Mathematical analysis of nanoparticle type and volume fraction on heat transfer efficiency of nanofluids

Abstract Nanofluids (NFs) have been implemented in several areas to increase heat transfer efficiency. Thus, efficiency for heat energy can be achieved. In this study, the effect of nanoparticle (NP) type, volume fraction, and Re number on the heat transfer efficiency of NFs were analyzed numerically, statistically, and theoretically. Heat transfer coefficient, number of transfer units, wall shear stress, and friction factor were selected as the heat transfer efficiency of NFs. ANSYS Fluent software was utilized to carry out computational fluid dynamics analyses. The numerical calculation scheme was implemented by employing the Taguchi L9 orthogonal array with three decisive factors. NP type, volume fraction, and Re number were assumed as decisive factors with three levels. Signal-to-noise ratio analysis was utilized to determine the direction of impact and ideal levels of each decisive factor on the heat transfer efficiency of NFs. Significance levels and contribution rates of the decisive factors on the heat transfer efficiency were calculated with Analysis of Variance. According to the mathematical responses, the most effective NPs on h and τ w were identified as Gr, Al 2 O 3 , and Cu, respectively, whereas the most effective NPs on number of transfer units are found to be Cu, Al 2 O 3 , and Gr, respectively. In addition, the increase in Re number from 6,000 to 8,000 causes an increase in h and τ w and a decrease in NTU and f . The data achieved from the mathematical research may be utilized to be a guide paper in experimental analyses.