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Modeling of Pore Structure Evolution Between Bundles of Plain Woven Fabrics During Chemical Vapor Infiltration Process: The Influence of Preform Geometry

Authors

  • Kang Guan,

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    • Science and Technology on Thermostructure Composite Materials Laboratory, Northwestern Polytechnical University, Xi'an, Shaanxi, China
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  • Laifei Cheng,

    1. Science and Technology on Thermostructure Composite Materials Laboratory, Northwestern Polytechnical University, Xi'an, Shaanxi, China
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  • Qingfeng Zeng,

    1. Science and Technology on Thermostructure Composite Materials Laboratory, Northwestern Polytechnical University, Xi'an, Shaanxi, China
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  • Litong Zhang,

    1. Science and Technology on Thermostructure Composite Materials Laboratory, Northwestern Polytechnical University, Xi'an, Shaanxi, China
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  • Juanli Deng,

    1. Science and Technology on Thermostructure Composite Materials Laboratory, Northwestern Polytechnical University, Xi'an, Shaanxi, China
    2. School of Materials Science and Engineering, Chang'an University, Xi'an, Shaanxi, China
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  • Kaiyuan Li,

    1. Science and Technology on Thermostructure Composite Materials Laboratory, Northwestern Polytechnical University, Xi'an, Shaanxi, China
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  • Hui Li

    1. School of Materials Science and Engineering, Chang'an University, Xi'an, Shaanxi, China
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Author to whom correspondence should be addressed. e-mail: kangguan123@gmail.com

Abstract

A three-dimensional model combining the nonconservative level set method and the steady-state diffusion equation is presented for studying evolving pore structure between bundles of plain woven fabrics during chemical vapor infiltration process. Parametric studies have been conducted to determine the sensitivity of the evolution of structural properties and effective gas diffusion coefficients to the geometric parameters of the preform. Based on our results, some analytical expressions are proposed to estimate the evolution of structural properties and the tortuosity. The prediction of the presented models is well consistent with the existing data of experiments.

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