Rapid and versatile numerical simulations of acoustic agglomeration by the fixed pivot-based population balance modeling

Abstract

Acoustic agglomeration (AA), which harnesses sound waves to enhance particle collision for agglomeration, is a promising technology for aerosol emission control. In addition to direct experimentation, numerical modeling has become another type of useful auxiliary toolsets for AA research. However, the existing modeling methods are computationally demanding and fail to precisely capture critical information on evolution of particle statistics with a versatile manner. Here, we present a novel temporal population balance modeling (PBM) methodology for AA processes using an efficient fixed-pivot strategy, which is capable of implementing rapid and versatile numerical simulations to investigate time-domain evolution of statistical properties of aerosol particles under sound waves. The reliability of the algorithm is validated through a classic analytical solution and a set of experimental results from a previous study. Furthermore, by incorporating multiple AA kernels, we cover a wide spectrum of aerosol and acoustic conditions and numerically investigate and analyze a series of simulation cases. This universal and robust PBM tool based on the fixed pivot method provides a rapid approach to theoretically predict, visualize, and understand sound-induced evolution behaviors of aerosol particle populations, which could further be favored by other physical aerosol agglomeration topics.