Thermal Conductivity Of Nanofluids Research Articles

The Stability and Thermal Conductivity of Cobalt Nano fluids in Base Liquid Water and Glycerol Mixture

The present work involves in determination of a suitable ratio of glycerol-water (GW) mixture for the preparation of nanofluid. The base liquid with 30% weight of glycerol is selected, based on the thermo-physical properties for dispersion of spherical cobalt (Co) nanoparticles of 80nm size, for a maximum concentration of 2% by weight. The stability investigation of prepared nanofluids is done by measuring Zeta potential and SEM imaging. The prepared cobalt nanofluid thermal conductivity is measured by maintaining 7.5pH as that concerned value has maximum Zeta potential. The maximum increment in thermal conductivity is found to be 38.4% for 2% nanofluid concentration is determined at a temperature of 60oC. The data obtained for the base liquid are in fine concurrence with the published data.

Predicting thermophysical properties and flow characteristics of nanofluids using intelligent methods: focusing on ANN methods

Nanofluids have attracted much attention of researchers during the past years due to its excellent properties. Albeit many theoretical and experimental examinations were conducted to evaluate the thermophysical properties of dissimilar nanofluids, researchers are not successful to find good theories for determining the viscosity and thermal conductivity of nanofluids. Although experimental approaches are more reliable compared to theoretical methods, they are usually difficult to do due to the need for specific equipment. The goal of this study is to review summaries of the most important work performed in the field of various nanofluid properties. In addition, the neural network application in predicting the nanofluid properties in different equipments has been studied. Artificial neural networks (ANNs) are one of the artifact intelligent branches that are inspired by the human brain function in identifying phenomena. They can be used to predict and model the phenomena. One of its applications is to predict and model the nanofluid thermophysical properties. This paper introduces the development of neural networks initially. Then, a summary of recent studies on the prediction modeling of nanofluid physical properties based on ANNs is reported. According to enough samples, it seems that ANN is an operational method to predict the nanofluid thermophysical properties. Finally, a general model for all nanofluids and the effect of all conditions on the nanofluid properties are proposed for future research.

Experimental investigation on the thermal performance and new correlation for thermal conductivity of aqueous copper oxide-doped MCM-41 nanofluids

In the present study, the pure MCM-41- and CuO-doped MCM-41 nanoparticles with various mass fractions of CuO were synthesized and used for the preparation of water-based nanofluids. The obtained nanoparticles were characterized using small-angle X-ray scattering, scanning electron microscopy, transmission electron microscopy and N2 adsorption/desorption analysis. The thermal conductivity of the water-based nanofluids with various mass fractions of nanoparticles including 0.1, 0.5 and 1 mass% was measured by KD2-Pro thermal analyzer. A new correlation is developed for the thermal conductivity of the nanofluid with a reasonably good accuracy (± 5%) when comparing to the experimental data. The thermal performance of these nanofluids together with hydraulic features such as friction factor and heat transfer coefficient was investigated using a mini-channel heat exchanger. The obtained results revealed that the thermal conductivity can be enhanced by 13.1% which belonged to the nanofluid with 1 mass% of CuO-doped MCM-41 nanoparticles. The maximum heat transfer coefficient enhancement was 31% and belonged to the nanofluid containing 50% CuO@MCM-41 nanoparticles at 0.5 mass%. The performance evaluation criterion (PEC) of the various nanofluids was also calculated, and it was identified that the nanoparticles with 50% CuO@MCM-41 dispersed in water have the largest PEC, 16.7% over the base fluid. The friction factor increases by adding the nanoparticles to the pure water. For example, at Re = 1200, the friction factor increases about 36.84% by using the 50%CuO@MCM-41 nanoparticles with 0.5 mass% as compared with the pure water. The friction factor decreases with increasing the Reynolds number. For example, for 50%CuO@MCM-41 and 0.5 mass%, the friction factor decreases up to 34.17% as the Reynolds number increases in the range of 400–1200.

Thermal conductivity of nanofluids: A comparison of EMD and NEMD calculations

Extensive studies on the effective thermal conductivity (ETC) of nanofluids have been conducted thus far; however, the mechanisms behind the change in ETC remain unclear. In the present study, we investigated the components of the ETC of nanofluids, based on equilibrium molecular dynamics (EMD) and non-EMD (NEMD) calculations, to elucidate more accurately the mechanisms responsible for change in ETC. Until now, the factors affecting ETC have not been revealed clearly, and not compared quantitatively by the EMD and NEMD. This study also aimed to compare the constituents of ETC quantitatively to validate the NEMD calculation as a method for determining the ETC of nanofluids. Our detailed results demonstrate that the primary factors contributing to the change in ETC are thermal transport in liquid–liquid interactions and the solid–solid interactions in nanoparticles. The ETC components related to liquid exhibit consistency between the EMD and NEMD calculations, with less than 5.0% difference. Conversely, although the components related to nanoparticles result in differences of more than 10%, these properties do not appear to have a significant impact on ETC. On the basis of the findings of this study, we are able to calculate consistent results for not only ETC but also its components, via EMD and NEMD, for a liquid system with a spherical nanoparticle.

Experimental Investigation on the Influence of Graphene Nanoplatelets Dispersion on the Thermal Conductivity of Sunflower Oil

In the past decades, considerable efforts have been made for the development of energy-efficient and eco-friendly convective heat transfer and lubricating agents because of growing energy demands, precision manufacturing, miniaturization and sustainability issues. In this study, different concentrations of graphene–sunflower oil nanofluid were prepared and their thermal conductivity was experimentally investigated and compared with the correlations of similar researches found in the literature. The morphology of graphene nanoplatelets was appraised by X-ray diffractometer (XRD) and scanning electron microscope (SEM). The results show that the thermal conductivity of nanofluid was enhanced with temperature and nanoparticles weight fraction. The nanoconvection at high temperatures, less meandering mobility of graphene nanoplatelets and high kinematic viscosity of graphene nanofluids at low temperatures were identified as the key factors for the thermal conductivity enhancement. Further, the concentration and temperature-dependent theoretical correlation were proposed for estimating the thermal conductivity of graphene nanofluids using backpropagation algorithm of artificial neural network (ANN) with the minimum margin of deviation.

Experimental Study for the Thermal Conductivity of Micro and Nano fluids by Using Copper and Titanium Oxide

In this article experimentally investigations have been carried out to study the effect of the size and type particles on the thermal conductivity of micro and nanofluids. The study investigated nanofluids and microfluids which containing copper (Cu) and titanium oxide (TiO2) as well as in the size and type of micro and nanoparticles in distilled water as base fluid with different particles size and concentrations. The experimental results emphasized the enhancement of the thermal conductivity due to the nanoparticles presence in the fluid greater than microfluids, also shown the effect of the particle size and concentration on the thermal conductivity. It has been recognized that the addition of highly conductive particles can significantly increase the thermal conductivity of heat – transfer fluids. Particles in the micro and nano – size range have attracted the most interest because of their enhanced stability against sedimentation and, as a result, reduction in potential for clogging a flow system. Furthermore the results showed that, they obtained thermal conductivities doubtlessly revealed that a size and type particle was a key factor affecting conductive heat transport in suspensions. These results show noticeable enhancement in the thermal conductivity were evaluated to be (7.66 %, 2.35 %) for the Cu, TiO2 – distilled water nanofluid while reaches to (3.23 %, 1.02 %) for the Cu, TiO2 – distilled water micro fluids at the concentration of (5 vol. %) and at the room temperature. Moreover thermal conductivity was increased for the nanofluids and microfluids which contains large particle size compared with that contains small particle size. A good agreement was found between the experimental obtained data for this paper and other results from published papers

Modelling and optimization of thermophysical properties of aqueous titania nanofluid using response surface methodology

Nanofluid is a colloidal solution of nano-sized particles which enhances the thermal performance, but their viscosity put the limitation for their effective use in many industrial applications as it adversely affects the pumping power. In the present work, response surface methodology (RSM) with three-level factorial design and desirability approach was utilized to optimize thermal conductivity and viscosity of TiO2-water nanofluid simultaneously. In this regard, the impact of temperature of nanofluid (T), the concentration of nanoparticle in base fluid (ϕ) and diameter of nanoparticle (dp) on the viscosity (ν) and thermal conductivity (k) of TiO2-water nanofluid was studied. According to the results, the predicted values were in smart agreement with the experimental data. The maximum percentage error was found to be only 1.67 and 8.23 for viscosity and thermal conductivity of nanofluid, respectively, which demonstrate the preciseness of the applied model. The most dominant factors which affect the viscosity and thermal conductivity of nanofluids were found to be the temperature, concentration and the diameter of nanoparticles. The optimized values of the temperature, concentration of nanoparticles and diameter of nanoparticle obtained according to desirability approach for multi-objective optimization were T = 60 °C, ϕ = 1.41 vol% and dp = 60 nm.

Intelligent assessment of effect of aggregation on thermal conductivity of nanofluids—Comparison by experimental data and empirical correlations

Improving the performance of energy-based systems is a key factor for potential applications of nanofluids. Nanoparticle aggregation is a complicate phenomenon that changes thermal conductivity of nanofluids and their heat transfer performances. There’s available no accurate correlation for accounting effects of aggregation on thermal conductivity of nanofluids over wide ranges of operating conditions. Therefore, we focus on designing accurate artificial intelligence (AI) approaches for prediction the effects of aggregation on thermal conductivity of different nanofluids. 1065 experimental data were collected from 36 different literatures and used for designing the AI-based approaches. Thermal conductivity of aggregated/non-aggregated nanofluids is related to temperature, nanoparticle size, aggregation size, nanoparticle concentration in base fluid, and synthesis procedure of nanofluid. Different AI-based paradigms including multilayer perceptron (MLP), radial basis function (RBF), generalized regression (GR), and cascade feedforward backpropagation (CFB) were examined and their performances were compared. Statistical analyses confirmed that the CFB neural network with eight hidden neurons outperforms all other considered models. Both computational effort and accuracy margin revealed that the Levenberg-Marquardt is the best training algorithm. This model predicted the experimental data with absolute average relative deviation (AARD) of 1.37%, mean square errors (MSE) of 4.75 × 10−4, and coefficient of determination (R2) of 0.98452.

Molecular dynamics study of thermal properties of nanofluids composed of one-dimensional (1-D) network of interconnected gold nanoparticles

In this Letter, Equilibrium Molecular Dynamics (EMD) simulations were used to investigate the nanoparticle volume fraction-dependent thermal conductivity of nanofluids composed of one-dimensional (1-D) network of interconnected gold nanoparticles (IAuNPs) within the Green–Kubo formula. Thermal conductivities of nanofluids containing spherical AuNPs were also studied for comparison. The results showed that the 1D network shape of IAuNPs improves the nanofluid thermal conductivity better than the spherical shape of AuNP. The mechanism responsible for this finding is discussed using the Mean-Square-Displacement (MSD) analysis and the Radial-Distribution-Function (RDF).

Nonlinear Radiation in Bioconvective Casson Nanofluid Flow

We investigate the impact of nonlinear thermal radiation and variable transport properties on the two-dimensional flow of an electrically conducting Casson nanofluid containing gyrotactic microorganisms along a moving wedge. In some previous studies, it has been assumed that the fluid viscosity and thermal conductivity are temperature dependent. However, this study assumes that the fluid viscosity, thermal conductivity, and the nanofluid properties, are dependent on the solute concentration. Some experimental studies have shown that the viscosity and thermal conductivity of nanofluids are strongly dependent on the volume fraction of nanoparticles rather than just the temperature. The spectral local linearization method is used to solve the conservation equations. We compare our results with those in the literature, and we discuss the convergence and accuracy of the spectral local linearization method. The impact of some parameters on the skin friction, heat and microorganisms mass transport is discussed.

Non-equilibrium natural convection in a differentially-heated nanofluid cavity partially filled with a porous medium

Purpose This study aims to numerically analyze natural convection of alumina-water nanofluid in a differentially-heated square cavity partially filled with a heat-generating porous medium. A single-phase nanofluid model with experimental correlations for the nanofluid viscosity and thermal conductivity has been considered for the description of the nanoparticles transport effect in the present study. Local thermal non-equilibrium approach for the porous layer with the Brinkman-extended Darcy model has been used. Design/methodology/approach Dimensionless governing equations formulated using stream function, vorticity and temperature have been solved by the finite difference method. The effects of the Rayleigh number, Ostrogradsky number, Nield number and nanoparticles volume fraction on nanofluid flow, heat and mass transfer have been analyzed. Findings It has been revealed that the dimensionless heat transfer coefficient at the fluid/solid matrix interface can be a very good control parameter for the convective flow and heat transfer intensity. The present results are original and new for the study of non-equilibrium natural convection in a differentially-heated nanofluid cavity partially filled with a porous medium. Originality/value The results of this paper are new and original with many practical applications of nanofluids in the modern industry.

Investigation of thermal conductivity property of plasmonic nanofluids based on gold nanorods prepared by seed-mediated growth method

In this paper, nanofluids were prepared based on gold nanorods in basic fluid, water, by single-stage chemical reduction and in different volume fractions and the used gold nanorods were synthesized by seed-mediated growth method in different dimensional ratios. The properties of the prepared nanoparticles, including crystalline size, aspect ratio, surface properties, nanoparticle purity, shape and morphology of nanostructures were investigated using x-ray diffraction, UV-vis spectroscopy, FT-IR, and transmitted electron microscopy. The effect of changing parameters of Nano rod dimensions, changes in Nano rod volume fraction in water and also the effect of temperature on the nanofluid thermal conductivity coefficient were investigated using transient hot wire method. The results showed that reducing the aspect ratio, increasing the volume fraction and increasing the temperature increase the thermal conductivity. In fact, results show that an increase in the nanorods aspect ratio with a constant volume fraction of 1:50 of gold in water nanorod and at room temperature leads to a decrease in the thermal conductivity of the nanofluid. Also, increasing the two parameters of volume fraction and temperature significantly increases the thermal conductivity coefficient.

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

AbstractIn 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 nanofluids increases with temperature and nanoparticles volume fraction but decreases when nanoparticle size intensifies. Furthermore, it is found that the nanoparticles volume fraction has a great impact on the nanofluids thermophysical properties. Finally, the obtained results confirm that the proposed model is considerably accurate and capable of predicting nanofluids thermal conductivity and that it can be used with ease as an alternative to many other models.

An Experimental Investigation of Properties of Nanofluid and Its Performance on Thermosyphon Cooled by Natural Convection

An attempt is made here to characterize thermal conductivity of water-based Al2O3 nanofluid and then use the same in a circular finned thermosyphon (TPCT) to measure its thermal performance. The concentration of Al2O3 nanofluid is varied within 0.05–0.25% by volume. The thermal conductivity of nanofluid is increased with concentration of Al2O3 nanoparticles as well as with temperature. A maximum of 26.7% enhancement of thermal conductivity is observed at 45 °C for 0.25% concentration by volume of nanofluid in comparison to that of de-ionized (DI) water. Variations of surface tension and contact angle of Al2O3 nanofluid are also compared with DI water. One of the smallest TPCT with different heat inputs (4 W, 8 W, and 12 W) and different inclinations (30 deg, 45 deg, 60 deg, and 90 deg) is tested for different concentration of Al2O3 nanofluid, which will find application in smaller electronic units. It is found that use of nanofluid decreases the wall temperature distribution of TPCT. Thermal resistance of TPCT decreases whenever TPCT is filled with nanofluid and a maximum of 36.4% reduction in thermal resistance is noted for 0.25% volume of nanoparticles at 4 W with an inclination of 60 deg. It is also found that performance of TPCT is higher at 60 deg inclination compared to other inclinations, especially for lower heat input.

RETRACTED: Numerical modelling of free convection in a symmetrical cone using MRT LB model combined with experimental properties of TiO2-water

Abstract In this paper, a numerical simulation on free convection flow and heat transfer in a symmetrical cone is carried out. The Multi-Relaxation-Time lattice Boltzmann method is utilized to simulate the flow and temperature fields. The cone is filled with TiO 2 -water nanofluid, and the dynamic viscosity and thermal conductivity of nanofluid are measured using modern devices including KD2 PRO thermal properties analyzer (Decagon devices, Inc., USA) and Brookfield viscometer of Brookfield engineering laboratories of USA, respectively. The experimental data are used in the numerical simulations to increase the reliability of the results. The radial coordinate is used into the temperature distribution function. As a result, only one source term is required for the present lattice Boltzmann model. On the other hand, the macro cylindrical energy equation is exactly recovered using Chapman–Enskog analysis. The flow structure, temperature distribution, heat transfer rate using average Nusselt number, second law analysis using local/volumetric entropy generation and Bejan number are provided.

Investigation of natural convection of magnetic nanofluid in an enclosure with a porous medium considering Brownian motion

In this work, magnetic nanofluid natural convection in the porous enclosure considering Brownian motion is studied numerically using Control Volume Finite Element Method (CVFEM). The effective viscosity and thermal conductivity of nanofluid are specified by Corcione correlation in which the impact of Brownian motion on the thermal conductivity is considered. The considered porous enclosure is heated from the inner circular side and it cooled from the outer circular side while two other walls are insulated. The impacts of various controlling parameters such as the Rayleigh number, Hartmann number, Darcy number and angle of magnetic field parameter are examined on the streamlines and isotherms. In addition, the average and local Nusselt numbers are obtained and presented via figures. The results state that the intensity of the convective flow has a direct relationship with the Rayleigh number and Darcy number while it has a reverse relationship with the Hartmann number and inclination angle of the magnetic field.

Study of acoustic and thermodynamic factors of synthesized ZnO-water nanofluid by ultrasonic technique

This paper is focused on acoustic and thermodynamic factors of as-synthesized ZnO nanoparticles (NPs) dispersed in water as base fluid. NPs were characterized by means of powder XRD, SEM, PSA and UV. Ultrasonic studies were performed by nanofluid interferometer provides information on understanding the molecular behavior and intermolecular interactions, which have been interpreted with respect to change in concentration of solute at different temperatures (30–80 °C). The experimental data was used to explore adiabatic compressibility (β), bulk modulus (K), intermolecular free path length (L), impedance (Z) and Thermal Conductivity (K) of nanofluids was predicted by using ultrasonic velocity.

Comparison of Experimental Measurements of Thermal Conductivity of Fe2O3 Nanofluids Against Standard Theoretical Models and Artificial Neural Network Approach

In the present work, the practicability of Fe2O3 nanofluids for heat transfer applications has been examined. Nanofluids performance, in terms of modulation of thermal conductivity, has been investigated with increasing concentration of Fe2O3 nanoparticles in water and ethylene glycol base fluids at 10, 20, 30, 40, 50, 60 and 70 °C. Fe2O3 nanoparticles have been synthesized using the wet chemical method and characterized using TEM, SEM, XRD and UV–Vis. The characterization results revealed a face-centered cubic structure having alpha phase and particle size in the range of 40-55 nm for the synthesized Fe2O3 nanoparticles. Thermal conductivity measurement results show increases in thermal conductivity with the increase in concentration and temperature of nanofluids. 16.45 and 19.76% enhancement in thermal conductivity have been observed for Fe2O3–water and Fe2O3–ethylene glycol nanofluids of 2 vol.% at 70 °C compared to water and ethylene glycol base fluids at 10 °C, respectively. Results of the ANN approach are in good agreement with experimental results, and H–C model gives better predictions compared to other standard models. The study gives clear insights into improved heat transfer performance by material engineering.

Prediction of Thermal Conductivity of Various Nanofluids with Ethylene Glycol using Artificial Neural Network

The nanofluid has been widely used in many heat transfer areas due to its significant enhancement effect on the thermal conductivity. Therefore, the methods that can accurately predict their thermal conductivities are very important to evaluate and analyze the heat transfer process. In this paper, a novel artificial neural network (ANN) model was proposed to predict the thermal conductivity of nanofluids with ethylene glycol and could be used in a wide range with excellent accuracy. A total of 391 experimental data with a wide range of temperatures (4°C~90°C), nanoparticles (metal, metal oxide, etc.), volume concentrations (0.05%~10%), and particle sizes (2 nm ~ 282 nm) were collected. To build the ANN model, the temperature, thermal conductivities of the base fluid and nanoparticles, the size and volume concentration of the nanoparticles were selected and used as the input parameters. There were 5 nodes, 10 nodes and 1 node in input layer, hidden layer and output layer, respectively. The predicted results of the ANN model coincided with the experimental data very well with the correlation coefficient and mean square error (MSE) were 0.9863 and 3.01×10-5, respectively. The relative deviations of 99.74% data were within ±5%. The model was expected to be a good practical method to predict the thermal conductivity of nanofluids with ethylene glycol.

Experimental study on heat transfer enhancement of nanofluid flow through helical tubes

Fluid flow and heat transfer characteristics of nanofluids flowing through helically coiled tubes under uniform heat flux condition are studied experimentally. The turbulent flow of two different kinds of nanofluids, i.e. Ag-water and SiO2-water, are examined. Three different helically coiled tubes along with straight ones are constructed to investigate the effects of geometrical parameters such as pitch circle diameter and helical pitch as well as nanoparticle volume concentration. The viscosity and thermal conductivity of nanofluids are determined experimentally in different volume fractions and temperatures. The range of Reynolds number is from 8900 to 11970. The experimental outcomes show that using nanoparticles in coiled tubes can be more effective in improving the heat transfer rate than the straight tube. Empirical correlations are extracted based on experimental data to predict the Nusselt number and friction factor of turbulent nanofluids flow through helically coiled tubes.