Statistical analysis of fine particle resuspension from rough surfaces by turbulent flows

You, S. and Wan, M. P. (2017) Statistical analysis of fine particle resuspension from rough surfaces by turbulent flows. Aerosol and Air Quality Research, 17(4), pp. 843-856. (doi: 10.4209/aaqr.2016.03.0106)

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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.

Item Type:Articles
Additional Information:This study is jointly supported by the Republic of Singapore’s Ministry of Education (MOE) through grant nos. RG 190/14 and MOE2016-T2-1-063 and National Research Foundation (NRF) through a grant to the Berkeley Education Alliance for Research in Singapore (BEARS) for the Singapore-Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) Program.
Glasgow Author(s) Enlighten ID:You, Dr Siming
Authors: You, S., and Wan, M. P.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Aerosol and Air Quality Research
Publisher:Taiwan Association for Aerosol Research
ISSN (Online):2071-1409

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