Magneto-radiative modelling and artificial neural network optimization of biofluid flow in a stenosed arterial domain
Journal:
arXiv
Published Date:
Jul 8, 2025
Abstract
The increasing complexity of cardiovascular diseases and limitations in
traditional healing methods mandate the invention of new drug delivery systems
that assure targeted, effective, and regulated treatments, contributing
directly to UN SDGs 3 and 9, thereby encouraging the utilization of sustainable
medical technologies in healthcare. This study investigates the flow of a
Casson-Maxwell nanofluid through a stenosed arterial domain. The quantities,
such as skin friction and heat transfer rate, are analysed in detail. The
Casson-Maxwell fluid shows a lower velocity profile than the Casson fluids,
which indicates the improved residence time for efficient drug delivery. The
heat transfer rate shows an increase with higher volume fractions of copper and
aluminium oxide nanoparticles and a decrease with higher volume fractions of
silver nanoparticles. The skin friction coefficient decreases by 219% with a
unit increase in the Maxwell parameter, whereas it increases by 66.1% with a
unit rise in the Casson parameter. This work supports SDGs 4 and 17 by
fostering interdisciplinary learning and collaboration in fluid dynamics and
healthcare innovation. Additionally, the rate of heat flow was forecasted (with
an overall R-value of 0.99457) using the Levenberg-Marquardt backpropagation
training scheme under the influence of magneto-radiative, linear heat source
and Casson-Maxwell parameters along with the tri-metallic nanoparticle volume
fractions. It is also observed that the drag coefficient is most sensitive to
the changes in the Maxwell parameter.
