, vol. 12(19), pages 1-20, October.
    7. Takanori Uchida & Yasushi Kawashima, 2019. "New Assessment Scales for Evaluating the Degree of Risk of Wind Turbine Blade Damage Caused by Terrain-Induced Turbulence," Energies, MDPI, vol. 12(13), pages 1-27, July.
    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. Takanori Uchida, 2020. "Effects of Inflow Shear on Wake Characteristics of Wind-Turbines over Flat Terrain," Energies, MDPI, vol. 13(14), pages 1-31, July.
    2. Takanori Uchida & Tadasuke Yoshida & Masaki Inui & Yoshihiro Taniyama, 2021. "Doppler Lidar Investigations of Wind Turbine Near-Wakes and LES Modeling with New Porous Disc Approach," Energies, MDPI, vol. 14(8), pages 1-33, April.
    3. Takanori Uchida & Susumu Takakuwa, 2020. "Numerical Investigation of Stable Stratification Effects on Wind Resource Assessment in Complex Terrain," Energies, MDPI, vol. 13(24), pages 1-32, December.

    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. Takanori Uchida, 2020. "Effects of Inflow Shear on Wake Characteristics of Wind-Turbines over Flat Terrain," Energies, MDPI, vol. 13(14), pages 1-31, July.
    2. Takanori Uchida & Kenichiro Sugitani, 2020. "Numerical and Experimental Study of Topographic Speed-Up Effects in Complex Terrain," Energies, MDPI, vol. 13(15), pages 1-38, July.
    3. Tristan Revaz & Fernando Porté-Agel, 2021. "Large-Eddy Simulation of Wind Turbine Flows: A New Evaluation of Actuator Disk Models," Energies, MDPI, vol. 14(13), pages 1-22, June.
    4. 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.
    5. 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.
    6. Kang, Yujoo & Kim, Hyebin & Lee, Sang, 2023. "Benefits of individual pitch control on offshore wind turbine submerged in upstream wake," Renewable Energy, Elsevier, vol. 217(C).
    7. 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.
    8. Zhang, Huan & Ge, Mingwei & Liu, Yongqian & Yang, Xiang I.A., 2021. "A new coupled model for the equivalent roughness heights of wind farms," Renewable Energy, Elsevier, vol. 171(C), pages 34-46.
    9. Hegazy, Amr & Blondel, Frédéric & Cathelain, Marie & Aubrun, Sandrine, 2022. "LiDAR and SCADA data processing for interacting wind turbine wakes with comparison to analytical wake models," Renewable Energy, Elsevier, vol. 181(C), pages 457-471.
    10. Takanori Uchida & Tadasuke Yoshida & Masaki Inui & Yoshihiro Taniyama, 2021. "Doppler Lidar Investigations of Wind Turbine Near-Wakes and LES Modeling with New Porous Disc Approach," Energies, MDPI, vol. 14(8), pages 1-33, April.
    11. Mou Lin & Fernando Porté-Agel, 2019. "Large-Eddy Simulation of Yawed Wind-Turbine Wakes: Comparisons with Wind Tunnel Measurements and Analytical Wake Models," Energies, MDPI, vol. 12(23), pages 1-18, November.
    12. Zhang, Ziyu & Huang, Peng & Bitsuamlak, Girma & Cao, Shuyang, 2024. "Large-eddy simulation of upwind-hill effects on wind-turbine wakes and power performance," Energy, Elsevier, vol. 294(C).
    13. Takanori Uchida, 2019. "Numerical Investigation of Terrain-Induced Turbulence in Complex Terrain Using High-Resolution Elevation Data and Surface Roughness Data Constructed with a Drone," Energies, MDPI, vol. 12(19), pages 1-20, October.
    14. 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.
    15. 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.
    16. 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.
    17. De-Zhi Wei & Ni-Na Wang & De-Cheng Wan, 2021. "Modelling Yawed Wind Turbine Wakes: Extension of a Gaussian-Based Wake Model," Energies, MDPI, vol. 14(15), pages 1-26, July.
    18. Ge, Mingwei & Zhang, Shuaibin & Meng, Hang & Ma, Hongliang, 2020. "Study on interaction between the wind-turbine wake and the urban district model by large eddy simulation," Renewable Energy, Elsevier, vol. 157(C), pages 941-950.
    19. Takanori Uchida & Susumu Takakuwa, 2020. "Numerical Investigation of Stable Stratification Effects on Wind Resource Assessment in Complex Terrain," Energies, MDPI, vol. 13(24), pages 1-32, December.
    20. 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).

    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:12:p:3197-:d:373938. 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.