Abstract:Hypersonic internal compression inlets are typically designed using streamline tracing technology based on the basic flowfield, and the quality of the basic flowfield design directly determines the performance metrics of the inlet. In this study, the quasi-uniform B-spline method is applied to achieve the parametric design of the internal compression basic flowfield. A fast prediction model for the internal compression basic flowfield is established based on the residual neural network architecture of deep learning, realizing the goal of "parametric design-flowfield prediction." The predicted flowfield cloud images are quantitatively evaluated using image quality assessment methods, and key flowfield characteristic parameter distributions are extracted to enable rapid acquisition of flowfield cloud images and characteristic parameter distributions based on design parameters. The results show that the developed basic flowfield fast prediction model achieves high prediction accuracy for flowfields corresponding to given geometric parameters. The average peak signal-to-noise ratio (PSNR) of the predicted total flowfield is 42.51 dB, and the average structural similarity index (SSIM) is 0.9973. Additionally, the model can effectively extract key flowfield characteristic parameter distributions from the predicted results, providing robust support for the rapid design and optimization of internal compression basic flowfield.

Abstract:In order to improve the heat dissipation capacity of the electronic equipment system of the near space vehicle and solve the problem of high heat flux of integrated and miniaturized electronic devices，the paper had a study on the heat transfer performance of dual synthetic jets actuators, by making use of two-phase flow composed of micron-particle and water and their combination. The mechanism of enhancing heat transfer capacity with dual synthetic jets actuator is was studied and analyzed. The flow process of the dual synthetic jets actuator and Cu-water micron particle fluid in the tube is was modeled, and the influence of five particle concentrations on the enhanced heat transfer capacity of the fluid is was simulated by the single Euler model. CUO-water and Al2O3-water micron particle fluids are were simulated. The results show that the heat transfer capacity of fluid can be enhanced by the dual synthetic jets. The heat transfer capacity in-creases with the increase of micron particle concentration. The two-phase flow heat transfer capacity of different metal particles varies with the thermal conductivity of metal particles. In the example, a microparticle fluid of Cu particles with a dual synthetic jets actuator on and a particle concentration of 8% is synthesized, and the chip temperature is reduced from 328.225K to 303.816K.

Abstract:Traditional methods often exhibit low efficiency in addressing multi-objective and multi-constraint optimization problems, failing to meet the requirements of dynamic and complex environments. In this case, a hybrid cooperative co-evolution algorithm was proposed based on cooperative co-evolution mechanisms, zebra optimization algorithms, and differential game theory. A phased optimization strategy was adopted to dynamically and adaptively optimize trajectories and strategies, while a multi-population co-evolution mechanism was introduced to enhance global exploration capability and local convergence performance. Differential game theory was integrated to improve the stability and reliability of game strategies. Simulation results demonstrate that this method significantly improves mission completion efficiency under multi-constraint conditions. It effectively balances dynamic strategy adjustments for both pursuers and evaders, providing an effective solution for satellite pursuit-evasion games in space-based target reconnaissance and surveillance missions.

Abstract:High-speed vehicles are characterized by a wide speed range and lightweight structures, and they face complex aerodynamic and thermal environments as well as structural stability challenges. Under the coupled effects of fluid-structure-thermal interactions, aerothermoelastic problems have become a major focus of attention. However, in the calculation of high-speed aerodynamic and thermal loads, engineering algorithms offer high computational efficiency but lack accuracy, while numerical simulation methods provide high precision at a significantly higher computational cost. Therefore, a typical thermal protection system (TPS) panel of a High-speed vehicle was focused, and aerodynamic and aerothermal surrogate models based on the Kriging method were developed, which achieved a four-orders-of-magnitude improvement in computational efficiency. Based on these surrogate models, a computational framework for the aerothermoelastic analysis of the TPS panel was established using the finite element method and a self-developed heat conduction program. The aerothermoelastic behavior of the TPS panel was then analyzed within this framework. This research will provide an important theoretical foundation for the rapid and accurate prediction of aerodynamic and thermal loads, the design of thermal protection systems, and the flight safety assessment of High-speed vehicles.

Abstract:As regional security issues have become increasingly severe, the strike capability of individual missiles is gradually failing to meet operational demands, necessitating improved strike efficiency through multi-missile coordination. This paper investigated the control problem of coordinated multi-missile fencing and attack against unknown maneuvering targets and explored the design of the overload ratio, which represents the relationship between missile maneuverability and target maneuverability. Inspired by the self-organizing behavior of biological swarms, this study designed a multi-missile cooperative fencing algorithm using sliding mode control. The algorithm included an attraction term to the the target, a repulsion term between adjacent missiles, and a relative velocity convergence term between missiles and the target. The analysis showed that, under this algorithm, the upper bound of the overload ratio could be calculated based on the initial conditions and control parameters, providing technical support for missile formations with a low overload ratio. Numerical simulation results showed that the proposed algorithm effectively achieved multi-missile fencing and attack against unknown maneuvering targets with a low overload ratio. It maintained a safe distance during the fencing phase and rapidly reduced inter-missile spacing during the attack phase by removing the repulsion term to enable coordinated engagement.

Abstract:The unsteady aerodynamic effects on the flight performance of morphing aircraft remain unclear. The unsteady aerodynamic characteristics during morphing were investigated, and their impact on flight performance was quantitatively analyzed. A dynamic model incorporating unsteady aerodynamic effects was established, with morphing rate and flight velocity as key parameters. A qualitative comparison was conducted between the flight performance under unsteady and quasi-steady aerodynamic models. Two typical flight scenarios were designed, and the pseudo-spectral method was employed to quantify the influence of unsteady aerodynamic effects on mission performance in maximum range operations and no-fly zone avoidance. The results indicate that the unsteady aerodynamic model introduces deviations in flight state accuracy compared to the quasi-steady model, which correlate with morphing rate and flight velocity. These deviations predominantly occur in low-altitude, low-speed (below Mach 3) flight regimes. During no-fly zone avoidance, where morphing is more pronounced, a trajectory deviation of approximately 1800m accumulates within 250s. In contrast, maximum range operations exhibit a smaller deviation of around 350m over 1000s of flight.

Abstract:Aiming at issues such as the loss of complex wave system structural features in intelligent reconstruction methods for supersonic flow fields, along with the inability to effectively capture the temporal evolution characteristics of unsteady flow fields, which together lead to the inaccurate identification of the shock train leading edge,. A neural network model based on combined detail feature enhancement to address these issues was proposed. High-precision predictions of the density gradient field was achieved based on sparse pressure data. The main wave system structure features of the flow field was established by connecting multiple layers of convolutional networks in series. A residual network with skip connections was used to integrate features from receptive fields of different scales, enhancing the model's ability to express detail features in reconstructed flow fields. Validation was conducted using a dataset constructed from numerical simulations of ramjet engines. Compared to multilayer convolutional neural networks, this method improves the average Peak Signal-to-Noise Ratio across the entire test set by 9.5%. Moreover, the reconstructed flow field's STLE position closely matches the numerical computation results, further demonstrating the effectiveness of the proposed method.

Abstract:Aiming at the problem of maneuverability limitation in the process of penetration and interception, a game trajectory optimization strategy solution based on adaptive dynamic programming is proposed. By establishing an affine nonlinear differential game model and considering the limited maneuverability, the performance index function of the control energy term with integral form was designed. The saddle point control strategy of the game was derived based on the differential game theory, and an evaluation network was designed based on the adaptive dynamic programming algorithm to approximate the solution of the differential game strategy. The weight adaptive updating law of the evaluation neural network was given and its stability was proved. Simulation results show that the proposed strategy solving method can achieve anti-interception effect and accurately strike enemy targets under the circumstance of limited maneuverability.

Abstract:Orbital pursuit-evasion, as a research hotspot in the field of aerospace dynamics and control, has garnered increasing attention from a growing number of researchers. The paper addresses the issue of constructing the closed-loop equilibrium for close-range orbital pursuit-evasion games and proposes a computation method that integrates Bellman’s Principle of Optimality, the finite difference method, and interpolation techniques. A dimension-reduction dynamics of the game system in the line-of-sight coordinate frame is derived, establishing a close-range orbital pursuit-evasion game model and reducing the dimensionality of the system’s state space. Based on Bellman’s Principle of Optimality, the original problem is reformulated as a Hamilton-Jacobi-Isaacs (HJI) Partial Differential Equation (PDE) terminal value problem, enabling the simultaneous handling of multiple game scenarios through reverse-time analysis. The state space is discretized using Cartesian grids, and the finite difference method is employed to calculate the dynamic evolution process of the equilibrium driven by the dynamics, and analyze the game situation. Utilizing the relationship between control and the spatial gradient of the equilibrium, numerical interpolation is applied to construct the closed-loop control function. The effectiveness of the proposed method is demonstrated through numerical simulations.

Abstract:To enable efficient prediction of transitional heat flux fields under diverse freestream conditions, a generative transition heat flux prediction model based on variational autoencoder architecture was developed. The hypersonic cone configuration under different freestream conditions was selected as the research object, with numerical simulation method being employed to generate the transition heat flux dataset. A variational autoencoder model was constructed and was trained and validated on the transition heat flux dataset. The analysis of results demonstrates that the latent variables of the heat flux field can be effectively extracted by the variational autoencoder model, and the heat flux structure of the transition process induced by leeward-side streamwise vortices was accurately reconstructed. A fully connected neural network model was established to construct a nonlinear mapping relationship between the freestream conditions and the latent variables of the heat flux field. By connecting the fully connected neural network model with the decoder part of the variational autoencoder model, a hypersonic cone transition heat flux prediction model was developed. The prediction results indicate that this model effectively learns the characteristics of heat flux distribution under complex transition mechanisms, achieves high prediction accuracy for heat flux under various freestream conditions, with errors not exceeding 0.024.