Transient numerical simulations were conducted to investigate the influence of large amplitude and fast impact backpressure on a shock train. The fundamental problem consists of a shock train within a constant-area channel with a Ma=1.61 inflow and a pulse backpressure applied to the outlet. The pressure disturbance in the isolator has an intense forcing-response lag. From the moment of the backpressure peak appearance, it takes 36 times the backpressure duration for the pressure disturbance to reach the upstream end. It moves upstream with time in the form of a normal shock wave. As time progresses, the normal shock degenerates into a $\lambda $ shock and a compression wave behind due to the action of viscous dissipation in the boundary layer. Eventually, a multi-stage shock train is formed. The maximum backpropagation distance is a quadratic function of both the pulse backpressure peak and duration, and the relationship between these variables was determined by fitting. When the integral value of backpressure to time is fixed, reducing the backpressure peak while increasing the duration will reduce the backpressure pulsation at the isolator outlet, which will be more conducive to shortening the maximum backpropagation distance than reducing the duration and increasing the backpressure peak. The values of backpressure peak and duration are obtained from the detonation combustion case, which ensures the authenticity of backpressure characteristics. The relevant research conclusions can provide a reference for the design of the isolator of pulse detonation ramjet.

The ground delay program (GDP) is a commonly used tool in air traffic management. Developing a departure flight delay prediction model based on GDP can aid airlines and control authorities in better flight planning and adjusting air traffic control strategies. A model that combines the improved sparrow search algorithm (ISSA) and Multilayer Perceptron (MLP) has been proposed to minimise prediction errors. The ISSA uses tent chaotic mapping, dynamic adaptive weights, and Levy flight strategy to enhance the algorithm’s accuracy for the sparrow search algorithm (SSA). The MLP model’s hyperparameters are optimised using the ISSA to improve the model’s prediction accuracy and generalisation performance. Experiments were performed using actual GDP-generated departure flight delay data and compared with other machine learning techniques and optimisation algorithms. The results of the experiments show that the mean absolute error (MAE) and root mean square error (RMSE) of the ISSA-MLP model are 16.8 and 24.2, respectively. These values are 5.61%, 6.3% and 1.8% higher in MAE and 4.4%, 5.1% and 2.5% higher in RMSE compared to SSA, particle swarm optimisation (PSO) and grey wolf optimisation (GWO). The ISSA-MLP model has been verified to have good predictive and practical value.

This paper proposes a methodology to define and quantify the precision uncertainties in aerothermodynamic cycle model comparisons. The total uncertainty depends on biases and random errors commonly found in such comparisons. These biases and random errors are classified and discussed based on observations found in the literature. The biases account for effects such as differences in model inputs, the configurations being simulated, and thermodynamic packages. Random errors consider the effects on the physics modeling and numerical methods used in cycle models. The methodology is applied to a comparison of two cycle models, designated as the model subject to comparison and reference model, respectively. The former is the so-called Aerothermodynamic Generic Cycle Model developed in-house at the Laboratory of Applied Research in Active Control, Avionics and AeroServoElasticity (LARCASE); the latter is an equivalent model programmed in the Numerical Propulsion System Simulation (NPSS). The proposed methodology is intended to quantify the bias and random errors effects on different cycle parameters of interest, such as thrust, specific fuel consumption, among others. Each bias and random errors are determined by deliberately preventing the effects from other biases and random errors. The methodology presented in this paper can be extended to other cycle model comparisons. Moreover, the uncertainty figures derived in this work are recommended to be used in other model comparisons when no better reference is available.

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.

This paper proposes a robust generalised dynamic inversion (GDI) control system design with adaptive neural network (NN) estimation for spacecraft attitude tracking under the absence of knowledge of the spacecraft inertia parameters. The robust GDI control system works to enforce attitude tracking, and the adaptive NN augmentation compensates for the lack of knowledge of the spacecraft inertia parameters. The baseline GDI control law consists of a particular part and an auxiliary part. The particular part of the GDI control law works to realise a desired attitude dynamics of the spacecraft, and the auxiliary part works for finite-time stabilisation of the spacecraft angular velocity. Robustness against modeling uncertainties and external disturbances is provided by augmenting a siding mode control element within the particular part of the GDI control law. The singularity that accompanies GDI control is avoided by modifying the Moore-Penrose generalised inverse by means of a dynamic scaling factor. The NN weighting matrices are updated adaptively through a control Lyapunov function. A detailed stability analysis shows that the closed loop system is semi-global practically stable. For performance assessment, a spacecraft model is developed, and GDI-NN control is investigated for its attitude control problem through numerical simulations. Simulation results reveal the efficacy, robustness and adaptive attributes of proposed GDI-NN control for its application to spacecraft attitude control.

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.

In this study a representative sandwich panel is investigated statically in two different configurations under similar bending loads. In one configuration serrations are introduced in the honeycomb core while the other one has un-modified core. Three-point bend test (TPBT) has been performed on both configurations through Finite Element Analysis (FEA) technique using ANSYS Workbench considering American Society for Testing and Materials (ASTM) standards. In both configurations the same aluminium honeycomb core is modelled having an adhesive layer in between adjacent foils to simulate actual scenario instead of relying on the block properties. Honeycomb core offers highest strength in its thickness (T) direction or the z-direction by virtue of its shape. Any distortion in the shape of the honeycomb adversely affects its strength. During bending the honeycomb core witnesses multidirectional forces consequently leading to distortion or crumpling. The serrations in the structure allow bending of the honeycomb core with minimal loss of strength by limiting the deformation to a specific region consequently preserving the shape as well as the strength of the honeycomb core. The results of both samples are compared with respect to deflection, strain and reaction force. It proves that serrated core is more favourable to be used in bent or curved sandwich panels.

The objective of this research is to predict the delays in the departure of scheduled commercial flights through a methodology that uses predictive tools based on machine learning/deep learning (ML/DL), with supervised training in regression, based on the available flight datasets. Since the novel contribution of this work is, first, to make the comparison of the predictions in terms of means and statistical variance of the different ML/DL models implemented and, second, to determine the coefficients of the importance of the features or flight attributes, using ML methods known as permutation importance, it is possible to rank the importance of flight attributes by their influence in determining the delay time and reduce the problem of selecting the most important flight attributes. From the results obtained, it is worth mentioning that the model that presents the best performance is the ensemble or combinatorial method of random forest regressor models, with an acceptable prediction range (measured with the root-mean-square-error).

The distinct operational characteristics of military aircraft, relative to civil aircraft, have impeded the standardisation of airworthiness management practice across Europe. Standardisation has been further deterred by the intertwined certification and qualification activities specific to military aircraft. The management of airworthiness in European military aviation has undergone significant changes over the past 15 years, with the progress made attributed to the harmonisation efforts driven by the European Defence Agency (EDA). The creation of a Military Airworthiness Authorities Forum and the development of the European Military Airworthiness Requirements (EMAR) have been instrumental in creating a more homogenous regulatory landscape. The examples of five main players of the European aerospace sector, namely France, Italy, Germany, Spain and the United Kingdom, are examined from the point of view of adoption and implementation of an EMARs-based system. Their regulatory structures have revealed similarities and primary differences. The EMAR’s framework has enabled a gradual build-up of technical knowhow within the European countries who embraced this, civil-based, framework. All five countries have adopted EMARs, though through a variety of regulatory constructs. Their regulatory structures exhibit diverse practices, especially in how initial and continuing airworthiness is managed. Some countries have also elected to have more than one authority overseeing/been responsible for airworthiness. Closer collaboration between national Military Aviation Authorities (MAAs) can be achieved through standardisation at regulatory structure level. The establishment of a joint MAAs may be the next logical step in the harmonisation process, in line with EDA objective’ for a EU-wide authority with greater powers.

This paper presents some of the first results of global linear stability analyses performed using a bespoke eigensolver that has recently been implemented in the next generation flow solver framework CODA. The eigensolver benefits from the automatic differentiation capability of CODA that allows computation of the exact product of the Jacobian matrix with an arbitrary complex vector. It implements the Krylov–Schur algorithm for solving the eigenvalue problem. The bespoke tool has been validated for the case of laminar flow past a circular cylinder with numerical results computed using the TAU code and those reported in the literature. It has been applied with both second-order finite volume and high-order discontinuous Galerkin schemes for the case of laminar flow past a square cylinder. It has been demonstrated that using high-order schemes on coarser grids leads to well-converged eigenmodes with a shorter computation time compared to using second-order schemes on finer grids.

Throughout the course of a flight mission, a range of aerodynamic conditions, including design-point conditions and off-design conditions, are encountered. As the bypass ratio increases and the fan-pressure ratio decreases to reduce the engine’s specific fuel consumption, the engine diameters increase, which results in an increase in the nacelle weight and overall drag. To reduce its weight and drag, a shorter nacelle with a length-to-diameter ratio $L/D = 0.35$ is investigated. In this study, an adaptive cokriging-based multi-objective optimisation method is applied to the design of a short aero-engine nacelle. Two nacelle performance metrics were employed as the objective functions for the optimisation routine. The cruise drag coefficient is evaluated under cruise conditions, whereas the intake pressure recovery is evaluated under takeoff conditions. The cokriging metamodel are refined using an effective infilling strategy, where high-fidelity samples are infilled via the modified Pareto fitness, and low-fidelity samples are infilled via the Pareto front. By combining parameterised geometry generation, automated mesh generation, numerical simulations, surrogate model construction, Pareto front exploration based on the non-dominated sorting genetic algorithm-II and sample infilling, an integrated multi-objective optimisation framework for short aero-engine nacelles is developed. Two-objective and three-objective test functions are used to validate the effectiveness of the proposed framework. After the optimisation process, a set of non-dominated nacelle designs is obtained with better aerodynamic performance than the original design, demonstrating the effectiveness of the optimisation framework. Compared with the kriging-based optimisation framework, the cokriging-based optimisation framework outperforms the single-fidelity method with a higher hypervolume value at the same number of iteration loops.

In this paper, an adaptive backstepping controller of hypersonic flight vehicle (HFV) system is investigated in the presence of unknown actuator and sensor faults, unknown control direction and varying external disturbance, which expands traditional fault-tolerant control (FTC) approaches in HFV field. The control-oriented model is established with reasonable and comprehensive analysis of various fault types. Compared with the previous FTC research on HFV system, the time-varying and fixed fault types are simultaneously designed for actuator and sensors, and the control direction is unknown, which can more accurately simulate the real fault situations in the hypersonic flight environment. To avoid singularity problems caused by the unknown control direction, the Nussbaum gain function technique is adopted in the control algorithm, which is also capable of overcoming the influence of unknown time-varying fault parameters. The boundness and stability of system under the designed control scheme are theoretically verified. Finally, numerical simulation results demonstrate the effectiveness and superiority of the proposed control strategy.

The relevant growth of human-machine interaction (HMI) systems in recent years is leading to the necessity of being constantly aware of the cognitive workload level of an operator, especially in a safety-critical context such as aviation. Since the confusion in the definition of this concept, this paper clarifies this terminology and also highlights its relationship with stress. Thus, we analysed the state-of-the-art of cognitive workload evaluations, showing three up-to-date methodologies: subjective, behavioural and physiological. In particular, the physiological approach is increasingly gaining attention in the literature due to today’s exponential growth of biomedical sensors. Therefore, a review of the most adopted physiological signals in the workload evaluation is provided, focusing on the aeronautical field. We conclude by highlighting the necessity of a multimodal approach for mental workload assessment as a result of this analysis.

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.

In this paper, an adaptive neural output-constrained control algorithm is proposed for a class of non-affine kinetic kill vehicle (KKV) systems. The key point is that the non-affine control law can be designed and the output of the KKV system conform to the output limit with the aid of the proposed method. Due to the aerodynamic moments, the actual control torque is non-affine, which can be addressed by introducing an integral process to the design of the controller. Besides, in order to improve the control precision, a nonlinear mapping is put forward so that the output constraint can be transformed to the constraint of the introduced dynamic signal that can be simply achieved. From the simulation results it can be concluded that the states of the KKV system can track the desired trajectories in spite of different working conditions and the control precision is higher compared with other control methods.

In this paper, a new approach to modeling and controlling the problems associated with a morphing unmanned aerial vehicle (UAV) is proposed. Within the scope of the study, a dataset was created by obtaining a wide range of aerodynamic parameters for the UAV with Ansys Fluent under variable conditions using the computational fluid dynamics approach. For this, a large dataset was created that considered 5 different angles of attack, 14 different swept angles, and 5 different velocities. While creating the dataset, the analyses were verified by considering studies that have been experimentally validated in the literature. Then, an artificial intelligence-based model was created using the dataset obtained. Metaheuristic algorithms such as the artificial bee colony algorithm, ant colony algorithm and genetic algorithms are used to increase the modeling success of the adaptive neuro-fuzzy inference system (ANFIS) approach. A novel modeling approach is proposed that constitutes a new decision support system for real-time flight. According to the results obtained, all the ANFIS models based on metaheuristic algorithms were more successful than the traditional approach, the multilinear regression model. The swept angle that meets the minimum lift needed by the UAV for different flight conditions was estimated with the help of the designed decision support system. Thus, the drag force is minimised while obtaining the required lift force. The performance of the UAV was compared with the nonmorphing configuration, and the results are presented in tables and graphs.

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.

The present paper describes the results of an experimental wind tunnel test campaign aimed at investigating the aerodynamic performance and flow physics related to a wing section equipped with two propellers mounted on a boom. The configuration investigated is meant to be representative of a full-scale eVTOL aircraft in cruise flight condition. The use of full-scale components of an eVTOL aircraft made this setup a quite advanced experiment in the recent literature. Pressure measurements and an infrared thermography technique were used during the test campaign, respectively, to evaluate localised effects induced by the propeller blowing on the wing and to provide a quantitative evaluation of the amount of laminar flow on the wing surface with and without the influence of the propeller at different thrust conditions.

Traditional active flutter suppression controllers are designed based on model. However, as the aircraft becomes more and more powerful, the modeling of aeroelastic system becomes difficult and the model-free requirement of controller design becomes more and more urgent. The complexity of industrial processes has brought about massive operational data generated online. Aviation industry development has entered the era of big data. Breaking through the traditional theoretical framework, mining the correlation, evolution and dynamic characteristics of the system from the data is the inevitable choice to meet this demand. In this paper, a data-driven model-free controller is designed, which relies on ridge regression of the input and output variation at each operating point of the closed-loop controlled system to recursively derive the iterative format of the control signals and ensure the numerical stability of the signals. The controller can only use the real-time measurement of the system’s online input and output data for continuous correction, to achieve the purpose of flutter suppression. Then flutter suppression of a three-degree-of-freedom binary wing with a control surface is studied, and the superiority of model-free controller is demonstrated by comparing it with the optimal controller.

This paper aims to present a vertical take-off and landing unmanned aerial vehicle (VTOL UAV) configuration and numerically simulate its flight transition from hover to cruise and from cruise to hover. It can tilt the canard and wing along with two attached propellers. Additionally, two fixed front propellers are pointing upwards. Multi-body equations of motion are derived for this concept of aircraft, which are used to compute the flight transition trajectory from hover to cruise configuration. Furthermore, a transition control algorithm based on gain scheduling is described, which stabilises the aircraft while it accelerates from hover to cruise, gradually tilting the wing along with its propellers, sequentially switching between equilibrium states, as the stability cost functions thresholds are reached. The transition control algorithm of the conceptual aircraft model is numerically simulated.