A reconstruction of Hamilton-Crosser model for effective thermal conductivity of nanofluids based on particle clustering and nanolayer formation

Although many models have been proposed to estimate the effective thermal conductivity (ETC) of nanofluids, the thermal conduction mechanisms need to be further addressed to improve the prediction accuracy of ETC model. In this paper, by fully considering the effects of particle clustering, Brownian motion, Kapitza resistance and nanolayer of particle, Hamilton-Crosser model is reconstructed to establish an improved model for the ETC of nanofluids. To develop this model, the particle clustering is characterized by the particle size distribution analysis, and the thermal conductivity distribution in the nanolayer is represented as a specific function of the distance from the nanoparticle. The influences of temperature, viscosity, particle size and other factors on the ETC of nanofluids are also included in this model. The results show that the accuracy of this model can be improved as compared to those considering only several of these factors, and the maximum error is 3% against the available experimental data. With this model, the inconsistency phenomena in ETC data of nanofluids can be explained in the view of the agglomeration and Brownian motion with the system conditions.

Automated summary

By reconstructing the Hamilton-Crosser model with considerations of particle clustering and nanolayer, an improved model for the effective thermal conductivity (ETC) of nanofluids is established in this study. The model shows higher accuracy compared to previous ones and can explain the inconsistency phenomena in ETC data of nanofluids.