Chemical and statistical soot modeling

The combustion of petroleum based fuels like kerosene, gasoline, or diesel leads to the formation of several kind of pollutants. Among them, soot particles are particularly bad for their severe consequences on human health. Over the past decades, strict regulations have been placed on car and aircraft engines in order to limit these particulate matter emissions. Designing low emission engines requires the use of predictive soot models which can be applied to the combustion of real fuels. Towards this goal, the present work addresses the formation of soot particles both from a chemical and statistical point of view. As a first step, a chemical model is developed to describe the formation of soot precursors from the combustion of several components typically found in surrogates, including n-heptane, iso-octane, benzene, and toluene. The same mechanism is also used to predict the formation of large Polycyclic Aromatic Hydrocarbons (PAH) up to cyclopenta[cd]pyrene (C_(18)H_(10)). Then, a new soot model which represents soot particles as fractal aggregates is used. In this model, a soot particle is described by three variables: its volume (V), its surface area (S), and the number of hydrogen sites on the surface (H). The Direct Quadrature Method of Moments (DQMOM) is used as a precise representation of the population of soot particles which includes small spherical particles and large aggregates. This model is shown to predict accurately the formation of soot in a wide range of flames including premixed and counter flow diffusion flames, low and high temperature flames and for a wide range of fuels from ethylene to iso-octane. Finally, this model predicts several aggregate properties like the primary particle diameter and gives insight into the reactivity of the soot surface.