基于改进孔隙网络模型的织物增强体渗透率预测方法

doi:10.19936/j.cnki.2096-8000.20250528.013

摘要/Abstract

摘要： 建立了通过孔隙网络模型预测复合材料织物增强体面内渗透率的方法。首先通过显微计算机断层扫描技术(Micro-CT)对织物样品进行无损扫描得到三维图像序列,并重建织物结构模型,随后对图像进行二值化,分离孔隙区域,采用分水岭算法分割孔隙区域后提取孔隙网络模型,对比了两种分割方法对孔隙网络模型参数的影响。其次考虑了孔隙内部流动并修正孔隙网络单元的流域长度和半径等参数,采用截面渐变微管流动模型改进了孔隙网络模型渗透系数的算法。最后基于孔隙网络模型预测了三种织物的面内渗透率,结果表明2D织物孔隙网络模型预测面内渗透率的平均误差低于21.2%,渗透系数算法改进后预测误差降低27.1%。

关键词: 复合材料, 纤维增强体, 孔隙网络模型, 渗透率, 分水岭分割

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

中图分类号:

TB332

范奇, 王静, 杨斌, 王继辉, 倪爱清. 基于改进孔隙网络模型的织物增强体渗透率预测方法[J]. 复合材料科学与工程, 2025, 0(5): 96-107.

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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