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Lightweight composite aircraft wing design increasingly depends on combining multidisciplinary design optimization (MDO), aeroelastic tailoring, and stacking sequence optimization. However, an overview of these interconnected fields is lacking. This study applies a PRISMA-ScR-based scoping review of 54 selected articles to map current approaches, identify emerging trends, and highlight remaining gaps. Key findings indicate six MDO architectures—with hybrid methods being increasingly preferred—and demonstrate that aeroelastic tailoring (e.g., ply angle manipulation) enhances performance while reducing weight. Manufacturing constraints (ply continuity, blending, symmetry) are addressed in a subset of the reviewed literature, with opportunities for broader integration. Critical future priorities include integrating manufacturing process models into MDO and incorporating durability considerations (fatigue, impact). This work synthesizes current approaches, identifies emerging trends, and provides a roadmap for the development of next-generation lightweight, high-performance composite wings. Full article

The aim of this paper is to give an overview of emerging technologies for the greening of aviation, how they can be applied to different classes of aircraft, and the challenges to be overcome in achieving efficiency and environmental objectives. The following steps are part of the journey towards the greening of aviation: (i) developing and maturing new technologies, including electrification and sustainable fuels; (ii) where possible, using new technologies in the current fleet to maximize short-term benefits—i.e., EU Fit for 55; (iii) when it is not possible to retrofit new technologies to current aircraft, incorporating them into new next-generation aircraft designs from 2035; and (iv) replacing existing fleets with new, cleaner aircraft to meet the ICAO Net Zero 2050 goal. These technologies of prime importance will have to be supplemented by operational, regulatory, and economic enablers to support wide deployment. There will not be one solution that meets the requirements of all aircraft classes or mission profiles, but rather a combination of electrification, hydrogen propulsion, and sustainable aviation fuels will be required. Achievement of aviation’s environmental goals will hence not solely be a function of technological progress but also certification pathways, investment in infrastructure, and integrated policy strategies. Full article

Aircraft taxiing speed control along predefined airport surface routes is a constrained single-aircraft longitudinal control problem involving heterogeneous route geometry, action smoothness, and terminal velocity feasibility. Existing learning-based taxiing controllers commonly use a unified policy for the whole route, which may be insufficient for handling straight-segment propulsion, curved-segment speed regulation, and action discontinuities near straight–curve transitions. This paper proposes SegCoord-Taxi, a geometry-aware segmented deep reinforcement learning framework for taxiing speed control. The route is decomposed into straight segments, curved segments, and transition boundary zones. A Straight-Segment Policy (SSP) and a Curved-Segment Policy (CSP) generate geometry-dependent base acceleration commands, a Switch Residual Adapter (SRA) provides local residual correction near transition regions, and a Route-Level Feasibility Projection (RFP) maps the coordinated action into an executable acceleration satisfying route-level feasibility constraints. Experiments on departure taxiing routes at Chengdu Tianfu International Airport (ZUTF) included baseline comparison, ablation analysis, projection diagnostics, sensitivity analysis, and a trajectory-level case study. On the evaluated ZUTF case-study routes, SegCoord-Taxi achieves the lowest final velocity on the test set, 0.336 ± 0.017 m/s, compared with 0.732 ± 0.061 m/s for the unified Proximal Policy Optimization (PPO) controller and 0.586 m/s for the curvature-aware constrained optimizer. The complete framework also reduces switch action jump from 1.022 ± 0.017 m/s² to 0.429 ± 0.004 m/s² in the ablation study. These results indicate improved terminal feasibility and transition-region smoothness in the evaluated single-airport case-study setting under an explicit efficiency–smoothness–feasibility trade-off. Future work will extend the framework to multi-aircraft and multi-airport settings under operational uncertainty. Full article

High-fidelity computational fluid dynamics (CFD) remains computationally expensive for steady aerodynamic prediction under multi-condition and variable-geometry configurations, which limits rapid design iteration. To address this issue, this study proposes a data-driven surrogate framework for aircraft surface flow-field prediction on irregular meshes. The framework combines a geometry-unification strategy for variable rudder-deflection configurations with KM-GAT, a hybrid neural architecture that integrates graph attention and KAN-based nonlinear feature transformation. Geometry unification maps the surface flow fields associated with different rudder-deflection states onto a common zero-deflection reference template, thereby establishing consistent mesh correspondence and fixed prediction locations across samples while retaining the rudder angle as an operating-condition variable. The KM-GAT model further combines topology-aware message passing with localized nonlinear refinement, while the Huber loss is adopted to improve training robustness for CFD-derived data. Experiments on the F-22 research model show that the proposed framework achieves lower prediction errors and more concentrated error distributions than baseline MLP and GNN-based models. Qualitative comparisons further indicate that KM-GAT better preserves localized high-gradient structures, including pressure transitions and vortex-dominated regions. These results suggest that the proposed framework provides an effective surrogate modeling strategy for variable-geometry aerodynamic flow field prediction. Full article

In this study, twin screw extruded Polyetherimide (PEI)/Poly(ethylene terephthalate) (PET) polymer blends (90/10, 70/30, 50/50 w/w%) were investigated to elucidate the composition–property relationship governed by morphological, structural, rheological, thermomechanical, mechanical, and electromagnetic shielding (EMI) performance behavior. Among other polymer blends, the 70/30 blend exhibits superior thermomechanical stability with a significant glass transition temperature of 132.7 °C, where a robust confinement effect effectively restricts the mobility of PET chains. This morphology, characterized by a domain size of 562 nm, provides proof of concept for interface-driven attenuation, reaching a maximum EMI shielding effectiveness of 2.54 dB within the investigated blends. This performance is primarily governed by Maxwell–Wagner–Sillars polarization at the immiscible boundaries, alongside an optimized dielectric loss of tan δ ≈ 0.065. The design of these high-temperature PEI blends provides a proof of concept for interface-driven attenuation and demonstrates their potential for developing advanced EMI shielding matrices. Full article

Air Traffic Control Officers (ATCOs) are responsible for controlling air traffic and ensuring the safety of the aircraft. Capacity, understood as the maximum number of aircraft that can be safely managed for one hour, is calculated based on the workload of ATCOs. This calculation normally is based on a manual and tedious data collection process that demands a high consumption of human resources. To improve and relieve human re-sources, automation tools that automatically generate a preliminary annotation of Air Traffic Control (ATC) activity have been developed. This paper focuses on the feasibility of employing data-driven approaches using neural networks to classify ATC events, as well as if it is possible to improve the performance of these ATC-activity tools. Particularly, this approach seeks to infer ATC intent for separation actions, which are the most critical in terms of ATC workload. A modular methodology has been developed to include information from different sources: flight plans, radar trajectories, trajectory prediction, conflict detection and rule-based knowledge. Different experiments are evaluated based on the different input’s combination, as well as three neural networks (Multilayer Perceptron, Convolutional Neural Network and TabNet). Results show that TabNet is the best neural network option, reaching a similar performance in task classification than current ATC tools and improving classification metrics around 4% by employing the outputs of ATC tool metrics as inputs. Full article

This work examines a complex-valued data partition within the improved Loewner Framework to enhance the efficiency of modal parameter identification for aerospace structures. The method is applied to the General Dynamics F-16 Ground Vibration Test dataset, assessing accuracy and computational performance against the standard real-valued formulation. The complex-valued approach reduces execution time by an order of magnitude while preserving the quality of the identified poles. The extracted modal parameters align well with established benchmark results, confirming the suitability of the proposed formulation for reliable and scalable modal analysis of aircraft structures. Full article

Improving detection performance across diverse synthetic aperture radar (SAR) scenes remains challenging because different datasets exhibit different levels of target–background separability. To address this issue, we propose a constant false alarm rate (CFAR)-guided mode-selective network (CGMSN), which selects an appropriate feature-fusion mode according to the CFAR target–background separation margin. Specifically, CFAR is used as an interpretable statistical tool to construct an anomaly response map. The separation margin is then calculated by comparing the average CFAR anomaly responses of annotated target regions and their surrounding contextual backgrounds. Based on this indicator, a You Only Look Once version 8 (YOLOv8)-based mode-selective detector is constructed with three key components. First, a lightweight representation-enhanced backbone that integrates ResNet18 and a dilated convolutional spatial pyramid (DCSP) module is adopted to improve contextual representation while maintaining moderate model complexity. Second, a mode-selective neck (MSN) is designed with three predefined fusion modes, where the appropriate fusion depth is selected according to the CFAR-guided target–background separation margin of each dataset. Third, a complete intersection over the union modulated head (CMH) is developed to enhance classification-regression alignment and suppress clutter-induced responses. Experiments on SAR-Aircraft-1.0, High-Resolution SAR Images Dataset (HRSID), and SAR Ship Detection Dataset (SSDD) indicate that datasets with smaller CFAR target–background separation margins benefit from deeper fusion, while datasets with larger separation margins can adopt shallower fusion. Moreover, the proposed CGMSN achieves superior performance over representative detectors, demonstrating its effectiveness on the evaluated SAR datasets with diverse scene characteristics. Full article

In unmanned aerial vehicle (UAV) scenarios, RGB-T salient object detection faces several challenges, including cross-modal spatial misalignment, redundant multi-scale features, and weak responses of small objects in cluttered backgrounds, which together degrade fusion effectiveness and localization stability in complex environments. To address these issues, we propose a Hierarchical Alignment Fusion and Mapping Network (HAFM-Net), a misalignment-robust fusion framework, for unaligned RGB-T salient object detection. The proposed method does not rely on explicit pixel-level preregistration. Instead, it replaces registration-first preprocessing with implicit feature-domain alignment and misalignment-robust fusion, enabling saliency prediction from unregistered RGB-T inputs. Specifically, we design a hierarchical adjacent-scale interaction mechanism to enhance multi-scale contextual modeling while suppressing cross-scale redundancy. We further develop a Misalignment-Robust Correlation Fusion module to explore cross-modal correlations and enable robust feature interaction under positional variations. In addition, a semantic–spatial complementary enhancement is introduced to promote collaboration between high-level semantic cues and low-level spatial details, thereby improving the representation and boundary localization of small salient objects. Experimental results on the UAV RGB-T 2400 dataset and an additional weakly aligned benchmark demonstrate that HAFM-Net achieves competitive performance and exhibits strong robustness in challenging scenarios, such as blur, illumination variation, small-object cases, and foggy conditions. Full article

This paper addresses a human resource allocation problem with spatial-temporal constraints (HRAP-SC) in the parallel assembly of complex products, such as satellites and aircraft. It involves coordinating a limited pool of multi-skilled workers across geographically distributed workstations, subject to rigorous constraints including team collaboration requirements, operation priorities, technological tail times (e.g., curing), and strict 8 h workdays. Existing exact approaches typically fail to converge due to the combinatorial explosion arising from the strong coupling of shared resources across workstations, while meta-heuristic methods often suffer from performance instability caused by hyper-parameter sensitivity. To overcome these limitations, we propose a pattern-based decomposition algorithm (PDA), a novel parameter-free exact solution framework. By exploiting the inherent symmetry of identical jobs and parallel workstations, PDA defines a set of canonical patterns to drastically reduce the search space. It employs an efficient traversal mechanism reinforced by rigorous mathematical bounds and pruning rules to eliminate unpromising solutions. Computational experiments demonstrate that PDA significantly outperforms state-of-the-art Mixed-Integer Programming (MIP) and Constraint Programming (CP) solvers. Unlike standard solvers, which frequently time out (3600 s), PDA strictly evaluates only a single pattern when proving optimality, and robustly scales to large industrial instances (e.g., six jobs comprising 78 operations) to provide high-quality schedules. By successfully solving complex scheduling problems that remain intractable for monolithic solvers, PDA provides a robust and automated decision-support tool for production management in complex manufacturing systems. Full article

Copper and its alloys are widely used in electrical, automotive, aerospace, and energy applications because of their excellent thermal and electrical conductivity. However, the low hardness and poor wear resistance of pure Cu limit its use under tribologically demanding sliding conditions. In this study, Cu matrix composites reinforced with 1 wt.% boron carbide (B₄C), boron (B), chromium (Cr), cobalt (Co), alumina (Al₂O₃), and graphite (Gr) were fabricated by powder metallurgy and comparatively evaluated under identical processing and testing conditions. Phase constitution and microstructural characteristics were analyzed by XRD, SEM, and EDS, while mechanical and tribological behavior was assessed by Vickers hardness and dry sliding wear tests. All reinforcements improved the hardness of the Cu matrix compared with unreinforced Cu. The hardness increase followed the order Cu–B₄C (68.91%) > Cu–B (66.43%) > Cu–Gr (63.97%) > Cu–Al₂O₃ (61.79%) > Cu–Cr (42.69%) > Cu–Co (36.04%). Dry sliding wear tests, performed under a 10 N normal load, 0.05 m s⁻¹ sliding speed, and 1000 m sliding distance against a 316L stainless-steel ball, showed that all reinforced composites exhibited lower mass loss and more stable sliding behavior than pure Cu. Among all samples, Cu–B₄C displayed the best wear performance, with a 154.8% improvement in wear resistance relative to pure Cu. SEM analysis of the worn surfaces revealed that reinforcement addition reduced severe plastic deformation, groove formation, and delamination, leading to a more stable wear regime. Graphite- and boron-containing composites benefited from interfacial lubrication and contact stabilization, whereas B₄C and Al₂O₃ improved wear resistance through rigid-particle strengthening and enhanced load-bearing capacity. By comparing ceramic, metalloid, metallic, oxide, and solid-lubricating reinforcements at the same low addition level and under identical processing and testing conditions, this study provides a reinforcement-selection framework for Cu-based composites requiring improved hardness and dry-sliding durability. Full article

Graphene is a novel material that can bring several advantages in the composite materials manufacturing field, such as improved electrical and thermal properties, and high performance. In particular, functionalizing current composite materials can bring advantages in the aerospace field in thermal management for electric aircraft engines. This paper studies the addition of graphene particles into carbon fiber composites manufactured by the Resin Transfer Molding Process (RTM). Thermal and mechanical properties are evaluated and compared with a conventional composite laminate. Major improvements were achieved on the thermal behavior of the composite material while maintaining general properties, but in particular, the addition of graphene had a negative impact on transverse tensile and mode II fracture toughness due to agglomerates present in the fiber–resin interface. Full article

The performance of the aircraft anti-skid braking system is critical to the ground operational safety of an aircraft. Conventional Pressure Bias Modulation (PBM) can suffer from deep skidding under low runway friction coefficients or low aircraft speeds. To address these issues, a composite control strategy based on Gaussian Quantum Particle Swarm Optimization (GQPSO) is proposed. This strategy employs the GQPSO algorithm for offline Proportional–Integral–Derivative (PID) parameter optimization, followed by real-time adaptive scheduling through a lookup table to accommodate varying speed domains and runway conditions. Simultaneously, by integrating the main-wheel dynamics model and friction characteristics, a runway identification function based on a Back Propagation Neural Network (BPNN) is designed to provide runway status information. The stability of the controller is verified via phase-plane analysis and Monte Carlo simulation. Subsequently, comparative Hardware-in-the-Loop (HIL) tests are conducted among PBM, PSO-PID, and the proposed GQPSO-PID controller under various runway conditions. The experimental results demonstrate that this composite controller can adapt to different speed domains and runway conditions, stably track the target slip ratio, effectively suppress skidding, and significantly improve braking efficiency, as well as exhibiting excellent robustness and control performance. Full article

The increasing demand for higher efficiency and lower emissions in aircraft gas turbines motivates investigation of alternative thermodynamic cycle architectures. This study assesses the performance and nitrogen oxides (NOx) emission behavior of a triple-spool, separate-exhaust turbofan engine equipped with an interstage turbine burner (ITB). A baseline engine representative of the RB211 Trent 892 is first modeled at maximum takeoff, sea-level static conditions and verified against publicly available takeoff reference data. The cycle is then modified by introducing an isobaric secondary combustion process between the high-pressure and intermediate-pressure turbines. The effects of fan pressure ratio, bypass ratio, overall pressure ratio, high-pressure turbine inlet temperature, and ITB exit temperature are examined using two-parameter response surface sweeps. Main combustor NOx is estimated using an RQL-type cycle correlation, while the ITB contribution is represented using an engineering source–sink model accounting for new NOx formation and partial reburning of upstream NOx. The baseline model predicts specific thrust, thrust-specific fuel consumption (TSFC), and NOx emission index (EINOx) within ±8% of reference values. At a selected ITB operating point, specific thrust increases by 1.98%, TSFC increases by 9.84%, thermal efficiency decreases by 2.56%, and the adopted engineering source–sink model predicts a 20.03% reduction in fuel flow-weighted EINOx. The corresponding takeoff-mode NOx-per-thrust indicator decreases by approximately 12.1%. These results indicate that ITB integration introduces a coupled performance–emissions trade-off and should not be evaluated solely as a thrust augmentation method. Full article

Continuous population growth will lead to further expansion and densification of urban environments. In this context, Urban Air Mobility (UAM) has emerged as a new transportation solution through the use of Vertical Take-Off and Landing (VTOL) aircraft, more precisely, configurations such as lift-plus-cruise tilt-rotors. During the conceptual design phase, propeller design methodologies commonly reported in the literature rely on vortex-based approaches or actuator disk theory. However, the accuracy of these methods strongly depends on the inflow angle and operating conditions. This paper introduces an analytical model to predict propeller thrust at a 90° inflow angle (edgewise flight), based on a correction of the thrust under axial flight conditions and the propeller geometry evaluated at 75% span. The approach relies on local velocity and angle of attack estimations derived from classical Blade Element Momentum Theory (BEMT) with an additional correction to account for stall effects at high angles of attack. This capability is particularly relevant for modeling lift-plus-cruise tilt-rotor configurations cruise phase during early design stages while maintaining minimal computational cost. The proposed model is validated against wind tunnel measurements for several propellers tested at different global pitch angles, varying from 0 m/s to 9.1 m/s of windspeed and 1300 to 6200 rpms, demonstrating the applicability of the developed formulation for blades with twist angles up to 16°. Full article