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Wang, Z., Qiao, D., Tang, G., Wang, B., Yan, J. and Ou, J. (2022) An Identification Method of Floating Wind Turbine Tower Responses Using Deep Learning Technology in the Monitoring System. Ocean Engineering, 261, Article 112105.
https://doi.org/10.1016/j.oceaneng.2022.112105

has been cited by the following article:

• TITLE: Numerical Investigation of Wind Turbine Wake Characteristics under Different Wind Shear Conditions Using the Actuator Line Model

  AUTHORS: Chengbo Peng, Guoyi He, Kaixu Zhang

  KEYWORDS: Wind Turbine, Large-Eddy Simulation, Actuator Line Model, Wake Analysis

  JOURNAL NAME: Journal of Power and Energy Engineering, Vol.13 No.6, June 30, 2025

  ABSTRACT: For the ultra-high-power IEA 22 MW wind turbine, an actuator line model was first developed based on actuator line theory. This model was then integrated into a CFD flow field as a momentum source term for numerical simulation. Simulations were performed under different wind shear conditions to examine how the wind shear exponent affects the wake characteristics and power output of the turbine. The findings of this study provide a scientific foundation for wake modeling of ultra-large wind turbines and optimizing wind farm design.