Designing a committee of machines for modeling viscosity of water-based nanofluids

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 Al2O3, SiC, SiO2, TiO2, CuO, nanodiamond, and Fe3O4 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.