Monte Carlo modelling of particle resuspension on a flat surface

Benito, Jesica Gisele; Aracena, Karina A. Valenzuela; Uñac, Rodolfo Omar; Vidales, Ana María; Ippolito, I.

doi:10.1016/j.jaerosci.2014.10.006

Cited by 19 publications

(27 citation statements)

References 40 publications

(69 reference statements)

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“…The model used for the resuspension of particles from a surface is based on a Monte Carlo method (MC) (Benito et al, 2015(Benito et al, , 2016. Taking into account the similitude between the resuspension phenomenon and a desorption process from a heterogeneous surface; we assume that the rate equation follows the Arrhenius law (Hughes, 1971).…”

Section: Resuspension Modelmentioning

confidence: 99%

“…A detailed description of the MC method based on the Metropolis function can be found in Benito et al (2015Benito et al ( , 2016. Briefly, we recall that assuming a N-particle system and considering only one possible type of transition, i.e., a deposited particle can resuspend to the gas stream, the probability P(t)dt of the system to perform a transition during the interval (t; t+dt) is given by a Poisson process (Sales et al, 1996) (1) where is the total transition rate for the whole system of N particles and is the resuspension transition rate for a single particle.…”

Section: Resuspension Modelmentioning

confidence: 99%

“…Recently, we have proposed a model based on a Monte Carlo (MC) simulation that takes advantage of the phenomenological Arrhenius expression for the determination of the resuspension rate (Benito et al, 2015 and2016). We have shown that the Metropolis function (Binder and Heermann, 1992), used as the transition probability between configuration states, is able to describe the principal characteristics of the particle resuspension for the case of a monolayer of particles deposited on a flat surface and subjected to air flow.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we will focus on a detailed analysis of the influence of air flow acceleration using our MC numerical model developed earlier (Benito et al, 2015). A summary of the numerical model is presented in section 2, while the results obtained through the Monte Carlo model for different velocity rates are analyzed in section 3.…”

Section: Introductionmentioning

confidence: 99%

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Kinetic Programmed Resuspension - KPR technique

Benito

Uñac

Vidales

et al. 2018

Journal of Aerosol Science

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In particle resuspension phenomena, experimental and simulation evidence demonstrate that, as the acceleration increases, higher air velocities are needed for particle re-entrainment, and the process requires less time to develop. In order to describe this problem and to shed light to its understanding, we present in this paper a new analysis named Kinetic Programmed Resuspension (KPR). This new insight into the kinetics of the resuspension process provides a technique for determining some resuspension experimental parameters. Thereby, using a simple Monte Carlo model, we are able to reproduce experimental data and, from these results, to analyze the main kinetic parameters involved, just by analogy with the process of thermal desorption of molecules from surfaces.

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Section: Resuspension Modelmentioning

confidence: 99%

Section: Resuspension Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Kinetic Programmed Resuspension - KPR technique

Benito

Uñac

Vidales

et al. 2018

Journal of Aerosol Science

Self Cite

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“…The static force-balance approach (e.g., the models by Goldasteh et al (2013) and You and Wan (2014b)) assumes that particle resuspension occurs once the aerodynamic forces (moments) have exceeded the adhesion force (moment) between particles and surfaces. The kinetic PDF approach (e.g., the models by Wen and Kasper (1989), Benito et al (2015), and Reeks and Hall (2001)) models particle resuspension by simulating the PDF of some variables related to the process. The dynamic PDF approaches recently introduced by Guingo and Minier (2008) and , consider both the disruption of static equilibrium of particle-surface contact and particle motion along walls.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of Fine Particle Resuspension from Rough Surfaces by Turbulent Flows

You

Wan

2017

Aerosol Air Qual. Res.

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Particle resuspension plays a part in indoor aerosol dynamics and has received increasing attention due to its ability to prolong human exposure to airborne particles. A stochastic model of turbulence-induced particle resuspension from rough surfaces is proposed based on the statistical nature of the process. Deposited fine (micro-or nano-size) particles are generally immersed in the viscous sublayer of the incompressible turbulent boundary layer and are subjected to aerodynamic forces that can be approximated by log-normal distributions due to penetration of turbulent inrushes and bursts into the viscous sublayer. Similarly, the adhesion force between particles and surfaces could be approximated by statistical distributions according to the statistical nature of surface roughness. Three common types of adhesion force distributions, i.e. log-normal, Weibull, and Gaussian distributions, are specifically explored. Predicted resuspension fractions versus free stream velocity are in good agreement with experimental data reported in the literature. Using the proposed stochastic model, influences of various parameters (composite Young's modulus, surface energy, adhesion force distribution, velocity distribution, fluid density, and particle diameter) on the threshold friction velocity (u * 50 ) and friction velocity divergence (Δu * ) are analysed. The information sheds light onto the controlling of the particle resuspension process. The proposed model extends the current capability of modeling particle resuspension by considering different types of adhesion force distributions.

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