Measurement and Artificial Neural Network Modeling of Electrical Conductivity of CuO/Glycerol Nanofluids at Various Thermal and Concentration Conditions

Reza Aghayari, Heydar Maddah, Mohammad Hossein Ahmadi, Wei-Mon Yan and Nahid Ghasemi
Additional contact information
Reza Aghayari: Department of Chemistry, Payame Noor University (PNU), P.O. Box, Tehran 19395-3697, Iran
Heydar Maddah: Department of Chemistry, Payame Noor University (PNU), P.O. Box, Tehran 19395-3697, Iran
Mohammad Hossein Ahmadi: Faculty of Mechanical Engineering, Shahrood University of Technology, Shahrood 3619995161, Iran
Wei-Mon Yan: Department of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
Nahid Ghasemi: Department of Chemistry, Arak Branch, Islamic Azad University, Arak 38361119131, Iran

Energies, 2018, vol. 11, issue 5, 1-16

Abstract: In this work, the electrical conductivity of CuO/glycerol nanofluid was measured at a temperature range of 20–60 °C, volume fraction of 0.1–1.5% and nanoparticle size of 20–60 nm. The experimental data were predicted by the perceptron neural network. The results showed that the electrical conductivity increases with temperature, especially in higher volume fractions. These results are attributed to the accumulation of nanoparticles in the presence of the field and their Brownian motion at different temperatures and the reduction of electrical conductivity at higher nanoparticle sizes is attributed to the decreased mobility of nanoparticles as load carriers as well as to their decrease in volume unit per constant volume fraction. The results revealed that sonication time up to 70 min increases the nanofluid stability, while further increase in the sonication time decreases the nanofluid stability. In the modeling, input data to perceptron artificial neural network are nanofluid temperature, nanoparticle size, sonication time and volume fraction and electrical conductivity is considered as output. The results obtained from self-organizing map (SOM) showed that the winner neuron which has the most data is neuron 31. The values of the correlation coefficient (R 2 ), the mean of squared errors (MSE) and maximum error(e max ) used to evaluate the perceptron artificial neural network with 2 hidden layers and 31 neurons are 1, 2.3542 × 10 −17 and 0 respectively, indicating the high accuracy of the network.

Keywords: electrical conductivity; perceptron artificial neural network; nanofluid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.mdpi.com/1996-1073/11/5/1190/pdf (application/pdf)
https://www.mdpi.com/1996-1073/11/5/1190/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:11:y:2018:i:5:p:1190-:d:145240

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().