The ability of forecasting flapping frequency of flexible filament by artificial neural network.
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| Title: | The ability of forecasting flapping frequency of flexible filament by artificial neural network. |
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| Authors: | Fayed, M.1,2 (AUTHOR), Elhadary, M.1,2 (AUTHOR) mostafaelhadary@yahoo.com, Ait Abderrahmane, H.3 (AUTHOR), Zakher, Bassem Nashaat4 (AUTHOR) |
| Source: | Alexandria Engineering Journal. Dec2019, Vol. 58 Issue 4, p1367-1374. 8p. |
| Subjects: | Artificial neural networks, Amplitude modulation, Fibers |
| Abstract: | Artificial Neural Networks (ANNs) are reliable and computationally inexpensive compared to numerical methods such as CFD simulations and experimental investigations in aerodynamics research. In this article, an Artificial Neural Network (ANN) has been introduced to predict the flapping frequencies of a filament placed in a 2-D soap-film tunnel. The multi-layer perception (MLP) networks have been used in developing the Artificial Neural Network while the backpropagation Levenberg-Marquardt algorithm was used to perform the training of the ANN. A part of the experimental data was considered for the training process while the rest for the prediction test of the suggested ANN. The ANN results indicate that it can predict the frequencies of the periodic flapping with good accuracy. However, it fails when the flapping presents amplitude modulation. [ABSTRACT FROM AUTHOR] |
| Copyright of Alexandria Engineering Journal is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 140982760 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: The ability of forecasting flapping frequency of flexible filament by artificial neural network. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Fayed%2C+M%2E%22">Fayed, M.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Elhadary%2C+M%2E%22">Elhadary, M.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> mostafaelhadary@yahoo.com</i><br /><searchLink fieldCode="AR" term="%22Ait+Abderrahmane%2C+H%2E%22">Ait Abderrahmane, H.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zakher%2C+Bassem+Nashaat%22">Zakher, Bassem Nashaat</searchLink><relatesTo>4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Alexandria+Engineering+Journal%22">Alexandria Engineering Journal</searchLink>. Dec2019, Vol. 58 Issue 4, p1367-1374. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Amplitude+modulation%22">Amplitude modulation</searchLink><br /><searchLink fieldCode="DE" term="%22Fibers%22">Fibers</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Artificial Neural Networks (ANNs) are reliable and computationally inexpensive compared to numerical methods such as CFD simulations and experimental investigations in aerodynamics research. In this article, an Artificial Neural Network (ANN) has been introduced to predict the flapping frequencies of a filament placed in a 2-D soap-film tunnel. The multi-layer perception (MLP) networks have been used in developing the Artificial Neural Network while the backpropagation Levenberg-Marquardt algorithm was used to perform the training of the ANN. A part of the experimental data was considered for the training process while the rest for the prediction test of the suggested ANN. The ANN results indicate that it can predict the frequencies of the periodic flapping with good accuracy. However, it fails when the flapping presents amplitude modulation. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Alexandria Engineering Journal is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.aej.2019.11.007 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 1367 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Amplitude modulation Type: general – SubjectFull: Fibers Type: general Titles: – TitleFull: The ability of forecasting flapping frequency of flexible filament by artificial neural network. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Fayed, M. – PersonEntity: Name: NameFull: Elhadary, M. – PersonEntity: Name: NameFull: Ait Abderrahmane, H. – PersonEntity: Name: NameFull: Zakher, Bassem Nashaat IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 11100168 Numbering: – Type: volume Value: 58 – Type: issue Value: 4 Titles: – TitleFull: Alexandria Engineering Journal Type: main |
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