Intelligent computing through neural networks for numerical treatment of non-Newtonian wire coating analysis model

Abstract In the current study, a modern implementation of intelligent numerical computational solver introduced using the Levenberg Marquardt algorithm based trained neural networks (LMA-TNN) to analyze the wire coating system (WCS) for the elastic-viscous non-Newtonian Eyring–Powell fluid (EPF) wit...

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Autores principales:	Jawaher Lafi Aljohani, Eman Salem Alaidarous, Muhammad Asif Zahoor Raja, Muhammad Shoaib, Muhammed Shabab Alhothuali
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/343765989dce4b12959ae4fd897e4c71
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spelling	oai:doaj.org-article:343765989dce4b12959ae4fd897e4c712021-12-02T13:41:23ZIntelligent computing through neural networks for numerical treatment of non-Newtonian wire coating analysis model10.1038/s41598-021-88499-82045-2322https://doaj.org/article/343765989dce4b12959ae4fd897e4c712021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88499-8https://doaj.org/toc/2045-2322Abstract In the current study, a modern implementation of intelligent numerical computational solver introduced using the Levenberg Marquardt algorithm based trained neural networks (LMA-TNN) to analyze the wire coating system (WCS) for the elastic-viscous non-Newtonian Eyring–Powell fluid (EPF) with the impacts of Joule heating, magnetic parameter and heat transfer scenarios in the permeable medium. The nonlinear PDEs describing the WCS-EPF are converted into dimensionless nonlinear ODEs containing the heat and viscosity parameters. The reference data for the designed LMA-TNN is produced for various scenarios of WCS-EPF representing with porosity parameter, non-Newtonian parameter, heat transfer parameter and magnetic parameter for the proposed analysis using the state of the art explicit Runge–Kutta technique. The training, validation, and testing operations of LMA-TNN are carried out to obtain the numerical solution of WCS-EPF for various cases and their comparison with the approximate outcomes certifying the reasonable accuracy and precision of LMA-TNN approach. The outcomes of LMA-TNN solver in terms of state transition (ST) index, error-histograms (EH) illustration, mean square error, and regression (R) studies further established the worth for stochastic numerical solution of the WCS-EPF. The strong correlation between the suggested and the reference outcomes indicates the structure’s validity, for all four cases of WCS-EPF, fitting of the precision $$10^{-5}$$ 10 - 5 to $$10^{-9}$$ 10 - 9 is also accomplished.Jawaher Lafi AljohaniEman Salem AlaidarousMuhammad Asif Zahoor RajaMuhammad ShoaibMuhammed Shabab AlhothualiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-32 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jawaher Lafi Aljohani Eman Salem Alaidarous Muhammad Asif Zahoor Raja Muhammad Shoaib Muhammed Shabab Alhothuali Intelligent computing through neural networks for numerical treatment of non-Newtonian wire coating analysis model
description	Abstract In the current study, a modern implementation of intelligent numerical computational solver introduced using the Levenberg Marquardt algorithm based trained neural networks (LMA-TNN) to analyze the wire coating system (WCS) for the elastic-viscous non-Newtonian Eyring–Powell fluid (EPF) with the impacts of Joule heating, magnetic parameter and heat transfer scenarios in the permeable medium. The nonlinear PDEs describing the WCS-EPF are converted into dimensionless nonlinear ODEs containing the heat and viscosity parameters. The reference data for the designed LMA-TNN is produced for various scenarios of WCS-EPF representing with porosity parameter, non-Newtonian parameter, heat transfer parameter and magnetic parameter for the proposed analysis using the state of the art explicit Runge–Kutta technique. The training, validation, and testing operations of LMA-TNN are carried out to obtain the numerical solution of WCS-EPF for various cases and their comparison with the approximate outcomes certifying the reasonable accuracy and precision of LMA-TNN approach. The outcomes of LMA-TNN solver in terms of state transition (ST) index, error-histograms (EH) illustration, mean square error, and regression (R) studies further established the worth for stochastic numerical solution of the WCS-EPF. The strong correlation between the suggested and the reference outcomes indicates the structure’s validity, for all four cases of WCS-EPF, fitting of the precision $$10^{-5}$$ 10 - 5 to $$10^{-9}$$ 10 - 9 is also accomplished.
format	article
author	Jawaher Lafi Aljohani Eman Salem Alaidarous Muhammad Asif Zahoor Raja Muhammad Shoaib Muhammed Shabab Alhothuali
author_facet	Jawaher Lafi Aljohani Eman Salem Alaidarous Muhammad Asif Zahoor Raja Muhammad Shoaib Muhammed Shabab Alhothuali
author_sort	Jawaher Lafi Aljohani
title	Intelligent computing through neural networks for numerical treatment of non-Newtonian wire coating analysis model
title_short	Intelligent computing through neural networks for numerical treatment of non-Newtonian wire coating analysis model
title_full	Intelligent computing through neural networks for numerical treatment of non-Newtonian wire coating analysis model
title_fullStr	Intelligent computing through neural networks for numerical treatment of non-Newtonian wire coating analysis model
title_full_unstemmed	Intelligent computing through neural networks for numerical treatment of non-Newtonian wire coating analysis model
title_sort	intelligent computing through neural networks for numerical treatment of non-newtonian wire coating analysis model
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/343765989dce4b12959ae4fd897e4c71
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Intelligent computing through neural networks for numerical treatment of non-Newtonian wire coating analysis model

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