Computational Modeling of Multiscale Air Filter Media Consisting of Nano- and Microfibers

Withregard to air filtration, computational work has been performedto predict filter performance outcomes in different particulate scenarios,yet comparative verification of different modeling methods is rarelystudied. In this study, computational simulations with different modelingtechniques are demonstrated for filter media with various fiber diameters,thicknesses, and basis weights. For a microscale meltblown (Micro)filter web, a resolved model reconstructed by X-ray micro-computedtomography (X mu-CT) is generated. The representative volume elementsand the number of voxels are determined by examining the simulationaccuracy and the computational time. For a multiscale dual-layer webcomposed of nanofibers (Nano) and microscale spunbond fibers (SB),a parametric model and a porous plane model are generated. The accuracyof the models is verified in terms of the morphological parametersand flow resistance in comparison, with actual test results. The parametricmodel and porous plane model suitably predict the characteristicsof multiscale filter media. Simulated filtration is conducted forparticles of different sizes (0.05-1 mu m) in an effortto understand the relationships among the filter morphology, facevelocity (71 and 142 mm/s), and particle size. This study presentsrelevant modeling methods, specifically a resolved model, a parametricmodel, and a porous plane model for virtualizing filter media on variousscales. It provides an informative discussion of various modelingparameters for accurate predictions of filtering behaviors with potentialapplicability to the reverse-engineering of filter products.

Automated summary

This study investigates different modeling methods for air filtration and verifies their accuracy through computational simulations and comparisons with actual test results. The study shows that resolved models, parametric models, and porous plane models are suitable for predicting the characteristics of filter media on different scales. Furthermore, the study explores the relationships among filter morphology, face velocity, and particle size, providing useful insights for accurate predictions of filtering behaviors.