Different Volume Fractions Of Nanoparticles Research Articles

Mono and hybrid nanofluid analysis over shrinking surface with thermal radiation: A numerical approach

The study of magnetohydrodynamics (MHD) incompressible flow of a fluid having hybrid nanoparticles making the colloidal combination with base fluid is presented in this research. Comparative analysis is carried out for the nanofluids Al2O3/Kerosene and ZnO/Kerosene oil with the hybrid nanofluid Al2O3–ZnO/Kerosene oil. The subject flows are influenced with thermal radiation and viscous dissipation. A numerical technique Keller box is employed to examine the envision mathematical model. For computational procedure MATLAB software will be used. Tabulated and graphical outcomes of different effects are presented for the appraisal of velocity and temperature distributions. It is comprehend that the thermal radiation and viscous dissipation parameters possess up surging trends for the hybrid and nanofluid temperature profile but opposite trend has been observed for different volume fractions of nanoparticles.

The Impact of Nanofluid on Natural Convection in an Isosceles Rectangular Container with a Heat Source

The development of modern technology in microelectronics and power engineering requires the creation of efficient cooling systems. This is made possible by the use of special fin technology inside the cavity or special heat transfer Ethylene glycol-copper nanofluids to intensify the heat removal from the heat-generating elements. A numerical study of the natural convection of stationary laminar heat transfers in a closed rectangular cavity with a local source of internal volumetric heat generation. For different Rayleigh numbers and different volume fractions of nanoparticles. The system of equations governing the problem was solved numerically by the fluent computer code based on the method of finite volumes. Based on the Boussinesq approximation. Interior and exterior surfaces are maintained at a constant temperature. The study is carried out for Rayleigh numbers ranging from 104 to 106. The effects of different Rayleigh numbers and volume fractions of nanoparticles on natural convection have been studied. The results are presented as isotherms, isocurrents, and local and mean Nusselt numbers. The aim of this study is to see the influence of the thermal Rayleigh number and the volume fraction of the nanoparticles on the rate of heat transfer.

Enhanced thermal energy storage performance of molten salt for the next generation concentrated solar power plants by SiO2 nanoparticles: A molecular dynamics study

Chloride molten salt is the most promising thermal energy storage materials for the next generation concentrated solar power (CSP) plants. In this work, to enhance the thermal performance of KNaCl2 molten salts, composited thermal energy storage (CTES) materials based on amorphous SiO2 nanoparticles and KNaCl2 were proposed and designed under the guidance of the material composition design strategy. The molecular dynamics simulation method has been conducted to investigate the thermal storage properties and analyze the mechanism of heat transfer improvement from the perspective of microstructure evolution, thermal diffusion properties and energy changes. Thermal conductivity, viscosity, and specific heat capacity of CTES materials at high temperatures with different volume fractions of nanoparticles were predicted to provide reference data for the design of heat transfer systems in CSP plants. The study discovered that increasing the volume fraction of nanoparticles increases the thermal conductivity and specific heat capacity of the systems significantly, with the maximum increase of 25.28% and 7.87%, respectively. Moreover, the enhancement of heat transfer characteristics is the result of the formation of a compressed interface layer with a thickness 5 Å on the outer surface of SiO2 nanoparticles. This work has important guiding significance for the material selection and composition design of molten salt-nanoparticle composite materials used for next-generation CSP plants.

Oscillatory natural convection of Al2O3-water nanofluid near its density maximum in a narrow horizontal annulus

In order to reveal the oscillatory characteristics of the natural convection of nanofluid, this paper presents a comprehensive numerical work on the oscillatory natural convection of Al2O3-water nanofluid near its density maximum in a narrow horizontal annulus. The influences of nanoparticle volume fraction, Rayleigh number and density inversion parameter on the flow pattern evolution and heat transfer ability are analyzed. Results show that the density inversion of the working fluid affects the velocity and temperature distributions of the natural convection of Al2O3-water nanofluid significantly. Adding nanoparticles is beneficial to stabilizing the flow of nanofluid and keeping the symmetry of the flow structure. The convective heat transfer is enhanced with the increase of the volume fraction of nanoparticles. Furthermore, the evolution of the flow regime with the Rayleigh number at different volume fractions of nanoparticles is summarized.

Improving the melting performance of phase change materials using novel fins and nanoparticles in tubular energy storage systems

The present work proposes an integration of a novel fin structure and Al2O3 nanoparticles as an enhancement technology to improve the melting performance of phase change materials (PCMs) for latent heat thermal energy storage systems. A mathematical model of the melting process of PCMs with nanoparticles in a triple-tube heat exchanger is formulated and validated against the experimental data. The effect of different fin layouts and different volume fractions of nanoparticles on the melting process is discussed and reported, including the evolution and deformation of solid–liquid interfaces, the distribution of isotherms, and the time-varying profile of liquid fraction and average temperature over the entire melting process. The results indicate that the melting characteristic is improved by applying the enhanced strategies of novel fins and nanoparticles. Compared to the original structure, the melting time of four different novel fins is reduced by 80.35%, 77.62%, 77.33%, and 80.65%, respectively, which are attributed to the heat transfer enhancement by adding fin configurations to the system. Al2O3 nanoparticles (at 3%, 6%, and 9%) are integrated into the PCMs, and the results show that the melting time is decreased by 13.1%, 15.6%, and 18.8%, respectively. It can be concluded that the combination of fins and nanoparticles is an efficient way to enhance the meting process of phase change materials for thermal energy storage systems.

Modelling and Comparison of the Thermohydraulic Performance with an Economical Evaluation for a Parabolic Trough Solar Collector Using Different Nanofluids

This study examines the three-dimensional heat transfer and flow characteristics of nanofluids in a parabolic trough solar collector under turbulent flow conditions, whereas a non-uniform focused heat flux was applied to the absorption pipe. CuO/water, Al2O3/water, TiO2/water, and SiO2/water are studied numerically. The dynamic and thermal fields are determined by the Reynolds number varying between 50000 ≤ Re ≤ 250000, while the volume concentration of nanofluids is the following: 3% CuO, 6% Al2O3, 4.82% SiO2 and 3.15% TiO2, the nanoparticle size of 30 nm by means of Finite Element Method (FEM). Effects of various parameters such as volume fraction of nanoparticles (φ), various Reynolds numbers and type of nanoparticle on thermo-hydraulic performance of the parabolic solar collector are studied. The average Nusselt number, heat transfer coefficient, average friction factor, pressure drop, temperature and velocity distribution are illustrated using four different types of nanofluids and four different volume fractions of nanoparticles with various Reynolds numbers. According to the final results, both TiO2 and CuO nanofluids have better performance in terms of thermal and hydraulic efficiency and evaluation economic performance compared with other nanofluids studied.

Multiphase CFD Investigation on Convective Heat Transfer Enhancement for Turbulent Flow of Water-Al2O3 Nanofluid

Because of extraordinary heat transfer capability, nanofluids have become a potential interest in engineering sectors. Despite being a multiphase fluid, nanofluids were treated as single phase fluids in many previous studies and comparison between single and two phase models was drawn. Examining nanofluids capability to augment heat transfer is one of the keys to utilize them properly in the field of thermofluids. However, the optimal multiphase model to simulate nanofluids heat transfer enhancement is yet to be found out. In this study, the method of computational fluid dynamics has been used to simulate flow of water-Al2O3 nanofluid in a circular pipe in the purpose of identifying the best multiphase model to simulate heat transfer enhancement of nanofluids. Two multiphase models have been taken into account: Volume of Fluid and Mixture model. Three different volume fractions of nanoparticles in nanofluid have been tested for each of these models such as 1%,4% and 6% for highly turbulent flows where Reynolds number was ranged between 20000 to 80000. The standard k-ɛ turbulence model has been employed to model the flow of nanofluid with the mentioned multiphase models in the present study. The results have been carried out in forms of correlation between Re and Nu and have been compared with existing experimental results. The results showed that the heat transfer enhancement of nanofluid is mostly dominated by concentration of nanoparticles present in the fluid and suggested that Mixture model is suitable for predicting convective heat transfer enhancement of nanofluid for cases with high particle concentration though the necessity of further experimental study in some scopes has been detected.

A well-trained feed-forward perceptron Artificial Neural Network (ANN) for prediction the dynamic viscosity of Al2O3–MWCNT (40:60)-Oil SAE50 hybrid nano-lubricant at different volume fraction of nanoparticles, temperatures, and shear rates

In this study, the influence of volume fraction of nanoparticles (φ) and temperature on dynamic viscosity (μnf) of Al2O3 –MWCNT (40:60) - Oil SAE50 hybrid nano-lubricant was analyzed. For this reason, μnf of a new hybrid nano-lubricant has derived for 174 various experiments through a series of experimental tests including a combination of different φ, temperatures and shear rates (γ̇). A well trained Artificial Neural Network (ANN) is created using the trainbr/trainlm algorithm and showed an MSE value of 3.58 along 0.999 as correlation coefficient for prediction of μnf. Variant Error diagrams and error histograms proved the appropriateness of the ANN as a tool for determining the μnf and the capability of the used training algorithms. In this analysis, the most effective factor on the μnf is the temperature and a decrease in temperature value has an impressive growth of μnf value for all φ. The results show that the φ has a negligible effect on the output parameter (μnf) especially in higher temperatures in a manner that by enhancing the φ, the μnfwill increase for all temperatures, while this enhance is more noticeable in lower temperatures. For example, enhancing the φ from 0 to 1%, almost will not change the μnf while this change of φ causes around 40% increase of μnf in T = 60 °C. Also, γ̇has a non-uniform influence on the μnf, in a sense that in lower γ̇, the μnf has much higher values for different combinations of temperature and φ; on the other hand, by increasing the γ̇, the μnfhas much lower values with narrower bandwidth for various temperatures and φ.

Modeling and estimation of thermal performance factor of MgO-water nanofluids flow by artificial neural network based on experimental data

Using a simple computational tool with a very high connection and the determining role of connections between neurons in identifying network function are the two similarities between natural and Artificial Neural Networks (ANNs). In this article, the very significant subjects of nanofluids efficiency and the thermal performance factor of these fluids operating as heat transfer have been investigated. To model the data in this article, ANNs are used. This modeling is presented for different ϕand modeling results have been compared with experimental data for MgO-water nanofluids flow. The data relating to the efficiency of these nanofluids use complicated patterns, so a type of ANNs has been used with the ability to distinguish the number of neurons required for modeling and following the data pattern. To evaluate the accuracy of the modeling using ANN, the experimental data have been compared with ANN results in different volume fraction of nanoparticles (ϕ). The results show that ANN has a better agreement with experimental data in estimating the data with a higher Reynolds number.

Using perceptron feed-forward Artificial Neural Network (ANN) for predicting the thermal conductivity of graphene oxide-Al2O3/water-ethylene glycol hybrid nanofluid

In this paper, Artificial Neural Network (ANN) was used to investigate the influence of temperature and volume fraction of nanoparticles on the thermal conductivity of Graphene oxide-Al2O3/Water-Ethylene glycol hybrid nanofluid. Nanofluids were prepared with the volume fraction of nanoparticles 0.1, 0.2, 0.4, 0.8, and 1.6% in the temperature range of 25–55 °C. The nanofluid's thermal conductivity results were extracted from six different volume fractions of nanoparticles and seven different temperatures. Then, to generalize the data and obtain a function, the Perceptron feed-forward ANN was used, simulating the output parameter. The outcomes show that the ANN is well trained using the trainbr algorithm and has an average of 1.67e-6 for MSE and a correlation coefficient of 0.999 for thermal conductivity. Finally, we conclude that the effect of increasing the temperature of nanofluid is less against the volume fraction of nanoparticles, especially in low concentrations. This effect is negligible and in the absence of nanoparticles, increasing the temperature from 20 °C to 55 °C leads to an enhance in thermal conductivity of about 6%. However, at high concentrations of nanoparticles, increasing the temperature leads to further thermal conductivity. At volume fraction nanoparticles 1.6%, increasing the temperature from 20 °C to 55 °C increases the thermal conductivity from 0.45 to 0.54 W/m.K.

Numerical investigation of swirling flow and heat transfer of a nanofluid in a tube with helical ribs using a two-phase model

In this investigation, fluid flow and heat transfer of the nanofluid in a tube with helical ribs are surveyed at a different volume fraction of nanoparticles $$\phi=$$ 0%, 1%, 2%, and 3% and Eulerian–Eulerian two-phase approach (mixture model) is used for simulating the water/Al2O3 nanofluid. The fluid flow is laminar, and simulation is done at Re = 250, 500, 750, and 1000. Also, four different geometric cases, including a plain tube and a tube with one, two, and three helical ribs, are used in this research. The most important surveyed parameters are the Nusselt number and friction factor. The results revealed that as Reynolds number increases, heat transfer increases, and friction factor decreases. The relative increase in Nusselt number and the relative decrease in friction factor at Re = 1000 than Re = 250 were calculated, respectively, 82% and 72% at $$\phi=$$ 0% in the plain tube. Helical ribs enhance the Nusselt number and friction factor and relative increase in Nusselt number, and friction factor in the tube with three helical ribs than the plain tube was calculated, respectively, 40% and 86% at $$\phi =$$ 0% and Re = 250. Finally, using nanofluid causes to increase in heat transfer and friction factor, and the relative increase in Nusselt number at $$\phi=$$ 3% than $$\phi =$$ 0% was calculated 6% at Re = 250 in the plain tube.

NUMERICAL ANALYSIS OF NON-FOURIER PHASE-CHANGE HEAT TRANSFER IN BIOLOGICAL TISSUES WITH THE ADDITION OF NANOPARTICLES

An improved cryopreservation method is the need of the hour in the medical field to control the freezing rate to prevent the damage of biological tissues. The present article addresses two issues. First, a numerical model is developed for cryopreservation of biological tissues where non-Fourier heat conduction plays an important role in predicting the freezing rate. A new version of enthalpy method is then proposed and applied for capturing the freezing front. Secondly, with the addition of MgO nanoparticles, which are biodegradable, biocompatible, and nontoxic, the enhancement of freezing rate is explored. The temperature field at different points in the domain is studied. The effect of the different volume fraction of nanoparticles on the freezing rate is discussed in detail. It is observed that the blood perfusion rate has a significant role in predicting the freezing rate.

Application of Artificial Neural Networks (ANNs) for Predicting the Viscosity of Tungsten Oxide (WO3)-MWCNTs/Engine Oil Hybrid Nanofluid

In the current work, the viscosity of hybrid WO3-MWCNTs/Engine Oil nanofluids for the different volume fraction of nanoparticles (φ), temperatures, and shear rates has been measured, and then using Artificial Neural Network (ANN) has been predicted. The statistical reports of ANN have been presented in figures and tables, and it could be found that the designed ANN has an acceptable accuracy in predicting the viscosity of hybrid nanofluids. It can be seen that the optimum neuron number for this data set is 39 and the ANN outputs are very close to the experimental data points. Finally, it is found that in the fitting method, the correlation between experimental data points and the output values is 0.999 424, but in the ANN method, the correlation was 0.999 837 454.

Thermo-economic analysis of a novel parabolic trough solar collector equipped with preheating system and canopy

This paper presents a novel Parabolic Trough Solar Collector (PTSC) equipped with a preheating system and canopy. The thermal, hydraulic, and economic characteristics of the PTSC are numerically investigated to achieve maximum performance evaluation criteria (PEC) index and minimum material consumption for different volume fractions of nanoparticles at various Reynolds numbers. The PTSC is also compared with a reference system without preheating system and canopy. The results indicate that preheating system does not have an obvious effect on friction factor of solar collectors. The PEC index values of the novel system are about 53.0% higher than those of the reference system. Besides, using canopy may have a crucial impact on the improvement of collector efficiency. According to thermo-economic analysis, the length of the PTSC is smaller than that of the reference system when their energy efficiency is the same, leading to lower production costs. It is found that the consumption of stainless steel and carbon steel decreases about 14% and the consumption of glass and carbon steel increases about 6% for the glass coverage and canopy.

Nanofluid flooding for enhanced oil recovery in a heterogeneous two-dimensional anticline geometry

In the present study, we try to numerically simulate the nanofluid flooding method, which is one of the newest methods in this field. For this purpose, the nanofluid is considered as a single phase fluid that enters the porous petroleum reservoir containing oil. Since all porous petroleum reservoirs are heterogeneous, the porous grid is also considered heterogeneous and is solved using the finite element numerical method. The geometry considered in this study is a two-dimensional anticline medium that is heterogeneous. In this study, the effect of using different volume fractions of nanoparticles in nanofluid is studied. Capillary pressure or diffusion are considered in simulation and their behavior in the solution domain is explored. To obtain the capillary pressure in the problem, the logarithmic method is used due to its faster convergence. Another important parameter to be considered is the amount of input fluid into the reservoir (debit), which three different values are considered. Also, the amount of saturated oil remaining in the reservoir, which is one of the inherent characteristics of the reservoir, has been investigated for four different values.

Using of artificial neural networks (ANNs) to predict the rheological behavior of magnesium oxide‐water nanofluid in a different volume fraction of nanoparticles, temperatures, and shear rates

Laboratory studies are usually time‐consuming and costly; hence, soft computing methodology can be an attractive alternative for predicting results. In this study, the viscosity of MgO‐water nanofluid in a different volume fraction of nanoparticles, temperatures, and shear rates has been predicted by artificial neural networks (ANNs) and surface methods. In the ANN method, an algorithm is proposed to select the best neuron number for the hidden layer. In the fitting method, a surface is proposed for each volume fraction of nanoparticles, and finally, the results of the ANN and surface fitting method have been compared. It can be observed that increasing the volume fraction from 0.07% to 1.25% at temperatures of 25°C, 30°C, 40°C, 50°C, and 60°C resulted in about two‐fold increase in viscosity. Also, the best network has 24 neurons in the hidden layer. It can be seen that for a network with 24 neurons in the hidden layer has the best overall correlation, and this coefficient is 0.999035. The mean absolute value of errors in the ANN and fitting method are 0.0118 and 0.0206, respectively.

Cooling Performance of a Novel Circulatory Flow Concentric Multi-Channel Heat Sink with Nanofluids.

Heat rejection from electronic devices such as processors necessitates a high heat removal rate. The present study focuses on liquid-cooled novel heat sink geometry made from four channels (width 4 mm and depth 3.5 mm) configured in a concentric shape with alternate flow passages (slot of 3 mm gap). In this study, the cooling performance of the heat sink was tested under simulated controlled conditions.The lower bottom surface of the heat sink was heated at a constant heat flux condition based on dissipated power of 50 W and 70 W. The computations were carried out for different volume fractions of nanoparticles, namely 0.5% to 5%, and water as base fluid at a flow rate of 30 to 180 mL/min. The results showed a higher rate of heat rejection from the nanofluid cooled heat sink compared with water. The enhancement in performance was analyzed with the help of a temperature difference of nanofluid outlet temperature and water outlet temperature under similar operating conditions. The enhancement was ~2% for 0.5% volume fraction nanofluids and ~17% for a 5% volume fraction.

Computational Modeling on Heat Transfer Enhancement and Fluid Flow Behavior of Al 2 O 3 Water Nanofluid through a Circular Duct

In this work, heat transfer behaviour from a isothermal-walled micro channel has been investigated by using Al2O3-water nanofluid as working fluid. A computational simulation has been carried out for high Reynolds number flow, having different volume fraction of nanoparticles added to it such as; 0%, 1%, 4% and 6%. It has been observed that the properties such as; density, thermal conductivity and viscosity increase with increase in nanoparticles. On the other hand, with increase in the volume fraction of nanoparticles, the specific heat decreases. It also has been observed that the heat transfer from the hot wall increases with Reynolds number and addition of nanoparticles to the nanofluid as well. The friction factor for the flow system decreases with increase the Reynolds number, but for different nanofluids its value is very much closer to each other. With increase in volume fraction of nanoparticles, the pumping power increases. Moreover, with increase in Reynolds number, pumping power increases.

Predict the thermal conductivity of SiO2/water–ethylene glycol (50:50) hybrid nanofluid using artificial neural network

In the current work, after generating experimental data points for different volume fraction of nanoparticles ( $$\phi$$ ) and different temperatures, an algorithm to find the best neuron number in the hidden layer of artificial neural network (ANN) method is proposed to find the best architecture and then to predict the thermal conductivity ( $$k_{\text{nf}}$$ ) of SiO2/water–ethylene glycol (50:50) nanofluid. This ANN is a feed-forward network with Levenberg–Marquardt for the learning algorithm. Regarding the experimental data points, a third-order function is obtained. In the fitting method, the mean square error is 2.7547e−05, and the maximum value of error is 0.0125. The correlation coefficient of the fitting method is 0.9919. This surface also shows the behavior of nanofluid based on the $$\phi$$ and temperatures, and finally, the results of these methods have been compared. It can be seen that for 8 neuron numbers, the correlation coefficient for all outputs of ANN is 0.993861.

Computational modeling of porous medium inside a channel with homogeneous nanofluid

In the present study, the flow field and heat transfer have been simulated numerically inside a channel. The channel is under the constant heat flux. The main objective of this research is investigating the effect of the arrangement of the porous medium in water/Al2O3 nanofluid inside the channel on the flow and heat transfer characteristics. The effect of various arrangements of porous medium with two porosities and different volume fractions of nanoparticles has been investigated in a constant cross section of the porous medium. The results are validated with other references, and excellent consistency has been seen. The results show that using the porous medium has more effect on heat transfer than using nanofluid, and the arrangement of the porous medium has a strong influence on temperature profile and heat transfer. Based on the results, using stair arrangement of porous medium with the distance of 30 cm from each other and the length of 30 cm and using nanofluid with 0.02 volume fraction of Al2O3 nanoparticles have better performance than other considered arrangements.