Overview of mechanistic particle resuspension models: comparison with compilation of experimental data

Abstract

In this study, several mechanistic models for particle removal from substrates were introduced and their predictive ability was tested. Empirically optimized parameters for a selected set of particle-substrate combinations were obtained and the ability of these models to predict resuspension for a large number of particle-substrate combinations and flow conditions using the published experimental data was explored. Our analysis showed that accurate predictions for a broad spectrum of particle-substrate combinations require accounting for substrate roughness and particle non-sphericity. From analysis of our compiled experimental data set, it was determined that the shear velocities required for particle resuspension follow a log-normal distribution, with geometric standard deviations in the range of 1.4 to 1.7. From comparison of the calculated and measured critical shear velocities, we found that a critical roughness parameter value of Δ_c = 0.774 is a reasonable estimate for most real substrates. We show that the combination of parameters determined from our study and models that account for roughness and particle non-sphericity, it is possible to predict particle resuspension from most substrates with reasonable accuracy.

Keywords:

• Particle resuspension
• particle detachment
• particle adhesion
• surface roughness
• relative humidity
• turbulent flow
• data analysis

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors acknowledge funding support from the Defense Threat Reduction Agency (DTRA) under the contract number HDTRA1-14-C-0009.