二维扩压叶栅流场的数据同化研究

To predict the flow field of two-dimension compressor cascade accurately, a data assimilation framework for the compressor cascade inner flow field based on ensemble Kalman filter algorithm was performed. The framework has been applied on the flow fields of MAN-GHH compressor cascade at different working conditions. By correcting coming boundary conditions and coefficients of S-A turbulence models, the numerical simulation flow fields which are highly consistent with experiments measurements from the cascade wind tunnel were acquired. The results show that: the data assimilation can reduce the perdition error more than 60%; it is necessary to correct the coming boundary conditions; for most working conditions, the flow separation bubbles were over-predicted by the S-A turbulence model and coefficients correcting can improve the accuracy of flow separation prediction; the corrected turbulence model coefficients have some regularity.