Academic Journal
AI-driven investigation of heat transfer in radiative magnetized micropolar nanofluid flow between rotating disks.
| Τίτλος: | AI-driven investigation of heat transfer in radiative magnetized micropolar nanofluid flow between rotating disks. |
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| Συγγραφείς: | Algehyne, Ebrahem A., Ahmed, Safa Elshaikh Saad, Osman, Osman Abdalla Adam, Alamrani, Fahad Maqbul, Saeed, Anwar, Bognár, Gabriella |
| Πηγή: | Journal of Thermal Analysis & Calorimetry; Jul2026, Vol. 151 Issue 13, p10763-10781, 19p |
| Θεματικοί όροι: | Artificial neural networks, Computer simulation of heat transfer, Numerical solutions to boundary value problems, Reynolds number, Rotating disks, Porous materials, Nanofluids, Heat radiation & absorption |
| Περίληψη: | Predicting heat transfer in variable porosity medium with the neural network approach allows for more precise predictions, which can result in better cooling, better lubrication systems and better design of biomedical devices. Keeping these important applications in view, the current work examines the thermally radiative flow of micropolar nanofluid on a variable Darcy regime through two circular disks. The main equations are first solved using the bvp4c approach and then the data from the bvp4c approach is utilized as a fundamental data for the artificial neural network (ANN) approach. This study shows that the ANN model exhibits good convergence behaviour, in the sense that for all of the operational cases a stable value of the MSE was reached within an acceptable range of 10−7 to 10−10. Error histogram analysis provides a small error uncertainty range, and more than 95% of error deviations are tightly grouped near the centre within a narrow absolute range of uncertainty of ± 5 × 10−6. The correlation between the predicted and numerical target values is outstanding for both fitness and regression analyses, both of which give a correlation coefficient of (R = 1.0000), indicating the high degree of statistical certainty of the predictive model. Increase in the Reynolds number and nanoparticles concentration results in increase in axial and tangential velocities and decrease in magnetic parameter results in decrease in axial and tangential velocities. Thermal profiles increase with the increase in Reynolds number and the radiation parameter and decrease with the increase in the heat source factor and the increase in nanoparticles concentration. Lastly, a quantitative validation of the comparison results with literature benchmarks reveals a very good agreement, with a maximum difference of less than 0.1%. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: AI-driven investigation of heat transfer in radiative magnetized micropolar nanofluid flow between rotating disks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Algehyne%2C+Ebrahem+A%2E%22">Algehyne, Ebrahem A.</searchLink><br /><searchLink fieldCode="AR" term="%22Ahmed%2C+Safa+Elshaikh+Saad%22">Ahmed, Safa Elshaikh Saad</searchLink><br /><searchLink fieldCode="AR" term="%22Osman%2C+Osman+Abdalla+Adam%22">Osman, Osman Abdalla Adam</searchLink><br /><searchLink fieldCode="AR" term="%22Alamrani%2C+Fahad+Maqbul%22">Alamrani, Fahad Maqbul</searchLink><br /><searchLink fieldCode="AR" term="%22Saeed%2C+Anwar%22">Saeed, Anwar</searchLink><br /><searchLink fieldCode="AR" term="%22Bognár%2C+Gabriella%22">Bognár, Gabriella</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Thermal Analysis & Calorimetry; Jul2026, Vol. 151 Issue 13, p10763-10781, 19p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation+of+heat+transfer%22">Computer simulation of heat transfer</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="%22Reynolds+number%22">Reynolds number</searchLink><br /><searchLink fieldCode="DE" term="%22Rotating+disks%22">Rotating disks</searchLink><br /><searchLink fieldCode="DE" term="%22Porous+materials%22">Porous materials</searchLink><br /><searchLink fieldCode="DE" term="%22Nanofluids%22">Nanofluids</searchLink><br /><searchLink fieldCode="DE" term="%22Heat+radiation+%26+absorption%22">Heat radiation & absorption</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Predicting heat transfer in variable porosity medium with the neural network approach allows for more precise predictions, which can result in better cooling, better lubrication systems and better design of biomedical devices. Keeping these important applications in view, the current work examines the thermally radiative flow of micropolar nanofluid on a variable Darcy regime through two circular disks. The main equations are first solved using the bvp4c approach and then the data from the bvp4c approach is utilized as a fundamental data for the artificial neural network (ANN) approach. This study shows that the ANN model exhibits good convergence behaviour, in the sense that for all of the operational cases a stable value of the MSE was reached within an acceptable range of 10<superscript>−7</superscript> to 10<superscript>−10</superscript>. Error histogram analysis provides a small error uncertainty range, and more than 95% of error deviations are tightly grouped near the centre within a narrow absolute range of uncertainty of ± 5 × 10<superscript>−6</superscript>. The correlation between the predicted and numerical target values is outstanding for both fitness and regression analyses, both of which give a correlation coefficient of (R = 1.0000), indicating the high degree of statistical certainty of the predictive model. Increase in the Reynolds number and nanoparticles concentration results in increase in axial and tangential velocities and decrease in magnetic parameter results in decrease in axial and tangential velocities. Thermal profiles increase with the increase in Reynolds number and the radiation parameter and decrease with the increase in the heat source factor and the increase in nanoparticles concentration. Lastly, a quantitative validation of the comparison results with literature benchmarks reveals a very good agreement, with a maximum difference of less than 0.1%. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Thermal Analysis & Calorimetry is the property of Springer Nature 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: 10.1007/s10973-026-15809-5 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 10763 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Computer simulation of heat transfer Type: general – SubjectFull: Numerical solutions to boundary value problems Type: general – SubjectFull: Reynolds number Type: general – SubjectFull: Rotating disks Type: general – SubjectFull: Porous materials Type: general – SubjectFull: Nanofluids Type: general – SubjectFull: Heat radiation & absorption Type: general Titles: – TitleFull: AI-driven investigation of heat transfer in radiative magnetized micropolar nanofluid flow between rotating disks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Algehyne, Ebrahem A. – PersonEntity: Name: NameFull: Ahmed, Safa Elshaikh Saad – PersonEntity: Name: NameFull: Osman, Osman Abdalla Adam – PersonEntity: Name: NameFull: Alamrani, Fahad Maqbul – PersonEntity: Name: NameFull: Saeed, Anwar – PersonEntity: Name: NameFull: Bognár, Gabriella IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13886150 Numbering: – Type: volume Value: 151 – Type: issue Value: 13 Titles: – TitleFull: Journal of Thermal Analysis & Calorimetry Type: main |
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