Comparisons of novel and efficient approaches for permeability prediction based on the fabric architecture

Wong, C.C., Long, A.C., Sherburn, M., Robitaille, F., Harrison, P. and Rudd, C.D. (2006) Comparisons of novel and efficient approaches for permeability prediction based on the fabric architecture. Composites Part A: Applied Science and Manufacturing, 37(6), pp. 847-857. (doi: 10.1016/j.compositesa.2005.01.020)

Abstract

Permeability models based on the structure of the fabric are attractive for any design process to enhance the experimental permeability data. The authors have proposed two efficient numerical approaches to predict permeability based on the architecture of the fabric. The 'Stream Surface' method reduces the complexity of the flow domain by representing the 3D volumes with their 2D curvilinear mid-surfaces while retaining the 3D attributes. The second method, 'Grid Average', discretises the 3D domain into a 2D regular grid with weighted average permeabilities for the individual elements. Flow equations are solved for the reduced meshes generated from these two approaches to calculate the effective permeability. These approaches are applied firstly to a single tow model, and then to a 2×2 twill weave fabric, whereby the effects of in-plane shear and the statistical behaviour of fabrics is discussed. Comparisons with the computationally intensive CFD approach are favourable.

• Item Type: Articles
• Keywords: Fabrics/textiles, computational modelling, statistical properties methods, permeability model.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Harrison, Dr Philip
• Authors: Wong, C.C., Long, A.C., Sherburn, M., Robitaille, F., Harrison, P., and Rudd, C.D.
• Subjects: T Technology > TS Manufactures; T Technology > TA Engineering (General). Civil engineering (General)
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy; University Centres > Glasgow Materials Research Initiative
• Journal Name: Composites Part A: Applied Science and Manufacturing
• ISSN: 1359-835X