Effect of temperature on the effective thermal conductivity of n-tetradecane-based nanofluids containing copper nanoparticles

Nanofluids were prepared by dispersing Cu nanoparticles (∼20 nm) in n-tetradecane by a two-step method. The effective thermal conductivity was measured for various nanoparticle volume fractions (0.0001–0.02) and temperatures (306.22–452.66 K). The experimental data compares well with the Jang and Choi model. The thermal conductivity enhancement was lower above 391.06 K than for that between 306.22 and 360.77 K. The interfacial thermal resistance increased with increasing temperature. The effective thermal conductivity enhancement was greater than that obtained with a more viscous fluid as the base media at 452.66 K because of nanoconvection induced by nanoparticle Brownian motion at high temperature.     

Synthesis and thermal conductivity of microfluidic copper nanofluids

A microfluidic chemical solution method is developed for the synthesizing Cu nanofluids.The method replaces batch-based macroreactors in the conventional chemical solution method by continuous-flow microfluidic microreactors,thereby enabling the synthesis of nanofluids with various microstructures.The Cu nanofluids synthesized by this technology show a better stability,remaining stable even after more than 100h standing.The measured thermal conductivity shows that the presence of nanoparticles can either upgrade or downgrade fluid conductivity,a phenomenon predicted by the recent thermal-wave theory of nanofluids.     

A prediction model for the effective thermal conductivity of nanofluids considering agglomeration and the radial distribution function of nanoparticles

Conventional heat transfer fluids usually have low thermal conductivity, limiting their efficiency in many applications. Many experiments have shown that adding nanosize solid particles to conventional fluids can greatly enhance their thermal conductivity. To explain this anomalous phenomenon, many theoretical investigations have been conducted in recent years. Some of this research has indicated that the particle agglomeration effect that commonly occurs in nanofluids should play an important role in such enhancement of the thermal conductivity, while some have shown that the enhancement of the effective thermal conductivity might be accounted for by the structure of nanofluids, which can be described using the radial distribution function of particles. However, theoretical predictions from these studies are not in very good agreement with experimental results. This paper proposes a prediction model for the effective thermal conductivity of nanofluids, considering both the agglomeration effect and the radial distribution function of nanoparticles. The resulting theoretical predictions for several sets of nanofluids are highly consistent with experimental data.     

Effect of surface charge state on the thermal conductivity of nanofluids

Thermal conductivity enhancement of nanofluids is very attractive to thermal and heat transfer engineering, however its mechanism is not clear yet. In this study, it is proposed that the surface charge state of nanoparticles is to explain the thermal conductivity enhancement of nanofluids. By comparing to the previous reported results, it is shown that the interparticle interaction due to the surface charge state is the most important factor to increase of thermal conductivity of nanofluids.     

Role of interfacial layer and clustering on the effective thermal conductivity of CuO-gear oil nanofluids

Copper oxide nanoparticles (∼40 nm) are dispersed in gear oil (IBP Haulic-68) at different volume fractions (0.005-0.025) with oleic acid added as a surfactant to stabilize the system. Prepared nanofluids are characterized by Fourier Transform Infrared spectroscopy (FTIR) and Dynamic light scattering (DLS) measurements. DLS data confirmed the presence of agglomerated nanoparticles in the prepared nanofluids. Thermal conductivity measurements are performed both as a function of CuO volume fraction and temperature between 5 and 80 °C. An enhancement in thermal conductivity at 30 °C of 10.4% with 0.025 volume fraction of CuO nanoparticle loading is observed. Measured volume fraction dependence of the thermal conductivity enhancement at room temperature is predicted fairly well considering contributions from both nanolayer at the solid-liquid interface and particle agglomeration in the suspension, as visualized by Feng et al.     

Predicting thermal conductivity of liquid suspensions of nanoparticles (nanofluids) based on rheology

A methodology is proposed for predicting the effective thermal conductivity of dilute suspensions of nanoparticles (nanofluids) based on rheology.The methodology uses the rheological data to infer microstructures of nanoparticles quantitatively,which is then incorporated into the conventional Hamilton-Crosser equation to predict the effective thermal conductivity of nanofluids.The methodology is experimentally validated using four types of nanofluids made of titania nanoparticles and titanate nanotubes dispersed in water and ethylene glycol.And the modified Hamilton-Crosser equation successfully predicted the effective thermal conductivity of the nanofluids.     

Shear-dependent thermal conductivity of alumina nanofluids

The thermal conductivities (k) of aqueous alumina nanofluids of various particle shapes (rods, bricks, blades) were measured at the dynamic state for the first time. The dynamic k was measured under torsional flows by using a homemade parallel-plate system. The homemade system was validated by numerical simulations and experiments with homogeneous liquids. All the nanofluids tested here showed decreasing k with increasing shear rate. This newly observed phenomenon was named ‘shear-reducing thermal conductivity.’ The dispersion characteristics were characterized by the dynamic light scattering (DLS) and rheological techniques. From the rheological properties of nanofluids it was inferred that the alumina nanofluids should have network structures and these microstructures should be destroyed or deformed by shearing. But not all the networks were destroyed by shearing. The DLS data revealed that some nanoparticles in nanofluids should exist as individual particles. The effective medium theory cannot explain the shear-reducing characteristics of nanofluids at the dynamic state. The rheological data imply that the heat percolation through the network may not be the sole reason for heat transfer enhancement in nanofluids. It is suggested that the Brownian motion of the primary particles cannot be excluded in heat conduction through nanofluids.     

Dependence of particle size on the effective thermal diffusivity and conductivity of nanofluids: role of base fluid properties

Effect of nanoparticle size on effective thermal diffusivity and conductivity of polymeric and water based nanofluids are investigated following thermal wave interference technique. Two sets of nanofluids, prepared by dispersing TiO₂ nanoparticles, with average sizes in the range 5–100?nm, in polyvinyl alcohol and water show opposing particle size dependences. Variations are explained invoking effective medium theory, including size of nanoparticles, molecular weight of base fluid and effects associated with it.     

Stability and thermal conductivity of water-based carbon nanotube nanofluids

Water-based nanofluids were prepared with multi walled carbon nanotubes(MWCNTs) of different lengths in concentrations of 0.1,0.25 and 0.5 vol%.To improve their dispersibility,pristine MWCNTs were functionalized and cut into small lengths by reflux in an oxidizing mixture of 3:1 sulfuric and nitric acids.The initial length of the carbon nanotubes(CNTs;10-15 μm) was reduced to 203,171 and 134 nm after1,2 and 4h of reflux,respectively.Surface modification and the reduced length of the CNTs,improved the stability of the nanofluids with no significant sedimentation observed after 80 days.Furthermore,the thermal conductivities of nanofluids prepared using refluxed CNTs,were higher than that of the pristine CNTs.The thermal conductivity also increased with the nanofluid temperature.The nanofluid prepared with 1 h refluxed CNTs had the highest thermal conductivity.The enhanced thermal conductivity and stability of the nanofluids was attributed to the decreased length of CNTs.     

The direct effect of interfacial nanolayers on thermal conductivity of nanofluids

One of the most important features of nanofluids is their thermal conductivity. In this article, a new model for thermal conductivity is proposed based on the combination of a statistical model and thermal convection caused by Brownian motion of nanoparticles with considering the effect of interfacial nanolayers among nanoparticles and base fluids. This model is compared with Al₂O₃ in deionized water and CuO in deionized water (based nanofluids of spherical particles) using a number of theoretical and experimental thermal conductivity models, after that the experimental results have been made available in the open literature. In this model, an interfacial nanolayer is influenced directly on both parts of static and dynamic effective thermal conductivity. The present model shows good agreement with the experimental result of nanofluids and gives better predictions compared to models used for nanofluids in this article. This model is purely theoretical and in order to achieve it, experimental results have no effect.     

The role of several heat transfer mechanisms on the enhancement of thermal conductivity in nanofluids

A modelling of the thermal conductivity of nanofluids based on extended irreversible thermodynamics is proposed with emphasis on the role of several coupled heat transfer mechanisms: liquid interfacial layering between nanoparticles and base fluid, particles agglomeration and Brownian motion. The relative importance of each specific mechanism on the enhancement of the effective thermal conductivity is examined. It is shown that the size of the nanoparticles and the liquid boundary layer around the particles play a determining role. For nanoparticles close to molecular range, the Brownian effect is important. At nanoparticles of the order of 1–100 nm, both agglomeration and liquid layering are influent. Agglomeration becomes the most important mechanism at nanoparticle sizes of the order of 100 nm and higher. The theoretical considerations are illustrated by three case studies: suspensions of alumina rigid spherical nanoparticles in water, ethylene glycol and a 50/50w% water/ethylene glycol mixture, respectively, good agreement with experimental data is observed.     

Measurement of temperature-dependent thermal conductivity and viscosity of TiO2-water nanofluids

Nanofluid is an innovative heat transfer fluid with superior potential for enhancing the heat transfer performance of conventional fluids. Many attempts have been made to investigate its thermal conductivity and viscosity, which are important thermophysical properties. No definitive agreements have emerged, however, about these properties. This article reports the thermal conductivity and dynamic viscosity of nanofluids experimentally. TiO₂ nanoparticles dispersed in water with volume concentration of 0.2–2 vol.% are used in the present study. A transient hot-wire apparatus is used for measuring the thermal conductivity of nanofluids whereas the Bohlin rotational rheometer (Malvern Instrument) is used to measure the viscosity of nanofluids. The data are collected for temperatures ranging from 15 °C to 35 °C. The results show that the measured viscosity and thermal conductivity of nanofluids increased as the particle concentrations increased and are higher than the values of the base liquids. Furthermore, thermal conductivity of nanofluids increased with increasing nanofluid temperatures and, conversely, the viscosity of nanofluids decreased with increasing temperature of nanofluids. Moreover, the measured thermal conductivity and viscosity of nanofluids are quite different from the predicted values from the existing correlations and the data reported by other researchers. Finally, new thermophysical correlations are proposed for predicting the thermal conductivity and viscosity of nanofluids.     

Investigation of viscosity and thermal conductivity of alumina nanofluids with addition of SDBS

The present study focused on thermal conductivity and viscosity of alumina nanoparticles, at low volume concentrations of 0.01–1.0 % dispersed in the mixture of ethylene glycol and water (mass ratio, 60:40). Sodium dodeobcylbenzene sulfonate (SDBS) was applied for better dispersion and stability of alumina nanoparticles and study of its influence on both thermal conductivity and viscosity. The thermal conductivity established polynomial enhancement pattern with increase of volume concentration up to 0.1 % while linear enhancement was obtained at higher concentrations. In addition, thermal conductivity was enhanced with the rise of temperature. However, the augmentation was negligible compared to that obtained with increase of volume concentration. In contrast, viscosity data showed remarkable reduction with increase of temperature. Meanwhile, viscosity of nanofluids enhanced with loading of alumina nanoparticles. Thermal conductivity and viscosity measurements showed higher values over theoretical predictions. Results showed SDBS at different concentrations has distinct influence on thermal conductivity and viscosity of nanofluid.     

Thermocapillary drift velocity and effective thermal conductivity of a suspension

The thermocapillary drift of a homogeneous suspension of spherical drops with the constant properties of a carrier fluid and the fluid inside the drops is considered. Several formulas are obtained for the drift velocity and the effective thermal conductivity of the suspension.     

Model for predicting effective thermal conductivity of composites with aligned continuous fibers of graded conductivity

Governing differential equations in both transverse and longitudinal directions for predicting the effective thermal conductivities of composites with aligned, graded continuous fibers are derived. It is shown that the effective conductivities of composites with graded fibers are predicted by solving the equations. The results by the present approach are applicable to both dilute and non-dilute cases without additional procedures unlike other approaches. The results are compared with those in the literature, and the applicability of the present approach is justified. A solution by the present approach is obtained analytically or numerically as long as thermal conductivity profile of fibers is given.