Viscous Drag Reduction In Boundary Layers

Timely in two respects: the first is the growing awareness of the need for energy conservation in both transportation and process industries. The second is the recent emergence and application of several viscous drag reduction techniques. Laminar as well as turbulent flows are covered as are active

The survival of the Aeronautical Industries of Europe in the highly competitive World Aviation Market is strongly dependent on such factors as time-to-market of a new or derivative aircraft and on its manufacturing costs but also on the achievement of a competitive technological advantage by which an increased market share can be gained. Recognizing this, cooperative research is continuously encouraged and co-financed by the European Union in order to strengthen the scientific and technological base of the Aeronautical Industries thus providing - among others - the technological edge needed for survival. Corresponding targets of research within Area 3, Technologies for Transport Means, and here in particular Area 3A, Aeronautics Technologies, of the Industrial and Materials Technologies Program ( Brite -EuRam III, 1994 -1998) have been identified to be aircraft efficiency, cost effectiveness and environmental impact. Concerning aircraft efficiency - relevant to the present research - a reduction in aircraft drag of 10%, a reduction in aircraft fuel consumption of 30%, and a reduction in airframe, engine and system weight of 20% are envisaged. Meeting these objectives has, of course, also a strong positive impact on the environment.

A series of numerical experiments were performed to explore new control strategies suitable for distributed control. All numerical experiments were performed in a fully developed turbulent channel flow. Three different control schemes were developed. These include applications of a neural network, a suboptimal control theory and systems control theory. In all cases, substantial drag reductions were achieved. A simple feedback law obtained from the neural network was implemented into a chip, which contains microelectronics (for the control logic), microsensors and microactuators (cu M chip). All cases used surface blowing and suction as control input. Development of a new numerical method for simulating a turbulent flow over distributed microflap actuators is included.

Proceedings of the 4th European Drag Reduction Meeting