An efficient error-propagation-based reduction method for large chemical kinetic mechanisms

Production rates obtained from a detailed chemical mechanism are analyzed in order to quantify the coupling between the various species and reactions involved. These interactions can be represented by a directed relation graph. A geometric error propagation strategy applied to this graph accurately identifies the dependencies of specified targets and creates a set of increasingly simplified kinetic schemes containing only the chemical paths deemed the most important for the targets. An integrity check is performed concurrently with the reduction process to avoid truncated chemical paths and mass accumulation in intermediate species. The quality of a given skeletal model is assessed through the magnitude of the errors introduced in the target predictions. The applied error evaluation is variable-dependent and unambiguous for unsteady problems. The technique yields overall monotonically increasing errors, and the smallest skeletal mechanism that satisfies a user-defined error tolerance over a selected domain of applicability is readily obtained. An additional module based on life-time analysis identifies a set of species that can be modeled accurately by quasi-steady state relations. An application of the reduction procedure is presented for autoignition using a large iso-octane mechanism. The whole process is automatic, is fast, has moderate CPU and memory requirements, and compares favorably to other existing techniques.