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

Abstract

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