Bayesian calibration of a k-ε turbulence model for predictive jet-in-crossflow simulations

‡§ We propose a Bayesian method to calibrate parameters of a RANS model to improve its predictive skill in jet-in-crossflow simulations. The method is based on the hypotheses that (1) informative parameters can be estimated from experiments of flow configurations that display the same, strongly vortical features of jet-in-crossflow interactions and (2) one can construct surrogates of RANS models for certain judiciously chosen RANS outputs which serve as calibration variables (alternatively, experimental observables). We estimate three ke parameters (C∝, C 2 , C 1 ) from Reynolds stress measurements from an incompressible flowover-a-square-cylinder experiment. The k-e parameters are estimated as a joint probability density function. Jet-in-crossflow simulations performed with (C∝, C 2 , C 1 ) samples drawn from this distribution are seen to provide far better predictions than those obtained with nominal parameter values. We also find a (C∝, C 2 , C 1 ) combination which provides < 15% error in a number of performance metrics; in contrast, the errors obtained with nominal parameter values may exceed 60%.