Application of Levenberg-Marquardt artificial neural network to study nanoparticle aggregation phenomena in stagnation point flow towards an off-centered rotating disk
The off-centered stagnation point flow of nanofluid past a revolving disk has applications in numerous industrial and engineering procedures. This phenomenon is crucial in improving heat transport capability in cooling systems like heat exchangers, turbine blades, and high-performance electronics. I...
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Elsevier
2025-05-01
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| Series: | International Journal of Thermofluids |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266620272500223X |
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| author | Prateek Kattimani Koushik V. Prasad Talha Anwar Shakti Prakash Jena Aman Shankhyan R. Naveen Kumar |
| author_facet | Prateek Kattimani Koushik V. Prasad Talha Anwar Shakti Prakash Jena Aman Shankhyan R. Naveen Kumar |
| author_sort | Prateek Kattimani |
| collection | DOAJ |
| description | The off-centered stagnation point flow of nanofluid past a revolving disk has applications in numerous industrial and engineering procedures. This phenomenon is crucial in improving heat transport capability in cooling systems like heat exchangers, turbine blades, and high-performance electronics. In view of this, the current study inspects the off-centric stagnation point flow of nanofluid with the consequence of nonlinear thermal radiation and heat source/sink via a revolving disk. Additionally, the significance of nanoparticle aggregation is considered in analyzing the liquid flow and heat transport properties. Using appropriate similarity transformations, the governing partial differential equations (PDEs) are transformed into ordinary differential equations (ODEs). Further, the Runge-Kutta Fehlberg’s fourth-fifth order (RKF-45) technique is subsequently employed to solve the resultant ODEs numerically. Moreover, the Levenberg-Marquardt artificial neural network (LM-ANN) is implemented to assess the liquid flow and heat transport attributes. The Levenberg-Marquardt procedure builds and trains the artificial neural network technique for thermal and velocity profiles. The consequence of subsequent parameters on the thermal and velocity profiles is demonstrated in the graphs. For the radiation parameter, the heat transmission rate is around 4.99% for without aggregation case, and 7.18% for with aggregation case. An increment in the rotation parameter increases the radial velocity profile, whereas it reduces the azimuthal velocity profile. The heat source/sink and radiation parameters enhance the thermal profile. |
| format | Article |
| id | doaj-art-cacfe794e64045a2bd2cffb34dd06865 |
| institution | DOAJ |
| issn | 2666-2027 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
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| series | International Journal of Thermofluids |
| spelling | doaj-art-cacfe794e64045a2bd2cffb34dd068652025-08-20T03:10:41ZengElsevierInternational Journal of Thermofluids2666-20272025-05-012710127610.1016/j.ijft.2025.101276Application of Levenberg-Marquardt artificial neural network to study nanoparticle aggregation phenomena in stagnation point flow towards an off-centered rotating diskPrateek Kattimani0Koushik V. Prasad1Talha Anwar2Shakti Prakash Jena3Aman Shankhyan4R. Naveen Kumar5Department of Studies in Mathematics, Davangere University, Davangere, Karnataka, IndiaDepartment of Mechanical Engineering, School of Engineering and Technology, JAIN (Deemed to be University), Bangalore, Karnataka, IndiaSchool of Science, Walailak University, Nakhon Si Thammarat 80160, Thailand; Corresponding author.Department of Mechanical Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, IndiaCentre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, IndiaDepartment of Mathematics, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, IndiaThe off-centered stagnation point flow of nanofluid past a revolving disk has applications in numerous industrial and engineering procedures. This phenomenon is crucial in improving heat transport capability in cooling systems like heat exchangers, turbine blades, and high-performance electronics. In view of this, the current study inspects the off-centric stagnation point flow of nanofluid with the consequence of nonlinear thermal radiation and heat source/sink via a revolving disk. Additionally, the significance of nanoparticle aggregation is considered in analyzing the liquid flow and heat transport properties. Using appropriate similarity transformations, the governing partial differential equations (PDEs) are transformed into ordinary differential equations (ODEs). Further, the Runge-Kutta Fehlberg’s fourth-fifth order (RKF-45) technique is subsequently employed to solve the resultant ODEs numerically. Moreover, the Levenberg-Marquardt artificial neural network (LM-ANN) is implemented to assess the liquid flow and heat transport attributes. The Levenberg-Marquardt procedure builds and trains the artificial neural network technique for thermal and velocity profiles. The consequence of subsequent parameters on the thermal and velocity profiles is demonstrated in the graphs. For the radiation parameter, the heat transmission rate is around 4.99% for without aggregation case, and 7.18% for with aggregation case. An increment in the rotation parameter increases the radial velocity profile, whereas it reduces the azimuthal velocity profile. The heat source/sink and radiation parameters enhance the thermal profile.http://www.sciencedirect.com/science/article/pii/S266620272500223XNanofluidOff-centric stagnation point flowRotating diskThermal radiationLevenberg-Marquardt artificial neural network (LM-ANN) |
| spellingShingle | Prateek Kattimani Koushik V. Prasad Talha Anwar Shakti Prakash Jena Aman Shankhyan R. Naveen Kumar Application of Levenberg-Marquardt artificial neural network to study nanoparticle aggregation phenomena in stagnation point flow towards an off-centered rotating disk International Journal of Thermofluids Nanofluid Off-centric stagnation point flow Rotating disk Thermal radiation Levenberg-Marquardt artificial neural network (LM-ANN) |
| title | Application of Levenberg-Marquardt artificial neural network to study nanoparticle aggregation phenomena in stagnation point flow towards an off-centered rotating disk |
| title_full | Application of Levenberg-Marquardt artificial neural network to study nanoparticle aggregation phenomena in stagnation point flow towards an off-centered rotating disk |
| title_fullStr | Application of Levenberg-Marquardt artificial neural network to study nanoparticle aggregation phenomena in stagnation point flow towards an off-centered rotating disk |
| title_full_unstemmed | Application of Levenberg-Marquardt artificial neural network to study nanoparticle aggregation phenomena in stagnation point flow towards an off-centered rotating disk |
| title_short | Application of Levenberg-Marquardt artificial neural network to study nanoparticle aggregation phenomena in stagnation point flow towards an off-centered rotating disk |
| title_sort | application of levenberg marquardt artificial neural network to study nanoparticle aggregation phenomena in stagnation point flow towards an off centered rotating disk |
| topic | Nanofluid Off-centric stagnation point flow Rotating disk Thermal radiation Levenberg-Marquardt artificial neural network (LM-ANN) |
| url | http://www.sciencedirect.com/science/article/pii/S266620272500223X |
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