Search Results (104)

This paper presents an integrated approach to the mapping and structural-tectonic interpretation of lineaments in the Mangystau region using multispectral Landsat-8 OLI data and the medium-resolution Airbus WorldDEM4Ortho digital elevation model. Automatic extraction of linear structures has enabled the identification of over 35,000 lineaments of varying length and orientation, forming a network of intersecting zones that influence the distribution of sedimentary thicknesses, drainage directions, and the location of karst-suffosion depressions. The most prominent are the north-western and sub-latitudinal systems, closely correlated with zones of fracturing and faults, which confirms their tectonic origin. The spatial concentration of lineaments coincides with areas of increased permeability in carbonate and gypsum-bearing rocks and localizes the pathways of groundwater circulation, contributing to the development of karst-suffosion processes. The obtained results demonstrate the significance of structural influences on the region’s current geomorphological and hydrogeological conditions and also have practical importance for engineering-geological surveys, the assessment of geological risks, and the planning of sustainable land use. Full article

This paper presents a novel modular assembly concept for large-volume Carbon Fiber Reinforced Plastics (CFRP) hydrogen tanks, supporting the aviation sector’s transition toward sustainable propulsion. Adhering to VDI 2221 and 2222 design methodologies, four assembly concepts were developed and then evaluated by Airbus, FFT, and Fraunhofer IFAM, to determine the best fit for industrial application. The “Modular Assembly System on Linear Axes” was identified as the best solution, characterized by superior process robustness and efficiency. Utilizing dual linear axes for precise component handling and robotic guidance, this concept ensures structural integrity during joining while offering scalability and seamless integration into existing manufacturing infrastructures. Full article

Ice accretion can significantly degrade aircraft performance and hinder its operational capacities. The ability to detect and characterize ice formation in real time is paramount for enabling timely mitigation strategies. Existing solutions for in-flight ice detection are either physically intrusive, require dedicated hardware that offers only localized readings, or are operationally impractical, depending on complex dynamic models or flight maneuvers unsuitable for standard commercial use. This context highlights a pertinent need for non-intrusive and robust methodologies for detecting actual ice accretion on aircraft. This article proposes a novel, non-intrusive Artificial Intelligence (AI)-driven methodology for real-time aircraft icing detection through the leveraging of data obtained from existing control surface sensors, namely from the aircraft’s ailerons. A supervised learning database was compiled from an Airbus aircraft flight test campaign. In this dataset, flight tests with artificial ice shapes model aircraft behavior under icing conditions, while ice-free tests performed under analogous flight domains represent the nominal scenario. A gradient boosting model was trained on the dataset and evaluated for its performance in accurately identifying the presence of ice accretion. The research shows that aileron sensor data provides sufficient discriminating capacity for in-flight ice accretion detection. Full article

Providing regulation-compliant, high-fidelity training in aircraft maintenance remains challenging for institutions of education, where access to real aircraft, specialist tools, and operational environments is limited by cost, safety, and resource factors. This paper presents the design, in-house development, and pilot deployment of a virtual reality (VR) training system for an operationally critical maintenance procedure—Airbus A320 nose landing gear (NLG) wheel removal, strictly following the official Airbus Aircraft Maintenance Manual (AMM). Managed by an Agile-based methodology, the application, programmed with the Unity engine, uses full-size 3D CAD models and domain-expert input iteratively for quality-assured and rapid deployment. The system was piloted with aeronautical engineering students at the Vietnam Aviation Academy (VAA), achieving significant engagement and perceived gains for procedure knowledge and skill development. Positive comments emphasized the realistic, interactive, and repeatable quality of the simulation. Usability issues related to controller handling, cybersickness, and the absence of haptic feedback, however, suggest opportunities for refinement. This paper reports an early published case study of VR use in commercial aircraft maintenance training that is practically replicable and scalable, and developed in alignment with applicable civil aviation procedural requirements. It suggests that such a high-fidelity VR training platform can provide an accessible solution for aviation stakeholders to help bridge classroom training and real-world application in safety-critical training contexts. Full article

In this work we present the development and application of a mesh adaptation tool on hybrid unstructured meshes for immersed boundary volume penalization methods in the computational fluid dynamics software from ONERA, DLR, and Airbus. This mesh adaptation tool is capable of refining elements around geometries immersed in unstructured meshes made of different types of elements, like tetrahedra, hexahedra, prisms, and pyramids. This feature allows us to simulate fluid flow problems with the immersed boundary method not only on Cartesian meshes but on general hybrid unstructured meshes. Of special interest in this work is the simulation of turbulent fluid flows in aerodynamics through the numerical solution of the Reynolds-averaged Navier–Stokes equations either on unstructured meshes with only immersed geometries or on unstructured body-fitted meshes along with immersed geometries. As part of the benchmarking, we simulate the subsonic flow past the high-lift multi-element airfoil. The reported numerical simulations are in good agreement with their corresponding full body-fitted meshes. Full article

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

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

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

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

Unmanned helicopters play a critical role in various fields including defense, disaster response, and infrastructure inspection. Military platforms such as the MQ-8C Fire Scout represent high-value assets exceeding $40 million per unit including development costs, particularly when compared to expendable multicopter drones costing approximately $500–2000 per unit. Unexpected failures of these high-value assets can lead to substantial economic losses and mission failures, making the implementation of Health and Usage Monitoring Systems (HUMS) essential. However, the scarcity of failure data in unmanned helicopters presents significant challenges for HUMS development, while the economic feasibility of investing resources comparable to manned helicopter programs remains questionable. This study presents a novel cross-scale transfer learning framework for vibration-based anomaly detection in unmanned helicopters. The framework successfully transfers knowledge from a source domain (Airbus large manned helicopter) using publicly available data to a target domain (Stanford small RC helicopter), achieving excellent anomaly detection performance without labeled target domain data. The approach consists of three key processes. First, we developed a multi-task learning transformer model achieving an F-β score of 0.963 (β = 0.3) using only Airbus vibration data. Second, we applied CORAL (Correlation Alignment) domain adaptation techniques to reduce the distribution discrepancy between source and target domains by 79.7%. Third, we developed a Control Effort Score (CES) based on control input data as a proxy labeling metric for 20 flight maneuvers in the target domain, achieving a Spearman correlation coefficient ρ of 0.903 between the CES and the Anomaly Index measured by the transfer-learned model. This represents a 95.5% improvement compared to the non-transfer learning baseline of 0.462. Full article

From the 9th century onwards, Tāmdult was one of the three major caravan ports in the Western Maghreb, alongside Sijilmāssa and Nūl Lamṭa. By the mid-20th century, the remains of dwellings, metallurgical production sites and fortifications had been located a few kilometres south of the present-day oasis of Aqqa, which is irrigated by the resurgence of the wadi of the same name. In 1999, our research, which was based on field surveys and aerial photographs, revealed exceptionally well-preserved traces of a large-scale agricultural system and an irrigation canal network adjacent to the ruins. This completed the picture of this pre-Saharan oasis. An initial study was published in 2011. However, the question of the chronological relationship between the two oases, Tāmdult and Aqqa, remained unresolved. Processing recent satellite images (Airbus © 2023) of these two oases and creating a WebGIS interface now enables us to refine and correct our observations from 1999. This new data largely confirms our initial proposals, such as the joint development of an urban settlement and an agricultural area with an irrigation network. Furthermore, these new images show the branching structure of the various water distribution channels, the regularity of the agricultural land parcels and the existence of interstitial rural settlements. They thus reveal a hierarchy in this distribution that was perhaps insufficiently explored in our initial publication. Given the limited historical sources available, we can now make more informed arguments regarding the possibility of the two oases coexisting over time. We can also propose initial hypotheses about the main reasons for the abandonment of one of the oases and discuss the identity of their founders, which could be local tribal groups and/or branches of the Idrisid dynasty. The central issue of the dossier to which our contribution is addressed—‘The Role of Urban Elites in the Construction of Rural Landscape’—is adapted here to the specific characteristics of the pre-Saharan context in terms of both climate and settlement structure. Full article

The impact of the aviation sector on the Earth’s atmosphere and climate is not limited to the effects of CO₂ emissions generated by the combustion of hydrocarbon-based fuel in an aircraft engine. It is complemented by other combustion products and non-CO₂ emissions, such as CO, NO_x, unburnt hydrocarbons (UHCs), and soot, as well as the formation of condensation trails (contrails) as a result of emitted H₂O and condensation nuclei. To evaluate the overall atmospheric impact of an aircraft mission, it is necessary to model the aero engine and the combustion chamber in context with the atmospheric conditions over the course of the flight trajectory. Following that rationale, this paper presents the novel multidisciplinary ‘Modeling and System analysis of Aero Engines’ (MSAE) platform, aiming to evaluate the emission products over the flight trajectory with realistic atmospheric and operative boundary conditions. MSAE comprises an ambient condition model, an aircraft operating model, an aero engine performance model, and a combustion chamber model. The functionality of the individual models as well as their interconnections are demonstrated using the example of an Airbus A320 powered by an International Aero Engines V2500-A1 turbofan engine. Non-CO₂ emissions, including CO, NO_x, UHC, and soot emission indices, can be predicted at a selected operating point. Furthermore, an evaluation of contrail formation for both annually averaged and intraday ambient conditions is conducted, showing the benefit of considering ambient conditions in a finer temporal resolution. The results show the functionality of the presented MSAE platform and the necessity of performance and emission analysis under realistic conditions. Full article

This study critically examines the technological feasibility, regulatory challenges, and societal acceptance of Pilotless Passenger Aircraft (PPAs) in commercial aviation. A mixed-methods design integrated quantitative passenger surveys (n = 312) and qualitative pilot interviews (n = 15), analyzed using SPSS and NVivo to capture both statistical and thematic perspectives. Results show moderate public awareness (58%) but limited willingness to fly (23%), driven by safety (72%), cybersecurity (64%), and human judgement (60%) concerns. Among pilots, 93% agreed automation improves safety, yet 80% opposed removing human pilots entirely, underscoring reliance on human adaptability in emergencies. Both groups identified regulatory assurance, demonstrable reliability, and human oversight as prerequisites for acceptance. Technologically, this paper synthesizes advances in AI-driven flight management, multi-sensor navigation, and high-integrity control systems, including Airbus’s ATTOL and NASA’s ICAROUS, demonstrating that pilotless flight is technically viable but has yet to achieve the airline-grade reliability target of 10⁻⁹ failures per flight hour. Regulatory analysis of FAA, EASA, and ICAO frameworks reveals maturing but fragmented approaches to certifying learning-enabled systems. Ethical and economic evaluations indicate unresolved accountability, job displacement, and liability issues, with potential 10–15% operational cost savings offset by certification, cybersecurity, and infrastructure expenditures. Integrated findings confirm that PPAs represent a socio-technical challenge rather than a purely engineering problem. This study recommends a phased implementation roadmap: (1) initial deployment in cargo and low-risk missions to accumulate safety data; (2) hybrid human–AI flight models combining automation with continuous human supervision; and (3) harmonized international certification standards enabling eventual passenger operations. Policy implications emphasize explainable-AI integration, workforce reskilling, and transparent public engagement to bridge the trust gap. This study concludes that pilotless aviation will not eliminate the human element but redefine it, achieving autonomy through partnership between human judgement and machine precision to sustain aviation’s uncompromising safety culture. Full article