Bayesian methods for the quantification of uncertainties in syngas chemistry models

Abstract

Syngas chemistry modeling is an integral step toward the development of safe and efficient syngas combustors. Although substantial effort has been undertaken to improve the modeling of syngas combustion, models nevertheless fail in regimes important to gas turbine combustors, such as low temperature and high pressure. In order to investigate the capabilities of syngas models, a Bayesian framework for the quantification of uncertainties has been used. This framework, given a set of experimental data, allows for the calibration of model parameters, determination of uncertainty in those parameters, propagation of that uncertainty into simulations, as well as determination of model plausibility from a set of candidate syngas models. Three recently developed or updated syngas combustion models have been calibrated to a set of high pressure experimental laminar flame speeds. The form of the prior distributions, used as the initial estimates for the syngas model parameter distributions, is shown to have significant effect upon the parameter calibration when chosen poorly. Consequently, great care must be taken when involving prior knowledge when setting the prior distributions. After calibration the resulting uncertainty in the parameters is propagated forward into the simulation of laminar flame speeds. The resulting uncertainty in the flame speed calculations is used to identify whether the model is capturing all relevant physics for the pertinent experimental conditions.