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Peer-reviewedCC BY-NC-NDMethodsX

Thermo-diffusional influence on Cu₃Zn₂-Co nanomaterial with porous medium using intelligent Levenberg-Marquardt method.

  • Ilyas Khan1
  • Ilkhom Khaydarov2
  • Mustafa Bayram3
  • Munawar Abbas4,5
  • Ansar Abbas6
  • Hakim Al Garalleh7
  • Zuhair Jastaneyah8
  • Wei Sin Koh9
9 affiliations
  1. 1Department of Mathematics, College of Science Al-Zulfi, Majmaah University, Al-Majmaah, 11952, Saudi Arabia.
  2. 2School of Exact Sciences, National Pedagogical University of Uzbekistan named after Nizami.
  3. 3Department of Computer Engineering, Biruni University, Istanbul, 34010, Turkey.
  4. 4Department of Mathematics, Firat University, Elazig, Turkiye, 23119, Turkey.
  5. 5Mechanics and Mathematics Department of the Western Caspian University, Baku, Azerbaijan.
  6. 6Jadara University Research Center, Jadara University, Irbid, Jordan.
  7. 7Department of Mathematical Science, College of Engineering, University of Business and Technology, Jeddah, 21361, Saudi Arabia.
  8. 8Department of Mechanical Engineering, College of Engineering, University of Business and Technology, Jeddah, 21361, Saudi Arabia.
  9. 9INTI International University, Persiaran Perdana BBN Putra Nilai, Nilai, Negeri Sembilan, 71800, Malaysia.
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Abstract

The suggested model has important applicability in advanced heat and mass transfer systems using Cu3Zn2-Co / Distilled water nanomaterial in porous media. It is applicable to thermal energy storage, heat exchangers, catalytic reactors, filtration systems, geothermal engineering, and cooling technologies in which thermo-diffusion has a substantial impact on transport processes. This study paper presents an assessment of the effect of LTNE effects on Cu3Zn2-Co / Distilled water nanomaterial flow with Dufour and Soret impacts. The Bvp4c solver creates a reference dataset for training the algorithm of Levenberg-Marquardt optimizer with back propagation neural network (ALMO-BNN) model. The model's low mean squared error (MSE) suggests significant convergence and precision. A high degree of agreement among predicted and numerical results is indicated by the regression coefficient is near to unity. The thermal and concentration profiles rise as the values of Dufour and Soret numbers increase.

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Cite

BibTeXRIS
BibTeX
@article{khan2026thermodiffusional,
  title = {{Thermo-diffusional influence on Cu₃Zn₂-Co nanomaterial with porous medium using intelligent Levenberg-Marquardt method.}},
  author = {Ilyas Khan and Ilkhom Khaydarov and Mustafa Bayram and Munawar Abbas and Ansar Abbas and Hakim Al Garalleh and Zuhair Jastaneyah and Wei Sin Koh},
  journal = {MethodsX},
  year = {2026},
  month = aug,
  doi = {10.1016/j.mex.2026.104117},
  url = {https://europepmc.org/article/MED/42997783},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Thermo-diffusional influence on Cu₃Zn₂-Co nanomaterial with porous medium using intelligent Levenberg-Marquardt method.
AU  - Ilyas Khan
AU  - Ilkhom Khaydarov
AU  - Mustafa Bayram
AU  - Munawar Abbas
AU  - Ansar Abbas
AU  - Hakim Al Garalleh
AU  - Zuhair Jastaneyah
AU  - Wei Sin Koh
PY  - 2026
DA  - 2026/08/27
JO  - MethodsX
DO  - 10.1016/j.mex.2026.104117
UR  - https://europepmc.org/article/MED/42997783
AB  - The suggested model has important applicability in advanced heat and mass transfer systems using Cu3Zn2-Co / Distilled water nanomaterial in porous media. It is applicable to thermal energy storage, heat exchangers, catalytic reactors, filtration systems, geothermal engineering, and cooling technologies in which thermo-diffusion has a substantial impact on transport processes. This study paper presents an assessment of the effect of LTNE effects on Cu3Zn2-Co / Distilled water nanomaterial flow with Dufour and Soret impacts. The Bvp4c solver creates a reference dataset for training the algorithm of Levenberg-Marquardt optimizer with back propagation neural network (ALMO-BNN) model. The model's low mean squared error (MSE) suggests significant convergence and precision. A high degree of agreement among predicted and numerical results is indicated by the regression coefficient is near to unity. The thermal and concentration profiles rise as the values of Dufour and Soret numbers increase.
SN  - 2215-0161
ER  -