Numerical prediction of collection efficiency of a personal sampler based on cyclone principle

Antara Badhan, University of Texas at El Paso

Abstract

A personal bioaerosol sampler is a self-contained, operation flexible, high-efficient device for indoor air quality (IAQ) and health risk exposure monitoring and measurement. Bio-aerosols such germ-laden viruses, microbial species, airborne microorganisms and volatile organic compounds (VOC) are sucked into the sampler and are deposited on the inner wall surface based on cyclone principal. The major concern with bio-aerosol samplers is the collection efficiency. In this study, we used computational fluid dynamics (CFD) tools to evaluate key design parameters, specifically the inlet tube angle and collection tube inner wall roughness. 3D incompressible turbulent flow was simulated using commercial software ANSYS FLUENT. Reynolds stress model (RSM) was used to investigate the turbulence effect with the following boundary conditions (velocity-inlet boundary condition at inlet, outflow boundary condition at outlet and no slip at walls). The numerical approach for air-aerosol interaction is based on an Eulerian-Lagrangian fluid dynamics framework, where the particles or droplets trajectories are computed in a Lagrangian method (discrete phase element) and then conjugate these particles to the continuous phase in the Eulerian frame. The variation of inlet angle affects the collection efficiency of the cyclone sampler. In addition, the flow characterizations with different velocity fluctuation profiles validate the continuous phase model. The development and evolution of the vortex core regions for velocities are obtained and evaluated in the simulation of the cyclonic flow.^

Subject Area

Engineering, Mechanical

Recommended Citation

Badhan, Antara, "Numerical prediction of collection efficiency of a personal sampler based on cyclone principle" (2014). ETD Collection for University of Texas, El Paso. AAI1564661.
http://digitalcommons.utep.edu/dissertations/AAI1564661

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