Bayesian analysis of syngas chemistry models

Syngas chemistry modelling is an integral step toward the development of safe and efficient syngas combustors. Although substantial effort has been undertaken to improve the modelling 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 evidence from a set of candidate syngas models. Here, three syngas combustion models have been calibrated using laminar flame speed measurements from high pressure experiments. After calibration the resulting uncertainty in the parameters is propagated forward into the simulation of laminar flame speeds. The model evidence is then used to compare candidate models for the given set of experimental conditions and results. Additionally, the technique MUM-PCE, an interesting uncertainty minimisation method for kinetics models, has been compared to the Bayesian method for this application to the prediction of syngas laminar flame speeds. This comparison shows the importance of model form error and experimental error representations in the uncertainty quantification context, for these choices significantly affect uncertainty quantification results.