Publications Details

Bayesian calibration of a RANS model with a complex response surface-a case study with jet-in-crossflow configuration

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

We demonstrate a Bayesian method that can be used to calibrate computationally expensive 3D RANS models with complex response surfaces. Such calibrations, conditioned on experimental data, can yield turbulence model parameters as probability density functions (PDF), concisely capturing the uncertainty in the estimation. Methods such as Markov chain Monte Carlo construct the PDF by sampling, and consequently a quick-running surrogate is used instead of the RANS simulator. The surrogate can be very difficult to design if the model’s response i.e., the dependence of the calibration variable (the observable) on the parameters being estimated is complex. We show how the training data used to construct the surrogate models can also be employed to isolate a promising and physically realistic part of the parameter space, within which the response is well-behaved and easily modeled. We design a classifier, based on treed linear models, to model the “well-behaved region”. This classifier serves as a prior in a Bayesian calibration study aimed at estimating 3 k-ε parameters C = (Cμ, Cε2, Cε1) from experimental data of a transonic jet-in-crossflow interaction. The robustness of the calibration is investigated by checking its predictions of variables not included in the calibration data. We also check the limit of applicability of the calibration by testing at an off-calibration point.