Dynamic Viscosity Of Nanofluid Research Articles

Predicting thermal conductivity and dynamic viscosity of nanofluid by employment of Support Vector Machines: A review

Nanofluids are employed in variety of thermal energy systems owing to their modified properties and ability of enhancement in heat transfer. Among the properties of nanofluids, thermal conductivity and dynamic viscosity have significant role in the heat transfer characteristics and fluid flow specifications. Among the intelligent techniques, Support Vector Machines (SVMs) are attractive choices regarding their significant exactness in prediction and modeling. In this article, research works on the forecasting and modeling of the mentioned properties of nanofluids with SVMs are reviewed and their outcomes are presented. In accordance with the results of the reviewed works it can be stated that the developed approaches based on the SVM technique have shown significant performance in accurate modeling of both thermal conductivity and dynamic viscosity and can have superior performance in comparison with other intelligent methods in some cases. Furthermore, it can be pointed out that the performance of these systems is dependent on some elements like the employed optimization algorithms. Some recommendations are represented for future studies to reach models with more favorable performance.

Examining rheological behavior of CeO2-GO-SA/10W40 ternary hybrid nanofluid based on experiments and COMBI/ANN/RSM modeling

In this study, the rheological behavior and dynamic viscosity of 10W40 engine oil in the presence of ternary-hybrid nanomaterials of cerium oxide (CeO2), graphene oxide (GO), and silica aerogel (SA) were investigated experimentally. Nanofluid viscosity was measured over a volume fraction range of VF = 0.25–1.5%, a temperature range of T = 5–55 °C, and a shear rate range of SR = 40–1000 rpm. The preparation of ternary-hybrid nanofluids involved a two-step process, and the nanomaterials were dispersed in SAE 10W40 using a magnetic stirrer and ultrasonic device. In addition, CeO2, GO, and SA nanoadditives underwent X-ray diffraction-based structural analysis. The non-Newtonian (pseudoplastic) behavior of ternary-hybrid nanofluid at all temperatures and volume fractions is revealed by analyzing shear stress, dynamic viscosity, and power-law model coefficients. However, the nanofluids tend to Newtonian behavior at low temperatures. For instance, dynamic viscosity declines with increasing shear rate between 4.51% (at 5 °C) and 41.59% (at 55 °C) for the 1.5 vol% nanofluid. The experimental results demonstrated that the viscosity of ternary-hybrid nanofluid declines with increasing temperature and decreasing volume fraction. For instance, assuming a constant SR of 100 rpm and a temperature increase from 5 to 55 °C, the dynamic viscosity increases by at least 95.05% (base fluid) and no more than 95.82% (1.5 vol% nanofluid). Furthermore, by increasing the volume fraction from 0 to 1.5%, the dynamic viscosity increases by a minimum of 14.74% (at 5 °C) and a maximum of 35.94% (at 55 °C). Moreover, different methods (COMBI algorithm, GMDH-type ANN, and RSM) were used to develop models for the nanofluid's dynamic viscosity, and their accuracy and complexity were compared. The COMBI algorithm with R2 = 0.9995 had the highest accuracy among the developed models. Additionally, RSM and COMBI were able to generate predictive models with the least complexity.

Cooling of a periodic heat-generating solid element in an electronic cabinet using a non-Newtonian pseudoplastic nanofluid and a heat-conducting substrate

PurposeNowadays, the most important challenge in mechanical engineering, power engineering and electronics is a development of effective cooling systems for heat-generating units. Taking into account this challenge, this study aims to deal with computational investigation of thermogravitational energy transport of pseudoplastic nanoliquid in an electronic chamber with a periodic thermally producing unit placed on the bottom heat-conducting wall of finite thickness under an influence of isothermal cooling from vertical side walls.Design/methodology/approachThe control equations formulated using the Boussinesq approach, Ostwald–de Waele power law and single-phase nanofluid model with experimentally based correlations of Guo et al. for nanofluid dynamic viscosity and Jang and Choi for nanofluid thermal conductivity have been worked out by the in-house computational procedure using the finite difference technique. The impact of the Rayleigh number, nanoadditives concentration, frequency of the periodic heat generation from the local element and thickness of the bottom solid substrate on nanoliquid circulation and energy transport has been studied.FindingsIt has been found that a raise of the nanoadditives concentration intensifies the cooling of the heat-generating element, while a growth of the heat-generation frequency allows reducing the amplitude of the heater temperature.Originality/valueMathematical modeling of a pseudoplastic nanomaterial thermogravitational energy transport in an electronic cabinet with a periodic thermally generating unit, a heat-conducting substrate and isothermal cooling vertical surfaces to identify the possibility of intensifying heat removal from a heated surface.

A unique artificial intelligence approach and mathematical model to accurately evaluate viscosity and density of several nanofluids from experimental data

In the present research paper, a unique single feed-forward multilayer perceptron neural network (MLPNN) based on data transformation technique and mathematical models were proposed for forecasting density and dynamic viscosity of Alumina (Al2O3)/distilled water (DW), multiwall carbon nanotube (MWCNT)/DW, and Graphene nanoparticle (GNP)/DW nanofluids. The density and dynamic viscosity of nanofluids (0.1%, 0.25%, 0.5%, 0.75%, and 1%) were experimentally measured in temperature range 30–80 °C. Nanofluids, volume concentration, and temperature were given as input and density and dynamic viscosity as output from the network. The statistical analysis was performed to predict the accuracy of both models. The experiments revealed that density and viscosity increase with an increase in nanofluid concentration, and decrease with an increase in temperature. The maximum increase in dynamic viscosity and density of 41.59% and 5.06% was noticed for Al2O3/DW nanofluid. The outcome shows that the MLPNN model is well trained at 12 perceptrons in the hidden layer using the Levenberg-Marquardt algorithm. The output obtained from MLPNN and the mathematical model were compared with experimental results. The maximum error < 0.2% in density and < 1% in viscosity measurement was noticed for both models.

Experimental study and computational intelligence on dynamic viscosity and thermal conductivity of HNTs based nanolubricant

PurposeThis paper aims to evaluate the dynamic viscosity and thermal conductivity of halloysite nanotubes (HNTs) suspended in SAE 5W40 using machine learning methods (MLMs).Design/methodology/approachA two-step method with surfactant was selected to prepare nanolubricants in concentrations of 0.025, 0.05, 0.1 and 0.5 wt%. Thermal conductivity and dynamic viscosity of nanofluids were ascertained over the temperature range of 25–70 °C, with an increment step of 5 °C, using a KD2-Pro analyser device and a digital viscometer MRC VIS-8. Additionally, four different MLMs, including Gaussian process regression (GPR), artificial neural network (ANN), support vector machine (SVM) and decision tree (DT), were used for predicting dynamic viscosity and thermal conductivity by using nanoparticle concentration and temperature as input parameters.FindingsAccording to the achieved results, the dynamic viscosity and thermal conductivity of nanolubricants mostly increased with the rise of nanoparticle concentration in the base oil. All the proposed models, especially GPR with root mean square error mean values of 0.0047 for dynamic viscosity and 0.0016 for thermal conductivity, basically showed superior ability and stability to estimate the viscosity and thermal conductivity of nanolubricants.Practical implicationsThe results of this paper could contribute to optimising the cost and time required for modelling the thermophysical properties of lubricants.Originality/valueTo the best of the author’s knowledge, in this available literature, there is no paper dealing with experimental study and prediction of dynamic viscosity and thermal conductivity of HNTs-based nanolubricant using GPR, ANN, SVM and DT.

Properties of water-based fly ash-copper hybrid nanofluid for solar energy applications: Application of RBF model

The hybrid nanofluids were used as absorber fluids in solar energy applications, which could further increase the efficiency of solar devices. The use of nanofluids in solar devices with the laminar and turbulent flow has received much attention. Presently, the effect of temperature and concentration on thermal conductivity and viscosity of fly ash-copper (80:20% by volume) hybrid nanofluid is investigated. The thermal conductivity and viscosity measurements were carried in the temperature range of 30–60 °C for a concentration range of 0–4.0 vol%. The nanoparticles and nanofluids were characterized by XRF, XRD, SEM, TEM, zeta potential, and DLS techniques. The maximum augmentation in the hybrid nanofluid's dynamic viscosity and thermal conductivity at a concentration of 4 vol% is 45.18% and 49.8%, respectively, at 30 and 60 °C. Correlations to estimate the hybrid nanofluid's dynamic viscosity and thermal conductivity have been proposed considering the results obtained from the present study. A radial basis function-based neural network is used to model nanofluids' effective thermal conductivity and relative viscosity. The outcomes of the experiments were used to calculate the Mouromtseff number and heat transfer efficiency for solar energy applications. • HyNF shows Newtonian behavior in the studied range. • The maximum augmentation in viscosity is 45.2%. • The highest amplification in thermal conductivity is 49.8%. • Radial basis function based neural network was used.

Computational Analysis of Water/Cu Nano Fluid Dynamic Viscosity using Molecular Dynamic Simulations

To enhance thermal management for micro-electric devices, nanofluids become promising working fluids for many thermo-fluid applications. Thermal characteristics of the working fluids can be improved by nano particles additives dispersed in the working fluid such as Cu-nanoparticles in water. The nanoparticle additives manage to alter thermal and dynamic properties of the working fluids such as the dynamic viscosity which plays an important role in specifying thermal and dynamic behaviour of the working media. In order to understand the effect of modifying the dynamic viscosity of the working media, the effective value of this property must be determined. The molecular dynamic (MD) simulation has been used to estimate the Cu/water nanofluid dynamic viscosity at partial volume fractions of φ=0.0125 % and φ= 0.02478 %, and at working temperatures 293 K, 303 K, 313 K, 323 K and 333 K. The used spherical shape nanoparticles are made up of numbers of 0.3-nm-diameter Cu-atoms. The MD simulation results have been compared to reliable experimental and analytical results. The estimated values of the dynamic viscosity using MD simulations converge very well to the experimental and analytical values of the dynamic viscosity, which reveals the advantages of using MD simulations to determine physical properties of nanofluid working medias and hence to design more efficient working fluids. The RDF shows good results for the SPCE model

Evaluation of Physicothermal Properties of Solar Thermic Fluids Dispersed with Multiwalled Carbon Nanotubes and Prediction of Data Using Artificial Neural Networks

A review of multiwalled carbon nanotubes as solar thermic fluids and their thermophysical properties is done in this article. The basic fluids were ethylene glycol and water in ratios of 100 : 0, 90 : 10, and 80 : 20. To investigate how surface modification impacts thermophysical properties, three base fluids were combined with surfactant-assisted MWCNTs and oxidized MWCNTs in weight fractions of 0.125, 0.25, and 0.5 percent, respectively. It takes two months to check whether the dispersion stays constant. Thermal conductivity and viscosity measurement were done using heated discs and Anton Paar viscometers. Using oxidized MWCNTs to disperse, the base fluids increased thermal conductivity by 15% to 24%. Surfactant-assisted MWCNTs in nanofluids perform worse than oxidized MWCNTs. The dynamic viscosity of nanofluids is higher than that of basic fluids between 50 and 70°C. During a mathematical computation, all of the MWCNT weight fractions and ethylene glycol volume percentages are included. The correlation may be a good fit for the experimental data within limits. The characteristics are forecasted using feed-forward backpropagation. In this research, buried layer neurons and factors are examined.

Effect of C60-SiO2 hybrid nanoparticles on thermophysical and tribological properties of a multigrade engine oil: an experimental study

In this study, the thermophysical and tribological properties of a hybrid nanofluid containing fullerene-silica nanoparticles in a multigrade engine oil have been investigated. The nanoparticles were suspended in SAE 5W30 engine oil with a volume fraction of 0.1 to 1% using the two-step method. The results showed that thermal conductivity of nanofluid increased by approximately 9% at 1% volume fraction, and at ambient temperature, rather than the base oil. Investigation of shear stress variations for base fluid and nanofluids at different concentrations revealed that the rheological behavior of the nano-oil is non-Newtonian and shear thinning. Also, the results represented that by increasing the concentration, the nanofluid dynamic viscosity had an erratic trend. The highest increase was 14% and occurred at 1% volume fraction and 5 °C. Also, the behavior of nanofluids against temperature was in accordance with the conventional fluids. Besides, viscosity decreased after the enhancement of temperature in all volume fractions. The wear test was carried out using a pin-on-disk device based on the ASTMG99 standard. The results revealed that the wear rate decreased by 18% in comparison with the base oil at concentration of 1% and temperature of 25 °C. Finally, in order to predict the viscosity of nanofluid, a precise equation was presented based on the experimental data in terms of three variables of temperature, volume fraction and shear rate by using response surface methodology (RSM). Two-degree polynomial function with R2 = 0.9983 has been employed as the optimal function to model the viscosity of nanolubricant.

Experimental investigation of viscosity, enhanced thermal conductivity and zeta potential of a TiO2 electrolyte – based nanofluid

The development of long-time stable nanofluids for practical use in heat transfer processes is a tremendous scientific challenge because nanoparticles tend to precipitate and agglomerate when in a solution, affecting both their thermophysical properties and their stability. This work experimentally investigates the role of the electro-repulse force by electric charges around the nanoparticle, as a way of improving the stability of an electrolyte-based nanofluid. Nanofluid samples were prepared in a two-step method, with 1 wt% and 3 wt% concentrations (mass fraction) of titanium oxide (TiO2) nanoparticles added to a base fluid consisting of an electrolyte solution with a different concentration of potassium chloride (KCl) and deionized water. The pH of the base fluid was maintained constant, adding HEPES as a buffering agent. The stable condition of the nanofluid was established when the temporal variation of the thermal conductivity was negligible. When stability was established, the dynamic viscosity, zeta potential and the enhancement of the thermal conductivity were measured under controlled temperatures. Experimental results showed that the stable behavior of the nanofluid was directly influenced by the electric charge around the nanoparticles and the electro-repulse force between the nanoparticles (represented by the zeta potential), producing a consistent and homogenous stable condition for an extended 30-day period. Due to the greater number of nanoparticles in the 3 wt% solution, the dynamic viscosity of the nanofluid at 3 wt% was higher than at 1 wt%. It was noted that the addition of the nanoparticles did not affect the Newtonian nature of the fluid (except that it was slightly for higher KCl concentrations) and it produced an increase of a 41.75 ± 2.4% for 1 wt% and 59.32 ± 2.1% for 3 wt% of the nanofluid dynamic viscosity, with respect to that of the pure water. Significant enhancement of thermal conductivity enhancement was also obtained, ranging from 0.46 ± 0.11% to 1.47 ± 0.12% for the 1 wt%; and, 2.15 ± 0.11% to 4.7 ± 0.13% for the 3 wt% of nanoparticles added. This noteworthy improvement was attributed to the higher level of homogeneity of the nanofluid, caused by the high electro-repulse force between nanoparticles. Stable electrolyte-based nanofluids, such as KCl, which increase the electro-repulse forces between nanoparticles, can bolster the application of this type of nanofluid in energy conversion and electronic cooling. Enhanced stability properties (particularly in microchannel heat sinks) give these nanofluids the ability to use electric fields for the fluid motion, rather than traditional pumping devices.

Thermal Conductivity and Dynamic Viscosity of Nanofluids: A Review

Thermal conductivity is one of the rheology properties that vital for engineering fluid which indispensable for heat transfer enhancement. For this reason, nanofluid is getting wider attention nowadays due to the presence of nanoparticles in the base fluid can further improve thermal conductivity and dynamic viscosity. These are two important properties for new engineering fluid in providing better cooling and lubricating effects, especially in mechanical and tribology applications. In this paper, specifically, nanofluids thermal conductivity and dynamic viscosity are discussed comprehensively. Both properties’ thermal conductivity and viscosity of nanofluids are improved over the base fluid. Furthermore, these two properties increase when more volume concentrations of nanoparticles are added. In addition, the thermal conductivity also improved with increasing the temperature. From the literature review, the maximum enhancement of thermal conductivity for single nanofluid is recorded 36% of MWCNTs in distilled water. On the other hand, the maximum enhancement of viscosity is recorded 39% of Al2O3 in water-ethylene glycol over base fluid. The hybrid nanofluids that consist of more than one type of nanoparticles exhibit better thermal conductivity where the maximum enhancement is recorded 68% of CuTiO2 in deionized water. For dynamic viscosity measurement, the maximum enhancement of hybrid nanofluids is recorded 168% of MgO-MWCNT in ethylene glycol. Therefore, to sum up, hybrid nanofluids are really promising to enhance heat transfer performance especially for heating and cooling applications. The potential of these nanofluids should be explored extensively to discover its advantages over conventional working fluid.

Experimental research on the performance of nanofluid flow through conically coiled tubes

In this paper, the thermal and hydrodynamic characteristics of SiO2 / water nanofluids flow through conically coiled tubes are examined experimentally. An experimental rig is constructed and set of experiments were carried out in order to investigate the heat transfer and pressure drop of SiO2-water nanofluids flow under constant wall heat flux condition through various conically coiled tubes. To better understand the fluid flow physics, the thermal conductivity and dynamic viscosity of nanofluids in different temperatures and volume fractions are measured experimentally. The results show that both heat transfer rate and pressure drop increase with increment in nanoparticle concentration. Moreover, the effects of cone angle and coil pitch were studied on heat transfer and pressure drop. Results indicate that, lower cone angle brings about more heat transfer and pressure drop. The maximum increase of 26% and 117% in heat transfer and pressure drop were recorded, respectively. Eventually, empirical correlations were presented for prediction of Nusslet number and friction factor in conically coiled tubes.

An experimental/numerical hydrothermal analysis on natural convection and TiO2-SiO2/W-EG nanofluid’s properties in a hollow/finned cavity

Purpose This paper aims to provide an experimental/numerical analysis of free convection within a hollow/finned cavity. Design/methodology/approach The hollow square cavity is equipped with eight active fins which have a similar configuration and different temperatures. Furthermore, four different thermal arrangements are considered to determine the order of temperature for each fin. The coupled lattice Boltzmann method is used, which not only maintains the considerable advantages of standard lattice Boltzmann method such as accuracy but also enhances the stability of this method. Findings The cavity is filled with TiO2-SiO2/Water-Ethylene Glycol nanofluid. The thermal conductivity and dynamic viscosity of nanofluid are experimentally measured using high-precision devices in six concentrations of the nanoparticle. In this study, some main parameters, including a range of Rayleigh number (103 &lt; Ra &lt; 106), the concentration of nanofluid (0.5 to 3 Vol.%) and thermal arrangements of fins, are considered. The effects of these main parameters on the flow, isotherms, heat transfer performance and entropy generation are studied. Originality/value The originality of this paper is combining the numerical simulation (lattice Boltzmann method) using a modern approach with experimental observations of nanofluid’s properties.

Locally weighted moving regression: A non-parametric method for modeling nanofluid features of dynamic viscosity

The objective of this study is to evaluate the variations of CuO/ ethanol nanofluid dynamic viscosity with temperature and shear rate experimentally. In this study, the nanoparticles average size, their mass fraction, temperature, and shear rate are varying from 10 to 50 nm, 0.005 to 5 wt. %, 25 to 70°C, and 2.6 to 64.6 s−1, respectively. The obtained results showed that as the nanoparticles mass fraction increases and the temperature decreases, the nanofluid dynamic viscosity enhances. Then to predict the nanofluid dynamic viscosity as a function of temperature and nanoparticles mass fraction, the locally weighted moving regression (LWMR) method is applied to the data alongside with applying a classic cubic interpolation for comparison. It was found that although both methods have acceptable performance in dealing with the trained data, the LWMR model results in a more precise prediction. In addition, the numerical viscosities obtained by both methods were evaluated at the trained dataset. Finally, it was concluded that the LWMR may be a suitable model for the investigation of nanofluid features.

Two- and three-dimensional simulation of natural convection flow of CuO-water in a horizontal concentric annulus considering nanoparticles’ Brownian motion

A numerical study of two- and three-dimensional natural convection flows of copper oxide water-based nanofluid in a horizontal concentric annulus has been conducted to investigate the influence of Rayleigh number () and nanoparticles volume fraction () on both hydrodynamic and thermal behaviors, and further to examine the difference between two- and three-dimensional simulations. The Brownian motion of nanoparticles is considered in evaluating the effective thermal conductivity and dynamic viscosity of nanofluid. The mass, momentum and energy conservation equations are solved numerically by the finite volume method together with the artificial compressibility algorithm for pressure-velocity coupling. The obtained results indicate that the addition of nanoparticles in water leads to an existence of the general flow structure, but causes more complicated changes in flow intensity. The thermal performance of the nanofluid increases with increment of nanoparticles volume fraction up to a critical particle loading at which the maximum heat transfer occurs. The corresponding maximum heat transfer enhancement, at three different Rayleigh numbers (), is approximately 17.2% (19.0%), 11.2% (11.1%), and 13.6% (12.8%) for 2-D (3-D) configuration.

Multiple-relaxation-time lattice Boltzmann model for simulation of free convection in axisymmetric nanofluid-filled annulus-experimental and numerical observations

Purpose The MRT lattice Boltzmann simulation of natural convection in a confined environment is carried out. The flow and heat transfer during natural convection in a symmetrical annulus are studied. Design/methodology/approach The cavity is filled with TiO2-water nanofluid, and the thermal conductivity and dynamic viscosity of nanofluid are measured experimentally. The experimental data are utilized in the numerical simulations. The nanofluids are prepared at four different nanoparticle concentrations φ = 0, 0.1, 0.3 and 0.5. It is notable that 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. Findings Influence of some main parameters including Rayleigh number in range of 103 to 106, solid volume fraction of nanofluid in range of 0 to 0.5 and four different aspect ratios on the the nanofluid flow (i.e. streamlines), heat transfer (i.e. temperature distribution and average Nusselt number) and entropy generation (i.e. total entropy generation and Bejan number) are presented, quantitatively and graphically. It is found that adding TiO2 nanoparticles to the base fluid has considerable positive effect on the heat transfer performance and entropy generation. In addition, the configuration of the annulus can be good controlling parameter on the heat transfer rate during natural convection. Originality/value The originality of this work is using of a modern numerical method to simulate the free convection and conducting experimental observations to calculate the thermo-physical properties of nanofluid. In addition, the numerical and experimental works are combined to provide accurate results.

RETRACTED: Natural convection heat transfer in a circle-square annulus using lattice Boltzmann method- Treatment of curved boundary conditions

Abstract The lattice Boltzmann simulation of natural convection fluid flow and heat transfer in a square-circle annulus is carried out. A comprehensive discussion regarding the treatment of curved boundary condition for temperature and velocity is presented. The annulus is filled with CuO-water nanofluid. The dynamic viscosity of nanofluid is estimated using KKL (Koo-Kleinstreuer-Li) model. The second law analysis is added to the investigation order to show the effect of different parameters on the irreversibility. The Rayleigh number (103≤Ra≤106), nanoparticle concentration (0 ≤ ϕ ≤ 0.04) and the aspect ratio of inner square cylinder are the governing parameters. The influence of the mentioned parameters on the nanofluid flow, temperature field, local and total entropy generation, Bejan number and average Nusselt number are presented, comprehensively.

Effect of the second outlet location and the applied magnetic field within a ventilated cubic cavity crossed by a nanofluid on mixed convection mode: best configurations

The present numerical study consists of a mixed convection heat transfer, taking place within a ventilated cubic cavity crossed by an alumina–water nanofluid. The ventilation is assured by three openings, practiced on the walls of the cavity. So, the cold nanofluid enters by an opening placed at the top of the left vertical wall and exits by two opening: one is placed at the bottom of the right vertical wall, and the second opening occupied several possibility of location along the four walls of the cavity. All the enclosure’s walls are maintained at a same temperature, higher than that of the entering fluid, except of the side walls which are adiabatic. The governing equations are discretized using the finite volume method and the SIMPLER algorithm to treat the coupling velocity–pressure. The method line by line is used to solve iteratively the algebraic equations. The simulations were carried for different values of the Richardson number (0 ≼ Ri ≼ 10), the Hartmann number (0 ≼ Ha ≼ 100) and a solid volume fractions φ = 4% using Koo–Kleinstreuer–Lee model for the evaluation of effective thermal conductivity and dynamic viscosity of nanofluid. The results are presented in the form of streamlines, isotherms, velocity vectors and average Nusselt number.

An experimental study on hydraulic and thermal performances of hybrid nanofluids in mini-channel

In the present work, the thermo-physical properties and hydraulic and thermal performances of alumina/water, silica/water, and alumina–silica/water nanofluids were experimentally investigated. The thermal conductivity and dynamic viscosity of nanofluids were measured for the volume fractions in the range of 0–2% and the temperature in the range of 10–40 °C. Some new correlations were proposed for the hybrid nanofluid. Single and hybrid nanofluids at the volume fractions of 0.05%, 0.1%, and 0.2% and the Reynolds number in the range of 490–3100 were tested in a mini-channel. Measurements of the thermo-physical properties indicated that the hybrid nanofluid provided larger values of the thermal conductivity and viscosity in comparison with the single ones. The results also showed that the Nusselt number increased with increasing the Reynolds number and volume fraction of the nanoparticles for all nanofluids. Hybrid nanofluid with 75% alumina–25% silica and volume fraction of 0.2% and the single alumina nanofluid with volume fraction of 0.2% provided the highest and the lowest increments in the Nusselt number with the mean increment values of 46% and 11%, respectively. The hydraulic performance assessment revealed that adding nanoparticles to the base fluid increased the friction factor in the mini-channel from 10.4 to 65.2% based on the values of the volume fraction. However, the thermal performance evaluation criteria are always above the unity regardless the type of the nanofluid and among the nanofluids considered in this study, and the maximum performance evaluation criterion was recorded for the hybrid nanofluid with the value of 1.23 at volume fraction of 0.2%.

Nanofluid flow and heat transfer due to natural convection in a semi-circle/ellipse annulus using modified lattice Boltzmann method

Purpose This paper aims to numerically investigate the nanofluid flow, heat transfer and entropy generation during natural convection in an annulus. Design/methodology/approach The lattice Boltzmann method is used to simulate the velocity and temperature fields. Furthermore, some special modifications are applied to make the lattice Boltzmann method capable for simulation in the curved boundary conditions. The annulus is filled with CuO-water nanofluid. The dynamic viscosity of nanofluid is estimated using KLL (Koo-Kleinstreuer-Li) model, and the nanoparticle shape effect is taken account in calculating the thermal conductivity. On the other hand, the local/volumetric entropy generation is used to show the irreversibility under influence of different parameters. Findings The effect of considered governing parameters including Rayleigh number (103&lt;Ra &lt; 106); nanoparticle concentration (0&lt;&lt;0.04) and configuration of annulus on the flow structure; temperature field; and local and total entropy generation and heat transfer rate are presented. Originality/value The originality of this work is using of lattice Boltzmann method is simulation of natural convection in a curved configuration and using of Koo–Kleinstreuer–Li correlation for simulation of nanofluid.