Aerospace Systems最新文献

The propulsion systems of a multi-rotor unmanned aerial vehicle (UAV) is crucial, as it directly affects the UAV’s performance, efficiency, and safety. Since the components of the UAV propulsion system are highly interconnectioned, we developed a fuzzy fault tree analysis method to analysis the varying reliability under different fault conditions. Combining the fuzzy fault tree analysis of the T-S model and the UAV propulsion system model, we constructed a fuzzy fault tree of the T-S type for the system and performed a reliability analysis. This fuzzy fault tree allows us to model the system from two perspectives: fuzzy failure rate and failure degree. Consequently, two methods can be used for failure analysis of UAV systems. The first method involves calculating the system’s fuzzy failure rate based on the component’s fuzzy failure rate. The second method calculates the fuzzy failure rate of the system based on the failure degree of the component. The computational results indicate that both methods are well-suited for fault diagnosis in UAV propulsion systems. Compared to traditional fault tree analysis, which does not subdivide fault degrees, the proposed methods provide more accurate fault rate assessments.

Autonomous systems, particularly in multi-UAV maritime operations, are becoming increasingly complex, posing significant challenges to dynamically modeling based on traditional systems engineering modeling methods. This paper proposes an innovative data-driven approach that combines deep reinforcement learning and process mining with Department of Defense Architecture Framework (DoDAF) views to learn and extract dynamic multi-UAV collaborative behaviors. First, a hierarchical multi-agent reinforcement learning framework is developed to simulate high-value complex maritime UAV collaboration, where agents learn implicit high-level task selection patterns while executing predefined low-level behaviors. Then, a DoDAF-oriented process mining algorithm is designed, which is the key innovation, to automatically extract DoDAF operational view-5b diagrams from learned behavioral pattern data. The experimental validation demonstrates this method excels at systematically extracting dynamic multi-UAV collaborative behaviors. The proposed approach could effectively bridge the gap between AI-based implicit behavior pattern learning and system engineering-based explicit behavior modeling requirement, contributing to the development of interpretable autonomous system and discovering effective collaborative behavior tactics.

This paper proposes a model predictive control (MPC) algorithm for a small satellite to accomplish on-orbit inspection missions. The relative dynamics of satellite is modelled first. Then, multiple constraints are taken into account for the on-orbit inspection missions, including input saturation, obstacle avoidance, velocity limit, and task specifications. To precisely formulate the tasks, the signal temporal logic (STL) framework is employed, where an auxiliary function is required to be designed based on the robust semantics of STL formulas. Considering the impact of input saturation, the proposed algorithm designs the auxiliary function in the form of cube power function, and incorporate it into the optimization problem in MPC. After that, the terminal ingredients are designed, whose parameters can be efficiently calculated based on linear matrix inequality techniques. Finally, numerical simulation is applied to validate the effectiveness of the proposed control strategy.

The approaches to solving the problem of separate identification of thrust and drag force coefficient are discussed. For this purpose, the direct method of optimal control formation is used, and the types of flight maneuver are selected that allow improving the problem’s degree of conditionality. The complexity of the problem especially lies in the fact that, it is ill-conditioned due to the almost complete co-linearity between thrust and drag vectors at small angles of attack. The advantage of using the proposed approach in this paper is that it does not require the use of a thermodynamic model of the engine, which gives it versatility and relative simplicity. The results of the flight maneuver formation based on mathematical simulation data are presented.

This study explores the enhancement of scramjet engine performance through the implementation of different strut configurations, specifically the Left Diagonal (LD) and Right Diagonal (RD) models, operating at Mach 2 with hydrogen fuel. Numerical simulations were conducted using the k–ω SST turbulence model to evaluate and compare the combustion efficiency of these configurations against a baseline model. The results indicate that both LD and RD models exhibit improved combustion efficiency between 120 and 240 mm along the combustor length, primarily due to shock waves generated by the small strut. However, beyond 240 mm, the LD model experiences a decline in efficiency, concluding 2.06% lower than the baseline. In contrast, the RD model maintains its advantage, achieving a 2.6% higher combustion efficiency compared to the baseline. This improvement is attributed to the enhanced turbulence and wake regions created by the strut positioned just below the divergent section of the combustor. Furthermore, analysis of hydrogen mass fraction along the combustor length reveals more effective fuel mixing in the RD model, as evidenced by its lower residual H₂ mass fraction compared to the LD model. The optimized strut placement in the RD configuration contributes to more stable and efficient combustion, demonstrating its potential for improving supersonic combustion performance. These findings provide valuable insights into strut-based cavity design optimization for air-breathing propulsion systems, particularly for hypersonic applications.

To address the scheduling challenges of mixed regional missions with different priorities across multiple observation modes of the SAR satellite, this study presents a Hierarchical Adaptive Scheduling Method (HASM) for generating optimized booting and imaging commands that align with diverse application requirements. First, considering spaceborne storage and energy limitations, a weighted multi-objective optimization model, incorporating mission decomposition and scheduling, is developed to enhance the flexibility of imaging strip segmentation and insertion. The weighted objective function is designed to balance the prioritization of urgent targets, the rapid increase of revenues, and the optimal utilization of resources. Meanwhile, the HASM method is proposed to alleviate the complexity and computational burden of high-dimensional combinatorial optimization problems. Furthermore, hyperparameters are automatically configured through observation pattern selection and optimal weight determination methods to minimize the overall mission observation time. Finally, simulation results demonstrate that the proposed method enhances the efficiency of over 95% of missions in global multi-mode mixed mission scheduling, while reducing the overall coverage time by more than 20% for China’s multi-priority missions.

For large precision aircraft structures, the main method of obtaining the dynamic characteristics of the structure is modal testing. The most important step in modal testing is to find a set of optimal excitation forces with appropriate distribution and amplitude to obtain the normal modal of the structure. Most of the existing methods for adjusting the excitation force rely on the experience of the researchers, with problems of time-consuming and insufficient accuracy in obtaining the modal parameters. This paper proposes a hybrid automatic force adjustment method in normal modal testing of structures which can solve the problem of force adjustment in dense mode. This method firstly measures the frequency response function of the structure, identifies the modal parameters through the polyreference least squares complex frequency domain method (PolyMAX), then use these modal parameters as the initial value of the normal modal test to derive the initial optimal excitation force. Finally, the optimal excitation force and the corresponding modal parameters are obtained by frequency sweep and particle swarm algorithm. Result shows that the method proposed in this paper can effectively solve the problem of local optimal and insufficient indicator function, and can reduce the force adjustment time of normal modal tests for large precision structures and improve the force adjustment accuracy.

In this paper, a data-driven adaptive dynamic programming method is proposed to solve the thrust tracking control problem of a turbofan engine with unknown system dynamics. Firstly, the augmented system of the engine and the reference signal system is established. The thrust tracking problem can be transformed into an optimal control problem by this augmented system. Secondly, an augmented algebraic Riccati equation is derived under the performance index of the original dynamics, and an offline policy iteration method is design to solve the optimal thrust tracking problem. Thirdly, a new online data-driven adaptive dynamic programming method is proposed to solve the optimal thrust tracking problem when the engine dynamics is inaccessible. Finally, some numerical simulation results are given to demonstrate the effectiveness of the proposed method.

This paper presents a morphing missile with an expandable flared skirt, which aims to change aerodynamic shape according to the change of flight environment to improve flight efficiency. The morphing missile with an expandable flared skirt is regarded as a multi-rigid-body system, and the six-degree-of-freedom(6-DOF) nonlinear dynamic model of the missile is established considering the changes of aerodynamic force, mass distribution, and inertia characteristics during morphing. In order to investigate the longitudinal dynamic characteristics of the missile, the nonlinear dynamic model is de-coupled to obtain its longitudinal motion equation. The aerodynamic parameters of the missile in the whole flight state envelope are simulated by quasi-steady assumption, and the aerodynamic coefficients under different flight environments and flared opening angles are obtained. The aerodynamic coefficients obtained from the simulation at each operating point are fitted to the flight altitude, Mach number, and the morphing rate of the flared skirt by the least square method. Finally, the parametric model of the aerodynamic coefficients is obtained. Finally, the aerodynamic coefficient function model is introduced into the longitudinal motion equation of the missile, and the longitudinal dynamic characteristics of the missile in different flight conditions are obtained by numerical simulation. The simulation results show that the morph of the flared skirt has a significant influence on the longitudinal motion characteristics of the missile.

Ballistic missiles are perhaps the most critical and common flying devices today, flying at very high altitudes compared to aeroplanes. Among the important points about these flying devices is their very high speed in the final phase, usually several times the speed of sound. Choosing essential design parameters for making these devices has always been a significant challenge for designers. Structural, propulsion, aerodynamic, and control parameters are the most influential. The topic of nose cone aerodynamics is one of the essential design components to achieve the most reliable and optimal designs. Deciding the right nose cone has been one of the main challenges in recent years in this field. This article aims to review and analyse some examples of standard nose cones with this structure. For this purpose, first, the arrangement and layout of various designs and their specifications have been presented. The nose cone modelling has been performed for all modes, and each layout's results have been introduced. Finally, a model based on the results is proposed, the most optimal and reliable arrangement based on aerodynamic parameters.