Two-component Brownian coagulation: Monte Carlo simulation and process characterization--颗粒学报PARTICUOLOGY

Zhao, H., & Zheng, C. (2011). Two-component Brownian coagulation: Monte Carlo simulation and process characterization. Particuology, 9(4), 414–423. https://doi.org/10.1016/j.partic.2011.04.003

Two-component Brownian coagulation: Monte Carlo simulation and process characterization

Haibo Zhao^*, Chuguang Zheng

State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, Wuhan, 430074 Hubei, China

10.1016/j.partic.2011.04.003

Volume 9, Issue 4, August 2011, Pages 414-423

Received 17 November 2010, Accepted 21 April 2011, Available online 2 July 2011.

E-mail: klinsmannzhb@163.com

Highlights

Abstract

The compositional distribution within aggregates of a given size is essential to the functionality of composite aggregates that are usually enlarged by rapid Brownian coagulation. There is no analytical solution for the process of such two-component systems. Monte Carlo method is an effective numerical approach for two-component coagulation. In this paper, the differentially weighted Monte Carlo method is used to investigate two-component Brownian coagulation, respectively, in the continuum regime, the free-molecular regime and the transition regime. It is found that (1) for Brownian coagulation in the continuum regime and in the free-molecular regime, the mono-variate compositional distribution, i.e., the number density distribution function of one component amount (in the form of volume of the component in aggregates) satisfies self-preserving form the same as particle size distribution in mono-component Brownian coagulation; (2) however, for Brownian coagulation in the transition regime the mono-variate compositional distribution cannot reach self-similarity; and (3) the bivariate compositional distribution, i.e., the combined number density distribution function of two component amounts in the three regimes satisfies a semi self-preserving form. Moreover, other new features inherent to aggregative mixing are also demonstrated; e.g., the degree of mixing between components, which is largely controlled by the initial compositional mass fraction, improves as aggregate size increases.

Graphical abstract

Keywords

Multivariate population balance; Aggregation; Stochastic method; Mixing; Self-preserving

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