Studies of Aerodynamic Performance of Propeller Blades for Sensor Implementation to Study Plumes in Extreme Environments

Turbulent buoyant plumes occur due to the release of buoyant source fluid into an ambient fluid and the resulting buoyancy forces result in the formation and the spread of the plume. The plume is characterized by the plume width and the plume height, which depend on the source buoyancy flux. Sources of buoyant plumes include wildfires, which have been studied using computational simulations and satellite images. It is observed that turbulent plumes have a sharp boundary between the turbulent buoyant fluid and the surroundings, increasing their width through the processes of entrainment of external fluid across this boundary by large eddies of the turbulence, followed by a smaller-scale mixing through the central core. Plumes have been released at a maximum rate of 16 ft.

3/hr. The vertical velocity and the turbulence measured at a fixed point off the axis of the plume have an intermittent character, because the measuring instrument spends part of the time in the fully turbulent fluid and part outside it. To understand the behavior of the plumes generated from wildfires, an initial experiment implementing wind sensors on a UAV to collect wind and temperature data was conducted. The mounting location of sensing systems on multirotor unmanned aerial vehicles (UAV) is essential to perform accurate measurements. In an effort to reduce sensor noise interference caused by the turbulence generated by the propeller of the UAV, a tip vortex characterization was conducted, wherein quantitative and qualitative analysis was performed to install the sensing systems on the quadcopter. Multiple fluids, including butane and hot air were added to the environment to increment the refraction of the light and increase the contrast between the medium of the experiment and the vortices generated by the propeller. Preliminary results are presented for the analysis conducted on a DJI M100 drone to analyze vortex generation around the propellers using multiple sensing techniques, including the Wind Sensor Rev P, smoke release in controlled environment, LiDAR, cameras, and Schlieren imaging. Notably, tip vortex identification using Schlieren imaging was performed for the first time for a propeller blade of a UAV. In addition, a different experimental approach to characterize the DJI 1345s propeller has been conducted, to obtain all the forces affecting the airframe of the DJI Matrice 100. The development a robust system to obtain thrust and torque values at different rotational speeds in the range 2000-6000 RPM---so as to understand the effects of wind in different conditions---is discussed. The propeller is subjected to wind velocities up to 6 m/s with different angle inlets. A non-dimensional analysis is done by obtaining coefficient of thrust and coefficient of torque, to understand how the wind impacts the performance of the propeller with respect to its angular velocity. A theoretical model of the DJI 1345s propeller has been recreated using photogrammetry techniques, generating a 3D CAD file of the propeller. A sectioning of the 3D CAD file along it's radial distance was performed to obtain results using the blade element momentum theory. The results of the experiments show a constant change on the regression function for thrust, affected by the wind coming perpendicular to the propeller---as it happens during the ascent or descent of the UAV---. Similarly, it has been found that wind coming tangentially to the propeller, laterally to the drone---typically caused by wind gusts or crosswinds---has a major effect on the torque . An extensive analysis using the 3D CAD file reveals the angle of attack in a range of 0.7 to 14.0 degrees. In addition, the optimum coefficient of drag and coefficient of lift for the sections of the propeller are revealed to be in a range of 0.04 to 1.4. With the data obtained from the section of the 3D CAD file, a comparison from the experimental and theoretical data show a difference of 3.385% for the thrust, validating the experimental results as well as the procedure followed. These results enable implementation of ultrasonic anemometers in appropriate locations on the airframe for atmospheric boundary layer measurements. The use of tethered multirotor unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV) have recently received significant interest as a means of obtaining measurements in real-time. The development of new technologies of LiDARs have allowed the creation of autonomously piloted systems, as well as the capability to recreate 3D models of unknown environments. Creating a sensor integration on a UAV and a UGV, allows collection of data including wind velocities, temperature, environment characteristics as well as the terrain constrains and topology in circumstances where the human access is limited or almost impossible, as in the case of Mars. Using ROS, an integration of the UAV and the UGV has been implemented by providing a data feeding cabling through the tethering system to the DJI M100, and connecting to a ground station. Likewise, a high-speed, high-distance, wireless connection has been implemented in the ground station to provide a connection to the UGV. A sensor integration was implemented in the UGV, using LiDAR, stereoscopic cameras, and wind sensors to collect wind, temperature, RGB, thermal and topological data in real time, while monitoring the proper functioning of the system. Due to the removal of the propeller turbulence and the limited capability of driving in one plane, the sensor integration on the rover has been done successfully. A 3D model of different environments was generated from LiDAR points, having detected cars, light posts and buildings. Wind measurements have been collected alongside the telemetry of the UGV.