Investigation of fin model using RNN intelligent computing associated to physical parameters and hybrid nanomaterials.
8 affiliations
- 1Department of Mathematics, Mohi-ud-Din Islamic University, Nerian Sharif, 12080, AJ&K, Pakistan.
- 2Department of Networks and Cybersecurity, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan.
- 3Department of Electrical Engineering, Faculty of Engineering, Islamic University of Madinah, Madinah, 42351, Saudi Arabia.
- 4Department of Medical Laboratory Science, College of Health Science, Lebanese French University, Erbil, Iraq.
- 5Department of Mathematics, Jimma University, Jimma, Ethiopia. [email protected].
- 6Center for Scientific Research and Entrepreneurship, Northern Border University, Arar, 73213, Saudi Arabia. [email protected].
- 7Department of Applied Mathematics, National Chung Hsing University, Taichung, Taiwan.
- 8Department of Mathematics, Mohi-ud-Din Islamic University, Nerian Sharif, 12080, AJ&K, Pakistan. [email protected].
Abstract
The efficient thermal transport through trapezoidal fin surface remains a significant multi engineering problem because traditional fins configuration offers unsatisfactory heat transport subject to high thermal loads which limits the colling system's thermal performance. Although, the nanoparticles have potential role in the heat mechanism across trapezoidal fin but the combined contribution of hybrid nanofluid properties, magnetic field, wetting surface and temperature dependent thermal conductivity has not been adequately presented using AI based computational paradigm. Therefore, the present research aims to formulate a nonlinear energy model for heat transport through trapezoidal fin and to examine the model parameters influence on its thermal behaviour. The formulated problem handled by implementing the RNN (Recurrent neural network) coupled with bvp4c and the numerical experiment executed against different parameters to explore their role in the heat transfer. The achieved results determine that higher thermal conductivity parameter ([Formula: see text]) significantly improves the heat transfer while emissivity ([Formula: see text]) suppresses the heat mechanism under the conditions [Formula: see text] and [Formula: see text]. The conductive/convective effects ([Formula: see text]) and wetted surface help to regulate the temperature and highlighted lower heat transfer. The temperature index parameter [Formula: see text] increases thermal response of the fin. The convergence and accuracy of the RNN predictions are measured through error histogram, regression and MSE (mean squared error) which demonstrate better agreement between the predicted and numerical data. Further, the benchmark solution over the domain from [Formula: see text] to [Formula: see text] is provided for future replication of the results. These theoretical findings provided computational basis for better understanding and heat controlling parametric ranges with potential applications in solar energy systems, heat and cooling process of mining environment. Thermal transport across the trapezoidal shaped fin is relevant to the fins based heat exchangers using for thermal control and heat dissipation purposes in mining technology. The heat transfer through trapezoidal fin using nanofluids can enhance thermal dissipation from including motors, mining equipment, compressors, and drilling machinery operating under high thermal loads.Clinical trial number not applicable.
Related
- Artificial intelligent computing of advanced nanomaterials under electrothermal heating
- Soft Computing Optimization of Industrial Robot Inverse Kinematics for Intelligent Manufacturing: A Statistical Benchmark of Metaheuristic Algorithms
- Beyond the Smartphone: A General Framework for Intelligent, Autonomous, and Portable Computing and Communication
- Beyond the Computer: Toward an Intelligent and Autonomous General-Purpose Computing and Communication Device
- Beyond the GPU: Goal-Oriented Intelligent Computing as an Autonomous Instrument for Computational Outcomes
BibTeX
@article{shahbaz2026investigation,
title = {{Investigation of fin model using RNN intelligent computing associated to physical parameters and hybrid nanomaterials.}},
author = {Muneeba Shahbaz and Abdelrahman H Hussein and Mohamed Arbi Khlifi and Hemn A H Barzani and W D Mergia and Refka Ghodhbani and Sana Ullah Saqib and Adnan},
journal = {Discover nano},
year = {2026},
month = oct,
doi = {10.1186/s11671-026-04992-x},
url = {https://europepmc.org/article/MED/42873368},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Investigation of fin model using RNN intelligent computing associated to physical parameters and hybrid nanomaterials.
AU - Muneeba Shahbaz
AU - Abdelrahman H Hussein
AU - Mohamed Arbi Khlifi
AU - Hemn A H Barzani
AU - W D Mergia
AU - Refka Ghodhbani
AU - Sana Ullah Saqib
AU - Adnan
PY - 2026
DA - 2026/10/14
JO - Discover nano
DO - 10.1186/s11671-026-04992-x
UR - https://europepmc.org/article/MED/42873368
AB - The efficient thermal transport through trapezoidal fin surface remains a significant multi engineering problem because traditional fins configuration offers unsatisfactory heat transport subject to high thermal loads which limits the colling system's thermal performance. Although, the nanoparticles have potential role in the heat mechanism across trapezoidal fin but the combined contribution of hybrid nanofluid properties, magnetic field, wetting surface and temperature dependent thermal conductivity has not been adequately presented using AI based computational paradigm. Therefore, the present research aims to formulate a nonlinear energy model for heat transport through trapezoidal fin and to examine the model parameters influence on its thermal behaviour. The formulated problem handled by implementing the RNN (Recurrent neural network) coupled with bvp4c and the numerical experiment executed against different parameters to explore their role in the heat transfer. The achieved results determine that higher thermal conductivity parameter ([Formula: see text]) significantly improves the heat transfer while emissivity ([Formula: see text]) suppresses the heat mechanism under the conditions [Formula: see text] and [Formula: see text]. The conductive/convective effects ([Formula: see text]) and wetted surface help to regulate the temperature and highlighted lower heat transfer. The temperature index parameter [Formula: see text] increases thermal response of the fin. The convergence and accuracy of the RNN predictions are measured through error histogram, regression and MSE (mean squared error) which demonstrate better agreement between the predicted and numerical data. Further, the benchmark solution over the domain from [Formula: see text] to [Formula: see text] is provided for future replication of the results. These theoretical findings provided computational basis for better understanding and heat controlling parametric ranges with potential applications in solar energy systems, heat and cooling process of mining environment. Thermal transport across the trapezoidal shaped fin is relevant to the fins based heat exchangers using for thermal control and heat dissipation purposes in mining technology. The heat transfer through trapezoidal fin using nanofluids can enhance thermal dissipation from including motors, mining equipment, compressors, and drilling machinery operating under high thermal loads.Clinical trial number not applicable.
SN - 2731-9229
ER -