Browsing by Author "Boyle, Paul Martin"
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Item A Refined Parallel Simulation of Crossflow Membrane Filtration(2011) Boyle, Paul Martin; Houchens, Brent C.This work builds upon the previous research carried out in the development of a simulation that incorporated a dynamically-updating velocity profile and electric interactions between particles with a Force Bias Monte Carlo method. Surface roughness of the membranes is added to this work, by fixing particles to the membrane surface. Additionally, the previous electric interactions are verified through the addition of an allrange solution to the calculation of the electrostatic double layer potential between two particles. Numerous numerical refinements are made to the simulation in order to ensure accuracy and confirm that previous results using single-precision variables are accurate when compared to double-precision work. Finally, the method by which the particles move within a Monte Carlo step was altered in order to implement a different data handling structure for the parallel environment. This new data handling structure greatly reduces the runtime while providing a more realistic movement scheme for the particles. Additionally, this data handling scheme offers the possibility of using a variety ofn-body algorithms that could, in the future, improve the speed of the simulation in cases with very high particle counts.Item Coupling a dynamically updating velocity profile and electric field interactions with force bias Monte Carlo methods to simulate colloidal fouling in membrane filtration(2009) Boyle, Paul Martin; Houchens, Brent C.Work has been completed in the modeling of pressure-driven channel flow with particulate volume fractions ranging from one to ten percent. Transport of particles is influenced by Brownian and shear-induced diffusion, and convection due to the axial crossflow. The particles in the simulation are also subject to electrostatic double layer repulsion and van der Waals attraction both between particles and between the particles and channel surfaces. These effects are modeled using Hydrodynamic Force Bias Monte Carlo (HFBMC) simulations to predict the deposition of the particles on the channel surfaces. Hydrodynamics and the change in particle potential determine the probability that a proposed, random move of a particle will be accepted. These discrete particle effects are coupled to the continuum flow via an apparent local viscosity, yielding a dynamically updating quasi-steady-state velocity profile. Results of this study indicate particles subject to combined hydrodynamic and electric effects reach a highly stable steady-state condition when compared to systems in which particles are subject only to hydrodynamic effects.