Sixty uncrewed aerial vehicle (UAV) accident reports were analysed to identify causal and contributory factors leading to loss of control in flight – the most prominent category of UAV accident (36%). Design and manufacturing errors were dominant causal factors (22 events, 34%) and contributory factors (18 events, 22%). Recovery was not attempted in the majority of events (35 events, 55%). The relationship between operator age, total hours of experience, experience on type and recovery attempts were analysed. The number of accidents decreased as total hours of experience increased as well as attempted recovery. Using this data, existing accident analysis frameworks HFACS, AcciMap and Accident Route Matrix were applied to a sample of accidents and suitability compared. An adapted version of the Accident Route Matrix – Uncrewed Aerial Vehicle is proposed to assist current and future operators to understand causal and contributory factors to mitigate future loss of control in flight accidents and improve the likelihood of recovery. Using the results of the statistical analysis and the data gathered, a new definition for LOC-I for UAVs was defined by considering the different operating environment of UAVs.

To address the challenges of high-manoeuver targets and limited line-of-sight from the interceptor’s side window, this paper proposes a three-dimensional target manoeuver compensation control (TMCC) guidance law based on compensation function observe (CFO) and a method for studying the terminal guidance handover region. First, a relative model of the missile-target engagement is established. Secondly, the CFO is used to estimate the target manoeuver state, and the estimated information is fed back to the controller of the orbit control engine to make the interception more accurate. Considering the limited line of sight of the side window, the body line of sight angle is constrained by controlling the attitude control engine. Then, the problem description for solving the handover area and the definition of the terminal guidance handover area were provided, and the algorithm design for the handover area was conducted, simplifying the solving process through the concept of area substitution. Simulation results indicate that the proposed terminal guidance law offers higher interception accuracy compared to traditional proportional guidance, and effectively validates the accuracy of the handover region calculation.

This paper quantifies the impacts of the airframe configuration change on the performance differences between a tube-and-wing and a blended wing body aircraft. Both are sized for a 5,000 nmi design range carrying 225 passengers, initially using the same engine. Parametric geometry is created for both concepts based on relevant public information. The tube-and-wing notional geometry is derived from the existing Boeing 767-300ER, whereas JetZero’s concept inspires the blended wing body. These geometries are optimised using computational fluid dynamics and gradient-free approaches. Drag polars for each optimised model, spanning the expected operating envelope, are generated using computational fluid dynamics simulations and multi-fidelity surrogate models. Mission analysis is performed for the blended wing body, a conventional tube-and wing variant with metallic structures, and an advanced tube-and-wing with composite structures. The results show that the blended wing body operates with 15-20% higher lift-over-drag during the cruise, 24% lower fuel burn for the design mission, and 15% reduction in ramp weight relative to the conventional tube-and-wing. These differences drop to 20% for the design mission fuel burn and 10% for the ramp weight relative to the advanced tube-and-wing. When the engines are re-sized and optimised separately for each configuration, the blended wing body demonstrates a 25% improvement in block fuel and 16% reduction in ramp weight relative to the conventional tube-and-wing, which decreases to 21% and 10% relative to the advanced tube-and-wing. In both comparisons, the fuel efficiency advantage of the blended wing body decreases as the mission range is reduced.

Urban air mobility (UAM) utilising novel transportation tools is gradually being recognised as a significant means to alleviate ground transportation pressures, vertiports which serve as pivotal nodes in UAM require efficient methods for assessing its operational capacity to develop an appropriate operational strategy and help to design vertiport ground infrastructure scientifically. This study proposes a multi-dimensional assessment method for the capacity of vertiports considering throughput and quality of service based on genetic algorithm (CEGA). The method comprehensively considers constraints such as unmanned aerial vehicle (UAV) safety separation, battery endurance, number of landing vertipads and UAV speed. The experimental results indicate that the vertiport with the scheduling algorithm proposed by this study has a larger capacity and experiences fewer delay than the vertiport with first-come-first-served (FCFS) algorithm when the vertiport has the same limited number of vertipads. Different proportions of UAVs significantly affect the quality of service and the degree of operation delays. The weights of vertiport throughput and customer satisfaction are the parameters that represent the importance of throughput and customer satisfaction in the objective function of the capacity assessment model. When the weights of throughput and customer satisfaction are set to 0.8 and 0.2 respectively, the performance of this optimisation model is optimal. This study provides a novel solution for capacity assessment and operation scheduling of vertiports, laying the foundation for improving the efficiency of UAM operations.

The proportional–integral–derivative (PID) controller remains widely used in industrial applications today due to its simplicity and ease of implementation. However, tuning the controller’s gains is crucial for achieving desired performance. This study compares the performance of PID controllers within a cascade control architecture designed for both position and attitude control of a quadcopter. Particle swarm optimisation (PSO), grey wolf optimisation (GWO), artificial bee colony (ABC), and differential evaluation (DE) methods are employed to optimally tune the PID parameters. A set of PID gains is determined offline by minimising various modified multi-objective functions based on different fitness measures: IAE, ISE, ITAE and ITSE. These measures are adapted as fitness functions for position and attitude control. A simulation study is conducted to determine which fitness function yields the optimal PID gains, as evidenced by the lowest values of the objective functions. In these simulations, two different desired trajectories are designed, and the controllers are applied to ensure the quadcopter tracks these trajectories accurately. Additionally, to test the robustness of the flight control architecture and the finely tuned PID controllers against various environmental effects and parametric uncertainties, several case scenarios are also explored.

The dynamic behaviour of helicopter during water impact, considering variations in initial downward velocity and pitching angle, have been investigated numerically and theoretically in the present study. The air-water two-phase flows are simulated by solving unsteady Reynolds-averaged Navier-Stokes equations enclosed by standard $k - \omega $ turbulence model. A treatment for computational domain in combination with a global dynamic mesh technique is applied to deal with the relative motion between the helicopter and water. Results indicate that the initial downward velocity of helicopter exhibits behaviour similar to that of a V-shaped body impacting on water, as does the initial pitching angle. To extend the theoretical approach for predicting the kinematic parameters during helicopter ditching, a shape factor capturing the combined effect of various attributes and an average deadrise angle for asymmetric wedges are also introduced.

This study addresses the Aircraft Reactive Scheduling Problem (ARSP) on multiple parallel runways in response to operational disruptions. We specifically consider three disruptive event types; flight cancelations, delays and unexpected arrivals. Interruptions to aircraft schedules due to various reasons (e.g. bad weather conditions) may render the initial schedule not optimal or infeasible. In this paper, the ARSP is conceptualised as a multi-objective optimisation problem wherein considerations encompass not only the quality of the schedule but also its stability, defined as its conformity to an initial schedule, are of interest. A mixed-integer linear programming (MILP) model is introduced to obtain optimal solutions under different policies. Repair and regeneration heuristic approaches are developed for larger instances for which optimal solutions are time-consuming to obtain. While prevailing literature tends to concentrate on individual disruption types, our investigation diverges by concurrently addressing diverse disruption types through multiple disruptive events. We introduce alternative reactive scheduling methodologies wherein the model autonomously adapts by dynamically choosing from a range of candidate solution methods, considering conflicting objectives related to both quality and stability. A computational study is conducted, and we compare the solutions of heuristics to optimal solutions or the best solution found within a time limit, and their performances are assessed in terms of schedule stability, solution quality and computational time. We compare the solutions of heuristics and optimal solutions (i.e. the best solution found so far), and their performances are assessed in terms of schedule stability, solution quality and computational time.

Aiming to address the issue of low accuracy in model predictions obtained from fitting frequency domain response curves for small unmanned helicopters during the process of modeling their flight dynamics, this study proposes a system identification algorithm based on the combination of weighted least squares and improved grey wolf optimisation algorithm. The algorithm utilises the weighted least squares method to obtain the initial model structure, optimises the initial model parameters using the improved grey wolf optimisation algorithm, and enhances the local search and global optimisation ability of the grey wolf optimisation algorithm by introducing an improved grey wolf subgrouping rule, nonlinear convergence factor and dynamic cooperative rule. Ultimately, this approach establishes a dynamic model for small, unmanned helicopters. The identified model is validated using flight test data, with findings demonstrating that this method achieves higher accuracy in model identification and better fits to frequency domain response curves, thus providing a more accurate reflection of the flight dynamics of small unmanned helicopters.

Status information from avionics systems is typically transmitted to airlines, but aircraft cabin systems remain largely disconnected and frequently reliant on manual, paper-based logbooks for defect recording. This results in error-prone processes that compromise data consistency and complicate maintenance planning. Digitalisation offers solutions to these challenges by enabling predictive maintenance, real-time monitoring and streamlined data sharing, improving operational reliability and efficiency. However, developing such systems is inherently complex due to operational constraints and stringent safety and security regulations. Model-based Systems Engineering (MBSE) effectively manages complexity, provide standardised system visualisation and enhance multidisciplinary communication. From a methodological perspective, approaches from literature suitable for addressing aviation maintenance systems were selected and enhanced with allocation techniques and subsequently applied to create system models for both current and digitalised aircraft cabins. This paper showcases MBSE’s relevance to develop digitalised aircraft cabin systems by using the Systems Modeling Language (SysML) enabling stakeholders to visualise system architectures and to make better-informed design decisions. The analysis of the presented SysML models highlights the error-prone structure of current non-digitalised aircraft cabin systems while illustrating new use cases unlocked by digitalisation. A model-based comparison underscores the improved efficiency, reliability and predictive capabilities achieved through digital transformation. This study demonstrates that MBSE provides qualitative advantages in system development by enhancing stakeholder collaboration, clarifying complex system architectures, and providing actionable insights into system behaviour and improvements.

It is known that interval type-2 fuzzy logic controllers (IT2FLC) with footprint of uncertainty (FOU) in terms of membership function (MF) have been developed as an effective control method to ensure control in systems where uncertainties and nonlinear situations are high, such as quadrotor control, and have been the subject of many studies. Designing and optimising parameters of IT2FLC controllers is complex and time-consuming. To overcome this situation, an optimisation method based on NSGA-II (Non-dominated Sorting Genetic Algorithm) was applied. ITAE (Integral Time Absolute Error) was chosen as the performance criterion. IT2FLC-NSGA-PID and NSGA-PID controllers were compared and it was observed that the IT2FLC-NSGA-PID controller gave better results. As a result, the superiority of the proposed controller over the other controllers is a better overshoot ratio, a faster settling time, a lower steady state error and a robust system response against uncertainties and disturbances in nonlinear systems.

A terrain and path following control scheme is designed for ground detection mission of a fixed-wing unmanned aerial vehicle (UAV) considering the attitude constraint. The attitude of the UAV should be maintained for efficient exploration, leading to the degradation of mission performance. The proposed controller makes the attitude of the UAV remain in a desired range, which alleviates the mission performance degradation. The proposed algorithm consists of the guidance law and the nonlinear flight path controller. The guidance law is designed by combining a terrain-following altitude controller and a horizontal path following controller based on the Lyapunov control scheme. The generated command by the guidance law is used as a reference input to be followed in the flight path controller. The flight path controller is designed considering the attitude constraint. Especially, the roll and pitch angles of the UAV are considered as attitude constraints so that the angles remain within the desired range. To design a flight path controller satisfying the attitude constraint, the control system is decomposed into three feedback loops. State-feedback controllers are designed using the sliding mode control scheme for flight path control in the outermost loop as well as for angular rate control in the inner loop. In the second-outer loop, a quadratic programming (QP)-based controller is designed to control the sideslip angle while satisfying the attitude constraint. The control Lyapunov function is adopted to determine the QP constraint for the sideslip angle control, and the control barrier function is used to obtain the QP constraint for the attitude constraint. Numerical simulation is performed to demonstrate the effectiveness of the proposed algorithm.

For binary plug nozzle, the plug cone is exposed to high-temperature mainstream flow, making it one of the nozzle’s high-temperature components. This paper uses the Realizable k-ε turbulence model and the reverse Monte Carlo method to numerically investigate the aerodynamic and infrared radiation characteristics of the plug nozzle. Various slot cooling configurations were adopted to study the nozzle’s infrared radiation in detail. Results indicate that compared to the baseline nozzle, the plug nozzle’s performance is slightly reduced due to the decrease in effective area of flow over the plug cone. Introducing slot cooling at the rear edge provides significant infrared suppression benefits at low detection angles and notably reduces infrared radiation discrepancy with baseline nozzle at high detection angles. The cooling air from slots causes the nozzle jet to exhibit a ‘thermal layered’ feature. With the same total coolant mass flow, the ‘leading edge + trailing edge’ cooling configuration can lower the area-averaged wall temperature of the plug cone by 5.5% – 12.3%. However, its infrared radiation intensity at each detection angle on the pitch detection plane is higher than that of the ‘trailing edge’ configuration. The significance of leading-edge cooling is focused more on thermal protection for the plug. Thus, it is essential to balance coolant mass flow distribution between infrared radiation suppression and thermal protection.

Turbofan engines are having a growing role in modern aircraft maintenance. Due to this increase, estimation of remaining useful life (RUL) of these engines is an important area of study in the field of reliability and maintenance optimisation. In this work, we propose a hybrid approach that combines deep learning models with similarity-based methods for accurate RUL estimation. For a better comparison, we evaluate four architectures: dropout long short-term memory (LSTM), bidirectional LSTM, convolutional neural network 1D (CNN 1D), and multi-layer LSTM. The FD002 subset of NASA’s Commercial Modular Aero-Propulsion System Simulation dataset is used in the case study. Root mean square error (RMSE) and mean absolute error (MAE) were used for performance metrics. The main output of the study suggests that the dropout LSTM model achieves the best prediction accuracy with an RMSE score of 26.547 and a MAE score of 18.749. It is worth noting that these are achieved despite requiring higher computational resources compared to multi-layer LSTM. Furthermore, all models had difficulties with smaller test trajectory lengths such as 50–100 due to training data imbalance. Overall, the findings highlight the promise of hybrid deep learning and similarity-based approaches for RUL prediction. However, potential advancements such as hyperparameter optimisation and data augmentation still hold potential for further improvements.

Turbulent flow widely exists in the aerospace field, and it is still challenging to realise the accurate prediction in the numerical simulation. To realise the high-fidelity numerical simulation of compressible turbulent flow, a high-order accurate self-adaptive turbulence eddy simulation (SATES) method is developed on the PHengLEI-HyOrder open-source solver, combining with the high-order accurate weighted compact nonlinear schemes (WCNS). The compressible flow in the subsonic and transonic is numerically simulated, including some typical cases, such as subsonic flow past a circular cylinder and flow past a square cylinder, high-lift configuration DLR-F11, transonic flow around a circular cylinder. The results predicted by the current high-order accurate SATES are in good agreement with the available experimental and numerical data. The present numerical method can also accurately capture the interactions between shock waves and turbulence while accurately simulating flow separation, shear layer instability and large-scale vortex shedding. The results obtained show that the current high-order accurate SATES simulations based on PHengLEI-HyOrder solver can accurately simulate complex turbulent flows with high reliability.