Artificial neural network-based computational heat transfer analysis of Carreau fluid over a rotating cone

Abstract

Heat transport in a dynamically rotating cone immersed in a Carreau fluid is the subject of this investigation. The fluid is non-Newtonian, admired for its characteristics, and extensively utilized in numerous industrial domains. The study investigates the interplay between buoyancy and centrifugal forces within an analytical framework. The study employs sophisticated mathematical methods, including similarity transformations, to convert governing partial differential equations into nonlinear ordinary differential equations. These equations are then solved using the shooting method, a numerical technique that solves a boundary value problem by iteratively adjusting the initial conditions until the boundary conditions are satisfied. We employ an artificial neural network algorithm with backpropagation Levenberg–Marquardt scheme to analyze the heat transfer mechanism quantitatively. In conjunction with the shooting mechanism, we will use numerical simulation with an artificial neural network algorithm, namely the backpropagation Levenberg–Marquardt scheme. The results prove the enormous influence of centrifugation and buoyancy on complex fluid dynamics and heat exchange processes. Some critical parameters that govern the convective heat transport process are the Nusselt number, the Reynolds number, the Grashof number, and the fluid and cone rotational velocities. The research validates the requirement of considering non-Newtonian complexity and viscous dissipation when investigating heat transfer dynamics and fluid flow, facilitating more accurate expectations and improved efficiency in various industrial processes.