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Research Article

Non-similar simulations of Ellis model based ternary hybrid nanofluid flow through porous media

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Received 17 Jan 2024, Accepted 29 May 2024, Published online: 17 Jun 2024
 

Abstract

The investigation of nanofluid flow through porous media is an emerging area of research in the optimization of thermal processing and numerous thermodynamic processes. The principal objective of these thermal applications is to examine the movement of ternary hybrid nanofluid across a stretched sheet oriented vertically. The flow being examined is represented mathematically in order to incorporate the influences of magnetic fields, thermal radiation, and viscous dissipation. The ternary hybrid nanofluid thermal performance can be enhanced and surpassed by adding nanoparticles to the base fluid. The non-Newtonian Ellis model takes into account the fluid movement through the porous media. Ferrous oxide (Fe3O4), Zinc oxide (ZnO), and Molybdenum disulfide (MoS2) are regarded as nanoparticles, with ethylene glycol (EG) serving as the base fluid. To convert the governing equations into a dimensionless system, suitable non-similar transformations have been established. The local non-similarity (LNS) approach is used with the MATLAB built-in tool bvp4c up to the second truncation level. The physical impacts of dimensionless factors on the temperature and velocity profiles of the studied nanofluids are thoroughly investigated. When the magnetic and porosity parameters change, the velocity profile becomes smaller. The heat transmission rate declines when the assessments of the magnetic number and Eckert number increase. The skin friction coefficient experiences an increase as the estimates of magnetic properties and nanoparticle value rise. The current study holds various practical implications, encompassing Solar Thermal Energy Storage, Waste Heat Recovery, Advanced Cooling Technologies, Enhanced Geothermal Systems, and Enhanced Oil Recovery.

Disclosure statement

No potential conflict of interest was reported by the author(s).

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