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
Predicting Air Traffic Congestion under Uncertain Adverse Weather
Aerospace 2024, 11(3), 240; https://doi.org/10.3390/aerospace11030240 - 19 Mar 2024
Abstract
This paper presents an approach for integrating uncertainty information in air traffic flow management at the tactical phase. In particular, probabilistic methodologies to predict sector demand and sector congestion under adverse weather in a time horizon of 1.5 h are developed. Two sources
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This paper presents an approach for integrating uncertainty information in air traffic flow management at the tactical phase. In particular, probabilistic methodologies to predict sector demand and sector congestion under adverse weather in a time horizon of 1.5 h are developed. Two sources of uncertainty are considered: the meteorological uncertainty inherent to the forecasting process and the uncertainty in the take-off time. An ensemble approach is adopted to characterize both uncertainty sources. The methodologies rely on a trajectory predictor able to generate an ensemble of 4D trajectories that provides a measure of the trajectory uncertainty, each trajectory avoiding the storm cells encountered along the way. The core of the approach is the statistical processing of the ensemble of trajectories to obtain probabilistic entry and occupancy counts of each sector and their congestion status when the counts are compared to weather-dependent capacity values. A new criterion to assess the risk of sector overload, which takes into account the uncertainty, is also defined. The results are presented for a historical situation over the Austrian airspace on a day with significant convection.
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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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A Novel Optimal Sensor Placement Method for Optimizing the Diagnosability of Liquid Rocket Engine
by
Meng Ma, Zhirong Zhong, Zhi Zhai and Ruobin Sun
Aerospace 2024, 11(3), 239; https://doi.org/10.3390/aerospace11030239 - 19 Mar 2024
Abstract
There are hundreds of various sensors used for online Prognosis and Health Management (PHM) of LREs. Inspired by the fact that a limited number of key sensors are selected for inflight control purposes in LRE, it is practical to optimal placement of redundant
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There are hundreds of various sensors used for online Prognosis and Health Management (PHM) of LREs. Inspired by the fact that a limited number of key sensors are selected for inflight control purposes in LRE, it is practical to optimal placement of redundant sensors for improving the diagnosability and economics of PHM systems. To strike a balance between sensor cost, real-time performance and diagnosability of the fault diagnosis algorithm in LRE, this paper proposes a novel Optimal Sensor Placement (OSP) method. Firstly, a Kernel Extreme Learning Machine-based (KELM) two-stage diagnosis algorithm is developed based on a system-level failure simulation model of LRE. Secondly, hierarchical diagnosability metrics are constructed to formulate the OSP problem in this paper. Thirdly, a Hierarchy Ranking Evolutionary Algorithm-based (HREA) two-stage OSP method is developed, achieving further optimization of Pareto solutions by the improved hypervolume indicator. Finally, the proposed method is validated using failure simulation datasets and hot-fire test-run experiment datasets. Additionally, four classical binary multi-objective optimization algorithms are introduced for comparison. The testing results demonstrate that the HREA-based OSP method outperforms other classical methods in effectively balancing the sensor cost, real-time performance and diagnosability of the diagnosis algorithm. The proposed method in this paper implements system-level OSP for LRE fault diagnosis and exhibits the potential for application in the development of reusable LREs.
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(This article belongs to the Section Astronautics & Space Science)
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Adaptive Turbulence Model for Leading Edge Vortex Flows Preconditioned by a Hybrid Neural Network
by
Moritz Zieher and Christian Breitsamter
Aerospace 2024, 11(3), 238; https://doi.org/10.3390/aerospace11030238 - 18 Mar 2024
Abstract
Eddy-viscosity-based turbulence models provide the most commonly used modeling approach for computational fluid dynamics simulations in the aerospace industry. These models are very accurate at a relatively low cost for many cases but lack accuracy in the case of highly rotational leading edge
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Eddy-viscosity-based turbulence models provide the most commonly used modeling approach for computational fluid dynamics simulations in the aerospace industry. These models are very accurate at a relatively low cost for many cases but lack accuracy in the case of highly rotational leading edge vortex flows for mid to low aspect-ratio wings. An enhanced adaptive turbulence model based on the one-equation Spalart–Allmaras turbulence model is fundamental to this work. This model employs several additional coefficients and source terms, specifically targeting vortex-dominated flow regions, where these coefficients can be calibrated by an optimization procedure based on experimental or high-fidelity numerical data. To extend the usability of the model from single or cluster-wise calibrated cases, this work presents a preconditioning approach of the turbulence model via a neural network. The neural network provides a case-unspecific calibration approach, enabling the use of the model for many known or unknown cases. This extension enables aircraft design teams to perform low-cost Reynolds-averaged Navier–Stokes simulations with increased accuracy instead of complex and costly high-fidelity simulations.
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(This article belongs to the Special Issue Data-Driven Aerodynamic Modeling)
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Modeling Wind and Obstacle Disturbances for Effective Performance Observations and Analysis of Resilience in UAV Swarms
by
Abhishek Phadke, F. Antonio Medrano, Tianxing Chu, Chandra N. Sekharan and Michael J. Starek
Aerospace 2024, 11(3), 237; https://doi.org/10.3390/aerospace11030237 - 18 Mar 2024
Abstract
UAV swarms have multiple real-world applications but operate in a dynamic environment where disruptions can impede performance or stop mission progress. Ideally, a UAV swarm should be resilient to disruptions to maintain the desired performance and produce consistent outputs. Resilience is the system’s
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UAV swarms have multiple real-world applications but operate in a dynamic environment where disruptions can impede performance or stop mission progress. Ideally, a UAV swarm should be resilient to disruptions to maintain the desired performance and produce consistent outputs. Resilience is the system’s capability to withstand disruptions and maintain acceptable performance levels. Scientists propose novel methods for resilience integration in UAV swarms and test them in simulation scenarios to gauge the performance and observe the system response. However, current studies lack a comprehensive inclusion of modeled disruptions to monitor performance accurately. Existing approaches in compartmentalized research prevent a thorough coverage of disruptions to test resilient responses. Actual resilient systems require robustness in multiple components. The challenge begins with recognizing, classifying, and implementing accurate disruption models in simulation scenarios. This calls for a dedicated study to outline, categorize, and model interferences that can be included in current simulation software, which is provided herein. Wind and in-path obstacles are the two primary disruptions, particularly in the case of aerial vehicles. This study starts a multi-step process to implement these disruptions in simulations accurately. Wind and obstacles are modeled using multiple methods and implemented in simulation scenarios. Their presence in simulations is demonstrated, and suggested scenarios and targeted observations are recommended. The study concludes that introducing previously absent and accurately modeled disruptions, such as wind and obstacles in simulation scenarios, can significantly change how resilience in swarm deployments is recorded and presented. A dedicated section for future work includes suggestions for implementing other disruptions, such as component failure and network intrusion.
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(This article belongs to the Special Issue UAV System Modelling Design and Simulation)
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Generation of Secondary Space Debris Risks from Net Capturing in Active Space Debris Removal Missions
by
Michal Cuadrat-Grzybowski and Eberhard Gill
Aerospace 2024, 11(3), 236; https://doi.org/10.3390/aerospace11030236 - 18 Mar 2024
Abstract
Mitigation strategies to eliminate existing space debris, such as with Active Space Debris Removal (ASDR) missions, have become increasingly important. Among the considered ASDR approaches, one involves using a net as a capturing mechanism. A fundamental requirement for any ASDR mission is that
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Mitigation strategies to eliminate existing space debris, such as with Active Space Debris Removal (ASDR) missions, have become increasingly important. Among the considered ASDR approaches, one involves using a net as a capturing mechanism. A fundamental requirement for any ASDR mission is that the capture process itself should not give rise to new space debris. However, in simulations of net capturing, the potential for structural breaking is often overlooked. A discrete Multi-Spring-Damper net model was employed to simulate the impact of a 30 m × 30 m net travelling at 20 m/s onto an ESA Envisat mock-up. The Envisat was modelled as a two-rigid-body system comprised of the main body and a large solar array with a hinge connection. The analysis revealed that more than two significant substructures had a notable likelihood of breaking, prompting the recommendation of limiting the impacting velocity. The generation of secondary space debris indicates that net capturing is riskier than previously assumed in the literature.
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(This article belongs to the Special Issue Guidance, Navigation and Control Algorithms for Satellite Formation Flying)
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A Radio Frequency Fingerprinting-Based Aircraft Identification Method Using ADS-B Transmissions
by
Gursu Gurer, Yaser Dalveren, Ali Kara and Mohammad Derawi
Aerospace 2024, 11(3), 235; https://doi.org/10.3390/aerospace11030235 - 17 Mar 2024
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The automatic dependent surveillance broadcast (ADS-B) system is one of the key components of the next generation air transportation system (NextGen). ADS-B messages are transmitted in unencrypted plain text. This, however, causes significant security vulnerabilities, leaving the system open to various types of
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The automatic dependent surveillance broadcast (ADS-B) system is one of the key components of the next generation air transportation system (NextGen). ADS-B messages are transmitted in unencrypted plain text. This, however, causes significant security vulnerabilities, leaving the system open to various types of wireless attacks. In particular, the attacks can be intensified by simple hardware, like a software-defined radio (SDR). In order to provide high security against such attacks, radio frequency fingerprinting (RFF) approaches offer reasonable solutions. In this study, an RFF method is proposed for aircraft identification based on ADS-B transmissions. Initially, 3480 ADS-B samples were collected by an SDR from eight aircrafts. The power spectral density (PSD) features were then extracted from the filtered and normalized samples. Furthermore, the support vector machine (SVM) with three kernels (linear, polynomial, and radial basis function) was used to identify the aircraft. Moreover, the classification accuracy was demonstrated via varying channel signal-to-noise ratio (SNR) levels (10–30 dB). With a minimum accuracy of 92% achieved at lower SNR levels (10 dB), the proposed method based on SVM with a polynomial kernel offers an acceptable performance. The promising performance achieved with even a small dataset also suggests that the proposed method is implementable in real-world applications.
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Design and Analysis of the Two-Impulse Transfer Orbit for a Space-Based Gravitational Wave Observatory
by
Zhuo Li, Huixiang Ling and Xiao Zhao
Aerospace 2024, 11(3), 234; https://doi.org/10.3390/aerospace11030234 - 16 Mar 2024
Abstract
There are plans to set up a space-based gravitational wave observatory that will use an ultra-large-scale laser interferometer in space to detect medium- and low-frequency gravitational waves. Both heliocentric and geocentric formations adopt the method of launching three satellites with one rocket, which
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There are plans to set up a space-based gravitational wave observatory that will use an ultra-large-scale laser interferometer in space to detect medium- and low-frequency gravitational waves. Both heliocentric and geocentric formations adopt the method of launching three satellites with one rocket, which has high requirements in terms of the carrying capacity of the rocket. Therefore, a proper transfer design is a prerequisite for achieving space-based gravitational wave detection. In this paper, the transfer orbit for three satellites of the Taiji mission is designed based on the two-impulse transfer model. Moreover, the influence on orbit design of the position of the formation relative to Earth, the initial phase angle of the formation, and the initial time of transfer is analyzed. The Earth-leading and -trailing transfers show opposite patterns in the above three aspects. A smaller velocity increment is required if a proper initial time is selected. After taking into account the stability of the formation, C3, the required velocity increment, transfer time, and the distance to Earth, 20° is determined to be the optimal initial trailing/leading angle.
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(This article belongs to the Special Issue Advanced Technology of Distributed Space Systems: Formation-Flying, Swarms, and Constellations)
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Derivation and Flight Test Validation of Maximum Rate of Climb during Takeoff for Fixed-Wing UAV Driven by Propeller Engine
by
Katsumi Watanabe, Takuma Shibata and Masazumi Ueba
Aerospace 2024, 11(3), 233; https://doi.org/10.3390/aerospace11030233 - 15 Mar 2024
Abstract
In recent years, the use of fixed-wing Unmanned Aerial Vehicles (UAVs) has expanded, and the use of fixed-wing UAVs is expected to expand due to their usefulness for long-range operations. Different from manned aircraft, no provision is required regarding climb angle at takeoff
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In recent years, the use of fixed-wing Unmanned Aerial Vehicles (UAVs) has expanded, and the use of fixed-wing UAVs is expected to expand due to their usefulness for long-range operations. Different from manned aircraft, no provision is required regarding climb angle at takeoff for fixed-wing UAVs. Therefore, fixed-wing UAVs can take off by taking advantage of their performance. In addition, propeller engines are the propulsion device currently used by most fixed-wing UAVs. However, the thrust force generated by a propeller engine decreases as its airspeed increases. In such circumstances, this paper describes how to derive a maximum rate of climb in which the characteristics of the propeller engine are taken into account, with the aim of reducing takeoff time by maximizing the rate of climb during takeoff. The derivation uses optimization problems with a dependency of the thrust force on the airspeed. After the derivation of the maximum rate of climb, we first checked whether the maximum rate of climb obtained for the mass system was feasible for takeoff at the rate of climb by using a 6-DOF flight simulation, and then confirmed its validity through flight experiments.
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(This article belongs to the Special Issue Flight Control)
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Online Learning-Based Surrogate Modeling of Stratospheric Airship Solar Array Output Power
by
Kangwen Sun, Siyu Liu, Huafei Du, Haoquan Liang and Xiao Guo
Aerospace 2024, 11(3), 232; https://doi.org/10.3390/aerospace11030232 - 15 Mar 2024
Abstract
The stratospheric airship is a type of aerostat that uses solar energy as its power source and can fly continuously for months or even years in near space. The rapid and accurate prediction of the output power of its solar array is the
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The stratospheric airship is a type of aerostat that uses solar energy as its power source and can fly continuously for months or even years in near space. The rapid and accurate prediction of the output power of its solar array is the key to maintaining energy balance and extending flight time. This paper establishes an online learning model for predicting the output power of the solar array of stratospheric airships. The readings of radiometers arranged on the surface of the airship are used as features for training the model. The parameters of the model can be updated in real-time during the flight process without retraining the entire model. The effect of radiometer placement on the model accuracy was also analyzed. The results show that for the continuous flight of 40 days, the online learning model can achieve an accuracy of 88% after training with 10 days of flight data and the accuracy basically reaches its highest level after 20 days. In addition, placing the radiometers at the four corners of the array can achieve a higher prediction accuracy of 95%. The online model can also accurately identify and reflect the effect of module efficiency attenuation or damage and maintain high accuracy.
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(This article belongs to the Collection Unmanned Aerial Systems)
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Optimizing Space Telescopes’ Thermal Performance through Uncertainty Analysis: Identification of Critical Parameters and Shaping Test Strategy Development
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Uxia Garcia-Luis, Alejandro M. Gomez-San-Juan, Fermin Navarro-Medina, Carlos Ulloa-Sande, Alfonso Yñigo-Rivera and Alba Eva Peláez-Santos
Aerospace 2024, 11(3), 231; https://doi.org/10.3390/aerospace11030231 - 15 Mar 2024
Abstract
The integration of uncertainty analysis methodologies allows for improving design efficiency, particularly in the context of instruments that demand precise pointing accuracy, such as space telescopes. Focusing on the VINIS Earth observation telescope developed by the Instituto de Astrofísica de Canarias (IAC), this
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The integration of uncertainty analysis methodologies allows for improving design efficiency, particularly in the context of instruments that demand precise pointing accuracy, such as space telescopes. Focusing on the VINIS Earth observation telescope developed by the Instituto de Astrofísica de Canarias (IAC), this paper reports an uncertainty analysis on a thermal model aimed at improving cost savings in the future testing phases. The primary objective was to identify critical parameters impacting thermal performance and reduce overdesign. Employing the Statistical Error Analysis (SEA) method across several operational scenarios, the research identifies key factors, including the Earth’s infrared temperature and albedo, and the spacecraft’s attitude and environmental conditions, as the variables with major influences on the system’s thermal performance. Ultimately, the findings suggest that uncertainty-based analysis is a potent tool for guiding thermal control system design in space platforms, promoting efficiency and reliability. This methodology not only provides a framework for optimizing thermal design and testing in space missions but also ensures that instruments like the VINIS telescope maintain optimal operating temperatures in diverse space environments, thereby increasing mission robustness and enabling precise resource allocation.
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(This article belongs to the Special Issue Space Telescopes & Payloads)
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Electric Sail Test Cube–Lunar Nanospacecraft, ESTCube-LuNa: Solar Wind Propulsion Demonstration Mission Concept
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Andris Slavinskis, Mario F. Palos, Janis Dalbins, Pekka Janhunen, Martin Tajmar, Nickolay Ivchenko, Agnes Rohtsalu, Aldo Micciani, Nicola Orsini, Karl Mattias Moor, Sergei Kuzmin, Marcis Bleiders, Marcis Donerblics, Ikechukwu Ofodile, Johan Kütt, Tõnis Eenmäe, Viljo Allik, Jaan Viru, Pätris Halapuu, Katriin Kristmann, Janis Sate, Endija Briede, Marius Anger, Katarina Aas, Gustavs Plonis, Hans Teras, Kristo Allaje, Andris Vaivads, Lorenzo Niccolai, Marco Bassetto, Giovanni Mengali, Petri Toivanen, Iaroslav Iakubivskyi, Mihkel Pajusalu and Antti Tammadd
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Aerospace 2024, 11(3), 230; https://doi.org/10.3390/aerospace11030230 - 14 Mar 2024
Abstract
The electric solar wind sail, or E-sail, is a propellantless interplanetary propulsion system concept. By deflecting solar wind particles off their original course, it can generate a propulsive effect with nothing more than an electric charge. The high-voltage charge is applied to one
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The electric solar wind sail, or E-sail, is a propellantless interplanetary propulsion system concept. By deflecting solar wind particles off their original course, it can generate a propulsive effect with nothing more than an electric charge. The high-voltage charge is applied to one or multiple centrifugally deployed hair-thin tethers, around which an electrostatic sheath is created. Electron emitters are required to compensate for the electron current gathered by the tether. The electric sail can also be utilised in low Earth orbit, or LEO, when passing through the ionosphere, where it serves as a plasma brake for deorbiting—several missions have been dedicated to LEO demonstration. In this article, we propose the ESTCube-LuNa mission concept and the preliminary cubesat design to be launched into the Moon’s orbit, where the solar wind is uninterrupted, except for the lunar wake and when the Moon is in the Earth’s magnetosphere. This article introduces E-sail demonstration experiments and the preliminary payload design, along with E-sail thrust validation and environment characterisation methods, a cis-lunar cubesat platform solution and an early concept of operations. The proposed lunar nanospacecraft concept is designed without a deep space network, typically used for lunar and deep space operations. Instead, radio telescopes are being repurposed for communications and radio frequency ranging, and celestial optical navigation is developed for on-board orbit determination.
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(This article belongs to the Special Issue Advances in CubeSat Sails and Tethers)
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A Ball-Contacting Dynamic Vibration Absorber with Adjustable Stiffness and Nonlinear Characteristics
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Ziqiang Hu, Lei Wei, Lin Yang, Yansong Wang and Yuanpeng Fan
Aerospace 2024, 11(3), 229; https://doi.org/10.3390/aerospace11030229 - 14 Mar 2024
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Structural vibration has always been a major concern in the engineering field. A dynamic vibration absorber in the form of contacts with adjustable stiffness (CDVA) offers effective vibration suppression and can improve conventional dynamic vibration absorbers with high sensitivity to frequency deviation and
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Structural vibration has always been a major concern in the engineering field. A dynamic vibration absorber in the form of contacts with adjustable stiffness (CDVA) offers effective vibration suppression and can improve conventional dynamic vibration absorbers with high sensitivity to frequency deviation and difficulty in adjusting the frequency. In this research, first, based on the theoretical model of the contact between a rubber ball and an inner cone, the feasibility of changing the axial contact state to change the structure’s natural frequency was verified using an ANSYS simulation. A theoretical model of the static contact stiffness between the ball and the inner cone was constructed using Hertzian contact theory and Hooke’s law, and a theoretical model of the cubic nonlinear elastic restoring force was used to characterize the stiffness properties of the rubber ball during compressive rebound. The steady-state frequency response equations of the main vibration structure were derived using the averaging method in conjunction with the two-degree-of-freedom dynamics model, and the stability of the solutions to the frequency response equations was obtained in conjunction with the stability determination criterion. Then, the impact of the CDVA’s design parameters on the nonlinear dynamic response of the primary vibration structure was simulated and analyzed. The resulting findings can serve as guidance for designing dynamic vibration absorber parameters. Based on the principles of ball-inner cone contact, a dynamic vibration absorber structure was proposed. A design test was conducted to verify the correctness of the contact stiffness model, and an experimental study was carried out to investigate the law of change in the dynamic stiffness and damping of the principle structure of CDVA under dynamic excitation conditions. Finally, the vibration test platform of the solidly supported beam structure was constructed, and vibration suppression tests of the CDVA in different compression states were conducted to investigate the tunability and feasibility of CDVA vibration suppression. The results showed that the dynamic vibration absorber had good vibration absorption characteristics and could be used for single-mode vibration suppression of multimodal main structures.
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Learning Fuel-Optimal Trajectories for Space Applications via Pontryagin Neural Networks
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Andrea D’Ambrosio and Roberto Furfaro
Aerospace 2024, 11(3), 228; https://doi.org/10.3390/aerospace11030228 - 14 Mar 2024
Abstract
This paper demonstrates the utilization of Pontryagin Neural Networks (PoNNs) to acquire control strategies for achieving fuel-optimal trajectories. PoNNs, a subtype of Physics-Informed Neural Networks (PINNs), are tailored for solving optimal control problems through indirect methods. Specifically, PoNNs learn to solve the Two-Point
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This paper demonstrates the utilization of Pontryagin Neural Networks (PoNNs) to acquire control strategies for achieving fuel-optimal trajectories. PoNNs, a subtype of Physics-Informed Neural Networks (PINNs), are tailored for solving optimal control problems through indirect methods. Specifically, PoNNs learn to solve the Two-Point Boundary Value Problem derived from the application of the Pontryagin Minimum Principle to the problem’s Hamiltonian. Within PoNNs, the Extreme Theory of Functional Connections (X-TFC) is leveraged to approximate states and costates using constrained expressions (CEs). These CEs comprise a free function, modeled by a shallow neural network trained via Extreme Learning Machine, and a functional component that consistently satisfies boundary conditions analytically. Addressing discontinuous control, a smoothing technique is employed, substituting the sign function with a hyperbolic tangent function and implementing a continuation procedure on the smoothing parameter. The proposed methodology is applied to scenarios involving fuel-optimal Earth−Mars interplanetary transfers and Mars landing trajectories. Remarkably, PoNNs exhibit convergence to solutions even with randomly initialized parameters, determining the number and timing of control switches without prior information. Additionally, an analytical approximation of the solution allows for optimal control computation at unencountered points during training. Comparative analysis reveals the efficacy of the proposed approach, which rivals state-of-the-art methods such as the shooting technique and the adaptive Gaussian quadrature collocation method.
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(This article belongs to the Special Issue GNC for the Moon, Mars, and Beyond)
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Investigation of the Effect of Magnetic Field and Propellant on Hall Thruster’s Stability via a 0D Model
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Luca Leporini, Ferhat Yaman, Tommaso Andreussi and Vittorio Giannetti
Aerospace 2024, 11(3), 227; https://doi.org/10.3390/aerospace11030227 - 14 Mar 2024
Abstract
Hall thrusters are plasma-based devices that have established themselves as one of the most attractive and mature electric propulsion technologies for space applications. These devices often operate in a regime characterized by low frequency, large amplitude oscillations of the discharge current, which is
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Hall thrusters are plasma-based devices that have established themselves as one of the most attractive and mature electric propulsion technologies for space applications. These devices often operate in a regime characterized by low frequency, large amplitude oscillations of the discharge current, which is commonly referred to as the ‘breathing mode’. The intensity of these oscillations depends on the thruster’s design and operating conditions and can reach values of the order of the average discharge current, posing issues for the thruster’s performance and for coupling with the driving electronics. A 0D model of the thruster discharge was developed to investigate the core physical mechanisms leading to the onset and sustenance of the breathing mode. The model was found to be capable of reproducing oscillations with characteristics in line with those observed in the breathing mode. In this work, we extend the use of the 0D model to investigate the effect of the magnetic field intensity and of different propellants on the system stability.
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(This article belongs to the Special Issue Numerical Simulations in Electric Propulsion)
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Approach and Landing Energy Prediction Based on a Long Short-Term Memory Model
by
Yahui Hu, Jiaqi Yan, Ertai Cao, Yimeng Yu, Haiming Tian and Heyuan Huang
Aerospace 2024, 11(3), 226; https://doi.org/10.3390/aerospace11030226 - 14 Mar 2024
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The statistical analysis of civil aircraft accidents reveals that the highest incidence of mishaps occurs during the approach and landing stages. Predominantly, these accidents are marked by abnormal energy states, leading to critical situations like stalling and heavy landings. Therefore, it is of
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The statistical analysis of civil aircraft accidents reveals that the highest incidence of mishaps occurs during the approach and landing stages. Predominantly, these accidents are marked by abnormal energy states, leading to critical situations like stalling and heavy landings. Therefore, it is of great significance to accurately predict the aircraft energy state in the approach and landing stages to ensure a safe landing. In this study, a deep learning method based on time sequence data for the prediction of the aircraft approach and landing energy states is proposed. Firstly, by conducting an extensive overview of the existing literature, three characteristic parameters of altitude, velocity, and glide angle were selected as the indicators to characterize the energy state. Following this, a semi-physical simulation platform for a certain type of aircraft was developed. The approach and landing experiments were carried out with different throttle sizes and flap deflection under different wind speeds and wind directions. Then, a deep learning prediction model based on Long Short-Term Memory (LSTM) was established based on the experimental data to predict the energy state indicators during the approach and landing phases. Finally, the established LSTM model underwent rigorous training and testing under different strategies, and a comparative analysis was carried out. The results demonstrated that the proposed LSTM model exhibited high accuracy and a strong generalization ability in predicting energy states during the approach and landing phases. These results offer a theoretical basis for designing energy early warning systems and formulating the relevant flight control laws in the approach and landing stages.
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Experimental and Numerical Investigation of a Novel Vortex Reducer in a Co-Rotating Cavity of Aeroengines
by
Wenjie Shen, Suofang Wang, Mengyuan Wang, Jia Suo and Zhao Zhang
Aerospace 2024, 11(3), 225; https://doi.org/10.3390/aerospace11030225 - 13 Mar 2024
Abstract
Improving airflow pressure is of great significance for the cooling and sealing of aeroengines. In a co-rotating cavity with radial inflow, vortex reducers are used to decrease the pressure drop. However, the performance of traditional vortex reducers is limited by their drag reduction
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Improving airflow pressure is of great significance for the cooling and sealing of aeroengines. In a co-rotating cavity with radial inflow, vortex reducers are used to decrease the pressure drop. However, the performance of traditional vortex reducers is limited by their drag reduction mechanism and cannot meet the needs of next-generation aeroengines. In this study, a novel vortex reducer (NVR) consisting of de-swirl shroud orifices and fins is proposed. Meanwhile, a design strategy is developed to ensure the NVR provides steady airflow and excellent drag reduction performance. Furthermore, experiments and numerical simulations are utilized to investigate the flow characteristics and drag reduction mechanism of the NVR. The results reveal that the de-swirl jets created by the de-swirl shroud orifices limit the enhancement of the Ekman layers at large radii, while the fins break down the high-speed vortices at small radii. Compared to a traditional finned vortex reducer with identical fins, the pressure drop of the NVR is relatively reduced by 28.52%. Specifically, the pressure drop of the NVR is monotonous in the operating range, indicating its suitability for engineering. Finally, a surrogate model and particle swarm optimization (PSO) are utilized to identify the optimal parameters of the de-swirl shroud orifices in the design range. This study provides a potential solution for the design of next-generation vortex reducers.
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(This article belongs to the Section Aeronautics)
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Guidance and Control for Safe Contactless Plume Impingement Operations to Detumble an Uncooperative Spacecraft
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Giacomo Borelli, Gabriella Gaias and Camilla Colombo
Aerospace 2024, 11(3), 224; https://doi.org/10.3390/aerospace11030224 - 13 Mar 2024
Abstract
In recent years, the interest in proximity operations to uncooperative and non-collaborative objects has been growing and and demanding for specific technology advances to tackle these challenging cases of in-orbit servicing and removal missions. Indeed, these architectures hold a crucial role in guaranteeing
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In recent years, the interest in proximity operations to uncooperative and non-collaborative objects has been growing and and demanding for specific technology advances to tackle these challenging cases of in-orbit servicing and removal missions. Indeed, these architectures hold a crucial role in guaranteeing future sustainable and efficient space operations. One of the main challenges of conducting robotic operations with a chaser in close proximity to an uncooperative object stems from its rotational motion. A tumbling motion of a large target object may require a costly and complex synchronisation of the servicer relative trajectory to the capture point and hinder the safety of operations due to rotating appendages. In this paper, the plume impingement strategy is employed to control the target’s tumbling motion in a contactless fashion, thus guaranteeing feasible approach and capture operations. Specifically, guidance and control strategies to be employed during this delicate and complex operation are devised, focusing on improving the safety of the trajectory while maximising the efficiency of the impingement effect during proximity flight. Simulations discuss the detumbling of a satellite of a large constellation, critically comparing delta-v cost, trajectory safety and overall time of operations.
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(This article belongs to the Section Astronautics & Space Science)
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Open AccessArticle
Lateral-Directional Aerodynamic Optimization of a Tandem Wing UAV Using CFD Analyses
by
Ivan Kostić, Aleksandar Simonović, Olivera Kostić, Dušan Ivković and Dragoljub Tanović
Aerospace 2024, 11(3), 223; https://doi.org/10.3390/aerospace11030223 - 13 Mar 2024
Abstract
This paper presents the second stage of a tandem fixed-wing unmanned aerial vehicle (UAV) aerodynamic development. In the initial stage, the UAV was optimized by analyzing its characteristics only in symmetrical flight conditions. Posted requirements were that both wings should produce relevant positive
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This paper presents the second stage of a tandem fixed-wing unmanned aerial vehicle (UAV) aerodynamic development. In the initial stage, the UAV was optimized by analyzing its characteristics only in symmetrical flight conditions. Posted requirements were that both wings should produce relevant positive lift, the initial stall must occur on the front wing first, the center of pressure should be close to the center of gravity, and longitudinal static stability should be in the optimum range. Computational fluid dynamic (CFD) analyses were performed, where the applied calculation model was derived from the authors’ previous successful projects. The eighth version TW V8 has satisfied all longitudinal requirements. Lateral-directional CFD analyses of V8 showed that the ratio of the lateral and directional stability at the nominal cruising regime was optimal, but both lateral and directional static stabilities were too high. On further development versions, the lower vertical tail was eliminated, a negative dihedral was implemented on the front wing, and four inverted blended winglets were added. Version TW V14 has largely improved lateral and directional stability characteristics, while their optimum ratio at the cruising regime was preserved. Longitudinal characteristics were also well preserved. Maximum lift coefficient and lift-to-drag ratio were increased, compared to the V8.
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(This article belongs to the Special Issue Aerodynamic Numerical Optimization in UAV Design)
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Preliminary Nose Landing Gear Digital Twin for Damage Detection
by
Lucio Pinello, Omar Hassan, Marco Giglio and Claudio Sbarufatti
Aerospace 2024, 11(3), 222; https://doi.org/10.3390/aerospace11030222 - 12 Mar 2024
Abstract
An increase in aircraft availability and readiness is one of the most desired characteristics of aircraft fleets. Unforeseen failures cause additional expenses and are particularly critical when thinking about combat jets and Unmanned Aerial Vehicles (UAVs). For instance, these systems are used under
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An increase in aircraft availability and readiness is one of the most desired characteristics of aircraft fleets. Unforeseen failures cause additional expenses and are particularly critical when thinking about combat jets and Unmanned Aerial Vehicles (UAVs). For instance, these systems are used under extreme conditions, and there can be situations where standard maintenance procedures are impractical or unfeasible. Thus, it is important to develop a Health and Usage Monitoring System (HUMS) that relies on diagnostic and prognostic algorithms to minimise maintenance downtime, improve safety and availability, and reduce maintenance costs. In particular, within the realm of aircraft structures, landing gear emerges as one of the most intricate systems, comprising several elements, such as actuators, shock absorbers, and structural components. Therefore, this work aims to develop a preliminary digital twin of a nose landing gear and implement diagnostic algorithms within the framework of the Health and Usage Monitoring System (HUMS). In this context, a digital twin can be used to build a database of signals acquired under healthy and faulty conditions on which damage detection algorithms can be implemented and tested. In particular, two algorithms have been implemented: the first is based on the Root-Mean-Square Error (RMSE), while the second relies on the Mahalanobis distance (MD). The algorithms were tested for three nose landing gear subsystems, namely, the steering system, the retraction/extraction system, and the oleo-pneumatic shock absorber. A comparison is made between the two algorithms using the ROC curve and accuracy, assuming equal weight for missed detections and false alarms. The algorithm that uses the Mahalanobis distance demonstrated superior performance, with a lower false alarm rate and higher accuracy compared to the other algorithm.
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(This article belongs to the Special Issue Aircraft Structural Health Monitoring and Digital Twin)
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Research on Passenger Evacuation Behavior in Civil Aircraft Demonstration Experiments Based on Neural Networks and Modeling
by
Zhenyu Feng, Qianqian You, Kun Chen, Houjin Song and Haoxuan Peng
Aerospace 2024, 11(3), 221; https://doi.org/10.3390/aerospace11030221 - 12 Mar 2024
Abstract
Evacuation simulation is an important method for studying and evaluating the safety of passenger evacuation, and the key lies in whether it can accurately predict personnel evacuation behavior in different environments. The existing models have good adaptability in building environments but have weaker
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Evacuation simulation is an important method for studying and evaluating the safety of passenger evacuation, and the key lies in whether it can accurately predict personnel evacuation behavior in different environments. The existing models have good adaptability in building environments but have weaker adaptability to personnel evacuation in civil aircraft cabins with more obstacles and stronger hindrances. We target the narrow seat aisle environment on airplanes and use a BP neural network to establish a continuous displacement model for personnel evacuation. We compare the simulation accuracy of evacuation time with the social force model based on continuous displacement and further compare the similarity of personnel evacuation process behavior. The results show that both models were close to the experimental values in simulating evacuation time, while our BP neural network evacuation model based on experimental data was more accurate in predicting the personnel evacuation process, showing more realistic details such as the probability of conflicts and bottleneck evolution in the cross aisle.
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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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