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Direct Simulation Monte Carlo Method for Acoustic Agglomeration under Standing Wave Condition
Author(s) -
Fengxian Fan,
Mingjun Zhang,
Zhengbiao Peng,
Jun Chen,
Mingxu Su,
Behdad Moghtaderi,
Elham Doroodchi
Publication year - 2017
Publication title -
aerosol and air quality research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.866
H-Index - 55
eISSN - 2071-1409
pISSN - 1680-8584
DOI - 10.4209/aaqr.2016.07.0322
Subject(s) - economies of agglomeration , monte carlo method , brownian motion , diffusion , particle (ecology) , acoustic wave , sedimentation , statistical physics , mechanics , acoustics , physics , geology , mathematics , engineering , thermodynamics , statistics , chemical engineering , paleontology , oceanography , quantum mechanics , sediment
Acoustic agglomeration proves promising for preconditioning fine particles (i.e., PM2.5) as it significantly improves the efficiency of conventional particulate removal devices. However, a good understanding of the mechanisms underlying the acoustic agglomeration in the standing wave is largely lacking. In this study, a model that accounts for all of the important particle interactions, e.g., orthokinetic interaction, gravity sedimentation, Brownian diffusion, mutual radiation pressure effect and acoustic wake effect, is developed to investigate the acoustic agglomeration dynamics of PM2.5 in the standing wave based on the framework of direct simulation Monte Carlo (DSMC) method. The results show that the combination of orthokinetic interaction and gravity sedimentation dominates the acoustic agglomeration process. Compared with Brownian diffusion and the mutual radiation pressure effect, the acoustic wake plays a relatively more important role in governing the particle agglomeration. The phenomenon of particle agglomeration becomes more pronounced when the acoustic frequency and intensity are increased. The model is shown to be capable of accurately predicting the dynamic acoustic agglomeration process in terms of the detailed evolution of particle size and spatial distribution, which in turn allows for the visualization of important features such as “orthokinetic drift”. The prediction results are in good agreement with the experimental data

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