Bayesian Uncertainty Reduction of Generalised k-ω Turbulence Model for Prediction of Film-Cooling Effectiveness

Abstract

In this study, Reynolds-averaged Navier-Stokes (RANS) simulations were carried out to predict the film cooling effectiveness of an inclined round jet in crossflow using turbulence model parameters optimised based on measurement data. The posterior distributions of the generalised (GEKO) turbulence model parameters were estimated using a computationally efficient surrogate model with the Markov chain Monte Carlo method, which provides a framework for probabilistic parameter estimation based on measurement data. The results show that using the maximum a posterior parameters for a blowing ratio of 0.5 gives better predictions than using the default parameters of the GEKO model. The estimated parameters were then applied to flows with a higher blowing ratio and different hole geometry to evaluate the generalisation performance. In both cases, the results were improved by properly predicting the spread of the cooling flow.