Computational insights and artificial neural network modeling of radiative boundary layer flows in tangent hyperbolic nanofluid

Khalid Masood; Khalid Masood

doi:10.3934/math.20251259

AIMS Mathematics

2025, Volume 10, Issue 12: 28606-28628. doi: 10.3934/math.20251259

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Computational insights and artificial neural network modeling of radiative boundary layer flows in tangent hyperbolic nanofluid

Khalid Masood ^,

Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMISU), Riyadh 11623, Saudi Arabia

Received: 01 September 2025 Revised: 25 November 2025 Accepted: 27 November 2025 Published: 04 December 2025
MSC : 76A02, 76A05, 76A20, 76W05

This research examines the boundary-layer flow of a tangent hyperbolic nanofluid over a moving wedge, considering both viscous and radiative effects, in order to evaluate nanoparticle-enhanced thermal properties and non-Newtonian dynamics. The study combines nanofluid heat enhancement with non-Newtonian flow behavior, radiative thermal processes, and motile organism patterns to create an integrated mathematical framework that addresses current research gaps while proposing applications ranging from cooling systems to automotive thermal management, biomedical technology, and energy system design. The differential equations are transformed using similarity transformations before being solved numerically using MATLAB's fourth-order Runge-Kutta technique. The study uses artificial neural networks for prediction validation and findings via contour plots, three-dimensional graphs, and simplified visuals. The study shows that Weissenberg numbers increase fluid elasticity while decreasing drag, heat radiation effects expand temperature profiles while increasing thermal boundary thickness and shifts in thermophoresis, and Lewis numbers have a significant impact on chemical distributions by improving industrial studies of fluid dynamics.
- artificial neural network validation,
- boundary layer flow,
- tangent hyperbolic nanofluid,
- Runge–Kutta method
Citation: Khalid Masood. Computational insights and artificial neural network modeling of radiative boundary layer flows in tangent hyperbolic nanofluid[J]. AIMS Mathematics, 2025, 10(12): 28606-28628. doi: 10.3934/math.20251259

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Abstract

This research examines the boundary-layer flow of a tangent hyperbolic nanofluid over a moving wedge, considering both viscous and radiative effects, in order to evaluate nanoparticle-enhanced thermal properties and non-Newtonian dynamics. The study combines nanofluid heat enhancement with non-Newtonian flow behavior, radiative thermal processes, and motile organism patterns to create an integrated mathematical framework that addresses current research gaps while proposing applications ranging from cooling systems to automotive thermal management, biomedical technology, and energy system design. The differential equations are transformed using similarity transformations before being solved numerically using MATLAB's fourth-order Runge-Kutta technique. The study uses artificial neural networks for prediction validation and findings via contour plots, three-dimensional graphs, and simplified visuals. The study shows that Weissenberg numbers increase fluid elasticity while decreasing drag, heat radiation effects expand temperature profiles while increasing thermal boundary thickness and shifts in thermophoresis, and Lewis numbers have a significant impact on chemical distributions by improving industrial studies of fluid dynamics.

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