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In the present paper, the thermal conductivity of hybrid nanofluids is experimentally investigated. The studied nanofluid was produced using a two-step method by dispersing Cu and TiO 2 nanoparticles with average diameter of 70 and 40 nm in a binary mixture of water/EG (60:40). The properties of this nanofluid were measured in various solid concentrations (0.1, 0.2, 0.4, 0.8, 1, 1.5, and 2%) and temperatures ranging from 30 to 60 °C. Next, two new correlations for predicting the thermal conductivity of studied hybrid nanofluids, in terms of solid concentration and temperature, are proposed that use an artificial neural network (ANN) and are based on experimental data. The results indicate that these two new models have great ability to predict thermal conductivity and show excellent agreement with the experimental results. [ABSTRACT FROM AUTHOR]

Abstract: The term of nanofluid refers to a solid–liquid mixture with a continuous phase which is a nanometer sized nanoparticle dispersed in conventional base fluids. In order to study the heat transfer behavior of the nanofluids, precise values of thermal and physical properties such as specific heat, viscosity and thermal conductivity of the nanofluids are required. There are a few well-known correlations for predicting the thermal and physical properties of nanofluids which are often cited by researchers to calculate the convective heat transfer behaviors of the nanofluids. Each researcher has used different models of the thermophysical properties in their works. This article aims to summarize the various models for predicting the thermophysical properties of nanofluids which have been commonly cited by a number of researchers and use them to calculate the experimental convective heat transfer coefficient of the nanofluid flowing in a double-tube counter flow heat exchanger. The effects of these models on the predicted value of the convective heat transfer of nanofluid with low nanoparticle concentration are discussed in detail. [Copyright &y& Elsevier]

This paper presents an investigation on the thermal conductivity of nanofluids using experimental data, neural networks, and correlation for modeling thermal conductivity. The thermal conductivity of Mg(OH) 2 nanoparticles with mean diameter of 10 nm dispersed in ethylene glycol was determined by using a KD2-pro thermal analyzer. Based on the experimental data at different solid volume fractions and temperatures, an experimental correlation is proposed in terms of volume fraction and temperature. Then, the model of relative thermal conductivity as a function of volume fraction and temperature was developed via neural network based on the measured data. A network with two hidden layers and 5 neurons in each layer has the lowest error and highest fitting coefficient. By comparing the performance of the neural network model and the correlation derived from empirical data, it was revealed that the neural network can more accurately predict the Mg(OH) 2 –EG nanofluids' thermal conductivity. [ABSTRACT FROM AUTHOR]

Abstract This experimental study includes measurement of thermal conductivity of distilled water-based CNT-SiO 2 hybrid nanofluids. Nanofluids were prepared by using two-step method, 3 different concentrations and 4 different mass range of CNT-SiO 2. SiO 2 has 2200 kg m−3 density, 1.4 W m−1 K−1 thermal conductivity and 7 nm average particle size. CNT has 2620 kg m−3 density, 25 W m−1 K−1 thermal conductivity and 6–10 nm average particle size. Samples were placed in ultrasonic homogenizer maximum power capacity for 3 h. Throughout sonication process temperature of nanofluids have been kept under control in order not to chance volumetric fraction of nanofluids. All measurements of thermal conductivity were done by using thermal conductivity meter. Thermal conductivity meter was calibrated by di-water. Measurements of thermal conductivity was done range from 25 °C to 60 °C for every 5 °C. Validation of measurements had been performed by using di-water and shown in a thermal conductivity-temperature figure. Minimum and maximum thermal conductivity enhancements were revealed in detail. Alteration of the thermal conductivity with temperature according to various volumetric fractions were in literature rated and it is found that the thermal conductivity increases with temperature and vol. fraction clearly. Enhancement on the thermal conductivity to di-water were also depicted for various temperatures and vol. fraction in figures. Almost well-known correlations in the literature were given with their predictable rates. Moreover, comparisons with other studies were provided in this present study. A practical correlation was proposed for other researchers. [ABSTRACT FROM AUTHOR]

Abstract The present paper is an overview of the latest developments regarding the application of nanofluids in boiling critical heat flux by means of both pool- and convective-flow boiling. Boiling heat transfer is a significant field in thermal engineering systems, and it is especially used for boiling in power plants as well as the cooling of nuclear reactors and high-tech electronic systems. This concise review contains efforts to show how nanofluids could play an essential role in achieving high heat flux with small temperature differences during the boiling process—which, in turn, improves the critical heat flux (boiling crisis) for such an operation and makes the heat-exchange system's performance safer and more durable. It is also expected that this work could be a helpful new reference guide that will allow investigators to update their knowledge on the topic of boiling critical heat flux using nanofluids. In addition, this work contains concise recommendations for future study directions. [ABSTRACT FROM AUTHOR]

Abstract The hybrid nanofluids were obtained by dispersing Silicon dioxide and Graphite nanoparticles in distilled water. Viscosity is regarded as internal resistant of a fluid, and it affects the pumping power and the convective heat transfer coefficient. This hybridization requires to be experimentally determined since its properties are unknown. For viable real life applications viscosity and density parameters are significant, owing to their influence on energy and cost saving of heat exchangers by affecting pressure drops. The viscosity measurements of hybrid nanofluids were performed using capillary tube viscometer, within a temperature range of 15 °C to 60 °C at several volume concentrations up to 2%. Furthermore, the volumetric fraction ratios within nano composites have also been changed, since there is more than one nano particle. The highest increment of viscosity was observed to be 36.12% in the nanofluid of 2 vol% at 15 °C. The conclusion was supported with SEM images and a regression correlation was proposed and afterward the results were compared with relevant literature. It is able to estimate the viscosity of hybrid nanofluid with 2.4% accuracy. [ABSTRACT FROM AUTHOR]

Water-based graphene quantum dots (GQD) suspension has great potential for different heat transfer applications as a novel coolant due to their unique colloidal stability, high thermal conductivity and low penalty for rheological properties once loading GQD. To this end, graphene quantum dots were firstly prepared through a new and cost-effective exfoliation procedure. Based on the morphological characterization, the average thickness and diameter of the synthesized amine treated-GQD (AGQD) were determined as mostly less than 1 nm and in the range of 5–20 nm, respectively. Case studies show that water-based AGQD nanofluid at very low weight fractions shows a considerably higher thermal conductivity than that of base fluid. In a detailed rheological investigation of the water-based AGQD nanofluid, no noteworthy increase was observed in comparison with the base fluid, which is considered as a major benefit for this novel generation of coolants. The water-based AGQD nanofluids were also found to be especially more effective in the thermosyphon in terms of overall thermal properties such as net heat transfer, and thermal efficiency, and rheological property such as effective viscosity, as well as, total pressure drop in comparison to the distilled water. Since the water-based AGQD nanofluids show no sedimentation, high thermal conductivity and fairly no effect on rheological properties, it would provide an economical approach for enhancing the performance of industrial heat pipes and thermosyphons. [ABSTRACT FROM AUTHOR]

In order to avoid the costs of experimental evaluations, soft computing methods like artificial neural network (ANN) and genetic algorithm have remarkably grabbed the attentions of investigators for predicting the hydrothermal characteristics of different types of nanofluids. In this paper, the implementation of ANN and genetic algorithm for modeling and multi-criteria optimizing the hydrothermal behavior of SiO 2 /water nanofluid has been investigated. Using the data obtained from the experimental analysis, an ANN model is developed to estimate the pressure drop and Nusselt number as a function of volume concentration, Reynolds number, and inlet temperature. Different network structures were assessed and it has been achieved that a network with 2 hidden layers and 6 neurons in every layer provides the most accurate prediction. It is revealed that the developed network is satisfactorily accurate to determine the Nusselt number and pressure drop of SiO 2 /water nanofluid compared to the empirical correlations. To optimize the hydrothermal behavior of the nanofluid (i.e. to find the optimal cases with highest Nusselt number and the relatively least pressure drop), the genetic algorithm coupled with compromise programming approach has been implemented considering decision maker's attitude. [ABSTRACT FROM AUTHOR]

The present paper deals with the artificial neural network modeling (ANN) of heat transfer coefficient and Nusselt number in TiO 2 /water nanofluid flow in a microchannel heat sink. The microchannel comprises of 40 channels; each channel has a length of 4 cm, a width of 500 μm, and a height of 800 μm. In the ANN modeling of heat transfer coefficient and Nusselt number 23 and 72 datasets have been used, respectively. The experimental Nusselt number has been calculated based on three different thermal conductivity models, four volume fractions of 0, 0.5, 1, and 2%, two values of Reynolds number i.e. 400 and 1200 and three different heating rates including 50.6, 60.7, and 69.1 W. Therefore, the inputs that are introduced to the neural network are volume fraction of nanoparticles, Reynolds number, heating rate, and model number while the output of network is the Nusselt number. It is elucidated that an appropriately trained network can act as a good alternative for costly and time-consuming experiments on the nanofluid flow in microchannels. The average relative errors in the prediction of Nusselt number and heat transfer coefficients were 0.3% and 0.2%, respectively. [ABSTRACT FROM AUTHOR]

Nitrogen-doped graphene (NDG) nanofluids are prepared using a two-step method in an aqueous solution of 0.025 wt.% Triton X-100 as a surfactant with various nanosheets at several concentrations (0.01, 0.02, 0.04, 0.06 wt.%). This paper reports results of experiments on thermal conductivity, specific heat capacity, and viscosity of the NDG nanofluids, as well as their convective heat transfer behavior flowing in a double-pipe heat exchanger. To assess the thermal properties, we used various water-based nanofluids as coolants to analyze the total heat transfer coefficient, convective heat transfer coefficient, the percentage of wall temperature reduction, pressure drop, and pumping power in a counter-flow double-pipe heat exchanger. A novel MATLAB code carried out the calculations for Reynolds numbers between 5000 and 15,000 (turbulent flow) and nanosheet weight percentages between 0.00% and 0.06%. An increase in Reynolds number or the percentage of nanomaterial could perhaps enhance the heat transfer of the working fluid. As an example, using 0.06 wt.% nanomaterial in the base fluid led to 15.86% enhancement of the convective heat transfer coefficient in comparison with water. Nonetheless, the penalty in terms of the rise in the pumping power was rather small. For a particular material, increasing Reynolds number or nanomaterial weight percentage would augment pumping power. Power consumption, heat removal, and heat transfer rate were greater for nanofluids than for water in all investigated cases, for a particular pumping power. The average increase in heat transfer coefficient was nearly 16.2%. As a result, choosing NDG/water as the working fluid can improve the performance of double-pipe heat exchangers. [ABSTRACT FROM AUTHOR]

In this paper, at first, a new correlation was proposed to predict the relative viscosity of MWCNTs-SiO 2 /AE40 nano-lubricant using experimental data. Then, considering minimum prediction error, an optimal artificial neural network was designed to predict the relative viscosity of the nano-lubricant. Forty-eight experimental data were used to feed the model. The data set was derived to training, validation and test sets which contained 70%, 15% and 15% of data points, respectively. The correlation outputs showed that there is a deviation margin of 4%. The results obtained from optimal artificial neural network presented a deviation margin of 1.5%. It can be found from comparisons that the optimal artificial neural network model is more accurate compared to empirical correlation. [ABSTRACT FROM AUTHOR]

This paper includes an examination of the dynamic viscosity of single-wall carbon nanotubes (SWCNTs) in ethylene glycol (EG) at temperatures ranging from 30 °C to 60 °C for various suspensions at solid volume fractions of 0.0125%, 0.025%, 0.05%, 0.075%, and 0.1%. Experimental findings revealed that SWCNTs/EG nanofluid behaves as a Newtonian fluid at solid volume fractions of 0.1% and at all considered temperatures. The measurements also indicated that dynamic viscosity increases with increasing solid volume fraction and decreases with increasing temperature. Moreover, relative viscosity results showed that that the viscosity of the nanofluid increases to 3.18 times that of the base fluid at a temperature of 30 °C and a solid volume fraction of 0.1%. Finally, using experimental data to estimate the dynamic viscosity of SWCNTs/EG nanofluid, a new correlation with acceptable accuracy was suggested. [ABSTRACT FROM AUTHOR]

During the past decade, nanotechnology with its rapid development has grabbed the attention of scientists, scholars, and engineers. Nanofluids are one of the surprising outcomes of this technology that could increase the efficiency of thermal systems remarkably. Nanofluids containing solid nanoparticles have a higher viscosity than common working fluids; hence, measuring the viscosity is necessary for designing thermal systems and estimating the required pumping power. In the current review study, an attempt has been made to cover the latest experimental studies performed on the viscosity of nanofluids. An experimental investigation is very vital for the analysis since the theoretical models usually underestimate the nanofluid viscosity. Through experiments, the real effects of volume fraction, temperature, particle size, and shape on the viscosity of nanofluids will be determined. [ABSTRACT FROM AUTHOR]

The present paper deals with the design, development and the measurement of thermal conductivity of graphene–water nanofluid using a transient hot wire technique at temperatures below and above ambient conditions ranging from 10 °C to 50 °C. The equipment is designed to measure the thermal conductivity using a single platinum wire of diameter 50 μm and 100 mm length. The platinum micro-wire acts both as a temperature sensor and heating element. Low volume concentrations (0.05, 0.1 and 0.15%) of graphene, having the size less than 100 nm, dispersed in 100 ml of water with SDBS (sodium dodecyl benzene sulfonate) as surfactant, for prolonged stability, is used in the present study. The results showed an enhancement in the thermal conductivity of 37.2% for 0.15% volume concentration of graphene at 50 °C when compared with that of the water at the same temperature. An interesting observation from this study is that the average thermal conductivity enhancement percentage with the increase in volume concentration (say from 0.05% to 0.15%) is found to be 3.3% higher when compared with that of the average enhancement with the increase in temperature from 10 °C to 50 °C. [ABSTRACT FROM AUTHOR]

Application of nanofluids in thermal energy devices such as solar collectors is developing day by day. This paper reports the results of experiments on a flat plate solar collector where the working fluid is SiO 2 /ethylene glycol (EG)–water nanofluid with volume fractions up to 1%. The thermal efficiency and performance characteristics of solar collector are obtained for mass flow rates between 0.018 and 0.045 kg/s. The curve characteristics of solar collector indicate that the effects of particle loading on the thermal efficiency enhancement are more pronounced at higher values of heat loss parameter. The results of this work elucidate the potential of SiO 2 nanoparticles to improve the efficiency of solar collectors despite its low thermal conductivity compared to other usual nanoparticles. [ABSTRACT FROM AUTHOR]

In the present study, the thermal conductivity of CuO/EG–water nanofluid in different solid concentrations and temperatures has been experimentally investigated. Using a two-step method, the nanofluid has been produced in different solid concentrations ranging from 0.1% to 2% and temperatures up to 50 °C. The thermal conductivity of the nanofluid has been experimentally measured using the KD2 Pro instrument. Based on the experimental data, new correlations for predicting the thermal conductivity of CuO/EG–water at different temperatures have been proposed. The results show that with the increase of the solid concentration, the thermal conductivity of the nanofluid increases. Furthermore, the thermal conductivity of the nanofluid increases while the temperature increases. This increase is by far more noticeable in higher solid concentrations compared with lower solid volume fraction. This means that it is the presence of nanoparticles in the base fluid that causes the increase of the effect of temperature on the thermal conductivity. [ABSTRACT FROM AUTHOR]

In this paper, the efficiency of Fe/ethylene glycol nanofluids as coolants is investigated in both laminar and turbulent flow regimes. The effects of particle size, temperature and concentration of nanofluids on the efficiency are examined. To determine the efficiency of nanofluids, the thermal conductivity and viscosity of the nanofluids with concentrations up to 3% in the temperature range of 26–55 °C are measured. Three different average particle sizes including 40 nm, 70 nm, and 100 nm are utilized. The results show that, in laminar flow, the nanofluids with low concentrations and containing particles with big size are not useful. For the turbulent flow, the efficiency analysis reveals that the efficiency of nanofluids increases with an increase in the temperature and solid volume fraction. The nanoparticles with the average size of 70 nm are found to be the best option in the turbulent flow and at the concentration of 3%, while in lower concentrations, the nanoparticles with the average size of 40 nm provide the maximum efficiency. [ABSTRACT FROM AUTHOR]

This paper is a continuation of the authors' previous work on the thermophysical properties, heat transfer, and pressure drop of nanofluids [Experimental Thermal and Fluid Science 52 (2014) 68–78]. In this paper, an experimental study is carried out to study the turbulent flow of COOH-functionalized multi-walled carbon nanotubes/water nanofluid flowing through a double tube heat exchanger. For this purpose, first, the thermophysical properties of the nanofluid, including the thermal conductivity and dynamic viscosity, have been measured at various temperatures and concentrations. Using the measured data, new correlations as a function of temperature and concentration are presented to predict the thermophysical properties. In the next step, the effects of low volume fractions of the nanofluid (from 0.05% to 1%) on the heat transfer rate are studied at the Reynolds numbers between 5000 and 27,000. The experimental results reveal that with increasing the nanofluid concentration, the heat transfer coefficient and thermal performance factor increase. On average, a 78% increase in heat transfer coefficient, a 36.5% increase in the average Nusselt number, and a 27.3% penalty in the pressure drop are recorded for the highest concentration of MWCNTs in water. [ABSTRACT FROM AUTHOR]

An experimental investigation has been carried out to compare the enhancement in the thermal performance of sintered and mesh wick heat pipes by varying the working fluid, inclination angle and heat input. Similar geometrical specifications of 12, 330 and 1 mm respectively are selected for the outer diameter, length and wick thickness and kept constant for both sintered and mesh wick heat pipes. The study focuses on changes in surface temperature distribution, thermal resistance and effective thermal conductivity of heat pipes. The results showed that the maximum reduction in surface temperature is obtained for sintered wick heat pipe at 45° tilt angle and 60° for mesh wick heat pipe with CuO/DI water nanofluid concentration at 1.0 wt.% for both the cases. The reduction in thermal resistance of sintered wick heat pipe is 13.92% higher compared with mesh wick heat pipe for the same heat input, mass concentration and inclination angle. Presence of CuO nanoparticles in DI water and increasing heat input tremendously increases the thermal conductivity of heat pipes. An important observation from this study is the sole effect of sintered wick in heat pipe not only reduces the thermal resistance but also increases the heat transport capacity up to 20 W compared with that of mesh wick. [ABSTRACT FROM AUTHOR]

Abstract: In this investigation, a numerical model having two-dimensional equations was obtained by a CFD program and authors' experimental data were evaluated for the verification procedure of the numerical outputs. The experimental case study includes the single-phase flow of pure water in plain and micro-fin pipes whereas the numerical one has the simulated results of TiO2 particles suspended in single phase water flow in equivalent pipes at a constant heat flux. Hydrodynamics and thermal behaviors of the water–TiO2 flow were calculated by constant heat flux and temperature-dependent settings. Physical specifications of nanofluids were calculated by means of the results of authors' previous ANN analyses. This study illustrates local and average values of temperature, pressure, and velocity distributions in the tested pipes; furthermore, comparisons of pressure drop characteristics are given in terms of nanoparticle concentrations and tube types. [Copyright &y& Elsevier]

Abstract: In this paper, the performance of a microchannel heat sink using TiO2/water nanofluid is experimentally investigated. The dimensions of the microchannel are 500μm width, 800μm height, and 40mm length, where the number of flowing channels is 40.The effects of uncertainties in thermophysical properties on the Nusselt number and friction factor are investigated by using three different sets of thermophysical models, which are based on experimental and theoretical relations. It is concluded that the use of the model which is based on experimental data is very important to estimate the friction factor, while the use of different models to calculate of thermal conductivity has no considerable effect on the prediction of Nusselt number. [Copyright &y& Elsevier]