A numerical investigation of forced convection heat transfer in a three-dimensional T-shaped bifurcating channel with an upstream rotating cylinder and a downstream vibrating wavy wall is presented. The working fluid is a ternary hybrid nanofluid (Fe2O3, CuO, MoS2 in water) exhibiting Casson rheology under an inclined magnetic field. The novelty of this work lies in the first integrated configuration combining these simultaneous mechanical, magnetic, and non-Newtonian effects. Using COMSOL Multiphysics, 413 parametric combinations of Reynolds number, Hartmann number, Casson parameter, nanoparticle shape and volume fraction, magnetic field angle, cylinder rotation speed, wall amplitude (Am), and period were solved. Average Nusselt and Bejan numbers quantified heat transfer enhancement and thermodynamic irreversibility. To interpret the high-dimensional parameter space and to circumvent the prohibitive computational cost of additional 3D magnetohydrodynamics simulations, machine learning (XGBoost) models were developed to rank feature importance and provide fast, accurate surrogate predictions (R2 > 0.99). Cylinder rotation dominates heat transfer, increasing the Nusselt number by over 980% (feature importance 0.42) with a modest entropy penalty. Nanoparticle volume fraction reduces the Nusselt number via viscous damping. Magnetic field parameters negligibly affect heat transfer but strongly influence entropy generation; a perpendicular field recovers up to 97% thermal efficiency at high Hartmann numbers.
Alshammari et al. (2026) studied this question.