Artificial neural network algorithm for hybrid nanofluid in Jeffery-Hamel flow under Thompson and Troian velocity slip effects: Comparison of Xue and Yamada-Ota models
•Artificial Neural Network Algorithm for hybrid nanofluid in Jeffery-Hamel flow.•Thomson and Troian Velocity Slip Effects.•Comparison of Xue and Yamada-Ota models.•The governing equations solved using the Explicit Runge-Kutta Method (ERKM).•Advanced thermal management optimization with hybrid nanofl...
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| Published in: | Chemical engineering science Vol. 321; p. 122785 |
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01.02.2026
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| Abstract | •Artificial Neural Network Algorithm for hybrid nanofluid in Jeffery-Hamel flow.•Thomson and Troian Velocity Slip Effects.•Comparison of Xue and Yamada-Ota models.•The governing equations solved using the Explicit Runge-Kutta Method (ERKM).•Advanced thermal management optimization with hybrid nanofluids.
Thermal management of fluid flow in convergent-divergence was a significant issue in thermo-physical applications and especially a problem of optimizing thermo-response and heat transfer process under the condition of magnetic fields and slip flow. The research focuses on the overall impacts of the Thomson-Troian velocity slip and magnetic fields on the hybrid nanofluids (Fe3O4-CoFe2O4/EG-water) in Jeffery-Hamel flow, and the impacts of nanofluids in presenting results on determining the thermal conductivity between Xue and Yamada-Ota with the study intending to fill a gap in clarifying the behavior of nanofluids under studied complex conditions. We then solve by using Explicit Runge-Kutta Method (ERKM) and Artificial Neural Network (ANN) algorithms to assess the transformed governing equations to determine flow and heat transfer characterizing these effects. Comprehensive results indicate that as the concentration of nanoparticles increases, the Nusselt number increases considerably for both convergent and divergent channels in accordance with the Xue and Yamada-Ota models. Further, by increasing Thompson slip parameter (ω1) and Troian (ω2) slip parameters, the velocity profiles increase in both convergent and divergent channels. In addition, the increase of Hartmann number makes grow the velocity and thus preventing the reversal flow. Xue model is always better than Yamada-Ota with higher Nusselt number Nu in convergent channels compared to Nusselt number Nu by the Yamada-Ota formulation showing that Xue model is much better in capturing interfacial effects. The presently-obtained results give practical information in designing the high-efficiency thermal system utilizing the hybrid nanofluids under magnetic and slip conditions. Carbon-based hybrid nanoparticles, experimental confirmation of Xue/Yamada-Ota models in the industrial context, and the use of machine learning in predictive nanofluids modelling of complex geometries to optimize the performance of the thermal systems could be the future working topics. |
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| AbstractList | •Artificial Neural Network Algorithm for hybrid nanofluid in Jeffery-Hamel flow.•Thomson and Troian Velocity Slip Effects.•Comparison of Xue and Yamada-Ota models.•The governing equations solved using the Explicit Runge-Kutta Method (ERKM).•Advanced thermal management optimization with hybrid nanofluids.
Thermal management of fluid flow in convergent-divergence was a significant issue in thermo-physical applications and especially a problem of optimizing thermo-response and heat transfer process under the condition of magnetic fields and slip flow. The research focuses on the overall impacts of the Thomson-Troian velocity slip and magnetic fields on the hybrid nanofluids (Fe3O4-CoFe2O4/EG-water) in Jeffery-Hamel flow, and the impacts of nanofluids in presenting results on determining the thermal conductivity between Xue and Yamada-Ota with the study intending to fill a gap in clarifying the behavior of nanofluids under studied complex conditions. We then solve by using Explicit Runge-Kutta Method (ERKM) and Artificial Neural Network (ANN) algorithms to assess the transformed governing equations to determine flow and heat transfer characterizing these effects. Comprehensive results indicate that as the concentration of nanoparticles increases, the Nusselt number increases considerably for both convergent and divergent channels in accordance with the Xue and Yamada-Ota models. Further, by increasing Thompson slip parameter (ω1) and Troian (ω2) slip parameters, the velocity profiles increase in both convergent and divergent channels. In addition, the increase of Hartmann number makes grow the velocity and thus preventing the reversal flow. Xue model is always better than Yamada-Ota with higher Nusselt number Nu in convergent channels compared to Nusselt number Nu by the Yamada-Ota formulation showing that Xue model is much better in capturing interfacial effects. The presently-obtained results give practical information in designing the high-efficiency thermal system utilizing the hybrid nanofluids under magnetic and slip conditions. Carbon-based hybrid nanoparticles, experimental confirmation of Xue/Yamada-Ota models in the industrial context, and the use of machine learning in predictive nanofluids modelling of complex geometries to optimize the performance of the thermal systems could be the future working topics. |
| ArticleNumber | 122785 |
| Author | Rashid, Farhan Lafta Nacereddine, Mohamed Kherief Ali Yousif, Badria Almaz Mahariq, Ibrahim Shah, Nehad Ali Kezzar, Mohamed Usman Sari, Mohamed Rafik |
| Author_xml | – sequence: 1 givenname: Mohamed Kherief surname: Nacereddine fullname: Nacereddine, Mohamed Kherief email: kherief2006@yahoo.fr organization: Normal High School of Technology Education ENSET-azzaba Skikda Algeria, Algeria – sequence: 2 orcidid: 0000-0002-5989-3670 surname: Usman fullname: Usman email: usman.malik.ms@gmail.com organization: School of Qilu Transportation, Shandong University, Jinan, China – sequence: 3 givenname: Farhan Lafta orcidid: 0000-0002-7609-6585 surname: Rashid fullname: Rashid, Farhan Lafta email: farhan.lefta@uokerbala.edu.iq organization: Petroleum Engineering Department, University of Kerbala, Karbala 56001, Iraq – sequence: 4 givenname: Nehad Ali surname: Shah fullname: Shah, Nehad Ali email: nehadali199@yahoo.com organization: Departement of Mechanical Engineering, Sejong University, Seoul 05006, South Korea – sequence: 5 givenname: Badria Almaz surname: Ali Yousif fullname: Ali Yousif, Badria Almaz email: B.Yousif@qu.edu.sa organization: Department of Mathematics College of Science Qassim University Buraydah 51452, Saudi Arabia – sequence: 6 givenname: Ibrahim surname: Mahariq fullname: Mahariq, Ibrahim email: ibmahariq@gmail.com organization: Department of Mathematics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 602105 Tamil Nadu, India – sequence: 7 givenname: Mohamed surname: Kezzar fullname: Kezzar, Mohamed email: kezzar_m@yahoo.com organization: Materials and Energy Engineering Laboratory (LMGE), Technology Department, Faculty of Technology, 20 Aout 1955 University of Skikda, P.O. Box 26, 21000, Skikda, Algeria – sequence: 8 givenname: Mohamed Rafik surname: Sari fullname: Sari, Mohamed Rafik email: mohamed-rafik.sari@univ-annaba.dz organization: Mechanics of Materials and Plant Maintenance Research Laboratory (LR3MI), Mechanical Engineering Department, Faculty of Technology, Badji Mokhtar-Annaba University, P.O. Box 12, Annaba, 23000, Algeria |
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| Keywords | Explicit Runge-Kutta method Artificial neural network algorithm Thomson-Troian slip Hybrid nanofluid Xue and Yamada-Ota models |
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| Title | Artificial neural network algorithm for hybrid nanofluid in Jeffery-Hamel flow under Thompson and Troian velocity slip effects: Comparison of Xue and Yamada-Ota models |
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