This paper studies the internal flow dynamics of a continuous-discrete pulsing jet fluidic oscillator and the influence of splitter on its outlet peak velocity and oscillating frequency. For that purpose, two-dimensional unsteady numerical simulations were conducted, covering five different inlet mass flowrates $\dot{m}$ and three oscillator models (two continuous-discrete pulsing oscillators with different splitting distances and a continuous sweeping oscillator).Results show that there are at least three different velocity peaks in one period at both outlets of continuous-discrete pulsing oscillators. When the splitting distance increases, the first peak velocity (U_max,1,O) decreases, the third one (U_max,3,O) increases, while the second peak velocity (U_max,2,O) is less affected; the three peak velocities generally satisfy U_max,3,O＞U_max,1,O＞U_max,2,O , unless $\dot{m}$ is extremely large or small.When the splitting distance increases, oscillating frequency decreases at first and then increases, but the frequency of continuous sweeping oscillator is always the highest and the difference of the other two keeps rising as $\dot{m}$ increases.Results also show that when the splitting distance was increased or $\dot{m}$ was reduced to a certain extent, the outlet velocity’s oscillating pattern changes, including the significant velocity fluctuations both before and after U_max,1,O, the phase shift and the great amplitude increase of U_max,2,O.The research results are of great importance in supporting the design optimization of the continuous-discrete pulsing oscillator and the development of flow control method with this kind of fluidic actuators.

Real-time reconstructing a complete picture of the flow field around aircraft by limited measurements is fundamental to real-time control. This paper proposes a method for real-time reconstruction of unsteady airfoil surface pressure based on the compressed sensing and Kalman filter. The method mainly consists of offline database construction and online real-time perception. Firstly, the full-field sampling of airfoil surface pressure is carried out by particle image velocimetry, pressure-sensitive paint, and computational fluid dynamics (CFD). The dimensionality of the measured full-field pressure data is reduced by the proper orthogonal decomposition, yielding a database containing the dominant coherent flow structures and the state transition relationships among the modal time coefficients. Secondly, based on the compressed sensing, the time coefficients of each dominant mode are preliminarily obtained by solving the optimization problem under the L₁-norm using the measurements of discrete pressure sensors and the dominant pressure modes. Finally, the state transition relationships among modal time coefficients obtained offline are put into the Kalman filter for prediction. The modal time coefficients obtained online are further corrected by the Kalman filter. CFD simulation results are used as validation to evaluate the performance of this method. Results show that the reconstruction error of this method is 64.79% lower than that of using compressed sensing only.

Though particle image velocimetry (PIV) has a high spatial resolution, it is often limited by the temporal resolution, which is typically below 15 Hz and difficult to acquire high-frequency flow information. Compressive sensing (CS) is able to capture the high-frequency information based on sparse sampled data, but if directly employed to all spatial points, the massive data poses a great computational cost. In this study, a high-frequency response flow field reconstruction method via coupling POD and CS is proposed to solve the above difficulty. POD is used first to reduce the dimensionality of the sampled PIV data, and the spatial modes and corresponding time coefficients are calculated at the same time. By taking the time coefficients at the sub-Nyquist sampling points in CS as the observable and selecting an appropriate sparse basis, the high-frequency mode coefficients can be computed via the basic pursuit method. The unsteady flow field with high-frequency temporal information can then be reconstructed via combining the spatial modes and the corresponding resolved time coefficients. Two cases, i.e. the periodic oscillator flow and the non-periodic double-cylinder wake flow, are used to test the adaptivity of the proposed method. The results show that without the aid from contact measurement, the proposed method can accurately reconstruct the original flow field for period flow and the reconstruction error is less than 3%, while for nonperiodic complex flow, the error is significantly increased due to high-frequency noise. Overall, the proposed method can be used for periodic flow to improve the temporal resolution of the measured experimental data.