Micro-particle adhesion, detachment and resuspension from surfaces have attracted considerable attention due to their numerous applications in semiconductor, xerographic, and pharmaceutical industries, and, more recently, in understanding indoor air quality. However, most earlier studies have focused on idealized spherical particles and smooth surfaces, and the effects of particle irregularities and surface roughness on the rate of particle removal and resuspension are not well understood. In this work, a Monte Carlo simulation of particle resuspension from a surface under turbulent flow conditions was developed and resuspension of nearly spherical and irregular shaped particles with rough surfaces from substrates under turbulent flow condition was studied. Following our earlier approach, compact irregular shaped particles were modeled as spherical particles with a number of hemispherical bumps. It was assumed that the bump surfaces also have fine roughness. The extended Johnson-Kendall-Roberts (JKR) adhesion theory for rough surfaces was used to model the particle adhesion and detachment.
A number of assumptions were made to apply the model. It was assumed that the particles have a Gaussian size distribution. The number of bumps of the irregular particles and surface roughness values of particle are assumed to be random, respectively, with Poisson and log-normal distributions. For particle detachment from the surface, the theory of critical moment was used. The effects of particle size, turbulent flow, particle irregularity and surface roughness on particle detachment and resuspension were studied for different cases. The Monte Carlo model predictions show probabilistic distributions of the particle resuspension. The simulation results are compared with the available experimental data and good agreement was found. The study provided information on the random nature of particle resuspension due to the randomness in the airflow, particle size distribution and surface roughness.
Particle adhesion to rough surfaces
