Mixed-convection MHD non-Newtonian fluid with entropy generation

We analyze the magnetohydrodynamic (MHD) mixed convection flow of a viscoelastic fluid with entropy generation. MHD mixed convection with entropy generation describes fluid flow in which both buoyancy-driven and externally driven convection occur simultaneously under the influence of a magnetic field. This study focuses on thermodynamic irreversibility, which is quantified as entropy generation within the system. This topic is significant in various engineering applications, such as nuclear reactors and electronic cooling devices. Research in this area usually utilizes analytical simulations to explore how key parameters, such as the curvature parameter, viscoelastic parameter, magnetic parameter, mixed convection parameter, and Brinkman number, affect heat transfer and entropy generation. The viscoelastic behavior of the fluid is modeled using the Walters’-B constitutive equation. The flow is induced by a stretching cylindrical surface, and viscous dissipation is incorporated into the energy equation. Scientists and engineers worldwide have shown growing interest in flows with entropy generation due to their unique properties in automotive engineering, metal spinning, heat transfer and storage systems, power generation, and renewable energy applications. Among these, heat transfer plays a crucial role in determining the efficiency and quality of the final product. Appropriate similarity modifications are introduced to reduce the governing “partial differential equations” (PDEs) into a system of “ordinary differential equations” (ODEs). Analytical solutions for the resulting nonlinear flow and thermal equations are obtained using the homotopy analysis method. Values for the “skin friction” coefficient and “local Nusselt” number are computed and analyzed. The physical behavior of key parameters is illustrated through graphical representations and supported by tabulated data. A notable aspect of this study is the detailed analysis of entropy generation. Observations indicate that the curvature and mixed convection parameters enhance both the flow and heat transfer rates near the cylinder surface. The viscoelastic parameter significantly influences both the temperature and velocity profiles. Entropy generation increases significantly with higher values of the magnetic field strength and Brinkman number, while it decreases with higher values of the temperature ratio parameter and the Weissenberg number. These parameters can, thus, be effectively used to control the ¸entropy generation process.

Authors

Institutions

Publication Details

Journal
Frontiers in Physics
Published
2026-04-28
DOI
https://doi.org/10.3389/fphy.2026.1732634
Primary Topic
Nanofluid Flow and Heat Transfer
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Mixed-convection MHD non-Newtonian fluid with entropy generation

Sadia Asad
Frontiers in Physics
Nanofluid Flow and Heat Transfer
article

Mixed-convection MHD non-Newtonian fluid with entropy generation

Sadia Asad
article en

Abstract

We analyze the magnetohydrodynamic (MHD) mixed convection flow of a viscoelastic fluid with entropy generation. MHD mixed convection with entropy generation describes fluid flow in which both buoyancy-driven and externally driven convection occur simultaneously under the influence of a magnetic field. This study focuses on thermodynamic irreversibility, which is quantified as entropy generation within the system. This topic is significant in various engineering applications, such as nuclear reactors and electronic cooling devices. Research in this area usually utilizes analytical simulations to explore how key parameters, such as the curvature parameter, viscoelastic parameter, magnetic parameter, mixed convection parameter, and Brinkman number, affect heat transfer and entropy generation. The viscoelastic behavior of the fluid is modeled using the Walters’-B constitutive equation. The flow is induced by a stretching cylindrical surface, and viscous dissipation is incorporated into the energy equation. Scientists and engineers worldwide have shown growing interest in flows with entropy generation due to their unique properties in automotive engineering, metal spinning, heat transfer and storage systems, power generation, and renewable energy applications. Among these, heat transfer plays a crucial role in determining the efficiency and quality of the final product. Appropriate similarity modifications are introduced to reduce the governing “partial differential equations” (PDEs) into a system of “ordinary differential equations” (ODEs). Analytical solutions for the resulting nonlinear flow and thermal equations are obtained using the homotopy analysis method. Values for the “skin friction” coefficient and “local Nusselt” number are computed and analyzed. The physical behavior of key parameters is illustrated through graphical representations and supported by tabulated data. A notable aspect of this study is the detailed analysis of entropy generation. Observations indicate that the curvature and mixed convection parameters enhance both the flow and heat transfer rates near the cylinder surface. The viscoelastic parameter significantly influences both the temperature and velocity profiles. Entropy generation increases significantly with higher values of the magnetic field strength and Brinkman number, while it decreases with higher values of the temperature ratio parameter and the Weissenberg number. These parameters can, thus, be effectively used to control the ¸entropy generation process.

Frontiers in PhysicsVol. 14
Majmaah University (SA)
Affordable and clean energy
Openalex Percentile: Top 13%
Nanofluid Flow and Heat Transfer
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.