One of the main goals of deep space exploration is asteroid exploration, which has great scientific significance. Faced with the challenges of long distances and limited knowledge of asteroid exploration targets, the rapidly evolving technology of artificial intelligence (AI) has emerged as a promising complement to existing space mission implementation paradigms. In addition to introducing the current state of research on the application of AI methods in asteroid exploration, this paper analyzes the methods of AI systems used in space exploration missions and asteroid ground observation systems. It also summarizes the challenges that these methods face. It also proposes key technologies with developmental potential for asteroid exploration.

The sustained escalation in aero-engine thrust-to-weight ratios and turbine inlet temperatures has established thermal barrier coatings (TBCs) as an indispensable solution for surpassing the intrinsic temperature limits of hot-section components and ensuring their long-term service reliability. Nevertheless, the service response of TBCs involves exceedingly complex thermo-mechanical-chemical (TMC) multi-field coupling and cross-scale damage evolution, which poses severe fundamental challenges to conventional single-scale experimental and analytical methodologies. This review systematically summarizes the advances in conventional and advanced modeling, simulation, and analysis techniques for TBC and further extends the discussion to the application of artificial intelligence (AI) technologies. The study aims to provide a systematic overview of analysis methods for TBC service behavior and structural optimization strategies for coatings and to promote the implementation of proactive maintenance strategies. The technical methods covered include thermal conductivity modeling, thermal stress simulation, crack propagation analysis, and mechanical behavior studies of TBC systems, which predominantly rely on diverse techniques such as multiscale modeling and finite element analysis. The paper focuses on the use of AI in TBC simulation, including data-driven modeling approaches and machine learning algorithms such as support vector machines, random forests, neural networks, and deep learning. The findings indicate that AI holds the potential to revolutionize TBC technology in multiple respects, encompassing material and structural design, performance optimization and prediction, condition monitoring, fault diagnosis and health management, and remaining useful life (RUL) prediction, while significantly improving prediction accuracy. Novel TBCs are anticipated to offer robust support for next-generation aero-engines to transcend temperature limits and achieve improved efficiency and reliability with ongoing advancements in the intelligent design of new materials.

In the many applications of lithium-ion batteries, the problem of thermal failure presents a significant safety risk. This failure is characterized by a sequence of events: the degradation of interface thermal stability, the onset of thermal runaway triggered by internal short circuits within a cell, and the subsequent thermal propagation at both the module and system levels. The coupled electro-thermal catastrophe serves as both a critical dimension in characterizing the thermal failure logic of lithium-ion battery systems and a key breakthrough in managing these thermal failures. This paper provides a thorough analysis of the thermal behaviors, such as self-generated heat, Joule heating, and heat conduction, that affect electrical behaviors during the solid electrolyte interphase (SEI) layer decomposition of a single cell, the separator melting, and heat propagation at the module level for electro-thermal coupling catastrophe in lithium-ion battery systems over long cycles and across multiple scales. The heat generation and heat transfer behaviors associated with electrical phenomena are also analyzed, such as overcharging, overdischarging and battery aging. The paper also reviews the most prominent electro-thermal coupling models for both individual cells and modules, providing a synthesis of the mechanisms that govern the electro-thermal interactions in lithium-ion batteries. By exploring these interactions from both microscopic and macroscopic viewpoints, the study aims to shed light on the future advancements in the management of electro-thermal coupling in battery modules. The goal of this research is to improve the safety and dependability of lithium-ion battery systems through design, operation, and maintenance.

To achieve an objective assessment of driving comfort, this study developed and systematically compared multiple machine learning models based on multimodal physiological signals. 64 individuals had their electrocardiograms (ECGs), surface electromyograms (sEMGs), muscle oxygen saturation (SmO₂), and arterial blood flow signals recorded while they drove. Using comfort scores as labels, six types of models, including support vector machine (SVM), random forest (RF), and adaptive Boosting (AdaBoost), were constructed and optimized with principal component analysis, synthetic minority oversampling technique (SMOTE), and grid search. Results showed that multimodal signal fusion significantly improved classification performance. The SVM model achieved optimal performance with the feature set containing arterial blood flow parameters, reaching an average area under the curve (AUC) of 0.97 and an accuracy of 0.90. RF models consistently exceeded 0.95 AUC with fused signals. Practical signal combinations for portable monitoring were identified: "sEMG + ECG" dual-signal combination (average AUC is 0.90) and "SmO₂ + ECG + sEMG" triple-signal combination (average AUC is 0.92). This study offers empirical support for model selection and signal setup in objective driving comfort evaluation, providing insightful information for the development of in-car health monitoring systems.

Superelasticity is one of the most important functional characteristics of shape memory alloys, which essentially originates from stress-induced reversible thermoelastic martensitic transformation. The superelastic working temperature range is mainly influenced by the synergistic effect of alloy chemical composition and microstructure. The development status of several shape memory alloy systems with broad temperature range superelastic properties over the last ten years is methodically reviewed in this article, with an emphasis on contrasting the features and variations of various alloys in important performance parameters like recoverable strain, temperature dependence of critical stress, and superelastic temperature window. This article uses comparative analysis to support the design, preparation, and engineering application of novel wide-temperature-range superelastic shape memory alloys theoretically and practically, as well as to offer opportunities for the field's future development.

The groove drag reduction technology based on the shark skin ridge structure has been widely concerned and studied by scientists because of its simple and easy operation and low energy consumption rate. It has shown great potential in marine ships, aerospace, pipeline transportation and other fields. The state of research and advancements in bionic groove drag reduction technologies during the previous two decades are examined. Firstly, three widely recognized drag reduction mechanisms are reviewed and other drag reduction mechanisms are supplemented. The design and optimization of the groove shape drag reduction as well as the current state of processing technology research are then discussed, along with a summary of the effects of the groove’s position and arrangement, dimensionless size, and yaw angle on the drag reduction performance. Finally, the practical application of bionic grooves in many fields is briefly described, and the research on drag reduction of bionic grooves is summarized and prospected.

To address the conflict between the limited payload capacity of a single launch vehicle and the substantial deployment demands of large-scale target payloads, this paper employs a two-phase manned lunar landing mission architecture, where on-orbit refueling connects Earth ascent and space transfer. Mission scale analysis and calculations were performed for manned lunar landing utilizing on-orbit refueling. Dynamic models for the four main phases—launch, rendezvous, Earth-Moon transfer, and lunar landing—were established, and overall mission design simulation analysis was completed. A mass estimation method was employed to compare the total mission mass and the scale of each stage between the traditional mode and the on-orbit refueling mode. In order to define phase-specific limitations and develop a mass model that takes into account in-orbit evaporation losses of cryogenic propellant, a mission model covering the four phases of launch, rendezvous and docking, Earth-Moon transfer, and lunar landing was built. Subsequently, mission windows were calculated, and full-process orbital simulations were conducted to analyze trajectory parameters and propellant consumption for each phase. Genetic algorithms were used to optimize parameters for the lunar landing phase. Mass estimation results show that, compared to the thousands of tonnes launch mass required by the traditional mode, the total launch mass for the on-orbit refueling scheme is below 500 tonnes, and the space transfer stage mass is on the order of tens of tonnes. The full-process simulation findings provide crucial references for engineering design, further elucidating the propellant consumption for each stage of the on-orbit refueling strategy.

To meet the growing demand for precision protection in advanced high-performance fighter aircraft and the increasing life-support requirements of pilots, intelligent oxygen systems integrating physiological monitoring, state prediction, and active intervention are expected to become a key component of next-generation life-support systems. Accordingly, the evolution of airborne oxygen systems and recent advances in intelligent oxygen technologies worldwide were systematically reviewed. Particular attention was given to the application potential and major limitations of three enabling technologies: electronic oxygen regulation, multimodal physiological monitoring, and physiological state prediction. Current evidence indicates that electronic oxygen regulators provide the engineering foundation for incorporating physiological information into the oxygen-supply control loop. However, sensor reliability under complex flight conditions, individualized prediction, appropriate matching of intervention strategies to physiological risks, closed-loop stability, and in-flight validation remain major barriers to practical implementation. Future research should integrate multimodal physiological information, hybrid physiology-data modeling, and staged experimental validation to achieve coordinated regulation of pilot physiological status, cockpit environment, and oxygen-supply equipment.

The rapid development of spaceflight has significantly advanced science and technology, but it also poses non-negligible environmental challenges. This study focuses on major emissions from rocket launches, including CO₂, black carbon (BC), H₂O, Al₂O₃, and chlorine species (Cl_y). To assess the atmospheric influence of space activities, their horizontal and vertical spatial distributions are examined using worldwide launch data from 2020 to 2024. The results indicate that gaseous emissions are dominated by CO₂ and H₂O, with total quantities of approximately 2.71×10⁸ kg and 1.44×10⁸ kg, respectively. In contrast, particulate emissions consist mainly of BC and Al₂O₃, with estimated releases of 6.70×10⁶ kg and 7.80×10⁶ kg, respectively. Horizontally, the emissions exhibit strong regional concentration, with the United States and China accounting for the majority of CO₂, H₂O, and BC emissions. Al₂O₃ and Cl_y emissions are mainly concentrated in China, France, the United States, and India. Vertically, CO₂ emissions are concentrated in the troposphere, while BC emissions peak in the stratosphere and mesosphere. Due to the persistence of BC in the upper atmosphere, a near twofold increase in net radiative forcing can be observed in these regions. This suggests that because of their high-altitude BC emissions, space launch operations are more dangerous to the climate than aircraft and conventional industrial sources that are limited to the troposphere.

This paper addresses the critical guidance problem for launch vehicles performing trans-lunar injection missions with low thrust-to-weight ratios, where the extended flight duration and substantial multi-source uncertainties present formidable challenges to conventional closed-loop guidance methods. A novel hybrid guidance approach integrating second-order cone programming with neighboring optimal control is developed to overcome these challenges. The proposed methodology begins with establishing a comprehensive trans-lunar injection problem model that explicitly accounts for multi-source uncertainties. A hybrid guidance strategy based on real-time trajectory planning is formulated to handle these uncertainties effectively. The main innovation is the application of a real-time trajectory planning technique that uses second-order cone programming in conjunction with Gauss pseudospectral discretization. By synergistically applying the costate mapping theorem and exploiting the dual output of second-order cone programming, the method achieves high-precision costate estimation, which provides the essential foundation for the neighboring optimal control component. A trajectory correction method based on neighboring optimal control is developed to address situations where uncertainties cause significant trajectory deviations. This method generates high-quality initial references for the second-order cone programming solver, substantially improving convergence characteristics and computational efficiency. Extensive numerical experiments demonstrate the superiority of the proposed guidance method in trans-lunar injection missions. The computational performance shows remarkable improvement, with the average computation time reduced to 75.353 milliseconds per guidance cycle, representing a 30.67% reduction. In comparison to current methods, this hybrid guidance method’s successful demonstration offers a novel and feasible technical pathway for trans-lunar injection missions.