Search Results (1,943)

The cooperative airdrop of UAV swarms by multiple transport aircraft creates a large-scale multi-agent planning problem. The mission involves heterogeneous aircraft, multi-visit airdrop areas, strict time windows, and threat-aware flight paths. To address these challenges, this work develops an integrated framework for both global task allocation and real-time replanning in complex three-dimensional operational environments. First, for the combinatorial optimization of task execution sequences across multiple aircraft, a static task assignment method is proposed. This method employs a Hybrid-encoding Constrained Black-winged Kite Algorithm (HCBKA), which incorporates optimization metrics such as mission execution time, completion rate, and load-balancing symmetry among aircraft. The HCBKA aims to find a task assignment scheme that achieves a comprehensive optimum across multiple objectives through efficient model solving. Second, to handle potential real-time dynamic changes during mission execution, a rapid-response and generalizable replanning mechanism is developed. This mechanism utilizes an event-triggered strategy based on a Time-window aware Dynamic Auction Algorithm (TDAA). It ensures that the system can promptly initiate and execute online task reallocation in response to contingencies such as changing mission requirements or losses within its own drone swarm, thus maintaining the adaptability and robustness of the overall plan. Simulation results show that the proposed framework produces high-quality global solutions and maintains strong robustness under dynamic changes. The approach provides an effective and scalable solution for coordinated multi-aircraft swarm airdrop missions. Full article

Aircraft brake torque tubes are safety-critical components subject to combined torsional and thermal loading. As such, in aging aircraft, fatigue cracks frequently occur at the side walls of the grooves near the fillet transitions. This study presents a detailed analysis of the stress–strain state of the torque tube support section using a thermo-mechanically coupled finite element model (FEM) developed in ANSYS 2023 R2 Workbench. The model parameters are based on operational and design data provided by Röder Component Service Center Ltd. Unlike previous studies using idealized models, this approach integrates real-world non-destructive testing (NDT) evidence to identify critical areas with high stress concentrations. The model evaluates stress distributions under normal and emergency braking. Results show that the baseline 1 mm groove fillet exhibits pronounced stress peaks, correlating with observed crack initiation sites. Increasing the fillet radius to 3 mm reduces peak equivalent stress and improves the safety-factor distribution, significantly lowering crack-initiation propensity. These findings demonstrate that even minor local geometric refinements can enhance the structural robustness of torque-transmitting components. This FE–inspection integration framework offers a transferable method for reliability assessment and design improvement in aging aircraft fleets. Full article

This paper presents an integrated assessment of aircraft noise in flight and ground operations within the EFACA project, supporting the roadmap toward ACARE Flightpath-2050 noise goals. It summarizes required reductions, evaluates current technology readiness, and analyzes contributions from advanced propulsion concepts, propeller-noise modeling, and operational procedures. New seven-bladed propeller designs, validated through semi-empirical, analytical, and CAA methods, demonstrate substantial tonal-noise improvements, influencing the aircraft noise reductions by 2–4 dB depending on the fight stage, and during the ground operation by up to 5 dB. Full article

This work presents the numerical assessment of a civil aircraft horizontal tailplane (HTP) using a fully parametric structural model developed through the Ansys Parametric Design Language (APDL). The objective is to evaluate the structural integrity, efficiency, and dynamic behavior of the HTP under realistic operational conditions within the HERFUSE Clean Aviation framework. The study includes linear static analyses for load distribution and critical stress regions, modal analysis for dynamic response characterization, and linear buckling analyses to determine stability assessment. Safety margins are computed for representative load cases across spars, skins, and ribs. The workflow will be integrated and connected to Multidisciplinary Optimization (MDO) loops for higher-level design trade-offs. Full article

The pitot tube is the core sensor for aircraft to obtain external atmospheric data, and its failure has a very important impact on flight safety. However, as its structure and principle are relatively simple, all manufacturers have not adopted available monitoring methods for its health status due to the perspective of cost and complexity reduction. The pitot tube fault warning method is conducted in this paper with a fully connected neural network (FCNN) method based on the data collected by the pitot tube itself. By constructing and selecting parameters and extracting fault features from flight record data, a pitot tube fault warning model based on an FCNN is constructed. The effectiveness of the proposed method is verified through pitot tube fault warning experiments based on actual flight record data, which can provide technical reference for pitot tube fault warning during aircraft route operation in the future. Full article

Emergency locator transmitters (ELTs) are key safety systems for post-crash aircraft localization and search-and-rescue operations. In more electric aircraft (MEA), however, their design and operation are increasingly influenced by complex electrical architectures, tighter equipment integration, and more demanding electromagnetic environments. This paper presents a narrative literature review of ELT technology from a MEA-oriented perspective. A practice-oriented narrative approach is adopted, examining ELTs through a dual lens: the evolution of the search and rescue (SAR) ecosystem and the progressive electrification of aircraft systems. The review addresses ELT fundamentals, classifications, operating principles, and interaction with the Cospas-Sarsat infrastructure, and examines the transition from legacy analog beacons to modern 406 MHz digital systems incorporating GNSS positioning, MEOSAR capabilities, second-generation beacon functionalities, and distress tracking features. Particular attention is given to integration challenges in MEA platforms, including autonomous energy supply, battery endurance, power quality disturbances, electromagnetic compatibility, installation robustness, antenna survivability, and certification constraints. The analysis highlights that ELT performance in MEA depends not only on the beacon itself, but also on the coupled interaction among device design, installation conditions, and the electrical environment. Finally, the review outlines research priorities for next-generation ELTs, including improved survivability assessment, energy-aware architectures, integration strategies based on electromagnetic compatibility, and certification-ready solutions compatible with future aircraft platforms. Full article

The research aimed to create a hybrid model for assessing the carbon footprint of pilots’ education at a flight school, taking into account the level of implementation of green infrastructure by the educational institution, while excluding indirect emissions from the model. The study implemented an approach that combines fuzzy set theory with expert evaluation methods, utilizing membership functions and convolution mechanisms to incorporate subjective expert assessments into formalized numerical measures. The research was focused on two research questions: Does the proposed hybrid model allow for a practical assessment of a pilot’s carbon footprint during his training? Does the hybrid model provide the ability to automatically determine the level of carbon footprint of an aviation educational institution and generate substantiated recommendations for the strategic management of sustainable development of the educational process? The resulting model enables a quantitative assessment of individual CO₂ emissions during pilot training and provides collective insights into the overall carbon footprint, accounting for the green infrastructure’s level of implementation. The hybrid model was tested and validated using real data from the Technical University of Košice (Slovakia) within the “PILOT” study program (2022–2025). The experimental calculations are based on the Viper SD4, a homogeneous aircraft type. The model is designed to account for multiple aircraft types through weighted aggregation, a feature that can be used in future institutional implementations. These recommendations are practical for managers and specialists at aviation educational institutions, environmental analysts, curriculum developers, and policymakers focused on sustainable development. At the current stage, the model primarily captures direct training-related and institution-level operational emissions, while indirect emissions were included only to a limited extent because of insufficiently available and non-systematically recorded data. Therefore, the proposed framework should be interpreted as an operational decision-support model rather than a full greenhouse gas inventory covering all indirect emission sources. Full article

This paper develops new empirical relationships to estimate FAA/EASA- and MIL-3013B-rules-compliant landing-field performance of multi-engine transport aircraft. Widely cited textbooks date from an era when inferior tire and braking capability limited aircraft performance. Today, the use of overly pessimistic conceptual design-level performance estimates may lead concept-design teams to advocate for unnecessary engineering solutions (for example, more complex flaps) to solve “problems” which do not actually exist. Moreover, today’s aircraft designer is likely to face customer-imposed wet and/or contaminated runway performance requirements, where the classic books only discussed dry-weather operations. Taken together, the design community needs a collection of revised empirical equations to estimate landing distances for dry and wet runways. The empirical relationships published here are based upon modern flight-manual data augmented by a calibrated physics-based numerical simulation applied to a wide range of possible vehicle configurations. They offer improved accuracy, compared to earlier methods. The new method, when applied to FAA rules for aircraft operating on dry runways, predicts the substantially shorter “real-world” certified landing distances attainable by modern aircraft. Full article

Reliable short-term air traffic forecasts are important for operational planning in national airspace systems. This study develops a transparent forecasting framework for Türkiye’s monthly aircraft movements using publicly available data from the General Directorate of State Airports Authority (DHMİ) for 2018–2025. Because DHMİ releases may follow cumulative within-year reporting, month-specific increments are reconstructed through within-year differencing and checked through simple audit procedures. The empirical analysis compares seasonal naïve, ETS, and a constrained SARIMA family under leakage-free evaluation, combining a strict 2025 holdout with expanding-window rolling-origin validation. Forecast performance is assessed using standard accuracy metrics and complemented by Diebold–Mariano comparisons, which are interpreted cautiously, given the short holdout length. To examine instability around the pandemic period, this study also reports structural-break and stability diagnostics as supportive evidence rather than definitive identification. Uncertainty is evaluated through backtested 80% and 95% prediction intervals, comparing nominal SARIMA intervals, parametric bootstrap, split conformal prediction, and adaptive conformal inference (ACI). The results show that SARIMA provides the strongest point-forecast performance among the benchmarked models, while adaptive conformal calibration offers a useful balance between empirical coverage and interval width under changing conditions. Overall, this study provides a reproducible and operationally interpretable baseline for national air traffic forecasting in Türkiye and a clear benchmark for future multivariate extensions. Full article

Currently, airport fleet maintenance is characterized by diverse aircraft models, densely concurrent tasks, and tightly coupled professional skills. Traditional specialized personnel allocation models often lead to severe human resource redundancy and high costs, whereas fully generalized models struggle to meet the strict safety and professional qualification requirements for complex maintenance. Scientifically allocating maintenance personnel to balance support costs and operational efficiency under the constraints of broad task scopes, varied personnel qualifications, and strict time limits has become a critical issue for ensuring stable fleet operations and overall airport efficiency. To address this issue, this study proposes an optimization method for aircraft fleet maintenance support personnel allocation based on an Improved Genetic Algorithm. First, the practical challenges are analyzed and a modeling framework is established by systematically examining the implementation processes, professional skill requirements, and time-consuming characteristics of common maintenance tasks across various aircraft models. Next, the minimum required number of maintenance personnel is calculated as a baseline constraint based on the man-hour method. Subsequently, incorporating practical engineering constraints such as cross-type aircraft support, an Improved Genetic Algorithm integrating adaptive crossover/mutation operators and an elite local hill-climbing strategy is designed to solve the allocation optimization problem. Finally, case studies under four allocation schemes and multiple ablation experiments are performed to comprehensively verify the reliability and rationality of the proposed method. The experimental results demonstrate that the optimal personnel allocation scheme obtained with this method saves approximately 10% to 11% in total human resource costs, when compared to the traditional independent support model. This study provides a scientific decision-making basis and technical support for the refined allocation of human resources in the context of fleet maintenance. Full article

The development of More Electric Aircraft (MEA) necessitates that Electro-Hydrostatic Actuator (EHA) controllers achieve exceptional power density within rigorously constrained volumes. However, the compact layout design of these controllers constitutes a challenging NP-hard problem, characterized by strong multi-physics coupling—such as electromagnetic, thermal, and structural fields—and complex nonlinear constraints. Traditional meta-heuristic algorithms frequently suffer from premature convergence and struggle to balance global exploration with local exploitation. To address these challenges, the core contribution of this paper is the proposal of a novel Fractional-Order Anteater Foraging Optimization Algorithm (AFO), which is successfully applied to an established EHA controller layout optimization model. At the algorithmic level, by incorporating the Grünwald–Letnikov fractional derivative, the algorithm exploits the inherent memory property of fractional calculus to dynamically adjust the search step size and direction based on historical evolutionary information, thereby preventing stagnation in local optima. At the engineering application level, a high-fidelity mathematical model of the EHA controller is established, comprising 11 design variables and 10 critical physical constraints, including parasitic inductance minimization, thermal radiation efficiency, and electromagnetic interference (EMI) isolation. Extensive validation against the CEC2005 and CEC2022 benchmark functions demonstrates the superior convergence accuracy and stability of the AFO algorithm. In a specific EHA case study, the proposed method reduced the controller volume by 33.9% while strictly satisfying all multi-physics constraints, compared to traditional methods. Furthermore, a physical prototype was fabricated based on the optimized layout, and experimental tests confirmed its stable operation and excellent thermal performance. The results validate the efficacy of incorporating fractional calculus into bio-inspired algorithms to solve complex, high-dimensional engineering optimization problems. Full article

This study proposes a scalable, AI-driven, and Geographic Information System (GIS)-integrated framework for intelligent route-level classification in large-scale airport transportation networks to support airport operations, logistics planning, and network-level decision-making. The framework addresses the need for practical artificial intelligence applications that combine spatial network analysis with supervised machine learning to improve route assessment and resource allocation in complex air transport systems. A structured dataset was developed using operational and traffic-related attributes, including route distance, aircraft capacity, weekly frequency, annual passenger volume, demand variability, and route performance indicators, with additional normalized features to improve data representation. A Gradient Boosting ensemble classifier was trained to categorize routes into high-, medium-, and low-priority classes. The model achieved strong predictive performance, with a testing area under the ROC curve of 0.961, accuracy of 0.922, F1-score of 0.915, precision of 0.918, and a recall of 0.922. Feature importance analysis identified demand variability and route-density indicators as the main drivers of classification, enhancing interpretability and practical trust. The proposed framework demonstrates the real-world potential of AI for scalable, explainable, and efficient decision support in airport logistics and transportation network management. Full article

In this study, we determined the distribution profile of different mineral fertilizers applied by a DJI Agras T50 remotely piloted aircraft (RPA) under different flight heights and speeds. The experiment was conducted in a randomized block design in a 3 × 3 × 3 factorial scheme, involving three fertilizers (urea, potassium chloride, and single superphosphate), three flight heights (4, 6, and 8 m), and three flight speeds (16, 18, and 20 km h⁻¹). The methodology included laboratory characterization of the physical properties of the fertilizers and the determination of the transverse distribution profile under field conditions. The data were processed using Adulanço software version 4.0 and subjected to statistical analyses (p-value < 0.05). The results indicated that flight height stood out as the main factor, increasing the total and effective swath widths; however, it reduced deposition per unit area and increased the relative error as height increased. The combination of 20 km h⁻¹ with flight heights of 4 and 6 m maximized deposition within the effective swath and provided theoretical operational capacities greater than 8 ha h⁻¹, regardless of the fertilizers. Correlation analysis indicated an operational trade-off, showing that fertilizers with different physical properties respond differently to flight height and flight speed. Full article

Hybrid-electric propulsion and alternative energy carriers are being considered to mitigate the climate impact of short-range regional aviation. Within this framework, the HERA (Hybrid Electric Regional Architecture) project investigates advanced propulsion architectures for a next-generation 72 passenger regional platform. This work presents a cradle-to-grave Life Cycle Assessment of two HERA reference configurations and compares them with a conventional 70 passenger turboprop representative of current service aircraft. The analysis focuses on lithium–sulphur batteries, proton exchange membrane fuel cells, liquid hydrogen storage tanks, and electric motors. The assessment is implemented through a parametric LCA tool supported by a detailed Life Cycle Inventory based on Ecoinvent v3.8 and evaluated using ReCiPe 2016 midpoint indicators. The system boundary includes raw material extraction, manufacturing and assembly, operation under defined mission profiles, maintenance with component replacement, and End-of-Life (EoL) treatment. Results show that the operational phase remains the main driver of climate change impacts, exceeding 95% of total CO₂ equivalent emissions across configurations. The battery-based hybrid reduces fuel consumption but increases manufacturing and maintenance burdens. The fuel cell configuration shows a more balanced life cycle profile, with platinum identified as a critical hotspot. Full article

The continued advancement of small unmanned aircraft systems (UASs) has resulted in growing concerns regarding the potential threat that UASs present. To deal with harmful or disruptive drones, techniques that can be performed using affordable, widely distributed sensor platforms would provide an immense benefit. One such sensor platform is Android smartphones, which continue to see improved sensor quality and orientation estimation while being prevalent worldwide. In this work, the results of crowdsourced drone localization experiments using a custom-built Android smartphone app will be presented. Using GPS positions and angular measurements collected from human-operated smartphones, the ability to localize a static and dynamic target will be demonstrated, as the positions of these targets are estimated from the intersection of line-of-sight vectors. The results from these tests show that the position of these targets can be computed to below 10 m using correction techniques to alleviate measurement errors introduced by environmental or human factors. The results from these tests validate the potential of using readily available smartphones as sensor platforms as an alternative to specially designed localization technology. The inclusion of environmental and human errors can significantly influence the resulting solution, but steps can be taken to alleviate their impact. Full article