Prediction and Optimization of the Thermal Properties of TiO2/Water Nanofluids in the Framework of a Machine Learning Approach

Abstract

In this study, comparing multiple models of machine learning, a multiple linear regression (MLP), multilayer feed-forward artificial neural network (BP) model, and a radial-basis feed-forward artificial neural network (RBF-BP) model are selected for the optimization of the thermal properties of TiO₂/water nanofluids. In particular, the least squares support vector machine (LS-SVM) method and radial basis support vector machine (RB-SVM) method are implemented. First, curve fitting is performed by means of multiple linear regression in order to obtain bivariate correlation functions for thermal conductivity and viscosity of the nanofluid. Then the aforementioned models are used for a predictive analysis of the dependence of its thermal conductivity and viscosity on temperature and volume fraction. The results show that the least squares support vector machine (LS-SVM) has a prediction accuracy higher than the other models. The model predicts the thermal conductivity of TiO₂/water MSE = 1.0853 × 10⁻⁶, R²= 0.99864, MAE = 0.00092, RMSE = 0.00104, and the viscosity of TiO₂/water MSE = 8.1397 × 10⁻⁶, R²= 0.99995, MAE = 0.00074, RMSE = 0.0009.