Modelling of acoustic agglomeration processes using the direct simulation Monte Carlo method

Sheng, Changdong; Shen, Xiangqian

doi:10.1016/j.jaerosci.2005.03.004

Cited by 58 publications

(51 citation statements)

References 37 publications

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“…If the selected event is agglomeration, a particle pair is randomly selected from the particle array based on a MC algorithm, which was detailed and tested by Sheng and Shen (2006) and is described briefly here. The first particle is picked up by generating a random number R 2 between 0 and 1, and finding a particle numbered i (i < N − 1) such that…”

Section: Dsmc Algorithmmentioning

confidence: 99%

“…Nevertheless, it overestimates the particle number concentration during the late stages. This overestimation can be attributed to the König model used in the modelling, which does not include the hydrodynamic forces induced by particle entrainment and viscous wave, thus underestimates the contribution of hydrodynamic interaction during the later stages when the particle concentration is very low (Sheng and Shen 2006). It can also be seen in Figure 1 that, for D f = 3.0, i.e., assuming that two particles agglomerate to form a coalesced particle, the simulation significantly over-predicts the particle number concentration during the whole process (see the fine solid curve in Figure 1), implying a significant underestimation of the agglomeration rate.…”

Section: Simulation Of Acoustic Agglomeration and Effectsmentioning

confidence: 99%

“…After a certain period, the particle size becomes distributed and the orthokinetic mechanism comes into action, therefore, the predicted agglomeration rate gradually increases. For poly-disperse particles, the orthokinetic mechanism plays a dominant role in acoustic agglomeration at the beginning (Sheng and Shen 2006) because of the wide particle size difference involved (say the standard deviation is 1.7). Therefore, the prediction with the polydispersity yields a faster agglomeration rate during the early stages than that with the mono-disperse assumption.…”

Section: Comparison Of the Predictions Of Usingmentioning

confidence: 99%

“…Besides, because the scale of the agglomeration chamber used in the experiment is usually small, particle deposition on the chamber wall may be considerable during the acoustic treatment (Boulaud et al 1984;Magill 1988), So particle loss due to wall deposition is also included. The DSMC method has previously been used to simulate acoustic agglomeration process of liquid particle aerosols and to evaluate the mechanistic models (Sheng and Shen 2006). In the present work, these models have been extended to describe acoustic agglomeration of poly-disperse solid particles by modifying the agglomeration kernels to consider the nature of the irregular aggregates formed during the agglomeration process.…”

Section: Introductionmentioning

confidence: 99%

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Simulation of Acoustic Agglomeration Processes of Poly-Disperse Solid Particles

Sheng

Shen

2007

Aerosol Science and Technology

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This article presents the simulation of acoustic agglomeration of poly-disperse solid particles with the direct simulation Monte Carlo method. The modelled processes include the agglomeration due to the orthokinetic and hydrodynamic mechanisms, Brownian coagulation and wall deposition. The aggregates formed during the agglomeration process were characterised as mass fractal aggregates with an equivalent radius to estimate the average radius of the primary particles in individual aggregates. Acoustic agglomeration of fly ash with a lognormal size distribution and TiO 2 particles with a bimodal size distribution was simulated and validated against the experimental data in the literature. It was found that the acoustic agglomeration process of solid particles could be represented with a modified version of Song's orthokinetic model and König's hydrodynamic equation that account for the fractal-like morphology of the aggregates. The fractal dimensions of around 1.8 and 2.2 were obtained for the fly ash and TiO 2 particles, respectively, consistent with the values reported for the aggregates in the literature. The poly-disperse nature of the primary particles is essential to the simulation; assuming mono-disperse primary particles leads to a significant underestimation of the agglomeration rate and the particle size growth particularly during the early stages of the acoustic agglomeration process. Particle deposition on the chamber walls also has some effect on acoustic agglomeration.

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Section: Dsmc Algorithmmentioning

confidence: 99%

Section: Simulation Of Acoustic Agglomeration and Effectsmentioning

confidence: 99%

Section: Comparison Of the Predictions Of Usingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simulation of Acoustic Agglomeration Processes of Poly-Disperse Solid Particles

Sheng

Shen

2007

Aerosol Science and Technology

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“…Acoustic agglomeration is governed by complex interaction mechanisms. The most significant ones are orthokinetic and hydrodynamic interactions [2]. Orthokinetic interaction is the most obvious mechanism.…”

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Numerical simulation of acoustic wake effect in acoustic agglomeration under Oseen flow condition

et al. 2012

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We numerically simulate the hydrodynamic interaction of aerosol particles due to the acoustic wake effect under the Oseen flow condition. Attraction is found for two nearby particles with an orientation angle of 0 to 50° with respect to the acoustic field, and weak repulsion is found outside this range. Good agreement is obtained between the numerical results and experiments in the literature. We study the influence of particle size, sound wave frequency and the particle separation. The result shows that the acoustic wake effect plays a significant role in acoustic agglomeration. It could be either the major agglomeration mechanism of monodisperse aerosols or the major refill mechanism for polydisperse aerosols to supplement orthokinetic interaction. Acoustic agglomeration is a process in which intense sound waves produce relative motions and collisions among aerosol particles. It can significantly shift the particle size distribution of an aerosol from smaller to larger sizes in a short time of the order of 1 s. Acoustic agglomeration has potential use in air pollution control as an aerosol preconditioning process to enhance the performance of conventional particle filtering devices, which are inefficient for retaining particles smaller than 2.5 m [1]. Acoustic agglomeration is governed by complex interaction mechanisms. The most significant ones are orthokinetic and hydrodynamic interactions [2]. Orthokinetic interaction is the most obvious mechanism. Particles with different sizes are entrained differently into the oscillating motion of the medium because of the differences in particle inertia. The relative motion leads to the approach and collision between particles. In one acoustic cycle, each larger particle sweeps a certain volume by its motion relative to smaller particles, and collects all the smaller particles in it. This volume is defined as the agglomeration volume. The mechanism of orthokinetic interaction was well summarized by Mednikov [3] and improved by other researchers [4][5][6]. Nevertheless, this mechanism cannot explain agglomeration of monodisperse aerosols or the way in which the agglomeration volume is refilled once it is emptied after one cycle.Hydrodynamic mechanisms are those which produce particle interactions through the surrounding medium because of hydrodynamic forces and the asymmetry of the flow field around the particle [1]. These mechanisms are generally considered to be the main acoustic agglomeration mechanisms for monodisperse aerosols because they are also active for same-sized particles [7]. Hydrodynamic mechanisms mainly include mutual radiation pressure and the acoustic wake effect [5,6,8,9]. The mutual radiation pressure is the effect due to the nonlinear interactions produced between the particle scattering wave and the incident field. However, so far, there is still no experiment to confirm this effect as a cause of agglomeration, and theoretical studies show that the mutual radiation pressure is much less important than the acoustic wake effect in acoustic agglome...

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Agglomeration Modelling of Sub-Micron Particle during Coal Combustion Based on the Flocculation Theory

Zhang

et al. 2009

Electrostatic Precipitation

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Modelling of acoustic agglomeration processes using the direct simulation Monte Carlo method

Cited by 58 publications

References 37 publications

Simulation of Acoustic Agglomeration Processes of Poly-Disperse Solid Particles

Simulation of Acoustic Agglomeration Processes of Poly-Disperse Solid Particles

Numerical simulation of acoustic wake effect in acoustic agglomeration under Oseen flow condition

Agglomeration Modelling of Sub-Micron Particle during Coal Combustion Based on the Flocculation Theory

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