Deep learning accelerated prediction of the permeability of fibrous microstructures

Permeability of fibrous microstructures is a key material property for predicting the mold fill times and resin flow path during composite manufacturing. In this work, we report an efficient approach to predict the permeability of 3D microstructures from deep learning based permeability predictions of 2D cross-sections combined via a circuit...

In-series sample methodology for permeability characterization demonstrated on carbon nanotube-grafted alumina textiles

In-plane permeability of small area (100 × 50 mm) alumina fiber woven fabrics grafted with aligned carbon nanotubes (CNT) was quantified by placing them in series with a glass mat of known permeability during a flow experiment. The methodology was first validated on a reference woven textile. Permeability values matched those obtained by a...