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Numerical Modeling of International Variations in Diesel Spray Combustion with Evaporation Surrogate and Virtual Species Conversion
Technical Paper
2017-01-0582
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
A methodology for simulating effect of international variations in fuel compositions on spray combustion is proposed. The methodology is validated with spray combustion experiments with real fuels from three different countries. The compositions of those fuels were analyzed through GC×GC and H-NMR. It was found that ignition delay times, flame region and flame luminosity were significantly affected by the compositional variations. For the simulation, an evaporation surrogate consisting of twenty two species, covering basic molecular types and a wide range of carbon numbers, is developed. Each species in the evaporation surrogate is then virtually converted to a reaction surrogate consisting of n-hexadecane, methylcyclohexane and 1,2,4-trimethyl benzene so that combustion reactions can be calculated with a published kinetic model. The virtual species conversion (VSC) is made so as to take over combustion-related properties of each species of evaporation surrogates. Thus, in the simulation, light components evaporate at lower temperature than heavy components, and an upstream mixture should contain more light components while a downstream one contain more heavy components, which is consistent with the distillation curve of the original fuel. The simulations are performed using commercial software, and compared with the experiments. We concluded that the simulation precisely reproduces the magnitude relationship in the spray ignition delay time between fuels with different compositions. However, the traditional soot model cannot reproduce the effect of the compositional variations on the flame luminosity.
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Kurimoto, N., Watanabe, N., Hoshi, S., Sasaki, S. et al., "Numerical Modeling of International Variations in Diesel Spray Combustion with Evaporation Surrogate and Virtual Species Conversion," SAE Technical Paper 2017-01-0582, 2017, https://doi.org/10.4271/2017-01-0582.Data Sets - Support Documents
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