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.

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.

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.

A TDOA joint optimization model (TDOA-PSO-NI-GD) that integrates Particle Swarm Optimization (PSO) and Newton's Iteration-Gradient Descent (NI-GD) method is proposed in response to the problem of insufficient positioning accuracy of multi-stage rocket debris, which improves the localization performance through the synergistic mechanism of global search and local optimization and constructs the multi-debris signal separation constraint model and the environmental interference compensation model. Experiments show that the model reduces the positioning error from 1~10 km to less than 0.5 km in the traditional single-stage optimization algorithm in the rocket debris recovery mission, and the multi-debris signal separation rate reaches 96.2%, and maintains the sub-kilometer accuracy under the mountainous terrain and strong wind disturbances; the validation of the Chan-Taylor algorithm combined with the least-squares method shows that its anti-jamming and positioning reliability are significantly better than that of the existing methods. The algorithm can be extended to mobile communications, unmanned vehicles and other fields, with both theoretical innovation and engineering application value.

Hydrogen-oxygen mixtures are widely used as aerospace propellant. Their thermodynamic properties, transport properties and chemical reaction processes are far more complex than pure air, and it is more difficult to analyze and predict their flow process. The self-developed device-detonation tunnel is used to provide high temperature hydrogen-oxygen combustion gas. Combined with CFD, the flow and aerodynamic heating characteristics of air and combustion gas under the same total temperature and total pressure are analyzed. It is found that, when flowing in the nozzle, the combustion gas has higher temperature, speed and smaller Mach number; the standoff distance and stagnation pressure of the combustion gas are smaller than air; the skin friction and wall heat flux are greater than air. Active chemical reactions in combustion gas make aerodynamic heating more serious.

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 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.

The e ^N method based on linear stability analysis is currently one of the most widely used methods for transition prediction; however, the selection of the transition criterion N_T value shows significant scatter at high Mach numbers. The linear stability analysis and e ^N method are used to conduct stability analysis and N-factor envelope calculations for the lifting calibration model at Mach numbers of 5 and 8, and for Reentry F at a Mach number of 20. Combining the transition onset locations obtained from wind tunnel results and flight data, the calibration of the transition criterion N_T value at high Mach numbers is carried out. The results show that for the lifting calibration model, the transition criterion N_T is approximately 5.3 to 6.5 under different angles of attack at Mach number of 5, and approximately 4.8 to 5.3 at Mach number of 8. For Reentry F, the transition onset locations vary significantly at different altitudes, but the transition criterion N_T does not change significantly, being approximately 9.55 to 9.73.

International Space Station has been in orbit for more than 20 years, forming a relatively mature cargo transport system and exploring the development path of low-cost and highly reliable commercial cargo transport system. As China's Space Station enters the stage of application and development, the demand for materials up and down will significantly increase. It will be particularly important to form a rich and stable cargo transport system and significantly reduce cargo costs under normal operating modes. The development and current situation of cargo transport system of the International Space Station are analyzed, the composition of the International Space Staion, commercial orbital transportation service plans, and recent cargo transportation situations are focused on, providing reference and suggestions for China's Space Station's cargo transport system.

In the final assembly process of various solid and liquid rocket models, there are many tasks involved, such as the docking of the rocket body and the transfer of the entire rocket body workstation. Currently, the completion of this series of complex tasks mainly relies on manual guarantee. When the operator's skills are insufficient, the factory quality of the rocket body cannot be guaranteed. And in recent years, with the new production of larger tonnage and diameter rocket models, it has caused a significant physical consumption of workers and also reduced labor productivity. Through long-term technological breakthroughs and physical verification experiments, a large tonnage electric propulsion device with lightweight, ultra overload, stepless speed regulation, and adjustable interface tooling has been developed, which solves the problems of difficult docking of most sections of the rocket body, high labor intensity of the entire rocket body workstation transfer, and intermittent overload of the rocket conveying pipe during cabin penetration in the overall aerospace assembly.