The purpose of this paper is to investigate the feasibility of using dielectric barrier discharge plasma actuation for the pitch stability control of a National Advisory Committee for Aeronautics 0012 pitch oscillating airfoil at low Reynolds number. The effects of the plasma actuator on the flow are incorporated into Navier–Stokes equations as a body force source term. The plasma body force is modeled by a phenomenological approach. Solutions are obtained by two-dimensional numerical simulations using the γ−Reθ transition model. The flow control effects of plasma actuator locations, strengths, force directions, and laminar separation flutter (LSF) conditions are investigated. The energy extracted by the airfoil from the flow over one oscillation cycle is defined to evaluate the control performance. The LSF analysis based on energy maps is also implemented. The control effect of co-flow plasma configuration on pitch instability is better than the counterflow configuration. The co-flow plasma actuator located at 0.6c reaches the best control performance and the energy extracted by the airfoil from the flow over one oscillation cycle reduces by 726% relative to the baseline case. The flow feature inducing the pitch instability and the flow control mechanism of co-flow and counterflow plasma actuator is analyzed in terms of the flow structure and pressure distribution, respectively. The results show that the plasma flow control is effective to mitigate the pitch instability across a wide range of Reynolds numbers at which laminar separation flutter occurs without control.

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In this paper, a reduced-order model (ROM) based on data-driven machine learning algorithm is constructed to identify the aerodynamic forces of airfoil undergoing large-amplitude pitching oscillation. Strong nonlinearity and unsteadiness in aerodynamics is a major challenge in the prediction of aerodynamic forces. To deal with this problem, the recurrent neural network (RNN) with gated recurrent unit (GRU) is applied for nonlinear and unsteady aerodynamic identification. A motion input signal which covers a wide range of frequency and amplitude is designed to enable the ROM with generalization capability. Shear stress transport (SST) model with low-Reynolds number modification is introduced into the computational fluid dynamics (CFD) method to calculate the aerodynamic forces as the training data. The time step size and lag order of the model are determined by the frequency domain characteristics of the training data. The results suggest that the proposed ROM has a high identification precision on nonlinear unsteady aerodynamics. The well-trained ROM could accurately predict the aerodynamic forces of airfoil undergoing sinusoidal oscillations with various frequencies and amplitudes. The proposed ROM shows advantages in accuracy over other ROM techniques. The calculation speed of ROM is 69 times faster than that of CFD method on the premise of accuracy, which can be expected a good application in engineering.