EAGER: Improving the Data Quality of Measurements Collected with Drone-Mounted Sensors: A Fluid Dynamics Perspective with Guidelines for Optimum Sensor Placement and Housing

EAGER：提高无人机安装传感器收集的测量数据质量：流体动力学视角以及最佳传感器放置和外壳指南

• 批准号: 2125997
• 负责人: Tony Saad
• 金额: $ 17.45万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2125997&HistoricalAwards=false
• 关键词: EAGER; Improving; Data; Quality; Measurements

Drones are routinely used to conduct measurements in the atmosphere and other difficult to access locations such as tunnels and large pipes. They can be used to measure air pollution, smoke, contaminants, etc. However, recent experimental data shows that measurements using drones can be compromised by the complex air flow created by the drone as well as the design of the sensor housing. For example, a recent study showed a nearly 100% overestimation of particle concentrations due to a drone’s induced rotors. This project aims to understand how drone airflow affects sensor measurements and to develop mitigation strategies for ideal sensor placement and design. The outcomes and products of this research will affect numerous sub-disciplines including environmental engineering, forest service, fire monitoring, contaminant tracking, agriculture, etc. that use drones for observation, measurement, and intervention. The overarching objective of this project is to characterize the mixing induced by drone airflow and its impact on on-board sensor measurements. The work will use computational fluid dynamics (CFD) and wind tunnel and open-air experiments to characterize the airflow around drones and its effects on sensor measurements of suspended particulates. Quadrotor and hexarotor drones will be considered in this work as those are the most commonly used types of drones to conduct airborne measurements. CFD calculations using Large Eddy Simulation will first be conducted to simulate different sampling scenarios such as across and into a plume as well as confined and well-mixed environments. Particles will be represented as scalar tracers because of their very low Stokes number. In addition, the work will consider sensor housing and orientation to quantify its impact on the final measurements. Experimental measurements will then be conducted to validate the CFD proposed guidelines. This research will enhance our fundamental understanding of the interaction between the airflow created by a drone and measurements of suspended particulate matter and gases. The research will introduce innovative tools for better understanding of drone-based sampling as well as guidelines for ideal sensor placement on the fuselage and sensor housing design.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.

无人机通常用于在大气层和其他难以进入的位置（如隧道和大型管道）进行测量。它们可用于测量空气污染、烟雾、污染物等。然而，最近的实验数据表明，使用无人机进行测量可能会受到无人机产生的复杂气流以及传感器外壳设计的影响。例如，最近的一项研究表明，由于无人机的诱导转子，颗粒浓度被高估了近100%。该项目旨在了解无人机气流如何影响传感器测量，并为理想的传感器放置和设计制定缓解策略。这项研究的成果和产品将影响许多子学科，包括环境工程，森林服务，火灾监测，污染物跟踪，农业等，这些学科使用无人机进行观察，测量和干预。 该项目的首要目标是表征无人机气流引起的混合及其对机载传感器测量的影响。这项工作将使用计算流体动力学（CFD）、风洞和露天实验来表征无人机周围的气流及其对悬浮颗粒物传感器测量的影响。在这项工作中将考虑四旋翼和六旋翼无人机，因为它们是进行空中测量最常用的无人机类型。首先将使用大涡模拟进行计算流体动力学计算，以模拟不同的采样情况，例如穿过和进入羽流以及封闭和混合良好的环境。粒子将被表示为标量示踪剂，因为它们的斯托克斯数非常低。此外，这项工作将考虑传感器外壳和方向，以量化其对最终测量的影响。然后将进行实验测量，以验证CFD建议的指南。这项研究将增强我们对无人机产生的气流与悬浮颗粒物和气体测量之间相互作用的基本理解。该研究将引入创新工具，以更好地了解无人机采样以及机身上理想传感器放置和传感器外壳设计的指导方针。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Investigating Errors Observed during UAV-Based Vertical Measurements Using Computational Fluid Dynamics

• DOI: 10.3390/drones6090253
• 发表时间: 2022-09-01
• 期刊: DRONES
• 影响因子: 4.8
• 作者: Hedworth, Hayden;Page, Jeffrey;Saad, Tony
• 通讯作者: Saad, Tony