Numerical and neural-network analysis of Casson hybrid nanofluid flow and heat transfer in a sinusoidally stenosed artery.
5 affiliations
- 1Centre for Advanced Studies in Pure and Applied Mathematics, Bahauddin Zakariya University, Multan, 60000, Pakistan.
- 2Department of Basic Sciences and Humanities, MNS University of Engineering and Technology, Multan, 60000, Pakistan.
- 3Department of Mathematics and Statistics, University of Southern Punjab, Multan, 60000, Pakistan.
- 4Department of Mathematics, Physics and Statistics, The University of Faisalabad, Faisalabad, Pakistan.
- 5Department of Mathematical Sciences, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates. [email protected].
Abstract
We investigate unsteady flow and heat transfer in a blood-based Casson hybrid nanofluid containing Al₂O₃ and Fe₃O₄ nanoparticles as it passes through a rigid artery with a mild sinusoidal stenosis. The formulation includes a transverse magnetic field, Hall-current effects, viscous dissipation, and Joule heating. Under the long-wavelength approximation, the governing equations reduce to local radial initial-boundary-value problems. These problems are mapped onto a fixed domain and solved with a forward-time central-space (FTCS) scheme that uses a separate stencil at the centreline. Particular attention is given to the effects of the Casson, magnetic, Hall, Reynolds, Prandtl, and Eckert parameters, as well as the two nanoparticle volume fractions, on the velocity, temperature, wall shear stress, and local Nusselt number. We interpret the observed trends through the balance among viscous resistance, Lorentz damping, radial diffusion, internal heating, and the effective properties of the suspension. Parameter-specific Levenberg-Marquardt backpropagation neural networks are also trained to interpolate the FTCS data. In most of the sampled cases, the network follows the numerical profiles closely. However, a small number of negative centreline-temperature predictions make clear that a low overall error does not always imply physically acceptable behaviour at every point. The neural-network results should therefore be viewed as interpolation within the sampled ranges, while the physical conclusions remain tied to the assumptions of the idealized single-phase arterial model. Clinical trial registration: Not applicable.
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BibTeX
@article{zeshan2026numerical,
title = {{Numerical and neural-network analysis of Casson hybrid nanofluid flow and heat transfer in a sinusoidally stenosed artery.}},
author = {Muhammad Zeshan and Kashif Ali and Shahzad Ahmad and Huma Gull and Muhammad Jawad and Qasem M Al-Mdallal},
journal = {Discover nano},
year = {2026},
month = oct,
doi = {10.1186/s11671-026-04991-y},
url = {https://europepmc.org/article/MED/42900435},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Numerical and neural-network analysis of Casson hybrid nanofluid flow and heat transfer in a sinusoidally stenosed artery.
AU - Muhammad Zeshan
AU - Kashif Ali
AU - Shahzad Ahmad
AU - Huma Gull
AU - Muhammad Jawad
AU - Qasem M Al-Mdallal
PY - 2026
DA - 2026/10/20
JO - Discover nano
DO - 10.1186/s11671-026-04991-y
UR - https://europepmc.org/article/MED/42900435
AB - We investigate unsteady flow and heat transfer in a blood-based Casson hybrid nanofluid containing Al₂O₃ and Fe₃O₄ nanoparticles as it passes through a rigid artery with a mild sinusoidal stenosis. The formulation includes a transverse magnetic field, Hall-current effects, viscous dissipation, and Joule heating. Under the long-wavelength approximation, the governing equations reduce to local radial initial-boundary-value problems. These problems are mapped onto a fixed domain and solved with a forward-time central-space (FTCS) scheme that uses a separate stencil at the centreline. Particular attention is given to the effects of the Casson, magnetic, Hall, Reynolds, Prandtl, and Eckert parameters, as well as the two nanoparticle volume fractions, on the velocity, temperature, wall shear stress, and local Nusselt number. We interpret the observed trends through the balance among viscous resistance, Lorentz damping, radial diffusion, internal heating, and the effective properties of the suspension. Parameter-specific Levenberg-Marquardt backpropagation neural networks are also trained to interpolate the FTCS data. In most of the sampled cases, the network follows the numerical profiles closely. However, a small number of negative centreline-temperature predictions make clear that a low overall error does not always imply physically acceptable behaviour at every point. The neural-network results should therefore be viewed as interpolation within the sampled ranges, while the physical conclusions remain tied to the assumptions of the idealized single-phase arterial model. Clinical trial registration: Not applicable.
SN - 2731-9229
ER -