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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Veröffentlicht in:	Chemical engineering science Jg. 321; S. 122785
Hauptverfasser:	Nacereddine, Mohamed Kherief, Usman, Rashid, Farhan Lafta, Shah, Nehad Ali, Ali Yousif, Badria Almaz, Mahariq, Ibrahim, Kezzar, Mohamed, Sari, Mohamed Rafik
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.02.2026
Schlagworte:	Artificial neural network algorithm Explicit Runge-Kutta method Hybrid nanofluid Thomson-Troian slip Xue and Yamada-Ota models Explicit Runge-Kutta method Artificial neural network algorithm Thomson-Troian slip Hybrid nanofluid Xue and Yamada-Ota models
ISSN:	0009-2509
Online-Zugang:	Volltext
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Zusammenfassung:	•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.
ISSN:	0009-2509
DOI:	10.1016/j.ces.2025.122785