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Flutter Mechanisms Characterization Using Distributed Aeroelastic Energy Analysis

机译:分布式气弹能量分析的颤振机理表征

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The problem of flutter mechanism classification is important particularly for modern fighter aircrafts, for which the amount of aircraft configurations which require analysis is typically large. Common flutter mechanism classification and characterization techniques rely on characteristics which represent the structural motions at flutter conditions rather than a quantitative measure of the instability severity or its origins. Such classifications may be somewhat subjective, require tedious treatment by the analyst and may in some cases lead to wrong conclusions. The current study aims to enhance flutter mechanism characterization and classification processes by using an aeroelastic energy balance analysis which is formulated in structural coordinates. The method may be easily applicable to typical industrial flutter analysis data and enables to identify distributed aeroelastic energy patterns which are visualized on the configuration structural model. This analysis approach is shown to enable straightforward identification of the structural parts which are the dominant contributors to the unstable coupling and thereby to distinguish between physically dissimilar flutter cases. The application of aeroelastic energy-based parameters for flutter mechanism classification is presented in this study for a representative dataset including thousands of wing flutter cases of the F-16 aircraft with various stores. Using the suggested approach, six basic flutter mechanism groups are identified in the examined dataset. Over 98% of the examined flutter cases may be automatically classified to one of these mechanism groups, which demonstrates the potential of this approach.
机译:颤振机构分类的问题对于现代战斗机尤其重要,因为现代战斗机通常需要分析大量的飞机配置。常见的颤振机制分类和表征技术依赖于代表颤振条件下结构运动的特征,而不是对不稳定性严重程度或其来源进行定量测量。这种分类可能有些主观,需要分析人员进行乏味的处理,并且在某些情况下可能导致错误的结论。当前的研究旨在通过使用在结构坐标中制定的气动弹性能平衡分析来增强颤振机理的表征和分类过程。该方法可以容易地应用于典型的工业颤振分析数据,并且使得能够识别在结构结构模型上可视化的分布式气弹能量模式。该分析方法显示出能够直接识别构成不稳定耦合的主要因素的结构部件,从而区分出物理上不相似的颤动情况。这项研究针对具有代表性的数据集,包括基于气动弹性能的参数在扑扑机构分类中的应用,该数据集包括数千个F-16飞机机翼扑扑案例,并有不同的商店。使用建议的方法,在检查的数据集中确定了六个基本的颤振机制组。超过98%的扑扑病例可以自动归类为这些机制组之一,这证明了这种方法的潜力。

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