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

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  • 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
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    References listed on IDEAS

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    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).
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    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).

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