This article examines the effectiveness of using virtual reality training environments for procedural training in fourth generation airliners. It is based on a study that assessed whether the training outcomes from a current recurrent training course for FAA certificated airframe and power plant technicians, which used a full flight simulator (FFS) to deliver and assess training, differed from the same training delivered using a virtual reality (VR) device. The study used an experimental design with three groups, and two within-group measures of training effectiveness. The control group followed the current training programme and was assessed in the FFS, while the second group was trained using a VR device and was subsequently assessed in the FFS. Training effectiveness was assessed using a modified Global Evaluative Assessment of Robotic Skills (GEARS) tool that measured both cognitive and psychomotor aspects of learning alongside the time to successful completion of the assessed task was also measured. The population sampled for the study were all Federal Aviation Administration (FAA) certificated airframe and power plant technicians who were engine-run qualified; a total sample of 100 was used to achieve a 95% confidence interval (p < 0.05). The hypothesis under test was that there is no difference in test performance between the three groups. A multivariate analysis of covariance (MANCOVA) analysis was performed using the GEARS scores and time to completion as variables, and the null hypothesis was retained. The VR system, as tested, was found to provide equivalent task performance to the traditional training method. Recommendations for future research and ongoing application of the specific experimental methodology were provided.

Aircraft maintenance is a multifaceted process that requires highly skilled, qualified and experienced personnel. Effective maintenance processes optimise aircraft operational lifespan, minimise lifecycle costs and improve reliability by reducing the probability of unexpected maintenance events. The initial diagnostic phase relies on detailed visual inspections conducted by certified technicians. Following inspections, data assessment leads to the development of a comprehensive maintenance plan, along with the sourcing of necessary resources and spare parts. As the maintenance, repair and overhaul (MRO) sector transitions into the era of Industry 4.0, there is a growing emphasis on integrating data analytics and cyber-physical systems into maintenance practices. A key objective in this evolution is the adoption of robotic systems for inspection tasks. This shift requires the reconfiguration of formal inspection procedures to ensure compatibility with robotic operations. Moreover, it is critical to address the specific requirements of robotics and to incorporate smart hangar technologies that take advantage of real-time data to improve both efficiency and effectiveness in maintenance operations. This study provides a comprehensive review of the MRO landscape and maintenance checks, with a particular focus on robotic aircraft inspection systems, navigation and smart hangar infrastructure. The discussion concludes with an examination of defect detection methods using machine vision along with relevant metrics to compare with human performance.

This paper presents a comprehensive approach for mitigating noise pollution from unmanned aerial vehicles (UAVs) in urban environment through path planning using reinforcement learning (RL). The study focuses on Turin, Italy, leveraging its diverse urban architecture to develop a comprehensive model. A detailed 3D occupancy grid map, based on OpenStreetMap data, was created to represent buildings’ locations and heights while a population density map was developed to account for demographic variances. The research develops a dynamic noise source model that adjusts noise emission levels based on UAV velocity, ensuring realistic noise impact predictions. Acoustic ray tracing techniques are utilised to simulate noise propagation, accounting for atmospheric absorption and reflections from urban structures, providing a detailed analysis of noise distribution. The core of this work is the application of the deep deterministic policy gradient (DDPG) algorithm within the RL framework. The algorithm is tailored to optimise flight paths by minimising noise impact while balancing other factors like path length and energy efficiency. The RL agent learns to navigate complex urban landscapes, integrating penalties for idling, excessive path length and abrupt manoeuvers to refine its path planning strategy. Simulation results with several maps unseen during training reveal that the RL-based approach effectively reduces noise impact in urban settings, making it a viable solution for better integrating UAVs into urban air mobility (UAM) systems. The methodology is scalable and adaptable, with potential applications in various urban environments globally. This research contributes to the development of sustainable drone operations in UAM context by addressing the critical issue of noise pollution, enhancing public acceptance and regulatory compliance.

Human-centric uncertainty remains one of the most persistent yet least quantified sources of risk in aviation maintenance. Although established safety frameworks such as SMS (safety management system), STAMP (Systems-Theoretic Accident Model and Processes), and FRAM (Functional Resonance Analysis Method) have advanced systemic oversight, they fall short in capturing the dynamic, context-dependent variability of human performance in real time. This study introduces the uncertainty quantification in aircraft maintenance (UQAM) framework – a novel, predictive safety tool designed to measure and manage operational uncertainty at the task level. The integrated uncertainty equation (IUE) is central to the model, a mathematical formulation that synthesises eight empirically derived uncertainty factors into a single, actionable score. Using a mixed-methods design, the research draws on thematic analysis of 49 semi-structured interviews with licensed maintenance engineers, followed by a 12-month field validation across four distinct maintenance tasks. Results demonstrate that the IUE effectively distinguishes between low, moderate and high-risk scenarios while remaining sensitive to procedural anomalies, diagnostic ambiguity and environmental complexity. Heatmap visualisations further enable supervisory teams to identify dominant uncertainty drivers and implement targeted interventions. UQAM enhances predictive governance, supports real-time decision-making and advances the evolution of next-generation safety systems in high-reliability aviation environments by embedding quantitative uncertainty metrics into existing safety architectures.

Aeroelastic analyses are part of the design of modern propeller blades. Most of the time, advanced numerical simulations are used, involving computational fluid dynamics. However, the coupling between fluid and structure may be missing. In this paper, two coupled fluid-structure interaction methods are presented, namely the modal time-marching and the quasi-static approach. An in-house aeroelastic tool, analysing an in-house blade design, is used. A limited number of experiments are available, and this was alleviated using new experiments as part of the Numerical and Experimental Study of Propeller Aeroelasticity project. In this work, 3D finite element models (FEM) were used to represent the blade structure. Time-marching and quasi-steady results were compared, and this is the first time that this is reported in the literature. It was found that regardless of the differences in the aerodynamic loads between time-marching and quasi-static computations, the final blade deformations were comparable. Time-marching computations using a modal representation of the blade, obtained from 3D FEM, showed that the blade deformation and vibration were driven by the stalled flow. This observation was verified by comparing the blade response with the flow off-the-blade. The harmonic content of the results includes the propeller blade passing frequency and its natural frequencies, but also additional frequencies related to the flow shedding and vortical content of the stalled part of the blade. To the best of our knowledge, this has not been reported in the open literature.

Control of small turbojet engines is challenging due to the small number of sensors and actuators. In these engines, typically the spool speed and exhaust gas temperature are the measured variables and the fuel flow is the only manipulated variable. However, the thrust command must be achieved and the engine’s structural and operational limitations must be safeguarded. In this research, a minimum selector control structure with a saturation function is presented for controlling small turbojet engines. One control loop is considered to control the spool speed and another loop is used to manage the exhaust gas temperature. The output of the control loops is the fuel flow rate and the minimum value is selected between them. To prevent the compressor surge and combustor blow-out during engine acceleration/deceleration, a fuel flow rate saturation is defined. Due to the switching structure of the proposed controller and existence of the saturation function, stability analysis is a critical issue. Therefore, a methodology is presented to analyse the stability of the proposed structure. In simulation study, a nonlinear thermodynamic model that matches more than 90% with the test data is used and the response of the proposed controller is compared with a proportional integral (PI) controller. In a comprehensive scenario, the throttle degree varies from 75% to 100%. Using the PI controller, some outputs have some overshoot and the exhaust gas temperature exceeds the corresponding constraint by 40K. While the proposed minimum selector controller, in addition to accurately fulfilling the thrust command, fully protects the limitations governing engine variables.

Autonomous manoeuvre decision-making is essential for enhancing the survivability and operational effectiveness of unmanned aerial vehicles in high-risk and dynamic air combat scenarios. To address the limitations of traditional air combat decision-making methods in dealing with complex and rapidly changing environments, this paper proposes an autonomous air combat decision-making algorithm based on hybrid temporal difference error-reward prioritised experience replay with twin delayed deep deterministic policy gradient. This algorithm constructs a closed-loop learning system from environmental interaction to policy optimisation, addressing the key challenges of slow convergence and insufficient identification of critical tactical decisions in autonomous air combat. A hybrid priority metric leveraging reward backpropagation and temporal difference error filter is introduced to optimise the learning of high-value experiences while balancing sample diversity and the reuse of critical experiences. To reduce excessive trial and error in the initial phase, an integrated reward function combining task rewards and auxiliary guidance rewards is designed using the reward reshaping method to guide the agent on how to choose a manoeuvre strategy. Based on the established three-dimensional close-range air combat game model, simulation validations were conducted for both basic manoeuvre and expert system engagements. The results demonstrate that the proposed autonomous air combat manoeuvre decision-making algorithm exhibits higher learning efficiency and convergence stability. It can rapidly identify high-value manoeuvres and effectively formulate rational yet superior tactical strategies in the face of complex battlefield scenarios, demonstrating obvious benefits in enhancing combat effectiveness and tactical adaptability.

The rapid development of AI has resulted in an unprecedented paradigm shift across various industries, with aerospace among the laureates of this transformation. This review paper attempts to explore and provide comprehensive overview of the aerospace research imperatives from the AI perspective, detailing the technical sides of the full lifecycle from vehicle design and operational optimisation to advanced air traffic management systems. By examining real-world engineering implementations, the review demonstrates how AI-driven solutions are directly addressing longstanding challenges in aerospace, such as optimising flight performance, reducing operational costs and improving system reliability. A significant emphasis is placed on the crucial roles of AI in health monitoring and predictive maintenance, areas that are pivotal for ensuring the safety and longevity of aerospace endeavors, and which are now increasingly adopted in industry for remaining useful life (RUL) forecasting and condition-based maintenance strategies. The paper also discusses AI embedded in quality control and inspection processes, where it boosts accuracy, efficiency and fault detection capability. The review provides insight into the state-of-the-art applications of AI in planetary exploration, particularly within the realms of autonomous scientific instrumentation and robotic prospecting, as well as surface operations on extraterrestrial bodies. An important case study is India’s Chandrayaan-3 mission, demonstrating the application of AI in both autonomous navigation and scientific exploration within the challenging environments of space. By furnishing an overview of the field, the paper frames the ever-important, increasing domains of AI as the forefront in the advancement of aerospace engineering and opens avenues for further discussion regarding the limitless possibilities at the juncture of intelligent systems and aerospace innovation.

The guidance law design is considered for an engagement scenario of a missile attacking an actively defended target. A three-player nonzero-sum differential game is formulated to describe players’ respective behaviours including the attacking missile’s evading the target’s defender and thereafter attacking the target, the target’s evading the attacking missile under the assistance of the defender, and the defender’s intercepting the attacking missile to assist the target evading the attacking missile. It is considered that the missile hits the target at a specific terminal angle to enhance the destructive effect by attacking the vulnerable regions of the target. The advantages of norm-bounded and linear quadratic differential game guidance laws are combined and the cost functions segmented by relative distances between the players are proposed. The equilibrium solutions of the nonzero-sum differential game are derived and used as the respective guidance laws of the players. Simulations in various engagement scenarios are conducted to verify the reliability and robustness of the design.

Vibration is defined as oscillation away from equilibrium and is a significant problem in aviation. Vibration is transmitted to the flight crew through all contact surfaces, including flight controls, floor and seats. Various effects are known to occur on flight crew exposed to vibration, with fatigue and low back pain being the most common vibration-related health complaints. Studies have shown that prolonged exposure to vibration or accumulated vibration can increase the risk of chronic low back pain and injury by increasing the exposure dose. In particular, there is data that helicopter pilots have more low back pain than fixed wing pilots. This is due to the fact that helicopters have much more vibration-generating factors due to the working principle of helicopters and the posture of helicopter pilots is slightly forward-leaning. In this study, an isolator cushion with a quasi-zero stiffness mechanism was developed, manufactured and tested to reduce the transmission of vibration to the pilot and flight crew during the operation of the Sikorsky UH-60 helicopter. The use of the specially designed cushion led to a noticeable reduction in vibration exposure under various flight conditions.

This work presents detailed 3D modelling and simulation of the mechanical effects induced by lightning strikes in protected carbon fibre-reinforced polymer laminates. Firstly, physically based models that represent the mechanical overpressure that results from a lightning strike are revisited. In particular, this paper compares the implementation of an analytical strong shock wave approximation with the solutions obtained from computational fluid dynamics (CFD), considering different equations of state, to represent the supersonic expansion of the hot plasma channel when simulating the mechanical damage induced by lightning strikes. The assessment of the pressure profiles, the numerical predictions of the displacement and velocity fields and the analysis of the predicted damage maps show that, for two lightning protection layers, the effects of the supersonic plasma expansion loads obtained from the strong shock wave approximation compare reasonably well with those obtained from CFD, independently of the equation of state solved numerically. Subsequently, the predictions of the 3D modelling strategy of the mechanical response of composite laminates subjected to lightning strike employing the strong shock wave approximation are compared with mechanical deformation measurements obtained from lab-scale lightning test results. Accurate deflection and out-of-plane velocity fields are predicted, validating the 3D modelling strategy. Moreover, the predicted damage maps correlate well with the (bulk) damage identified by C-scan (considering only the damaged area below the second ply).

Air transportation significantly contributes to air pollution, making the reduction of fuel consumption and emissions critical for sustainable development. Continuous climb operations (CCO) have emerged as a promising approach to minimise the environmental impact of aviation, particularly during the climb phase, where fuel burn is highest. This study assesses the efficiency of vertical climb profiles using flight data from departures at Trabzon Airport. The analysis incorporates maximum thrust and maximum rate of climb constraints alongside clean configuration to design CCO profiles. These profiles are compared with actual flight data, revealing that CCO implementation could result in average savings of 5.1% in time, 8% in fuel and 7.5% in HC, 8.8% in CO and 5.8% in ${\rm{N}}{{\rm{O}}_{\rm{x}}}$ emissions. The study highlights challenges in CCO implementation, particularly due to speed regime changes and flight procedure constraints, emphasising the need for enhancements in air traffic management to maximise CCO benefits in complex airspace environments.

The rapid growth of civil aviation has posed significant challenges to air traffic management (ATM), highlighting the need for accurate aircraft trajectory prediction (TP). Due to the scarcity of relevant data and the resulting class imbalance in the sample, aircraft TP under severe weather conditions faces significant challenges. This paper proposes an aircraft TP method framework consisting of trajectory data augmentation and TP networks to address this issue. To validate the effectiveness of this framework in solving the TP problem in severe weather, we propose an improved conditional tabular generative adversarial networks (CTGAN)-long short-term memories (LSTMs) hybrid model. We conduct comparative experiments of four LSTM-based models (LSTM, convolutional neural network (CNN)-LSTM, CNN-LSTM-attention, and CNN-BiLSTM) under this framework. The improved CTGAN is also compared with the commonly used data augmentation method, the Synthetic Minority Oversampling Technique (SMOTE). The results show that the TP accuracy can be effectively improved by enhancing the minority-class sample data; compared with SMOTE, the improved CTGAN is more suitable for minority-class sample data augmentation for aircraft TP, and it also shows that for minority-class sample data augmentation, data distribution characteristics are more important than the simple trajectory point accuracy. The hybrid modeling approach with the improved CTGAN as the data augmentation network proposed in this study provides valuable insights into addressing the data imbalance problem in aircraft TP.

This work presents a novel dynamic inflow modeling approach for single- and multirotor systems. The proposed model is derived from first principles using a parametrisation that guarantees accurate results at steady-state. In an effort to simplify and unite previous works in the field, it is shown how two important works from the dynamic inflow modeling literature can be recreated using the present framework. The utility of the model is further illustrated through analysis and comparisons to well-established theories and empirical results in both steady-state and dynamic scenarios. Overall, the aim is to simplify and unite dynamic inflow models.

The Monte Carlo methods are frequently employed to evaluate the overall characteristics of non-monotonic, non-linear, non-superpositional performance functions. However, the multi-parameter, multi-objective spacecraft separation dynamics model is not amenable to decoupling to produce a result. This paper presents a parametric objective function that can be sampled. It combines the reliability analysis of the complex non-linear spacecraft separation model with Automated Dynamic Analysis of Mechanical Systems (ADAMS) and uses the Monte Carlo method to obtain the separation performance of the spacecraft separation system reliability profile, that is to say, the distribution of separation performance. The performance distribution of the spacecraft separation system was determined and parameters such as spring separation force, spring line of action, module mass and module centre of mass position were found to have a significant effect on the spacecraft separation dynamics by Adaboost machine learning regression.