We're at the Kentland Experimental Aerial Systems Lab, or KEAS Lab, which supports uncrewed aircraft experimentation. The original focus of the lab was really on advanced flight control - how to make airplanes fly better. When an aircraft designer develops a concept for an aircraft, they typically focus on a particular flight condition, where the airplane flies at a constant speed with small turns. And a lot of the theory that people develop to control aircraft is based on these small perturbation assumptions. But there are applications where it would be advantageous to allow the aircraft to behave more aggressively. To support those applications, we have to develop new theory. That's what the graduate students do as part of their dissertation research. But what this lab enables us to do is to come out and test those algorithms that they've developed, the theory that they've developed, on actual aircraft. I'm an international student from Ethiopia. I've always loved the aerospace world. I guess I got that influence from my parents. Ironically, my father was a pilot and my mom was a flight attendant. Now they're retired. Eventually, as I grew, I realized that I had more interest other than just flying. And I wanted to understand how aircraft work and this field allowed me to understand that. There's a lot of interest in the aerospace industry right now in a concept called air mobility. And the idea is, these smaller, electric powered, vertical-takeoff and landing aircraft that can operate out of cities and fly back and forth from cities to suburbs or between industrial sites in an urban area. The challenge there is that the wind conditions are very strong and unpredictable. We have done a very good job as an aerospace community to understand the weather patterns where big aircraft fly, but we don't have an understanding where small aircraft will fly. By small, I mean drones. This research is essentially saying there's some gaps within that region, one of which is understanding wind velocity. And how do we assimilate those to larger weather predicting models? If we can collect more data down low and provide that data to numerical weather modelers, they can make better forecasts. And if we can make better forecasts of the weather conditions in those kinds of environments, that will be enabling for this new technology.
