Bayesian Model Calibration Using High-Fidelity Simulations of a Mach 8 Scramjet Isolator and Combustor

Abstract

ABSTRACT The development of complex physical systems, such as scramjets, requires comprehensive assessments of the relevant performance envelopes. Because high-fidelity simulations and experiments are generally too expensive to perform over a wide range of operating conditions, reduced models can be especially useful for exploring the design space. However, these models often involve empirical parameters which must be calibrated using high-fidelity data sets. The present work develops a calibration procedure for a one-dimensional model of the wall pressure in a scramjet flowpath. Using wall pressure measurements from three-dimensional simulations, the six model parameters are tuned using a Bayesian methodology. The results show that the tuned model is largely able to capture the mean wall pressure in both the isolator and combustor. The calibration procedure is able to converge for both uniform and Gaussian priors, provided that the Gaussian priors are sufficiently wide. Physically-consistent correlations between parameters are obtained, which could inform further reductions in the model’s dimensionality. Application of the model to additional flow conditions shows good agreement to simulations not used in the calibration procedure.