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.

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.

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.

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.

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.

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.

This paper aims to explore the feasibility of providing boundary layer propulsion and flow control by means of embedded aerofoils that are oscillating in the pure plunge mode. To this end, Navier-Stokes calculations of the low-speed flow over a flat plate with an oscillating small foil in close vicinity to the plate were performed to determine the influence of the wall distance, Reynolds number, and reduced frequency on the aerofoil thrust. The simulations were extensively validated against water tunnel experiments at Reynolds numbers between 440 to 5,940. Good agreement was obtained in terms of mean streamwise velocity profiles and the vortical wake patterns. Results indicate that the thrust increases from its value in unbounded flow with decreasing distance from the plate. The propulsive efficiency exhibits a consistent peak at a non-dimensional plunge velocity of about 0.55. For wall distances between one-half to one chord lengths, vortex pairs are shed in a slightly upward deflected direction independent of the starting motion of the aerofoil. As the wall distance increases further, these vortex pairs change into the well-known reverse Karman vortex street. Example calculations for a flat plate with two foils mounted close to the plate trailing edge and oscillating in counterphase confirm the device’s efficacy.

A simplified configuration was developed to facilitate the mode transition process within an over-under Turbine-Based Combined Cycle (TBCC) inlet. Leveraging dynamic mesh technology, an unsteady numerical simulation of the mode transition was conducted, emphasising the flow characteristics of the mode transition and the impact of key similarity criteria numbers. The findings indicate that at an incoming Mach number of 2.0, the mode transition is paired with a continuous alteration in the capture mass flow of the high-speed duct. This continual change instigates the inlet unstarting, with subsequent flow characteristics being contingent on the historical effect, exhibiting a degree of hysteresis characteristics. When the scale effect is considered, it is observed that a larger model scale results in higher Reynolds (Re) and Strouhal (St) numbers. This directly contributes to a notable delay in the unstart moment, a decrease in the unstart interval, and an enlargement of the hysteresis loop. An examination of control variables reveals that the Re number marginally influences mode transition characteristics, while the St number’s effect constitutes approximately 90% of the scale effect. This conclusively demonstrates that the St number is the predominant similarity criterion number in the mode transition process.

This paper presents the concept of a lifting-wing quadcopter unmanned aerial vehicle (UAV), a vertical take-off and landing vehicle (VTOL) with a rear wing, a canard at its front and four propellers. The aerodynamic surfaces are designed so that their mounting angle can be adjusted and fixed before flight, so its performance in transition flight can be studied for a combination of wing and canard mounting angles. A dynamic model using rigid-body equations of motion is presented, which is used to compute the transition flight trajectory from hover to cruise in horizontal flight. The trim conditions were computed for a range of fixed wing and canard mounting angles to study the effects of these variables on transition trajectory parameters such as required power, body pitch angle and propeller rotation speeds as a function of flight speed. Furthermore, a transition flight control algorithm is presented, which has a cascaded PID controller and a reference scheduler to switch between the proper reference states, controls and control allocation matrix. Finally, the transition control algorithm of the conceptual UAV is numerically simulated. Results show that this configuration can perform a fast and smooth transition from hover to cruise flight using the proposed flight control algorithm, substantially reducing required propulsive power in cruise of up to 64%. The application of the control algorithm made notable a transition manoeuver that consists of negatively inclining the aircraft at a negative pitch angle, initially at high intensity, and as the final cruising speed approaches, the inclination is attenuated until the equilibrium pitch angle is reached. Simultaneously with the negative inclination of the pitch angle, there is a slight drop in altitude, which is quickly resumed as the trajectory develops until the final cruising speed. Lastly, this aircraft configuration can be widely used in applications where performance gains in operations currently carried out by multicopters, which cover large distances and need long flight time, would bring great operational advantages.

The main purpose of this article is to present the nonlinear unsteady behaviour for jet transport aircraft response to serious atmosphere turbulence in cruise flight and to provide the appropriate mitigation concepts for pilots in the pilot training course of the IATA – Loss of Control In-flight (LOC-I) program. The flight data of a twin-jet and a four-jet transport aircraft encountered serious atmosphere turbulence are the study cases for this article. This study uses flight data mining and fuzzy-logic modeling of artificial intelligence techniques to establish nonlinear unsteady aerodynamic models. Since the rapid change of aerodynamic characteristics in turbulence, so the study uses decoupled longitudinal and lateral-directional motion to identify various eigenvalue motion modes of nonlinear unsteady behaviour through digital 6-DOF flight simulation. It is found that the changes of the main flight variables in the aerodynamic scene and flight environment of the two aircraft are different, but the profiles of five eigenvalue motion modes are actually similar. Those similar eigenvalue motion modes can formulate preventive actions related to the flight handling quality for safe and efficient control by pilots to execute the flight tasks. The one with a large drop height during the ups and downs motion between the two is chosen to construct the movement mechanism of nonlinear unsteady behaviours. The assessments of dynamic stability characteristics of nonlinear unsteady behaviour based on the approaches of oscillatory motion and eigenvalue motion modes related to loss of control will be demonstrated in this article. To develop preventive actions, the situation awareness response to the induced mutation of nonlinear unsteady behaviour on the pilot’s operations will be a further research task in the future.

This paper presents a comprehensive evaluation of reduced-order models (ROMs) for the determination of pressure coefficient distributions on supersonic and hypersonic bodies. The study investigates the limitations, aerodynamic precision and computational performance associated with various methodologies, ranging from simplistic Newtonian theory-based approaches to more advanced first and second-order shock-expansion theories. Validation is performed by comparing computed results with experimental and computational data for pressure distributions, drag and lift coefficients and centres of pressure for fundamental geometries and authentic vehicle design over a wide range of freestream conditions. The study also includes a comprehensive computational complexity analysis, demonstrating the superiority of finite-element ROM approaches over traditional finite-volume computational fluid dynamics (CFD) simulations. The primary objective of this paper is to scrutinise the extension of these methodological classes to the low supersonic regime. Hence, thermo-chemical reactions within the flow are disregarded, and the ideal gas law is adopted. A value of $\gamma = 1.4$ is chosen for consistency and comparability across the analyses. The proposed ROMs show remarkable potential for reducing high-speed simulation execution times by four orders of magnitude, maintaining accuracy within 20 per cent and as low as 1 per cent. The study unveils three key findings: first, the accuracy degradation of Newtonian-based theories for inclined elements, particularly around 45 degrees, and their reduced dependency on Mach number at large inclination. Secondly, the study presents novel insights into the impact of shock-wave-Mach-wave interactions on pressure distribution calculations, emphasising the Mach number as a crucial metric governing recompression effects. Lastly, the study demonstrates the exceptional accuracy of DeJarnette’s method, providing ${C_P}$ results within 2 per cent for a wide range of conditions, offering an attractive alternative to the Taylor-Maccoll equation.

Cost efficiency is a critical factor in the competitive aviation sector. These efficiency factors force airline operators to develop new approaches in their organizations. Predictive maintenance helps to build scheduling maintenance programs for airline operators or MROs. Scheduled maintenance programs benefit cost efficiency in the aviation sector. Predictive maintenance methods predict the failure time of any equipment. Predictions can be made by analyzing the sensor values from equipment.

In this paper, we predicted the remaining useful life (RUL) of turbofan engines using machine learning models and a similarity-based approach. Sensor datasets from the Prognostics Data Repository of NASA, called CMAPPS, were utilized. Using the FD0002 sub-dataset, a health index (HI) was created, and models were trained. Once the models were trained, train and test HIs were estimated. The predicted test HI was matched with the predicted train HI based on a similarity-based approach, and then a RUL prediction was made. The results obtained were compared with the actual results to calculate the accuracy, and the algorithm that resulted in the maximum accuracy was identified.

We selected six machine learning algorithms and also created an ensemble model by averaging the predictions of six machine learning algorithms for comparing prediction accuracy. The different algorithms were compared to obtain the prediction model with the closest prediction of remaining useful lifecycle in terms of the number of life cycles. This experiment showed us the effect of the similarity-based approach on the basic version of machine learning models for RUL prediction.

Estimating airspace capacity under convective weather conditions is crucial for ensuring the efficiency and safety of air traffic operations. Sector route segments, which are essential components of flight routes, require timely capacity predictions during operationally critical periods. In this paper, initially, an enhanced Recursive Feature Elimination algorithm is used to select meteorological data and develop predictive features. Subsequently, the CWSRC model is established using the RF supervised learning algorithm. Finally, the paper takes ENH-YIH segment as an example to predict the capacity. Compared with other machine learning algorithms, the residual percentages for KNN, MLP and RF are 86.03%, 77.37% and 93.40%, respectively, within the range of [−0.2, 0.2]. In three separate day cases, results show that the CWSRC model’s MAE, MSE, RMSE and R2 significantly outperform traditional methods like Maxflow/Mincut and scanning line. The results confirm the CWSRC model’s superior predictive capabilities.