Statistical analysis of fine particle resuspension from rough surfaces by turbulent flows
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 th...
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sg-ntu-dr.10356-852502023-03-04T17:15:04Z Statistical analysis of fine particle resuspension from rough surfaces by turbulent flows You, Siming Wan, Man Pun School of Mechanical and Aerospace Engineering Fine Particles DRNTU::Engineering::Mechanical engineering Stochastic Analysis 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. NRF (Natl Research Foundation, S’pore) MOE (Min. of Education, S’pore) Published version 2019-05-14T05:29:19Z 2019-12-06T16:00:25Z 2019-05-14T05:29:19Z 2019-12-06T16:00:25Z 2017 Journal Article You, S., & Wan, M. P. (2017). Statistical analysis of fine particle resuspension from rough surfaces by turbulent flows. Aerosol and Air Quality Research, 17(4), 843-856. doi:10.4209/aaqr.2016.03.0106 1680-8584 https://hdl.handle.net/10356/85250 http://hdl.handle.net/10220/48186 10.4209/aaqr.2016.03.0106 en Aerosol and Air Quality Research © 2017 Taiwan Association for Aerosol Research. All rights reserved. This paper was published in Aerosol and Air Quality Research and is made available with permission of Taiwan Association for Aerosol Research. 14 p. application/pdf |
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Fine Particles DRNTU::Engineering::Mechanical engineering Stochastic Analysis You, Siming Wan, Man Pun Statistical analysis of fine particle resuspension from rough surfaces by turbulent flows |
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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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School of Mechanical and Aerospace Engineering |
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School of Mechanical and Aerospace Engineering You, Siming Wan, Man Pun |
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Article |
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You, Siming Wan, Man Pun |
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You, Siming |
title |
Statistical analysis of fine particle resuspension from rough surfaces by turbulent flows |
title_short |
Statistical analysis of fine particle resuspension from rough surfaces by turbulent flows |
title_full |
Statistical analysis of fine particle resuspension from rough surfaces by turbulent flows |
title_fullStr |
Statistical analysis of fine particle resuspension from rough surfaces by turbulent flows |
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Statistical analysis of fine particle resuspension from rough surfaces by turbulent flows |
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statistical analysis of fine particle resuspension from rough surfaces by turbulent flows |
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2019 |
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https://hdl.handle.net/10356/85250 http://hdl.handle.net/10220/48186 |
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1759858204171304960 |