Bi-modal moment model for predicting the formation and growth of soot aggregate particles

Abstract

Numerical simulations of the formation and growth of soot particles in combustion processes are important for estimating the emission rate of particulate air pollutants and predicting the accurate size and morphology of industrial commodity particles. A new bi-modal soot aerosol model called the Soot Aerosol Moment Model (SAMM) was developed. The SAMM can predict the changes in the soot particle size distribution and morphology simultaneously by monitoring only five variables evolving due to nucleation, surface reaction, PAH condensation, coagulation, sintering, and condensational obliteration. The performance of the SAMM was evaluated by comparing its predictions with those of a sectional soot model and available measurements. The SAMM provided comparable information on the soot particle size distribution and morphology with a more than 100-fold shorter computation time than the sectional model, suggesting that it can be used effectively to develop and test sophisticated chemical mechanisms for soot formation.