Experimental study on thermal conductivity of DWCNT-ZnO/water-EG nanofluids

Esfe, Mohammad Hemmat; Yan, Wei‐Mon; Akbari, Mohammad; Karimipour, Arash; Hassani, Mohsen

doi:10.1016/j.icheatmasstransfer.2015.09.001

Cited by 164 publications

(11 citation statements)

References 38 publications

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“…However, Li and Peterson [85] concluded that dependence of the effective thermal conductivity on the bulk temperature is much weaker than the dependence on the volume fraction. This conclusion was confirmed later by [107,108] results. In the same context, Beck et al [96] results indicated that the thermal conductivity enhancement behavior over temperature variation followed closely that of the base fluid.…”

Section: Nanoparticle Thermal Conductivitysupporting

confidence: 63%

“…Most of them found that the more nanoparticles concentration, the more enhanced thermal conductivity of the suspension. Some of researchers found the relation to be nonlinear [25,79,86,87], while others found it linear [22,23,[26][27][28][29]31,32,34,35,39,40,55,[69][70][71][72][73][74][75][76][77][78]82,84,85,[88][89][90][91][92][93][94][95][96][97][98][99][100][101][102][104][105][106][107][108]…”

Section: Parameters Affecting Nanofluids Thermal Conductivitymentioning

confidence: 99%

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Experimental studies of nanofluid thermal conductivity enhancement and applications: A review

Tawfik

2017

Renewable and Sustainable Energy Reviews

265

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In many applications, there is a critical need for enhancing the poor thermal conductivity of conventional fluids in order to develop efficient heat transfer fluids. This requirement can be met through dispersing nanometric particles in a given base fluid such as water, ethylene glycol, oil or air. The resulting nanofluids enhanced thermal conductivity of the base fluids. In order to evaluate this enhancement, nanofluid thermal conductivity is required to be measured. Several methods and techniques are covered in the present contribution. In addition, enhancements recorded experimentally are reviewed and summarized. Different parameters affecting on such enhancement are covered, including: nanoparticle concentration, size, shape and thermal conductivity. In addition, base fluid type, nanofluid bulk temperature and dispersion techniques are also covered parameters. However, nanofluids have the potential to contribute in several practical applications including solar thermal, transportation, electronic cooling, medical, detergency and military applications. In the present work, a brief overview of evolution in the use of nanofluids in some applications has been presented. According to this contribution, there is a critical need for further fundamental and applications of nanofluids studies in order to understand the physical mechanisms of using nanofluids as well as explore different aspects of applications of nanofluids.

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Section: Nanoparticle Thermal Conductivitysupporting

confidence: 63%

Section: Parameters Affecting Nanofluids Thermal Conductivitymentioning

confidence: 99%

Experimental studies of nanofluid thermal conductivity enhancement and applications: A review

Tawfik

2017

Renewable and Sustainable Energy Reviews

265

View full text Add to dashboard Cite

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“…The validation results of the present study are in good agreement with the results of Dixit and Babu 4 and Bouhalleb and Abbassi. 29 T A B L E 2 Thermophysical properties of water and Ag at 300 K 33 34 Radial basis function artificial neural networks (RBF-ANN) is one of the favorable neural networked, which has simple implementation with good generalization and strong tolerance to input noise. It was first introduced by Broomhead and Lowe 35 in 1988.…”

Section: Solving Procedure Results Independency From the Grid And mentioning

confidence: 99%

“…As can be seen, the minimum entropy generation occurs in the vertical enclosure (angle of 0°) and the maximum entropy generation occurs for the enclosure with the angle of 75°. As it is evidenced in the equation (34), the total entropy generation included two parameters of thermal and frictional By enhancing the volume fraction, the temperature gradients decreases and the conductivity increases. When the conductivity enhancement is more impressive than the reduction of temperature gradients, thermal entropy generation increases (and vice versa).…”

Section: Entropy Generationmentioning

confidence: 87%

“…ANN is a high‐performance computing unit inspired from biological neural structures. It has an impressive role in figuring out the intricate nonlinear relevance of process parameters attributed to the training function and optimized weights . Radial basis function artificial neural networks (RBF‐ANN) is one of the favorable neural networked, which has simple implementation with good generalization and strong tolerance to input noise.…”

Section: Designing Methods Based On Radial Basis Function Artificial Nmentioning

confidence: 99%

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Study of flow field, heat transfer, and entropy generation of nanofluid turbulent natural convection in an enclosure utilizing the computational fluid dynamics‐artificial neural network hybrid method

Sepehrnia

Sheikhzadeh

Abaei

et al. 2019

Heat Trans. Asian Res.

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In this study, the turbulent natural convection of Ag‐water nanofluid in a tall, inclined enclosure has been investigated. The main objective of this study is finding the optimized angle of the enclosure with operational boundary condition in cooling from ceiling utilizing the computational fluid dynamics‐artificial neural network (CFD‐ANN) hybrid method, which has not been noticed in previous studies. To achieve this, we proposed two approaches. First, the simulations have been done with a deviation angle of 0 to 90° by using water and Ag‐water nanofluid. And second, a new prediction approach is proposed based on radial basis function artificial neural networks (RBF‐ANN) to predict the mean Nusselt number and entropy generation with the variation of Rayleigh numbers, deviation angles, and volume fractions as inputs. The results from the first approach indicate that the Rayleigh number has a considerable function in the determination of optimized angle. The results from the second approach, which used the first approach simulation results as training data set, could predict the mean Nusselt number and entropy generation with 1.4577e−022 and 1.552e−015 mean square error, respectively. Moreover, a new set of data for Rayleigh numbers, deviation angles, and volume fractions were used to test the performance of the prediction model, which shows promising and superior prospects for RBF‐ANN.

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Optimization and modeling of thermal conductivity and viscosity of Cu/engine oil nanofluids by NSGA‐II using RSM

Esfe

Motallebi

2020

Math Methods in App Sciences

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This study provides the optimization of the thermophysical properties of Cu/engine oil nanofluid. In this optimization, the objective functions were determined using response surface methodology (RSM) to analyze the experimental data of nanofluid viscosity and thermal conductivity (TC). Two equations are presented for the accurate prediction of TC and viscosity data. The nondominated sorting genetic algorithm II (NSGA‐II) method was used for multi‐objective optimization (Mo‐O), and Pareto's front was introduced to study optimal viscosity and TC. According to the results, the highest TC and the lowest viscosity occur when the temperature and solid volume fraction of the nanofluid are at their maximum values.

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Experimental study on thermal conductivity of DWCNT-ZnO/water-EG nanofluids

Cited by 164 publications

References 38 publications

Experimental studies of nanofluid thermal conductivity enhancement and applications: A review

Experimental studies of nanofluid thermal conductivity enhancement and applications: A review

Study of flow field, heat transfer, and entropy generation of nanofluid turbulent natural convection in an enclosure utilizing the computational fluid dynamics‐artificial neural network hybrid method

Optimization and modeling of thermal conductivity and viscosity of Cu/engine oil nanofluids by NSGA‐II using RSM

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