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.

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.

Maintaining high-complexity aircraft requires resilient and data-driven maintenance planning. This article presents the efficient task allocation and packing problem solver (ETTAPS), a novel framework that integrates predictive analytics and optimisation models to generate adaptive maintenance schedules. ETTAPS employs a trial-and-error approach to optimise maintenance intervals, leveraging a branch-and-cut solver combined with first-fit decreasing (FFD) task grouping to minimise costs and enhance aircraft availability. Additionally, a random forest model, retrained using a rolling 24-month data window, continuously refines predictions, leading to progressive cost reductions and improved system reliability over multiple maintenance cycles. Our results demonstrate that ETTAPS significantly reduces maintenance costs and increases aircraft availability by efficiently grouping tasks and incorporating real-world constraints, such as mechanic skill levels, task dependencies and resource limitations. The framework addresses key gaps in MSG-3 and certification analysis, improving task scheduling efficiency and ensuring long-term operational resilience. Furthermore, ETTAPS lays the groundwork for integration with digital twins, real-time anomaly detection and flight planning systems, supporting a more intelligent and proactive approach to aircraft maintenance. This research advances resilience and sustainable aviation maintenance planning by optimising costs, reducing downtime and proactively adapting to operational demands. By aligning with Industry 4.0 and aviation sustainability goals for 2050, ETTAPS contributes to the next generation of intelligent maintenance systems.

This paper develops a novel full-state-constrained intelligent adaptive control (FIAC) scheme for a class of uncertain nonlinear systems under full state constraints, unmodeled dynamics and external disturbances. The key point of the proposed scheme is to appropriately suppress and compensate for unmodeled dynamics that are coupled with other states of the system under the conditions of various disturbances and full state constraints. Firstly, to guarantee that the time-varying asymmetric full state constraints are obeyed, a simple and valid nonlinear error transformation method has been proposed, which can simplify the constrained control problem of the system states into a bounded control problem of the transformed states. Secondly, considering the coupling relationship between the unmodeled dynamics and other states of the controlled system such as system states and control inputs, a decoupling approach for coupling uncertainties is introduced. Thereafter, owing to the employed dynamic signal and bias radial basis function neural network (BIAS-RBFNN) improved on traditional RBFNN, the adverse effects of unmodeled dynamics on the controlled system can be suppressed appropriately. Furthermore, the matched and mismatched disturbances are reasonably estimated and circumvented by a mathematical inequality and a disturbance observer, respectively. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed FIAC strategy.

In this study, measurements and numerical analyses of the temperature distribution of exhaust gas passing through two types of mixers using a micro turbojet engine were performed to investigate the flow mixing performance based on the shape of the mixer, which mixes the bypass air and core air in a gas turbine turbofan engine. To study the mixing characteristics of the mixer, compressed air was supplied through an external duct mounted on a micro turbojet engine to simulate bypass flow, and a system in which hot gas and compressed air were mixed and ejected into the atmosphere was fabricated. A confluent-type mixer and a mixer with 8-lobed mixer channels in the form of a sine wave were used for the experiment. The exhaust gas temperature was measured based on the distance from the nozzle outlet at bypass ratios of 0.5, 1.0 and 1.4. The results showed that the lobed mixer is more effective than the confluent mixer in lowering the exhaust gas temperature as the bypass ratio increased. Numerical analysis results indicated that, in the case of the confluent mixer, flow mixing is primarily performed by shear flow owing to the velocity difference between the core gas and the bypass air. In contrast, in the case of a lobed mixer, flow mixing is achieved through rotational motion and transverse flow. In addition, when the number of lobe channels increased from 8 to 12, the rotational motion increased and the mixing performance improved. Furthermore, infrared signal calculation results confirmed that, as the number of lobe channels increased, improved flow mixing effectively reduced the infrared signal. We conclude that this study helps understand the mixing characteristics of the flow according to the shape of the mixer at various bypass ratios and determine their effect on the characteristics of the infrared signal.

This paper presents the concept of a lifting-wing quadcopter unmanned aerial vehicle (UAV), a vertical take-off and landing vehicle (VTOL) with a rear wing, a canard at its front and four propellers. The aerodynamic surfaces are designed so that their mounting angle can be adjusted and fixed before flight, so its performance in transition flight can be studied for a combination of wing and canard mounting angles. A dynamic model using rigid-body equations of motion is presented, which is used to compute the transition flight trajectory from hover to cruise in horizontal flight. The trim conditions were computed for a range of fixed wing and canard mounting angles to study the effects of these variables on transition trajectory parameters such as required power, body pitch angle and propeller rotation speeds as a function of flight speed. Furthermore, a transition flight control algorithm is presented, which has a cascaded PID controller and a reference scheduler to switch between the proper reference states, controls and control allocation matrix. Finally, the transition control algorithm of the conceptual UAV is numerically simulated. Results show that this configuration can perform a fast and smooth transition from hover to cruise flight using the proposed flight control algorithm, substantially reducing required propulsive power in cruise of up to 64%. The application of the control algorithm made notable a transition manoeuver that consists of negatively inclining the aircraft at a negative pitch angle, initially at high intensity, and as the final cruising speed approaches, the inclination is attenuated until the equilibrium pitch angle is reached. Simultaneously with the negative inclination of the pitch angle, there is a slight drop in altitude, which is quickly resumed as the trajectory develops until the final cruising speed. Lastly, this aircraft configuration can be widely used in applications where performance gains in operations currently carried out by multicopters, which cover large distances and need long flight time, would bring great operational advantages.

Current literature offers limited mass estimation methodologies and their application in the conceptual or preliminary design stages of moderate to high aspect ratio wings with electric, hydrogen or distributed propulsions. This study presents the development and application of a quasi-analytical wing mass estimation method to address this limitation. The proposed method is distinguished from the existing mass estimation methods by its expanded realistic load cases, sensitivity to several design parameters, improved accuracy with short computational time and capabilities for future applications. To achieve these features, new geometric models are introduced; 483 load cases including symmetric manoeuvre, rolling, and combined cases are covered following airworthiness requirements; the structural elements are idealised and sized with strength and buckling criteria; existing methods are evaluated and integrated cautiously for secondary structures and non-optimum masses. A computation time of 0.1s is accomplished for one load case. The developed method achieved the highest accuracy with an average error of -2.2% and a standard error of 1.8% for wing mass estimates compared with six existing methods, benchmarked against thirteen wings of different aircraft categories. The effects of engine numbers with dual- to 16-engine setups and the dry wing concepts on the wing mass are investigated. The optimised number of engines and their locations decreased the wing mass of the high aspect ratio wing significantly. In contrast, the dry wing design increased the wing masses of all baseline aircraft. The future applications and improvements of the presented method in novel configurations and multidisciplinary designed optimisation studies are explained.

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.

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.

The aeronautical telecommunication network (ATN) aims to provide reliable end-to-end communications even for those including the air-to-ground segment and in particular for data link applications. The existing ATN, known as ATN/OSI, is based on OSI protocols since its first deployment. The OSI model implementation in ATN communicating entities causes great complexity in network management, particularly in terms of Internet network interoperability. Therefore, since 2010, the International Civil Aviation Organization (ICAO) proposed a migration to ATN over Internet protocol suite (IPS), called ATN/IPS. Thus, this research work focuses on specifying the reliability mechanisms required for air ground data link applications in future ATN/IPS. To achieve this, the transport protocols performance is assessed based on simulations using an ATN model developed considering the ICAO standards. The modeled legacy application enables to generate traffic based on real controller-pilot data link communications (CPDLC) log files from French area control centre (ACC). The air-to-ground subnetworks are characterised using time series delay induced from previously modeled VDL Mode 2 data link analysis. As proof-of-concept, CPDLC messages exchange from aircraft to controller and future applications that transmits heavier files from ground-to-board are simulated. Transport protocols performance are evaluated with respect to the most constraining requirements. The simulation results highlighted the limitations of both connection-oriented transport protocol class 4 (COTP4) and TCP. This enabled to provide a preliminary overview of a new QUIC-like reliable protocol that should meet the heterogeneous requirements of the legacy and the eventual future ATN/IPS applications.

Ensuring safety and security in urban air mobility is of utmost significance. As air traffic becomes more concentrated in urban regions, instances of flight conflicts are on the rise. The complex urban morphology, micro-environmental factors and various flight risks significantly impact flight safety. This paper introduces a comprehensive framework to address these challenges. The framework incorporates random 3D city layouts, encompassing city buildings and terrains to establish a corridor graph structure. Leveraging this graph, a multi-view representation learning approach is proposed, which employs graph neural networks, recurrent neural networks (RNN) and contrastive learning to effectively manage tactical conflicts. Through rigorous testing across diverse scenarios, including the incorporation of uncertainties such as wind turbulence, the model’s performance is extensively evaluated. The conclusive results underscore the robustness and efficacy of the proposed approach in ensuring safety and resolving conflicts within the dynamic urban air mobility landscape.

Actuator faults in unmanned aerial vehicles (UAVs) can have significant and potentially adverse effects on their safety and performance, highlighting the critical importance of fault diagnosis in UAV design. Ensuring the reliability of these systems in various applications often requires the use of advanced diagnostic algorithms. Artificial intelligence methods, such as deep learning and machine learning techniques, enable fault diagnosis through sample-based learning without the need for prior knowledge of fault mechanisms or physics-based models. However, UAV fault datasets are typically small due to stringent safety standards, which presents challenges for achieving high-performance fault diagnosis. To address this, deep reinforcement learning (DRL) algorithms offer a unique advantage by combining deep learning’s automatic feature extraction with reinforcement learning’s interactive learning approach, improving both learning capabilities and robustness. In this study, we propose and evaluate two DRL-based fault diagnosis models, which demonstrate remarkable accuracy in fault diagnosis, consistently exceeding $99{\rm{\% }}$. Notably, under small sample scenarios, the proposed models significantly outperform traditional classifiers such as decision trees, support vector machines, and multilayer perceptron neural networks. These findings suggest that the integration of DRL enhances fault diagnosis performance, particularly in data-limited environments.

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.

With the development of overall design methodologies for hypersonic vehicles and their propulsion systems, nozzles should expand airflow in a short length and provide sufficient thrust. Therefore, the large expansion ratio single expansion ramp nozzle (LSERN) is widely used. The form of the overexpanded flow field in the nozzle is complex, under the conditions of nozzle start-up, low speed and low nozzle pressure ratio (NPR), thereby negatively influencing the entire propulsion system. Thus, the nozzle flow separation pattern and the key factors affecting the flow separation pattern also deserve considerable attention. In this study, the design of SERN is completed using the cubic curve design method, and the model is numerically simulated for specific operating conditions to study the flow separation patterns and the transition processes of different patterns. Furthermore, the key factors affecting the various flow separation patterns in the nozzle are investigated in detail. Results show that the LSERN in different NPRs appeared in two types of restricted shock separation (RSS) pattern and free shock separation (FSS) pattern, as well as their corresponding flow separation pattern transition processes. The initial expansion angle and the nozzle length affect the range of NPRs maintained by the FSS pattern. The initial expansion angle affects the pattern of flow separation, whereas the nozzle length remarkably influences the critical NPR during transition.

This paper presents the results of reverse-engineering (RE) strategies, surface roughness and computational fluid dynamics (CFD) modelling for a Wren100 micro gas turbine (MGT). Utilising silicone moulds and resin tooling, precise blade geometry capture was achieved for 3D reconstruction allowing for discrete and parametric geometric models to be created. Using these geometries, CFD simulations employing both Reynolds-averaged Navier–Stokes (RANS) and large eddy simulation (LES) models, alongside experimental wind tunnel cascade tests, were used to evaluate these reverse engineering strategies. The results show that while the parametric model captures overall MGT performance with fewer parameters, the discrete model provides enhanced accuracy, highlighting its suitability for detailed aerodynamic analyses. Contrary to initial expectations, surface roughness exhibited a noticeable impact on performance despite the lower Reynolds numbers (40,000), as demonstrated by the CFD model and wind tunnel experiments. The results indicate that surface roughness can reduce laminar separation bubbles on the blade leading edge, delay the onset of transition, and mitigate secondary flow losses. Overall, this study contributes to knowledge advancement in turbine blade reverse engineering and aerodynamics by detailing the impact of surface roughness on performance.

In model-based diagnostics, a simulation model is used to simulate the same operating conditions as the system to be diagnosed to detect and identify anomalies. For this type of analysis, the diagnostic results may be affected by multiple sources of uncertainty. The most common uncertainty to consider is measurement noise. Other sources of uncertainties may originate from the simulation model, instrumentation setup and numerical issues, such as tolerances. While these are often overlooked, they may affect the result to various extent.

In this paper, a multi-point model-based gas path analysis method is proposed and evaluated in the presence of both measurement noise and model uncertainties. The multi-point algorithm addresses the issue of the diagnostic system being underdetermined, having more health parameters than measurements available for diagnostics. It obtains a unique solution through an optimization, where the deviation in health parameter estimation for the operating conditions going into the analysis is minimised. Model uncertainties are introduced in the system by intentionally skewing the characteristics of the rotating components. The objective function is then reconfigured with a, for the gas turbine diagnostic field, novel method taking model uncertainties of the component maps into account. Through this it is possible to reduce the effect of model uncertainties on the diagnostic result. The study shows that through this approach, the uncertainties in diagnostic results are reduced by $3.7{\rm{\% }}$ for the evaluated operating conditions.

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.

To meet the development needs of aeroengines for high thrust-to-weight ratios and fuel-air ratios, a high temperature rise triple-swirler main combustor was designed with a total fuel-air ratio of 0.037, utilising advanced technologies including staged combustion, multi-point injection and multi-inclined hole cooling. Fluent software was used to conduct numerical simulations under both takeoff and idle conditions, thereby obtaining the distribution characteristics of the velocity and temperature fields within the combustor, as well as the generation of pollutants. The simulation results indicate that under takeoff conditions, the high temperature rise triple-swirler combustor achieves a total pressure loss coefficient of less than 6% and a combustion efficiency exceeding 99%. Under takeoff conditions, the OTDF and RTDF values are 0.144 and 0.0738, respectively. The mole fraction of NOx emissions is 3,700ppm, while the mole fraction of soot emissions is 2.55×10−5ppm. Under idle conditions, the triple-swirler combustor maintains a total pressure loss coefficient of less than 6% and a combustion efficiency greater than 99.9%. The OTDF and RTDF values are 0.131 and 0.0624, respectively. The mole fractions of CO and UHC emissions are both 0×10−32ppm at the calculation limit of Fluent software.

With the rapid expansion of the aviation industry, an increasing number of Close Spaced Parallel Runway (CSPR) airports are either planning or constructing End Around Taxiways (EAT) to alleviate field operation pressures and enhance safety. Taking Shanghai Hongqiao Airport’s typical CSPR EAT configuration as a case study, this research integrates the airport’s current operational status with the anticipated requirements for future structural renovations and increased flight volumes. Various operational scenarios are established, and simulation research on optimising EAT operations is conducted in advance. The simulation study proceeds as follows: first, an AirTOP simulation model is constructed based on Hongqiao Airport’s actual operational construction. Subsequently, leveraging existing operational scenarios, five simulation scenarios are devised by activating EATs at the departure and approach ends of the eastern zone. The merits and drawbacks of these scenarios are thoroughly analysed. The findings indicate that, with escalating flight volumes, the utilisation of EAT for larger aircraft can curtail their holding duration by nearly 8 min, consequently reducing overall arrival holding duration by 6 min. Departures from gates proximate to T1 experience a 3-min reduction in holding duration through the adoption of EAT at the approach end. Despite an increase in taxi distance due to a higher proportion of aircraft taxiing around, the overall taxi time is diminished. Activating EATs at the departure and approach ends of the eastern zone effectively mitigates the adverse effects of heightened flight volumes on field operational efficiency.

This paper considers the guidance issue for attackers against aircraft with active defense in a two-on-two engagement, which includes an attacker, a protector, a defender and a target. A cooperative line-of-sight guidance scheme with prescribed performance and input saturation is proposed utilising the sliding mode control and line-of-sight guidance theories, which guarantees that the attacker is able to capture the target with the assistance of the protector remaining on the line-of-sight between the defender and the attacker in order to intercept the defender. A fixed-time prescribed performance function and first-order anti-saturation auxiliary variable are designed in the game guidance strategy to constrain the overshoot of the guidance variable and satisfy the requirement of an overload manoeuver. The proposed guidance strategy alleviates the influence of external disturbance by implementing a fixed-time observer and the chattering phenomenon caused by the sign function. Finally, nonlinear numerical simulations verify the cooperative guidance strategies.