ANN-based two hidden layers computational procedure for analysis of heat transport dynamics in polymer-based trihybrid Carreau nanofluid flow over needle geometry
Significance: Nanofluids over a continuously moving thin needle play a crucial role in thermal transport processes in various situations. This geometry facilitates the heat transfer mechanism, which could be crucial in many real-world applications such as cooling of electronic devices, heat exchange...
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Elsevier
2025-06-01
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| Series: | Hybrid Advances |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773207X2500020X |
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| author | Adil Darvesh Fethi Mohamed Maiz Basma Souayeh Luis Jaime Collantes Santisteban Hakim AL. Garalleh Afnan Al Agha Lucerito Katherine Ortiz García Nicole Anarella Sánchez-Miranda |
| author_facet | Adil Darvesh Fethi Mohamed Maiz Basma Souayeh Luis Jaime Collantes Santisteban Hakim AL. Garalleh Afnan Al Agha Lucerito Katherine Ortiz García Nicole Anarella Sánchez-Miranda |
| author_sort | Adil Darvesh |
| collection | DOAJ |
| description | Significance: Nanofluids over a continuously moving thin needle play a crucial role in thermal transport processes in various situations. This geometry facilitates the heat transfer mechanism, which could be crucial in many real-world applications such as cooling of electronic devices, heat exchangers and advanced manufacturing techniques. Purpose: A novel investigation of polymer-based trihybrid Carreau nanofluid flow subjected to thermal radiation and magnetohydrodynamic consequences (MHD) over a continuously moving thin needle has been made in this research attempt. Velocity of fluid is scrutinized through magnetic aspect and transport of heat is inspected through thermal radiation and heat sink source. In addition, implementing advance ANN-based computational procedures such as multi layers neural networks (MLNNs) provide valuable aid in unmatched capability for capturing the high complexity of heat transfer in fluid flow problems. Their advantages in handling nonlinearities and modeling high-dimensional data through integrating physical laws make them far superior to simpler machine learning and other traditional techniques, despite requiring greater data and computational resources. Methodology: The physical model is originally formed with the help of partial differential equations (PDEs), that are formulated with pre-defined assumption of fluid flow mechanism. These governing system is transformed into ordinary differential equations (ODEs) via appropriate similarity transformations. Numerical computation of ODEs is made by a well-known bvp4c scheme and then an advanced artificial neural network (ANN) computational framework is integrated to train the resulting dataset, which is based on scaled conjugate gradient neural network (SCG-NN) to facilitate predictions regarding advanced solutions. Findings: The velocity profile of a trihybrid nanofluid decreases with an increasing values of Weissenberg number and magnetic parameter but in case of numeric growth in Carreau index parameter, the magnitude of velocity is increasing due to shear-thinning behavior. On the other hand, temperature profile of a polymer-based trihybrid nanofluids decreased with augmented values of radiation parameter and heat generation parameter due to the enhanced radiative heat transfer and the specific thermal properties of the nanofluid as well as generated amount of heat respectively. |
| format | Article |
| id | doaj-art-6bbc051bed1540cd8ff590ffe024cab2 |
| institution | Kabale University |
| issn | 2773-207X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Hybrid Advances |
| spelling | doaj-art-6bbc051bed1540cd8ff590ffe024cab22025-02-05T04:32:54ZengElsevierHybrid Advances2773-207X2025-06-019100396ANN-based two hidden layers computational procedure for analysis of heat transport dynamics in polymer-based trihybrid Carreau nanofluid flow over needle geometryAdil Darvesh0Fethi Mohamed Maiz1Basma Souayeh2Luis Jaime Collantes Santisteban3Hakim AL. Garalleh4Afnan Al Agha5Lucerito Katherine Ortiz García6Nicole Anarella Sánchez-Miranda7Department of Mathematics and Statistics, Hazara University Mansehra, 21300, PakistanKing Khalid University, Faculty of Science, Physics Department, P.O. Box 9004, Abha, Saudi ArabiaDepartment of Physics, College of Science, King Faisal University, PO Box 400, Al-Ahsa, 31982, Saudi Arabia; Laboratory of Fluid Mechanics, Physics Department, Faculty of Sciences of Tunis, University of Tunis El Manar, 2092, Tunis, Tunisia; Corresponding author. Department of Physics, College of Science, King Faisal University, PO Box 400, Al-Ahsa, 31982, Saudi Arabia.Department of Mathematics, Universidad Nacional Pedro Ruiz Gallo, Lambayeque, PeruDepartment of Mathematical Science, College of Engineering, University of Business and Technology, Jeddah, 21361, Saudi ArabiaDepartment of Mathematical Science, College of Engineering, University of Business and Technology, Jeddah, 21361, Saudi ArabiaUniversidad de San Martin de Porres, Chiclayo, PeruUniversidad Nacional Pedro Ruiz Gallo, Lambayeque, PeruSignificance: Nanofluids over a continuously moving thin needle play a crucial role in thermal transport processes in various situations. This geometry facilitates the heat transfer mechanism, which could be crucial in many real-world applications such as cooling of electronic devices, heat exchangers and advanced manufacturing techniques. Purpose: A novel investigation of polymer-based trihybrid Carreau nanofluid flow subjected to thermal radiation and magnetohydrodynamic consequences (MHD) over a continuously moving thin needle has been made in this research attempt. Velocity of fluid is scrutinized through magnetic aspect and transport of heat is inspected through thermal radiation and heat sink source. In addition, implementing advance ANN-based computational procedures such as multi layers neural networks (MLNNs) provide valuable aid in unmatched capability for capturing the high complexity of heat transfer in fluid flow problems. Their advantages in handling nonlinearities and modeling high-dimensional data through integrating physical laws make them far superior to simpler machine learning and other traditional techniques, despite requiring greater data and computational resources. Methodology: The physical model is originally formed with the help of partial differential equations (PDEs), that are formulated with pre-defined assumption of fluid flow mechanism. These governing system is transformed into ordinary differential equations (ODEs) via appropriate similarity transformations. Numerical computation of ODEs is made by a well-known bvp4c scheme and then an advanced artificial neural network (ANN) computational framework is integrated to train the resulting dataset, which is based on scaled conjugate gradient neural network (SCG-NN) to facilitate predictions regarding advanced solutions. Findings: The velocity profile of a trihybrid nanofluid decreases with an increasing values of Weissenberg number and magnetic parameter but in case of numeric growth in Carreau index parameter, the magnitude of velocity is increasing due to shear-thinning behavior. On the other hand, temperature profile of a polymer-based trihybrid nanofluids decreased with augmented values of radiation parameter and heat generation parameter due to the enhanced radiative heat transfer and the specific thermal properties of the nanofluid as well as generated amount of heat respectively.http://www.sciencedirect.com/science/article/pii/S2773207X2500020XComputational mechanismAN-Based simulationHeat transport dynamicsCarreau fluidTernary nanoparticlesNeedle geometry |
| spellingShingle | Adil Darvesh Fethi Mohamed Maiz Basma Souayeh Luis Jaime Collantes Santisteban Hakim AL. Garalleh Afnan Al Agha Lucerito Katherine Ortiz García Nicole Anarella Sánchez-Miranda ANN-based two hidden layers computational procedure for analysis of heat transport dynamics in polymer-based trihybrid Carreau nanofluid flow over needle geometry Hybrid Advances Computational mechanism AN-Based simulation Heat transport dynamics Carreau fluid Ternary nanoparticles Needle geometry |
| title | ANN-based two hidden layers computational procedure for analysis of heat transport dynamics in polymer-based trihybrid Carreau nanofluid flow over needle geometry |
| title_full | ANN-based two hidden layers computational procedure for analysis of heat transport dynamics in polymer-based trihybrid Carreau nanofluid flow over needle geometry |
| title_fullStr | ANN-based two hidden layers computational procedure for analysis of heat transport dynamics in polymer-based trihybrid Carreau nanofluid flow over needle geometry |
| title_full_unstemmed | ANN-based two hidden layers computational procedure for analysis of heat transport dynamics in polymer-based trihybrid Carreau nanofluid flow over needle geometry |
| title_short | ANN-based two hidden layers computational procedure for analysis of heat transport dynamics in polymer-based trihybrid Carreau nanofluid flow over needle geometry |
| title_sort | ann based two hidden layers computational procedure for analysis of heat transport dynamics in polymer based trihybrid carreau nanofluid flow over needle geometry |
| topic | Computational mechanism AN-Based simulation Heat transport dynamics Carreau fluid Ternary nanoparticles Needle geometry |
| url | http://www.sciencedirect.com/science/article/pii/S2773207X2500020X |
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