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首页> 外文OA文献 >Coordinated Unmanned Aircraft System (UAS) and Ground-Based Weather Measurements to Predict Lagrangian Coherent Structures (LCSs)
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Coordinated Unmanned Aircraft System (UAS) and Ground-Based Weather Measurements to Predict Lagrangian Coherent Structures (LCSs)

机译：协调无人驾驶飞机系统（UAS）和基于地面的天气测量，以预测拉格朗日相干结构（LCSS）

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Concentrations of airborne chemical and biological agents from a hazardous release are not spread uniformly. Instead, there are regions of higher concentration, in part due to local atmospheric flow conditions which can attract agents. We equipped a ground station and two rotary-wing unmanned aircraft systems (UASs) with ultrasonic anemometers. Flights reported here were conducted 10 to 15 m above ground level (AGL) at the Leach Airfield in the San Luis Valley, Colorado as part of the Lower Atmospheric Process Studies at Elevation—a Remotely-Piloted Aircraft Team Experiment (LAPSE-RATE) campaign in 2018. The ultrasonic anemometers were used to collect simultaneous measurements of wind speed, wind direction, and temperature in a fixed triangle pattern; each sensor was located at one apex of a triangle with ∼100 to 200 m on each side, depending on the experiment. A WRF-LES model was used to determine the wind field across the sampling domain. Data from the ground-based sensors and the two UASs were used to detect attracting regions (also known as Lagrangian Coherent Structures, or LCSs), which have the potential to transport high concentrations of agents. This unique framework for detection of high concentration regions is based on estimates of the horizontal wind gradient tensor. To our knowledge, our work represents the first direct measurement of an LCS indicator in the atmosphere using a team of sensors. Our ultimate goal is to use environmental data from swarms of sensors to drive transport models of hazardous agents that can lead to real-time proper decisions regarding rapid emergency responses. The integration of real-time data from unmanned assets, advanced mathematical techniques for transport analysis, and predictive models can help assist in emergency response decisions in the future.
机译：来自危险释放的空气传播化学和生物药物的浓度不会均匀地分布。相反，由于局部大气流动条件，有更高的浓度区域，部分浓度可以吸引药剂。我们配备了一个地面站和两个旋翼无人驾驶飞机系统（uass），具有超声波风管。这里报告的航班在科罗拉多州圣路易斯谷的Leach机场在地面（AGL）上方进行了10至15米，作为高度大气过程研究的一部分 - 一支远程推出的飞机团队实验（流逝 - 利率）运动2018年，超声波风量计用于在固定三角形图案中收集风速，风向和温度的同时测量;根据实验，每个传感器位于三角形的三角形的顶点上，根据实验。 WRF-LES模型用于确定采样域中的风力字段。来自地面传感器的数据和两个uass用于检测吸引区域（也称为拉格朗日相干结构，或LCSS），其具有运输高浓度的药剂。这种检测的高浓度区域的独特框架是基于水平风梯度张量的估计。为了我们的知识，我们的工作代表了使用传感器团队的大气中LCS指示器的首次直接测量。我们的最终目标是使用来自传感器的群体的环境数据来驱动有危险药物的运输模型，这可能导致关于快速紧急响应的实时正确决策。从无人资产的实时数据的整合，运输分析的高级数学技术以及预测模型可以帮助在未来协助应急响应决策。

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