Exploring Convective Heat Transfer Mechanism with a BLMS-ANN Analysis of Carreau Fluid over a Rotating Cone

Abstract This study seeks to investigate the phenomenon of heat transfer in a rotating cone submerged in a non-Newtonian Carreau fluid. The Carreau fluid is highly regarded for its shear-thinning behavior and its wide range of practical applications. Using a shooting method for numerical simulation and an Artificial Neural Network algorithm (ANN), specifically the back-propagation Levenberg-Marquardt Scheme (BLMS), we thoroughly analyze the intricate interplay between buoyancy and centrifugal forces through a rigorous analytical approach. The findings of our study make valuable contributions to the understanding of the fundamental significance of centrifugal forces and buoyancy in the domains of fluid dynamics and heat transfer. An in-depth analysis is conducted on the convective heat transfer process, considering crucial factors like the Nusselt number, Reynolds number, Grashof number, fluid velocity, and cone rotation. This work emphasizes the importance of further research into fluid flow and heat transfer dynamics. It specifically focuses on the interaction between various parameters, including thermal radiation, Thermophoresis, Brownian motion, viscous dissipation, and non-Newtonian complexity. This thorough investigation has the potential to significantly enhance precision and efficiency in various manufacturing applications.