Magneto-radiative modelling and artificial neural network optimization of biofluid flow in a stenosed arterial domain

TL;DR

This study models biofluid flow in stenosed arteries using magneto-radiative effects and neural networks, revealing how nanoparticle types and parameters influence heat transfer and skin friction, aiding targeted drug delivery.

Contribution

It introduces a combined magneto-radiative and neural network approach to optimize biofluid flow in stenosed arteries, incorporating novel nanofluid parameters and predictive modeling.

Findings

Casson-Maxwell fluid exhibits lower velocity, improving drug residence time.

Heat transfer increases with copper and aluminium oxide nanoparticles, decreases with silver.

Skin friction decreases significantly with Maxwell parameter, increases with Casson parameter.

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.