Search Results (283)

The paper presents a study regarding the tool-life of uncoated and DLC-coated cutting inserts used for machining aluminum–lithium components used in the structure of the Airbus A350 aircraft. The experiment was conducted in an industrial environment that produced aircraft parts, using industrial equipment, under serial processing conditions during 5874 machining hours, resulting in 1440 samples. The experimental results were used as the input data for obtaining predictive models for the estimation of the tool-life machining supervised learning from MATLAB 2025b based on four machine-learning algorithms: trainlm and trainbr (artificial neural networks), fitrtree (decision trees), and fitrensemble (ensemble methods) respectively. The models were evaluated and compared in terms of their performance, which determined the best option. Also, a sensitive analysis of the five predictors was performed. The validation of the four learning algorithms was performed based on a separate set of experimental data, which was not used in learning. The analysis between the experimental results and those predicted by the learning models confirmed their robustness. The analysis between the experimental results and those predicted concluded the best model. Full article

The accurate anticipation of passenger demand across specific origin–destination (OD) airport routes is a cornerstone of strategic and operational decision-making within the global aviation sector, including airlines optimizing fleet and route management, airports planning infrastructure development, and regulatory bodies overseeing airspace efficiency. However, conventional forecasting techniques frequently encounter limitations when confronted with the inherent complexities and non-linear interdependencies that characterize air travel demand patterns. These patterns are shaped by an array of dynamic variables, including macroeconomic trends, population dynamics, distinct seasonal variations, and emergent phenomena. This investigation evaluates the utility of Artificial Intelligence (AI) paradigms in constructing predictive models for monthly passenger volumes between international OD airport pairs. This work highlights the ongoing transformative impact of AI methodologies on forecasting tasks within the aviation industry. Full article

Over the last decades, progress in modal analysis has enabled the increasingly routine use of modal parameters for applications such as structural health monitoring and finite element model updating. For output-only identification, or operational modal analysis (OMA), widely adopted approaches include stochastic subspace identification (SSI) methods and the Natural Excitation Technique, combined with the Eigensystem Realization Algorithm (NExT-ERA). Nevertheless, SSI-based techniques may become cumbersome on large systems, while NExT-ERA fitting can struggle when measurements are contaminated by noise. To alleviate these, this work investigates an OMA frequency-domain formulation for aeronautical structures by coupling the Loewner Framework (LF) with NExT, yielding the proposed NExT-LF method. The method exploits the computational efficiency of LF, due to the effectiveness of tangential interpolation, together with the impulse response function retrieval enabled by NExT. NExT-LF is assessed on two experimental benchmarks: the eXperimental BeaRDS 2 high-aspect-ratio wing main spar and an Airbus Helicopters H135 bearingless main rotor blade. The identified modal parameters are compared against available experimental references and results obtained via SSI with a Canonical Variate Analysis and NExT-ERA. The results show that the modes identified by NExT-LF correlate well with benchmark data, particularly for high-amplitude tests and in the low-frequency range. Full article

Transonic buffet is a critical self-sustained shock/boundary-layer instability limiting the flight envelope of modern transport aircraft. This study investigates the interaction between shock buffet and forced wing motion on the Airbus XRF-1 wind tunnel model, using unsteady Reynolds-Averaged Navier–Stokes (URANS) simulations with the DLR TAU code. The investigation is carried out in deep buffet condition ($\mathrm{Ma}=0.84$, $\alpha =4.5^{\circ }$, $\mathrm{Re}=25\times {10}^6$) and validated against wind tunnel data at the same flow condition. The buffet flow is superimposed with forced wing motions derived from a symmetric wing eigenmode at $\mathrm{Sr}=0.164$. Two different amplitudes scaled with the half-span s are considered: $A_{tip}=0.0025·s$ and $0.01·s$. The baseline no-forcing URANS captures the buffet flow quite well with only small deviations in the standard deviation of the surface pressure coefficient $c_{p,rms}$. A special variant of the Discrete Fourier Transformation for the whole wing upper surface $c_p$ distribution revealed that the typical buffet frequencies are also matched. The analysis of the forced simulations revealed a strong influence of the local wing motion on the increase of $c_{p,rms}$. The spectral content showed a shift and damping or amplification of different buffet modes, which is relevant for the interaction of motion induced and buffed induced aerodynamic forces. Full article

Accurate structural analysis is essential for the design and optimization of aircraft wings; however, repeated high-fidelity finite element analysis (FEA) becomes computationally expensive when embedded in iterative design loops. This study presents a machine learning-based surrogate modeling framework for the efficient analysis and optimization of metallic commercial wing structures. A detailed Airbus A320-like wing model was developed and analyzed in ANSYS 2023 R1 under modal, static, and eigenvalue buckling conditions. The general dimensions of the Airbus A320 wing were used only as a reference; the resulting model is a conceptual benchmark rather than a one-to-one geometric replica or a validated digital twin of a specific aircraft wing. Using Latin Hypercube Sampling, 340 high-fidelity samples were generated, with 300 samples used for training and validation and 40 retained as an independent holdout set. The proposed Pyramidal Deep Regression Network (PDRN), a deep learning-based surrogate model whose architecture is automatically tuned using Bayesian Optimization, was benchmarked against Artificial Neural Networks (ANNs), Ensemble Learning, Support Vector Regression (SVR), and Gaussian Process Regression (GPR). On the unseen test set, the PDRN achieved the best overall predictive performance, with RMS errors of 0.8% for mass, 3.1% for the first natural frequency, 11.5% for load factor, and 11.4% for safety factor. To evaluate its practical utility, the trained PDRN was embedded into a PSO-based optimization framework for mass minimization under minimum safety factor, load factor, and first-frequency constraints. The surrogate-guided optimum was verified in ANSYS and remained feasible, yielding a mass of 10,485 kg, a first natural frequency of 1.4142 Hz, a load factor of 1.307, and a safety factor of 1.158. Compared with direct ANSYS in-the-loop optimization, the proposed workflow reached a comparable feasible design with substantially fewer high-fidelity evaluations. These results demonstrate that the PDRN provides an accurate and computationally efficient surrogate for rapid wing analysis and constraint-driven structural optimization. Full article

In the early stages of concurrent engineering, the ability to assess design change impact is fundamentally limited by the availability of expert knowledge. Knowledge-Based Engineering (KBE) provides structured approaches for the capture, formalization, management, and diffusion of knowledge within complex organizations. KBE has increasingly turned toward ontology-based methodologies, leveraging their robust framework for shared conceptualization and reasoning capabilities. Integrated with Model-Based Systems Engineering (MBSE), such Ontology-Based Engineering (OBE) methodologies provide the necessary infrastructure for knowledge-driven workflows in a Digital Engineering (DE) context. Such integration is critical for complex engineering sectors such as the aerospace industry. However, the traditional knowledge acquisition process is expert-centric and, consequently, resource-intensive. The digital transformation of the industry has led to an explosion of data volumes, and raised concerns toward statistical approaches. This study implements a hybrid knowledge acquisition method within the OBE framework and MBSE environment. Specifically, this method combines human expertise and interpretable machine learning techniques to formalize knowledge models and instantiate them with concrete design rules. Applied in a real-world use-case involving workload estimation, this paper aims to enhance cross-domain collaboration during the conceptual design phase of new aircrafts. Full article

The utilisation of composite materials has the potential to play a vital role in the development of lightweight structures for future generations of aircraft, with the objective to reduce emissions. Ultrasonic welding is a process that has been proven to exhibit advantageous qualities, including the capacity to achieve welds with a comparatively short process time. Furthermore, its capacity to function as both a static and a continuous process makes it a viable candidate for facilitating the realisation of this objective. The present study investigates the potential of a novel explicit modelling approach for the static ultrasonic welding process to more accurately represent the welding process by incorporating a more precise representation of the hammering effect. The hammering effect describes the partial loss of contact between the sonotrode and the upper adherend. The model’s validation was achieved through a multifaceted approach that incorporates high-speed camera recording, encompassing digital image correlation, laser displacement sensor measurements, and static ultrasonic welding experiments. These experiments encompassed varying welding times, followed by fracture surface analysis. The findings showed that an explicit time-domain model can effectively represent the static welding process of unidirectional materials utilising a film energy director. The experimental validation demonstrated a high degree of correlation between the thermal behaviour of the welding interface and the simulation results. The study demonstrated that the neutral position of the sonotrode exhibited an increase during the initial phase of the welding process due to dynamic stresses. This phenomenon enables reduced constraint movement of the adherends and the energy director, which results in the disconnection of the sonotrode from both the upper adherend and the energy director, as well as the adherends and the anvil. The higher neutral position of the sonotrode was then implemented in an explicit simulation of the static ultrasonic welding process. Full article

The Galileo Signal Authentication Service (SAS) is the next new feature to be offered by Galileo, the European GNSS. Its signal-in-space initial capability is expected already in the next months of 2025, starting with the L3 (Launch 3) Galileo elliptical-orbit satellites. It is the first-ever navigation signal authentication feature offered globally and openly. Galileo SAS uses the existing Galileo E6-C signal to be encrypted, in combination with OSNMA (Open Service Navigation Message Authentication), through the so-called semi-assisted authentication concept. In this concept, portions of the E6-C are re-encrypted with OSNMA future keys and published in a server. The concept allows signal authentication openly and for free, and without private key management by users. In exchange, the time between authentications is 30 s, inherited from OSNMA, and it introduces a latency between the E6-C signal reception and its authentication down to a few seconds. This work presents the status of Galileo SAS. It outlines its latest technical definition, already shared in previous publications. It will also present the MMARIO (Message and Measurement Authentication Receiver for Initial Operations) project, developing the first SAS server, receiver and testing platform. The paper also outlines the Galileo SAS plans for the near future, up to the Initial Service Declaration. Full article

This paper presents how the combination of Vision-based Navigation (VBN), Terrain Referenced Navigation (TRN) and Star Navigation complement each other to tackle the challenge of GNSS-denied navigation for aeronautics covering a wide range of environmental and operational conditions. Moreover, Collaborative Navigation contributes to GNSS-denied navigation capability by distributing the position information within the group of collaborating platforms in a stochastically optimal way using an Extended Kalman Filter. Full article

The degradation of wetlands and forests is still a threat to the supply and recovery of ecosystem services in the tropics. Studies comparing restoration measures and ecosystem service recoveries are fragmented. This study investigated the spatial extent and drivers of wetland/forest degradation, and assessed the effects of restoration measures on the recovery of ecosystem services and resilient livelihoods. A cross-sectional household survey was conducted targeting households adjacent to restored and unrestored wetland/forest ecosystems. The data was analyzed using a Binary Logistic regression to characterize earlier and recovered ecosystem services between forest and wetland ecosystems. High spatial-resolution optical satellite imagery from the Airbus constellation was obtained and analyzed to examine wetland and forest degradation. Our findings revealed that the spatial extent of degraded land under wetlands and forests decreased between 2023 and 2025. Ecosystem service degradation was primarily driven by chronic poverty, excessive water abstraction, population growth, burning practices, overharvesting of resources, overgrazing, cultivation, infrastructure development, and the invasion of alien species (p < 0.05). The counteractive ecosystem restoration activities undertaken included mobilization and sensitization of communities on wetland restoration, wetland demarcation, revegetation, establishment of flood control measures, and provision of alternative livelihoods (p ≤ 0.05). The multiple direct and indirect ecosystem service recoveries reported were provisioning services (increases in pasture, enhanced livestock production, increased soil productivity, health-related benefits from crops and livestock products) and regulating services (improved water quality/quantity). The ecosystem service recoveries were more significant in the restored wetlands than the forests. The indicators of enhanced ecosystem-based resilient livelihoods included increased household incomes, higher livestock yields, increased crop productivity, improved health from crop/livestock products, improved water quality/quantity, and enhanced scenic beauty and tourism (p < 0.05). The restoration activities in degraded wetland systems had more potential to facilitate full recovery of the wetland ecosystem compared to the absence of interventions. This evidence highlights the need to restore high-ecological-sensitive ecosystems to sustain the delivery of ecosystem services for community and environmental resilience. Full article

The development of modern production systems involves numerous interdependent disciplines, heterogeneous data sources, and frequent design iterations, making the conceptual design phase particularly complex and error-prone. Model-Based Systems Engineering (MBSE) provides a promising approach to manage this complexity by enabling consistent and structured system representations. While domain-specific modeling languages (DSMLs) can tailor MBSE methods to specific domains, existing approaches often lack standardized semantics, user guidance, and tool support to ensure consistent model creation and verification. This paper introduces a DSML framework tailored for the conceptual design of production systems, integrating both methodological guidance and standard-based domain knowledge. The approach builds upon the Software Platform Embedded Systems (SPES) framework and extends Systems Modeling Language (SysML) through the Unified Modeling Language (UML) profile mechanism, providing clear modeling constructs, viewpoint-specific diagram types, and automated consistency checks. To enhance comprehensibility and domain alignment, the framework incorporates supplementary DSMLs that capture structures and semantics from established industrial standards. The proposed method is evaluated using an aircraft production case study, demonstrating improved applicability of MBSE for the conceptual design of complex production systems. Full article

This paper presents the design, manufacturing, instrumentation and validation by tests (ground and icing wind tunnel) of a full-scale Hybrid Laminar Flow Control (HLFC) leading-edge demonstrator based on Airbus A330 outer wing plan-form. The Ground-Based Demonstrator (GBD) was developed to reproduce a full-scale, realistic wing section integrating into the leading-edge three key systems: micro-perforated skin for the hybrid laminar flow control suction system (HLFC), the hot-air Wing Ice Protection System (WIPS) and a folding “bull nose” Krueger high-lift device. The demonstrator combines a superplastic-formed and diffusion-bonded (SPF/DB) perforated titanium skin mounted on aluminum ribs jointed with a carbon-fiber-reinforced polymer (CFRP) wing box. Titanium internal ducts were designed to ensure uniform hot-air distribution and structural compatibility with composite components. Manufacturing employed advanced aeronautical processes and precision assembly under INCAS coordination. Ground tests were performed using a dedicated hot-air and vacuum rig delivering up to 200 °C and 1.6 bar, thermocouples and pressure sensors. The results confirmed uniform heating (±2 °C deviation) and stable operation of the WIPS without structural distortion. Relevant tests were performed in the CIRA Icing Wind Tunnel facility, verifying the anti-ice protection system and Krueger device. The successful design, fabrication, testing and validation of this multifunctional leading edge—featuring integrated HLFC, WIPS and Krueger systems—demonstrates the readiness of the concept for subsequent aerodynamic testing. Full article

The energy transition, driven by the global shift toward renewable and electrification, necessitates accurate and efficient prediction of electrical load profiles to quantify energy consumption. Therefore, the systematic literature review (SLR), followed by PRISMA guidelines, synthesizes hybrid architectures for sequential electrical load profiles, aiming to span statistical techniques, machine learning (ML), and deep learning (DL) strategies for optimizing performance and practical viability. The findings reveal a dominant trend towards complex hybrid models leveraging the combined strengths of DL architectures such as long short-term memory (LSTM) and optimization algorithms such as genetic algorithm and Particle Swarm Optimization (PSO) to capture non-linear relationships. Thus, hybrid models achieve superior performance by synergistically integrating components such as Convolutional Neural Network (CNN) for feature extraction and LSTMs for temporal modeling with feature selection algorithms, which collectively capture local trends, cross-correlations, and long-term dependencies in the data. A crucial challenge identified is the lack of an established framework to manage adaptable output lengths in dynamic neural network forecasting. Addressing this, we propose the first explicit idea of decoupling output length predictions from the core signal prediction task. A key finding is that while models, particularly optimization-tuned hybrid architectures, have demonstrated quantitative superiority over conventional shallow methods, their performance assessment relies heavily on statistical measures like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). However, for comprehensive performance assessment, there is a crucial need for developing tailored, application-based metrics that integrate system economics and major planning aspects to ensure reliable domain-specific validation. Full article

The establishment of a sustained human presence on the Moon is critically dependent on the ability to utilize local resources, primarily the production of oxygen for life support and propellant. The ROXY (Regolith to Oxygen and metals conversion) process is a molten salt electrolysis technology designed for this purpose. This paper presents an economic analysis of a ROXY pilot plant capable of producing over one ton of oxygen per year. We evaluate the economic viability by analyzing development, transportation, and operational costs against the potential revenue from selling oxygen and metals within a nascent lunar economy. A key aspect of this analysis is the perspective of an early customer in habitation life support systems preceding that of much higher propellant production demand. The analysis contextualizes this paradigm by recognizing that the primary economic driver for oxygen production is the larger future market for propellant; however, early life support demand may incentivize a paradigm-shift from Earth-based consumable resupply. Scenarios based on varying transportation costs and development timelines are evaluated to determine the internal rate of return (IRR) and time to break even (TTBE). The results indicate that the ROXY pilot plant is economically viable, particularly in near-term scenarios with higher transportation costs, achieving a positive IRR of up to 47.4% when both oxygen and metals are sold. The analysis identifies facility mass, driven by the robotics subsystem, as the primary factor for future cost-reduction efforts, concluding that ROXY is a technically and economically sound pathway toward sustainable lunar operations. Full article

Cybersecurity education requires practical activities such as malware analysis, phishing detection, and Capture the Flag (CTF) challenges. These exercises enable students to actively apply theoretical concepts in realistic scenarios, fostering experiential learning. This article introduces an innovative pedagogical approach relying on gamification in cybersecurity courses, combining technical problem-solving with human factors such as social engineering and risk-taking behavior. By integrating interactive challenges into the courses, engagement and motivation have been enhanced, while addressing both technological and managerial dimensions of cybersecurity. Observations from course implementation indicate that students demonstrate higher involvement when participating in supervised offensive security tasks and social engineering simulations within controlled environments. These findings highlight the potential of gamified strategies to strengthen cybersecurity competencies and promote ethical awareness, paving the way for future research on long-term cybersecurity learning outcomes. Full article