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Peer-reviewedCC BY-NC-ND 4.0Discover Nano

Artificial intelligent computing of advanced nanomaterials under electrothermal heating

  • Iqra Karim1
  • Mustefa Abduletif Mamud2
  • Mohamed Arbi Khlifi3
  • Adnan1
  • Walid Abdelfattah4
  • Heba G. Mohamed5
  • Yasir Khan6
  • Muhammad Nasir Bashir7
7 affiliations
  1. 1Department of Mathematics, Mohi-ud-Din Islamic University, Nerian Sharif AJ&K, Pakistan
  2. 2Department of Mathematics, College of Natural Sciences, Jimma University, Jimma, Ethiopia
  3. 3Department of Electrical Engineering, Faculty of Engineering, Islamic University of Madinah, Madinah, Saudi Arabia
  4. 4Humanities and Social Research Center, Northern Border University, Arar, Saudi Arabia
  5. 5Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
  6. 6Department of Mathematics, University of Hafr Al Batin, Hafr Al Batin, Saudi Arabia
  7. 7Department of Mechanical Engineering, College of Engineering, King Faisal University, Al-Ahsa, Saudi Arabia
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Abstract

Hybrid nanofluid technologies incorporating multiple nanoparticles have become increasingly important for enhancing thermal transport in contemporary engineering systems. In this work, the transport characteristics of hybrid nanofluid are explored in the presence of Joule heating, magnetic field effects, thermal radiation, through vertically stretched surface. The physical model is formulated to examine the coupled momentum and heat transfer mechanisms associated with electrically conducting nanofluids under dissipative energy effects. By employing suitable similarity transformations and thermophysical relations, the governing nonlinear equations are converted into a dimensionless mathematical system. The computational investigation performed through an Artificial Neural Network (ANN) framework, which provides efficient prediction and analysis of the transport phenomena with high computational accuracy. The analysis reveals that the stretching of the surface (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}λ =0.1,0.2,0.3,0.4\end{document}) intensifies the fluid motion, whereas Hartman number (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}M=0.2,0.4,0.6,0.8\end{document}) opposes the flow and reduces the velocity distribution due to electromagnetic resistance. Additionally, stronger Joule heating, radiation (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}Rd=1.0,3.0,5.0,7.0\end{document}), and Eckert number (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}Ec=0.1,0.2,0.3,0.4\end{document}) effects produce considerable enhancement in the thermal distribution. The role of concentration \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}ϕ i\end{document} for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}i=1,2,3,4,5\end{document} in the range of 0.1% to 0.4% is observed beneficial for the heat mechanism. These momentum and heat mechanisms together with these effects provide a theoretical framework for improving thermal electromagnetic control in high temperature stretching surface processes, coatings, material processing and extrusion.

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Cite

BibTeXRIS
BibTeX
@article{karim2026artificial,
  title = {{Artificial intelligent computing of advanced nanomaterials under electrothermal heating}},
  author = {Iqra Karim and Mustefa Abduletif Mamud and Mohamed Arbi Khlifi and Adnan and Walid Abdelfattah and Heba G. Mohamed and Yasir Khan and Muhammad Nasir Bashir},
  journal = {Discover Nano},
  year = {2026},
  month = oct,
  doi = {10.1186/s11671-026-05073-9},
  url = {https://doi.org/10.1186/s11671-026-05073-9},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Artificial intelligent computing of advanced nanomaterials under electrothermal heating
AU  - Iqra Karim
AU  - Mustefa Abduletif Mamud
AU  - Mohamed Arbi Khlifi
AU  - Adnan
AU  - Walid Abdelfattah
AU  - Heba G. Mohamed
AU  - Yasir Khan
AU  - Muhammad Nasir Bashir
PY  - 2026
DA  - 2026/10/31
JO  - Discover Nano
DO  - 10.1186/s11671-026-05073-9
UR  - https://doi.org/10.1186/s11671-026-05073-9
AB  - Hybrid nanofluid technologies incorporating multiple nanoparticles have become increasingly important for enhancing thermal transport in contemporary engineering systems. In this work, the transport characteristics of hybrid nanofluid are explored in the presence of Joule heating, magnetic field effects, thermal radiation, through vertically stretched surface. The physical model is formulated to examine the coupled momentum and heat transfer mechanisms associated with electrically conducting nanofluids under dissipative energy effects. By employing suitable similarity transformations and thermophysical relations, the governing nonlinear equations are converted into a dimensionless mathematical system. The computational investigation performed through an Artificial Neural Network (ANN) framework, which provides efficient prediction and analysis of the transport phenomena with high computational accuracy. The analysis reveals that the stretching of the surface (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}λ =0.1,0.2,0.3,0.4\end{document}) intensifies the fluid motion, whereas Hartman number (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}M=0.2,0.4,0.6,0.8\end{document}) opposes the flow and reduces the velocity distribution due to electromagnetic resistance. Additionally, stronger Joule heating, radiation (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}Rd=1.0,3.0,5.0,7.0\end{document}), and Eckert number (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}Ec=0.1,0.2,0.3,0.4\end{document}) effects produce considerable enhancement in the thermal distribution. The role of concentration \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}ϕ _i\end{document} for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}i=1,2,3,4,5\end{document} in the range of 0.1% to 0.4% is observed beneficial for the heat mechanism. These momentum and heat mechanisms together with these effects provide a theoretical framework for improving thermal electromagnetic control in high temperature stretching surface processes, coatings, material processing and extrusion.
SN  - 2731-9229
ER  -