Modelling of gasoline fuel droplets heating and evaporation

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Abstract The paper presents a new approach to modelling of the heating and evaporation of gasoline fuel droplets with a specific application to conditions representative of internal combustion engines. A number of the components of gasoline with identical chemical formulae and close thermodynamic and transport properties are replaced with characteristic components leading to reducing the original composition of gasoline fuel (83 components) to 20 components only. Furthermore, the approximation to the composition of gasoline with these components is replaced with a smaller number of hypothetical quasi-components/components as previously suggested in the multi-dimensional quasi-discrete (MDQD) model. The transient diffusion of quasi-components and single components in the liquid phase as well as the temperature gradient and recirculation inside the droplets, due to the relative velocities between the droplets and the ambient air, are accounted for in the model. In the original MDQD model, n-alkanes and iso-alkanes are considered as one group of alkanes. In this new approach, the contributions of these two groups are taken into account separately. The values for the initial model parameters were selected from experimental data measured in a research engine prior to combustion. The results are compared with the predictions of the single-component model in which the transport and thermodynamic properties of components are averaged, diffusion of species is ignored and liquid thermal conductivity is assumed to be infinitely large, or approximated by those of iso-octane. It is shown that the application of the latter models leads to an under-prediction of the droplet evaporation time by approximately 67% (averaged) and 47% (iso-octane), respectively, compared to those obtained using the discrete component model, taking into account the contributions of 20 components. It is shown that the approximation of the actual composition of gasoline fuel by 6 quasi-components/components, using the MDQD model, leads to an under-prediction of the estimated droplet surface temperatures and evaporation times by approximately 0.9% and 6.6% respectively, for the same engine conditions. The application of the latter model has resulted in an approximately 70% reduction in CPU processor time compared to the model taking into account all 20 components of gasoline fuel.