Academic Journal
Numerical simulation of MHD Carreau bio-magnetic dusty hybrid nano fluid flow over a rotating disk: machine learning approach.
| Τίτλος: | Numerical simulation of MHD Carreau bio-magnetic dusty hybrid nano fluid flow over a rotating disk: machine learning approach. |
|---|---|
| Συγγραφείς: | Karuppiah, Santhi, Govindaram, Anitha, Reddy Reddisekhar Reddy, Seethi, Jayapal, Jegan |
| Πηγή: | International Journal of Computer Mathematics; Aug2026, Vol. 103 Issue 8, p1583-1611, 29p |
| Θεματικοί όροι: | Nanofluids, Fluid flow, Fluid dynamics, Numerical solutions to boundary value problems, Heat flux, Heat transfer coefficient, Rotating disks, Computational fluid dynamics |
| Περίληψη: | Machine learning is an effective method for addressing complicated fluid dynamics issues and evaluating enormous data sets. This work investigates the thermophysical behaviour of Ag-Au/blood hybrid nanofluids on a spinning disk utilizing a two-phase MHD Carreau model and a non-Fourier heat flux technique. The nonlinear governing equations for momentum, temperature, skin friction, and Nusselt number were numerically solved with the MATLAB bvp4c function. Tables and graphs show how important factors affect biomagnetic flow. Temperature increases with increased nanoparticle concentration and radiation, but velocity decreases with increasing porosity and magnetic field intensity. Under favourable conditions, the interaction between dust and fluid improves heat transmission by increasing skin friction and Nusselt number. Radiation increases the Nusselt number by around 19%, while particle contact decreases it by almost 12%. Machine learning integration enables predictive modelling and optimization of biomedical systems such as artificial organs, lab-on-a-chip devices, and medication delivery. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Computer Mathematics is the property of Taylor & Francis Ltd 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=00207160&ISBN=&volume=103&issue=8&date=20260801&spage=1583&pages=1583-1611&title=International Journal of Computer Mathematics&atitle=Numerical%20simulation%20of%20MHD%20Carreau%20bio-magnetic%20dusty%20hybrid%20nano%20fluid%20flow%20over%20a%20rotating%20disk%3A%20machine%20learning%20approach.&aulast=Karuppiah%2C%20Santhi&id=DOI:10.1080/00207160.2026.2637674 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Header | DbId: edb DbLabel: Complementary Index An: 195622017 RelevancyScore: 1082 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1082.42456054688 |
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| Items | – Name: Title Label: Title Group: Ti Data: Numerical simulation of MHD Carreau bio-magnetic dusty hybrid nano fluid flow over a rotating disk: machine learning approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Karuppiah%2C+Santhi%22">Karuppiah, Santhi</searchLink><br /><searchLink fieldCode="AR" term="%22Govindaram%2C+Anitha%22">Govindaram, Anitha</searchLink><br /><searchLink fieldCode="AR" term="%22Reddy+Reddisekhar+Reddy%2C+Seethi%22">Reddy Reddisekhar Reddy, Seethi</searchLink><br /><searchLink fieldCode="AR" term="%22Jayapal%2C+Jegan%22">Jayapal, Jegan</searchLink> – Name: TitleSource Label: Source Group: Src Data: International Journal of Computer Mathematics; Aug2026, Vol. 103 Issue 8, p1583-1611, 29p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Nanofluids%22">Nanofluids</searchLink><br /><searchLink fieldCode="DE" term="%22Fluid+flow%22">Fluid flow</searchLink><br /><searchLink fieldCode="DE" term="%22Fluid+dynamics%22">Fluid dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+solutions+to+boundary+value+problems%22">Numerical solutions to boundary value problems</searchLink><br /><searchLink fieldCode="DE" term="%22Heat+flux%22">Heat flux</searchLink><br /><searchLink fieldCode="DE" term="%22Heat+transfer+coefficient%22">Heat transfer coefficient</searchLink><br /><searchLink fieldCode="DE" term="%22Rotating+disks%22">Rotating disks</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+fluid+dynamics%22">Computational fluid dynamics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Machine learning is an effective method for addressing complicated fluid dynamics issues and evaluating enormous data sets. This work investigates the thermophysical behaviour of Ag-Au/blood hybrid nanofluids on a spinning disk utilizing a two-phase MHD Carreau model and a non-Fourier heat flux technique. The nonlinear governing equations for momentum, temperature, skin friction, and Nusselt number were numerically solved with the MATLAB bvp4c function. Tables and graphs show how important factors affect biomagnetic flow. Temperature increases with increased nanoparticle concentration and radiation, but velocity decreases with increasing porosity and magnetic field intensity. Under favourable conditions, the interaction between dust and fluid improves heat transmission by increasing skin friction and Nusselt number. Radiation increases the Nusselt number by around 19%, while particle contact decreases it by almost 12%. Machine learning integration enables predictive modelling and optimization of biomedical systems such as artificial organs, lab-on-a-chip devices, and medication delivery. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of International Journal of Computer Mathematics is the property of Taylor & Francis Ltd 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00207160.2026.2637674 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 1583 Subjects: – SubjectFull: Nanofluids Type: general – SubjectFull: Fluid flow Type: general – SubjectFull: Fluid dynamics Type: general – SubjectFull: Numerical solutions to boundary value problems Type: general – SubjectFull: Heat flux Type: general – SubjectFull: Heat transfer coefficient Type: general – SubjectFull: Rotating disks Type: general – SubjectFull: Computational fluid dynamics Type: general Titles: – TitleFull: Numerical simulation of MHD Carreau bio-magnetic dusty hybrid nano fluid flow over a rotating disk: machine learning approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Karuppiah, Santhi – PersonEntity: Name: NameFull: Govindaram, Anitha – PersonEntity: Name: NameFull: Reddy Reddisekhar Reddy, Seethi – PersonEntity: Name: NameFull: Jayapal, Jegan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00207160 Numbering: – Type: volume Value: 103 – Type: issue Value: 8 Titles: – TitleFull: International Journal of Computer Mathematics Type: main |
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