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.     

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.     

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.     

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.     

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.     

Effect of Clusters on Thermal Conductivity in Nanofluids

We propose a new model for the effective thermal conductivities of nanottuids, which is derived from the fact that nanoparticles and clusters coexist in the fluids. The effects of the compactness and the perfectness of the contact between nanoparticles in clusters on the effective thermal conductivity of nanofluids are analysed. The proposed model indicates that the effective thermal conductivity of nanofluids decreases with the increasing concentration of clusters. The model predictions are compared with and are in good agreement with the available experimental data.     

Experimental investigation into the convective heat transfer and system-level effects of Al2O3-propanol nanofluid

It has been speculated that the application of nanofluids in real systems could lead to smaller, more compact heat exchangers and reductions in material cost. However, few studies have been conducted which have carefully measured the thermo-physical properties and thermal performance of these fluids as well as examine the system-level effects of using these fluids in traditional cooling systems. In this study, dilute suspensions of 10 nm aluminum oxide nanoparticles in propanol (0.5, 1, and 3 wt%) were investigated. Changes in density, specific heat, and thermal conductivity with particle concentration were measured and found to be linear, whereas changes in viscosity were nonlinear and increased sharply with particle loading. Nanofluid heat transfer performance data were generally commensurate with that measured for the baseline. For the 1 wt% concentration, a small but significant enhancement in the heat transfer coefficient was recorded for 1800 < Re < 2800, which is attributed to an earlier transition to turbulent flow. In the case of high particle loading (i.e. 3 wt%), the thermal performance was observed to deteriorate with respect to the baseline case. Discoloration of the fluid was also observed after being cycled at high flow rates and increased temperature.     

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%.     

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.     

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.     

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.     

Heat transfer enhancement by application of nano-powder

In this investigation, laminar flow heat transfer enhancement in circular tube utilizing different nanofluids including Al₂O₃ (20 nm), CuO (50 nm), and Cu (25 nm) nanoparticles in water was studied. Constant wall temperature was used as thermal boundary condition. The results indicate enhancement of heat transfer with increasing nanoparticle concentrations, but an optimum concentration for each nanofluid suspension can be found. Based on the experimental results, metallic nanoparticles show better enhancement of heat transfer coefficient in comparison with oxide particles. The promotions of heat transfer due to utilizing nanoparticles are higher than the theoretical correlation prediction.     

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.     

Effect of Nanoparticles on Thermal Properties Enhancement in Different Oils – A Review

The development of stable dispersion of nanoparticles in different oils is gaining momentum for close circuit applications as most of the mineral oils used are not very good thermal conductors. The enhancement of thermal conductivity with optimum enhancement of viscosity of oil with nanoparticles poses a serious challenge for use of such fluids in cooling. Transformer oil, mineral oil, silicon oil, hydrocarbon fuels, biodiesel, and some organic solutions have been used as the base fluids for studying the effect of nanoparticles for improving thermal efficiency. Innovative heat transfer fluids are produced by suspending metallic or nonmetallic nanometer-sized solid particles. Although a large number of sources are available on water-based nanofluids, the number of such reports on oil-based nanofluids is rather limited. The aim of this article is to summarize recent developments on the preparation methods of nanofluids based on oil, its stability, thermal conductivity enhancement, nanoparticle effect on viscosity, heat transfer characteristics, breakdown voltage, dielectric properties, and applications of such nanofluids.     

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.     

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.     

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.     

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.     

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.