For particle deposition from log-normal polydispersed aerosol streams by one or more of several mechanisms described by piecewise-power-law mass transfer coefficients, w e derive useful relations between actual total mass deposition rates and t h e 'reference' rate one would calculate by imagining that all particles in t h e mainstream population had t h e average particle volume V (=&/N,,). Included here are diffusion or inertial mechanisms for laminar-or turbulent-boundary layers, free-molecular or continuum diffusion at high Peclet numbers of d e n s e spherical particles, or fractal agglomerates. The mainstream particle volume distribution is considered to b e log-normal with arbitrary 'spread' parameter, thereby generalizing earlier results for "coagulation-aged'' (self-preserving) distributions. Further generalizations include transitions between important particle transport mechanisms, opening t h e way t o efficient, finite-analytic methods for predicting mass deposition rates for arbitrary, size-dependent particle capture efficiencies.
The important connection between particulate deposit properties and deposition mechanism remains poorly understood and only scarcely studied. Accordingly, in this research, we develop a discrete stochastic model to simulate particulate deposition processes resulting from realistic combinations of deposition mechanisms. Particle motion is assumed to be determined by the superposition of a deterministic force that acts toward the collecting surface and a random force, which produces Brownian diffusion. We characterize the resulting deposit microstructure via porosity and pore sizelarea distribution, surface area; and we examine the evolution of these descriptors with time (number of particles deposited) for different deposition mechanisms. We also examine the effect of particle polydispersity, spatial orientation (for nonspherical particles), and mean-free-path on the resulting deposit structure.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.