Aerospace Systems最新文献

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.

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.

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.

The processing and application of time series are widespread, including tasks like weather forecasting, traffic flow prediction and intention recognition. However, in reality, missing data often occurs due to target occlusion or sensor failures. Many deep learning models are designed for uniformly sampled complete data and cannot be directly applied to scenarios with missing values. Traditional data preprocessing methods, such as imputation and interpolation, introduce additional noise. To address these challenges, we propose an end-to-end model with Learnable Embedding and capture Multidimensional Features (LEMF). LEMF can directly handle real-world time series with missing values. We utilize the LE module to extract richer temporal information, compensating for the limitations of missing data. The MF module can extract features related to the relationships between variables. We leverage these hidden representations for intention recognition, which is the time series classification task. We thoroughly evaluate our model on a self-constructed intention dataset. Compared to baseline model, the LEMF model achieved an average of 10% higher accuracy at each missing ratio. Additionally, we validate the model’s generalization capabilities on two real-world datasets. Our model also shows optimal or suboptimal performance.

This paper is the first part of article series devoted to the computational study of thick composite panels buckling under compressive and shear loads taking into account through-the-thickness shear strains. In the first part of the study, an approximation is carried out taking into account the numerical solutions of the buckling problem. The first part presents a numerical study of orthotropic composite panels of a wide-body aircraft wing of large thicknesses, during which analytical dependencies are derived that determine the critical force for buckling under compressive and shear loads, taking into account through-the-thickness shear strains.

To determine the critical forces at which the panel buckles, the authors used a numerical modeling approach using the Finite Element Method (FEM) and Bubnov-Galerkin method (a method of aircraft structural mechanics). For this purpose, shell models of wing skin panels with orthotropic composite lay-up were made using a layer-by-layer modeling approach. A review of existing analytical dependencies for determining the critical forces for buckling of composite panels taking into account the through-the-thickness shear strains during compression was also carried out.

After validating the computational models, the authors conducted a series of virtual tests and analytical calculations for panels with different aspect ratios and thicknesses ranging from 1.8 mm to 24 mm in 2 mm increments. Based on the data obtained, the influence of through-the-thickness shear strains under compressive and shear loads was studied empirically, and an analytical relationship was obtained for assessing buckling of composite panels of large thicknesses under shear load.

The scientific novelty of this study is the identification of an empirical relationship for problems of composite panels of large thicknesses buckling under the influence of shear load, taking into account through-the-thickness shear strain.

Space exploration demands robust spacecraft(SC) subsystems to endure the harsh conditions of space and ensure mission success. Attitude determination and control subsystems (ADCS), as a significant subsystem within SC, are essential for providing the necessary pointing accuracy and stability, and failures in the ADCS can lead to mission failure. Therefore, robust design, thorough testing, and Fault Detection, Isolation and Identification(FDII) techniques are crucial for spacecraft operations. This paper focuses on developing advanced FDII techniques for reaction wheels(RW) within ADCS, evaluating the Prony-based FDII technique for RW, considering its accuracy, time complexity, and memory usage, and Additionally, it introduces new machine learning-based FDII techniques, including enhancements to the Prony-based FDII technique, to manage single faults more effectively. The new proposed techniques used to explore the novel area of multiple faults within the same subsystem. Results indicate that the proposed FDII techniques significantly improve fault detection accuracy, isolation time, and memory efficiency compared to traditional techniques. These advancements enhance the reliability and longevity of spacecraft missions, ensuring that critical subsystems like ADCS operate effectively in the challenging conditions of space. The contributions presented in the paper are introducing three different FDII machine learning-based techniques that support identifying five types of single faults in spacecraft ADCS RW, outperform the Prony-based FDII technique for spacecraft ADCS RW in terms of time and memory complexity, and Finally, improves the fault tolerance of the spacecraft system by detecting Multiple fault combinations that may occur at the same time in one system.

A comprehensive simulation model is established to design the altitude adjustment of the stratospheric airship with the application of the adjustable ballonets for pitch control. A series of mathematical models, including atmosphere, thermal, dynamics and kinematics, airship pressure and pitch control, are developed to achieve the altitude adjustment when the stratospheric airship flying at the stationary phase. The altitude adjustment strategy takes the thermodynamics, dynamics, and pressure control requirements together into consideration, to better fulfill the realistic flight conditions. Based on these models, the characteristics of stratospheric airship’s flight performance are simulated and discussed in detail. The results show that taking adjustable ballonets as the actuator can realize the pitch and pressure control simultaneously and satisfy the requirements of the flight missions. Furthermore, stratospheric airship can achieve altitude adjustment with the application of adjustable ballonets and propulsion system coordinately. Moreover, the simulation model can accurately present the interaction of thermodynamics, pressure, and dynamics, which better satisfies the realistic flight situation. The results and conclusions presented herein contribute to the design and operation of stratospheric airship.

Trajectory prediction (TP) of ballistic missile (BM) is a critical task in the field of military and defense security. However, influenced by various external factors, including target maneuverability, interference, and atmospheric conditions, BMs encounter complex forces during the boost flight phase, making their trajectories complex and variable. Furthermore, the conventional numerical integration and polynomial fitting TP methods are highly dependent on fixed motion models and precise initial observations, so they tend to engender error accumulation and inaccurate predictions. Thus, in terms of this issue, this paper proposed a TP method based on adaptive tracking and Gaussian Process Regression (GPR) to achieve stability in short-term TP for boost phase BM. Specifically, a database of trajectories for boost phase BM is created and used for training GPR predictive models, in which the unknown noise's covariance matrix is dynamically adjusted according to the statistical characteristics of observations to provide more precise trajectory data for model training. At the same time, incremental learning is adopted to add tracking results from real-time tests to improve further and update the predictive model. Additionally, the output uncertainty of GPR is also beneficial for decision-making systems usually making decisions in accordance with the uncertainty. Simulation results based on the GEO dual-satellite positioning system show that the absolute average TP RMSE of the boost phase BM of our proposed method can be less than 0.35 km, 0.51 km, and 0.62 km in future 20 s, 40 s, and 60 s, which outperforms those of the numerical integration method of 2.1 km, 3.7 km, and 6.9 km and the function approximation method of 0.89 km, 3.1 km, and 6.1 km. This paper provides a significant reference for the positioning, tracking, and prediction of BM in boost phase.

This paper introduces the current and new Satellite solutions for local and global tracking of ships for enhanced Ship Traffic Control (STC) and Ship Traffic Management (STM) at sea, in sea passages, approaching to the anchorages and inside of seaports. All transportation systems and especially for maritime applications require far more sophisticated technology solutions, networks and onboard equipment for modern Satellite ship tracking than current standalone the US Global Positioning System (GPS) or Russian Global Navigation Satellite System (GLONAS) networks. The forthcoming Global Ship Tracking (GST), Satellite Data Link (SDL), Maritime GNSS Augmentation SDL (GASDL) and Maritime Satellite Automatic Dependent Surveillance-Broadcast (SADS-B) networks with Space and Ground Segment infrastructures for all three systems are discussed including benefits of these new technologies and solution for improved STC.