A hydrogen-oxygen mixture spontaneously ignites when initially slow thermal reactions suddenly give way to chain-branching reactions that proliferate radicals, sustaining and accelerating the overall oxidation reaction. At high pressure and temperature, estimates of the ignition times are possible from reactive molecular dynamics simulations. These atomistic models of hydrogen oxidation do not require prior knowledge of possible mechanisms or intermediates and can be validated against electronic structure calculations. While there are not currently model-independent methods for the identification of the causal chemical mechanisms hidden within the emergent dynamics of ignition phenomena, these simulations are a natural starting point for the development of data-driven methods of analysis. Here, we demonstrate a machine-learning methodology for dynamical processes, based on Koopman mode analysis, to extract dynamic modes for the kinetic stability and instability of reacting mixtures out of chemical and thermal equilibrium and apply it to extensive atomistic simulations of hydrogen oxidation. By defining persistent dynamic modes, we have developed an automated means to extract persistent local (in time) composite reactions along with a fuel-agnostic measure of ignition-delay time that correlates well with empirical measures.

Dynamic modes of ignition phenomena: Learning chemistry from data

2019 Fall National Meeting

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