Search Results (573)

This review paper presents a comprehensive synthesis of current scientific knowledge on the integration of low-emission technologies into airport operational models. Attention is also given to the role of artificial intelligence techniques in predicting environmental risks, optimizing energy system design, and enhancing operational safety. The primary objective of the study is to evaluate the synergy between renewable energy sources (solar and wind energy) and emerging propulsion technologies in aviation (hydrogen and electrification) from the perspective of safety and operational stability. The methodology is based on a systematic review of 78 scientific studies identified in the Scopus and Web of Science databases. The analysis identifies critical technical and operational barriers, including electromagnetic interference caused by wind turbines, optical hazards associated with photovoltaic systems, and stability challenges in airport microgrids under peak loads resulting from the charging of electric aircraft. Particular attention is given to the safety of hydrogen infrastructure, where findings from the literature indicate the need to revise separation distances and highlight the potential reduction of airport stand capacity by 5% to 16%. The study synthesizes these findings into a strategic framework for “Smart Green Airports”, proposing solutions such as adaptive infrastructure design, the deployment of predictive models based on artificial intelligence, and the implementation of inherently safe energy storage systems. The paper concludes that achieving airport energy self-sufficiency while maintaining the integrity of flight operations is feasible only through the holistic integration of technical measures, simulation-based planning, and strict compliance with updated safety regulations. Full article

A novel semi-analytical computational approach is formulated and assessed for the dynamic aeroelastic stability analysis of flexible slender thin wings in incompressible flow, which can boost the preliminary airframe design and optimisation of lightweight aircraft, offering both theoretical and practical insights. Hencky’s bar-chain model is explicitly adopted as a discrete numerical implementation of the Euler–Bernoulli continuous beam idealisation for the flexible wing structure and its deformation, resulting in a linear system of coupled ordinary differential equations for its bending and torsion dynamics. Modified strip theory is employed for the unsteady sectional airload, where approximate yet effective analytical expressions are efficiently adopted for its build-up and distribution, combining two- and three-dimensional effects in subsonic potential flow. Once the natural vibration modes of the wing are obtained from its physical model, a reduced set is selected, and a modal approach is then employed to perform its aeroelastic stability analysis with either “p-k” or “p” method, depending on the aerodynamic model. Numerical results from such a reduced-order model are critically assessed for the flutter analysis of Goland’s, Loring’s, and Pazy wings and demonstrate excellent agreement with literature results for two- and three-dimensional airflow, also for the case of the swept wing. Full article

Lift-plus-cruise electric vertical takeoff and landing (eVTOL) aircraft exhibit complex stability characteristics during transition flight, when rotor-borne and wing-borne regimes coexist. This work investigates the dynamic stability of a lift-plus-cruise eVTOL using a nonlinear six-degree-of-freedom model incorporating aerodynamic forces, tractor propulsion, and vertical lifter dynamics. Linearization about representative trimmed conditions enables longitudinal and lateral–directional modal analysis. The results identify a critical near-stall region where lift-curve slope reduction markedly decreases short-period damping. Residual lifter authority partially compensates for this degradation, improving stability in the transition regime. To ensure smooth control transfer, an airspeed-dependent blending strategy between hover and fixed-wing controllers is implemented. Comparative analyses show that a sigmoid blending law improves the minimum short-period damping ratio relative to a linear strategy while preserving similar overall damping variation. Closed-loop simulations of a complete mission profile demonstrate the effectiveness of the proposed approach and reveal an asymmetric dynamic response between hover-to-forward and forward-to-hover transitions. These findings provide a physically grounded explanation for stability degradation during transition and establish practical guidelines for control authority blending in lift-plus-cruise eVTOL aircraft. Full article

This paper proposes an optimal tracking control framework for a permanent magnet synchronous motor (PMSM) drive integrated with a quasi-Z-source (QZS) inverter for all-electric aircraft applications. Two tracking control strategies are developed: (i) an online adaptive optimal control (OAC) method for tracking motor speed and (ii) a linear quadratic integral (LQI) controller for regulating the peak DC-link voltage (PDV) of the QZS. Due to the nonlinear characteristics, parameter uncertainties, and external disturbances inherent in PMSM systems, achieving accurate speed tracking and stable DC-link voltage (DCV) regulation using a PDV control strategy under varying power flow conditions remains a significant challenge. In this study, the PMSM model is represented as a nonlinear system with strict feedback. Augmented feedforward control signals are incorporated to restructure the conventional cascade control architecture into a novel optimal control framework. Based on this formulation, a saturated adaptive optimal control law is proposed, relying on a near-optimal solution to the Hamilton–Jacobi–Isaacs (HJI) equation. This solution is approximated using an online approximator combined with an integral reinforcement learning technique. Meanwhile, an LQI controller is employed to regulate the PDV and suppress voltage fluctuations in the QZS. Simulation results demonstrate that the proposed approach significantly improves speed tracking accuracy, DCV stability, and disturbance rejection capability while improving the overall performance and reliability of PMSM drive systems. The simulation results demonstrate that the proposed control strategies have strong potential for effective application in all-electric aircraft systems, meeting the requirements of high performance and energy efficiency. Full article

Aircraft maintenance scheduling is a critical task in air transportation and national defense security, characterized by complex multi-step procedures, strict precedence dependencies, and multi-resource constraints involving personnel skills and equipment availability. Traditional scheduling methods and standard metaheuristic algorithms often suffer from insufficient model adaptability, poor population diversity, premature convergence, and complex encoding schemes that require frequent feasibility checks. To address these challenges, this paper proposes a comprehensive optimization framework based on the Resource-Constrained Project Scheduling Problem (RCPSP) model. A decimal priority-based encoding method is introduced to replace traditional integer permutation encoding, significantly reducing computational complexity and enhancing search space continuity. Furthermore, an improved hybrid Particle Swarm Optimization algorithm integrating reverse learning and partial random operations (RL-PSO) is developed. The reverse learning mechanism expands the global search space by generating reverse particles, while partial random operations maintain population diversity and prevent premature convergence. The proposed framework converts priority encoding into feasible schedules through a priority sorting and left-shift resource allocation strategy. Simulation experiments on maintenance tasks involving up to 50 aircraft demonstrate that RL-PSO achieves optimization accuracy of 332 min, convergence speed of 92.07 s, and stability of 2.8843 min in standard deviation, which are superior compared to standard PSO, Simulated Annealing, and Teaching–Learning-Based Optimization combined with the serial schedule generation scheme (SSGS). The method effectively balances global exploration and local exploitation, making it suitable for complex, large-scale aircraft maintenance scenarios. Future work will extend the framework to multi-objective optimization and dynamic scheduling environments. Full article

Understanding the variations in the bidirectional reflectance distribution function (BRDF) and albedo over snow surface under various conditions is important for interpreting the surface–atmosphere processes of the cryosphere, and the kernel-driven model is among the most popular methods to obtain this information for a comprehensive analysis. Recently, the RossThick-LiSparseReciprocal-Snow (RTLSRS) model was developed to better characterize the anisotropic reflectance of snow and shows strong potential for integration into operational remote sensing algorithms for snow BRDF/albedo retrieval. To comprehensively test the ability of the RTLSRS model to reproduce snow reflectance, the fitting accuracy to different multi-angular data derived from ground, tower, aircraft, and satellite platforms across the full optical wavelength range were demonstrated in this study. Special attention in this study was directed to analyzing the model performance under extreme illumination observation geometries, particularly with respect to the retrieval accuracy and stability under large Solar Zenith Angles (SZAs) and different Relative Azimuth Angles (RAAs). The model performance for silt-polluted snow surface with different concentrations is also assessed to provide necessary supplementation, relative to “pure” snow surface in the previous study. The main findings of this study are summarized as follows: (1) The RTLSRS model exhibits strong robustness under various SZAs; even when the SZA exceeds 80°, the model maintains high accuracy in BRDF reconstruction, with root mean square error (RMSE) values below 0.05. (2) The model also demonstrates satisfactory inversion capability when observations deviate from the principal plane (PP); the model can achieve fitting accuracy with R² approaching 0.5 and RMSE below 0.05 for MODIS data. (3) In the spectral range below 1300 nm, the RTLSRS model effectively reconstructs the scattering characteristics of snow surfaces with light impurity levels (<20 g/0.5 m²). (4) The spectral shape of snow reflectance remains consistent across different view zenith angles (VZAs) in general. However, the variations caused by different SZAs can be as high as 38.49% and such SZA-induced difference can result in WSA estimation discrepancy of up to 63.43%. This comprehensive assessment further affirms and demonstrates the applicability of the RTLSRS model for the first time in fitting observations across different platforms with various optical wavelengths and geometries, and provides an improved understanding to analyze BRDF variations for the user community. Full article

To address the recognition challenges caused by blurred state boundaries and the limitations of single monitoring modalities during aircraft skin laser paint stripping, this study proposes a multimodal data fusion method for state recognition based on laser-induced breakdown spectroscopy (LIBS) and surface imaging. By constructing a synchronous monitoring platform, a dataset covering five key physical states, namely topcoat (Tc), topcoat–primer transition (Tc-Pr), primer (Pr), primer–substrate transition (Pr-As), and substrate damage (As), was established. The proposed gated weighted multimodal fusion network (PGMF-Net) employs SE-ResNet1D to capture variations in elemental composition features from the spectra and integrates ResNet18 to extract changes in surface morphology from the images. The experimental results show that the proposed model outperforms the single-modal methods as well as the compared early-fusion and late-fusion methods, achieving a recognition accuracy of 94.12% on the test set and an average accuracy of 94.87% in stratified cross-validation. The bootstrap-based confidence interval analysis further verifies the stability of this method under the current dataset conditions. Further analysis indicates that the single-spectrum model has difficulty effectively distinguishing coating transition states because different transition states contain identical or highly similar characteristic peak information. The single-vision model, however, shows insufficient sensitivity to subtle substrate damage, whereas multimodal fusion enables complementary representation of material composition information and surface morphological information. Experimental validation under different power conditions further confirms that the model outputs are generally consistent with the macroscopic morphological evolution observed on the sample surface. This method compensates for the limitations of traditional single-source monitoring and provides a methodological foundation for online monitoring and state feedback during the laser paint stripping process. Full article

Efficient and reliable cooling is essential for ensuring the safety and performance of battery packs in electric vertical takeoff and landing (eVTOL) aircraft. To address the limitations of existing cooling methods in cooling capability and structural integration, this study proposes a hybrid cooling system combining air cooling, high-thermal-conductivity plates (HCPs), and phase-change material (PCM). The power demand in different eVTOL flight phases is first analyzed. A single-cell simulation model is then developed and validated through experiments. The effects of three key structural parameters on system performance are investigated, and their relative importance is quantified using sensitivity analysis. A multi-objective evaluation framework is further established to compare the proposed system with no cooling, passive cooling, and liquid cooling strategies. The adaptability of the hybrid cooling system under different operating conditions is also evaluated. Finally, an air-cooling intervention strategy is proposed based on the PCM liquid fraction. The results show that the optimized hybrid cooling system limits the maximum battery temperature and maximum temperature difference to 37.9 °C and 3.1 °C, respectively. Compared with passive cooling, the proposed system improves temperature stability by 44.6%. Compared with the liquid cooling system, space occupancy is reduced by 19.5%, and the grouping efficiency is increased by 22.4%. The adaptability analysis indicates that the optimized system is suitable for ambient temperatures not exceeding 30 °C. In addition, the proposed air-cooling intervention strategy reduces the air-cooling energy consumption by 43.3% compared with continuous air cooling, while maintaining temperature uniformity. These findings provide a numerical reference for the preliminary design of eVTOL battery cooling systems. Full article

Conventional direct communication in Manned–Unmanned Teaming (MUM-T) suffers from fundamental scalability and security limitations. As the number of Unmanned Aerial Vehicles (UAVs) increases, the communication burden on the manned aircraft (MA) grows significantly, while security threats originating from UAVs may directly propagate to the MA. To address these challenges, this paper proposes a hierarchical communication architecture that introduces dedicated Network Drones (NDs) as intermediate communication mediators and trust boundaries between the MA and multiple UAV swarms. In the proposed design, the MA interacts exclusively with NDs, while UAV swarms communicate through ND-mediated links, effectively bounding the number of MA-facing connections and enabling scalable communication. Building on this structured communication model, a message-level Zero-Trust framework is enforced at the MA–ND interface. Each message is evaluated using a multi-dimensional risk model that incorporates authentication consistency, behavioral consistency, content validity, and contextual information, enabling early detection and containment of compromised UAV behavior. Furthermore, the architecture incorporates backup planning mechanisms, including dynamic reassociation and hot-standby operation, to ensure robust communication under ND failure conditions. Experimental results demonstrate that the proposed approach reduces MA-facing communication overhead, stabilizes end-to-end latency, and improves detection performance in terms of false positives and false negatives, while maintaining system robustness under failure scenarios. Full article

To simulate the erosion damage behavior of thermal barrier coatings (TBCs) under actual service conditions in an aircraft engine environment, this study developed a multi-factor coupled test setup capable of simulating combined loading under high-temperature (1150 °C), high-speed (0.4 Mach), and solid-particle erosion conditions. Yttria-stabilized zirconia (YSZ) TBCs were prepared using electron beam physical vapor deposition (EB-PVD). For different erosion durations (2 h, 5 h, 8 h, 12 h), the evolution of macroscopic and microscopic morphologies as well as the development of residual stresses in the thermally grown oxide (TGO) layer were systematically investigated. The results indicate that the erosion process of the YSZ coating can be divided into three stages. During the initial high-erosion-rate stage (8.17 g/kg), erosion damage was confined to the grain tips of the columnar crystals, primarily caused by brittle fracture at the grain tips, and the TGO stress was relatively low (−0.6 GPa). During the intermediate stage, the erosion rate was lower (2.74 g/kg). Impact stresses induced microcracks within the columnar grains, which gradually connected to form intergranular fractures. This led to the expansion of localized spalling pits. The interface began to wrinkle, and the stress rose to −2.2 GPa. In the final accelerated failure stage (5.88 g/kg), horizontal cracks fully propagated, leading to large-scale peeling of the coating. The stress was released to −0.9 GPa. The coating failure mechanism evolves from surface damage to interfacial peeling, which is closely related to the coating structure, stress evolution, and interfacial state. Full article

This paper introduces an adaptive prescribed performance control (PPC) methodology designed to achieve appointed-time stabilization for morphing aircraft. The proposed approach ensures accurate attitude tracking despite challenges posed by time-varying dynamic constraints, structural deformation perturbations, abrupt aerodynamic disturbances, and rapid variations in attitude commands. Specifically, a novel appointed-time control law is developed using the back-stepping framework to enable precise adjustment of the stabilization time. Then, an adaptive performance boundary adjustment function is introduced. This function not only constrains the system state error but also adapts based on the distance between the state error and the real-time boundary, as well as command variations. This mitigates the fragility issues associated with traditional PPC methods. To further address the ‘differential explosion’ problem, an adaptive appointed-time filter is constructed in which the filter error can be stabilized for an appointed time. The unknown and total perturbations are estimated via adaptive neural networks. The designed controller is shown to guarantee the appointed time stability for all closed-loop signals and ensure that the system state error stays inside the prescribed bounds based on the stability analysis. Lastly, numerical simulations are performed to verify the advantages and effectiveness of the proposed method. Full article

Few-shot fine-grained image classification aims to recognize subcategories with high visual similarity using only a limited number of annotated samples. Existing metric learning-based methods typically rely solely on spatial-domain features. Confined to this single perspective, models inevitably suffer from inherent texture biases, entangling essential structural details with high-frequency background noise. Furthermore, lacking cross-view geometric constraints, single-view metrics tend to overfit this noise, resulting in structural instability under few-shot conditions. To address these issues, this paper proposes the Dual-Subspace Network (DSNet). Specifically, DSNet utilizes the discrete cosine transform (DCT) and a low-pass filtering mechanism to explicitly isolate low-frequency global structural components from spatial features, thereby suppressing background interference. Truncated Singular Value Decomposition (SVD) is employed to construct independent, low-rank linear subspaces for both spatial texture and frequency structural features. An adaptive gating mechanism is designed to dynamically fuse the projection distances from these dual views. This strategy leverages the structural stability of the frequency subspace to prevent the spatial subspace from overfitting to background features. Extensive experiments on four benchmark datasets—CUB-200-2011, Stanford Cars, Stanford Dogs, and FGVC-Aircraft—demonstrate that DSNet exhibits excellent classification performance and robustness, achieving highly competitive results compared to existing metric learning algorithms. Complexity analysis further confirms that the proposed network achieves a favorable balance between high accuracy and computational efficiency, providing an effective new paradigm for few-shot fine-grained visual recognition. Full article

To improve coating adhesion and tribological stability on aircraft-grade aluminum, this work utilizes periodic fiber-laser microtexts as a surface-engineering pre-treatment before applying an epoxy primer. AA2024-T3 panels were imprinted with rhombus, hexagon, and circular lattices (scale factors 100–250 µm; scan speeds 250–750 mm s⁻¹), then primed with an aerospace epoxy primer and evaluated within reciprocating sliding wear tests. Areal profilometry and sessile-drop goniometry measured topography and wettability, whereas friction–distance traces and scratch-track metrology resolved interfacial integrity. The textures expanded surface area and modified energy states in a geometry- and scale-dependent fashion, producing stable friction plateaus and smaller, less-lateral scratch scars compared to the untextured reference. Circular dimples reliably provided the best damage-tolerant behavior, a function of improved mechanical interlocking and debris/film management (reservoir and micro-trap effects), whereas polygonal lattices evidenced greater sensitivity to both scale and speed. Factorial analyses disclosed prevalent interaction effects amongst geometry, scale, and scan speed, reinforcing the notion that performance arises from co-optimized texture architecture rather than a single parameter. In systemic terms, laser-defined microtexts complemented with aerospace-standard primers represent a controllable pathway to vary friction, dampen wear, and improve coating–substrate adhesion. These results provide practical selection guides; and a broad selection prefers larger, well-spaced circular dimples for best-in-class performance and a transferable framework for designing texture-coating systems across aerospace and allied manufacturing contexts. 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

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