Effect of microstructure on porosity of random fibrous networks

Abstract

Porosity of fibrous networks plays an important role in many processes, especially in filtration and separation applications. The paper aims to statistically analyze characteristics of porosity for random fibrous networks employing analytical and numerical approaches. An analytical model evaluating the number of pores related to microstructural parameters (e.g., fiber diameter) is proposed and validated with a numerical approach. An image processing of numerically generated fibrous networks was first performed to reveal pore-size distributions and then to calculate the number of pores with high accuracy.The obtained analytical results are in good agreement with those from image analysis based numerical realizations of fibrous networks. Additionally, analyses on orientation and crimp of fibers and their effects on parameters of porosity were carried out. These studies indicate that as more fibers are aligned in a principal direction, the number of pores decreases, and this induces the increase in the average pore size.The investigations also demonstrate that crimp does not affect pore characteristics in random distributions considerably; however, the number of pores decreases with fiber crimp as alignment increases. Generally, this study demonstrates vividly that characteristics of porosity such as areal number density of pores, mean pore size and its distribution are governed by microstructure of fibrous networks.The developed approach based on image analysis is capable of high-accuracy, assessment of distribution of pore size and number of pores in a random fiber network.

Microstructural evaluation and recommendations for face masks in community use to reduce the transmission of respiratory infectious diseases

“…First, mock-up models of two layer face masks emulating actual microstructure extracted from CT images were generated with finite-element simulations, where 3D fibers were stacked on the top of each other in the through-thickness direction (TD). To have a realistic microstructure, constituent fibres were in contact and possible penetrations were minimised with contact algorithms [76] , [77] , [78] , [79] . Using in-house algorithms written in Python, the generated models in FE environment were converted into CAD models in Autodesk Fusion to be imported in Star CCM+ for CFD simulations.…”