Pressure drop through textile fabrics - experimental data modelling using classical models and neural networks

This work studies pressure drops through several textile fabrics. A prelimi nary study of cloth characteristics, including scanning electron micrograph s, shows their specificities towards particular media. For 20 different clo ths, in terms of weave and raw material (rayon or activated carbon fibers), an experimental study is carried out using a pilot-unit, in order to measu re air and water pressure drops through one layer of each cloth. Fluid Reyn olds numbers range from 0 up to 2500 for both fluids. This experimental stu dy shows the influence of specific parameters of cloths (like weave) on the ir dynamic behavior. Furthermore, the swelling phenomenon of fibers in wate r is considered. Goodings' model is set up for woven structures and it enab les the fabric opening diameter to be calculated around 10 mu m. Experiment al data are then modelled, firstly using classical models set up for partic ular porous media (Ergun, Carman's dimensionless model, Comiti-Renaud), and then using a statistical tool, neural networks. These models are tested us ing three different definitions for the specific surface area, on the fabri c, yarn, and opening scale, respectively. Whichever the definition used, th ey are not suitable to describe the flow through woven structures. However, they enable the swelling phenomenon of fibers in water to be confirmed, an d the flow into the fabric yarn to be located. The experimental study, coup led with these modelling results, leads to the choice of input neurons in t he neural network (fluid properties - mu, rho, Re - and fabric characterist ics - thickness, density, number of openings N-o, S-o, and raw material) in order to predict pressure drops as the output neuron. The statistical resu lts obtained with this architecture are satisfactory and a variable analysi s carried out with connection weight values enables the influence of specif ic parameters of cloths (like N-o) on pressure drops to be quantified. (C) 2000 Elsevier Science Ltd. All rights reserved.