Artificial neural network simulation with numerical computation for targeted modulation of blood cells in hemodynamically stenosed anisotropic artery.
Saved in:
| Title: | Artificial neural network simulation with numerical computation for targeted modulation of blood cells in hemodynamically stenosed anisotropic artery. |
|---|---|
| Authors: | Batool, Maria1 (AUTHOR) mariamwu21@outlook.com, Rubbab, Qammar1 (AUTHOR), Akbar, Noreen Sher1,2 (AUTHOR) nakbar@pmu.edu.sa, Farooq, Muhammad Asif3 (AUTHOR), Muhammad, Taseer4 (AUTHOR) |
| Source: | International Communications in Heat & Mass Transfer. Apr2026, Vol. 173, pN.PAG-N.PAG. 1p. |
| Subjects: | Artificial neural networks, Magnetohydrodynamics, Computer simulation, Biomedical engineering, Mass transfer, Nanofluids, Heat transfer, Non-Newtonian fluids |
| Abstract: | This study investigates steady magnetohydrodynamic (MHD) flow and transport characteristics of a tetra-hybrid nanofluid through a stenosed artery modeled as a Cross non-Newtonian fluid. The problem is motivated by biomedical applications such as magnetic drug targeting, hyperthermia therapy, and controlled blood flow regulation. An extended Tiwari–Das nanofluid model incorporating titanium dioxide, gold, silver, and aluminium oxide nanoparticles is employed to enhance heat and mass transfer. The governing momentum, energy, and concentration equations account for nonlinear thermal radiation, Joule heating, viscous dissipation, non-uniform heat generation/absorption, chemical reaction, and Soret effects. Using similarity transformations, the resulting dimensionless system is solved numerically via MATLAB's bvp4c solver. To improve computational efficiency, Artificial Neural Networks (ANNs) and Physics-Informed Neural Networks (PINNs) are developed as surrogate models. Results indicate that increasing the Weissenberg number reduces wall shear stress, while magnetic effects suppress velocity by approximately 28%. Temperature rises by about 22% due to combined thermal effects, whereas concentration increases near the wall with higher Soret number and decreases with stronger chemical reactions. Enhanced nanoparticle volume fraction significantly improves Nusselt and Sherwood numbers. Excellent agreement between numerical and machine-learning results is observed, with ANN showing superior accuracy and faster convergence. The study demonstrates that integrating MHD control with AI-driven modeling offers an efficient framework for optimizing thermal and mass transport in non-Newtonian biomedical flows. [ABSTRACT FROM AUTHOR] |
| Copyright of International Communications in Heat & Mass Transfer is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 192228746 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Artificial neural network simulation with numerical computation for targeted modulation of blood cells in hemodynamically stenosed anisotropic artery. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Batool%2C+Maria%22">Batool, Maria</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mariamwu21@outlook.com</i><br /><searchLink fieldCode="AR" term="%22Rubbab%2C+Qammar%22">Rubbab, Qammar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Akbar%2C+Noreen+Sher%22">Akbar, Noreen Sher</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> nakbar@pmu.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Farooq%2C+Muhammad+Asif%22">Farooq, Muhammad Asif</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Muhammad%2C+Taseer%22">Muhammad, Taseer</searchLink><relatesTo>4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Communications+in+Heat+%26+Mass+Transfer%22">International Communications in Heat & Mass Transfer</searchLink>. Apr2026, Vol. 173, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetohydrodynamics%22">Magnetohydrodynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Biomedical+engineering%22">Biomedical engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Mass+transfer%22">Mass transfer</searchLink><br /><searchLink fieldCode="DE" term="%22Nanofluids%22">Nanofluids</searchLink><br /><searchLink fieldCode="DE" term="%22Heat+transfer%22">Heat transfer</searchLink><br /><searchLink fieldCode="DE" term="%22Non-Newtonian+fluids%22">Non-Newtonian fluids</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study investigates steady magnetohydrodynamic (MHD) flow and transport characteristics of a tetra-hybrid nanofluid through a stenosed artery modeled as a Cross non-Newtonian fluid. The problem is motivated by biomedical applications such as magnetic drug targeting, hyperthermia therapy, and controlled blood flow regulation. An extended Tiwari–Das nanofluid model incorporating titanium dioxide, gold, silver, and aluminium oxide nanoparticles is employed to enhance heat and mass transfer. The governing momentum, energy, and concentration equations account for nonlinear thermal radiation, Joule heating, viscous dissipation, non-uniform heat generation/absorption, chemical reaction, and Soret effects. Using similarity transformations, the resulting dimensionless system is solved numerically via MATLAB's bvp4c solver. To improve computational efficiency, Artificial Neural Networks (ANNs) and Physics-Informed Neural Networks (PINNs) are developed as surrogate models. Results indicate that increasing the Weissenberg number reduces wall shear stress, while magnetic effects suppress velocity by approximately 28%. Temperature rises by about 22% due to combined thermal effects, whereas concentration increases near the wall with higher Soret number and decreases with stronger chemical reactions. Enhanced nanoparticle volume fraction significantly improves Nusselt and Sherwood numbers. Excellent agreement between numerical and machine-learning results is observed, with ANN showing superior accuracy and faster convergence. The study demonstrates that integrating MHD control with AI-driven modeling offers an efficient framework for optimizing thermal and mass transport in non-Newtonian biomedical flows. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Communications in Heat & Mass Transfer is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=192228746 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.icheatmasstransfer.2026.110721 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Magnetohydrodynamics Type: general – SubjectFull: Computer simulation Type: general – SubjectFull: Biomedical engineering Type: general – SubjectFull: Mass transfer Type: general – SubjectFull: Nanofluids Type: general – SubjectFull: Heat transfer Type: general – SubjectFull: Non-Newtonian fluids Type: general Titles: – TitleFull: Artificial neural network simulation with numerical computation for targeted modulation of blood cells in hemodynamically stenosed anisotropic artery. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Batool, Maria – PersonEntity: Name: NameFull: Rubbab, Qammar – PersonEntity: Name: NameFull: Akbar, Noreen Sher – PersonEntity: Name: NameFull: Farooq, Muhammad Asif – PersonEntity: Name: NameFull: Muhammad, Taseer IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 07351933 Numbering: – Type: volume Value: 173 Titles: – TitleFull: International Communications in Heat & Mass Transfer Type: main |
| ResultId | 1 |
