Computational analysis of the magnetohydrodynamic hybrid nanofluid flow in a porous circular cavity

The mixture of silver nanoparticles (Ag) and zinc oxide nanoparticles (ZnO) in terms of the hybrid nanofluid in a circular, and semicircular porous cavity offers enhanced heat transfer performance, making them suitable for various applications such as electronic cooling, thermal management in engines, and heat exchangers. A water-based hybrid nanofluid composition of Ag and ZnO is used for heat transfer (HT) performance applications. The porous cavity is considered for the flow field under the inspiration of an applied magnetic field. The problem of interest is tackled through the control volume finite element method “CVFEM.” The artificial neural network (ANN) is also applied to handle the obtained results in terms of validation, testing, and training. Auto-encoder (AE) performance has been used for the impacts of different emerging factors of Ha and Ra. The interaction of the magnetic field and magnetic nanoparticles in the hybrid nanofluid causes magnetohydrodynamic (MHD) effects like Lorentz force and magnetization. Nanoparticle concentration, magnetic field intensity (Ha), and cavity porosity (Ra) are the parameters that affect the analysis. Varying these parameters allows for the estimation of the effects of the HNF to evaluate the heat transfer optimization. It is observed that ZnO enhances the HT rate by 14% and Ag + ZnO enhances the HT rate by up to 16% using the 5% nanoparticle volume fraction. Increased Rayleigh numbers enhance natural convection (HT) and encourage more energetic flow behavior, resulting in better heat transfer. Enhanced convective heat transfer is a result of Hartmann numbers, strong Lorentz forces, and metallic and oxide nanoparticles.