Pressure Drop Of Nanofluid Research Articles

Numerical Study of Flow and Heat Transfer Characteristics for Al2O3 Nanofluid in a Double-Pipe Helical Coil Heat Exchanger.

To numerically investigate the flow and heat transfer characteristics of a water/Al2O3 nanofluid in a double-pipe helical coil heat exchanger, we simulated a two-phase Eulerian model to predict the heat transfer coefficient, Nusselt number, and pressure drop at various concentrations (i.e., volume fraction) and under diverse flow rates at the steady state. In this simulation, we used the k-epsilon turbulence model with an enhanced wall treatment method. The performance factor of the nanofluid was evaluated by accounting for the heat transfer and pressure drop characteristics. As a result, the heat transfer was enhanced by increasing the nanofluid concentration. The 1.0 vol.% nanofluid (i.e., the highest concentration) showed a heat transfer coefficient 1.43 times greater than water and a Nusselt number of 1.38 times greater than water. The pressure drop of nanofluids was greater than that of water due to the increased density and viscosity induced using nanoparticles. Based on the relationship between the Nusselt number and pressure drop, the 1.0 vol.% nanofluid was calculated to have a performance factor of 1.4 relative to water, indicating that the enhancement rate in heat transfer performance was greater than that in the pressure drop. In conclusion, the Al2O3 nanofluid shows potential as an enhanced working fluid in diverse heat transfer applications.

Study on heat transfer and pressure drop characteristics for nanofluids in microchannel heat exchangers

The microchannel heat exchanger (MCHE) is considered the next-generation heat exchanger owing to its high-performance thermal management systems and fabrication processes. The MCHE is used in many cooling applications, such as x ray medical devices, high-power microelectronics, and high-heat flux devices. Comprehensive exploration of different nanofluids, their concentrations, and impacts on pressure drop and heat transfer within the context of MCHEs was the main focus of the current study. The experiments were conducted at Reynolds numbers ranging from 100 to 500 for laminar flow. Additionally, nanoparticles (np) such as Al2O3, CuO, and carbon nanotubes (CNT) were added to de-ionized water at weight percentages of 0.01, 0.03, and 0.05. The results indicated that heat transfer significantly increased at a particle concentration 0.05 by wt. % for all nanofluids (nf). Furthermore, at a particle of 0.05 by wt. %, the heat transfer of the CNT-based nf increased by 37%, whereas those of the Al2O3- and CuO-based nf increased by 24.01% and 6.23%, respectively. The nanofluid pressure drop (PD) increases with an increase in the Reynolds number owing to the increase in the viscosity of the liquid compared to de-ionized water (base fluid) and requires more pumping power.

Experimental and numerical investigation of heat transfer and flow of water-based graphene oxide nanofluid in a double pipe heat exchanger using different artificial neural network models

In this study, a water-based graphene oxide nanofluid was utilized in a counter-current flow double pipe heat exchanger (DPHE). The inner pipe carried the hot fluid (deionized water-based fluid). In the outer pipe, the cold fluid was flown, in which the experiments were performed once for deionized water-based fluid and once for graphene oxide nanofluid. These experiments were conducted at various inlet hot fluid temperatures of 35, 45, and 55 °C, volumetric concentrations of 0.01, 0.055, and 0.1% for nanofluid, and cold fluid flow rates of 14.4, 18.9, and 23.4 ml/s to evaluate the thermohydraulics of the DPHE. The results demonstrated that an increase in the flow rate and concentration of nanoparticles led to an improvement in the heat transfer coefficient (HTC) . It is also observed that inlet hot fluid temperature had a negligible effect on this coefficient compared to the concentration and flow rate. Additionally, the results showed that the friction factor and pressure drop of nanofluid were higher than those of the basefluid and their values were increased by increasing the concentration. The maximum increase compared to the basefluid was 72% for the friction factor and 111%, for the pressure drop. Besides, radial basis function neural network (RBFNN) and multi-layer perceptron neural network (MLPNN) models were designed for estimating the HTC. Both models accurately predicted the coefficient, but the RBFNN model exhibited higher accuracy than the MLPNN model. The results further indicated that the nanofluid outperformed the basefluid in terms of HTC. Due to the exceptionally high thermal conductivity of graphene nanoparticles, the HTC of the nanofluid was improved by up to 85% compared to the basefluid. The optimum value of HTC was achieved at a temperature of 55 °C, a volume concentration of 0.1%, and a flow rate of 23.4 ml/s.

Numerical study of heat transfer and pressure drop of nanofluids in a combined porous media of hydrophobic and hydrophilic surfaces

The present study provides a 3D numerical research on the hydro-thermal performance of water-Al2O3/CuO nanofluids and water/Al2O3-CuO hybrid nanofluid inside a U-bend tube incorporating a porous medium with both hydrophilic and hydrophobic surfaces. Furthermore, a single-phase flow is adopted to simulate the nanofluid flow. Taking into consideration the equation of the Darcy-Brinkman-Forchheimer, to simulate the flow of fluids in the porous media. The influence of Dean number on hydro-thermal performance in the range of (600–1750) was investigated. And according to the results, Dn = 600 had the best performance. Considering the outcomes, a completely hydrophobic wall has a better hydro-thermal performance than a completely hydrophilic one and other hydrophilicity and hydrophobicity combinations. As well, improvement in heat transfer efficiency is considerably influenced by the Darcy number (10−4–10−1) and the augmentation of the porous thickness ratio. Additionally, average Nusselt number and pressure losses for any and all types of nanofluids show an incremental schema with augmenting volume fraction, while PEC exhibits a reducing pattern. Besides this, structures with permeable porous media or ones with Da = 0.1 and rp = 0.6 present the highest PEC. Also, among the reviewed nanoparticles, Al2O3 with a volume percentage of 1% had the maximum PEC.

Experimental study of convective heat transfer in Fe3O4-H2O nanofluids in a grid-shaped micro-channel under magnetic field

Experimental study of convective heat transfer with Fe3O4-H2O (1 vol%) nanofluids was examined when the nanofluids flowed through a gridded micro-channel under a perpendicularly oriented magnetic field of 0-700 G strength. The results show that, compared to deionized water, nanofluids reduces chip temperature by 2.11?C and increases the convective heat transfer coefficient by 30.43% when no magnetic field is present. Under magnetic field conditions, the chip temperature was maximally reduced by 3.2?C, while the convective heat transfer coefficient is improved up to 65% in comparison to deionized water. With increasing magnetic field strength, nanofluids pressure drop and flow resistance showed an overall decreasing trend, and the pressure drop at 500 G and 700 G were reduced by 19.3% and 14.51%, respectively, compared to that at 0 G. In terms of overall performance, improved heat transfer in the presence of a magnetic field outper-forms heat transfer in the absence of a magnetic field. The intensive heat transfer factor of nanofluids under magnetic field conditions is greater than one when the Reynolds number exceeds 400. The best overall performance and the highest intensive heat transfer factor are observed at a magnetic field strength of 300 G.

Heat Transfer and Pressure Drop of Nanofluid with Rod-like Particles in Turbulent Flows through a Curved Pipe.

Pressure drop, heat transfer, and energy performance of ZnO/water nanofluid with rodlike particles flowing through a curved pipe are studied in the range of Reynolds number 5000 ≤ Re ≤ 30,000, particle volume concentration 0.1% ≤ Φ ≤ 5%, Schmidt number 104 ≤ Sc ≤ 3 × 105, particle aspect ratio 2 ≤ λ ≤ 14, and Dean number 5 × 103 ≤ De ≤ 1.5 × 104. The momentum and energy equations of nanofluid, together with the equation of particle number density for particles, are solved numerically. Some results are validated by comparing with the experimental results. The effect of Re, Φ, Sc, λ, and De on the friction factor f and Nusselt number Nu is analyzed. The results showed that the values of f are increased with increases in Φ, Sc, and De, and with decreases in Re and λ. The heat transfer performance is enhanced with increases in Re, Φ, λ, and De, and with decreases in Sc. The ratio of energy PEC for nanofluid to base fluid is increased with increases in Re, Φ, λ, and De, and with decreases in Sc. Finally, the formula of ratio of energy PEC for nanofluid to base fluid as a function of Re, Φ, Sc, λ, and De is derived based on the numerical data.

Numerical simulation and multi-objective optimization of heat transfer of Al2O3/water nanofluid in rectangular ducts

Laminar flow of Al2O3/water nanofluids in rectangular ducts with different aspect ratios (AR) were simulated numerically. Both heat transfer coefficient and pressure drop of nanofluids are higher than the base fluid and increase with nanofluid concentration in ducts of all aspect ratios. For a duct with AR = 2, the heat transfer coefficient of nanofluids with a concentration of 5% vol. is on average 22% higher than the base fluid. Its pressure drop is about 2.5 times of the base fluids. The effect of nanofluid concentration on the heat transfer coefficient is more significant in ducts with a lower aspect ratio. The heat transfer coefficient of a 5% vol. nanofluid in a duct with AR = 1 is about 24% larger than the base fluid; this value is about 18% for a duct with AR = 6. The effect of nanofluid concentration on the pressure drop of all ducts is identical. The results of the numerical simulation were used for training a multi-Layer perceptron artificial neural network (MLP-ANN) model. The ANN model predicts the pressure drop and heat transfer coefficient of nanofluids in rectangular ducts very accurately. The maximum differences between the values of heat transfer coefficient predicted by the ANN and those obtained from the numerical simulation is about 0.11%, and that of the pressure drop is about 0.65%. Multi-objective NSGA-II optimization algorithm was applied to optimize the nanofluid flow in rectangular ducts. In optimization, the goal was to maximize the heat transfer coefficient and minimize the pressure drop. The values of objective functions were evaluated by the resulting ANN model. Optimization for ducts with each aspect ratio was performed. Optimization was also done for all channels. Pareto fronts were obtained from optimization. The best solution for each optimization is determined by two decision-making methods. For the overall optimal solution, nanofluid concentration, Reynolds number, and the duct aspect ratio are 2.28% vol., 400, and 6, respectively which are the same as the optimal solution of the duct with an aspect ratio of six.

Silver-Water Nanofluid for Heat Transfer Applications

Introduction: Nanoscale materials have attracted a great deal of attention of many researchers and academician in mechanical engineering because of their size and shape dependent thermal properties. The attractiveness of its superior thermal properties compared to its base materials is the main reason for the effective utilization of nanomaterials for the energy systems. The rapid development and researches in nanotechnology led to the development of new class of heat transfer fluid called nanofluid for heat transfer enhancement. The nanoparticles dispersed in the fluid increase the surface area, heat capacity and thermal conductivity of the fluid making it very suitable for heat transfer enhancement (1). Methods: Silver being a safe material with higher thermal conductivity of 429 W/mK can be extensively used for the heat transfer applications. Silver-water nanofluid was prepared by dispersing silver nanoparticles of sizes 20, 30-50 and 50-80 nm in the volume concentrations of 0.01, 0.05 and 0.1%. Polyvinyl pyrrolidone (PVP) has been used as surfactant to stabilize the nanofluid. Thermal conductivity of nanofluid was measured by kD2 Pro thermal analyzer. The stability of nanofluid was analyzed by sedimentation technique, pH measurement, zeta potential and particle size distribution. The effect of surfactant on the thermal conductivity enhancement and stability of nanofluid was studied (1,2). Then the investigations on the heat transfer and pressure drop of silver-water nanofluid in a circular tube under constant wall heat flux condition were carried out in laminar and turbulent flow regimes. Results & Discussions: Among the three sizes of silver nanoparticles used in the investigation, nanofluid with 20 nm exhibited better thermal conductivity enhancement and stability due to lower size. The addition of surfactant improved the stability of nanofluid but caused reduction in thermal conductivity enhancement slightly. Increase of concentration of nanoparticles from 0.01 to 0.1% improved the thermal conductivity of nanofluid up to 50% (1). Experimental study on use of silver-water nanofluid for heat transfer applications showed enhancement in heat transfer coefficient of nanofluid up to 36% and 74% respectively for laminar and turbulent flow conditions. However, the addition of nanoparticles increases the pressure drop slightly. But the higher enhancement of heat transfer overcomes the increase of pressure drop of nanofluid. The proposed correlations for the prediction of Nusselt number and friction factor showed good agreement with the experimental results (3.4). Conclusions: Lower size and lower concentrations of silver water showed better hydrothermal performance of heat transfer systems. The effect of pressure drop and erosion problem due to the addition of nanoparticles will be minimized with low concentrations and lower size of silver nanoparticles. It can be a suitable nanofluid for the thermal energy systems requiring improvement in their performance. Keywords: Silver Nanoparticles, Nanofluid, Thermal conductivity, Heat transfer

CFD study on heat transfer and pressure drop of nanofluids (SiO2/H2O, Al2O3/H2O, CNTs/H2O) in a concentric tube heat exchanger

In this work, a computational fluid dynamics (CFD) analysis has been carried out over a concentric tube heat exchanger for analysing the overall heat transfer coefficient, heat transfer rate and pressure loss of SiO2/water, Al2O3/water and CNTs/water nanofluids at different volume concentrations (1 %, 2% and 3%) under turbulent flow condition (Reynolds number range 2000–10000). Analysis resulted in 23.6%, 26.83% and 37.25% enhancement of the overall heat transfer coefficient and 20.71%, 23.72% and 32.65% enhancement in the heat transfer rate with 3% volume concentration of SiO2/water nanofluid, Al2O3/water nanofluid and CNTs/water nanofluid when compared with base fluid, respectively. Despite all these enhancements, a little increment in pressure loss is also observed. It has been concluded that the overall heat transfer coefficient and heat transfer rate increases with the increase in the Reynolds number as well as volume concentration of nanofluids.

Modeling and Multi-objective Optimization of Heat Transfer Characteristics and Pressure Drop of Nanofluids in Microtubes

Multi-objective optimization of nanofluids in a microchannel heat sink was conducted to determine the highest Nusselt number and the lowest pressure drop. This was done by creating a design study consisting of four nanoparticles of Al2O3, CuO, ZnO and SiO2 with four diameters of 10, 30, 60 and 100 nm, nanoparticle volumetric concentrations of 0.001, 0.005, 0.01 and 0.05 and four base fluids of water (W), ethylene glycol (EG), W/EG (50/50) and W/EG (60/40). Every possible combination of these four input parameters created 256 design points for the numerical simulation. The outcome of the numerical simulation depended on the performance of heat transfer and pressure drop. A modified non-dominated sorting genetic algorithm was then used to determine the highest Nusselt number and the lowest pressure drop based on the numerical results of the 256 design points. The outcome of the optimization was that silicon dioxide–water (SiO2) nanofluid gave the optimal combination of heat transfer and pressure drop. The results also showed that the nanoparticle diameter had a small effect on the pressure drop in the microchannel heat sink and an increase in the nanoparticle volumetric concentration increased the heat transfer coefficient.

Evaluation of different numerical models for prediction of pressure drop in laminar nanofluid flows

ABSTRACT Pressure drop and increase of pumping power is a major issue associated with the applications of nanofluids in thermal systems. Accordingly, the accurate prediction of the pressure drop is important for the optimization of these systems. In this paper, predictions of three numerical models (the discrete phase model (DPM), two-phase mixture model, and the effective single-phase model) for the pressure drop of the alumina-water nanofluid flow through a tube were compared. The volume fraction of nanoparticles, , was varied from 0.1% to 6%. The nanofluid flows with the Re numbers of 450 and 900 were considered in the performed simulations. The results were compared with the available experimental data. Accordingly, it was concluded that the DPM provides more accurate results for , while the mixture and single-phase models have better predictions for . At ϕ = 1%, DPM provided more accurate predictions for the nanoparticle sizes smaller than or equal to 40 nm. However, the other two models led to better predictions for the nanoparticle sizes greater than or equal to 50 nm. Moreover, a sharp reduction in the nanofluid pressure drop was observed when the nanoparticle diameter rose from 40 nm to 50 nm at .

Experimental assessment of heat transfer and pressure drop of nanofluid as a coolant in corrugated channels

Over the last few decades, tremendous consideration is drawn towards corrugation surfaces because of their advantages over the improvement in thermal performance for different engineering applications. An experimental investigation is carried out to compare the effects of combined corrugated walls and turbulent nanofluid flow on thermo-hydraulic performance in corrugated channels over Reynolds number ranges of 10,000–30,000 and constant heat flux of 1 × 104 W m−2. Three shapes, namely semicircle corrugated channel, trapezoidal corrugated channel (TCC), and straight channel, are fabricated and tested with 1% and 2% volume fraction of Al2O3–water nanofluids. Al2O3 nanoparticles suspended in water with two volume fractions ( $$\phi$$ ) of 1.0% and 2.0% are successfully prepared and tested. The experimental findings demonstrate that employing corrugated channel (TCC) improves heat transfer levels by up to 63.59%, pressure drop by 1.37 times, and thermal performance by up to 2.22 times compared to straight channels. Furthermore, heat transfer increased as Al2O3’s volume fraction increases due to the thermal conductivity boost. The use of the tested channels and alumina nanofluid at a volume fraction of 2.0% caused an increase in the heat transfer ratio of around 7.9–8.3% compared to the utilization of the same channels with base fluid. New empirical correlations of corrugated channels with alumina nanofluid are also developed and reported for heat transfer applications.

Experimental and Numerical Investigations on Hydrodynamics of Nanofluid Flowing in Coiled Tubes

A numerical model was developed to analyze the hydrodynamics of TiO2-water nanofluids flowing through straight and coiled tubes in laminar flow regime. Pressure drop of nanofluid predicted from this model was validated with experiments for identical dimensions of tubes and process conditions. The velocity contours of flow in straight as well as coiled tube were examined. The present study revealed that there was 74% decrease in pressure drop when the straight tube diameter was increased from 0.003 m to 0.0072 m. There was 46–51% decrease in friction factor when curvature ratio (Dc/dt) was increased from 7.54 to 12.24 for 200<De < 850.

Prediction of the pressure drop for CuO/(Ethylene glycol-water) nanofluid flows in the car radiator by means of Artificial Neural Networks analysis integrated with genetic algorithm

In this investigation, neural networks were used to predict pressure drop of CuO-based nanofluid in a car radiator. For this purpose, the neural network with the multilayer perceptron structure was used to formulate a model for estimating the pressure drop In this way, different concentrations of copper oxide-based nanofluid were prepared. The base fluid was the mixture of ethylene glycol and pure water (60:40 wt%) which usually used as the cooling fluid in automotive industries. The prepared nanofluid samples were used in a car radiator and the pressure drop of nanofluid flows in the system at different Reynolds were measured. The main purpose of this study was developing the optimized neural networks for predicting the pressure drop of the system with sufficient precision. For the aim of designing the model’s structure, different neural networks were constructed and applied by changing the adjustable parameters (containing the transfer function, training rule, momentum’s amount, hidden layers’ number and the neurons’ number in hidden layer). In each case, the structure with the highest correlation coefficient was chosen as the final model. The selection of each parameters in the neural network model requires repeated tests and errors. So, genetic algorithm was used to optimize these parameters. Additionally, the pressure drop in the radiator of this method was investigated in neural network optimization. The outcomes indicated which a high accuracy in modeling and estimating the pressure drop of nanofluid flows in the studied system can be achieved by the neural network.

Experimental analyses on heat transfer performance of TiO2–water nanofluid in double-pipe counter-flow heat exchanger for various flow regimes

Nanofluids are widely used in heat transfer applications. This article presents the effect of heat transfer and pressure drop of the TiO2–water nanofluids flowing in a double-tube counter-flow heat exchanger with various flow patterns. In this experimental work, performance of TiO2–water nanofluid on heat transfer in three different cases such as laminar, transition and turbulent flow region were analyzed. TiO2 nanoparticles with average diameters of 20 nm dispersed in water with three volume concentrations of 0.1, 0.3 and 0.5 vol% were used as the test fluid. The results show that the heat transfer of nanofluids is higher than that of the base liquid (water) and increased with the increase in Reynolds number and particle concentrations. The heat transfer rate of nanofluid with 0.5 vol% was 25% greater than that of base liquid, and the results also show that the heat transfer coefficient of the nanofluids at a volume concentration of 0.5 vol% was 15% higher than that of base fluid at given conditions. Pressure drop of nanofluid was increased with increase in volume concentration, and it is slightly higher than that of the base fluid.

Nanofluids as coolant in a shell and tube heat exchanger: ANN modeling and multi-objective optimization

In the present study, an artificial neural network (ANN) was developed to predict the thermal and hydrodynamic behavior of two types of Newtonian nanofluids used as coolants in a shell and tube heat exchanger (STHE). Inputs of the ANN model are nanoparticle volume concentration, Reynolds number, nanoparticle thermal conductivity, and Prandtl number. Results indicate that the ANN model predicts the experimental data with very high accuracy. Values of Nusselt number resulted from experiments and those obtained from the ANN have at most 9% difference, this value is 9.6% for the pressure drop. Multi-objective optimization was implemented with the aim of minimizing the total pressure drop and maximizing the nanofluids Nusselt number in the STHE according to NSGA-II algorithm. In optimization procedure nanofluids pressure drop and the Nusselt number (tube-side) was evaluated by the ANN model. To find the shell-side pressure drop method of Delaware was employed. Nanofluids concentration and Reynolds number were selected as decision parameters. The Pareto front was obtained. The best solution adopted from points on the Pareto front by two well-known decision-making methods LINMAP and TOPSIS. The Nusselt number of optimal solutions are about 30% greater than the base fluid and pressure drop of optimal solutions are about 10% lower than the base fluid.

Investigation of heat transfer and fluid flow behaviors of CuO/(60:40)% ethylene glycol and water nanofluid through a serpentine milichannel heat exchanger

PurposeThis paper aims to investigate the three-dimensional (3D) numerical simulations, based on the Navier–Stokes equations and the energy equation. Forced convection of a mixture of (60:40) percent ethylene glycol and water, was used as the base fluid and CuO nanoparticles, through a serpentine minichannel.Design/methodology/approachIn this simulation, a serpentine mini-channel heat exchanger was simulated. The fluid studied in this simulation was composed of a mixture of (60:40) per cent ethylene glycol and water, was used as the base fluid and CuO nanoparticles. Four slabs and three serpentines were used in this study. The serpentine section is connected to the slab. Three equidistant circular channels (1 mm in diameter) were implemented inside the slab.FindingsResults show that nanoparticles increase the fluid pressure drop and by changing volume fraction of nanoparticles from 0 to 1 per cent, the pressure drop of nanofluids increases between 42and 47 per cent, for Reynolds numbers from 100 to 500. The existence of serpentine bend in the minichannel heat exchanger causes the heat transfer rate to increase. Increase the volume fraction of nanoparticles reduces the fluid temperature at the outlet of the heat exchanger. The numerical results show that in Re = 500, at the beginning of the last slab in middle channel by changing volume fraction of nanoparticles from 0 to 2 per cent, local Nusselt number 57.40 per cent increase. The existence of the serpentine bend causes the heat transfer rate to increase.Originality/valueForced convection of a mixture of (60:40) per cent ethylene glycol and water by using of 3D numerical simulations, based on the Navier–Stokes equations.

An experimental study on heat transfer performance and pressure drop of nanofluids using graphene and multi-walled carbon nanotubes based on diesel oil

The present study deals with the investigation of the heat transfer performance of nanofluids using diesel oil as base fluid and multi-walled carbon nanotubes (MWCNT) and graphene nanoplatelets (GNP) as nanoparticles with different concentrations and flow rates. These nanoparticles have been functionalized covalently with hexylamine (HA) and non-covalently with oleic acid (OA) to prepare nanofluids to include OA-MWCNT, HA-MWCNT, OA-GNP.HA-GNP and OA-MWCNT/GNP (1–1 Hybrid of each of nanoparticles) at 5 weight percentages include 0.05–0.5 wt%. An experimental setup designed and constructed and heat transfer performance of the above-mentioned nanofluids are investigated under laminar flow conditions at 4 flow rates. In order to utilize nanofluids in practical applications, the main purpose of this study is focused on investigating if the addition of nanoparticles can enhance the heat transfer performance of diesel oil.In addition, the pressure drop and performance index of nanofluids measured in a closed loop. The results revealed that heat transfer properties of nanofluids improved compared to the base oil. Also according to the results, by increasing the velocity of nanofluid, the convective heat transfer coefficient increases at all weight coefficient of nanoparticles and an increase in pressure drop for nanofluids than pure oil is not perceptible. The performance index of nanofluids, except for the two cases that are related to the hybrid of MWCNT and GNP, in other cases is more than one. Therefore, using nanofluids leads to an increase in the heat transfer coefficient more than increasing the pressure drop, and are suitable for use in the industrial units.

Experimental study on heat transfer and pressure drop of in-house synthesized graphene oxide nanofluids

In this article, first, graphene oxide nanosheets were synthesized in-house according to the modified Hummers method, and these nanosheets were used to prepare graphene oxide nanofluids at two concentrations. Then the thermophysical properties of nanofluids were characterized using X-ray diffraction analysis, a scanning electron microscope, and UV–Vis spectrophotometry. The particle size distribution was investigated using dynamic light scattering. Then, a fundamental study was conducted on the thermal-hydraulic characteristics of graphene oxide nanofluids flowing through a straight copper tube. An experimental setup was developed to find the heat transfer characteristics and pressure drop of nanofluids in the test section consisting of a copper tube with constant heat flux. The flow regimes and associated pressure drop and heat transfer characteristics at varying flow rate were investigated at three different heat flux conditions of 7.4, 9.1, and 12.6 kW/m2. Due to the increase in viscosity, flowrate and Reynolds number decreased from 0.01 to 0.1 wt% of graphene oxide nanofluids at constant pump frequency. Experimental data obtained for water were validated with the findings from the literature, and the correlations were formulated for the Nusselt number and Reynolds number by considering the multiple regression analysis. The convective heat transfer coefficient for graphene oxide at 0.01 wt% was higher when compared to graphene oxide at 0.1 wt% and water. The variation of Nusselt number with the heat flux and velocity was insignificant.

Experimental investigation on heat transfer and flow resistance of drag-reducing alumina nanofluid in a fin-and-tube heat exchanger

One of the main drawbacks of using nanofluids as a heat transfer media which limited its application in industries is higher pressure drop in comparison with the conventional fluids. In this research, it is the aim to decrease the nanofluid pressure drop using drag reducing agent (DRA). Water based γ-alumina nanofluids at different concentrations were mixed with different concentrations of cetyltrimethylammonium chloride (CTAC) surfactant to reduce the nanofluid pressure drop. On the other hand, it is very important to know that the addition of DRA could decrease the heat transfer performance of the system. So, it is a complicated problem since we have two objective functions (pressure drop and heat transfer) which are varied inversely. The addition of nanoparticles increases both objective functions while the addition of DRA decreases both of them. Results showed that the use of 0.2 %wt nanofluid (without the addition of DRA) causes %20 heat transfer enhancement with the penalty of %5 increase in pressure drop. It was also obtained that at the optimum condition, which was the highest concentration of CTAC (100 ppm) and the highest concentration of nanoparticle (0.2 %wt), the overall heat transfer coefficient enhancement of %17.2, and the friction factor reduction of %4.8 could be obtained in comparison with pure water.