Publications Details

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

Ray, Jaideep R.; Lefantzi, Sophia L.; Arunajatesan, Srinivasan A.; DeChant, Lawrence J.

We propose a Bayesian method to calibrate parameters of a k-∈ 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 judiciously chosen outputs which serve as calibration observables. We estimate three k - ∈ parameters, (Cμ,C∈2,C∈1), from Reynolds stress measurements obtained from an incompressible flow-over-a-square-cylinder experiment. The k - ∈ parameters are estimated as a joint probability density function. Jet-in-crossflow simulations performed with (Cμ,C∈2,C∈) samples drawn from this distribution are seen to provide far better predictions than those obtained with nominal parameter values. We also find a (Cμ,C∈2,C∈1) combination which provides less than 15% error in a number of performance metrics. In contrast, the errors obtained with nominal parameter values may exceed 60%.