ANN-based analysis of thin film Maxwell fluid dynamics with electro-osmotic and nonlinear thermal effects

An intelligent Levenberg-Marquardt Technique (LMT) with artificial neural network (ANN) backpropagation (BP) has been used to analyze the thermal heat and mass transfer of unsteady magnetohydrodynamics (MHD) thin film Maxwell fluid flow in a porous inclined sheet with an emphasis on the influence of...

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Main Authors: Irfan Saif Ud Din, Imran Siddique, Zohaib Zahid, Muhammad Nadeem, S. Islam
Format: Article
Language:English
Published: Elsevier 2025-08-01
Series:Alexandria Engineering Journal
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Online Access:http://www.sciencedirect.com/science/article/pii/S1110016825005769
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author Irfan Saif Ud Din
Imran Siddique
Zohaib Zahid
Muhammad Nadeem
S. Islam
author_facet Irfan Saif Ud Din
Imran Siddique
Zohaib Zahid
Muhammad Nadeem
S. Islam
author_sort Irfan Saif Ud Din
collection DOAJ
description An intelligent Levenberg-Marquardt Technique (LMT) with artificial neural network (ANN) backpropagation (BP) has been used to analyze the thermal heat and mass transfer of unsteady magnetohydrodynamics (MHD) thin film Maxwell fluid flow in a porous inclined sheet with an emphasis on the influence of electro-osmosis. The activation energy, chemical reaction, mixed convection, melting heat, joule heating, nonlinear thermal radiation, variable thermal conductivity and thermal source/sink effect are taken into account for transport expressions. Appropriate similarity transformations were used to translate partial differential equations (PDEs) into ordinary differential equations (ODEs). After that, the built-in MATLAB BVP4C method was used for a data set assessed using the LMT-ANN strategy to solve these ODEs. The physical significance of the designed parameters is thoroughly discussed in both tabular and graphical form. The observed R-squared value is 1, and the mean square error up to 10−15 demonstrates the LMT-ANN's precise and accurate computing capability. The model’s validity is also confirmed by the strong agreement between the obtained predicted findings and numerical results, which shows a high degree of accuracy within the range of 10−8 to 10−11. It was revealed that radiative heat considerably increases surface heat energy through accumulation, improving heat transfer qualities, whereas fluid temperature is raised by Joule dissipation, variable thermal conductivity, and heat source. Electro-osmosis and magnetic fields reduce fluid velocity by generating opposing forces that resist the flow. This problem works best in microscale fluid transport systems and drilling operations, where magnetic and electro-osmotic control are crucial. These systems include micro-electromechanical systems, lab-on-a-chip devices, porous geological formations, and thin film coating technologies.
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spelling doaj-art-3ddd1e5a979c48e09c501f8736845f8d2025-08-22T04:55:16ZengElsevierAlexandria Engineering Journal1110-01682025-08-0112739241010.1016/j.aej.2025.04.084ANN-based analysis of thin film Maxwell fluid dynamics with electro-osmotic and nonlinear thermal effectsIrfan Saif Ud Din0Imran Siddique1Zohaib Zahid2Muhammad Nadeem3S. Islam4Department of Mathematics, University of Management and Technology, Lahore 54770, PakistanDepartment of Mathematics, University of Management and Technology, Lahore 54770, Pakistan; Department of Mathematics, University of Sargodha, Sargodha 40100, Pakistan; Mathematics in Applied Sciences and Engineering Research Group, Scientific Research Center, Al-Ayen University, Nasiriyah 64001, IraqDepartment of Mathematics, University of Management and Technology, Lahore 54770, PakistanFederal University of Technology Paran UTFPR, Mechanical Engineering Department DAMEC, Postgraduate Program in Mechanical and Materials Engineering PPGEM, Research Center for Rheology and Non-Newtonian Fluids CERNN, R. Deputado Heitor Alencar Furtado, 5000 - Bloco N - Ecoville, Curitiba, PR 81280-340, BrazilDepartment of Mechanical Engineering, Prince Mohammad Bin Fahd University, P.O Box 1664, Al Khobar 31952, Saudi Arabia; Corresponding author.An intelligent Levenberg-Marquardt Technique (LMT) with artificial neural network (ANN) backpropagation (BP) has been used to analyze the thermal heat and mass transfer of unsteady magnetohydrodynamics (MHD) thin film Maxwell fluid flow in a porous inclined sheet with an emphasis on the influence of electro-osmosis. The activation energy, chemical reaction, mixed convection, melting heat, joule heating, nonlinear thermal radiation, variable thermal conductivity and thermal source/sink effect are taken into account for transport expressions. Appropriate similarity transformations were used to translate partial differential equations (PDEs) into ordinary differential equations (ODEs). After that, the built-in MATLAB BVP4C method was used for a data set assessed using the LMT-ANN strategy to solve these ODEs. The physical significance of the designed parameters is thoroughly discussed in both tabular and graphical form. The observed R-squared value is 1, and the mean square error up to 10−15 demonstrates the LMT-ANN's precise and accurate computing capability. The model’s validity is also confirmed by the strong agreement between the obtained predicted findings and numerical results, which shows a high degree of accuracy within the range of 10−8 to 10−11. It was revealed that radiative heat considerably increases surface heat energy through accumulation, improving heat transfer qualities, whereas fluid temperature is raised by Joule dissipation, variable thermal conductivity, and heat source. Electro-osmosis and magnetic fields reduce fluid velocity by generating opposing forces that resist the flow. This problem works best in microscale fluid transport systems and drilling operations, where magnetic and electro-osmotic control are crucial. These systems include micro-electromechanical systems, lab-on-a-chip devices, porous geological formations, and thin film coating technologies.http://www.sciencedirect.com/science/article/pii/S1110016825005769Maxwell fluidMagnetic effectElectro-osmosis effectVariable thermal conductivityHeat sourceChemical reaction
spellingShingle Irfan Saif Ud Din
Imran Siddique
Zohaib Zahid
Muhammad Nadeem
S. Islam
ANN-based analysis of thin film Maxwell fluid dynamics with electro-osmotic and nonlinear thermal effects
Alexandria Engineering Journal
Maxwell fluid
Magnetic effect
Electro-osmosis effect
Variable thermal conductivity
Heat source
Chemical reaction
title ANN-based analysis of thin film Maxwell fluid dynamics with electro-osmotic and nonlinear thermal effects
title_full ANN-based analysis of thin film Maxwell fluid dynamics with electro-osmotic and nonlinear thermal effects
title_fullStr ANN-based analysis of thin film Maxwell fluid dynamics with electro-osmotic and nonlinear thermal effects
title_full_unstemmed ANN-based analysis of thin film Maxwell fluid dynamics with electro-osmotic and nonlinear thermal effects
title_short ANN-based analysis of thin film Maxwell fluid dynamics with electro-osmotic and nonlinear thermal effects
title_sort ann based analysis of thin film maxwell fluid dynamics with electro osmotic and nonlinear thermal effects
topic Maxwell fluid
Magnetic effect
Electro-osmosis effect
Variable thermal conductivity
Heat source
Chemical reaction
url http://www.sciencedirect.com/science/article/pii/S1110016825005769
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AT zohaibzahid annbasedanalysisofthinfilmmaxwellfluiddynamicswithelectroosmoticandnonlinearthermaleffects
AT muhammadnadeem annbasedanalysisofthinfilmmaxwellfluiddynamicswithelectroosmoticandnonlinearthermaleffects
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