IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v246y2022ics0360544222003024.html
   My bibliography  Save this article

The balance effects of momentum deficit and thrust in cumulative wake models

Author

Listed:
  • Barasa, Maulidi
  • Li, Xuemin
  • Zhang, Yi
  • Xu, Weiming

Abstract

Due to the intricacies of wind wake transition, it is necessary to estimate both the atmospheric boundary layer (ABL) contributions as well as the momentum deficit and thrust balancing. Wake superposition models such as Linear and Katic produce inconsistent wake predictions that are empirically derived. Designing an explicit cumulative model based on the momentum deficit and thrust budget analysis is not feasible. We use the analytical and machine learning approach to predict and correct inconsistencies between momentum deficits and thrust. The Mass Flow Conservation Superposition (MFCS) model is derived using the Linear Momentum equation, and the Mass Flow and Thrust Conservation Superposition (MFTCS) model is derived by fitting the Gaussian Process Regression (GPR) model to Large Eddy Simulation (LES) data. Noticeably from our results, the MFCS underestimates the thrust magnitude and the wake recovery rates. The MFTCS model based on the GPR model fit correctly estimates the momentum deficit and thrust balance. The GPR model assesses the smoothness and differentiability of the LES data to predict wake recovery. The MFTCS outperforms Katic and MFCS results but is more accurate than the LES data based on the goodness of fit criteria.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:246:y:2022:i:c:s0360544222003024
    DOI: 10.1016/j.energy.2022.123399
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222003024
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.123399?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Keane, Aidan & Nisbet, Iain & Calvo, Gabriele & Pickering, George & Tulloch, Jake & More, Graham & Koronka, Neil, 2022. "Wind farm cumulative induction zone effect and the impact on energy yield estimation," Renewable Energy, Elsevier, vol. 181(C), pages 1209-1222.
    2. Ti, Zilong & Deng, Xiao Wei & Yang, Hongxing, 2020. "Wake modeling of wind turbines using machine learning," Applied Energy, Elsevier, vol. 257(C).
    3. 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).
    4. 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.
    5. Sun, Haiying & Gao, Xiaoxia & Yang, Hongxing, 2020. "Experimental study on wind speeds in a complex-terrain wind farm and analysis of wake effects," Applied Energy, Elsevier, vol. 272(C).
    6. 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).
    7. Zhang, Jincheng & Zhao, Xiaowei, 2022. "Wind farm wake modeling based on deep convolutional conditional generative adversarial network," Energy, Elsevier, vol. 238(PB).
    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. Zhang, Ziyu & Huang, Peng, 2023. "Prediction of multiple-wake velocity and wind power using a cosine-shaped wake model," Renewable Energy, Elsevier, vol. 219(P1).
    2. 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).

    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. Amiri, Mojtaba Maali & Shadman, Milad & Estefen, Segen F., 2024. "A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    2. He, Ruiyang & Sun, Haiying & Gao, Xiaoxia & Yang, Hongxing, 2022. "Wind tunnel tests for wind turbines: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    3. 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).
    4. 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).
    5. Ti, Zilong & Deng, Xiao Wei & Zhang, Mingming, 2021. "Artificial Neural Networks based wake model for power prediction of wind farm," Renewable Energy, Elsevier, vol. 172(C), pages 618-631.
    6. 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).
    7. Chen, Zhenyu & Lin, Zhongwei & Zhai, Xiaoya & Liu, Jizhen, 2022. "Dynamic wind turbine wake reconstruction: A Koopman-linear flow estimator," Energy, Elsevier, vol. 238(PB).
    8. Pawar, Suraj & Sharma, Ashesh & Vijayakumar, Ganesh & Bay, Chrstopher J. & Yellapantula, Shashank & San, Omer, 2022. "Towards multi-fidelity deep learning of wind turbine wakes," Renewable Energy, Elsevier, vol. 200(C), pages 867-879.
    9. Zhiwen Deng & Chang Xu & Zhihong Huo & Xingxing Han & Feifei Xue, 2023. "Yaw Optimisation for Wind Farm Production Maximisation Based on a Dynamic Wake Model," Energies, MDPI, vol. 16(9), pages 1-20, May.
    10. Yang, Kun & Deng, Xiaowei & Ti, Zilong & Yang, Shanghui & Huang, Senbin & Wang, Yuhang, 2023. "A data-driven layout optimization framework of large-scale wind farms based on machine learning," Renewable Energy, Elsevier, vol. 218(C).
    11. 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).
    12. 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.
    13. Yang, Xuefeng & Yu, Peining & Sui, Yi & Chen, Shengli & Xing, Jiuxing & Li, Lei, 2024. "A numerical study of rainfall effects on wind turbine wakes," Renewable Energy, Elsevier, vol. 230(C).
    14. Sun, Haiying & Yang, Hongxing, 2023. "Wind farm layout and hub height optimization with a novel wake model," Applied Energy, Elsevier, vol. 348(C).
    15. 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).
    16. Jia, Rui & Ge, Mingwei & Zhang, Ziliang & Li, Xintao & Du, Bowen, 2024. "A numerical simulation framework for wakes downstream of large wind farms based on equivalent roughness model," Energy, Elsevier, vol. 307(C).
    17. He, Ruiyang & Yang, Hongxing & Sun, Haiying & Gao, Xiaoxia, 2021. "A novel three-dimensional wake model based on anisotropic Gaussian distribution for wind turbine wakes," Applied Energy, Elsevier, vol. 296(C).
    18. 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.
    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. 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.

    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:eee:energy:v:246:y:2022:i:c:s0360544222003024. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.