By Konstantin Volkov
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Additional resources for Aerosols: Science and Case Studies
Shohat and J. D. Tamarkin. The Problem of Moments. American Mathematical Society, Providence, Rhode Island, revised edition, 1970. 29 30 Aerosols - Science and Case Studies  Douglas L. Wright Jr. Numerical advection of moments of the particle size distribution in Eulerian models. Journal of Aerosol Science, 38:352–369, 2007.  D. T. Gillespie. An exact method for numerically simulating the stochastic coalescence process in a cloud. Journal of the Atmospheric Sciences, 32(10):1977–1989, 1975.
Once the closed moment governing equations were established, they can be solved by some common 35 36 Aerosols - Science and Case Studies numerical techniques, such as the fourth‐order Runge-Kutta method. In the past, some techniques have been proposed to implement the method of moments. In this section, we only focus on coagulation and present how these different methods of moments are implemented for resolving this issue. In order to represent the evolution of particle number, it is necessary to define particle concentration as a function of time and particle volume.
Xiao, and F. Wei. Simulation of soot size distribution in a counterflow flame. International Journal of Chemical Reactor Engineering, 12(2):1–7, 2014. 5772/65565 Abstract The study on aerosol dynamics processes, such as formation of nano/microscale aerosol particle and its subsequent growth in quiescent or evolving flows, has received much attention from both chemical engineering and atmospheric environment communities. The suitable theoretical method for resolving aerosol dynamical processes is widely known as population balance modeling (PBM), which is based on solving the popula‐ tion balance equation (PBE) in terms of particle number concentration.
Aerosols: Science and Case Studies by Konstantin Volkov