Designing a committee of machines for modeling viscosity of water-based nanofluids. Issue 1 (1st January 2021)

• Record Type: Journal Article
• Authors: Hemmati-Sarapardeh, Abdolhossein; Hatami, Sobhan; Taghvaei, Hamed; Naseri, Ali; Band, Shahab S.; Chau, Kwok-wing
• Is Part Of: Engineering applications of computational fluid mechanics. Volume 15:Issue 1(2021)
• Page Start: 1967
• Page End: 1987
• Publication Date: 2021-01-01
• Subjects: Water-based nanofluid -- viscosity -- machine learning -- artificial intelligence -- LSSVM -- CMIS; Computational fluid dynamics -- Periodicals; 620.10640285
• DOI: 10.1080/19942060.2021.1979099
• Languages: English
• ISSNs: 1994-2060

Abstract

Abstract : Viscosity is a crucial thermophysical feature of a substance that must be accurately determined before designing a system with nanofluid as the working fluid. In this study, the modern technique of committee machine intelligent system (CMIS) is used for establishing a predictive model for the relative viscosity of the water-based nanofluids. The model was developed by considering 1440 experimental data points of different types of water-based nanofluids containing Al2 O3, SiC, SiO2, TiO2, CuO, nanodiamond, and Fe3 O4 nanoparticles. The CMIS model combines three intelligent models including a multilayer perceptron (MLP) model trained with Levenberg-Marquardt (LM), an MLP model trained by Bayesian Regularization (BR) and a radial basis function (RBF) approach to estimate the relative viscosity of different water-based nanofluids. Statistical and graphical error criteria revealed that the CMIS technique successfully estimates the relative viscosity of all data points over the whole ranges of operational conditions with a mean absolute relative error of approximately 1.25%. According to their precision and performance, the established CMIS system provides the best performance, followed by the BR-MLP, LM-MLP, and RBF models. Moreover, the performance and estimation capability of the CMIS model was verified against 13 theoretical and empirical models.