School of Engineering
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Shahab Mirjalili
Physical Science Research Scientist
Current Research and Scholarly InterestsBroadly, 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