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A new analytical model for wind turbine wakes based on Monin-Obukhov similarity theory

Citations

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  1. 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.
  2. Zhang, Shaohai & Gao, Xiaoxia & Ma, Wanli & Lu, Hongkun & Lv, Tao & Xu, Shinai & Zhu, Xiaoxun & Sun, Haiying & Wang, Yu, 2023. "Derivation and verification of three-dimensional wake model of multiple wind turbines based on super-Gaussian function," Renewable Energy, Elsevier, vol. 215(C).
  3. Mingqiu Liu & Zhichang Liang & Haixiao Liu, 2022. "Numerical Investigations of Wake Expansion in the Offshore Wind Farm Using a Large Eddy Simulation," Energies, MDPI, vol. 15(6), pages 1-19, March.
  4. Zhang, Shaohai & Duan, Huanfeng & Lu, Lin & He, Ruiyang & Gao, Xiaoxia & Zhu, Songye, 2024. "Quantification of three-dimensional added turbulence intensity for the horizontal-axis wind turbine considering the wake anisotropy," Energy, Elsevier, vol. 294(C).
  5. Wu, Yan & Zhang, Shuai & Wang, Ruiqi & Wang, Yufei & Feng, Xiao, 2020. "A design methodology for wind farm layout considering cable routing and economic benefit based on genetic algorithm and GeoSteiner," Renewable Energy, Elsevier, vol. 146(C), pages 687-698.
  6. Dong, Guodan & Qin, Jianhua & Wu, Chutian & Xu, Chang & Yang, Xiaolei, 2026. "Reinforcement learning-enhanced genetic algorithm for wind farm layout optimization," Renewable Energy, Elsevier, vol. 259(C).
  7. Gao, Xiaoxia & Zhang, Shaohai & Li, Luqing & Xu, Shinai & Chen, Yao & Zhu, Xiaoxun & Sun, Haiying & Wang, Yu & Lu, Hao, 2022. "Quantification of 3D spatiotemporal inhomogeneity for wake characteristics with validations from field measurement and wind tunnel test," Energy, Elsevier, vol. 254(PA).
  8. Yang, Shanghui & Deng, Xiaowei & Yang, Kun, 2024. "Machine-learning-based wind farm optimization through layout design and yaw control," Renewable Energy, Elsevier, vol. 224(C).
  9. Xiao, Pengcheng & Tian, Linlin & Zhao, Ning & Song, Yilei & Wang, Zhenming & Lu, Xiyun, 2026. "An integrated model for predicting wind turbine wake velocity and turbulence intensity under different atmospheric stability regimes," Renewable Energy, Elsevier, vol. 256(PA).
  10. 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.
  11. Chang, Hao & Wang, Zengqiang & Peng, Guangjie & Shi, Weidong & Lin, Renyong & Zhou, Ling, 2025. "Role of wall roughness on energy dissipation and vortex dynamics in self-priming centrifugal pump: A study via entropy generation and Liutex vortex identification," Energy, Elsevier, vol. 337(C).
  12. Meng, Hang & Li, Li & Zhang, Jinhua, 2020. "A preliminary numerical study of the wake effects on the fatigue load for wind farm based on elastic actuator line model," Renewable Energy, Elsevier, vol. 162(C), pages 788-801.
  13. Zhichang Liang & Jingjing Zhang & Xinru Guo & Haixiao Liu, 2025. "Investigation of Wake Expansion for Spanwise Arranged Turbines in the Offshore Wind Farm by Large Eddy Simulation," Energies, MDPI, vol. 18(11), pages 1-24, June.
  14. Souaiby, Marwa & Porté-Agel, Fernando, 2026. "Physics-based and stability-aware analytical modeling of wind turbine wakes," Applied Energy, Elsevier, vol. 410(C).
  15. Wang, Yangwei & Lin, Jiahuan & Zhang, Jun, 2022. "Investigation of a new analytical wake prediction method for offshore floating wind turbines considering an accurate incoming wind flow," Renewable Energy, Elsevier, vol. 185(C), pages 827-849.
  16. Xiong, Xue-Lu & Lyu, Pin & Chen, Wen-Li & Li, Hui, 2020. "Self-similarity in the wake of a semi-submersible offshore wind turbine considering the interaction with the wake of supporting platform," Renewable Energy, Elsevier, vol. 156(C), pages 328-341.
  17. 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.
  18. Brogna, Roberto & Feng, Ju & Sørensen, Jens Nørkær & Shen, Wen Zhong & Porté-Agel, Fernando, 2020. "A new wake model and comparison of eight algorithms for layout optimization of wind farms in complex terrain," Applied Energy, Elsevier, vol. 259(C).
  19. Xingxing Han & Tongguang Wang & Xiandong Ma & Chang Xu & Shifeng Fu & Jinmeng Zhang & Feifei Xue & Zhe Cheng, 2024. "A Nonlinear Wind Turbine Wake Expansion Model Considering Atmospheric Stability and Ground Effects," Energies, MDPI, vol. 17(17), pages 1-24, September.
  20. Gao, Xiaoxia & Li, Bingbing & Wang, Tengyuan & Sun, Haiying & Yang, Hongxing & Li, Yonghua & Wang, Yu & Zhao, Fei, 2020. "Investigation and validation of 3D wake model for horizontal-axis wind turbines based on filed measurements," Applied Energy, Elsevier, vol. 260(C).
  21. Li, Ming-Wei & Wang, Yu-Tian & Yan, Shi-Jia & Geng, Jing & Wang, Xiao-Hang, 2026. "An optimization method for tidal current turbine array layout based on particle swarm optimization," Energy, Elsevier, vol. 348(C).
  22. Feng, Dachuan & Li, Larry K.B. & Gupta, Vikrant & Wan, Minping, 2022. "Componentwise influence of upstream turbulence on the far-wake dynamics of wind turbines," Renewable Energy, Elsevier, vol. 200(C), pages 1081-1091.
  23. Liu, Guangyi & Yang, Qingshan, 2026. "A super-Gaussian theoretical model to calculate the near wake velocity distribution of wind turbine," Renewable Energy, Elsevier, vol. 273(C).
  24. Adam Zagubień & Katarzyna Wolniewicz & Jakub Szwochertowski, 2024. "Analysis of Wind Farm Productivity Taking Wake Loss into Account: Case Study," Energies, MDPI, vol. 17(23), pages 1-14, November.
  25. Kuichao Ma & Huanqiang Zhang & Xiaoxia Gao & Xiaodong Wang & Heng Nian & Wei Fan, 2024. "Research on Evaluation Method of Wind Farm Wake Energy Efficiency Loss Based on SCADA Data Analysis," Sustainability, MDPI, vol. 16(5), pages 1-16, February.
  26. Guo, Nai-Zhi & Shi, Ke-Zhong & Li, Bo & Qi, Liang-Wen & Wu, Hong-Hui & Zhang, Zi-Liang & Xu, Jian-Zhong, 2022. "A physics-inspired neural network model for short-term wind power prediction considering wake effects," Energy, Elsevier, vol. 261(PA).
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