Data assimilation method and application of shear stress transport turbulence model for complex separation of internal shock boundary layer flow

Abstract

Traditional turbulence models suffer from low accuracy and weak applicability when predicting complex separated flows, such as those that occur in shock boundary layers. To overcome this problem, the present paper considers a cavity-ramp structure and calibrates the turbulence model parameters using a deep neural network (DNN) surrogate model and a genetic algorithm (GA). The non-intrusive polynomial chaos expansion method is used to quantify the uncertainty of the shear stress transport (SST) turbulence model parameters and determine the effects of these parameters on the wall pressure, allowing suitable feature identification parameters to be selected for the DNN turbulence surrogate model. The DNN is compared with the traditional polynomial chaos expansion method, and the results highlight the advantages of using the DNN method to construct the surrogate model. Finally, a GA is used to optimize and calibrate the SST turbulence model parameters based on the surrogate model and experimental data. Experimental results show that the DNN turbulence surrogate model is highly accurate, with the predicted wall pressure, achieving a coefficient of determination above 0.998. The DNN has higher precision, stronger feature extraction ability, and faster prediction times than the traditional polynomial chaos expansion method. The calibrated SST turbulence model produces wall pressures that are close to the experimental data, verifying the feasibility of the proposed method. It is expected that the approach proposed in this paper will improve the calculation accuracy of the SST turbulence model.