Flutter flight test involves the evaluation of the airframes aeroelastic stability by applying artificial excitation on the aircraft lifting surfaces. The subsequent responses are captured and analyzed to extract the frequencies and damping characteristics of the system. However, noise contamination, turbulence, non-optimal excitation of modes, and sensor malfunction in one or more sensors make it time-consuming and corrupt the extraction process. In order to expedite the process of identifying and analyzing aeroelastic modes, this study implements a time-delay embedded Dynamic Mode Decomposition technique. This approach is complemented by Robust Principal Component Analysis methodology, and a sparsity promoting criterion which enables the automatic and optimal selection of sparse modes. The anonymized flutter flight test data, provided by the fifth author of this research paper, is utilized in this implementation. The methodology assumes no knowledge of the input excitation, only deals with the responses captured by accelerometer channels, and rapidly identifies the aeroelastic modes. By incorporating a compressed sensing algorithm, the methodology gains the ability to identify aeroelastic modes, even when the number of available sensors is limited. This augmentation greatly enhances the methodology's robustness and effectiveness, making it an excellent choice for real-time implementation during flutter test campaigns.

翻译：颤振飞行试验通过在人造激励作用下评估飞行器升力面的气动弹性稳定性。系统响应被采集并分析以提取系统的频率和阻尼特性。然而，噪声干扰、湍流、模态激励非最优以及一个或多个传感器的故障均会导致提取过程耗时且结果失真。为加快气动弹性模态识别与分析进程，本研究采用了一种时延嵌入动态模态分解技术。该方法结合了稳健主成分分析技术及一种稀疏促进准则，能够自动且最优地选取稀疏模态。研究使用了本文第五作者提供的匿名化颤振飞行试验数据。该方法无需已知输入激励信息，仅利用加速度计通道捕获的响应，即可快速识别气动弹性模态。通过引入压缩感知算法，该方法即使在可用传感器数量有限的情况下也能识别气动弹性模态。这一增强显著提升了方法的鲁棒性和有效性，使其成为颤振试验期间实时实施的理想选择。