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.
