在数字孪生试飞中，要采用高质量的试飞数据进行数字模型与实际试飞数据的融合。试飞过程中飞机不可避免地会受到大气紊流的持续激励，为了消除紊流激励的影响，提高后续信号处理结论的准确性，本文提出了一种基于颤振试飞数字孪生数据重构的扫频响应模态参数估计方法。首先对实测响应进行时域重构，将其分离为纯粹因扫频引起的结构响应和由紊流激励的结构响应两部分，进而分别应用子空间算法分别对分离后两种响应数据进行模态参数辨识，最后通过仿真和实测数据对所提出方法进行了验证。结果表明，本文方法可以获得满足数字孪生中虚实融合要求的高质量试飞数据；数据重构后可以获得更加准确、可信的辨识结果；同时由于紊流响应的分离和紊流独特的宽频特性，方法对扫频范围以外的模态也可进行有效辨识。
During the digital twinning flight test, high quality flight test data is used to fuse the digital model with the actual flight test data. In order to eliminate the effect of turbulence excitation and improve the accuracy of subsequent signal processing con-clusions, a new method for estimating swept frequency response modal parameters is proposed based on the reconstruction of flutter flight test digital twinning data. Firstly, the measured response is reconstructed in the time domain, and it is separated into two parts: the structural response caused purely by the swept and the structural response excited by turbulence. Then, the subspace algorithm is used to identify the modal parameters of the two separated response data respectively. Finally, the proposed method is verified by simulation and measured data. The results show that the proposed method can obtain high-quality test flight data that meets the requirements of virtual-real fusion in digital twins; after data reconstruction, more ac-curate and reliable identification results can be obtained; at the same time, due to the separation of turbulent response and the unique broadband characteristics of turbulence, the method can also effectively identify modes outside the swept fre-quency range.
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