A new methodology to calculate process rates in a kinetic Monte Carlo model of PAH growth
| Autor: | Jethro Akroyd, Sebastian Mosbach, Gustavo Leon, Nick A. Eaves, Markus Kraft |
|---|---|
| Přispěvatelé: | School of Chemical and Biomedical Engineering, Cambridge Centre for Advanced Research and Education in Singapore (CARES), Akroyd, Jethro [0000-0002-2143-8656], Mosbach, Sebastian [0000-0001-7018-9433], Kraft, Markus [0000-0002-4293-8924], Apollo - University of Cambridge Repository |
| Rok vydání: | 2019 |
| Předmět: |
Materials science
010304 chemical physics Stochastic process General Chemical Engineering Chemical engineering [Engineering] General Physics and Astronomy Energy Engineering and Power Technology Thermodynamics PAH Kinetic Monte Carlo 02 engineering and technology General Chemistry 021001 nanoscience & nanotechnology 01 natural sciences Modelling Fuel Technology 0103 physical sciences Aromatic site Polycyclic Aromatic Hydrocarbon 0210 nano-technology Simulation |
| Zdroj: | Combustion and Flame |
| ISSN: | 0010-2180 |
| DOI: | 10.1016/j.combustflame.2019.07.032 |
| Popis: | This paper develops a new methodology to calculate the process rates in a kinetic Monte Carlo (KMC) model of polycyclic aromatic hydrocarbon (PAH) growth. The methodology uses a combination of the steady-state and partial-equilibrium approximations. It shows good agreement with the results from simulations using a detailed chemical mechanism under conditions relevant to flames (temperatures between 1000 and 2500 K, equivalence ratios between 0.5 and 10). The new methodology is used to calculate the rate of different stochastic processes in KMC simulations of PAH growth of premixed ethylene-oxygen flames. The resulting rates are only a function of temperature and the main gas-phase species present in the flame environment. The results of the KMC model are shown to be consistent with the concentrations of species calculated using a well-established mechanism for the growth of small PAH species. National Research Foundation (NRF) Accepted version This work was partly funded by the National Research Foundation (NRF), Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement no. 724145. Gustavo Leon is funded by a CONACYT Cambridge Scholarship and wishes to acknowledge both institutions, the National Council of Science and Technology and the Cambridge Commonwealth Trust. Markus Kraft acknowledges the support of the Alexander von Humboldt foundation |
| Databáze: | OpenAIRE |
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