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.

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.

This CFD study investigates the aerodynamic characteristics of various internal grid patterns of grid fins without missile bodies, focusing on their influence on aerodynamic performance in subsonic flight regimes. Research examines four distinct grid fin lattices through comprehensive numerical simulations at Mach numbers 0.1, 0.3 and 0.5, for varying high angles of attack ranging between 0o ≤ α ≤ +45o. For validation, comparison between current numerical results and a previous experimental study results was carried out (good agreement within 9.8% error). The numerical results reveal that the diamond cell pattern demonstrates increased lift generation but incurs higher drag, while the square pattern minimises drag and pitching moment, favouring weight-efficient designs. The hexagonal grid fin excels in stall delay and post-stall performance, enhancing manoeuverability and stability at the expense of increased pitching moments. The triangular pattern offers balanced aerodynamic performance with high aerodynamic efficiency at lower speeds, making it versatile for various applications. The analysis offers key insights into grid cell pattern’s aerodynamic effects, aiding grid fin design optimisation for improved aerodynamic efficiency and controllability across various flight conditions and high angles of attack.

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.

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.

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.

This paper studies the adaptive distributed consensus tracking control framework for hypersonic gliding vehicles (HGVs) flying in tight formation. The system investigated in this paper is non-affine and subjected to multisource disturbances and mismatched uncertainties caused by a dramatically changing environment. Firstly, by refining the primary factors in the three-dimensional cluster dynamics, a non-affine closed-loop control system is summarised. Note that actual control is coupled with states, an additional auxiliary differential equation is developed to introduce additional affine control inputs. Furthermore, by employing the hyperbolic tangent function and disturbance boundary estimator, time-varying multisource disturbances can be handled. Several radial base function neural networks (RBFNNs) are utilised to approximate unknown nonlinearities. Furthermore, a generalised equatorial coordinate system is proposed to convert the longitudinal, lateral and vertical relative distances in the desired formation configuration into first-order consensus tracking error, such as latitude, longitude and height deviations. Analysis based on the Lyapunov function illustrates that variables are globally uniformly bounded, and the output tracking error of followers exponentially converges to a small neighbourhood. Finally, numerical simulations of equilibrium glide and spiral diving manoeuvers are provided to demonstrate the validity and practicability of the proposed approach.

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.

Manned lunar landers must ensure astronaut safety while enhancing payload capacity. Due to traditional landers being weak in high-impact energy absorb and heavy payload capacity, a Starship-type manned lunar lander is proposed in this paper. Firstly, a comprehensive analysis was conducted on the traditional cantilever beam cushioning mechanism for manned lander. Subsequently, a 26-ton manned lander and its landing mechanism were designed, and a rigid-flexible coupling dynamic analysis was performed on the compression process of the primary and auxiliary legs. Secondly, the landing performance of the proposed Starship-type manned lunar lander was compared with the traditional 14-ton manned lander in multiple landing conditions. The results indicate that under normal conditions, the largest acceleration of the proposed 26-ton Starship-type manned lander decreases more than 13.1%. It enables a significant increase in payload capacity while mitigating impact loads under various landing conditions.

The integration of unmanned aerial vehicles (UAVs) into agriculture has emerged as a transformative approach to enhance resource efficiency and enable precision farming. UAVs are used for various agricultural tasks, including monitoring, mapping and spraying of pesticides, providing detailed data that support targeted and sustainable practices. However, effective deployment of UAVs in these applications faces complex control challenges. This paper presents a comprehensive review of UAVs in agricultural applications, highlighting the sophisticated control strategies required to address these challenges. Key obstacles, such as modelling inaccuracies, unstable centre of gravity (COG) due to shifting payloads, fluid sloshing within pesticide tanks and external disturbances like wind, are identified and analysed. The review delves into advanced control methodologies, with particular focus on adaptive algorithms, backstepping control and machine learning-enhanced systems, which collectively enhance UAV stability and responsiveness in dynamic agricultural environments. Through an in-depth examination of flight dynamics, stability control and payload adaptability, this paper highlights how UAVs can achieve precise and reliable operation despite environmental and operational complexities. The insights drawn from this review underscore the importance of integrating adaptive control frameworks and real-time sensor data processing, enabling UAVs to autonomously adjust to changing conditions and ensuring optimal performance in agriculture. Future research directions are proposed, advocating for the development of control systems that enhance UAV resilience, accuracy and sustainability. By addressing these control challenges, UAVs have the potential to significantly advance precision agriculture, offering practical and environmental benefits crucial to sustaining global food production demands.

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.

This article aims to introduce a novel synthesis optimisation framework based on a performance prediction model for a space propulsion system. The structure of this research is divided into five steps for optimisation. In the first step, the optimisation problem is defined, and the objective functions, design parameters and constraints of the problem are determined. This system consists of five sub-systems including (gas tank, liquid fuel tank, injector, catalytic bed and nozzle). In this optimisation, the objective functions include increasing the specific impulse and reducing the overall mass of the system. Also, the design parameters and limitations of each subsystem are mentioned in the text. The optimisation space is extracted in the second step using the design of experiments (DoE) tool and the Latin hypercube sampling (LHS) method. In the third stage, using the design points obtained from the DoE, the metamodel of the mass of each of the sub-systems and the metamodel of the specific impulse of the whole system are produced by the Kriging method. In the fourth step, the metamodels produced are optimised using the SHERPA algorithm based on the objective functions of the problem. In the fifth step, the results are derived by comparing two objective functions (minimum mass and maximum specific impulse for the entire system) using the Pareto front diagram. Finally, by comparing the optimal results with an existing thruster sample, it shows that the specific impulse has increased by 6% and the total mass of the system has decreased by 15.8%.

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.

There are multiple equilibrium points in the launching and unfolding process of the multi-body aircraft. Different equilibrium points exhibit different stability characteristics and change with parameters such as connection method. The changes in stability characteristics can also lead to the inability of multi-body aircraft to achieve stable deployment. To solve these problems, the dynamic stability of multi-body aircraft during falling is analysed based on bifurcation theory in this paper. In this paper, Lagrange multiplier method is used to establish the multi-body dynamics model of the multi-body aircraft, and the curly spring torque model is added. In order to consider the coupling effect between the wings and the influence of the relative motion between the flight units on the aerodynamic force, the reference angle-of-attack, the reference sideslip angle, the relative attitude angle and the relative attitude angular velocity between the flight units were introduced as new variables to establish the aerodynamic model of the multi-body aircraft. Based on the equilibrium equations, the equilibrium curve of the two-body aircraft is obtained by using the joint stiffness coefficient as the continuous variable parameter. The stability of the equilibrium point domain on each equilibrium curve was analysed by using linearised theory. The dynamic characteristics of the launching and unfolding process of the two-body aircraft were analysed using bifurcation theory, and the stable domain was obtained regarding the initial folding angle and connection stiffness coefficient. The influence of initial folding angle and connection stiffness coefficient on the dynamic characteristics of the launching and unfolding process and the meaning of the stability domain were analysed through numerical simulation calculations. Finally, the correctness of the analysis conclusion was verified through experiments on the two-body aircraft, accumulating the technical foundation for subsequent research on high-altitude deployment.