An optimization approach to kinetic model reduction for combustion chemistry

TL;DR

This paper reviews an optimization-based method for reducing complex combustion models by identifying slow invariant manifolds, enabling faster simulations while maintaining accuracy, and demonstrates its effectiveness on realistic chemical reaction mechanisms.

Contribution

It extends trajectory-based model reduction to realistic combustion models with thermochemistry, proving existence of solutions for various model dimensions.

Findings

Successfully applied to ozone decomposition, hydrogen, and syngas combustion models.

Demonstrated the method's applicability to realistic reaction mechanisms.

Compared favorably with existing reduction techniques.

Abstract

Model reduction methods are relevant when the computation time of a full convection-diffusion-reaction simulation based on detailed chemical reaction mechanisms is too large. In this article, we review a model reduction approach based on optimization of trajectories and show its applicability to realistic combustion models. As most model reduction methods, it identifies points on a slow invariant manifold based on time scale separation in the dynamics of the reaction system. The numerical approximation of points on the manifold is achieved by solving a semi-infinite optimization problem, where the dynamics enter the problem as constraints. The proof of existence of a solution for an arbitrarily chosen dimension of the reduced model (slow manifold) is extended to the case of realistic combustion models including thermochemistry by considering the properties of proper maps. The model reduction approach is finally applied to three models based on realistic reaction mechanisms: 1. ozone decomposition as a small test case; 2. simplified hydrogen combustion for comparison with another model reduction method; 3. syngas combustion as a test case including all features of a detailed combustion mechanism.