Measurement of the thermal conductivity of MWCNT-CuO/water hybrid nanofluid using artificial neural networks (ANNs)

Rostami, Sara; Toghraie, Davood; Shabani, Behrooz; Sina, Nima; Barnoon, Pouya

doi:10.1007/s10973-020-09458-5

Cited by 118 publications

(30 citation statements)

References 23 publications

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“…As a result of the study, it was found that the margin of error is less about the estimated data of the ANN method. In addition, it has been stated that the increase in the volume fraction of the nanoparticle at constant temperature causes an increase in its thermal conductivity [23]. Another MWCNT experiment was conducted by Afshari et al In this study, MWCNT-Alumina/water (80%)ethylene glycol (20%) mixture was tested in different solid volume fractions and temperatures, and the effects of hybrid nanofluid on viscosity were investigated [24].…”

Section: Introductionmentioning

confidence: 95%

Comparison of effects of nanofluid utilization (Al2O3, SiO2, TiO2) with reference water in automotive radiators on exergetic properties of diesel engines

2021

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In this study, nanofluids formed by using ethylene glycol and three kinds of nanoparticles such as Al2O3, SiO2, and TiO2 were added to the four-stroke internal combustion engine radiator and compared with the conventional coolant (pure water). This comparison is based on the exergy performances which are the main theme of the second law of thermodynamics. The tests were carried out at a fixed engine speed of 1800 rpm using diesel fuel, and the outputs were obtained from the test setup experimentally. A total of six nanofluid tests were performed on two different dispersions (0.2% and 0.4%). As a result of this study, the best exergy efficiency was obtained by using TiO2 particles with a 35.67% value. Increasing the percentage of nanoparticles in the fluid from 0.2 to 0.4 positively affected efficiency. Pure water generally lagged behind nanofluid performance in experimental parameters. Compared to conventional coolant (pure water), the lowest exhaust temperature value was measured by using an Al2O3/Ethylene Glycol mixture with a difference of 59 K. Also, by using Al2O3 nanoparticles as a coolant, 8.858 kW of exergy exhaust value was obtained. This is the best emission value measured in the experimental study. While calculating values close to each other in the use of other nanoparticles, the worst exergy exhaust results were obtained by using the conventional refrigerant. Consequently, in this paper, exergetic outputs such as exergetic efficiency, exergy destruction, exergy heat, exergy work, exergy total exhaust, and entropy production rate were calculated for pure water and each nanofluid.

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Section: Introductionmentioning

confidence: 95%

Comparison of effects of nanofluid utilization (Al2O3, SiO2, TiO2) with reference water in automotive radiators on exergetic properties of diesel engines

2021

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“…43,44 Exploration related to comparative study on the rheological behavior of hybrid nanofluids with graphene, natural circulation loop with hybrid-nanofluid along single-phase, and measurement of the thermal conductivity CuO/water hybrid nanofluid using latest technique artificial neural networks is made by several investigators. [45][46][47] Study related to the magnet when the field of the magnet and the velocity of electrically conducted fluid are coupled is called magnetohydrodynamic (MHD) flow. There is induction of current during the magnetic field and a force is created, which alters the magnetic field itself called Lorentz force.…”

Section: Introductionmentioning

confidence: 99%

Nanoscale energy transport of inclined magnetized 3D hybrid nanofluid with Lobatto IIIA scheme

et al. 2021

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Key developments in the field of nanotechnology have drawn the attention of many scholars toward the interaction of nanoparticles due to their capturing applications in solar energy systems and thermal engineering. Larger consumption of energy posed a challenge for thermal science, so thermal engineering is trying to solve this issue by increasing the thermal conductivity of the fluid. The thermal conductivity of conventional fluid is increased by incorporating the nanoparticles in the base fluid. Keeping this in mind, the present research project addresses the utilization of nanoparticles in a steady three-dimensional rotating flow of magnetohydrodynamic water-based hybrid fluid over an extending sheet. Nanoparticles of aluminum oxide (Al 2 O 3 ) and silver (Ag) are being used with water (H 2 O) as base fluid. The velocity of nanoparticles is being captured under the influence of an inclined magnetic field and the transport of heat is scrutinized through thermal radiation. The physical model generates partial differential equations and then transported into an equivalent set of a nonlinear ordinary differential equations. The purpose of numerical computation is made by the Lobatto IIIA method, which is

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“…Artificial neural network modeling is known as a powerful tool in various advanced scientific and engineering topics to solve complicated problems. Regarding its high running speed, widespread capacity and also simplicity of applying ANNs in comparison with classic methods, numerous scientists tend to use this modeling method to predict thermophysical [25][26][27][28][29], viscosity [30][31][32] and thermal conductivity [33][34][35][36] properties of nanofluids.…”

Section: Introductionmentioning

confidence: 99%

A hybrid genetic–BP algorithm approach for thermal conductivity modeling of nanofluid containing silver nanoparticles coated with PVP

Paknezhad

Vakili

Bozorgi

et al. 2020

J Therm Anal Calorim

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Since nanoparticles play a significant role in increasing the thermal conductivity of fluids, the present study aims to predict the thermal conductivity of silver nanofluid coated with polyvinylpyrrolidone (PVP) by the combinational model of multilayer perceptron artificial neural network and genetic algorithm. For modeling, the results of experimental measurements have been used for thermal conductivity of nanofluid containing PVP-coated silver nanoparticle-based deionized water at 25-55 °C in volume fraction of 250 ppm, 500 ppm and 1000 ppm. Henceforth, genetic algorithm is applied to improve learning process in the artificial neural network. It is in this way that the masses were chosen for each neuron's communications as well as their bias happens according to optimization performed by the genetic algorithm. To evaluate the accuracy of the model in predicting thermal conductivity of nanofluid, mean absolute percentage error, root mean square error, coefficient of determination (R 2 ) and mean bias error have exerted indices which are 1.202, 0.345, 0.989 and − 0.016, respectively. The results of the indices and predictions, compared to the experimental results, show high accuracy and reliable combinational model of artificial neural network and genetic algorithm.

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Measurement of the thermal conductivity of MWCNT-CuO/water hybrid nanofluid using artificial neural networks (ANNs)

Cited by 118 publications

References 23 publications

Comparison of effects of nanofluid utilization (Al2O3, SiO2, TiO2) with reference water in automotive radiators on exergetic properties of diesel engines

Comparison of effects of nanofluid utilization (Al2O3, SiO2, TiO2) with reference water in automotive radiators on exergetic properties of diesel engines

Nanoscale energy transport of inclined magnetized 3D hybrid nanofluid with Lobatto IIIA scheme

A hybrid genetic–BP algorithm approach for thermal conductivity modeling of nanofluid containing silver nanoparticles coated with PVP

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