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

• Published in: Energies (Basel) Vol. 11; no. 5; p. 1190
• Main Authors: Aghayari, Reza, Maddah, Heydar, Ahmadi, Mohammad, Yan, Wei-Mon, Ghasemi, Nahid
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2018
• Subjects: Copper; Correlation coefficient; Correlation coefficients; Electrical conductivity; Electrical resistivity; Glycerol; Modelling; nanofluid; Nanofluids; Nanoparticles; Neural networks; Oxides; perceptron artificial neural network; Self organizing maps; Sonication; Stability; Temperature; Temperature effects; Temperature perception
• DOI: 10.3390/en11051190
• ISSN: 1996-1073

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 (R2), the mean of squared errors (MSE) and maximum error(emax) 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.