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Multimodal Estimation of Sine Dwell Vibrational Responses from Aeroelastic Flutter Flight Tests

机译:气动弹性颤振飞行试验正弦驻留振动响应的多模态估计

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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.
机译:在新的机翼下存储安装过程中,飞机包络膨胀是一个具有挑战性的问题,主要与气动弹性颤振现象有关。气动弹性模型通常很难建模,因此通常需要飞行测试来验证气动弹性模型的预测,考虑到达到颤振点的灾难性后果,这是一个重要问题。这种约束有利于使用像正弦停留这样的短时间激励来执行飞行测试,以便飞机尽可能少地保持在颤振点附近,但短时间数据意味着光谱分辨率较差,因此导致不准确和非重复的结果。本文将解决与处理气动弹性颤振飞行试验中的正弦驻留信号有关的问题,其特点是数据长度非常短(小于5 s)和低频(小于10 Hz),用于识别与结构相关的自然模式。特别是,将介绍一种新的鲁棒技术,即PRESTO算法,并与基于拉普拉斯小波的匹配追求估计进行比较。这两种技术都被证明是非常短时间(正弦驻留)信号上非常准确和稳健的程序,其特殊之处在于,拉普拉斯小波估计已经在F-18真实颤振飞行测试数据上得到了验证,如不同论文中所述。然而,PRESTO算法提高了拉普拉斯小波处理的性能和准确性,同时保持了其在真实和模拟数据上的鲁棒性。

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