Calibration of the k-ω shear stress transport turbulence model for shock wave boundary layer interaction in a SERN using machine learning

Abstract

The Single Expansion Ramp Nozzle, which is called SERN, is commonly used in supersonic and hypersonic aircraft, taking into account the design of integrated aircraft/propulsion systems/nozzles. Under the conditions of low Mach number and low nozzle pressure ratio, the flow in this device became severely over-expanded, internal resistance was clearly enhanced, and the quality and performance of the flow deteriorated sharply. In general, the exact prediction of shock wave structure and its interaction with the boundary layer in operating conditions plays an important role in the design of the protective structures. To this end, this paper uses machine learning techniques to calibrate the constant coefficient of the k-ω SST turbulence model. Furthermore, recognizing the effective factors in the performance reduction of the SERNs in different working conditions can be very helpful in optimizing them. Therefore, the second aim of this work is to investigate the effects of the main working parameters on the behavior of the supersonic flow of a SERN. It was shown that using the calibrated k-ω SST turbulence model improved the accuracy of computations by 4.5% when compared to using the model with its default constant coefficients. After that, the effects of inlet temperature on the flow behavior in the SERN have been studied. The results showed that The shock position shifts by 0.33% away from the throat and 0.17% closer to the SERN center when the inlet flow temperature is raised from 250 to 325 K.