Numerical micro-scale modelling of the mechanical loading of woven fabrics equipped with particles

Abstract

The equipping of polyester multifilament fabrics with functionalized micro particles for partial and targeted reduction of the size of continuous inter-yarn pores in the fabric, utilizing the filtering effect of textile surfaces, has proven to be a very effective method for increasing the barrier properties of fabrics for use as surgical reusable textiles, cleanroom clothing or cleanable filter media. The objectives of this study were to understand the interrelationships of weaving parameters and fabric properties and to model the porous fabrics under loading conditions. Artificial Neural Networks were trained and tested in order to link the weaving and processing parameters with the fabric properties using the experimental data. Mean flow pore sizes and permeability values of woven fabrics under biaxial loading conditions were measured according to a method previously developed using a special sample holder connected to a conventional porosimetry. Finite element models of unit cells were developed and loaded virtually. Those models were transferred into voxel models and imported into software DNSlab, where they were virtually equipped with particles. For the simulation of the fluid flow, the Navier-Stokes equations were solved numerically by the Lattice-Boltzmann method. The experimental and numerical studies show that pore size of barrier fabrics can be tailored using the developed methods and models. Important parameters are the fabric construction parameters and the particle size.