Tamer Zaki, a professor of mechanical engineering, focuses on theoretical and computational innovations in the field of turbulence and solutions to the challenges that are created when turbulence meets momentum, heat, and mass. He is also a member of JHU’s Institute for Data Intensive Engineering and Science (IDIES), the Center for Environmental and Applied Fluid Mechanics (CEAFM), and the Hopkins Extreme Materials Institute (HEMI).
Zaki’s work offers novel applications for hydro and aero-dynamics, environmental flow dynamics, energy systems, and medical interventions. His research and the work of his lab, Johns Hopkins’ Flow Science and Engineering (FSE), address a classic, complex mechanics problem—chaotic fluid motion caused by infinitesimal disturbances. To capture these detailed flow instabilities and the resulting turbulence, Zaki creates high-fidelity, scalable, numerical simulations with complementary analytical and semi-analytical techniques. Zaki’s FSE group is recognized for its seminal contributions in the fields of data assimilation, where scarce sensor measurements are augmented using numerical simulations and machine learning algorithms in order to discover the entire flow field that generated the sensor signals. Current research areas include modeling and controlling the transition to turbulence in extreme flow regimes, flow manipulation and turbulent drag reduction technologies, and the stability of complex fluids.
Zaki received the Office of Naval Research Young Investigator Award and is the recipient of the 2016 and 2017 visiting fellowship from the Japanese Society for the Promotion of Science. He is the 2017 recipient of JHU William H. Huggins Excellence in Teaching Award and has garnered several awards at Imperial College London. He is a life member and Fellow of the American Physical Society.
Author of numerous journal articles and book chapters, Zaki is a member of the editorial advisory board of Flow, Turbulence and Combustion and is an associate editor of the Journal of Fluid Mechanics.
He received a BSc in mechanical engineering from Penn State University in 1998, and, from Stanford University, an MSc in mechanical engineering in 2001 and a PhD in flow physics and computational engineering in 2005.
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$4.8 million grant promotes research on scientific machine learning focused on greater predictive capabilities.
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For his “fundamental contributions to linear and non-linear theories of bypass transition, novel applications of data science to fluid mechanics, and innovative numerical simulations of Newtonian and non-Newtonian flows with practical applications.”