2D-C/SiC复合材料热传导系数演化模型

Evolution model of thermal conductivity for 2D-C/SiC composites

  • 摘要: 热导率是评估复合材料高温服役性能的一个重要热物理参数,其具有显著的温度和损伤相关性。针对2D-C/SiC复合材料面内热导率的表征预测,首先基于同心圆柱体剪滞模型、热阻串/并联法则以及均匀化方法给出了含基体裂纹和界面脱粘损伤纤维束的纵向热导率之封闭解,同时根据有效介质理论模型给出了含界面脱粘损伤纤维束的横向热导率之解析表达式。其次,考虑平纹编织陶瓷基复合材料的细观结构及其制备工艺特点,采用理想化平纹织物单胞模型,并基于热阻网格法给出了该类材料宏观等效热导率的分析预测模型。最后,基于2D-C/SiC复合材料的实验数据对理论模型进行了验证,并分析讨论了热导率的主要影响因素。参数分析表明,纤维体积分数、孔洞含量、织物的几何结构参数、基体开裂和界面脱粘等因素均会影响2D-C/SiC复合材料的热导率;验证结果表明,本模型具有合理性和准确性,其预测值与实验数据吻合良好。

     

    Abstract: Thermal conductivity is an important thermophysical parameter for evaluating the high-temperature service performance of composites, with significant temperature and damage dependence. This paper aims at characterization and prediction of thermal conductivity of 2D-C/SiC composites. Firstly, the closed solution of the longitudinal thermal conductivity of fiber bundle with matrix cracks and interface debonding damage is presented based on concentric cylinder shear-lag model, thermal resistance series/parallel law and homogenization method. Meanwhile, the analytical equation of the transverse thermal conductivity of fiber bundle with debonded interface is provided according to the effective medium theory. Subsequently, an idealized single-cell model was established with consideration of the microstructure of plain-woven ceramic matrix composites and the characteristics of their preparation techniques, and an analytical prediction model for the macroscopic equivalent thermal conductivity of the composites is developed by the thermal resistance grid method. Finally, the theoretical model is validated based on the in-plane experimental data of 2D-C/SiC, and the main influencing factors of the thermal conductivity were analyzed and discussed. The parametric analysis shows that the fiber volume fraction, pore content, geometrical structural parameters of the fabric, matrix crack spacing and interfacial debonding rate all affect the thermal conductivity of 2D-C/SiC composites. The verification results show that the present model is reasonable and accurate, and its predicted values are in good agreement with the experimental data.

     

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