Analysis of Swirling Flow of Hybrid Nanofluids Due to a Moving Cylinder With Temperature‐Dependent Thermal Conductivity Through Machine Learning Algorithms.
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| Title: | Analysis of Swirling Flow of Hybrid Nanofluids Due to a Moving Cylinder With Temperature‐Dependent Thermal Conductivity Through Machine Learning Algorithms. |
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| Authors: | Niaz, Sidra1 (AUTHOR), Azhar, Muhammad1 (AUTHOR) azharmath@aus.edu.pk, Mujahid, Zeeshan1 (AUTHOR), Abbas, Syed Zaheer2 (AUTHOR) zaheer@hu.edu.pk, Ayed, Hamdi3 (AUTHOR) |
| Source: | ZAMM -- Journal of Applied Mathematics & Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik. Apr2026, Vol. 106 Issue 4, p1-28. 28p. |
| Subjects: | Nanofluids, Machine learning, Numerical analysis, Thermal conductivity, Non-Newtonian fluids, Heat transfer, Swirling flow, Energy dissipation |
| Abstract: | The current work is motivated to study the flow characteristics and transfer of heat in the occurrence of tiny‐sized solid particles, which are utilized to improve the thermo‐physical features of the base fluid through machine learning. In this work, the significant consequences of viscous dissipation are due to a swirling flow of hybrid nano‐fluid on a stretched cylinder. The non‐Newtonian working medium's effect on the flow structure, heat transfer, and entropy generation performance is investigated by means of partial differential governing equations. The thermal radiation impacts and temperature‐dependent variable conductivity are also characterized. Swirling flow and slip impacts are considered in this study. The base fluid (ethylene glycol C6H6O2)${{C}_6}{{H}_6}{{O}_2})$ with nanoparticles of copper (Cu$Cu$) and titanium dioxide (TiO2$Ti{{O}_2}$) is adopted in this communication. The boundary flow equations formulated in the present model are converted into ODEs through the application of appropriately modified variables. Furthermore, two distinct numerical techniques are applied to solve the system of ODEs derived from modeled PDEs. A bvp4c algorithm built‐in computational technique in MATLAB is used to numerically calculate the dimensionless nonlinear ODEs. For comparative analysis, artificial intelligence based neural network back propagation Levenberg Marquardth scheme is used. The two methods yielded matching numerical and visual outcomes, confirming the strong consistency and dependability of the computational system. The physical inspirations of prominent parameters against the velocity profile and temperature distribution are analyzed through graphs and tabular data. [ABSTRACT FROM AUTHOR] |
| Copyright of ZAMM -- Journal of Applied Mathematics & Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik is the property of Wiley-Blackwell 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 193323485 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Analysis of Swirling Flow of Hybrid Nanofluids Due to a Moving Cylinder With Temperature‐Dependent Thermal Conductivity Through Machine Learning Algorithms. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Niaz%2C+Sidra%22">Niaz, Sidra</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Azhar%2C+Muhammad%22">Azhar, Muhammad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> azharmath@aus.edu.pk</i><br /><searchLink fieldCode="AR" term="%22Mujahid%2C+Zeeshan%22">Mujahid, Zeeshan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Abbas%2C+Syed+Zaheer%22">Abbas, Syed Zaheer</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> zaheer@hu.edu.pk</i><br /><searchLink fieldCode="AR" term="%22Ayed%2C+Hamdi%22">Ayed, Hamdi</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22ZAMM+--+Journal+of+Applied+Mathematics+%26+Mechanics+%2F+Zeitschrift+für+Angewandte+Mathematik+und+Mechanik%22">ZAMM -- Journal of Applied Mathematics & Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik</searchLink>. Apr2026, Vol. 106 Issue 4, p1-28. 28p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Nanofluids%22">Nanofluids</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+analysis%22">Numerical analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Thermal+conductivity%22">Thermal conductivity</searchLink><br /><searchLink fieldCode="DE" term="%22Non-Newtonian+fluids%22">Non-Newtonian fluids</searchLink><br /><searchLink fieldCode="DE" term="%22Heat+transfer%22">Heat transfer</searchLink><br /><searchLink fieldCode="DE" term="%22Swirling+flow%22">Swirling flow</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+dissipation%22">Energy dissipation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The current work is motivated to study the flow characteristics and transfer of heat in the occurrence of tiny‐sized solid particles, which are utilized to improve the thermo‐physical features of the base fluid through machine learning. In this work, the significant consequences of viscous dissipation are due to a swirling flow of hybrid nano‐fluid on a stretched cylinder. The non‐Newtonian working medium's effect on the flow structure, heat transfer, and entropy generation performance is investigated by means of partial differential governing equations. The thermal radiation impacts and temperature‐dependent variable conductivity are also characterized. Swirling flow and slip impacts are considered in this study. The base fluid (ethylene glycol C6H6O2)${{C}_6}{{H}_6}{{O}_2})$ with nanoparticles of copper (Cu$Cu$) and titanium dioxide (TiO2$Ti{{O}_2}$) is adopted in this communication. The boundary flow equations formulated in the present model are converted into ODEs through the application of appropriately modified variables. Furthermore, two distinct numerical techniques are applied to solve the system of ODEs derived from modeled PDEs. A bvp4c algorithm built‐in computational technique in MATLAB is used to numerically calculate the dimensionless nonlinear ODEs. For comparative analysis, artificial intelligence based neural network back propagation Levenberg Marquardth scheme is used. The two methods yielded matching numerical and visual outcomes, confirming the strong consistency and dependability of the computational system. The physical inspirations of prominent parameters against the velocity profile and temperature distribution are analyzed through graphs and tabular data. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of ZAMM -- Journal of Applied Mathematics & Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik is the property of Wiley-Blackwell 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: 10.1002/zamm.70378 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 28 StartPage: 1 Subjects: – SubjectFull: Nanofluids Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Numerical analysis Type: general – SubjectFull: Thermal conductivity Type: general – SubjectFull: Non-Newtonian fluids Type: general – SubjectFull: Heat transfer Type: general – SubjectFull: Swirling flow Type: general – SubjectFull: Energy dissipation Type: general Titles: – TitleFull: Analysis of Swirling Flow of Hybrid Nanofluids Due to a Moving Cylinder With Temperature‐Dependent Thermal Conductivity Through Machine Learning Algorithms. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Niaz, Sidra – PersonEntity: Name: NameFull: Azhar, Muhammad – PersonEntity: Name: NameFull: Mujahid, Zeeshan – PersonEntity: Name: NameFull: Abbas, Syed Zaheer – PersonEntity: Name: NameFull: Ayed, Hamdi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00442267 Numbering: – Type: volume Value: 106 – Type: issue Value: 4 Titles: – TitleFull: ZAMM -- Journal of Applied Mathematics & Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik Type: main |
| ResultId | 1 |
