Cite this article: Fan, F., Zhang, M., Peng, Z., Chen, J., Su, M., Moghtaderi, B. and Doroodchi, E. (2017). Direct Simulation Monte Carlo Method for Acoustic Agglomeration under Standing Wave Condition.
Aerosol Air Qual. Res.
17: 1073-1083. https://doi.org/10.4209/aaqr.2016.07.0322
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.
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: Numerical simulation; Fine particles (PM2.5); Acoustic agglomeration; Standing wave; Direct simulation Monte Carlo (DSMC) method