In response to the current reality that cryogenic liquid launch vehicles have become the mainstay of space missions and the urgent need to enhance personnel competency, this study proactively addresses the training requirements of launch sites for the upcoming universal ground-based testing, launch, and control systems. By fully considering scenarios such as normal testing and launch procedures, troubleshooting under abnormal conditions, and emergency response operations, research, design, and development of a simulation and training system for cryogenic liquid launch vehicle testing and launch procedures are conducted. This system thoroughly analyzes the universal characteristics of the product and aligns with the training needs of launch sites for the soon-to-be-deployed universal ground-based testing, launch, and control systems. It develops hardware equipment and testing software consistent with actual products, covering all phases of testing operations across launch site systems. It supports comprehensive, full-process operational training for all systems and positions, fully meeting the training needs of personnel involved in cryogenic launch vehicle testing and launch operations. The system is capable of fostering a more technically proficient testing and launch team, thereby comprehensively supporting the foundational capacity building of a leading spacefaring nation.

This article takes the real-time intelligent reconnaissance of the American "Black Swift" hypersonic intelligent aircraft as an example to analyze the demand for strong real-time, high-energy efficiency intelligent computing of typical intelligent hypersonic vehicles. On this basis, it analyzes in detail how to build a strong real-time, high-energy efficiency intelligent computing system for intelligent hypersonic vehicles from three levels: intelligent model lightweight, software and hardware collaborative compilation and optimization, and ultra-heterogeneous integrated computing hardware. Furthermore, a system integration example is provided to illustrate the practical application of these principles. In the future, with the loading of the strong real-time, high-energy efficiency intelligent computing system, intelligent hypersonic aircraft will be more autonomous, reliable and capable of group collaboration, and will drive the intelligent upgrade of aerospace cross-domain flight and global rapid transit.

The rolling screw transmission mechanism is a key transmission component of the servo mechanism. Monitoring its operating state is crucial for the normal operation of the servo control system. Currently, there are still difficulties in the state monitoring of rolling screw transmission mechanisms, such as low integration and wiring difficulties. A multi-parameter wireless micro-nano sensing monitoring system for rolling screw transmission mechanisms is designed. The sensing module, MCU module, wireless module and power management module are integrated in this system, which can be integrated with the nut of the rolling screw transmission mechanisms directly and flexibly, improving integration and reducing wiring complexity. The sensing module contains three types of MEMS sensors for temperature, acceleration, and acoustics to meet the condition monitoring requirements of multi-point distributed and multi-parameter sensing on the surface and interior of the nut. This system has advantages of small size, easy installation, flexibility and high integration, effectively monitoring the working state of rolling screw transmission mechanisms.

The stability of lubricating grease performances is a significant factor that restricts the long-term storage reliability of aerospace electro-mechanical servo actuators. The rheological properties of lubricating grease can directly affect the transition characteristics of servo actuators. Accelerated degradation test at high temperature stresses is designed and carried out. Three grades of lubricating grease with different base oil systems, including soap based, non-soap based and mixed lubricating grease are investigated for their theological properties. The rheological properties of different lubricating grease changed in thermal aging time and temperature stress are analyzed. It is found that the viscosity of lubrication dose not change unidirectionally with the increase of thermal aging time. In addition, the rheological properties of mixed lubricating grease at high temperature is better than that of soap based lubricating grease and non-soap based lubricating grease, which is valuable to evaluate the properties of lubricating grease during long-term storage of aerospace electromechanical servo system.

Numerical investigation on the characteristics of a two-dimensional, mixed-compression inlet with various leading edge bluntness is presented. Effects of leading edge bluntness on the self-starting ability, aerodynamic performance of the inlet at design and off design operations are acquired. Results indicate that, with the increase of blunted radius, the self-starting ability and mass flow capture of the inlet are deteriorated, the backpressure tolerance capability and critical total pressure recovery coefficient goes down, while the drag coefficient rises obviously. 5% decrease of mass flow ratio is observed while the backpressure tolerance and critical total pressure recovery coefficient drop off at least 8.5% for the design operating point. Complex shock wave interference pattern forms due to the oblique shock waves from the external compression of the inlet intersecting the bow shock wave produced in front of the leading edge with the variation of freestream Mach number and the angle of attack. Diminution of flow separation in the inner side of the inlet lip is observed with the increase of blunted radius at a high Mach number condition. As the angle of attack rises, the influence of the bow shock wave induced by the blunted leading edge on the performance of the inlet is found to be weaken.

With the increasing frequency of human space activities, there are surviving debris of re-entry spacecraft and debris that impact the Earth's surface, causing typical damage cases and affecting the future technological development of spacecraft. Firstly typical re-entry damage cases and disposal methods of foreign spacecraft that have attracted significant social attention are analyzeds. Secondly the main international norms and standards for re-entry casualty risk are introduced. Then those assessment methods of re-entry casualty risk are studied together with re-entry risk analysis tools of NASA and ESA. Finally it's looked forward to the furture development and existing problems of major space powers in the technical directions of active deorbit, assisted deorbit, large-scale reentry, and reusable spacecraft. Research shows that although the re-entry casualty risk of spacecraft and debris is still difficult to predicte very accurately, it is expected to be moderately controlled by the design of risk-reduction measures along with technological development.

To address the issue of harmonic concentration near fixed switching frequencies caused by high-speed switching actions in traditional PWM strategy, the random modulation strategies in the PMSM servo drive system are studied. On the basis of conventional RSF-PWM strategy, DRM-PWM strategy enhances EMC by introducing randomization of the zero-vector action time. However, this strategy struggles to balance EMI suppression effectiveness with system control performance. Subsequently, MARSF-SVPWM strategy is investigated, with a focus on comparing and analyzing the two modulation strategies. Compared to DRM-PWM strategy, MARSF-SVPWM strategy maintains overall spread spectrum range and average switching frequency while narrowing the distribution range of differences between adjacent switching frequencies, thereby further reducing harmonic peaks. Simulation and experimental results demonstrate that MARSF-SVPWM strategy achieves better harmonic dispersion performance compared to DRM-PWM, and maintains a lower impact on system control performance, optimizing the EMI suppression effect.

To address the limitations of existing methods for underwater unmanned vehicle (UUV) motor fault diagnosis, which rely on manual feature extraction and do not fully leverage the potential of intelligent diagnosis, a two-stream CNN-LSTM fault diagnosis model is proposed. The model employs convolutional neural networks as feature extractor, which can learn the low frequency trend and high frequency detail features of the original signal without complex pre-processing steps, making real-time motor status monitoring possible. Afterwards, the classifier based on the long short-term memory network uses these features to explore temporal dependencies and identify motor faults. Experiments are conducted on a self-constructed UUV motor fault simulation platform, and the performance of the model is validated by setting multiple speeds and load conditions. The results show that this method can efficiently diagnose six typical states in UUV motors and achieve an average diagnostic accuracy of 97.22%. These findings demonstrate the model's effectiveness and robustness in UUV motor fault diagnosis.

As artificial intelligence technology developing rapidly, the intelligence level of unmanned systems is much increasing. Specially, intelligent reconnaissance technology is more mature and widely used. To solve the above problems, an adversarial patch attack based camouflage and deception method is proposed. Convolutional neural network is used to build a classifier as the attack object, and a novel patch generation method and loss function are designed to attack target samples, which effectively maps the attacked target samples to the specified wrong target category. A directed evaluation method and wealthy experiments are provided to verify the advancement and effectiveness of this method.

The guidance system, a core subsystem of launch vehicles, is crucial for the successful launch. Perturbation guidance, as the primary guidance method for domestic rockets within the atmosphere, relies on the design efficiency and precision of guidance parameters to determine the overall system performance. Traditionally, the parameter design is carried out by designers through extensive trial calculations and experience-based adjustments, resulting in low efficiency and high costs. To address this issue, an automatic optimization method for perturbation guidance parameters based on adjoint sensitivity and the Adam gradient descent algorithm is proposed. By constructing a point-mass dynamic model of the launch vehicle, the guidance parameter design is transformed into a constrained optimization problem. The adjoint sensitivity method is employed to efficiently compute the gradient of the objective function with respect to parameters, while the Adam algorithm adaptively adjusts the learning rate to achieve parameter auto-tuning. A two-stage launch vehicle is tested under random wind field disturbances. The perturbation guidance control parameters are optimized using a cost function that combines trajectory tracking errors and terminal range deviations. Simulation results show that the compared with the traditional manual tuning method, the proposed method can quickly find suitable control parameters. It provides an efficient and precise solution for the design of perturbation guidance parameters in launch vehicles, significantly reducing simulation time and design costs. The method also offers engineering reference value for improving the optimization efficiency of complex dynamic systems