https://doi.org/10.1140/epjp/s13360-024-05625-x
Regular Article
Prediction of Cattaneo–Christov heat flux with thermal slip effects over a lubricated surface using artificial neural network
1
Department of Mathematics and Statistics, International Islamic University Islamabad, 44000, Islamabad, Pakistan
2
Faculty of Energy and Power Engineering, School of Chemical Engineering and Energy Technology, Dongguan University of Technology, Dongguan, China
3
Department of Chemical Engineering and Energy Technology, University of Science and Technology, Langfang, China
4
Department of Mechanical Engineering, Taibah University, 42353, Medina, Saudi Arabia
5
Mechanical Engineering Department, College of Engineering, King Khalid University, 61421, Abha, Asir, Saudi Arabia
6
Research Center for Advanced Materials Science (RCAMS), King Khalid University, 61413, Guraiger, Abha, Asir, Saudi Arabia
7
Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES), Research Institute, King Fahd University of Petroleum and Minerals (KFUPM), 31261, Dhahran, Saudi Arabia
8
Department of Mechanical Engineering, College of Engineering, King Faisal University, P. O. Box 380, 31982, Al-Ahsa, Saudi Arabia
b hasanshahzad@dgut.edu.cn, hasanshahzad99@hotmail.com
Received:
9
April
2024
Accepted:
8
September
2024
Published online:
24
September
2024
The lubricated systems containing fluid lubricants have the load-carrying ability. Suitable lubrication permits smooth, incessant operation of machine elements. The significant applications in engineering and industry are drag reduction, cooling of electronic devices and cooling of nuclear reactors, and many other hydrodynamic processes. In the industries, lubricants frequently exhibit non-Newtonian properties and conform to various constitutive relations. One prevalent type of lubricant is the power law fluid, which adheres to the Ostwald procedure. The present investigation focuses on the analysis of fluid flow in the purlieu of a lubricated surface, where a thin layer of variable-thickness power law fluid is used for lubrication. The effects of velocity and thermal slip with Cattaneo–Christov heat transfer are taken into account. A conversion from partial to ordinary system of equations is happened utilizing similarities. To acquire a dataset, the shooting method is utilized. An artificial neural network procedure is utilized to envisage the fluid flow by solving the governing system of partial differential equations, and testing, training, and validation procedures are arranged to generate results under different circumstances and cases of Levenberg–Marquardt backpropagation neural network. The precision of the proposed model is established by comparing the outcomes with the reference dataset. The Levenberg–Marquardt backpropagation neural network output is evaluated using mean regression illustrations, analysis of error histograms, mean square error, and dynamics of state transition. The results indicate that developed neural network models can accurately envisage thermal analysis. Furthermore, compared to other numerical performances, the current artificial neural network model can be employed in more complicated scientific models while decreasing the time and processing ability needed to solve the problem.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
