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Research Abstracts Online
January 2009 - March 2010

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University of Minnesota Twin Cities
Institute of Technology
Department of Electrical and Computer Engineering

PI: Mihailo Jovanovic

Modeling and Control of Turbulent Wall-bounded Shear Flows

This project introduces new methods for modeling and control of turbulent wall-bounded shear flows. Transition in these flows is crucial to the technologically relevant problem of skin-friction drag reduction. This form of drag results from the no-slip boundary conditions on the surface of a submarine or the fuselage and the wings of an aircraft; it approximately accounts for 50% of the overall drag of a subsonic aircraft at cruise conditions, and 90% of the overall drag of an underwater vehicle. Progress in these problems has been hindered by the lack of understanding of turbulent flows and the absence of tractable models and theoretical tools for analysis and control thereof. Since skin-friction drag directly translates into large fuel consumption for air and water vehicles, there is a critical demand for development and utilization of advanced theoretical and computational techniques in this area.

During the last period, the researchers have developed a framework for design of a sensorless flow control strategy for preventing transition in a channel flow. The theoretical predictions are confirmed by direct numerical simulations of the nonlinear Navier-Stokes equations, which are conducted on MSI’s computing resources.

Further work will develop new control-oriented models of turbulent wall-bounded shear flows; these models will contain appropriate statistical description of flow disturbances that approximate turbulent flow statistics in the least-squares sense. The flow estimators and controllers designed using these models will have superior performance compared to their counterparts designed without an accurate statistical description of flow disturbances. This approach will enable successful skin-friction drag reduction at higher Reynolds numbers than currently possible.

Group Members

Binh Lieu, Graduate Student
Rashad Moarref, Graduate Student