Jinping Li

To address the issue that conventional plasma actuation struggles to achieve both high-frequency continuous disturbance and high-energy deposition simultaneously in high Mach number supersonic shock wave/boundary layer interaction (SWBLI), an alternating discharge circuit based on electronic switches is designed in this paper. Schlieren experiments are conducted in a Mach 3 compression corner flow field to compare the control effects of high-frequency discharge, high-energy discharge, and alternating discharge on SWBLI. The control mechanism is revealed by combining fast Fourier transform and proper orthogonal decomposition analysis. Experimental results show that high-frequency discharge has insufficient single-pulse energy and limited control effect on strong shock waves. Although high-energy discharge provides high disturbance intensity, it is difficult to achieve sustained control due to the constraint of the charging cycle. The proposed alternating discharge maintains high-energy pulses while realizing 20 kHz continuous actuation, leading to a significantly improved control effect. It can effectively reduce the inclination angle of the main shock, weaken the shock intensity, and lift the shock intersection position. The control mechanism is characterized by the coupling of instantaneous disturbance, temporal accumulation, and high-frequency fluctuation effects, which can significantly suppress the low-frequency unsteady motion of shock waves. The findings can provide experimental support for supersonic flow control technology.

Cavity oscillation is a significant and challenging issue in aerospace engineering. Surface dielectric barrier discharge (SDBD) plasma actuators can induce electrical body forces instantaneously in the flow, and have been proven to be effective in suppressing the noise caused by cavity oscillation. This research focuses on the steady and unsteady operation modes of SDBD plasma actuators. A systematic parametric study of the cavity flow is conducted experimentally, and the flow characteristics are analyzed through acoustic and particle image velocimetry measurements. Results indicate that under steady control, increased peak-to-peak voltage enhances control effectiveness, and the reduction of wall-normal velocity fluctuation within the shear layer is the primary cause of noise suppression. Under unsteady control, the effects of dimensionless modulation frequency Stm are analyzed in detail. The alteration of vortex evolution patterns leads to two distinct flow regimes, with Stm = 1.6 as the dividing line. When Stm < 1.6, only the frequency information related to plasma actuation is observed in the sound pressure spectrum. Although the peak sound pressure level (SPL) increases, the overall SPL decreases owing to the narrow bandwidth at the peak frequency. When Stm > 1.6, the frequency information of the original flow is also retained. The case of Stm = 2.4 achieves optimal noise suppression, employing the same control mechanism as steady actuation.

In this experimental study, a value-based reinforcement learning algorithm (deep Q-network, DQN) is used to control the flow separation behind a backward facing step at a Reynolds number of 2.9 × 104. The flow is forced by a dielectric barrier discharge (DBD) plasma actuator pasted at the upstream of the step edge, and the feedback information of the separation zone is provided by a hotwire sensor submerged in the downstream shear layer. The control law represented by a deep neural network is implemented on a field programable gate array (FPGA), able to execute in real-time at a frequency as high as 1000 Hz. Results show that both open-loop periodical control and DQN control can effectively reduce the reattachment length and the recirculation area. Compared with the former, which requires dozens of trail-and-error measurements lasting for hours, the latter is able to find an optimal control law in only two minutes, achieving a long-term reward 7% higher. Moreover, by introducing a weak penalty term for plasma actuation, the mean actuator power consumption in DQN can be cut down to only 60% of that in the optimal open-loop control, meanwhile sacrificing a negligible amount of control effectiveness. Physically, the open-loop periodical control destabilizes the shear layer earlier, increasing both the area and the peak amplitude of the high turbulent kinetic energy (TKE) zone, whereas under DQN control, only a slight increase in the TKE peak is observed, and the overall spatial distribution remains the same as baseline.

Oscillation characteristics in a cavity are investigated under real experimental conditions through unsteady numerical simulations of the time-evolving oscillatory damping of a high-Mach-number freestream over a two-dimensional forward-facing cavity. The post-disturbance flow field is taken as the initial condition. Temporal variations in the flow field and wall resistance coefficient are obtained. The forward-facing cavity experiences underdamped oscillatory behavior when subjected to disturbances. The convergence of the oscillations is influenced by the cavity volume, with significant reductions in cavity damping observed when stagnation regions develop within the cavity. During the initial phase of disturbance, each oscillation cycle consists of gas injection and jet phases. In the former, external gas stagnates within the cavity, resulting in a gradual increase in internal density and pressure. High-temperature regions extend from the external flow into the cavity, and bow shocks approach the cavity wall, adversely affecting aerodynamic drag reduction and thermal protection for aircrafts. In the jet phase, the flow field structure resembles the opposing jet. As the gas is expelled, the internal cavity pressure decreases, forming a cold jet that envelops the cavity's surface. The temperature within the boundary layer on the surface decreases, and bow shocks are pushed away from the wall, resulting in thermal-protection and drag-reduction effects. Transitions between phases induce instability in the internal flow states within the cavity. During the transition from the gas injection phase to the jet phase, the wall drag coefficient reaches its peak value; the reverse transition results in the lowest wall drag coefficient.

Aerodynamic/stealth design is becoming an important factor in the advanced airfoil design. In this work, a supervised machine learning method is proposed for aerodynamic and stealth integrated airfoil design. The conditional generative adversarial network (CGAN) is constructed for the multidisciplinary design of airfoil. Then, the generator and discriminator simply using deep neural network have good robustness and stability in training. The CGAN model also shows good generalization capability in the test set, with less than 1% error in fitting to the airfoil profile data, and the generated airfoils are within 10% error compared to the test airfoil aerodynamic stealth characteristics. In addition, the optimization results based on the CGAN model demonstrate that aerodynamic performance improvement would increase the airfoil camber and stealth performance improvement would sharpen the airfoil leading edge.