Thermal transport and fluid flow analysis of magneto-Sisko fluid through porous medium under radiation effects using ANN-based and numerical approaches

This study investigates the magnetized viscoelastic fluid flow and thermal transport around a porous stretching cylinder. A subclass of generalized Newtonian fluids described by the Sisko fluid model represents non-Newtonian fluids that exhibit both shear-thinning and shear-thickening behaviors, characterized by yield stress. This behavior directly influences fluid dynamics and heat transfer performance, making it critically important in various industrial and engineering applications. It enhanced the efficiency, control, and design of mechanical, chemical, thermal, and biological engineering systems. The governing partial differential equations of the power-law model are transformed into ordinary differential equations through the application of similarity variables. The resulting equations, along with their corresponding boundary conditions, are numerically solved using the fifth-order Runge-Kutta integration scheme in MATLAB. Additionally, numerical predictions are generated through an artificial neural network based on the Levenberg–Marquardt technique (LMT). Key temperature and flow velocity features are illustrated through graphs under various parameter settings, accompanied by a comparative analysis. The dataset is divided into three sections: 70% for training, 15% for validation, and 15% for testing. The performance of the proposed AI-based LMS-ANN model is assessed using the mean squared error under different scenarios. The results indicate that a magnetic field significantly reduces fluid velocity, while curvature and material parameters increase it. Additionally, higher values of the radiation parameter and the Eckert number elevate the thermal profile. A comparison with earlier research is presented to illustrate how physical characteristics influence the skin friction coefficient and local Nusselt number. The flow of magnetized viscoelastic Sisko fluid and thermal transport around a porous stretched cylinder are graphically depicted in the abstract. With velocity arrows indicating the moving fluid layers surrounding it, the central cylindrical surface represents the stretching wall.

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Publication Details

Journal
Case Studies in Thermal Engineering
Published
2025-12-25
DOI
https://doi.org/10.1016/j.csite.2025.107584
Primary Topic
Nanofluid Flow and Heat Transfer
Type
article
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article

Thermal transport and fluid flow analysis of magneto-Sisko fluid through porous medium under radiation effects using ANN-based and numerical approaches

Ibrahim Alraddadi, Syed Modassir Hussain, Muhammad, Masood Khan
Case Studies in Thermal Engineering
Nanofluid Flow and Heat Transfer
article

Thermal transport and fluid flow analysis of magneto-Sisko fluid through porous medium under radiation effects using ANN-based and numerical approaches

Ibrahim Alraddadi, Syed Modassir Hussain, Muhammad, Masood Khan
article en

Abstract

This study investigates the magnetized viscoelastic fluid flow and thermal transport around a porous stretching cylinder. A subclass of generalized Newtonian fluids described by the Sisko fluid model represents non-Newtonian fluids that exhibit both shear-thinning and shear-thickening behaviors, characterized by yield stress. This behavior directly influences fluid dynamics and heat transfer performance, making it critically important in various industrial and engineering applications. It enhanced the efficiency, control, and design of mechanical, chemical, thermal, and biological engineering systems. The governing partial differential equations of the power-law model are transformed into ordinary differential equations through the application of similarity variables. The resulting equations, along with their corresponding boundary conditions, are numerically solved using the fifth-order Runge-Kutta integration scheme in MATLAB. Additionally, numerical predictions are generated through an artificial neural network based on the Levenberg–Marquardt technique (LMT). Key temperature and flow velocity features are illustrated through graphs under various parameter settings, accompanied by a comparative analysis. The dataset is divided into three sections: 70% for training, 15% for validation, and 15% for testing. The performance of the proposed AI-based LMS-ANN model is assessed using the mean squared error under different scenarios. The results indicate that a magnetic field significantly reduces fluid velocity, while curvature and material parameters increase it. Additionally, higher values of the radiation parameter and the Eckert number elevate the thermal profile. A comparison with earlier research is presented to illustrate how physical characteristics influence the skin friction coefficient and local Nusselt number. The flow of magnetized viscoelastic Sisko fluid and thermal transport around a porous stretched cylinder are graphically depicted in the abstract. With velocity arrows indicating the moving fluid layers surrounding it, the central cylindrical surface represents the stretching wall.

Case Studies in Thermal EngineeringVol. 77
Quaid-i-Azam University (PK), Islamic University of Madinah (SA)
Openalex Percentile: Top 42%
Nanofluid Flow and Heat Transfer
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