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.     

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.     

Predicting the thermal conductivity enhancement of nanofluids using computational intelligence

Nanofluids, composed of nanoparticles in base liquids, have drawn increasing attention in heat transfer applications due to their anomalously increased thermal conductivity. Pertinent parameters, including the base liquid thermal conductivity, particle thermal conductivity, particle size, particle volume fraction, and temperature, have been shown to have significant but complex effects on thermal performance of nanofluids, which is commonly characterized by the thermal conductivity enhancement, E%. In this work, machine learning is used to develop the Gaussian process regression model to find statistical correlations between E% and aforementioned physical parameters among various types of nanofluids. Nearly 300 nanofluid samples, dispersions of metal and ceramic nanoparticles in water, ethylene glycol, and transformer oil, are explored for this purpose. The modeling approach demonstrates a high degree of accuracy and stability, contributing to efficient and low-cost estimations of E%.     

Investigation of thermal transport in colloidal silica dispersions (nanofluids)

Thermal conductivity enhancement in colloidal silica dispersions (nanofluids) is investigated experimentally using a novel optical technique. The effects of nanoparticle size, concentration, and state of aggregation are examined. New data on well dispersed systems are compared to published data obtained using the more conventional transient hot-wire technique and good agreement was found. Experimental results are also compared with model predictions for relative thermal conductivity based on effective medium theory. For systems composed of larger diameter nanoparticles (~30 nm), good agreement was found between the measured thermal conductivity enhancement and that predicted by the classical Maxwell-Garnett model. For systems composed of smaller nanoparticles (∼10 and 20 nm), thermal conductivity enhancement was reduced by as much as 10%, presumably because interfacial thermal resistance effects become important. Measurements on two systems that were induced to form gels exhibited an increase in thermal conductivity of approximately 5% relative to the well-dispersed systems. The observed increase in thermal conductivity is larger than that predicted by a recently proposed model for aggregated nanofluids.     

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.     

Pumping power of nanofluids in a flowing system

Nanofluids have the potential to increase thermal conductivities and heat transfer coefficients compared to their base fluids. However, the addition of nanoparticles to a fluid also increases the viscosity and therefore increases the power required to pump the fluid through the system. When the benefit of the increased heat transfer is larger than the penalty of the increased pumping power, the nanofluid has the potential for commercial viability. The pumping power for nanofluids has been considered previously for flow in straight tubes. In this study, the pumping power was measured for nanofluids flowing in a complete system including straight tubing, elbows, and expansions. The objective was to determine the significance of two-phase flow effects on system performance. Two types of nanofluids were used in this study: a water-based nanofluid containing 2.0–8.0 vol% of 40-nm alumina nanoparticles, and a 50/50 ethylene glycol/water mixture-based nanofluid containing 2.2 vol% of 29-nm SiC nanoparticles. All experiments were performed in the turbulent flow region in the entire test system simulating features typically found in heat exchanger systems. Experimental results were compared to the pumping power calculated from a mathematical model of the system to evaluate the system effects. The pumping power results were also combined with the heat transfer enhancement to evaluate the viability of the two nanofluids.     

Flow boiling heat transfer of alumina nanofluids in single microchannels and the roles of nanoparticles

This study investigates flow boiling heat transfer of aqueous alumina nanofluids in single microchannels with particular focuses on the critical heat flux (CHF) and the potential dual roles played by nanoparticles, i.e., (i) modification of the heating surface through particle deposition and (ii) modification of bubble dynamics through particles suspended in the liquid phase. Low concentrations of nanofluids (0.001–0.1 vol.%) are formulated by the two-step method and the average alumina particle size is ~25 nm. Two sets of experiments are performed: (a) flow boiling of formed nanofluids in single microchannels where the effect of heating surface modification by nanoparticle deposition is apparent and (b) bubble formation in a quiescent pool of alumina nanofluids under adiabatic conditions where the role of suspended nanoparticles in the liquid phase is revealed. The flow boiling experiments reveal a modest increase in CHF by nanofluids, being higher at higher nanoparticle concentrations and higher inlet subcoolings. The bubble formation experiments show that suspended nanoparticles in the liquid phase alone can significantly affect bubble dynamics. Further discussion reveals that both roles are likely co-existent in a typical boiling system. Properly surface-promoted nanoparticles could minimize particle deposition hence little modification of the heating surface, but could still contribute to the modification in heat transfer through the second mechanism, which is potentially promising for microchannel applications.     

Rheological behaviour of ethylene glycol-titanate nanotube nanofluids

Experimental work has been performed on the rheological behaviour of ethylene glycol based nanofluids containing titanate nanotubes over 20–60 °C and a particle mass concentration of 0–8%. It is found that the nanofluids show shear-thinning behaviour particularly at particle concentrations in excess of ~2%. Temperature imposes a very strong effect on the rheological behaviour of the nanofluids with higher temperatures giving stronger shear thinning. For a given particle concentration, there exists a certain shear rate below which the viscosity increases with increasing temperature, whereas the reverse occurs above such a shear rate. The normalised high-shear viscosity with respect to the base liquid viscosity, however, is independent of temperature. Further analyses suggest that the temperature effects are due to the shear-dependence of the relative contributions to the viscosity of the Brownian diffusion and convection. The analyses also suggest that a combination of particle aggregation and particle shape effects is the mechanism for the observed high-shear rheological behaviour, which is also supported by the thermal conductivity measurements and analyses.     

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.     

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.     

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.     

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.     

Experimental investigation into the pool boiling heat transfer of aqueous based γ-alumina nanofluids

This paper is concerned about pool boiling heat transfer using nanofluids, a subject of several investigations over the past few years. The work is motivated by the controversial results reported in the literature and the potential impact of nanofluids on heat transfer intensification. Systematic experiments are carried out to formulate stable aqueous based nanofluids containing γ-alumina nanoparticles (primary particle size 10–50 nm), and to investigate their heat transfer behaviour under nucleate pool boiling conditions. The results show that alumina nanofluids can significantly enhance boiling heat transfer. The enhancement increases with increasing particle concentration and reaches ∼ ∼40% at a particle loading of 1.25% by weight. Discussion of the results suggests that the reported controversies in the thermal performance of nanofluids under the nucleate pool boiling conditions be associated with the properties and behaviour of the nanofluids and boiling surface, as well as their interactions.     

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.     

Molecular dynamics simulation of thermal conductivity of Cu–Ar nanofluid using EAM potential for Cu–Cu interactions

Mechanism of heat conduction in copper-argon nanofluids is studied by molecular dynamics simulation and the thermal conductivity was obtained using the Green–Kubo method. While the interatomic potential between argon atoms is described using the well-known Lennard–Jones (L–J) potential, a more accurate embedded atom method (EAM) potential is used in describing the interatomic interaction between copper atoms. It is found that the heat current autocorrelation function obtained using L–J potential to describe the copper-copper interatomic interaction fluctuates periodically due to periodic oscillation of the instantaneous microscopic heat fluxes. Thermal conductivities of nanofluids using EAM potentials were calculated with different volume fractions but the same nanoparticle size. The results show that thermal conductivity of nanofluids are almost a linear function of the volume fraction and slightly higher than the results predicted by the conventional effective media theory for a well-dispersed solution. A solid-like base fluid liquid layer with a thickness of 0.6 nm was found in the simulation and this layer is believed to account for the small discrepancy between the results of MD simulation and the conventional effective media theory.     

Computational analysis of factors influencing thermal conductivity of nanofluids

Numerical investigations are conducted to study the effect of factors such as particle clustering and interfacial layer thickness on thermal conductivity of nanofluids. Based on this, parameters including Kapitza radius and fractal and chemical dimension which have received little attention by previous research are rigorously investigated. The degree of thermal enhancement is analyzed for increasing aggregate size, particle concentration, interfacial thermal resistance, and fractal and chemical dimensions. This analysis is conducted for water-based nanofluids of Alumina (Al₂O₃), CuO, and Titania (TiO₂) nanoparticles where the particle concentrations are varied up to 4 vol%. Results from the numerical work are validated using available experimental data. For the case of aggregate size, particle concentration, and interfacial thermal resistance, the aspect ratio (ratio of radius of gyration of aggregate to radius of primary particle, R _g/a) is varied from 2 to 60. It was found that the enhancement decreases with interfacial layer thickness. Also the rate of decrease is more significant after a given aggregate size. For a given interfacial resistance, the enhancement is mostly sensitive to R _g/a < 20 indicated by the steep gradients of data plots. Predicted and experimental data for thermal conductivity enhancement are in good agreement. On the influence of fractal and chemical dimensions (d _l and d _f) of Alumina–water nanofluid, the R _g/a was varied from 2 to 8, d _l from 1.2 to 1.8, and d _f from 1.75 to 2.5. For a given concentration, the enhancement increased with the reduction of d _l or d _f. It appears a distinctive sensitivity of the enhancement to d _f, in particular, in the range 2–2.25, for all values of R _g/a. However, the sensitivity of d _l was largely depended on the value of R _g/a. The information gathered from this study on the sensitivity of thermal conductivity enhancement to aggregate size, particle concentration, interfacial resistance, and fractal and chemical dimensions will be useful in manufacturing highly thermally conductive nanofluids. Further research on the refine cluster evolution dynamics as a function of particle-scale properties is underway.     

Experimental investigation into the pool boiling heat transfer of aqueous based γ-alumina nanofluids

This paper is concerned about pool boiling heat transfer using nanofluids, a subject of several investigations over the past few years. The work is motivated by the controversial results reported in the literature and the potential impact of nanofluids on heat transfer intensification. Systematic experiments are carried out to formulate stable aqueous based nanofluids containing γ-alumina nanoparticles (primary particle size 10–50 nm), and to investigate their heat transfer behaviour under nucleate pool boiling conditions. The results show that alumina nanofluids can significantly enhance boiling heat transfer. The enhancement increases with increasing particle concentration and reaches ∼ ∼40% at a particle loading of 1.25% by weight. Discussion of the results suggests that the reported controversies in the thermal performance of nanofluids under the nucleate pool boiling conditions be associated with the properties and behaviour of the nanofluids and boiling surface, as well as their interactions.This revised version was published online in August 2005 with a corrected issue number.     

Size control synthesis of starch capped-gold nanoparticles

Metallic gold nanoparticles have been synthesized by the reduction of chloroaurate anions [AuCl₄]⁻ solution with hydrazine in the aqueous starch and ethylene glycol solution at room temperature and at atmospheric pressure. The characterization of synthesized gold nanoparticles by UV–vis spectroscopy, high resolution transmission electron microscopy (HRTEM), electron diffraction analysis, X-ray diffraction (XRD), and X-rays photoelectron spectroscopy (XPS) indicate that average size of pure gold nanoparticles is 3.5 nm, they are spherical in shape and are pure metallic gold. The concentration effects of [AuCl₄]⁻ anions, starch, ethylene glycol, and hydrazine, on particle size, were investigated, and the stabilization mechanism of Au nanoparticles by starch polymer molecules was also studied by FT-IR and thermogravimetric analysis (TGA). FT-IR and TGA analysis shows that hydroxyl groups of starch are responsible of capping and stabilizing gold nanoparticles. The UV–vis spectrum of these samples shows that there is blue shift in surface plasmon resonance peak with decrease in particle size due to the quantum confinement effect, a supporting evidence of formation of gold nanoparticles and this shift remains stable even after 3 months.     

Physics and mechanics of heat exchange processes in nanofluid flows

Nanofluids present a new type of dispersed fluids consisting of a carrier fluid and solid nanoparticles. Unusual properties of nanofluids, particularly high thermal conductivity, make them eminently suitable for many thermophysical applications, e.g., for cooling of equipment, designing of new heat energy transportation and production systems and so on. This requires a systematic study of heat exchange properties of nanofluids. The present paper contains the measurement results for the heat transfer coefficient of the laminar and turbulent flow of nanofluids on the basis of distilled water with silica, alumina and copper oxide particles in a minichannel with circular cross section. The maximum volume concentration of particles did not exceed 2%. The dependence of the heat transfer coefficient on the concentration and size of nanoparticles was studied. It is shown that the use of nanofluids allows a significant increase in the heat transfer coefficient as compared to that for water. However, the obtained result strongly depends on the regime of flow. The excess of the heat transfer coefficient in the laminar flow is only due to an increase in the thermal conductivity coefficient of nanofluid, while in the turbulent flow the obtained effect is due to the ratio between the viscosity and thermal conductivity of nanofluid. The viscosity and thermal conductivity of nanofluids depend on the volume concentration of nanoparticles as well as on their size and material and are not described by classical theories. That is why the literature data are diverse and contradictory; they do not actually take into account the influence of the mentioned factors (size and material of nanoparticles). It has been shown experimentally and by a molecular dynamics method that the nanofluid viscosity increases while the thermal conductivity decreases with the decreasing dispersed particle size. It is found experimentally for the first time that the nanofluid viscosity coefficient depends on the particle material. The higher is the density of particles, the higher is the thermal conductivity coefficient of nanofluid.     

Brownian dynamic simulation for the prediction of effective thermal conductivity of nanofluid

Nanofluid is a colloidal solution of nanosized solid particles in liquids. Nanofluids show anomalously high thermal conductivity in comparison to the base fluid, a fact that has drawn the interest of lots of research groups. Thermal conductivity of nanofluids depends on factors such as the nature of base fluid and nanoparticle, particle concentration, temperature of the fluid and size of the particles. Also, the nanofluids show significant change in properties such as viscosity and specific heat in comparison to the base fluid. Hence, a theoretical model becomes important in order to optimize the nanofluid dispersion (with respect to particle size, volume fraction, temperature, etc.) for its performance. As molecular dynamic simulation is computationally expensive, here the technique of Brownian dynamic simulation coupled with the Green Kubo model has been used in order to compute the thermal conductivity of nanofluids. The simulations were performed for different concentration ranging from 0.5 to 3 vol%, particle size ranging from 15 to 150 nm and temperature ranging from 290 to 320 K. The results were compared with the available experimental data, and they were found to be in close agreement. The model also brings to light important physical aspect like the role of Brownian motion in the thermal conductivity enhancement of nanofluids.