IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i14p5200-d865356.html
   My bibliography  Save this article

Method to Predict Outputs of Two-Dimensional VAWT Rotors by Using Wake Model Mimicking the CFD-Created Flow Field

Author

Listed:
  • Jirarote Buranarote

    (Department of Mechanical and Aerospace Engineering, Tottori University, 4-101 Koyama-Minami, Tottori 680-8552, Japan)

  • Yutaka Hara

    (Advanced Mechanical and Electronic System Research Center, Faculty of Engineering, Tottori University, 4-101 Koyama-Minami, Tottori 680-8552, Japan)

  • Masaru Furukawa

    (Advanced Mechanical and Electronic System Research Center, Faculty of Engineering, Tottori University, 4-101 Koyama-Minami, Tottori 680-8552, Japan)

  • Yoshifumi Jodai

    (Department of Mechanical Engineering, Kagawa National Institute of Technology (KOSEN), Kagawa College, 355 Chokushi, Takamatsu 761-8058, Japan)

Abstract

Recently, wind farms consisting of clusters of closely spaced vertical-axis wind turbines (VAWTs) have attracted the interest of many people. In this study, a method using a wake model to predict the flow field and the output power of each rotor in a VAWT cluster is proposed. The method uses the information obtained by the preliminary computational fluid dynamics (CFD) targeting an isolated single two-dimensional (2D) VAWT rotor and a few layouts of the paired 2D rotors. In the method, the resultant rotor and flow conditions are determined so as to satisfy the momentum balance in the main wind direction. The pressure loss of the control volume (CV) is given by an interaction model which modifies the prepared information on a single rotor case and assumes the dependence on the inter-rotor distance and the induced velocity. The interaction model consists of four equations depending on the typical four-type layouts of selected two rotors. To obtain the appropriate circulation of each rotor, the searching range of the circulation is limited according to the distribution of other rotors around the rotor at issue. The method can predict the rotor powers in a 2D-VAWT cluster including a few rotors in an incomparably shorter time than the CFD analysis using a dynamic model.

Suggested Citation

  • Jirarote Buranarote & Yutaka Hara & Masaru Furukawa & Yoshifumi Jodai, 2022. "Method to Predict Outputs of Two-Dimensional VAWT Rotors by Using Wake Model Mimicking the CFD-Created Flow Field," Energies, MDPI, vol. 15(14), pages 1-29, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5200-:d:865356
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/14/5200/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/14/5200/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zanforlin, Stefania & Nishino, Takafumi, 2016. "Fluid dynamic mechanisms of enhanced power generation by closely spaced vertical axis wind turbines," Renewable Energy, Elsevier, vol. 99(C), pages 1213-1226.
    2. Guo-Wei Qian & Takeshi Ishihara, 2018. "A New Analytical Wake Model for Yawed Wind Turbines," Energies, MDPI, vol. 11(3), pages 1-24, March.
    3. Ziyu Zhang & Peng Huang & Haocheng Sun, 2020. "A Novel Analytical Wake Model with a Cosine-Shaped Velocity Deficit," Energies, MDPI, vol. 13(13), pages 1-20, June.
    4. Zhichang Liang & Haixiao Liu, 2022. "Layout Optimization of a Modular Floating Wind Farm Based on the Full-Field Wake Model," Energies, MDPI, vol. 15(3), pages 1-15, January.
    5. Amin Niayifar & Fernando Porté-Agel, 2016. "Analytical Modeling of Wind Farms: A New Approach for Power Prediction," Energies, MDPI, vol. 9(9), pages 1-13, September.
    6. Masaru Furukawa & Yutaka Hara & Yoshifumi Jodai, 2022. "Analytical Model for Phase Synchronization of a Pair of Vertical-Axis Wind Turbines," Energies, MDPI, vol. 15(11), pages 1-19, June.
    7. Fernando Porté-Agel & Yu-Ting Wu & Chang-Hung Chen, 2013. "A Numerical Study of the Effects of Wind Direction on Turbine Wakes and Power Losses in a Large Wind Farm," Energies, MDPI, vol. 6(10), pages 1-17, October.
    8. Yoshifumi Jodai & Yutaka Hara, 2021. "Wind Tunnel Experiments on Interaction between Two Closely Spaced Vertical-Axis Wind Turbines in Side-by-Side Arrangement," Energies, MDPI, vol. 14(23), pages 1-19, November.
    9. Vergaerde, Antoine & De Troyer, Tim & Standaert, Lieven & Kluczewska-Bordier, Joanna & Pitance, Denis & Immas, Alexandre & Silvert, Frédéric & Runacres, Mark C., 2020. "Experimental validation of the power enhancement of a pair of vertical-axis wind turbines," Renewable Energy, Elsevier, vol. 146(C), pages 181-187.
    10. Jennifer Marie Rinker & Esperanza Soto Sagredo & Leonardo Bergami, 2021. "The Importance of Wake Meandering on Wind Turbine Fatigue Loads in Wake," Energies, MDPI, vol. 14(21), pages 1-18, November.
    11. Göçmen, Tuhfe & Laan, Paul van der & Réthoré, Pierre-Elouan & Diaz, Alfredo Peña & Larsen, Gunner Chr. & Ott, Søren, 2016. "Wind turbine wake models developed at the technical university of Denmark: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 752-769.
    12. Yutaka Hara & Yoshifumi Jodai & Tomoyuki Okinaga & Masaru Furukawa, 2021. "Numerical Analysis of the Dynamic Interaction between Two Closely Spaced Vertical-Axis Wind Turbines," Energies, MDPI, vol. 14(8), pages 1-23, April.
    13. Jeffrey E. Silva & Louis Angelo M. Danao, 2021. "Varying VAWT Cluster Configuration and the Effect on Individual Rotor and Overall Cluster Performance," Energies, MDPI, vol. 14(6), pages 1-22, March.
    14. Carl R. Shapiro & Genevieve M. Starke & Charles Meneveau & Dennice F. Gayme, 2019. "A Wake Modeling Paradigm for Wind Farm Design and Control," Energies, MDPI, vol. 12(15), pages 1-19, August.
    15. Peng, H.Y. & Han, Z.D. & Liu, H.J. & Lin, K. & Lam, H.F., 2020. "Assessment and optimization of the power performance of twin vertical axis wind turbines via numerical simulations," Renewable Energy, Elsevier, vol. 147(P1), pages 43-54.
    16. Javier Serrano González & Bruno López & Martín Draper, 2021. "Optimal Pitch Angle Strategy for Energy Maximization in Offshore Wind Farms Considering Gaussian Wake Model," Energies, MDPI, vol. 14(4), pages 1-18, February.
    17. Feng, Ju & Shen, Wen Zhong, 2015. "Solving the wind farm layout optimization problem using random search algorithm," Renewable Energy, Elsevier, vol. 78(C), pages 182-192.
    18. Wim Munters & Johan Meyers, 2018. "Dynamic Strategies for Yaw and Induction Control of Wind Farms Based on Large-Eddy Simulation and Optimization," Energies, MDPI, vol. 11(1), pages 1-32, January.
    19. Nicolas Kirchner-Bossi & Fernando Porté-Agel, 2018. "Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model," Energies, MDPI, vol. 11(12), pages 1-26, November.
    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. Daniel Micallef, 2023. "Advancements in Offshore Vertical Axis Wind Turbines," Energies, MDPI, vol. 16(4), pages 1-3, February.

    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. Nicolas Kirchner-Bossi & Fernando Porté-Agel, 2021. "Wind Farm Area Shape Optimization Using Newly Developed Multi-Objective Evolutionary Algorithms," Energies, MDPI, vol. 14(14), pages 1-25, July.
    2. Masaru Furukawa & Yutaka Hara & Yoshifumi Jodai, 2022. "Analytical Model for Phase Synchronization of a Pair of Vertical-Axis Wind Turbines," Energies, MDPI, vol. 15(11), pages 1-19, June.
    3. Dongqin Zhang & Yang Liang & Chao Li & Yiqing Xiao & Gang Hu, 2022. "Applicability of Wake Models to Predictions of Turbine-Induced Velocity Deficit and Wind Farm Power Generation," Energies, MDPI, vol. 15(19), pages 1-26, October.
    4. 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).
    5. Ziyu Zhang & Peng Huang & Haocheng Sun, 2020. "A Novel Analytical Wake Model with a Cosine-Shaped Velocity Deficit," Energies, MDPI, vol. 13(13), pages 1-20, June.
    6. Yoshifumi Jodai & Yutaka Hara, 2023. "Wind-Tunnel Experiments on the Interactions among a Pair/Trio of Closely Spaced Vertical-Axis Wind Turbines," Energies, MDPI, vol. 16(3), pages 1-27, January.
    7. Nicolas Kirchner-Bossi & Fernando Porté-Agel, 2018. "Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model," Energies, MDPI, vol. 11(12), pages 1-26, November.
    8. Keane, Aidan, 2021. "Advancement of an analytical double-Gaussian full wind turbine wake model," Renewable Energy, Elsevier, vol. 171(C), pages 687-708.
    9. Ni, Lulu & Miao, Weipao & Li, Chun & Liu, Qingsong, 2021. "Impacts of Gurney flap and solidity on the aerodynamic performance of vertical axis wind turbines in array configurations," Energy, Elsevier, vol. 215(PA).
    10. Dhiman, Harsh S. & Deb, Dipankar & Foley, Aoife M., 2020. "Lidar assisted wake redirection in wind farms: A data driven approach," Renewable Energy, Elsevier, vol. 152(C), pages 484-493.
    11. Zhenzhou Shao & Ying Wu & Li Li & Shuang Han & Yongqian Liu, 2019. "Multiple Wind Turbine Wakes Modeling Considering the Faster Wake Recovery in Overlapped Wakes," Energies, MDPI, vol. 12(4), pages 1-14, February.
    12. Ji Hao Zhang & Fue-Sang Lien & Eugene Yee, 2022. "Investigations of Vertical-Axis Wind-Turbine Group Synergy Using an Actuator Line Model," Energies, MDPI, vol. 15(17), pages 1-22, August.
    13. Zhichang Liang & Haixiao Liu, 2023. "Layout Optimization Algorithms for the Offshore Wind Farm with Different Densities Using a Full-Field Wake Model," Energies, MDPI, vol. 16(16), pages 1-15, August.
    14. Qian, Guo-Wei & Ishihara, Takeshi, 2021. "Wind farm power maximization through wake steering with a new multiple wake model for prediction of turbulence intensity," Energy, Elsevier, vol. 220(C).
    15. Matthias Ritter & Simone Pieralli & Martin Odening, 2017. "Neighborhood Effects in Wind Farm Performance: A Regression Approach," Energies, MDPI, vol. 10(3), pages 1-16, March.
    16. Carl R. Shapiro & Genevieve M. Starke & Charles Meneveau & Dennice F. Gayme, 2019. "A Wake Modeling Paradigm for Wind Farm Design and Control," Energies, MDPI, vol. 12(15), pages 1-19, August.
    17. Pollini, Nicolò, 2022. "Topology optimization of wind farm layouts," Renewable Energy, Elsevier, vol. 195(C), pages 1015-1027.
    18. Ti, Zilong & Deng, Xiao Wei & Yang, Hongxing, 2020. "Wake modeling of wind turbines using machine learning," Applied Energy, Elsevier, vol. 257(C).
    19. Kuang, Limin & Katsuchi, Hiroshi & Zhou, Dai & Chen, Yaoran & Han, Zhaolong & Zhang, Kai & Wang, Jiaqi & Bao, Yan & Cao, Yong & Liu, Yijie, 2023. "Strategy for mitigating wake interference between offshore vertical-axis wind turbines: Evaluation of vertically staggered arrangement," Applied Energy, Elsevier, vol. 351(C).
    20. Li, Xuyang & Qiu, Yingning & Feng, Yanhui & Wang, Zheng, 2021. "Wind turbine power prediction considering wake effects with dual laser beam LiDAR measured yaw misalignment," Applied Energy, Elsevier, vol. 299(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:15:y:2022:i:14:p:5200-:d:865356. 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.