Flight Data Monitoring (FDM) programmes have become a key part of every major airline’s safety management system. They are primarily based on learning from unwanted deviations in flight parameters encountered during normal flight operations. Owing to its unique nature, anomaly detection of FDM presents distinct problem complexities from the majority of analytical and learning tasks. This methodology, while useful, concentrates only on a small part of the operation, leaving most of the data unprocessed, and does not allow for analysing events that had the potential to go wrong but were recovered in time by the crews. This research focused on analysing an FDM dataset of 1332 approaches between January 2018 and July 2022 at Tenerife South Airport (Spain), where there is a known phenomenon of increasing headwinds during the final approach. The flights were clustered using self-organising maps (SOM) by patterns of increasing headwinds, and the clusters were assessed in terms of clustering performance. The clusters were well differentiated. A further comparison between the results from the airline showed that 88 flights were affected by wind shifts, while 27 flights were picked up by the airline. The results demonstrate that SOMs are a meaningful tool for clustering flight data and can complement the current FDM analysis methodology. Combining both methodologies could shift FDM data analysis to look beyond exceedances into what went well, thus shifting the FDM paradigm towards a more safety-II-based method.

Rotorcraft engines are highly complex, nonlinear thermodynamic systems operating under varying environmental and flight conditions. Simulating their dynamics is crucial for design, fault diagnostics and deterioration control, requiring robust control systems to estimate performance throughout the flight envelope. Numerical simulations provide accurate assessments in both steady and unsteady scenarios through physics-based and mathematical models, although their development is challenging due to the engine’s complex physics and strong dependencies on environmental conditions. In this context, data-driven machine-learning techniques have gained significant interest for their ability to capture nonlinear dynamics and enable online performance estimation with competitive accuracy. This work explores different neural network architectures to model the turboshaft engine of Leonardo’s AW189P4 prototype, aiming to predict engine torque. The models are trained on a large database of real flight tests, covering a variety of operational manoeuvers under different conditions, thus offering a comprehensive performance representation. Additionally, sparse identification of nonlinear dynamics (SINDy) is applied to derive a low-dimensional model from the available data, capturing the relationship between fuel flow and engine torque. The resulting model highlights SINDy’s ability to recover underlying engine physics and suggests its potential for further investigations into engine complexity. The paper details the development and prediction results of each model, demonstrating that data-driven approaches can exploit a broader range of parameters compared to standard transfer function-based methods, enabling the use of trained schemes to simulate nonlinear effects in different engines and helicopters.

Technological developments and affordable price structures have increased the usage of unmanned aerial vehicles (UAVs) across almost all sectors, hence increasing demand. Since UAVs can fly and perform various tasks without requiring a human operator, the most dangerous and time-consuming tasks previously performed by humans in many sectors are now accomplished by using UAVs. The increased use of UAVs has also introduced critical safety and security risks, including airspace congestion, collisions and malicious use, and therefore, identifying and assessing the risks associated with UAVs and finding ways to mitigate them is of great importance. This qualitative study investigates the safety and security risks posed by the increased use of UAVs and discusses ways to mitigate these risks. Semi-structured interviews with aviation professionals, including pilots, air traffic controllers and academicians, were conducted, and the collected data were analysed by using MAXQDA 24 qualitative analysis software. The results indicate that 86% of participants emphasised air traffic density as a major safety concern, while 71% underlined the need for dedicated air corridors and robust legal frameworks to reduce collision risks. These insights suggest that the safe integration of UAVs into current aviation systems demands a multifaceted strategy involving regulatory interventions, such as clearly defined UAV flight zones and essential technological enhancements. Overall, the study underscores the urgent need for coordinated efforts–legal, technological, and inter-institutional–to ensure the secure incorporation of UAVs into national airspace.

A path being planned for an unmanned aerial vehicle (UAV) or its armed conformation called the unmanned combat aerial vehicle (UCAV) has a critical importance on the flight safety and success of the task including reconnaissance, surveillance, monitoring or destroying. The Back-and-Forth (BaF) algorithm is one of the most recent greedy techniques that executes a heuristic approach for generating two backbone candidates and a combination procedure for bringing advantageous segments of them in order to solve the geometric description of the path planning problem. This study was devoted to a thread-based parallel implementation of the BaF algorithm, also named the multi-threaded BaF (tBaF for short). The threads of the tBaF algorithm increase their search bounds gradually according to the assigned thread indexes and execute the heuristic approach and combination procedure of the BaF for calculating paths. A set of detailed experiments was carried out with the aim of evaluating the performance of the tBaF, and its results were compared with the BaF and 14 other meta-heuristic-based path planners over three battlefield scenarios and their 12 test cases containing the preidentified enemy threats. Comparative studies showed that the different local and global search capabilities of the threads gained with the newly introduced thread-based organisation give a significant contribution to the path planning performance and allow the tBaF to be ranked among the top three planners even though it requires at least from four to eight times less function calls than the tested meta-heuristics.

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.

This paper examines the aeroelastic stability of uniform flexible wings imperfectly supported at one end and free at the other. Real-world aircraft wings inevitably exhibit imperfections, including non-ideal end supports. This work is motivated by the critical need to fundamentally understand how end-support imperfections influence the aeroelastic behaviour of fixed wings. The equations of motion are obtained via the extended Hamilton’s principle. The bending-torsional dynamics of the wing is approximated using the Euler-Bernoulli beam theory. The aerodynamic lift and pitching moment are modelled using the unsteady aerodynamics for the arbitrary motion of thin aerofoils in the time domain, extended by the strip flow theory. The imperfect support is modelled via rotational springs (with linear stiffness) for both bending and torsional degrees of freedom. The Galerkin method is used for the spatial discretisation. The stability analysis is performed by solving the resulting eigenvalue problem, and the numerical results are presented in Argand diagrams. The numerical results presented in this study are novel and offer great insights. It is demonstrated that support imperfections can substantially influence the critical flow velocity for both flutter and divergence, as well as alter the sequence of instabilities and the unstable mode. The extent of these effects directly depends on the magnitude of the imperfections. Interestingly–and counterintuitively–in certain cases, a reduction in the flutter speed is observed as the imperfections decrease.

Micro-UAV systems used for metric purposes are highly capable of capturing relatively high-resolution, chromatically stable aerial images at low altitudes. In micro-UAV-based aerial imaging-based structure-from-motion (a-SfM) applications, the flight mission planning problem can be customised to achieve different objectives. The requirement for minimising the time spent in the air, which is crucial for energy conservation, can be achieved by designing the shortest possible flight path. Spatial resolution in the captured aerial images can be significantly preserved by maintaining the ground sampling distance (GSD) value within a 95${\rm{\% }}$ confidence interval throughout the flight path. Fuel efficiency can be improved by minimising the number of turning manoeuvers required to follow the flight path during the flying mission. In this paper, four distinct flight mission planning processes are delineated to enable the energy-efficient and effective implementation of aerial imaging missions, with their associated parameters optimised using the colony-based search algorithm (CSA). The obtained experimental results demonstrate that the proposed flight mission planning processes are highly successful in the energy-efficient and effective execution of aerial imaging missions.

The aerodynamic performance of an ultra-high aspect ratio strut-braced wing design is assessed for flight at cruise. The sensitivity of a selected airframe design from a recent CleanSky2 project to operating conditions around the design point is quantified using the adaptive-cut high-dimensional model representation (HDMR) method, which allows for the decomposition of the parameter space into smaller subdomains to isolate the parameter interactions and influence on the aerodynamic forces. A comparative analysis with a cantilever wing configuration is performed to identify the role of the strut on the sensitivity of the design. Insight into the transonic performance is gained by characterisation of buffet limits and drag rise. Results show that, for the selected optimised airframe configuration, small changes in freestream parameters can lead to significant reduction in performance due to drag divergence triggered by the shock wave generated at the strut-wing junction and at the fuselage-strut intersection. Cruise conditions can be achieved without buffet onset throughout much of the parameter space. Safety margins associated with buffeting are satisfied, but sensible limits are imposed on the flight envelope for this configuration.

The cavities over the re-entry vehicle alter the aerothermodynamic properties, leading to enhanced thermal protection as well as effective aerothermodynamic performance. This paper investigates the estimation of aerothermodynamic properties over a re-entry vehicle with different types of cavities on the frontal face of the vehicle. The direct simulation Monte Carlo (DSMC) simulation of hypersonic flow over the Crew module Atmospheric Re-entry Experiment (CARE) capsule was simulated with the re-entry velocity of 7,422 m/s and the freestream temperature of 225 K at an altitude of 110 km. A transient flow Knudsen number of 0.1 and air consists of 78.09% of ${N_2}$ and 21.91% of ${O_2}$ are used in the simulations. Two types of cavities, namely trapezoidal and the semi-circular cavity on the frontal face of the re-entry vehicle with different length to depth ratios, are analysed. The simulation results show that the recirculation regions are formed at the base of the cavity in the case of a cavity with sharp corners, whereas in the case of a cavity with rounded corners, the recirculation formed at the lip of the cavity for both trapezoidal and the semi-circular cavities. Increasing the length and depth of the cavity leads to smaller decrement in the drag when compared to the capsule without cavity for both trapezoidal and the semi-circular cavities. The heat flux is low for a cavity with the small L/D ratio (L/D = 0.5) for both fixed length and depth for trapezoidal-type cavity, whereas for large L/D ratio (L/D = 1.5) increasing the length of the cavity increases the overall heat flux.

With numerous applications of coilable masts in high-precision space application scenarios, there are also greater demands on the accuracy of their dynamic modelling and analysis. The modelling of hinges is a critical issue in the dynamic modelling of coilable masts, which significantly affects the accuracy of the dynamic response analysis. For coilable masts, the rotational effect is the most important problem in hinge modelling. However, few studies have focused on this topic. To address this problem, the concept of hinge equivalent rotational stiffness is proposed in this paper to describe the rotational effect of the coilable mast hinges. After that, a new coilable mast dynamic model containing the undetermined hinge equivalent rotational stiffness is introduced, and an identification method for the hinge equivalent rotational stiffness based on the hammer test is proposed. Finally, the dynamic modelling method is validated through an actual coilable mast example, and the analysis and test results show that the accuracy of the dynamic model established by the proposed method in this paper is greater than that of the traditional model.

This study explored mental workload recognition methods for carrier-based aircraft pilots utilising multiple sensor physiological signal fusion and portable devices. A simulation carrier-based aircraft flight experiment was designed, and subjective mental workload scores and electroencephalogram (EEG) and photoplethysmogram (PPG) signals from six pilot cadets were collected using NASA Task Load Index (NASA-TLX) and portable devices. The subjective scores of the pilots in three flight phases were used to label the data into three mental workload levels. Features from the physiological signals were extracted, and the interrelations between mental workload and physiological indicators were evaluated. Machine learning and deep learning algorithms were used to classify the pilots’ mental workload. The performances of the single-modal method and multimodal fusion methods were investigated. The results showed that the multimodal fusion methods outperformed the single-modal methods, achieving higher accuracy, precision, recall and F1 score. Among all the classifiers, the random forest classifier with feature-level fusion obtained the best results, with an accuracy of 97.69%, precision of 98.08%, recall of 96.98% and F1 score of 97.44%. The findings of this study demonstrate the effectiveness and feasibility of the proposed method, offering insights into mental workload management and the enhancement of flight safety for carrier-based aircraft pilots.

In this paper, an intricate autopilot architecture is built for a highly nonlinear aircraft. The mode control of the autopilot is illustrated with state transition diagram. The fundamental concept of this architecture is a model-free design, which means that aerodynamic derivatives and model linearisation are not required. However, only the dynamic control allocation (CA) technique involves the use of derivatives for aerodynamic control. With the use of the Incremental Nonlinear Dynamic Inversion (INDI) in this framework, reduced dependency on models can be achieved. The algorithm addresses system nonlinearities. Our study simulates the approach, and the typical terminal arrival phase of the flight. For lateral navigation, a line-of-sight (LOS) based guidance approach is employed. The principle of the LOS is look ahead-based steering and, mainly the cross-track error is primarily used in the literature. The cross-track error is not the only error for establishing the predefined path. The error between the aircraft heading and the path course is required to describe how fast heading change occurs to re-establish the path again. This proposed modified LOS improves not only the path following ability, but also fault tolerance during aileron or rudder malfunctions. In addition, control surface lock-in-place failures, right/left engine flames-out, and turbulence/wind cases are studied. The objective is a safe approach without reconfiguration. The effectiveness of the proposed control architecture is demonstrated with numerical simulations for the NASA Generic Transport Model (GTM) T-2.

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 study investigates the dynamics of water droplets within a Batchelor vortex. Such an analytically described flow structure serves here as a model that may capture the essence of a trailing vortex. A Lagrangian approach is used to analyse the coupling between droplet motion and the flow field generated by the vortex. Under certain thermodynamic and hydrodynamic conditions, droplets may undergo evaporation and condensation when circulating the vortex core due to sharp changes in the environmental conditions induced by the vortex. The vortex-induced pressure drop is quantified using a non-dimensional vortex Euler number, revealing conditions required for condensation initiation within the vortex core. The onset of condensation is characterised by defining a mass transfer coefficient, indicating the direction and extent of mass transfer to the droplets. Our study uncovered a distinct clustering phenomenon linked to the initial Stokes number, with droplets showing a tendency to aggregate at higher Stokes numbers. The presented model may offer valuable insights into droplet dynamics within trailing vortices, contributing to improved modelling and prediction of droplet transport phenomena near trailing vortices.

Aerodynamic investigations are crucial for the efficient design of Lighter-than-Air (LTA) systems. This study explores the aerodynamic characteristics of conventional and multi-lobed airships, motivated by the growing interest in LTA systems due to advancements in materials science, energy sources, aerodynamics, propulsion technology and control systems. The study employs the k-epsilon turbulence model, which is well-suited for turbulent flow simulations, and the Semi-Implicit Method for Pressure Linked Equations (SIMPLE) algorithm, known for its effectiveness in pressure-velocity coupling in fluid dynamics simulations. The results indicate that multi-lobed airships offer enhanced aerodynamic efficiency over conventional designs. Detailed analyses of lift and drag coefficients provide insights into aerodynamic performance, guiding the optimisation of airship designs for improved efficiency. The findings of this study support the development of more aerodynamically efficient airship designs, which can serve as cost-effective, energy-efficient and quieter alternatives to traditional aircraft, particularly for applications such as surveillance, cargo transport and scientific research.

This paper investigates a method for estimating emissions from hybrid-electric aircraft, providing a qualitative assessment of their potential environmental benefits. Limited data availability poses challenges, discussed in this study, for its rigorous evaluation. Proper characterisation of both ${{C}}{{{O}}_{{2}}}$ and non-${{C}}{{{O}}_{{2}}}$ emissions across the operating envelope is crucial. Existing databases and predictive models for turboshaft engines for full-thermal regional aircraft may not be reliable for hybrid-electric configurations, which could operate under uncommon conditions, as low thermal power supply during specific flight phases. This uncertainty highlights the limitations of current emission prediction models. The first of the present study examines non-${{C}}{{{O}}_{{2}}}$ emissions (${{N}}{{{O}}_{{x}}}$, HC, ${{S}}{{{O}}_{{2}}}$, CO) from hybrid-electric aircraft optimised for minimum block fuel, comparing them with those from thermal competitors. The second part evaluates ${{C}}{{{O}}_{{2}}}$ emissions, considering both in-flight and electricity generation contributions. Results indicate that hybrid-electric configurations could reduce non-${{C}}{{{O}}_{{2}}}$ emissions for both the entire mission and the landing-take-off cycle. However, ${{C}}{{{O}}_{{2}}}$ reductions are only significant for short design ranges and could be marginal if electricity generation does not shift to fully renewable sources.

To capture the airspeed-dependent dynamics of flexible aircraft, high-order aeroservoelastic systems can generally be expressed as linear parameter-varying (LPV) models. This paper presents a comprehensive model order reduction and control design process for grid-based LPV systems, and takes the flexible aircraft FLEXOP as an example for verification. The LPV model order reduction method is extended from projection-based linear time-invariant methods through construction of continuous transformations. The corresponding algorithm can be programmed to automatically perform the model order reduction for LPV systems and simultaneously ensure the state consistency between grid points and the continuity of state-space data interpolation. By applying this method, a 680th-order high-fidelity LPV model of the FLEXOP aircraft is reduced to a control-oriented model with only 19 states. Considering that the frequencies of rigid-body and flexible modes are close under certain parameter conditions, an integrated design approach for rigid-flexible coupling control is employed in this paper. Instead of separately designing a baseline rigid-body flight controller and a flutter suppression controller for each unstable flexible mode, a parameter-dependent dynamic output-feedback controller is designed. The resulting controller effectively expands the flutter-free flight envelope, ensuring rigid-body attitude and velocity tracking performance while stabilising the two originally unstable flutter modes.

A quadrotor was modified by adding wings to the frame to directly compare the flight dynamics characteristics as well as the stability and control derivatives of the quadrotor and its biplane tailsitter variant. The on-axis response of the quadrotor and a biplane tailsitter variant were measured through flight tests, and a frequency domain system identification was used for non-parametric and parametric model identification. Identification of the full vehicle dynamics also demonstrated that identifying the motor torque and back-EMF constants from no-load measurements and the remaining motor parameters from a rotor-motor test stand provided the most accurately identified full vehicle model. The motor dynamics were shown to add a pole to the thrust-based responses (roll, pitch and heave), while the torque-based response (yaw) included a pole and a zero. This approach was then used to identify and compare the quadrotor dynamics, tailsitter dynamics and the total impact of canting the motors. It was found that the presence of the wing added pitch damping to the dynamics and pitch stability became negative. The yaw axis saw an increase in yaw damping derivative, and a reduction in the yaw control derivative to the point where it became difficult to control the aircraft. By introducing cant, both the quadrotor and tailsitter saw large increases in the yaw control derivative. Further, the rotor thrust-based moment generation due to cant resulted in the yaw response zero being canceled by the motor dynamics, resulting in a purely first-order yaw response. Neither the wing nor cant produced any change in the lateral and heave axes.

Today, innovative vehicle designs explore the possibility of employing distributed propulsion systems with multiple rotors and propellers. Distributed propulsion systems create complex interactional aerodynamics, that remain largely unexplored and not fully understood. This paper presents a high-fidelity aerodynamic analysis of a tip-mounted propeller combined with over-the-wing propellers. Different configurations were tested using fully resolved simulations with the HMB3 CFD solver. The results indicate that interactional effects in all configurations influence the propeller and wing performance. The proposed configuration, featuring a tip-mounted propeller and over-the-wing propellers, produced 3.5% more thrust compared to the configuration with only a tip-mounted propeller, while also enhancing efficiency. Wing performance was also improved, yielding more lift and less drag, resulting in a higher lift-to-drag ratio. These benefits were due to the thrust and power distributions, and of favourable propeller slipstream/wing interactions. The over-the-wing distributed propulsion system resulted in higher pitching moments, suggesting that moment balancing using different thrust settings and propeller installation positions, or the entire vehicle should be considered to pave the way for practical applications.

This publication presents the results of a numerical analysis aimed at assessing the applicability of thermal tip clearance control (TCC) to the fore-to-last stage of a 10-stage high-pressure compressor system. The chosen geometry is representative for the HPC rear stages of a modern middle-sized turbofan, designed for large business jets and regional airliners. Simplified models for two TCC concepts were implemented, isolated and in combination: external impingement cooling and heat pipes. The analysis was performed by means of finite-element thermostructural simulations. Transient operational cycles, derived from a meanline model, along with empiric correlations for heat convection provided the required boundary conditions. Qualitative similarity to selected previous works in terms of temperature, stress and clearance evolutions was achieved. The combination of concepts demonstrated its potential as a TCC system with up to 0.45% reductions in rotor and stator clearances. Calculated heat pipe temperatures and heat fluxes were inside the estimated operational limitations. Regarding stresses, some local concentrations were observed, without significant impact in critical stress regions. A slam cycle analysis showed that, while blade rubbing remains a possibility, it can be mitigated by robust cooling control. All in all, the concept was deemed worthy of more detailed studies.