For efficient altitude transition and cruise maintenance, the wing-in-ground craft (WIG) quickly adjusts to and then stabilises at the target flight altitude, with minimal deviation during the fastest transition. Conventional methods typically rely on multi-objective functions to minimise settling time (ST) and integrated absolute error (IAE). These methods achieve the target trajectory through iterative trial-and-error adjustment on their weights that balance the two objectives, leading to time-consuming tuning. A reward function is proposed to prioritise minimising ST first, followed by minimising IAE. When trajectory points deviate from the target altitude, rewards are calculated based on absolute error (AE) values. Once the target altitude is reached, the reward takes the value of an exponential based on the current step number. The proposed reward function ensures that positive rewards for reducing ST outweigh the reward losses from adjusting previous trajectory points, and it is mathematically validated. Example of a WIG’s altitude change compares the proposed reward function and conventional multi-objective methods via deep reinforcement learning. Results show that the proposed reward function directly plans the trajectory that prioritises fast-settling requirements first and then minimising deviation, without needing to introduce or tune any parameter.

Predictive maintenance in safety-critical systems like turbofan engines increasingly relies on machine learning (ML) models to estimate remaining useful life (RUL), but the ‘black box’ nature of these models hinders their adoption and trustworthiness. While traditional ex-ante prognostic metrics (e.g. monotonicity, trendability) are used to pre-screen sensor data, a systematic comparison against the post-hoc explanations of what a model actually learns is lacking. We explore the application of SHapley Additive exPlanations (SHAP) from explainable artificial intelligence (XAI) to investigate feature importance in engine failure prediction using the second dataset of the Commercial Modular Aero-Propulsion System Simulation (CMAPSS). The preprocessing pipeline includes z-score normalisation of sensor data and the calculation of a health index (HI) to quantify system degradation. A power-law fit is applied to the HI to capture the underlying trends of engine wear and failure progression. We use the normalisation data to calculate prognostic feature selection metrics: monotonicity, trendability and prognosability. Then, we train two machine learning models – random forest (RF) regressor and gradient boosting (GB) method – directly from the raw data to predict the RUL based on the actual sensor readings. The SHAP values generated for both models are analysed to identify the features with the most significant impact on RUL predictions. By comparing the SHAP value distributions across models and prognostic predictors, we highlight feature robustness and their relative influence on engine degradation and failure prediction. This work provides insights into the interpretability of machine learning models in prognostics and enhances the understanding of sensor contributions to engine health monitoring. The results demonstrate the effectiveness of SHAP in elucidating feature importance, supporting the development of more transparent and reliable prognostic systems.

The increase in activities related to unmanned aircraft systems and the implementation of this new ecosystem have introduced new hazards, impacting the operational safety of air traffic, particularly near airports, creating risks and disruptions in the flow of aircraft. The establishment of airspace for unmanned traffic management has required the integration of this new airspace with the existing one, bringing the potential for issues from this integration. A method was identified as needed to guide the detection of hazards posed to air traffic control activities and the consequent implementation of required mitigation measures. The aim of this work is to propose a framework for identifying hazards introduced to air traffic control, with a view to ensure the safe transition of this process. The method involved consulting air traffic operational safety specialists via a questionnaire, presenting the hazards highlighted in the literature concerning the integration of new airspace concepts within air traffic control activities. The results, obtained through a Delphi consultation, were analysed based on the most frequently assigned scores (mode) to reflect expert consensus. The results were organised into the proposed framework, establishing a guide to risk management activities aimed at implementing the change. The resulting structure was re-submitted to specialists and validated based on the Delphi method. Contributions to society include a guide for this process and potential future implementations, while the literature gap was addressed by adding knowledge to the scientific process.

To investigate the stall mechanisms of a multi-stage axial compressor under different rotational speeds and identify the initial stall stages, this study focuses on a high-load nine-stage axial compressor, validated through experimental data. The results reveal that at 100% corrected rotational speed, flow instability is primarily triggered by corner separation in the front four stators (S1–S4). At 80% corrected rotational speed, the instability stems from the interaction between the first rotor (R1) tip leakage vortex and the main flow, coupled with the front four stators’ corner separation. Precise identification of initial stall locations in multi-stage axial compressors is imperative. The study first employs qualitative flow-field analysis to identify initial stall locations by comparing meridional mass flux variation contour maps and axial velocity iso-surfaces. The results show that the stall inception occurs at the S2 root under 100% corrected rotational speed, while at 80% corrected rotational speed, stall initiates simultaneously at both the S2 root and the R1 tip. Furthermore, an innovative three-dimensional flow blockage quantification method was established to systematically evaluate blockage severity within multi-stage blade passages. This approach utilises relative blockage variation metrics to quantitatively identify regions of rapid flow deterioration, achieving remarkable consistency with qualitative flow-field analysis. The qualitative and quantitative analysis results have been mutually corroborated. The proposed blockage quantification approach enables precise evaluation across stages without complex flow fields comparisons, allowing rapid identification of stall-initiating locations and supporting subsequent stability enhancement optimization.

This study proposes a geometric solution to the norm differential game design problem in target-attacker-defender (TAD) engagements, addressing key limitations of conventional zero-effort-miss approaches. By leveraging the geometric analogy between guidance-law-generated trajectories and Dubins paths, we reformulate the derivation of zero-effort-miss-based guidance laws as a Nash equilibrium optimisation problem, with optimal strategies determined through reachable set analysis of Dubins path frontier. The resulting model is a non-convex optimisation problem, which prevents the derivation of traditional state-feedback control laws. To overcome this limitation and enable real-time implementation, we develop a custom back propagation neural network, enhanced with a relaxation factor method for output filtering, a Holt linear trend model for outlier compensation and a saturation function for oscillation suppression. Extensive simulations demonstrate that the proposed framework significantly outperforms baseline methods. These results validate the effectiveness and robustness of our approach for high-performance TAD applications.

In this study, self-piercing riveting (SPR) connection, which is one of the joining techniques of aluminum alloys, is investigated. SPR is a cold mechanical joining process used to join two or more sheets of material by pushing the rivet that pierces the upper sheet under the guidance of a suitable mold and then locking it to the lower sheet. The SPR process was carried out with the split Hopkinson pressure par test system. The bar inside the cylinder, accelerated by pressure, performs the riveting process by hitting the surface of the mold developed for SPR. In this study, different numbers of slots were opened on the rivet tail, and the process was carried out using SPRs at different deformation rates. A powerful tomography scanner device designed for 3D metrology was used to visualise the SPR joining mechanisms without cutting. Tensile-shear tests were applied to the samples made with rivets with different numbers of slots and different pressures, and cross-tension tests were also applied to the samples prepared with rivets with different numbers of slots. The opened slots caused a decrease in the maximum strength of the samples. It was understood that the appropriate riveting pressure could change the connection strength by approximately 50%. In general, the force values decreased as the number of slots increased.

This review examines the critical role of meteorological data in optimising flight trajectories and enhancing operational efficiency in aviation. Weather conditions directly influence fuel consumption, delays and safety, making their integration into flight planning increasingly vital. Understanding these dynamics becomes essential for risk mitigation as climate change drives more frequent and severe weather events. Synthesising insights from 57 studies published between 2001 and 2024, this article highlights key variables – such as wind, temperature and convective weather – significantly impacting flight operations. A framework is proposed to improve air traffic management’s safety, efficiency and cost-effectiveness. The findings emphasise the need for systematically incorporating meteorological inputs into trajectory optimisation models, such as wind shear, convective storms and temperature gradients. This integration improves operational predictability and safety while advancing sustainability goals by reducing fuel consumption and CO2 emissions – an increasingly important priority amid rising climate variability and global air traffic demand.

Strategies for optimising air-fuel interaction are critical in supersonic combustion. This research alters the fuel injector design by adjusting the strut corner base angle, allowing the fuel to contact the air transversely. This computational analysis uses the Reynolds-averaged Navier-Stokes (RANS) equations in conjunction with the Shear Stress Transport (SST) k-omega turbulence model and the eddy dissipation turbulence chemistry model. The validation has been conducted for the present simulation with the experimental data, comparing the pressure, temperature and Schlieren images. The standard DLR scramjet combustor model consists of a single strut (fuel injector) injecting parallel to the air stream, but in this research, the design of the strut base is changed to angles 30, 45 and 60 degrees to inject the fuel in a new method. This slanted strut base aids fuel injection into the airstream and permits the mixture to generate swirls behind the strut base, resulting in better mixing and 35% greater turbulence. This modification improves the reaction process’s spontaneity and generates 37% higher temperatures, increasing mixing and combustion efficiency by about 37% and 23%, respectively.

The role of pylon-induced vortex structures on flame stabilisation within a supersonic pylon-cavity flameholder is numerically investigated. The study examines how the fuel jet interacts with the vortices produced by three distinct pylon-cavity flameholder geometries labelled as P0, P1 and P2. P0 represents the pyramidal-shaped baseline pylon configuration, whereas P1 and P2 consist of parallel and slanted grooves on the pylon slant surfaces with respect to the supersonic crossflow for the generation of instream vortices. The selection criteria for P1 and P2 are based on reduced effective blockage area compared with P0. The inlet flow Mach number used for the investigation is 2.2. A sonic ${{\rm{H}}_2}$ fuel injection at 2.5 bar and 250 K is used for all the test cases. Steady RANS reactive flow simulations are used for the assessments. An 18-step Jachimowski chemical kinetic scheme is used to model ${{\rm{H}}_2}$-air reaction mechanism. The flowfield structures within the pylon-cavity flameholder are categorised into two, (i) pylon-cavity geometry-induced vortex structures (II, III, and IV) and (ii) fuel jet vortex pairs (FJVP and SFJVP). The study shows that the interaction between these two decides the reactant mixture formation within the flameholder and the flame stabilisation. This study identifies four different flame-holding locations – L1, L2, L3, and L4 – and their strength depends on the pylon configuration. Overall the P2 configuration is found to perform better than the others in terms of high heat release magnitude and flame spread within the combustor.

This study presents a surrogate-model-assisted Quasi-Newton optimisation framework for simultaneously improving the aerodynamic performance and radar stealth characteristics of an unmanned aerial vehicle (UAV). High-fidelity computational fluid dynamics (CFD) and computational electromagnetics (CEM) simulations are integrated through surrogate models generated via a face-centred central composite design within a design of experiments framework. Quadratic polynomial response surface equations are constructed for key aerodynamic and radar cross-section (RCS) metrics, enabling analytical gradient evaluation. A gradient-based quasi-Newton method with Broyden–Fletcher–Goldfarb–Shanno Hessian updates is employed to minimise a scalarised objective function combining normalised maximum lift coefficient, overall RCS and frontal RCS. Constraints are imposed on the lift-to-drag ratio ($L/D \geq 10$) and static longitudinal stability (${C_{m0}} \geq 0$). Analytical derivatives from the response surface equations (RSEs) eliminate the need for direct numerical differentiation of CFD/CEM outputs, reducing computational cost and eliminating simulation noise. An interior-point sequential quadratic programming strategy is used to ensure satisfaction of nonlinear constraints during the optimisation process. The optimised UAV design demonstrates a $12{\rm{\% }}$ increase in maximum lift coefficient and a $30{\rm{\% }}$ reduction in both overall and frontal RCS compared to the baseline configuration. The results are confirmed through high-fidelity CFD and RCS simulations and are further validated experimentally in an anechoic chamber, with close agreement across all measured frequencies. The proposed methodology provides an efficient and experimentally verified approach for integrated aerodynamic and stealth optimisation in UAV design.

In this paper, a sliding mode guidance law for impact angle control without violating a seeker’s field-of-view limits is proposed against targets with various motions and unknown acceleration, including stationary, constant-velocity moving and manoeuvring targets. To develop the guidance law, the kinematic conditions for engagement geometry are defined, and the sliding mode control is applied to satisfy the homing constraint and impact angle control. Then, the relation between look angle, the desired line-of-sight angle and desired impact angle is established to guarantee the target in the field of the missile’s view. The stability of the proposed approach is analysed using Lyapunov theory. Furthermore, the look angle is examined to verify the field-of-view constraint, and a capturability analysis is conducted. To evaluate the performance of the proposed law, numerical simulations demonstrate that the proposed approach achieves satisfactory miss distance and impact angle error while adhering to the field-of-view limit.

The improvement of the accuracy and real-time performance of sector traffic flow prediction is of great significance to air traffic management decision-making. Sectors operate under complex spatial structures and time dimensions. Some neural network methods adopt sequence order to gradually transmit information, which makes it difficult to achieve complete parallel training. Not only does it take too long to train, resulting in low training efficiency, but it is also easy to lose the effective correlation information of long sequence data. To this end, a sector traffic flow prediction method based on attention-improved graph convolutional transformer (AGC-T) network is proposed to improve the current traffic prediction problem for sectors. First, the graph structure information and historical traffic data of the sector are input into the graph convolutional network improved based on the attention mechanism to fully capture the spatial relationship with sectors as nodes. Combined with the transformer’s multi-head self-attention mechanism, it can directly focus on the sequence data at any position without gradually transmitting information. Not only does it improve efficiency through parallel training, but the encoder-decoder structure can also mine the information features in the traffic data, focus on the traffic data features of key nodes and more accurately predict sector traffic. Finally, the operation traffic data of sectors in typical areas in central and southern China are taken as an example to analyse the model. The results show that compared with other prediction models, the AGC-T model $RSME$, $MAE$ and ${R^2}$ are 45.16%, 46.78% and 2.63% higher than the GCN model in the 15-min single-day traffic prediction task, and 41.74%, 35.27% and 1.20% higher than the GRU model. In the single-week traffic prediction task, $RSME$, $MAE$ and ${R^2}$ are 37.12%, 40.54% and 3.55% higher than the GCN model, and 35.15%, 35.17% and 0.65% higher than the GRU model, respectively, showing better prediction performance. This study will help air navigation service providers (ANSP) to make sector traffic predictions more accurately, thereby implementing more scientific and reasonable traffic management measures.

Aerodynamic modeling of tiltrotor aircraft is inherently limited by incomplete aerofoil data for blades constructed from multiple aerofoil sections. This data deficiency leads fundamental errors into the blade element theory (BET) inputs, resulting in inaccurate induced velocity and unsteady load predictions that compromise flight control design. To overcome this limitation, a corrective induced inflow factor k is introduced into an augmented Pitt–Peters dynamic inflow model. For fixed tilt angles, k is efficiently identified via an optimisation framework. The fmincon algorithm attains the optimal k with a computational speed 5–6 times faster than the genetic algorithm, while maintaining thrust coefficient errors within 1.26%. For continuous tilting, a direction-aware, time-varying quadratic model for k(t) is proposed, which uses the Fréchet distance metric to accurately reconstruct the aerodynamic hysteresis loop and eliminate spurious load predictions during rotation reversal. Validated through wind tunnel tests, the framework reduces the maximum thrust prediction error in dynamic tilting from 34.10 to 10.70%. The corrected aerodynamic model supports real-time simulation and provides a reliable foundation for advanced tiltrotor control system design.

Electrical contacts are critical components in all connector systems, as they enable the flow of electrical current. However, power contacts are increasingly subjected to various forms of degradation due to the high input power demands of modern electrical circuits. One of the primary causes of damage is the electrical arc, which can lead to erosion and oxidation of contact surfaces, ultimately resulting in electrical insulation failure. In industries such as aerospace, this type of failure can be mission-critical, especially in scenarios where in-orbit repair is not possible. Therefore, the design and choice of contact material must be carefully considered. Based on theoretical studies of arc-related phenomena, we conducted experimental tests focusing on the optimisation of hemispherical contact using samples made from various pure and coated materials. The contact surfaces in these tests were composed of high-conductivity base such as copper (Cu) and aluminum (Al), and were also coated with noble metals such as gold (Au) and silver (Ag). These materials are commonly used in sectors including aerospace, automotive and general industrial applications. To ensure a fair comparison, all contact samples were manipulated and tested under consistent conditions that reflect their real-world operational environments. The resulting arc parameters were identified and analysed through modeling to determine the most suitable contact design and material most capable of withstanding arc-related degradation over the mission duration, thus ensuring longer service life in applications were a continuous monitoring and repair are not feasible. The results show that repetitive exposure to high input power significantly damages contacts surfaces. Furthermore, the use of coated materials effectively extends the lifespan of the electrical contacts. Scanning electron microscope (SEM) and energy-dispersive X-ray spectroscopy (EDX) analyses support these findings, revealing that high-power input increases erosion rates and leaves pronounced marks on the contact surfaces.

Reinforcement learning (RL) has demonstrated computational efficiency and autonomy in solving unmanned aerial vehicle (UAV) obstacle avoidance problems. However, practical applications still remain challenges, such as three-dimensional manoeuvres, dynamic obstacles and kinematic constraints. This paper proposes a real-time obstacle avoidance method based on RL and a kinematic model, where the RL framework outputs 3D-axis velocity to enable continuous UAV manoeuver control. To perceive large-scale, highly dynamic obstacles, we establish a 3D safety separation model and construct a modular observation matrix to enhance perception capability. The Soft Actor-Critic (SAC) algorithm is adopted to enhance stochastic exploration in high-dimensional state space while incorporating flight uncertainty. Under simulation, the proposed method effectively avoids dynamic obstacles. The optimised state space boosts learning speed and performance. This provides an effective solution for UAV autonomous obstacle avoidance in complex environments.