Heat Transfer Performance Of Nanofluids Research Articles

Thermal Conductivity and Rheology of Graphene Oxide Nanofluids and a Modified Predication Model

In order to reveal the heat transfer performance of nanofluids in solar collectors, the thermal conductivity and dynamic viscosity of five kinds of graphene oxide nanofluids, with a mass fraction of 0.002% to 0.01%, were studied in the temperature range of 25–50 °C. To ensure the dispersion and stability of the prepared nanofluids, UV–Vis absorption spectrum, zeta potential and particle size distribution were employed for nanofluid characterization. Agglomeration and sedimentation of the prepared nanofluids after standing for 20 days were observed, showing the good stability of the prepared graphene oxide–water nanofluid. The dynamic viscosity and thermal conductivity were measured. They show that with the increase in temperature, the dynamic viscosity of nanofluids decreases and the thermal conductivity increases. With the increase in mass concentration, the viscosity and thermal conductivity are improved. The highest thermal conductivity increase is obtained when the nanofluid concentration is 0.01% and the temperature is 50 °C. Finally, and most importantly, considering the inaccuracy of the existing experimental correlations to the predicted values of thermal conductivity, we propose our new mathematical model of correlation and carry out a series of tests to verify its reliability. The experimental correlations with temperature and concentration as independent variables show good agreement and accuracy with the experimental data.

Estimation of thermophysical property of hybrid nanofluids for solar Thermal applications: Implementation of novel Optimizable Gaussian Process regression (O-GPR) approach for Viscosity prediction.

Solar energy technologies represent a viable alternative to fossil fuels for meeting increasing global energy demands. However, to increase the production of solar technologies in the global energy mix, the cost of production should be as competitive as other sources. This study focuses on the implementation of machine learning for estimating the thermophysical properties of nanofluids for nanofluid-based solar energy technologies as this would make the synthesis of nanofluids cost-effective. The prediction of thermal conductivity has gained a lot of research attention, whereas, the viscosity of nanofluids has less concentration of studies. The accurate prediction of the viscosity of hybrid nanofluids is important in estimating the heat transfer performance of nanofluids as regards their pump power requirements and convective heat transfer coefficient in several applications. The rigor of experimentations of hybrid nanofluids has necessitated the need for developing efficient and robust machine learning models for accurately estimating the viscosity of hybrid nanofluids for solar applications. Several studies were aimed at developing a predictive model for the viscosity of nanofluids; however, these models are limited to specific types of nanofluids. This study is aimed at developing a robust machine learning algorithm for predicting the viscosity of several hybrid nanofluids from reliable experimental data (700 datasets) culled from literature. This study implements a novel optimizable Gaussian process regression (O-GPR), which have not been previously used in this area, and compares the result with other commonly used machine learning algorithms like, Boosted tree regression (BTR), Artificial neural network (ANN), support vector regression (SVR), to accurately predict the viscosity of a wide range of Newtonian-based hybrid nanofluid. The input parameters used in training the machine learning models were temperature (T), volume fraction (VF), the acentric factor of the base fluid (ACF), nanoparticle size (NS), and nanoparticle density (ND). The prediction performance of the machine learning algorithms was tested using statistical metrics and was compared with theoretical models. The O-GPR model showed superior predictive performance with an R2 of 0.999998 and an MSE of 0.0002552. The study conclusively states that the high accuracy prediction of thermophysical properties of nanofluid using robust machine learning models makes the design of nanofluid-based solar energy technologies more cost-effective.

Heat transfer enhancement of Fe 3 O 4 ‐water nanofluid by the thermo‐magnetic convection and thermophorestic effect

The promotion of the heat transfer by ferro-nanofluid under a magnetic field has caught more extensive attention in the field of nuclear industry and energy engineering. Firstly, a modified model based on the Buongiorno model was proposed to investigate the heat transfer considering the Kelvin force in the presence of an inhomogeneous magnetic field to elevate the heat transfer performance of nanofluid using thermomagnetic convection. Secondly, the influence of Kelvin force, thermophoresis, and Brownian motion of nanoparticles on the heat transfer at a moderate Reynolds number was investigated. The numerical results indicate that the chaotic convection induced by the Kelvin force and the thermophoretic effect are much important factors. Thirdly, the numerical results demonstrate that the NSN and NSN/SNS arrangements of magnets should be the preferential selection. Besides, the more powerful magnetic field and greater volume fraction of nanoparticle can produce better heat transfer performance. Finally, the discussions on the underneath mechanisms for heat transfer augment are conducted, and it can be concluded the thermomagnetic convection induced the combination of the magnetic effect and thermophoretic effect may be the key mechanism.

Investigation of Performance on Photovoltaic/Thermal (PV/T) System Using Magnetic Nanofluids

Photovoltaic/Thermal (PV/T) systems provide hot fluid (usually water) production as well as electrical energy production. In addition, since the overheating of the PV systems is prevented by the heat drawn by the thermal system, the electricity production performance of the PVs increases. Nanofluids are offered as a solution to increase the heat absorbed in the thermal system. The main physical events that lead to a significant improvement in the heat transfer performance of nanofluids can be summarized as follows: (i) The thermal conductivity of the prepared nanofluid increases at certain rates because the thermal conductivity of the solid metal is higher than that of the basic fluid, (ii) The heat transfer surface area increases due to the increase in the thermal conductivity of the fluid, (iii) Increase in the effective thermal capacity of the fluid, (iv) Increase in the thermal conductivity of the fluid and turbulent volume due to high fluid activity. In this study, by using nanofluids obtained by adding 1% Fe2O3, Fe3O4 and NiFe2O4 magnetic nanoparticles by weight to the basic fluid water, a bidirectional performance increase was achieved by increasing the thermal heat transfer of the PV/T system while providing more cooling of the PV system. In the experimental study, a 14% improvement in electricity production was achieved in NiFe2O4 nanofluid by drawing more heat from the heated PV panels by utilizing the high thermal conductivity of nanofluids. Since the amount of heat absorbed in the thermal system is high, an average of 104% temperature (∆T) increase in the hot fluid temperature compared to the base fluid water was obtained in the NiFe2O4 nanofluid.

Size, interface and temperature effects on specific heat capacities of Cu-water nanofluid and Cu nanoparticle: A molecular analysis

Accurate estimation of specific heat capacity (Cp) is of essential importance for characterization of the heat transfer performance of nanofluids in many applications. In this study, the particle size, interface, and temperature effects on the Cp of Cu-water nanofluid and Cu nanoparticle are systematically studied. Also, the TIP4P rigid and the SPC/Fw flexible water models are compared to demonstrate their influence on the estimation of Cp. The investigation is performed by using the molecular dynamics simulation method, at the mean temperatures of 300 K, 350 K, and 400 K. The results show that the Cp increases with increasing nanoparticles size, but increases with decreasing temperature. The increase is attributed to the interface effect demonstrated by the vibrational density of state (VDOS). The VDOS mismatch is close to zero with the increase in the Cp of the Cu-water nanofluids. In contrast, there is a substantial divergence of the Cp of the nanoparticles from the theoretical values. The reason can be owing to the effect of particle size and the interaction with the surrounding water molecules. Compared to the TIP4P model, the SPC/Fw model shows an increase in the Cp. It is attributed to the intramolecular degrees of freedom that existed in the model, since they provide a small, but probably significant, contribution to intermolecular interactions. This work is helpful for understanding the enhancement mechanisms of specific heat capacity for nanofluids and the suspended nanoparticles used in, e.g., solar thermal applications.

Modeling and optimization the effective parameters of nanofluid heat transfer performance using artificial neural network and genetic algorithm method

The heat transfer coefficient and, as a result, the Nusselt (Nu) number for nanofluids are affected by parameters such as thermal conductivity, thermal capacity of the fluid and nanoparticles, flow pattern, nanofluid viscosity, volume fraction of suspended particles, particle shape, and size. Since the parameters affecting the heat transfer coefficient of nanofluids are more than two or three parameters, and optimization requires calculating the value of the function at different points, on the other hand, there are no suitable equations for the relationship between these parameters, so for this purpose, existing experimental data was modeled using neural networks. After validating the created networks, using these networks, the optimal points in the existing information range have been determined using the genetic algorithm optimization method. All leading networks, with an intermediate layer, have the Levenberg–Marquardt (LM) training method, and the method of least sum of squares error has been used for validation. In addition, other networks were trained using data for all three nanofluids (Al2O3/water, CuO/water, and Cu/water nanofluids) by adding a particle size parameter. The obtained networks were also optimized in the genetic algorithm. The results showed that increasing Reynolds (Re) from 729 to 1995 at a concentration of 1 wt%, T = 96 °C, and Prandtl (Pr) = 2.97 increased the Nu by nearly 82% for Al2O3/water nanofluids. For CuO/water nanofluids, with increasing Reynolds from 617 to 2053 at a concentration of 3 wt%, T = 95.9 °C, and Pr = 4.59, the variations of the ratio of convection heat transfer coefficient of nanofluid to water increased by almost 38%. For Cu/water nanofluids, increasing the Reynolds number from 626 to 1895 at a concentration of 3 wt%, T = 65 °C, and Pr = 2.95, increased the Nu by nearly 77%.

CFD simulation of laminar free convection flows of nanofluids in a cubical enclosure

Understanding the physics behind heat transfer within an enclosure especially in the natural convection domain has many important applications. The heat transfer performance of nanofluids inside a cubical enclosure with an internal heat source fixed on the side-wall of the enclosure will be investigated under natural convection flow condition in this paper. A three-dimensional two-phase mixture model will be developed and validated for this investigation. Computational fluid dynamic (CFD) simulations will be undergone using an in-house solver developed on simplified marker and cell (SMAC) algorithm within a collocated grid by finite difference method. The time dependent flow and characteristics of heat transfer for nanofluids will be investigated with pure water as the base fluid dispersed with different nanoparticles. In addition, parametric studies will also be performed by varying the particle volume concentrations (0%≤Φ ≤ 3%), particle diameters (25 nm ≤ Dp ≤ 50 nm) and Grashof numbers (103 ≤ Gr ≤ 106). The particle volume concentration when increased in a nanofluid gives more affinity to heat absorption. Velocity increase of particles leads to the efficient mixing of nanofluid and so an increase in Grashof number also leads to heat transfer enhancement. Decrease in particle diameter is the final parameter which shows a heat transfer increase.

Forced convection heat transfer of non-Newtonian MWCNTs nanofluids in microchannels under laminar flow

Heat transfer process in micro/mini-scales becomes an urgent need for many applications where size and weight are severe constraints such as electronics. However, the latter led to developing new thermal fluids, the so-called nanofluids, with enhanced thermophysical properties to cover the high heat dissipation requirements due to the devices' miniaturization. In this work, a validated computational model is used to establish guidelines for the compact heat exchanger with microchannels, as well as to assist nanofluid properties tailoring. A finite volume method (FVM) approach is built considering laminar fluid flow conditions in a circular microchannel. The latter is used and validated against available experimental data, and the influence of temperature-dependent thermophysical properties and non-Newtonian behaviour of the MWCNTs (Multi-walled carbon nanotubes) nanofluids are considered. The results show that MWCNTs nanofluids enhance the heat transfer performance of the micro heat exchanger by up to 33%. Moreover, particular operating conditions are seen to improve the system energy efficiency, mainly near to Reynolds number (Re) equal to 1000, when taking into account both heat transfer performance and pressure drop. Additionally, a correlation of the average Nusselt number is proposed to estimate the heat transfer performance of nanofluids in microchannels.

Numerical investigation of nanoparticles slip mechanisms impact on the natural convection heat transfer characteristics of nanofluids in an enclosure

This study intends to give qualitative results toward the understanding of different slip mechanisms impact on the natural heat transfer performance of nanofluids. The slip mechanisms considered in this study are Brownian diffusion, thermophoretic diffusion, and sedimentation. This study compares three different Eulerian nanofluid models; Single-phase, two-phase, and a third model that consists of incorporating the three slip mechanisms in a two-phase drift-flux. These slip mechanisms are found to have different impacts depending on the nanoparticle concentration, where this effect ranges from negligible to dominant. It has been reported experimentally in the literature that, with high nanoparticle volume fraction the heat transfer deteriorates. Admittingly, classical nanofluid models are known to underpredict this impairment. To address this discrepancy, this study focuses on the effect of thermophoretic diffusion and sedimentation outcome as these two mechanisms turn out to be influencing players in the resulting heat transfer rate using the two-phase model. In particular, the necessity to account for the sedimentation contribution toward qualitative modeling of the heat transfer is highlighted. To this end, correlations relating the thermophoretic and sedimentation coefficients to the nanofluid concentration and Rayleigh number are proposed in this study. Numerical experiments are presented to show the effectiveness of the proposed two-phase model in approaching the experimental data, for the full range of Rayleigh number in the laminar flow regime and for nanoparticles concentration of (0% to 3%), with great satisfaction.

Experimental Study on the Specific Heat Capacity Measurement of Water- Based Al2O3-Cu Hybrid Nanofluid by using Differential Thermal Analysis Method

Background: Researchers working in the field of nanofluid have done many studies on the thermophysical properties of nanofluids. Among these studies, the number of studies on specific heat is rather limited. In the study of the heat transfer performance of nanofluids, it is essential to raise the number of specific heat studies, whose subject is one of the important thermophysical properties. Objective: The authors aimed to measure the specific heat values of Al2O3/water, Cu/water nanofluids and Al2O3-Cu/water hybrid nanofluids using the DTA procedure, and compare the results with those frequently used in the literature. In addition, this study focuses on the effect of temperature and volume concentration on specific heat. Methods: The two-step method was tried to have nanofluids. The pure water selected as the base fluid was mixed with the Al2O3 and Cu nanoparticles and Arabic Gum as the surfactant, firstly mixed in the magnetic stirrer for half an hour. It was then homogenized for 6 hours in the ultrasonic homogenizer. Results: After the experiments, the specific heat of nanofluids and hybrid nanofluid were compared and the temperature and volume concentration of specific heat were investigated. Then, the experimental results obtained for all three fluids were compared with the two frequently used correlations in the literature. Conclusion: Specific heat capacity increased with increasing temperature, and decreased with increasing volume concentration for three tested nanofluids. Cu/water has the lowest specific heat capacity among all tested fluids. Experimental specific heat capacity measurement results are compared by using the models developed by Pak and Cho and Xuan and Roetzel. According to experimental results, these correlations can predict experimental results within the range of ±1%.

A Review on Stability and Heat Transfer Performance of Nanofluid Using Surfactants

Nanofluid had been widely used in heat transfer applications due to its better thermophysical properties. However, nanofluid had a problem in the stability of nanoparticles suspended in the based fluid. Several ways had been done to increase the stability of nanofluids including using surfactants. The purpose of this review is to uncover the stability and heat transfer performance of nanofluid using surfactants. A systematic review was used to collect the related articles for this review. This review shows the mechanism of two types of surfactants that had been used which are ionic and non-ionic. Furthermore, the stability of nanofluid is very important to enhance the thermal performance of nanofluid. The recommendations are highlighted to study the optimum amount of surfactant for respective nanofluids.

A Review on Heat Transfer of Nanofluids by Applied Electric Field or Magnetic Field.

Nanofluids are considered to be a next-generation heat transfer medium due to their excellent thermal performance. To investigate the effect of electric fields and magnetic fields on heat transfer of nanofluids, this paper analyzes the mechanism of thermal conductivity enhancement of nanofluids, the chaotic convection and the heat transfer enhancement of nanofluids in the presence of an applied electric field or magnetic field through the method of literature review. The studies we searched showed that applied electric field and magnetic field can significantly affect the heat transfer performance of nanofluids, although there are still many different opinions about the effect and mechanism of heat transfer. In a word, this review is supposed to be useful for the researchers who want to understand the research state of heat transfer of nanofluids.

Effects of nanoparticle dispersion on turbulent mixed convection flows in cubical enclosure considering Brownian motion and thermophoresis

This paper reports on the numerical investigation of assisting and opposing mixed convection flows and heat transfer performance of nanofluids in a cubical enclosure under turbulent conditions. The enclosure side wall consists of a partially heated heat source and the nanofluid is pumped into the enclosure through the inlet port. A three-dimensional, turbulent two-phase mixture model is developed to investigate the thermal performance of nanofluids by considering the effects of Brownian motion and thermophoresis. The base fluid is considered as water into which different nanoparticles such as aluminum oxide (Al2O3), copper (Cu) and silver (Ag) are dispersed for different particle volume fractions (ϕ) varied from 0% to 4%. The Richardson number (Ri) and Reynolds number (Re) are varied between 0.01 ≤ Ri ≤ 10 and 3.1 × 103 ≤ Re ≤ 105 and the Grashof number (Gr) is maintained at a constant value of 108. The nanoparticles are considered as monodispersed spherical particles with a uniform diameter of 100 nm. The results indicate that the temperature and turbulent kinetic energy distributions for opposing flows are higher than assisting flows. It also found that the average heat transfer rate increases with decrease in Richardson number and increase in volume fraction for both assisting and opposing flows. The effects of Brownian motion and thermophoresis are more significant in assisting flows than opposing flows.

Convective Heat Transfer Coefficient Model Under Nanofluid Minimum Quantity Lubrication Coupled with Cryogenic Air Grinding Ti–6Al–4V

Under the threat of serious environmental pollution and resource waste, sustainable development and green manufacturing have gradually become a new development trend. A new environmentally sustainable approach, namely, cryogenic air nanofluid minimum quantity lubrication (CNMQL), is proposed considering the unfavorable lubricating characteristic of cryogenic air (CA) and the deficient cooling performance of minimum quantity lubrication (MQL). However, the heat transfer mechanism of vortex tube cold air fraction by CNMQL remains unclear. The cold air fraction of vortex tubes influences the boiling heat transfer state and cooling heat transfer performance of nanofluids during the grinding process. Thus, a convective heat transfer coefficient model was established based on the theory of boiling heat transfer and conduction, and the numerical simulation of finite difference and temperature field in the grinding zone under different vortex tube cold air fractions was conducted. Simulation results demonstrated that the highest temperature initially declines and then rises with increasing cold air fraction. Afterward, this temperature reaches the lowest peak (192.7 °C) when the cold air fraction is 0.35. Experimental verification was conducted with Ti–6Al–4V to verify the convective heat transfer coefficient model. The results concluded that the low specific grinding energy (66.03 J/mm3), high viscosity (267.8 cP), and large contact angle (54.01°) of nanofluids were obtained when the cold air fraction was 0.35. Meanwhile, the lowest temperature of the grinding zone was obtained (183.9 °C). Furthermore, the experimental results were consistent with the theoretical analysis, thereby verifying the reliability of the simulation model.

Experimental Research and Development on the Natural Convection of Suspensions of Nanoparticles-A Comprehensive Review.

Suspensions of nanoparticles, widely known as nanofluids, are considered as advanced heat transfer media for thermal management and conversion systems. Research on their convective thermal transport is of paramount importance for their applications in such systems such as heat exchangers and solar collectors. This paper presents experimental research on the natural convection heat transfer performances of nanofluids in different geometries from thermal management and conversion perspectives. Experimental results and available experiment-derived correlations for the natural thermal convection of nanofluids are critically analyzed. Other features such as nanofluid preparation, stability evaluation and thermophysical properties of nanofluids that are important for this thermal transfer feature are also briefly reviewed and discussed. Additionally, techniques (active and passive) employed for enhancing the thermo-convection of nanofluids in different geometries are highlighted and discussed. Hybrid nanofluids are featured in this work as the newest class of nanofluids, with particular focuses on the thermophysical properties and natural convection heat transfer performance in enclosures. It is demonstrated that there has been a lack of accurate stability evaluation given the inconsistencies of available results on these properties and features of nanofluids. Although nanofluids exhibit enhanced thermophysical properties such as viscosity and thermal conductivity, convective heat transfer coefficients were observed to deteriorate in some cases when nanofluids were used, especially for nanoparticle concentrations of more than 0.1 vol.%. However, there are inconsistencies in the literature results, and the underlying mechanisms are also not yet well-understood despite their great importance for practical applications.

Experimental Investigation into Natural Convection of Zinc Oxide/Water Nanofluids in a Square Cavity

The public domain is inundated with discrepancies in numerical and experimental findings on the natural convection heat transfer performance of nanofluids in a cavity. This paper presents the experimental investigation of the natural convection of deionized water (DIW)-based zinc oxide (ZnO) nanofluid in a rectangular cavity. The ZnO nanoparticles (20 nm) were dispersed in DIW to formulate nanofluids at various volume concentrations (0.10, 0.18, 0.36, 0.50 and 1.0 vol.%). The spectrophotometer and zeta potential were used to verify the stability of ZnO/DIW nanofluid at various temperatures and concentrations. ZnO/DIW nanofluids and DIW were charged into a rectangular cavity with the opposite vertical walls under varying temperature differences. The natural convection of ZnO/DIW nanofluid was performed at Rayleigh number range of 7.45 × 107 and 9.20 × 108. Zeta potential values revealed stable nanofluids with no sedimentation of nanoparticles observed within 24 h. At 0.10 vol.% and temperature difference of 32 °C, the ZnO/DIW nanofluid was observed to enhance the heat transfer coefficient by 9.14% relative to DIW. Further increase in volume concentration resulted in the attenuation of heat transfer. Additionally, the Nusselt number and heat transfer rate were augmented by 8.42% and 6.75% at 0.10 vol.%, respectively.

Integrated Taguchi-GRA-PCA for optimising the heat transfer performance of nanofluid in an automotive cooling system

PurposeIn the present study, the thermal performance of engine radiator using conventional coolant and nanofluid is determined experimentally for the different flow rates. Further, the study implemented the Integrated Taguchi-GRA-PCA for optimising the heat transfer performance.Design/methodology/approachNanofluids were prepared by taking ethylene glycol and water (25:75 by volume) with volume fraction of 0.01, 0.03 and 0.05% of TiO2 nanopowder. Experimental Data were collected based on the design of experiments (DOE) L9 orthogonal array using Taguchi method. Statistical analysis via Grey relation analysis (GRA) and principal component analysis (PCA) were done to determine the role of experimental parameters on heat transfer coefficient and rate of heat transfer. Impact of three control factors, vol. % of TiO2 concentration (φ), flow rate (LPH), and sonication time (min) on the performance characteristics on heat transfer coefficient and ratio of heat transfer rate is analysed to get the best combination of the parameters involved.FindingsAnalysis revealed the importance of parameters on heat transfer coefficient and can be sorted in terms of contributions from higher to lower degree. Finally, ANOVA test has been conducted to validate the effect of process parameters. The major controllable parameter is φ (concentration), contributing about 32.74%, then flow rate contributing 32.5% and finally sonication time showing small contribution of 18.57%.Originality/valueA grey relational analysis integrated with principal component analyses (PCA) are implemented to get the optimum heat transfer coefficient and ratio of heat transfer rate. The novelty of the work is to adopt and implement the Integrated Taguchi-GRA-PCA first time for the purpose of thermal performance analysis of engine nano-coolant for radiator.

Numerical investigation of molten salt-based nanofluid laminar heat transfer in a circular tube using Eulerian-Lagrangian method

Molten salt-based nanofluid in a laminar region of a circular tube with constant wall heat flux was numerically investigated. An Eulerian-Lagrangian method, discrete phase model, was used to predict the heat transfer performance of nanofluid, considering the factors of inlet Reynolds number, the mass concentration of the nanoparticles, and nanoparticles diameter. Validation results were found in a good match with experimental results obtained from the literature. Numerical results showed that the heat transfer performance of nanofluid was considerably better than that of pure molten salt. The local heat transfer coefficient and Nusselt number of nanofluid are about 30% higher than these of pure molten salt and increase with an increase of Reynolds number and nanoparticles concentration. Moreover, the heat transfer performance of nanofluid with the small size of the nanoparticles (10~100 nm) is improved significantly.

Numerical investigation of jet impingement flows with different nanofluids in a mini channel using Eulerian-Eulerian two-phase method

An in–house Fortran based two-phase Eulerian-Eulerian solver is used to numerically study an incompressible confined slot jet impinging with nanofluid on an iso-thermal hot flat surface of a mini channel in the laminar regime. The governing equations of liquid and solid phases are solved using finite difference method. Heat transfer performance of water-based nanofluid with different nanoparticles such as Al2O3, SiO2, Cu, Fe, diamond and Ag is studied. Heat transfer performance of nanofluids cannot be predicted solely from the thermal conductivity values of suspended nanoparticles as observed that Al2O3 water nanofluid shows better performance than iron-water nanofluid. Since the iron-water nanofluid has only 5% more average Nusselt number but 7% more pressure drop compared to Al2O3-water nanofluid for jet Reynolds number 300 and particle concentration 5%. Heat transfer performance of Cu-water nanofluid is studied and reported for different parameters such as jet Reynolds number, particle concentration and channel height to jet width ratios. Results show a significant change in heat transfer characteristics with respect to jet Reynolds number, particle concentration, and height to jet width ratio. The heat transfer enhancement is 25% for Al2O3 water nanofluid and 60% for Cu- water nanofluid compared to pure water at jet Reynolds number 300 and particle concentration 5%. An empirical correlation is developed to predict the average Nusselt number of the jet flow with Cu-water nanofluid impinging on a hot surface. Present results are validated with numerical and experimental results available in the literature.

A review of magnetic field influence on natural convection heat transfer performance of nanofluids in square cavities

The emergence of nanofluids as high-performance thermal transport media has drawn great research attention in the field of heat transfer. Owning to the huge importance of natural convection applications in environmental, agricultural, manufacturing, electronics, aviation, power plants, and industrial processes, heat transfer and flow characteristics of these special fluids in various cavities have been extensively researched. This review paper has paid serious attention to the benefits of controlling the natural convection heat transfer and flow performance of nanofluids in square cavities using magnetic field sources in addition to the aspect ratio, porous media, cavity and magnetic field inclination, hybrid nanofluids, etc. The influence of several variables such as heat distribution methods, thermal and concentration boundary conditions, governing parameters, magnetic field types, numerical schemes, thermophysical correlation types, nanofluid types, slip conditions, Brownian motion, and thermophoresis on the magnetohydrodynamic (MHD) natural convection behaviours of nanofluids in square cavities has been reviewed. The paper focused on the application of numerical and experimental methods to hydromagnetic behaviours of nanofluids in square-shaped enclosures. The concept of bioconvection, bio-nanofluid (green nanofluid), ionic nanofluid, and hybrid nanofluid has also been reviewed in relation to natural convection for the first time. Special cases of MHD natural convection in cavities involving micropolar and hybrid nanofluids are also presented herein. Convective heat transfer in square cavities has been demonstrated to be altered due to the presence of magnetic fields.