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.

The effect of tab orientation on the mixing characteristics of a Mach 1.6 circular jet issuing from a convergent-divergent (C-D) nozzle is studied experimentally. The tabs used in this study are triangular tabs with sharp and truncated vertices positioned upright and in inverse orientations, along diametrically opposite locations at the nozzle exit. Mixing characteristics of the jet operated at nozzle pressure ratios (NPRs) 3 to 6, in step 1, were studied. For inverse triangular and inverse truncated triangular tabs, a maximum core length reduction of about 87% is achieved at NPR 3. The corresponding core length reductions caused by the upright triangular and truncated triangular, and rectangular tabs are 71, 81 and 84%, respectively. It is found that the jet mixing is strongly influenced by the combined effect of tab geometry, tip effectand the pressure gradient at the nozzle exit. The pressure distribution in the directions along and perpendicular to the tab, at different axial locations, was used to discern the evolution and spread of the jet. The pressure profiles for all tab geometries show that the jet spread perpendicular to the tab is more than that along the tab at all the NPRs studied. The shadowgraph images of the jets show that the waves present in the controlled jet are weaker than those in the uncontrolled jet.

Liquid hydrogen (LH2) is a promising energy carrier to decrease the climate impact of aviation. However, the inevitable formation of hydrogen boil-off gas (BOG) is a main drawback of LH2. As the venting of BOG reduces the overall efficiency and implies a safety risk at the airport, means for capturing and re-using should be implemented. Metal hydrides (MHs) offer promising approaches for BOG recovery, as they can directly absorb the BOG at ambient pressures and temperatures. Hence, this study elaborates a design concept for such an MH-based BOG recovery system at hydrogen-ready airports. The conceptual design involves the following process steps: identify the requirements, establish a functional structure, determine working principles and combine the working principles to generate a promising solution.

Unstable approaches contribute significantly to accidents during the critical approach and landing phases of flight, many of which could have been prevented by executing a go-around. This review investigates cognitive lockup, a tendency to adhere to task completion despite shifting priorities, and its role in aviation incidents. Specifically, we explore the psychological underpinnings of cognitive lockup, the influence on pilot decision-making and potential mitigation strategies. We examine factors such as task completion bias, framing effects and the perceived cost of task switching, and provide recommendations for training and policy modifications to reduce cognitive lockup. Aviation safety in critical flight phases can be improved through enhanced pilot training, mindfulness techniques, positive policy framing and AI-based alert systems.

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.

This paper provides a preliminary investigation into the use of a novel passive aircraft flight loads alleviation device called the Superelastic Monostable Spoiler. The work focuses primarily on understanding the behaviour of such a device and the related loads alleviation performance during dynamic gust events. A number of different design parameters are explored, such as the trigger condition and the activation speed. The main aim of the paper is to define the preliminary operational requirements of such a device in order to guide the future detailed design, which is not addressed here. It was found that the Superelastic Monostable Spoiler could potentially provide loads alleviation performance comparable with typical gust loads alleviation technologies currently used in modern civil aviation based on the use of ailerons and spoilers.

This paper presents a novel artificial intelligence-based autopilot control system designed for the Cessna Citation X (CCX) aircraft longitudinal motion during cruise. In this control methodology, the unknown aircraft dynamics in the state-space representations of each vertical speed (VS) mode and altitude hold (AH) mode were approximated by two multiplayer fuzzy recurrent neural networks (MFRNNs) trained online using a novel approach based on particle swarm optimisation and backpropagation algorithms. These MFRNNs were used with two sliding mode controllers to guarantee the robustness of both VS and AH modes. In addition, a novel fuzzy logic-based transition algorithm was proposed to efficiently switch the controller between these autopilot modes. The performance of the controllers was evaluated with a nonlinear simulation platform developed for the CCX based on data from a Level D research aircraft flight simulator certified by the FAA. The system stability and robustness were proved by the Lyapunov theorem. The simulation, tested under 925 flight conditions, demonstrated the controllers exceptional tracking capability in a variety of uncertainties, including turbulent and non-turbulent flight scenarios. In addition, the design ensured that the smoothness of the control input signals was maintained in order to preserve the mechanical integrity of the elevator actuation system.

The aeroelastic behaviour of a flapped-wing with freeplay and reduced stiffness is highly nonlinear. The optimal control of a nonlinear system is desired to optimise a given structural performance. In this paper, two novelties (a new method to solve state dependent Riccati equation, and inclusion of damage effects on aero-servo-elastic system) are developed to optimally control the nonlinear aeroelastic behaviour of a flapped-wing section including freeplay and reduced stiffness. To design the optimal controller, the State Dependent Riccati Equation (SDRE) is utilised based on a combination of the Hamiltonian matrix and the Schur method. A three degree of freedom (DoF) aeroelastic wing structure with a control surface is mathematically modelled, including freeplay in control surface, cubic nonlinear spring for description of the torsional stiffness and reduced stiffness factor in torsional spring due to damage. The effect of freeplay, reduced stiffness and concentrated nonlinearity in torsional spring are analysed on aeroelastic response. The system response is determined by time marching of the governing equations using a Matlab code. Various simulations results for multiple flow velocities and nonlinear parameters prove the effectiveness of this control method in flutter suppression. It is also shown that the control surface freeplay leads to limit cycle oscillation at speeds less than flutter speed. Furthermore, the simulation results show that the presence of a damage – which reduces the stiffness in torsional spring – leads to an increase in the oscillation amplitudes.

Airbreathing waveriders often use the fuselage forebody as the pre-compression surface of the inlet, which tends to encounter complex internal-external flow coupling issues. First, the osculating cone method is employed, and a wide-speed-range airbreathing waverider is designed by partitioning it into the waverider forebody, elongated body and waverider aft body, achieving full waverider characteristics. Next, the configuration is optimised to address the internal-external flow coupling issue. The calculations show that the optimised configuration improves the lift-to-drag ratio by more than 20% and the total pressure recovery coefficient by more than 30% in both operating conditions compared to the baseline configuration. Finally, data mining techniques are applied to analyse the data from the optimisation process. It reveals the interdependent relationship between the vehicle’s internal and external flow performance, with the cone shock wave angle and wing extension line length having the most significant impact on aerodynamic performance, thereby generating design knowledge. The content of this paper covers configuration design, optimisation and data mining. The entire process is highly generalisable and can serve as a reference for other aircraft configuration design optimisation tasks. The resulting design knowledge can also provide valuable insights for researchers in future airbreathing waverider designs.

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.

The conceptual design of mission-tailored aircraft is increasingly shifting towards system of systems (SoS) perspectives that account for system interactions using a holistic view. Agent-based modelling and simulation (ABMS) is a common approach for analysing an SoS, but the behaviour of its agents tends to be defined by rigid behaviour trees. The present work aims to evaluate the suitability of a prompt-engineered large language model (LLM) acting as the Incident Commander (IC), replacing the fixed behaviour trees that govern the agents’ decisions. The research contributes by developing a prompting framework for operational guidelines, constraints, and priorities to obtain an LLM commander within a wildfire suppression, SoS capable of replicating human decisions. By enabling agents in a simulation model with decision-making capabilities closer to those expected from humans, the commander’s decisions and potential emergent patterns can be translated into more defined requirements for aircraft conceptual design (ACD) (e.g., endurance, payload, sensors, communications, or turnaround requirements). Results showed that an LLM commander facilitated adaptive and context-aware decisions that can be analysed via decision logs. The results allow designers to derive aircraft requirements for their specific roles from operational outcomes rather than a priori assumptions, linking SoS mission needs and ACD parameters.

Renewed interest in supersonic air travel has prompted researchers to reconsider the design and operation of supersonic transport aircraft. Previously, such aircraft were restricted to overwater routes due to the disturbances caused by their sonic booms. Now, however, low-boom designs and overland flight at marginally supersonic Mach numbers are seen as potential enablers for widespread supersonic air travel. As a result, the trajectories that next-generation supersonic transports may fly are likely to be less constrained than for previous types, and in the last decade there has been a noticeable increase in research focusing on trajectory planning for such aircraft. This paper reviews the different methods that have been used to generate and optimise the flight paths of past and future supersonic transports. The challenges associated with optimising trajectories for aircraft that do not yet exist are discussed, and suggestions for future research activity are presented. Climate-optimal trajectory planning and development of detailed, non-proprietary supersonic aircraft performance models are identified as two key areas for future work.

Considering the performance of the standard KF (Kalman filter)degrades significantly when subjected to a hybrid attack, this paper investigates the state estimation problem for a class of stochastic systems under randomly occurring hybrid cyber attacks, which involves denial-of-service (DoS) attack and deception attack obeying unknown Gaussian distribution. First, to consider the DoS attack and deception attack in a unified model, a categorical distributed vector is employed to establish a new measurement model including hybrid cyber attacks. Next, the conjugate prior distributions for the unknown attack parameters are determined, in which the attack probabilities are modeled as Dirichlet distribution, and the deception attack is described by Gaussian-inverse-Wishart distribution. Then, based on variational Bayesian (VB) inference, a RKF (robust Kalman filter) is designed to simultaneously estimate the state and unknown attack parameters. Finally, the estimation performance of the proposed filter is illustrated through a simulation example.

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.

The success of cooperative unmanned aerial vehicle (UAV) missions relies on effective multi-UAV path planning. To address the issues of limited individual diversity and susceptibility to local optima during the population initialisation phase of the traditional grey wolf optimiser (GWO), this paper proposes an improved chaotic grey wolf optimiser (CGWO). The algorithm enhances population diversity by introducing chaotic initialisation to generate more uniformly distributed initial solutions. Combined with a chaotic local search strategy, it employs a dynamic balancing mechanism that allows individuals in the population to strike a balance between global exploration and local refinement, thereby breaking free from local optima constraints and accelerating optimal solution convergence. The algorithm was validated by using the CEC2017 benchmark functions and simulations of multi-UAV mission scenarios. The results clearly demonstrate that the improved algorithm significantly outperforms the original GWO and other common optimisation algorithms in terms of convergence accuracy and speed during benchmark testing. In multi-UAV mission scenarios, the enhanced algorithm excels in trajectory planning, flexibly avoiding obstacles while maintaining smooth flight paths for UAVs. Overall, this research provides a practical solution for coordinated multi-UAV operational path planning.

To investigate the advantages and disadvantages of two multi-swirl fuel-rich dome configurations, namely the triple-swirler and double-swirler, for a novel high-temperature rise centre-staged combustor, this study employed ANSYS Fluent software. Utilising the Reynolds-averaged Navier-Stokes (RANS) equation as the governing equation, three-dimensional numerical simulations were conducted using the Realisable k-ε turbulence model and non-premixed probability density function (PDF) combustion model to analyse the flow and combustion characteristics of both configurations. A comparative study was then performed to evaluate the performance differences between the two dome configurations under take-off and idle conditions. The results demonstrate that, under both conditions, the fuel-air mixing in the triple-swirler combustor occurs faster and more uniformly. Specifically, during takeoff, the primary zone temperature distribution in the triple-swirler combustor is more uniform, while during idle, the fuel-rich combustion region is more symmetrical. Furthermore, across both conditions, the outlet temperature distribution of the triple-swirler combustor is of superior quality, albeit with equivalent combustion efficiency. Notably, the formation of NOx and soot in the triple-swirler combustor, during takeoff conditions, exceeds that of the double-stage combustor along the flow path, whereas the generation of CO and UHC, during idle conditions, is lower in the former.

This paper presents a detailed robustness analysis of three nonlinear filtering algorithms: the unscented Kalman filter, the cubature Kalman filter, and the ensemble Kalman filter, applied to aircraft state estimation for fixed-wing flight dynamics. The study focuses on estimating critical longitudinal flight parameters such as true airspeed, angle-of-attack, pitch angle and pitch rate using pitch angle measurements. A nonlinear aircraft model is formulated, and each filtering technique is implemented and evaluated under multiple scenarios, including sensor noise, initial state mismatches and plant-model uncertainties. Simulation results across four cases, ranging from ideal conditions to $95{\mathrm{\% }}$ mismatch, demonstrate that the unscented Kalman filter consistently delivers the most accurate and robust estimates, especially for velocity and pitch rate. The cubature Kalman filter offers a trade-off between estimation accuracy and computational efficiency, while the ensemble Kalman filter shows significant sensitivity to uncertainties but performs relatively better in estimating the angle-of-attack under severe mismatch conditions. This comparative study provides valuable insights for selecting appropriate filtering strategies in aerospace applications, particularly where robustness and reliability under uncertainty are crucial.

Systems are holistic that represent complex structures in which different components interact. Systems thinking plays an important role as a methodology used to solve and analyse these complex problems. This approach emphasises addressing problems holistically rather than simply breaking them down into parts, providing a deeper understanding of identifying and resolving root causes. The article aims to explain the conceptual framework of systems thinking by discussing the basic concepts and principles of systems thinking in detail. In this context, the literature focuses on reaching a common definition of the term ‘systems’ and discusses practical approaches to the use of systems thinking in aircraft design. It also includes analysis of the application of systems thinking through examples of catastrophic accidents resulting from the misunderstanding or mismanagement of complex systems in engineering studies. The change in aircraft design process over the years has been examined, and a new categorisation method is proposed. By integrating systems thinking into the aircraft design process, it examines the advantages it will provide in understanding and optimising the interaction of components, saving time and costs. This study aims to deal with the systems thinking perspective of aircraft design. The importance of the system concept in aviation is emphasised with concrete examples, and its applicability is examined. Thus, a base is formed for its use in aviation.

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.

Birds and bats are extremely adept flyers: whether in hunting prey, or evading predators, post-stall manoeuvrability is a characteristic of vital importance. Their performance, in this regard, greatly exceeds that of uncrewed aerial vehicles (UAVs) of similar scale. Attempts to attain post-stall manoeuvrability, or supermanoeuvrability, in UAVs have typically focused on thrust-vectoring technology. Here we show that biomimetic wing morphing offers an additional pathway to classical supermanoeuvrability, as well as novel forms of bioinspired post-stall manoeuvrability. Using a state-of-the-art flight simulator, equipped with a multibody model of lifting surface motion and a delay differential equation (Goman-Khrabrov) dynamic stall model for all lifting surfaces, we demonstrate the capability of a biomimetic morphing-wing UAV for two post-stall manoeuvres: a classical rapid nose-pointing-and-shooting (RaNPAS) manoeuvre; and a wall landing manoeuvre inspired by biological ballistic transitions. We show that parametric variation of nonlinear longitudinal stability profiles is an effective open-loop strategy to explore the space of post-stall manoeuvres in these types of UAVs; and it yields insight into effective morphing kinematics to enable these manoeuvres. Our results demonstrate the capability of morphing-based control of nonlinear longitudinal stability to enable complex forms of transient supermanoeuvrability in UAVs.