So much has been written about Frederick W. Lanchester over the years, it is hard to imagine finding something new to discuss about his efforts in aerodynamics. Many of the previous Lanchester Memorial Lectures discussed topics such as wing aerodynamics, aircraft concepts and design, unsteady rotor aerodynamics, aerodynamics research and a wide variety of other related aerodynamic topics. However, there has never been a lecture about Lanchester’s book Aerodynamics as a tool for aerodynamics education in the early 20th century. The lecture will discuss his book relative to other aerodynamics books before and after 1907, and uncover how Lanchester’s book had a very distinct, and important, contribution to make for aerodynamic education.

Measuring the aerodynamics and stability characteristics of small unmanned aerial vehicles (sUAVs) operating at Reynolds numbers below $60,000$ is a challenge. Conventional measurement methods can be impractical and costly due to the vehicle’s size and the considerably low forces and moments involved. To overcome these limitations, the current study aims at utilising an existing motion tracking system to conduct off-board aerodynamic measurements of sUAVs. Six sUAVs, with varying wing aspect ratios, are investigated in un-powered, glide flight mode to establish the utility of the motion capture system as an aerodynamic characterisation system and understand the low Reynolds number effects on the flight dynamics. The trajectory tracking system was thoroughly validated through a series of static and dynamic tests to account for uncertainties and errors. Subsequently, flight trajectory data was collected and processed to extract the aircraft’s force and moment characteristics under quasi-steady conditions. The measured lift, drag and moment data compared well with existing literature and theoretical predictions. Longitudinal, lateral and dynamic stability derivatives were also accurately captured. Key findings from the current work included an inverse relationship between the wing aspect ratio and lift curve slope and substantially lower Oswald efficiency factors, both of which were attributed to low Reynolds number effects.

This article presents a systematic review on the use of eye-tracking technology to assess the mental workload of unmanned aircraft system (UAS) operators. With the increasing use of unmanned aircraft in military and civilian operations, understanding the mental workload of these operators has become essential for ensuring mission effectiveness and safety. The review covered 26 studies that explored the application of eye-tracking to capture nuances of visual attention and assess cognitive load in real-time. Traditional methods such as self-assessment questionnaires, although useful, showed limitations in terms of accuracy and objectivity, highlighting the need for advanced approaches like eye-tracking. By analysing gaze patterns in simulated environments that reproduce real challenges, it was possible to identify moments of higher mental workload, areas of concentration and sources of distraction. The review also discussed strategies for managing mental workload, including adaptive design of human-machine interfaces. The analysis of the studies revealed a growing relevance and acceptance of eye-tracking as a diagnostic and analytical tool, offering guidelines for the development of interfaces and training that dynamically respond to the cognitive needs of operators. It was concluded that eye-tracking technology can significantly contribute to the optimisation of UAS operations, enhancing both the safety and efficiency of military and civilian missions.

In recent times, there has been increased focus on the utilisation of virtual reality flight simulators in flight training, driven by their advantages compared to conventional methods. However, a paucity of empirical evidence has prevented their widespread introduction and regulatory approval. Existing research focuses on single-user simulators, leaving a gap in studies of collaborative training within virtual environments. Consequently, this paper investigates evidence-based simulator training within a collaborative virtual environment.

A mixed methods approach was adopted, where behaviours related to industry-standard competencies were observed in a virtual reality complex aircraft and thematic analysis applied to a post-experiment participant debrief. The findings showcase the feasibility of utilising a collaborative virtual environment for evidence-based training purposes in scenarios aligned to typical initial First Officer airline training programmes, which is a precursor to supplementing traditional professional pilot training techniques. In addition, the study found that the visual barriers imposed by head-mounted displays were overcome through the adoption of refined communication strategies, thus laying the groundwork for physically separated multi-crew pilot training.

Unmanned aerial vehicle (UAV) formations for bearing-only passive detection are increasingly important in modern military confrontations, and the array of the formation is one of the decisive factors affecting the detection accuracy of the system. How to plan the optimal geometric array in bearing-only detection is a complex nondeterministic polynomial problem, and this paper proposed the distributed stochastic subgradient projection algorithm (DSSPA) with layered constraints to solve this challenge. Firstly, based on the constraints of safe flight altitude and fixed baseline, the UAV formation is layered, and the system model for bearing-only cooperative localisation is constructed and analysed. Then, the calculation formula for geometric dilution of precision (GDOP) in the observation plane is provided, this nonlinear objective function is appropriately simplified to obtain its quadratic form, ensuring that it can be adapted and used efficiently in the system model. Finally, the proposed distributed stochastic subgradient projection algorithm (DSSPA) combines the idea of stochastic gradient descent with the projection method. By performing a projection operation on each feasible solution, it ensures that the updated parameters can satisfy the constraints while efficiently solving the convex optimisation problem of array planning. In addition to theoretical proof, this paper also conducts three simulation experiments of different scales, validating the effectiveness and superiority of the proposed method for bearing-only detection array planning in UAV formations. This research provides essential guidance and technical reference for the deployment of UAV formations and path planning of detection platforms.

Reynolds-Averaged Navier–Stokes (RANS) simulations, both steady and unsteady, are used to investigate supersonic, chemically reacting, flow fields inside a strut-stabilised supersonic combustion ramjet (scramjet) engine operating under different fuel flow rates. Fully supersonic, fully subsonic and mixed modes of operations inside the combustor, obtained at different fuel flow rates, are studied numerically through shock wave visualisations and top-wall static-pressure probing. The effect of changing fuel flow rates, imposed both suddenly and gradually, on the behaviour of shock waves and wall pressure profiles are studied in detail. For certain modes of combustion characterised by the presence of oblique shocks at the strut, shockwaves in the combustor respond predictably to an increase or decrease in fuel flow rate attaining the steady state flow fields as predicted by RANS simulations for those fuel flow rates. For certain other modes of combustion, characterised by the presence of shockwaves in the isolator and the absence of oblique shocks at the leading edge of the strut, shockwaves in the flow field appear unstable to fuel flow rate modulations. For such cases, any change in fuel flow rates, sudden or gradual, increase or decrease, causes the isolator shocks to immediately move upstream and eventually out of the isolator. A plausible physics-based explanation of the observed phenomena is presented.

With the rapid increase in the use of drones in various applications, including commercial and governmental, and the increasing probability of communication failures and contingencies, research becomes critical to ensure the safety and efficiency of their operations. The aim of this research is to develop adaptive drone flight control algorithms capable of operating effectively under conditions of limited communication and incomplete information to ensure reliable and safe autonomous operation of these systems. The applied methods include computer modelling and simulation, analytical, statistical, functional, deductive and descriptive methods. The study found that the use of performance evaluation methods for complex systems enables the identification of safety and performance criteria for drones, and drone flight control provides basic principles and methods that can be adapted for drones, including autopiloting and navigation. In addition, analyses of satellite communication and navigation prove the need to consider the limitations of this technology when developing drone control algorithms. The combination of these techniques allows for more robust and adaptive drone control systems that can function effectively in complex environments such as communication limitations and incomplete information. Additionally, it was found that the integration of adaptive control algorithms based on these methods allows drones to effectively adapt to variable environmental conditions and make decisions quickly even when communication is lost or information is limited.

If an aircraft’s initial mass, the variation of true airspeed, true rate of climb, wind speed and wind direction with time and the relationship between barometric altitude and local temperature are known, the performance along the entire flight path can be determined. Previously published work has provided the building blocks for a simple, fast, open-source and transparent method to estimate the instantaneous fuel flow rate and the engine overall efficiency, plus several other performance characteristics for turbofan powered, civil transport aircraft. The flight phases of primary interest are the climb, cruise, descent and holding, when the flaps and undercarriage are fully retracted and the engine is providing significant, positive thrust. However, for completeness, an approximate relation is provided for the engine’s ‘flight idle’ condition, together with simple estimates for fuel use during take-off and landing, plus a factor to allow for in-service deterioration. Detailed consideration is also given to the operating limits and relations are developed for the estimation of their location in Mach number and flight level space. To apply the method, a series of characteristic coefficients and constants must be known. Estimates for these quantities have been progressively improved and extended over time. Initially, results were published for 53 aircraft types and variants. The data base has now been extended to 67 entries and this is given in tabular form. Finally, to demonstrate the method’s accuracy, estimates of fuel flow rate are compared with flight data recorder values for 20 complete flights of six different aircraft types.

Rapid and comprehensive fighter optimisation is an important part of modern combat decision-making. However, due to the numerous influencing factors, it is difficult for decision-makers to consider comprehensively and specify the optimal decision, and it is highly subjective, which leads to different decision conclusions from person to person. Therefore, to solve the above deficiencies in fighter selection, this paper proposes a sequential decision-making framework that comprehensively considers the effectiveness, maintenance, support capability and health status of the fighter aircraft. Based on the multi-dimensional state, it provides comprehensive and credible auxiliary support for commanders. The sequential decision-making framework (called GRA-VIKOR-IFNs) uses the combination of equation and fuzzy multi-criteria decision-making (FMCDM) to evaluate the effectiveness, support capability and health in turn, to complete the step-by-step selection of fighter models, troops and sorties. The evaluation equation is for the effectiveness evaluation and a hybrid method using the extended grey correlation analysis (GRA) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method based on intuitionistic fuzzy numbers (IFNs) is for the support capability and health evaluation. The proposed strategy is in line with the logic and demand of actual combat and training decision-making and takes into account the influence of uncertain factors. Finally, a comparison with some classical methods is carried out, such as the full consistency method (FUCOM), the technique for order of preference by similarity to ideal solution (TOPSIS) and so on. The GRA-VIKOR-IFNs method is consistent with the results of other methods and the result sort resolution is 0.0619 and at least 40% higher than other methods, which can lead the commanders to a more reliable and clear decision.

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.

To investigate the flame acceleration to detonation in 2.0 and 0.5 mm planar glass combustion chambers, the experiments have been conducted utilising ethylene/oxygen mixtures at atmospheric pressure and temperature. The high-speed camera has been used to record the revolution of flame front and pressure inside the combustion chamber. Different equivalence ratios and ignition locations have been considered in the experiments. The results show that the detonation pressure in the 2 mm thick chamber is nearly three times of Chapman-Jouguet pressure, while detonation pressure in the 0.5 mm thick chamber is only 45.7% of the Chapman-Jouguet value at the stoichiometric mixture. This phenomenon is attributed to the larger pressure loss in the thinner chamber during the detonation propagation. As the value of equivalence ratio is 2.2, the detonation cannot be produced in the 2 mm thick chamber, while the detonation can be generated successfully in the 0.5 mm thick chamber. This phenomenon indicates that the deflagration is easily to be accelerated and transformed into the detonation, due to a larger wall friction and reflection. Besides, the micro-obstacle has been added into the combustor can shorten the detonation transition time and reduces the distance of the detonation transition.

Effusion cooling is the state-of-the-art cooling technology for gas turbine hot-gas path components. Typically, effusion cooling holes across the entire combustor liner are aligned with the combustor axis, rendering a nominal zero compound angle between highly directional miniature effusion cooling jets and the main flow direction. The pitch of effusion cooling holes is optimised accordingly. However, the swirling main flow results in a non-zero compound angle and an effectively different pitch from the design. The directional effect of effusion cooling as a result of swirling main flow on the adiabatic film cooling effectiveness (AFE) is a combined effect of a non-zero compound angle and a varied pitch. The current experimental study aims to investigate the isolated effects of compound angle on AFE by excluding the influences of varying pitch. With an improved understanding of the sole effects of non-zero compound angles on AFE, the roles that a varied pitch plays in modifying AFE are further discussed to guide future effusion cooling designs under swirling main flow conditions. Binary pressure sensitive paint (PSP) was used to determine AFE experimentally.

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.

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.

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.

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 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.