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Volume 17, No. 4, April 2017, Pages 1073-1083 PDF(443 KB)  
doi: 10.4209/aaqr.2016.07.0322   

Direct Simulation Monte Carlo Method for Acoustic Agglomeration under Standing Wave Condition

Fengxian Fan1,2, Mingjun Zhang1,2, Zhengbiao Peng3, Jun Chen1,2, Mingxu Su1,2, Behdad Moghtaderi3, Elham Doroodchi3

1 School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
2 Shanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
3 Discipline of Chemical Engineering, School of Engineering, The University of Newcastle, NSW 2308, Australia

 

Highlights
  • Acoustic agglomeration of PM2.5 in the standing wave acoustic field is modeled.
  • The effects of mutual radiation pressure and acoustic wake are taken into account.
  • The DSMC method reveals “orthokinetic drift” in particle agglomeration.
  • The DSMC method predicts the detailed evolution of particle size distribution.
  • The simulations provide the influence of key parameters on acoustic agglomeration.

Abstract

 

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.

 

 

Keywords: Fine particles (PM2.5); Acoustic agglomeration; Standing wave; Direct simulation Monte Carlo (DSMC) method; Numerical simulation.

 

 

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