Using of Artificial Neural Networks (ANNs) to predict the thermal conductivity of Zinc Oxide–Silver (50%–50%)/Water hybrid Newtonian nanofluid

He, Wei; Ruhani, Behrooz; Toghraie, Davood; Izadpanahi, Niloufar; Esfahani, Navid Nasajpour; Karimipour, Arash; Afrand, Masoud

doi:10.1016/j.icheatmasstransfer.2020.104645

Cited by 117 publications

(37 citation statements)

References 32 publications

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“…There are many studies conducted by researchers in the literature about ANNs that have these advantageous features. In a study by He et al, 17 an ANN was developed to estimate the thermal conductivity of water‐based ZnO ‐ Ag nanofluids. After obtaining the functioning and correlation coefficients for ANN, experimental data were positioned on the surface formed using the fitting method.…”

Section: Introductionmentioning

confidence: 99%

A novel comparative investigation of the effect of the number of neurons on the predictive performance of the artificial neural network: An experimental study on the thermal conductivity of ZrO ₂ nanofluid

Çolak

2021

Int J Energy Res

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Summary In this study, the effect of the number of neurons on the predictive performance of artificial neural networks (ANN) has been investigated using experimental data. For this purpose, 6 different ANN have been developed by using a total of 60 experimental data of ZrO2/water nanofluid obtained from the literature. In ANN developed with the number of 5, 10, 15, 20, 25, and 30 neurons, all other parameters have been kept constant, and the effect of only the number of neurons on the prediction performance has been investigated. The performance of each ANN has been calculated separately and then their performance has been analyzed by comparing them with each other. As a consequence of the study, it has been seen that the model with the most ideal predictive performance has been developed with 5 neurons with an average error rate of 0.001%, and the highest margin of error the model has been developed with 15 neurons and had an error rate of 0.026%. In the light of the obtained data, it has been concluded that ANN are generally high performance predictive tools, and it is not possible to reach a standard correlation to regulate the number of neurons to be used in the optimization of ANN.

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Section: Introductionmentioning

confidence: 99%

A novel comparative investigation of the effect of the number of neurons on the predictive performance of the artificial neural network: An experimental study on the thermal conductivity of ZrO ₂ nanofluid

Çolak

2021

Int J Energy Res

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“…The procedure of finding optimal ANN is illustrated in Figure 2. Many researchers have used a similar method to find the best network 34‐55 …”

Section: Resultsmentioning

confidence: 99%

Applying artificial neural networks to predict the enhanced thermal conductivity of a phase change material with dispersed oxide nanoparticles

Motahar

Sadri

2021

Intl J of Energy Research

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The weak thermal conductance of a phase change material (PCM) can be intensified by dispersing nanostructured materials called nano-PCM. Accurate thermal conductivity (TC) prediction of nano-PCM is essential to evaluate heat transport during phase change processes, namely, melting and solidification. The present study develops an artificial neural network (ANN) to forecast the TC of n-octadecane as a PCM with dispersed oxide nanoparticles. A total of 122 experimental datasets from existing literature with a wide range of temperatures (5-60 C), nanoparticles (CuO, Al 2 O 3 , TiO 2 , and mesoporous SiO 2 ), nanoparticle mass fractions (0.5-12 wt%) are compiled to train a multi-layered feed-forward ANN with Levenberg-Marquardt back-propagation algorithm.An optimal architecture of the neural network is acquired by varying the number of network hidden layers, the number of neurons in each layer, and the transfer function of layers. The minimum mean square error (MSE) of 1.3512 Â 10 À5 is obtained for the best developed ANN. Results show that average absolute deviation (AAD) of 0.002458, mean absolute percentage error (MAPE) of 0.8260%, and correlation coefficient (R) of 0.999964948 are achieved for training data. Moreover, MAPE, AAD, and R values are, respectively, 0.9478, 0.002167, and 0.9999715861 for testing data. The maximum percentage errors of ANN computed values are 2.31%, and 0.812% for liquid and solid phases, respectively. This indicates that the ANN model accurately predicts the enhanced TC of nano-PCM across various oxide nanoparticles, temperatures, and nanoparticle loadings.

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“…The main structure of the ANN models developed is shown in Figure 3. In the developed ANN models, the Levenberg–Marquardt training algorithm, which is frequently used by researchers, is preferred 58 . In hidden layers of developed MLP networks, Tan‐Sig function has been used as a transfer function and Purelin function in output layers 59 .…”

Section: Artificial Neural Network Designmentioning

confidence: 99%

Designing artificial neural network of nanoparticle diameter and solid–fluid interfacial layer on single‐walled carbon nanotubes/ethylene glycol nanofluid flow on thin slendering needles

Shafiq

Çolak

Sindhu

2021

Numerical Methods in Fluids

115

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In this study, an artificial neural network (ANN) has been developed to predict the boundary layer flow of a single‐walled carbon nanotubes nanofluid toward three different nonlinear thin isothermal needles of paraboloid, cone, and cylinder shapes with convective boundary conditions. Different effects of particle diameter and solid–fluid interface coating have been taken into account in the thermal conductivity model of nanofluid in which ethylene glycol has been used as the base fluid. Single and dual phase approach is used to establish the management model under the phenomenon of zero heat and mass flux. A dataset has been developed for different scenarios of the fluid model by changing the relevant parameters with the Runge–Kutta based shooting technique. Two different ANN models have been developed to predict Nusselt number and skin friction coefficient (SFC) values. The values obtained from ANN models have been compared with the numerical data, which are the target values. In addition, mean square error and R values have also been examined in order to analyze the prediction performance of ANN models more comprehensively. The calculated R values for Nusselt number and SFC were obtained as 0.9999. The results obtained showed that ANN can predict Nusselt number and SFC values with high accuracy.

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Using of Artificial Neural Networks (ANNs) to predict the thermal conductivity of Zinc Oxide–Silver (50%–50%)/Water hybrid Newtonian nanofluid

Cited by 117 publications

References 32 publications

A novel comparative investigation of the effect of the number of neurons on the predictive performance of the artificial neural network: An experimental study on the thermal conductivity of ZrO ₂ nanofluid

A novel comparative investigation of the effect of the number of neurons on the predictive performance of the artificial neural network: An experimental study on the thermal conductivity of ZrO ₂ nanofluid

Applying artificial neural networks to predict the enhanced thermal conductivity of a phase change material with dispersed oxide nanoparticles

Designing artificial neural network of nanoparticle diameter and solid–fluid interfacial layer on single‐walled carbon nanotubes/ethylene glycol nanofluid flow on thin slendering needles

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Using of Artificial Neural Networks (ANNs) to predict the thermal conductivity of Zinc Oxide–Silver (50%–50%)/Water hybrid Newtonian nanofluid

Cited by 117 publications

References 32 publications

A novel comparative investigation of the effect of the number of neurons on the predictive performance of the artificial neural network: An experimental study on the thermal conductivity of ZrO 2 nanofluid

A novel comparative investigation of the effect of the number of neurons on the predictive performance of the artificial neural network: An experimental study on the thermal conductivity of ZrO 2 nanofluid

Applying artificial neural networks to predict the enhanced thermal conductivity of a phase change material with dispersed oxide nanoparticles

Designing artificial neural network of nanoparticle diameter and solid–fluid interfacial layer on single‐walled carbon nanotubes/ethylene glycol nanofluid flow on thin slendering needles

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Product

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A novel comparative investigation of the effect of the number of neurons on the predictive performance of the artificial neural network: An experimental study on the thermal conductivity of ZrO ₂ nanofluid

A novel comparative investigation of the effect of the number of neurons on the predictive performance of the artificial neural network: An experimental study on the thermal conductivity of ZrO ₂ nanofluid