journal article·2026-05-28Open access

Electromagnetohydrodynamic flow and heat transfer of a dusty hybrid nanofluid over a rotating disk in a porous medium

D. Serafin Grace, P. Durgaprasad

Discover Nano· Volume 21· Issue 1

Abstract

Momentum and thermal transport characteristics of a two-phase dusty Eyring-Powell hybrid nanofluid induced by a rotating disk in a Darcy-Forchheimer porous medium are numerically analysed under electromagnetohydrodynamic conditions. The working fluid comprises blood filled with Ag-Cu hybrid nanoparticles, while shear-dependent viscosity is modelled through the Eyring-Powell constitutive relation. To account for dust-fluid interactions, a two-phase model incorporates interphase momentum and heat transfer processes. The governing partial differential equations are transformed into a system of nonlinear ordinary differential equations using appropriate similarity transformations. The resulting boundary value problem is solved numerically using the MATLAB built-in solver bvp4c. The effects of porous resistance, inertial drag, electromagnetic field intensity, dust-interaction parameters, thermal Biot number, internal heat generation, and nanoparticle concentration on velocity distributions, temperature fields, skin friction coefficients, and heat-transfer rates are systematically examined. The numerical results show that while surface convection and more nanoparticles improve heat-transfer efficiency, porous drag, electromagnetic forces, and non-Newtonian effects significantly reduce momentum transport. Effective interphase interaction is essential for the momentum and heat transfer between the fluid and dust phases. These results provide a physical understanding of EMHD-controlled transport phenomena in rotating porous configurations pertinent to magnetically regulated blood transport in rotating biomedical devices.
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Research context

OpenAlex topics: Nanofluid Flow and Heat Transfer · Heat and Mass Transfer in Porous Media · Heat Transfer Mechanisms

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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