Nolan Peter J, McClelland Hunter G, Woolsey Craig A, Ross Shane D
Engineering Mechanics Program, Virginia Tech, Blacksburg, VA 24061, USA.
Kevin T. Crofton Department of Aerospace and Ocean Engineering, Virginia Tech, Blacksburg, VA 24061, USA.
Sensors (Basel). 2019 Apr 3;19(7):1607. doi: 10.3390/s19071607.
The transport of material through the atmosphere is an issue with wide ranging implications for fields as diverse as agriculture, aviation, and human health. Due to the unsteady nature of the atmosphere, predicting how material will be transported via the Earth's wind field is challenging. Lagrangian diagnostics, such as Lagrangian coherent structures (LCSs), have been used to discover the most significant regions of material collection or dispersion. However, Lagrangian diagnostics can be time-consuming to calculate and often rely on weather forecasts that may not be completely accurate. Recently, Eulerian diagnostics have been developed which can provide indications of LCS and have computational advantages over their Lagrangian counterparts. In this paper, a methodology is developed for estimating local Eulerian diagnostics from wind velocity data measured by a single fixed-wing unmanned aircraft system (UAS) flying in a circular arc. Using a simulation environment, driven by realistic atmospheric velocity data from the North American Mesoscale (NAM) model, it is shown that the Eulerian diagnostic estimates from UAS measurements approximate the true local Eulerian diagnostics and also predict the passage of LCSs. This methodology requires only a single flying UAS, making it easier and more affordable to implement in the field than existing alternatives, such as multiple UASs and Dopler LiDAR measurements. Our method is general enough to be applied to calculate the gradient of any scalar field.
物质在大气中的传输是一个对农业、航空和人类健康等众多领域都有广泛影响的问题。由于大气的不稳定特性，预测物质如何通过地球风场传输具有挑战性。拉格朗日诊断方法，如拉格朗日相干结构（LCSs），已被用于发现物质聚集或扩散的最重要区域。然而，拉格朗日诊断计算可能耗时，且通常依赖可能不完全准确的天气预报。最近，已开发出欧拉诊断方法，它可以提供LCS的指示，并且在计算上比拉格朗日方法具有优势。在本文中，我们开发了一种方法，用于根据在圆弧飞行的单架固定翼无人机系统（UAS）测量的风速数据估计局部欧拉诊断。使用由北美中尺度（NAM）模型的实际大气速度数据驱动的模拟环境，结果表明，无人机测量的欧拉诊断估计值接近真实的局部欧拉诊断，并且还能预测LCS的通过。这种方法只需要一架飞行的无人机，与现有的替代方法（如多架无人机和多普勒激光雷达测量）相比，在现场实施起来更容易且成本更低。我们的方法具有足够的通用性，可用于计算任何标量场的梯度。
