A unique multilayer perceptron model (ANN) for different oxide/EG nanofluid’s viscosity from the experimental study

Devendra Yadav, Prabhat Dansena, Subrata Kumar Ghosh and Pawan Kumar Singh

Physica A: Statistical Mechanics and its Applications, 2020, vol. 549, issue C

Abstract: In this work, different oxide-based nanofluids in ethylene glycol base fluid were prepared. The volumetric concentration of 0.2%, 0.5%, 0.8%, 1% and 1.5% of Al2O3, CeO2, and CuO nanofluid samples were used for viscosity experiment. The viscosity tests were performed on Anton Paar (SVM 3000 Stabinger) viscometer for the temperature range of 20 °C–80 °C. With the increase in temperature, the exponential variation in viscosity was observed, while the linear variation in its density. Since these oxide nanofluids have the same tendency of change in viscosity with temperature, therefore, a unique multilayer perceptron artificial neural network was utilized to predict the different oxide based nanofluid viscosity. This neural network can predict the viscosity for the different oxides nanofluids subjected to various volume concentrations, temperature, and size. The R2 value for training and testing data were 0.9998 and 0.9999. The model was validated by predicting the viscosity results from the experimental work of other researchers.

Keywords: Viscosity; Oxides; ANN; Concentration; Learning rate; Transfer function (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:549:y:2020:i:c:s0378437119322290

DOI: 10.1016/j.physa.2019.124030

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