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Application of Operational Modal Analysis for Wind-Tunnel Testing of an Aircraft Wing Model with Control-Surface

机译:控制表面飞机翼模型风隧道试验运算模态分析

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Operational modal analysis (OMA) is applied to experimental data from a transonic wind-tunnel test of an aircraft wing model with control-surface. Airflow turbulence and control-surface excitation via hydraulic rotational actuators driven by shaped and burst random signals are employed as natural and artificial excitation for the model structure. Altogether 27 sets of data were measured and processed with different test conditions, e.g. Mach numbers, angles of attack of wing section and angles of the control-surface, static pressure, etc. A novel narrow-band OMA procedure based on frequency-spatial domain decomposition (SFDD) is adopted to identify modal parameters from all different data sets. Mode shapes are identified by frequency domain (FD) principle component analysis (PCA) via SVD of processed power spectrum density (PSD) matrix. A spatial domain modal filter is devised to isolate individual modes, followed by FD curve fitting to estimate relevant modal frequencies and damping ratios from enhanced PSD in the selected narrow-band. Altogether 8 to 12 modes are clearly defined and modal parameters are then identified in the frequency band of 50~350 Hz, including all the "rigid-body" modes of the 2-D airfoil plus flexible modes exhibiting 3-D motion of the assumed 2-D model. Modal parameter changes, especially for modal damping and frequencies, with different wind-tunnel test conditions are clearly observed and their physical reasoning is discussed.
机译:操作模态分析(OMA)应用于具有控制表面的飞机翼模型的跨音频风隧道试验的实验数据。气流湍流和控制表面激发通过成形和突发随机信号驱动的液压旋转致动器被用作模型结构的天然和人为激励。通过不同的测试条件测量并处理了27组数据,例如,使用不同的测试条件。马赫数,翼片段的攻击角和控制表面的角度,静压等。采用基于频率空间域分解(SFDD)的新型窄带OMA过程来识别来自所有不同数据集的模态参数。模式形状由处理的功率谱密度(PSD)矩阵的SVD识别通过频域(FD)原理分量分析(PCA)。设计空间域模滤波器以隔离各个模式,然后是FD曲线拟合,以估计所选窄带中的增强型PSD的相关模态频率和阻尼比。完全8至12种模式明确定义,然后在50〜350Hz的频带中识别模态参数,包括所有“刚体”模式,包括2-D翼型的所有“刚体”模式加上假定的3-D运动的柔性模式2-D型号。模态参数改变,特别是对于模态阻尼和频率,清楚地观察到不同的风洞测试条件,并讨论其物理推理。

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