Applications of feedforward multilayer perceptron artificial neural networks and empirical correlation for prediction of thermal conductivity of Mg(OH)2–EG using experimental data

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.     

Designing an artificial neural network to predict thermal conductivity and dynamic viscosity of ferromagnetic nanofluid

This paper focuses on designing an artificial neural network which can predict thermal conductivity and dynamic viscosity of ferromagnetic nanofluids from input experimental data including temperature, diameter of particles, and solid volume fraction. The experimental data were extracted and they were used as learning dataset to train the neural network. To find a proper architecture for network, an iteration method was used. Based on the results, there was no over-fitting in designed neural network and the neural network was able to track the data. ANN outputs showed that the maximum errors in predicting thermal conductivity and dynamic viscosity are 2% and 2.5%, respectively. Based on the ANN outputs, two sets of correlations for estimating the thermal conductivity and dynamic viscosity were presented. The comparisons between experimental data and the proposed correlations showed that the presented correlations were in an excellent agreement with experimental data.     

Experimental investigation on the thermal conductivity and shear viscosity of viscoelastic-fluid-based nanofluids

Viscoelastic-fluid-based nanofluids with dispersion of copper (Cu) nanoparticles in viscoelastic surfactant solution (aqueous solution of cetyltrimethylammonium chloride/sodium salicylate) were prepared. A comparative study of thermal conductivity and viscosity between viscoelastic-fluid-based Cu nanofluids and distilled water based nanofluids was then performed experimentally. Different concentrations of viscoelastic base fluid and volume fraction of Cu nanoparticles were matched in order to check their influences on fluid’s thermal conductivity and viscosity. The experimental results show that the viscoelastic-fluid-based Cu nanofluids have a higher thermal conductivity than viscoelastic base fluid, and its thermal conductivity increases with increasing temperature and increasing particle volume fraction. Furthermore, the viscoelastic-fluid-based Cu nanofluid shows a non-Newtonian behavior in its viscosity, and the viscosity increases with the increase of Cu nanoparticle concentration and decrease of temperature.     

A Complete Experimental Investigation on The Rheological Behavior of Silver Oil Based Nanofluid

In this study, thermo‐physical properties including thermal conductivity, viscosity, density and specific heat capacity of an oil based nanofluid including silver as to be nanoparticles have been experimentally studied. The results indicate an enhancement in thermal conductivity which was depended on bulk temperature and volume fraction of utilized nanofluids. Viscosity data show a significant increment through volume fraction increasing. In addition, the specific heat capacity and density of nanofluids were studied experimentally and it was found that, all measured rheological properties of these nanofluids, were not in agreement to published correlations.     

Buoyancy-Driven Heat Transfer in Nanofluid-Filled Trapezoidal Enclosure with Variable Thermal Conductivity and Viscosity

Heat transfer performance utilizing nanofluids in a trapezoidal enclosure is investigated taking into account variable thermal conductivity and viscosity. Transport equations are modelled by a stream-vorticity formulation, and are solved numerically by the finite difference method. The effects of the Rayleigh number, base angle, volume fraction, and size of nanoparticles on flow and temperature patterns as well as the heat transfer rate are presented. We found that the effect of the viscosity was more dominant than the thermal conductivity, and there is almost no improvement in heat transfer performance utilizing nanofluids.     

Stability and thermophysical properties of fly ash nanofluid for heat transfer applications

Improving the performance of heat transfer fluids is altogether significant. The best approach for improving the thermal conductivity is the addition of nanoparticles to the base fluid. In the present study, specific heat, dynamic viscosity, and thermal conductivity of water-based Indian coal fly ash stable nanofluid for 0.1% to 0.5% volume concentration in the temperature range of 30 to 60°C has been investigated. To evaluate an average particle diameter of 11.5 nm, the fly ash nanoparticles were characterized with scanning electron microscopy and dynamic light scattering. Using zeta potential, the stability of nanofluid in the presence of surfactant Triton X-100 was tested. Thermal conductivity and viscosity of fly ash nanofluid increased, while specific heat decreased as volume concentration increased. The effect of temperature on the fly ash nanofluid was directly proportional to its thermal conductivity and specific heat and inversely proportional to viscosity.     

Effect of nanofluid variable properties on natural convection in enclosures

In this work, the heat transfer enhancement in a differentially heated enclosure using variable thermal conductivity and variable viscosity of Al₂O₃–water and CuO–water nanofluids is investigated. The results are presented over a wide range of Rayleigh numbers (Ra = 10³–10⁵), volume fractions of nanoparticles (0 ≤ φ ≤ 9%), and aspect ratios (½ ≤ A ≤ 2). For an enclosure with unity aspect ratio, the average Nusselt number of a Al₂O₃–water nanofluid at high Rayleigh numbers was reduced by increasing the volume fraction of nanoparticles above 5%. However, at low Rayleigh numbers, the average Nusselt number was slightly enhanced by increasing the volume fraction of nanoparticles. At high Rayleigh numbers, CuO–water nanofluids manifest a continuous decrease in Nusselt number as the volume fraction of nanoparticles is increased. However, the Nusselt number was not sensitive to the volume fraction at low Rayleigh numbers. The Nusselt number demonstrates to be sensitive to the aspect ratio. It was observed that enclosures, having high aspect ratios, experience more deterioration in the average Nusselt number when compared to enclosures having low aspect ratios. The variable thermal conductivity and variable viscosity models were compared to both the Maxwell-Garnett model and the Brinkman model. It was found that at high Rayleigh numbers the average Nusselt number was more sensitive to the viscosity models than to the thermal conductivity models.     

Experimental investigation on the thermo-physical properties of Al2O3 nanoparticles suspended in car radiator coolant

Nanofluid is a new type of heat transfer fluid with superior thermal performance characteristics, which is very promising for thermal engineering applications. This paper presents new findings on the thermal conductivity, viscosity, density, and specific heat of Al₂O₃ nanoparticles dispersed into water and ethylene glycol based coolant used in car radiator. The nanofluids were prepared by the two-step method by using an ultrasonic homogenizer with no surfactants. Thermal conductivity, viscosity, density, and specific heat have been measured at different volume concentrations (i.e. 0 to 1 vol.%) of nanoparticles and various temperature ranges (i.e. from 10 °C to 50 °C). It was found that thermal conductivity, viscosity, and density of the nanofluid increased with the increase of volume concentrations. However, specific heat of nanofluid was found to be decreased with the increase of nanoparticle volume concentrations. Moreover, by increasing the temperature, thermal conductivity and specific heat were observed to be intensified, while the viscosity and density were decreased.     

Experimental Investigation and Model Development for Thermal Conductivity of Glycerol–MgO Nanofluids

This paper presents experimental and theoretical determination of the effective thermal conductivity of three magnesium oxide (MgO) nanoparticles of different sizes dispersed in glycerol. The glycerol-based nanofluids were prepared at volume fractions ranging from 0.5% to 4% and no surfactant. The nanoparticles were dispersed and deagglomerated for 2 hours using an ultrasonic probe. The effective thermal conductivity of nanofluids was measured from 20°C to 45°C using a thermal conductivity analyzer. The experimental results show an increase in the thermal conductivity of MgO–glycerol nanofluids with increasing volume fraction of nanoparticles. The thermal conductivity ratio is unaffected as the temperature increases. In the given volume fraction and temperature range, the thermal conductivity ratio of MgO–glycerol nanofluids decreases with increasing particle size. The obtained experimental data were also compared with some existing theoretical and empirical models that may work for glycerol-based nanofluids. The comparison of experimental data with these available models shows that the data do not agree with the models. Therefore, a new empirical correlation was developed for the MgO–glycerol nanofluids.     

Experimental investigation of the thermal transport properties of graphene oxide/Co3O4 hybrid nanofluids

The in-situ growth and chemical co-precipitation method was used for the synthesis of uniform dispersion of Co₃O₄ nanoparticles on the graphene oxide (GO) nanosheet. The reductions of aqueous cobalt chloride in the presence of GO with sodium borohydrate result in the formation of hybrid GO/Co₃O₄ nanoparticles. The synthesized GO/Co₃O₄ nanoparticles were characterized using X-ray power diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, transmission electron microscopy (TEM) and vibrating sample magnetometer (VSM). The hybrid nanofluids were prepared by dispersing synthesized GO/Co₃O₄ nanoparticles in water, ethylene glycol, and ethylene glycol/water mixtures. The properties such as thermal conductivity and viscosity were estimated experimentally at different volume concentrations and temperatures. The thermal conductivity enhancement of water-based nanofluid is 19.14% and ethylene glycol-based nanofluid is 11.85% at 0.2% volume concentration and at a temperature of 60 °C respectively compared to their respective base fluids. Similarly, the viscosity enhancement of water-based nanofluid is 1.70-times and ethylene glycol-based nanofluid is 1.42-times at 0.2% volume concentration and at a temperature of 60 °C respectively. The obtained thermal conductivity and viscosity data is compared with the literature values.     

A numerical investigation of transient natural convection heat transfer of aqueous nanofluids in a differentially heated square cavity

Transient natural convection heat transfer of aqueous nanofluids in a differentially heated square cavity is investigated numerically. The effective thermal conductivity and dynamic viscosity of nanofluids are predicted by using the proposed models that take the contribution of Brownian motion of nanoparticles into account. Three different Rayleigh numbers and five different volume fractions of nanoparticles are considered. The development of natural convection is presented through the evolutions of the average Nusselt number along the cold side wall. The predicted flow development times and time-averaged Nusselt numbers are scaled with Rayleigh number. In addition, the time-averaged Nusselt numbers are presented in terms of volume fraction of nanoparticles. It is shown that at constant Rayleigh numbers, the time-averaged Nusselt number is lowered with increasing volume fraction of nanoparticles.     

Thermal conductivity measurement of methanol-based nanofluids with Al2O3 and SiO2 nanoparticles

In this study, the methanol-based nanofluids with Al₂O₃ and SiO₂ nanoparticles are prepared by dispersing nanoparticles in pure methanol using an ultrasonic equipment. The main objective of this paper is to measure the thermal conductivity of the methanol-based nanofluids. We have also measured the zeta potential, particle size and Tyndall effect for the present nanofluids. The transient hot-wire method is applied for measuring the thermal conductivity of methanol-based nanofluids. The measurement uncertainty in repeatability is obtained as 1.95% for deionized (DI) water and 1.34% for pure methanol, respectively. The effective thermal conductivity of methanol-based nanofluids is measured at a temperature of 293.15 K. The results show that the thermal conductivity increases with an increase of the nanoparticle volume fraction, and the enhancement is observed to be 10.74% and 14.29% over the basefluid at the volume fraction of 0.5vol% for Al₂O₃ and SiO₂ nanoparticles, respectively. Clustering of nanoparticles is considered to be the main reason for the thermal conductivity enhancement.     

Unexpected sharp peak in thermal conductivity of carbon nanotubes water-based nanofluids

We report here the thermal conductivity measurement of carbon nanotubes water-based nanofluids stabilized by sodium dodecylbenzene sulfonate as a function of volume fraction and temperature. For the first time, we further show the existence of a sharp peak in thermal conductivity at very small volume fraction below theoretical percolation threshold which is temperature independent. This preliminary study evidences the potential of promising and useful nanofluid for practical applications in cooling and energy systems and heat exchangers, as viscosity penalty is obviously vanished at this concentration.     

Experimental exploration and theoretical certainty of thermal conductivity and viscosity of MgO-therminol 55 nanofluid

In this study, a combination of thermal conductivity, viscosity, and density characteristics are experimentally probed for attaining maximum heat transfer using MgO-Therminol 55 as nanofluid is reported. Recent studies proved that nanofluids have miserable properties that make them feasibly useful in many applications in heat transfer compared to base fluid.MgO-Therminol 55 nanofluid is synthesized by diffusion of MgO nanoparticles of size 160–190 nm in Therminol 55 at different concentrations (0.05%–0.3%). Thermal conductivity and viscosity are calculated at a temperature range of 30–60°C using kd₂ analyzer and Fenske viscometer. Data obtained from the experimental results reveals that when volume concentration is increased with respect to that thermal conductivity increases, viscosity decreases and density decreases at different temperatures. The proposed models were supportive to the experimental data.     

Simulation of heat transfer enhancement in nanofluids using dissipative particle dynamics

The current paper applied dissipative particle dynamics (DPD) approach to investigate heat transfer within nanofluids. The DPD approach was applied to study natural convection in a differential heated enclosure by considering the viscosity and the thermal conductivity of the nanofluid to be dual function of temperature and volume fraction of nanoparticles. Experimental data for viscosity and thermal conductivity are incorporated in the current DPD model to mimic energy transport within nanofluids. This incorporation is done through the modification of the dissipative weighting function that appears in the dissipative force vector and the dissipative heat flux. For the entire range of Rayleigh number considered in this study, it was found that the DPD results show a deterioration in heat transfer in the enclosure due to the presence of nanoparticles for φ > 4%. However, some slight enhancement is shown to take place for small volume fraction of nanoparticles, φ ≤ 4%. The DPD results experienced some degree of compressibility at high values of Rayleigh number Ra ≥ 10⁵.     

Experimental investigation and development of new correlation for thermal conductivity and viscosity of BioGlycol/water based SiO2 nanofluids

Nanofluids are a new class of engineered heat transfer fluids which exhibit superior thermophysical properties and have potential applications in numerous important fields. In this study, nanofluids have been prepared by dispersing SiO₂ nanoparticles in different base fluids such as 20:80% and 30:70% by volume of BioGlycol (BG)/water (W) mixtures. Thermal conductivity and viscosity experiments have been conducted in temperatures between 30 °C and 80 °C and in volume concentrations between 0.5% and 2.0%. Results show that thermal conductivity of nanofluids increases with increase of volume concentrations and temperatures. Similarly, viscosity of nanofluid increases with increase of volume concentrations but decreases with increase of temperatures. The maximum thermal conductivity enhancement among all the nanofluids was observed for 20:80% BG/W nanofluid about 7.2% in the volume concentration of 2.0% at a temperature of 70 °C. Correspondingly among all the nanofluids maximum viscosity enhancement was observed for 30:70% BG/W nanofluid about 1.38-times in the volume concentration of 2.0% at a temperature of 70 °C. The classical models and semi-empirical correlations failed to predict the thermal conductivity and viscosity of nanofluids with effect of volume concentration and temperatures. Therefore, nonlinear correlations have been proposed with 3% maximum deviation for the estimation of thermal conductivity and viscosity of nanofluids.     

Estimation of thermal conductivity of CNTs-water in low temperature by artificial neural network and correlation

An accurate artificial neural network (ANN) model and new correlation are developed to predict thermal conductivity of functionalized carbon nanotubes (MWNT-10 nm in diameter)-water nanofluid based on experimental data. Experimental values of thermal conductivity are in six concentrations of nanoparticles from 0.005% up to 1.5%. The temperatures were changed within 10–60 °C. In order to estimate the thermal conductivity, a feed-forward three-layer neural network is utilized. The obtained results exhibited that the new correlation and ANN model have a good agreement with the experimental data. The maximum values of deviation and mean square error of neural network outputs were 2% and 8.2E − 05, respectively. The findings illustrated that the artificial neural network can estimate and model the thermal conductivity of CNTs-water nanofluid very efficiently and accurately.     

Numerical investigation of fluid flow and heat transfer around a solid circular cylinder utilizing nanofluid

In this study, flow-field and heat transfer through a copper–water nanofluid around circular cylinder has been numerically investigated. Governing equations containing continuity, N–S equation and energy equation have been developed in polar coordinate system. The equations have been numerically solved using a finite volume method over a staggered grid system. SIMPLE algorithm has been applied for solving the pressure linked equations. Reynolds and Peclet numbers (based on the cylinder diameter and the velocity of free stream) are within the range of 1 to 40. Furthermore, volume fraction of nanoparticles (φ) varies within the range of 0 to 0.05. Effective thermal conductivity and effective viscosity of nanofluid have been estimated by Hamilton–Crosser and Brinkman models, respectively. The effect of volume fraction of nanoparticles on the fluid flow and heat transfer characteristics are investigated. It is found that the vorticity, pressure coefficient, recirculation length are increased by the addition of nanoparticles into clear fluid. Moreover, the local and mean Nusselt numbers are enhanced due to adding nanoparticles into base fluid.     

Investigation of thermal conductivity and viscosity of Fe3O4 nanofluid for heat transfer applications

Experimental investigations and theoretical determination of effective thermal conductivity and viscosity of magnetic Fe₃O₄/water nanofluid are reported in this paper. The nanofluid was prepared by synthesizing Fe₃O₄ nanoparticles using the chemical precipitation method, and then dispersed in distilled water using a sonicator. Both experiments were conducted in the volume concentration range 0.0% to 2.0% and the temperature range 20 °C to 60 °C. The thermal conductivity and viscosity of the nanofluid were increased with an increase in the particle volume concentration. Viscosity enhancement was greater compared to thermal conductivity enhancement under at same volume concentration and temperature. Theoretical equations were developed to predict thermal conductivity and viscosity of nanofluids without resorting to the well established Maxwell and Einstein models, respectively. The proposed equations show reasonably good agreement with the experimental results.     

Latest developments on the viscosity of nanofluids

The past decade has seen the rapid development of nanofluids science in many aspects. Number of research is conducted that is mostly focused on the thermal conductivity of these fluids. However, nanofluid viscosity also deserves the same attention as thermal conductivity. In this paper, different characteristics of viscosity of nanofluids including nanofluid preparation methods, temperature, particle size and shape, and volume fraction effects are thoroughly compiled and reviewed. Furthermore, a precise review on theoretical models/correlations of conventional models related to nanofluid viscosity is presented. The existing experimental results about the nanofluids viscosity show clearly that viscosity augmented accordingly with an increase of volume concentration and decreased with the temperature rise. However, there are some contradictory results on the effects of temperature on viscosity. Moreover, it is shown that particle size has some noteworthy effects over viscosity of nanofluids.