Addressing the issues of low reliability and severe wear of dynamic seals in high-speed aircraft under extreme thermal environments, this study focuses on high-temperature dynamic sealing performance testing technology and develops an integrated testing system to validate the performance of novel multi-material composite seals. An independently designed integrated testing platform for high-temperature wear and sealing is developed to comprehensively evaluate the friction coefficient, sealing performance, and compression resilience of seals under high-temperature conditions. Experiments demonstrate that the developed testing system operates stably in high-temperature environments up to 800 ℃, yielding highly reproducible test data. The system provides an effective experimental means for performance evaluation and optimization of high-temperature dynamic seals.

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.

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.

The sparse reconstruction theory can obtain distance information in the emitters localization. In the case of traditional emitter sparse reconstruction poor algorithm performance and off grid, an Off-Grid and multiple emitters direct localization algorithm are proposed based on GDP distribution and Sparse Bayesian Learning (SBL). This GDP-SBL-DPD algorithm assumes that the reconstructed signal follows a Generalized Double Pareto (GDP) distribution and leverages a coarse-to-fine search and signal hyper parameter quadratic updating method to enhance the performance of Emitters Direct in off-grid scenarios. Simulation results demonstrate that the GDP-SBL-DPD algorithm outperforms the grid mismatch algorithms based on Orthogonal Matching Pursuit (OMP), Alternating DirectionMethod of Multipliers (ADMM), and SBL coarse-to-fine search in multiple emitters grid mismatch scenarios, with higher accuracy and stronger robustness.

Aiming at the problem of rapid repair of the debonding defect between the solid rocket motor insulation layer and the case in the battlefield environment, a comparative performance analysis of the bonding materials required for the rapid repair of the debonding between the nitrile rubber insulation layer and the steel case is carried out through experiments, and the adhesives suitable for the corresponding conditions and performance requirements are screened out. Firstly, combined with the requirements for the rapid repair of the debonding between the solid rocket motor insulation layer and the case, the curing conditions and performance indicators required for the adhesives used for rapid repair are analyzed. Further, mechanical property and ablation tests of 11 kinds of adhesives from three major categories, namely acrylate, epoxy resin, and silicone, are conducted. Finally, a grouting machine for rapid repair is designed and a fluidity test is carried out. The test results show that the X701-1 adhesive can meet the curing conditions and performance requirements for the rapid repair of the debonding between the nitrile rubber insulation layer and the steel case.

The missile cabin structure is used to form the shape, connect and install the subsystems, and bear various loads. The loads that the missile needs to bear in the flight stage mainly includes shear load, bending moment, axial load, external pressure, thermal load, ect. In order to verify the structural stability and strength performance of the missile cabin structure under multi-load cooperation, it is necessary to carry out the research on the structural strength test. The equivalent treatment method of test load and the design of coupled loading scheme are focused on. The study of structural strength test method of a certain missile cabin under multi-load is carried out, which provides a reliable test basis for the calculation, optimization and modification of the cabin structural strength.

With the wide application of cognitive technology in the military field, cognitive Electronic Warfare (EW) has become the inevitable trend of future military war. Given the development of EW combat object in the cognitive direction leading to a decline in the operational effectiveness of traditional EW, an in-depth exploration and analysis of key technologies in cognitive electronic warfare are conducted, including cognitive reconnaissance, cognitive jamming, jamming evaluation, and dynamic knowledge base. Moreover, the basic concepts of cognitive EW are sorted out, by analyzing the numerous challenges to traditional EW. Then, the system architecture and composition of cognitive EW are studied. On this foundation, the key technologies of cognitive EW are summarized. Finally, based on the characteristics of cognitive EW, the development direction of cognitive EW has been prospected.

Aerodynamic configuration design is an important technology for hypersonic vehicles. Combined with other relative specialties, it is the key to obtain good flight performance and ensure essential flight quality. As a fundamental research area of modern hypersonic vehicles, air-breathing vehicle has become an indispensable way to realize the sustainable hypersonic flight. The typical hypersonic air-breathing vehicles are focused on and the design concept of configurations is investigated. Proposed from the perspective of combined-cycle propulsion systems, the relationship between propulsion systems and aerodynamic configuration is studied which provides a reference for the aerodynamic configuration design work.

Establishing an accurate aerodynamic model is of great significance for analyzing the aerodynamic characteristics of aircraft and designing reliable flight control systems during the aircraft design process. Multi-source data fusion of aerodynamic data from different sources, such as wind tunnel tests and flight tests, is currently a popular method for unsteady aerodynamic modeling by intelligent algorithms. However, traditional fusion algorithms have shortcomings such as high requirements for flight data sources and weak generalization capabilities. Thus an improved intelligent modeling method based on Physics-Informed Neural Networks (PINNs) is proposed to integrate static wind tunnel test data and flight data. Compared to traditional PINNs, the physical loss constraints in improved PINN are reversely constructed to enable feature extraction from different and discrete flight data. The static wind tunnel data are incorporated into both the input and loss function of neural network to construct residual estimates. So the differences between ground and flight aerodynamic data are effectively corrected. The predictive aerodynamic characteristics for different motion forms demonstrate that the improved PINN not only has high aerodynamic prediction accuracy but also exhibits excellent generalization capabilities.

The nodes of the unmanned aircraft are fast maneuvering and confronting fiercely in the complex environment. There are bad conditions such as position change and link interference suppression. The robust routing technology adapted to wireless network is studied in order to solve the problem of network resource management caused by the frequent change of wireless network topology and the sharp increase of management and control cost, and realize the goal of tenacious, timely and accurate service carrying of wireless communication service in the specific environment. A lightweight hybrid routing scheme with layered management and control is constructed, and topology awareness and fault detection technologies are used to detect network changes quickly and accurately. The damaged residual network is quickly optimized scheduling and compensation through layered route construction, intra-segment route repair and inter-segment route repair, and the communication service capability of the damaged network is improved.