Chaotic, swirling motion, called turbulence, in fluid flows near solid bodies is a strong source of undesirable noise. The impact of this noise creates a negative experience for people who live near airports, wind turbine farms, and military bases, often creating the need for restrictive rules. The study of this noise, called aeroacoustics, has been the subject of extensive research in recent decades, but our ability to make accurate predictions of flow noise is still extremely poor. Six peer-reviewed and widely accepted models have been proposed, but the differences between them are so great they are practically unusable; the disagreement between the highest and lowest predicting models is a factor of fifteen, meaning our ability to predict the noise on an aircraft is only accurate to the range between a garbage disposal and a rock concert. One of the reasons for the lack of significant progress in this research area is the nearly infinite number of variables that may contribute to the production of aerodynamic noise. Finding an organized way to generate and characterize all of these variables has presented a huge challenge, but it’s critical for advancing the field of aeroacoustics and improving human quality of life. My team has designed a novel wind tunnel experiment that manages to neatly divide up the different variables of the problem in controllable and repeatable ways by using a rotating airplane wing model to change the conditions on the test surface. The preliminary results of these experiments show that it is in fact possible to control and study this phenomenon in a systematic way, which we believe will help reveal the underlying physics and improve our ability to make accurate noise predictions.