Evaluation of reduced combustion kinetic mechanisms using global sensitivity-based similarity analysis (GSSA)

Reduced combustion kinetic mechanisms, instead of detailed ones, are often used in computational fluid dynamics (CFD) simulations for reduced and frequently even affordable computational cost. The criterion for the evaluation of a reduced mechanism usually focuses on its prediction error for the global properties such as the ignition delay time, while ignoring the detailed features of reaction kinetics such as reaction pathways. In our opinion, good reduced mechanisms should have similar predicting behaviors as the detailed ones, and these behaviors include model predictions for specific targets, prediction error bars, and uncertainty sources for the errors. In this work, a new approach using global sensitivity-based similarity analysis (GSSA) is proposed to compare reduced mechanisms with detailed ones. The similarity coefficient for the reduced mechanism is calculated by similarity method based on Euclidean distance between sensitivity indices of the reduced mechanism and those of the detailed mechanism. The larger the similarity coefficient, the higher the degree of similarity between the reduced and detailed mechanisms. To demonstrate this similarity method, directed relation graph with error propagation (DRGEP) is employed to simplify both the GRI 3.0 mechanism without the NOx chemistry and the JetSurF mechanism consisting of 1459 reactions, resulting in reduced mechanisms with different sizes which can accurately predict the ignition delay times for corresponding fuel mixtures. Similarity analysis is then employed to evaluate these reduced mechanisms. The result shows that the actual reaction kinetic features cannot be replicated by some of the reduced mechanisms. First, the rankings of the important reactions obtained by reduced mechanisms are not consistent with those obtained by the detailed mechanism. Second, by investigating the sensitive reactions, the actual impact of uncertainties in reaction rates on the ignition delay times cannot be presented by reduced mechanisms. The similarity analysis on reduced mechanisms can be used to select a reduced mechanism which shows much better performance to replicate the actual combustion reaction kinetics. GSSA can provide information on the uncertainty sources induced by the reactions parameters of reduced mechanisms for target predictions, which is important for further reduced model optimization and for the sensitivity analysis of CFD simulations.