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Effects of variable Darcy regime on magnetized radiative Casson hybrid nanofluid flow on stretching sheet with Joule heating and heat source effects

2026/01/01 by Saima Noor, Humaira Yasmin · 1 voice
Engineering · #Nanofluid Flow and Heat Transfer #Fluid Dynamics and Vibration Analysis #Heat and Mass Transfer in Porous Media

paper · doi:10.1515/ntrev-2025-0314

openalex publication_date 2026/01/01 · openalex created_date 2026/05/21 · openalex updated_date 2026/06/26

Abstract

Abstract Magnetized hybrid nanofluid modeling with Joule heating improves heat transfer control, thermal management, and efficiency in advanced industrial energy systems today. It further has applications in the cooling of electronic devices, solar collectors, polymer extrusion, metallurgical processing, and porous media filtration. For these impressive applications, the current study numerically analyzes the fluid flow of rotational Casson hybrid nanofluid on a linearly expanding sheet. The fluid is exposed to magnetic fields, which induce both resistive Lorentz forces and significant Joule heating. The flow saturates a porous medium modeled with a variable Darcy regime. Thermal transport is further influenced by radiative heat flux and the existence of an internal thermal source/sink. The main equations have been converted to ODEs. Furthermore, the ODEs are then solved by using a numerical technique bvp4c approach. As an outcome of the study, it has been publicized that in this work, with progress in CuO/TiO 2 -nanoparticles concentration, magnetic factor, and Casson parameter, there is a lessening in primary velocity while an expansion in secondary velocity. Thermal panel augmented with a surge in CuO/TiO 2 -nanoparticles concentration, magnetic factor, radiation parameter, Eckert number, and thermal Biot number, while decayed with a surge in variable porous factor. Augmentation in the variable porous factor causes a decline in secondary velocity and growth in primary velocity. Comparative analysis has been conducted with published works, and exposed a fine promise among all datasets.

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