A control system verification framework is presented for unmanned aerial vehicles using theorem proving. The framework’s aim is to set out a procedure for proving that the mathematically designed control system of the aircraft satisfies robustness requirements to ensure safe performance under varying environmental conditions. Extensive mathematical derivations, which have formerly been carried out manually, are checked for their correctness on a computer. To illustrate the procedures, a higher-order logic interactive theorem-prover and an automated theorem-prover are utilised to formally verify a nonlinear attitude control system of a generic multi-rotor UAV over a stability domain within the dynamical state space of the drone. Further benefits of the procedures are that some of the resulting methods can be implemented onboard the aircraft to detect when its controller breaches its flight envelope limits due to severe weather conditions or actuator/sensor malfunction. Such a detection procedure can be used to advise the remote pilot, or an onboard intelligent agent, to decide on some alterations of the planned flight path or to perform emergency landing.

Depending on magnitude and duration, any manoeuvering overload can damage the structure of an aircraft and adversely affect the pilot’s concentration and reaction time. These are all threats to flight safety. The flight safety envelope estimation method based on the classical reachable set cannot take into account the effect of manoeuvering overload. To overcome this limitation, a generalized reachable set known as a cost-limited reachable set is introduced into the computation of flight safety envelopes in this paper. It differs from the classical reachable set in that the performance index of the system can be set as the time integral of a running cost, and it can discuss the ability to reach the trim set before the performance index grows to the admissible cost. When computing the flight safety envelope, the running cost is set as a weighted sum of time consumption and manoeuver overload factor, and the flight safety envelope is defined as a cost-limited reachable set of the trim set. The flight safety envelopes and optimal control laws under the different weight of manoeuver overload factors are analyzed.

In recent years, there has been significant momentum in applying deep learning (DL) to machine health monitoring (MHM). It has been widely claimed that DL methodologies are superior to more traditional techniques in this area. This paper aims to investigate this claim by analysing a real-world dataset of helicopter sensor faults provided by Airbus. Specifically, we will address the problem of machine sensor health unsupervised classification. In a 2019 worldwide competition hosted by Airbus, Fujitsu Systems Europe (FSE) won first prize by achieving an F1-score of 93% using a DL model based on generative adversarial networks (GAN). In another comprehensive study, various modified and existing image encoding methods were compared for the convolutional auto-encoder (CAE) model. The best classification result was achieved using the scalogram as the image encoding method, with an F1-score of 91%. In this paper, we use these two studies as benchmarks to compare with basic statistical analysis methods and the one-class supporting vector machine (SVM). Our comparative study demonstrates that while DL-based techniques have great potential, they are not always superior to traditional methods. We therefore recommend that all future published studies of applying DL methods to MHM include appropriately selected traditional reference methods, wherever possible.

Although the problem of locked-in deep stall is well documented over many years, there currently exists no consistent procedure that can guarantee recovery. Past studies have suggested that it might be possible to rock the aircraft in pitch to destabilise the statically stable deep stall trim point, thereby gaining enough momentum to push the nose down. However, the methods used in these studies are either of preliminary or empirical nature and cannot guarantee recovery. In this paper, we use bifurcation analysis to derive a recovery manoeuvre, specifically by assessing the aircraft’s nonlinear frequency response under an elevator forcing. The ensuing nonlinear Bode plot detects unstable (divergent) solutions near resonance that contribute to a successful deep stall recovery. Moreover, the nonlinear resonant frequency is slightly lower than the result obtained using linear analysis, and time simulation shows that relying on the linear result does not lead to a successful recovery. It is also found that at the high angles of attack associated with deep stall, the frequency separation between the short period and phugoid mode is significantly reduced, leading to only one visible peak in the frequency response. This feature is also reflected in the time-domain step response.

Flight crews’ capacity to conduct take-off and landing in near zero visibility conditions has been partially addressed by advanced surveillance and cockpit display technology. This capability is yet to be realised within the context of manoeuvring aircraft within airport terminal areas. In this paper the performance and workload benefits of user-centre designed visual and haptic taxi navigational cues, presented via a head-up display (HUD) and active sidestick, respectively, were evaluated in simulated taxiing trials by 12 professional pilots. In addition, the trials sought to examine pilot acceptance of side stick nose wheel steering. The HUD navigational cues demonstrated a significant task-specific benefit by reducing centreline deviation during turns and the frequency of major taxiway deviations. In parallel, the visual cues reduced self-report workload. Pilot’s appraisal of nose wheel steering by sidestick was positive, and active sidestick cues increased confidence in the multimodal guidance construct. The study presents the first examination of how a multimodal display, combining visual and haptic cues, could support the safety and efficiency in which pilots are able to conduct a taxi navigation task in low-visibility conditions.

For financial and operational reasons many aircraft manufacturers are working on the development of single-pilot commercial aircraft. It is suggested that cargo operations may commence in the early 2030s followed by passenger flights later that decade. Two technological approaches for the development of single-pilot airliners are being developed either based upon extant technology and operating concepts derived from uninhabited aviation systems and military aircraft, or alternatively based upon high levels of onboard autonomy/automation. This review considers the economic, technological, regulatory (safety) and societal acceptance of the single-pilot airliner, and examines some of the operational challenges that airlines may face. It is suggested that while the technological and safety challenges may be resolved, it is the operational challenges that may determine if the concept is ultimately viable.

In an aircraft design, optimisation has become a common practice, especially when structural and aerodynamics interactions are considered. Performance measures often used in an industrial setting include structural weight, drag, lift to drag ratio, fuel burn or maximum range. It is a common practice to evaluate such performance indicators only on a handful of sample points. To achieve a truly economical aircraft design it is necessary to include a fully integrated mission analysis during a multidisciplinary structural optimisation, as there is a strong coupling between a flight behaviour and actual operational conditions of an aircraft. This paper makes a case for a modular approach to a mission analysis implementation that could utilise a variety of physical models and their combinations, offsetting some of the computational demands related to a fully integrated mission analysis and allowing to focus resources where they are needed.

The evaluation of the on-ground casualty risk assessments due to a controlled or uncontrolled re-entry is highly sensitive to the accurate prediction of fragmentation events during an atmospheric re-entry. The main objective of this study is an investigation into the use of peridynamics (PD) to improve the analysis of fragmentation during atmospheric re-entry with respect to currently adopted semi-empirical approaches. The high temperatures characterising such scenarios may substantially impact fragmentation, which requires appropriate modelling of the damage process within the PD method. The damage models in PD require experimentally determined fracture mechanical properties that are unavailable as a function of temperature. This work proposes a numerical methodology to estimate the PD damage parameters changes with temperature to enable the study of fragmentation during atmospheric re-entry. Initially, tensile-testing simulation experiments are performed in peridynamics to calibrate material parameters for steel and aluminium alloys as a function of temperature. Then, a parametric study is carried out to evaluate the temperature-dependent damage model parameters for the same materials. The applicability of the proposed methodology is showcased using a re-entry test case scenario.

For a class of uncertain systems with large-error sensing, the low-order stable signal corrector and observer are presented for signal correction and uncertainty estimation according to completely decoupling estimation. The model-free signal corrector can reject the bounded stochastic disturbance/error in global position sensing, and system uncertainty can be estimated by the observer, even the existence of large disturbance in position sensing. Furthermore, a general form of signal corrector is given. The describing function method is used to analyse the robustness of the corrector in frequency domain, and the parameter selection rules are presented. The merits of the signal corrector includes its model free, gain-bounded stable structure, sufficient rejection of bounded stochastic disturbance/error in sensing and ease of parameters’ regulation. The corrector and observer are applied to a UAV navigation and control for large disturbance/error corrections in position/attitude angle and the uncertainties estimation in the UAV flight dynamics. The control laws are designed according to the correction-estimation results. Finally, experiments demonstrate the effectiveness of the proposed method.

A new measurement technique to reconstruct the density field of the shock-wave/boundary-layer interaction (SWBLI) in a confined duct is proposed. With this technique, it is possible to quantitatively capture in detail the structures of the density field both in the regions of the shock-systems in the central core and boundary-layer flows near the duct wall concurrently. The novel feature of the proposed technique is to make use of the schlieren images with the rainbow filters of the vertical and horizontal cutoff settings and then to reconstruct the two-dimensional density field integrated over the line-of-sight direction using the corresponding filter calibration curves. The proposed technique is applied for the first time to a shock train in a constant-area straight duct under the upstream condition of the shock train: the freestream Mach number is 1.42, the incoming boundary layer thickness normalised by the duct half height is 0.175, and the corresponding unit Reynolds number $Re/m$ is $2.99 \times 10^7$ m-1. The calculated isopycnic field depicts the streamwise and transverse density variations inside the shock train, the mixing region after the shock train, and the boundary-layer of the interaction region. This technique is shown to be capable of identifying the locations of shocks in a shock train more precisely than a conventional approach measuring the static pressure distribution along the duct wall. In addition, various quantitative visual representations such as a shadowgraphy and a bright-field schlieren can be extracted from the density field acquired by the present approach, and the spatial evolution of the shape and strength of each shock constituting the shock train as well as the boundary layer flow properties can be quantitatively clarified.

A multiple-vehicles time-coordination guidance technique based on deep learning is suggested to address the cooperative guiding problem of hypersonic gliding vehicle entry phase. A dual-parameter bank angle profile is used in longitudinal guiding to meet the requirements of time coordination. A vehicle trajectory database is constructed along with a deep neural network (DNN) structure devised to fulfill the error criteria, and a trained network is used to replace the conventional prediction approach. Moreover, an extended Kalman filter is constructed to detect changes in aerodynamic parameters in real time, and the aerodynamic parameters are fed into a DNN. The lateral guiding employs a logic for reversing the sign of bank angle, which is based on the segmented heading angle error corridor. The final simulation results demonstrate that the built DNN is capable of addressing the cooperative guiding requirements. The algorithm is highly accurate in terms of guiding, has a fast response time, and does not need inter-munition communication, and it is capable of solving guidance orders that satisfy flight requirements even when aerodynamic parameter disruptions occur.

In this work, the key role of the upper-deck design including engine installation as a potential source of tail-shake is at focus. The work is based on a Wind-Tunnel Test (WTT) campaign performed at the Airbus Helicopters’ Marignane wind-tunnel facilities on a high-fidelity minibody fuselage at scale 1:3.5 representing a generic heavy-helicopter upper deck.

Two different engine intake installations for a Power Unit (PU) have been investigated; in a first configuration, the air intake is implemented at the pylon-fairing trailing edge. The second configuration consists in positioning two air intakes on each side of the pylon fairing, close to the maximum cross-section location. Different measurement methods to evaluate aerodynamic interactions and wake sources are proposed: flow-separation assessments from surface oil flow visualisations, time-resolved Particle Image Velocimetry (PIV) measurements and unsteady skin-pressure measurements at the cowlings. Tail-shake-related indicators are then proposed. Basically, a configuration that produces strong vortices characterised by a broadband spectral signature is believed to gather all the conditions for tail-shake to emerge.

The flow over the clean configuration is first analysed for various combinations of angle-of-attack and sideslip, highlighting four different areas of flow separation at the cowlings. The complex flow topology around the upper deck is then assessed, which includes a spectral analysis of the flow in the PIV planes. The influence of the air intakes (operating or not) is then evaluated. When located at the pylon-fairing trailing edge and operating, the air intake has a spectacular impact on the flow-field topology. It is responsible for the generation of an intense broadband wake interacting with the pylon-fairing lip vortices, which is believed to be a potential source of tail-shake. The second air-intake configuration is also not favourable, as it requires enlarging the pylon fairing by 100mm, which causes an intense wake similarly to a blunt body. At last, a mitigation mean is proposed for the first configuration. It demonstrates a significant reduction of the wake intensity and broadband signature at the source.

In this paper, to address the cooperative localisation of a heterogeneous UAV swarm in the GNSS-denied environment, an adaptive simulated annealing-particle swarm optimisation (SA-PSO) cooperative localisation algorithm is proposed. Firstly, the forming principle of the communication and measurement framework is investigated in light of a heterogeneous UAV swarm composition. Secondly, a reasonably cooperative localisation function is established based on the proposed forming principle, which can minimise the relative localisation error with limited available information. Then, an adaptive weight principle is incorporated into the particle swarm optimisation (PSO) for better performance. Furthermore, in order to overcome the drawbacks of PSO algorithm easily falling into the local extreme point, an adaptive SA-PSO algorithm is improved to promote the convergence speed of cooperative localisation. Finally, comparative simulations are performed among the adaptive SA-PSO, adaptive PSO, and PSO algorithms to demonstrate the feasibility and superiority of the proposed adaptive SA-PSO algorithm. Simulation results show that the proposed algorithm has better performance in convergence speed, and the cooperative localisation precision can be guaranteed.

Laser-directed energy deposition (L-DED) is a key enabling technology for the repair of high-value aerospace components, as damaged regions can be removed and replaced with additively deposited material. While L-DED repair improves strength and fatigue performance compared to conventional subtractive techniques, mechanical performance can be limited by process-related defects. To assess the role of oxygen on defect formation, local and chamber-based shielding methods were applied in the repair of 300M high strength steel. Oxidation between layers for locally shielded specimens is confirmed to cause large gas pores which have deleterious effects on fatigue life. Such pores are eliminated for chamber shielded specimens, resulting in an increased ductility of ∼15%, compared to ∼11% with chamber shielding. Despite this, unmelted powder defects are not affected by oxygen content and are found in both chamber- and locally shielded samples, which still have negative consequences for fatigue.

In January 2017, a business jet flew in Norway on a short repositioning flight with two pilots onboard, no passengers or cargo. Initially, the take-off proceeded as normal but as the landing gear was retracted both pilots observed that the airspeed was rapidly approaching the flap limiting speed of 200kts. When the flaps were fully retracted at a height of approximately 2,100ft above ground level, the crew experienced a violent nose-down pitch motion. Control was regained at a height of approximately 170ft above ground level and, following the accident, data from the flight data recorder showed that the aircraft experienced –2.62G during the pitch upset. A tailplane stall due to icing was suspected; however, the flight data recorder, being limited to 36 parameters, was not able to confirm this. For expediency during the accident investigation process, a simplified, linear flight dynamics model was developed using Matlab/Simulink to assess static and dynamic stability for a range of tailplane efficiency factors to simulate the effects of tailplane icing.

The wavy leading edge (WLE, also known as leading edge protuberances) is a passive flow control device inspired by the humpback whale pectoral flippers. It reduces the flow of three-dimensional effects on wings and increases their aerodynamic performance at high angles of attack. Despite the numerous studies on its aerodynamic benefits, research on its possible applications is still incipient. Therefore, this article addresses an evaluation of the WLE effects on the aerodynamic performance of a winglet. A rectangular wing, a base smooth leading edge winglet, and a winglet with WLE were designed and manufactured for CFD simulations and wind tunnel measurements. The winglet with WLE increased the maximum aerodynamic efficiency, i.e. this configuration reduced the induced drag by increasing wingtip vortex dissipation at a given angle-of-attack. Such results were used in re-evaluations of the aerodynamic performance of an original agricultural aircraft initially configured with a multi-winglet device. The winglet with WLE showed to be effective at increasing the aircraft operational time and range under a simulated actual condition.

Currently there is lack of knowledge on how new types of alternative fuels and their storage conditions change the droplet evaporation characteristics. Liquid fuel is commonly stored in wide varieties of containers, where fuel characteristics may change because of the exposure to the material of the container. This study evaluates the impact of different storage containers on droplet evaporation characteristics of different fuels. It was found that there is a distinct phase transition between high volatility to low volatility phase in each fuel stored in steel drums verses fuel that is stored in plastic drums. The type of fuel contaminated by polymer additive has a high impact on fuel droplet burn rates. Polymer additives also have an impact on nucleate boiling, sub-droplets and soot particles. The burning rate constant, K, of selected pure aromatics, various fuel mixtures and Jet A with different cetane numbers have been evaluated. Research has shown that in the low volatility combustion phase a trend reduction of lowest boiling point pure aromatic burning rate to the highest boiling point pure aromatic burning rate is obvious. Irregular change in droplet diameter between the high volatility phase and low volatility phase during the combustion of aromatics blend was observed. This work has also evaluated the relationship between burning rates and cetane numbers.

Collaborative planning for multiple hypersonic vehicles can effectively improve operational effectiveness. Time coordination is one of the main forms of cooperation among multi-hypersonic glide vehicles, and time cooperation trajectory optimisation is a key technology that can significantly increase the success rate of flight missions. However, it is difficult to obtain satisfactory time as a constraint condition during trajectory optimisation. To solve this problem, a multilayer Perceptrona is trained and adopted in a time-decision module, whose input is a four-dimensional vector selected according to the trajectory characteristics. Additionally, the MLP will be capable of determining the optimal initial heading angle of each aircraft to reduce unnecessary manoeuvering performance consumption in the flight mission. Subsequently, to improve the cooperative flight performance of hypersonic glide vehicles, the speed-dependent angle-of-attack and bank command were designed and optimised using the Artificial Bee Colony algorithm. The final simulation results show that the novel strategy proposed in this study can satisfy terminal space constraints and collaborative time constraints simultaneously. Meanwhile, each aircraft saves an average of 13.08% flight range, and the terminal speed is increased by 315.6m/s compared to the optimisation results of general purpose optimal control software (GPOPS) tools.

FFD (free-form deformation method) is one of the most commonly used parameterisation methods at present. It places the parameterised objects inside the control volume through coordinate system transformation, and controls the control volume through control points, thus realising the deformation control of its internal objects. Firstly, this paper systematically analyses and compares the characteristics and technical requirements of Bernstein, B-spline and NURBS (non-uniform rational b-splines) basic functions that can be adopted by FFD, and uses the minimum number of control points required to achieve the specified control effect threshold to express the control capability. Aiming at the problem of discontinuity at the right end in the actual calculation of B-spline basis function, a method of adding a small epsilon is proposed to solve it. Then, three basic functions are applied to the FFD parameterisation method, respectively, and the differences are compared from two aspects of the accurate expression of the model and the ability of deformation control. It is found that the BFFD (b-spline free-form deformation) approach owns better comprehensive performance when the control points are distributed correctly. In this paper, the BFFD method is improved, and a p-BFFD (reverse solution points based BFFD) method based on inverse solution is proposed to realise the free distribution of control points under the specified topology. Further, for the lifting body configuration, the control points of the p-BFFD method are brought closer to the airframe forming the EDGE-p-BFFD (edge constraints based p-BFFD) method. For the case in this paper, the proposed EDGE-p-BFFD method not only has fairly high parameterisation accuracy, but also reduces the expression error from 1.01E-3 to 1.25E-4, which is nearly ten times. It can also achieve effective lifting body guideline constraints, and has the ability of local deformation adapting to the configuration characteristics. In terms of the proportion of effective control points, the EDGE-p-BFFD method increases the proportion of effective control points from 36.7% to 50%, and the more control points, the more obvious the proportion increase effect. The new method also has better effect on the continuity of geometric deformation. At the same time, this paper introduces the independent deformation method of the upper and lower surfaces based on the double control body frames, which effectively avoids the deformation coupling problem of the simultaneous change of the upper and lower surfaces caused by the movement of control points in the traditional single control framework.

The aerodynamic modelling is one of the challenging tasks that is generally established using the results of the computational fluid dynamic software and wind tunnel analysis performed either on the scaled model or the prototype. In order to improve the confidence of the estimates, the conventional parameter estimation methods such as equation error method (EEM) and output error method (OEM) are more often applied to extract the aircraft’s stability and control derivatives from its respective flight test data. The quality of the estimates gets influenced due to the presence of the measurement and process noises in the flight test data. With the advancement in the machine learning algorithms, the data driven methods have got more attention in the modelling of a system based on the input-output measurements and also, in the identification of the system/model parameters. The research article investigates the longitudinal stability and control derivatives of the aerodynamic models by using an integrated optimisation algorithm based on a recurrent neural network. The flight test data of Hansa-3 and HFB 320 aircraft were used as case studies to see the efficacy of the parameter estimation algorithm and further, the confidence of the estimates were demonstrated in terms of the standard deviations. Finally, the simulated variables obtained using the estimates demonstrate a qualitative estimation in the presence of the noise.