Abstract
A good understanding of woven fiber preform permeabilities is critical in the design and optimization of the composite molding processes encountered in resin transfer molding (RTM); yet these issues remain unresolved in the literature. Many have attempted to address permeability predictions for flat undeformed fiber preform, but few have investigated permeability variations for complex geometries of porous fibrous media. In this study, the objectives are to: (i) provide a brief review of existing methods for the prediction of the fiber mat permeability; (ii) postulate a more realistic representation of a unit cell to account for such fabric structures as crimp, tow spacing and the like; and (iii) apply computational approximations to predict effective permeabilities for use in modeling of structural composites manufacturing processes. The Stokes equation is used to model the flow in the inter-tow region of the unit cell, and in the intra-tow region, the Brinkman's equation is used. Initial permeability calculations are performed for a three-dimensional unit cell model representative of the PET-61 woven fabric composite. The results show good agreement with experimental data published in the literature.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 3135-3145 |
| Number of pages | 11 |
| Journal | International Journal of Heat and Mass Transfer |
| Volume | 44 |
| Issue number | 16 |
| DOIs | |
| State | Published - Jun 19 2001 |
Bibliographical note
Funding Information:The authors are very pleased to acknowledge support in part by Battelle/US Army Research Office (ARO) Research Triangle Park, North Carolina, under grant number DAAH04-96-C-0086, and by the Army High Performance Computing Research Center (AHPCRC) under the auspices of the Department of the Army, Army Research Laboratory (ARL) cooperative agreement number DAAH04-95-2-0003/contract number DAAH04-95-C-0008. The content does not necessarily reflect the position or the policy of the government, and no official endorsement should be inferred. Support in part by Dr. Andrew Mark of the Integrated Modeling and Testing (IMT) Computational Technical Activity and the ARL/MSRC facilities is also gratefully acknowledged. Special thanks are due to the CICC Directorate and the Materials Division at the US Army Research Laboratory (ARL), Aberdeen Proving Ground, Maryland. Other related support in the form of computer grants from the Minnesota Supercomputer Institute (MSI), Minneapolis, Minnesota is also gratefully acknowledged.