Application of support vector machines for accurate prediction of convection heat transfer coefficient of nanofluids through circular pipes

Abstract

Purpose Convection is one of the main heat transfer mechanisms in both high to low temperature media. The accurate convection heat transfer coefficient (HTC) value is required for exact prediction of heat transfer. As convection HTC depends on many variables including fluid properties, flow hydrodynamics, surface geometry and operating and boundary conditions, among others, its accurate estimation is often too hard. Homogeneous dispersion of nanoparticles in a base fluid (nanofluids) that found high popularities during the past two decades has also increased the level of this complexity. Therefore, this study aims to show the application of least-square support vector machines (LS-SVM) for prediction of convection heat transfer coefficient of nanofluids through circular pipes as an accurate alternative way and draw a clear path for future researches in the field. Design/methodology/approach The proposed LS-SVM model is developed using a relatively huge databank, including 253 experimental data sets. The predictive performance of this intelligent approach is validated using both experimental data and empirical correlations in the literature. Findings The results show that the LS-SVM paradigm with a radial basis kernel outperforms all other considered approaches. It presents an absolute average relative deviation of 2.47% and the regression coefficient (R2) of 0.99935 for the estimation of the experimental databank. The proposed smart paradigm expedites the procedure of estimation of convection HTC of nanofluid flow inside circular pipes. Originality/value Therefore, the focus of the current study is concentrated on the estimation of convection HTC of nanofluid flow through circular pipes using the LS-SVM. Indeed, this estimation is done using operating conditions and some simply measured characteristics of nanoparticle, base fluid and nanofluid.