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

Category: Aerosol Physics and Instrumentation

Volume: 17 | Issue: 4 | Pages: 843-856
DOI: 10.4209/aaqr.2016.03.0106
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Siming You, Man Pun Wan

  • School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798, Singapore


A stochastic model of turbulence-induced particle resuspension is proposed.
Model Predictions are in good agreement with reported experimental data.
Observed phenomena are successfully explained by the stochastic model.
The influences of modeling parameters on particle resuspension are investigated.
The model extends the current capability of modeling particle resuspension.


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.


Stochastic analysis Fine particles Resuspension Turbulence Parametric analysis

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