Thermal conductivity prediction of nanoparticle packed beds by using modified Johnson-Kendall-Roberts model

TL;DR

This paper introduces a modified Johnson-Kendall-Roberts model that incorporates size effects to improve the accuracy of thermal conductivity predictions in nanoparticle packed beds, especially at low porosity.

Contribution

A modified JKR model including size effects of Young's modulus is integrated into the EMA framework for better thermal conductivity prediction of NPB.

Findings

Enhanced prediction accuracy for solid phase thermal conductivity

Particularly effective for low-porosity nanoparticle packed beds

Provides a theoretical basis explaining experimental results

Abstract

Nanoparticle packed beds (NPBs) have demonstrated the potential for thermal insulation, and further reducing thermal conductivity (k) requires a theoretical understanding of the thermal conduction in them. Till now, the theoretical models under the framework of effective medium approach (EMA) have been widely developed for the thermal conductivity (k) prediction of NPB. In these models, corresponding architecture parameters are usually evaluated by the classical Johnson-Kendall-Roberts (JKR) model. Unfortunately, the size effect is usually ignored in JKR model, resulting in the inferior ability to accurately predict the geometrical information of NPB. In this work, the modified JKR model including the size effect of Young's modulus is integrated in the EMA model for k prediction, and experimental results in [Int. J. Heat Mass Tran., 2019, 129, 28-36] was further explained. As a result, the developed model illustrates the advantages on the prediction of solid phase thermal conductivity (k_s), especially for the NPB with a low porosity. This work provides a modified JKR model to improve the accuracy of existing EMA model in the k prediction of NPB.