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

Abstract

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.

Developing an ANFIS-based swarm concept model for estimating the relative viscosity of nanofluids

“…Lately, heuristic models such as genetic programing, neural networks, and fuzzy logic have generated a lot of attention for their ability to predict thermophysical properties without the need for experimental verification (Aghayari, Maddah, Ahmadi, Yan, & Ghasemi, 2018;Ahmadi, Ahmadi, Mehrpooya, & Rosen, 2015;Ahmadi, Ahmadi, Sadatsakkak, & Feidt, 2015;Ahmadi, Tatar, et al, 2018;Ahmadi, Sorouri Ghare Aghaj, & Nazeri, 2013;Baghban, Bahadori, Mohammadi, & Behbahaninia, 2017;Baghban, Pourfayaz, et al, 2018;Baghban, Jalali, Mohammadi, & Habibzadeh, 2018;Baghban, Kardani, & Habibzadeh, 2017;Baghban, Mohammadi, & Taleghani, 2017;Faizollahzadeh Ardabili et al, 2018;Fotovatikhah et al, 2018;Ghazani, Baghban, Mohammadi, & Habibzadeh, 2018;Kazemi et al, 2018;Li, Zhang, & Liu, 2017;Loni, Asli-Ardeh, Ghobadian, Ahmadi, & Bellos, 2018;Pourkiaei, Ahmadi, & Hasheminejad, 2016;Taormina, Chau, & Sivakumar, 1985;Wu & Chau, 2011;S. Zhang & Chau, 2009;Z.…”

Heat Transfer Prediction of Supercritical Carbon Dioxide in Vertical Tube Based on Artificial Neural Networks

“…Ahmadi et al [17] accurately estimated the pressure drop of nanofluid flow in a car radiator by optimized ANN model with genetic algorithm. Reza et al [18] also successfully predicted the electrical conductivity of CuO/glycerol nanofluid by ANN model with 2 hidden layers and 31 neurons. Among various models, Mensah et al [19] recommended ANN model using group method of data handling (GMDH) to predict flammability parameters.…”

Position and mass identification in nanotube mass sensor using neural networks

“…A review on the parametric detection based on vibration analysis using ANNs was presented. 24 ANNs were used for bearing fault detection, 25 structure damage diagnosis, 26,27 crack detection in beam-like structure, 28 impact location identification in composites, 29,30 thermal resistance prediction of pulsating heat pipes with ethanol, 31 determination of thermal conductivity ratio of CuO/ethylene glycol, electrical conductivity of CuO/glycerol, convective heat transfer coefficient, and thermal conductivity ratio of Al 2 O 3 /water nanofluids, 32–35 determination of elastic constants of anisotropic laminated plates, 36 and natural frequencies determination of axially moving beams. 37 ANNs were also used to predict the fundamental vibration frequencies of the pristine CNTs having different diameters and lengths.…”