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Open Access Publications from the University of California

Vortex-Based Aero- and Hydrodynamic Estimation

  • Author(s): Hemati, Maziar Sam
  • Advisor(s): Eldredge, Jeff D
  • Speyer, Jason L
  • et al.
Abstract

Flow control strategies often require knowledge of unmeasurable quantities, thus presenting a need to reconstruct flow states from measurable ones. In this thesis, the modeling, simulation, and estimator design aspects of flow reconstruction are considered. First, a vortex-based aero- and hydrodynamic estimation paradigm is developed to design a wake sensing algorithm for aircraft formation flight missions. The method assimilates wing distributed pressure measurements with a vortex-based wake model to better predict the state of the flow. The study compares Kalman-type algorithms with particle filtering algorithms, demonstrating that the vortex nonlinearities require particle filters to yield adequate performance. Furthermore, the observability structure of the wake is shown to have a negative impact on filter performance regardless of the algorithm applied. It is demonstrated that relative motions can alleviate the filter divergence issues associated with this observability structure.

In addition to estimator development, the dissertation addresses the need for an efficient unsteady multi-body aerodynamics testbed for estimator and controller validation studies. A pure vortex particle implementation of a vortex panel-particle method is developed to satisfy this need. The numerical method is demonstrated on the impulsive startup of a flat plate as well as the impulsive startup of a multi-wing formation. It is clear, from these validation studies, that the method is able to accommodate the unsteady wake effects that arise in formation flight missions.

Lastly, successful vortex-based estimation is highly dependent on the reliability of the low-order vortex model used in representing the flow of interest. The present treatise establishes a systematic framework for vortex model improvement, grounded in optimal control theory and the calculus of variations. By minimizing model predicted errors with respect to empirical data, the shortcomings of the baseline vortex model can be revealed and reconciled. Here, the method is demonstrated on an impulse matching model for canonical unsteady wing maneuvers and reveals the shortcomings of the Kutta condition in such flows. The resulting analysis sheds light on the governing physical processes and provides guidance for model improvement for the unsteady aerodynamics associated with these canonical wing maneuvers.

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