A computational role of blood nanofluid induced by a stenosed artery with porous medium and thermophoretic particle deposition effects
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 ther...
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Elsevier
2025-02-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824014236 |
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| author | Shivalila Hangaragi N. Neelima N. Beemkumar Ankur Kulshreshta Umair Khan Noreen Sher Akbar Mohammad Kanan Mona Mahmoud |
| author_facet | Shivalila Hangaragi N. Neelima N. Beemkumar Ankur Kulshreshta Umair Khan Noreen Sher Akbar Mohammad Kanan Mona Mahmoud |
| author_sort | Shivalila Hangaragi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e7e52109e6334c879b9e28e5d9c67caf |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-e7e52109e6334c879b9e28e5d9c67caf2025-02-07T04:46:59ZengElsevierAlexandria Engineering Journal1110-01682025-02-011133243A computational role of blood nanofluid induced by a stenosed artery with porous medium and thermophoretic particle deposition effectsShivalila Hangaragi0N. Neelima1N. Beemkumar2Ankur Kulshreshta3Umair Khan4Noreen Sher Akbar5Mohammad Kanan6Mona Mahmoud7Department of Electronics and Communication, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bangalore, IndiaDepartment of Electronics and Communication, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bangalore, IndiaDepartment of Mechanical Engineering, School of Engineering and Technology, JAIN (Deemed to be University), Bangalore, Karnataka, IndiaDepartment of Mechanical & Aerospace Engineering, NIMS Institute of Engineering & Technology, NIMS University Rajasthan, Jaipur, IndiaDepartment of Mathematics, Faculty of Science, Sakarya University, Serdivan, Sakarya 54050, Turkey; Department of Mechanics and Mathematics, Western Caspian University, Baku 1001, Azerbaijan; Corresponding author at: Department of Mathematics, Faculty of Science, Sakarya University, Serdivan, Sakarya 54050, Turkey.Department of Mechanical Engineering, College of Engineering, Prince Mohammad Bin Fahd University, Al Khobar 31952, Saudi ArabiaDepartment of Industrial Engineering, College of Engineering, University of Business and Technology, Jeddah 21448, Saudi Arabia; Department of Mechanical Engineering, College of Engineering, Zarqa University, Zarqa, JordanDepartment of Physics, College of Science, King Khalid University, P.O. Box 9004, Abha 61413, Saudi ArabiaThe 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.http://www.sciencedirect.com/science/article/pii/S1110016824014236Stenosis arteryDeep learningNanofluidThermal radiationThermophoretic particle deposition |
| spellingShingle | Shivalila Hangaragi N. Neelima N. Beemkumar Ankur Kulshreshta Umair Khan Noreen Sher Akbar Mohammad Kanan Mona Mahmoud A computational role of blood nanofluid induced by a stenosed artery with porous medium and thermophoretic particle deposition effects Alexandria Engineering Journal Stenosis artery Deep learning Nanofluid Thermal radiation Thermophoretic particle deposition |
| title | A computational role of blood nanofluid induced by a stenosed artery with porous medium and thermophoretic particle deposition effects |
| title_full | A computational role of blood nanofluid induced by a stenosed artery with porous medium and thermophoretic particle deposition effects |
| title_fullStr | A computational role of blood nanofluid induced by a stenosed artery with porous medium and thermophoretic particle deposition effects |
| title_full_unstemmed | A computational role of blood nanofluid induced by a stenosed artery with porous medium and thermophoretic particle deposition effects |
| title_short | A computational role of blood nanofluid induced by a stenosed artery with porous medium and thermophoretic particle deposition effects |
| title_sort | computational role of blood nanofluid induced by a stenosed artery with porous medium and thermophoretic particle deposition effects |
| topic | Stenosis artery Deep learning Nanofluid Thermal radiation Thermophoretic particle deposition |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824014236 |
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