Drone to Fly In Greenland to Collect Glacier Movement

In spring semester of my second year in college (2020), I was selected through the Equity Research Experience for Undergraduates Program to conduct research with Dr. Langelaan in the Aerospace Department at The Pennsylvania State University. This program awarded a scholarship to carefully selected students where they perform 10 hours of research a week and present their findings in a poster format to judges along with completing an abstract that is published in the Penn State records.

My research goal was to create an Unmanned Aerial Vehicle (UAV) with a purpose of collecting data from a network of sensors deployed on the Helheim Glacier in Greenland (Figure 1). This mission requires a drone capable of flying several kilometers as well as hovering over sensors in the network.

Figure 1. Location on Google Earth of Glacier

Figure 2 is the current drone in Greenland, but the goal includes improving the design of this model. Since it needs to be transported over a long distance, folding/disassembling capabilities were considered for the new design. Using previous information from research on this topic, calculations were made to vary factors such as flight time or total weight with disc loading (Figure 3). These equations then produced graphs in MATLAB to visually compare the different variables and create a better understanding of the size, shape, and modifications of the drone.

Figure 2. Picture of current drone flying by Helheim Glacier, Greenland

Figure 3. Graph of Total Mass vs Disc Loading

The graph in Figure 3 shows that a lighter vehicle is desired for the best results.

Rotor and motor combinations based on commercial off-the-shelf equipment formed into four different groups comparing quadrotors with hexrotors. From these four groups, information gathered gave the opportunity to perform calculations resulting in estimated flight time, battery weight, and total weight of all combinations (Figure 4). Varying the anticipated structure weight, all the combinations were then graphed to compare sensitivity of weight with flight time (Figure 5).

Figure 4. Table of Different Rotor Combinations

Figure 5. Graph of the 4 combinations tested at different weights to show sensitivity to flight time

One group specifically stood out with a higher flight time and less sensitivity to changes in design parameters. Quadrotor and hexrotor prices were then compared on a bar graph to notice if the small variations in flight time between the two are worth the extra cost (Figure 6). The four-rotor combination of this group proved to be the best fit after this comparison.

It is shown that the hex-rotor design has the highest flight time. The cost is then compared to see if the extra flight time is worth the cost:

Figure 6. Cost Comparison for 4 vs 6 Rotors

It is concluded that, even though the 6 rotor has a slightly higher flight time, it is not worth the extra cost compared to a 4 rotor.

After the semester ended and the pandemic began, I was unable to continue with this project since I could not work in person to physically build the drone model that was determined. I switched to other research projects the next year but still keep in touch with the professor involved on progress for the drone.

This project shows a much more technical design using coding languages and mathematics versus my other projects that are based more on physical prototypes.