Abstract
Nanofluids have found extended applications in different industrial and engineering systems nowadays. This study aims to investigate the accurate prediction of SiO2 nanofluid effect on the heat transfer performance, specifically convective heat transfer coefficient (H), of a quadrangular cross-section channel by considering affecting fluid flow specifications factors of Re, Pr, and concentration of nanoparticles (x) in the employing working fluid. An experimental setup is used to prepare a database consisting of 270 data points on the H, of SiO2 nanofluids. These data are then applied to develop predictive models based on three intelligent algorithms, namely multi-layer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS), and least squares support vector machine (LSSVM), respectively. Graphical and statistical error criterions are carried out to evaluate the credibility of the proposed approaches. The LSSVM method had the precise performance regarding the mean squared error (MSE) and the coefficient of determination (R-2) of 59.7 and 0.9992, respectively. A sensitivity analysis is also carried out to assess the impact of different parameters on the H demonstrating that the Prandtl number has the highest impact with a relevancy factor (r) of 0.524.