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

Urban air mobility (UAM) utilising novel transportation tools is gradually being recognised as a significant means to alleviate ground transportation pressures, vertiports which serve as pivotal nodes in UAM require efficient methods for assessing its operational capacity to develop an appropriate operational strategy and help to design vertiport ground infrastructure scientifically. This study proposes a multi-dimensional assessment method for the capacity of vertiports considering throughput and quality of service based on genetic algorithm (CEGA). The method comprehensively considers constraints such as unmanned aerial vehicle (UAV) safety separation, battery endurance, number of landing vertipads and UAV speed. The experimental results indicate that the vertiport with the scheduling algorithm proposed by this study has a larger capacity and experiences fewer delay than the vertiport with first-come-first-served (FCFS) algorithm when the vertiport has the same limited number of vertipads. Different proportions of UAVs significantly affect the quality of service and the degree of operation delays. The weights of vertiport throughput and customer satisfaction are the parameters that represent the importance of throughput and customer satisfaction in the objective function of the capacity assessment model. When the weights of throughput and customer satisfaction are set to 0.8 and 0.2 respectively, the performance of this optimisation model is optimal. This study provides a novel solution for capacity assessment and operation scheduling of vertiports, laying the foundation for improving the efficiency of UAM operations.

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.

A three-dimensional robust nonlinear cooperative guidance law is proposed to address the challenge of multiple missiles intercepting manoeuvering targets under stringent input constraints and thruster failure. The finite-time convergence theory is used to design a distributed nonlinear sliding mode guidance law, ensuring that the system converges in finite time, with the upper limit of convergence time related to the initial state. A nonlinear sliding surface is adopted to mitigate actuator saturation issues. Then, considering thruster failure, a robust cooperative guidance law is further introduced, ensuring mission completion through the reconstruction of the guidance law. The closed-loop system is proven to be stable using Lyapunov theory, and the influence of hyperparameters on the cooperative guidance law is analysed. Additionally, the results of numerical simulations and hardware-in-the-loop experiments demonstrate the effectiveness and robustness of the proposed algorithm in dealing with stringent input saturation and various disturbances.

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.

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

In this paper, we designa robust Successive Generalised Dynamic Inversion (SGDI) flight control system for high-performance trajectory tracking of target sun-synchronous orbit Satellite Launch Vehicles (SLVs). The robust SGDI control system is designed to track an optimal reference trajectory such that the desired orbital terminal conditions of the ascent flight phase are achieved. The proposed SGDI is composed of two loops. The attitude control loop employs Dynamically Scaled Generalised Inversion (DSGI) of Servo Constraint Dynamics (SVD) in the deviations of Euler attitude angles from their desired optimal trajectories. The inner-dynamics control loop employs DSGI of an SVD in the SLV angular velocity components. Robustification control elements are augmented within the two loops of the baseline SGDI control system to overcome control performance degradation due to dynamic scaling of the Moore-Penrose generalised inverse, modeling and parametric uncertainties, and exogenous disturbances. The robust SGDI control system works to enforce global convergence of the SLV attitude trajectories to the reference trajectories. The high-performance attributes of the robust SGDI control system are verified via comparisons with a classical sliding mode control system, and by performing numerous runs of Monte Carlo simulations under various types of uncertainties and external wind disturbances.

Low-flying aircraft are susceptible to attacks by ground-launched infrared (IR)-guided man portable air defence system (MANPADS) and surface-to-air missiles (SAM). When seen from direct below, a dual band sensor can lock on to either exhaust plume or aircraft surfaces. Based on the magnitude of the IR signature, the missile can use any one source for the terminal guidance. In this study, the IR signature of the aircraft surface and potential plume core is analysed and compared from direct bottom view in different IR bands. In the Long Wave Infrared (LWIR) band, the surface emission is higher and in the Medium Wave Infrared (MWIR) band the plume emission is higher. The plume (MWIR) emission is higher than the surface (LWIR) emission for low Mach numbers, but as the Mach number increases the plume (MWIR) to surface (LWIR) emission ratio decreases, and at supersonic Mach numbers the surface LWIR signature is higher than the plume MWIR signature. The plume MWIR to surface LWIR ratio further depends on the engine power, altitude of operation and the emissivity of the aircraft surface. In the reheat mode, plume MWIR emission is always higher than the surface LWIR emission. The dual band IR detector can be a combination of short wave infrared (SWIR)-MWIR, SWIR-LWIR, and the MWIR-LWIR band. The MWIR-LWIR dual band combination is the best suited combination of IR windows for a dual band IR sensor/detector for aircraft application.

In this article, a genetic algorithm (GA) is proposed as a solution for the path planning of unmanned aerial vehicles (UAVs) in 3D, both static and dynamic environments. In most cases, genetic algorithms are utilised for optimisation in offline applications; however, this work proposes an approach that performs real-time path planning with the capability to avoid dynamic obstacles. The proposed method is based on applying a genetic algorithm to find optimised trajectories in changing static and dynamic environments. The genetic algorithm considers genetic operators that are employed for path planning, along with high mutation criteria, the population of convergence, repopulation criteria and the incorporation of the destination point within the population. The effectiveness of this approach is validated through results obtained from both simulations and experiments, demonstrating that the genetic algorithm ensures efficient path planning and the ability to effectively avoid static and dynamic obstacles. A genetic algorithm for path planning of UAVs is proposed, achieving optimised paths in both static and dynamic environments for real-time tasks. In addition, this path planning algorithm has the properties to avoid static and moving obstacles in real-time environments.

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.

The rising demand for air traffic will inevitably result in a surge in both the number and complexity of flight conflicts, necessitating intelligent strategies for conflict resolution. This study addresses the critical challenges of scalability and real-time performance in multi-aircraft flight conflict resolution by proposing a comprehensive method that integrates a priority ranking mechanism with a conflict resolution model based on the Markov decision process (MDP). Within this framework, the proximity between aircraft in a multi-aircraft conflict set is dynamically assessed to establish a conflict resolution ranking mechanism. The problem of multi-aircraft conflict resolution is formalised through the MDP, encompassing the design of state space, discrete action space and reward function, with the transition function implemented via simulation prediction using model-free methods. To address the positional uncertainty of aircraft in real-time scenarios, the conflict detection mechanism introduces the aircraft’s positional error. A deep reinforcement learning (DRL) environment is constructed incorporating actual airspace structures and traffic densities, leveraging the Actor Critic using Kronecker-factored Trust Region (ACKTR) algorithm to determine resolution actions. The experimental results indicate that with 20–30 aircraft in the airspace, the success rate can reach 94% for the training set and 85% for the test set. Furthermore, this study analyses the impact of varying aircraft numbers on the success rate within a specific airspace scenario. The outcomes of this research provide valuable insights for the automation of flight conflict resolution.

Bio-inspiration can be used to improve the aerodynamic performance of commercial multirotor propellers. In the present study, insect wings are used as a source of inspiration, and the effects of inspiration from insect’s wing shape on the propeller performance–, especially this effect on parameters like thrust, torque, and propeller efficiency, are investigated. Six insect species have been selected as inspiration: Hemiptera, Orthoptera, Neuroptera, Mantodea, Odonata and Hymenoptera. The analyses have been done using the numerical simulation of flow and the moving reference frame (MRF) method alongside the SST k-ω turbulence model. The simulations were carried out over a range of rotational speeds, varying from 4,000 to 8,000 rpm, for propellers with a diameter of 0.24 m. All propellers utilised the Eppler E63 airfoil. To ensure the accuracy of the present numerical simulation results, validation was done by comparing them with experimental data from the DJI Phantom-3 propeller. The results of validation showed significant agreement with the experimental data. The results indicated that the insect-inspired propellers generate higher thrust compared to conventional propellers. Additionally, for a constant thrust force, the inspired propellers exhibit lower rotational speeds. Moreover, in terms of thrust, the Hemiptera insect-inspired propeller outperforms the DJI Phantom-3 propeller, achieving a notable average improvement of 34.182%.

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

The Water-Enhanced Turbofan is a promising aero engine propulsion concept that could reduce the climate impact of aviation significantly by combining the conventional Joule/Brayton cycle with a Clausius-Rankine steam cycle. One important component with a high impact on the overall performance is the condenser, a heat exchanger cooling the core exhaust for water recovery. The design conditions for the Water-Enhanced Turbofan condenser have not been analysed from a system perspective in previous studies. Therefore, these operating conditions, which can be decisive for the dimensioning of the condenser, are investigated in the present work. These operating conditions include ambient temperature variations, different cruise altitudes, maximum cruise thrust and contrail avoidance. A conceptual design engine model is set up in the Numerical Propulsion System Simulation (NPSS) framework, incorporating a multi-point design scheme. The heat exchangers are modelled using a neural network surrogate model. The results show a trade-off between engine fuel burn and the cold size frontal area of the condenser, the latter being an indication for the integrability. It is shown that high ambient temperatures pose a challenge to the condenser design, necessitating consideration of such operating conditions in new engine concepts based on heat exchange with the environment. The condenser designed for typical cruise at 15K above standard atmosphere at 35,000 ft cruise altitude, enables sustained water cycle operations down to 10,000 ft under standard day conditions. Additionally, the complete cruise segment of the design mission can be flown with sustained water cycle operations at 10K above standard atmosphere conditions. A positive side effect of the condenser sizing for hot day conditions is that the probability of contrail formation is reduced because the condenser design results in excess water recovery at colder ambient conditions.

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

In order to improve the performance of $k - \omega $ SST model in turbomachinery, previous studies have used the machine-learning (ML) technique to obtain turbulence models (for example, the ML-RANS EQ model). However, these models do not lead to satisfactory results in complex flows in turbomachinery. In this study, we use non-equilibrium training dataset to obtain a new turbulence model (i.e., the ML-RANS TR-NE-EQ model). Calculations in various cases of turbine cascade flows show that ML-RANS TR-NE-EQ model performs obviously better than ML-RANS EQ model as well as $k - \omega $ SST model.

The proportional–integral–derivative (PID) controller remains widely used in industrial applications today due to its simplicity and ease of implementation. However, tuning the controller’s gains is crucial for achieving desired performance. This study compares the performance of PID controllers within a cascade control architecture designed for both position and attitude control of a quadcopter. Particle swarm optimisation (PSO), grey wolf optimisation (GWO), artificial bee colony (ABC), and differential evaluation (DE) methods are employed to optimally tune the PID parameters. A set of PID gains is determined offline by minimising various modified multi-objective functions based on different fitness measures: IAE, ISE, ITAE and ITSE. These measures are adapted as fitness functions for position and attitude control. A simulation study is conducted to determine which fitness function yields the optimal PID gains, as evidenced by the lowest values of the objective functions. In these simulations, two different desired trajectories are designed, and the controllers are applied to ensure the quadcopter tracks these trajectories accurately. Additionally, to test the robustness of the flight control architecture and the finely tuned PID controllers against various environmental effects and parametric uncertainties, several case scenarios are also explored.

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.

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.

The dynamic model of the distributed propulsion vehicle faces significant challenges due to several factors. The primary difficulties arise from the strong coupling between multiple power units and aerodynamic rudder surfaces, the interaction between thrust and vehicle dynamics, and the complexity of the aerodynamic model, which includes high-dimensional and high-order variables. To address these challenges, wind tunnel tests are conducted to analyse the aerodynamic characteristics and identify variables affecting the aerodynamic coefficients. Subsequently, a deep neural network is employed to investigate the influence of the power system and aerodynamic rudder on the aerodynamic coefficients. Based on these findings, a multi-dynamic coupled aerodynamic model is developed. Furthermore, a control-oriented nonlinear dynamics model for the distributed propulsion vehicle is established, and a flight controller is designed. Finally, closed-loop simulations for the climb, descent and turn phases are performed, validating the effectiveness of the established model.