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室内可吸入颗粒物运动特性的格子 Boltzmann方法模拟
引用本文:鄢仁樵,陈丽萍,周斌. 室内可吸入颗粒物运动特性的格子 Boltzmann方法模拟[J]. 计算物理, 2016, 33(6): 698-706. DOI: 10.3969/j.issn.1001-246X.2016.06.010
作者姓名:鄢仁樵  陈丽萍  周斌
作者单位:南京工业大学城市建设学院, 南京 210009
基金项目:江苏省自然科学基金(BK20130946),江苏省高等学校青蓝工程资助项目
摘    要:为探究室内可吸入颗粒物的运动特性,使用格子Boltzmann方法,在颗粒物运动概率模型的基础上考虑布朗力对颗粒物的作用,利用改良的LB-CA(Lattice Boltzmann-cellular automata)模型,模拟了Re数分别为400、1 000和2 000时粒径为0.01 μm、0.1 μm和1 μm的颗粒物在上送上回和上送侧回两种回风形式中的运动特性.结果显示:颗粒物的空间分布范围随着Re数的增大而增大,小粒径的颗粒物受到气流湍动和扩散作用的影响更明显;颗粒物的均方位移(mean square displacement,MSD)与Re数、颗粒物粒径的大小成反比,而同样的Re数下,颗粒物在上送侧回的回风形式中其MSD较大.总体上看上送侧回的回风形式具有较低的悬浮颗粒数和更高的室内空气品质.

关 键 词:室内可吸入颗粒物  运动特性  格子Boltzmann方法  
收稿时间:2015-09-17
修稿时间:2016-02-13

Lattice Boltzmann Simulation on Motion Characteristics of Indoor Respirable Particles
YAN Renqiao,CHEN Liping,ZHOU Bin. Lattice Boltzmann Simulation on Motion Characteristics of Indoor Respirable Particles[J]. Chinese Journal of Computational Physics, 2016, 33(6): 698-706. DOI: 10.3969/j.issn.1001-246X.2016.06.010
Authors:YAN Renqiao  CHEN Liping  ZHOU Bin
Affiliation:College of Urban Construction, Nanjing University of Technology, Nanjing 210009, China
Abstract:To explore motion characteristics of indoor respirable particles, Brownian force on particles is considered in motion probability model. Motion characteristics of 0.01 μm, 0.1 μm and 1 μm particles under conditions of up supply with up return and up supply with side return air forms were simulated with lattice Boltzmann method taking Re as 400, 1 000 and 2 000, respectively. It shows that range of particle spacial distribution increases with Re, and smaller particles affected more obvious by air turbulence and diffusion effect. Mean square displacement (MSD) of particles is inversely proportional to Re and diameter of particles. At same Re, MSD of particles is greater under up supply with side return air form, thus lower suspended particles and higher indoor air quality appears in up supply with side return air form.
Keywords:indoor respirable particles  motion characteristic  lattice Boltzmann method
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