Modeling electrical properties of nanofluids using artificial neural network

Abstract

This paper presents a theoretical study of the electrical properties of two different samples of nanofluids (MgO and Si–TiO2 nanoparticles in ethylene glycol EG as the base fluid) using an artificial neural network (ANN) model. Experimental data were extracted from previous experimental studies and used as inputs. A learning ANN method was applied based on the back propagation technique. The optimal network structure, which produces the most acceptable performance, was attained. Electrical conductivity and permittivity in terms of nanoparticle concentration, temperature and frequency were simulated and predicted using the ANN model. A new nonlinear equation describing the behavior of electrical properties of nanofluids was obtained. The simulation results of the ANN model are highly accurate in comparison with experimental data. Predictions of values that are not involved in the experimental data range were carried out and provide excellent results. The mean squared error and regression coefficient were also determined. Their values support the success of the ANN model. The main purpose of the paper is to show the ability and power of an ANN model in estimating and predicting electrical properties of nanofluids. According to this research, the ANN model can be utilized as an efficient tool to predict the electrical properties of nanofluids. It can also achieve great links between practical and theoretical branches.

A Review on Electrical Conductivity of Nanoparticle-Enhanced Fluids

“…Anyhow, most of the researchers (see [19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35]) have explained the increase in electrical conductivity mainly by EDL formation; that is, the structure of charge accumulation and charge separation that always occurs at the interface when an electrode (in this case solid nanoparticles) is immersed into an electrolyte solution (i.e., the base fluid).…”

“…Mohamed [23] performed a theoretical study, using an artificial neural network (ANN) model, of the electrical properties of two nanofluids based on EG with MgO and Si-TiO nanoparticles. Electrical conductivity was simulated using the ANN model in regard to both nanoparticle concentration and temperature influence.…”

Predicting the AC conductivity of semiconductor composition thin films using ANFIS model; an integrated experimental and theoretical approach

“…Where O i 5, is the final outcome [10]. According to the theoretical basis of ANFIS, it is clear that ANFIS considered a powerful predictive modeling tool that offers many benefits, including; Improved accuracy, reduced training time, ability to handle non-linear problems, and stable results.…”

“…A successful model is characterized by its ease of understanding and use, and it should accurately represent the system's behavior. Different mathematical models exist, and they are used in a wide variety of applications [4][5][6][7][8][9][10][11][12][13]. ANFIS is an intelligent technique utilized to model and predict complex systems.…”

Duan–Rach Approach to Study Al2O3-Ethylene Glycol C2H6O2 Nanofluid Flow Based upon KKL Model

“…Rana and Nawaz [10] investigated the Sutterby nanofluids with the Koo-Kleinstreuer-Lee (KKL) model under the heat transfer mechanism. Mohamed [11] studied the electrical features of nanofluids via an artificial neural network.…”