Hard landings are a perennial issue for airlines, resulting in lost aircraft utilisation, ground delays and landing gear damage. With the Boeing 787 series in widespread use with airlines globally, this study aims to quantify the influence of several flight parameters on the vertical load factor at touchdown for the Boeing 787 using data from the aircraft’s quick access recorder (QAR). A hierarchical regression analysis was performed on 13 variables that were grouped into three sets: (A) Aircraft and Environmental Conditions, (B) Flare Parameters and (C) Final Manoeuvres. These sets were entered sequentially to predict touchdown load factor in Gs. The final model was statistically significant (p < 0.001), explaining 14% of the variance in touchdown G. Final Manoeuvres (Set C) was the largest unique contributor, accounting for 5% of the variance. Three flight parameters were found to be significant predictors: windspeed, vertical speed at 20ft AGL and stick pitch (forward). For the latter, pitch-down control input resulted in an average increase of 0.08G compared to a stick-neutral input.

This paper presents the design of a nonlinear adaptive flight control system for the Cessna Citation X longitudinal dynamics. The aircraft pitch rate is controlled using a combination of recursive least squares-based nonlinear dynamic inversion and an adaptive neural network controller. The recursive least squares algorithm provides online parameter estimates to support the inversion, while the neural network compensates for residual modeling errors through online weight adaptation. To enhance robustness and ensure stability, a fixed-gain proportional integral derivative controller is integrated into the control structure. Unlike conventional gain-scheduled controllers, where PID gains vary with flight condition, the proposed adaptive controller uses a single baseline set of fixed gains. The adaptive component updates the control action online, enabling the same controller configuration to operate effectively across all 64 cruise conditions without any gain scheduling. A systematic tuning methodology is introduced for initialising the recursive least squares, selecting forgetting factors and applying covariance resets to ensure accurate adaptation. The controller is able to track a pitch-rate reference model that satisfies longitudinal flight quality requirements. Robustness is assessed under realistic disturbances, including wind gusts, Dryden turbulence, actuator loss-of-effectiveness and actuator noise. Simulation results demonstrate that the controller achieves precise reference tracking while maintaining Level 1 flight qualities. Stability is formally guaranteed using Lyapunov-based analysis. The findings highlight the ability of the designed hybrid adaptive controller to overcome limitations of linearisation, gain scheduling and estimator sensitivity, forecasting a practical and certifiable method for the integration of intelligent adaptive flight control systems into commercial aircraft.

The accuracy, robustness and affordability of localisation are fundamental to autonomous robotic inspection within aircraft maintenance, repair and overhaul (MRO) hangars. Hangars typically have high ceilings and are predominantly steel-framed structures with metal cladding. Because of this, they are regarded as GPS-denied environments, characterised by significant multipath effects and strict operational constraints, which together form a unique challenging setting. The lack of comparative techno-economic benchmarks for localisation technologies in such environments remains a critical gap. Addressing this, the paper presents the first techno-economic analysis that benchmarks motion capture (MoCap), ultra-wideband (UWB) and a ceiling-mounted camera (CMC) system across three operational scenarios: robot localisation, asset monitoring and surface defect detection within a single-bay hangar. A two-stage optimisation framework for camera selection and placement is introduced, which couples market-based camera-lens selection with an optimisation solver, producing camera layouts that minimise hardware while meeting accuracy and coverage targets. The consolidated blueprints provide quantification of the required equipment and its performance: 15 global-shutter GigE cameras are adequate for drone localisation, 9 cameras meet the requirements for on-bay monitoring and 49 high-resolution cameras facilitate defect mapping of the upper airframe surfaces for midsize defects. Across these scenarios, the study reports indicative performance and cost envelopes: a MoCap installation delivers submillimeter localisation at an estimated £190k per bay, UWB delivers centimetre-level tracking for around £49k and the proposed CMC system layouts achieve task-specific coverage with costs in the £9k–£77k range. The analysis equips MRO planners with an actionable method to balance accuracy, coverage and budget, demonstrating that an optimised CMC system can deliver robust and cost-effective sensing for next-generation smart hangars.

Future space-terrestrial networks demand autonomy, sustainability, and trust-aware intelligence. This paper proposes a Neuro-Symbolic Digital Twin (NSDT) architecture tailored for geostationary (GEO) satellites. By fusing neural inference with symbolic reasoning, NSDT delivers interpretable, resilient decision-making under uncertain or hostile conditions. Key innovations include a semantic trust layer, green beamforming and a novel energy metric ${\varepsilon _{NS}}$. High-fidelity MATLAB simulations demonstrate 35–40% energy savings, 3–5 dB SINR gains, 0.8–0.9 resilience, and latency of 120–160 ms versus conventional approaches.

This study proposes a radial basis function neural network disturbance observer- (RBFNNDO) based anti-saturation backstepping controller for hypersonic vehicles with input saturations and multiple disturbances. Firstly, in response to the problem of ‘exploding complexity’ in backstepping controller, we adopt finite-time tracking differentiators (FTD), which realise higher tracking accuracy and tracking speed than those of the existing methods. Secondly, we develop multivariable neural network disturbance observers to estimate the lumped disturbances involving aerodynamic uncertainties and external disturbances, thereby improving the robustness of the proposed controller. Thirdly, in order to alleviate the input saturation and minimise the duration time, we use an adaptive fixed-time anti-saturation compensator (AFAC). The simulation results have proven that our proposed backstepping controller outperforms other existing methods in terms of control performance and saturation time.

This paper presents a comprehensive experimental investigation into the shock characteristics associated with a low-thrust, low-shock separation mechanism incorporating Mild Detonating Cord (MDC) within a rubber bellow interface. Two test configurations were developed with varying explosive charge masses to study their influence on pressure generation and shock propagation. Linear accelerometers and high-speed pressure transducers were employed to capture transient dynamic responses at both piston and cylinder interfaces. The results demonstrate a significant reduction in peak pressure and shock levels, especially in the second test configuration, where the explosive mass was reduced to 60% of the initial configuration. The shock response spectrum (SRS) analysis confirms that the lower charge mass leads to proportionally reduced shock amplitudes across the frequency range of interest. Furthermore, comparative assessment of shock levels reveals a significant reduction of shock levels as compared to conventional separation mechanisms, such as a flexible linear-shaped explosive charge (FLSC) mass or a separation bolt actuated with a pyro cartridge. The experimental pressure values are shown to correlate well with theoretical predictions, validating the design approach. These findings provide critical insights into tailoring explosive-based separation mechanisms for sensitive payload environments, highlighting the importance of confined detonation and charge optimisation in mitigating pyroshock.

Collaborative robots (cobots) have emerged as a pivotal paradigm for the upcoming leap to Industry 5.0. In recent years, the range of applications has expanded significantly, particularly in assembly tasks within the manufacturing industry. The primary goal of this paper is to review the application of cobots in industrial assembly tasks, highlighting possibilities for innovative research in smart robotics, including prospects for challenging applications in aircraft final assembly processes. The paper systematically reviews recent literature to analyse the use of collaborative robotics in industrial assembly tasks, encompassing characterisation of application environments, motivations, characteristics and outcomes of relevant use cases across various industrial segments. Additionally, it reviews a set of innovative technological patents issued by the aeronautical industry over the past 14 years, highlighting trending projects in industry. The investigation reveals that the automotive and electronics industries remain at the forefront of cobot applications, mainly for tasks like pick-and-place operations and component manipulation. Applications in open work cells, where humans and robots operate at supportive or sequential interaction levels, using conventional communication interfaces and camera-assisted technologies, have been the most prevalent. The review identifies potential opportunities and key aspects from future application scenarios for cobots. The findings are relevant to the industrial robotics community, emphasising the need for novel applied research on human–robot colla boration in aeronautical industry.

This study presents a comprehensive analysis of the frequency response characteristics in a gas generator cycle liquid rocket engine, employing modular decomposition and linearised frequency-domain modeling to simulate dynamic behaviours under forced oscillations. The engine is dissected into key subsystems, including liquid pipelines, turbopump assembly, valves, flow regulation components, thrust chamber, gas generator and pyrotechnic starter, highlighting features such as centrifugal pump pressurisation, staged combustion and cavitation mitigation via venturis. Three oscillation scenarios are examined: supply system responses to thrust chamber pressure disturbances, combustion component responses to fluid disturbances and combustion component responses to pump speed disturbances. Simulations over 0–2000 Hz reveal acoustic-dominated traits in the thrust chamber with oxidiser pathway dominance, low-frequency emphasis in the gas generator driven by fuel disturbances, and heightened instability risks from pump pulsations. Parametric analyses demonstrate that increased pipeline lengths shift resonant frequencies downward, elevated injector pressure drops enhance stability margins by 1.6% with a 20% pressure drop increase, and chamber structural/gas parameter variations erode system stability. These insights, validated against benchmark models, inform strategies for mitigating combustion instability, optimising design parameters, and improving reliability in high-thrust propulsion applications.

Hydrogen is a leading candidate for zero-emission propulsion in aviation, particularly when stored and utilised in its liquid form. However, key components such as composite cryogenic pressure vessels remain at low Technology Readiness Levels (TRL), requiring further investigation into their structural performance under realistic operational conditions. The present work aims to provide a validated numerical methodology for simulating the thermomechanical behaviour and the progressive damage evolution of composite cryogenic hydrogen tanks. The finite element framework incorporates ply-level failure criteria and stiffness degradation laws to capture intra-laminar damage mechanisms under combined pressure and temperature loads. The modelling approach is validated against experimental data from coupon-level open-hole tension tests and subcomponent-scale composite pipes burst tests, demonstrating strong correlation in terms of failure onset and progression.

The validated methodology is subsequently applied to a demonstrator, comprising a composite liquid hydrogen tank, subjected to three representative loading scenarios: internal pressure, cryogenic temperature and combined cryogenic-mechanical loading. Results reveal that matrix-dominated damage initiates near the cylinder – dome interfaces of the tank and propagates across the laminate, while fibre failure is not observed in the investigated load cases. This suggests that potential hydrogen leakage is the initial critical failure condition that occurs before any other important structural damage of the tank, highlighting the need for appropriate tank design. The performed study contributes to the understanding of structural integrity of composite cryogenic tanks and offers a computational basis for future design and certification efforts in hydrogen aviation systems.

Design optimisation of hybrid airships consisting of multi-lobed configurations is being projected as the next paradigm shift in implementing sustainable flight operations within the aeronautical industry. To that end, this paper discusses the effect of varying the relative placement of side-lobes pertaining to a tri-lobed airship hull geometry with respect to the middle-lobe. The paper makes use of a validated OpenFOAM® solver to underscore the aerodynamic impact of shifting the side-lobes in upstream, downstream, upward and downward directions with respect to the middle-lobe while retaining the same volume. These tri-lobed airship hull variants called as skewed tri-lobed hulls, have been comprehensively investigated through the usage of numerical solver (Reynolds-averaged Navier-Stokes) at high Reynolds number flow across various angles of attack. The investigations delineate significant impact of skewed side-lobes on the overall aerodynamics of the tri-lobed airships. Skewing the side-lobes in fore direction leads to drag mitigation at the expense of degraded aerodynamic efficiency owing to lift reduction. Contrarily, aft-skewness amounts to an aerodynamic efficiency enhancement of $ \approx 17{\rm{\% }}$ and improved pitch stability while marginally increasing the pressure drag liability. Aerodynamic efficiency enhancement is attributed to increased lifting force. Skewed upward and downward variants present an overall aerodynamic efficiency reduction. The paper further made use of detailed flow-field visualistion as well as pressure-coefficient distribution plots to underscore the underlying flow-physics related to aforementioned aerodynamic trends. These investigations emphasised the presence of varying three-dimensional relieving effect, intermixing between the three lobes as well as diverse flow separation characteristics downstream of the maximum diameter region leading to the aerodynamic variations thereof. The paper enhances aerodynamic understanding related to tri-lobed geometry that will be crucial in implementing future design changes to the baseline model for improved aerodynamic performance. Amalgamation of these inferences with an optimisation scheme could be implemented in future aerodynamic investigations to optimise tri-lobed geometry for enhanced aerodynamic utility.

Strategic trajectory planning (STP) is critical for improving flight efficiency and ensuring operational safety, particularly in large-scale flight operations. Given the long lead time of STP, accurately analysing wind forecast uncertainty is essential to enhancing the quality of planned trajectories. However, most existing research overlooks the time-variant nature of wind forecast uncertainty. This may lead to significant discrepancies between planned and actual flight trajectories, increasing operational costs and conflict risks. Therefore, this paper proposes a novel bilevel STP framework for large-scale flights that explicitly accounts for time-variant wind forecast uncertainty. The upper-level model optimises trajectories across multiple flights to minimise total flight time, based on the departure times determined by the lower-level model. The lower-level model mitigates potential conflicts by adjusting the departure times according to the trajectories selected by the upper level. To solve this problem efficiently, a time-variant A* algorithm (TVA*) and a multi-objective cooperative co-evolution algorithm (MOCCEA) are developed, supported by static expectation (SE) and dynamic equilibrium grouping (DEG) strategies to accelerate computation. Experimental results confirm that the proposed method yields consistently dominant Pareto fronts, significantly enhancing flight efficiency while ensuring operational safety and fairness.

Passive gust load alleviation systems have the potential to significantly reduce airframe mass without reliance on complex systems of sensors and actuators. Recent experimental work by the authors has shown that a passive, strain-actuated spoiler can rapidly reduce the lift coefficient of an aerofoil. In this work, we numerically investigate the efficacy of a strain-actuated spoiler in alleviating loads within the wider airframe. The airframe is represented by a beam model which is exposed to a series of One-Minus-Cosine gusts. The effect of the spoiler on the wing is captured by locally reducing lift when wingbox strains meet a triggering condition. The model spoiler is shown to be capable of reducing the sizing wing root bending moment by up to $17$% for the airframe and spoiler parameters considered. In addition, the sensitivity of this load alleviation to key spoiler design parameters is investigated. It is found that deploying the spoiler as early as possible in the gust provides the best load alleviation performance. In a few cases, the spoiler is found to induce a limit cycle oscillation in the wing by repeatedly deploying and stowing. This may be an artefact caused by the low fidelity structural model employed in this work. Nonetheless, two ways of preventing this behaviour are demonstrated. Our work demonstrates for the first time that a strain-actuated spoiler is capable of alleviating loads at the scale of a full aircraft.

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.

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 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.