Abstract

We are working on implementing a Jacobian preconditioning method to accelerate chemical kinetics for large mechanisms in Cantera. The specific technique is referred to as adaptive preconditioning by those who developed it at LANL (see references below). This method has been shown to provide speed up for kinetic models with more than roughly 100 species. It specifically preconditions with a reduced model that neglects third-body efficiencies and pressure dependence in reactions, and reduces the number of iterations necessary to obtain the Jacobian matrix used in implicit integration.

Motivation

Using kinetic models with a large number of species is computationally expensive. The goal of this implementation is to make simulations requiring larger and more complicated mechanisms more feasible. This could accelerate autoignition simulations for large models by one or two orders of magnitude. In addition, another application of interest is applying this to modeling atmospheric chemistry. Models for volatile organic carbons (VOC) and second organic aerosols (SOA) in the atmosphere can involve thousands of species, but current atmospheric simulations only use a few hundred species due to computational expense. This change will impact anyone interested in resolving large detailed kinetic models for any application. It is a good solution because it has shown potential to provide substantial speedup over traditional methods.

Description

A detailed description of the work being done. This may include examples of how the new feature will be used, intended use cases, and pseudo-code illustrating its use.
Jacobian preconditioning is a method used to accelerate solutions to systems of ODEs that require inversion of the Jacobian matrix. It works by applying an educated guess to the Jacobian prior to it being inverted which reduces the number of iterations necessary to invert the matrix. LANL’s method uses a reduced mechanism to precondition the Jacobian and leverage matrix sparsity. Their study showed promising results we intend to implement more generally for more cases.

References

McNenly, M. J., Whitesides, R. A., & Flowers, D. L. (2015). Faster solvers for large kinetic mechanisms using adaptive preconditioners. Proceedings of the Combustion Institute, 35(1), 581-587.

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