中国科学院大学学报2017，Vol.34Issue(2)：204-209,6.DOI:10.7523/j.issn.2095-6134.2017.02.013
双组分颗粒团聚过程中组分混合程度的预测
Predictions of compositional mixing degree in two-component aggregation
摘要
Abstract
Multi-component particle aggregation is one of the main physical mechanisms in the process of particle growth.For two-component aggregation,the compositional mixing degree x is an important criterion and the key to determination of compositional distribution.Now the dynamic evolution of x before the attainment of a steady-state value is beyond numerical modeling and theoretical research.In this work,the fast differentially-weighted Monte Carlo method for population balance modeling was used to predict the dependence of time-varied x on initial feeding conditions through hundreds of systematically varied simulations.It is found that x is subject to exponential decay in the free molecular regime and the continuum regime.By using the explored exponential formulas for the dynamic mixing degree,it is possible to achieve an optimum control over the compositional distributions during two-component aggregation processes through selecting the initial feeding parameters.关键词
颗粒群平衡模拟/双组分凝并/Monte Carlo方法/混合程度Key words
population balance modeling/two-component aggregation/Monte Carlo method/compositional mixing degree分类
数理科学引用本文复制引用
赵翰卿,徐祖伟,赵海波..双组分颗粒团聚过程中组分混合程度的预测[J].中国科学院大学学报,2017,34(2):204-209,6.基金项目
国家自然科学基金(51390494,51276077)资助 （51390494,51276077）
