Wide-parameter multi-resolution transition path analysis of ignition process: A case study in coarse-grained methane fueled system

A novel coarse-grained analysis approach is proposed to represent detailed combustion mechanism in wide operating conditions range. Based on the concepts from complex network and machine learning method of unsupervised clustering, coarse-grained combustion states can be defined by identifying communities formed by intensively interacted groups of species. The evolution of these coarse-grained states in wide parameter range are then analyzed statistically by introducing nonequilibrium transition path method to get an overview of the behavior of the analyzed detailed mechanism at different resolutions. The comparison of the results in the literature confirms its ability to identify the most relevant species and the reaction pathways and how they change with the parameters both at equilibrium conditions and during the transient evolution from initial conditions. The method is revealed to be effective in identifying the optimal resolution level to get a simplified representation of the mechanism.