Data-driven modeling of pitching and plunging tandem wings in hovering flight

The current paper addresses the challenges related to low-order modeling of a domain with moving boundaries from experimentally obtained flow data. In particular, a modeling methodology is implemented on measured flow field data of pitching and plunging wings in a tandem configuration. The applied method uses a Eulerian–Lagrangian method to map a deforming domain to a domain with stationary boundaries. This mapping allows the use of modeling approaches based on the proper orthogonal decomposition (POD) or dynamic mode decomposition. In this paper, the velocity field associated with the wings’ flapping motion was mapped and modeled using POD-based approach. The flow field dynamics was approximated by a linear model based on four POD modes. Since the state of the low-order model is physically impossible to measure directly, a Kalman filter was implemented. The Kalman filter used the signals from two low-profile strain gauge sensors located at the root of the hindwing to evaluate the reduced-order state of the system. Then, the full state of the system was estimated using the POD approximation. Therefore, using only two strain sensors’ signals, the vortex dynamics associated with the tandem wings motion was successfully modeled.