State-to-State Master Equation and Direct Molecular Simulation Study of Energy Transfer and Dissociation for the N2-N System. MacDonald, R. L., Torres, E., Schwartzentruber, T. E., & Panesi, M. Journal of Physical Chemistry A, 124(35):6986–7000, American Chemical Society, September, 2020. doi abstract bibtex We present a detailed comparison of two high-fidelity approaches for simulating non-equilibrium chemical processes in gases: The state-to-state master equation (StS-ME) and the direct molecular simulation (DMS) methods. The former is a deterministic method, which relies on the pre-computed kinetic database for the N2-N system based on the NASA Ames ab initio potential energy surface (PES) to describe the evolution of the molecules' internal energy states through a system of master equations. The latter is a stochastic interpretation of molecular dynamics relying exclusively on the same ab initio PES. It directly tracks the microscopic gas state through a particle ensemble undergoing a sequence of collisions. We study a mixture of nitrogen molecules and atoms forced into strong thermochemical non-equilibrium by sudden exposure of rovibrationally cold gas to a high-temperature heat bath. We observe excellent agreement between the DMS and StS-ME predictions for the transfer rates of translational into rotational and vibrational energy, as well as of dissociation rates across a wide range of temperatures. Both methods agree down to the microscopic scale, where they predict the same non-Boltzmann population distributions during quasi-steady-state dissociation. Beyond establishing the equivalence of both methods, this cross-validation helped in reinterpreting the NASA Ames kinetic database and resolve discrepancies observed in prior studies. The close agreement found between the StS-ME and DMS methods, whose sole model inputs are the PESs, lends confidence to their use as benchmark tools for studying high-temperature air chemistry.
@article{macdonald2020,
title = {State-to-{State} {Master} {Equation} and {Direct} {Molecular} {Simulation} {Study} of {Energy} {Transfer} and {Dissociation} for the {N2}-{N} {System}},
volume = {124},
doi = {10.1021/ACS.JPCA.0C04029/ASSET/IMAGES/LARGE/JP0C04029_0013.JPEG},
abstract = {We present a detailed comparison of two high-fidelity approaches for simulating non-equilibrium chemical processes in gases: The state-to-state master equation (StS-ME) and the direct molecular simulation (DMS) methods. The former is a deterministic method, which relies on the pre-computed kinetic database for the N2-N system based on the NASA Ames ab initio potential energy surface (PES) to describe the evolution of the molecules' internal energy states through a system of master equations. The latter is a stochastic interpretation of molecular dynamics relying exclusively on the same ab initio PES. It directly tracks the microscopic gas state through a particle ensemble undergoing a sequence of collisions. We study a mixture of nitrogen molecules and atoms forced into strong thermochemical non-equilibrium by sudden exposure of rovibrationally cold gas to a high-temperature heat bath. We observe excellent agreement between the DMS and StS-ME predictions for the transfer rates of translational into rotational and vibrational energy, as well as of dissociation rates across a wide range of temperatures. Both methods agree down to the microscopic scale, where they predict the same non-Boltzmann population distributions during quasi-steady-state dissociation. Beyond establishing the equivalence of both methods, this cross-validation helped in reinterpreting the NASA Ames kinetic database and resolve discrepancies observed in prior studies. The close agreement found between the StS-ME and DMS methods, whose sole model inputs are the PESs, lends confidence to their use as benchmark tools for studying high-temperature air chemistry.},
number = {35},
journal = {Journal of Physical Chemistry A},
publisher = {American Chemical Society},
author = {MacDonald, Robyn L. and Torres, Erik and Schwartzentruber, Thomas E. and Panesi, Marco},
month = sep,
year = {2020},
pages = {6986--7000},
}
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