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In this research, an experimental evaluation is conducted on the hydrothermal behavior of water-based manganese ferrite nanofluid flowing in a metal foam tube. For this purpose, manganese ferrite nanoparticles are synthesized, and X-ray diffraction and scanning electron microscopy are implemented to specify the samples for determination of phase and size of nanoparticles. The effects of Reynolds number, Prandtl number, and presence of MnFe2O4 nanoparticles inside the water on the Nusselt number and friction factor have been studied. The experimental analysis shows that the increment of Reynolds number, Prandtl number, and nanoparticles concentration improve the heat transfer performance. The maximum of 19.1% and 10.5% increase in Nusselt number and friction factor have been achieved respectively by dispersion of 2 wt% manganese ferrite nanoparticles inside the deionized water at Reynolds number of 1,000. A hydrothermal index is proposed to consider the thermal and hydrodynamic characteristics of the nanofluid, and it is attained that the convection heat transfer improvement dominates the pressure drop in this work. According to the experimental results, the Nusselt number and friction factor of the nanofluid is modeled as a function of Reynolds number, Prandtl number, and nanoparticles concentration using artificial neural network with an acceptable precision. [ABSTRACT FROM AUTHOR]

This paper assesses the contribution of TiO2 nanoparticles on thermal performance of a 0.5 mass% aqueous solution of carboxymethyl cellulose (CMC) in a square channel. In this regard, a neural network model is firstly developed for modeling of power law index, consistency index, and thermal conductivity of the aqueous solution of TiO2/CMC-water non-Newtonian nanofluid in terms of the nanoparticle concentration and temperature. Then, an attempt is made to evaluate the friction factor and heat transfer coefficient relative values. According to the results, it is found that the friction factor ratio is directly proportional to the temperature and nanoparticle content, while it inversely varies relative to the shear rate. Moreover, heat transfer coefficient ratio is improved at elevated nanoparticle content, and this improvement is much more profound at higher temperature conditions. For practical purposes, the nanofluid hydrothermal performance index is examined since the addition of nanoparticles increases both heat transfer and friction factor. The corresponding data disclose that the performance index is directly proportional to the nanoparticle content, especially at decreased shear rate and elevated temperature conditions. The application of TiO2/CMC-water nanofluid is found to be more favorable for applications with elevated shear rate conditions. [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 costly and time-consuming determination of thermophysical properties of nanofluids through the experimental analysis leads the current investigations to use the soft computing methods like correlating, artificial neural network (ANN) and genetic algorithm. In this study, the application of ANN, empirical correlations and genetic algorithm for modeling and multi-criteria optimization of the thermophysical properties of clove-treated MWCNTs nanofluid which has been synthesized through a facile and eco-friendly procedure has been investigated. In this contribution, totally 6 structures are assessed: networks including one and two hidden layers with 2, 4, and 6 neurons. From assessment of the ANN, it is found that the network including two hidden layers with 4 neurons in every layer results in the least difference between the network outputs and the experimental data, providing the best performance. It is concluded that the optimal ANN model is a more precise and accurate way to predict the thermal conductivity and viscosity of environmentally friendly C-MWCNT/water nanofluid compared to empirical correlations obtained from non-linear regression method. Moreover, the evolutionary algorithm has been implemented for achieving the optimal conditions to maximize the thermal conductivity and to minimize the viscosity of nanofluid. In this regard, based on real-world engineering experience, the final optimal solutions opted from several distinguished procedures of decision-making including the Bellman-Zadeh, TOPSIS and LINMAP approaches has been investigated in parallel. The results of this study revealed that the obtained outputs using TOPSIS and LINMAP procedures are the closest to ideal solution. [ABSTRACT FROM AUTHOR]

This study presents the application of a modified single-phase method by thermal dispersion model incorporating heterogeneous distribution of nanoparticle concentration for evaluating thermal performance of a nanofluid in a circular porous metal foam tube. Numerical approach was conducted for Re = 200–1000, mean concentration of 0.5–2%, and metal foam porosity of 0.7–0.9. It is observed that the predicted data by application of the thermal dispersion approach are in satisfactory agreement with those obtained experimentally, whereas applying the general homogeneous model results in an underestimation. The results reveal that the heterogeneity of the concentration distribution is directly proportional to nanoparticle mean concentration, Reynolds number, and the metal foam porosity. The velocity and temperature profiles at a cross section have been found to be flatter in dispersion model compared to those obtained from the homogeneous model. Furthermore, it is achieved that the Nusselt number varies directly relative to mean concentration and Reynolds number, whereas it inversely alters relative to the porosity. This reduction is found to be more profound at lower porosities. [ABSTRACT FROM AUTHOR]

In this work, a two-phase mixture model for evaluation of flow and heat transfer performance of SiO 2 /water nanofluids under turbulent flow was proposed by considering the heterogeneity of concentration due to crossed effect and the influences of shear rate, viscosity gradient, thermophoresis and Brownian motion on the diffusion of the nanoparticles. The effects of Peclet number, Reynolds number, nanoparticle size and nanofluid mean concentration on the distribution of nanoparticles have been evaluated. The values of thermal conductivity and viscosity as the main thermophysical properties of nanofluids changed across different layers of the liquid due to the heterogeneous distribution of concentration. It was observed that an increase in the Peclet number caused heterogeneity in the distribution of the properties. The achieved nanoparticle distribution has been implemented for analysis of nanofluid using two-phase mixture model. It was found that the effect of nanofluid concentration on the Nusselt number was more noticeable in lower Reynolds numbers due to the insignificant effect of flow momentum on heat transfer. The maximum of 43.9% enhancement in convection heat transfer was achieved by dispersion of 4% SiO 2 nanoparticles inside DI-water at Re = 25,000. [ABSTRACT FROM AUTHOR]

In this paper, an experimental evaluation on the viscosity of water-based manganese ferrite nanofluid with and without magnetic field with 100, 200, 300, and 400 G intensities has been conducted. The Brookfield DV-I PRIME viscometer is implemented to measure the MnFe 2 O 4 /water nanofluid viscosity and to evaluate the influence of different volume concentrations (from 0.25% to 3%) and various temperatures (from 20 to 60 °C) on the viscosity. According to the measurements, viscosity incrementally increases with the augmentation of nanoparticles concentration while it remarkably decreases at higher temperatures under absence and attendance of magnetic field. The maximum viscosity ratio of 1.14 is achieved at 3 vol% of nanoparticles and 20 °C under no magnetic field, whereas it increments to maximum viscosity ratio of 1.75 at 3 vol% of nanoparticles and 40 °C under 400 G magnetic field. Furthermore, new correlation is proposed for determination of viscosity of MnFe 2 O 4 /water nanofluids in terms of magnetic field intensity, volume concentration and temperature. [ABSTRACT FROM AUTHOR]

An experimental study is conducted to investigate the convection heat transfer of Fe 3 O 4 /water nanofluid through a porous metal foam tube with uniform heat flux under the influence of constant magnetic field. This magnetic field is generated by four identical electromagnets. The electromagnets sequence is one of the most important parameters that has considerable effect on the convection heat transfer in this subject. Therefore, four different configurations of electromagnets are considered and the efficient arrangement is obtained by a numerical simulation using single-phase method in order to reach the higher heat transfer rate. Furthermore, the effects of Reynolds number, nanofluid weight fraction and magnetic field intensity on the forced convection heat transfer in the porous metal foam tube are examined under presence and absence of constant magnetic field. The experimental results show that the application of Fe 3 O 4 /water nanofluid enhances the convection heat transfer compared to DI-water. Additionally, implement of constant magnetic field leads to improve the convection heat transfer of nanofluid through the porous metal foam tube. This heat transfer improvement is more intensified in the lower fluid velocities as well as higher nanoparticle weight fractions. The maximum of 23.4% heat transfer enhancement is obtained by dispersion of 2% weight fraction of nanofluid inside the DI-water at Re = 200 under applied magnetic field of 200 G (0.02 T) intensity. A new correlation has been proposed for the Nusselt number of Fe 3 O 4 /water nanofluid in terms of Reynolds number, Prandtl number, nanoparticle weight fraction and magnetic field intensity which predicts the experimental data accurately. [ABSTRACT FROM AUTHOR]

In this work, the evaluation of thermal conductivity of MnFe 2 O 4 /water nanofluids in attendance of magnetic field is conducted. The MnFe 2 O 4 /water is synthesized and prepared in different nanoparticles concentrations, from 0.25% to 3%. The thermal conductivity of nanofluids has been determined at various nanofluid concentrations and temperatures with and without magnetic field. It is found that the thermal conductivity of nanofluid remarkably improves with augmentation of solid volume fraction. Moreover, by incrementing the nanofluid temperature, the thermal conductivity improves under no magnetic field, while it decreases at higher temperatures in attendance of magnetic field. The highest thermal conductivity improvement is achieved 26.4% at 60 °C and 3 vol.% of nanoparticles under no magnetic field and 36.5% under 400 G magnetic field which occurs at 20 °C and 3 vol.% of nanoparticles. Furthermore, an empirical correlation is derived to estimate thermal conductivity of MnFe 2 O 4 /water nanofluid in terms of magnetic field intensity, volume concentration, and temperature. [ABSTRACT FROM AUTHOR]

Abstract Modeling and simulation of nanofluid flows is crucial for applications ranging from the cooling of electronic devices to solar water heating systems, particularly when compared to the high expense of experimental studies. Accurate simulation of a thermal-fluid system requires a deep understanding of the underlying physical phenomena occurring in the system. In the case of a complex nanofluid-based system, suitable simplifying approximations must be chosen to strike a balance between the nano-scale and macro-scale phenomena. Based on these choices, the computational approach – or set of approaches – to solve the mathematical model can be identified, implemented and validated. In Part I of this review (Mahian et al., 2019), we presented the details of various approaches that are used for modeling nanofluid flows, which can be classified into single-phase and two-phase approaches. Now, in Part II, the main computational methods for solving the transport equations associated with nanofluid flow are briefly summarized, including the finite difference, finite volume, finite element, lattice Boltzmann methods, and Lagrangian methods (such as dissipative particle dynamics and molecular dynamics). Next, the latest studies on 3D simulation of nanofluid flow in various regimes and configurations are reviewed. The numerical studies in the literature mostly focus on various forms of heat exchangers, such as solar collectors (flat plate and parabolic solar collectors), microchannels, car radiators, and blast furnace stave coolers along with a few other important nanofluid flow applications. Attention is given to the difference between 2D and 3D simulations, the effect of using different computational approaches on the flow and thermal performance predictions, and the influence of the selected physical model on the computational results. Finally, the knowledge gaps in this field are discussed in detail, along with some suggestions for the next steps in this field. The present review, prepared in two parts, is intended to be a comprehensive reference for researchers and practitioners interested in nanofluids and in the many applications of nanofluid flows. [ABSTRACT FROM AUTHOR]

Abstract It has been more than two decades since the discovery of nanofluids-mixtures of common liquids and solid nanoparticles less than 100 nm in size. As a type of colloidal suspension, nanofluids are typically employed as heat transfer fluids due to their favorable thermal and fluid properties. There have been numerous numerical studies of nanofluids in recent years (more than 1000 in both 2016 and 2017, based on Scopus statistics). Due to the small size and large numbers of nanoparticles that interact with the surrounding fluid in nanofluid flows, it has been a major challenge to capture both the macro-scale and the nano-scale effects of these systems without incurring extraordinarily high computational costs. To help understand the state of the art in modeling nanofluids and to discuss the challenges that remain in this field, the present article reviews the latest developments in modeling of nanofluid flows and heat transfer with an emphasis on 3D simulations. In part I, a brief overview of nanofluids (fabrication, applications, and their achievable thermo-physical properties) will be presented first. Next, various forces that exist in particulate flows such as drag, lift (Magnus and Saffman), Brownian, thermophoretic, van der Waals, and electrostatic double layer forces and their significance in nanofluid flows are discussed. Afterwards, the main models used to calculate the thermophysical properties of nanofluids are reviewed. This will be followed with the description of the main physical models presented for nanofluid flows and heat transfer, from single-phase to Eulerian and Lagrangian two-phase models. In part II, various computational fluid dynamics (CFD) techniques will be presented. Next, the latest studies on 3D simulation of nanofluid flow in various regimes and configurations are reviewed. The present review is expected to be helpful for researchers working on numerical simulation of nanofluids and also for scholars who work on experimental aspects of nanofluids to understand the underlying physical phenomena occurring during their experiments. [ABSTRACT FROM AUTHOR]

The second law attributes including entropy generation, exergy destruction and second law efficiency for flow a novel nanofluid containing graphene nanoplatelets decorated with silver nanoparticles are investigated in a micro double-pipe heat exchanger. The effects of viscous dissipation are considered in the simulation. Frictional entropy generation intensifies with increase in Reynolds number and concentration. Moreover, with the concentration increment, thermal entropy generation rate increases. The frictional and thermal exergy destruction rates intensify in both tube and annulus sides by increasing either Reynolds number or concentration. The results show that the friction has a more significant role than heat transfer in exergy destruction occurred in the heat exchanger. By increasing the Reynolds number, contribution of the nanofluid side to the exergy destruction increases in comparison with that of the water side. In addition, the second law efficiency decreases by increasing either Reynolds number or concentration. [ABSTRACT FROM AUTHOR]

Highlights • Transient characteristics of nanofluid in a minichannel heat sink with MEPCM layer in its ceiling are studied. • The wall temperature rises sharply after sudden heat-pulse and then tend to rise slowly. • The dispersion of nanoparticles inside water leads to the clear decrement of wall and temperature. Abstract In this work, transient thermal characteristics of pure water or Al 2 O 3 /water nanofluid with 2–8 wt% concentrations as coolant flowing through a minichannel heat sink with MEPCM layer in its ceiling are experimentally investigated. The heat sink consists of eight minichannels with length, height, and width of 50 mm, 3 mm, and 1 mm for each minichannel, respectively. A uniform heat flux in the range of 1.2–6.8 W/cm2 is imposed to heat the heat sink and a heat pulse amplitude of 8 W/cm2 is suddenly applied for 10 min after steady-state heating condition and then the heating power is cut off immediately. The dimensionless wall temperature distribution, temperature of MEPCM layer, maximum wall temperature suppression, and heat transfer effectiveness of the system are assessed under sudden heat-pulse conditions. The measured results reveal that the wall temperature and MEPCM layer temperature rise sharply after sudden heat-pulse and then tend to rise slowly. Moreover, it is observed that the latent heat absorbed by the MEPCM layer during melting process can reduce the severe temperature variation caused by sudden heat-pulse. Furthermore, it is concluded that the dispersion of alumina nanoparticles inside pure water and the increase of its concentration lead to the significant decrement of wall temperature and MEPCM layer temperature and increment of the maximum wall temperature suppression. Finally, the comparison of the results obtained from the employment of nanofluid and pure water in the heat sink shows that the nanofluid-cooled heat sink with MEPCM layer in its ceiling outperforms the water-cooled one with/without MEPCM layer. [ABSTRACT FROM AUTHOR]

In this study, the rheological behavior and dynamic viscosity of 5 W30 engine oil based ZnO-MWCNT (30:70) hybrid nanofluid with various volume fractions (VFs) ranging from 0.05 to 1 vol%, temperatures in the range of 5–55 °C, and shear rates altering from 50 to 1000 rpm are experimentally evaluated. The measured viscosity values at different shear rates (SRs) and temperatures revealed that the hybrid nanofluid under study has a non-Newtonian behavior. For example, with increasing SR from 50 to 300 rpm, the maximum viscosity reduction (= 25.6%) occurs at temperature of 5 °C and VF of 1%, while with increasing SR from 700 to 1000 rpm, the minimum viscosity reduction (= 7.6%) occurs at temperature of 55 °C and VF of 0.05%. Moreover, the power law index is observed less than unity indicating pseudoplastic behavior of hybrid nanofluid under study in all VFs and temperatures. The experimental results show that the increase of the VF of nanoparticles leads to the elevated viscosity. By raising the nanofluid temperature, the viscosity of hybrid nanofluid reduces, while engine oil without additives has a high viscosity at elevated temperatures. As an example, considering constant SR of 300 rpm, the increase of temperature from 5 to 55 °C leads to the 78.4% and 85.5% viscosity reduction for the hybrid nanofluid with VF of 0.5% and 0.75%, respectively. In order to predict the experimental data, a three-variable correlation (depending on the temperature, VF of nanoparticles, and SR) and artificial neural network modeling are developed. It is concluded that an excellent agreement exists between the experimental data, correlation results, and predicted data by neural network, however, neural network modeling demonstrated its capability to estimate the viscosity of 5 W30 engine oil based ZnO-MWCNT hybrid nanofluid more precisely rather than the proposed correlation. Hence, the neural network modeling is highly recommended for prediction of viscosity of the hybrid nanofluid under study. [Display omitted] • Rheological behavior of ZnO-MWCNT (30:70)/5 W30 hybrid nanofluid is evaluated. • Effects of concentration, temperature, and shear rates on the viscosity is studied. • A three-variable correlation for viscosity estimation is developed. • Comparison of correlation results with those of available correlations is done. • A neural network modeling is developed for predicting the experimental data. [ABSTRACT FROM AUTHOR]