The design process for bearings typically involves design, simulation and testing. However, the influence of paired bearings and actual support is not considered during the design process, which results in an incomplete match between design parameter considerations and tester parameters. This, in turn, reduces the efficiency of the design process. In addressing these issues, this paper focuses on cylindrical roller bearings as the research object, conducting dynamics investigation and comparison under single bearing and tester systems. Utilising the theory of finite element analysis, finite element dynamics models of cylindrical roller bearings under single bearing and tester systems are established, respectively. The dynamic characteristic parameters of stress, contact force, roller deflection angle and vibration acceleration are extracted to obtain the vibration characteristics of the bearings. The differences in the dynamic characteristics of cylindrical roller bearings under the same load and rotational speed in the two working conditions are then compared. The results demonstrate that with an increase in load and rotational speed, there is an increase in stress, contact force, vibration acceleration and maximum deflection angle of cylindrical roller bearing. Besides, it is evident that the dynamic parameters in the tester system are larger than those in the single-bearing system. However, the average deflection angle shows a decreasing trend with the increase of rotational speed and an increasing trend with the increase of load.
Trailing edge morphing technology offers strong potential for enhancing the aerodynamic performance of airfoils under varying flight conditions. This study investigates the effect of trailing edge deformation (50% of the chord) on the aeroelastic response of a NACA0012 airfoil, focusing on parameters such as lift, drag, flutter behavior, and overall stability. A fully coupled fluid–structure interaction (FSI) approach is employed, solving the Navier–Stokes equations using URANS, combined with a finite element model of the airfoil structure. Simulations were conducted across different values of dimensionless stiffness k * and stiffness ratios φ , representing the Young’s modulus relationship between the morphing and rigid segments, to analyze the influence of morphing on the dynamic response of the system. The results show that low stiffness configurations exhibit amplified oscillations and stall-flutter phenomena, whereas higher stiffness values lead to a damped response. A dynamic instability region was identified for k * < 270 . The optimal configuration ( k * = 2.50 , φ = 0.1 ) yields a mean drag reduction of approximately 86 % compared to the fully unstable case, with a lift-to-drag ratio c l / c d ranging between −30 and 30, while the unstable configuration ( k * = 2.48 , φ = 0.1 ) produces peak drag coefficients above c d = 0.1 due to massive flow separation. These findings establish that unrestrained trailing-edge morphing accelerates flutter onset rather than suppressing it, demonstrating that passive morphing alone is insufficient and must be combined with active stiffness control to exploit the aerodynamic benefits of flexible trailing edges.
Specific aircraft services, such as transport, exist in a wide range of safety targets. This paper analyses a safety assessment method applicable to both manned and unmanned aircraft, offering the following contributions. First, different aircraft types, services, and current specifications have been reviewed and consolidated into exhaustive lists. This is followed by determining the most stringent service alongside the existing gaps, particularly the lack of a universal safety assessment method. To address these identified gaps, this paper assesses safety and the impact of its allocated performance, validated by the most stringent service. The primary significance of this paper lies in its contribution to academic literature, presenting innovative methods for deriving safety targets and apportioning risk, which are crucial for determining the required navigation performance parameters of accuracy, integrity, continuity and availability. This research provides foundational support for studies into integrity and continuity risk issues. The second significance lies in the advancement of practice, as service providers and policymakers can utilise the findings to establish relevant specifications including safety targets. To support industry and standardisation sectors in aircraft transport, an alert limit has been established at 3 m, accompanied by a safety target of 1.0 × 10 -5 per hour. Although these values provide a general benchmark, their applicability typically depends on both the type of service and the specific phase of operation.
The search for Multi-UAV cooperative targets in complex environments remains challenging because the joint action space increases rapidly with swarm size, while sensor observations are constrained by limited range, field of view, and obstacle-induced sight occlusion. To address the common limitations of search redundancy and local-optimal traps in existing methods, this study proposes an integrated multi-UAV active cooperative search framework driven by EIG-TS. The framework is underpinned by the symbiotic synergy between macro-space decoupling and micro-heuristic exploration. Specifically, a dynamic Voronoi partitioning mechanism utilizes real-time swarm positions to mathematically decouple the high dimensional joint action space into subspaces, eliminating cooperative search redundancy at the macro level. Within each dynamically assigned region, the proposed EIG-TS strategy combines expected entropy reduction, Beta Thompson sampling, and distance-cost scoring to balance exploitation, uncertainty driven exploration, and flight cost control, while suppressing micro level local-optimal behavior. Simulation results demonstrate that the proposed integrated strategy achieves a target detection success rate of 96.67% and reduces invalid flight distance by 89.1% compared with conventional baselines. Ablation experiments verify that the active exploration engine and the partitioning mechanism improve the final uncertainty accuracy by 33.7%, demonstrating superior environmental adaptability and improving the robustness of multi-UAV search.
This paper investigates the pursuit-evasion-defense (PED) game problem for spacecraft, where the defender cooperating with the evader is to escape from the pursuer. To achieve this task, a cooperation game strategy is proposed based on the linear quadratic differential game (LQDG) and the state estimation algorithm. Considering the actual situation that the target cannot obtain complete information about the pursuer, an extended-state sliding mode observer is designed to estimate the incomplete information, including the velocity and the controller. Based on the estimated information, a control compensation game strategy is derived to weaken the impact of incomplete information on the game outcome. Furthermore, the cooperative game strategy based on the LQDG is developed, which aims at improving the escape success rate of the target under weak maneuvering conditions. The cooperative strategy can entice the pursuer to approach the defender by driving the target to maneuver behind the defender. This can increase the capture probability against the pursuer and shorten the game time. Finally, numerical simulations are performed to evaluate the proposed game strategy. The results demonstrate that the cooperative game strategy can drive the defender to intercept the pursuer while reducing energy costs and game time. Moreover, the proposed method exhibits robustness with respect to variations in orbital altitude.
The dynamic reconfigurability of civil aircraft flight control systems (FCS) enhances mission adaptability yet introduces significant operational uncertainty. Traditional static safety assurance methods often fail to cope with such real-time variations. To address this, this paper proposes an integrated safety assurance framework that synergistically combines uncertainty quantification, resilience engineering, and multi-criteria decision-making. First, this paper establishes a multidimensional uncertainty ontology encompassing environmental, system-internal, human–machine, and reconfiguration factors, which is quantified using hybrid probabilistic graphical models. Building on this foundation, this paper develops a closed-loop resilience architecture that embeds the four core capabilities of resilience engineering (anticipating, monitoring, responding, learning) to enable dynamic risk perception and adaptive reconfiguration. Specifically, this paper utilizes a time-evolving multi-attribute utility function coupled with online optimization to support worst-case-oriented robust decision-making. Central to the approach is a time-evolving multi-attribute utility function coupled with online optimization, which supports worst-case-oriented robust decision-making. Validation through a dual-disturbance scenario (severe weather and sensor degradation) demonstrates significant improvements in decision quality and operational resilience. Compared with a traditional fault tree analysis (FTA) baseline under identical simulation settings, the proposed framework demonstrates up to a 25%–35% improvement in the timeliness of risk state identification and an approximately 30–40% enhancement in decision robustness under sensor degradation and dynamic environmental uncertainty.
This paper presents a non-oscillatory Hermite-Simpson convex framework for solving nonsmooth convex optimal control problems. A separated Hermite-Simpson transcription is formulated to provide a fourth-order discretization for convex optimal control, resulting in a sparse formulation well suited for second-order cone programming. To preserve the accuracy and polynomial consistency of the Hermite-Simpson scheme, piecewise quadratic control reconstruction is employed, and a specially designed midpoint-control constraint is incorporated to suppress interpolation-induced oscillations. The proposed method is evaluated on two representative trajectory optimization problems: Mars powered descent and landing and fuel-optimal low-thrust transfer. Numerical simulations demonstrate that the proposed method effectively suppresses interpolation-induced control oscillations, ensures consistency between discrete and propagated trajectories, and achieves high computational efficiency. Comparative studies show superior propagation accuracy, competitive computational efficiency, and robust numerical performance. The low-thrust transfer example additionally confirms the method’s applicability to challenging long-duration trajectory optimization problems with nonsmooth control profiles.
Wing-in-Ground effect (WIG) craft represents a promising mode of high-speed, low-altitude transportation that harnesses the aerodynamic benefits of flying close to the ground or water surface. Operating typically within half the wingspan above the surface, WIG vehicles benefit from a ground-induced high-pressure cushion that enhances lift and reduces drag. WIG craft are perfect for emergency rescue, military missions, coastal logistics, and island commuting due to their increased speed and fuel economy over traditional marine vessels. This review summarizes crucial aerodynamic factors, including ground clearance ratio (h/c = 0.05–0.5), angle of attack (0°–12°), aspect ratio (2.5–8), cruising speeds (8–40 m/s), and wave amplitudes (up to 0.3 m). According to experimental and numerical research, performance benefits include lift-to-drag ratio increases of 20–35%, drag reductions of up to 30%, and lift coefficient enhancements of 0.3–0.6 when compared with aircraft flying at a desired altitude that is outside the ground effect zone. Aerodynamic behaviour in ground effect, stability and control mechanisms, and the impact of external variables such as gusts and sea conditions are some of the major subjects covered. Additionally, recent developments in autonomous control, including deep reinforcement learning, Model Predictive Control (MPC)/H-infinity techniques, and hybrid PID–fuzzy logic, are studied. In addition to outlining potential research and innovation possibilities for WIG systems for both civilian and military uses, the article discusses current issues such as gust sensitivity, sea-state limitations, and energy constraints.
This paper presents a data-driven adaptive dynamic programming (ADP) control law for gust response alleviation in active camber morphing wings. The controller is synthesized using input-output data generated from a reduced-order aeroelastic model, which is validated across a broad operational envelope, including inflow velocities V = 15-25 m/s, sinusoidal gusts with frequency f g = 1-3 Hz, and gust amplitude A g = 2 m/s. Simulation results demonstrate that the ADP controller adaptively tunes its parameters to achieve a peak alleviation efficiency of 89.6% at f g = 2.4 Hz. Moreover, the control law also performs well in V = 20 m/s, a sinusoidal gust of f g = 2 Hz, A g = 2 m/s with 10 dB Gaussian white noise and “1-cos” discrete gust, respectively. Compared to the GPC-based (Generalized Predictive Control, GPC) controller, the proposed ADP controller achieves superior alleviation performance with significantly reduced training data requirements. Furthermore, a fluid-structure-control interaction (FSCI) framework is developed based on the high-fidelity computational fluid dynamics (CFD) simulation embedded with ADP control to investigate the alleviation mechanism under sinusoidal gusts with large gust ratios (GRs). The mechanism reveals that the ADP controller modulates trailing-edge morphing to generate a counter-acting pressure zone and inhibit the formation of the leading-edge vortex (LEV). This dual action directly suppresses the sources of unsteady aerodynamic forces, thereby reducing the generalized forces and effectively attenuating the structural response.
The evolution of aviation engine fan blade leading-edge erosion characteristics with operating time was studied. Identifying the evolutionary features of the eroded leading edges facilitates the investigation of their impact on aerodynamic performance throughout the blade’s lifecycle. This paper constructed four distinct evolution models for eroded leading edges of subsonic and supersonic fan blades based on experimental measurement data. The underlying flow mechanisms were analyzed through unsteady numerical simulations. The results demonstrated that the combined effect of the irregular leading-edge geometry and chord reduction significantly influences the total pressure loss in the flow field. For subsonic airfoils: as erosion severity increases, the energy proportion of higher harmonics in the lower-order modes rises. Large-scale vortex structures near the pressure-side leading edge weaken, whereas small-scale wake vortices strengthen. The mutual induction and stretching between vortices intensify, leading to increased flow unsteadiness. For supersonic airfoils: the eroded profiles SUP-STG2 and SUP-STG3 generate additional coherent structures downstream of the passage shock. The oscillation of the normal shock is also more intense. The flow downstream of the shock exhibits a broad spectrum of high-frequency fluctuations. This reduces the energy of the wake vortices and enhances vortex induction, resulting in greater shock and viscous loss.
This study presents a reduced-order engineering framework for the system-level assessment of dual-circuit pneumatic systems operating under degraded supply conditions. Such systems are relevant to aerospace support-system applications, where the ability to maintain functional capability under loss-of-supply conditions is of practical engineering importance. In contrast to conventional approaches focused on detailed actuator dynamics, the proposed method enables quantitative evaluation of system functionality based on a working-cycle-based performance indicator. The model was implemented in MATLAB and internally verified against a reference schematic simulation developed in FluidSIM. The FluidSIM model was used as a schematic system-level reference rather than as an experimental or high-fidelity validation source. Additionally, engineering consistency was assessed using catalog-based actuator characteristics to ensure realistic force levels and geometric representation. The analysis was conducted for multiple operating scenarios, including nominal conditions, loss of primary supply, and emergency operation, as well as for different numbers of active actuators. The results demonstrate a strong dependence of functional capacity on the available stored energy and the number of active receivers, revealing a nonlinear threshold behavior with respect to external load. The proposed framework provides a computationally efficient tool for preliminary system-level analysis, degraded-mode assessment, and engineering decision support. While the model does not aim to provide high-fidelity prediction of local flow phenomena, it captures the dominant mechanisms governing functional endurance of pneumatic systems under limited supply conditions.
The integration of serpentine nozzles into Blended Wing Body (BWB) aircraft enhances stealth capability, yet the flattened afterbody mandates an asymmetric integrated outlet whose single-sided expansion induces flow distortion and thrust misalignment. The exit Aspect Ratio (AR) thus emerges as a critical design parameter governing nozzle curvature, outlet flattening, and integration quality. Using numerical methods validated against experimental pressure data and schlieren visualization, this study examines the influence of exit AR on internal flow, wave structures, and jet evolution under sea-level static and high-altitude cruise conditions. Increasing the AR enhances internal flow uniformity by mitigating longitudinal pressure variations, albeit at the cost of elevated wall temperatures from coolant-layer thinning. The asymmetric outlet generates complex three-dimensional bowl-shaped expansion waves and arcuate shock fronts; higher AR configurations suppress shock-induced boundary layer separation, reducing the thrust misalignment angle by 3.6°. Externally, the AR exerts a regime-dependent bifurcated influence on jet evolution: a higher AR shortens the jet core via mixing-dominated mechanisms in attached flow, whereas it prolongs the core by inhibiting separation-induced dissipation in separated flow. The “axis switching” arising under aft-deck and sidewall confinement yields a distinctive “Y”-shaped cross-section, enlarging the lateral projected area and modifying the detectable signature. By uncovering this regime-dependent bifurcation, the study establishes a quantitative trade-off framework in which moderately high aspect ratios optimally balance thrust stability and plume compactness against the thermal penalty, providing actionable guidance for integrated serpentine nozzle design.
Reliable bearing fault diagnosis is essential for predictive maintenance of rotating machinery, particularly in applications where contact-based vibration sensors are difficult to install or maintain. This study proposes an explainable acoustic fault diagnosis framework based on Mel-spectrogram representations and a hybrid Convolutional Neural Network–Gated Recurrent Unit (CNN–GRU) architecture. Acoustic emission signals collected from a controlled bearing fault simulator are first segmented and transformed into Mel-spectrograms to represent the time–frequency structure of normal and faulty bearing conditions. The CNN module extracts localized spectral patterns from the Mel-spectrogram images, while the GRU module models temporal dependencies along the spectrogram time axis with fewer recurrent parameters than conventional LSTM-based designs. To address the interpretability limitations of deep learning models, Grad-CAM, Integrated Gradients, and SHAP are employed to analyze the frequency–time regions contributing to the model decisions. In addition, the explanation maps are compared with fault-relevant spectral regions derived from fault characteristic frequency analysis to evaluate whether the model focuses on physically meaningful patterns rather than arbitrary image regions. The proposed framework achieved an high accuracy of in the initial experiment and was further evaluated through repeated validation to assess performance stability under small-sample conditions. The results demonstrate that acoustic sensing combined with explainable CNN–GRU learning can provide a non-contact and interpretable alternative for bearing fault diagnosis. However, the limited dataset size remains an important constraint, and future studies should validate the framework on larger record-level and cross-domain datasets.
This paper focuses on aviation energy conservation and emission reduction as primary objectives for hybrid electric propulsion aircraft. By summarizing current research in these areas, it provides an overview of key technologies related to hybrid electric propulsion system’s energy management. Initially, the study designs the system operation modes based on the operational conditions of large-load hybrid electric propulsion aircraft and establishes an energy change model for each mode. Subsequently, it conducts a thorough transient management of the energy converters, including engines, generators, batteries, and motors--within the hybrid system, while designing an energy control law aimed at energy savings and emission reductions. Next, the paper introduces a hybrid power system energy management strategy that utilizes reinforcement learning to optimize the distribution of engine and motor torque, thereby enhancing energy utilization and fuel economy. Finally, a hardware-in-the-loop verification system is designed for the energy management strategy to experimentally validate the research findings. The findings of this research can offer foundational design concepts and analytical methods for the design and energy management of hybrid electric propulsion systems.
Traffic management for uncrewed aerial vehicles (UAVs) is crucial in low-altitude uncontrolled airspace. As UAV deployments grow in complexity, the need for structured trajectory planning becomes increasingly important to mitigate potential conflicts in designated flight paths. This work presents a comprehensive framework for pre-flight trajectory planning, designed to streamline UAV traffic coordination by integrating key elements such as corridor-based navigation, identification of conflict-prone areas, and conflict mitigation. The entire process flow within the ground control station (GCS) is proposed using algorithms that work in conjunction with a UAV database. Furthermore, a scheduling algorithm with applicable velocity and acceleration constraints is proposed that generates deconflicted trajectories for UAVs operating within designated airspace. The framework was tested in simulations and on Crazyflie nano drone hardware for experiments involving cases with different intersection types and multiple UAV types, demonstrating its capability to enhance UAV coordination and help boost overall operational efficiency in the real world.
Explosive separation represents one of the most severe mechanical environments encountered by missile/rocket structures during service. The associated high-amplitude broadband transient shocks may cause severe damage to onboard equipment, making accurate simulation of explosive separation environment is essential. The explosive separation environment is typically characterized by the shock response spectrum (SRS) measured using a pyrotechnic shock simulator. However, due to limited analytical methods, existing experimental studies still rely mainly on iterative parameter adjustment, leading to long test cycles, high costs, and insufficient quantitative understanding of simulator parameters. To address this gap, a high-precision finite element model was developed and validated based on the fundamental principles of the plate-type pyrotechnic shock simulator, and a systematic parametric study was conducted to comprehensively investigate the influence of projectile parameters on SRS characteristics. Building upon this foundation, an orthogonal experimental design (OED) was formulated with three factors—projectile impact velocity, impact angle, and projectile length—to conduct explicit dynamic simulations. This approach systematically investigated the influence of these parameters on the shock response spectrum (SRS). Results indicate that impact velocity exerts the most significant influence on the deformation of the response plate and fixture center, followed by projectile length, while impact angle has the least effect. Increasing impact velocity substantially amplifies response spectrum amplitude. A larger impact angle primarily attenuates high-frequency amplitude. Projectile length elongation causes upward curvature in the slope of low-frequency spectral lines. This study establishes and validates an explosion shock simulation apparatus while providing quantitative parameter relationships. The study explicitly identifies impact velocity as the dominant control parameter determining response spectrum amplitude, enabling efficient device calibration and rapid parameter adjustment. This methodology provides a reliable basis for accurately and economically replicating typical explosive shock environments in engineering applications.
Accurate identification of the inertia tensor of combined spacecraft after capturing a non-cooperative target is crucial for stable attitude takeover in on-orbit servicing. Conventional estimators often degrade under abrupt inertia variations and measurement noise. This paper presents a compact inertia identification approach, termed Adaptive Entropy-Structured Pruned LSTM (AESP-LSTM), designed for real-time onboard execution under limited computational resources. In the offline stage, an over-parameterized model is trained on diverse post-capture scenarios and pruned to suppress redundant neurons that amplify noise, while a Dynamic Inertia Transfer Ratio (DITR) provides excitation-aware prior features. The strategy reduces model size by over 29%, lowering storage to 719 KB. In the online stage, the pruned network runs on a processor with millisecond-level inference 200-300 ms and a rolling adaptation scheme to preserve accuracy under time-varying inertia. Simulations in disturbance-rich orbital environments demonstrate superior accuracy, robustness, and efficiency compared with classical RLS, RPBSID, and FNN estimators. Notably, more than 90% of the predictions generated by this method fall within a 5% relative error margin, the proposed AESP-LSTM framework thus ensures high-accuracy, lightweight, and real-time inertia identification, offering a practical solution for reliable attitude takeover in future on-orbit servicing missions.
The matching of a radial inflow turbine with a piston engine, while ensuring torque backup across ranges of engine speeds and loads, is a complex task due to the nature of exhaust gas flows. This paper investigates the effects of pulsating exhaust gas flows on a single-entry radial turbine when coupled with a spark-ignition piston engine. The transient behaviour is analyzed using a combination of computational fluid dynamics (CFD) and an analytical model under both steady and unsteady conditions. This study also examines how the pulsating gas flows affect the turbine performance and the transient response when paired with an internal combustion engine (ICE) under various operating conditions. When matched with an ICE, the unsteady performance of a turbocharger differs significantly from the steady-state one due to highly pulsating gas flows at the inlet of the turbine. This behaviour is characterized by a hysteresis loop in the turbine’s performance, which stems from the filling and emptying of gases within the volute. This work essentially demonstrates that the unsteadiness from the turbine-side propagates through the whole turbocharging system, inducing a shift in the compressor’s operating line. For the case of single-cylinder engine, the operating points are moved towards the surge limit at low engine speeds—with observed deviations of up to 4% in the pressure ratio and 4.9% in the swallowing capacity—a critical surge risk that the conventional steady-state matching approaches fail to predict. The contribution of this work lies in integrating a system-level analysis, which uniquely quantifies the substantial impacts of the radial turbine’s unsteady behaviour on the matching with a spark-ignition engine and the operational stability of the entire turbocharging system.
With the rapid advancement of intelligent and clustered unmanned technologies, the autonomous decision-making and confrontation of multiple unmanned aerial vehicles (UAVs) has emerged as a prominent research focus among major military powers worldwide. Multi-UAV confrontation environments are characterized by high-dimensional action spaces, nonlinearity, and stringent real-time decision-making requirements, which pose significant challenges to existing decision-making algorithms. Therefore, this paper addresses the problem of the real-time maneuvering decision-making problem of multiple UAVs in the context of 2v2 close-range air combat. Firstly, a multi-UAV confrontation simulation environment based on the agent-environment cyclic (AEC) game model is developed to resolve issues of ambiguous reward allocation and dynamic variations in the number of intelligent agents. Secondly, a multi-agent soft actor-critic deep reinforcement learning method is proposed within a centralized training-distributed execution (CTDE) framework, supplemented by a strategy training and optimization approach incorporating curriculum learning. Furthermore, by integrating mainline and process rewards, collaborative rewards are introduced to strengthen tactical coordination among UAVs and enhance the effectiveness of adversarial strategies. Finally, three-dimensional simulation experiments validate the effectiveness and stability of the proposed method.
Focusing on the application of stealth aircraft maneuvering tactics, this study investigates the influence of maneuvering flight on electromagnetic scattering characteristics. A motion trajectory scattering (MTS) calculation method based on flight dynamics principles (FDP) and dynamic characteristic calculation (DCC) is proposed. The maneuvering trajectory model is developed using a 6-DOF model, and the changes in radar line-of-sight angle and aircraft attitude angle during maneuvering flight are derived. The rotation matrix formulas of aileron, rudder and elevator are presented. Based on these rotation matrices, a hybrid grid matrix representing the deflection states of the aircraft’s various control surfaces is established. The calculation of the dynamic stealth characteristics of the aircraft is accomplished through the dynamic RCS simulation model. Several typical maneuvering actions are selected to validate the calculation method for maneuvering aircraft. The simulation results demonstrate that this method is applicable to dynamic RCS calculation of diverse flight trajectories. The deflection of the control surfaces has a significant impact on the dynamic RCS of the aircraft, and it is closely related to the flight attitude and the relative position of the radar. In the design and tactical application of aircraft stealth, these factors must be taken into account to reduce the risk of being detected.
