A computational role of blood nanofluid induced by a stenosed artery with porous medium and thermophoretic particle deposition effects

Abstract

The rising prevalence of cardiovascular disorders highlights the need for a deeper understanding of blood flow dynamics in the stenotic arteries to improve diagnostic and therapeutic approaches. This investigation is motivated by the potential of the Casson nanofluids, which exhibit exceptional thermal properties, offering promising applications in medical treatments such as targeted drug delivery and hyperthermia therapy. The present work focuses on understanding the flow behavior of the Casson nanofluids through the stenosed artery under the influence of porosity, thermal radiation, thermophoretic particle diffusion and Stefen blowing. The study makes certain key assumptions, including the consideration of the porous nature of the arterial walls and the impacts of external thermal influences. Based on these assumptions, the governing equations are formulated and transformed into a system of ordinary differential equations using appropriate similarity transformations. These reduced equations are solved numerically using the Runge-Kutta-Fehlberg fourth-fifth-order schemes. The important nondimensional factors affecting fluid velocity, thermal, and concentration profiles are analyzed. Further, the investigation utilizes advanced methods of deep learning to create models and forecast the intricate relationships among various variables, offering a thorough analysis. Escalated values of radiation and curvature parameters will enhance the temperature profile. Deep learning models demonstrate significant efficacy in analyzing and predicting stenotic conditions. The novel outcomes of this research highlight the effectiveness of deep learning models in predicting and analyzing stenotic blood flow conditions, contributing to improved diagnostic approaches to improve the patient's healthcare and reduce the mortality rate.