基 于 数 据 同 化 的 试 验 数 据 驱 动 的 叶 栅 流 场 预 测

To predict the cascade flow field more accurately, an experimental data driven prediction framework based on data assimilation with a two-step ensemble Kalman filter method was developed. The ensemble Kalman filtering algorithm was first verified by a test function and the selection criteria of hyperparameters were then discussed. The S-A and SST turbulence models were applied on the MAN GHH cascade under the design Mach number and different angles of attack to perform the experimental data driven flow field prediction, which shows that the predicted flow fields are highly consistent with the experimental measurement. The results indicate that compared with the predicted results under the original parameters, the errors between the flow field corrected by data assimilation and the experimental measurement results are reduced by nearly 70%； for most working conditions, the sizes of separation bubbles at the suction surface of the blade tail shrink obviously and the separation starting points move to downstream. The corrected coming boundary conditions and the physical quantities in the corrected flow field predicted by the two turbulence models are almost the same, indicating that the flow fields driven by the experimental data are nearly independent of turbulence models.