Artificial intelligent computing of hybrid nano suspension for enhanced thermal mechanism in Jeffery Hamel configuration.
8 affiliations
- 1Department of Mathematics, Mohi-ud-Din Islamic University, Nerian Sharif, 12080, AJ&K, Pakistan. [email protected].
- 2Communications and Computer Engineering Department, Faculty of Engineering, Hourani Center for Applied Scientific Research, Amman, Jordan.
- 3Department of Electrical Engineering, Faculty of Engineering, Islamic University of Madinah, Madinah, 42351, Saudi Arabia.
- 4Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, Erbil, Kurdistan Region, Iraq.
- 5Department of Mathematics, Ambo University, Ambo, Ethiopia. [email protected].
- 6Center for Scientific Research and Entrepreneurship, Northern Border University, Arar, 73213, Saudi Arabia. [email protected].
- 7Department of Mechanical Engineering, College of Engineering, King Faisal University, Al-Ahsa, 31982, Saudi Arabia. [email protected].
- 8Department of Applied Mathematics, National Chung Hsing University, Taichung, Taiwan.
Abstract
Nanofluids have potential role in the heat transfer enhancement for engineering applications. These fluids together with physical controls change the dynamics of flow inside oblique channel. Thus, this work focuses on the organization of hybrid model using dissipation function and enhanced properties nanoparticles (NiZnFe₂O₄, and MnZnFe₂O₄). The similarity transforms used in the governing model and obtain a new model for hybrid nanofluids. The output estimated in the form of graphical results using Levenberg Marquardt Back Propagation scheme under multiple variations. It is noticed that the velocity is maximum towards the middle line of the channel and backflow observed in the surrounding of walls. Higher concentration declines the velocity due to improved density while Reynolds number enhances it. Further, the temperature is also scrutinized optimum about [Formula: see text] for Eckert and Reynolds number, which represents the central part of the physical domain. The skin friction improves from 3.87182 to 3.88703, 0.76311 to 0.77658 against [Formula: see text] to [Formula: see text]. The significant increase (7.63195 to 8.09306, and 4.68020 to 4.96782) in the heat transfer gradient is observed for 1.0% to 4.0% concentration factor. The convergence of the model achieved excellently in the form of error histogram ([Formula: see text]), MSE from [Formula: see text] to [Formula: see text], best validation performance from [Formula: see text] to [Formula: see text] (velocity case), [Formula: see text] to [Formula: see text] (temperature case) at epochs 1000. The heat transport through such configuration has potential relevance to the thermal transport in cooling of drilling, mining machinery, and mines equipment. Such problems can also be useful for heat transport systems associated with deep mine energy and waste heat recovery which required effective heat removal.
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BibTeX
@article{adnan2026artificial,
title = {{Artificial intelligent computing of hybrid nano suspension for enhanced thermal mechanism in Jeffery Hamel configuration.}},
author = {Adnan and Ali Khwaldeh and Mohamed Arbi Khlifi and Ahmed Najat Ahmed and Ayele Tulu and Taoufik Saidani and Muhammad Nasir Bashir and Sana Ullah Saqib},
journal = {Discover nano},
year = {2026},
month = oct,
doi = {10.1186/s11671-026-04980-1},
url = {https://europepmc.org/article/MED/42897353},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Artificial intelligent computing of hybrid nano suspension for enhanced thermal mechanism in Jeffery Hamel configuration.
AU - Adnan
AU - Ali Khwaldeh
AU - Mohamed Arbi Khlifi
AU - Ahmed Najat Ahmed
AU - Ayele Tulu
AU - Taoufik Saidani
AU - Muhammad Nasir Bashir
AU - Sana Ullah Saqib
PY - 2026
DA - 2026/10/19
JO - Discover nano
DO - 10.1186/s11671-026-04980-1
UR - https://europepmc.org/article/MED/42897353
AB - Nanofluids have potential role in the heat transfer enhancement for engineering applications. These fluids together with physical controls change the dynamics of flow inside oblique channel. Thus, this work focuses on the organization of hybrid model using dissipation function and enhanced properties nanoparticles (NiZnFe₂O₄, and MnZnFe₂O₄). The similarity transforms used in the governing model and obtain a new model for hybrid nanofluids. The output estimated in the form of graphical results using Levenberg Marquardt Back Propagation scheme under multiple variations. It is noticed that the velocity is maximum towards the middle line of the channel and backflow observed in the surrounding of walls. Higher concentration declines the velocity due to improved density while Reynolds number enhances it. Further, the temperature is also scrutinized optimum about [Formula: see text] for Eckert and Reynolds number, which represents the central part of the physical domain. The skin friction improves from 3.87182 to 3.88703, 0.76311 to 0.77658 against [Formula: see text] to [Formula: see text]. The significant increase (7.63195 to 8.09306, and 4.68020 to 4.96782) in the heat transfer gradient is observed for 1.0% to 4.0% concentration factor. The convergence of the model achieved excellently in the form of error histogram ([Formula: see text]), MSE from [Formula: see text] to [Formula: see text], best validation performance from [Formula: see text] to [Formula: see text] (velocity case), [Formula: see text] to [Formula: see text] (temperature case) at epochs 1000. The heat transport through such configuration has potential relevance to the thermal transport in cooling of drilling, mining machinery, and mines equipment. Such problems can also be useful for heat transport systems associated with deep mine energy and waste heat recovery which required effective heat removal.
SN - 2731-9229
ER -