Cargando…
Supervised Learning Algorithm to Study the Magnetohydrodynamic Flow of a Third Grade Fluid for the Analysis of Wire Coating
In the present study, modeling of intelligent numerical computing through Levenberg–Marquardt back propagation-based supervised neural network (LMB-SNN) is incorporated to analyze the magnetohydrodynamic flow of a third grade fluid for wire coating analysis (MHD-TGFWCA). The original mathematical fo...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8479500/ https://www.ncbi.nlm.nih.gov/pubmed/34603928 http://dx.doi.org/10.1007/s13369-021-06212-3 |
_version_ | 1784576271047458816 |
---|---|
author | Aljohani, Jawaher Lafi Alaidarous, Eman Salem Raja, Muhammad Asif Zahoor Alhothuali, Muhammed Shabab Shoaib, Muhammad |
author_facet | Aljohani, Jawaher Lafi Alaidarous, Eman Salem Raja, Muhammad Asif Zahoor Alhothuali, Muhammed Shabab Shoaib, Muhammad |
author_sort | Aljohani, Jawaher Lafi |
collection | PubMed |
description | In the present study, modeling of intelligent numerical computing through Levenberg–Marquardt back propagation-based supervised neural network (LMB-SNN) is incorporated to analyze the magnetohydrodynamic flow of a third grade fluid for wire coating analysis (MHD-TGFWCA). The original mathematical formulations in terms of partial differential equations for MHD-TGFWCA are converted into a system of ordinary differential equations through dimensionless parameters and a suitable transformation mechanism. A reference dataset for the LMB-SNNs scheme is created with Adam’s numerical technique for various scenarios by variation of different physical quantities such as third grade fluid parameter, magnetic parameter, and the velocity ratio parameter. To compute the approximate solution for MHD-TGFWCA in terms of various scenarios, the training, testing, and validation operations are carried out in parallel to adjust neural networks by developing the mean square error function (MSEF) through Levenberg–Marquardt back-propagation. The comparative analyses and performance studies through outputs of MSEF, regression illustrations, and error histograms validate the effectiveness of the suggested solver LMB-SNNs. The method’s precision is verified by the closest numerical outputs of both built and dataset values with similar levels [Formula: see text] to [Formula: see text] . |
format | Online Article Text |
id | pubmed-8479500 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-84795002021-09-29 Supervised Learning Algorithm to Study the Magnetohydrodynamic Flow of a Third Grade Fluid for the Analysis of Wire Coating Aljohani, Jawaher Lafi Alaidarous, Eman Salem Raja, Muhammad Asif Zahoor Alhothuali, Muhammed Shabab Shoaib, Muhammad Arab J Sci Eng Research Article-Physics In the present study, modeling of intelligent numerical computing through Levenberg–Marquardt back propagation-based supervised neural network (LMB-SNN) is incorporated to analyze the magnetohydrodynamic flow of a third grade fluid for wire coating analysis (MHD-TGFWCA). The original mathematical formulations in terms of partial differential equations for MHD-TGFWCA are converted into a system of ordinary differential equations through dimensionless parameters and a suitable transformation mechanism. A reference dataset for the LMB-SNNs scheme is created with Adam’s numerical technique for various scenarios by variation of different physical quantities such as third grade fluid parameter, magnetic parameter, and the velocity ratio parameter. To compute the approximate solution for MHD-TGFWCA in terms of various scenarios, the training, testing, and validation operations are carried out in parallel to adjust neural networks by developing the mean square error function (MSEF) through Levenberg–Marquardt back-propagation. The comparative analyses and performance studies through outputs of MSEF, regression illustrations, and error histograms validate the effectiveness of the suggested solver LMB-SNNs. The method’s precision is verified by the closest numerical outputs of both built and dataset values with similar levels [Formula: see text] to [Formula: see text] . Springer Berlin Heidelberg 2021-09-29 2022 /pmc/articles/PMC8479500/ /pubmed/34603928 http://dx.doi.org/10.1007/s13369-021-06212-3 Text en © King Fahd University of Petroleum & Minerals 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Research Article-Physics Aljohani, Jawaher Lafi Alaidarous, Eman Salem Raja, Muhammad Asif Zahoor Alhothuali, Muhammed Shabab Shoaib, Muhammad Supervised Learning Algorithm to Study the Magnetohydrodynamic Flow of a Third Grade Fluid for the Analysis of Wire Coating |
title | Supervised Learning Algorithm to Study the Magnetohydrodynamic Flow of a Third Grade Fluid for the Analysis of Wire Coating |
title_full | Supervised Learning Algorithm to Study the Magnetohydrodynamic Flow of a Third Grade Fluid for the Analysis of Wire Coating |
title_fullStr | Supervised Learning Algorithm to Study the Magnetohydrodynamic Flow of a Third Grade Fluid for the Analysis of Wire Coating |
title_full_unstemmed | Supervised Learning Algorithm to Study the Magnetohydrodynamic Flow of a Third Grade Fluid for the Analysis of Wire Coating |
title_short | Supervised Learning Algorithm to Study the Magnetohydrodynamic Flow of a Third Grade Fluid for the Analysis of Wire Coating |
title_sort | supervised learning algorithm to study the magnetohydrodynamic flow of a third grade fluid for the analysis of wire coating |
topic | Research Article-Physics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8479500/ https://www.ncbi.nlm.nih.gov/pubmed/34603928 http://dx.doi.org/10.1007/s13369-021-06212-3 |
work_keys_str_mv | AT aljohanijawaherlafi supervisedlearningalgorithmtostudythemagnetohydrodynamicflowofathirdgradefluidfortheanalysisofwirecoating AT alaidarousemansalem supervisedlearningalgorithmtostudythemagnetohydrodynamicflowofathirdgradefluidfortheanalysisofwirecoating AT rajamuhammadasifzahoor supervisedlearningalgorithmtostudythemagnetohydrodynamicflowofathirdgradefluidfortheanalysisofwirecoating AT alhothualimuhammedshabab supervisedlearningalgorithmtostudythemagnetohydrodynamicflowofathirdgradefluidfortheanalysisofwirecoating AT shoaibmuhammad supervisedlearningalgorithmtostudythemagnetohydrodynamicflowofathirdgradefluidfortheanalysisofwirecoating |