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An efficient multi-component fuel droplet vaporization model has been developed in this work using discrete approach. The precise modeling of droplet vaporization process is divided into two parts: vapor-phase and liquid-phase sub-models. Temporal evolution of flow inside the droplet is considered to describe the transient behavior introduced by the slow diffusion process. In order to account for the internal circulation motion, surface regression and finite diffusion without actually resolving the spatial governing equations within the liquid phase, a set of ordinary differential equations is applied to describe the evolution of the non-uniform distributions of universal diffusional variables, i.e. temperature and species mass fraction. The differences between the droplet surface and bulk mean states are modeled by constructing a quasi-1D frame; the effect of the internal circulations is taken into consideration by using the effective diffusivity rather than physical diffusivity.

Inverted phi (ϕ)-sensitivity is a new approach of NOx reduction in compression-ignition (C.I.) engines. Previously, pure ethanol (E100) was selected as the preliminary test fuel in a single injection compression-ignition engine, and was shown to have good potential for low engine-out NOx emissions under low and medium load conditions due to its inverted ignition sequence. Under high load, however, the near-stoichiometric and non-homogeneous fuel/air distribution removes the effectiveness of the inverted ϕ-sensitivity. Therefore, it is desirable to recover the combustion sequence in the chamber such that the leaner region is burned before the near-stoichiometric region. When the combustion in near-stoichiometric region is inhibited, the temperature rise of that region is hindered and the formation of NOx is suppressed.