Multimodal Estimation of Sine Dwell Vibrational Responses from Aeroelastic Flutter Flight Tests
Aircraft envelope expansion during new underwing stores installation is a challenging problem, mainly related to the aeroelastic flutter phenomenon. Aeroelastic models are usually very hard to model, and therefore flight tests are usually required to validate the aeroelastic model predictions, which...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
MDPI AG
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/391bc721668c41b08fb168578f142502 |
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| Sumario: | Aircraft envelope expansion during new underwing stores installation is a challenging problem, mainly related to the aeroelastic flutter phenomenon. Aeroelastic models are usually very hard to model, and therefore flight tests are usually required to validate the aeroelastic model predictions, which given the catastrophic consequences of reaching the flutter point pose an important problem. This constraint favors using short time excitations like Sine Dwell to perform the flight tests, so that the aircraft stays close to the flutter point as little time as possible, but short time data implies a poor spectrum resolution and therefore leads to inaccurate and non repetitive results. The present paper will address the problem related to processing Sine Dwell signals from aeroelastic Flutter Flight Tests, characterized by very short data length (less than 5 s) and low frequency (less than 10 Hz) and used to identify the natural modes associated with the structure. In particular, a new robust technique, the PRESTO algorithm, will be presented and compared to a Matching Pursuit estimation based on Laplace Wavelet. Both techniques have demonstrated to be very accurate and robust procedures on very short time (Sine Dwell) signals, with the particularity that the Laplace Wavelet estimation has already been validated over F-18 real Flutter Flight Test data as described in different papers. However, the PRESTO algorithm improves the performance and accuracy of the Laplace Wavelet processing while keeping its robustness, both on real and simulated data. |
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