The focus of this paper was to develop a comprehensive nanofluid thermal conductivity model that can be applied to nanofluids with any number of distinct nanoparticles for a given base fluid, concentration, temperature, particle material, and particle diameter. For the first time, this model permits a direct analytical comparison between nanofluids with a different number of distinct nanoparticles. It was observed that the model’s average error was ~5.289% when compared with independent experimental data for hybrid nanofluids, which is lower than the average error of the best preexisting hybrid nanofluid model. Additionally, the effects of the operating temperature and nanoparticle concentration on the thermal conductivity and viscosity of nanofluids were investigated theoretically and experimentally. It was found that optimization of the operating conditions and characteristics of nanofluids is crucial to maximize the heat transfer coefficient in nanofluidics and microfluidics. Furthermore, the existing theoretical models to predict nanofluid thermal conductivity were discussed based on the main mechanisms of energy transfer, including Effective Medium Theory, Brownian motion, the nanolayer, aggregation, Molecular Dynamics simulations, and enhancement in hybrid nanofluids. The advantage and disadvantage of each model, as well as the level of accuracy of each model, were examined using independent experimental data.

The focus of this paper is to investigate the effects of the addition of a connector between two serial microchannels. The idea of adding connector at the inlet of microchannels to enhance the random motion of molecules or nanoparticles in low Reynolds numbers was developed in our research group for the first time. It was experimentally determined that the shape of a connector between two microchannels has a significant impact on the enhancement of the random motion of molecules or nanoparticles. Consequently, the heat transfer coefficient is improved inside the second microchannel. The connector is large enough to refresh the memory of the fluid before entering the second channel, causing a higher maximum heat transfer coefficient in the second channel. It was also observed that the heat transfer coefficient can be increased at the end of the channel when the outlet temperature is relatively high. This may be explained by the fact that as temperature increases, the fluid viscosity tends to decrease, which generally drives an increase in the local random motion of base fluid molecules and nanoparticles. This causes an increase in the microchannel heat transfer coefficient. It was found that the addition of nanoparticles significantly modified the impact of the connector on the microchannel heat transfer coefficient. In addition, the effects of changing the Reynolds number and the shape of the connector were investigated through use of computational fluid dynamics (CFD) calculations. It was found that both factors have an important impact on the variation of velocity and enhancement of random motion of molecules and consequently significantly affect the heat transfer coefficient.

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