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.     

The Specific Heat Capacity,Effective Thermal Conductivity,Density, and Viscosity of Coolants Containing Carboxylic Acid Functionalized Multi-Walled Carbon Nanotubes

Obviously, the behavior of thermophysical properties of covalently functionalized CNT-based water and -based ethylene glycol (EG) nanofluids cannot be predicted from the predicted models. We present a study of the specific heat capacity, effective thermal conductivity, density, and viscosity of coolants containing functionalized multi-walled carbon nanotubes (CNT-COOH) with carboxylic acid groups at different temperatures. After synthesizing of CNT-COOH-based water and CNT-COOH-based EG nanofluids, measurements on the prepared coolant were made at various concentrations by different experimental methods. While the thermal conductivity of both nanofluids illustrated a significant increase, the specific heat capacity of both samples showed a downward behavior with increasing temperature. Although the thermal conductivity of CNT-COOH-based water nanofluids is bigger than CNT-COOH-based EG nanofluids, CNT-COOH-based water has weaker temperature dependence than that of the CNT-COOH-based EG nanofluids. The viscosity was investigated in different shear rates and temperatures. It is noteworthy that CNT-COOH-based EG nanofluids show relatively a non-Newtonian behavior. Interestingly, specific heat capacities of both prepared nanofluids were decreased with increasing concentration. Also, the density of the CNT-COOH-based water and -based EG nanofluids increased and decreased smoothly with increasing CNT-COOH concentration and temperature, respectively.     

A new semi-analytical model for effective thermal conductivity of nanofluids

A new theoretical model for thermal conductivity of nanofluids is developed incorporating effective medium theory, interfacial layer, particle aggregation and Brownian motion-induced convection from multiple nanoparticles/aggregates. The predicated result using aggregate size, which represents the particle size in the actual condition of nanofluids, fits well with the experimental data for water-, R113- and ethylene glycol (EG)-based nanofluids. The present model also gives much better predictions compared to the existing models. A parametric analysis, particularly particle aggregation, is conducted to investigate the dependence of effective thermal conductivity of nanofluids on the properties of nanoparticles and fluid. Aggregation is the main factor responsible for thermal conductivity enhancement. The dynamic contribution of Brownian motion on thermal conductivity enhancement is surpassed by that of static mechanisms, particularly at high volume fraction. Predication also indicated that the viscosity increases faster than the thermal conductivity, causing the highly aggregated nanofluids to become unfavourable, especially for d_f = 1.8.     

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.     

Thermal conductivity and specific heat capacity measurements of CuO nanofluids

A study of thermal properties of CuO dispersed in water and ethylene glycol as a function of the particle volume fraction and at temperatures between 298 and 338 K has been performed. Thermal conductivities have been studied by the steady-state coaxial cylinders method, using a C80D microcalorimeter (Setaram, France) equipped with special calorimetric vessels. Heat capacities have been measured with a Micro DSC II microcalorimeter (Setaram, France) with batch cells designed in our laboratory and the “scanning or continuous method.” Results for thermal conductivities can be well justified using a classical model (Hamilton–Crosser), and experimental measurements of heat capacities can be justified with a model of particles in thermal equilibrium with the base fluid.     

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.     

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.     

In situ reduction of graphitic oxide by amorphization of magnesium diboride for the superior thermo-optical property based nanofluid applications

The reaction of magnesium diboride with water results in an intermediate borohydride product which leads to the simultaneous reduction of graphitic oxide (GO) and the formation of magnesium hydroxide. In this work, the thermo-optical properties of magnesium diboride modified reduced graphene oxide–based nanofluids have been explored. The study primarily focuses on the reaction mechanism of magnesium diboride and GO by using liquid exfoliation technique. Suspension after liquid exfoliation mainly consisted of a turbid supernatant and precipitate which was composed of boron-based nanosheets (CBNs) and a composite of magnesium hydroxide and reduced graphene oxide (CBNs-rGO), respectively. Nanofluids were subsequently formulated from the obtained products of the reaction. CBNs form a stable suspension in water and ethylene glycol because of its attached borohydrides and hydroxyl hydrophilic sites. CBNs nanofluids show good thermal conductivity with poor light absorption properties in the visible wavelength range. Whereas, CBNs-rGO nanofluids show ~95% attenuation in the radiation with a significant enhancement of ~30% and 20% in thermal conductivity as compared with Deionized water– and ethylene glycol–based fluids, respectively.     

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.     

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.     

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.     

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.     

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.     

Ionic-Liquid-Based Nanofluids and Their Heat-Transfer Applications: A Comprehensive Review

Due to the improved thermophysical characteristics of ionic liquids (ILs), such as their strong ionic conductivity, negligible vapor pressure, and thermal stability at high temperatures, they are being looked at viable contender for future heat transfer fluids. Additionally, the dispersing nanoparticles can further improve the thermophysical characteristics and thermal performance of ionic liquids, which is one of the emerging research interests to increase the heat transfer rates of the thermal devices. The latest investigations about the utilization of ionic liquid nanofluids as a heat transfer fluid is summarized in this work. These summaries are broken down into three types: (a) the thermophysical parameters including thermal conductivity, viscosity, density, and specific heat of ionic liquids (base fluids), (b) the thermophysical properties like thermal conductivity, viscosity, density, and viscosity of ionic liquids based nanofluids (IL nanofluids), and (iii) utilization of IL nanofluids as a heat transfer fluid in the thermal devices. The techniques for measuring the thermophysical characteristics and the synthesis of IL nanofluids are also covered. The suggestions for potential future research directions for IL nanofluids are summarized.     

Thermo-physical and stability properties of raw and functionalization of graphene nanoplatelets-based aqueous nanofluids

This research aimed to evaluate the thermal viscosity, stability, conductivity and density of coolants including PEG-functionalized graphene nanoplatelets (GNPs) and gum Arabic (GA)-treated GNPs as a base fluid at various temperatures and concentrations. The present study explores the impacts of GNPs functionalized with poly ethylene glycol (PEG) on the colloidal stability and thermophysical properties of water-based PEG-functionalized GNPs suspensions as a new generation of heat transfer fluids. To this end, PEG-functionalized GNPs as a covalent sample and GA-treated GNPs were synthesized and their colloidal stabilities were traced via UV–vis spectrometry. After functionalized, colloidal stability results indicate less sedimentation for covalent samples (less than 10%) that that of noncovalent one (almost 20%) after a 15-day period. In addition, all the thermophysical properties e.g. thermal conductivity, density and viscosity were measured experimentally. Further, it has shown that by loading PEG-functionalized GNPs in the water, the increasing rate of the density and viscosity is not significant, while water-based GA-treated GNPs nanofluids showed higher rates of increase. Interestingly, the water-based PEG-functionalized GNP nanofluids at very low concentration significantly increase the thermal conductivity in comparison with that of non-covalent nanofluid at the same concentration and temperature and defiantly water.     

A review on molten-salt-based and ionic-liquid-based nanofluids for medium-to-high temperature heat transfer

Molten-salt-based nanofluids and ionic-liquid-based nanofluids are developed for thermal storage and heat transfer at relatively high temperatures, in the past few years. Preparation and stabilization techniques are briefly introduced firstly, and then, thermal properties, e.g., specific heat, thermal conductivity and viscosity, are summarized and discussed in detail. The properties are not only affected by the characteristics of nanomaterials and base fluids, but also affected by the synthesis method, such as the sonication intensity and duration. Some of the thermophysical property data are still incomplete, especially the thermal conductivity of molten-salt-based nanofluids, and properties of ionic-liquid-based nanofluids at high temperatures. While several literature works show that the Krieger–Dougherty model can well predict the viscosity, no general models for thermal conductivity and specific heat have been developed yet for both types of nanofluids.

     

A comprehensive investigation in determination of nanofluids thermophysical properties

In recent years, research on heat transfer and related equipment has been one of the topics of interest in many different industries. The use of conventional fluids in heat transfer due to their low thermal properties has created problems in this area, so the use of nanofluids in many cases has been a solution to overcome this problem. The parameters affecting the thermophysical and thermal properties of nanofluids are temperature, concentration, size, shape, pH, surfactant and ultrasonic time, among which temperature and concentration have the greatest effect. Existing models and studies in the field of nanofluids are limited to the type of nanoparticles and base fluids and their operating range, and there is no comprehensive model for predicting thermal properties. In the present study, models and theories regarding the determination of thermal conductivity of nanofluids and other thermophysical properties have been comprehensively compiled and the mechanisms for increasing the thermal properties as well as the effective parameters and the effect of each of them on improving the properties are presented. In general, the results showed that thermal properties improve with increasing concentration and temperature. Finally, the role of nanofluids effect on thermal performance in the heat exchangers is studied and the results are summarized.     

ANN modelling and experimental investigation on effective thermal conductivity of ethylene glycol:water nanofluids

Journal of Thermal Analysis and Calorimetry - In this study, ethylene glycol (EG)–water (35:65 %v)-based nanofluids have been prepared to study enhancement in thermal conductivity....     

Thermal conductivity of dry anatase and rutile nano-powders and ethylene and propylene glycol-based TiO2 nanofluids

Thermal conductivity behaviour was studied for two TiO₂ nano-powders with different nanocrystalline structures, viz. anatase and rutile, as well as nanofluids formulated as dispersions of these two oxides up to volume concentrations of 8.5% in two different glycols, viz. ethylene and propylene glycol. Because it is known that titanium dioxide can exhibit three different crystalline structures, the dry nano-powders were analysed using X-ray Diffraction to determine the nanocrystalline structure of the powders. Two different techniques were employed in the thermal conductivity study of the materials. Dry nano-powders, with and without compaction, were analysed at room temperature by using a device based on the guarded heat flow meter method. Nanofluids and base fluids were studied with a transient hot wire technique over the temperature range from (283.15 to 343.15) K. The base fluid propylene glycol was measured by using both techniques in order to verify the good agreement between both sets of results. The experimental measurements presented in this work were compared with other literature data for TiO₂ nanofluids in order to understand the thermal conductivity enhancement as a function of nanoparticle concentration. Different theoretical or semi-theoretical approaches such as Maxwell, Peñas et al., Yu-Choi were evaluated comparing with our experimental values. A parallel model was used to predict thermal conductivities employing experimental values for dry nanopowder.     

A comparative study in the prediction of thermal conductivity enhancement of nanofluids using ANN-MLP,ANN-RBF,ANFIS, and GMDH methods

In this work, four types of data mining methods, namely adaptive neuro-fuzzy inference system, artificial neural network—multilayer perceptron algorithm (ANN-MLP), artificial neural network—radial basis function algorithm (ANN-RBF), and group method of data handling (GMDH) have been used to predict the enhancement of the relative thermal conductivity of a wide range of nanofluids with different base fluids and nanoparticles. The total number of experimental data used in this work is 483 from 18 different nanofluids. The input parameters are thermal conductivity of base fluid and nanoparticles, volume fraction percent, the average size of nanoparticles, and temperature. Although the results showed that all four models are in relatively good agreement with experimental data, the ANFIS method is the best. The average absolute relative deviations (AARD%) between the experimental data and those of obtained using ANFIS, ANN-MLP, ANN-RBF, and GMDH methods were calculated as 2.7, 2.8, 4.2, and 4.3, respectively, for the test sets and as 1.1, 2.4, 3.9, and 4.5, respectively, for the training sets. Comparison between the predictions of the proposed ANN-MLP, ANN-RBF, ANFIS, and GMDH models and those predicted by traditional models, namely Maxwell and Bruggeman models showed that much better agreements can be obtained using the four models especially ANFIS model. Accordingly, the ANFIS method can able us to predict the relative thermal conductivity of new nanofluids in different conditions with good accuracy.