首页|Dynamic pore network model to predict residual saturation and pressure drop in mist oleo-phobic filters
Dynamic pore network model to predict residual saturation and pressure drop in mist oleo-phobic filters
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A dynamic pore network modelling (DPNM) framework is used to evaluate pressure drop and residual saturation in oleo-phobic nonwoven media in mist filters. The advanced fully-resolved VOF simulations along with μ-CT measurements are also carried out to substantiate the validity of the DPNM simulations. The results of the study show that PNM could be a beneficial and reliable tool for predicting oil saturation and pressure drop during early-stage design of oleo-phobic filters with nonwoven media. Given the considerably higher efficiency of PNM models compared to CFD-based techniques, some parametric studies were also conducted. Our findings suggest that, for a given set of operating conditions, efficiency/performance can be improved by appropriate choice of filters with optimized properties (fibre diameter, packing density, contact angle, etc.) for which the overall oil saturation and pressure drop in the filter attain minimum, may offer a self-cleaning mist filter. Finally, a correlation was developed for evaluating the performance of oleo-phobic media using the filter specifications and the data of steady state saturation and pressure drop found in open literature. The obtained correlation shows a good conformity with the experimental measurements with an average of 12.1% error for about 93% of data out of 68 data sets collected with a correlation coefficient of (R~2) of 0.934.
Mist filtrationOleo-phobic filterDynamic pore network modelCFDμ-CTNonwoven media
A. Azarafza、A.J.C. King、R. Mead-Hunter
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School of Civil and Mechanical Engineering, Curtin University, Australia
Fluid Dynamics Research Group and The Curtin Institute for Computation, Curtin University, Australia
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