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

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.

Automated summary

Numerical simulations of soot particle formation and growth in combustion processes are crucial for assessing environmental pollution and predicting industrial particle characteristics. The Soot Aerosol Moment Model (SAMM) has been developed to predict the changes in particle size distribution and morphology, offering comparable information with significantly reduced computation time compared to previous models. SAMM can effectively contribute to the study of chemical mechanisms for soot formation.