A combined model for the effective thermal conductivity of nanofluids

This paper presents a combined static and dynamic mechanisms-based model for predicting the effective thermal conductivity of nanofluids. The model includes the effects of particle size, nanolayer, Brownian motion, and particle surface chemistry and interaction potential which are the static and dynamic mechanisms responsible for the enhanced effective thermal conductivity of nanofluids. Present model shows reasonably good agreement with the experimental results of several types of nanofluids and gives better predictions compared to the existing models.     

Experimental investigation on the thermal conductivity and shear viscosity of viscoelastic-fluid-based nanofluids

Viscoelastic-fluid-based nanofluids with dispersion of copper (Cu) nanoparticles in viscoelastic surfactant solution (aqueous solution of cetyltrimethylammonium chloride/sodium salicylate) were prepared. A comparative study of thermal conductivity and viscosity between viscoelastic-fluid-based Cu nanofluids and distilled water based nanofluids was then performed experimentally. Different concentrations of viscoelastic base fluid and volume fraction of Cu nanoparticles were matched in order to check their influences on fluid’s thermal conductivity and viscosity. The experimental results show that the viscoelastic-fluid-based Cu nanofluids have a higher thermal conductivity than viscoelastic base fluid, and its thermal conductivity increases with increasing temperature and increasing particle volume fraction. Furthermore, the viscoelastic-fluid-based Cu nanofluid shows a non-Newtonian behavior in its viscosity, and the viscosity increases with the increase of Cu nanoparticle concentration and decrease of temperature.     

Enhancement of thermal conductivity with carbon nanotube for nanofluids

Thermal conductivity enhancements in ethylene glycol and synthetic engine oil in the presence of multi-walled carbon nanotubes (MWNTs) are investigated. CNT nanofluids are prepared using a two-step method. The volume concentration of CNT–ethylene glycol suspensions is below 1.0 vol.% and that of CNT–synthetic engine oil suspensions is below 2.0 vol.%. The thermal conductivities of the CNT suspensions are measured with a modified transient hot wire method. The results show that CNT–ethylene glycol suspensions have noticeably higher thermal conductivities than the ethylene glycol base fluid without CNT. The results for CNT–synthetic engine oil suspensions also exhibit the same trend. For CNT–ethylene glycol suspensions at a volume fraction of 0.01 (1 vol.%), thermal conductivity is enhanced by 12.4%. On the other hand, for CNT–synthetic engine oil suspension, thermal conductivity is enhanced by 30% at a volume fraction of 0.02 (2 vol.%). The rates of increase are, however, different for different base fluids. The CNT–synthetic engine oil suspension has a much higher enhanced thermal conductivity ratio than the CNT–ethylene glycol suspension.     

Synthesis and thermal conductivity of Cu2O nanofluids

We apply the chemical solution method to synthesize Cu₂O nanofluids: suspensions of cuprous-oxide (Cu₂O) nanoparticles in water, and experimentally study the effect of reactant molar concentration and nanofluid temperature on the thermal conductivity. Substantial conductivity enhancement up to 24% is achievable with the synthesized nanofluids. The nanoparticle shape is variable by adjusting some synthesis parameters. The thermal conductivity shows both sensitivity and nonlinearity to the reactant molar concentration and the nanofluid temperature.     

New temperature dependent thermal conductivity data for water-based nanofluids

This paper presents effective thermal conductivity measurements of alumina/water and copper oxide/water nanofluids. The effects of particle volume fraction, temperature and particle size were investigated. Readings at ambient temperature as well as over a relatively large temperature range were made for various particle volume fractions up to 9%. Results clearly show the predicted overall effect of an increase in the effective thermal conductivity with an increase in particle volume fraction and with a decrease in particle size. Furthermore, the relative increase in thermal conductivity was found to be more important at higher temperatures. Obtained results compare favorably with certain data sets and theoretical models found in current literature.     

Discussion of proposed mechanisms of thermal conductivity enhancement in nanofluids

Based upon Green–Kubo linear response theory, we use the exact expression for the heat flux vector of the base fluid plus nanoparticle system to estimate the contribution of nanoparticle Brownian motion to thermal conductivity. We find that its contribution is too small to account for abnormally high reported values. The possibility of convection caused by Brownian particles is also found to be unlikely. We have estimated the mean free path and the transition speed of phonons in nanofluid through density functional theory. We found a layer structure can form around the nanoparticles and the structure does not further induce fluid–fluid phase transition in the bulk fluid. By analyzing the compressibility of the fluid, we have also investigated the sound speed in the nanofluid. For the models of an asymmetric hard sphere mixture representing the single spherical nanoparticles and a mixture of rods and hard spheres representing aggregates, both suspended in the fluid, we found that for the very low volume fraction cases, the compressibility changes little. This shows that the speed of phonon transition does not change due to the addition of nanoparticles of either type. Our results indicate that, besides the enhancement due to the high thermal conductivity of nanoparticles themselves, fluid molecules make no evident contribution to the enhancement of thermal conductivity attributable to the presence of the nanoparticles at volume fractions less than 5%.     

Effect of aggregation and interfacial thermal resistance on thermal conductivity of nanocomposites and colloidal nanofluids

We analyzed the role of aggregation and interfacial thermal resistance on the effective thermal conductivity of nanofluids and nanocomposites. We found that the thermal conductivity of nanofluids and nanocomposites can be significantly enhanced by the aggregation of nanoparticles into clusters. The value of the thermal conductivity enhancement is determined by the cluster morphology, filler conductivity and interfacial thermal resistance. We also compared thermal conductivity enhancement due to aggregation with that associated with high-aspect ratio fillers, including fibers and plates.     

The combined analysis of phonon and electron heat transfer mechanism on thermal conductivity for nanofluids

The paper features the mathematical model representing the analytical calculation of phonon and electron heat transfer analysis of thermal conductivity for nanofluids. The mathematical model was developed on the basis of statistical nanomechanics. We have made the detailed analysis of the influence of temperature dependence on thermal conductivity for nanofluids. On this basis are taken into account the influences such as formation of nanolayer around nanoparticles, the Brown motion of solid nanoparticles and influence of diffusive-ballistic heat transport.The analytical results obtained by statistical mechanics are compared with the experimental data and they show relatively good agreement.     

A simple analytical model for calculating the effective thermal conductivity of nanofluids

A simple mathematical model for calculating the effective thermal conductivity of nanofluids has been developed based on the thermal resistance approach. The model is developed by considering both effects of a solid‐like nanolayer and convective heat transfer caused by Brownian motion which have not been considered simultaneously by most available models in the literature. In addition the correlation of Prasher and Phelan for the convective heat transfer coefficient is modified to take into account the effect of the solid‐like nanolayer. In addition a general value for n (different from the one presented by Tillman and Hill) is introduced to modify the thickness of the solid‐like nanolayer. The latter is done by considering both conduction and convection heat transfer mechanisms. Comparisons with previously published experimental results and other mathematical models show that the presented model could well predict a nanofluids effective thermal conductivity as a function of the nanoparticles mean diameter, volume fraction, and temperature for different kinds of nanofluids. © 2010 Wiley Periodicals, Inc. Heat Trans Asian Res; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/htj.20290     

Modeling the effective thermal conductivity of nanofluids using full factorial design analysis

In this paper, a full factorial design analysis is proposed for predicting nanofluid thermal conductivity ratio (TCR) as well as determining the effects of critical factors and their interactions. A statistical design of experiment approach with three variables (volume fraction, temperature, and nanoparticle diameter) at two levels is carried out. Three types of oxide‐water nanofluids (Al₂O₃‐water, CuO‐water, and TiO₂‐water) are used to evaluate the effectiveness of the proposed mathematical model. The significance and adequacy of the regression model were evaluated by the analysis of variance. The predicted model has a root mean square error equals to 0.0074, R² = 0.99, and P < .0013, thus showing good results compared to a set of experimental data as well as other mathematical model results. The results illustrate that the TCR of metallic oxide nano?uids increases with temperature and nanoparticles volume fraction but decreases when nanoparticle size intensi?es. Furthermore, it is found that the nanoparticles volume fraction has a great impact on the nano?uids thermophysical properties. Finally, the obtained results confirm that the proposed model is considerably accurate and capable of predicting nano?uids thermal conductivity and that it can be used with ease as an alternative to many other models.     

The role of different parameters on the stability and thermal conductivity of carbon nanotube/water nanofluids

It is obvious that the applicability and efficiency of nanofluids (suspensions contained nanoparticles) are related to their high heat transfer coefficients, especially thermal conductivity. Many parameters affect this property including size, shape and source of nanoparticles, surfactants, power of ultrasonic, time of ultrasonication, elapsed time after ultrasonication, pH, temperature, particle concentration and surfactant concentration. Some of these parameters may have interaction effects. An accepted way for obtaining the optimized condition is based on the design of experiments and statistical analysis. In this paper we investigate the stability and thermal conductivity of carbon nanotube (CNT)/water nanofluids and propose the optimum condition for the production and application of nanofluids. It has been shown that the significant factors on the thermal conductivity and stability are not precisely similar to one another.     

Prediction of thermal conductivity of various nanofluids using artificial neural network

This paper presents a 5-input artificial neural network (ANN) model for the prediction of the thermal conductivity ratio of nanofluids to the base fluid (k_nf/k_f) of various nanofluids based on water and ethylene glycol (EG) and a type of transformer oil. The studied nanofluids are Al₂O₃–Water, Al–Water, TiO₂–Water, Cu–Water, Cuo–Water, ZrO₂–Water, Al₂O₃–EG, Al–EG, Cu–EG, Cuo–EG, Mg(OH)₂–EG, Al₂O₃–Oil, Al–Oil, Cuo–Oil and Cu–Oil (15 nanofluids). The network is designed and trained using a total of 776 experimental data points collected from 21 sources of experimental data available in the literature. Average diameter, volume fraction, thermal conductivity of nanoparticles and temperature as well as some appropriated numbers for both nanoparticle and base fluid are chosen as input variables of the network, whereas the corresponding value of (k_nf/k_f) is selected as its target. The developed optimal ANN model shows a reasonable agreement in predicting experimental data with mean absolute percent error of 1.26% and 1.44% and correlation coefficient of 0.995 and 0.993 for training and testing data sets, respectively.     

Unexpected sharp peak in thermal conductivity of carbon nanotubes water-based nanofluids

We report here the thermal conductivity measurement of carbon nanotubes water-based nanofluids stabilized by sodium dodecylbenzene sulfonate as a function of volume fraction and temperature. For the first time, we further show the existence of a sharp peak in thermal conductivity at very small volume fraction below theoretical percolation threshold which is temperature independent. This preliminary study evidences the potential of promising and useful nanofluid for practical applications in cooling and energy systems and heat exchangers, as viscosity penalty is obviously vanished at this concentration.     

Experimental investigation of nanofluids on sintered heat pipe thermal performance

Dilute dispersion of silver nano-particles in pure water was employed as the working fluid for conventional 1 mm wick-thickness sintered circular heat pipe. The nanofluid used in present study is an aqueous solution of 10 and 35 nm diameter silver nano-particles.The experiment was performed to measure the temperature distribution and compare the heat pipe temperature difference using nanofluid and DI-water. The tested nano-particle concentrations ranged from 1, 10 and 100 mg/l. The condenser section of the heat pipe was attached to a heat sink that was cooled by water supplied from a constant temperature bath maintained at 40 °C.At a same charge volume, the measured nanofluids filled heat pipe temperature distribution demonstrated that the temperature difference decreased 0.56–0.65 °C compared to DI-water at an input power of 30–50 W. In addition, the nanofluid as working medium in heat pipe can up to 70 W and is higher than pure water about 20 W.     

Effective thermal conductivity of wet particle assemblies

The effective thermal conductivity of mono- and poly-dispersed random assemblies of spherical particles and irregular crystals, both dry and partially or fully saturated by wetting and non-wetting liquids, has been determined computationally by numerical solution of the Fourier’s law on 3-D reconstructed media and experimentally by the transient hot wire method. The effect of spatial distribution and volume fractions of the vapour, liquid, and solid phases on effective thermal conductivity was systematically investigated. A power-law correlation for estimating the effective conductivity, valid over a wide range of phase volume fractions and relative conductivities of components, has been proposed.     

Experimental studies on thermal conductivity of blends of ethylene glycol-water-based TiO2 nanofluids

A set of three nanofluids of different blends were prepared with ethylene glycol–water and TiO₂ nanoparticles and are characterized for thermal conductivity as a function of temperature and volume concentration of nanoparticles. The measurements were taken in the temperature range from 30 °C to 70 °C, which happens to be most widely used range of temperature for many cooling applications in heat transfer equipment. Nanofluids were prepared by dispersing the nanoparticles in base fluids such as (1) water, (2) ethylene glycol plus water in the ratio of 40%:60% and 3) ethylene glycol plus water in the ratio of 50%:50% by weight. Based on the experimental results, it is observed that the thermal conductivity of TiO₂ nanofluids, considered in the present investigation, increases with increase in percentage of volume concentration of TiO₂ and also with temperature. Current experimental investigation presents valuable data on the measured thermal conductivity of TiO₂ nanofluids for very low volume concentrations from 0.2% to 1.0% of nanoparticles in the temperature range of 30 °C–70 °C.     

The effect of alumina/water nanofluid particle size on thermal conductivity

This study examines the effect of particle size, temperature, and weight fraction on the thermal conductivity ratio of alumina(Al₂O₃)/water nanofluids. A Al₂O₃/water nanofluid produced by the direct synthesis method served as the experimental sample, and nanoparticles, each of a different nominal diameter (20, 50, and 100 nm), were dispersed into four different concentrations (0.5, 1.0, 1.5, and 2.0 wt%). This experiment measured the thermal conductivity of nanofluids with different particle sizes, weight fractions, and working temperatures (10, 30, 50 °C). The results showed a correlation between high thermal conductivity ratios and enhanced sensitivity, and small nanoparticle size and higher temperature. This research utilized experimental data to construct a new empirical equation, taking the nanoparticle size, temperature, and lower weight fraction of the nanofluid into consideration. Comparing the regression results with the experimental values, the margin of error was within ?3.5% to +2.7%. The proposed empirical equation showed reasonably good agreement with our experimental results.