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.

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.

The purpose of this study was to explore the electroencephalogram (EEG) features sensitive to situation awareness (SA) and then classify SA levels. Forty-eight participants were recruited to complete an SA standard test based on the multi-attribute task battery (MATB) II, and the corresponding EEG data and situation awareness global assessment technology (SAGAT) scores were recorded. The population with the top 25% of SAGAT scores was selected as the high-SA level (HSL) group, and the bottom 25% was the low-SA level (LSL) group. The results showed that (1) for the relative power of $\beta$1 (16–20Hz), $\beta$2 (20–24Hz) and $\beta$3 (24–30Hz), repeated measures analysis of variance (ANOVA) in three brain regions (Central Central-Parietal, and Parietal) × three brain lateralities (left, midline, and right) × two SA groups (HSL and LSL) showed a significant main effect for SA groups; post hoc comparisons revealed that compared with LSL, the above features of HSL were higher. (2) for most ratio features associated with $\beta$1 ∼ $\beta$3, ANOVA also revealed a main effect for SA groups. (3) EEG features sensitive to SA were selected to classify SA levels with small-sample data based on the general supervised machine learning classifiers. Five-fold cross-validation results showed that among the models with easy interpretability, logistic regression (LR) and decision tree (DT) presented the highest accuracy (both 92%), while among the models with hard interpretability, the accuracy of random forest (RF) was 88.8%, followed by an artificial neural network (ANN) of 84%. The above results suggested that (1) the relative power of $\beta$1 ∼ $\beta$3 and their associated ratios were sensitive to changes in SA levels; (2) the general supervised machine learning models all exhibited good accuracy (greater than 75%); and (3) furthermore, LR and DT are recommended by combining the interpretability and accuracy of the models.

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.

The effect of tube depth, the separation distance between the tube and nozzle exit, and the nozzle pressure ratio on the characteristics of the flow coming out of the Hartmann tube was studied experimentally. The configuration used in this work consists of an underexpanded sonic jet emanating from a convergent nozzle directed into a closed-ended cylindrical tube of the same diameter (D) as the nozzle exit. The nozzle was operated at two levels of underexpansion corresponding to nozzle pressure ratio (NPR) 3 and 5. The distance (S) from nozzle exit and tube inlet was varied from 0.4D to 4D. Discrete high-amplitude tones (the jet regurgitant, JRG) were produced, only at certain (periodic) intervals (near the shock-cell terminations) of spacing for NPR 3, while for NPR 5 the JRG tones are produced at all points beyond the first shock-cell. For locations other than these, high-frequency tones (screech mode) were observed. The connection between the jet structure and operating modes revealed that the location of standoff shock ahead of the tube with respect to the jet structure plays a dominant role in the observed ‘modes’ rather than the nozzle tube separation. The results reveal that the frequency response of longer tubes in JRG mode approaches their quarter wave frequencies. The high-frequency oscillations observed in the screech mode showed independency with cavity (pipe) depth, contrary to the available literature, the transition between ‘different modes’ oscillation is a function of cavity depth.

Establishing aerothermal criteria for swept leading-edge hypersonic vehicle design is the predominant purpose of this work. This study is focused on two different configurations of vehicles based on the swept-back angle ($\varLambda$) viz. minimum drag ($\varLambda$Drag-min), and minimum heat transfer to vehicle ($\varLambda$HT-min). Maximum wall temperatures obtained from the simulation performed in ANSYS 2020 with the k-epsilon turbulence model are 1,013 and 970K for $\varLambda$Drag-min and $\varLambda$HT-min, respectively. These temperatures are used to obtain the corresponding thicknesses of thermal protection systems to maintain inner wall temperature at 323K. Further study is divided into two cases depending on the direction of thickness of thermal protection system with respect to vehicle body. For constant payload capacity, the direction of thickness is outside; whereas for constant overall volume case, direction of thickness is inside. For constant payload volume case, the percentage weight reduction of thermal protection system is 4.8%. For constant overall volume case, the percentage payload capacity increases with design at $\varLambda$HT-min by 4.04% in addition to thermal protection system weight reduction. The lift-induced drag on vehicles with design at $\varLambda$HT-min is significantly reduced for both cases, by 47.68% (for constant payload volume) and 45.27% (for constant overall volume).

In this paper, the results of an experimental investigation for a Y-shaped engine inlet are presented. The experiment is performed at subsonic flow conditions. The main focus is given to time-dependent total pressures measured at the aerodynamic interface plane. Distinctive frequencies carrying high energy contents of the fluctuating total pressures are given and the relation between time-dependent and time-average performance parameters is presented. The cross-correlation coefficients of the high frequency probe readings distributed through the aerodynamic interface plane are also investigated.

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, we present a detailed experimental investigation mainly on the vortical flow fields and the associated vortex breakdown phenomena over a non-slender flying wing (sweep angle, ${\rm{\Lambda }}$ = 53°). In the process, the aerodynamic coefficients were also determined using a six-component force balance. Surface oil flow visualisation, surface pressure measurements and particle image velocimetry (PIV) measurements, in various crossflow planes and in a longitudinal plane passing through the leading-edge vortex core, were carried out at various Reynolds numbers to understand the flow field over the non-slender flying wing. Aerodynamic characteristics of the flying wing show local peaks and valleys in the pitching moment coefficient. The surface flow visualisation reveals that the nonlinearity of the pitching moment curve is due to the complex nature of vortical flow structures. The flow visualisation also demonstrates the presence of a wave-like surface pattern, and its size is found to reduce with increasing Reynolds numbers. The present PIV measurements confirm that this wave-like surface pattern is associated with vortex breakdown phenomena. These measurements also reveal that the vortex breakdown has not reached the apex of the wing, even at post-stall angle-of-attack. For pre-stall ($\alpha $ = 20°) flow regimes, it is observed that the location of the vortex breakdown moves downstream as the Reynolds number increases, but this influence is minimised at near-stall ($\alpha $ = 25°) and post-stall ($\alpha $ = 30°) flow regimes. Reconstructed velocity field using the first 10 dominant proper orthogonal decomposition (POD) modes reveals that the nature of the vortex breakdown over the flying wing is a spiral-type vortex breakdown.

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.

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.

Open rotors can play a critical role towards transitioning to a more sustainable aviation by providing a fuel-efficient alternative. This paper considers the sensitivity of an open-rotor engine to variations of three operational parameters during take-off, focusing on both aerodynamics and aeroacoustics. Via a sensitivity analysis, insights to the complex interactions of aerodynamics and aeroacoustics can be gained. For both the aerodynamics and aeroacoustics of the engine, numerical methods have been implemented. Namely, the flowfield has been solved using unsteady Reynolds Averaged Navier Stokes and the acoustic footprint of the engine has been quantified through the Ffowcs Williams-Hawking equations. The analysis has concluded that the aerodynamic performance of the open rotor can decisively be impacted by small variations of the operational parameters. Specifically, blade loading increased by 9.8% for a 5% decrease in inlet total temperature with the uncertainty being amplified through the engine. In comparison, the aeroacoustic footprint of the engine had more moderate variations, with the overall sound pressure level increasing by up to 2.4dB for a microphone lying on the engine axis and aft of the inlet. The results signify that there is considerable sensitivity in the model and shall be systematically examined during the design or optimisation process.

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.

This study investigates the combustion characteristics (lean blowoff (LBO), flashback, and combustion instability) of sustainable aviation fuels (SAFs) using a two-staged combustor arranged in tandem. The combustor consists of a first (main) burner with an axial swirler and a second burner (afterburner) with a V-gutter bluff-body. The main burner utilises a constant flow rate of CH4/Air as reactants, while the afterburner operates on the combustion product from the first burner, additional dilution air, and one of the six liquid test fuels (Jet-A, A-2, C-1, C-5, C-9, and JP-8/HRJ) as reactants. When test fuels with higher derived cetane number (DCN) are used in the afterburner, it is observed that LBO occurs at lower afterburner global equivalence ratios (${\phi _{af}}$). This is due to the smaller chemical time scale of those test fuels, which enables the flame to be sustained under higher flow speeds. This is consistent with the results of previous studies. Additionally, this study shows that flashback also occurs at lower equivalence ratio for test fuels with higher DCN. This is a new finding and can be explained in a similar way to LBO. As the operating conditions approach the LBO or exceed flashback limits, the reactivity of shear layer also decreases, creating favourable conditions for them to occur. Moreover, for test fuels with low DCN (C-1 and C-5), blowoff occurs instead of flashback as ${\phi _{af}}$ increases. This can be attributed to the stronger pressure perturbation resulting from combustion instability in these flames compared to others. It demonstrates a stronger correlation between the pressure perturbation and the heat release rate fluctuation for C-1 and C-5 than that of other test fuels at the same equivalence ratio. In conclusion, it was found that test fuels with higher DCN have advantages in terms of LBO and stability, however, they are more prone to flashback.

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 aerodynamic performance of a wing model with a row of distributed engines are investigated at the vertical take-off condition. The engines are installed near the trailing edge of the wing. During vertical take-off, the jets exit from the engines and impinge perpendicularly to the ground, providing a thrust for the aircraft. Due to the ground effects, complex vortex structures are induced by the jets. The vortices are categorised into the spanwise vortices and the chordwise vortices. The underwing vortices can lead to low-pressure regions on the lower surface of the wing, resulting in an undesirable downward force. The underwing vortex structures are affected by the ratio of the engine distance to the engine diameter ($S/D$). At a small $S/D$ = 1.10, the flow field is dominated by the spanwise vortices; at a large $S/D$ = 2.78, the flow field is dominated by the chordwise vortices. The range and strength of the spanwise vortices are affected by the vortices interaction. Competition mechanism exists between the range and strength effects, which results in the non-linear variation of the wing lift coefficient with engine spacing. The details of the flow physics underneath the wing and its mechanism on the lift of the wing during take-off are investigated.

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.

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.

Lateral jets are used to control the missiles and re-entry vehicles at high altitudes. The objective of the research paper is to investigate the effect of lateral jet interaction with the external flow on a blunted nose cone re-entry vehicle configuration and its flight speed. Structured mesh is used for the simulations, and the computational analysis is carried out by Ansys Fluent solver. The simulation results are validated, and the same methodology used for the parametric analysis. Simulations have been carried out at an external flow Mach number 6, 8.1, 12 and 16 at five degree angle-of-attack for jet-off and jet-on conditions. At 8.1 Mach number, the normal force coefficient is decreased by 45.6% due to jet interaction. The lateral jet interaction effectively reduces the nose down pitching moment. At 8.1 Mach number, the pitching moment coefficient was reduced by 48% with the jet-on condition compared to the jet-off condition.

Large truss structures have many potential applications in space, such as antennas, telescopes and space solar power plants. In this scenario, a natural concern is the susceptibility of these lightweight structures to be damaged during their operational life, due to impacts, transient thermal states and fatigue phenomena. The inclusion of active elements, equipped with sensor/actuator systems capable of modulating their shape and strength, makes it possible to transform the truss into a smart structure capable of remedying the damage, once it is detected. In this paper, a procedure is described that is capable of restoring at least the basic functionality of a composite truss for space applications, starting with the observation that damage has occurred, regardless of its specific location. The system eigenstructure is used as a benchmark for damage detection, as well as a target characteristic for the subsequent restoration activity. The observer/Kalman filter identification algorithm (OKID), in cascade with the eigensystem realization algorithm (ERA), is adopted to reconstruct, from sensor recordings, the dynamic response of the truss in terms of system state-space representation and eigen-characteristics. Finally, a static output feedback control is developed to recover the low-frequency dynamic behaviour of the truss. The entire procedure is tested using finite element analysis. All activities are coordinated in an innovative procedure that, within a unique Python language code, automatically generates finite element (FE) models, launches finite element analysis (FEA), extracts output data, implements OKID-ERA, processes the control law and applies it to the final FE simulation.