Machine learning simulation for radiation effects on MoS₂-Al₂O₃ hybrid nanoliquid with local thermal non-equilibrium conditions.
6 affiliations
- 1Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia.
- 2Department of Mechanical Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia.
- 3School of Exact Sciences, National Pedagogical University of Uzbekistan named after Nizami, Tashkent, Uzbekistan.
- 4Department of Computer Engineering, Biruni University, Istanbul, 34010, Turkey.
- 5Department of General Subjects, University of Business and Technology, Jeddah, 21361, Saudi Arabia.
- 6Chemical Engineering Department, Faculty of Engineering, Alexandria University, Alexandria, 21544, Egypt.
Abstract
The current study examines the Soret-Dufour influence on a MoS₂-Al₂O₃ hybrid nanomaterial with LTNECs, using a novel intelligent numerical computing paradigm. The current study uses a simple mathematical model to investigate the features of heat transmission in the absence of LTECs. It can help forecast and optimize medication delivery since temperature and concentration gradients can better direct nanoparticles in blood. The model also aids in understanding heat and mass transmission in stenosed arteries, which is critical for designing medical remedies like hyperthermia therapy and preventing arterial blockages. It also sheds light on biomedical device design, such as stents or targeted nanofluid-based cooling systems, by accurately simulating blood flow under complex temperature and concentration scenarios. The ODEs are then numerically solved using MATLAB's built-in bvp4c solver. Comprehensive error analysis, histogram evaluations, regression metrics, and mean squared error (MSE) figures of merit were routinely employed to address fluid model variants, allowing us to assess the efficacy of the newly given ANN-BLMA methodology. As the interphase heat transfer parameter increases, the solid phase thermal profile falls while the liquid phase thermal profile rises.
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BibTeX
@article{khadimalah2026machine,
title = {{Machine learning simulation for radiation effects on MoS₂-Al₂O₃ hybrid nanoliquid with local thermal non-equilibrium conditions.}},
author = {Mohamad Amin Khadimalah and Elimem Ali and Joffin J Ponnore and Farkhod Alisherov and Saba Liaqat and Mohamed Abbas El-Naggar},
journal = {MethodsX},
year = {2026},
month = oct,
doi = {10.1016/j.mex.2026.104193},
url = {https://europepmc.org/article/MED/42903680},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Machine learning simulation for radiation effects on MoS₂-Al₂O₃ hybrid nanoliquid with local thermal non-equilibrium conditions.
AU - Mohamad Amin Khadimalah
AU - Elimem Ali
AU - Joffin J Ponnore
AU - Farkhod Alisherov
AU - Saba Liaqat
AU - Mohamed Abbas El-Naggar
PY - 2026
DA - 2026/10/07
JO - MethodsX
DO - 10.1016/j.mex.2026.104193
UR - https://europepmc.org/article/MED/42903680
AB - The current study examines the Soret-Dufour influence on a MoS₂-Al₂O₃ hybrid nanomaterial with LTNECs, using a novel intelligent numerical computing paradigm. The current study uses a simple mathematical model to investigate the features of heat transmission in the absence of LTECs. It can help forecast and optimize medication delivery since temperature and concentration gradients can better direct nanoparticles in blood. The model also aids in understanding heat and mass transmission in stenosed arteries, which is critical for designing medical remedies like hyperthermia therapy and preventing arterial blockages. It also sheds light on biomedical device design, such as stents or targeted nanofluid-based cooling systems, by accurately simulating blood flow under complex temperature and concentration scenarios. The ODEs are then numerically solved using MATLAB's built-in bvp4c solver. Comprehensive error analysis, histogram evaluations, regression metrics, and mean squared error (MSE) figures of merit were routinely employed to address fluid model variants, allowing us to assess the efficacy of the newly given ANN-BLMA methodology. As the interphase heat transfer parameter increases, the solid phase thermal profile falls while the liquid phase thermal profile rises.
SN - 2215-0161
ER -