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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Reducing the design variable space is crucial in multi-objective airfoil profile optimization to improve optimization efficiency and reduce computational costs. Based on random forest and deep neural networks (DNNs), this work performs range reduction on ten design variables obtained through a fourth-order class shape transformation parameterization method for subsonic airfoil profiles. Three aerodynamic performance objectives (lift coefficient, drag coefficient, and lift-to-drag ratio) are evaluated using the Reynolds-averaged Navier–Stokes equations, and two radar stealth performance objectives (horizontal and vertical polarization radar cross sections) are assessed through the method of moments. By combining a DNN architecture with an improved regression prediction capability, predictive models are trained for mapping design variables to design objectives. The prediction errors are below 3% for the aerodynamic performance design objectives and below 1% for the stealth performance design objectives. The particle swarm optimization algorithm provides optimized airfoil profiles for three scenarios. First is a higher lift coefficient with a lower radar cross section. Second is a lower radar cross section. Third is a higher lift coefficient. Increasing the airfoil curvature and reducing the maximum thickness improves the lift coefficient by 386 counts and reduces the drag coefficient by 17 counts. By curving the airfoil leading edge, the radar cross section for the transverse electric and transverse magnetic polarizations decreased by 2.78 and 2.09 dBsm, respectively. [ABSTRACT FROM AUTHOR]

Spanwise wall oscillation is an easily implemented active control scheme. It can delay the transition to turbulent flows in incompressible boundary layers under appropriate control parameters. However, this delayed transition in compressible boundary layers has not yet been confirmed numerically. In this paper, we perform a series of direct numerical simulations of a spatially developing subsonic boundary layer with different spanwise wall oscillation periods and amplitudes. We numerically confirm the delay in the transition under appropriate control parameters. The delayed transition can be explained by the Stokes boundary layer induced by the spanwise wall oscillation affecting the streamwise vortices. The superposed spanwise velocity and streamwise vorticity observed in the Stokes boundary layer can weaken the development of instability in the original streamwise vortices under appropriate control parameters, leading to a delayed transition. Furthermore, the spanwise wall oscillation changes the flow compressibility, as can be inferred from the equation for the velocity divergence. The enhanced compressibility around the pre-transition regions also stabilizes the development of the streamwise vortices and causes a delayed transition. [ABSTRACT FROM AUTHOR]