Numerical Optimization of Internal Cooling Structure Placement for MHD Mixed Convection Using Multi-Nanoparticle Fluids

This study conducts a comprehensive numerical investigation of magnetohydrodynamic (MHD) mixed convection and entropy generation in a two-dimensional square cavity filled with a ternary hybrid nanofluid. The working fluid consists of Multi-Walled Carbon Nanotubes (MWCNT), Copper (Cu), and Ferric Oxide (Fe₃O₄) nanoparticles dispersed in water, selected for their superior thermal properties. Two vertically aligned, saw-tooth-shaped cooling structures are embedded along the left and right walls of the cavity, with four distinct configurations considered based on their vertical positioning. An externally imposed uniform magnetic field is applied to assess its influence on fluid flow, heat transfer, and thermodynamic irreversibility. The governing nonlinear partial differential equations accounting for mass, momentum, energy, and entropy generation are solved using the Finite Volume Method (FVM) in conjunction with a Full Multigrid Algorithm to enhance computational efficiency. The study systematically examines the effects of key dimensionless parameters, including the Hartmann number (Ha), Richardson number (Ri), Reynolds number (Re), nanoparticle volume fraction (φ), and structural configuration, on flow dynamics, thermal performance, and entropy generation. The results provide valuable insights into the optimization of heat transfer systems through geometrical and thermophysical enhancements under MHD conditions. Results reveal that among the configurations studied, the position (P3) configuration featuring asymmetrical placement of the internal saw-tooth cooling structures demonstrates the highest thermal performance, achieving an average Nusselt number of 63.698 at a nanoparticle volume fraction of φ = 12% and Richardson numbers in the range of Ri = 60–80. This superior performance is attributed to enhanced convective mixing and optimal disruption of thermal boundary layers without excessive entropy generation.