Bayesian calibration of a methane-air global scheme and uncertainty propagation to flame-vortex interactions

• Published in: Combustion and flame 2021-12, Vol.234, p.111642, Article 111642
• Main Authors: Armengol, Jan Mateu, Le Maître, Olivier, Vicquelin, Ronan
• Format: Article
• Language: English
• Subjects: Applications; Bayesian inference; Calibration; Computational fluid dynamics; Computer simulation; Computing costs; Confidence intervals; Direct numerical simulation; Engineering Sciences; Flame speed; Flame-vortex interaction; Flame-vortex interactions; Fluids mechanics; Laminar premixed flame; Markov chains; Mathematical models; Mathematics; Mechanics; Methane; Methane-air global scheme; Parameter uncertainty; Polynomials; Premixed flames; Principal components analysis; Propagation; Reaction mechanisms; Reactive fluid environment; Sampling methods; Statistics; Thickness; Two dimensional models; Uncertainty propagation; Uncertainty quantification; Vortices
• ISSN: 0010-2180; 1556-2921
• DOI: 10.1016/j.combustflame.2021.111642

Simplified chemistry models are commonly used in reactive computational fluid dynamics (CFD) simulations to alleviate the computational cost. Uncertainties associated with the calibration of such simplified models have been characterized in some works, but to our knowledge, there is a lack of studies analyzing the subsequent propagation through CFD simulation of combustion processes. This work propagates the uncertainties - arising in the calibration of a global chemistry model - through direct numerical simulations (DNS) of flame-vortex interactions. Calibration uncertainties are derived by inferring the parameters of a two-step reaction mechanism for methane, using synthetic observations of one-dimensional laminar premixed flames based on a detailed mechanism. To assist the inference, independent surrogate models for estimating flame speed and thermal thickness are built taking advantage of the Principal Component Analysis (PCA) and the Polynomial Chaos (PC) expansion. Using the Markov Chain Monte Carlo (MCMC) sampling method, a discussion on how push-forward posterior densities behave with respect to the detailed mechanism is provided based on three different calibrations relying (i) only on flame speed, (ii) only on thermal thickness, and (iii) on both quantities simultaneously. The model parameter uncertainties characterized in the latter calibration are propagated to two-dimensional flame-vortex interactions using 60 independent samples. Posterior predictive densities for the time evolution of the heat release and flame surface are consistent, being that the confidence intervals contain the reference simulation. However, the two-step mechanism fails to reproduce the flame response to stretch as it was not considered in the calibration. This study highlights the capabilities and limitations of propagating chemistry-models uncertainties to CFD applications to fully quantify posterior uncertainties on target quantities.