Prediction of residual saturation and pressure drop during coalescence filtration using dynamic pore network model

Local pressure and residual saturation in the polyester filter predicted by PNM and CFD. [Display omitted] •Pore network model is used to predict pressure drop and saturation in mist filtration.•Watershed-based algorithm SNOW in PoreSpy is used to extract the filter networks.•Micro-CT experiments are performed to obtain local saturation inside the filters.•Single phase and dynamic PNM are validated against present pore-scale CFD and experiments.•Pressure drop and saturation predicted by PNM are in good agreement with CFD and experiments. Microstructure optimization of mist and dust filter media used in many applications including respirators, lubricated machining, compressors and engine crankcases is increasingly becoming reliant on computational simulations of dry and wet filtration processes. Computational fluid dynamics (CFD) based full-morphology simulations with advanced interface capturing techniques offer unique advantages in enabling accurate characterization of the pore-scale transport processes, yet demand substantial computational effort especially for fine filters. In this work, a dynamic pore network modeling (DPNM) framework is developed and evaluated for the prediction of residual saturation and pressure drop during mist filtration, and applied to an oil-air system. Two nonwoven filters (stainless steel and polyester) with different fibre diameters and packing densities are considered. The pore network is extracted using a watershed-based algorithm, from realistic virtual filter geometries (Abishek et al., 2017), and validated for macroscopic properties such as packing density. The validity of the steady PNM model is assessed by comparison of the dry (air) pressure drop against CFD and experimental data in the literature. The validity of the DPNM model is assessed by comparison of multiphase pressure drops and residual saturation against micro-CT and gravimetric experiments carried out in this work, as well as pore-scale CFD simulations using the volume-of-fluid model. It is found that the dynamic pore network model is able to successfully predict the critical properties of the filtration process such as saturation (local and total) and pressure drop to within 15% of experiment and CFD. Considering that the overall computational cost of DPNM is about 2–3 orders of magnitude lower than fully resolved CFD, DPNM stands as a potential tool for the early-stage design and optimization of mist filtration media.