My research focuses on developing physics-based, reduced-order models that can be used to understand, predict, and control the turbulent flow of fluids in various engineering systems. My approach revolves around 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 disproportionately 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 goal will require the development of accurate, robust reduced-order models that describe the coherent structures to provide a basis for modern control strategies. I approach model development using a diverse set of tools and ideas from a wide range of disciplines, including hydrodynamic stability theory, advanced large-scale data mining, statistics, applied and computational mathematics, and optimal control theory; a recurring theme in my work is a symbiotic relationship between basic theory and experimental and numerical data.
Some of my main projects are described below.