ANN modeling, cost performance and sensitivity analyzing of thermal conductivity of DWCNT–SiO2/EG hybrid nanofluid for higher heat transfer

2017 ◽  
Vol 131 (3) ◽  
pp. 2381-2393 ◽  
Author(s):  
Mohammad Hemmat Esfe ◽  
Ali Akbar Abbasian Arani ◽  
Rasool Shafiei Badi ◽  
Mousa Rejvani
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Hybrid Nanofluid ◽  
Cost Performance ◽  
Ann Modeling

scholarly journals Thermal Performance of Hybrid-Inspired Coolant for Radiator Application

Nanomaterials ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 1100
Author(s):  
F. Benedict ◽  
Amit Kumar ◽  
K. Kadirgama ◽  
Hussein A. Mohammed ◽  
D. Ramasamy ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Heat Capacity ◽  
Specific Heat ◽  
Specific Heat Capacity ◽  
Volume Concentration ◽  
The Other ◽  
Hybrid Nanofluid ◽  
Hybrid Nanofluids ◽  
Tio2 Nanofluids

Due to the increasing demand in industrial application, nanofluids have attracted the considerable attention of researchers in recent decades. The addition of nanocellulose (CNC) with water (W) and ethylene glycol (EG) to a coolant for a radiator application exhibits beneficial properties to improve the efficiency of the radiator. The focus of the present work was to investigate the performance of mono or hybrid metal oxide such as Al2O3 and TiO2 with or without plant base-extracted CNC with varying concentrations as a better heat transfer nanofluid in comparison to distilled water as a radiator coolant. The CNC is dispersed in the base fluid of EG and W with a 60:40 ratio. The highest absorption peak was noticed at 0.9% volume concentration of TiO2, Al2O3, CNC, Al2O3/TiO2, and Al2O3/CNC nanofluids which indicates a better stability of the nanofluids’ suspension. Better thermal conductivity improvement was observed for the Al2O3 nanofluids in all mono nanofluids followed by the CNC and TiO2 nanofluids, respectively. The thermal conductivity of the Al2O3/CNC hybrid nanofluids with 0.9% volume concentration was found to be superior than that of the Al2O3/TiO2 hybrid nanofluids. Al2O3/CNC hybrid nanofluid dominates over other mono and hybrid nanofluids in terms of viscosity at all volume concentrations. CNC nanofluids (all volume concentrations) exhibited the highest specific heat capacity than other mono nanofluids. Additionally, in both hybrid nanofluids, Al2O3/CNC showed the lowest specific heat capacity. The optimized volume concentration from the statistical analytical tool was found to be 0.5%. The experimental results show that the heat transfer coefficient, convective heat transfer, Reynolds number and the Nusselt number have a proportional relationship with the volumetric flow rate. Hybrid nanofluids exhibit better thermal conductivity than mono nanofluids. For instance, a better thermal conductivity improvement was shown by the mono Al2O3 nanofluids than the CNC and TiO2 nanofluids. On the other hand, superior thermal conductivity was observed for the Al2O3/CNC hybrid nanofluids compared to the other mono and hybrid ones (Al2O3/TiO2).

scholarly journals Investigation of the Thermal Conductivity, Viscosity, and Thermal Performance of Graphene Nanoplatelet-Alumina Hybrid Nanofluid in a Differentially Heated Cavity

2021 ◽  
Vol 9 ◽  
Author(s):  
Adeola O. Borode ◽  
Noor A. Ahmed ◽  
Peter A. Olubambi ◽  
Mohsen Sharifpur ◽  
Josua P. Meyer
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Heat Transfer Performance ◽  
Temperature Gradients ◽  
Hybrid Nanofluid ◽  
Mixing Ratio ◽  
Graphene Nanoplatelet ◽  
Hybrid Nanomaterial ◽  
Mixing Ratios ◽  
Hybrid Nanofluids

This paper investigates the thermophysical properties and heat transfer performance of graphene nanoplatelet (GNP) and alumina hybrid nanofluids at different mixing ratios. The electrical conductivity and viscosity of the nanofluids were obtained at temperatures between 15–55°C. The thermal conductivity was measured at temperatures between 20–40°C. The natural convection properties, including Nusselt number, Rayleigh number, and heat transfer coefficient, were experimentally obtained at different temperature gradients (20, 25, 30, and 35°C) in a rectangular cavity. The Mouromtseff number was used to theoretically estimate all the nanofluids’ forced convective performance at temperatures between 20–40°C. The results indicated that the thermal conductivity and viscosity of water are increased with the hybrid nanomaterial. On the other hand, the viscosity and thermal conductivity of the hybrid nanofluids are lesser than that of mono-GNP nanofluids. Notwithstanding, of all the hybrid nanofluids, GNP-alumina hybrid nanofluid with a mixing ratio of 50:50 and 75:25 were found to have the highest thermal conductivity and viscosity, enhancing thermal conductivity by 4.23% and increasing viscosity by 15.79%, compared to water. Further, the addition of the hybrid nanomaterials improved the natural convective performance of water while it deteriorates with mono-GNP. The maximum augmentation of 6.44 and 10.48% were obtained for Nuaverage and haverage of GNP-Alumina (50:50) hybrid nanofluid compared to water, respectively. This study shows that hybrid nanofluids are more effective for heat transfer than water and mono-GNP nanofluid.

scholarly journals Investigating and Modeling the Factors Affecting Thermal Optimization and Dynamic Viscosity of Water Hybrid Nanofluid/Carbon Nanotubes via MOPSO using ANN

2020 ◽  
Vol 2 (3) ◽  
pp. 108-114
Author(s):  
Amin Moslemi Petrudi ◽  
Ionut Cristian Scurtu
Keyword(s):  
Thermal Conductivity ◽  
Carbon Nanotubes ◽  
Volume Fraction ◽  
Optimization Process ◽  
Hybrid Nanofluid ◽  
Engineering Systems ◽  
Factors Affecting ◽  
Multi Objective ◽  
Ann Modeling ◽  
Artificial Neural Network Ann

Optimization is to find the best answer among existing situations. Optimization is used in the design and maintenance of many engineering systems to minimize costs or maximize profits. Due to the widespread use of optimization in engineering, this topic has grown a lot. In this paper, the optimization of multi-objective of Water Hybrid Nanofluid/Carbon Nanotubes is investigated. Multi-Objective Particle Swarm Optimization (MOPSO) algorithm has been used in order to maximize thermal conductivity and minimum viscosity by changing the temperature (300 to 340 ºk) and the volume fraction (0.01 to 0.4%) of nanofluid. Artificial Neural Network (ANN) modeling of experimental data has been used to obtain the values. Parto fronts, the optimal points and different values are 20 members and 15 iterations, and in order to compare the results optimization process on the first, fifth, tenth fronts, a relation has been proposed to predict the viscosity and Parto fronts in the optimization process. The aim of the study was to optimize nanofluid to reduce viscosity and increase thermal conductivity.

scholarly journals CFD Analysis of Enhancement of Heat Transfer of Automobile Radiator with Hybrid Nanofluid as a Coolant

2021 ◽  
Vol 9 (9) ◽  
pp. 367-376
Author(s):  
Megha Zanzote
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Nusselt Number ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Volume Flow Rate ◽  
Inlet Temperature ◽  
Hybrid Nanofluid ◽  
Cfd Analysis ◽  
Maximum Enhancement

Abstract: The performance of the radiator depends on the fluid used in it as a coolant. The conventional fluids like water, ethylene glycol used as a coolant have low thermal conductivity and are not enough for transferring the heat to more extend. Nanoparticles because of their high thermal conductivity enhances the performance of the radiator when added into the base fluid. In the present work Al2O3-CuO/ Water based hybrid nanofluid is used as a coolant for the CFD analysis of automobile radiator. Different mixing ratios (80:20, 60:40,50:50,40:60 and 20:80) of Al2O3-CuO nanoparticles are used in water with 1% volume concentration. The inlet temperature and volume flow rate of fluid are kept constant. The nanofluid with 20:80 mixing ratio of Al2O3-CuO gives maximum enhancement in heat transfer coefficient and Nusselt number than water by 72% and 65% respectively. Keywords: Coolant, Heat Transfer Coefficient, Nusselt Number, Hybrid Nanofluids, Radiator

MHD flow of a Newtonian fluid in symmetric channel with ABC fractional model containing hybrid nanoparticles

2021 ◽  
Vol 24 ◽  
Author(s):  
Muhammad Danish Ikram ◽  
Muhammad Asjad Imran ◽  
Yu Ming Chu ◽  
Ali Akgül
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Velocity Fields ◽  
Engine Oil ◽  
Hybrid Nanoparticles ◽  
Hybrid Nanofluid ◽  
Fractional Operators ◽  
Parallel Plates ◽  
Viscous Fluid Model ◽  
Temperature And Velocity Fields

Introduction: The nanofluid is novelty of nanotechnology to overcome the difficulties of heat transfer in several manufacturing and engineering areas. Fractional calculus has many applications in nearly all fields of science and engineering which comprises electrochemistry, dispersion and viscoelasticity. Objectives: This paper focused on the heat transfer of hybrid nanofluid in two vertical parallel plates and presented a comparison between fractional operators. Methods: The fractional viscous fluid model is considered with physical initial and boundary conditions for the movement occurrences. The analytical solutions were obtained via Laplace transform method for the concentration, temperature and velocity fields. After that we presented a comparison between Atangana-Baleanu (ABC), Caputo (C) and Caputo-Fabrizio (CF) fractional operators. Results: The comparison of different base fluids (Water, kerosene, Engine Oil) is discussed graphically for temperature and velocity. It is resulted that due to high thermal conductivity in water, temperature and velocity are high. While engine oil has maximum viscosity than water and kerosene, so temperature and velocity are very low. Due to the thermal conductivity improving with the enrichment of hybrid nanoparticles, so the Temperature is increased and since viscosity increased, so the velocity is reduced. Conclusion: Atangana-Baleanu (ABC) fractional operator gives better memory effect of concentration, temperature and velocity fields than Caputo (C) and Caputo-Fabrizio (CF). Temperature and velocity of water with hybridized nanoparticles is high in comparison with kerosene and engine oil.

Entropy generation analysis during MHD natural convection flow of hybrid nanofluid in a square cavity containing a corrugated conducting block

2019 ◽  
Vol 30 (3) ◽  
pp. 1115-1136 ◽  
Author(s):  
Tahar Tayebi ◽  
Ali J. Chamkha
Keyword(s):  
Magnetic Field ◽  
Heat Transfer ◽  
Thermal Conductivity ◽  
Entropy Generation ◽  
Thermal Conductivity Ratio ◽  
Hybrid Nanofluid ◽  
Conductivity Ratio ◽  
Square Cavity ◽  
Content Type ◽  
Solid Block

Purpose The purpose of this paper is to study the influence of magnetic field on entropy generation and natural convection inside an enclosure filled with a hybrid nanofluid and having a conducting wavy solid block. Also, the effect of fluid–solid thermal conductivity ratio is investigated. Design/methodology/approach The governing equations that are formulated in the dimensionless form are discretized via finite volume method. The velocity–pressure coupling is assured by the SIMPLE algorithm. Heat transfer balance is used to verify the convergence. The validation of the numerical results was performed by comparing qualitatively and quantitatively the results with previously published investigations. Findings The results indicate that the magnetic field and the conductivity ratio of the wavy solid block can significantly affect the dynamic and thermal field and, consequently, the heat transfer rate and entropy generation because of heat transfer, fluid friction and magnetic force. Originality/value To the best of the authors’ knowledge, the present numerical study is the first attempt to use hybrid nanofluid for studying the entropy generation because of magnetohydrodynamic natural convective flow in a square cavity with the presence of a wavy circular conductive cylinder. Irreversibilities due to magnetic effect are taken into account. The effect of fluid–solid thermal conductivity ratio is considered.

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