Direct simulation Monte Carlo method for particle coagulation and aggregation

Kruis, Frank Einar; Maisels, A.; Fißan, H.

doi:10.1002/aic.690460905

Cited by 171 publications

(138 citation statements)

References 18 publications

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“…For example, Ezekoye and Wibowo (1999) used the sectional algorithm. In the present work, we apply the direct simulation Monte Carlo method (Smith and Matsoukas 1998;Kruis et al 2000;Rosner and Yu 2001;Kostoglou and Konstandopoulos 2001;Lin, Lee, and Matsoukas 2002;Friesen and Dabros 2003;Maisels, Kruis and Fissan 2004;Sun, Axelbaum, and Huertas 2004). By this method particle behaviors, such as agglomeration and deposition, are treated as probabilistic events.…”

Section: Dsmc Algorithmmentioning

confidence: 99%

“…It should be noted that both the agglomeration and the deposition lead to a continuous decrease of the particle number in the simulated volume. In order to avoid the increase in statistical error associated with decreasing N , the constant-volume MC approach is applied following Liffman (1992) and Kruis, Maisels and Fissan (2000). The simulated volume V S is kept constant during the simulation.…”

Section: Dsmc Algorithmmentioning

confidence: 99%

“…The agglomeration of solid particle aerosols subjected to travelling sound waves in a typical experimental agglomeration chamber is modelled by the direct simulation Monte Carlo (DSMC) method. The constant-volume Monte Carlo (MC) approach is applied following Liffman (1992) and Kruis, Maisels, and Fissan (2000). In the modelling, both the orthokinetic and hydrodynamic mechanisms and Brownian coagulation are considered to formulate the agglomeration kernel.…”

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: Dsmc Algorithmmentioning

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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“…Since the GDE can hardly be solved analytically, many methods were developed to solve it in the last century. Among them, the moment method (Hulburt and Katz 1964;Lee and Chen 1984;Yu et al 2008;Xie 2014;Yu et al 2015), the sectional method (Gelbard and Seinfeld 1980;Landgrebe and Pratsinis 1990;Friedlander 2000) and the direct simulation Monte Carlo (DSMC) method (Kruis et al 2000;Loyalka 2003;Sheng and Shen 2007) are the most important ones. The DSMC is high efficient for multi-component aerosol dynamics and easy to couple with chemical or nuclear reactions; so it has been applied in many fields such as aerosol reactors and nuclear reactor safety (Efendiev and Zachariah 2002;Loyalka 2003).…”

Section: Introductionmentioning

confidence: 99%

Decay of High-Concentration Aerosol in a Chamber

Liu

Yang

et al. 2017

Aerosol Sci Eng

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High-concentration aerosol is supposed to be crucial in some air pollution events. Aerosol always decays due to such mechanisms as coagulation, gravitational settling and diffusion deposition in a chamber; so its concentration needs to be calibrated in chamber experiments, especially for high-concentration aerosol. Two methods including the wall-loss correction and the direct simulation Monte Carlo (DSMC) were used to describe decay of the aerosol concentration. The parameters of diffusion deposition were fitted by experimental data. The results by both methods were compared to the experimental results of high-concentration aerosol decay process. The results show that the wall-loss correction under the present conditions is valid when the initial total aerosol concentration is less than 10 4 cm −3 , and the DSMC results are consistent to the experimental data for concentration ranging from 10 4 cm −3 to more than 10 6 cm −3 .

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“…By not considering the spatial position of the particles, the DSMC method consumes much less CPU time than the classical Monte Carlo method. Since this work, various versions of the Monte Carlo method have been applied to study particle size distribution undergoing coagulation and/or aggregation (Vemury et al 1994;Smith and Matsoukas 1998;Tandon and Rosner 1999;Kruis et al 2000;Rosner and Yu 2001;Benson et al 2004), crystallization (van Peborgh Gooch andHounslow 1996), fragmentation (Shah et al 1977;Liffman 1992) and polymerization (Spouge 1985). Recently, Frenklach (1998, 2003) incorporated surface growth into the Monte Carlo method to study particle aggregation with simultaneous surface growth.…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo Simulation of Multicomponent Aerosols Undergoing Simultaneous Coagulation and Condensation

Sun

Axelbaum

Huertas

2004

Aerosol Science and Technology

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A Monte Carlo method was developed to simulate multicomponent aerosol dynamics, specifically with simultaneous coagulation and fast condensation where the sectional method suffers from numerical diffusion. This method captures both composition and size distributions of the aerosols. In other words, the composition distribution can be obtained as a function of particle size. In this method, particles are grouped into bins according to their size, and coagulation is simulated by statistical sampling. Condensation is incorporated into the Monte Carlo method in a deterministic way. If bins with fixed boundaries are used to simulate the condensation process numerical dispersion occurs, and thus a moving bins approach was developed to eliminate numerical dispersion. The method was validated against analytical solutions, showing excellent agreement. An example of the usefulness of this model in understanding aerosol evolution is presented. The effects of the number of particles and number of bins on the accuracy of the numerical results are also discussed. It was found that with 20 bins per decade and 10 5 particles in the control volume results with less than 5% error can be obtained. The results are further improved to within 2% error by filtering the statistical noise with a cubic spline algorithm.

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Direct simulation Monte Carlo method for particle coagulation and aggregation

Cited by 171 publications

References 18 publications

Simulation of Acoustic Agglomeration Processes of Poly-Disperse Solid Particles

Simulation of Acoustic Agglomeration Processes of Poly-Disperse Solid Particles

Decay of High-Concentration Aerosol in a Chamber

Monte Carlo Simulation of Multicomponent Aerosols Undergoing Simultaneous Coagulation and Condensation

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