A numerical study is conducted to reveal the roles of the acoustic impedance ratio Z and acoustic velocity ratio X on semiconfined cellular detonations propagating through a layer of reactant bounded by inert gas. A series of cases are simulated based on the control variate method in two initial conditions. The simulation results unveil significant differences in the roles of Z and X in the layered detonation propagation. As the Z increases (at X≡1), the layered detonation propagation regime remains in an oblique-shock-attached detonation structure whose shock wave angle generally remains the same while the interface angle slightly decreases, resulting in reduced detonation area expansion. However, as the X increases (at Z≡1), the layered detonation propagation regime is more complicated, whose shock wave angle significantly increases and even becomes an overshooting detached shock to generate a compression effect on the detonation front. In cases with a high X of 3–4, the overshooting detached shock forms an inverse-Mach reflection or double-Mach reflection shock system, leading to a detonation area decrement and overdriven propagation. Furthermore, a semi-empirical prediction model for the detonation velocity deficit subject to both detonation area expansion and decrement is established in a unified manner and validated through existing experimental data. This study first clarifies the respective roles of two parameters and elucidates the underlying mechanisms, which further deepens the understanding of the layered detonation propagation.

This paper presents a computational-fluid-dynamics-driven machine learning framework to enhance Reynolds-averaged Navier–Stokes turbulence models for complex delta wing flows at high Reynolds numbers and transonic speeds. Building upon the coupled Spalart–Allmaras model framework, this study represents the first application of an interpretable machine learning framework to such complex three-dimensional vortex-dominated external aerodynamic applications. The key contribution lies in applying gene expression programming to learn optimal anisotropy tensor corrections for delta wing flows, where the Boussinesq approximation is extended with nonlinear stress–strain terms and coupled to a redefined production term in the transport equation for turbulent viscosity. Trained on a single-delta wing configuration, the proposed approach is validated on double- and triple-delta wing geometries under challenging transonic flow conditions, demonstrating significant improvements in the prediction of aerodynamic coefficients and showcasing both accuracy and robustness. The use of gene expression programming enables the automated discovery of models, generating explicit, physically interpretable expressions that provide deeper insight into vortex-dominated flow physics and facilitate generalization across related flow regimes. While the present results are specific to delta wing configurations and remain most accurate near the training conditions, they demonstrate the potential of physics-informed machine learning as a promising direction for advancing turbulence modeling in such complex three-dimensional flow regimes.

This study examines the laser ablation (LA) ignition process in a scramjet combustor through integrated experimental and numerical methods. The effects of laser energy, ignition location, and cavity geometry on ignition performance are systematically investigated. High-speed CH* chemiluminescence imaging was employed to visualize flame kernel development, while Reynolds-averaged Navier–Stokes (RANS) simulations were utilized to characterize the underlying flowfield. Results demonstrate that both laser energy and ignition location exert significant influence on flame kernel formation and subsequent flame stabilization. A modified cavity configuration featuring a secondary recess integrated into the base wall of the primary cavity was proposed and evaluated. This design achieved a 43% reduction in minimum ignition energy and markedly enhanced ignition stability. Numerical analyses further revealed that the secondary cavity attenuates thermal losses, promotes the formation of localized low-speed recirculation zones, and improves fuel–air mixing, collectively establishing a more conducive environment for ignition. The proposed geometry offers a novel and effective strategy for optimizing ignition in scramjet combustors.

This paper presents a new theoretical approach for accurately evaluating the tangential velocity gradient at the stagnation-point boundary-layer (BL) edge, a key parameter for predicting stagnation point heat flux. Traditional methods often use inviscid stagnation point values, which this study shows can introduce significant errors in practical scenarios. Both the BL edge and inviscid tangential velocity gradients decrease with increasing thermochemistry. Higher viscosity increases the difference between these gradients for a given gas model but reduces the disparity between edge values across different gas models. Additionally, neither the edge nor inviscid stagnation point values show significant thermochemical nonequilibrium effects, and nonequilibrium values are well approximated by equilibrium values. The proposed approach derives the BL edge tangential velocity gradient by considering the linear variation of the inviscid gradient along the stagnation streamline, the absence of BL displacement, and the ratio of BL thickness to shock standoff distance, δ/Δ, for which a new analytical expression including real-gas effects is developed. Validation is performed using two-dimensional axisymmetric Navier–Stokes simulations across a range of gas models, gas mixtures, and high-enthalpy freestream conditions for both cold (300 K) and hot (2000 K) walls. This approach significantly improves theoretical prediction accuracy.

The shock-wave/boundary-layer interaction that is generated by a sharp fin at incidence on a flat plate is studied for the case of a transitional incoming boundary layer in a Mach-6 shock tunnel. Novel variations of several diagnostic techniques are employed to explore the mean and unsteady flow features induced near the surface of the plate. Fast-response temperature-sensitive paint (TSP) measurements are used to derive time-resolved heat-flux distributions in the interaction region. Plan-view high-speed schlieren visualizations are obtained through the surface of the plate through the use of flush-mounted windows; these visualizations provide a qualitative picture of the flow development and allow the behavior of high-frequency disturbances to be examined. Finally, multipoint wall-normal focused laser differential interferometry (FLDI) provides high-bandwidth, pointwise measurements of disturbance velocities, propagation angles, and turbulence intensities in the interaction region. Although the schlieren, TSP, and FLDI results show the flow to be transitional in nature, no strong evidence of modal content at any specific frequency is detected. Despite the transitional nature of the boundary layer, the mean heat-flux features determined by the TSP show good collapse when plotted about a virtual conical origin (as in the turbulent case); however, significant levels of unsteadiness are seen throughout the interaction and, in particular, in the inception region and close to the separation line.

Operating satellites in the very-low-Earth-orbit (VLEO) region has a number of advantages. However, a primary challenge to the sustained operation of VLEO satellites is the significant atmospheric drag resulting from the higher ambient density, which leads to rapid orbital decay and eventual reentry. Therefore, reducing aerodynamic drag is essential for extending the operational lifetime of VLEO satellites. At the same time, it is important to consider the internal volume to ensure that the satellite maintains its functional capabilities. This work presents a shape optimization framework for VLEO satellites aimed at minimizing the aerodynamic drag without compromising the internal volume. The optimization is performed using a genetic algorithm, with drag force calculated via the panel method and validated by the direct simulation Monte Carlo simulations. Under a fixed-volume constraint, the optimal satellite geometry achieves a maximum drag reduction of 60.3% compared to the reference satellite. Furthermore, this study employs multi-objective optimization to simultaneously minimize aerodynamic drag and maximize internal volume. The results demonstrate that the notable improvements in both drag and volume can be simultaneously achieved through shape optimization. Compared to the reference satellite, the optimal satellite geometry offers a drag reduction of 53.9% and a volume increase of 7.6%.

As advances and simulation prediction design become more prevalent, the methods for solving rotor dynamics response are challenged to meet higher standards for precision, efficiency, and convergence. To address the limitations of current approaches like Newmark and Runge–Kutta in solving nonlinear responses of rotor dynamics in engineering applications, this paper introduces an improved differential-quadrature precision method. This method fits the Duhamel term with a nonequidistant differential-quadrature technique and uses Taylor and Lagrange formulas for self-starting predictions, balancing accuracy with computational efficiency. Furthermore, the paper presents an iterative strategy that effectively addresses the convergence issues of precise integration methods under large step sizes for nonlinear response solutions. Comparative analysis with real aeroengine gas turbine rotor models demonstrates the performance differences between the improved precise method and Newmark method. The result shows that in steady-state response solutions the improved precise method achieves greater precision at twice the step size of the Newmark method. In transient response solutions, the improved precise method outperforms Newmark in terms of computational efficiency, and the iterative strategy proves both reliable and effective.

We train active neural-network flow controllers using a deep learning PDE augmentation method to optimize lift-to-drag ratios in turbulent airfoil flows at Reynolds number 5×104 and Mach number 0.4. Direct numerical simulation and large-eddy simulation are employed to model compressible, unconfined flow over two-dimensional (2D) and three-dimensional (3D) semi-infinite NACA 0012 airfoils at angles of attack α=5, 10, and 15 deg. Control actions, implemented through a blowing/suction jet at a fixed location and geometry on the upper surface, are adaptively determined by a neural network that maps local pressure measurements to optimal jet total pressure, enabling a sensor-informed control policy that responds spatially and temporally to unsteady flow conditions. The sensitivities of the flow to the neural network parameters are computed using the adjoint Navier–Stokes equations, which we construct using automatic differentiation applied to the flow solver. The trained flow controllers significantly improve the lift-to-drag ratios and reduce flow separation for both 2D and 3D airfoil flows, especially at α=5 and 10 deg. The 2D-trained models remain effective when applied out-of-sample to 3D flows, which demonstrates the robustness of the adjoint-trained control approach. The 3D-trained models capture the flow dynamics even more effectively, which leads to better energy efficiency and comparable performance for both adaptive (neural network) and offline (simplified, constant-pressure) controllers. These results underscore the effectiveness of this learning-based approach in improving aerodynamic performance.