A comprehensive study on the effective thermal conductivity of random hybrid polymer composites. (January 2022)

• Record Type: Journal Article
• Authors: Yang, Mingshan; Li, Xiangyu; Yuan, Jianghong; Wen, Zefeng; Kang, Guozheng
• Is Part Of: International journal of heat and mass transfer. Volume 182(2022)
• Publication Date: 2022-01
• Subjects: Reconstruction of random media -- Lattice Boltzmann method -- Prediction model -- Thermal management; Heat -- Transmission -- Periodicals; Mass transfer -- Periodicals; Chaleur -- Transmission -- Périodiques; Transfert de masse -- Périodiques; Electronic journals; 621.4022
• DOI: 10.1016/j.ijheatmasstransfer.2021.121936
• Languages: English
• ISSNs: 0017-9310

Abstract

Highlights: Hybrid polymer composites are reconstructed by the random digital models. Systematic experimental and numerical studies for thermal conductivity are conducted. A prediction model is proposed based on the extensive numerical results. An interesting experiment is performed under the guidance of the prediction model. Abstract: Polymer composites have a wide range of applications in frontier science and technology, such as flexible electronic packaging and thermally actuated soft robotics. However, most of the theoretical and numerical researches on the thermal properties of polymer composites have been focusing on the composites with homogenous mono-fillers. In this paper, a comprehensive study on the effective thermal conductivity of the composites containing both particulate and fibrous fillers is performed through experimental, numerical and theoretical approaches. Firstly, the PDMS composites with aluminum nitride particles and/or multiwalled carbon nanotubes are prepared. Then, a series of random digital models are constructed by the simulated annealing method and monte carlo method to characterize the microstructure of such composites. And then, the lattice Boltzmann method is used to calculate the effective thermal conductivity of the random microstructure. The present numerical results are fully verified by experiments. Finally, a phenomenological prediction model is developed by the nonlinear regression method based on the systematic numerical simulations. To show the versatility of the present model, an interesting experiment on thermal management materials is designed and performed. By virtue of this model, quantitatively controlling the temperature of electronic devices may be fulfilled.