Review Article

A Review on Nanofluids: Fabrication, Stability, and Thermophysical Properties

Table 6

Examples of different effective thermal conductivity correlations available in literatures.

ResearchersModelRemarks

Hamilton and Crosser [52]Modified Maxwell model that determines the effective thermal conductivity of nonspherical particles using a shape factor (), where and (cylindrical particles) or (spherical particles). The model is shown to take into account the particle shape, particle distribution, particle shell structure, high volume fraction, and interface contact resistance. At and by a factor of 100, the model has shown good agreement with the experimental data.

Wasp et al. [53]Spherical case of the Hamilton and Crosser model (i.e., ) with the interfacial layer thickness results having a higher thermal conductivity than the basefluid and a larger effective volume concentration of the particle-liquid layered structure which tends to improve the thermal conductivity prediction.

Yu and Choi [54]Another modified Maxwell model where all volume fraction and the combination of nanolayer and nanoparticles thermal conductivity are taken into account. The thermal conductivity of the nanolayer () needs to be less than 10 to obtain a good prediction. The used in the equation represents the ratio of the nanolayer thickness to the nanoparticle diameter.

Xuan et al. [55]Modified Maxwell model that takes into consideration the Brownian motion effect and the aggregation structure of nanoparticles clusters. The model was found to yield incorrect units in the Brownian motion as described by different researchers [56, 57]. The temperature of the fluid, density of the nanoparticles, specific heat of the nanoparticles, Boltzmann constant, viscosity of the basefluid, and the mean radius of the cluster are represented as , , , , , and , respectively, in the model.

Koo and Kleinstreuer [58]This model considers the kinetic energy of the nanoparticles that is produced from the Brownian movement in addition to the effects of particle vol%, particle size, basefluid properties, and temperature dependence. The thermal conductivity of both Brownian motion () and static dilute dispersion () was combined (). The diameter of the nanoparticle, density of the basefluid, specific heat of the basefluid, hydrodynamic interaction between particles affected fluid, and augmented temperature dependence via particle interactions are shown as , , , , and , respectively. Using experimental data of Das et al. [59] for CuO nanofluids, an empirical equation was established as . The equation is valid in the range of 27°C < < 52°C and 0.01 < < 0.04.

Xue and Xu [60]An implicit model that assumes the existing of nanoparticles shells which cover the solid particle and interact with the surrounding basefluid. The model was developed based on the data of effective thermal conductivity of CuO/H2O and CuO/EG, where , and both and represent the thermal conductivity and thickness of the interfacial shell, respectively.

Prasher et al. [61]This model uses the effect of Brownian motion as a correction factor to the Maxwell correlation to predict the enhancement in thermal conductivity caused from the nanoparticles local convection mechanism, where the is the Brownian-Reynolds number, is the matrix conductivity, is the nanoparticle Biot number, is the interfacial thermal resistance between nanoparticle and the surrounding fluid, Pr is the Prandtl number, and % for H2O based nanofluid.

Jang and Choi [62]This model takes into account the relation between the kinetic theory and Nusselt number for flow past a sphere. The symbol in the equation is a constant associated with the Kapitza resistance (0.01) per unit area, , and . For water at 27°C,  nm and  nm.