Aerospace Systems最新文献

The paper presents an overview of manipulators and robots that can be used as part of a service spacecraft to implement methods for the active removal of space debris from near-Earth orbits as well as for structure assembly, and spacecraft maintenance. Some of the robots and manipulators described in the paper were originally intended for assembling various structures in weightlessness, for refueling spacecraft (SC) or for conducting inspections as a free-flying spacecraft. The feasibility of their use for active space debris removal should be further investigated, especially with regard to large and heavy structures. Many of the described manipulators and robots are already actively used in space and have proven themselves well.

Electromechanical actuators (EMAs) are extensively employed in small aircraft. However, inherent structural nonlinearities such as freeplay, arising from limitations in production and manufacturing processes, can adversely affect the dynamic behavior of fin-actuator systems. This paper studies the correlation between modal frequencies and freeplay through the developed simulation model of the fin-actuator, and the modal frequencies are obtained by the impact hammer test. Both freeplay and friction effects are incorporated into the model to evaluate the influence of freeplay gap length and external loads on the frequency response function (FRF). Comparative analyses reveal that, under consistent impact load, significant differences exist in the vibration frequency responses for a fin with/without an external weight load. These results suggest a positive correlation between the frequency discrepancy and the magnitude of the freeplay, offering a reference for the detection of freeplay in fin-actuator systems.

Controlling the landing path trajectories of a vertical takeoff and landing aircraft is a challenge that requires an accurate design algorithm. This study introduces an innovative approach to investigate the dynamic of such vehicles and implement an automatic controller such that the aircraft follows a desired landing path trajectory. By linearizing the governing equations of the aircraft around a specific reference path, the linear model of the aircraft system is established. The bank angle is used as the primary control input to adjust landing profile and ballistic paths, while landing speed is treated as an independent state variable. This design algorithm involves creating a linear quadratic regulator controller gain that minimizes the cost function of the aircraft system, establishing dynamic state equations. The Hamiltonian function is employed to generate and solve both state and co-state equations under specific boundary conditions, culminating in the solution of the Riccati matrix. Finally, the controller's performance is tested with different initial state values. Results reveal significant improvements in stability and performance considering the landing path. However, challenges such as conflicts between system states, control gain saturation, and abrupt state changes remain key design hinders. This study offers a sufficient method for controlling such an aircraft using linear control algorithms, which can be more efficient and cost-effective than complex nonlinear algorithms. By simplifying control systems, this approach ensures stable and effective flight operations, making it a valuable advancement in the field of vertical takeoff and landing aircraft technology.

Modern space exploration requires superior Propelling systems and dual bell nozzles present a promising solution for enhancing rocket propulsion system performance across varied flight regimes. This study offers a comprehensive optimization and analysis of dual bell nozzle design for advanced rockets. By employing Machine Learning with an Artificial Neural Network model, we developed a novel approach to rapidly optimize dual bell nozzle geometry for a specified exit Mach number, addressing the complex calculations typically associated with nozzle design. The algorithm generated a nozzle configuration capable of efficient operation in both low and high-altitude conditions. To validate results, we conducted detailed computational simulations using ANSYS Fluent. The analysis corroborated the model predictions, revealing key performance characteristics including a maximum exhaust velocity of approximately 2200 m/s and an exit Mach number of 5.8, aligning closely with the optimization. Our study contributes to the advancement of space propulsion technology by demonstrating the potential of AI-driven optimization in nozzle design.

Software and hardware loosely coupled systems, characterized by their critical role in various high-reliability applications, require robust fault tolerance mechanisms due to their complexity and the intertwined nature of software and hardware components. However, the tight integration of diverse functions within the system-wide computing environment, coupled with the unclear mechanism of fault propagation, presents significant challenges in enhancing system reliability. Modern avionics systems, as a prominent example, are also inherently software-hardware loosely coupled systems, and they face similar challenges in ensuring fault tolerance. In response to these challenges, this paper proposes a fault propagation analysis method that comprehensively considers both temporal and spatial dimensions. Through in-depth analysis of dependency, fault probability, and fault propagation capability, the paper constructs a fault propagation model for software and hardware loosely coupled systems, providing a precise description of fault information. In the spatial dimension, the efficiency of fault propagation analysis is enhanced using the ant colony algorithm, while in the temporal dimension, task modeling is performed using the directed acyclic graph (DAG) model to improve the adaptability of fault propagation methods to real-time task requirements. The experimental results validate the effectiveness and efficiency of the proposed fault propagation method, demonstrating that the temporal dimension of fault propagation can effectively complement the shortcomings of spatial dimension fault propagation in meeting real-time task requirements.

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.