Numerical study and optimization of important parameters of non-Newtonian nanofluid turbulent flow in a channel with triangular vortex generators using artificial neural network

Abstract

The current research investigated the turbulent flow field as well as heat transfer rate (HTR) of the non-Newtonian H2O-Al2O3-carboxymethyl (CMC) in some channel having vortex generators (VGs). The partial differential equations were solved using the finite volume approach and the SIMPLE algorithm. The results also revealed that the VG depth change, which was in a few millimeters range, had no considerable impacts on the Nu as well as pressure decrease. In addition, the rise of Re (higher turbulent flow) remarkably increased HTR. Furthermore, due to the formation of vortex movements, local Nu variations revealed a significant rise near the considered VG. With the increase of the VG attack angle, there was a sudden the fluid flow change in the VG zone, resulting a larger DP, especially for the angle of attack of 60°For the 60° attack angle, a number of vortexes were formed behind the VGs, resulting a sudden variation of velocity on the contact surface of the VG. The greatest Nusselt number was 51.321, and the maximum pressure drop was 94993, according to the artificial neural network findings. As a consequence of the Pareto findings, there is a greater pressure decrease when there are more Nusselt.