The new aeronautical structures have become more flexible and light weight to reduce energy consumption, emissions, and noise, and to operate at high altitudes for long periods in the air, such as those required in the NASA Helios project. The increased structural flexibility of these aircraft has reignited concerns related to aeroelastic instabilities, such as flutter. Improving the techniques and methods used in aircraft certification flights is an important concern of the aeronautical community, because current standards and procedures do not provide recommendations and guidelines for aircraft with a high degree of flexibility. The techniques traditionally used in commercial airplanes cannot be used in this new aircraft concept, because they have a high degree of non-linearity in their flight dynamics. Current research indicates an increasing awareness about the importance of vision in the monitoring of UAV structural health. This work presents a new methodology to measure natural frequencies of aeronautical structures using a computer vision system. We also discusses new approaches to sense and acquire vibration data on aeroelastic certification flights test. These new approaches aim to reduce both the time required to identify the aeroelastic phenomenon and the size of the hardware that must be boarded on the aircraft, thus minimizing the risks and costs of the vibration tests. The advance of computer vision systems enables the use of cameras as a motion tracker sensor with millimeter precision and accuracy. Non-contact sensors are suitable for flutter analysis because they do not interfere with the dynamics of the aircraft. Therefore, this new methodology is able to process the obtained images and provide the user with the data about movements in a ready to use vector, at a reasonable cost. Using the data provided by this methodology the natural frequencies of the first bending modes were identified. This new methodology can be a user-friendly tool to support the Brazilian National Civil Aviation Agency - ANAC program called iBR 2020, which aims to certify small aircraft.

中文翻译：

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基于激光和计算机视觉技术的气动弹性振动测量

新的航空结构变得更加灵活和重量更轻，以减少能源消耗、排放和噪音，并在高空长时间空中运行，例如 NASA Helios 项目所需的结构。这些飞机增加的结构灵活性重新引发了对气动弹性不稳定性（例如颤振）的担忧。改进飞机认证飞行中使用的技术和方法是航空界的一个重要关注点，因为当前的标准和程序没有为具有高度灵活性的飞机提供建议和指南。传统上用于商用飞机的技术不能用于这种新的飞机概念，因为它们的飞行动力学具有高度的非线性。当前的研究表明，人们越来越意识到视觉在无人机结构健康监测中的重要性。这项工作提出了一种使用计算机视觉系统测量航空结构固有频率的新方法。我们还讨论了在气动弹性认证飞行测试中感知和获取振动数据的新方法。这些新方法旨在减少识别气动弹性现象所需的时间以及必须安装在飞机上的硬件尺寸，从而最大限度地降低振动测试的风险和成本。计算机视觉系统的进步使相机能够用作具有毫米级精度和准确度的运动跟踪器传感器。非接触式传感器适用于颤振分析，因为它们不会干扰飞机的动力学。所以，这种新方法能够处理获得的图像，并以合理的成本向用户提供有关现成矢量中运动的数据。使用该方法提供的数据，确定了第一弯曲模式的固有频率。这种新方法可以成为一种用户友好的工具，以支持巴西国家民航局 - 名为 iBR 2020 的 ANAC 计划，该计划旨在认证小型飞机。