The intelligent networks for double-diffusion and MHD analysis of thin film flow over a stretched surface

Abstract This study presents a novel application of soft-computing through intelligent, neural networks backpropagated by Levenberg–Marquardt scheme (NNs-BLMS) to solve the mathematical model of unsteady thin film flow of magnetized Maxwell fluid with thermo-diffusion effects and chemical reaction (...

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Autores principales:	Iftikhar Uddin, Ikram Ullah, Muhammad Asif Zahoor Raja, Muhammad Shoaib, Saeed Islam, M. S. Zobaer, K. S. Nisar, C. Ahamed Saleel, Saad Alshahrani
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1bfa1dfa6fdf48a28268fab8faf51529
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spelling	oai:doaj.org-article:1bfa1dfa6fdf48a28268fab8faf515292021-12-02T18:51:15ZThe intelligent networks for double-diffusion and MHD analysis of thin film flow over a stretched surface10.1038/s41598-021-97458-22045-2322https://doaj.org/article/1bfa1dfa6fdf48a28268fab8faf515292021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97458-2https://doaj.org/toc/2045-2322Abstract This study presents a novel application of soft-computing through intelligent, neural networks backpropagated by Levenberg–Marquardt scheme (NNs-BLMS) to solve the mathematical model of unsteady thin film flow of magnetized Maxwell fluid with thermo-diffusion effects and chemical reaction (TFFMFTDECR) over a horizontal rotating disk. The expression for thermophoretic velocity is accounted. Energy expression is deliberated with the addition of non-uniform heat source. The PDEs of mathematical model of TFFMFTDECR are transformed to ODEs by the application of similarity transformations. A dataset is generated through Adams method for the proposed NNs-BLMS in case of various scenarios of TFFMFTDECR model by variation of rotation parameter, magnetic parameter, space dependent heat sink/source parameter, temperature dependent heat sink/source parameter and chemical reaction controlling parameter. The designed computational solver NNs-BLMS is implemented by performing training, testing and validation for the solution of TFFMFTDECR system for different variants. Variation of various physical parameters are designed via plots and explain in details. It is depicted that thin film thickness increases for higher values of disk rotation parameter, while it diminishes for higher magnetic parameter. Furthermore, higher values of Dufour number and the corresponding diminishing values of Soret number causes enhancement in fluid temperature profile. Further the effectiveness of NNs-BLMS is validated by comparing the results of the proposed solver and the standard solution of TFFMFTDECR model through error analyses, histogram representations and regression analyses.Iftikhar UddinIkram UllahMuhammad Asif Zahoor RajaMuhammad ShoaibSaeed IslamM. S. ZobaerK. S. NisarC. Ahamed SaleelSaad AlshahraniNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-20 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Iftikhar Uddin Ikram Ullah Muhammad Asif Zahoor Raja Muhammad Shoaib Saeed Islam M. S. Zobaer K. S. Nisar C. Ahamed Saleel Saad Alshahrani The intelligent networks for double-diffusion and MHD analysis of thin film flow over a stretched surface
description	Abstract This study presents a novel application of soft-computing through intelligent, neural networks backpropagated by Levenberg–Marquardt scheme (NNs-BLMS) to solve the mathematical model of unsteady thin film flow of magnetized Maxwell fluid with thermo-diffusion effects and chemical reaction (TFFMFTDECR) over a horizontal rotating disk. The expression for thermophoretic velocity is accounted. Energy expression is deliberated with the addition of non-uniform heat source. The PDEs of mathematical model of TFFMFTDECR are transformed to ODEs by the application of similarity transformations. A dataset is generated through Adams method for the proposed NNs-BLMS in case of various scenarios of TFFMFTDECR model by variation of rotation parameter, magnetic parameter, space dependent heat sink/source parameter, temperature dependent heat sink/source parameter and chemical reaction controlling parameter. The designed computational solver NNs-BLMS is implemented by performing training, testing and validation for the solution of TFFMFTDECR system for different variants. Variation of various physical parameters are designed via plots and explain in details. It is depicted that thin film thickness increases for higher values of disk rotation parameter, while it diminishes for higher magnetic parameter. Furthermore, higher values of Dufour number and the corresponding diminishing values of Soret number causes enhancement in fluid temperature profile. Further the effectiveness of NNs-BLMS is validated by comparing the results of the proposed solver and the standard solution of TFFMFTDECR model through error analyses, histogram representations and regression analyses.
format	article
author	Iftikhar Uddin Ikram Ullah Muhammad Asif Zahoor Raja Muhammad Shoaib Saeed Islam M. S. Zobaer K. S. Nisar C. Ahamed Saleel Saad Alshahrani
author_facet	Iftikhar Uddin Ikram Ullah Muhammad Asif Zahoor Raja Muhammad Shoaib Saeed Islam M. S. Zobaer K. S. Nisar C. Ahamed Saleel Saad Alshahrani
author_sort	Iftikhar Uddin
title	The intelligent networks for double-diffusion and MHD analysis of thin film flow over a stretched surface
title_short	The intelligent networks for double-diffusion and MHD analysis of thin film flow over a stretched surface
title_full	The intelligent networks for double-diffusion and MHD analysis of thin film flow over a stretched surface
title_fullStr	The intelligent networks for double-diffusion and MHD analysis of thin film flow over a stretched surface
title_full_unstemmed	The intelligent networks for double-diffusion and MHD analysis of thin film flow over a stretched surface
title_sort	intelligent networks for double-diffusion and mhd analysis of thin film flow over a stretched surface
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1bfa1dfa6fdf48a28268fab8faf51529
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The intelligent networks for double-diffusion and MHD analysis of thin film flow over a stretched surface

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