Soft computing paradigm for Ferrofluid by exponentially stretched surface in the presence of magnetic dipole and heat transfer

In the presented research article, the intelligence based numerical computation of artificial neural network backpropagated with Levenberg-Marquardt algorithm has been developed to analyze the novel ferrofluid flow model in the presence of magnetic dipole. Heat transfer effects are also incorporated...

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Autores principales:	Muhammad Shoaib, Muhammad Asif Zahoor Raja, Imrana Farhat, Zahir Shah, Poom Kumam, Saeed Islam
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Ferrofluid flow Magnetic dipole Numerical computation Backpropagated neural network Levenberg Marquardt Algorithm Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/412f9164756247569760ba7861fa23fd
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spelling	oai:doaj.org-article:412f9164756247569760ba7861fa23fd2021-11-18T04:45:34ZSoft computing paradigm for Ferrofluid by exponentially stretched surface in the presence of magnetic dipole and heat transfer1110-016810.1016/j.aej.2021.06.060https://doaj.org/article/412f9164756247569760ba7861fa23fd2022-02-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1110016821004233https://doaj.org/toc/1110-0168In the presented research article, the intelligence based numerical computation of artificial neural network backpropagated with Levenberg-Marquardt algorithm has been developed to analyze the novel ferrofluid flow model in the presence of magnetic dipole. Heat transfer effects are also incorporated along the horizontal. The designed fluid flow model initially represented by system of partial differential equations are converted into system of non-linear ordinary differential equations through suitable similarity transformations. The reference dataset of the possible outcomes is obtained from Adam numerical solver for the different scenarios of flow model by variation of co-efficient of the thermal expansion, Eckert number, suction parameter, magnetization and radiation parameter. The approximated solutions are interpreted for designed model by testing, training and validation process of backpropagated neural networks. Furthermore, the comparative studies and performance analysis of used algorithm is validated through regression analysis, histogram studies, correlation index and results of mean square error.Muhammad ShoaibMuhammad Asif Zahoor RajaImrana FarhatZahir ShahPoom KumamSaeed IslamElsevierarticleFerrofluid flowMagnetic dipoleNumerical computationBackpropagated neural networkLevenberg Marquardt AlgorithmEngineering (General). Civil engineering (General)TA1-2040ENAlexandria Engineering Journal, Vol 61, Iss 2, Pp 1607-1623 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Ferrofluid flow Magnetic dipole Numerical computation Backpropagated neural network Levenberg Marquardt Algorithm Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Ferrofluid flow Magnetic dipole Numerical computation Backpropagated neural network Levenberg Marquardt Algorithm Engineering (General). Civil engineering (General) TA1-2040 Muhammad Shoaib Muhammad Asif Zahoor Raja Imrana Farhat Zahir Shah Poom Kumam Saeed Islam Soft computing paradigm for Ferrofluid by exponentially stretched surface in the presence of magnetic dipole and heat transfer
description	In the presented research article, the intelligence based numerical computation of artificial neural network backpropagated with Levenberg-Marquardt algorithm has been developed to analyze the novel ferrofluid flow model in the presence of magnetic dipole. Heat transfer effects are also incorporated along the horizontal. The designed fluid flow model initially represented by system of partial differential equations are converted into system of non-linear ordinary differential equations through suitable similarity transformations. The reference dataset of the possible outcomes is obtained from Adam numerical solver for the different scenarios of flow model by variation of co-efficient of the thermal expansion, Eckert number, suction parameter, magnetization and radiation parameter. The approximated solutions are interpreted for designed model by testing, training and validation process of backpropagated neural networks. Furthermore, the comparative studies and performance analysis of used algorithm is validated through regression analysis, histogram studies, correlation index and results of mean square error.
format	article
author	Muhammad Shoaib Muhammad Asif Zahoor Raja Imrana Farhat Zahir Shah Poom Kumam Saeed Islam
author_facet	Muhammad Shoaib Muhammad Asif Zahoor Raja Imrana Farhat Zahir Shah Poom Kumam Saeed Islam
author_sort	Muhammad Shoaib
title	Soft computing paradigm for Ferrofluid by exponentially stretched surface in the presence of magnetic dipole and heat transfer
title_short	Soft computing paradigm for Ferrofluid by exponentially stretched surface in the presence of magnetic dipole and heat transfer
title_full	Soft computing paradigm for Ferrofluid by exponentially stretched surface in the presence of magnetic dipole and heat transfer
title_fullStr	Soft computing paradigm for Ferrofluid by exponentially stretched surface in the presence of magnetic dipole and heat transfer
title_full_unstemmed	Soft computing paradigm for Ferrofluid by exponentially stretched surface in the presence of magnetic dipole and heat transfer
title_sort	soft computing paradigm for ferrofluid by exponentially stretched surface in the presence of magnetic dipole and heat transfer
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/412f9164756247569760ba7861fa23fd
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Soft computing paradigm for Ferrofluid by exponentially stretched surface in the presence of magnetic dipole and heat transfer

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