Advances in Astronautics Science and Technology最新文献

The orbital inspection game (OIG) is characterized by its strong coupling with observational conditions and higher complexity compared to pursuit-evasion games. This study employs reinforcement learning techniques to investigate the OIG problem. Building upon the Markov decision process and its hybrid variant, a two-stage decision-making model for spacecraft is proposed, decomposing the OIG into approach and inspection tasks. With a specifically designed hierarchical network architecture and gradient-driven exploration strategy, the gradient-driven exploration-based serial solution method (GDE-SSM) is developed. GDE-SSM stratifies the agent’s exploration space through model switching and task decomposition, thereby significantly improving training effectiveness. Compared to the prediction-reward-detection multi-agent deep deterministic policy gradient algorithm and the deep deterministic policy gradient algorithm, the defender trained via GDE-SSM exhibits a tendency to adopt more aggressive maneuvering strategies, resulting in average success rate improvements of 57.8% and 21.8%, respectively. Generalization and robustness analyses demonstrate that GDE-SSM achieves excellent robustness against uncertainties arising from environmental parameters, suboptimal observation conditions, and abrupt disturbances.

The concept of spacecraft modularization has gradually attracted attention. Due to the problems of low efficiency and high transfer consumption in the current large-scale reconfiguration research of space modular self-reconfigurable satellites (SMSRS) in orbit, we propose a distributed self-reconfigurable motion planning optimization method. Firstly, the real-time minimum map and the corresponding undirected connected graph are established based on the connection condition and the limited observation constraint condition. Then, using the Dijkstra search algorithm, we create the real-time minimum map and the connected graph, leading to a new local optimal reconfiguration path algorithm that combines minimum map and shortest path planning, which significantly cuts down on the total calculations and the number of steps needed for reconfiguration in large-scale planning. Simulation results show that the new method is better than the traditional centralized graph-based method in the on-orbit reconfiguration mission of random configuration with 10–500 module scale, about 11.2% transfer steps and 35.7% reconfiguration planning time were saved.

Ammonium dinitramide (ADN)-based thrusters are pivotal for future spacecraft propulsion due to their low toxicity, adjustable specific impulse, and environmental benefits. However, complex fault patterns observed during ground tests challenge traditional fault detection methods, which struggle with high-dimensional, nonlinear time-series data. This study proposes a machine learning-based approach for robust fault diagnosis in ADN-based thrusters. Using 189 real engine test time-series datasets, we performed systematic preprocessing and feature engineering to extract statistical and correlation characteristics inside experimental data, creating a standardized dataset of normal and faulty conditions. Ten algorithms—six traditional machine learning and four deep learning—were evaluated for fault identification. The multilayer perceptron achieved 98.2% accuracy and 100% recall, while random forest and XGBoost, attained accuracies of 99.1% and 98.2% respectively, with superior computational efficiency. Deep learning excels in complex scenarios but demands longer training, whereas traditional methods suit real-time applications. Feature analysis highlighted pre-injection pressure and capillary outlet temperature as key fault indicators. A Simcenter AMESim-based simulation model further augmented the dataset, supporting fault mechanism studies. This approach enhances fault diagnosis, health monitoring, and design optimization for ADN-based thrusters, offering significant engineering value.

A numerical investigation is conducted to analyze the impact of increasing inflow air total temperatures on a scramjet combustor featuring a strut injector and cavity integration. The AnsysFluent tool is used for the computational analysis, employing RANS equations and an SST k-ω turbulence model. The cavities, which are fastened symmetrically to combustor walls, are situated downstream of the strut injector. To evaluate the performance of the cavity-coupled DLR scramjet model under varying inflow air total temperatures, flow patterns, and flow property distributions are analyzed. The cavity configurations, compared to the baseline model, enhance combustion efficiency by achieving complete combustion while reducing the length of the combustion chamber. However, the introduction of additional shock waves from the cavities results in an increased overall pressure drop. Additionally, as the inflow air total temperature rises, the combustion zone extends further along the flow direction, contributing to a prolonged combustion process.

This paper presents a stochastic control strategy for fixed-time safe attitude control of rigid bodies on SO(3), addressing stochastic disturbances while ensuring safety constraints are met with probabilistic guarantees. A stochastic fixed-time control Lyapunov function (SFxTCLF) is designed to ensure that the attitude error converges to zero in probability with fixed-time stability guarantees. To enforce safety constraints on attitude and angular velocity, stochastic control barrier functions are formulated and combined with the SFxTCLF as a quadratic programming framework, which synthesizes an optimal control input that satisfies both stability and safety conditions simultaneously while respecting actuator limits. Extensive numerical simulations validate the efficacy and resilience of the proposed approach, demonstrating superior convergence speed and safety enforcement compared to existing stochastic attitude control methods.

The article delineates an innovative ball-type locking device designed for the separation of structural elements in promising launch vehicles. It further substantiates the necessity of this mechanism by addressing the stringent requirements for reliable operation. A mathematical model has been developed to analyze the dynamics of the relative motion between the separable stages of the launch vehicle utilizing the ball-type locking device. Key parameters for ensuring reliable stage separation with minimal time delay have been identified. Comprehensive numerical simulations have been conducted to evaluate the stress-strain characteristics of structural elements within the ball-type locking device. Additionally, the rational quantity of the balls within the design of this device has been established.

Aiming at the problems of life cycle cost estimation and reasonable design of key parameters for cost reduction in engineering design of Reusable Launch vehicle (RLV), a comparative cost modeling method for RLV benchmark against Expendable Launch Vehicle (EELV) of the same scale is proposed, with which it is carried out the economic analysis of RLV projection such as Space Shuttle, SpaceX Falcon 9 and reusable conceptual configuration. It is found that: reusable launchers with reasonable parameter can significantly reduce the cost of EELV, but the goal of reducing the cost by one order of magnitude is difficult to realize with restricted by the theoretical upper limit; the life cycle cost of RLV tends towards constant values or exhibit extremum points; the reusable recovery and refurbishment cost is a key parameter affecting the life cycle cost, cost-effectiveness and maintainability constitute defining characteristics of RLV, necessitating per-flight refurbishment expenditure to be maintained at ultralow levels—ideally not exceeding 5% of the Theoretical First Unit (TFU) manufacturing cost—to ensure economic viability; boundary conditions are given for the economic superiority of full reuse over first-stage partially reuse.

Shock wave generator is an effective device for mixing enhancement in scramjet combustors. This paper employs the Reynolds-averaged Navier-Stokes (RANS) numerical simulation method to investigate the effects of different parameters of a wall-mounted ramp, which serves as a shock wave generator, on cold flow, reacting flow fields and the vorticity transport within the turbulent mixing region in a strut-based scramjet combustor. The parameters examined include the ramp angle (15°, 20°, 25°, 30°) and its position (110, 130, 150 mm). The results demonstrate that the introduction of the shock wave generator alters the shock wave distribution within the strut-based combustor, promoting rapid mixing between the hydrogen jet and the incoming air flow. In the cold flow field, volumetric expansion and diffusion are identified as the dominant mechanisms for vorticity transport in the mixing layer, whereas in the reacting flow field, the baroclinic torque and diffusion emerge as the primary mechanisms. Moreover, the shock wave generator with different structural parameters exhibit a more pronounced influence about shock wave and vorticity distribution on the reacting flow compared to the cold flow. Notably, in reacting flow field, compared to a single-sided shock wave, the simultaneous incidence of shock waves from both the upper and lower sides into the mixing layer and their interaction significantly enhances the baroclinic term and the diffusion term.

MXene has become a potential electromagnetic absorbing material due to its controllable and excellent electromagnetic parameters, which have attracted extensive attention from researchers. However, due to the two-dimensional nanosheet structure of MXene and the rich polar functional groups on its surface, the dispersion of MXene in the process of assembling composites becomes a challenge. Herein, from the perspective of preparation methods, the micromorphology design ideas and the corresponding electromagnetic absorbing performance of MXene-based absorbing composite materials in recent years are reviewed. Through the meticulous material structure design strategy, MXene-based composites are expected to become tunable electromagnetic wave absorbing materials with superior performance.

Space-based multi-target tracking is one of the key enabling technologies for space situational awareness. As to solve the multi-target passive tracking problem by a cooperative dual-observer system, a centralized parallel fusion method that considers measurement association probability is developed. Firstly, an orbital dynamics model perturbed by Earth’s oblateness and lunar gravity, together with a line-of-sight (LOS) angles measurement model are established. Secondly, a new centralized parallel fusion multi-target tracking frame considering measurement association probabilities is designed for dual-observer cooperative system. In detail, a dual-observer LOSs association evaluation metric and a two-stage measurements-to-measurements association algorithm are developed, where the measurement association probabilities are calculated to achieve high-precision cooperative data association. Subsequently, a centralized parallel tracking filter is proposed for accurately estimating the targets’ orbital states based on the frame of probability hypothesis density (PHD) filter. Multiple sets of augmented measurements based on the measurement association results are constructed, of which the associated measurement probabilities are incorporated into the Gaussian Mixture PHD (GM-PHD) filter to adjust the weights of the Gaussian components. Finally, numerical simulations are conducted with a GEO-type mission, where a 10-target’s tracking case is taken for validation. The results show that the accurate tracking is achieved, where the performance advantage is analyzed and confirmed by comparing with traditional distributed tracking methods.