Aerospace Systems最新文献

Acoustic field reconstruction for aircraft engine fans is essential for effective noise reduction. However, the use of a limited number of far-field measurement points in the reconstruction process exacerbates the ill-posedness issues, which necessitates the adoption of regularization techniques. The hierarchical Bayesian-based regularisation method has recently been applied to solve the equivalent source intensity distribution and reconstruct the sound field. However, previous methods have failed to accurately obtain the acoustic modal coefficients of the sound source, which are essential for determining the radiation type and directivity. This paper proposes a sound field reconstruction method that applies the hierarchical Bayesian algorithm to the source modal coefficient solution. Firstly, the deconvolution beamforming method obtains the sound source position. Subsequently, the hierarchical Bayesian algorithm is employed to obtain the source modal coefficients of the sound field, thereby completing the reconstruction of the sound field in the far-field region. Experimental results indicate that the proposed algorithm is highly effective in reconstructing far-field sound fields. Under free-field conditions and in mid-high frequency ranges, the average reconstruction error can be significantly reduced by using a few far-field microphones compared to traditional methods.

The article analyzes the main options for design solutions of separation devices of promising launch vehicles and payloads without using pyrotechnic elements, such as the “Clamp Band” type, “ball” and “petal” types, as well as the bandage-based separation device. Parametric redundancy has been performed to achieve the required level of the reliability, providing the necessary coefficient of parametric margin. Engineering Critical Assessment of structural elements of the proposed separation devices by using the scorecard method is analyzed and considered. The reliability requirements are considered, which must be confirmed during complex experimental processing, including laboratory debugging, control developing, control sampling, control finishing and manufacturing tests of individual large-sized elements and subsystems included in the proposed separation devices, as well as recommendations for ensuring the reliability of their operation at the design development stage.

Unmanned aerial vehicles (UAVs) propelled by electricity have emerged as a prominent concept in aviation due to their eco-friendly and stealth characteristics. To address the limitations of Polymer Membrane Fuel Cell (PMFC), which serve as the primary power source but exhibit sluggish responses to sudden load changes, this research proposes a novel hybrid power system incorporating a Li-Ion battery. This hybrid setup ensures superior dynamic response while maintaining high power-to-weight efficiency. This paper presents an intelligent energy management system (EMS), which effectively regulates power flow between the PMFC and Li-Ion battery through a multi-input multi-output (MIMO) control framework. The uniqueness of this study lies in the comparative evaluation of two advanced EMS control strategies: Fuzzy Logic Control and the Adaptive Neuro-Fuzzy Inference System (ANFIS), under multiple flight modes. By thoroughly analyzing system transients and dynamic behaviors using MATLAB/SIMULINK, this work provides a detailed insight into optimizing UAV power efficiency. Unlike previous studies, this research highlights the distinct advantages and limitations of each control strategy for different flight phases, providing a comprehensive benchmark for future EMS designs in UAV applications.

This study investigates the potential of Fish Bone Morphing (FBM) technology for enhancing the aerodynamic performance of aerofoils. FBM is a bio-inspired concept that incorporates flexible structural elements to facilitate morphing of the aerofoil shape in response to varying flight conditions. The NACA 2412 aerofoil is chosen for its camber adaptability, and CFD simulations are employed to assess the efficacy of FBM integration. The k–ω SST turbulence model is adopted for its ability to combine the strengths of the k–ω and k–ε models. The investigation encompasses a systematic exploration of geometric configurations, including trailing edge deflection at various chord lengths (0.6c, 0.65c, 0.70c, 0.75c, and 0.80c) and deflection angles (4°, 8°, and 12°). The results reveal that FBM aerofoils exhibit a consistent increase in maximum lift coefficient compared to conventional aerofoils across all deflection points and angles. Additionally, improvements in lift-to-drag ratio are observed. Furthermore, the stalling angle remains unaffected by deflection point variations, while deflection angle increments lead to corresponding increases in maximum lift coefficient. The morphing aerofoil with a 0.60c deflection point demonstrates the most significant enhancement in maximum lift coefficient, achieving a 13% increase at a 12° deflection angle. These findings establish the aerodynamic efficiency of FBM aerofoils, characterized by superior lift-to-drag ratios and increased maximum lift coefficients.

Dynamic multi-robot task allocation (MRTA) requires real-time responsiveness and adaptability to rapidly changing conditions. Existing methods, primarily based on static data and centralized architectures, often fail in dynamic environments that require decentralized, context-aware decisions. To address these challenges, this paper proposes a novel graph reinforcement learning (GRL) architecture, named Spatial-Temporal Fusing Reinforcement Learning (STFRL), to address real-time distributed target allocation problems in search and rescue scenarios. The proposed policy network includes an encoder, which employs a Temporal-Spatial Fusing Encoder (TSFE) to extract input features and a decoder uses multi-head attention (MHA) to perform distributed allocation based on the encoder’s output and context. The policy network is trained with the REINFORCE algorithm. Experimental comparisons with state-of-the-art baselines demonstrate that STFRL achieves superior performance in path cost, inference speed, and scalability, highlighting its robustness and efficiency in complex, dynamic environments.

Stability is essential for the safety of Unmanned Aerial Vehicles (UAVs) and holds paramount importance in their design. This study focuses on the longitudinal stability of twin-boom UAVs with inverted V-tail and inverted U-tail configurations. Computational fluid dynamics (CFD) method and longitudinal perturbed equations of motion were employed to comprehensively analyze the stability and flight performance of these UAVs. Results indicate that the inverted U-tail configuration exhibits 23.6% higher longitudinal static stability than the inverted V-tail under small perturbations. In Phugoid mode, the inverted U-tail UAV also demonstrates superior performance. These findings provide valuable insights for the design and optimization of UAV tail configurations.

This paper proposes an intermittent measurement-based attitude tracking control strategy for spacecraft operating in the presence of sensor-actuator faults. A sampled-data (self-)learning observer is developed to estimate both the spacecraft’s states and lumped disturbances, effectively mitigating the impact of faults. This observer acts as a virtual predictor, reconstructing states and actuator fault deviations using only intermittent measurement data, addressing the limitations imposed by sensor failures. The control scheme incorporates compensation based on the predictor’s estimates, ensuring robust attitude tracking despite the presence of faults. We provide the first proof of bounded stability for this learning observer utilizing intermittent information, expanding its applicability. Numerical simulations demonstrate the effectiveness of this innovative strategy, highlighting its potential for enhancing spacecraft autonomy and reliability in challenging operational scenarios.

The aerodynamic benefits of non-smooth surfaces, such as drag reduction, are well-established, but accurately simulating their effects poses significant challenges due to increased modeling complexity and computational demands. This paper introduces an enhanced simulation method tailored for analyzing the near-wall flow fields of non-smooth surfaces. By developing a modified wall function, the proposed method replicates the flow characteristics of non-smooth surfaces on smooth walls, thereby simplifying the simulation process. The results demonstrate a marked improvement in computational efficiency, with a reduction in simulation time by more than 20%, without compromising accuracy. This approach offers a robust and efficient tool for aerodynamic optimization in engineering applications.

Use of operational data such as those from QAR (Quick Access Recorder) has recently attracted interest in building high-accuracy flight fuel models. This is often combined with applying some machine learning algorithms to improve the model’s fidelity. However, the data-based approach lacks the physical characteristics of the aircraft flight performance models and is challenging to interpret and use in optimizing aircraft designs. This paper proposes a collaborative optimization process based on a physics-based aircraft multidisciplinary sizing tool and a data model built from flight data. First, an enhanced aircraft sizing tool is used to provide initial estimation of the aircraft design parameters based on the top-level requirements. Unknown parameters in the sizing model are determined using data-based approach which include both aircraft operational and flight parameters. Aircraft operational parameters include actual passenger weight, cargo weight, fuel weight, cruising Mach number, and other essential operational parameters. Aircraft flight parameters include information on aircraft, route, and weather etc., derived from QAR data and open-source flight databases. Aircraft design, operation, and flight parameters are coupled with an aircraft performance model, which can be used in a collaborative multi-parameter optimization framework to optimize aircraft design and operations for improved fuel performance.

This review presents a groundbreaking approach for investigating low-satellite orbits through the derivation of comprehensive equations governing their motions. The present work also presents some of the forces affecting this motion at low satellite orbit levels. This paper also presents different numerical methods for solving the equations governing two-body problems. The goal is to develop a strong mathematical model for the satellite to find a suitable path for orbital movement. Due to the effects on the orbit, the orbit must be controlled. For this purpose, orbital control uses orbital maneuvers to move the satellite to the desired location. Some modern technology (intelligent modeling) was used to create a simulator to increase the mathematical accuracy of the model and control its orbit. The objective is to develop a comprehensive mathematical model of orbital motion. This includes the design of a control unit for satellite orbits and the application of optimization algorithms. Furthermore, it involves developing a neural network-based model for the orbital control system. This study aims to achieve the desired outcomes in satellite orbital motion control by integrating these components.