Evaluation of thermal conductivity of MgO-MWCNTs/EG hybrid nanofluids based on experimental data by selecting optimal artificial neural networks

Vafaei, Masoud; Afrand, Masoud; Sina, Nima; Kalbasi, Rasool; Sourani, Forough; Teimouri, Hamid

doi:10.1016/j.physe.2016.08.020

Cited by 221 publications

(48 citation statements)

References 46 publications

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“…With the end goal of calculating the most precise results, minimization of the cost function is needed. Equation 16 represents the constraints which are applied to the cost function: (16) where n th input is denoted by xn and its output is expressed by yn. ε is the maximum acceptable error for the function, and and * show the margin of acceptable error.…”

Section: Least Squares Support Vector Machine (Lssvm)mentioning

confidence: 99%

“…One of the auspicious nanomaterials which has exceptionally enticed researchers is carbon nanotubes (CNTs) as the result of their excellent thermal properties. Different energy systems and industrial processes involve with heat transfer by using working fluids [16,17]. Under this condition, the fluids' thermal properties perform a crucial part in providing equipments with energy-effective heat transfer.…”

Section: Introductionmentioning

confidence: 99%

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Soft Computing Approaches for Thermal Conductivity Estimation of CNT/Water Nanofluid

Ahmadi¹,

Ghazvini²,

Baghban³

et al. 2019

RCMA

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One of the auspicious nanomaterials which has exceptionally enticed researchers is carbon nanotubes (CNTs) as the result of their excellent thermal properties. In this investigation, an experiment was carried out on three kinds of CNTs-nanofluids with various CNTs added to de-ionized water to compared and analyze their thermal conductivity properties. The main purpose of this study was to introduce a combination of experimental and modelling approaches to forecast the amount of thermal conductivity using four different neural networks. Between MLP-ANN, ANFIS, LSSVM, and RBF-ANN Methods, it was found that the LSSVM produced better results with the lowest deviation factor and reflected the most accurate responses between the proposed models. the regression diagram of experimental and estimated values shows an R2 coefficient of 0.9806 and 0.9579 for training and testing sections of the ANFIS method in part a, and in the b, c and d parts of the diagram, coefficients of determination were 0.9893& 0.9967 and 0.9974 & 0.9992 and 0.9996& 0.9989 for training and testing part of MLP-ANN, RBF-ANN and LSSVM models. Also, the effect of different parameters was investigated using a sensitivity analysis method which demonstrates that the temperature is the most affecting parameter on the thermal conductivity with a relevancy factor of 0.66866.

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Section: Least Squares Support Vector Machine (Lssvm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Soft Computing Approaches for Thermal Conductivity Estimation of CNT/Water Nanofluid

Ahmadi¹,

Ghazvini²,

Baghban³

et al. 2019

RCMA

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show abstract

“…Additionally, some studies 2 of 14 have focused on the prediction of the thermal conductivity ratio associated with various nanofluids with the help of using experiments and artificial neural networks [20][21][22][23][24][25][26][27][28][29][30][31]. Vafaei et al [32] predicted the thermal conductivity ratio of MgO-MWCNTs/EG hybrid nanofluids by using ANN (artificial neural network) at the temperature range of 25-50 • C. According to the results, the best performance belonged to the neural network with 12 neurons in the hidden layer. Also, an investigation has been carried out by Afrand et al [33] to estimate the thermal conductivity of MgO/water nanofluid.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network

et al. 2020

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In this paper, an artificial neural network is implemented for the sake of predicting the thermal conductivity ratio of TiO2-Al2O3/water nanofluid. TiO2-Al2O3/water in the role of an innovative type of nanofluid was synthesized by the sol–gel method. The results indicated that 1.5 vol.% of nanofluids enhanced the thermal conductivity by up to 25%. It was shown that the heat transfer coefficient was linearly augmented with increasing nanoparticle concentration, but its variation with temperature was nonlinear. It should be noted that the increase in concentration may cause the particles to agglomerate, and then the thermal conductivity is reduced. The increase in temperature also increases the thermal conductivity, due to an increase in the Brownian motion and collision of particles. In this research, for the sake of predicting the thermal conductivity of TiO2-Al2O3/water nanofluid based on volumetric concentration and temperature functions, an artificial neural network is implemented. In this way, for predicting thermal conductivity, SOM (self-organizing map) and BP-LM (Back Propagation-Levenberq-Marquardt) algorithms were used. Based on the results obtained, these algorithms can be considered as an exceptional tool for predicting thermal conductivity. Additionally, the correlation coefficient values were equal to 0.938 and 0.98 when implementing the SOM and BP-LM algorithms, respectively, which is highly acceptable.

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“…Adding the solid nanoparticles into the fluids is one of the ways for increasing the heat transfer rate. In spite of the fact that by adding the solid nanoparticles to the fluids, the thermal conductivity of the fluid increases, it also increases the viscosity of the fluid [9][10][11][12][13][14][15][16][17][18]. In the natural convection flow, increasing the fluid viscosity leads to the decrease of the fluid flow; thus, it has a negative effect on the heat transfer rate.…”

Section: Introductionmentioning

confidence: 99%

“…Where the Rayleigh number, Ra, Prandtl number, Pr, and the Reynolds number, Re, are defined as: (13) Moreover, by applying the dimensionless variables, the boundary conditions are given as:…”

mentioning

confidence: 99%

Mixed convection in a rotating eccentric annulus containing nanofluid using bi-orthogonal grid types: A finite volume simulation

Teimouri

Sheikhzadeh

Afrand

et al. 2017

Journal of Molecular Liquids

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Evaluation of thermal conductivity of MgO-MWCNTs/EG hybrid nanofluids based on experimental data by selecting optimal artificial neural networks

Cited by 221 publications

References 46 publications

Soft Computing Approaches for Thermal Conductivity Estimation of CNT/Water Nanofluid

Soft Computing Approaches for Thermal Conductivity Estimation of CNT/Water Nanofluid

Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network

Mixed convection in a rotating eccentric annulus containing nanofluid using bi-orthogonal grid types: A finite volume simulation

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