Abstract The development and reattachment of the free shear layer in supersonic flows have been a widely encountered phenomenon in engineering, and are gaining increasing interest from researchers who investigate such complex flow structures using numerical simulation methods. Among these methods, Reynolds Averaged Navier-Stokes (RANS) still plays an important role in application problems. However, the uncertainty of the parameters in RANS turbulence model affects the reliability and accuracy of predictions. Therefore, the purpose of this study is to conduct a detailed uncertainty analysis of the parameters in the shear-stress transport (SST) turbulence model for the free shear layer in a cavity-ramp flow. In the current study, uncertainty quantification was conducted with the construction of a surrogate model between the parameters and calculation results using the non-intrusive polynomial chaos (NIPC) method. Next, for sensitivity analysis, the Sobol index of each parameter was calculated to identify the key parameters mainly responsible for the uncertainty. Based on the above results, Bayesian inference was employed to calibrate these key parameters by leveraging experimental data. The results show that the calculated wall pressure and skin friction of the ramp suffer a non-negligible uncertainty caused by the model parameters, and parameters $a_1$, ${\sigma }_{\omega 2}$, ${\beta }_2$, $\kappa$, and ${\beta }_1$ are identified as the key parameters in this type of flow. Subsequently, the calibrated values for the key parameters are obtained through Bayesian inference, which could significantly improve prediction results in the original case. Furthermore, the applicability of the SST model with calibrated parameters is verified under different free-stream conditions.

Highlights • Quantify the uncertainty of SST turbulence model's parameters in cavity-ramp flow. • Key parameters in SST model are identified for predicting cavity-ramp flow. • Improve model's performance by calibrating key parameters via Bayesian inference.