The increase in CO2 emissions due to the significant growth of the level of air traffic expected in the next 40 years can be tackled with new technologies able to reduce the skin friction drag of the new generation aircraft. The ACARE (Advisory Council for Aeronautical Research in Europe), within the Flightpath 2015 Visions, has established stringent targets for drag reduction, which can be achieved only with innovative flow control methods. Synthetic jets are a promising method of flow control, especially for their ability to control the flow without the need of a bleed air supply. The application of synthetic jets for flow separation control has been already proven. Their application can also be extended to skin friction drag reduction in a turbulent flow. Indeed, most of turbulence production in a turbulent boundary layer is related to the dynamics of streamwise streaks and vortices in the near-wall region. Synthetic jets can be used to weaken these structures, to reduce turbulence production and consequently skin friction drag. The effectiveness of synthetic jets for skin friction drag reduction in a turbulent boundary layer has already been explored in a few works. However, there is a lack of understanding on the physical mechanism by which this effect is achieved. The aim of this work is to provide further insight on this. A series of experimental investigations are carried out, using three main measurement techniques: Particle Image Velocimetry, Liquid Crystal Thermography and Constant Temperature Anemometry. The effectiveness of a single round synthetic jet in controlling near-wall streamwise streaks and vortices in a laminar environment, in particular those that develop downstream of a circular cylinder, is verified. Turbulent boundary layer forcing is attempted using a synthetic jet array that produces coherent structures of the same scale as the streamwise vortices and streaks of a turbulent boundary layer. The synthetic jet array is able to create regions of lower velocity in the near-wall and of lower skin friction. A possible physical mechanism behind this has been proposed. With a few minor modification, it is believed that the performance of the synthetic jet array could be significantly improved. This can be achieved especially if the array is installed in a feed-forward control unit, which is only briefly explored in this work. In this case the information on the flow field gathered real-time with wall sensors can help to consistently improve the synthetic jet array performance in terms of skin friction drag reduction.

This dissertation presents an experimentally supported multi-physics model of a dielectric barrier discharge boundary layer flow control actuator. The model is independent of empirical data about the specific behavior of the system. This model contributes to the understanding of the specific mechanisms that enable the actuator to induce flow control. The multi-physics numerical model couples a fluid model, a chemistry model, and an electrostatics model. The chemistry model has been experimentally validated against known spectroscopic techniques, and the fluid model has been experimentally validated against the time-resolved shadowgraphy. The model demonstrates the capability to replicate emergent flow structures near a wall. These structures contribute to momentum transport that enhance the boundary layer’s wall attachment and provide for better flow control. An experiment was designed to validate the model predictions. The spectroscopic results confirmed the model predictions of an electron temperature of 0.282eV and an electron number density of 65.5 × 10⁻¹²kmol/m³ matching to within a relative error of 12.4% and 14.8%, respectively. The shadowgraphic results also confirmed the model predicted velocities of flow structures of 3.75m/s with a relative error of 10.9%. The distribution of results from both experimental and model velocity calculations strongly overlap each other. This validated model provides new and useful information on the effect of Dielectric Barrier Discharge actuators on flow control and performance. This work was supported in part by NSF grant CNS-0960081 and the HokieSpeed supercomputer at Virginia Tech. / Ph. D.