https://doi.org/10.1140/epjs/s11734-025-01497-z
Regular Article
Data-driven joint noise reduction strategy for flutter boundary prediction
1
School of Mathematics and Statistics, Northwestern Polytechnical University, 710129, Xi’an, China
2
MOE Key Laboratory for Complexity Science in Aerospace, Northwestern Polytechnical University, 710072, Xi’an, China
3
Department of Systems and Control Engineering, Tokyo Institute of Technology, 152-8552, Tokyo, Japan
4
School of Mathematics and Statistics, Shaanxi Normal University, 710119, Xi’an, China
5
Potsdam Institute for Climate Impact Research, 14412, Potsdam, Germany
6
Department of Physics, Humboldt University Berlin, 12489, Berlin, Germany
Received:
18
October
2024
Accepted:
27
January
2025
Published online:
14
February
2025
Flutter test data processing is crucial for modal parameter identification, which facilitates flutter boundary prediction. However, the response signals acquired from real experiments have difficulties due to non-smoothness, multimodal mixing and low signal-to-noise ratio. A direct analysis and prediction will often lead to low accuracy on the predictions and seriously threaten flight safety. Therefore, this paper proposes a data-driven joint noise reduction strategy to improve the performance of flutter boundary prediction. Particularly, a variational mode decomposition is substantially improved by introducing an optimization algorithm. The decomposed effective signal components are reprocessed via a wavelet threshold denoising method with a soft-hard compromise threshold function. Then, based on the matrix pencil method, the modal parameters of original turbulence response signals are identified from the impulse responses generating by deep learning. The effectiveness of the presented method is verified by a comparative analysis with conventional methods.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
