Calibration of RANS model constant based on data assimilation and accurate simulation of separated flow

To improve the prediction accuracy of separated flow based on Reynolds Averaged Navier–Stokes model, model constants baseline stress are calibrated by ensemble Kalman filter data assimilation method. The in a diffuser is taken as object, and wall pressure coefficients used driving data. results show that method recalibrates easy to implement an effective separation regions predicted with default deviate greatly from experimental observations. By recalibrating constants, improved. This provides idea for accurate simulation engineering applications.