Coordinated Unmanned Aircraft System (UAS) and Ground-Based Weather Measurements to Predict Lagrangian Coherent Structures (LCSs)
Coordinated Unmanned Aircraft System (UAS) and Ground-Based Weather Measurements to Predict Lagrangian Coherent Structures (LCSs)
复制标题
DOI：
10.3390/s18124448
复制
发表时间：
2018-12-01
期刊：
影响因子：
3.9
通讯作者：
Schmale, David G., III
中科院分区：
文献类型：
--
作者：
Nolan, Peter J.;Pinto, James;Schmale, David G., III
危险释放产生的气载化学剂和生物剂的浓度分布不均匀。相反，存在较高浓度的区域，部分原因是当地的大气流动条件可以吸引药剂。我们为一个地面站和两个装有超声波风速计的旋翼无人机系统（UAS）配备了设备。这里报告的飞行是在科罗拉多圣路易斯山谷的利奇机场进行的，距离地面10至15米，作为2018年高空低大气过程研究-远程驾驶飞机团队实验（LAPSE-RATE）活动的一部分。超声波风速计用于收集固定三角形模式中风速、风向和温度的同时测量值;每个传感器位于三角形的一个顶点，每边近似100至200米，具体取决于实验。一个WRF-LES模式被用来确定整个采样域的风场。来自地面传感器和两个UAS的数据被用来检测吸引区域（也称为拉格朗日相干结构或LCS），这些区域有可能运输高浓度的制剂。这种独特的框架检测高浓度区域的基础上的水平风梯度张量的估计。据我们所知，我们的工作是第一次使用一组传感器直接测量大气中的LCS指标。我们的最终目标是使用来自传感器群的环境数据来驱动危险物质的运输模型，从而能够做出有关快速应急响应的实时正确决策。来自无人资产的实时数据、用于运输分析的先进数学技术和预测模型的集成可以帮助未来的应急响应决策。
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 similar to 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.