Thermo-Magnetic Interaction and Mixed Convection Dynamics of Casson Fluid over a Stretching Surface
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146-158
Digital Object Identifier:
10.58578/mjms.v4i1.7852
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Authors:
D. E. Bamidele1, D. O. Olumuji2*, M. O. Afolabi3, O. A. Olajide4, I. T. Adeyemi5, A. A. Akindele6 
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Abstract
Although thermo-magnetic Casson fluid flow over stretching surfaces has received increasing attention in previous studies, the combined influence of mixed convection, magnetic field strength, and boundary-layer control mechanisms on heat and mass transfer characteristics remains underexplored. This study aims to analyze the interactive effects of magnetic induction, Casson parameter, and convection-related factors on the thermal and velocity profiles of a Casson fluid subjected to thermo-magnetic forces. A quantitative computational approach was employed, in which the governing partial differential equations were transformed into coupled nonlinear ordinary differential equations and solved numerically using MATLAB’s bvp4c solver. The model incorporated key dimensionless parameters, including the magnetic field intensity, Eckert number, Prandtl number, porosity, and inclination angle, to capture mixed convection and thermo-magnetic effects over a porous stretching surface. The findings indicate that increasing magnetic parameter values suppress the velocity profile while enhancing the thermal boundary-layer thickness, reflecting the retarding Lorentz force and associated thermal buildup. Similarly, higher Eckert numbers intensify viscous dissipation, leading to increased temperature fields, whereas larger Prandtl numbers reduce temperature distribution due to diminished thermal diffusivity. These results contribute to the theoretical development of magnetohydrodynamic Casson fluid dynamics and extend understanding of thermo-magnetic interactions in non-Newtonian heat transfer systems. The study concludes that magnetic field modulation and convective parameters play crucial roles in controlling Casson fluid behavior and boundary-layer structure, and recommends that future models incorporate nanoparticle effects and biological considerations to improve prediction accuracy. The implications span applied mathematics, heat transfer modeling, and industrial fluid engineering, with potential applications in cooling systems, polymer processing, and energy devices.
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