A generalized correlation for predicting thermal conductivity of filled composite with high filler content. (July 2021)

• Record Type: Journal Article
• Authors: Wang, Xiaojian; Niu, Xiaohu; Kang, Wensheng; Wang, Xiaoxue; Wang, Liangbi
• Is Part Of: International communications in heat and mass transfer. Volume 126(2021)
• Publication Date: 2021-07
• Subjects: Filled composite -- Thermal conductivity -- High volume content -- Filler contact -- Interface thermal resistance; Heat -- Transmission -- Periodicals; Mass transfer -- Periodicals; Chaleur -- Transmission -- Périodiques; Transfert de masse -- Périodiques; Heat -- Transmission; Mass transfer; Periodicals; 621.4022
• DOI: 10.1016/j.icheatmasstransfer.2021.105454
• Languages: English
• ISSNs: 0735-1933

Abstract

Abstract: The heat conductive materials with high filler content are widely used in various heat-dissipating fields. However, previous theoretical models and empirical correlations cannot accurately predict the thermal conductivity of composite with high filler content. In this study, the effects of filler contact, interface thermal resistance, thermal conductivity ratio and filler content are investigated. The results indicate that the effect of the thermal conductivity ratio on the thermal conductivity of composite is related to the filler content. The difference that caused by thermal conductivity ratio is increased rapidly with filler content. When the filler content is greater than 40%, the effects of thermal conductivity ratio cannot be ignored even the thermal conductivity ratio greater than 100. The filler content of 40% is the percolation threshold of composite that has filler contact. The effect of interfacial thermal resistance decrease rapidly with interfacial thermal resistance. A generalized correlation is derived to predict the effective thermal conductivity of filled composite with high filler content. This correlation is not only used widely (0 ≤ V f ≤ 60%), but also has higher consistency with experimental values than other theoretical models and empirical correlations.