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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.06273 |
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| _version_ | 1866915391628902400 |
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| author | Shivakumar, S P Ramasekhar, Gunisetty Nimmy, P Areekara, Sujesh Thanuja, L Smitha, T V Devanathan, S Naik, Ganesh R Nagaraja, K V |
| author_facet | Shivakumar, S P Ramasekhar, Gunisetty Nimmy, P Areekara, Sujesh Thanuja, L Smitha, T V Devanathan, S Naik, Ganesh R Nagaraja, K V |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06273 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Magneto-radiative modelling and artificial neural network optimization of biofluid flow in a stenosed arterial domain Shivakumar, S P Ramasekhar, Gunisetty Nimmy, P Areekara, Sujesh Thanuja, L Smitha, T V Devanathan, S Naik, Ganesh R Nagaraja, K V Medical Physics Artificial Intelligence Numerical Analysis Biological Physics 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. |
| title | Magneto-radiative modelling and artificial neural network optimization of biofluid flow in a stenosed arterial domain |
| topic | Medical Physics Artificial Intelligence Numerical Analysis Biological Physics |
| url | https://arxiv.org/abs/2507.06273 |