In this study, comprehensive study of laminar flow and heat transfer of pseudo-plastic non-Newtonian nanofluid (Al2O3+CMC) within the porous circular concentric region is presented. The effect of ...volume fraction of nanoparticles, Reynolds number, Darcy number, thickness ratio is studied. Simulations for different Reynolds numbers and Darcy numbers in the range of 100≤Re≤300and 10−4≤Da≤10−2 are done. The results show that the effect of the porous layer on increasing the convective heat transfer coefficient is larger than the Reynolds number, since, at a given volume fraction, the porous medium plays a greater role in increasing the heat transfer compared to the increasing Reynolds number. Also, at a given volume fraction and for a fixed porosity, decreases in the permeability leads to increased Darcy velocity and, consequently, velocity profile. As the thickness of the porous layer increases at fixed values of permeability and porosity, the velocity of the nanofluid is also increased in a constant Reynolds number, by increasing the thickness of the porous media, heat transfer coefficient increases. In addition, at a specified thickness and constant Reynolds number, by increasing the Darcy number, the heat transfer coefficient and the Nusselt number increases. Moreover, as the thickness of the porous layer increases at fixed values of permeability and porosity, the velocity of the nanofluid is also increased; this consequently maximizes the pressure drop.
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•Comprehensive study of non-Newtonian nanofluid.•Study the effect of volume fraction, Reynolds number and Darcy number.•Effect of the porous layer on convective heat transfer coefficient is larger than the Re.
In this paper, entropy generation analysis of different nanofluid flows in the space between two concentric horizontal pipes in the presence of magnetic field by using of single-phase and two-phase ...approaches was carried out. Single-phase model and two-phase model (mixture) are utilized to model the flow and heat transfer for Newtonian nanofluids in the space between two concentric horizontal tubes subjected to the magnetic field. The Reynolds and Hartman numbers ranges are 500 ≤Re≤ 1500 and 0 ≤Ha≤ 20, respectively. In this study, heat transfer of various nanofluids (Al2O3, TiO2, ZnO and SiO2) and their entropy generation have been investigated. The effect of diameter of particles (water-Al2O3 nanofluid) on heat transfer and entropy generation has also been studied. Average Nusselt number in terms of Hartman number and Reynolds number for different nanofluids for single-phase and two-phase models in various volume fractions, entropy generation due to friction, magnet and heat transfer in terms of radial direction for different Hartman numbers, Reynolds number and different nanofluids with different diameter of particles were obtained. We found that in all states, the Nusselt number is higher in two-phase model than in single-phase model. The maximum pressure difference for single- and two-phase models occurs at maximum volume fractions and Hartman number. Also, as the diameter of the nanoparticle increases, the result will be an increase in the temperature of the walls, leading to an increase in entropy generation. Also, as the Hartman number increases, the amount of entropy generation increases.
In this paper, the thermal conductivity of Fe3O4 magnetic nanofluids has been investigated experimentally. The nanofluid samples were prepared using a two-step method by dispersing Fe3O4 ...nanoparticles into the water with the solid volume fractions of 0.1%, 0.2%, 0.4%, 1%, 2% and 3%. Thermal conductivity measurements were performed by employing a KD2 Pro thermal properties analyser under temperatures ranging from 20°C to 55°C. Then, using experimental data, a new correlation was proposed to predict the thermal conductivity ratio of the magnetic nanofluid. Finally, an optimal artificial neural network was designed to predict the thermal conductivity ratio of the magnetic nanofluid. The experimental results indicated that the maximum enhancement of thermal conductivity of nanofluid was about 90%, which occurred at solid volume fraction of 3.0% and temperature of 55°C. The comparative results showed that there are deviations of 5% and 1.5%, respectively, for correlation and ANN from the experimental data. It was found from comparisons that the optimal artificial neural network model is more accurate compared to empirical correlation.
In the present study, the nanofluid flow and heat transfer in a shell and tube heat exchanger have been simulated in three dimensions. The hot fluid is the combustion gaseous product of a diesel ...engine, and the coolant is considered the shell of pure water, seawater, and CuO-water nanofluid with
φ
=
2 and 4%. The present study aims to reduce the temperature of exhaust gases from the heat exchanger, which is used at the inlet of the diesel engine gas recirculation system. The velocities of the hot gases and the coolant liquid based on Reynolds numbers, (respectively, Re
gas
and Re
water
), various fluids, and the volume fraction of the nanofluid
φ
are studied in this research. Moreover, the effect of twisted tape and baffle on the thermal performance of the heat exchanger is investigated. Examination of the base fluid shows that the change of the base fluid from pure water to seawater decreases the thermal efficiency of the heat exchanger by 3% and also increases the outlet gas temperature. Also, the pressure drop due to seawater is 16% higher than that of pure water. According to the results, the effect of twisted tape on improving thermal performance is higher than baffles. The analysis of thermal performance shows that the maximum convective heat transfer coefficient is obtained in the condition that the twisted tape with TR = 4 is used in the shell part, which is 119 W m
−2
K
−1
. According to the results, the use of nanoparticles in the base fluid with twisted tape, despite the increase in heat transfer, reduces the thermal performance. The use of nanoparticles in seawater also increases heat transfer; however, it considerably increases the friction factor.
Abstract
Mixed convection of nanofluid in a 2D square enclosure with a porous block in its center and four rotating cylinders, which are forced by a simple harmonic function, was studied numerically. ...The porous zone was studied by considering the Forchheimer–Brinkman-extended Darcy model. Effects of various parameters including Darcy number (10
–5
≤ Da ≤ 10
–2
), porosity (0.2 ≤ ɛ ≤ 0.7), Richardson number (0.1 ≤ Ri ≤ 10), and volume fraction of nanoparticles (0 ≤ ϕ ≤ 0.03), on heat transfer, entropy generation, PEC, velocity, streamline and isotherm contours were demonstrated. The results show that decreasing the Darcy number as well as reducing the Richardson number leads to an increase in the average Nusselt number. However, porosity changes had no decisive effect on heat transfer. Maximize the volume fraction of copper nanoparticles in the base fluid enhanced heat transfer. In the case of the high permeability of the porous medium, the impact of the harmonic rotation of the cylinders on the flow patterns became more pronounced.
Abstract
This study presents the design of an artificial neural network (ANN) to evaluate and predict the viscosity behavior of Al
2
O
3
/10W40 nanofluid at different temperatures, shear rates, and ...volume fraction of nanoparticles. Nanofluid viscosity (
$${\mu }_{nf}$$
μ
nf
) is evaluated at volume fractions (
$$\varphi$$
φ
=0.25% to 2%) and temperature range of 5 to 55 °C. For modeling by ANN, a multilayer perceptron (MLP) network with the Levenberg–Marquardt algorithm (LMA) is used. The main purpose of this study is to model and predict the
$${\mu }_{nf}$$
μ
nf
of Al
2
O
3
/10W40 nanofluid through ANN, select the best ANN structure from the set of predicted structures and manage time and cost by predicting the ANN with the least error. To model the ANN,
$$\varphi$$
φ
, temperature, and shear rate are considered as input variables, and
$${\mu }_{nf}$$
μ
nf
is considered as output variable. From 400 different ANN structures for Al
2
O
3
/10W40 nanofluid, the optimal structure consisting of two hidden layers with the optimal structure of 6 neurons in the first layer and 4 neurons in the second layer is selected. Finally, the R regression coefficient and the MSE are 0.995838 and 4.14469E−08 for the optimal structure, respectively. According to all data, the margin of deviation (MOD) is in the range of less than 2% < MOD < + 2%. Comparison of the three data sets, namely laboratory data, correlation output, and ANN output, shows that the ANN estimates laboratory data more accurately.
In the current work, we investigate the dynamic viscosity of Ag/Ethylene glycol nanofluid within the temperature range of 25–55 ° C with volume fraction of nanoparticles range of 0.2%–2%. The ...experimental data includes 42 samples. At first, an Artificial Neural Network (ANN) is designed to predict the dynamic viscosity of this nanofluid and finally the results of ANN and correlation has been compared. The algorithm of generating the best architecture of ANN has been proposed and the best ANN has been used to predict the dynamic viscosity of Silver/Ethylene glycol nanofluid. It is found that the ANN can predict the viscosity of Ag/Ethylene glycol nanofluid with good precision compared to the correlation method. Also, in the correlation method, MSE is 0.0012, SSE is 0.0512 and the maximum value of error is 0.0858.
In this research, the effect of utilizing semi-attached rib on heat transfer and liquid turbulent flow of nanofluid water–copper oxide in three-dimensional rectangular microchannel has been ...investigated. The results of numerical examination of this study in comparison to those of smooth channel have also been evaluated. The range of Reynolds numbers is between 10,000 and 60,000 and the volume fraction of copper oxide nanoparticle in 0%, 2%, and 4% was examined. In this numerical simulation, the effects of the changes in parameters such as dimensions of semi-attached rib, volume fraction of the nanoparticle, and Reynolds number were considered. The results of this study showed that utilizing semi-attached rib in microchannel with a ratio of 0 < R/W ≤ 0.325 in producing stronger vortices, which causes better mixture in fluid layers, is weaker than that with tooth mode of R/W = 0 ratio. However, the main advantage of using tooth with a ratio of 0 < R/W ≤ 0.325 in comparison to ordinary tooth is the increase in heat transfer and reduction in coefficient friction and pumping power.
A molecular dynamics simulation study is performed to predict the glass transition temperature ( Tg) and the volumetric coefficient of thermal expansion (CTE) of thermoset polymer based nanocomposite ...reinforced by carbon nanotube (CNT). An atomistic model of cross-linked Diglycidyl ether bisphenol A (DGEBA) epoxy and Diethylenetriamine (DETA) was built as a matrix by employingCondensed-phase optimized molecular potentials for atomistic simulation (COMPASS27) force field. Different molecular models were constructed with various types of CNT embedded in epoxy simulation boxes. Tg was determined based on density variation with temperature. Furthermore, a new method is proposed to compute the CTE based on density variation with temperature. The effects of CNT diameter, volume fraction and chirality on Tg and CTE of nanocomposites were investigated using molecular dynamics simulation. For all cases, studied and CTE were less than pure epoxy (between 3.77% to 10.05% for Tg and respectively 14.24% to 32.23% and 23.82% to 41.65% for CTE below and above of Tg ). Increasing the CNT diameter in nanocomposite increases Tg and CTE (5.0% for Tg and 20.0% for CTE when the diameter of CNT changed 7.8A0 to 15.6A0). On the other hand increasing volume fraction of CNT in the nanocomposite decreases Tg and CTE (2.7% for Tg and 13.8% for CTE when the volume fraction of CNT in the nanocomposites changed 3.36% to 5.23%). Chirality studies under constant weight fraction of nanocomposites show that applying armchair CNT instead of zigzag CNT, decreases Tg and increases CTE (2.1% for Tg and 5.8% for CTE)
•A study on the glass transition temperature and volumetric thermal expansion coefficient.•Using of thermoset polymer based Epoxy nanocomposite.•By increasing the CNT diameter, Tg and CTE increase.•By increasing volume fraction of CNT, Tg and CTE decreases.
•The effect of solid volume fraction and Re on heat transfer coefficient and pressure drop of nanofluid is investigated.•Heat transfer coefficient and Nusselt number increases with an increase in ...solid volume fraction and Re.•The effect of increasing percentage of nanoparticle in low Reynolds number is stronger than that of high Reynolds number.
This study presents an experimental study of the effect of solid volume fraction and Reynolds number on heat transfer coefficient and pressure drop of CuO–Water nanofluid. Pure Water and nanofluid with particle volume fractions of 0.0625%, 0.125%, 0.25%, 0.5%, 1%, 1.5% and 2% are used as working fluids. Nanofluids were flowed inside a horizontal double-tube counter flow heat exchanger under turbulent flow regime. Flow Reynolds numbers of each volume fraction of nanofluid were between from 2900 to 18,500 during the experiments. The Result shows that generally heat transfer coefficient of nanofluids is higher than that of base fluid. Moreover, it is observed that heat transfer coefficient and Nusselt number of nanofluids increases with an increase in solid volume fraction and Reynolds number. But the rate of this increase in low Reynolds numbers was more than that at high Reynolds numbers. The measurements also show that the pressure drop of nanofluid is slightly higher than that of the base fluid and increases with an increase in the nanoparticles volume fraction. But the rate of this increase in low Reynolds numbers was more than that at high Reynolds numbers. Therefore, it can be concluded that the effect of increasing percentage of nanoparticle in low Reynolds number of this research is stronger than that of high Reynolds number. Moreover, friction factors were calculated and compared with blasious correlation. Finally, in order to find the optimum condition of this nanofluid for practical applications, thermal performance factor was defined to consider increasing Nusselt ratio besides increasing friction ratio simultaneously. The results show that the maximum thermal performance factor of this nanofluid was 1.266, which was calculated for 2% nanoparticle volume fraction at Reynolds number 3677.
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