Composite materials are enjoying an increasing use in aircraft structures. As more fleets transition from metal to more composite aircraft, the practice of maintenance is also adjusting. Handling, inspection and repair of aircraft composites following non-visible or barely visible damage are among the areas of concern, due to associated cost and safety implications. A pilot study was performed to explore the level of awareness and understanding of aviation maintenance practitioners around these issues. In addition, this research project sought to identify factors related to the technical/engineering judgement capacity of the personnel working with composites and to gauge the need for specialised education and training. A questionnaire survey was administered to a group of 40 professionals working for an aircraft maintenance, repair and overhaul (MRO) organisation. Descriptive statistics in conjunction with analysis of variance (ANOVA) were used to analyse the results. The sources of impact and common areas affected on the aircraft have been identified, with situational awareness suggested as the most important mitigator against impact damage. Over 70% of the participants would refer to their engineering manager for instructions on how to handle composite damage. The need for higher standardisation for composites’ maintenance, repair and handling issues emerged as a common theme across different sections of the survey. Almost all respondents agree on the need for specialised knowledge and training for the handling, repair and inspection of composites.

Accurate and reliable computation of the aerodynamic characteristics of wind turbines is very important for the development of new efficient designs. The flow around a wind turbine is modeled by a permeable disc (PD), solved through the Unsteady Reynolds-Averaged Navier–Stokes equations (URANS), here named PD/URANS method. The finite volume method and a total variation diminishing (TVD) scheme solve numerically the flow governing equations. The turbulent flow in the wake of the wind turbine is simulated utilising a one-equation turbulence model. The Glauert correction calculation considers a uniform normal force distribution (CT) on the virtual permeable disc applied to the flow, while the axial induction factor is obtained directly from the numerical solution of the URANS equations. The numerical axial induction factor obtained agrees fairly well with Glauert correction, except if the flow behind the turbine is highly unsteady and Reynolds number dependent.

With the increase of air transportation, some crossing waypoints (CWPs) are becoming bottlenecks in the operation of air traffic networks. This paper presents a CWP operation optimisation framework based on a two-stage optimisation method. First, we considered the interests of airlines and air traffic controllers and established a flight-level dynamic allocation model for the CWP to minimise the flight-level deviation and the number of flight conflicts. A multi-objective, self-adaptive differential evolution-local search hybrid algorithm was used to solve the model in a parallel computing manner. Subsequently, a flight conflict resolution algorithm based on the Monte-Carlo tree search was designed for flight conflicts that existed after the optimisation. Finally, based on real operation data, four experimental scenarios were constructed, and the air traffic operation simulation system was used for experimental validation. For daily traffic and 1.2 times peak traffic scenarios, the average flight-level deviation reduction rates after optimisation were 53% and 39%, and the successful flight conflict resolution rates reached 89% and 75%, respectively. The experimental results showed that this optimisation framework can effectively balance the number of flight conflicts with the efficiency of flight-level usage and directly improve the capacity of the CWP, which can be used as a reference for air traffic control auxiliary decision support systems.

Every manufacturing procedure is subject to tolerance variations. Over the years, a set of key characteristic features (KCF) that can explain the effect of manufacturing variations on the aero-mechanical performance of a fan blade has been devised and monitored to ensure conformality and good performance. The KCFs are derived from a cloud of coordinate measurement machine (CMM) points and are defined on approved engineering drawings for the manufactured part. In this paper, it is demonstrated that some of the traditional, common wisdom KCFs are not adequate to explain the engine performance deviation behaviour on a test bed at the sea-level condition. On the other hand, good correlation is found by analysing a set of engineering parameters drawn from a new inverse-mapping procedure of the CMM data. It is further demonstrated that a deviation measured via CMM or 3D structured light (GOM) data in cold conditions can be translated to a variation in the hot running shape of the blade. Having identified the key blade features, a cheap alternative to modifying the manufacturing procedure is devised to recover the fan performance by optimising its leading-edge shape.

In this paper, a super-twisting disturbance observer (STDO)-based adaptive reinforcement learning control scheme is proposed for the straight air compound missile system with aerodynamic uncertainties and unmodeled dynamics. Firstly, neural network (NN)-based adaptive reinforcement learning control scheme with actor-critic design is investigated to deal with the tracking problems for the straight gas compound system. The actor NN and the critic NN are utilised to cope with the unmodeled dynamics and approximate the cost function that are related to control input and tracking error, respectively. In other words, the actor NN is used to perform the tracking control behaviours, and the critic NN aims to evaluate the tracking performance and give feedback to actor NN. Moreover, with the aid of the STDO disturbance observer, the problem of the control signal fluctuation caused by the mismatched disturbance can be solved well. Based on the proposed adaptive law and the Lyapunov direct method, the eventually consistent boundedness of the straight gas compound system is proved. Finally, numerical simulations are carried out to demonstrate the feasibility and superiority of the proposed reinforcement learning-based STDO control algorithm.

A synthetic flow angle sensor, able to estimate angle-of-attack and angle-of-sideslip, can exploit different methods to solve a set of equations modelling data fusion from other onboard systems. In operative scenarios, measurements used for data fusion are characterised by several uncertainties that would significantly affect the synthetic sensor performance. The off-line use of neural networks is not a novelty to model deterministic synthetic flow angle sensors and to mitigate issues arising from real flight applications. A common practice is to train the neural network with corrupted data that are representative of uncertainties of the current application. However, this approach requires accurate tuning on the target aircraft and extensive flight test campaigns, therefore, making the neural network tightly dependent on the specific aircraft. In order to overcome latter issues, this work proposes the use of neural networks to solve a model-free scheme, derived from classical flight mechanics, that is independent from the target aircraft, flight regime and avionics. It is crucial to make use of a training dataset that is not related to any specific aircraft or avionics to preserve the generality of the scheme. Under these circumstances, global and local neural networks are herein compared with an iterative method to assess the neural capabilities to generalise the proposed model-free solver. The final objective of the present work, in fact, is to select the neural technique that can enable a flow angle synthetic sensor to be used on board any flying body at any flight regime without any further training sessions.

It is envisaged that future civil aero-engines will operate with ultra-high bypass ratios to reduce the specific fuel consumption. To achieve the expected benefits from the new engine cycles, these new powerplants may mount compact nacelles. For these new configurations the aerodynamic coupling between the powerplant and the airframe may increase. For this reason, it is required to quantify and further understand the effects of aircraft integration for compact aero-engine nacelles. This study provides an insight of the changes in flow aerodynamics as well as quantification of the most relevant performance metrics of the powerplant, airframe and the combined aircraft system across a range of different installation positions. Relative to a conventional architecture, there is an aerodynamic benefit in net vehicle force of about 1.2% for a compact powerplant when installed in forward positions. This is the same improvement that was identified when the aero-engine nacelles were in isolation. However, for close-coupled installation positions, the aerodynamic benefit in net vehicle force erodes to 0.44% due to the larger effects of aircraft integration on compact nacelles.

Global air traffic demand has shown rapid growth for the last three decades. This growth led to more delays and congestion within terminal manoeuvring areas (TMAs) around major airports. The efficient use of airport capacities through the careful planning of air traffic flows is imperative to overcome these problems. In this study, a mixed-integer nonlinear programming (MINLP) model with a multi-objective approach was developed to solve the aircraft sequencing and scheduling problem for mixed runway operations within the TMAs. The model contains fuel cost functions based on airspeed, altitude, bank angle, and the aerodynamic characteristics of the aircraft. The optimisation problem was solved by using the $\varepsilon$-constraint method where total delay and total fuel functions were simultaneously optimised. We tested the model with different scenarios generated based on the real traffic data of Istanbul Sabiha Gökçen Airport. The results revealed that the average total delay and average total fuel were reduced by 26.4% and 6.7%, respectively.

The control of a wing-in-ground craft (WIG) usually allows for many needs, like cruising, speed, survival and stealth. Various degrees of emphasis on these requirements result in different trajectories, but there has not been a way of integrating and quantifying them yet. Moreover, most previous studies on other vehicles’ multi-objective trajectory is planned globally, lacking for local planning. For the multi-objective trajectory planning of WIGs, this paper proposes a multi-objective function in a polynomial form, in which each item represents an independent requirement and is adjusted by a linear or exponential weight. It uses the magnitude of weights to demonstrate how much attention is paid relatively to the corresponding demand. Trajectories of a virtual WIG model above the wave trough terrain are planned using reward shaping based on the introduced multi-objective function and deep reinforcement learning (DRL). Two conditions are considered globally and locally: a single scheme of weights is assigned to the whole environment, and two different schemes of weights are assigned to the two parts of the environment. Effectiveness of the multi-object reward function is analysed from the local and global perspectives. The reward function provides WIGs with a universal framework for adjusting the magnitude of weights, to meet different degrees of requirements on cruising, speed, stealth and survival, and helps WIGs guide an expected trajectory in engineering.

Evaluation of the power required in level flight is essential to any new or modified helicopter performance flight-testing effort. The conventional flight-test method is based on an overly simplification of the induced and profile power components required for a helicopter in level flight. This simplistic approach incorporates several drawbacks that not only make execution of flight sorties inefficient and time consuming, but also compromise the level of accuracy achieved. This paper proposes an alternative flight-test method for evaluating the level-flight performance of a conventional helicopter while addressing and rectifying all identified deficiencies of the conventional method. The proposed method, referred to as the corrected-variables screening using dimensionality reduction (CVSDR), uses an original list of 36 corrected variables derived from basic dimensional analysis principles. This list of 36 corrected variables is reduced using tools of dimensionality reduction to keep only the most effective level-flight predictors. The CVSDR method is demonstrated and tested in this paper using flight-test data from a MBB BO-105 helicopter. It is shown that the CVSDR method predicts the power required for level flight about 21% more accurately than the conventional method while reducing the required flight time by an estimate of at least 60%. Unlike the conventional method, the CVSDR is not bounded by the high-speed approximation associated with the induced power estimation, therefore it is also relevant to the low airspeed regime. This low-airspeed relevancy allows the CVSDR method to bridge between the level-flight regime and the hover. Although demonstrated in this paper for a specific type of helicopter, the CVSDR method is applicable for level-flight performance flight testing of any type of conventional helicopter.