This work addresses the construction and use of low-dimensional invariant manifolds to simplify complex chemical kinetics. Typically, chemical kinetic systems have a wide range of time scales. As a consequence, reaction trajectories rapidly approach a hierarchy of attracting manifolds of decreasing dimension in the full composition space. In previous research, several different methods have been proposed to identify these low-dimensional attracting manifolds. Here we propose a new method based on an invariant constrained equilibrium edge (ICE) manifold. This manifold (of dimension nr) is generated by the reaction trajectories emanating from its (nr−1)-dimensional edge, on which the composition is in a constrained equilibrium state. A reasonable choice of the nr represented variables (e.g., nr “major” species) ensures that there exists a unique point on the ICE manifold corresponding to each realizable value of the represented variables. The process of identifying this point is referred to as species reconstruction. A second contribution of this work is a local method of species reconstruction, called ICE-PIC, which is based on the ICE manifold and uses preimage curves (PICs). The ICE-PIC method is local in the sense that species reconstruction can be performed without generating the whole of the manifold (or a significant portion thereof). The ICE-PIC method is the first approach that locally determines points on a low-dimensional invariant manifold, and its application to high-dimensional chemical systems is straightforward. The “inputs” to the method are the detailed kinetic mechanism and the chosen reduced representation (e.g., some major species). The ICE-PIC method is illustrated and demonstrated using an idealized H2∕O system with six chemical species. It is then tested and compared to three other dimension-reduction methods for the test case of a one-dimensional premixed laminar flame of stoichiometric hydrogen/air, which is described by a detailed mechanism containing nine species and 21 reactions. It is shown that the error incurred by the ICE-PIC method with four represented species is small across the whole flame, even in the low temperature region.

A cell agglomeration algorithm is proposed to mitigate the computational cost of incorporating detailed chemical kinetics in multi-dimensional Computational Fluid Dynamics (CFD) simulations. Cells that are close in species and energy composition space are agglomerated before calling the reaction integrator, substantially reducing the number of chemistry integrations. The algorithm is generalized and applicable to any reacting flow configuration, and the accuracy is fully controllable. A dynamic hash table is used to efficiently bin cells into high dimensional hyper-cubes in composition space. The method is applied to four different CFD simulations and the speed-up and incurred error are assessed for a range of agglomeration tolerances and table dimensions. The proposed approach exhibits up to an order of magnitude speed-up with a relatively moderate decrease in accuracy.