Community Reaction Network Reduction for Constructing a Coarse-Grained Representation of Combustion Reaction Mechanisms

Abstract

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.

Wide-parameter coarse-grained state mechanism analysis in the methane combustion system

“…To do this, it is necessary to first determine the critical condition on the parameter diagram of interest according to the bifurcation analysis. 16,23…”

“…The concept of coarse-grained state arises from the analysis of the community partitions resulting in different values of the system parameters α and time t. A key point is that the investigation of the communities by using a blind spanning of the values of α will not be effective in revealing how the structure of the community changes with α. Firstly, because community detection is computationally demanding, 16,25 requiring the solution of eqn (1) over the entire range of the values of α of interest, but especially because meaningful variations of the structure of the community tend to appear where (for the value of α) significant changes in the system behavior are observed, calling for the use of analysis tools leading to the identification of these critical values of α.…”

“…For combustion processes, it is proved that the rate-limiting processes or the driving reactions could not be a single reaction but are the synergistic effect of a number of closely coupled species. 15 In our previous work, 16 a coarse-grained method was employed to represent the driving kinetics for the global reactive character based on the identification of functional groups of species that have dense inner connections and their synergistic effects.…”

“…In this work, our previously proposed coarse graining method for combustion reactions 16 is further developed by combining with bifurcation techniques to identify the dominant mechanisms of ignition. The methane combustion system is described by the GRI-3.0 mechanism.…”

Identifying Coarse-Grained Representations for Electronic Predictions

“…[1][2][3] While these methodologies are well developed, the task of selecting the "good" CG maps remains challenging, with chemical intuition or convenience dictating most practical choices. [4][5][6] A number of systematic schemes for generating CG maps have been introduced that leverage essential dynamics, 7-10 graph theory, [11][12][13][14] and machine learning (ML), [15][16][17][18] but a dominant approach has yet to emerge.…”