Bayesian uncertainty analysis of SA turbulence model for backward-facing step simulations

The Reynolds averaged Navier-Stokes models are still the workhorse in current engineering applications due to its high efficiency and robustness. However, the closure coefficients (also known as model parameters) of turbulence models are calibrated by model builders according to some fundamental flows, and the values suggested by the model builders may not be applicable to all flow types. In this work, the Bayesian method is applied to recalibrate the closure coefficients of SA turbulence model to improve its performance in backward-facing step problem. The results show that the four parameters Cb1, Cw3, Cv1 and ? are well informed by the experimental data of skin friction coefficient. The recalibrated model parameters show better performance than the nominal values in the prediction of skin fiction coefficient.