Methodologies: Computational Fluid Dynamics, Data Mining, Machine Learning

Aaron Towne

Assistant Professor, Mechanical Engineering

Aaron Towne is an Assistant Professor in the Department of Mechanical Engineering. His research develops simple models that can be used to understand, predict, and control turbulent fluid dynamical systems. His approach focuses on identifying and modeling coherent flow structures, i.e., organized motions within otherwise chaotic flows. These structures provide building blocks for an improved theoretical understanding of turbulence and also contribute significantly to engineering quantities of interest such as drag, heat transfer, and noise emission. Consequently, strategically manipulating coherent structures can potentially lead to vast performance improvements in a wide range of engineering applications. Realizing this potential requires new data mining and analysis methods that can be used to identify and extract these organized motions from the large data sets produced by high fidelity simulations and experiments, as well as new theoretical and computational approaches for modeling and controlling them. Aaron’s research focuses on developing these tools for turbulent flow applications, while also contributing more broadly to the emerging areas of large-scale data mining and machine learning.

Temperature (color) and pressure (gray scale) from a simulation of a turbulent jet. The pressure field exhibits organized patterns (alternating black and white regions) that can be leveraged to understand, predict, and control the noise produced by the jet. Image courtesy of Guillaume Brès.