A workflow for automatic generation and efficient refinement of individual pressure-dependent networks

Johnson, Matthew S.; Grinberg Dana, Alon; Green, William H.

doi:10.1016/j.combustflame.2022.112516

A workflow for automatic generation and efficient refinement of individual pressure-dependent networks

Journal Article · Thu Dec 08 00:00:00 EST 2022 · Combustion and Flame

DOI:https://doi.org/10.1016/j.combustflame.2022.112516· OSTI ID:1899711

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Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Massachusetts Institute of Technology (MIT)
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Technion-Israel Institute of Technology, Haifa (Israel)
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Manual chemical kinetic model construction often requires modelists to at least implicitly guess all possible decay pathways and their relative fluxes for all important species. We present a workflow and associated tool for automatic generation and re finement of pressure-dependent networks that should enable modelists to efficiently and comprehensively identify decay pathways and estimate respective parameters for chemical species. This tool combines the capabilities of the Reaction Mechanism Generator (RMG) software to generate possible reaction paths, determine thermochemistry, approximate rate coefficients and estimate frequencies with the capabilities of the Arkane software to use quantum chemical parameters to compute thermochemistry and pressure dependent rates. A flux-based algorithm is used to decide which isomers to add to the network. Isomers added to the network are reacted to form other channels. When enough of the flux is accounted for by the isomers and bimolecular product channels, the generation process terminates to yield a comprehensive network. Network sensitivity analysis is applied to the network to identify the most important wells and barriers that can be refined using quantum chemistry calculations. Iterative refinement can then be used to achieve accuracy. The comprehensive network can be easily reduced using energy and flux-based algorithms to the most important channels and isomers in the network.

View Accepted Manuscript (DOE)

Research Organization:: Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division

Grant/Contract Number:: SC0014901

OSTI ID:: 1899711

Journal Information:: Combustion and Flame, Journal Name: Combustion and Flame Vol. 257; ISSN 0010-2180

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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