Thermal Conductivity Modeling of Aqueous CuO Nanofluids by Adaptive Neuro-Fuzzy Inference System (ANFIS) Using Experimental Data

Abstract

In this article, thermal conductivity data of aqueous nanofluids of CuO have been modeled through one of the instruments of empirical data modeling. The input data of 5 different volume fractions of nanofluid obtained in four temperatures Keywords nanofluids, fuzzy networks, thermal conductivity, ANFIS IntroductionIncrease in energy cost in long time and growing need for energy have made scientists look for ways to conserve energy. One way for conserving energy in heat transfer field is to use operating fluids of heat transfer with better and more efficient transfer properties. Around 20 years ago, Choi [1] in his report proposed his solution for this problem by introducing suspensions called nanofluids. After scientists' familiarity with these fluids, a great deal of attention was drawn to them. When many researchers of heat transfer and mass field observed nanofluids potential in reducing energy consumption, they embarked on their researches on these new fluids and thousands of scientific articles in this field have been published so far.These articles have different subjects such as nanofluids thermal conductivity [2][3][4][5][6][7], viscosity [8][9][10][11], heat transfer coefficient [12][13][14][15][16][17][18] and the other subjects about nanofluids.In addition to experimental researches, a large number of analytical and numerical researches have been conducted on this field. Beyond this level, some researchers have begun studies on experimental data modeling. These researches are conducted with the purpose of nanofluids behavior modeling in thermophysical and hydrodynamic terms.A report of the studies conducted to model nanofluids behavior is provided in Table 1.In this article, thermal behavior of aqueous nanofluids containing CuO nanoparticles has been modeled by ANFIS network. The nanofluids were prepared through a two-step method and the thermal conductivity data were measured based on previous work [21,22] in five volume fractions and four temperatures and the modeling results were compared with experimental data. It should be noted that in this article Sugeno method has been used for modeling the data through ANFIS network. Based on the author's knowledge, there are no similar studies in literature on modeling the thermal conductivity by Adaptive Neuro-Fuzzy Inference System (ANFIS).

Experimental study and ANFIS modelling of the thermophysical properties and efficacy of GNP-Al2O3 hybrid nanofluids of different concentrations and temperatures

“…This is because the error points for the ANFIS prediction of all the thermophysical properties sit closer to the reference line of zero than that of the linear regression model. This confirms the efficacy of using ANFIS for the modelling of thermophysical properties, as illustrated in previous studies [33,52,53].…”

“…Neuro-fuzzy logic provides a useful tool for predicting the behaviour of complex issues that are extremely difficult to model using conventional mathematical techniques. The ANFIS technique consists of five different levels [33,34], as illustrated in Fig. 2.…”

Nonlinear function estimation fuzzy system (NFEFS) as a novel statistical approach to estimate nanofluids’ thermal conductivity according to empirical data

“…Several numerical models have been presented to predict the nanofluid thermo-physical properties, such as viscosity, density, conductivity and so on (Heidari et al , 2016; Karimi Darvanjooghi et al , 2017; Sadi, 2017; Karimipour et al , 2018c; Adio et al , 2016; Darvanjooghi et al , 2018; Akbari et al , 2017; Balla et al , 2013; Hosseini et al , 2017; Esfe, 2018; Esfahani et al , 2017). It should be noted that some deviations are observed in previous reported results for various types of mixtures, which encourage researchers to develop some correlations according to the empirical achievements for any kind of nanofluids, which means the predicted results accuracy increases.…”

Fault prognostics of rolling element bearing based on feature extraction and supervised machine learning: Application to shaft wind turbine gearbox using vibration signal

“…22 Gradientbased methods are widely used to fit both preceding and subsequent parameters in the ANFIS model. 45 One of the problems with gradient methods is that the response is situated in the local optimality, and the rate of convergence is too slow. The algorithms of metaheuristic optimization such as PSO is used as an efficient solution into problems related to methods that are based on gradient.…”