Textile reinforcement permeability prediction based on improved pore network model

doi:10.19936/j.cnki.2096-8000.20250528.013

Abstract

Abstract: A method for predicting the in-plane permeability of composite fabric reinforcements through a pore network model was established. First, a three-dimensional image sequence was obtained by non-destructive scanning of the fabric sample using micro-computed tomography (Micro-CT), and the image was binarized, separated into pore areas, and segmented using watershed segmentation to extract the pore network model. The influence of two segmentation methods on the parameters of the pore network model was compared. Secondly, considering the flow inside the pores and modifying the parameters such as the length and radius of the pore network unit, a fictionally-graded micro-tube flow model was adopted to improve the algorithm for calculating the permeability coefficient of the pore network model. Finally, based on the pore network model, the in-plane permeability of three fabrics was predicted. The results showed that the error in predicting the in-plane permeability of 2D fabric pore network model was less than 21.2%, and the improved hydraulic conductance algorithm reduces the prediction error by 27.1%.

Key words: composites, fiber-reinforced materials, pore network models, permeability, watershed segmentation

CLC Number:

TB332

FAN Qi, WANG Jing, YANG Bin, WANG Jihui, NI Aiqing. Textile reinforcement permeability prediction based on improved pore network model[J]. COMPOSITES SCIENCE AND ENGINEERING, 2025, 0(5): 96-107.

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