A species-clustered ODE solver for large-scale chemical kinetics using detailed mechanisms

TL;DR

This paper introduces a species-clustered ODE solver for large-scale chemical kinetics that uses graph partitioning to improve computational efficiency in detailed mechanisms, validated on auto-ignition problems.

Contribution

It presents a novel operator-splitting method based on graph partitioning for efficient integration of large chemical kinetic systems.

Findings

Significant speedup in computational efficiency observed.

Effective clustering reduces complexity of large systems.

Validated on multiple auto-ignition problems with varying scales.

Abstract

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.