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首页> 外文会议>AIAA/ASCE/AHS/ASC structures, structural dynamics and materials conference;AIAA SciTech Forum >Efficient Clustering Algorithm Using Modal Assurance Criterion for System Identification
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Efficient Clustering Algorithm Using Modal Assurance Criterion for System Identification

机译：基于模态保证准则的高效聚类算法

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It is critical to identify and understand how the structural modes interact with the aerodynamics at different flight conditions because often times this interaction can becomes unstable causing excessive oscillations of the air vehicle. To this end, ZONA has been developing a system identification toolset within IADS flight test control system, designated as ZAMS+. ZAMS+ utilizes the Covariance-Driven Subspace System (CDSS) identification method to extract the mode shapes and modal parameters (frequency and damping ratio) based on the accelerometer measurements, and then display the identified mode shapes on a realistic three-dimensional air vehicle model. To mitigate the effects of a single user predefined system order selection for CDSS, the CDSS identification is applied over a range of system order selections. With the identified results for all the system orders, the clustering algorithm can be applied thereafter to extract/separate the physical modes from the mathematical ones. This paper focuses on the usage of Modal Assurance Criterion (MAC) as the third parameter in addition to the frequency and damping for the unproved efficiency of clustering algorithm to take out the outlier data points. Thus not only the accuracy of the identified damping ratio but also the identified mode shape can be greatly improved. Two numerical examples, a lumped mass system and real flight test data, are carried out to demonstrate the effects of MAC towards clustering algorithm.
机译：识别和理解结构模式在不同飞行条件下如何与空气动力学相互作用至关重要，因为这种相互作用经常会变得不稳定，从而导致飞行器过度振荡。为此，ZONA一直在IADS飞行测试控制系统（称为ZAMS +）中开发系统识别工具集。 ZAMS +利用协方差驱动子空间系统（CDSS）识别方法来提取基于加速度计测量的模式形状和模态参数（频率和阻尼比），然后在真实的三维飞机模型上显示识别出的模式形状。为了减轻CDSS的单个用户预定义系统订单选择的影响，将CDSS标识应用于一系列系统订单选择。利用针对所有系统订单的识别结果，此后可以应用聚类算法从数学模式中提取/分离物理模式。本文将重点放在模态保证准则（MAC）作为除频率和阻尼之外的第三个参数上，以提高聚类算法未经证实的效率来提取异常数据点。因此，不仅可以大大提高所确定的阻尼比的精度，而且可以大大提高所确定的模态形状。进行了两个数值示例，集总质量系统和实际飞行测试数据，以证明MAC对聚类算法的影响。

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