Stability Wind Tunnel project seeks better data for aerodynamic models

This is a project that's funded by NASA. We've been working on it for about five years now. The project itself is intended to help the industry, NASA, government, and even academia to be able to compute the air dynamics of aircraft at a much higher confidence level. You're testing full scale aircraft to the cost of tens and hundreds of millions of dollars. How can you turn that from being a test, being a more reliable computation? The students are those who are coming up with the ideas for the experiments that we're going to run. They're coming up and proposing the measurements that they want to take. The breadth of the measurements that's going on in this experiment is unparalleled. We have a student for each type of measurement that we're taking. I'm actually working on the PIV aspect of this project. So, that's particle imaging velocimetry. Our laser forms a sheet that we point at our measurement volume. Our cameras look at that sheet and when we run the wind tunnel, we see the flow of the fog. The particles are illuminated by the laser and the camera takes pictures and picks that up. From there, we can calculate all three components of velocity. And that allows us to look at the boundary layer in the flow and anything that happens in the flow. So we're really trying to understand how the flow interacting with the vehicle itself. And, with the wind tunnel testing we're able to verify if those datas are valid or not. With today's technology, a lot of fluid dynamic has been done computationally. Unfortunately, when it comes to complex geometry objects, the CFD stands for computational fluid dynamics is not really able to predict a lot of flow separation regents where it's happening. In any given year, up to about 400 undergraduate students who are doing research with this very experiment, making measurements as part of their undergraduate pedagogy where their undergraduate lab experience involves them coming in, observing the experiment. But not just that, going in and understanding and using the equipment. Students get all that exposure, they get access to the data. And then meanwhile, they're helping us to get more and more data sets. Every one of their lab sections is gathering more data, which in lots of cases are repeat sets, which is critical to this high standard that we have of getting very confident data that computational modelers can use.