双组分颗粒团聚过程中组分混合程度的预测

doi:10.7523/j.issn.2095-6134.2017.02.013

中国科学院大学学报 ›› 2017, Vol. 34 ›› Issue (2): 204-209.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

赵翰卿, 徐祖伟, 赵海波

华中科技大学煤燃烧国家重点实验室, 武汉 430074

收稿日期:2016-04-18 修回日期:2016-06-30 发布日期:2017-03-15
通讯作者: 赵海波,E-mail:klinsmannzhb@163.com
基金资助:
国家自然科学基金（51390494，51276077）资助

Predictions of compositional mixing degree in two-component aggregation

ZHAO Hanqing, XU Zuwei, ZHAO Haibo

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

Received:2016-04-18 Revised:2016-06-30 Published:2017-03-15

摘要/Abstract

摘要： 多组分颗粒凝并是颗粒长大过程的主要物理机制之一。对于双组分凝并，混合程度χ为一重要衡量标准，是预测组分分布的关键参数。针对双组分颗粒团聚的非稳态过程研究混合程度随时间的变化关系，采用颗粒群平衡模拟异权值快速Monte Carlo方法，进行模拟，最终预测出χ与初始条件的关系，得到自由分子区布朗凝并与连续区布朗凝并的指数形式预测公式，并进行验证，从而可以通过合理选择初始参数，优化控制组分分布和实现颗粒定向功能制备。

关键词: 颗粒群平衡模拟, 双组分凝并, Monte Carlo方法, 混合程度

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 χ is an important criterion and the key to determination of compositional distribution. Now the dynamic evolution of χ 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 χ on initial feeding conditions through hundreds of systematically varied simulations. It is found that χ 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.

Key words: population balance modeling, two-component aggregation, Monte Carlo method, compositional mixing degree

中图分类号:

O359

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.

ZHAO Hanqing, XU Zuwei, ZHAO Haibo. Predictions of compositional mixing degree in two-component aggregation[J]. , 2017, 34(2): 204-209.

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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

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