Cavity shape optimization to maximize thermal efficiency in natural convective GO-MgO/silicone oil hybrid nanofluid flow under periodic field influence

Abstract

As global energy consumption continues to rise, the world's primary energy demand has reached unprecedented levels. A critical step in addressing this challenge involves enhancing the efficiency of thermal systems. This can be achieved through the optimal selection of cavity shape and utilizing a hybrid nanofluid as the working fluid to enhance heat transfer performance. This study investigates fluid flow and heat transfer characteristics of Graphene Oxide (GO)-Magnesium Oxide (MgO)-silicone oil hybrid nanofluid in h-shape and square cavities to choose the optimum cavity shape. Seven different types of nanoparticle shapes were assessed to determine which offers the best heat transfer performance. Additionally, it examines how an inclined periodic magnetic field, thermal radiation, and heat source or sink influence the flow field and heat transfer. Four Machine Learning (ML) models (Multiple Linear Regression (MLR), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF)) are adopted to select optimal ML model to predict characteristics of average rate of heat transfer and most influential pertinent parameter in cavity. The governing equations are solved using the finite difference approach. The outcomes show that suspending lamina-shaped nanoparticles in the base fluid provides 37.87% and 32.52% higher average heat transfer rates than that of spherical nanoparticles in the h-shape and square cavities, respectively. Compared with other pertinent parameters, radiation and heat source and sink parameters have a dominant impact in determining the heat transfer rate in the h-shape cavity, while Rayleigh number and radiation parameter have a dominant impact in the square cavity.