Broadly, my research lies in the intersection of fluid mechanics, scientific computing, and machine learning. My work aims to develop and use computational methods to provide a predictive understanding of complex flow problems, including those involving multi-physics couplings and multiphase dynamics across a wide range of scales and Reynolds numbers. In this vein, I develop physically consistent models, robust numerical schemes, and high-performance computing (HPC) software that enable high-fidelity simulations of flows involving complex multi-physics effects. These developments build upon my novel work on modeling multiphase flows and my high-performance multiphase, multi-physics software. In addition to simulations, I use asymptotic analyses and machine learning (ML) to construct reduced-order models (ROMs) that can be used for engineering analysis, control, design, and especially optimization. I am interested in a wide range of applications involving impactful problems. In particular, I am passionate about improving the predictive understanding of multiphase flows in:
Propulsion and energy conversion/storage
Additive manufacturing processes
Biophysical systems
Environmental flows