Numerical Modeling of Droplet Aerosol Coagulation, Condensation/Evaporation and Deposition Processes

Abstract

The differentially weighted operator-splitting Monte Carlo (DWOSMC) method is further developed to describe the droplet aerosol dynamic behaviors, including coagulation, deposition, condensation, and evaporation processes. It is first proposed that the droplet aerosols will experience firstly condensation and then evaporation, and this phenomenon is first implemented into the Monte Carlo method and sectional method with considering coagulation, deposition, and condensation/evaporation processes in both single-component and two-component aerosol particle systems. It is found that the calculated results of the DWOSMC method agree well with both the analytical solutions and the sectional method. The further developed DWOSMC method can predict the variation of particle number density, total particle volume, mean particle diameter, particle size distributions, and the component-related particle volume densities in both single component and two-component droplet aerosol systems considering coagulation, deposition, and condensation/evaporation processes.

A new sorting algorithm-based merging weighted fraction Monte Carlo method for solving the population balance equation for particle coagulation dynamics

“…These existing analytical solutions are of great significance and can be used as a useful benchmark for validating different numerical methods. Different numerical approaches aiming at different problems of aerosol dynamics are developed to approximate the solution of the GDE for an aerosol system of interest, such as the sectional method (SM) (Gelbard et al , 1980; Prakash et al , 2003; Zhang et al , 2020; Wu et al , 2022), method of moments (MOMs) (Frenklach and Harris, 1987; McGraw, 1997; Yu et al , 2008; Yu and Chan, 2015; Chan et al , 2018; Li et al , 2019; Liu et al , 2019c; Shen et al , 2020; Yang et al , 2020; Jiang et al , 2021; Shen et al , 2022) and Monte Carlo (MC) method (Gillespie, 1975; Garcia et al , 1987; Liffman, 1992; Smith and Matsoukas, 1998; Kruis et al , 2000; Lin et al , 2002; Zhao et al , 2009; Xu et al , 2014; Kotalczyk and Kruis, 2017; Liu and Chan, 2017; Liu and Chan, 2018a, 2018b; Liu et al , 2019a, 2019b; Liu and Chan, 2020; Liu et al , 2021; Jiang and Chan, 2021; Liu et al , 2022). As the discrete nature of the MC method perfectly matches the stochastic properties of particle motion, it can be used to closely simulate the behaviour of particles.…”