In recent years, the utilization of space has expanded rapidly across communications and broadcasting, positioning, meteorological observation, and remote sensing, resulting in a sharply increasing demand. Under these circumstances, the number of satellites has surged, elevating the risk of collisions and raising concerns about the growing amount of space debris generated by such collisions. Satellites use liquid propellants for orbital insertion and attitude control, but they reach the end of their operational life when propellant is depleted. Many satellites remain in orbit for extended periods after the end of life, and the need to launch replacement satellites further increases the number of objects in orbit, contributing to the growth of space debris. Conversely, if on-orbit propellant refilling can be realized, it may be possible to extend satellite lifetimes and thereby suppress both the need for replacement launches and the associated increase in space debris.

The ultimate goal of the present study is to establish on-orbit propellant resupply technology, and the objective of this work is to develop in-tank gas–liquid separation techniques during propellant transfer under microgravity conditions, which constitute one of the core enabling technologies for achieving that goal. As the first step in the development of this technology, the behavior of liquid in a small, spherical mock-up tank equipped with a vane-type propellant acquisition mechanism utilizing surface tension was observed during filling with simulated propellant under short-duration microgravity conditions generated using a drop tower. Additionally, a variant of the vane-type propellant acquisition mechanism, with baffles added to the central and top sections of the support rods that install the vanes, was also fabricated and tested. It was found that the gas–liquid separation performance of the configuration with top-mounted baffles was superior to that of the other designs.

Among the most versatile lighter-than-air platforms, unmanned airships are currently being designed mostly for either low-altitude missions for close-distance surveillance, in competition with multi-copter drones, or for high-altitude missions in the stratospheric layer, thus ideally complementing the role of space satellites. Correspondingly, algorithms to automatically compute global values like the volume and mass of an airship, for a desired mission performance and for assumed technologies of the components (like the materials employed for the envelope or the construction of the gondola), have been experimented with and are already documented in the literature.

Building on this base, this research proposes a method where not just the parameters mostly typical to preliminary design are solved, but a unified automatic approach is employed for taking into account requirements on static balance as well as dynamic performance. This modular method links three original tools, developed in-house respectively for preliminary sizing, lofting and inertial modeling, and dynamic analysis. The synergistic use of the first two allows the simultaneous computation of not just the weight and volume of the machine for a specific mission, but also a detailed static balance problem, by suitably arranging the masses of the components onboard. The last module further tweaks the positioning of selected components to obtain better flying qualities. The latter are measured by means of damping and the characteristic time of specific eigenmodes of the system, in turn obtained after drawing a linearized model of airship dynamics from a fully non-linear one.

The outcome of the overall unified sizing procedure, numerically configured as an automatically solved optimal problem, accounts not just for the requirements of the mission profile, but also potentially for static balance and for a desired level of flying qualities. The full procedure is demonstrated on the data of an existing small-scale airship prototype.

During flight, helicopter rotor blades generate significant vibrations and noise due to aerodynamic loads. These effects limit maximum flight speed, increase operating costs, and reduce the fatigue life of structural members. The use of active control systems for helicopter rotor blades is a current scientific trend of research into noise and vibration reduction. Several methods exist for actively controlling helicopter rotor blades. One control strategy applied to suppress vibrations is Higher Harmonic Control (HHC) and Individual Blade Control (IBC). Currently, several methods of helicopter blade control are being researched with the development of piezoelectric fibres: Active Trailing Edge (ATE) and Active Twist (AT).

Active Twist is based on the fact that the actuator control elements can be located on the blade's load-bearing skin surface. The orientation of the piezoelectric fibres in the piezoelectric actuator on the top and bottom surfaces of the skin is ±45°, resulting in dynamic blade twisting when the piezoelectric actuators are activated. Thus, Active Twist can be integrated into the existing rotor blades without significant design changes.

In the present work, an analysis of a numerical study of a helicopter blade with the piezoelectric actuators integrated into the skin of the main rotor blade was performed. The results of static blade twisting as a function of the piezoelectric actuator chord-wise length are presented. Additionally, the effect of changing the geometry of the blade's cross-section on the twist angle was examined. The influence of piezoelectric actuators and changing the geometry of the blade's cross-section on the stiffness characteristics of the helicopter blade are demonstrated.

Additive manufacturing (AM), defined as a layer-by-layer fabrication process driven by digital design data, enables the realisation of geometrically complex structures that are unattainable through conventional subtractive or formative methods. By allowing material placement only where structurally necessary, AM provides unprecedented control over internal architecture, mass distribution, and functional integration. Lightweight structural design is a critical requirement for modern aerospace systems, particularly for unmanned aerial vehicles (UAVs) and small-scale platforms where mass directly governs endurance, payload capacity, and operational efficiency. Topology optimisation combined with additive manufacturing offers a promising route to overcome the inherent limitations of polymer materials by enabling highly efficient internal material architectures.

This study investigates the experimental performance of topology-optimised lattice structures manufactured using fused deposition modelling (FDM) for lightweight aerospace applications. A function representation (F-rep)-based parametric design framework was developed to generate cylindrical lattice architectures with controlled lattice frequency and vertical phase shift. Seventy-eight (78) specimens were fabricated from polylactic acid (PLA) across three infill regimes (30%, 70%, and 100%) and tested under quasi-static axial compression in accordance with ASTM 1621-16. Mechanical performance was evaluated in terms of maximum compressive load and strength-to-mass ratio.

The results show that topology-optimised lattice structures significantly outperform solid reference specimens in mass-specific mechanical performance. The greatest enhancement was observed at 70% infill, where lattice structures achieved improvements in strength-to-mass ratio of up to 68.42% compared to solid specimens. Lattice architectures also exhibited progressive collapse behaviour, indicating improved damage tolerance.

These findings demonstrate that topology-optimised, additively manufactured polymer lattice structures provide a viable and scalable pathway for developing lightweight, structurally efficient aerospace components, particularly for UAV and small-platform applications.

The EU EFACA project considers two conceptual aircraft design configurations for cleaning European air traffic in future decades. The EU SENECA project considered four conceptual configurations for supersonic aircraft design—it is also expected to impact air traffic in future decades. Recently, several different technologies have led to propulsion designs that have the potential to reduce greenhouse gas emissions and replace existing conventions, including jet fuel technology. On one hand, H₂-powered aviation just recently regained significant attention from the industry, e.g., Airbus launched the ZEROe program, where they pledged to develop the world’s first zero-emission commercial aircraft by 2035. On the other hand, sustainable aviation fuels or biofuels have been identified as an alternative option, also with the potential for use in supersonic flights. In both projects, the results of the assessment of environmental factors are considered using a multidimensional approach, ranging from aircraft certification requirements to regional/global assessment of new designs in air traffic. Any factor reduction technology is simulated and compared to a reference, usually the current best in its aircraft class, providing the possibility to assess its efficiency for necessary certification requirements and for real or forecasted operational conditions, in particular due to the ACARE goals assessment in mid-2035 and long-term 2050 terms.

The increasing proliferation of space debris poses a significant and growing threat to operational spacecraft and future space missions. Our research aims to address the growing concern about space debris and the need for accurate trajectory predictions to ensure the safety and sustainability of space operations. Our approach combines machine learning for space object classification with classical filtering techniques for trajectory prediction, resulting in an interactive visualization of the spatial environment. The initial phase of our research consisted of applying a Random Forest Classifier for the accurate detection and classification of space objects, distinguishing between active satellites and space debris. Subsequently, our research used a Kalman filter to predict the trajectories of both active satellites and space debris. This allowed us to obtain dynamic and precise position informations for these space objects. Finally, a 3D visualization has been developed to illustrate the behavior and movement of both debris and active satellites. Preliminary results, obtained by extracting orbital parameters such as semi-major axis, inclination, right ascension of the ascending node (RAAN), argument of perigee, and mean anomaly from Two-line element (TLE) data, indicated a good classification accuracy of approximatively 98% for distinguishing between different types of space objects during the training phase.

Introduction: Structural health monitoring (SHM) in aeronautical components is challenged by the high cost, downtime, and limited real-time capability of conventional inspection methods such as ultrasonic testing or Eddy current evaluation. To address these limitations, this work proposes an innovative and scalable strain-based monitoring approach for crack propagation in aircraft-grade aluminum structures.

Methods: Aluminum 6082-T6 fuselage-representative specimens were experimentally tested under both static tensile loading and fatigue cycling. Samples included unnotched, notched, and pre-cracked configurations to replicate stress concentration effects typical of riveted aircraft panels. A network of six strategically positioned strain gauges was implemented to capture localized strain evolution during crack growth under cyclic loads of 3, 3.5, and 4 kN. Crack length estimations from strain signals were validated through periodic Eddy current inspections. Based on linear fracture mechanics principles, a novel mathematical formulation was developed to directly correlate strain ratios with crack length without requiring full load knowledge.

Results: The proposed predictive model demonstrated high reliability, achieving correlation coefficients above 0.97 between estimated and measured crack lengths across fatigue conditions. The method provided an average absolute error of 2.23 mm, enabling continuous crack sizing throughout propagation. Higher load levels significantly reduced fatigue life, confirming accelerated crack kinetics under increased stress amplitudes.

Conclusions: This study introduces a cost-effective, real-time SHM methodology integrating conventional strain gauge networks with predictive fracture modeling. The approach offers strong potential for deployment in critical fuselage regions between rivets, minimizing aircraft maintenance interruptions while enhancing operational safety and efficiency.

In the scope of the Clean Aviation (CA) Strategic Research and Innovation Agenda (SRIA), Airbus Defence and Space (ADS) participates in the development of technologies for the future Ultra-Efficient Regional Aircraft (UERA) and Hybrid Electric Regional Architecture (HERA) UCB. In particular, it is anticipated that higher-efficiency aircraft concepts such as UERA and UCB will have higher aspect ratio wings, more flexible structures and, in general, higher aeroelastic phenomena, influencing concept design and production. To evaluate these novel features of future aircraft, it is necessary to adopt new conceptual loads and aeroelastics methods and tools.

In early design and feasibility studies of conventional aircraft, semiempirical methods based on similar aircraft can usually be used to estimate critical load magnitudes. However, as new designs differ considerably from existing aircraft, model-based analysis is required from the early design stages. In this paper, the UCB conceptual loads model is presented as a parametric conceptual loads loop, where early design in the CA HERFUSE project has benefited from a full flight and ground conceptual loads database. In addition, the different methods and approaches followed for model property estimation will be reviewed.

In summary, this parametric definition of the loads model contributes to next-generation aircraft design by giving quick conclusions for different trade-off scenarios in geometry, mass, and structural properties. This allows us to reduce uncertainty in early design stages and to evaluate different configurations in an agile manner.

The aviation sector faces an urgent mandate to decarbonize while simultaneously limiting the emission of harmful pollutants. This work presents a Mixed-Integer Nonlinear Programming (MINLP) framework for the design of next-generation gas turbine engines, specifically targeting the trade-off between COâ‚‚ reduction and NOx mitigation. The model encompasses advanced architectures featuring Rotating Detonation Combustion (RDC), intercooling, heat recuperation, and three-stream flow management, enabling transitions from conventional Joule–Brayton to complex Joule–Humphrey cycles.

The modeling framework integrates semi-empirical correlations linking local thermodynamic conditions—specifically flame temperature and residence time—to NOx formation rates via the Zeldovich mechanism, while COâ‚‚ emissions are minimized through specific fuel consumption optimization. These emission models are coupled with a rigorous thermodynamic cycle solver, yielding a bi-objective formulation that simultaneously minimizes the two primary environmental contributors over a representative cruise mission, without the confounding influence of weight penalties.

Optimization is performed using a Multi-Start Physics-Acquainted Branch and Bound (MS-PABnB) algorithm, which exploits interdependencies among binary architectural decisions to efficiently navigate the non-convex design space. Pareto fronts are generated for three thrust classes (10 kN, 30 kN, 50 kN), strictly quantifying the environmental cost of disparate architectural choices. Our results demonstrate that Optimized Novel Architecture Engine Designs (ONAEDs) can unlock unique design points where advanced cooling and reheating strategies mitigate the traditional penalty of rising NOx associated with high-efficiency cycles, offering a pathway toward truly sustainable propulsion.

Introduction
The increasing use of machine learning in aerospace control systems poses critical questions related to explainability, verification, and certification. While black-box approaches can achieve high performance, their limited transparency remains a major barrier for deployment in safety-critical applications. This work addresses this gap by focusing on interpretable machine-learning methods for nonlinear control, aiming to balance learning-based performance with the explainability and analysability required in aerospace engineering.

Methods
An interpretable control framework is considered, based on a deliberate separation between optimal control generation and control-law learning. Open-loop optimal trajectories are first computed for nonlinear systems and subsequently used to identify closed-loop feedback laws via symbolic regression. Two complementary approaches are employed: (i) genetic programming with integrated continuous parameter optimisation, enabling compact symbolic controllers, and (ii) Kolmogorov–Arnold-based decompositions that reduce high-dimensional learning problems into structured combinations of univariate functions. This decomposition is particularly attractive for explainability, as it exposes the functional role of individual state variables within the control law.

Results
The approach is demonstrated on textbook nonlinear control problems, where fully interpretable feedback laws achieve performance comparable to reference optimal solutions and classical controllers. For aeronautical applications, the framework is applied to stability augmentation and tracking tasks on a nonlinear aircraft model, with emphasis on controller transparency rather than aggressive performance tuning. Results indicate that Kolmogorov–Arnold representations offer improved scalability and readability compared to direct symbolic regression, enabling meaningful inspection of control structure and sensitivities. Initial closed-loop validations and limited robustness analyses support the practical relevance of the method.

Conclusions
This work contributes to the development of explainable learning-based control for aerospace systems, offering a viable pathway toward controllers that are not only effective but also interpretable and verifiable. By prioritising transparency and structural insight, the proposed approach aligns with emerging certification and assurance needs for autonomous and highly nonlinear aerospace systems.