Application of Deep Learning to Identify Flutter Flight Testing Signals Parameters and Analysis of Real F-18 Flutter Flight Test Data
Aircraft envelope expansion during the installation of new underwing stores presents significant challenges, particularly due to the aeroelastic flutter phenomenon. Accurate modeling of aeroelastic behavior often necessitates flight testing, which poses risks due to the potential catastrophic conseq...
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MDPI AG
2025-01-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/12/1/34 |
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| author | Sami Abou-Kebeh Roberto Gil-Pita Manuel Rosa-Zurera |
| author_facet | Sami Abou-Kebeh Roberto Gil-Pita Manuel Rosa-Zurera |
| author_sort | Sami Abou-Kebeh |
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| description | Aircraft envelope expansion during the installation of new underwing stores presents significant challenges, particularly due to the aeroelastic flutter phenomenon. Accurate modeling of aeroelastic behavior often necessitates flight testing, which poses risks due to the potential catastrophic consequences of reaching the flutter point. Traditional methods, like frequency sweeps, are effective but require prolonged exposure to flutter conditions, making them less suitable for transonic flight validations. This paper introduces a robust deep learning approach to process sine dwell signals from aeroelastic flutter flight tests, characterized by short data lengths (less than 5 s) and low frequencies (less than 10 Hz). We explore the preliminary viability of different deep learning networks and compare their performances to existing methods such as the PRESTO algorithm and Laplace Wavelet Matching Pursuit estimation. Deep learning algorithms demonstrate substantial accuracy and robustness, providing reliable parameter identification for flutter analysis while significantly reducing the time spent near flutter conditions. Although the results with the networks trained show less accuracy than the PRESTO algorithm, they are more accurate than the Laplace Wavelet estimation, and the results are promising enough to justify extended investigation on this area. This approach is validated using both synthetic data and real F-18 flight test signals, which highlights its potential for real-time analysis and broader applicability in aeroelastic testing. |
| format | Article |
| id | doaj-art-e5663cc38dbc457787d6dc2f30a0a486 |
| institution | Kabale University |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-e5663cc38dbc457787d6dc2f30a0a4862025-01-24T13:15:33ZengMDPI AGAerospace2226-43102025-01-011213410.3390/aerospace12010034Application of Deep Learning to Identify Flutter Flight Testing Signals Parameters and Analysis of Real F-18 Flutter Flight Test DataSami Abou-Kebeh0Roberto Gil-Pita1Manuel Rosa-Zurera2Signal Theory and Communications Department, Universidad de Alcalá, 28805 Alcalá de Henares, SpainSignal Theory and Communications Department, Universidad de Alcalá, 28805 Alcalá de Henares, SpainSignal Theory and Communications Department, Universidad de Alcalá, 28805 Alcalá de Henares, SpainAircraft envelope expansion during the installation of new underwing stores presents significant challenges, particularly due to the aeroelastic flutter phenomenon. Accurate modeling of aeroelastic behavior often necessitates flight testing, which poses risks due to the potential catastrophic consequences of reaching the flutter point. Traditional methods, like frequency sweeps, are effective but require prolonged exposure to flutter conditions, making them less suitable for transonic flight validations. This paper introduces a robust deep learning approach to process sine dwell signals from aeroelastic flutter flight tests, characterized by short data lengths (less than 5 s) and low frequencies (less than 10 Hz). We explore the preliminary viability of different deep learning networks and compare their performances to existing methods such as the PRESTO algorithm and Laplace Wavelet Matching Pursuit estimation. Deep learning algorithms demonstrate substantial accuracy and robustness, providing reliable parameter identification for flutter analysis while significantly reducing the time spent near flutter conditions. Although the results with the networks trained show less accuracy than the PRESTO algorithm, they are more accurate than the Laplace Wavelet estimation, and the results are promising enough to justify extended investigation on this area. This approach is validated using both synthetic data and real F-18 flight test signals, which highlights its potential for real-time analysis and broader applicability in aeroelastic testing.https://www.mdpi.com/2226-4310/12/1/34aeroelastic flutterflight testingdeep learningconvolutional neural networkdeep neural networkmulti-layer perceptron |
| spellingShingle | Sami Abou-Kebeh Roberto Gil-Pita Manuel Rosa-Zurera Application of Deep Learning to Identify Flutter Flight Testing Signals Parameters and Analysis of Real F-18 Flutter Flight Test Data Aerospace aeroelastic flutter flight testing deep learning convolutional neural network deep neural network multi-layer perceptron |
| title | Application of Deep Learning to Identify Flutter Flight Testing Signals Parameters and Analysis of Real F-18 Flutter Flight Test Data |
| title_full | Application of Deep Learning to Identify Flutter Flight Testing Signals Parameters and Analysis of Real F-18 Flutter Flight Test Data |
| title_fullStr | Application of Deep Learning to Identify Flutter Flight Testing Signals Parameters and Analysis of Real F-18 Flutter Flight Test Data |
| title_full_unstemmed | Application of Deep Learning to Identify Flutter Flight Testing Signals Parameters and Analysis of Real F-18 Flutter Flight Test Data |
| title_short | Application of Deep Learning to Identify Flutter Flight Testing Signals Parameters and Analysis of Real F-18 Flutter Flight Test Data |
| title_sort | application of deep learning to identify flutter flight testing signals parameters and analysis of real f 18 flutter flight test data |
| topic | aeroelastic flutter flight testing deep learning convolutional neural network deep neural network multi-layer perceptron |
| url | https://www.mdpi.com/2226-4310/12/1/34 |
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