A threshold soot index-based fuel surrogate formulation methodology to mimic sooting tendency of real fuels in 3D-CFD simulations

Abstract

Particulate Matter emission is an increasing concern for engine manufacturers due to the strong limits imposed by worldwide regulations. Fuel composition plays a key role in determining the extent to which soot is formed during the combustion process. The availability of advanced multidimensional computational fluid-dynamics soot models incorporating soot chemistry pushed researchers to formulate fuel surrogates able to represent sooting tendency of real fuels in the numerical framework. Such studies, which provide information to target research grade fuels, are scarcely present in literature. A methodology is proposed to estimate sooting tendency of commercial gasolines, based on Threshold Soot Index and basic composition information, as well as to formulate tailored ethanol-toluene reference fuel surrogates. The technique relies on a purely mathematical approach to estimate Threshold Soot Index of individual compounds and blends using a structural group contribution-based approach, available in literature, which allows to adapt fuel surrogate palette as needed. Firstly, this approach is demonstrated by means of constant pressure reactor simulations using the Method of Moments. Secondly, a methodology is proposed to formulate fuel surrogates simultaneously targeting the main chemical and physical auto-ignition characteristics and estimated Threshold Soot Index. Several oxygenated and non-oxygenated commercial gasolines available on the market are targeted to provide a wide number of validation cases. Finally, surrogates are used to generate a selection of constant pressure-based soot libraries tested in conjunction with the Sectional Method on a 3D-CFD model of a single-cylinder optically accessible gasoline engine. Surrogates exhibit the same qualitative ranking estimated based on Threshold Soot Index and retain a quantitative scaling in terms of particulate matter formation.