Prediction of Cattaneo–Christov heat flux with thermal slip effects over a lubricated surface using artificial neural network

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,...

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Vydáno v:	European physical journal plus Ročník 139; číslo 9; s. 851
Hlavní autoři:	Sadiq, M. N., Shahzad, Hasan, Alqahtani, Hassan, Tirth, Vineet, Algahtani, Ali, Irshad, Kashif, Al-Mughanam, Tawfiq
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 24.09.2024 Springer Nature B.V
Témata:	Accuracy Applied and Technical Physics Artificial neural networks Atomic Back propagation networks Complex Systems Condensed Matter Physics Constitutive relationships Cooling Datasets Differential equations Drag reduction Engineering Error analysis Fluid flow Fluid mechanics Heat conductivity Heat flux Heat transfer Lubricants Lubricants & lubrication Lubrication Mathematical and Computational Physics Molecular Neural networks Nuclear power plants Nuclear reactors Optical and Plasma Physics Optimization techniques Ostwald ripening Partial differential equations Physics Physics and Astronomy Power law Regression models Regular Article Shear stress Temperature Theoretical Thermal analysis Variable thickness Velocity
ISSN:	2190-5444, 2190-5444
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Abstract	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. Graphic abstract
AbstractList	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.Graphic abstract 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. Graphic abstract
ArticleNumber	851
Author	Shahzad, Hasan Irshad, Kashif Tirth, Vineet Algahtani, Ali Al-Mughanam, Tawfiq Sadiq, M. N. Alqahtani, Hassan
Author_xml	– sequence: 1 givenname: M. N. surname: Sadiq fullname: Sadiq, M. N. organization: Department of Mathematics and Statistics, International Islamic University Islamabad – sequence: 2 givenname: Hasan orcidid: 0000-0001-9154-5791 surname: Shahzad fullname: Shahzad, Hasan email: hasanshahzad@dgut.edu.cn, hasanshahzad99@hotmail.com organization: Faculty of Energy and Power Engineering, School of Chemical Engineering and Energy Technology, Dongguan University of Technology, Department of Chemical Engineering and Energy Technology, University of Science and Technology – sequence: 3 givenname: Hassan surname: Alqahtani fullname: Alqahtani, Hassan organization: Department of Mechanical Engineering, Taibah University – sequence: 4 givenname: Vineet surname: Tirth fullname: Tirth, Vineet organization: Mechanical Engineering Department, College of Engineering, King Khalid University, Research Center for Advanced Materials Science (RCAMS), King Khalid University – sequence: 5 givenname: Ali surname: Algahtani fullname: Algahtani, Ali organization: Mechanical Engineering Department, College of Engineering, King Khalid University, Research Center for Advanced Materials Science (RCAMS), King Khalid University – sequence: 6 givenname: Kashif surname: Irshad fullname: Irshad, Kashif organization: Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES), Research Institute, King Fahd University of Petroleum and Minerals (KFUPM) – sequence: 7 givenname: Tawfiq surname: Al-Mughanam fullname: Al-Mughanam, Tawfiq organization: Department of Mechanical Engineering, College of Engineering, King Faisal University
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Cites_doi	10.1016/j.flowmeasinst.2016.04.003 10.1088/0256-307X/29/2/024702 10.1016/0017-9310(67)90100-7 10.1016/j.aml.2014.07.013 10.1002/er.5680 10.1016/j.physe.2014.07.013 10.1016/j.powtec.2015.03.005 10.1016/j.apacoust.2021.108022 10.1016/j.csite.2024.104024 10.1016/j.apacoust.2020.107829 10.1007/s40430-018-1560-3 10.1016/j.ijmecsci.2011.07.012 10.1016/j.mechrescom.2008.11.003 10.1016/j.mechrescom.2010.06.002 10.1007/s12046-019-1093-1 10.1016/j.physa.2019.123520 10.1007/s42452-020-3156-7 10.1007/s10973-021-10889-x 10.1016/j.mechrescom.2019.06.003 10.1016/j.applthermaleng.2016.01.063 10.1016/j.molliq.2016.05.051 10.4236/jamp.2019.76092 10.1007/s00707-007-0484-2 10.1038/s41598-021-93790-9 10.1007/s10973-021-10568-x 10.1007/s40819-015-0032-z 10.1140/epjp/i2017-11572-y 10.1016/j.powtec.2014.06.062 10.1016/S1364-0321(01)00006-5
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SubjectTerms	Accuracy Applied and Technical Physics Artificial neural networks Atomic Back propagation networks Complex Systems Condensed Matter Physics Constitutive relationships Cooling Datasets Differential equations Drag reduction Engineering Error analysis Fluid flow Fluid mechanics Heat conductivity Heat flux Heat transfer Lubricants Lubricants & lubrication Lubrication Mathematical and Computational Physics Molecular Neural networks Nuclear power plants Nuclear reactors Optical and Plasma Physics Optimization techniques Ostwald ripening Partial differential equations Physics Physics and Astronomy Power law Regression models Regular Article Shear stress Temperature Theoretical Thermal analysis Variable thickness Velocity
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Title	Prediction of Cattaneo–Christov heat flux with thermal slip effects over a lubricated surface using artificial neural network
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