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.

Future projections indicate that continued population growth will lead to further expansion and densification of urban environments, thereby increasing transportation demands and associated challenges. In this context, Urban Air Mobility (UAM) has emerged as a promising solution, enabling new intra- and inter-urban transportation services through the use of Vertical Take-Off and Landing (VTOL) aircraft, more precisely configurations such as lift and cruise tiltrotors which combine the hovering capability of conventional helicopters with the cruise speed and range of fixed-wing aircraft by means of tilting propulsion mechanisms. Optimizing the aircraft design process is essential to reduce overall development time and cost. During the conceptual design phase, propeller design methodologies commonly reported in the literature rely on vortex-based approaches or actuator disk theory to estimate the main propeller characteristics. However, the accuracy of these methods strongly depends on the inflow angle and operating conditions, with discrepancies increasing as the inflow angle and advance ratio grow. This paper introduces an analytical model to predict propeller thrust at a 90° inflow angle (pure lateral flow), based on a correction of the thrust under perpendicular flow conditions and the propeller geometry evaluated at 75% span. The approach relies on local velocity and angle of attack estimations derived from classical Blade Element Momentum Theory (BEMT). The propeller lift coefficient is obtained by representing the blades as thin airfoils, with an additional correction to account for stall effects at high angles of attack, while the drag coefficient is calculated based on the known thrust values. The induced velocity, required for local flow calculations, is estimated from known thrust values and discretizing the propeller disk according to the number of blades. This capability is particularly relevant for modeling lift and cruise tilt rotor configurations cruise phase during early design stages while maintaining minimal computational cost. The proposed model is validated against wind tunnel measurements for several propellers tested at different global pitch angles, demonstrating the applicability of the developed formulation for blades with twist angles up to 16°.

The adoption of liquid hydrogen (LHâ‚‚) as a sustainable aviation fuel presents unique challenges, particularly regarding fuel tank dynamics during flight. Sloshing-induced thermodynamic changes in cryogenic tanks can significantly impact pressure stability and fuel management systems. This study addresses the critical need to model and understand these complex interactions to ensure safe and efficient LHâ‚‚ storage in dynamic flight environments.

An integrated computational framework was developed using Cranfield University's in-house thermal-fluid code as the foundation. Ludwig's thermal diffusivity model, originally validated for liquid nitrogen (LNâ‚‚), was adapted from a temperature-based to an energy-based approach and incorporated into the thermal-fluid model to capture sloshing dynamics. The enhanced model underwent validation against experimental LNâ‚‚ data before being applied to LHâ‚‚ systems under the assumption of similar cryogenic fluid behaviour. A comprehensive three-phase methodology encompassed validation, LHâ‚‚ case studies, and parametric analysis examining initial pressure, fill levels, and excitation characteristics.

Model validation achieved agreement with experimental data, showing maximum deviation of only 7%. LHâ‚‚ simulations revealed a sloshing-induced pressure drop of approximately 100 kPa over 40 seconds, attributed to enhanced condensation and turbulence at the liquid–vapour interface. Parametric analysis demonstrated that higher initial pressures and fill levels (85%) increased pressure drop rates by 5-8%. Notably, chaotic sloshing patterns produced substantially faster pressure decrease (5.2 kPa/s) compared to planar wave sloshing (3.2 kPa/s).

This research demonstrates the capability of the enhanced thermal-fluid model to simulate complex cryogenic tank behaviour under dynamic conditions. The findings provide valuable design insights for optimizing LHâ‚‚ tank configurations and operational strategies in future hydrogen-powered aircraft, contributing to the advancement of sustainable aviation technologies.

Aerospace engineering applications for air and space solutions have been becoming increasingly complex. They are required to provide high performance while keeping sustainable safety in place, particularly when human beings are involved in their operations. Multidisciplinary development teams of engineers, system architects, and other stakeholders typically work together to design such sophisticated man-made systems. They use computer models that have different views of the same aerospace system under development. However, technical collaboration between interested parties becomes a deadlock when representations from each collaborator must be cross-checked across multiple models (from other developers) to assess the impact of diverse viewpoints, e.g., how changes in the software design models affect control design models. Later changes in modelling augment risks and costs as they require synchronization of blueprints by updating, and then re-verifying as well as re-validating each of the model representations.

This paper presents details of the design process of a planet rover in which diverse stakeholders deal with aspects of the space ground vehicles. It includes preliminary results from requirements analysis and system design that are used to interlink system models to set an autonomous crosschecked-model design process. The approach is carried out by a multidisciplinary design framework for development of aerospace systems that is meant to reduce collaborative design efforts by enabling multiple developers to minimize potential design inconsistencies (that ultimately produce downtimes) by means of interconnecting their system models. The framework methodology is based on an Artificial Intelligence (AI) support to autonomously link the parametrical notations from different models for modelling analytics, and for further advice on dependencies between models and actions to be taken. Concluding remarks and future research are also presented.

Multirotor unmanned aerial vehicles increasingly require design optimisation balancing aerodynamic efficiency, noise emissions, and operational constraints. While machine learning surrogates enable rapid performance prediction, their opacity limits physical insight into how design variables, such as rotor speed, blade pitch, and geometry, interact to shape system behaviour. For safety-critical applications, this lack of transparency complicates certification and informed design decisions. This work presents a framework combining High Dimensional Model Representation (HDMR) and Kolmogorov–Arnold Modelling (KAM) to extract interpretable structure from rotor aerodynamic performance models.

HDMR provides variance-based sensitivity indices and quantifies parameter interactions through additive decomposition. KAM complements this with a compositional representation that identifies localised functional regimes and reveals how variable importance shifts across the design space. The methodology is first validated on analytical benchmark functions exhibiting tuneable interaction structures, then applied to a parametric study of rotor thrust and efficiency generated using a mid-fidelity vortex particle method. The framework provides a foundation for extending the analysis to multi-rotor configurations where parameter interactions become increasingly complex.

The results will demonstrate how HDMR effectively ranks dominant parameters globally, while KAM uncovers regime transitions corresponding to shifts in dominant parameter interactions across the design space. The combined approach supports transparent design exploration and provides a starting point for regime-aware optimisation strategies in multirotor development.

CubeSat development has historically been mission-specific, with each spacecraft built as a unique integration of avionics, structure, EPS, and ADCS hardware. While this enables tailored optimization, it also increases non-recurring engineering effort, extends integration schedules, and complicates qualification and acceptance testing for every new flight article. As small satellite missions expand beyond academic demonstration toward sustained science and commercial services, the lack of a standardized, reusable bus architecture has become a limiting factor for program throughput and cost efficiency. This work presents a 3U common CubeSat bus designed to interface directly with a 3U payload, forming a complete 6U spacecraft without requiring new bus-level avionics layout, system harnessing, or mechanical redesign for each mission.

The architecture establishes consistent mechanical mounting features, power distribution topology, thermal sink paths, compute resources, and safe-to-mate inhibit logic, enabling payloads to integrate through defined electrical and structural interfaces rather than bespoke bus adaptation. Ten 6U missions launched between 2018 and 2025 were evaluated to derive power, pointing, mass, and data-rate envelopes that guided sizing of solar generation, battery capacity, OBC throughput, and ADCS accommodation. Results indicate that a single, openly defined 3U bus can support multiple payload classes including imaging, RF communications, atmospheric science, and technology demonstration with minimal configuration changes.

This paper argues that the primary value of the proposed bus is not reliability as an outcome, but architectural standardization as the mechanism enabling repeatable manufacturing, faster ATP flow, and reduced development friction across mission sets.

Introduction: The red-tailed hawk (RTH) is a remarkable soaring raptor with a broad wingspan that grants it impressive aerodynamic efficiency, allowing for effortless gliding and rapid transitions into high-speed dives for instant prey capture. These capabilities make the RTH an ideal biological model for developing a feathered, morphing drone that is capable of achieving glider-like aerodynamic efficiency while retaining the maneuverability of quadcopters. Methods: This work presents “SMART Hawk,” a biomimetic, non-flapping Unmanned Aerial Vehicle (UAV) with wing- and tail-morphing capabilities. SMART Hawk’s target weight and size are based on the physical characteristics of a female RTH. A mathematical model was first developed in MachUpX to guide the selection of the design parameters for optimal aerodynamic performance at various morphing configurations. SMART Hawk’s wing incorporates artificial composite feathers that are distributed along the wingspan and connected to a linkage mechanism modeled after avian wing bones, allowing the wing to sweep in a way that mimics the natural deformation of the hawk’s wing natural. The wing’s airfoil profile was extracted from the cross-section of the RTH obtained through 3D scanning of an RTH cadaver. A compact tail mechanism was designed to actuate three degrees of freedom: pitching, tilting, and feather expansion. Composite shells, balsa wood, and 3D-printed ASA plastic were used strategically for the structural and load-bearing components. Propulsion was achieved via a single electric motor located at the nose of the fuselage. Computational fluid dynamics (CFD) and finite element analysis (FEA) simulations were performed to validate the design of all UAV components. Results: A proof-of-concept prototype was built and flight tests were performed to prove the effectiveness of the proposed design. Conclusions: The proposed biomimetic morphing UAV design can replicate the aerodynamic qualities of the RTH. The selected materials and servomotors enabled the achievement of the design objectives.