Efficient treatment of secondary kinetic processes for pre-partitioned adaptive chemistry approaches

Probability Density Function (PDF) methods, which allow for the direct integration of chemical kinetics, are well established to accurately simulate turbulent flames with strong turbulence-chemistry interactions. While adaptive chemistry techniques have been proven effective in reducing the high CPU cost and memory requirements associated with the handling of chemistry in such simulations, performance metrics have mostly been focussed on the primary oxidation pathways converting fuel to major products. In contrast, this work investigates the ability of adaptive techniques, in this case, the pre-partitioned adaptive chemistry (PPAC) approach, to handle secondary kinetics pathways that are parallel, but tightly coupled to the main oxidation process, taking formation as a case study. PPAC relies on a partitioning of the composition space into a user-specified number of regions, on which specialised reduced models are generated using the Directed Relation Graph with Error Propagation (DRGEP) reduction technique. The direct application of that methodology to a mix of hydrocarbon oxidation and nitrogen-related targets is shown to yield excessively detailed region-specific reduced mechanisms in order to properly capture both the main oxidation and the secondary formation processes, thereby decreasing the benefits of the adaptive approach. To address this issue, a sequential approach is proposed for the generation of the region-specific reduced mechanisms, in which the primary combustion pathways relevant for each region are identified first, followed by the selective addition, directly at the reduced level, of any secondary pathways relevant for that region using a recently developed build-up technique. This new strategy is assessed in the context of propane combustion in a partially stirred reactor (PaSR) and methane combustion in the Sandia Flame D configuration, demonstrating in both cases the benefits of the sequential approach for reduced model generation.