Predicting MHD mixed convection in a semicircular cavity with hybrid nanofluids using AI

This study presents a numerical analysis of magnetohydrodynamic (MHD) mixed convection in a semicircular enclosure containing a rotating inner cylinder and filled with nanofluids and hybrid nanofluids. The investigation explores the effects of Al₂O₃-TiO₂-SWCNT-water hybrid nanofluids with varying nanoparticle compositions, as well as Al₂O₃-water, TiO₂-water, and SWCNT-water nanofluids. The analysis includes the development of an artificial neural network (ANN) model to predict outcomes, achieving 97.34 % accuracy in training and 97.41 % in testing for the average Nusselt number. The study examines the impact of Reynolds number (Re), Richardson number (Ri), Hartmann number (<i>Ha</i>), cylinder rotation speed (Ω), cylinder size, and nanoparticle volume fraction (φ) on heat transfer and fluid flow. Key findings include a 6.98 % increase in heat transfer for SWCNT-water nanofluid from Ri = 1 to Ri = 10, a reduction in heat transfer with higher Hartmann numbers, and a significant 21.12 % enhancement when cylinder speed increases to Ω = 10 compared to a stationary cylinder. Larger cylinder sizes also improve convective heat transfer, with a 66.14 % increase for SWCNT-water nanofluid. Additionally, higher concentrations of SWCNT and Al₂O₃ in hybrid nanofluids enhance heat transfer performance.