A surrogate fuel formulation to characterize heating and evaporation of light naphtha droplets

Abstract

Predictive simulations of realistic combustion engines commonly employ surrogate fuels consisting of a small number of pure components to represent the targeted real fuels in terms of their physical and chemical kinetic characteristics. In this paper, a systematic procedure to develop a physical surrogate of multicomponent real fuels which matches the heating and evaporation of a fuel droplet is described. A one-dimensional heating and evaporation model is used to select the surrogate components of a multicomponent fuel. Components with similar evaporation behavior are grouped and represented by the component with the highest initial mass fraction in the detailed composition of fuel. This systematic procedure is applied to light naphtha (LN) to develop two possible surrogates of three and five components, which are compared to previously developed chemical surrogates. The new surrogates also target matching the hydrogen-to-carbon (H/C) ratio and research octane number (RON). The heating and evaporation model, referred to as effective thermal conductivity/effective diffusivity (ETC/ED) model, is based on the analytical solutions of heat conduction and species diffusion equations inside a liquid droplet, and accounts for finite thermal conductivity, mass diffusivity and recirculation inside the droplet. The model is then implemented into CONVERGE software to predict the behavior of the developed surrogates in sprays, as an improvement over the widely used infinite thermal conductivity/infinite diffusivity (ITC/ID) model. The predicted spray penetration lengths for the developed surrogates show good agreement with the experimental data for light naphtha at ambient conditions. At engine-like conditions, the ETC/ED model predicts higher vapor mass than the ITC/ID model, hence showing the expected trend by incorporating the realistic droplet heating process.

An Improved Prediction of Pre-Combustion Processes, Using the Discrete Multicomponent Model

“…One can see that the difference in peak pressures predicted by the two CONVERGE versions is larger for the early injection stage than for the late injection. This agrees with our findings presented in [12] and highlights how important the new model is in the simulation of combustion processes in PPCI engines. Liquid fuel mass versus time as predicted by both versions of CONVERGE are shown in Figure 10.…”

“…The difference in predicted vapour mass for both models increases at longer times, as shown in Figure 11. This agrees with the numerical predictions for vaporising sprays at different ambient conditions presented by Kabil et al [12] using both heating and evaporating models. In the latter analysis, a spray of five-component light naphtha fuel surrogate at engine-like conditions was modelled.…”

“…The new version of CONVERGE predicted slightly shorter penetration. This was demonstrated in [12] where different fuels were tested for hollow-cone spray setup using the two evaporation models. In contrast, a larger difference is revealed in the vapour masses predicted by two versions of CONVERGE, as shown in Figure 3.…”

“…The discrete multicomponent (DMC) model (see [7][8][9] for details) was a response to those drawbacks. A distinctive feature of this model is that it considers the effects of temperature gradient, species diffusion, and recirculation inside droplets using the effective thermal conductivity (ETC) and effective diffusivity (ED) models [10][11][12][13].…”

Heating and Evaporation of Droplets of Multicomponent and Blended Fuels: A Review of Recent Modeling Approaches

“…Fuel surrogates usually include far fewer components than the full composition of commercial fuels such as petrol and diesel. A wide range of fuel surrogates has been formulated in the past (e.g., refs , , and − ) to replicate the physical and chemical behaviors of the real fuels using a small number of components. The limitations of researchers’ knowledge of the chemistry of combustion processes and limitations on computational resources stimulated the approximation of fuels by single components.…”