Thermal conductivity of Al2O3/water nanofluids

A considerable number of studies can be found on the thermal conductivity of nanofluids in which Al₂O₃ nanoparticles are used as additives. In the present study, the aim is to measure the thermal conductivity of very narrow Al₂O₃ nanoparticles with the size of 5 nm suspended in water. The thermal conductivity of nanofluids with concentrations up to 5 % is measured in a temperature range between 26 and 55 °C. Using the experimental data, a correlation is presented as a function of the temperature and volume fraction of nanoparticles. Finally, a sensitivity analysis is performed to assess the sensitivity of thermal conductivity of nanofluids to increase the particle loading at different temperatures. The sensitivity analysis reveals that at a given concentration, the sensitivity of thermal conductivity to particle loading increases when the temperature increases.     

Microwave synthesis of silver nanofluids with polyvinylpyrrolidone (PVP) and their transport properties

Microwave synthesis has been applied to prepare stable silver nanofluids in ethanol by reduction of AgNO₃ with polyvinylpyrrolidone (PVP), used as stabilizing agent, having Ag concentrations of 1% by volume. The nanofluids were characterized by UV-vis spectroscopy, Fourier transform infrared, energy-dispersive X-ray spectroscopy, and transmission electron microscopy and systematically investigated for refractive index, electrical and thermal conductivity, and viscosity for different polymer concentrations. The size of nanoparticles was found to be in the range of 30–60 nm for two different salt-to-PVP ratios. For higher concentration of polymer in nanofluid, nanoparticles were 30 nm in size showing increase in thermal conductivity but a decrease in viscosity and refractive index, which is due to the polymer structure around nanoparticles. Thermal conductivity measurements of nanofluids show substantial increment in the thermal conductivity of nanofluid relative to the base fluid and nonlinear enhancement over the 283–323 K temperature range. Rheology of nanofluids was studied at room temperature showing effect of polymer on viscosity and confirming the Newtonian behavior of nanofluid.     

Thermal conductivity modeling of MgO/EG nanofluids using experimental data and artificial neural network

The application of nanofluids in energy systems is developing day by day. Before using a nanofluid in an energy system, it is necessary to measure the properties of nanofluids. In this paper, first the results of experiments on the thermal conductivity of MgO/ethylene glycol (EG) nanofluids in a temperature range of 25–55 °C and volume concentrations up to 5 % are presented. Different sizes of MgO nanoparticles are selected to disperse in EG, including 20, 40, 50, and 60 nm. Based on the results, an empirical correlation is presented as a function of temperature, volume fraction, and nanoparticle size. Next, the model of thermal conductivity enhancement in terms of volume fraction, particle size, and temperature was developed via neural network based on the measured data. It is observed that neural network can be used as a powerful tool to predict the thermal conductivity of nanofluids.     

Ionic liquid-based stable nanofluids containing gold nanoparticles

A one-phase and/or two-phase method were used to prepare the stable ionic liquid-based nanofluids containing same volume fraction but different sizes or surface states of gold nanoparticles (Au NPs) and their thermal conductivities were investigated in more detail. Five significant experiment parameters, i.e. temperature, dispersion condition, particle size and surface state, and viscosity of base liquid, were evaluated to supply experimental explanations for heat transport mechanisms. The conspicuously temperature-dependent and greatly enhanced thermal conductivity under high temperatures verify that Brownian motion should be one key effect factor in the heat transport processes of ionic liquid-based gold nanofluids. While the positive influences of proper aggregation and the optimized particle size on their thermal conductivity enhancements under some specific conditions demonstrate that clustering may be another critical effect factor in heat transport processes. Moreover, the remarkable difference of the thermal conductivity enhancements of the nanofluids containing Au NPs with different surface states could be attributed to the surface state which has a strong correlation with not only Brownian motion but also clustering. Whilst the close relationship between their thermal conductivity enhancements and the viscosity of base liquid further indicate Brownian motion must occupy the leading position among various influencing factors. Finally, a promisingly synergistic effect of Brownian motion and clustering based on experimental clues and theoretical analyses was first proposed, justifying different mechanisms are sure related. The results may shed lights on comprehensive understanding of heat transport mechanisms in nanofluids.     

Determination of nanolayer thickness and effective thermal conductivity of nanofluids

Nanofluids having high thermal conductivity enhancement relative to conventional pure fluids are fluids engineered by suspending solid nanoparticles into base fluids. In the present study, calculating the Van der Waals interaction energy between a nanoparticle and an ordered liquid nanolayer around it, the nanolayer thickness was determined, the average velocity of the Brownian motion of nanoparticles in a fluid was estimated, and by taking into account both the aggregation of nanoparticles and the presence of a nanolayer a new thermal conductivity model for nanofluids was proposed. It has been shown that the nanolayer thickness in nanofluids is independent on the radius of nanoparticles when the radius of the nanoparticles is much greater than the nanolayer thickness and determines by the specific interaction of the given liquid and solid nanoparticle through the Hamaker constant, the surface tension and the wetting angle. It was proved that the frequency of heat exchange by fluid molecules is two orders of magnitude higher than the frequency of heat transfer by nanoparticles, so that the contribution due to the Brownian motion of nanoparticles in the thermal conductivity of nanofluids can be neglected. The predictions of the proposed model of thermal conductivity were compared with the experimental data and a good correlation was achieved.     

Synthesis of colloidal dispersions of NiAl, ZnAl, NiCr, ZnCr, NiFe, and MgFe hydrotalcite-like nanoparticles

Nanometric silver dispersed water based nanofluids have been prepared by a single-step chemical process. The crystallite/particle size, morphology and purity of nanoparticles were characterized using standard microscopic, diffraction and spectroscopic techniques. The thermal conductivity enhancement (with respect to the base fluid) has been determined as a function of concentration and size of silver particles using transient hot-wire technique. The accurate fitting of the experimental data of thermal conductivity enhancement with a theoretical model developed by Patel et al. predicts that high specific surface area of the particles, layering at the liquid-solid interface and Brownian motion may be responsible for enhancement.     

Thermal conductivity and specific heat capacity measurements of Al2O3 nanofluids

Thermal conductivities and specific heat capacities of nanoparticles of Al₂O₃ dispersed in water and ethylene glycol as a function of the particle volume fraction and at temperatures between 298 and 338 K were measured. The steady-state coaxial cylinders method, using a C80D microcalorimeter (Setaram, France) equipped with special calorimetric vessels, was used for the thermal conductivities measurements. The heat capacities were measured with a Micro DSC II microcalorimeter (Setaram, France) with batch cells designed in our laboratory and the “scanning or continuous method.” The Hamilton–Crosser model properly accounts for the thermal conductivity of the studied nanofluids. Assuming that the nanoparticles and the base fluid are in thermal equilibrium, the experimental specific heat capacities of nanofluids are correctly justified.     

Effect of Reynolds number on heat transfer and flow for multi-oxide nanofluids using numerical simulation

A numerical simulation model for laminar flow of nanofluids in a pipe with constant heat flux at the wall has been built to study the effect of Reynolds number on heat transfer and pressure loss. The investigation was performed for metallic oxide and multi-oxide nanoparticles suspended in water. The thermal conductivity and dynamic viscosity were measured for a range of temperature (10–60 °C) and volume fraction of multi-oxide nanofluid. Comparison of the thermal conductivity for monocular oxide and multi-oxide nanofluids reveals a new way to control the enhancement in nanofluid conductivity. The numerical results obtained were compared with existing well-established correlations. The predictions of the Nusselt number for nanofluids are in agreement with the Shah correlation, and the deviation in the results is less than 1 %. It is found that the pressure loss increases with the Reynolds number, nanoparticle density, and volume fraction for multi-oxide nanoparticles. However, the flow demonstrates enhancement in heat transfer which improves with increasing Reynolds number of the flow.     

The noble effect of aging on the thermal conductivity of modified CNTs-ethylene glycol nanofluids

In order to improve the heat transfer process by using nanofluids, different nanoparticles and base fluids have been studied. In this work, stability and effect of aging and temperature on the thermal conductivity of CNTs-ethylene glycol (EG) nanofluids were investigated. Chemical functionalisation was used to oxidise the surface of CNTs. The functionalised CNTs were used to prepare the nanofluids by a two-step method. The stability of nanofluids was measured by UV-vis spectroscopy and the results showed that the nanofluids had a good stability over several days. Immediately after nanofluid preparation not too much increase was observed for thermal conductivity but the nanofluid aging had a great influence on the improvement of the thermal conductivity, as after 65 days, about 50% increase was observed. The increase has been attributed to forming an ordered nanolayer of EG molecules around the CNTs. Also no significant temperature dependence of thermal conductivity was observed up to 50°C possibly due to the lack of temperature dependence of CNTs Brownian motions.     

Preparation of Silver Nanofluids with High Electrical Conductivity

Silver nanofluids have been prepared by single-step chemical reduction method starting with silver nitrate metal precursor. Electrical conductivity of nanofluids has been investigated, as it has largely been overlooked despite immense technological importance. Extremely low yield nanofluid (0.013 wt%) is found to give high electrical conductivity attributed to smaller size monodisperse nanoparticles obtained (16.3 nm). Increased precursor concentration has lead to high yield and high electrical conductivity. Larger particle sizes obtained are optimized by reducing the yield at high concentration, as well as by dilution. The stability is exceptionally higher than the reported results for copper nanofluids.     

The thermal conductivity of alumina nanoparticles dispersed in ethylene glycol

The thermal conductivities of several nanofluids (dispersions of alumina nanoparticles in ethylene glycol) were measured at temperatures ranging from 298 to 411 K using a liquid metal transient hot wire apparatus. Our measurements span the widest range of temperatures that have been investigated to date for any nanofluid. A maximum in the thermal conductivity versus temperature behavior was observed at all mass fractions of nanoparticles, closely following the behavior of the base fluid (ethylene glycol). Our results confirm that additional temperature contributions inherent in Brownian motion models are not necessary to describe the temperature dependence of the thermal conductivity of nanofluids. Our results also show that the effect of mass or volume fraction of nanoparticles on the thermal conductivity of nanofluids can be correlated using the Hamilton and Crosser or Yu and Choi models with one adjustable parameter (the shape factor in the Hamilton and Crosser model, or the ordered liquid layer thickness in the Yu and Choi model).     

The thermal conductivities enhancement of mono ethylene glycol and paraffin fluids by adding β-SiC nanoparticles

Changes in the thermal conductivities of paraffin and mono ethylene glycol (MEG) as a function of β-SiC nanoparticle concentration and size was studied. An enhancement in the effective thermal conductivity was found for both fluids (i.e., both paraffin and MEG) upon the addition of nanoparticles. Although an enhancement in thermal conductivity was found, the degree of enhancement depended on the nanoparticle concentration in a complex way. An increase in particle-to-particle interactions is thought to be the cause of the enhancement. However, the enhancement became muted at higher particle concentrations compared to lower ones. This phenomenon can be related to nanoparticles interactions. An improvement in the thermal conductivities for both fluids was also found as the nanoparticle size shrank. It is believed that the larger Brownian motion for smaller particles causes more particle-to-particle interactions, which, in turn, improves the thermal conductivity. The role that the base-fluid plays in the enhancement is complex. Lower fluid viscosities are believed to contribute to greater enhancement, but a second effect, the interaction of the fluid with the nanoparticle surface, can be even more important. Nanoparticle-liquid suspensions generate a shell of organized liquid molecules on the particle surface. These organized molecules more efficiently transmit energy, via phonons, to the bulk of the fluid. The efficient energy transmission results in enhanced thermal conductivity. The experimentally measured thermal conductivities of the suspensions were compared to a variety of models. None of the models proved to adequately predict the thermal conductivities of the nanoparticle suspensions.     

A simple economic and heat transfer analysis of the nanoparticles use

In this paper, a review of the impact of most common nanoparticles on the Leidenfrost temperature T _Leid in heat transfer applications is delivered. Moreover, a simple economic analysis of the nanoparticles use is proposed. When coolant is distilled water, T _Leid can range 150–220 °C; occasionally, it can even amount to over 400 °C. When the base liquid is modified by additives, considerable changes in the character of heat transfer are observed. Out of five nanofluids under consideration in this study, the best thermal effect (up to 50%) is obtained when Al₂O₃ nanofluid having particle sizes ~39 nm and volume concentration of 0.1% is used. Conversely, the fluid containing TiO₂ particles, 20–70 nm in size, seems to be the worst of the analysed fluid, giving only 7% heat transfer enhancement in comparison with water. However, when TiO₂ nanoparticles are far smaller, very good thermal effects are obtained (23–25%). In a majority of the cases analysed, the temperature that marks the onset of film boiling is inversely proportional to concentrations of nanoparticles, which is relevant from the economic standpoint.     

A proposed model to predict thermal conductivity ratio of Al2O3/EG nanofluid by applying least squares support vector machine (LSSVM) and genetic algorithm as a connectionist approach

In this study, a model is proposed by applying the least squares support vector machine (LSSVM). In addition, genetic algorithm is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. In addition to temperature and concentration of nanoparticles, the parameters which are used in most of the modeling procedures for thermal conductivity, the effect of particle size is considered. By considering the size of nanoparticles as one of the input variables, a more comprehensive model is obtained which is applicable for wider ranges of influential factor on the thermal conductivity of the nanofluid. The coefficient of determination (R²) for the introduced model is equal to 0.9902, and the mean squared error is 8.64 × 10^?4 for the thermal conductivity ratio of Al₂O₃/EG.

     

Effect of copper nanoparticle aggregation on the thermal conductivity of nanofluids

The thermal conductivity of water and glycerol is investigated via the transient hot wire method by adding small amounts of copper nanoparticles to solutions. At a 0.2% copper nanoparticle concentration, the thermal conductivity coefficient rises to 25% for the Cu + glycerol system, and to 35% for Cu + water system. A mechanism and mathematical model for describing the nanoparticle aggregation effect on the thermal properties of nanofluids are proposed, based on an analysis of the accumulated experimental data. It is shown that the enhancement of nanofluid thermal conductivity at low nanoparticle concentrations is directly proportional to their volume fraction and thermal conductivity coefficient, and (in accordance with the literature data) is inversely proportional to the radius and the aggregation ratio. The proposed model describes the existing experimental data quite well. The results from this work can be applied to the rapid cooling of electronic components, in the power engineering for ensuring the rapid and effective transfer of thermal energy in a nuclear reactor, and in the oil industry for thermal stimulation.     

Thermophysical properties of interfacial layer in nanofluids

Although recent experiments have revealed that nanofluids have superior thermal conductivities to base fluids, the inherent physics are not fully understood. In this study, an interfacial layer, competing with Brownian motion as a corresponding mechanism, is conceptually connected with the surface-charge-induced electrical double layer. By applying colloidal science, the first explicit equations for the thickness and thermal conductivity of the layer are obtained. A fractal model including the new concept of the layer is developed. The model predictions are compared with experimental data for effects of pH, temperature, volume fraction, and primary particle size of CuO-water nanofluids.     

Thermal analysis for heat transfer enhancement in electroosmosis-modulated peristaltic transport of Sutterby nanofluids in a microfluidic vessel

Viscosity plays a crucial role in the flow and heat transfer process of nanofluids. To effectively calculate and predict the changing characteristics of nanofluids viscosity, this study presents a theoretical model combining the static interface layer and dynamic Brownian motion mechanisms of spherical nanoparticles for water-based Newtonian nanofluids. The model describes the reasonable dependences of nanofluids viscosity on physical properties of nanoparticles (density, volume fraction, size) and base fluid (temperature, viscosity, density). Taking four kinds of typical water-based Newtonian nanofluids containing spherical oxide nanoparticles (Al₂O₃, CuO, SiO₂ and TiO₂) as examples, the prediction performance of different viscosity models is analyzed in detail. From the comparison studies, it is demonstrated that the new viscosity model developed in this paper can exhibit better prediction performance than many well-known theoretical models and empirical correlations. Not only do the predicted results of model agree well with the experimental data from various studies, but also the effects of different factors are reflected effectively.

     

A model of nanofluids effective thermal conductivity based on dimensionless groups

Thermal conductivity is an important parameter in the field of nanofluid heat transfer. This article presents a novel model for the prediction of the effective thermal conductivity of nanofluids based on dimensionless groups. The model expresses the thermal conductivity of a nanofluid as a function of the thermal conductivity of the solid and liquid, their volume fractions, particle size and interfacial shell properties. According to this model, thermal conductivity changes nonlinearly with nanoparticle loading. The results are in good agreement with the experimental data of alumina-water and alumina-ethylene glycol based nanofluids.     

Viscosity and thermal conductivity of MgO–EG nanofluids

Nanofluids are a group of novel engineering materials that are increasingly being used, particularly in the processes of heat exchange. One of the most promising materials in this group is magnesium oxide–ethylene glycol (MgO–EG) nanofluid. The literature informs that this material is characterized by an significant increase in thermal conductivity with low dynamic viscosity increase. The aim of this paper is to provide experimental data on the dynamic viscosity and thermal conductivity of nanofluids containing MgO nanoparticles with 20 nm average size and ethylene glycol as base fluid. To determine dynamic viscosity and thermal conductivity of samples, a HAAKE MARS 2 rheometer (Thermo Electron Corporation, Karlsruhe, Germany) and KD2 Pro Thermal Properties Analyzer (Decagon Devices Inc., Pullman, Washington, USA) were used. Additionally, a comparison of the experimental results and the predictions of theoretical models was presented. It was presented that the vast majority of theoretical models does not describe in a correct way both viscosity and thermal conductivity. It was also shown that the enhancement of this basic physical properties might be described with good result with second degree polynomials. Finally, evaluation of the heat transfer performance was presented.     

Synthesis and thermo-physical properties of water-based novel Ag/ZnO hybrid nanofluids

The comparative study on the thermo-physical properties of water-based ZnO nanofluids and Ag/ZnO hybrid nanofluids is reported in the present study. The outer surface of ZnO nanoparticles was modified with a thin coating of Ag nanoparticles by a wet chemical method for improved stability and heat transfer properties. The ZnO and Ag/ZnO nanofluids were prepared with varying volume concentration (??=?0.02–0.1%). The synthesized nanoparticles and nanofluids were characterized with different characterization methods viz., scanning electron microscopy, X-ray diffraction, dynamic light scattering, thermal conductivity measurement, and viscosity measurement. Results show that thermal conductivity of Ag/ZnO hybrid nanofluids is found to be significantly higher compared to ZnO nanofluids. The maximum thermal conductivity an enhancement for Ag/ZnO nanofluid (??=?0.1%) is found to 20% and 28% when it compared with ZnO nanofluid (??=?0.1%) and water, respectively.