A demonstration of a fully onboard method for generating background oriented schlieren (BOS) data on a jet exhaust is presented. Readily available commercial camera equipment is used to capture in-flight imagery of a miniature jet engine exhaust mounted on a custom-built model aircraft. The setup for image acquisition and processing algorithms are described. A new process for registration of images to reduce the degrading effects of vibration and flexure of the airframe are developed and presented along with the underpinning BOS algorithm. Results show that jet flows can be visualised using this technique using a contained system on a single aircraft and demonstrate how a simple technique, such as BOS, can be democratised to such an extent that the cost of conducting in-flight jet measurements can be reduced to the budget of any model aircraft flyer.

Unmanned aerial vehicles (UAVs) with fully electric propulsion systems mainly use lithium-based batteries. However, using fuel cells, hybrid propulsion systems are created to improve the flight time and payload capacity of the UAVs. Energy management and energy-aware path planning are important aspects to be explored in hybrid-propulsion powered UAV configurations. These facilitate optimal power distribution among energy sources and motion planning considering energy consumption, respectively. In the literature, although there are many studies on the energy management of hybrid-powered UAVs and path planning of only battery-powered UAVs, there are research gaps in energy-aware path planning of hybrid-powered UAVs. Additionally, the energy management of hybrid-powered UAVs is usually considered independent of path planning in the literature. This paper thoroughly reviews recent energy-aware path planning for small UAVs to address the listed critical challenges above, providing a new perspective and recommendations for further research. Firstly, this study evaluates the recent status of path planning, hydrogen-based UAVs, and energy management algorithms and identifies some challenges. Later, the applications of hydrogen-powered UAVs are summarised. In addition, hydrogen-based hybrid power system topologies are defined for small UAVs. Then, the path-planning algorithms are classified, and existing studies are discussed. Finally, this paper provides a comprehensive and critical assessment of the status of energy-aware path planning of UAVs, as well as detailed future work recommendations for researchers.

The experimental 7AD rotor blade is assessed for flutter stability in hover to identify the influence of aerodynamic contributions related to blade aerofoil, rotor inflow and wake periodicity on flutter onset. For the aeroelastic analyses, the multibody model is tightly coupled with an unsteady aerodynamic model based on Wagner’s function and related enhancements for the general motion of an aerofoil section considering heave and pitch. The mathematical setup of the approximated Wagner function in state space is extended for axial flow to include unsteady effects related to rotor inflow and wake periodicity. Since the aerodynamic model is based on indicial response functions, a separation of these contributions is possible and allows for the study of their impact on rotor blade flutter. The according flutter results are extracted in terms of frequency and damping behaviour for three test cases that differ in the unsteady aerodynamic model for circulation comprising blade aerofoil, rotor inflow and wake periodicity. As known for articulated rotor blades, also the 7AD blade exhibits a classical bending-torsion coupling. The lowest flutter onset is found for unsteady aerodynamics limited to blade aerofoil, whilst the cases with added rotor inflow and wake periodicity show both the same flutter onset at a 5% larger rotor speed. Here, the influence of rotor inflow plays the major role, since it increases the torsion damping within the critical flutter coupling. Added wake periodicity neither changes frequency nor damping and, hence, does not affect the aeroelastic coupling.

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.

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.

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 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.

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 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.

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.

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.