@article{MENG2024, 
author = {Lingting MENG and Junwei YANG and Hua YANG},
title = {Research on data assimilation of wind turbine airfoils in stall},
year = {2024},
journal = {Acta Aerodynamica Sinica},
volume = {42},
number = {4},
pages = {37-45},
keywords = {data assimilation, ensemble Kalman filter, stall, S-A turbulence model, pressure distribution},
url = {https://www.sciopen.com/article/10.7638/kqdlxxb-2023.0113},
doi = {10.7638/kqdlxxb-2023.0113},
abstract = {In the present paper, we utilize the ensemble Kalman filter (EnKF) method to optimize parameters of the turbulence model Spalart-Allramas (S-A) to simulate flow fields around three wind turbine airfoils (NACA63415, S809, and DU97W300) at the same stall degree. The optimized parameters of the medium-thickness airfoil S809 are further applied to the other two airfoils to investigate their performance on airfoils with various thicknesses. For each airfoil, comparisons to experimental data regarding the pressure coefficient distribution and separation location show that using the optimized parameters can reduce numerical errors significantly. Even though applying the optimized parameters of S809 to the other two can also considerably reduce the numerical errors, the errors are slightly larger than those obtained by the optimized parameters of their own. In addition, the model parameters with significant changes after assimilation are  Cb1,  Cυ1, and σ, of which  Cb1 undergoes the most prominent change and decreases posterior of assimilation for all three airfoils. It is speculated that  Cb1 is crucial for the model parameters' universality, at least for the present three airfoils, of the model parameters.}
}