A community-reaction network reduction (CNR) approach is presented for mechanism reduction on the basis of a network-based community detection technique, a concept related to pre-equilibrium in chemical kinetics. In this method, the detailed combustion mechanism is first transformed into a weighted network, in which communities of species that have dense inner connections under the critical ignition conditions are identified. By analyzing the community partitions in different regions, we determine the effective functional groups and driving processes. Then, a skeletal model for the overall mechanism is deduced according to the network centrality data, including transition pathway identification and reaction-path flux. The CNR method is illustrated on the hydrogen autoignition system which has been extensively investigated, and a new reduced mechanism involving seven processes is proposed. Dynamics simulations employing the present CNR model show that the computed ignition time and distribution of major species on a wide range of temperature and pressure conditions are in accord with the experiments and results from other methods.

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Based on the coarse graining idea, network community detection and bifurcation techniques are combined to analyze detailed combustion models.

A new method is proposed for the reduction mechanism used in the low-temperature negative temperature coefficient region on the basis of the statistical degree screening (SDS) method. Dynamic information is used to redefine network structure and exclude the influence of very weak interactions on node degree according to the statistics character of their distribution as edge weight. Representative low-temperature conditions are used to set weight thresholds to redefine the network structure so that an effective low-temperature oxidation mechanism is covered while negligible interactions are overlooked. Then, the reduction mechanism is obtained by the SDS method through screening out the redundant species and corresponding reactions according to the scale-free character of the degree distribution. This weighted network degree screening (WNDS) method is demonstrated in the n-heptane system. The performance of the reduced mechanism is evaluated in a closed homogeneous reactor for the fuel over T = 600–1000 K, P = 1–30 atm, and φ = 0.5–2. Results show WNDS yields a skeletal mechanism with comparable or even better prediction ability over a wide parameter range than those generated by directed relation graph. WNDS is a novel statistical property-based reduction method that is suitable for low-temperature oxidation reduction. Its good reduction application indicates a brand-new angle for large combustion mechanism reduction.