EAGER: A New Aerial Platform for Turbulence Measurements in the Atmospheric Surface Layer

EAGER：用于测量大气表层湍流的新型空中平台

• 批准号: 2332467
• 负责人: Stephan De Wekker
• 金额: $ 12.61万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2332467&HistoricalAwards=false
• 关键词: EAGER; New; Aerial; Platform; Turbulence

This project seeks to advance research in the measurement of winds. Why is it important? Having refined measurements of winds improves weather forecasting. For instance, if we can better predict how winds behave during disaster situations such as wildfires and train derailments that involve toxic gas pollution, we can forecast the behavior and impact of wind factors in local areas and prevent or minimize health risks to the public. This would allow better coordination of emergency response and resources and minimize the economic impact at the local, state, and national levels. To achieve these goals, drone technology will be used in this project. One major benefit of drones is that they can access locations in the atmosphere near the ground that are unsafe to humans or difficult to access otherwise, such as mountainous terrain, lakes, and glaciers. Another benefit of drone technology is that a drone can hover over difficult terrain for a long time, allowing data collection to develop and test wind forecasting models. This type of in-person research in difficult terrain is currently impossible or very difficult to achieve, which has resulted in major weaknesses in wind behavior understanding and in wind forecasting models. Collecting wind data with drones is not only very novel, but also cost-effective. The wind collection technology using drones would address the current research weaknesses and help protect the public in their daily lives or in emergency situations. The project aims to explore the ability of commercial off-the-shelf (COTS) multirotor drones to measure turbulence. The approach is innovative because it only relies on the use of easily retrievable data from COTS drones. There is no need for custom-built frames and tuning of flight control parameters, making the approach cost-efficient and easily accessible to the scientific community without an engineering and/or technical background. The major goals are to investigate 1) the accuracy of these drone-based turbulence measurements, and 2) the dependency of the accuracy on drone size and weight. Measurements will be performed next to a large array of start-of-the-art sonic anemometers over flat terrain in the NSF funded M2HATS field study, offering an unprecedented opportunity for an intercomparison experiment. Eight multi-rotor vehicles will be used, including two different types of COTS multirotor drones to retrieve data necessary to estimate turbulence.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目旨在进步在测量风中的研究。为什么重要？通过进行了精致的测量，可以改善天气预报。例如，如果我们能够更好地预测涉及有毒气体污染的灾难情况下风的行为如何，我们可以预测风因素在当地地区的行为和影响，并防止或最大程度地减少向公众的健康风险。这样可以更好地协调紧急响应和资源，并最大程度地减少地方，州和国家一级的经济影响。为了实现这些目标，该项目将使用无人机技术。无人机的主要好处是，他们可以进入地面附近大气中的位置，这些位置对人类不安全或难以进入，例如山地，湖泊，湖泊和冰川。 无人机技术的另一个好处是，很长一段时间以来，无人机可以在困难的地形上徘徊，从而使数据收集可以开发和测试风预测模型。目前在困难的地形上进行这种类型的面对面研究是不可能或很难实现的，这导致了风行行为理解和风预测模型的主要弱点。用无人机收集风数据不仅非常新颖，而且具有成本效益。使用无人机收集技术将解决当前的研究弱点，并在日常生活或紧急情况下帮助保护公众。该项目旨在探讨商业现成的（COTS）多趋势无人机测量湍流的能力。该方法具有创新性，因为它仅依赖于从COTS无人机中易于检索的数据。不需要定制的框架和飞行控制参数的调整，这使得在没有工程和/或技术背景的情况下，科学社区的方法效率且易于访问。主要目标是研究1）这些基于无人机的湍流测量的准确性，以及2）准确性对无人机尺寸和重量的依赖性。在NSF资助的M2HATS现场研究中，将在扁平地形上进行大量的开始声音障碍仪旁边进行测量，为进行比较实验提供了前所未有的机会。将使用八辆多旋翼车辆，包括两种不同类型的COTS多旋转无人机来检索所需的数据以估算湍流。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响来通过评估来获得支持的审查标准。