摘要
A method for predicting effective thermal conductivities(ETCs) of three-dimensional five-directional(3D5D) braided composites is presented. The effective thermal conductivity prediction method contains a digital image processing technology. Multiple scanning electron microscopy(SEM)images of composites are analyzed to obtain actual microstructural features. These actual microstructural features of 3D5D braided composites are introduced into representative volume element(RVE) modeling. Apart from applying actual microstructural features,compression effects between yarns are considered in the modeling of RVE,making the RVE more realistic. Therefore,the ETC prediction method establishes a representative unit cell model that better reflects the true microstructural characteristics of the 3D5D braided composites. The ETCs are predicted with the finite element method. Then thermal conductivity measurements are carried out for a 3D5D braided composite sample.By comparing the predicted ETC with the measured thermal conductivity, the whole process of the ETC prediction method is proved to be effective and accurate,where a relative error of only 2.9 % is obtained.Furthermore,the effects of microstructural features are investigated,indicating that increasing interior braiding angles and fiber fill factor can lead to higher transverse ETCs. Longitudinal ETCs decrease with increasing interior braiding angles,but increase with increasing fiber fill factor. Finally,the influence of variations of microstructure parameters observed in digital image processing are investigated. To explore the influence of variations in microstructural features on variations in predicted ETCs,the actual probability distributions of microstructural features obtained from the 3D5D braided composite sample are introduced into the ETC investigation. The results show that,compared with the interior braiding angle,variations in the fiber fill factor exhibit more significant effects on variations in ETCs.
A method for predicting effective thermal conductivities(ETCs) of three-dimensional five-directional(3D5D) braided composites is presented. The effective thermal conductivity prediction method contains a digital image processing technology. Multiple scanning electron microscopy(SEM)images of composites are analyzed to obtain actual microstructural features. These actual microstructural features of 3D5D braided composites are introduced into representative volume element(RVE) modeling. Apart from applying actual microstructural features,compression effects between yarns are considered in the modeling of RVE,making the RVE more realistic. Therefore,the ETC prediction method establishes a representative unit cell model that better reflects the true microstructural characteristics of the 3D5D braided composites. The ETCs are predicted with the finite element method. Then thermal conductivity measurements are carried out for a 3D5D braided composite sample.By comparing the predicted ETC with the measured thermal conductivity, the whole process of the ETC prediction method is proved to be effective and accurate,where a relative error of only 2.9 % is obtained.Furthermore,the effects of microstructural features are investigated,indicating that increasing interior braiding angles and fiber fill factor can lead to higher transverse ETCs. Longitudinal ETCs decrease with increasing interior braiding angles,but increase with increasing fiber fill factor. Finally,the influence of variations of microstructure parameters observed in digital image processing are investigated. To explore the influence of variations in microstructural features on variations in predicted ETCs,the actual probability distributions of microstructural features obtained from the 3D5D braided composite sample are introduced into the ETC investigation. The results show that,compared with the interior braiding angle,variations in the fiber fill factor exhibit more significant effects on variations in ETCs.
