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

Lin Ji, Yue Li, Jie Wang, An Ning, Naixin Zhang, Shengyao Liang, Jiyun He, Tianyu Zhang, Zexing Qu, Jiali Gao

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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.

Original languageEnglish (US)
JournalJournal of Chemical Information and Modeling
Volume62
Issue number10
DOIs
StateAccepted/In press - 2022

Bibliographical note

Funding Information:
This work was supported by the Natural Science Foundation of China Program (grant numbers 91841301 and 21773158).

Publisher Copyright:
© 2022 American Chemical Society.

Keywords

  • Chemical Phenomena
  • Hydrogen
  • Temperature

PubMed: MeSH publication types

  • Research Support, Non-U.S. Gov't
  • Journal Article

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