Enhanced heat transport in magneto-nanofluidic thermal systems: adiabatic block effects in grooved channels and ANN modeling

This study investigates heat transfer enhancement in magneto-nanofluidic systems through the strategic placement of adiabatic blocks in grooved channels. Using CuO H 2 O nanofluid in a bottom-heated channel with circular expansion, we examine the complex interactions between forced convection, magnetic fields, and buoyancy effects. Through systematic numerical analysis, we explore the combined influences of Rayleigh, Reynolds, and Hartmann numbers on thermal performance. Our findings reveal significant heat transfer enhancement (up to 137 %) under optimal conditions, particularly with vertical magnetic field orientation at Re = 100 and Ha = 30. The results demonstrate how adiabatic blocks modify flow structures, with larger blocks diminishing vortex intensity while elevated Ra generates secondary vortices that interact with primary circulations. Magnetic field effects show notable dependence on orientation, with vertical fields generally promoting better heat transfer than horizontal configurations. To complement the numerical analysis, we develop a predictive model using Artificial Neural Network (ANN) for Nusselt numbers across various operating conditions, achieving over 99 % accuracy. The integrated computational-ANN approach offers significant advancements in optimizing thermal systems in various areas, ranging from electronics cooling to microfluidic devices.