Open Access

ARTICLE

On Heat Transfer in Oblique Stagnation Point Nanofluid Flow with Temperature Dependent Viscosity

Rabail Tabassum¹, M. Kamran¹, Khalil Ur Rehman^2,*, Wasfi Shatanawi^2,3, Rashid Mehmood⁴

1 Department of Mathematics, Faculty of Basic and Applied Sciences, Air University, Islamabad, 44000, Pakistan
2 Department of Mathematics and Sciences, College of Humanities and Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia
3 Department of Mathematics, Faculty of Science, The Hashemite University, Zarqa, 13133, Jordan
4 Department of Mathematics, Faculty of Applied Sciences, HITEC University, Rawalpindi, 47080, Pakistan

* Corresponding Author: Khalil Ur Rehman. Email:

(This article belongs to the Special Issue: Advances in Computational Thermo-Fluids and Nanofluids)

Frontiers in Heat and Mass Transfer 2025, 23(2), 577-599. https://doi.org/10.32604/fhmt.2025.059466

Received 09 October 2024; Accepted 15 January 2025; Issue published 25 April 2025

Abstract

This study aims to elucidate the connection between the shape factor of GO (graphene oxide) nanoparticles and the behavior of blood-based non-aligned, 2-dimensional, incompressible nanofluid flow near stagnation point, under the influence of temperature-dependent viscosity. Appropriate similarity transformations are employed to transform the non-linear partial differential equations (PDEs) into ordinary differential equations (ODEs). The governing equations are subsequently resolved by utilizing the shooting method. The modified Maxwell model is used to estimate the thermal efficiency of the nanofluid affected by different nanoparticle shapes. The impact of various shapes of GO nanoparticles on the velocity and temperature profiles, along with drag forces and heat flux at the stretching boundary, are examined with particular attention to factors such as viscosity changes. Numerical findings are based on the constant concentration of with nanoparticles measuring 25 nm in size. The influence of different shapes of GO nanoparticles is analyzed for velocity, temperature distributions, as well as drag forces, and heat transfer at the stretching boundary. The velocity profile is highest for spherical-shaped nanoparticles, whereas the blade-shaped particles produced the greatest temperature distribution. Additionally, it was observed that enhancing the nanoparticles’ volume fraction from to significantly improved the temperature profile. Streamline trends are more inclined to the left when the stretching ratio parameter is applied, and a similar pattern is noted for the variable viscosity case with . Furthermore, the blade-shaped nanoparticles exhibit the highest thermal conductivity, while the spherical-shaped nanoparticles display the lowest.

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