Journal Description
Aerospace
Aerospace
is a peer-reviewed, open access journal of aeronautics and astronautics published monthly online by MDPI. The European Aeronautics Science Network (EASN), and the ECATS International Association are affiliated with Aerospace and their members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Inspec, and other databases.
- Journal Rank: JCR - Q1 (Engineering, Aerospace) / CiteScore - Q2 (Aerospace Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 22.3 days after submission; acceptance to publication is undertaken in 2.7 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journal: Astronomy.
Impact Factor:
2.6 (2022);
5-Year Impact Factor:
2.6 (2022)
Latest Articles
An Anomaly Detection Method for UAV Based on Wavelet Decomposition and Stacked Denoising Autoencoder
Aerospace 2024, 11(5), 393; https://doi.org/10.3390/aerospace11050393 (registering DOI) - 14 May 2024
Abstract
The paper proposes an anomaly detection method for UAVs based on wavelet decomposition and stacked denoising autoencoder. This method takes the negative impact of noisy data and the feature extraction capabilities of deep learning models into account. It aims to improve the accuracy
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The paper proposes an anomaly detection method for UAVs based on wavelet decomposition and stacked denoising autoencoder. This method takes the negative impact of noisy data and the feature extraction capabilities of deep learning models into account. It aims to improve the accuracy of the proposed anomaly detection method with wavelet decomposition and stacked denoising autoencoder methods. Anomaly detection based on UAV flight data is an important method of UAV condition monitoring and potential abnormal state mining, which is an important means to reduce the risk of UAV flight accidents. However, the diversity of UAV mission scenarios leads to a complex and harsh environment, so the acquired data are affected by noise, which brings challenges to accurate anomaly detection based on UAV data. Firstly, we use wavelet decomposition to denoise the original data; then, we used the stacked denoising autoencoder to achieve feature extraction. Finally, the softmax classifier is used to realize the anomaly detection of UAV. The experimental results demonstrate that the proposed method still has good performance in the case of noisy data. Specifically, the Accuracy reaches 97.53%, the Precision is 97.50%, the Recall is 91.81%, and the F1-score is 94.57%. Furthermore, the proposed method outperforms the four comparison models with more outstanding performance. Therefore, it has significant potential in reducing UAV flight accidents and enhancing operational safety.
Full article
(This article belongs to the Special Issue Computing Methods for Aerospace Reliability Engineering)
Open AccessArticle
Enhancing Small Object Detection in Aerial Images: A Novel Approach with PCSG Model
by
Kang An, Huiping Duanmu, Zhiyang Wu, Yuqiang Liu, Jingzhen Qiao, Qianqian Shangguan, Yaqing Song and Xiaonong Xu
Aerospace 2024, 11(5), 392; https://doi.org/10.3390/aerospace11050392 (registering DOI) - 14 May 2024
Abstract
Generalized target detection algorithms perform well for large- and medium-sized targets but struggle with small ones. However, with the growing importance of aerial images in urban transportation and environmental monitoring, detecting small targets in such imagery has been a promising research hotspot. The
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Generalized target detection algorithms perform well for large- and medium-sized targets but struggle with small ones. However, with the growing importance of aerial images in urban transportation and environmental monitoring, detecting small targets in such imagery has been a promising research hotspot. The challenge in small object detection lies in the limited pixel proportion and the complexity of feature extraction. Moreover, current mainstream detection algorithms tend to be overly complex, leading to structural redundancy for small objects. To cope with these challenges, this paper recommends the PCSG model based on yolov5, which optimizes both the detection head and backbone networks. (1) An enhanced detection header is introduced, featuring a new structure that enhances the feature pyramid network and the path aggregation network. This enhancement bolsters the model’s shallow feature reuse capability and introduces a dedicated detection layer for smaller objects. Additionally, redundant structures in the network are pruned, and the lightweight and versatile upsampling operator CARAFE is used to optimize the upsampling algorithm. (2) The paper proposes the module named SPD-Conv to replace the strided convolution operation and pooling structures in yolov5, thereby enhancing the backbone’s feature extraction capability. Furthermore, Ghost convolution is utilized to optimize the parameter count, ensuring that the backbone meets the real-time needs of aerial image detection. The experimental results from the RSOD dataset show that the PCSG model exhibits superior detection performance. The value of mAP increases from 97.1% to 97.8%, while the number of model parameters decreases by 22.3%, from 1,761,871 to 1,368,823. These findings unequivocally highlight the effectiveness of this approach.
Full article
Open AccessArticle
ARCnet: A Multi-Feature-Based Auto Radio Check Model
by
Weijun Pan, Yidi Wang, Yumei Zhang and Boyuan Han
Aerospace 2024, 11(5), 391; https://doi.org/10.3390/aerospace11050391 - 14 May 2024
Abstract
Radio checks serve as the foundation for ground-to-air communication. To integrate machine learning for automated and reliable radio checks, this study introduces an Auto Radio Check network (ARCnet), a novel algorithm for non-intrusive speech quality assessment in civil aviation, addressing the crucial need
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Radio checks serve as the foundation for ground-to-air communication. To integrate machine learning for automated and reliable radio checks, this study introduces an Auto Radio Check network (ARCnet), a novel algorithm for non-intrusive speech quality assessment in civil aviation, addressing the crucial need for dependable ground-to-air communication. By employing a multi-scale feature fusion approach, including the consideration of audio’s frequency domain, comprehensibility, and temporal information within the radio check scoring network, ARCnet integrates manually designed features with self-supervised features and utilizes a transformer network to enhance speech segment analysis. Utilizing the NISQA open-source dataset and the proprietary RadioCheckSpeech dataset, ARCnet demonstrates superior performance in predicting speech quality, showing a 12% improvement in both the Pearson correlation coefficient and root mean square error (RMSE) compared to existing models. This research not only highlights the significance of applying multi-scale attributes and deep neural network parameters in speech quality assessment but also emphasizes the crucial role of the temporal network in capturing the nuances of voice data. Through a comprehensive comparison of the ARCnet approach to traditional methods, this study underscores its innovative contribution to enhancing communication efficiency and safety in civil aviation.
Full article
(This article belongs to the Special Issue Space-Air-Ground-Integrated Communications in the New Era: Applications, Challenges, and Countermeasures)
Open AccessArticle
Speech Recognition for Air Traffic Control Utilizing a Multi-Head State-Space Model and Transfer Learning
by
Haijun Liang, Hanwen Chang and Jianguo Kong
Aerospace 2024, 11(5), 390; https://doi.org/10.3390/aerospace11050390 - 14 May 2024
Abstract
In the present study, a novel end-to-end automatic speech recognition (ASR) framework, namely, ResNeXt-Mssm-CTC, has been developed for air traffic control (ATC) systems. This framework is built upon the Multi-Head State-Space Model (Mssm) and incorporates transfer learning techniques. Residual Networks with Cardinality (ResNeXt)
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In the present study, a novel end-to-end automatic speech recognition (ASR) framework, namely, ResNeXt-Mssm-CTC, has been developed for air traffic control (ATC) systems. This framework is built upon the Multi-Head State-Space Model (Mssm) and incorporates transfer learning techniques. Residual Networks with Cardinality (ResNeXt) employ multi-layered convolutions with residual connections to augment the extraction of intricate feature representations from speech signals. The Mssm is endowed with specialized gating mechanisms, which incorporate parallel heads that acquire knowledge of both local and global temporal dynamics in sequence data. Connectionist temporal classification (CTC) is utilized in the context of sequence labeling, eliminating the requirement for forced alignment and accommodating labels of varying lengths. Moreover, the utilization of transfer learning has been shown to improve performance on the target task by leveraging knowledge acquired from a source task. The experimental results indicate that the model proposed in this study exhibits superior performance compared to other baseline models. Specifically, when pretrained on the Aishell corpus, the model achieves a minimum character error rate (CER) of 7.2% and 8.3%. Furthermore, when applied to the ATC corpus, the CER is reduced to 5.5% and 6.7%.
Full article
(This article belongs to the Special Issue Application of Multidisciplinary Optimization and Artificial Intelligence Techniques to Aerospace Engineering (Volume II))
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Open AccessArticle
Investigation on the Aerodynamic Performance and Flow Mechanism of Transonic Ultra-Highly Loaded Tandem-Rotor Stage
by
Shilong Yuan, Yunfeng Wu, Shengfeng Zhao, Xingen Lu and Ge Han
Aerospace 2024, 11(5), 389; https://doi.org/10.3390/aerospace11050389 - 13 May 2024
Abstract
The compressor serves as a crucial component that influences the performance of the gas turbine engine. Researchers have been endeavoring to explore compressor types that possess a high loading level and high-efficiency characteristics concurrently. In this study, tandem blade technology was applied to
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The compressor serves as a crucial component that influences the performance of the gas turbine engine. Researchers have been endeavoring to explore compressor types that possess a high loading level and high-efficiency characteristics concurrently. In this study, tandem blade technology was applied to a transonic ultra-highly loaded axial compressor, and the Baseline single-blade rotor was replaced by a tandem rotor to take into account the loading level and compressor performance. Detailed investigations were carried out to identify the effects on the aerodynamic performance of the ultra-highly loaded stage and the fundamental flow mechanism within the tandem-rotor stage. This paper presents original design maps for the tandem-rotor stage, and the selection criteria for tandem parameters in tandem-rotor stage are refined. The results indicate that the peak efficiency improved by 0.83%, the stall margin increased by 2.16%, and the choke flow rate rose by 0.30% for the optimal tandem-rotor configuration. The meridional division position of the rotor primarily affects the ratio of loading of the front and rear blades, while the circumferential relative position of the tandem rotor mainly influences the channel types formed by the front and rear blades. Larger values for the meridional division position parameter and smaller values for circumferential relative position parameter should be selected for the tandem rotor design to optimize both the isentropic efficiency and total pressure ratio. This investigation offers the theoretical foundation for the design of a transonic ultra-highly loaded tandem-rotor compressor.
Full article
(This article belongs to the Section Aeronautics)
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Open AccessArticle
Experimental Investigation of Impulsive Coupling Characteristics of Asteroid Simulants Based on Laser Ablation Propulsion
by
Yingjie Ma, Hao Chang, Weijing Zhou and Zhilong Jian
Aerospace 2024, 11(5), 388; https://doi.org/10.3390/aerospace11050388 - 13 May 2024
Abstract
The ablation impulse of typical asteroid simulants irradiated by a nanosecond pulsed laser has been investigated in a vacuum environment. A torsional pendulum measurement system was constructed to calculate the impulse of laser ablation. A 10 ns pulsed laser was used, with a
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The ablation impulse of typical asteroid simulants irradiated by a nanosecond pulsed laser has been investigated in a vacuum environment. A torsional pendulum measurement system was constructed to calculate the impulse of laser ablation. A 10 ns pulsed laser was used, with a 1064 nm wavelength, a 900 mJ maximum pulse energy, and a millimeter-scale ablation spot diameter. Impulsive coupling characteristics of six typical targets that imitate the substance of asteroids with various laser fluences were analyzed. Furthermore, the impulse coupling coefficient curves of different materials were fitted. The results reveal that the minimum laser fluence corresponding to a measurable ablation impulse is approximately 2.5 J/cm2, and the optimum laser fluence corresponding to the maximum impulse coupling coefficient is approximately 14.0 J/cm2. The trends of the laser ablation impulse coupling curves are roughly consistent for the six materials. Impulse coupling characteristics of the six typical materials can be represented by the same polynomial within a 95% confidence interval, so a unified rule has been given. In actual deflection tasks of asteroids, the unified impulse coupling characteristic can be used to implement laser deflection techniques, especially when the material of the asteroid cannot be accurately judged in time.
Full article
(This article belongs to the Special Issue Laser Propulsion Science and Technology)
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Open AccessReview
A Survey of Flow Field and Combustion Characteristics under Subatmospheric Pressure
by
Guoyu Ding, Zhaohui Yao, Zhixiang Zhu and Yakun Huang
Aerospace 2024, 11(5), 387; https://doi.org/10.3390/aerospace11050387 - 13 May 2024
Abstract
This paper presents a summary of and introduction to research on high-altitude and subatmospheric combustion concerning turbine and scramjet engines. The investigation includes theoretical analysis, experimental studies, and numerical simulations. The analysis encompasses the flow field structure, fuel atomization, and combustion performance. Subsequently,
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This paper presents a summary of and introduction to research on high-altitude and subatmospheric combustion concerning turbine and scramjet engines. The investigation includes theoretical analysis, experimental studies, and numerical simulations. The analysis encompasses the flow field structure, fuel atomization, and combustion performance. Subsequently, recent research on the combustion performance of liquid fuels, solid fuels, and gaseous fuels under high-altitude and low-pressure plateau environments is reviewed. This includes an evaluation of flame height, flame temperature, combustion rate, fire spread rate, and heat radiation flux. Additionally, combustion performance prediction models for high-altitude environments based on experimental and theoretical analysis have been introduced. Lastly, issues in subatmospheric combustion in the aerospace and plateau fire fields are presented based on the current research.
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(This article belongs to the Section Aeronautics)
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Open AccessArticle
A DRL-Based Satellite Service Allocation Method in LEO Satellite Networks
by
Yafei Zhao, Jiaen Zhou, Zhenrui Chen and Xinyang Wang
Aerospace 2024, 11(5), 386; https://doi.org/10.3390/aerospace11050386 - 13 May 2024
Abstract
Satellite computing represents a recent computational paradigm in the development of low Earth orbit (LEO) satellites. It aims to augment the capabilities of LEO satellites beyond their current transparent relay functions by enabling real-time processing, thereby providing low-latency computational services to end users.
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Satellite computing represents a recent computational paradigm in the development of low Earth orbit (LEO) satellites. It aims to augment the capabilities of LEO satellites beyond their current transparent relay functions by enabling real-time processing, thereby providing low-latency computational services to end users. In LEO constellations, a significant deployment of computationally capable satellites is orchestrated to offer enhanced computational resources. Challenges arise in the optimal allocation of terminal services to the most suitable satellite due to overlapping coverage among neighboring satellites, compounded by constraints on satellite energy and computational resources. The satellite service allocation (SSA) problem is recognized as NP-hard, yet assessing allocation methods through results allows for the application of deep reinforcement learning (DRL) to obtain improved solutions, partially addressing the SSA challenge. In this paper, we introduce a satellite computing capability model to quantify satellite computational resources. A DRL model is proposed to address service demands, computational resources, and resolve service allocation conflicts, strategically placing each service on appropriate servers. Through simulation experiments, numerical results demonstrate the superiority of our proposed method over baseline approaches in service allocation and satellite resource utilization, showcasing advancements in this field.
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(This article belongs to the Section Astronautics & Space Science)
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Open AccessArticle
Design and Analysis of a Base Bleed Unit for the Drag Reduction of a High-Power Rocket Operating at Transonic Speeds
by
Petros Famellos, Athanasios Skevas, Asterios Koutsiadis, Christos Koutsouras and Pericles Panagiotou
Aerospace 2024, 11(5), 385; https://doi.org/10.3390/aerospace11050385 - 12 May 2024
Abstract
In the present study, a passive flow device is considered for drag reduction purposes through implementation in a transonic high-power rocket. The high-power rocket serves as a reference platform that, apart from the operating conditions, enforces several constraints in terms of available volume
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In the present study, a passive flow device is considered for drag reduction purposes through implementation in a transonic high-power rocket. The high-power rocket serves as a reference platform that, apart from the operating conditions, enforces several constraints in terms of available volume and placement locations. A step-by-step methodology is suggested, where the unit is initially broken down into an inlet and an outlet component. The flow field is investigated by means of computational modeling (CFD), where the Reynolds-averaged Navier–Stokes equations are solved coupled with turbulence models that vary depending on the design phase and the individual component. In the first design phase, the best alternative configuration is selected for each component by comparing mass flow rates and discharge coefficients. In the second design phase, each component is analyzed in greater detail based on the first phase results. Indicatively, the protruding inlet diffuser-type channel is converted into a protruding inlet nozzle-type channel to avoid choked flow phenomena, and a nozzle geometry is selected as the outlet amongst the other considered scenarios. The two components are eventually integrated into a common base bleed unit and a final assessment is made. The computational results are used to predict the performance and trajectory of the rocket through a well-established trajectory software. The overall methodology is validated against full-scale test flight data. The results show that the base bleed unit developed in the framework of this study yields a drag reduction of approximately 15% at transonic speeds without impacting the rocket mass and stability.
Full article
(This article belongs to the Section Aeronautics)
Open AccessArticle
Implementation and Verification of a Micro-Jet-Vane System of a Solid Rocket Motor for a Micro-Nano Satellite
by
Gang Zhang, Wen Feng, Youwen Tan, Yang Liu and Weihua Hui
Aerospace 2024, 11(5), 384; https://doi.org/10.3390/aerospace11050384 - 10 May 2024
Abstract
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To achieve rapid vector maneuvering of a space micro-nano satellite, a micro-sized solid rocket motor was utilized as its propulsion system, and a micro-jet-vane-thrust-vector control system was devised. Computational fluid dynamics (CFD) numerical simulations were conducted on the designed micro-vane structure at various
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To achieve rapid vector maneuvering of a space micro-nano satellite, a micro-sized solid rocket motor was utilized as its propulsion system, and a micro-jet-vane-thrust-vector control system was devised. Computational fluid dynamics (CFD) numerical simulations were conducted on the designed micro-vane structure at various deflection angles to ascertain the lateral force and flow field characteristics. The motor’s combustion temperature is 1380 K. Therefore, materials such as 45 steel, alumina ceramics, and tungsten–molybdenum alloy were chosen for the jet vanes to carry out ground-based-motor-jet-ablation experiments and measure the ablation amount. Concurrently, experimental data, including lateral force, were gathered. The tests demonstrated that despite 45 steel having a higher melting point than the combustion temperature significant ablation still occurred. Alumina ceramics exhibited defects and experienced ablation and fragmentation post-test. In contrast, tungsten–molybdenum alloy, being a refractory metal, showed minimal ablation after testing, making it an ideal material for micro-jet vanes. At a 20° deflection of the jet vanes, the lateral force calculated via numerical simulation was 3.76 N, whereas the lateral force obtained from the test was approximately 3.8 N, resulting in an error within 1% and validating the numerical simulation’s validity and accuracy. The jet vanes can generate a maximum steering angle of 8°, thus ensuring the micro-nano satellite’s swift vector maneuvering at large angles.
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Open AccessArticle
Validating Synthetic Data for Perception in Autonomous Airport Navigation Tasks
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Miguel Ángel de Frutos Carro, Carlos Cerdán Villalonga and Antonio Barrientos Cruz
Aerospace 2024, 11(5), 383; https://doi.org/10.3390/aerospace11050383 - 10 May 2024
Abstract
Autonomous navigation within airport environments presents significant challenges, mostly due to the scarcity of accessible and labeled data for training autonomous systems. This study introduces an innovative approach to assess the performance of vision-based models trained on synthetic datasets, with the goal of
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Autonomous navigation within airport environments presents significant challenges, mostly due to the scarcity of accessible and labeled data for training autonomous systems. This study introduces an innovative approach to assess the performance of vision-based models trained on synthetic datasets, with the goal of determining whether simulated data can train and validate navigation operations in complex airport environments. The methodology includes a comparative analysis employing image processing techniques and object detection algorithms. A comparative analysis of two different datasets was conducted: a synthetic dataset that mirrors real airport scenarios, generated using the Microsoft Flight Simulator 2020®video game, and a real-world dataset. The results indicate that models trained on a combination of both real and synthetic images perform much better in terms of adaptability and accuracy compared to those trained only on one type of dataset. This analysis makes a significant contribution to the field of autonomous airport navigation and offers a cost-effective and practical solution to overcome the challenges of dataset acquisition and algorithm validation. It is thus believed that this study lays the groundwork for future advancements in the field.
Full article
(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management (2nd Edition))
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Open AccessArticle
Operational Reliability Analysis of Turbine Blisk Using an Enhanced Moving Neural Network Framework
by
Xiao Liang, Wei Sun, Qingchao Sun and Chengwei Fei
Aerospace 2024, 11(5), 382; https://doi.org/10.3390/aerospace11050382 - 9 May 2024
Abstract
As one of the key components of an aeroengine, turbine blisk endures complex coupling loads under a harsh operational environment so that the reliability of turbine blisk directly influences the safe operation of aeroengine. It is urgent to precisely perform the reliability estimation
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As one of the key components of an aeroengine, turbine blisk endures complex coupling loads under a harsh operational environment so that the reliability of turbine blisk directly influences the safe operation of aeroengine. It is urgent to precisely perform the reliability estimation of a complex blisk structure. To address this issue, an enhanced Moving Neural Network Framework (MNNF) is proposed by integrating compact support region theory, improve sooty tern optimization algorithm (ISTOA), and Bayesian regularization strategy into artificial neural network. The compact support region theory is applied to select the efficient samples for modeling from the training samples set, the ISTOA is to determine the optimal compact support region, and Bayesian regularization thought is utilized to improve the generalization ability of neural network model. The operational reliability assessment of aeroengine blisk is performed with the consideration of transient loads to verify the proposed MNNF method. It is shown that the reliability degree of turbine blisk stain is 0.9984 when the allowable value is 5.2862 × 10−3 m. In line with the comparison of methods, the developed MNNF approach has 0.99738 in root means square error, 3.1634 × 10−4 m in goodness of fit, 0.423 s in modeling time, 99.99% in simulation precision, and 0.496 s in simulation time under 10,000 simulations, which are superior to all other methods (i.e., 99.96%, 99.91%, 99.93%, 99.97%, and 99.97% in simulation precision and 16.27%, 4.82%, 30.07%, 39.87%, and 23.59% in simulation efficiency, for the response surface method (RSM), Kriging, support vector machine (SVM), back propagation-artificial neural network (BP-NN), and BP-NN based on particle swarm optimization (BP-PSO) methods, respectively). It is demonstrated that the MNNF method holds excellent modeling and simulation performances. The efforts of this study provide promising tools and insights into the reliability design of complex structures, and enrich and develop reliability theory.
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(This article belongs to the Section Aeronautics)
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Open AccessArticle
Mechanism of Evolution of Shock Wave of Muzzle Jet under Initial Interference and Its Simplified Model
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Zijie Li and Hao Wang
Aerospace 2024, 11(5), 381; https://doi.org/10.3390/aerospace11050381 - 9 May 2024
Abstract
Large-caliber and long-barrel weapons are important experimental devices for exploring the impact resistance and reliability of warheads. The force of impact of the muzzle jet has a significant influence on the overload resistance of the warhead and surrounding devices. The mechanism of motion
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Large-caliber and long-barrel weapons are important experimental devices for exploring the impact resistance and reliability of warheads. The force of impact of the muzzle jet has a significant influence on the overload resistance of the warhead and surrounding devices. The mechanism of motion of the body inside the tube cannot be ignored owing to the high kinetic energy at the muzzle. In this study, we designed the relevant experiment and a simulation model to analyze the structural characteristics and mechanism of evolution of the shock wave and the vortex structure in a muzzle jet. The aim was to examine the evolution of the shock wave with initial jet-induced interference. And we established three other simulation models to compare the similarities and differences between the results of the models. The results showed that, in the original complex model, the initial jet restricted the free expansion of the muzzle jet, and this led to many shock–shock collisions that retarded the development of the shock waves. Multiple reflected shock waves were thus formed under a high local pressure that distorted the shock structure, while the structure of the shock wave in the simplified models was clear and simple. The parameters of motion of the body changed by a little when the initial jet-induced interference was ignored. The difference in values of the strongest impact force measured at monitoring points far from the muzzle was small, with an error of about 2%, such that the simplified model without the initial jet could be used in place of the original complex model. The other simplified models yielded significant differences. Our results provide an important theoretical basis for further research on the muzzle jet and its applications in engineering.
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(This article belongs to the Special Issue Shock-Dominated Flow)
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Open AccessArticle
Rapid Approximation of Low-Thrust Spacecraft Reachable Sets within Complex Two-Body and Cislunar Dynamics
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Sean Bowerfind and Ehsan Taheri
Aerospace 2024, 11(5), 380; https://doi.org/10.3390/aerospace11050380 - 9 May 2024
Abstract
The reachable set of controlled dynamical systems is the set of all reachable states from an initial condition over a certain time horizon, subject to operational constraints and exogenous disturbances. In astrodynamics, rapid approximation of reachable sets is invaluable for trajectory planning, collision
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The reachable set of controlled dynamical systems is the set of all reachable states from an initial condition over a certain time horizon, subject to operational constraints and exogenous disturbances. In astrodynamics, rapid approximation of reachable sets is invaluable for trajectory planning, collision avoidance, and ensuring safe and optimal performance in complex dynamics. Leveraging the connection between minimum-time trajectories and the boundary of reachable sets, we propose a sampling-based method for rapid and efficient approximation of reachable sets for finite- and low-thrust spacecraft. The proposed method combines a minimum-time multi-stage indirect formulation with the celebrated primer vector theory. Reachable sets are generated under two-body and circular restricted three-body (CR3B) dynamics. For the two-body dynamics, reachable sets are generated for (1) the heliocentric phase of a benchmark Earth-to-Mars problem, (2) two scenarios with uncertainties in the initial position and velocity of the spacecraft at the time of departure from Earth, and (3) a scenario with a bounded single impulse at the time of departure from Earth. For the CR3B dynamics, several cislunar applications are considered, including L1 Halo orbit, L2 Halo orbit, and Lunar Gateway 9:2 NRHO. The results indicate that low-thrust spacecraft reachable sets coincide with invariant manifolds existing in multi-body dynamical environments. The proposed method serves as a valuable tool for qualitatively analyzing the evolution of reachable sets under complex dynamics, which would otherwise be either incoherent with existing grid-based reachability approaches or computationally intractable with a complete Hamilton–Jacobi–Bellman method.
Full article
(This article belongs to the Topic Innovation and Inventions in Aerospace and UAV Applications)
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Open AccessArticle
Preliminary Performance Analysis of Medium-Range Liquid Hydrogen-Powered Box-Wing Aircraft
by
Giuseppe Palaia, Karim Abu Salem and Erasmo Carrera
Aerospace 2024, 11(5), 379; https://doi.org/10.3390/aerospace11050379 - 9 May 2024
Abstract
This paper proposes a performance analysis of a medium-range airliner powered by liquid hydrogen (LH2) propulsion. The focus is on operating performance in terms of achievable payload and range. A non-conventional box-wing architecture was selected to maximize operating performance. An optimization-based
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This paper proposes a performance analysis of a medium-range airliner powered by liquid hydrogen (LH2) propulsion. The focus is on operating performance in terms of achievable payload and range. A non-conventional box-wing architecture was selected to maximize operating performance. An optimization-based multidisciplinary design framework was developed to retrofit a baseline medium-range box-wing aircraft by designing and integrating the fuel tanks needed to store the LH2; several solutions were investigated for tank arrangement and layout by means of sensitivity analyses. As a main outcome, a performance analysis of the proposed LH2-powered box-wing aircraft is provided, highlighting the impact of the introduction of this energy carrier (and the integration of the related tank systems) on aircraft operating performance; a comparative study with respect to a competitor LH2-retrofitted tube-and-wing aircraft is also provided, to highlight the main possible operating differences between the two architectures. The findings reveal that the retrofitted box-wing can achieve long-range flights at the cost of a substantially reduced payload, mainly due to the volume limitations imposed by the installation of LH2 tanks, or it can preserve payload capacity at the expense of a significant reduction in range, as the trade-off implies a reduction in on-board LH2 mass. Specifically, the studied box-wing configuration can achieve a range of 7100 km transporting 150 passengers, or shorter ranges of 2300 km transporting 230 passengers. The competitor LH2-retrofitted tube-and-wing aircraft, operating in the same category and compatible with the same airport apron constraints, could achieve a distance of 1500 km transporting 110 passengers.
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(This article belongs to the Special Issue Multidisciplinary Design Optimization for Climate-Neutral Transport Aviation)
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Open AccessArticle
A Study on Disrupted Flight Recovery Based on Logic-Based Benders Decomposition Method
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Yunfang Peng, Xuechun Hu and Beixin Xia
Aerospace 2024, 11(5), 378; https://doi.org/10.3390/aerospace11050378 - 9 May 2024
Abstract
Aiming at the disrupted flight recovery problem, this paper established a mixed-integer programming model based on the resource assignment model to minimize the recovery cost. To deal with the large-scale examples, the Logic-Based Benders decomposition algorithm is designed to divide the problem into
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Aiming at the disrupted flight recovery problem, this paper established a mixed-integer programming model based on the resource assignment model to minimize the recovery cost. To deal with the large-scale examples, the Logic-Based Benders decomposition algorithm is designed to divide the problem into a master problem and sub-problems. The algorithm uses MIP in the master problem to determine flight cancellations and aircraft replacements. In the sub-problems, MIP or CP is used to determine the departure time of delayed flights. Later, incorporating sectional constraints into the main problem and iterating until an optimal solution is obtained. Furthermore, the added cutting plane constraint in the iterations of the Benders decomposition algorithm are strengthened to eliminate more inferior solutions. By comparing the results of CPLEX, the Logic-Based Benders decomposition algorithm, and the enhanced Benders decomposition algorithm, it is verified that the improved Benders decomposition algorithm can solve large-scale examples more efficiently with a faster time and fewer iterations.
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(This article belongs to the Special Issue Advances in Air Traffic and Airspace Control and Management (2nd Edition))
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Fast Aerodynamic Prediction of Airfoil with Trailing Edge Flap Based on Multi-Task Deep Learning
by
Chi Zhang, Zhiyuan Hu, Yongjie Shi and Guohua Xu
Aerospace 2024, 11(5), 377; https://doi.org/10.3390/aerospace11050377 - 9 May 2024
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Conventional methods for solving Navier–Stokes (NS) equations to analyze flow fields and aerodynamic forces of airfoils with trailing edge flaps (TEFs) are known for their significant time cost. This study presents a Multi-Task Swin Transformer (MT-Swin-T) deep learning framework tailored for swift prediction
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Conventional methods for solving Navier–Stokes (NS) equations to analyze flow fields and aerodynamic forces of airfoils with trailing edge flaps (TEFs) are known for their significant time cost. This study presents a Multi-Task Swin Transformer (MT-Swin-T) deep learning framework tailored for swift prediction of velocity fields and aerodynamic coefficients of TEF-equipped airfoils. The proposed model combines a Swin Transformer (Swin-T) for flow field prediction with a multi-layer perceptron (MLP) dedicated to lift coefficient prediction. Both networks undergo gradient updates through the shared encoder component of the Swin Transformer. Such a trained network model for computational fluid dynamics simulations is both effective and robust, significantly improving the efficiency of complex aerodynamic shape design optimization and flow control. The study further investigates the impact of integrating multi-task learning loss functions, skip connections, and the network’s structural design on prediction accuracy. Additionally, the effectiveness of deep learning in improving the aerodynamic simulation efficiency of airfoils with TEF is examined. Results demonstrate that the multi-task deep learning approach provides accurate predictions for TEF airfoil flow fields and lift coefficients. The strategic combination of these tasks during network training, along with the optimal selection of loss functions, significantly enhances prediction accuracy compared with the single-task network. In a specific case study, the MT-Swin-T model demonstrated a prediction time that was 1/7214 of the time necessitated by CFD simulation.
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Open AccessArticle
Minimum-Data-Driven Guidance for Impact Angle Control
by
Chang Liu, Jiang Wang, Hongyan Li and Weipeng Liu
Aerospace 2024, 11(5), 376; https://doi.org/10.3390/aerospace11050376 - 8 May 2024
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This paper investigates the impact-angle-control guidance problem for varying-speed flight vehicles with constrained acceleration. A learning-based bias proportional navigation guidance (L-BPN) law is proposed to achieve impact-angle-constrained impact by constructing a deep neural network (DNN) for nonlinear mapping between the impact angle and
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This paper investigates the impact-angle-control guidance problem for varying-speed flight vehicles with constrained acceleration. A learning-based bias proportional navigation guidance (L-BPN) law is proposed to achieve impact-angle-constrained impact by constructing a deep neural network (DNN) for nonlinear mapping between the impact angle and the bias term. During the process of dataset establishment, the impact of state variables is evaluated by sensitivity analysis to minimize the quantity of training data. This approach also effectively accelerates sample generation and improves the training efficiency. The simulation results verify the effectiveness of the proposed L-BPN law and demonstrate its advantages over the existing algorithms.
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Open AccessArticle
Dynamic Encircling Cooperative Guidance for Intercepting Superior Target with Overload, Impact Angle and Simultaneous Time Constraints
by
Dengfeng Yang and Xiaodong Yan
Aerospace 2024, 11(5), 375; https://doi.org/10.3390/aerospace11050375 - 8 May 2024
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This paper proposes a dynamic encircling cooperative guidance (DECG) law to enable multiple interceptors to cooperatively intercept a superior target, considering low velocity, limited overload, impact angle and simultaneous arrival constraints. First, the feasible escaping area of the target is analyzed and a
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This paper proposes a dynamic encircling cooperative guidance (DECG) law to enable multiple interceptors to cooperatively intercept a superior target, considering low velocity, limited overload, impact angle and simultaneous arrival constraints. First, the feasible escaping area of the target is analyzed and a dynamic encircling strategy for the target is established. This strategy efficiently provides virtual escaping points, allowing interceptors to dynamically encircle the target without excessive energy consumption, ultimately leading to a successful interception. Second, to enhance the physical feasibility of the kinematic equations governing the interaction between interceptors and target at the virtual escaping points, the independent variable is substituted and the kinematic equations are remodeled. Convex optimization is employed to address the multi-constraint optimal guidance problem for each interceptor, thereby facilitating simultaneous interception. Compared with the existing guidance laws, DECG has a more practical and feasible cooperative strategy, is able to handle more constraints including the interceptor’s own constraints and cooperative constraints, and does not rely on the precise calculation of explicit remaining flight time in the guidance law implementation. Lastly, the effectiveness, superiority and robustness of the DECG law are evaluated through a series of numerical simulations, and its performance is compared with that of the cooperative proportional navigation guidance law (CPNG).
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Open AccessArticle
Broadband Noise Reduction of a Two-Stage Fan with Wavy Trailing-Edge Blades
by
Ruibiao Gao, Weijie Chen, Hang Tong, Jianxin Lian and Weiyang Qiao
Aerospace 2024, 11(5), 374; https://doi.org/10.3390/aerospace11050374 - 8 May 2024
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In this paper, a numerical investigation is performed to study the broadband noise of a fan stage with wavy trailing-edge blades. A study of the wavelength and ratio of amplitude to wavelength (H/L) is conducted to better understand the noise reduction effect of
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In this paper, a numerical investigation is performed to study the broadband noise of a fan stage with wavy trailing-edge blades. A study of the wavelength and ratio of amplitude to wavelength (H/L) is conducted to better understand the noise reduction effect of wavy trailing-edge blades. A rotor–stator interaction broadband noise prediction method based on the result of a Reynolds-averaged Navier–Stokes equation is used. The results show that all wavy trailing-edge configurations reduce the sound power level of the fan stage. The noise reduction effect of H20L10 is the best among all the wavy trailing-edge configurations, and the sound power level is reduced by 2.4 dB at 1000 Hz. When the H/L remains unchanged, the noise reduction effect of the wavy trailing-edge configuration increases with the increase in wavelength. When the wavelength remains unchanged, the noise reduction effect of the wavy trailing-edge configuration with an H/L of 2 is the best. The use of wavy trailing-edge configurations reduces the turbulent kinetic energy and turbulent integral length scale upstream of the stator by changing the wake of the rotor, thereby reducing the rotor–stator interaction broadband noise of the fan stage.
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