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.

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.

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.

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.

In response to the requirements for assessing the impact safety of aero-engines, a high-fidelity numerical simulation method based on overset mesh technology for six-degree-of-freedom rigid body motion is proposed. A gas-solid two-phase flow model is established, coupling two types of ice-debris (externally ingested ice and internally delaminated ice) with air, to analyse their behaviour in a dorsal S-shaped inlet with a diffusion ratio of 1.3. Results indicate that the ice-debris entering from the upper region of the entrance section exerts the most significant distortion on the total-pressure at the engine inlet. Additionally, the behaviour of ice-debris is determined by its angle with respect to the incoming flow direction and the shape of ice. Furthermore, although the ice-debris detached from the entrance section poses no immediate threat to the engine, the prolonged acceleration by high-speed airflow, with velocity increments exceeding 45 m/s, results in a higher kinetic energy carried upon impact with the inlet walls. Regarding externally ingested ice-debris, a smaller initial velocity corresponds to a higher probability of impacting the engine, accompanied by a significant increase in velocity. For instance, the irregular ice-debris ingested at an initial velocity of 6 m/s can experience velocity amplification exceeding 590%.

The dynamic model of the distributed propulsion vehicle faces significant challenges due to several factors. The primary difficulties arise from the strong coupling between multiple power units and aerodynamic rudder surfaces, the interaction between thrust and vehicle dynamics, and the complexity of the aerodynamic model, which includes high-dimensional and high-order variables. To address these challenges, wind tunnel tests are conducted to analyse the aerodynamic characteristics and identify variables affecting the aerodynamic coefficients. Subsequently, a deep neural network is employed to investigate the influence of the power system and aerodynamic rudder on the aerodynamic coefficients. Based on these findings, a multi-dynamic coupled aerodynamic model is developed. Furthermore, a control-oriented nonlinear dynamics model for the distributed propulsion vehicle is established, and a flight controller is designed. Finally, closed-loop simulations for the climb, descent and turn phases are performed, validating the effectiveness of the established model.

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.

A numerical simulation of a two-dimensional rectangular supersonic intake has been performed in a steady-state condition to understand the effect of separation bubble size and its position on the intake performance. Diverse characteristics of separation bubbles in terms of their position, size and quantity have been systematically investigated under varying cowl deflections ranging between $\alpha $ = 0${{\rm{\;}}^ \circ }$ and 4${{\rm{\;}}^ \circ }$ within the intake system. The study encompasses a range of Mach numbers, specifically between M = 1.5 and 3, allowing for comprehensive comparisons of pressure recovery and flow distortions associated with each configuration. The flow field is generated using compressible Reynolds-averaged Navier-Stokes equations along with k-$\omega $ turbulence model. The numerical model is validated using previous experimental and numerical results. Intake unstart is observed when a large separation bubble forms on the ramp surface. The separation bubble shifts from the ramp and moves towards the exit as the Mach number and cowl angle change, resulting in the intake restart. The performance of the intake is observed to be degrading as separation size increases with changes in Mach number and cowl angles. At fixed Mach number, pressure recovery of the intake is observed to be improving with increase in cowl deflection owing to the reduction in net separation bubble size. Maximum TPR of 0.772 is observed at M = 2.2 with 4${{\rm{\;}}^ \circ }$ cowl deflection characterised by net separation of 1.65 mm. Flow distortion is found to be dependent on separation size, position and number of separation bubbles.

In this study, we developed an adaptive gain-scheduling algorithm for hypersonic flight vehicles operating across wide altitude-Mach number envelopes. First, we employed a gap metric-based nominal point selection algorithm to establish a linear parameter-varying (LPV) model more accurate than the traditional Jacobian linearisation method. Active disturbance rejection control (ADRC) was then applied to cope with disturbances and uncertainties, and control gains were scheduled using the Guardian maps (GM) method to adapt to the wide envelope of velocity and altitude. The simulation results demonstrate that under all operating conditions, the proposed algorithm can automatically iterate to obtain a gain-scheduling strategy that meets the flying qualities requirements. Notably, the proposed algorithm exhibited an integral of the time absolute error approximately half of that of the traditional ADRC and significantly lower than that of the GM-LQR method in the ascent phase, demonstrating its excellent control performance and robustness.

Unmanned aerial vehicles (UAVs) with fully electric propulsion systems mainly use lithium-based batteries. However, using fuel cells, hybrid propulsion systems are created to improve the flight time and payload capacity of the UAVs. Energy management and energy-aware path planning are important aspects to be explored in hybrid-propulsion powered UAV configurations. These facilitate optimal power distribution among energy sources and motion planning considering energy consumption, respectively. In the literature, although there are many studies on the energy management of hybrid-powered UAVs and path planning of only battery-powered UAVs, there are research gaps in energy-aware path planning of hybrid-powered UAVs. Additionally, the energy management of hybrid-powered UAVs is usually considered independent of path planning in the literature. This paper thoroughly reviews recent energy-aware path planning for small UAVs to address the listed critical challenges above, providing a new perspective and recommendations for further research. Firstly, this study evaluates the recent status of path planning, hydrogen-based UAVs, and energy management algorithms and identifies some challenges. Later, the applications of hydrogen-powered UAVs are summarised. In addition, hydrogen-based hybrid power system topologies are defined for small UAVs. Then, the path-planning algorithms are classified, and existing studies are discussed. Finally, this paper provides a comprehensive and critical assessment of the status of energy-aware path planning of UAVs, as well as detailed future work recommendations for researchers.

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.

A three-dimensional robust nonlinear cooperative guidance law is proposed to address the challenge of multiple missiles intercepting manoeuvering targets under stringent input constraints and thruster failure. The finite-time convergence theory is used to design a distributed nonlinear sliding mode guidance law, ensuring that the system converges in finite time, with the upper limit of convergence time related to the initial state. A nonlinear sliding surface is adopted to mitigate actuator saturation issues. Then, considering thruster failure, a robust cooperative guidance law is further introduced, ensuring mission completion through the reconstruction of the guidance law. The closed-loop system is proven to be stable using Lyapunov theory, and the influence of hyperparameters on the cooperative guidance law is analysed. Additionally, the results of numerical simulations and hardware-in-the-loop experiments demonstrate the effectiveness and robustness of the proposed algorithm in dealing with stringent input saturation and various disturbances.

Gaseous hydrogen chemically reacting with air in lean premixed mode yields essentially water vapour enabling to decarbonise aeronautical propulsion systems. When hydrogen fuel is produced by electrolysis, the impact on Earth is neutral on a life-cycle basis. Hydrogen fuel, combined to swirled premixed combustion mode, is a sustainable method for thermal-powered aviation. Knowledge gaps have hindered progress in the field and no laboratory-scale demonstrations have been made to date in the specific 100% H2/Air swirled premixed regime. This study describes an experiment established to: (1) demonstrate this highly swirled lean fully premixed H2/Air combustion mode and (2) -describe the underlying flame stabilisation principle. Theoretical results enable pioneering the first-to-date experimental stabilisation for these flames. Measurements with optical diagnostics including chemiluminescence and shadowgraphy direct imaging provide insights into the flame position and the flame regime. This experimental demonstration confirms that the kinematic balance between the flame displacement speed and the flow velocity is critical along with the flame-wall interaction at the bluff-body. It is shown that flashback can be mitigated. The present experiment can be replicated and utilised for application in several scientific disciplines and for advancing technologies. The experimental demonstration, regime characterisation method and mechanism description documented here open the perspective to deploy clean hydrogen combustion to decarbonise aviation with low nitrogen oxides emissions. The combination of high-swirl fully premixed H2/Air experimental data and the theoretical results are unique.

In recent years, the rapid convergence of artificial intelligence (AI) and low-altitude flight technology has driven significant transformations across various industries. These advancements have showcased immense potential in areas such as logistics distribution, urban air mobility (UAM) and national defense. By adopting the AI technology, low-altitude flight technology can achieve high levels of automation and operate in coordinated swarms, thereby enhancing efficiency and precision. However, as these technologies become more pervasive, they also raise pressing ethical or moral concerns, particularly regarding privacy, public safety, as well as the risks of militarisation and weaponisation. These issues have sparked extensive debates. In summary, while the integration of AI and low-altitude flight presents revolutionary opportunities, it also introduces complex ethical challenges. This article will explore these opportunities and challenges in depth, focusing on areas such as privacy protection, public safety, military applications and legal regulation, and will propose strategies to ensure that technological advancements remain aligned with ethical or moral principles.

The Water-Enhanced Turbofan is a promising aero engine propulsion concept that could reduce the climate impact of aviation significantly by combining the conventional Joule/Brayton cycle with a Clausius-Rankine steam cycle. One important component with a high impact on the overall performance is the condenser, a heat exchanger cooling the core exhaust for water recovery. The design conditions for the Water-Enhanced Turbofan condenser have not been analysed from a system perspective in previous studies. Therefore, these operating conditions, which can be decisive for the dimensioning of the condenser, are investigated in the present work. These operating conditions include ambient temperature variations, different cruise altitudes, maximum cruise thrust and contrail avoidance. A conceptual design engine model is set up in the Numerical Propulsion System Simulation (NPSS) framework, incorporating a multi-point design scheme. The heat exchangers are modelled using a neural network surrogate model. The results show a trade-off between engine fuel burn and the cold size frontal area of the condenser, the latter being an indication for the integrability. It is shown that high ambient temperatures pose a challenge to the condenser design, necessitating consideration of such operating conditions in new engine concepts based on heat exchange with the environment. The condenser designed for typical cruise at 15K above standard atmosphere at 35,000 ft cruise altitude, enables sustained water cycle operations down to 10,000 ft under standard day conditions. Additionally, the complete cruise segment of the design mission can be flown with sustained water cycle operations at 10K above standard atmosphere conditions. A positive side effect of the condenser sizing for hot day conditions is that the probability of contrail formation is reduced because the condenser design results in excess water recovery at colder ambient conditions.

Safe and efficient flight operations depend on effective air traffic management and the decision-making skills of air traffic control officers (ATCOs). However, managing air traffic in terminal control areas (TMAs), especially in approach control units, is challenging due to the complexity of the airspace. This is particularly evident in metroplex airspaces like the Istanbul TMA, which features multiple civil and military airports, diverse approach systems, and heavy traffic volumes, all contributing to an exceptionally complex operational environment. This study examines how experienced ATCOs perceive airspace complexity, focusing on approach control units within TMAs. Using Istanbul TMA as a case study, the research combines qualitative and quantitative methods to analyse the factors contributing to complexity. In the first phase, the Content Validity Method (CVM) is used to identify and confirm the key factors influencing airspace complexity. In the second phase, the Best-Worst Method (BWM) is applied to measure the importance of these factors. The study involves two groups of ATCOs: 40 in the first group and 20 in the second. The results reveal that ‘conflicts’ are the most critical factor affecting airspace complexity, highlighting the importance of conflict resolution in air traffic control. Other significant factors include rules and procedures, airspace design and traffic density. This study provides clear insights into the challenges of managing TMA, especially in metroplex airspace. Identifying and analysing the key factors offers valuable guidance for improving air traffic management and supporting ATCOs in making better decisions.

With developing technology, the usage areas of aircraft are constantly expanded. In aircraft designed for different missions, it is an important issue to evaluate many design possibilities and make optimum designs by taking into account many parameters that are not directly connected to each other with equal importance. In this context, issues such as safety and performance come to the fore in aircraft designs. One of the critical situations affecting flight safety is the takeoff and landing phases of the aircraft. The speed changes that occur in these stages are an important issue. In this study, takeoff speed was predicted with different machine learning algorithms using takeoff speed data of the Boeing B-737-300 type aircraft. Linear regression, support vector regression, classification and regression trees, random forest regression, Extreme Gradient Boosting algorithms were selected from machine learning algorithms for takeoff speed prediction. Base models were created with these selected algorithms and the takeoff speed was predicted by training the data. Considering the obtained results, feature engineered was applied to minimise the error values of the proposed base models. In models developed by applying feature engineered, error values were reduced and better performance was observed in takeoff speed prediction. Takeoff speed values obtained with the developed models and actual flight speed values are presented comparatively for the first time in the literature. The simulation results emphasise that the developed models can be used as an effective and alternative method for takeoff speed prediction.

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.