Prediction of certain conjugate convective flow of a micropolar nanofluid in an inclined enclosure with the Lorentz force and porous medium by virtue of the artificial neural networks

Background: Inclined square cavities play a critical role in engineering applications, particularly in thermal management, energy storage and electronic cooling, where inclination angles influence convective heat transfer. This study examines conjugate convective heat transfer within an inclined square cavity filled with micropolar nanofluids under a tilted Lorentz force using a two-phase nanofluid model. The system includes a heat-generating porous medium under thermal nonequilibrium, introducing complex dynamics. Factors such as thermal buoyancy, fluid–solid heat transfer coefficient and micropolar fluid properties are analyzed for their impact on heat transfer efficiency. Methods: The study uses the finite difference method (FDM) to solve nonlinear equations governing convective flow and heat transfer. Physical parameters, including thermal buoyancy, micropolar properties and thickness of the solid walls, are incorporated. The setup features conductive horizontal walls and adiabatic vertical walls to simulate realistic thermal conditions. An artificial neural network (ANN) trained on FDM data predicts local Nusselt numbers, leveraging multiple input factors. This hybrid numerical-AI approach enhances efficiency, reducing computational time while maintaining accuracy. Significant findings: The inclination angle, buoyancy ratio and micropolar effects significantly influence heat transfer efficiency by driving convective behavior within the cavity and affecting the local Nusselt number distribution. The ANN demonstrates high prediction accuracy, providing a fast and reliable tool for analyzing heat transfer in complex systems. Increasing the thickness of the solid walls from 0.05 to 0.3 results in a 35.78% reduction in flow activity. Additionally, when the fluid type is changed from Newtonian ([Formula: see text]) to non-Newtonian ([Formula: see text]), the heat transfer rate decreases by 50.63%.