IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i3p1130-d1041821.html
   My bibliography  Save this article

Deep Learning-Based Prediction of Unsteady Reynolds-Averaged Navier-Stokes Solutions for Vertical-Axis Turbines

Author

Listed:
  • Chloë Dorge

    (Department of Mechanical Engineering, University of Manitoba, 75 Chancellors Cir, Winnipeg, MB R3T 5V6, Canada)

  • Eric Louis Bibeau

    (Department of Mechanical Engineering, University of Manitoba, 75 Chancellors Cir, Winnipeg, MB R3T 5V6, Canada)

Abstract

The following study investigates the effectiveness of a deep learning-based method for predicting the flow field and flow-driven rotation of a vertical-axis hydrokinetic turbine operating in previously unseen free-stream velocities. A Convolutional Neural Network (CNN) is trained and tested using the solutions of five two-dimensional (2-D), foil-resolved Unsteady Reynolds-Averaged Navier-Stokes (URANS) simulations, with free-stream velocities of 1.0, 1.5, 2.0, 2.5, and 3.0 m/s. Based on the boundary conditions of free-stream velocity and rotor position, the flow fields of x-velocity, y-velocity, pressure, and turbulent viscosity are inferred, in addition to the angular velocity of the rotor. Three trained CNN models are developed to evaluate the effects of (1) the dimensions of the training data, and (2) the number of simulations used as training cases. Reducing data dimensions was found to diminish mean relative error in predictions of velocity and turbulent viscosity, while increasing it in predictions of pressure and angular velocity. Increasing the number of training cases from two to three was found to reduce relative error for all predicted unknowns. With the best achieved CNN model, the variables of x-velocity, y-velocity, pressure, turbulent viscosity, and angular velocity were inferred with mean relative errors of 6.93%, 9.82%, 10.7%, 7.48%, and 0.817%, respectively.

Suggested Citation

  • Chloë Dorge & Eric Louis Bibeau, 2023. "Deep Learning-Based Prediction of Unsteady Reynolds-Averaged Navier-Stokes Solutions for Vertical-Axis Turbines," Energies, MDPI, vol. 16(3), pages 1-33, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1130-:d:1041821
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/3/1130/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/3/1130/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Rui & Zhang, Jincheng & Zhao, Xiaowei, 2022. "Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data," Energy, Elsevier, vol. 258(C).
    2. Ti, Zilong & Deng, Xiao Wei & Yang, Hongxing, 2020. "Wake modeling of wind turbines using machine learning," Applied Energy, Elsevier, vol. 257(C).
    3. Ian D. Brownstein & Nathaniel J. Wei & John O. Dabiri, 2019. "Aerodynamically Interacting Vertical-Axis Wind Turbines: Performance Enhancement and Three-Dimensional Flow," Energies, MDPI, vol. 12(14), pages 1-23, July.
    4. Ahmadi-Baloutaki, Mojtaba & Carriveau, Rupp & Ting, David S-K., 2016. "A wind tunnel study on the aerodynamic interaction of vertical axis wind turbines in array configurations," Renewable Energy, Elsevier, vol. 96(PA), pages 904-913.
    5. 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.
    6. Zhang, Jincheng & Zhao, Xiaowei, 2022. "Wind farm wake modeling based on deep convolutional conditional generative adversarial network," Energy, Elsevier, vol. 238(PB).
    7. 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.
    8. Laurie Jégo & Sylvain S. Guillou, 2021. "Study of a Bi-Vertical Axis Turbines Farm Using the Actuator Cylinder Method," Energies, MDPI, vol. 14(16), pages 1-23, August.
    9. Rezaeiha, Abdolrahim & Kalkman, Ivo & Blocken, Bert, 2017. "CFD simulation of a vertical axis wind turbine operating at a moderate tip speed ratio: Guidelines for minimum domain size and azimuthal increment," Renewable Energy, Elsevier, vol. 107(C), pages 373-385.
    10. Hansen, Joachim Toftegaard & Mahak, Mahak & Tzanakis, Iakovos, 2021. "Numerical modelling and optimization of vertical axis wind turbine pairs: A scale up approach," Renewable Energy, Elsevier, vol. 171(C), pages 1371-1381.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Li, Siyi & Zhang, Mingrui & Piggott, Matthew D., 2023. "End-to-end wind turbine wake modelling with deep graph representation learning," Applied Energy, Elsevier, vol. 339(C).
    3. 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.
    4. Barnes, Andrew & Marshall-Cross, Daniel & Hughes, Ben Richard, 2021. "Towards a standard approach for future Vertical Axis Wind Turbine aerodynamics research and development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    5. Zhou, Lei & Wen, Jiahao & Wang, Zhaokun & Deng, Pengru & Zhang, Hongfu, 2023. "High-fidelity wind turbine wake velocity prediction by surrogate model based on d-POD and LSTM," Energy, Elsevier, vol. 275(C).
    6. 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).
    7. Yadav, Sandeep & Veeravalli, Srinivas V. & Singh, Sidh Nath, 2024. "Effect of rotor spacing, overlapping and non-overlapping, on the performance of a coupled counter-rotating twin-rotor VAWT using CFD," Renewable Energy, Elsevier, vol. 221(C).
    8. Li, Rui & Zhang, Jincheng & Zhao, Xiaowei, 2022. "Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data," Energy, Elsevier, vol. 258(C).
    9. Luo, Zhaohui & Wang, Longyan & Xu, Jian & Wang, Zilu & Yuan, Jianping & Tan, Andy C.C., 2024. "A reduced order modeling-based machine learning approach for wind turbine wake flow estimation from sparse sensor measurements," Energy, Elsevier, vol. 294(C).
    10. Peng, H.Y. & Liu, M.N. & Liu, H.J. & Lin, K., 2022. "Optimization of twin vertical axis wind turbines through large eddy simulations and Taguchi method," Energy, Elsevier, vol. 240(C).
    11. Posa, Antonio, 2022. "Wake characterization of paired cross-flow turbines," Renewable Energy, Elsevier, vol. 196(C), pages 1064-1094.
    12. 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.
    13. Li, Siyi & Robert, Arnaud & Faisal, A. Aldo & Piggott, Matthew D., 2024. "Learning to optimise wind farms with graph transformers," Applied Energy, Elsevier, vol. 359(C).
    14. Xu, Zhongyun & Chen, Jian & Li, Chun, 2023. "Research on the adaptability of dynamic pitch control strategies on H-type VAWT close-range arrays by simulation study," Renewable Energy, Elsevier, vol. 218(C).
    15. 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.
    16. Barasa, Maulidi & Li, Xuemin & Zhang, Yi & Xu, Weiming, 2022. "The balance effects of momentum deficit and thrust in cumulative wake models," Energy, Elsevier, vol. 246(C).
    17. 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.
    18. Reddy, Sohail R., 2021. "A machine learning approach for modeling irregular regions with multiple owners in wind farm layout design," Energy, Elsevier, vol. 220(C).
    19. Cheng, Biyi & Du, Jianjun & Yao, Yingxue, 2022. "Machine learning methods to assist structure design and optimization of Dual Darrieus Wind Turbines," Energy, Elsevier, vol. 244(PA).
    20. Hassan, Syed Saddam ul & Javaid, M. Tariq & Rauf, Umar & Nasir, Sheharyar & Shahzad, Aamer & Salamat, Shuaib, 2023. "Systematic investigation of power enhancement of Vertical Axis Wind Turbines using bio-inspired leading edge tubercles," Energy, Elsevier, vol. 270(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:16:y:2023:i:3:p:1130-:d:1041821. 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.