颤振速度估算中的阻尼识别灵敏度
Damping Identification Sensitivity in Flutter Speed Estimation
AI总结：
本研究评估FRVF和LF两种频域识别方法用于柔性机翼颤振速度预测的效果，发现其预测偏差小，凸显阻尼识别精度的影响，可为初步气动弹性评估提供高效替代方案。
摘要：
颤振预测仍是气动弹性研究中的一项核心挑战，部分模型依赖于固有频率、阻尼比等模态参数。这类模型在早期设计阶段或小型无人机（最大起飞质量低于7 kg）的研发中尤为实用。本研究评估了两种频域系统识别方法——Fast Relaxed Vector Fitting（FRVF，快速松弛矢量拟合）和Loewner Framework（LF，勒夫纳框架）——用于预测柔性机翼模型的颤振起始速度。两种方法均被用于从地面振动试验数据中提取模态参数，随后利用这些参数建立两自由度降阶模型。结果表明，基于FRVF和LF的模型可提供可靠的颤振速度，其预测结果与基于N4SID的基准值相比偏差分别不超过3%（FRVF）和5%（LF）。研究结果凸显了颤振速度预测对阻尼比识别精度的敏感性，并证明了这些方法作为初步气动弹性评估的高效计算替代方案的潜力。
英文摘要：
Predicting flutter remains a key challenge in aeroelastic research, with certain models relying on modal parameters, such as natural frequencies and damping ratios. These models are particularly useful in early design stages or for the development of small Unmanned Aerial Vehicles (maximum take-off mass below 7 kg). This study evaluates two frequency-domain system identification methods, Fast Relaxed Vector Fitting (FRVF) and the Loewner Framework (LF), for predicting the flutter onset speed of a flexible wing model. Both methods are applied to extract modal parameters from Ground Vibration Testing data, which are subsequently used to develop a reduced-order model with two degrees of freedom. Results indicate that FRVF and LF-informed models provide reliable flutter speed, with predictions deviating by no more than 3% (FRVF) and 5% (LF) from the N4SID-informed benchmark. The findings highlight the sensitivity of flutter speed predictions to damping ratio identification accuracy and demonstrate the potential of these methods as computationally efficient alternatives for preliminary aeroelastic assessments.
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