With the rapid increase in the use of drones in various applications, including commercial and governmental, and the increasing probability of communication failures and contingencies, research becomes critical to ensure the safety and efficiency of their operations. The aim of this research is to develop adaptive drone flight control algorithms capable of operating effectively under conditions of limited communication and incomplete information to ensure reliable and safe autonomous operation of these systems. The applied methods include computer modelling and simulation, analytical, statistical, functional, deductive and descriptive methods. The study found that the use of performance evaluation methods for complex systems enables the identification of safety and performance criteria for drones, and drone flight control provides basic principles and methods that can be adapted for drones, including autopiloting and navigation. In addition, analyses of satellite communication and navigation prove the need to consider the limitations of this technology when developing drone control algorithms. The combination of these techniques allows for more robust and adaptive drone control systems that can function effectively in complex environments such as communication limitations and incomplete information. Additionally, it was found that the integration of adaptive control algorithms based on these methods allows drones to effectively adapt to variable environmental conditions and make decisions quickly even when communication is lost or information is limited.

The world is currently undergoing a technological transformation with numerous innovative concepts emerging. This shift is driven by remarkable advancements in artificial intelligence and the urgent need for decarbonisation. With this comes a growing demand for skilled engineers who can actively contribute at any stage within the life cycle of a product. This can be the generation of new concepts at low Technology Readiness Levels or contributing actively to their development and operational safety. This paper explores the integration of a 1-day practical activity to reinforce theoretical concepts learned within a classroom-based environment. Small groups of students were given the opportunity of engaging with a small helicopter engine (Rolls-Royce Gnome engine) through the disassembly and reassembly of the exhaust and power turbine section while following the manufacturer’s manual and ensuring industrial norms for safe practice. This hands-on activity included an introduction to tooling, a Gnome familiarisation activity, and an introduction to inspection techniques. Based on the feedback recorded, the students experienced a notable improvement in their basic understanding by effectively reinforcing knowledge acquired within the classroom through active engagement with an actual gas turbine engine.

The main purpose of this article is to present the nonlinear unsteady behaviour for jet transport aircraft response to serious atmosphere turbulence in cruise flight and to provide the appropriate mitigation concepts for pilots in the pilot training course of the IATA – Loss of Control In-flight (LOC-I) program. The flight data of a twin-jet and a four-jet transport aircraft encountered serious atmosphere turbulence are the study cases for this article. This study uses flight data mining and fuzzy-logic modeling of artificial intelligence techniques to establish nonlinear unsteady aerodynamic models. Since the rapid change of aerodynamic characteristics in turbulence, so the study uses decoupled longitudinal and lateral-directional motion to identify various eigenvalue motion modes of nonlinear unsteady behaviour through digital 6-DOF flight simulation. It is found that the changes of the main flight variables in the aerodynamic scene and flight environment of the two aircraft are different, but the profiles of five eigenvalue motion modes are actually similar. Those similar eigenvalue motion modes can formulate preventive actions related to the flight handling quality for safe and efficient control by pilots to execute the flight tasks. The one with a large drop height during the ups and downs motion between the two is chosen to construct the movement mechanism of nonlinear unsteady behaviours. The assessments of dynamic stability characteristics of nonlinear unsteady behaviour based on the approaches of oscillatory motion and eigenvalue motion modes related to loss of control will be demonstrated in this article. To develop preventive actions, the situation awareness response to the induced mutation of nonlinear unsteady behaviour on the pilot’s operations will be a further research task in the future.

This paper investigates the take-off performance of a single engine battery-electric aeroplane, using the example of the 300kg Sherwood eKub. It shows analysis of take-off performance of such an aeroplane must include as a minimum two new parameters not normally considered: time at full throttle and state of charge. It was shown in both ground and flight test that the state of available power reduces both as the throttle is fully open, and as battery charge is consumed, although recovers partially when power is reduced for a period. It is possible to schedule take-off performance as a function of the usual parameters plus state of charge. Because of the reducing climb performance with use of state of charge, and the requirement in airworthiness standards for minimum climb performance being available, it becomes necessary to introduce the concept of minimum-indicated state of charge for take-off, SoCiMTO; means to calculate that are shown for compliance with both microlight aeroplane standards and larger aeroplane standards, and the calculations are demonstrated for the eKub. Conclusions are also drawn about the use of commercial products SkyDemon and Google Earth for recording and analysing aeroplane performance data.

Aircraft play a major role in meeting the fast and efficient transportation needs of modern society, thanks to their advanced features. However, gas turbine engines used in aircraft have many negative effects on human health. One of the negative effects is the exhaust gases released by these engines to nature. In this study, it is discussed to present alternative models based on heuristic methods to reduce the emission values of the synthetic fuel mixture used in the combustion chamber of gas turbine engines. For this purpose, a model based on artificial neural networks (ANN) based on the back-tracking search optimisation (BSO) algorithm is proposed by using experimentally obtained emission values found in the literature. In the proposed model, the parameters of the optimum ANN structure are first determined by the BSO algorithm. Then, by using the optimum ANN structure, the most appropriate input values were found with the BSO algorithm, and the emission values were reduced. The simulation results have shown that the proposed method will be a fast and safe alternative method for reducing emission values.

This paper presents a comprehensive evaluation of reduced-order models (ROMs) for the determination of pressure coefficient distributions on supersonic and hypersonic bodies. The study investigates the limitations, aerodynamic precision and computational performance associated with various methodologies, ranging from simplistic Newtonian theory-based approaches to more advanced first and second-order shock-expansion theories. Validation is performed by comparing computed results with experimental and computational data for pressure distributions, drag and lift coefficients and centres of pressure for fundamental geometries and authentic vehicle design over a wide range of freestream conditions. The study also includes a comprehensive computational complexity analysis, demonstrating the superiority of finite-element ROM approaches over traditional finite-volume computational fluid dynamics (CFD) simulations. The primary objective of this paper is to scrutinise the extension of these methodological classes to the low supersonic regime. Hence, thermo-chemical reactions within the flow are disregarded, and the ideal gas law is adopted. A value of $\gamma = 1.4$ is chosen for consistency and comparability across the analyses. The proposed ROMs show remarkable potential for reducing high-speed simulation execution times by four orders of magnitude, maintaining accuracy within 20 per cent and as low as 1 per cent. The study unveils three key findings: first, the accuracy degradation of Newtonian-based theories for inclined elements, particularly around 45 degrees, and their reduced dependency on Mach number at large inclination. Secondly, the study presents novel insights into the impact of shock-wave-Mach-wave interactions on pressure distribution calculations, emphasising the Mach number as a crucial metric governing recompression effects. Lastly, the study demonstrates the exceptional accuracy of DeJarnette’s method, providing ${C_P}$ results within 2 per cent for a wide range of conditions, offering an attractive alternative to the Taylor-Maccoll equation.

Reynolds-Averaged Navier–Stokes (RANS) simulations, both steady and unsteady, are used to investigate supersonic, chemically reacting, flow fields inside a strut-stabilised supersonic combustion ramjet (scramjet) engine operating under different fuel flow rates. Fully supersonic, fully subsonic and mixed modes of operations inside the combustor, obtained at different fuel flow rates, are studied numerically through shock wave visualisations and top-wall static-pressure probing. The effect of changing fuel flow rates, imposed both suddenly and gradually, on the behaviour of shock waves and wall pressure profiles are studied in detail. For certain modes of combustion characterised by the presence of oblique shocks at the strut, shockwaves in the combustor respond predictably to an increase or decrease in fuel flow rate attaining the steady state flow fields as predicted by RANS simulations for those fuel flow rates. For certain other modes of combustion, characterised by the presence of shockwaves in the isolator and the absence of oblique shocks at the leading edge of the strut, shockwaves in the flow field appear unstable to fuel flow rate modulations. For such cases, any change in fuel flow rates, sudden or gradual, increase or decrease, causes the isolator shocks to immediately move upstream and eventually out of the isolator. A plausible physics-based explanation of the observed phenomena is presented.

The axisymmetric nozzle mechanism is the core part for thrust vectoring of aero engine, which contains complex rigid-flexible coupled multibody system with joints clearance and significantly reduces the efficiency in modeling and calculation, therefore the kinematics and dynamics analysis of axisymmetric vectoring nozzle mechanism based on deep neural network is proposed. The deep neural network model of the axisymmetric vector nozzle is established according to the limited training data from the physical dynamic model and then used to predict the kinematics and dynamics response of the axisymmetric vector nozzle. This study analyses the effects of joint clearance on the kinematics and dynamics of the axisymmetric vector nozzle mechanism by a data-driven model. It is found that the angular acceleration of the expanding blade and the driving force are mostly affected by joint clearance followed by the angle, angular velocity and position of the expanding blade. Larger joint clearance results in more pronounced fluctuations of the dynamic response of the mechanism, which is due to the greater relative velocity and contact force between the bushing and the pin. Since axisymmetric vector nozzles are highly complex nonlinear systems, traditional numerical methods of dynamics are extremely time-consuming. Our work indicates that the data-driven approach greatly reduces the computational cost while maintaining accuracy, and can be used for rapid evaluation and iterative computation of complex multibody dynamics of engine nozzle mechanism.

This study presents a model that aims to optimise the sequencing arrival aircraft around the terminal manoeuvring area (TMA). The model considers the transit passenger counts of these aircraft and employs the point merge at Sabiha Gokcen Airport. In this study, aircraft were categorised into two groups, namely ‘High or Low Transit Passenger (HTP/LTP)’. Subsequently, multi-objective models were employed to solve the test problems. Weighted sum scalarisation (WSS), conic scalarization (CS), and epsilon constraint (EC) models were utilised to increase robustness and their results were compared with a single-objective optimisation model. This approach aims to provide decision-makers with a variety of outcomes, thus expanding their options. Simultaneously, efforts are made within the model to allow aircraft with HTP counts to have minimal delays. Additionally, emission calculations were conducted to offer a critical perspective on the environmental implications, and the delay results of the multi-objective optimisation (MOO) models underwent statistical analysis.

Effusion cooling is the state-of-the-art cooling technology for gas turbine hot-gas path components. Typically, effusion cooling holes across the entire combustor liner are aligned with the combustor axis, rendering a nominal zero compound angle between highly directional miniature effusion cooling jets and the main flow direction. The pitch of effusion cooling holes is optimised accordingly. However, the swirling main flow results in a non-zero compound angle and an effectively different pitch from the design. The directional effect of effusion cooling as a result of swirling main flow on the adiabatic film cooling effectiveness (AFE) is a combined effect of a non-zero compound angle and a varied pitch. The current experimental study aims to investigate the isolated effects of compound angle on AFE by excluding the influences of varying pitch. With an improved understanding of the sole effects of non-zero compound angles on AFE, the roles that a varied pitch plays in modifying AFE are further discussed to guide future effusion cooling designs under swirling main flow conditions. Binary pressure sensitive paint (PSP) was used to determine AFE experimentally.

Damage initiation hotspots around features, such as bolts and ply drops, must be investigated during the preliminary design phase of large composite structures, such as composite airframes. A global-local modelling approach is commonly employed to perform this investigation, whereby a global low-fidelity model is used to drive high-fidelity local models around the features of interest. However, this methodology is slow, repetitive and expert-dependent. In this investigation, we address these issues by applying machine learning techniques to this global-local modelling framework and demonstrate the time-saving benefit when predicting damage initiation of bolted composite joints. Feature engineering of model inputs and outputs, and appropriate customisation of machine learning methods enables damage initiation prediction. Special consideration is given to the boundary conditions that must be varied to simulate the response of the bolted composite joints. Results show over three orders of magnitude time-saving benefit and satisfactory accuracy of the proposed methodology. This indicates its potential to be developed further into a rapid design and optimisation tool.

Critical space assets require continuous monitoring to prevent potential losses. Auxiliary satellites protect these assets by observing and tracking approaching targets. Although observation satellites can make rapid autonomous onboard decisions, they face challenges due to limited computational capacity. The two mainstream command and control methods currently available do not meet the demands of onboard decision-making. Highly procedural decision-making methods require extended decision times, while rapid-response intuitive or heuristic methods carry significant error risks. To address this, this paper proposes a multi-granularity decision-making method for optical space surveillance satellites. First, multi-granularity relative orbit determination algorithm models and multi-granularity impulsive orbit manoeuver algorithm models were developed. Based on these models, a granularity selection method for sequential three-way decisions is proposed. In non-emergency situations, fine-granularity models are preferred to conserve fuel, while in emergency situations, coarse-granularity models are used to enhance decision-making speed and reduce positional deviations caused by the manoeuvering game. In random multi-scenario tests, the proposed method demonstrates lower average terminal positional deviations and fuel consumption compared to single-granularity (highly procedural or rapid-response) and random-granularity methods.

The Aerospace Integration Research Centre (AIRC) at Cranfield University offers industry and academia an open environment to explore the opportunities for efficient integration of aircraft systems. As a part of the centre, Cranfield University, Rolls-Royce, and DCA Design International jointly have developed the Future Systems Simulator (FSS) for the purpose of research and development in areas such as human factors in aviation, single-pilot operations, future cockpit design, aircraft electrification, and alternative control approaches. Utilising the state-of-the-art modularity principles in simulation technology, the FSS is built to simulate a diverse range of current and novel aircraft, enabling researchers and industry partners to conduct experiments rapidly and efficiently. Central to the requirement, a unique, user-experience-centred development and design process is implemented for the development of the FSS. This paper presents the development process of such a flight simulator with an innovative flight deck. Furthermore, the paper demonstrates the FSS’s capabilities through case studies. The cutting-edge versatility and flexibility of the FSS are demonstrated through the diverse example research case studies. In the final section, the authors provide guidance for the development of an engineering flight simulator based on lessons learned in this project.

The objective of this study was to investigate the effects of different cockpit primary flight display (PFD) interface designs on pilot cognitive efficiency and cognitive load. This study designed five optimised PFD interfaces and conducted interface cognition experiments to assess cognitive responses across six different PFD interface designs, including the original design. It compared various subjective and objective metrics across different interface designs and evaluated the impact of each design factor on cognitive task performance. The experimental results show that the PFD interface in the original interface design performs better under different flight symbol designs, and the interface with 50% increase in font size performs better among interface designs with different font sizes with relatively lower cognitive load. This study provides experimental support and optimization suggestions for the optimal design of cockpit PFD interface, which can help improve pilots’ perception and operational capabilities, and thus enhance task performance efficiency and flight safety. Future research can investigate the effects of various design factors on the cognitive effects of the interface to enhance the ongoing improvement and optimisation of interface design.

The fault-tolerant control issue of aircraft engines with actuator dynamics and faults is investigated in this paper. By proposing a novel intelligent sliding mode fault-tolerant control (ISMFTC) method, which combines an adaptive dynamic programming (ADP) sliding surface with Grey Wolf Optimizer (GWO) for controller parameter optimisation, the goal is to achieve quality steady-state and dynamic performance in aircraft engines while maintaining strong fault-tolerance properties. Firstly, by considering not only actuator dynamics but also actuator faults, an uncertain nonlinear cascaded model of aircraft engines is developed according to characteristic of aircraft engines and their actuators. Secondly, an ADP-based sliding surface is proposed for considered aircraft engine uncertain nonlinear cascaded system. It can obtain a certain sense of optimised performance, and could be solved by ADP strategy off-line as well. Thirdly, fault-tolerant controller is obtained on the basis of sliding mode theory and adaptive fault estimation law, namely, ADP-based ISMFTC controller. Meanwhile, GWO is integrated into the investigation of ADP-based ISMFTC controller, optimised designable control parameters are obtained subsequently. Besides, robustness analysis is elaborated according to Lyapunov theory, fault estimation error is bounded and states of closed-loop system are uniformly ultimately bounded. Simulation on a twin-shaft turbofan aircraft engine, indicates the effect of proposed ADP-based ISMFTC method.

In this paper, a brand-new adaptive fault-tolerant non-affine integrated guidance and control method based on reinforcement learning is proposed for a class of skid-to-turn (STT) missile. Firstly, considering the non-affine characteristics of the missile, a new non-affine integrated guidance and control (NAIGC) design model is constructed. For the NAIGC system, an adaptive expansion integral system is introduced to address the issue of challenging control brought on by the non-affine form of the control signal. Subsequently, the hyperbolic tangent function and adaptive boundary estimation are utilised to lessen the jitter due to disturbances in the control system and the deviation caused by actuator failures while taking into account the uncertainty in the NAIGC system. Importantly, actor-critic is introduced into the control framework, where the actor network aims to deal with the multiple uncertainties of the subsystem and generate the control input based on the critic results. Eventually, not only is the stability of the NAIGC closed-loop system demonstrated using Lyapunov theory, but also the validity and superiority of the method are verified by numerical simulations.

Cost efficiency is a critical factor in the competitive aviation sector. These efficiency factors force airline operators to develop new approaches in their organizations. Predictive maintenance helps to build scheduling maintenance programs for airline operators or MROs. Scheduled maintenance programs benefit cost efficiency in the aviation sector. Predictive maintenance methods predict the failure time of any equipment. Predictions can be made by analyzing the sensor values from equipment.

In this paper, we predicted the remaining useful life (RUL) of turbofan engines using machine learning models and a similarity-based approach. Sensor datasets from the Prognostics Data Repository of NASA, called CMAPPS, were utilized. Using the FD0002 sub-dataset, a health index (HI) was created, and models were trained. Once the models were trained, train and test HIs were estimated. The predicted test HI was matched with the predicted train HI based on a similarity-based approach, and then a RUL prediction was made. The results obtained were compared with the actual results to calculate the accuracy, and the algorithm that resulted in the maximum accuracy was identified.

We selected six machine learning algorithms and also created an ensemble model by averaging the predictions of six machine learning algorithms for comparing prediction accuracy. The different algorithms were compared to obtain the prediction model with the closest prediction of remaining useful lifecycle in terms of the number of life cycles. This experiment showed us the effect of the similarity-based approach on the basic version of machine learning models for RUL prediction.

The high-altitude balloon proposed in this paper is a long-life balloon carrying a payload through a cable that flies at 20km altitude in near space. A dynamic model of the system, including the thermodynamics of the buoyancy body coupled with a hanging model of the pod, is developed using the Newton–Euler method. The buoyancy body consists of a helium balloon and a ballonet. A differential pressure difference-based altitude adjustment is achieved by tracking the pressure difference at the target altitude. A dynamic simulation of the buoyancy body with a slung pod in autonomous vertical takeoff and altitude regulation processes is presented. The internal thermodynamic variations and pressure differential of the buoyancy body are given. The air mass exchange and blower flow control of the ballonet are validated. The altitude holding error is analysed. The maximum pull force that the cable can withstand is calculated, and the maximum attitude angles of the pod during the ascending and descending processes are depicted. Simulation results provide basic knowledge for the structural design and payload installation of pods.

Aircraft ground taxiing contributes significantly to carbon emissions and engine wear. The electric towing tractor (ETT) addresses these issues by towing the aircraft to the runway end, thereby minimising ground taxiing. As the complexity of ETT towing operations increases, both the towing distance and time increase significantly, and the original method for estimating the number of ETTs is no longer applicable. Due to the substantial acquisition cost of ETT and the need to reduce waste while ensuring operational efficiency, this paper introduces for the first time an ETT quantity estimation model that combines simulation and vehicle scheduling models. The simulation model simulates the impact of ETT on apron operations, taxiing on taxiways and takeoffs and landings on runways. Key timing points for ETT usage by each aircraft are identified through simulation, forming the basis for determining the minimum number of vehicles required for airport operations using a hard-time window vehicle scheduling model. To ensure the validity of the model, simulation model verification is conducted. Furthermore, the study explores the influence of vehicle speed and airport scale on the required number of ETTs. The results demonstrate the effective representation of real-airport operations by the simulation model. ETT speed, airport runway and taxiway configurations, takeoff and landing frequencies and imbalances during peak periods all impact the required quantity of ETTs. A comprehensive approach considering these factors is necessary to determine the optimal number of ETTs.

The threat of GNSS interference poses a great danger to many critical infrastructure systems including air navigation. With a focus on mitigating this threat, this paper proposes a methodology for detecting GNSS interference. The methodology utilises the quality indicator NACp transmitted in ADS-B messages and GPS almanac data for interference detection. The NACp indicator enables estimation of the position error derived from GPS, which is compared with the HDOP value of the GPS satellite constellation. Based on this comparison, the developed detection algorithm determines whether the aircraft is affected by jamming. The detection methodology is evaluated on datasets obtained during deliberate experiments with GPS jamming. The proposed methodology provides a way to detect GNSS interference, facilitating mitigation of its impact on air traffic operation.