The enhancement of jet engine components may result in the expansion of the established design space. In particular, the trend towards short and therefore highly aggressive inter compressor ducts (ICD) extends the traditional design space. The potential for fuel savings resulting from a reduction in engine weight is in contrast to the emergence of a more complex flow field. Many studies consider the secondary flow system of highly aggressive ICDs at the design point, but there is a lack of off-design considerations. To fill this gap, the present study investigates in detail the off-design performance of the new German Aerospace Center (DLR) test case. Firstly, computational fluid dynamics (CFD) simulations of different typical operating points allow detailed considerations of the flow field under off-design conditions. Secondly, a variation of the inlet conditions describes the sensitivity of highly aggressive ICDs to different low-pressure compressor operating points. Finally, the comparison of the CFD stagnation pressure loss with the loss predicted by a preliminary off-design method validates the use of traditional off-design prediction during the preliminary design of highly aggressive ICDs.

This paper presents a study of the effects of the durability and level of energy storage technology on energy management strategies and the performance of hybrid electric turboprops. The results highlight the key role of battery energy density on the durability of the battery pack and the viability of the concept of hybrid electric aircraft. Additionally, the trade-off between zero-day environmental compatibility and battery lifetime is identified, caused by the size of the pack. The effective energy density would decrease with the aging of the cells, leaving a significant inert mass and increasing fuel consumption. Optimal energy management strategies are suggested in light of this new information. Higher specific energy of the pack would mitigate this aspect, along with a reduction in fuel consumption and ${\rm{N}}{{\rm{O}}_{\rm{x}}}$ emissions. Indeed, the improvement of environmental compatibility was found to be nonlinear with a positive rate, suggesting high returns in investing in great improvements in energy density over a gradual increase. This result relates to the results of the statistical technological forecast presented in this study, which, without an increase in funding, predicts the availability of the specific energy required to match the fuel-only baseline in the 2040–2050 decade.

The axisymmetric nozzle mechanism is the core part for thrust vectoring of aero engine, which contains complex rigid-flexible coupled multibody system with joints clearance and significantly reduces the efficiency in modeling and calculation, therefore the kinematics and dynamics analysis of axisymmetric vectoring nozzle mechanism based on deep neural network is proposed. The deep neural network model of the axisymmetric vector nozzle is established according to the limited training data from the physical dynamic model and then used to predict the kinematics and dynamics response of the axisymmetric vector nozzle. This study analyses the effects of joint clearance on the kinematics and dynamics of the axisymmetric vector nozzle mechanism by a data-driven model. It is found that the angular acceleration of the expanding blade and the driving force are mostly affected by joint clearance followed by the angle, angular velocity and position of the expanding blade. Larger joint clearance results in more pronounced fluctuations of the dynamic response of the mechanism, which is due to the greater relative velocity and contact force between the bushing and the pin. Since axisymmetric vector nozzles are highly complex nonlinear systems, traditional numerical methods of dynamics are extremely time-consuming. Our work indicates that the data-driven approach greatly reduces the computational cost while maintaining accuracy, and can be used for rapid evaluation and iterative computation of complex multibody dynamics of engine nozzle mechanism.

In this paper, we designa robust Successive Generalised Dynamic Inversion (SGDI) flight control system for high-performance trajectory tracking of target sun-synchronous orbit Satellite Launch Vehicles (SLVs). The robust SGDI control system is designed to track an optimal reference trajectory such that the desired orbital terminal conditions of the ascent flight phase are achieved. The proposed SGDI is composed of two loops. The attitude control loop employs Dynamically Scaled Generalised Inversion (DSGI) of Servo Constraint Dynamics (SVD) in the deviations of Euler attitude angles from their desired optimal trajectories. The inner-dynamics control loop employs DSGI of an SVD in the SLV angular velocity components. Robustification control elements are augmented within the two loops of the baseline SGDI control system to overcome control performance degradation due to dynamic scaling of the Moore-Penrose generalised inverse, modeling and parametric uncertainties, and exogenous disturbances. The robust SGDI control system works to enforce global convergence of the SLV attitude trajectories to the reference trajectories. The high-performance attributes of the robust SGDI control system are verified via comparisons with a classical sliding mode control system, and by performing numerous runs of Monte Carlo simulations under various types of uncertainties and external wind disturbances.

In order to improve the performance of $k - \omega $ SST model in turbomachinery, previous studies have used the machine-learning (ML) technique to obtain turbulence models (for example, the ML-RANS EQ model). However, these models do not lead to satisfactory results in complex flows in turbomachinery. In this study, we use non-equilibrium training dataset to obtain a new turbulence model (i.e., the ML-RANS TR-NE-EQ model). Calculations in various cases of turbine cascade flows show that ML-RANS TR-NE-EQ model performs obviously better than ML-RANS EQ model as well as $k - \omega $ SST model.

In the past few decades, substantial work has been directed towards the design of aircraft structures that maximise fuel efficiency, improve performance and curtail emissions. Aeroelastic optimisation offers an effective way to devise lightweight and fuel efficient structures, with structural stability constraints often driving the design. To date, the aeroelastic optimisation community has relied mostly on linear buckling predictions for the evaluation of structural stability constraints, mainly because of their conservativeness, computational efficiency and simplicity of implementation. This approach typically leads to overly conservative buckling margins, and this over-conservativeness places a glass ceiling over the load carrying capacity of wing structures, consequently restricting the exploration of regions within the design space where considerable weight savings could be achieved.

By contrast to previous works that predominantly rely on linear buckling constraints, the present paper introduces a method to incorporate nonlinear structural stability analysis into aeroelastic optimisations of wingbox-like structures. The method relies on the evaluation of the positive-definiteness of the tangent stiffness matrix, which is an indicator of structural stability. The sign of the stiffness eigenvalues is monitored while tracing the load-displacement equilibrium paths by means of the arc-length method, thus pinpointing the onset of instability. The proposed constraint is tested in a proof of concept structural optimisation of an idealised version of the CRM wingbox. This optimisation shows a $10.9{\rm{\% }} $ reduction in mass with respect to a baseline design that is optimal with a linear buckling approach, promising great potential for application to more realistic aeroelastic optimisations.

The current emphasis in aerospace component development is on creating safe, reliable and cost-effective technologies. However, the intricate design of stage separation systems renders component reliability a critical factor in determining mission success or failure. One of the technical challenges involves the development of various aerospace mechanisms, such as payload separation, heavy propulsion system separation, ejection of auxiliary components and detachment of rigid components. These stage separation mechanisms commonly employ pyrotechnic devices, which, by their operational nature, impart shock to the spacecraft, potentially causing damage or adverse effects on flight instruments. Therefore, it is imperative to explore multiple viable concepts aimed at reducing shock and experimentally ascertain the impact of shock using diverse shock attenuation techniques. While existing literature primarily addresses shock attenuation with distance from the shock source, limited attention has been given to diminishing shock at the location of the shock-generating element. This study employed various shock-attenuating devices, including dampers, metallic foam structures, viscous materials and dampeners, to assess the effectiveness of shock reduction. Furthermore, the study investigated shock reduction resulting from the elimination of rigid connections, such as bolted joints, from pyro-actuated mechanisms. Through a series of experiments, a conclusive analysis was conducted to determine the approach for achieving a substantial reduction in pyro shock.

This paper develops a novel full-state-constrained intelligent adaptive control (FIAC) scheme for a class of uncertain nonlinear systems under full state constraints, unmodeled dynamics and external disturbances. The key point of the proposed scheme is to appropriately suppress and compensate for unmodeled dynamics that are coupled with other states of the system under the conditions of various disturbances and full state constraints. Firstly, to guarantee that the time-varying asymmetric full state constraints are obeyed, a simple and valid nonlinear error transformation method has been proposed, which can simplify the constrained control problem of the system states into a bounded control problem of the transformed states. Secondly, considering the coupling relationship between the unmodeled dynamics and other states of the controlled system such as system states and control inputs, a decoupling approach for coupling uncertainties is introduced. Thereafter, owing to the employed dynamic signal and bias radial basis function neural network (BIAS-RBFNN) improved on traditional RBFNN, the adverse effects of unmodeled dynamics on the controlled system can be suppressed appropriately. Furthermore, the matched and mismatched disturbances are reasonably estimated and circumvented by a mathematical inequality and a disturbance observer, respectively. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed FIAC strategy.

Actuator faults in unmanned aerial vehicles (UAVs) can have significant and potentially adverse effects on their safety and performance, highlighting the critical importance of fault diagnosis in UAV design. Ensuring the reliability of these systems in various applications often requires the use of advanced diagnostic algorithms. Artificial intelligence methods, such as deep learning and machine learning techniques, enable fault diagnosis through sample-based learning without the need for prior knowledge of fault mechanisms or physics-based models. However, UAV fault datasets are typically small due to stringent safety standards, which presents challenges for achieving high-performance fault diagnosis. To address this, deep reinforcement learning (DRL) algorithms offer a unique advantage by combining deep learning’s automatic feature extraction with reinforcement learning’s interactive learning approach, improving both learning capabilities and robustness. In this study, we propose and evaluate two DRL-based fault diagnosis models, which demonstrate remarkable accuracy in fault diagnosis, consistently exceeding $99{\rm{\% }}$. Notably, under small sample scenarios, the proposed models significantly outperform traditional classifiers such as decision trees, support vector machines, and multilayer perceptron neural networks. These findings suggest that the integration of DRL enhances fault diagnosis performance, particularly in data-limited environments.

To meet the development needs of aeroengines for high thrust-to-weight ratios and fuel-air ratios, a high temperature rise triple-swirler main combustor was designed with a total fuel-air ratio of 0.037, utilising advanced technologies including staged combustion, multi-point injection and multi-inclined hole cooling. Fluent software was used to conduct numerical simulations under both takeoff and idle conditions, thereby obtaining the distribution characteristics of the velocity and temperature fields within the combustor, as well as the generation of pollutants. The simulation results indicate that under takeoff conditions, the high temperature rise triple-swirler combustor achieves a total pressure loss coefficient of less than 6% and a combustion efficiency exceeding 99%. Under takeoff conditions, the OTDF and RTDF values are 0.144 and 0.0738, respectively. The mole fraction of NOx emissions is 3,700ppm, while the mole fraction of soot emissions is 2.55×10−5ppm. Under idle conditions, the triple-swirler combustor maintains a total pressure loss coefficient of less than 6% and a combustion efficiency greater than 99.9%. The OTDF and RTDF values are 0.131 and 0.0624, respectively. The mole fractions of CO and UHC emissions are both 0×10−32ppm at the calculation limit of Fluent software.

This study investigates the influence of release timing on the trajectory of internal store separation through numerical solutions of continuity, momentum, energy equations and six degrees of freedom equations in a coupled manner. The internal store separation process in advanced fighter aircraft is analysed using computational fluid dynamics (CFD) and six degrees of freedom equations of motion. Initially, the equations of motion are validated by reenacting the Eglin Air Force Base study, an external store separation example with documented experimental results. Subsequently, validation is extended to the M219 cavity problem. In the internal store separation analysis, a cavity with an L/D ratio of 5, a freestream velocity of 0.85 Mach, and a generic store are utilised. Detached eddy simulation (DES) is applied using both static and dynamic mesh techniques in all numerical solutions. The generic store, positioned within a clean cavity with a 90-degree flap angle, was released at two distinct times, corresponding to the points of maximum and minimum gravitational forces. Interestingly, the results show that releasing the store when the normal force acting on it is at its maximum does not necessarily provide an optimal separation. Specifically, when the force coefficient was at its maximum (0.14), the store collided with the cavity door flap after 0.171465 seconds. In contrast, when the force coefficient was at its minimum (-0.04), the store contacted the cavity door after 0.170295 seconds at the same location. Despite the differences in force magnitudes, the trajectories were nearly identical, suggesting that the timing of the release may not have a significant effect on preventing collision. This further emphasises the need for flow control methods to ensure safe and effective store separation.

Typically, the fuselage of a modern military aircraft is designed in such a way that the propulsion system is integrated into it. The main reasons are reduction of installation space and minimisation of radar signature. Those requirements can be achieved by using highly bent engine intakes, which are occluding a direct line of sight to the compressor system. Depending on their design, secondary flows and flow separation can be expected due to the strong curvature of the intake system. In this study, a serpentine intake in front of the Larzac 04 test engine is investigated experimentally and its performance compared with and without flow stabilising measures. In detail, a configuration with vortex generators was compared experimentally with a configuration of active flow control by injected air. In order to analyse and compare the efficiency of both systems, the dimensionless total pressure coefficient, the distortion coefficient (DC60) and detailed surface pressure distributions as well as the aerodynamic interface plane are evaluated. In addition, different throttle lines were recorded for surge line evaluation of the low pressure compressor of the Larzac engine and compared for each flow-stabilising measure investigated. It was found that the application of injecting air showed a larger improvement in surge margin and reduction in distortion coefficients compared to the passive flow control.

The challenging tracking control issue for a space manipulator subject to parametric uncertainty and unknown disturbance is addressed in this paper. An observer-based fixed-time terminal sliding mode control methodology is put forward. Firstly, a nonlinear disturbance observer is introduced for exactly reconstructing the lumped uncertainty without requiring any prior knowledge of the lumped uncertainty. Meanwhile, the estimation time’s upper bound is not only irrelevant to the initial estimation error but can be directly predicted in advance via a specific parameter in the observer. Invoking the estimated information, a fast fixed-time tracking controller with strong robustness is designed, where a novel sliding mode surface incorporated enables faster convergence. The globally fixed-time stability of the closed-loop tracking system is rigorously demonstrated through Lyapunov stability analysis. Finally, numerical simulations and comparisons verify the validity and superiority of the suggested controller.