There are numerous studies on understanding the mechanisms of thermal conductivity enhancement.
The most accepted mechanisms are Brownian motion of nanoparticles, clustering of nanoparticles,
nano-layering of the liquid at the liquid-nanoparticle interface, and ballistic transport[13][14]. Most of
the studies focus on the discussion of the roles of the Brownian motion of the nanoparticles,
molecular-scale layering at liquid/particle interface (nano-layer) and nanoparticle clustering. These are
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Fig 2-20 Effect of particle material type on ethylene glycol based nanofluids: (a) low concentration, (b) high concentration. [114]
Brownian Motion
The effect of particle Brownian motion is often neglected due to the large sized particles. As particle
size reduces, particularly when particles approach the nanometer scale, the particle Brownian motion (a)
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and its effect on the surrounding liquids could become significant. The contribution of the Brownian
motion enhance thermal conduction could occur in two ways. First, the movement of the nanoparticles
transfers heat via convection., Second, the motion of particles induces fluid motion and hence heat
transfer around individual nanoparticles. The first one has been shown theoretically to be
negligible[117]. The second one has also been shown to have a minor effect on the thermal
conductivity by theoretical analysis[118].
Keblinski et al.[119]performed molecular dynamics (MD) simulations to calculate the thermal
conductivity of a nanofluid using simplified intermolecular potential model. Their results suggested
insignificant effect of nanoparticle Brownian motion on thermal conductivity enhancement. This is
consistent with the ratio of thermal diffusion to the Brownian diffusion would indicate. Keblinski et al.
also indicated that the Brownian motion may have an effect on the formation of particle cluster, which
could improve the thermal conductivity of naonfluids.
Nano-layer
The nano-layer refers to a solid-like liquid layer at the interface between the solid nanoparticles and the
surrounding base liquids[120]. An interfacial thermal resistance is known to be present at such
interfaces, which is termed as the Kapitza resistance[121]. Although liquid molecules close to a solid
surface are easier to form a layered solid-like structure, little is known about the thermal properties of
such a nano-layer and interaction between this layer and the base fluid and the solid. According to Yu
et al.[122], the layered molecules are in an intermediate physical state between a solid and a base fluid.
Therefore, the solid-like nano-layer of liquid molecules would be expected to lead to a higher thermal
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thermal bridge between a solid nanoparticle and a base fluid, leading to one of the explanation of the
experimentally observed thermal conductivity enhancement.
Although the nanolayer effect is thought by some researchers to play an important role in the thermal
conductivity enhancement of nanofluids, experiments and simulations have shown that the thickness of
such a layer is only in the order of a few atomic distances (0.5 to 1 nm)[123]. This length scale is
smaller than the mean free path of phonons, and hence is not expected to give a dominant effect rather
than enhancement.
Clustering
As discussed earlier, nanoparticles are prone to aggregation due to van der Waals forces. This
aggregated structure could act as a local percolation mechanism, leading to local enhancement of the
effective thermal conductivity of nanofluids.
Clearly, a nanofluid consisting of all nanoparticle clusters would give a high extent of thermal
conductivity enhancement, which is difficult to realize in practice, particularly when the fluid is under
shear and the cluster can be destroyed. However, local clustering is possible and they have been
experimentally observed. These clusters are more thoroughly conductive than the nanofluid made of
well dispersed nanoparticles. As a result, the volume fraction of the highly conductive phase is larger
than the actual solid volume fraction and may significantly increase the thermal conductivity.
Wang et al.[124] proposed a new model for the thermal conductivity of nanofluids based on the
effective medium approximation and the fractal theory for accounting for the effect of nanoparticle
clusters. They considered that size effect and the nano-layer in the model. Although their model agrees
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Karthikeyan et al[125]. experimentally studied the effect of nanoparticle clustering on the thermal
conductivity of CuO nanoparticles based aqueous nanofluid. They showed that the cluster size had a
significant effect on the thermal conductivity of the nanofluids, which decreases with time due to the
clustering of CuO nanoparticles. Furthermore, they indicated that the finer the particle size with
monodispersity, the larger the enhancement in the thermal conductivity of nanofluids. They also noted
that the nanoparticle clustering may exert a negative effect on heat transfer enhancement when the
clusters start ot settle in the fluid.
Prasher et al[126], by using the effective medium theory, showed that the thermal conductivity of
nanofluids can be significantly enhanced by the aggregation of nanoparticles into clusters. They
claimed that the observed thermal conductivity of nanofluids can be explained by aggregation kinetics.
Their predictions using the effective medium theory were in excellent agreement with detailed
numerical calculations on model nanofluids involving fractal clusters and showed the importance of
cluster morphology on thermal conductivity enhancements.
Although a lot of work have been done on the development of prediction models for thermal
conductivity of nanofluids, most of the models are only valid under their own experimental condition
and they are in most cases in disagreement with other literature data.
2.4 Viscosity of nano suspensions
Viscosity is an important fluid property that affects pressure drop and hence pumping power and
convective heat transfer of a fluid flows. There have been numerous studies on the area and they are
summarized in the following according to the effects of particle shape, particle size, volume fraction
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