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Open AccessArticle

Feasibility of ANFIS-PSO and ANFIS-GA Models in Predicting Thermophysical Properties of Al2O3-MWCNT/Oil Hybrid Nanofluid

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Department of Mechanical and Industrial Engineering, College of Engineering, Majmaah University, Al-Majmaah 11952, Saudi Arabia
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Division of Computational Physics, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
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Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
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Center for Advanced Laser Manufacturing, School of Mechanical Engineering, Shandong University of Technology, Shandong 255049, China
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Author to whom correspondence should be addressed.
Materials 2019, 12(21), 3628; https://doi.org/10.3390/ma12213628
Received: 2 September 2019 / Revised: 9 October 2019 / Accepted: 30 October 2019 / Published: 4 November 2019
(This article belongs to the Special Issue Nanofluids: From Fundamental Sciences to Applications)
The main purpose of the present paper is to improve the performance of the adaptive neuro-fuzzy inference system (ANFIS) in predicting the thermophysical properties of Al2O3-MWCNT/thermal oil hybrid nanofluid through mixing using metaheuristic optimization techniques. A literature survey showed that the use of an artificial neural network (ANN) is the most widely used method, although there are other methods that showed better performance. Moreover, it was found in the literature that artificial intelligence methods have been widely used for predicting the thermal conductivity of nanofluids. Thus, in the present study, genetic algorithms (GAs) and particle swarm optimization (PSO) have been utilized to search and determine the antecedent and consequent parameters of the ANFIS model. Solid concentration and temperature were considered as input variables, and thermal conductivity, dynamic viscosity, heat transfer performance, and pumping power in both the internal laminar and turbulent flow regimes were the outputs. In order to evaluate and compare the performance of the models, two statistical indices of root mean square error (RMSE) and determination coefficient (R) were utilized. Based on the results, both of the models are able to predict the thermophysical properties appropriately. However, the ANFIS-PSO model had a better performance than the ANFIS-GA model. Finally, the studied thermophysical properties were developed by the trained ANFIS-PSO model. View Full-Text
Keywords: thermophysical properties; ANFIS; PSO; GA; MWCNT-Al2O3 nanoparticles; dynamic viscosity; thermal conductivity; heat transfer performance thermophysical properties; ANFIS; PSO; GA; MWCNT-Al2O3 nanoparticles; dynamic viscosity; thermal conductivity; heat transfer performance
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Alarifi, I.M.; Nguyen, H.M.; Naderi Bakhtiyari, A.; Asadi, A. Feasibility of ANFIS-PSO and ANFIS-GA Models in Predicting Thermophysical Properties of Al2O3-MWCNT/Oil Hybrid Nanofluid. Materials 2019, 12, 3628.

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