Abstract
For the application of feedback flow control, surface mounted sensors are needed to accurately, efficiently and robustly estimate the flow state. The ability to estimate the flow state in real time is crucial for state based feedback flow control. This paper explores three different methods of state estimation: linear stochastic estimation, artificial neural network estimation, and wavenet estimation. Sensor locations are chosen based on Proper Orthogonal Decomposition of surface flow information. Parameters such as the number of sensors and training database are varied with the goal of developing a highly accurate, robust flow state estimation method for a given flow state basis. The state estimation methods are then applied to the unstable shear layer. Wavenets prove to be a very useful identification method for frequency rich, highly non-linear, periodic behavior.
Published Version
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