The scramjet engine usually employs a fuel regeneration cooling system to cool the walls. As a highly efficient thermal engine, the supercritical carbon dioxide (S-CO₂) cycle system can recover and utilize heat energy to generate electricity to power equipment while reducing the amount of fuel needed for cooling and effectively enhancing the engine's overall performance. An overall parametric analysis of the cooling system for the scramjet engine is conducted based on the supercritical CO₂ cycle power generation system, obtaining a system-generated power output of 65kW and a cycle efficiency of 11.75%. Additionally, the impact of various parameters is evaluated such as compressor pressure ratio, turbine inlet temperature, engine heat input, compressor inlet temperature, compressor isentropic efficiency, and turbine isentropic efficiency on system power generation and efficiency. Using a multi-objective genetic algorithm, the maximum power output and efficiency of the system are assessed, and preliminary designs for key components including the precooler and compressor are conducted.

To address the challenges of strong nonlinearity, high uncertainty, and rapid time-varying parameters during the reentry phase of high-speed vehicles, this study proposes an end-to-end intelligent attitude control method based on an improved Twin Delayed Deep Deterministic Policy Gradient algorithm, aligned with the demands of intelligent spacecraft development. To overcome the issues of training instability and convergence difficulties in TD3-based attitude control learning, two key innovations are introduced: a hybrid reward mechanism combining continuous tracking error penalties and sparse task-completion rewards is designed within the Markov Decision Process framework to synergistically guide agent convergence. Prior knowledge constraints derived from modern control theory are incorporated into the training process, proposing a behavior cloning-based optimization strategy for the Actor network to balance expert experience imitation and cumulative reward maximization. Simulation results show that the proposed method can accurately track the three-channel attitude commands under 14 combinations of parameter deviations.

In remote sensing images of complex scenes, ships exhibit significant scale variations. In particular, their key regions are represented by only a few pixels, making direct detection methods susceptible to background noise interference, which results in insufficient accuracy and robustness. To address these challenges, a hierarchical detection method based on a Multi-level Detection Network (MDNet) is proposed. In the first stage, which is built upon Cascade R-CNN, a global context module is integrated to enhance scene discrimination capability. Furthermore, deformable convolutional heads are employed to adapt to the geometric variations of objects, through which precise coarse localization of ships is achieved. Following automated cropping and enhancement via Gamma Correction, a dual attention mechanism is utilized in the second stage to focus on the weak features within local image patches, whereby fine-grained identification of the key regions is performed. Through this method, complex background noise can be effectively filtered, and salient features in key regions can be focused on. A significant improvement in average precision is thus achieved compared to direct detection methods.

Aiming at the deformation matching damage problem of the arrayed bonded thermal insulation tile assembly, this study extracts four typical aircraft skin deformation conditions, combines with Digital Image Correlation (DIC) technology and force-displacement synchronous measurement methods, conducts experiments and obtains quantitative results.​The results show that when the skin is depressed in a large area, the 80% compression of the gap between tiles is the compaction threshold of the filler strip, exceeding this threshold will cause local extrusion damage at the edge of the thermal insulation tile, and this threshold can be used as the safety upper limit for the gap design of thermal insulation tiles. When the discontinuous skin undergoes relative deformation, the thermal insulation tile will flip and become unstable after 60% compression of the gap between tiles, and debonding failure occurs in the bonding layer; the corresponding deformation can guide the skin deformation control of the docking areas such as the cabin door and the airframe. When the skin is locally depressed, debonding failure occurs in the bonding layer and spreads rapidly from the middle of the thermal insulation tile to the edge within 2 seconds. When the skin is locally bulged, debonding damage occurs in the bonding layer and spreads rapidly from the edge of the thermal insulation tile to the center after approximately 10 seconds; the corresponding deformation thresholds can guide the stiffness design of the skin in local areas such as access panels. The results of this study can guide the deformation matching design of thermal insulation tile assemblies, and the experimental scheme can provide references for similar designs and verifications.

Regarding the low-visibility issue of aerospace ground equipment in the visible light band, regulating the spectrum by using optical microstructures is one of the approaches to achieve stealth. Through reasonable structural design and optimization, the band can also be extended to infrared or even microwave, so as to realize multi-spectrum compatible stealth. Inspired by the periodic microstructures of butterfly wing scales, the inclined ridge-rib microstructures and nano-hole structures in butterfly wings are numerically characterized based on the spatial trigonometric function model, and the anti-reflective optical characteristics are analyzed by using the FDTD method. The results show that the two structures can provide solutions for the design of stealth metamaterials for aerospace equipment. Using a generalized model to digitize the structural characteristics is helpful for the subsequent efficient optimization and selection, so as to quickly obtain the optimal target optical performance.

In response to the requirements for rapid resilience reconstruction of C2 in group equipment, research on resilient C2 technologies for group equipment in complex dynamic environments is conducted. Addressing the challenges of task execution and group topology recovery in the command process of group equipment, a collaborative reconstruction mechanism for group command is developed through leveraging task decomposition and topology segmentation. Focusing on the state deviation of group members caused by internal and external disturbances, an adaptive task-state disturbance rejection control algorithm is designed for group equipment under strong interference conditions. Aiming at the secure handling of abnormal nodes, a security resilience regulation method which focusing on information screening of abnormal nodes is proposed. Finally, a multi-level resilient command and control reconstruction and strategy framework for group equipment has been established, supporting resilient command and control of group equipment in highly dynamic, strongly disturbed, and multi-domain scenarios.

EEPROM as a core non-volatile memory device in critical systems such as aerospace, automotive electronics, and industrial control, its long-term reliability is directly related to the data security of the equipment. Traditional static burn-in (BI) technology only use high temperature and bias voltage to accelerate failure, which is difficult to simulate the electrical stress damage under the actual dynamic working state, and the fault coverage is limited. Dynamic BI technology can effectively activate early failures by simulating real workloads, thereby improving the reliability of the device. XX28C010 device is taken as the research object. Firstly, the working principles of dynamic and static BI, device working principles and graphic algorithm are introduced, then the system architecture, test algorithm program and hardware design are introduced, and finally the test results are analyzed, which provide a reference for the research on dynamic BI technology of EEPROM.

Achieving low cost, high frequency, and rapid-response launche remains the core objective of space transportation development. Since the mid-20th century, the United States pioneered reusable launch vehicle (RLV) development. Government agencies like NASA and the U.S. Air Force led numerous flight test programs and engineering initiatives, progressing through multiple phases: early exploration, the Space Shuttle era, spaceplane concepts, and second-generation RLV development. Breakthroughs in key reusable technologies were ultimately achieved by commercial entities, notably SpaceX. This evolution exhibits multiple iterative cycles and parallel development paths. Systematically analyzing the U.S. RLV development route, including key projects and technical strategies, offers valuable insights for China's reusable launch vehicle advancement, supporting the planning and execution of major national projects.

This study investigates the distribution patterns and computational methods for shock wave overpressure in static explosions of elliptical explosive charges through simulations and experiments. Static detonations of elliptical charges with varying aspect ratios (major-to-minor axes) are simulated and experimentally conducted, with shock wave overpressure data captured at multiple distances and positions. A computational method for determining the overpressure distribution of elliptical charges is established based on classical shock wave overpressure calculation formulas. The results demonstrate that the overpressure distribution of elliptical charges is non-uniform and exhibits significant directionality, with higher overpressure observed along the minor axis than along the major axis. As propagation distance increases, the overpressure distribution gradually becomes more uniform, while larger aspect ratios lead to greater non-uniformity in overpressure distribution. Good agreement is shown between the proposed computational method and experimental results, indicating its reliability for supporting overpressure prediction and analysis in engineering applications.

This research addresses the challenge of high computational resource demands and extended simulation cycles associated with three-dimensional aerothermal numerical simulations for complex-shaped hypersonic vehicles. To overcome this limitation, the application of machine learning-based multi-source data fusion methods in aerodynamic thermal design is investigated, utilizing substantial datasets accumulated during past development projects. The characteristics of various data types, including aerodynamic thermal engineering/numerical simulation and ground/flight test data, are analyzed. Employing Latin hypercube sampling and batch submission techniques, a numerical simulation dataset is constructed, and a multi-source heterogeneous aerodynamic thermal database is established. Grid normalization algorithms for configurations involving rudder rotation and localized deformation are developed. Based on clustering and region matching algorithms, simulation data are partitioned, extracted, and statistically analyzed. Deep learning-based approaches for aerodynamic thermal data fusion and intelligent agent modeling are researched, with predictive accuracy validated using a specific lifting body aerodynamic configuration.