Graham Smith

Currently, Unmanned Aerial Vehicles (UAVs) are at a critical stage of development, where there are enough current UAV designs and predictions about future battery technology and its related performance that forecasts on future UAV performance can be made based on this public data. These forecasts are important because they could be used to optimize support infrastructure for UAVs used for package delivery. Previously, research on predicting electric performance had focused on larger, passenger-carrying, fixed-wing aircraft, and the results were not transferable to smaller UAVs. One reason that research hasn’t focused on these smaller UAVs is a lack of data. 

Therefore, I manually collected data online on 40 UAV designs as there was no dataset for the designs and specifications that I needed, which was used for training the design tool. Then, I came up with a very computationally simple way to create these forecasts, which was by creating a sizing model for electric UAVs. A sizing model is a method for estimating certain characteristics of a potential aircraft design, and it is usually the first step in the design process for any aerial vehicle. I developed the UAV sizing model by altering a common plane design tool to work with electric UAVs and use recent UAV data. I determined the accuracy of my UAV design tool by comparing it to items in my database and comparing it to a similar model for regular planes. 

My tool was slightly less accurate than the comparable model, with an 18 percentage point higher error, which was considered a good result considering the lower amount of data relating to electric drones. I then varied the different battery technologies and inputted the assumed values into my tool to predict the future performance of drones. I showed the relationship between range, which is how far the UAV can travel, its total weight, and how much it can carry. In summary, I made a uniquely computationally simple and general UAV design tool.