Species selection for automatic chemical kinetic mechanism generation

Abstract

Many important chemical kinetic systems require detailed chemical kinetic models to resolve. These detailed kinetic models can involve thousands of species and hundreds of thousands of chemical reactions, making them difficult to construct by hand. Modern automatic mechanism generation algorithms can mostly be divided into two classes: rule and rate based. Rule-based generators choose species based on user defined constraints on species and reaction classes. Rate-based generators generate a much larger set of potentially important species and reactions and then choose which ones to add based on running simulations of species and reactions deemed important and calculating the flux to potentially important species. In principle, the latter is preferable, as it requires the user to make far fewer assumptions about what is important in the system. However, while the effectiveness of the rate-based approach has been demonstrated in a wide variety of systems, it has also been demonstrated to have difficulty picking up important low-flux chemistries. Here we present a discussion of the challenges associated with rate-based mechanism generation and new algorithms that are able to efficiently mitigate these challenges improving species selection during mechanism generation in a set of case studies.