Computational Turbulence and Multiphysics Group
The Computational Turbulence and Multiphysics Group is a multidisciplinary research team that develops computational techniques to address the entire spectrum of research, including discovery, analysis, predictive modeling, optimization, and control of turbulent flows and their multiphysical interactions.
As a world-leading group in this domain, we develop and apply computational techniques to address the entire spectrum of research, including discovery, analysis, predictive modeling, optimization, and control of turbulent flows and their multiphysical interactions. Our goal is to expand the boundaries of knowledge in real-world applications, from advancing aerospace, marine, and nuclear sciences to improving energy efficiency and environmental sustainability.
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Computational Turbulence and Multiphysics Group Members
Jesse Capecelatro
Assoc. Professor, Mechanical Engineering & Aerospace Engineering
Multiphase/multi-physics flow lab
Multiphase and Granular flow, Turbulence, Optimization, HPC, Energy
Karthik Duraisamy
Professor, Aerospace Engineering & Mechanical Engineering & Nuclear Engineering; Director, MICDE
Computational Aerosciences Laboratory
Computational science, Data Driven & reduced order modeling, Numerical methods, Turbulence modeling, Aeromechanics
Eric Johnsen
Professor, Mechanical Engineering
Scientific Computing and Flow Physics Laboratory
Multiphase flows, plasmas, Numerical methods, High energy density physics
Krishnan Mahesh
Professor, Naval Architecture and Marine Engineering & Aerospace Engineering
Computational Fluids Laboratory
LES, Marine Propulsors, Cavitation, Hydroacoustics, FSI, HPC
Venkat Raman
Professor, Aerospace Engineering & Mechanical Engineering
Advanced Propulsion Concepts Lab
Turbulent Combustion modeling, LES, HPC, Extreme events, Propulsion
Aaron Towne
Asst. Professor, Mechanical Engineering
Aeroacoustics, Reduced order modeling, Dynamical systems, Flow instabilities
Ricardo Vinuesa
Associate Professor and Associate Chair for Research, Aerospace Engineering
Computational methods, Deep learning, Artificial Intelligence, Turbulence, Flow control