  In this study, a species-clustered ordinary differential equations (ODE)
solver for chemical kinetics with large detailed mechanisms based on
operator-splitting is presented. The ODE system is split into clusters of
species by using graph partition methods which has been intensively studied in
areas of model reduction, parameterization and coarse-graining, etc. , such as
diffusion maps based on the concept of Markov random walk. Definition of the
weight (similarity) matrix is application-driven and according to chemical
kinetics. Each cluster of species is then integrated by VODE, an implicit
solver which is intractable and costly for large systems of many species and
reactions. Expected speedup in computational efficiency is observed by
numerical experiments on three zero-dimensional (0D) auto-ignition problems,
considering the detailed hydrocarbon/air combustion mechanisms in varying
scales, from 53 species with 325 reactions of methane to 2115 species with 8157
reactions of n-hexadecane.
