Publicou 18 edições por ano
ISSN Imprimir: 1064-2285
ISSN On-line: 2162-6561
Indexed in
PREDICTION OF DYNAMIC VISCOSITY OF A NEW NON-NEWTONIAN HYBRID NANOFLUID USING EXPERIMENTAL AND ARTIFICIAL NEURAL NETWORK (ANN) METHODS
RESUMO
In this paper, an artificial neural network (ANN) has been studied for the viscosity of MWCNTs-ZnO/water-ethylene glycol (80:20 vol.%) non-Newtonian nanofluid. To evaluate the rheological behavior of the nanocoolants, for each solid volume fraction and temperature, all experiments were repeated at different shear rates. After generating the experimental data, an ANN method is applied. The ANN is selected based on the different generating architectures (neuron numbers). The algorithm for choosing the best ANN is presented. Also, using the correlation method, the viscosity of nanofluid is predicted. Finally, ANN and correlation results are compared with the obtained data from the correlation method. It was found that the ANN had a better ability in predicting the viscosity of nanofluid compared with the correlation method because the (MSE) of ANN was 0.0885, and the MSE of the correlation method was 0.9531. However, both approaches are useful, but ANN had a better ability to model the viscosity of nanofluid based on the input values.
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Esfahani, M.A. and Toghraie, D., Experimental Investigation for Developing a New Model for the Thermal Conductivity of Silica/Water-Ethylene Glycol (40%-60%) Nanofluid at Different Temperatures and Solid Volume Fractions, J. Mol. Liq., vol. 232, pp. 105-112, 2017.
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Esfahani, N.N., Toghraie, D., and Afrand, M., A New Correlation for Predicting the Thermal Conductivity of ZnO-Ag (50%-50%)/Water Hybrid Nanofluid: An Experimental Study, Powder Technol., vol. 323, pp. 367-373, 2018.
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Kumar, S., Kothiyal, A., Singh Bisht, M., and Kumar, A., Effect of Nanofluid Flow and Protrusion Transverse Ribs on Thermal and Hydrodynamic Performance in Square Channel: An Experimental Investigation, J. Enhanced Heat Transf., vol. 26, no. 1, pp. 75-100, 2019.
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Naphon, P., Wiriyasart, S., and Arisariyawong, T., Artificial Neural Network Analysis the Pulsating Nusselt Number and Friction Factor of TiO2/Water Nanofluids in the Spirally Coiled Tube with Magnetic Field, Int. Commun. Heat Mass Transf., vol. 118, pp. 1152-1159, 2018.
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Pal, S.K. and Bhattacharyya, S., Enhanced Heat Transfer of Cu-Water Nanofluid in a Channel with Wall Mounted Blunt Ribs, J. Enhanced Heat Transf., vol. 25, no. 1, pp. 61-78, 2018.
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Rahman, A.A. and Zhang, X., Prediction of Oscillatory Heat Transfer Coefficient for a Thermoacoustic Heat Exchanger through Artificial Neural Network Technique, Int. J. Heat Mass Transf., vol. 124, pp. 1088-1096, 2018.
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Sandhu, H., Gangacharyulu, D., and Singh, M.K., Experimental Investigations on the Cooling Performance of Microchannels Using Alumina Nanofluids with Different Base Fluids, J. Enhanced Heat Transf., vol. 25, no. 3, pp. 283-291, 2018.
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Zhi, L.H., Hu, P., Chen, L.X., and Zhao, G., Viscosity Prediction for Six Pure Refrigerants Using Different Artificial Neural Networks, Int. J. Refrig., vol. 88, pp. 432-440, 2018.
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Qing Haohua, Hamedi Sajad, Eftekhari S. Ali, Alizadeh S.M., Toghraie Davood, Hekmatifar Maboud, Ahmed Ahmed Najat, Khan Afrasyab, A well-trained feed-forward perceptron Artificial Neural Network (ANN) for prediction the dynamic viscosity of Al2O3–MWCNT (40:60)-Oil SAE50 hybrid nano-lubricant at different volume fraction of nanoparticles, temperatures, and shear rates, International Communications in Heat and Mass Transfer, 128, 2021. Crossref
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Soltani Farid, Hajian Mehdi, Toghraie Davood, Gheisari Ali, Sina Nima, Alizadeh As'ad, Applying Artificial Neural Networks (ANNs) for prediction of the thermal characteristics of engine oil –based nanofluids containing tungsten oxide -MWCNTs, Case Studies in Thermal Engineering, 26, 2021. Crossref
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Derikvand Mohammad, Solari Mojtaba Shams, Toghraie Davood, Numerical investigation of the effect of a porous block and flow injection using non-Newtonian nanofluid on heat transfer and entropy generation in a microchannel with hydrophobic walls, The European Physical Journal Plus, 136, 8, 2021. Crossref
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Hemmat Esfe Mohammad, Kamyab Mohammad Hassan, Alirezaie Ali, Toghraie Davood, Using radial basis function network to model the heat transfer and pressure drop of water based nanofluids containing MgO nanoparticles, Case Studies in Thermal Engineering, 28, 2021. Crossref
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Fan Guangli, A.S. El-Shafay, Eftekhari S. Ali, Hekmatifar Maboud, Toghraie Davood, Mohammed Amin Salih, Khan Afrasyab, A well-trained artificial neural network (ANN) using the trainlm algorithm for predicting the rheological behavior of water – Ethylene glycol/WO3 – MWCNTs nanofluid, International Communications in Heat and Mass Transfer, 131, 2022. Crossref
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Ali Aatif, Ahammad N. Ameer, Tag-Eldin Elsayed, Gamaoun Fehmi, Daradkeh Yousef Ibrahim, Yassen Mansour F., MHD williamson nanofluid flow in the rheology of thermal radiation, joule heating, and chemical reaction using the Levenberg–Marquardt neural network algorithm, Frontiers in Energy Research, 10, 2022. Crossref
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