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 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.

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.

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.

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.

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.

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.

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 behaviour of helicopter during water impact, considering variations in initial downward velocity and pitching angle, have been investigated numerically and theoretically in the present study. The air-water two-phase flows are simulated by solving unsteady Reynolds-averaged Navier-Stokes equations enclosed by standard $k - \omega $ turbulence model. A treatment for computational domain in combination with a global dynamic mesh technique is applied to deal with the relative motion between the helicopter and water. Results indicate that the initial downward velocity of helicopter exhibits behaviour similar to that of a V-shaped body impacting on water, as does the initial pitching angle. To extend the theoretical approach for predicting the kinematic parameters during helicopter ditching, a shape factor capturing the combined effect of various attributes and an average deadrise angle for asymmetric wedges are also introduced.

This paper describes a reverse engineering methodology so as to accomplish an aero-propulsive modelling (APM) through implementing a drag polar estimation for a case study jet aircraft in case of the absence of the thrust data of the aircraft’s engine. Since the available thrust force can be replaced by the required thrust force for the sustained turn, this approach allows the elimination for the need of the thrust parameter in deriving an aero-propulsive model utilising equations of motion. Two different modelling approaches have been adopted: (i) implementing the 6-DOF model data for sustained turn and climb flight to achieve induced drag model; and then incorporating the glide data to obtain the total drag polar model; (ii) using the 6-DOF model data together with introducing the effect of CL-α dependency. The error assessments showed that the derived CSA models were able to predict the drag polar values accurately, providing linear correlation coefficient (R) values equal to 0.9982 and 0.9998 for the small α assumption and CL-α dependency, respectively. A direct comparison between the trimmed CD values of 6-DOF model and the values predicted by the CSA model was accomplished, which yielded highly satisfactory results within high subsonic and transonic CL values.

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.

Contrails are a major contributor to the climate effect of aviation. Mitigation efforts and technological improvements aim to reduce the contrail climate effect. Many currently discussed innovations (like using sustainable aviation fuels (SAFs) or hydrogen) affect the physical processes and phenomena during contrail formation. Hence, understanding and analysing contrail formation is of great importance in the context of climate research. Ice crystal formation in a nascent contrail is completed within the first seconds after the engine exhaust is emitted. In the past, numerical models treating this early stage typically involved either a 3D or 0D approach. Whereas 3D models are computationally expensive, restricting the number of simulations that could be performed, less expensive 0D models allow to explore a larger parameter space but neglect plume heterogeneity and use a prescribed plume dilution. We present the new dynamical framework RadMod for contrail formation simulations that describes the evolution of a turbulent round jet emitted from an aircraft engine. Relative to large-eddy simulation (LES) or Reynolds-averaged Navier-Stokes (RANS) 3D models of contrail formation, our model is computationally less expensive, enabling extensive parameter studies. The model accounts for the mixing of the hot and moist exhaust air with the cold ambient air through the solution of the two-dimensional advection-diffusion equation of momentum, temperature, and water vapour. The validation of our model is conducted through comparisons with empirical relationships and CFD results. In the near future, this model will be combined with an existing microphysical model, resulting in a contrail formation model of intermediate complexity.

A distributed cooperative guidance law without numerical singularities is proposed for the simultaneous attack a stationary target by multiple vehicles with field-of-view constraints. Firstly, the vehicle engagement motion model is transformed into a multi-agent model. Then, based on the state-constrained consensus protocol, a coordination control law with field-of-view (FOV) constraints is proposed. Finally, the cooperative guidance law has been improved to make it more suitable for practical application. Numerical simulations verified the effectiveness and robustness of the proposed guidance law in the presence of acceleration saturation, communication delays and measurement noise.

The variable stator vanes (VSV) are a set of typical spatial linkage mechanisms widely used in the variable cycle engine compressor. Various factors influence the angle adjustment precision of the VSV, leading to the failure of the mechanism. The reliability analysis of VSV is a complex task due to the involvement of multiple components, high dimensionality input and computational inefficiency. Considering the hierarchical characteristics of VSV structure, we propose a novel multi-layer Kriging surrogate (MLKG) for the reliability analysis of VSV. The MLKG combines multiple Kriging surrogate models arranged in a hierarchical structure. By breaking the problem down into more minor problems, MLKG works by presenting each small problem as a Kriging model and reducing the input dimension of the sub-layer Kriging model. In this way, the MLKG can capture the complex interactions between the inputs and outputs of the problem while maintaining a high degree of accuracy and efficiency. This study proves the error propagation process of MLKG. To evaluate MLKG’s accuracy, we test it on two typical high-dimensional non-linearity functions (Rosenbrock and Michalewicz function). We compared MLKG with some contemporary KG surrogate modeling techniques using mean squared error (MSE) and R square (${R^2}$). Results show that MLKG achieves an excellent level of accuracy for reliability analysis in high-dimensional problems with a small number of sample points.

In the past few decades, substantial work has been directed towards the design of aircraft structures that maximise fuel efficiency, improve performance and curtail emissions. Aeroelastic optimisation offers an effective way to devise lightweight and fuel efficient structures, with structural stability constraints often driving the design. To date, the aeroelastic optimisation community has relied mostly on linear buckling predictions for the evaluation of structural stability constraints, mainly because of their conservativeness, computational efficiency and simplicity of implementation. This approach typically leads to overly conservative buckling margins, and this over-conservativeness places a glass ceiling over the load carrying capacity of wing structures, consequently restricting the exploration of regions within the design space where considerable weight savings could be achieved.

By contrast to previous works that predominantly rely on linear buckling constraints, the present paper introduces a method to incorporate nonlinear structural stability analysis into aeroelastic optimisations of wingbox-like structures. The method relies on the evaluation of the positive-definiteness of the tangent stiffness matrix, which is an indicator of structural stability. The sign of the stiffness eigenvalues is monitored while tracing the load-displacement equilibrium paths by means of the arc-length method, thus pinpointing the onset of instability. The proposed constraint is tested in a proof of concept structural optimisation of an idealised version of the CRM wingbox. This optimisation shows a $10.9{\rm{\% }} $ reduction in mass with respect to a baseline design that is optimal with a linear buckling approach, promising great potential for application to more realistic aeroelastic optimisations.

This paper presents the development and implementation of a comprehensive system for acquiring telemetry from Alsat-2A and Alsat-2B satellites, whose orbits are phased 180 degrees apart, utilising the CDM600 demodulator. Integral to this system is an automatic learning module leveraging machine learning algorithms to optimise circular polarisation selection based on reception conditions. The software segment manages the demodulator, user interface, and coordinates the machine learning algorithm, drawing insights from historical polarisation data to construct predictive models for optimal polarisation selection. Through the integration of machine learning, this system aims to enhance telemetry signal reception quality, contributing to the success (Alsat-2A was launched on 12 July 2010, and Alsat-2B on 26 September 2016) of satellite missions.

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.

Aiming at the problem of fast and consensus obstacle avoidance of multiple unmanned aerial systems in undirected network, a multi-quadrotor unmanned aerial vehicles UAVs (QUAVs) finite-time consensus obstacle avoidance algorithm is proposed. In this paper, multi-QUAVs establish communication through the leader-following method, and the formation is led by the leader to fly to the target position automatically and avoid obstacles autonomously through the improved artificial potential field method. The finite-time consensus protocol controls multi-QUAVs to form a desired formation quickly, considering the existence of communication and input delay, and rigorously proves the convergence of the proposed protocol. A trajectory segmentation strategy is added to the improved artificial potential field method to reduce trajectory loss and improve the task execution efficiency. The simulation results show that multi-QUAVs can be assembled to form the desired formation quickly, and the QUAV formation can avoid obstacles and maintain the formation unchanged while avoiding obstacles.