Pitch Perfect - tuning low-order aerodynamic models for non-periodic flows

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Unpiloted aerial vehicles (UAVs) can be expected to encounter external disturbances and to maneuver around obstacles during its journey. As a result of the vehicle deviating from steady level flight, the flow around it is no longer statistically stationary. Without a well-defined mean (time-averaged) flow, it is more challenging to develop reduced-order models that capture coherent flow structures around the vehicle. This difficulty is compounded by the fact that most sensors onboard a UAV only measure flow quantities near a solid boundary. If the UAV had knowledge of the surrounding velocity field, it would have better control authority during violent maneuvers or in the face of gusts. The objective of this project, therefore, is to exploit machine learning and reduced-order modeling techniques that convert limited surface sensor measurements into an accurate estimation of the velocity field around the vehicle. This can be interpreted as a super resolution problem for non-stationary flows. It is expected that the results from this project could be extended further to more complex maneuvers, e.g. pitching and surging, that are inherent to large-scale flow applications such as helicopter blades and wind turbines undergoing dynamic stall.