Search Results (144)

Accurate prediction of air traffic noise is critical for advancing environmentally sustainable operations in high density terminal areas. Conventional noise prediction models often exhibit significant limitations due to discrepancies between actual and nominal flight trajectories. To overcome this challenge, this study introduces a probabilistic framework that integrates real air-traffic-flow data to generate realistic flight trajectory distributions. The proposed methodology extracts key operational features—including trajectory distribution probabilities, and essential trajectory operation features—within a machine learning architecture. Furthermore, we develop a dedicated air traffic noise prediction model for clustered flight paths that explicitly incorporates traffic flow patterns, enabling high-fidelity simulation of noise propagation under actual air traffic operation. The framework is validated using a QAR (Quick Access Recorder) dataset from the terminal area of Changsha Huanghua International Airport. Experimental results demonstrate the model’s high predictive accuracy for both air traffic noise distribution and its influence, coupled with computational efficiency and practical applicability. The findings indicate that the proposed approach successfully addresses the challenge of predicting air traffic noise from divergent, real-world flight trajectories, offering a robust method for supporting noise-abatement strategies and sustainable aviation-planning initiatives. Full article

The aerodynamic behavior of small-scale UAV propellers operating under non-axial inflow conditions poses a significant prediction challenge due to the presence of strong azimuthal asymmetries, inherently unsteady flow phenomena, and Reynolds number effects that dominate forward flight conditions. Although numerical models based on the Moving Reference Frame (MRF) formulation combined with steady RANS solvers are widely used in engineering practice because of their low computational cost, the precise limits of their applicability in crossflow configurations remain poorly defined. This work conducts a comprehensive numerical investigation that systematically compares steady RANS–MRF predictions against time-accurate URANS simulations across a wide range of advanced ratios and rotor tilt angles. Rigorous validation of the computational framework against experimental data in axial and near-axial regimes demonstrates excellent agreement, with deviations below 5% in propulsive efficiency. The results clearly identify the operational envelope within which MRF-based steady models remain valid under non-axial inflow. In particular, the steady approach exhibits robust performance for low-to-moderate advance ratios, where global errors in thrust and power remain below 10% for $\mu =0.40$. However, the fidelity of the method deteriorates sharply under extreme edgewise-flight conditions ($\mu =0.70)$, in which the crossflow component dominates the aerodynamic field, the “frozen-rotor” assumption progressively loses mathematical consistency, and the solver may converge toward steady solutions that no longer represent a physically meaningful flow state. The URANS analysis further reveals two critical phenomena that cannot be captured by steady-state models. First, at high advance ratios, the retreating blade encounters an extensive region of reverse flow, which induces negative sectional thrust and strongly anharmonic load waveforms. This behavior has direct implications for structural design: the peak-to-peak amplitude of thrust oscillation in edgewise flight can exceed the mean thrust level, implying extreme cyclic loading and a high risk of high-cycle fatigue. Second, the simulations quantify the emergence of off-axis parasitic moments (pitching and rolling), which are negligible in vertical flight but reach magnitudes comparable to the total aerodynamic torque in forward-flight conditions. Taken together, these findings highlight the need for a hybrid-fidelity strategy in UAV propulsion analysis: employing steady RANS–MRF within the validated domain for energetic assessments, while relying on time-accurate URANS for mandatory evaluation of structural loading, vibration, and control logic in critical high-speed regimes. Full article

This research presents the design, numerical analysis, and experimental validation of a compact radial turbine intended for mini-turbocharger applications in UAV power systems. To meet the stringent requirements of UAV propulsion—such as lightweight construction, high efficiency at small scales, and stable performance across varying operating altitudes—a test rig was constructed to experimentally estimate turbine torque and shaft power across selected operating conditions. Complementary CFD simulations were performed to evaluate aerodynamic behavior, including flow distribution, torque generation, and power output at multiple rotational speeds matched to experimental mass-flow rates. Additional high-speed CFD simulations were conducted to predict turbine performance in operational regimes typical of UAV engines, where experimental testing is challenging. The combined CFD–experimental methodology provides accurate performance prediction for micro-scale radial turbines across different volute geometries and operating conditions. The results contribute essential insights for the development of next-generation miniaturized turbochargers aimed at enhancing UAV engine efficiency, high-altitude capability, and overall flight endurance. Full article

The proliferation of air travel demand necessitates innovative solutions to enhance passenger experience while optimizing airport operational efficiency. This paper presents the pilot-scale implementation and evaluation of an AI-powered service robot ecosystem integrated with thermal cameras and 5G wireless connectivity at Athens International Airport. The system addresses critical challenges in passenger flow management through real-time crowd analytics, congestion detection, and personalized robotic assistance. Eight strategically deployed thermal cameras monitor passenger movements across check-in areas, security zones, and departure entrances while employing privacy-by-design principles through thermal imaging technology that reduces personally identifiable information capture. A humanoid service robot, equipped with Robot Operating System navigation capabilities and natural language processing interfaces, provides real-time passenger assistance including flight information, wayfinding guidance, and congestion avoidance recommendations. The wi.move platform serves as the central intelligence hub, processing video streams through advanced computer vision algorithms to generate actionable insights including passenger count statistics, flow rate analysis, queue length monitoring, and anomaly detection. Formal trial evaluation conducted on 10 April 2025, with extended operational monitoring from April to June 2025, demonstrated strong technical performance with application round-trip latency achieving 42.9 milliseconds, perfect service reliability and availability ratings of one hundred percent, and comprehensive passenger satisfaction scores exceeding 4.3/5 across all evaluated dimensions. Results indicate promising potential for scalable deployment across major international airports, with identified requirements for sixth-generation network capabilities to support enhanced multi-robot coordination and advanced predictive analytics functionalities in future implementations. Full article

This paper presents a unified aerodynamic design and optimization framework for morphing Blended-Wing-Body (BWB) Unmanned Aerial Vehicles (UAVs) operating in subsonic and near-transonic regimes. The proposed framework integrates parametric CAD modeling, Computational Fluid Dynamics (CFD), and surrogate-based optimization using Response Surface Methodology (RSM) to establish a generalized approach for geometry-driven aerodynamic design under multi-Mach conditions. The study integrates classical aerodynamic principles with modern surrogate-based optimization to show that adaptive morphing geometries can maintain efficiency across varied flight conditions, establishing a scalable and physically grounded framework that advances real-time, high-performance aerodynamic adaptation for next-generation BWB UAVs. The methodology formulates the optimization problem as drag minimization under constant lift and wetted-area constraints, enabling systematic sensitivity analysis of key geometric parameters, including sweep, taper, and twist across varying flow regimes. Theoretical trends are established, showing that geometric twist and taper dominate lift variations at low Mach numbers, whereas sweep angle becomes increasingly significant as compressibility effects intensify. To validate the framework, a representative BWB UAV was optimized at Mach 0.2, 0.4, and 0.8 using a parametric ANSYS Workbench environment. Results demonstrated up to a 56% improvement in lift-to-drag ratio relative to an equivalent conventional UAV and confirmed the theoretical predictions regarding the Mach-dependent aerodynamic sensitivities. The framework provides a reusable foundation for conceptual design and optimization of morphing aircraft, offering practical guidelines for multi-regime performance enhancement and early-stage design integration. Full article

This study employed a combined approach of computational fluid dynamics (CFD), numerical simulations, and wind tunnel tests to investigate droplet drift characteristics and develop prediction models in order to address the issues of low pesticide utilization rates and high drift risk, associated with droplet drift during agricultural unmanned aerial vehicle (UAV) spraying, as well as the unreliable results of field experiments. Firstly, a numerical model of the rotor wind field was established using the multiple reference frame (MRF) method, while the realizable k-ε turbulence model was employed to analyze the flow field. The model’s reliability was verified through wind field tests. Next, the Euler–Lagrange method was used to couple the wind field with droplet movement. The drift characteristics of two flat-fan nozzles (FP90-02 and F80-02) were then compared and analyzed. The results showed that the relative error between the simulated and wind tunnel test values was within 20%. Centrifugal nozzle experiments were carried out using single-factor and orthogonal designs to analyze the effects of flight height, rotor wind speed, flight speed, and droplet size on drift. The priority order of influence was found to be “rotor wind speed > flight height > flight speed”, while droplet size (D_V50 = 100–300 µm) was found to have no significant effect. Based on the simulation data, a multiple linear regression drift prediction model was constructed with a goodness of fit R² value of 0.9704. Under the verification condition, the relative error between the predicted and simulated values was approximately 10%. These results can provide a theoretical basis and practical guidance for assessing drift risk and optimizing operational parameters for agricultural UAVs. Full article

Aircraft with a twin vertical tail and leading-edge extension configuration may experience vertical tail buffeting during high-angle-of-attack maneuvering flight. This issue can lead to structural fatigue damage in the vertical tail, shortening its service life and increasing maintenance costs, ultimately compromising flight safety. Therefore, accurate prediction of buffeting loads and responses is essential during design. In the preliminary stage, wind tunnel testing is the primary means to obtain dynamic data such as fluctuating pressure and acceleration response, which can be transformed to full-scale conditions through similitude principles. However, the elastic scaling model used in buffeting tests is usually established for a specific flight condition. When the flow velocity or objective flight condition changes, the similitude relationship becomes invalid, limiting the applicability of test results and preventing full-envelope strength verification. To overcome this limitation, this study proposes a multi-objective point response prediction method for vertical tail buffeting. The method enables the prediction of full-scale responses at multiple objective flight conditions using wind tunnel data that do not strictly satisfy similitude criteria. A complete aircraft vertical tail buffet (rigid/elastic) hybrid model was developed for testing, and an Adjusted Model incorporating elastic scaling transformation was established. The proposed method was validated through experiments, demonstrating improved test data utilization and prediction accuracy across multiple-objective flight conditions. Full article

This paper reports on the study of turbulence at various locations in Hong Kong during Typhoon Wipha in July 2025, including turbulence intensity based on Doppler Light Detection and Ranging (LIDAR) systems and radiosondes, observations by microclimate stations, and low-level windshear and turbulence at the Hong Kong International Airport (HKIA) by LIDAR, flight data, and pilot reports. Although the observation period was primarily limited to 20 July 2025, passage of a typhoon over a densely instrumented urban area is uncommon; these observations on turbulent flow associated with typhoons therefore can serve as valuable benchmarks for similar studies on turbulent flow associated with typhoons in other coastal areas, particularly for operational alerts in aviation. To assess the predictability of turbulence, the eddy dissipation rate (EDR) was derived from a high-resolution numerical weather prediction (NWP) model using diagnostic and reconstruction approaches. Compared with radiosonde data, both approaches performed similarly in the shear-dominated low-level atmosphere, while the diagnostic approach outperformed when buoyancy became important. This result highlights the importance of incorporating buoyancy effects in the reconstruction approach if the EDR diagnostic is not available. The high-resolution NWP was also used to provide time-varying boundary conditions for computational fluid dynamics simulations in urban areas, and its limitations were discussed. This study also demonstrated the difficulty of capturing low-level windshear encountered by departing aircraft in an operational environment and demonstrated that a trajectory-aware method for deriving headwind could align more closely with onboard measurements than the standard fixed-path product. Full article

The application of unmanned vehicles in civilian and military fields is increasingly widespread. Traditionally, unmanned vehicles primarily rely on Global Positioning Systems (GPSs) for positioning; however, GPS signals can be limited or completely lost in conditions such as building obstructions, indoor environments, or electronic interference. In addition, countries are actively developing GPS jamming and deception technologies for military applications, making precise positioning and navigation of unmanned vehicles in GPS-denied or constrained environments a critical issue that needs to be addressed. In this work, authors propose a method based on Visual–Inertial Odometry (VIO), integrating the extended Kalman filter (EKF), an Inertial Measurement Unit (IMU), optical flow, and feature matching to achieve drone localization in GPS-denied environments. The proposed method uses the heading angle and acceleration data obtained from the IMU as the state prediction for the EKF, and estimates relative displacement using optical flow. It further corrects the optical flow calculation errors through IMU rotation compensation, enhancing the robustness of visual odometry. Additionally, when re-selecting feature points for optical flow, it combines a KAZE feature matching technique for global position correction, reducing drift errors caused by long-duration flight. The authors also employ an adaptive noise adjustment strategy that dynamically adjusts the internal state and measurement noise matrices of the EKF based on the rate of change in heading angle and feature matching reliability, allowing the drone to maintain stable positioning in various flight conditions. According to the simulation results, the proposed method is able to effectively estimate the flight trajectory of drones without GPS. Compared to results that rely solely on optical flow or feature matching, it significantly reduces cumulative errors. This makes it suitable for urban environments, forest areas, and military applications where GPS signals are limited, providing a reliable solution for autonomous navigation and positioning of drones. Full article

A time-stepping, lifting-line, computationally efficient tool for preliminary design applications is developed to predict the unsteady aerodynamic loads of rotors operating in hovering and axial flight. The velocity field induced by wake vorticity is computed using a free-wake vortex-lattice model, while sectional aerodynamic loads are evaluated through the application of Küssner and Schwarz’s airfoil theory. The vorticity released by the trailing edge is related to the distribution of bound circulation and is convected downstream to form the vortex-lattice wake. The local bound circulation is determined by applying the Kutta–Joukowski theorem for unsteady flows. The proposed unsteady aerodynamic solver is successfully validated by comparison with both experimental data available in the literature and numerical results obtained by a three-dimensional boundary element method computational tool for potential flow. It does not apply to rotors in edgewise flight conditions and when compressibility effects are not negligible. Full article

Reservoir simulation remains a major computational bottleneck for production optimization, history matching, and uncertainty quantification, particularly as geological models become increasingly detailed and recovery processes more complex. Coarse-grid network (CGNet) models have recently emerged as an efficient, physics-grounded proxy to full-physics simulations by solving the flow equations on a coarse network whose parameters are freely calibrated to reproduce fine-scale or observed well responses. In this study, we investigate how different coarse-partitioning strategies affect the accuracy and robustness of CGNet models. Four partitioning approaches are examined: a simple cookie-cutter partition, and three partitions based on cell-wise indicators—absolute permeability, velocity magnitude, and the product of forward and backward time-of-flight. Two test cases are considered: one using a single layer of the SPE10 benchmark dataset and the other using a sector model from the Norne field. Results show that, despite substantial differences in coarse-grid topology, the four CGNet models achieve comparable convergence behavior and predictive accuracy. For the SPE10 case, all models reproduce the fine-scale responses well, and no clear superiority among the partitioning methods. In the Norne case, the time-of-flight–based partition yields the lowest misfit and slightly better well-response predictions, although overall differences remain modest. These findings demonstrate that CGNet models are robust to coarse-grid topology and that incorporating flow-based indicators in partition generation can offer marginal improvements for complex geological systems. The results highlight the potential of CGNet as a cost-effective, physically consistent surrogate for large-scale reservoir applications. Full article

Airplane cabin temperature is a critical environmental factor governing the safety and reliability of airborne equipment. Compared with measuring temperature, predicting temperature is more cost- and time-saving and can cover an extreme flight envelope. Physics-informed neural networks (PINNs) offer a promising prediction solution whose performance hinges on the availability of precise governing differential equations. However, building governing differential equations between flight parameters and cabin temperature is a great challenge, as it is comprehensively influenced by aerodynamic heat, avionic heat, and internal flow. To solve this, a new PINN framework based on “a priori monotonicity” is proposed. Underlying physical trends (monotonicity) from flight data are extracted to construct the loss function as a data-driven constraint, thus eliminating the need for any governing equations. The new PINN is developed to estimate the seven cabin temperatures of an unmanned aerial vehicle. The model was trained on data from four flight sorties and validated on another four independent sorties. Results demonstrate that the proposed PINN achieves a mean absolute error of 1.9 and a root mean square error of 2.6, outperforming a conventional neural network by approximately 35%. The core value of this work is a new PINN framework that bypasses the development of complex governing equations, which enhances its practicality for engineering applications. Full article

Long-range rotating wrap-around-fin rockets may exhibit non-convergent conical motion at high Mach numbers, causing increased drag, reduced range, and potential flight instability. This study employs the implicit dual time-stepping method to solve the unsteady Reynolds-averaged Navier–Stokes (URANS) equations for simulating the flow field around a high aspect ratio wrap-around-fin rotating rocket at supersonic speeds. Validation of the numerical method in predicting aerodynamic characteristics at small angles of attack is achieved by comparing numerically obtained side force and yawing moment coefficients with experimental data. Analyzing the rocket’s angular motion process, along with angular motion equations, reveals the necessary conditions for the yawing moment to ensure stability during angular motion. Shape optimization is performed based on aerodynamic coefficient features and flow field structures at various angles of attack and Mach numbers, using the yawing moment stability condition as a guideline. Adjustments to parameters such as tail fin curvature radius, tail fin aspect ratio, and body aspect ratio diminish the impact of asymmetric flow induced by the wrap-around fin on the lateral moment, effectively resolving issues associated with near misses and off-target impacts resulting from dynamic instability at high Mach numbers. Full article

Aviation’s sustainability discourse often centres on flight emissions, but production and end-of-life phases also carry material, energy, and pollution impacts that are large enough to merit systematic intervention. With ~13,000 aircraft projected to retire over the next two decades—roughly 44% of the global fleet—the sector must scale responsible dismantling and material recovery to avoid lost opportunities for meeting future sustainability goals and to harness economic value from secondary parts and recycled feedstocks. Embedding major sustainability and circular economy principles into aircraft design, operations, and retirement can reduce waste, conserve critical materials, and lower lifecycle emissions while contributing directly to multiple SDGs. Furthermore, when considering particular aircraft types, thousands of narrow-body aircraft such as the Airbus A320 and Boeing 737 are due to reach their end of life over the next two decades. This research evaluates the economic and environmental feasibility of aluminium recycling from these aircraft, integrating material flow analysis, cost–benefit modelling, and a lifecycle emissions assessment. An economic assessment framework is developed and applied, with the results showing that approximately 24.7 tonnes of aluminium can be recovered per aircraft, leading to emissions savings of over 338,000 kg of CO₂e, a 95% reduction compared to primary aluminium production. However, scrap value alone cannot offset dismantling costs; the break-even scrap price is over USD 4200 per tonne. When additional revenue streams such as component resale and carbon credit incentives are incorporated, the model predicts a net profit of over USD 59,000 per aircraft. The scenario analysis confirms that aluminium recycling only becomes financially viable through multi-stream revenue models, supported by Extended Producer Responsibility (EPR) and carbon pricing. While barriers remain, aluminium recovery is a strategic opportunity to align aviation with circular economy and decarbonisation goals. Full article

This paper introduces a parallelized approach to reconstruct Koopman computational graphs from the perspective of parallel computing to address the computational efficiency bottleneck in approximating Koopman operators within high-dimensional spaces. We propose the KPA (Koopman Parallel Accelerator), a parallelized algorithm that restructures the Koopman computational workflow to transform sequential time-step computations into parallel tasks. KPA leverages GPU parallelism to improve execution efficiency without compromising model accuracy. To validate the algorithm’s effectiveness, we apply KPA to a flight trajectory prediction scenario based on the Koopman operator. Within the CUDA kernel implementation of KPA, several optimization techniques—such as shared memory, tiling, double buffering, and data prefetching—are employed. We compare our implementation against two baselines: the original Koopman neural operator for trajectory prediction implemented in TensorFlow (TF-baseline) and its XLA-compiled variant (TF-XLA). The experimental results demonstrate that KPA achieves a 2.47× speed up over TF-baseline and a 1.09× improvement over TF-XLA when predicting a 1422-dimensional flight trajectory. Additionally, an ablation study on block size and the number of streaming multiprocessors (SMs) reveals that the best performance is obtained with the block size of 16 × 16 and SM = 8. The results demonstrate that KPA can significantly accelerate Koopman operator computations, making it suitable for high-dimensional, large-scale, or real-time applications. Full article