Non-Similar Analysis of Boundary Layer Flow and Heat Transfer in Non-Newtonian Hybrid Nanofluid over a Cylinder with Viscous Dissipation Effects

Ahmed Zeeshan, Majeed Ahmad Yousif, Muhammad Imran Khan, Muhammad Amer Latif (), Syed Shahzad Ali and Pshtiwan Othman Mohammed ()
Additional contact information
Ahmed Zeeshan: Department of Mathematics & Statistics, Faculty of Sciences, International Islamic University Islamabad, H-10, Islamabad 44000, Pakistan
Majeed Ahmad Yousif: Department of Mathematics, College of Education, University of Zakho, Zakho 42002, Iraq
Muhammad Imran Khan: Department of Mathematics & Statistics, Faculty of Sciences, International Islamic University Islamabad, H-10, Islamabad 44000, Pakistan
Muhammad Amer Latif: Department of Mathematics, Faculty of Sciences, King Faisal University, Hofuf 31982, Saudi Arabia
Syed Shahzad Ali: Department of Mathematics & Statistics, Faculty of Sciences, International Islamic University Islamabad, H-10, Islamabad 44000, Pakistan
Pshtiwan Othman Mohammed: Department of Mathematics, College of Education, University of Sulaimani, Sulaimani 46001, Iraq

Energies, 2025, vol. 18, issue 7, 1-40

Abstract: Highlighting the importance of artificial intelligence and machine learning approaches in engineering and fluid mechanics problems, especially in heat transfer applications is main goal of the presented article. With the advancement in Artificial Intelligence (AI) and Machine Learning (ML) techniques, the computational efficiency and accuracy of numerical results are enhanced. The theme of the study is to use machine learning techniques to examine the thermal analysis of MHD boundary layer flow of Eyring-Powell Hybrid Nanofluid (EPHNFs) passing a horizontal cylinder embedded in a porous medium with heat source/sink and viscous dissipation effects. The considered base fluid is water ( H 2 O ) and hybrid nanoparticles titanium oxide ( T i O 2 ) and Copper oxide ( C u O ). The governing flow equations are nonlinear PDEs. Non-similar system of PDEs are obtained with efficient conversion variables. The dimensionless PDEs are truncated using a local non-similarity approach up to third level and numerical solution is evaluated using MATLAB built-in-function bvp4c. Artificial Neural Networks (ANNs) simulation approach is used to trained the networks to predict the solution behavior. Thermal boundary layer improves with the enhancement in the value of R d . The accuracy and reliability of ANNs predicted solution is addressed with computation of correlation index and residual analysis. The RMSE is evaluated [0.04892, 0.0007597, 0.0007596, 0.01546, 0.008871, 0.01686] for various scenarios. It is observed that when concentration of hybrid nanoparticles increases then thermal characteristics of the Eyring-Powell Hybrid Nanofluid (EPHNFs) passing a horizontal cylinder.

Keywords: Eyring-Powell fluid; machine learning; heat transfer; local non-similarity method; viscous dissipation; heat source/sink; mixed convection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/7/1660/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/7/1660/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:7:p:1660-:d:1621170

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().