High-fidelity simulation of an aircraft’s dynamic reaction characteristics under challenging flight conditions is made possible by numerical virtual flight (NVF) technology, which is essential for designing control laws and assessing flight quality. This paper systematically reviews the NVF technology coupled with aerodynamics, motion, and control, with an emphasis on the application of this coupling method in complex flight problems. This paper outlines the basic framework and technical workflow of NVF, elaborating on the evolution of dynamic mesh technology from single deformation to dynamic hybrid mesh systems, as well as its critical role in ensuring relative motion of components and computational stability, while also pointing out the enabling potential of machine learning in mesh quality control. Regarding unsteady flow field solutions, this paper discusses the coupling modes of aerodynamics, motion, and control, introduces the technical evolution from loose coupling to tightly coupled closed-loop solutions, and highlights that the tightly coupled method can significantly improve the simulation accuracy for high-dynamic flight states such as high angle of attack and post-stall. This paper elaborates on the application practices of this coupling method in three major scenarios: hypersonic multi-field coupling, morphing flight, and flexible bionics. NVF will advance toward effective, unified, and full-system multi-field coupling in the future thanks to artificial intelligence technologies, hastening the shift in aircraft development paradigms toward simulation-driven, digital-intelligent testing.

Filter-press electrochemical reactors have become the dominant reactor configuration for flow batteries owing to their simple structure, high modularity, and low cell voltage. They also show broad application potential in energy storage batteries, electrochemical water treatment, and electrosynthetic chemical processes. Among various flow battery systems, the all-vanadium redox flow battery represents the most commercially advanced and extensively studied technology. However, inadequate mass transfer continues to limit further performance development since it increases concentration polarization, reduces voltage efficiency, speeds up long-term performance degradation, and eventually raises system costs. This review focuses primarily on all-vanadium redox flow batteries and discusses mass-transfer enhancement from the perspectives of both key reactor components and system operation. Recent advances in the mass-transfer optimization of electrodes, flow fields, membranes, and other core components are systematically summarized. The effects of operating parameters, including flow rate, temperature, current density, electrolyte concentration, and tank-to-stack ratio, on mass-transfer behavior are analyzed. Based on this, coordinated optimization solutions including reactor structure and system operation are suggested, and the coupling relationship between component-level mass-transfer characteristics and total battery performance is elucidated. From an engineering perspective, mass-transfer optimization in all-vanadium redox flow batteries should not be limited to component modification alone. Instead, it should also account for stack scale-up effects, tank-stack matching, operating-parameter regulation, and the balance between electrochemical performance and system energy consumption. This review aims to provide theoretical guidance and technical references for the engineering design and performance improvement of filter-press flow battery reactors.

All-electric aircraft face limitations in achieving long range due to the energy density constraints of current lithium batteries. Range-extended aircraft, employing an "electric-dominant, fuel-supplement" powertrain architecture, offer a balance between environmental benefits and extended range, positioning them as a crucial transitional solution for aviation decarbonization. This review focuses on the range-extended technology pathway. It provides an overview of the technological roadmaps adopted by representative range-extended aircraft models both domestically and internationally; it compares and analyzes five typical hybrid powertrain architectures, elucidating the definition and operational principles of the range-extended concept. It explores the advances and challenges of key enabling technologies that are driving its development, including high-power-density permanent magnet motors, intelligent energy management systems, efficient thermal management systems, and high-voltage partial discharge monitoring and protection technologies. This review clarifies the current landscape and core barriers of range-extended technology. It provides essential insights to inform the technological evolution towards long-range adaptability for all-electric aircraft and serves as a valuable reference for formulating strategies aimed at the low-carbon transition of the aviation industry.

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 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.

The you only look once (YOLO) series of deep learning-based object identification algorithms has several advantages, including great adaptability, high detection accuracy, and quick detection speed. Make it the preferred choice for object detection in drone aerial images. Due to the complex and ever-changing nature of aerial images and videos captured by drones, the YOLO series object detection algorithms from the UAV vision have become a challenging problem. Therefore, it is necessary to comprehensively summarize and analyze the recent research and application achievements of YOLO series algorithms for object detection from the UAV vision. Firstly, the YOLO structural diagram is redrawn and the development, connection, and optimization features of the 10 fundamental versions of YOLO are summarized. The roles and objectives of various YOLO optimization strategies are pointed out, indicating the direction for further theoretical research. Secondly, the optimization methods of YOLO in the application field of drone object detection are summarized. This includes preprocessing before data training, optimization of training and inference, application of attention modules, convolutional optimization, detection heads, and optimization of loss functions. At the same time, some application datasets and data expansion methods from the UAV vision are sorted out. Through studies, the effectiveness of YOLO series algorithms on the UAV vision dataset and the influence of convolutional block attention module (CBAM) on various YOLO improvements are assessed. Finally, it is pointed out that there are still some problems and challenges in the research of YOLO series algorithms for object detection from the UAV vision. This article looks forward to possible development trends and research directions in this field.

Underwater high-precision autonomous navigation is a core enabling technology for ocean exploration and development. The positional error of an inertial navigation system (INS) builds up and diverges with time, requiring outside data to be corrected. As an inherent property of the Earth, the gravity field offers stability, autonomy, and passivity, making it an ideal information source. Consequently, gravity-aided inertial navigation technology holds significant research value. This paper systematically reviews the research progress of four important technologies: trajectory planning, gravity matching algorithms, gravity reference map construction, and gravity field adaptability analysis. It is based on an analysis of the system’s technical architecture and fundamental principles. Mainstream methods, such as multi-source data fusion, gridded interpolation, feature evaluation and matching area extraction, sequential correlation matching, nonlinear filtering, and intelligent path optimization, are comprehensively summarized. Furthermore, common unresolved issues in current technological development are thoroughly examined. Finally, in view of the technical bottlenecks at this stage, future research directions are discussed, aiming to provide theoretical references and technical guidance for the subsequent development of a high-precision, high-reliability, and high-autonomy underwater gravity-aided inertial navigation system (GAINS).

Due to their high energy density and zero emissions, hydrogen fuel cell UAVs have become a vital means of overcoming the endurance limitations of traditional lithium-battery-powered UAVs in the context of a growing low-altitude economy and the development of green aviation. This paper provides a comprehensive review and systematic categorization of the development and key technologies of hydrogen-fuel cell UAVs, along with a reasonable outlook on their future evolution, to serve as a valuable reference for related research. In particular, the paper starts with a summary of the scientific advancements and developmental history of hydrogen-powered aircraft, the inherent benefits of hydrogen fuel cell powertrain systems, and the state of the art in hydrogen fuel cell unmanned aerial vehicles. Subsequently, a systematic analysis is conducted on the core technologies involved in hydrogen fuel cell UAVs, including hydrogen fuel cell technology, multi-source hybrid energy systems, airborne hydrogen storage technology, energy management strategies, and thermal management strategies. Lastly, a summary of the study’s significant findings is provided, and future research directions are indicated by matching the state of these important technologies' research with the real-world difficulties they face.

This study suggests an approach that combines prioritized fictitious self-play (PFSP) with an adaptive winning-game weighting mechanism to address the delayed convergence and policy oscillation in self-play training for multi-aircraft cooperative beyond-visual-range (BVR) air combat. Firstly, a 4v4 BVR air combat simulation environment is established under a multi-agent reinforcement learning framework. Second, in order to break the policy cycle, the Elo method is developed to conduct non-uniform sampling of past opponents and dynamically assess policy capabilities. Meanwhile, a win-rate-based loss function weighting mechanism is designed to guide gradient updates when early high-value samples are scarce. According to experiments, the suggested approach greatly lessens later-stage oscillation and converges to the Nash equilibrium by the 200th episode, increasing convergence efficiency by 55% over conventional FSP. The weighting mechanism’s accelerating impact is further supported by ablation investigations. Lastly, agents educated using PFSP have strong air combat decision-making ability, winning 70.3% of the time against skilled opponents.

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.