Search Results (150)

This paper proposes a novel hierarchical control architecture that combines Model Predictive Control (MPC) with Explicit Model-Following Control (EMFC) to enable accurate and efficient trajectory tracking for quadrotor electric Vertical Takeoff and Landing (eVTOL) aircraft operating in urban environments. The approach addresses the challenges of strong nonlinear dynamics, multi-axis coupling, and stringent safety constraints by separating the planning task from the fast-response control task. The MPC layer generates constrained velocity and yaw rate commands based on a simplified inertial prediction model, effectively reducing computational complexity while accounting for physical and operational limits. The EMFC layer then compensates for dynamic couplings and ensures the rapid execution of commands. A high-fidelity simulation model, incorporating rotor flapping dynamics, differential collective pitch control, and enhanced aerodynamic interference effects, is developed to validate the controller. Four representative ADS-33E-PRF tasks—Hover, Hovering Turn, Pirouette, and Vertical Maneuver—are simulated. Results demonstrate that the proposed controller achieves accurate trajectory tracking, stable flight performance, and full compliance with ADS-33E-PRF criteria, highlighting its potential for autonomous urban air mobility applications. Full article

Three-dimensional (3D) radar imaging is essential for target detection and measurement of scattering characteristics. Cylindrical scanning, a prevalent spatial sampling technique, provides benefits in engineering applications and has been extensively utilized for assessing the radar stealth capabilities of large aircraft. Traditional cylindrical scanning generally utilizes highly sampled full-coverage techniques, leading to an excessive quantity of sampling points and diminished image efficiency, constraining its use for quick detection applications. This work presents an efficient 3D sampling strategy that integrates vertical linear scanning with horizontal rotating motion to overcome these restrictions. A joint angle–space sampling model is developed, and geometric constraints are implemented to enhance the scanning trajectory. The experimental results demonstrate that, compared to conventional techniques, the proposed method achieves a 94% reduction in the scanning duration while maintaining a peak sidelobe level ratio (PSLR) of 12 dB. Furthermore, this study demonstrates that 3D imaging may be accomplished solely by a “V”-shaped trajectory, efficiently determining the minimal possible sampling aperture. This approach offers novel insights and theoretical backing for the advancement of high-efficiency, low-redundancy 3D radar imaging systems. Full article

Accurate fire source location in an aircraft cargo compartment cannot be determined by common design practices. This study proposes an advanced fire location inversion framework based on a Convolutional Long-Short-Term Memory (ConvLSTM) network. A self-designed interpolation preprocessing module is introduced to realize the integration of spatial and temporal sensor data. The model was trained and validated using a comprehensive database generated from large-scale fire dynamics simulations. Hyperparameter optimization, including a learning rate of 0.001 and a 5 × 5 convolution kernel size, can effectively avoid the systematic errors introduced by interpolation preprocessing, further enhancing model robustness. Validation in simplified scenarios demonstrated a mean squared error of 0.0042 m and a mean positional deviation of 0.095 m for the fire source location. Moreover, the present study assessed the model’s timeliness and reliability in full-scale cabin complex scenarios. The model maintained high performance across varying heights within cargo compartments, achieving a correlation coefficient of 0.99 and a mean absolute relative error of 1.9%. Noteworthily, reasonable location accuracy can be achieved with a minimum of three detectors, even in obstructed environments. These findings offer a robust tool for enhancing fire safety systems in aviation and other similar complex scenarios. Full article

Emergencies can occur unexpectedly and require immediate action, especially in aviation, where time pressure and uncertainty are high. This study focused on improving emergency evacuation in airport and aircraft scenarios using real-time decision-making support. A system based on the Asynchronous Advantage Actor–Critic (A3C) algorithm, an advanced deep reinforcement learning method, was developed to generate faster and more efficient evacuation routes compared to traditional models. The A3C model was tested in various scenarios, including different environmental conditions and numbers of agents, and its performance was compared with the Deep Q-Network (DQN) algorithm. The results showed that A3C achieved evacuations 43.86% faster on average and converged in fewer episodes (100 vs. 250 for DQN). In dynamic environments with moving threats, A3C also outperformed DQN in maintaining agent safety and adapting routes in real time. As the number of agents increased, A3C maintained high levels of efficiency and robustness. These findings demonstrate A3C’s strong potential to enhance evacuation planning through improved speed, adaptability, and scalability. The study concludes by highlighting the practical benefits of applying such models in real-world emergency response systems, including significantly faster evacuation times, real-time adaptability to evolving threats, and enhanced scalability for managing large crowds in high-density environments including airport terminals. The A3C-based model offers a cost-effective alternative to full-scale evacuation drills by enabling virtual scenario testing, supports proactive safety planning through predictive modeling, and contributes to the development of intelligent decision-support tools that improve coordination and reduce response time during emergencies. Full article

This paper presents an integrated digital training twin framework for adaptive aircraft maintenance education, combining real-time competence modeling, algorithmic orchestration, and cloud–edge deployment architectures. The proposed system dynamically evaluates learner skill gaps and assigns individualized training resources through a multi-objective optimization function that balances skill alignment, Bloom’s cognitive level, fidelity tier, and time efficiency. A modular orchestration engine incorporates reinforcement learning agents for policy refinement, federated learning for privacy-preserving skill analytics, and knowledge graph-based curriculum models for dependency management. Simulation results were conducted on the Pneumatic Systems training module. The system’s validation matrix provides full-cycle traceability of instructional decisions, supporting regulatory audit-readiness and institutional reporting. The digital training twin ecosystem offers a scalable, regulation-compliant, and data-driven solution for next-generation aviation maintenance training, with demonstrated operational efficiency, instructional precision, and extensibility for future expansion. Full article

Hydrodynamic slamming loads during water landing are one of the main concerns for the structural design and wave resistance performance of large amphibious aircraft. However, current existing sensors are not used for full-scale hydrodynamic load flight tests on complex models due to their large size, fragility, intrusiveness, limited range, frequency response limitations, accuracy issues, and low sampling frequency. Fibre-optic sensors’ small size, immunity to electromagnetic interference, and reduced susceptibility to environmental disturbances have led to their progressive development in maritime and aeronautic fields. This research proposes a novel hydrodynamic profile encapsulation method using ultra-thin surface-mounted micro-electromechanical system (MEMS) fibre-optic Fabry–Pérot pressure sensors (total thickness of 1 mm). The proposed sensor exhibits an exceptional linear response and low-temperature sensitivity in hydrostatic calibration tests and shows superior response and detection accuracy in water-entry tests of wedge-shaped bodies. This work exhibits significant potential for the in situ monitoring of hydrodynamic loads during water landing, contributing to the research of large amphibious aircraft. Furthermore, this research demonstrates, for the first time, the proposed surface-mounted pressure sensor in conjunction with a high-speed acquisition system for the in situ monitoring of hydrodynamic pressure on the hull of a large amphibious prototype. Following flight tests, the sensors remained intact throughout multiple high-speed hydrodynamic taxiing events and 12 full water landings, successfully acquiring the complete dataset. The flight test results show that this proposed pressure sensor exhibits superior robustness in extreme environments compared to traditional invasive electrical sensors and can be used for full-scale hydrodynamic load flight tests. Full article

The operational integrity of the BeiDou-3 Navigation Satellite System (BDS-3) has been significantly challenged by electromagnetic interference, particularly from Distance Measuring Equipment (DME) ground beacons to the newly implemented B2a signal, since its full operational deployment in 2020. This study developed a comprehensive interference evaluation model based on receiver signal processing principles to quantify the degradation of B2a signal reception performance under DME interference scenarios. Leveraging empirical data from the DME beacon network in the Chinese mainland, we systematically analyzed the interference effects through an effective carrier-to-noise ratio ($C/N_0$), signal detection probability, carrier tracking accuracy, and demodulation bit error rate (BER). The results demonstrate that the effective $C/N_0$ of the B2a signal degrades by up to 3.25 dB, the detection probability decreases by 33%, and the carrier tracking errors and BER increase by 2.57° and 5.1%, respectively, in worst-case interference scenarios. Furthermore, significant spatial correlation was observed between the interference hotspots and regions of high aircraft density. DME interference adversely affected the accuracy, availability, continuity, and integrity of the airborne BeiDou navigation system, thereby compromising civil aviation flight safety. These findings establish a scientific foundation for developing Minimum Operational Performance Standards for B2a signal receivers and for strategically optimizing DME beacon deployment throughout the Chinese mainland. Full article

Aiming at the practical difficulties (e.g., high cost of full-scale tests) in testing the rolling resistance of aircraft wheels on unpaved runways, this study establishes a theoretical calculation formula for wheel rolling resistance based on the Bekker model, following an analysis of the development and application of wheel–soil interaction models. Global sensitivity analysis using the Sobol’ method was performed on the theoretical formula to derive a simplified calculation model. Aircraft load simulation tests under 80 kN, 100 kN, and 120 kN loading conditions were conducted using a specialized loading vehicle to determine parameters for the simplified prediction model. The resistance values obtained from this model were then applied to calculate aircraft takeoff roll distance. The accuracy of resistance estimation was verified by comparing the calculated results with takeoff distances reported in relevant literature. This research provides a novel approach for estimating wheel rolling resistance of transport aircraft on unpaved runways and offers valuable reference for determining the required length of unpaved runways. Full article

This study presents a surrogate modeling framework designed for the rapid yet reliable assessment of sonic boom impacts. The methodology is demonstrated through two case studies: a transatlantic flight from Milan to New York, highlighting the sonic boom impact along the route; and a representative supersonic overflight of Italy, quantifying the population exposure to varying noise levels. Aerodynamic numerical simulations were carried out using an open-source code to capture near-field pressure signatures at three critical mission points. These signatures were used to compute the Whitham F-functions, which were then propagated through a homogeneous atmosphere to the ground using the Whitham equal area rule. The resulting N-waves enabled the computation of aircraft shape factors, which were employed in a regression model to predict the sonic boom characteristics across the full mission profile. Finally, the integration of noise metrics and geographical information system software provided the evaluation of environmental impact and population noise exposure. Full article

To tackle climate change, aircraft designers envision new aircraft concepts which promise to reduce greenhouse gas emissions and enable greener flights. One option is hybrid-electric propulsion architectures. The University of Stuttgart has built and operates such an aircraft, called the e-Genius. This paper aims to demonstrate how far a digital twin is able to replicate a real-world flight using a simplified mission definition and to estimate the range limit for a high-performance hybrid-electric aircraft, lifting the operational constraints faced in the real-world environment. First a digital twin is built and compared to actual flight data to calibrate the model. Next, a comparison with a full flight is performed, using a long-range flight of 2000 km for this purpose. Due to the duration of this flight, weather conditions like wind need to be considered. Validation is performed by comparison to two additional missions, one 500 km mission flown at faster speed and a 1000 km mission flown at a similar speed. To estimate the maximum range based on this calibrated model, operational constraints like daylight and maximum flight time are lifted to see the further potential of the aircraft. This allows the aircraft to fly more slowly, at best cruise speed, and thus estimate the maximum range of the aircraft. Results show good agreement with flight tests for fuel burnt, highlighting however a need to measure additional parameters in future flights. Overall, the model allows us to plan future flights and assess the feasibility of new projects. Full article

To address the trade-off between safety levels and operational efficiency in the Bay Area, this study proposes a Multi-Stand Grouped Operations method based on deep reinforcement learning under the consideration of the safety domain. The full-process operation of aircraft within the Bay Area is analyzed to identify key operational spots. Safety domains are then established based on path conflicts arising from aircraft movements and safety conflicts caused by minimum separation distances and wake vortex effects. These domains are used to define corresponding safe operating spaces and construct an optimized operational model for the Bay Area. A multi-agent reinforcement learning algorithm is employed to solve the model, deriving an optimized stand allocation plan and Multi-Stand Grouped Operations strategy. To evaluate the effectiveness of the optimization, real flight data from the northwest Bay Area of Terminal 2 at Guangzhou Baiyun Airport are used for validation. Compared to the original stand allocation scheme, the optimized stand allocation and Multi-Stand Grouped Operations strategy reduce aircraft delay times by 62.45%, demonstrating that the proposed model effectively enhances operational efficiency in the Bay Area. Full article

To address the key challenges of poor real-time interaction, insufficient integration of operating rules, and limited virtual–physical synergy in current carrier-based aircraft scheduling simulations, this study proposes an immersive digital twin platform that integrates a two-layer genetic algorithm (GA) with hardware-in-the-loop (HIL) semi-physical validation. The platform architecture combines high-fidelity 3D visualization-based modeling (of aircraft, carrier deck, and auxiliary equipment) with real-time data exchange via TCP/IP, establishing a collaborative virtual–physical simulation environment. Three key innovations are presented: (1) a two-tier genetic algorithm (GA)-based scheduling model is proposed to coordinate global planning and dynamic execution optimization for carrier-based aircraft operations; (2) a systematic constraint integration framework incorporating aircraft taxiing dynamics, deck spatial constraints, and safety clearance requirements into the scheduling system, significantly enhancing tactical feasibility compared to conventional approaches that oversimplify multidimensional operational rules; (3) an integrated virtual–physical simulation architecture merging virtual reality interaction with HIL verification, establishing a collaborative digital twin–physical device platform for immersive visualization of full-process operations and dynamic spatiotemporal evolution characterization. Experimental results indicate that this work bridges the gap between theoretical scheduling algorithms and practical naval aviation requirements, offering a standardized testing platform for intelligent carrier-based aircraft operations. Full article

Scaled flight demonstrators have played an important part throughout the history of aviation. Ranging from aviation pioneers to renowned institutions like the National Aeronautics and Space Administration (NASA), many actors have relied on miniaturized models in both research and development. Despite the age of the method, sub-scale models are still being used as a low-cost option for flight tests in realistic flight conditions. One utilization aspect that is becoming increasingly popular is as a flying test platform for the development and testing of new aviation technologies or capabilities. By conducting flight tests in real atmospheric conditions, it enables a low-cost link between analytical studies and full-scale testing, consequently closing the gap between Technology Readiness Levels (TRLs) 4 and 6, which is both time- and cost-efficient. For this paper, the utilization of the e-Genius-Mod, a modular scaled version of the all-electric e-Genius aircraft, as a versatile platform for testing new technologies is being investigated. As a case study, a multi-hole probe (MHP) is installed onto the aircraft through a custom-made wing adapter and connected to an independent data collection system. By using Computational Fluid Dynamics (CFD) simulations and wind-tunnel tests, the probe installation is validated, paving the way for upcoming flight tests. Full article

This study presents the development of a real-time simulation model for electromechanical actuators tailored to a hybrid iron bird for next-generation regional turboprop aircraft. This iron bird is aimed at integrating real and virtual components, enabling advanced validation of flight control systems while balancing risk and cost. The mathematical models of actuators needed for the development and operation of the iron bird must comply with stringent requirements, especially in terms of computational cost. A novel two-step iterative methodology is proposed, combining bottom-up and top-down approaches. This process begins with simplified low-fidelity models. Then, the models are incrementally refined to capture complex dynamics while maintaining computational efficiency. Using the proposed approach, the computational time of the real-time model remained almost unvaried and consistent with the sampling frequency, while the number of state variables and the range of described phenomena grew significantly. The real-time model is validated against simulated data from a reference high-fidelity model and experimental data, achieving excellent agreement while reducing the computational time by 93%. The enhanced model incorporates selected failure modes equivalent models regarding the electric motor, power drive unit, and mechanical transmission, supporting possible future prognostics and health management (PHM) applications. These results showcase a scalable solution for integrating electromechanical actuation in modern aerospace systems, paving the way for full virtual iron birds and greener aviation technologies. Full article

Advances in material science and intelligent systems have led to an increasing use of large-deflection flexible structures in the aerospace industry, including flexible-wall wind tunnel nozzles, deformable wings, and variable nozzles for aircraft engines. These structures have attracted significant research interest due to their variable aerodynamic performance, functional diversity, and dynamic response characteristics that distinguish them from rigid structures. Large-deflection flexible aerodynamic structures typically consist of flexible structural surfaces and actuators. Precise deformation control and optimized structural design are crucial for achieving their full performance potential. However, few existing technological tools can effectively guide the implementation of such deformation control and optimized design. In this paper, we first established a mechanical model of a multi-pivot flexible nozzle based on a typical wind tunnel flexible nozzle. We then derived a theoretical model of beam deformation with multi-point dynamic constraints using the principle of variability. Next, we created a deformation solution method based on radial basis point interpolation to evaluate nozzle profile accuracy. Finally, we established a complete surrogate-based optimization process for a large-deflection flexible nozzle and experimentally verified it using a wind tunnel nozzle prototype equipped with laser tracking and flexible sensors. The results show that the nozzle’s profile accuracy remains within ±0.2 mm under specified operational conditions. Full article