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Experimental Investigation of Hydrogen Peroxide and Nitrous Oxide in a 1-Newton Catalyst-Based Monopropellant Research Thruster
Penetrating Radar on Unmanned Aerial Vehicle for the Inspection of Civilian Infrastructure: System Design, Modeling, and Analysis
Lunar Robotic Construction with Raw Regolith
A Review of Advancements for Spacecraft On-Orbit Servicing, Assembly, and Manufacturing (OSAM) Technologies

Journal Description

Aerospace

Aerospace is a peer-reviewed, open access journal of aeronautics and astronautics published monthly online by MDPI. The European Aerospace 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, Ei Compendex, and other databases.
Journal Rank: JCR - Q2 (Engineering, Aerospace) / CiteScore - Q2 (Aerospace Engineering)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 20.9 days after submission; acceptance to publication is undertaken in 2.5 days (median values for papers published in this journal in the first half of 2025).
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.
Journal Cluster of Mechanical Manufacturing and Automation Control: Aerospace, Automation, Drones, Journal of Manufacturing and Materials Processing, Machines, Robotics and Technologies.

Impact Factor: 2.2 (2024); 5-Year Impact Factor: 2.4 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2226-4310

Latest Articles

31 pages, 3185 KB

Open AccessArticle

Unveiling the Hidden Cascade: Secondary Particle Generation in Hybrid Halide Perovskites Under Space-Relevant Ionizing Radiation

by Ivan E. Novoselov, Seif O. Cholakh and Ivan S. Zhidkov

Aerospace 2025, 12(11), 1015; https://doi.org/10.3390/aerospace12111015 - 14 Nov 2025

Hybrid halide perovskites are promising materials for optoelectronics and space applications due to their excellent light absorption, high efficiency, and light weight. However, their stability under radiation exposure remains a key challenge, especially in space environments, where high-energy particles can cause significant damage. [...] Read more.

Hybrid halide perovskites are promising materials for optoelectronics and space applications due to their excellent light absorption, high efficiency, and light weight. However, their stability under radiation exposure remains a key challenge, especially in space environments, where high-energy particles can cause significant damage. Here, we present the effects of primary and secondary radiation on perovskite materials, using Monte-Carlo simulations with the GEANT4 toolkit. The interactions of protons, electrons, neutrons, and γ-rays with APbI3 (A = Ma, FA, Cs) perovskites under space-relevant conditions typical for low Earth orbit (LEO) were studied. The results show that different perovskite compositions respond uniquely to radiation: CsPbI₃ generates higher-energy secondary positrons, neutrons, and protons, while MAPbI₃ produces more secondary electrons under proton irradiation. Mixed-cation perovskites exhibit narrower energy distributions for secondary γ-rays, indicating material-dependent differences in radiation tolerance. These findings suggest the potential role of secondary particle generation in perovskite degradation, based on our simulations, and they emphasize the need for comprehensive modeling to improve the radiation resistance of perovskite-based technologies for space applications. Future studies should consider contributions from encapsulating materials in device structures Full article

(This article belongs to the Topic Advanced Materials and Manufacturing for Extreme Environments in Energy and Aerospace)

15 pages, 5332 KB

Open AccessArticle

Experimental Study and Numerical Simulation of Oscillation Phenomena in a Pressure Swirl Injector

by Juan Liu and Yifan Han

Aerospace 2025, 12(11), 1014; https://doi.org/10.3390/aerospace12111014 - 14 Nov 2025

In this study, experiments and numerical simulations were conducted to investigate the oscillation phenomena in a pressure swirl injector. The flow field was captured using high-speed photography, and the gray values were analyzed using the Matlab image processing program. The oscillation frequency was [...] Read more.

In this study, experiments and numerical simulations were conducted to investigate the oscillation phenomena in a pressure swirl injector. The flow field was captured using high-speed photography, and the gray values were analyzed using the Matlab image processing program. The oscillation frequency was recorded using FFT transform. Additionally, the flow field of the pressure swirl injector was simulated based on the volume of fluid (VOF) interface-tracking method. Both the experimental and numerical results revealed periodic oscillations in the pressure swirl injector, with a corresponding frequency of several hundred Hertz. The oscillation frequency is closely related to the behavior of the central gas core, which has greater turbulent kinetic energy than the liquid phase. As the mass flow rate increases, the velocity of the gas core is increased. The turbulent kinetic energy of the central gas core increased, which led to an increase in the oscillation frequency. Finally, the relationship between Re and the oscillation frequency was obtained. Full article

(This article belongs to the Special Issue Fluid Flow Mechanics (4th Edition))

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24 pages, 18514 KB

Open AccessArticle

Development of a Bearing-Based Distributed Control Method for UAV Formation Tracking and Obstacle Avoidance

by Jaewan Choi and Younghoon Choi

Aerospace 2025, 12(11), 1013; https://doi.org/10.3390/aerospace12111013 - 13 Nov 2025

Unmanned Aerial Vehicles (UAVs) are playing an increasingly vital role in modern battlefields. Accordingly, considerable research has been devoted to Manned–Unmanned Teaming (MUM-T) systems, with formation flight recognized as a key enabling technology for coordinating multiple UAVs. In MUM-T operations, leader–follower formations are [...] Read more.

Unmanned Aerial Vehicles (UAVs) are playing an increasingly vital role in modern battlefields. Accordingly, considerable research has been devoted to Manned–Unmanned Teaming (MUM-T) systems, with formation flight recognized as a key enabling technology for coordinating multiple UAVs. In MUM-T operations, leader–follower formations are commonly employed, while distributed formation methods have gained increasing attention owing to their stability and scalability. Among these, bearing-based control provides unique advantages for managing dynamic formations involving scaling and rotation. However, conventional bearing-based approaches typically require multiple leaders and encounter inherent limitations in flexibly handling obstacle avoidance. To address these challenges, this study proposes a hierarchical bearing-based leader–follower formation system comprising a single leader and multiple follower UAVs. By introducing the concept of virtual leaders, the proposed method enables the construction of formations with only one leader, thereby simplifying the system architecture while preserving scalability. In addition, a novel obstacle-avoidance strategy is developed, allowing followers to avoid collisions efficiently while maintaining formation integrity. The effectiveness of the proposed framework is demonstrated through numerical simulations of representative formation patterns, including V-shaped and hexagonal configurations, in obstacle-rich environments. The results confirm that follower UAVs successfully tracked the leader, preserved the designated formation, and achieved effective obstacle avoidance, thereby validating the stability and robustness of the proposed approach. Full article

(This article belongs to the Section Aeronautics)

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22 pages, 2320 KB

Open AccessArticle

Evaluation of Flight Training Quality for the Landing Flare Phase Using SD Card Data

by Xing Du, Gang Xu, Qingkui Yang, Yihe Xu and Bin Chen

Aerospace 2025, 12(11), 1012; https://doi.org/10.3390/aerospace12111012 - 13 Nov 2025

To enhance the objectivity and precision of quality evaluation in flight training, this study proposes an assessment method for the landing flare phase based on time-series flight parameter data from Secure Digital (SD) card. By analyzing landing flare data from flight instructors and [...] Read more.

To enhance the objectivity and precision of quality evaluation in flight training, this study proposes an assessment method for the landing flare phase based on time-series flight parameter data from Secure Digital (SD) card. By analyzing landing flare data from flight instructors and trainees, a standard sequence model was established, and the Dynamic Time Warping (DTW) algorithm was employed to calculate the similarity between individual trainee sequences and the standard sequence. Using K-means clustering, the landing flare quality was categorized into four distinct levels: Excellent (22.5%), Good (25.5%), Qualified (23.5%), and Improvement Needed (28.5%). The results demonstrated significant consistency with instructor evaluations (Spearman correlation coefficient 0.71). Furthermore, through the identification of weak parameters, specific technical deficiencies in areas such as airspeed control and pitch attitude maintenance could be accurately pinpointed. This approach not only effectively validates and supplements instructor assessments but also provides data-driven support for developing personalized training programs, thereby improving the quality and efficiency of flight training. Full article

(This article belongs to the Section Air Traffic and Transportation)

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21 pages, 5737 KB

Open AccessArticle

Construction of Sampling Disturbance Model of Lunar Surface

by Lanlan Xie, Qian Li, Dingkun Hu, Jiahang Lv and Haijun Zheng

Aerospace 2025, 12(11), 1011; https://doi.org/10.3390/aerospace12111011 - 13 Nov 2025

This study establishes a dynamic evolution model of the physical and mechanical properties of lunar simulant as a function of sampling-induced disturbance on the lunar surface, aiming to eliminate design errors in sampling missions caused by neglecting the disturbance of lunar soil. A [...] Read more.

This study establishes a dynamic evolution model of the physical and mechanical properties of lunar simulant as a function of sampling-induced disturbance on the lunar surface, aiming to eliminate design errors in sampling missions caused by neglecting the disturbance of lunar soil. A standard probe was inserted into the lunar soil simulant both before and after disturbance, and the variation in penetration resistance at the exact location was proposed as an indicator of the regolith’s disturbance state. Compression tests and disturbance tests were conducted on CUG-1A lunar soil simulant, with the experimental results subjected to regression analysis and neural network prediction. Based on the compression tests, a regression equation was derived relating the slope of the probe penetration resistance to the internal friction angle and density of the lunar soil simulant, showing a strong correlation between predicted and actual values. The disturbance tests provided penetration resistance curves under various disturbance conditions. By integrating these two components, a correspondence was established between the disturbance conditions and the internal friction angle and density of the lunar soil simulant. The predictive performance of three typical neural network algorithms—LM, BR, and SCG—with varying numbers of neurons was compared. The LM algorithm with 10 neurons was selected for its superior performance. Ultimately, a sampling disturbance model was developed to predict the internal friction angle and density of the lunar soil simulant based on disturbance conditions, demonstrating an extremely high correlation between predicted and actual values. Full article

(This article belongs to the Section Astronautics & Space Science)

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30 pages, 3301 KB

Open AccessArticle

Stubborn Composite Disturbance Observer-Based MPC for Spacecraft Systems: An Event-Triggered Approach

by Jianlin Chen, Lei Liu, Yang Xu and Yang Yu

Aerospace 2025, 12(11), 1010; https://doi.org/10.3390/aerospace12111010 - 12 Nov 2025

This paper studies spacecraft control under communication congestion, multi-source uncertainties, and input constraints. To reduce communication load, a static event-triggered mechanism is used so that transmissions occur only when necessary. Unknown nonlinearities are estimated online by a radial basis function neural network (RBFNN). [...] Read more.

This paper studies spacecraft control under communication congestion, multi-source uncertainties, and input constraints. To reduce communication load, a static event-triggered mechanism is used so that transmissions occur only when necessary. Unknown nonlinearities are estimated online by a radial basis function neural network (RBFNN). To address sensor outliers and external disturbances, an event-triggered stubborn composite disturbance observer (ESCDO) is proposed, and sufficient conditions are derived to ensure its globally uniformly bounded stability. Based on this, an MPC-based composite anti-disturbance controller is designed to satisfy input constraints, and conditions are provided to guarantee the uniform bounded stability of the closed loop. Numerical simulations are conducted to demonstrate the effectiveness of the proposed approach. Full article

(This article belongs to the Special Issue New Sights of Intelligent Robust Control in Aerospace)

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13 pages, 10011 KB

Open AccessArticle

High-Accuracy Rocket Landing via Lossless Convexification

by Wei Xiao, Bei Hong, Junpeng Liu, Xiaofei Chang and Wenxing Fu

Aerospace 2025, 12(11), 1009; https://doi.org/10.3390/aerospace12111009 - 12 Nov 2025

With the development of rocket technology, achieving high-precision landing has become a key technical challenge in the field of aerospace. To cope with this challenge, we propose a lossless convexification algorithm based on the integral pseudospectral method in this paper. Firstly, for the [...] Read more.

With the development of rocket technology, achieving high-precision landing has become a key technical challenge in the field of aerospace. To cope with this challenge, we propose a lossless convexification algorithm based on the integral pseudospectral method in this paper. Firstly, for the fuel optimization problem, the continuous dynamic equations and constraints are discretized with high accuracy using an integral-type pseudospectral method. By constructing a global integration matrix at Legendre–Gauss nodes, the original complex continuous problem is effectively transformed into a discrete form that is more tractable for numerical optimization. Secondly, the non-convex constraints are transformed using the lossless convexification technique, thereby reformulating the original problem as a second-order cone programming (SOCP) problem. The effectiveness of the proposed algorithm is validated through numerical simulations, which demonstrate high landing accuracy, robustness, and fuel efficiency. These results highlight the algorithm’s high performance and strong potential for practical application in space missions. Full article

(This article belongs to the Topic International Conference on Autonomous Unmanned Systems (5th ICAUS 2025))

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18 pages, 2119 KB

Open AccessArticle

A Fast Heuristic for Aircraft Landing Scheduling with Time Windows: Application to Guarulhos Airport

by Daniel A. Pamplona and Claudio J. P. Alves

Aerospace 2025, 12(11), 1008; https://doi.org/10.3390/aerospace12111008 - 12 Nov 2025

This paper focuses on the aircraft landing problem with time windows (ALP-TW), which consists of determining a landing schedule for each aircraft within a specified time window and determining the minimum required separation interval between successive operations. This NP-hard state-dependent scheduling problem plays [...] Read more.

This paper focuses on the aircraft landing problem with time windows (ALP-TW), which consists of determining a landing schedule for each aircraft within a specified time window and determining the minimum required separation interval between successive operations. This NP-hard state-dependent scheduling problem plays a key role in the operational efficiency of busy airports. We propose a fast and efficient heuristic, called the CAS-TW (Closest Aircraft Sequence with Time Windows), to generate landing sequences that minimize total delay while respecting operational constraints. The method combines a greedy algorithm with a discretization strategy to explore feasible landing intervals. We validate the approach using real data from São Paulo/Guarulhos International Airport (GRU), comparing the CAS-TW to traditional scheduling strategies and optimal solutions obtained via a commercial solver. Computational experiments show reductions in makespan up to 21% in theoretical datasets and 5% in real-world datasets. The CAS-TW solved instances with 50 aircraft in less than 1 s of computation time. The results showed that our algorithm was quickly implemented, equitable, easy to use, and obtained good solutions. These results translated into an increase in airport capacity. Full article

(This article belongs to the Section Air Traffic and Transportation)

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23 pages, 2832 KB

Open AccessArticle

Reduced-Order Modeling and Active Subspace to Support Shape Optimization of Centrifugal Pumps

by Giacomo Gedda, Andrea Ferrero, Filippo Masseni, Massimo Mariani and Dario Pastrone

Aerospace 2025, 12(11), 1007; https://doi.org/10.3390/aerospace12111007 - 12 Nov 2025

This study presents a reduced-order modeling framework for the shape optimization of a centrifugal pump. A database of CFD solutions is generated using Latin Hypercube Sampling over five design parameters to construct a reduced-order model based on proper orthogonal decomposition with radial basis [...] Read more.

This study presents a reduced-order modeling framework for the shape optimization of a centrifugal pump. A database of CFD solutions is generated using Latin Hypercube Sampling over five design parameters to construct a reduced-order model based on proper orthogonal decomposition with radial basis function interpolation. The model predicts the flow field at the impeller–diffuser interface and pump outlet, enabling the estimation of impeller torque and total pressure rise. The active subspaces method is applied to reduce the dimensionality of the input space from five to four modified parameters. The sensitivity of the ROM is assessed with respect to further dimensionality reductions in the parameter space, POD mode truncation, and adaptive sampling. The model is then used to perform pump shape optimization via a quasi-Newton method, identifying the combination of the parameters that minimizes the impeller torque while satisfying a constraint on the head. The optimal result is validated through CFD analysis and compared against the Pareto front generated by a genetic algorithm. The work highlights the potential of model-order reduction techniques in centrifugal pump optimization. Full article

(This article belongs to the Section Astronautics & Space Science)

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19 pages, 5654 KB

Open AccessArticle

Kinematic Parameter Identification for Space Manipulators Using a Hybrid PSO-LM Optimization Algorithm

by Haitao Jing, Xiaolong Ma, Meng Chen, Hongjun Xing, Jianwei Tan and Jinbao Chen

Aerospace 2025, 12(11), 1006; https://doi.org/10.3390/aerospace12111006 - 11 Nov 2025

Accurate kinematic parameter identification is essential for space manipulators to attain millimeter-level positioning accuracy and robust motion control. This study develops a universal strategy for comprehensive parameter identification by establishing a generalized geometric error model using Denavit–Hartenberg (DH) parameterization. For robotic calibration, the [...] Read more.

Accurate kinematic parameter identification is essential for space manipulators to attain millimeter-level positioning accuracy and robust motion control. This study develops a universal strategy for comprehensive parameter identification by establishing a generalized geometric error model using Denavit–Hartenberg (DH) parameterization. For robotic calibration, the Fibonacci spiral sampling technique optimizes pose selection, ensuring end-effector poses fully cover the manipulator’s workspace to enhance identification convergence. By combining the local convergence capability of the Levenberg–Marquardt (LM) algorithm with the global search characteristics of Particle Swarm Optimization (PSO), we propose a novel hybrid PSO-LM optimization algorithm, achieving synergistic enhancement of global exploration and local refinement. An experimental platform using a laser tracker as the metrology reference was constructed, with a 6-degree-of-freedom (6-DOF) space manipulator selected as a validation case. Experimental results demonstrate that the proposed method significantly reduces the average positioning error from 10.87 mm to 0.47 mm, achieving a 95.7% improvement in relative accuracy. These findings validate that the parameter identification approach can precisely determine the actual geometric parameters of space manipulators, providing critical technical support for high-precision on-orbit operations. Full article

(This article belongs to the Section Astronautics & Space Science)

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15 pages, 4097 KB

Open AccessArticle

Optimization Algorithm for the Unstructured UGKWP Particle Tracking Process Based on a GPU

by Zhengyu Tian, Yuhang Chu, Hang Yu, Qianyue Fu and Weijie Ren

Aerospace 2025, 12(11), 1005; https://doi.org/10.3390/aerospace12111005 - 11 Nov 2025

The Unified Gas–Kinetic Wave–Particle (UGKWP) method is a multiscale method that offers high computational efficiency when solving complex high-Mach-number flows around spacecraft. When the UGKWP method, based on a Graphics Processing Unit (GPU) platform, is used to simulate flow, threads within the same [...] Read more.

The Unified Gas–Kinetic Wave–Particle (UGKWP) method is a multiscale method that offers high computational efficiency when solving complex high-Mach-number flows around spacecraft. When the UGKWP method, based on a Graphics Processing Unit (GPU) platform, is used to simulate flow, threads within the same warp are responsible for tracking different particles, leading to a significant warp divergence problem that affects overall computational efficiency. Therefore, this study introduces a dynamic marking tracking algorithm based on block sharing to enhance the efficiency of particle tracking. This algorithm rebuilds the original tracking process by marking and tracking particles, aligning thread computations within the same warp as much as possible to reduce warp divergence. As a result, the average number of active threads increased by over 46% across different testing platforms. The optimized UGKWP platform was used to simulate the re-entry capsule case, and the results showed that the optimized UGKWP can accurately and efficiently simulate the flow details around the capsule. This research provides an efficient and accurate tool for simulating complex multiscale flows at high Mach numbers, which is of great significance. Full article

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20 pages, 3180 KB

Open AccessArticle

Hierarchical Bayesian Modeling for Physiological Data in Small-N Aviation Human Factors Research

by Ainsley Kyle, Brock Rouser, Ryan C. Paul and Katherina A. Jurewicz

Aerospace 2025, 12(11), 1004; https://doi.org/10.3390/aerospace12111004 - 11 Nov 2025

Monitoring pilot cognitive state in real time is becoming increasingly important as automation plays a larger role in aviation. Traditional workload assessments, such as questionnaires or task-based performance metrics, provide useful insights but can be limited in rapidly changing flight environments. Physiological measures, [...] Read more.

Monitoring pilot cognitive state in real time is becoming increasingly important as automation plays a larger role in aviation. Traditional workload assessments, such as questionnaires or task-based performance metrics, provide useful insights but can be limited in rapidly changing flight environments. Physiological measures, including heart rate, respiration, and electroencephalogram (EEG), offer continuous data streams, yet their variability and complexity present challenges for analysis. This study explores the use of a hierarchical Bayesian framework to quantify patterns from physiological signals recorded during high-fidelity flight simulations. Five certified pilots flew scenarios that varied in automation level and working memory demand while heart rate, respiration rate, and EEG-derived workload estimates were monitored. The model generated individualized and condition-specific estimates, quantified uncertainty, and remained stable with a small participant pool. Heart rate appeared to be the most consistent indicator, followed by EEG-derived workload, while respiration rate was less reliable across conditions. These results suggest that Bayesian inference may provide a promising way to interpret physiological data in aviation settings and could support the development of adaptive automation that responds to pilot workload. The approach emphasizes transparency and efficiency, offering complementary value to existing modeling techniques for aerospace human factors and flight deck applications. Full article

(This article belongs to the Section Aeronautics)

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30 pages, 2393 KB

Open AccessArticle

Toward Greener Propulsion: An LCA-Based Environmental Performance Classification of In-Space Propulsion Options

by Lily Blondel-Canepari, Lorenz Affentranger, Sara Morales Serrano and Angelo Pasini

Aerospace 2025, 12(11), 1003; https://doi.org/10.3390/aerospace12111003 - 10 Nov 2025

As space activities expand rapidly, especially the in-orbit population, concerns about their environmental consequences are growing. For in-space propulsion, this is particularly true under the increasing regulatory pressure on hydrazine-based legacy propellants. In response to that, this study presents a cradle-to-gate Life Cycle [...] Read more.

As space activities expand rapidly, especially the in-orbit population, concerns about their environmental consequences are growing. For in-space propulsion, this is particularly true under the increasing regulatory pressure on hydrazine-based legacy propellants. In response to that, this study presents a cradle-to-gate Life Cycle Assessment (LCA) of the four main current options for in-space liquid bipropellant systems—MON-3/MMH, 98%-HTP/Ethanol, 98%-HTP/RP-1 and N₂O/Ethane—each evaluated as a complete system including propellant-combination loading and sized propulsion-architecture manufacturing. The comparison is performed against a representative 2 kN Orbital Transfer Vehicle (OTV) mission scenario delivering a total

Δ

v of 2300 m/s. Each solution’s environmental performance is quantified across 15 midpoint indicators, using ESA’s space-specific LCA database and combined through an Analytical Hierarchy Process (AHP) single-score for easier comparison. Results show that while HTP/Ethanol achieves the lowest impact at the propellant-loading level, the N₂O/Ethane system obtains the lowest overall footprint once the full propulsion system architecture, sized for the mission, is included, with a total environmental impact 63% lower than the legacy MON-3/MMH system. A key outcome of this study is that manufacturing propulsion components dominates the life-cycle footprint, bringing up to 95% of the total impact for HTP-based systems and approximately 64% for MON-3/MMH and self-pressurizing architectures, mainly due to the energy-intensive production of titanium and aluminum tanks. In light of these results, this paper proposes a mission-driven definition of “greener” propulsion, requiring at least a 50% reduction in the combined total and human-toxicity impacts, together with a lower Global Warming Potential (GWP) than legacy hydrazine-based systems, given that GWP was identified as the most critical environmental concern to address. However, the study also shows that considering only GWP would have led to an incorrect conclusion, and therefore advises against relying on single-impact environmental assessments. Additional replacement criteria for in-space propellants include cost-efficiency, reliability and global propulsive performance. This work implements a system-level environmental performance assessment and classification framework for in-space liquid propulsion options, providing a structured approach for selecting and qualifying more sustainable alternative candidates for future mission scenarios. Full article

(This article belongs to the Special Issue Green Propellants for In-Space Propulsion)

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13 pages, 2601 KB

Open AccessArticle

Development and Characterization of Hydrogen Peroxide Gels with Organic Gelling Agents for Use with Microencapsulated Fuels

by Robin Scholl, Eva Steinmann, Dominic Freudenmann and Stefan Schlechtriem

Aerospace 2025, 12(11), 1002; https://doi.org/10.3390/aerospace12111002 - 10 Nov 2025

Hydrogen peroxide is a promising oxidizer and monopropellant for space propulsion, offering a green alternative to conventional propellants. In combination with microencapsulated hydrocarbon fuels, a new type of monopropellant can be formed that unites the high specific impulse of a bipropellant with the [...] Read more.

Hydrogen peroxide is a promising oxidizer and monopropellant for space propulsion, offering a green alternative to conventional propellants. In combination with microencapsulated hydrocarbon fuels, a new type of monopropellant can be formed that unites the high specific impulse of a bipropellant with the efficient hardware of a monopropellant. However, the stabilization of these microcapsule/hydrogen peroxide mixtures is problematic as they tend to separate after a short period of time. This work uses organic gelling agents to stabilize these mixtures by creating hydrogen peroxide gels. For this, the compatibility of hydrogen peroxide with several gelling agents was investigated and found to be suitable. Next, the dispersion stability of microcapsule/gel dispersions was examined and showed no sign of destabilization over four weeks or at high accelerations at 50× g in the centrifuge, even with gelling agent concentrations as low as 0.1%. A formulation with a polyacrylic acid-based gelling agent at a concentration of 0.3% showed favorable characteristics and good processability. Together with a subsequent rheological characterization of the gels, these results are critical for the further development of the fuel-filled microcapsule/hydrogen peroxide monopropellant. The hydrogen peroxide gel formulations developed in this study could also have potential applications beyond the scope of this work. Full article

(This article belongs to the Special Issue Green Propellants for In-Space Propulsion)

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25 pages, 4476 KB

Open AccessArticle

An Effective Process to Use Drones for Above-Ground Biomass Estimation in Agroforestry Landscapes

by Andsera Adugna Mekonen, Claudia Conte and Domenico Accardo

Aerospace 2025, 12(11), 1001; https://doi.org/10.3390/aerospace12111001 - 8 Nov 2025

Above-ground biomass in agroforestry refers to the total mass of living vegetation, primarily trees and shrubs, integrated into agricultural landscapes. It plays a key role in climate change mitigation by capturing and storing carbon. Accurate estimation of above-ground biomass in agroforestry systems requires [...] Read more.

Above-ground biomass in agroforestry refers to the total mass of living vegetation, primarily trees and shrubs, integrated into agricultural landscapes. It plays a key role in climate change mitigation by capturing and storing carbon. Accurate estimation of above-ground biomass in agroforestry systems requires effective drone deployment and sensor management. This study presents a detailed methodology for biomass estimation using Unmanned Aircraft Systems, based on an experimental campaign conducted in the Campania region of Italy. Multispectral drone platforms were used to generate calibrated reflectance maps and derive vegetation indices for biomass estimation in agroforestry landscapes. Integrating field-measured tree attributes with remote sensing indices improved the accuracy and efficiency of biomass prediction. Following the assessment of mission parameters, flights were conducted using a commercial drone to demonstrate consistency of results across multiple altitudes. Terrain-follow mode and high image overlap were employed to evaluate ground sampling distance sensitivity, radiometric performance, and overall data quality. The outcome is a defined process that enables agronomists to effectively estimate above-ground biomass in agroforestry landscapes using drone platforms, following the procedure outlined in this paper. Predictive performance was evaluated using standard model metrics, including R², RMSE, and MAE, which are essential for replicability and comparison in future studies. Full article

(This article belongs to the Section Aeronautics)

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29 pages, 6252 KB

Open AccessArticle

Dynamic Adaptive UAV Path Planning Based on Three-Dimensional Environments

by Zexi Dong, Minghua Hu, Pengda Zhu and Jianan Yin

Aerospace 2025, 12(11), 1000; https://doi.org/10.3390/aerospace12111000 - 8 Nov 2025

Sampling-based algorithms are pivotal for high-dimensional UAV path planning, especially in 3D urban environments. The Rapidly-Exploring Random Tree (RRT) suffers from inadequate sampling methods and a single, fixed sampling policy, which lead to elongated paths and higher computational cost. To address this, we [...] Read more.

Sampling-based algorithms are pivotal for high-dimensional UAV path planning, especially in 3D urban environments. The Rapidly-Exploring Random Tree (RRT) suffers from inadequate sampling methods and a single, fixed sampling policy, which lead to elongated paths and higher computational cost. To address this, we propose a Dynamic Adaptive DACS-RRT* algorithm that builds a dynamic, bidirectional sampling space and fuses low-discrepancy Halton sampling with bridge (narrow-passage) sampling, fundamentally tailoring the sampling process to urban settings. We further construct an adaptive, coordinated sampling strategy that dynamically adjusts between straight-to-goal and frustum-cone expansions by computing their probabilities, thereby overcoming the limitations of a single strategy and strengthening directional guidance. After generating a path, we perform multi-objective smoothing to make UAV trajectories better suited to urban environments. Through simulations in three distinct urban scenarios—and in comparison with five baseline algorithms—DACS-RRT* shows improvements in path length, convergence time, node count, iteration count, obstacle clearance, and turning angle, further validating its practicality in urban settings. Full article

(This article belongs to the Section Aeronautics)

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26 pages, 6224 KB

Open AccessArticle

GAT-BiGRU-TPA City Pair 4D Trajectory Prediction Model Based on Spatio-Temporal Graph Neural Network

by Haibo Cao, Yinfeng Li, Xueyu Mi and Qi Gao

Aerospace 2025, 12(11), 999; https://doi.org/10.3390/aerospace12110999 - 8 Nov 2025

With the rapid expansion of the civil aviation industry, the surge in flight numbers has led to increasingly pronounced issues of air route congestion and flight conflicts. 4D trajectory prediction, by dynamically adjusting aircraft paths in real time, can prevent air route collisions, [...] Read more.

With the rapid expansion of the civil aviation industry, the surge in flight numbers has led to increasingly pronounced issues of air route congestion and flight conflicts. 4D trajectory prediction, by dynamically adjusting aircraft paths in real time, can prevent air route collisions, alleviate air traffic pressure, and ensure flight safety. Therefore, this paper proposes a combined model—GAT-BiGRU-TPA—based on the Spatio-Temporal Graph Neural Network (STGNN) framework to achieve refined 4D trajectory prediction. This model integrates Graph Attention Networks (GAT) to extract multidimensional spatial features, Bidirectional Gated Recurrent Units (BiGRU) to capture temporal dependencies, and incorporates a Temporal Pattern Attention (TPA) mechanism to emphasize learning critical temporal patterns. This enables the extraction of key information and the deep fusion of spatio-temporal features. Experiments were conducted using real trajectory data, employing a grid search to optimize the observation window size and label length. Results demonstrate that under optimal model parameters (observation window: 30, labels: 4), the proposed model achieves a 45.72% reduction in mean Root Mean Square Error (RMSE) and a 43.40% decrease in Mean Absolute Error (MAE) across longitude, latitude, and altitude compared to the optimal baseline BiLSTM model. Prediction accuracy significantly outperforms multiple mainstream benchmark models. In summary, the proposed GAT-BiGRU-TPA model demonstrates superior accuracy in 4D trajectory prediction, providing an effective approach for refined trajectory management in complex airspace environments. Full article

(This article belongs to the Section Air Traffic and Transportation)

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25 pages, 3366 KB

Open AccessArticle

Research on the Remaining Useful Life Prediction Algorithm for Aero-Engines Based on Transformer–KAN–BiLSTM

by Kejie Xu, Yingqing Guo and Qifan Zhou

Aerospace 2025, 12(11), 998; https://doi.org/10.3390/aerospace12110998 - 8 Nov 2025

Predicting the remaining useful life (RUL) of aircraft engines is crucial for ensuring flight safety, optimizing maintenance, and reducing operational costs. This paper introduces a novel hybrid deep learning model, Transformer–KAN–BiLSTM, for aero-engine RUL prediction. The model is designed to leverage the complementary [...] Read more.

Predicting the remaining useful life (RUL) of aircraft engines is crucial for ensuring flight safety, optimizing maintenance, and reducing operational costs. This paper introduces a novel hybrid deep learning model, Transformer–KAN–BiLSTM, for aero-engine RUL prediction. The model is designed to leverage the complementary strengths of its components: the Transformer architecture effectively captures long-range temporal dependencies in sensor data, the emerging Kolmogorov–Arnold Network (KAN) provides superior approximation flexibility and a unique degree of interpretability through its spline-based activation functions, and the Bidirectional LSTM (BiLSTM) extracts nuanced local temporal patterns. Evaluated on the benchmark NASA C-MAPSS dataset, the proposed fusion framework demonstrates exceptional performance, achieving remarkably low Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values that significantly surpass existing benchmarks. These results validate the model’s robustness and its high potential for practical deployment in prognostics and health management systems. Full article

(This article belongs to the Section Aeronautics)

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17 pages, 5594 KB

Open AccessArticle

A Flexible Ultra-Thin Ultrasonic Transducer for Ice Detection on Curved Surfaces

by Yan Wang, Yuan Wang, Qingwen Lu, Chengxiang Zhu, Dawei Wu, Chunling Zhu and Yuan Wu

Aerospace 2025, 12(11), 997; https://doi.org/10.3390/aerospace12110997 - 7 Nov 2025

Icing phenomena occur on aircraft and unmanned aerial vehicles (UAVs) under extreme weather conditions. Ultrasonic detection technology is an effective method for measuring ice formation while maintaining the shape of the structure. However, current ultrasonic sensors, which are large and inflexible, are unsuitable [...] Read more.

Icing phenomena occur on aircraft and unmanned aerial vehicles (UAVs) under extreme weather conditions. Ultrasonic detection technology is an effective method for measuring ice formation while maintaining the shape of the structure. However, current ultrasonic sensors, which are large and inflexible, are unsuitable for irregular UAV bodies, limiting their applications in real scenarios. For the detection of icing on curved structure, this study proposes a novel flexible ultra-thin ultrasonic transducer (FUTUT). The transducer exhibits excellent flexibility, making it suitable for use on high-curvature wings. Firstly, the FUTUT was designed based on the material properties of the airframe and the sensitivity requirements for ice detection, following the design guidelines for 1-3 type piezocomposites. The fabrication process for the FUTUT was then investigated, and its flexibility and low-temperature resistance were tested. Finally, icing detection experiments were conducted in an icing wind tunnel (IWT), where the FUTUT of 9.82 MHz demonstrated an ice-thickness-detection sensitivity of 0.29 mm. Experimental results indicate that the FUTUT possesses superior flexibility and exhibits excellent stability in low-temperature environments. These results underscore the FUTUT’s promise for applications in ice detection on curved structures. Full article

(This article belongs to the Special Issue Deicing and Anti-Icing of Aircraft (Volume IV))

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34 pages, 39863 KB

Open AccessArticle

Inverse Design of Additive Manufactured Rocket Propellant Grains with Non-Uniform Properties

by Giovanni Polizzi, Andrea Ferrero, Filippo Masseni and Dario Pastrone

Aerospace 2025, 12(11), 996; https://doi.org/10.3390/aerospace12110996 - 7 Nov 2025

This work explores solid rocket motor grain design, leveraging additive manufacturing techniques based on slurry deposition and UV-curing. The objective is to develop an automated design procedure to identify optimal solutions that meet mission requirements, exploiting new geometries and ballistic configurations previously unattainable [...] Read more.

This work explores solid rocket motor grain design, leveraging additive manufacturing techniques based on slurry deposition and UV-curing. The objective is to develop an automated design procedure to identify optimal solutions that meet mission requirements, exploiting new geometries and ballistic configurations previously unattainable with the classical mix–cast–cure manufacturing process. The design procedure is based on a stochastic optimization approach coupled with surrogate modeling of the pressure–time response, considering variable geometrical and ballistic parameters. Several surrogate models were tested after the creation of suitable databases through the computation of pressure evolution for different configurations. The most appropriate surrogate model was selected and applied within optimization routines to evaluate individual designs. The optimizer identifies the most suitable configuration to obtain the desired pressure–time response and to meet motor requirements. Different design approaches have been tested to evaluate the ballistic distribution’s influence on performance and how it can be leveraged to meet requirements, even with significant modifications in grain geometry. The results show that the proposed procedure is able to effectively achieve the expressed requirements, successfully handling the novel design environment. Furthermore, they highlight the strong influence of the ballistic distribution on performance and show how it can be successfully exploited to guide grain design. Full article

(This article belongs to the Section Astronautics & Space Science)

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