Technology Category
Search and Optimization: Mixed Discrete/Continuous SearchCognitive Modeling & Cognitive Systems: Neural Spike CodingConstraint Satisfaction and Optimization: Satisfiability Modulo Theories
Magneto-radiative modelling and artificial neural network optimization of biofluid flow in a stenosed arterial domain
🤖 AI Summary
Low targeting efficiency of drug delivery in narrow arteries limits therapeutic efficacy.
Method: This study develops a multiphysics Casson–Maxwell non-Newtonian nanofluid model incorporating magnetic radiation and ternary metallic nanoparticles (Cu, Al₂O₃, Ag), integrated with a Levenberg–Marquardt backpropagation neural network for data-driven optimization. Linear thermal sources and radiative heat transfer are explicitly modeled to quantify the effects of Maxwell relaxation time and Casson yield stress on flow resistance and thermal transport.
Contribution/Results: Cu and Al₂O₃ nanoparticles significantly enhance thermal conductivity, whereas Ag suppresses heat transfer. Skin friction coefficient exhibits highest sensitivity to the Maxwell parameter. The optimized model achieves an R² of 0.99457 for heat flux prediction. Critically, the framework extends drug residence time in stenosed vessels and establishes a novel, interpretable, high-fidelity modeling paradigm for magnetically controlled precision drug targeting.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Investigates biofluid flow in stenosed arteries for drug delivery
Analyzes heat transfer and skin friction in Casson-Maxwell nanofluids
Optimizes magneto-radiative parameters using neural network modeling
Innovation
Methods, ideas, or system contributions that make the work stand out.
Magneto-radiative modeling optimizes biofluid flow
Neural network predicts heat flow accurately
Casson-Maxwell nanofluid enhances drug delivery
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