Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
669749 | International Journal of Thermal Sciences | 2009 | 12 Pages |
A nine-step phenomenological soot model has been implemented into the KIVA-3V code for predicting soot formation and oxidation processes in diesel engines. The model involves nine generic steps, i.e., fuel pyrolysis, precursor species (including acetylene) formation and oxidation, soot particle inception, particle coagulation, surface growth and oxidation. The fuel pyrolysis process leads to acetylene formation and it is described by a single-step reaction. The particle inception occurs via a generic gas-phase precursor species, and the precursor is the product of an irreversible reaction from acetylene. The acetylene addition reaction contributes to soot surface growth. The particle coagulation affects both particle size and number density. The oxidation of soot particles includes two mechanisms—Nagle and Strickland-Constable's O2 oxidation mechanism and Neoh et al.'s OH oxidation mechanism. The quasi-steady state assumption is applied to an H2–O2–CO system for calculating OH concentration. Both acetylene and precursor species have their own consumption paths, each of which is described by a single-step oxidation reaction.Validations of the model have been conducted over a wide range of engine conditions from conventional to PCCI-like combustion. Two engine examples (a heavy-duty diesel engine and a light-duty diesel engine) are presented in this paper. The predictions are compared against measurements, and the applicability of the model to multi-dimensional diesel simulations is assessed. The model's capability of predicting the soot distribution structure in a conventional diesel flame is included in discussion as well. The work reveals that the nine-step model is not only computationally efficient but also fundamentally sound. The model can be applied to diesel engine combustion analysis and, after calibration, is suitable to be integrated with genetic algorithms for system optimization over a controllable range of operations.