Reduced order models for control of fluids using the Eigensystem Realization Algorithm

In feedback flow control, one of the challenges is to develop mathematical models that describe the fluid physics relevant to the task at hand, while neglecting irrelevant details of the flow in order to remain computationally tractable. A number of techniques are presently used to develop such reduced-order models, such as proper orthogonal decomposition (POD), and approximate snapshot-based balanced truncation, also known as balanced POD. Each method has its strengths and weaknesses: for instance, POD models can behave unpredictably and perform poorly, but they can be computed directly from experimental data; approximate balanced truncation often produces vastly superior models to POD, but requires data from adjoint simulations, and thus cannot be applied to experimental data. In this paper, we show that using the Eigensystem Realization Algorithm (ERA) \citep{JuPa-85}, one can theoretically obtain exactly the same reduced order models as by balanced POD. Moreover, the models can be obtained directly from experimental data, without the use of adjoint information. The algorithm can also substantially improve computational efficiency when forming reduced-order models from simulation data. If adjoint information is available, then balanced POD has some advantages over ERA: for instance, it produces modes that are useful for multiple purposes, and the method has been generalized to unstable systems. We also present a modified ERA procedure that produces modes without adjoint information, but for this procedure, the resulting models are not balanced, and do not perform as well in examples. We present a detailed comparison of the methods, and illustrate them on an example of the flow past an inclined flat plate at a low Reynolds number.Comment: 22 pages, 7 figure

The art and science of flow control – case studies using flow visualization methods

Active flow control (AFC) has been the focus of significant research in the last decade. This is mainly due to the potentially substantial benefits it affords. AFC applications range from the subsonic to the supersonic (and beyond) regime for both internal and external flows. These applications are wide and varied, such as controlling flow transition and separation over various external components of the aircraft to active management of separation and flow distortion in engine components and over turbine and compressor blades. High-speed AFC applications include control of flow oscillations in cavity flows, supersonic jet screech, impinging jets, and jet-noise control. In this paper we review some of our recent applications of AFC through a number of case studies that illustrate the typical benefits as well as limitations of present AFC methods. The case studies include subsonic and supersonic canonical flowfields such as separation control over airfoils, control of supersonic cavity flows and impinging jets. In addition, properties of zero-net mass-flux (ZNMF) actuators are also discussed as they represent one of the most widely studied actuators used for AFC. In keeping with the theme of this special issue, the flowfield properties and their response to actuation are examined through the use of various qualitative and quantitative flow visualization methods, such as smoke, shadowgraph, schlieren, planar-laser scattering, and Particle image velocimetry (PIV). The results presented here clearly illustrate the merits of using flow visualization to gain significant insight into the flow and its response to AFC