Synthesis of metal-based nanofluids and their thermo-hydraulic performance in compact heat exchanger with multi-louvered fins working under laminar conditions

Present experimental investigation incorporates characterization of Al nanopowder, synthesis of Al/water nanofluids, and effect of these nanofluids on thermal performance of compact heat exchanger. Al nanoparticles are characterized using TEM and XRD. Al/water nanofluid is prepared by dispersing metal basis aluminium nanoparticles of average 100 nm size into double distilled water at two different particle volume concentrations of 0.1 and 0.2%. The nanofluids are prepared by two-step method and cetyl trimethyl ammonium bromide surfactant is used to stabilize the nanofluid. Thermo-physical properties of nanofluids at two different concentrations and their variation with fluid temperature are measured experimentally. It is examined that thermal conductivity, viscosity, and density of the nanofluid increased with the increase of volume concentrations. Furthermore, by increasing the fluid temperature, thermal conductivity is intensified, while the viscosity and density are decreased. Heat transfer parameters are strong functions of these thermo-physical properties. Therefore, comprehensive findings on heat transfer coefficient, Nusselt number, colburn factor, friction factor, and effectiveness are determined experimentally for prepared nanofluids passing under laminar conditions through single-pass cross-flow compact heat exchanger attached with multi-louvered fins.

     

Green synthesis,crystal growth,and some physicochemical studies on an inter-molecular compound of anthranilic acid and <Emphasis Type="Italic">m</Emphasis>-nitro-benzoic acid system

Nanofluids are prepared by suspending the nanoparticles in the base fluid and can be substantially enhanced the heat transfer rate compared to the pure fluids. In this paper, experimental investigation of the effects of volume concentration and temperature on dynamic viscosity of the hybrid nanofluid of multi-walled carbon nanotubes and aluminum oxide in a mixture of water (80%) and ethylene-glycol (20%) has been presented. The nanofluid was prepared with solid volume fractions between 0.0625 and 1%, and experiments were performed in the temperature range of 25–50 °C. The measurement results at different shear rates showed that the base fluid and nanofluid samples with solid volume fractions of less than 0.5% had Newtonian behavior, while those with higher solid volume fractions (0.75 and 1%) exhibit a pseudoplastic rheological behavior with a power law index of less than unity. The results showed that viscosity has a direct relationship with solid volume fraction of the nanofluid. The value of maximum enhancement is which occurred in 25 °C. Moreover, the consistency index and power law index have been obtained by accurate curve fitting for samples with non-Newtonian behavior of nanofluids. The results also revealed that the apparent viscosity generally increases with an increase in the solid volume fraction.     

Highly stable copper/carbon dot nanofluid

Copper/carbon dot nanohybrids (Cu/CD NHs) were prepared via a facile precipitation method through a disproportionation reaction. The surface characterization was performed by various techniques such as XRD, FTIR and TEM. Then, water-based nanofluids composed of Cu/CD NHs at 0.1 and 0.5 mass% were prepared, and their thermo-physical properties including thermal conductivity, viscosity, density and specific heat were evaluated at various temperatures. The water-based Cu/CD nanofluid demonstrated to be a potential heat transfer fluid with a high stability. It was found that the thermal conductivity can be enhanced by increasing the nanoparticle concentration and temperature. Almost 1.25-fold increase in thermal conductivity has been achieved by raising the temperature up to 50 °C and at the concentration of 0.5 mass%. The heat capacity was found to increase with increasing concentration. Moreover, by increasing temperature the density and viscosity of the as-prepared nanofluid decreased, whereas the heat capacity showed an increasing trend.     

Effect of Reynolds number on heat transfer and flow for multi-oxide nanofluids using numerical simulation

A numerical simulation model for laminar flow of nanofluids in a pipe with constant heat flux at the wall has been built to study the effect of Reynolds number on heat transfer and pressure loss. The investigation was performed for metallic oxide and multi-oxide nanoparticles suspended in water. The thermal conductivity and dynamic viscosity were measured for a range of temperature (10–60 °C) and volume fraction of multi-oxide nanofluid. Comparison of the thermal conductivity for monocular oxide and multi-oxide nanofluids reveals a new way to control the enhancement in nanofluid conductivity. The numerical results obtained were compared with existing well-established correlations. The predictions of the Nusselt number for nanofluids are in agreement with the Shah correlation, and the deviation in the results is less than 1 %. It is found that the pressure loss increases with the Reynolds number, nanoparticle density, and volume fraction for multi-oxide nanoparticles. However, the flow demonstrates enhancement in heat transfer which improves with increasing Reynolds number of the flow.     

Estimation of thermal conductivity of ethylene glycol-based nanofluid with hybrid suspensions of SWCNT–Al<Subscript>2</Subscript>O<Subscript>3</Subscript> nanoparticles by correlation and ANN methods using experimental data

In the present paper, the effects of temperature and volume fraction on thermal conductivity of SWCNT–Al₂O₃/EG hybrid nanofluid are investigated. Single-walled carbon nanotube with outer diameter of 1–2 nm and aluminum oxide nanoparticles with mean diameter of 20 nm with the ratio of 30 and 70%, respectively, were dispersed in the base fluid. The measurements were conducted on samples with volume fractions of 0.04, 0.08, 0.15, 0.3, 0.5, 0.8, 1.5 and 2.5. In order to investigate the effects of temperature on thermal conductivity of the nanofluid, this characteristic was measured in five different temperatures of 30, 35, 40, 45 and 50 °C. The results indicate that enhancement of nanoparticles’ thickness in low volume fractions and at any temperature causes a considerable increment in thermal conductivity of the nanofluid. In this study, the highest enhancement of thermal conductivity was 41.2% which was achieved at the temperature of 50 °C and volume fraction of 2.5%. Based on the experimental data, an experimental correlation and a neural network are presented and for thermal conductivity of the nanofluid in terms of volume fraction and temperature. Comparing outputs of the experimental correlation and the designed artificial neural network with experimental data, the maximum error values for the experimental correlation and the artificial neural network were, respectively, 2.6 and 1.94% which indicate the excellent accuracy of both methods in prediction of thermal conductivity.     

Effect of volume concentration and temperature on viscosity and surface tension of graphene–water nanofluid for heat transfer applications

In the present study, the effect of volume concentration (0.05, 0.1 and 0.15 %) and temperature (10–90 °C) on viscosity and surface tension of graphene–water nanofluid has been experimentally measured. The sodium dodecyl benzene sulfonate is used as the surfactant for stable suspension of graphene. The results showed that the viscosity of graphene–water nanofluid increases with an increase in the volume concentration of nanoparticles and decreases with an increase in temperature. An average enhancement of 47.12 % in viscosity has been noted for 0.15 % volume concentration of graphene at 50 °C. The enhancement of the viscosity of the nanofluid at higher volume concentration is due to the higher shear rate. In contrast, the surface tension of the graphene–water nanofluid decreases with an increase in both volume concentration and temperature. A decrement of 18.7 % in surface tension has been noted for the same volume concentration and temperature. The surface tension reduction in nanofluid at higher volume concentrations is due to the adsorption of nanoparticles at the liquid–gas interface because of hydrophobic nature of graphene; and at higher temperatures, is due to the weakening of molecular attractions between fluid molecules and nanoparticles. The viscosity and surface tension showed stronger dependency on volume concentration than temperature. Based on the calculated effectiveness of graphene–water nanofluids, it is suggested that the graphene–water nanofluid is preferable as the better coolant for the real-time heat transfer applications.     

Microwave synthesis of silver nanofluids with polyvinylpyrrolidone (PVP) and their transport properties

Microwave synthesis has been applied to prepare stable silver nanofluids in ethanol by reduction of AgNO₃ with polyvinylpyrrolidone (PVP), used as stabilizing agent, having Ag concentrations of 1% by volume. The nanofluids were characterized by UV-vis spectroscopy, Fourier transform infrared, energy-dispersive X-ray spectroscopy, and transmission electron microscopy and systematically investigated for refractive index, electrical and thermal conductivity, and viscosity for different polymer concentrations. The size of nanoparticles was found to be in the range of 30–60 nm for two different salt-to-PVP ratios. For higher concentration of polymer in nanofluid, nanoparticles were 30 nm in size showing increase in thermal conductivity but a decrease in viscosity and refractive index, which is due to the polymer structure around nanoparticles. Thermal conductivity measurements of nanofluids show substantial increment in the thermal conductivity of nanofluid relative to the base fluid and nonlinear enhancement over the 283–323 K temperature range. Rheology of nanofluids was studied at room temperature showing effect of polymer on viscosity and confirming the Newtonian behavior of nanofluid.     

Proposing a nano-approach to modify viscosity behavior of SAE 5W50 as light road vehicles lubricant

In this paper, viscosity of MWCNT (%50)–TiO₂ (%50)/5W50 is investigated in temperature range of 5–55 °C, solid volume fractions of 0.05%, 0.1%, 0.25%, 0.5%, 0.75% and 1%, and shear rate range of 666.5–10,664 (s^?1). Experimental results showed non-Newtonian behavior of enriched nano-engine oil. Nano-engine oil viscosity reduction (compared to 5W50 base oil) in some specific temperatures and solid volume fractions is one of the unique and interesting results of this research. Maximum viscosity reduction (??11%) occurred in 15 °C and solid volume fraction of 0.05%, and maximum viscosity enhancement (+?17%) was observed in 25 °C and solid volume fraction of 1%. The main goal of present study is to control viscosity increase of nanofluid after adding nanoparticles to the oil. Modeling and prediction of results were achieved via RSM and ANN methods.

     

Synthesis and thermo-physical properties of water-based novel Ag/ZnO hybrid nanofluids

The comparative study on the thermo-physical properties of water-based ZnO nanofluids and Ag/ZnO hybrid nanofluids is reported in the present study. The outer surface of ZnO nanoparticles was modified with a thin coating of Ag nanoparticles by a wet chemical method for improved stability and heat transfer properties. The ZnO and Ag/ZnO nanofluids were prepared with varying volume concentration (??=?0.02–0.1%). The synthesized nanoparticles and nanofluids were characterized with different characterization methods viz., scanning electron microscopy, X-ray diffraction, dynamic light scattering, thermal conductivity measurement, and viscosity measurement. Results show that thermal conductivity of Ag/ZnO hybrid nanofluids is found to be significantly higher compared to ZnO nanofluids. The maximum thermal conductivity an enhancement for Ag/ZnO nanofluid (??=?0.1%) is found to 20% and 28% when it compared with ZnO nanofluid (??=?0.1%) and water, respectively.     

The noble effect of aging on the thermal conductivity of modified CNTs-ethylene glycol nanofluids

In order to improve the heat transfer process by using nanofluids, different nanoparticles and base fluids have been studied. In this work, stability and effect of aging and temperature on the thermal conductivity of CNTs-ethylene glycol (EG) nanofluids were investigated. Chemical functionalisation was used to oxidise the surface of CNTs. The functionalised CNTs were used to prepare the nanofluids by a two-step method. The stability of nanofluids was measured by UV-vis spectroscopy and the results showed that the nanofluids had a good stability over several days. Immediately after nanofluid preparation not too much increase was observed for thermal conductivity but the nanofluid aging had a great influence on the improvement of the thermal conductivity, as after 65 days, about 50% increase was observed. The increase has been attributed to forming an ordered nanolayer of EG molecules around the CNTs. Also no significant temperature dependence of thermal conductivity was observed up to 50°C possibly due to the lack of temperature dependence of CNTs Brownian motions.     

Improving the heat transfer efficiency of synthetic oil with silica nanoparticles

The heat transfer properties of synthetic oil (Therminol 66) used for high temperature applications was improved by introducing 15 nm silicon dioxide nanoparticles. Stable suspensions of inorganic nanoparticles in the non-polar fluid were prepared using a cationic surfactant (benzalkonium chloride). The effects of nanoparticle and surfactant concentrations on thermo-physical properties (viscosity, thermal conductivity and total heat absorption) of these nanofluids were investigated in a wide temperature range. The surfactant-to-nanoparticle (SN) ratio was optimized for higher thermal conductivity and lower viscosity, which are both critical for the efficiency of heat transfer. The rheological behavior of SiO(2)/TH66 nanofluids was correlated to average agglomerate sizes, which were shown to vary with SN ratio and temperature. The conditions of ultrasonic treatment were studied and the temporary decrease of agglomerate size from an equilibrium size (characteristic to SN ratio) was demonstrated. The heat transfer efficiencies were estimated for the formulated nanofluids for both turbulent and laminar flow regimes and were compared to the performance of the base fluid.     

Thermal conductivity modeling of MgO/EG nanofluids using experimental data and artificial neural network

The application of nanofluids in energy systems is developing day by day. Before using a nanofluid in an energy system, it is necessary to measure the properties of nanofluids. In this paper, first the results of experiments on the thermal conductivity of MgO/ethylene glycol (EG) nanofluids in a temperature range of 25–55 °C and volume concentrations up to 5 % are presented. Different sizes of MgO nanoparticles are selected to disperse in EG, including 20, 40, 50, and 60 nm. Based on the results, an empirical correlation is presented as a function of temperature, volume fraction, and nanoparticle size. Next, the model of thermal conductivity enhancement in terms of volume fraction, particle size, and temperature was developed via neural network based on the measured data. It is observed that neural network can be used as a powerful tool to predict the thermal conductivity of nanofluids.     

Evaluation of the effects of the presence of ZnO -TiO2 (50 %–50 %) on the thermal conductivity of Ethylene Glycol base fluid and its estimation using Artificial Neural Network for industrial and commercial applications

In this study, the thermal conductivity (k_nf) of ZnO -TiO₂ (50 %–50 %)/ Ethylene Glycol hybrid nanofluid using Artificial Neural Networks (ANNs) was predicted. The nanofluid was prepared at different volume fractions (φ) of nanoparticles (φ = 0.001 to 0.035) and temperatures (T = 25 to 50 °C). In this study, an algorithm is presented to find the best neuron number in the hidden layer. Also, a surface fitting method has been applied to predict the k_nf of nanofluid. Finally, the correlation coefficients, performances, and Maximum Absolute Error (MAE) for both methods have been presented and compared. It could be understood that the ANN method had a better ability in predicting the k_nf of nanofluid compared to the fitting method. This method not only showed better performance but also reached a better MAE and correlation coefficient.     

Experimental study on the effect of copper oxide nanoparticles on thermophysical properties of ethylene glycol–water for using in indirect heater at city gate stations

This study aimed to investigate the increase in heat transfer in the indirect heater at a city gate station (CGS) with the addition of copper oxide (CuO) nanoparticles to water–ethylene glycol base fluids. Indirect heaters are typically used at CGSs to raise the heat transfer coefficient of output gas flow from ? 5 to 15 °C. Moreover, manufacturing laboratory equipment in the presence of water–ethylene glycol base fluid and the nanoparticle in volume fractions of 0.05, 0.1, 0.2, and 0.3 at a temperature of 40–70 °C was discussed using dimensional simulation and analysis. The physical properties of the base fluid and nanofluid were measured using precise devices. Heat transfer tests for the base and nanofluid, as well as replacing of the air by gas, were conducted in a simulated and developed device. According to the obtained results with respect to the changes in convection and conduction heat transfer, enhancement of temperature difference at a rate of 36% was observed in the indirect heater with nanoparticle volume concentration of 0.2% at a temperature of 70 °C. Moreover, the Nusselt number showed a relatively good agreement with theoretical discussions.

     

Experimental determination of thermal conductivity and viscosity of different nanofluids and its effect on a hybrid solar collector

In this research, three different volume concentrations (??=?0.05, 0.1 and 0.2%) of Al₂O₃/water, CuO/water and Al₂O₃–CuO/water (50:50) nanofluids are prepared by adopting a two-step nanofluid preparation method. Al₂O₃ and CuO nanoparticles with the average diameter of 50 nm and 27 nm were dispersed in distilled water. The thermal conductivity and viscosity of prepared nanofluids are measured for different temperatures by using KD2 Pro thermal property analyzed and Brookfield viscometer, respectively. The effects of nanofluids on the thermal, electrical and overall efficiency of photovoltaic thermal (PVT) solar collector are also studied. The experimental results revealed that the thermal conductivity and viscosity increase with the increase in percentage volume concentration and viscosity decreases with the increase in temperature. Furthermore, the obtained maximum thermal and electrical efficiencies of a PVT solar collector for 0.2% volume concentration of hybrid nanofluids are 82% and 15%, respectively, at peak solar radiation. The highest overall efficiency of a PVT collector with .2% volume concentration of hybrid nanofluid was 97% at peak solar radiation. Results recommend that nanofluids can be used as a heat transfer in PVT solar collector.

     

Thermal performance of stable SiO2 nanofluids and regression correlations to estimate their thermophysical properties

The present work investigates the best mix ratio of Glycerol in Water as a medium to prepare a stable nanofluid. Increasing the proportion of glycerol enhances the aqueous mix's dynamic viscosity and improves the prepared nanofluid's stability. The thermal conductivity and viscosity of the Glycerol and Water mixtures determination were undertaken at various Glycerol ratios. The best percentage of glycerol in the mixture is found to have the least amount of thermal conductivity loss and the optimum viscosity gain. Silica (SiO₂) nanofluid of 0.25%, 0.5%, 1%, and 1.5% weight concentrations was prepared with this optimal mixture of Glycerol and Water. The stability of these SiO₂ nanofluids is evaluated by determining the zeta potential at different time intervals. The nanofluids prepared were observed to be stable for one month. The thermal conductivity and viscosity of the nanofluids are measured between the temperature limits of 30°–70°C. A peak increment of 32.1%and 46.3% in thermal conductivity and viscosity is observed. Furthermore, when the percentage enhancement ratio (PER) and Mouromtseff ratio of these nanofluids is examined, it is observed that they have more excellent thermal performance at higher temperatures. Regression correlations are developed to estimate the thermal conductivity and viscosity of the prepared nanofluids with a maximum deviation of 9%.     

Thermal performance evaluation of a nanofluid‐based flat‐plate solar collector

The thermal performance of a flat-plate solar collector (FPSC) is investigated experimentally and analytically. The studied nanofluid is SiO₂/deionized water with volumetric concentration up to 0.6% and nanoparticles diameter of 20–30 nm. The tests and also the modeling are performed based on ASHRAE standard and compared with each other to validate the developed model. The dynamic model is based on the energy balance in a control volume. The system of derived equations is solved by employing an implicit finite difference scheme. Moreover, the thermal conductivity and viscosity of SiO₂ nanofluid have been investigated thoroughly. The measurement findings indicate that silica nanoparticles, despite their low thermal conductivity, have a great potential for improving the thermal performance of FPSC. Analyzing the characteristic parameters of solar collector efficiency reveals that the effect of nanoparticles on the performance improvement is more pronounced at higher values of reduced temperature. The thermal efficiency, working fluid outlet temperature and also absorber plate temperature of the modeling have been confirmed with experimental verification. A satisfactory agreement has been achieved between the results. The maximum percentage of deviation for working fluid outlet temperature and collector absorber plate temperature is 0.7% and 3.7%, respectively.

     

ANSYS simulation study of a low volume fraction CuO–ZnO/water hybrid nanofluid in a shell and tube heat exchanger

For the first time, the heat transfer performance of a CuO–ZnO (80:20)/water hybrid has been studied experimentally and numerically in a shell and tube heat exchanger under turbulent flow conditions nanofluid (STHE). All experiments are carried out with 0.01 ​vol% CuO–ZnO (80:20)/water hybrid nanofluid at Reynolds numbers (N_Re) ranging from 1900 to 17,500. The stabilized hybrid nanofluids (30 ​°C-Tube side) are then used as a coolant to reduce the hot fluid (60 ​°C-shell side) temperature using a STHE, with the results for the convective heat transfer coefficient, Nusselt number, friction factor, and pressure drop reported. The primary goal of this paper is to investigate the impact of hybrid nanoparticle mixing ratio optimization on STHE heat transfer efficiency under various operating conditions. According to the findings, the CuO–ZnO (80:20)/water hybrid nanofluid improved the heat transfer performance of the STHE at all Reynolds numbers. When using nanofluid over water, the Nusselt number and pressure drop were improved by approximately 33% and 13%, respectively. The hybrid nanofluid's maximum thermal performance factor and thermal efficiency enhancement were 1.45 and 7%, respectively, at N_Re ​= ​17,500. According to the study, the thermal conductivity of nanofluid varies by only 5% after ten trials. Furthermore, the ANSYS Fluent program was used to predict the behavior of the hybrid nanofluid in STHE, and the simulation results fit the experimental values very well.     

Experimental investigation and modeling of thermal conductivity of CuO–water/EG nanofluid by FFBP-ANN and multiple regressions

The purpose of this study is to predict the thermal conductivity of copper oxide (CuO) nanofluid by using feed forward backpropagation artificial neural network (FFBP-ANN). Thermal conductivity of CuO nanofluid is measured experimentally using transient hot-wire technique in temperature range of 20–60 °C and in volume fractions of 0.00125, 0.0025, 0.005 and 0.01% for neural network training and modeling. In addition, in order to evaluate accuracy of modeling in predicting the coefficient of nanofluid thermal conductivity, indices of root-mean-square error, coefficient of determination (R ²) and mean absolute percentage error have been used. FFBP-ANN with two input parameters (volume fraction and nanofluid temperature) and one output parameter (nanofluid thermal conductivity) in addition to two hidden layers and one outer layer which purelin, logsig and tansig functions are used was considered as the most optimum structure for modeling with neuron number of 4–10–1. In this study, among common methods of theoretical modeling of nanofluid thermal conductivity, theoretical method of Maxwell and also multivariate linear regression model was used for explaining the importance of modeling and predicting the results using neural network. According to this research, the results of indices and predictions show high accuracy and certainty of ANN modeling in comparison with empirical results and theoretical models.     

Theoretical -Experimental study of factors affecting the thermal conductivity of SWCNT-CuO (25:75)/water nanofluid and challenging comparison with CuO nanofluids/water

In this theoretical–experimental study, the basic parameters effect such as solid volume fraction (SVF or φ) and temperature on thermal conductivity (TC) of SWCNT-CuO (25:75)/water nanofluid (NF) has been investigated. The used NF in this study has been prepared and used for the first time. Monitoring and investigation of TC were done in T = 28 to 50?C and SVF = 0.03 % to 1.15 %. The role of SVF effective in relative thermal conductivity (RTC) with changing of the temperature shows the importance of this factor in improving the RTC; results show that the better TC is T = 50 °C compared to other temperatures. Also, the maximum enhancement of TC compared to the base fluid (BF) (36 %) was observed at the mentioned temperature. In addition to the laboratory tests such as the margin of deviation (MOD) and RTC sensitivity within the range of ?1.90 %<MOD < 1.42 % in the theoretical section, a new relationship was predicted using the response surface methodology (RSM). A comparison was also made between SWCNT-CuO (25:75)/water NF and other NFs at the same temperature and SVF, which shows the increased RTC of the NF after using SWCNT (25 %).