Influence of pH on Nanofluids' Viscosity and Thermal Conductivity

Aiming at the dispersion stability of nanopartieles regarded as the guide of heat transfer enhancement, we investigate the viscosity and the thermal conductivity of Cu and Al2O3 nanoparticles in water under different pH values. The results show that there exists an optimal pH value for the lowest viscosity and the highest thermal conductivity, and that at the optimal pH value the nanofluids containing a small amount of nanoparticles have noticeably higher thermal conductivity than that of the base fluid without nanoparticles. For the two nanofluids the enhancements of thermal conductivity are observed up to 13% （Al2O3-water） or 15% （Cu-water） at 0.4 wt%, respectively. Therefore, adjusting the pH values is suggested to improve the stability and the thermal conductivity for practical applications of nanofluid.     

Novel nanofluids based on mesoporous silica for enhanced heat transfer

Nanofluids, which are liquids with engineered nanometer-sized particles suspensions, have drawn remarkable attraction from the researchers because of their enormous potential to enhance the efficiency in heat-transfer fluids. In the present study, water-based calcined mesoporous silica nanofluids were prepared and characterized. The commercial mesoporous silica (MPSiO₂) nanoparticles were dispersed in deionized water by means of pH adjustment and ultrasonic agitation. MPSiO₂ nanoparticles were observed to have an average particle size of 350 ± 100 nm by SEM analysis. The concentration of MPSiO₂ was varied between 1 and 6 wt%. The physicochemical properties of nanofluids were characterized using various techniques, such as particle size analyzer, zeta-potential meter, TEM, and FT-IR. The thermal conductivity was measured by Transient Plane Source (TPS) method, and nanofluids showed a higher thermal conductivity than the base liquid for all the tested concentrations.     

Influence of the particle size distribution on the thermal conductivity of nanofluids

In a previous study, we have obtained an equation to predict the thermal conductivity of nanofluids containing nanoparticles with conductive interface. The model is maximal particle packing dependent. In this study, the maximal packing is obtained as a function of the particle size distribution, which is the Gamma distribution. The thermal conductivity enhancement depends on the averaged particle size. Discussion concerning the influence of the suspension pH on the particle packing is made. The proposed model is evaluated using number of sets from the published experimental data to the thermal conductivity enhancement for different nanofluids.     

Dispersion behavior and thermal conductivity characteristics of Al2O3–H2O nanofluids

Nanofluid is a kind of new engineering material consisting of solid nanoparticles with sizes typically of 1–100 nm suspended in base fluids. In this study, Al₂O₃–H₂O nanofluids were synthesized, their dispersion behaviors and thermal conductivity in water were investigated under different pH values and different sodium dodecylbenzenesulfonate (SDBS) concentration. The sedimentation kinetics was determined by examining the absorbency of particle in solution. The zeta potential and particle size of the particles were measured and the Derjaguin–Landau–Verwey–Overbeek (DLVO) theory was used to calculate attractive and repulsive potentials. The thermal conductivity was measured by a hot disk thermal constants analyser. The results showed that the stability and thermal conductivity enhancements of Al₂O₃–H₂O nanofluids are highly dependent on pH values and different SDBS dispersant concentration of nano-suspensions, with an optimal pH value and SDBS concentration for the best dispersion behavior and the highest thermal conductivity. The absolute value of zeta potential and the absorbency of nano-Al₂O₃ suspensions with SDBS dispersant are higher at pH 8.0. The calculated DLVO interparticle interaction potentials verified the experimental results of the pH effect on the stability behavior. The Al₂O₃–H₂O nanofluids with an ounce of Al₂O₃ have noticeably higher thermal conductivity than the base fluid without nanoparticles, for Al₂O₃ nanoparticles at a weight fraction of 0.0015 (0.15 wt%), thermal conductivity was enhanced by up to 10.1%.     

Particle agglomeration and properties of nanofluids

The present study demonstrates the importance of actual agglomerated particle size in the nanofluid and its effect on the fluid properties. The current work deals with 5 to 100 nm nanoparticles dispersed in fluids that resulted in 200 to 800 nm agglomerates. Particle size distributions for a range of nanofluids are measured by dynamic light scattering (DLS). Wet scanning electron microscopy method is used to visualize agglomerated particles in the dispersed state and to confirm particle size measurements by DLS. Our results show that a combination of base fluid chemistry and nanoparticle type is very important to create stable nanofluids. Several nanofluids resulted in stable state without any stabilizers, but in the long term had agglomerations of 250 % over a 2 month period. The effects of agglomeration on the thermal and rheological properties are presented for several types of nanoparticle and base fluid chemistries. Despite using nanodiamond particles with high thermal conductivity and a very sensitive laser flash thermal conductivity measurement technique, no anomalous increases of thermal conductivity was measured. The thermal conductivity increases of nanofluid with the particle concentration are as those predicted by Maxwell and Bruggeman models. The level of agglomeration of nanoparticles hardly influenced the thermal conductivity of the nanofluid. The viscosity of nanofluids increased strongly as the concentration of particle is increased; it displays shear thinning and is a strong function of the level of agglomeration. The viscosity increase is significantly above of that predicted by the Einstein model even for very small concentration of nanoparticles.     

Theory for anisotropic thermal conductivity of magnetic nanofluids

Effective thermal conductivity tensor for magnetic nanofluids containing magnetizable nanoparticles suspended in a base liquid is theoretically investigated with a two-step homogenization method. First, we adopt differential effective medium theory to determine the equivalent thermal conductivity of magnetizable nanoparticle chains. Second, we generalize self-consistent anisotropic effective medium theory to study the effective thermal conductivity tensors of magnetic nanofluids. Numerical results show that the aspect ratio of chain-like aggregated clusters plays an important role in enhancement of anisotropic thermal conductivity. In addition, our theoretical results on the elements of thermal conductivity parallel to the fields Kez and perpendicular to the fields Kex are in good agreement with experimental data. Furthermore, we predict the nonmonotonic dependence of effective thermal conductivity on magnetic field strength, in accordance with experimental reports.     

Application of SAXS to the study of particle-size-dependent thermal conductivity in silica nanofluids

Knowledge of the size and distribution of nanoparticles in solution is critical to understanding the observed enhancements in thermal conductivity and heat transfer of nanofluids. We have applied small-angle X-ray scattering (SAXS) to the characterization of SiO₂ nanoparticles (10–30 nm) uniformly dispersed in a water-based fluid using the Advanced Photon Source at Argonne National Laboratory. Size distributions for the suspended nanoparticles were derived by fitting experimental data to an established model. Thermal conductivity of the SiO₂ nanofluids was also measured, and the relation between the average particle size and the thermal conductivity enhancement was established. The experimental data contradict models based on fluid interfacial layers or Brownian motion but support the concept of thermal resistance at the liquid–particle interface.     

Effect of Brownian motion on flow and heat transfer of nanofluids over a backward-facing step with and without adiabatic square cylinder

A mathematical model to predict large enhancement of thermal conductivity of nanofluids by considering the Brownian motion is proposed. The effect of the Brownian motion on the flow and heat transfer characteristics is examined. The computations were done for various types of nanoparticles such as CuO, Al₂O₃, and ZnO dispersed in a base fluid (water), volume fraction of nanoparticles ? in the range of 1 % to 6 % at a fixed Reynolds number Re = 450 and nanoparticle diameter d_np = 30 nm. Our results demonstrate that Brownian motion could be an important factor that enhances the thermal conductivity of nanofluids. Nanofluid of Al₂O₃ is observed to have the highest Nusselt number Nu among other nanofluids types, while nanofluid of ZnO nanoparticles has the lowest Nu. Effects of the square cylinder on heat transfer characteristics are significant with considering Brownian motion. Enhancement in the maximum value of Nu of 29 % and 26 % are obtained at the lower and the upper walls of the channel, respectively, by considering the Brownian effects, with square cylinder, compared with that in the case without considering the Brownian motion. On the other hand, results show a marked improvement in heat transfer compared to the base fluid, this improvement is more pronounced on the upper wall for higher ?.     

Investigation on characteristics of thermal conductivity enhancement of nanofluids

It has been shown that a nanofluid consisting of nanoparticles dispersed in base fluid has much higher effective thermal conductivity than pure fluid. In this study, four kinds of nanofluids such as multiwalled carbon nanotube (MWCNT) in water, CuO in water, SiO₂ in water, and CuO in ethylene glycol, are produced. Their thermal conductivities are measured by a transient hot-wire method. The thermal conductivity enhancement of water-based MWCNT nanofluid is increased up to 11.3% at a volume fraction of 0.01. The measured thermal conductivities of MWCNT nanofluids are higher than those calculated with Hamilton–Crosser model due to neglecting solid–liquid interaction at the interface. The results show that the thermal conductivity enhancement of nanofluids depends on the thermal conductivities of both particles and the base fluid.     

The thermal conductivity of alumina nanofluids in water, ethylene glycol, and ethylene glycol + water mixtures

We present new data on the thermal conductivity of nanofluids consisting of alumina nanoparticles dispersed in water, ethylene glycol, and ethylene glycol + water mixtures. We also demonstrate that our previously published model is able to describe the temperature, particle size, and particle volume fraction dependence of these nanofluids without any adjustable parameters, irrespective of the base fluid used (water, ethylene glycol, or water + ethylene glycol mixtures). Furthermore, we demonstrate how the model may be used to check the consistency of literature data on all alumina nanofluids.     

Preparation and heat transfer properties of nanoparticle-in-transformer oil dispersions as advanced energy-efficient coolants

Three kinds of nanofluids are prepared by dispersing Al₂O₃ and AlN nanoparticles-in-transformer oil. The thermal conductivity of the nanoparticle–oil mixtures increases with particle volume fraction and thermal conductivity of the solid particle itself. The AlN nanoparticles at a volume fraction of 0.5% can increase the thermal conductivity of the transformer oil by 8% and the overall heat transfer coefficient by 20%. From the natural convection test using a prototype transformer, the cooling effect of Al₂O₃/AlN-oil nanofluids on the heating element and oil itself is confirmed. However, the excess quantity of surfactant has a harmful effect on viscosity, thermal property, chemical stability, and thus it is strongly recommended to control the addition of the surfactant with great care.     

Fabrication, characterization and measurement of thermal conductivity of Fe3O4 nanofluids

Magnetite Fe₃O₄ nanoparticles were synthesized by a co-precipitation method at different pH values. The products were characterized by X-ray diffraction, Fourier transform infrared spectroscopy, and transmission electronic microscopy. Their magnetic properties were evaluated on a vibrating sample magnetometer. The results show that the shape of the particles is cubic and they are superparamagnetic at room temperature. Magnetic nanofluids were prepared by dispersing the Fe₃O₄ nanoparticles in water as a base fluid in the presence of tetramethyl ammonium hydroxide as a dispersant. The thermal conductivity of the nanofluids was measured as a function of volume fraction and temperature. The results show that the thermal conductivity ratio of the nanofluids increases with increase in temperature and volume fraction. The highest enhancement of thermal conductivity was 11.5% in the nanofluid of 3 vol% of nanoparticles at 40 °C. The experimental results were also compared with the theoretical models.     

Modeling of thermal conductivity of nanofluids by modifying Maxwell’s equation using cell model approach

Nanofluid is an innovative heat transfer fluid with superior potential for enhancing the heat transfer performance of conventional fluids. Though many attempts have been made to investigate the abnormal high thermal conductivity of nanofluids, the existing models cannot precisely predict the same. An attempt has been made to develop a model for predicting the thermal conductivity of different types of nanofluids. The model presented here is derived based on the fact that thermal conductivity of nanofluids depends on thermal conductivity of particle and fluid as well as micro-convective heat transfer due to Brownian motion of nanoparticles. Novelty of the article lies in giving a unique equation which predicts thermal conductivity of nanofluids for different concentrations and particle sizes which also correctly predicts the trends observed in experimental data over a wide range of particle sizes, temperatures, and particle concentrations.     

The effect of particle size on the thermal conductivity of alumina nanofluids

We present new data for the thermal conductivity enhancement in seven nanofluids containing 8–282 nm diameter alumina nanoparticles in water or ethylene glycol. Our results show that the thermal conductivity enhancement in these nanofluids decreases as the particle size decreases below about 50 nm. This finding is consistent with a decrease in the thermal conductivity of alumina nanoparticles with decreasing particle size, which can be attributed to phonon scattering at the solid–liquid interface. The limiting value of the enhancement for nanofluids containing large particles is greater than that predicted by the Maxwell equation, but is predicted well by the volume fraction weighted geometric mean of the bulk thermal conductivities of the solid and liquid. This observation was used to develop a simple relationship for the thermal conductivity of alumina nanofluids in both water and ethylene glycol.     

Effective thermal conductivity of condensed polymeric nanofluids (nanosolids) controlled by diffusion and interfacial scattering

Thermal properties of polymeric nanosolids, obtained by condensing the corresponding nanofluids, are investigated using photothermal techniques. The heat transport properties of two sets of polyvinyl alcohol (PVA) based nanosolids, TiO₂/PVA and Cu/PVA, prepared by condensing the respective nanofluids, which are prepared by dispersing nanoparticles of TiO₂ and metallic copper in liquid PVA, are reported. Two photothermal techniques, the photoacoustic and the photopyroelectric techniques, have been employed for measuring thermal diffusivity, thermal conductivity and specific heat capacity of these nanosolids. The experimental results indicate that thermal conduction in these polymer composites is controlled by heat diffusion through the embedded particles and interfacial scattering at matrix–particle boundaries. These two mechanisms are combined to arrive at an expression for their effective thermal conductivity. Analysis of the results reveals the possibility to tune the thermal conductivity of such nanosolids over a wide range using the right types of nanoparticles and right concentration.     

Heat transfer enhancement of laminar nanofluids flow in a triangular duct using vortex generator

In this work, two dimensional laminar flow of different nanofluids flow inside a triangular duct with the existence of vortex generator is numerically investigated. The governing equations of mass, momentum and energy were solved using the finite volume method (FVM). The effects of type of the nanoparticles, particle concentrations, and Reynolds number on the heat transfer coefficient and pressure drop of nanofluids are examined. Reynolds number is ranged from 100 to 800. A constant surface temperature is assumed to be the thermal condition for the upper and lower heated walls. In the present work, three nanofluids are examined which are Al₂O₃, CuO and SiO₂ suspended in the base fluid of ethylene glycol with nanoparticles concentrations ranged from 1 to 6%. The results show that for the case of SiO₂–EG, at ? = 6% and Re = 800, it is found that the average Nusselt number is about 50.0% higher than the case of Re = 100.     

Dispersion stability and thermal conductivity of propylene glycol-based nanofluids

The dispersion stability and thermal conductivity of propylene glycol-based nanofluids containing Al₂O₃ and TiO₂ nanoparticles were studied in the temperature range of 20–80 °C. Nanofluids with different concentrations of nanoparticles were formulated by the two-step method and no dispersant was used. In contrast to the common belief, the average particle size of nanofluids was observed to decrease with increasing temperature, and nanofluids showed an excellent stability over the temperature range of interest. Thermal conductivity enhancement for both studied nanofluids was a nonlinear function of concentration and was temperature independent. Theoretical analyses were also performed using existing models compared with experimental results. The model based on the aggregation theory appears to be the best.     

A thermal conductivity model for nanofluids including effect of the temperature-dependent interfacial layer

The interfacial layer of nanoparticles has been recently shown to have an effect on the thermal conductivity of nanofluids. There is, however, still no thermal conductivity model that includes the effects of temperature and nanoparticle size variations on the thickness and consequently on the thermal conductivity of the interfacial layer. In the present work, the stationary model developed by Leong et al. (J Nanopart Res 8:245–254, 2006) is initially modified to include the thermal dispersion effect due to the Brownian motion of nanoparticles. This model is called the ‘Leong et al.’s dynamic model’. However, the Leong et al.’s dynamic model over-predicts the thermal conductivity of nanofluids in the case of the flowing fluid. This suggests that the enhancement in the thermal conductivity of the flowing nanofluids due to the increase in temperature does not come from the thermal dispersion effect. It is more likely that the enhancement in heat transfer of the flowing nanofluids comes from the temperature-dependent interfacial layer effect. Therefore, the Leong et al.’s stationary model is again modified to include the effect of temperature variation on the thermal conductivity of the interfacial layer for different sizes of nanoparticles. This present model is then evaluated and compared with the other thermal conductivity models for the turbulent convective heat transfer in nanofluids along a uniformly heated tube. The results show that the present model is more general than the other models in the sense that it can predict both the temperature and the volume fraction dependence of the thermal conductivity of nanofluids for both non-flowing and flowing fluids. Also, it is found to be more accurate than the other models due to the inclusion of the effect of the temperature-dependent interfacial layer. In conclusion, the present model can accurately predict the changes in thermal conductivity of nanofluids due to the changes in volume fraction and temperature for various nanoparticle sizes.     

Investigation of thermal conductivity and rheological properties of vegetable oil based hybrid nanofluids containing Cu–Zn hybrid nanoparticles

Vegetable oils (Ground nut) are being investigated to serve as a possible substitute for non-biodegradable mineral oils, which are currently being used as metal-cutting fluids in machining processes. In this study, thermophysical properties of hybrid nanofluids (vegetable oil) to be used as metalworking cutting fluids are investigated. In-situ synthesis of copper (Cu) and zinc (Zn) combined hybrid particles is performed by mechanical alloying with compositions of 50:50, 75:25, and 25:75 by weight. Characterizations of the synthesized powder were carried out using X-ray diffraction, a particle size analyzer, FE-SEM, and TEM. Hybrid nanofluids with all the three combinations of hybrid nanoparticles were prepared by dispersing them into a base fluid (vegetable oil). The thermophysical properties, such as thermal conductivity and viscosity, were studied for various volume concentrations and at a range of temperatures. Experimental results have shown enhancement in thermal conductivity in all cases and also an increase in viscosity. The enhancement in viscosity is similar in all three combinations, as the particle size and shape are almost identical. The enhancement in thermal conductivity is higher in Cu–Zn (50:50), resulting in better enhancement in thermal conductivity due to the Brownian motion of the particles and higher thermal conductivity of the nanoparticles incorporated.     

Comparison between Experimental and Theoretical Thermal Conductivity of Nanofluids Containing Multi-walled Carbon Nanotubes Decorated with TiO2 Nanoparticles

This article reports the thermal conductivity modeling of nanofluids containing decorated multi-walled carbon nanotubes with TiO₂ nanoparticles. TiO₂ nanoparticles and decorated multi-walled carbon nanotubes are synthesized with different amounts of TiO₂ nanoparticles. The experimental results show that the measured thermal conductivities of TiO₂ nanofluids and multi-walled carbon nanotube nanofluids are higher than the predicted values by theoretical models. The comparison results of multi-walled carbon nanotube nanofluids and multi-walled carbon nanotube–TiO₂ nanofluids reveal that the predicted values by the Xue model are closer to the measured values. In addition, the results show that the thermal conductivity of nanofluids containing multi-walled carbon nanotube–TiO₂ increases with respect to TiO₂ content of hybrid.