Application of Artificial Neural Networks (ANNs) for Predicting the Viscosity of Tungsten Oxide (WO3)-MWCNTs/Engine Oil Hybrid Nanofluid

Toghraie, Davood; Aghahadi, Mohammad; Sina, Nima; Soltani, Farid

doi:10.1007/s10765-020-02749-x

Cited by 18 publications

(8 citation statements)

References 24 publications

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“…Due to the non-Newtonian behavior of some of the nanofluids [85,86], it is crucial to consider the shear rate in measuring and reporting the DV. Contrary to the Newtonian fluids, shear rate affects the DV of non-Newtonian fluids.…”

Section: Dynamic Viscositymentioning

confidence: 99%

“…The proposed model had adequate exactness with a maximum error of 8%. Toghraie et al [86] suggested two models based on correlation and ANN for the DV of a non-Newtonian hybrid nanofluid composed of WO 3 and MWCNT. In their work, various numbers of neurons were tested to find the optimum structure, and it was found that using 39 neurons in the HL led to the best performance.…”

Section: Aghaei Et Al [97]mentioning

confidence: 99%

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Thermophysical Properties of Hybrid Nanofluids and the Proposed Models: An Updated Comprehensive Study

et al. 2021

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Thermal performance of energy conversion systems is one of the most important goals to improve the system’s efficiency. Such thermal performance is strongly dependent on the thermophysical features of the applied fluids used in energy conversion systems. Thermal conductivity, specific heat in addition to dynamic viscosity are the properties that dramatically affect heat transfer characteristics. These features of hybrid nanofluids, as promising heat transfer fluids, are influenced by different constituents, including volume fraction, size of solid parts and temperature. In this article, the mentioned features of the nanofluids with hybrid nanostructures and the proposed models for these properties are reviewed. It is concluded that the increase in the volume fraction of solids causes improvement in thermal conductivity and dynamic viscosity, while the trend of variations in the specific heat depends on the base fluid. In addition, the increase in temperature increases the thermal conductivity while it decreases the dynamic viscosity. Moreover, as stated by the reviewed works, different approaches have applicability for modeling these properties with high accuracy, while intelligent algorithms, including artificial neural networks, are able to reach a higher precision compared with the correlations. In addition to the used method, some other factors, such as the model architecture, influence the reliability and exactness of the proposed models.

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Section: Dynamic Viscositymentioning

confidence: 99%

Section: Aghaei Et Al [97]mentioning

confidence: 99%

Thermophysical Properties of Hybrid Nanofluids and the Proposed Models: An Updated Comprehensive Study

et al. 2021

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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%

“…They also used an ANN trained by the MLP algorithm to accurately predict the dynamic viscosity based on input parameters. Toghraie et al 40 measured the viscosity of Tungsten Oxide – MWCNTs/Engine Oil at various temperatures, volume fractions, and shear rates. They used an ANN model to predict the dynamic viscosity.…”

Section: Introductionmentioning

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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“…MSE and R 2 of this network were 0.00901 and 0.9954, respectively. The viscosity of Tungsten Oxide -MWCNTs/Engine Oil was measured at various temperatures, volume fraction and shear rates by Toghraie et al [18]. They used an ANN model to predict the dynamic viscosity.…”

Section: Introductionmentioning

confidence: 99%

A neural network approach to estimate non-Newtonian behavior of nanofluid phase change material containing mesoporous silica particles

Motahar

2021

IJE

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Neural networks are powerful tools for evaluating the thermophysical characteristics of nanofluids to reduce the cost and time of experiments. Dynamic viscosity is an important property in nanofluids that usually needs to be accurately computed in heat transfer and nanofluid flow problems. In this paper, the rheological properties of nanofluid phase change material containing mesoporous silica nanoparticles are predicted by the artificial neural networks (ANNs) method based on the experimental database reported in literature. Experimental inputs include nanoparticle mass fractions (0-5 wt.%), temperatures (35-55℃), shear rates (10-200 s -1 ), targets include dynamic viscosities and shear stresses. A multilayer perceptron feedforward neural network with Levenberg-Marquardt back-propagation training algorithm is utilized to predict rheological properties. The optimal network architecture consists of 22 neurons in the hidden layer based on the minimum mean square error (MSE). The results showed that the developed ANN has an MSE of 6.67×10 -4 and 6.55×10 -3 for the training and test dataset, respectively. The predicted dynamic viscosity and shear stress also have the maximum relative error of 6.26 and 0.418%, respectively.

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Application of Artificial Neural Networks (ANNs) for Predicting the Viscosity of Tungsten Oxide (WO3)-MWCNTs/Engine Oil Hybrid Nanofluid

Cited by 18 publications

References 24 publications

Thermophysical Properties of Hybrid Nanofluids and the Proposed Models: An Updated Comprehensive Study

Thermophysical Properties of Hybrid Nanofluids and the Proposed Models: An Updated Comprehensive Study

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

A neural network approach to estimate non-Newtonian behavior of nanofluid phase change material containing mesoporous silica particles

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