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Evaluation of Kinetics Process in CFD Model and Its Application in Ignition Process Analysis of a Natural Gas-Diesel Dual Fuel Engine
Technical Paper
2017-01-0554
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Computational fluid dynamics (CFD) model has been widely applied in internal combustion (IC) engine research. The integration of chemical kinetic model with CFD provides an opportunity for researchers to investigate the detailed chemical reactions for better understanding the combustion process of IC engines. However, the simulation using CFD has generally focused on the examination of primary parameters, such as temperature and species distributions. The detailed investigation on chemical reactions is limited.
This paper presents the development of a post-processing tool capable of calculating the rate of production (ROP) of interested species with the known temperature, pressure, and concentration of each species in each cell simulated using CONVERGE-SAGE CFD model. The Converge CFD model with a reduced primary reference fuel (PRF) mechanism was validated against the cylinder pressure, heat release rate and exhaust emissions measured using a single cylinder natural gas-diesel dual fuel engine. The validated Converge CFD code with the reduced PRF chemistry and the post-process tool developed was applied to investigate the impact of dilution, heat capacity, and chemical kinetics of methane on the low temperature combustion (LTC) during the ignition delay period of the NG-diesel dual fuel engine. The preliminary simulation results obtained over a representative line suggest that the higher heat capacity dominates the impact of methane on the LTC occurred during the ignition delay period of NG-diesel dual fuel engine. The contributions of the key radicals such as OH, HO2 and H to chemical reactions is also presented and discussed.
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Li, Y., Guo, H., and Li, H., "Evaluation of Kinetics Process in CFD Model and Its Application in Ignition Process Analysis of a Natural Gas-Diesel Dual Fuel Engine," SAE Technical Paper 2017-01-0554, 2017, https://doi.org/10.4271/2017-01-0554.Data Sets - Support Documents
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