Heat Transfer and Fluid Flow Optimization of Titanium Dioxide–Water Nanofluids in a Turbulent Flow Regime

Abstract

In this study, the convection heat transfer and pressure drop of titanium dioxide–water nanofluids were modeled by applying the fuzzy C-means adaptive neuro-fuzzy inference system approach for a completely developed turbulent flow based on experimentally obtained training and test datasets. Two models were proposed based on the effective parameters; one model was developed for the Nusselt number considering the effects of the Reynolds number, Prandtl number, nanofluid volume concentration and average nanoparticle diameter. Another model was suggested for the pressure drop of the nanofluid as a function of the Reynolds number, nanofluid volume concentration, and average nanoparticle diameter. The results of these two proposed models were compared with experimental data as well as the existing correlations in the literature. The validity of the proposed models was benchmarked by statistical criteria. Moreover, a modified non-dominated sorting genetic algorithm multiobjective optimization technique was applied to obtain the optimum design points, and the final result was shown in a Pareto front.