Search Results (108)

With the rapid development of electric Vertical Take-Off and Landing (eVTOL) aircraft for urban air mobility, ensuring safe operation in complex low-altitude environments remains a major challenge. In particular, interactions with non-cooperative airspace users introduce uncertainties that are difficult to fully handle with current autonomous systems. To better understand these risks, a Monte Carlo simulation framework is developed to model random encounters between an eVTOL and uncontrolled unmanned aerial vehicles. The results show a relatively low collision probability of approximately 0.18%. However, a large proportion of encounters fall within an intermediate separation range of 100–200 m, indicating a high-frequency conflict region that still requires continuous monitoring and decision-making. Based on these observations, Fault Tree Analysis (FTA) is further applied to evaluate system-level safety under different operational architectures, incorporating revised assumptions on human reliability and system interactions. The results suggest that the inclusion of human pilots can contribute to reducing the probability of catastrophic failure compared with fully autonomous configurations, particularly in uncertain and non-cooperative scenarios. These findings suggest that, although full autonomy is a long-term goal, current intelligent systems still face limitations in dealing with uncertain and non-cooperative scenarios in urban airspace. In such situations, human operators can provide additional situational awareness and flexible decision-making, improving overall system robustness. Overall, a phased transition toward full autonomy, starting from a human–machine collaborative approach, appears to be a practical path to ensure safety, support certification, and enable the deployment of eVTOL systems. Full article

The flying car, academically known as electric vertical takeoff and landing (eVTOL) aircraft, is one of the core vehicles for low-altitude transportation. The split-type tilt-rotor cargo flying vehicle that is composed of tilt rotors, a fixed wing, and a detachable cargo pod exhibits characteristics of rotor–wing coupling and significant changes in weight and center of gravity (CG). Therefore, empirical design rules for conventional aircraft are not directly applicable. This paper presents the stability analysis of two configurations, i.e., the aerial vehicle module (AVM) and the aerial cargo configuration (ACC). The dynamic model of the proposed cargo flying vehicle is developed. Based on test data from the tilt-rotor experimental bench, the CFD models of the rotor subsystems and the full vehicle were validated and subsequently used to simulate the aerodynamic performance and stability of the flying vehicle under various operating conditions. The results indicate that vertical takeoff and landing (VTOL) stability is highly sensitive to the rotor–CG lever arm. Under cruise conditions, the CG positions were tested within a range of 1.4–1.7 $c_A$ (mean aerodynamic chord) from the wing leading edge with the most favorable static stability observed at 1.62 $c_A$. Among the three proposed tilt-rotor strategies, initiating the secondary tilt rotors first while keeping the main tilt rotors vertical results in the weakest rotor–surface aerodynamic coupling, the lowest pitching-moment peaks, and favorable longitudinal static stability. These findings inform CG management, aerodynamic layout, and tilt-schedule design for split-type tilt-rotor cargo vehicles in low-altitude transportation. Full article

Intelligence, surveillance, and reconnaissance (ISR) platforms and electric vertical take-off and landing (eVTOL) aircraft demand onboard power conversion systems that simultaneously achieve high gravimetric power density, robustness, and fault-tolerance. In this context, modular battery architectures based on per-string power electronic interfaces emerge as a key enabler for voltage regulation, fault isolation, and in-flight reconfiguration. However, the stringent mass and volume constraints of electric aviation place magnetic components among the primary limiting factors of converter scalability. This paper presents a design methodology for interleaved converters with coupled inductors that explicitly decompose common-mode, differential-mode, and uncoupled inductance components. The proposed approach enables independent adjustment of current ripple and dynamic response, allowing zero-voltage switching (ZVS) operation while ensuring stable and controllable behavior under close-loop current regulation. The methodology is experimentally validated on a 4 kW two-phase interleaved GaN-based boost converter operating at 500 kHz. Experimental results demonstrate a peak efficiency of 97%, with less than 1% variation across the operating range, and stable dynamic behavior under load transients. These results confirm the effectiveness of the proposed design methodology as a scalable solution for high-power-density, high-reliability power converters in electric aviation battery systems. Full article

Rapid urban air mobility (UAM) developments and new classes of vertical takeoff and landing (eVTOL) aircraft have changed the safety paradigm in urban airspace. eVTOL aircraft operations in dense urban environments are characterized by increased variability of external factors, highly dynamic flight scenarios, and an increased likelihood of rare but potentially critical events. Traditional safety assessment approaches do not capture the specific features of eVTOL designs, power plants, autonomy algorithms, and urban air traffic characteristics; this results in low threat prediction accuracy and limited development of modern incident prevention systems. Herein, the risk profile of eVTOL aircraft is analyzed, accounting for the multifactorial nature of urban environments and the complexity of integrating such vehicles into existing UAM infrastructure. The need for quantitative methods for assessing the probability of critical situation risks is also substantiated. These methods provide a statistically accurate description of extreme events and enable the identification of hidden dependencies in complex technical and organizational systems. Approaches based on probabilistic models, extreme value analysis, and systemic processing of operational data are considered, providing increased risk assessment accuracy and a deeper understanding of mechanisms underlying hazardous events. Results demonstrate the importance of applying the extreme value theory (EVT)–Copula model, which enables the quantitative assessment of the probability of extreme situations and loss of stability of eVTOL vehicles in the context of developing UAM. This model can be employed to obtain realistic predictions of flight processes, reduce uncertainty, and create scientifically valid tools for developing effective measures to minimize the risks of extreme events—a key factor in ensuring the safety of eVTOL flights in urban airspace. Full article

Water scarcity and increasing climate variability have strengthened the demand for effective weather-modification technologies such as cloud seeding. In Thailand, conventional manned rainmaking aircraft remain constrained by operational range, safety risks, and sustainability considerations, motivating the development of electric vertical take-off and landing unmanned aerial vehicles (eVTOL-UAVs). This paper proposes a mission-driven conceptual design and optimization framework for a cloud-seeding eVTOL-UAV, and extends it to reliability-based design optimization (RBDO) under uncertainty. The design task is formulated as a five-objective many-objective optimization problem with the following objectives: minimizing take-off weight, turn radius, and probability of failure, while maximizing endurance and climb rate, subject to stability/control and performance constraints. Ten state-of-the-art many-objective metaheuristics are benchmarked and solve the problem, and their performance is assessed using hypervolume (HV), inverted generational distance (IGD), runtime, and Friedman rank statistics. Results show that AGEMOEAII and PREA consistently provide the most competitive solution-set quality (HV/IGD) with comparable computational cost across algorithms. A deterministic–reliability comparison further demonstrates a clear robustness gap. Five representative Pareto designs from the best-performing optimizer are reported to illustrate practical trade-offs and support decision-making for sustainable, autonomous cloud-seeding operations. Full article

Vertical takeoff and landing (VTOL) aircraft must balance the conflicting demands of hover and cruise performance. To address the lack of integrated design methodologies in the existing literature, a unified design-optimization framework is presented, coupling high-fidelity CFD simulations with a genetic algorithm to refine a gas-driven thrust fan (GDTF) VTOL nacelle. Key geometric parameters—fan pressure ratio pressure ratio, fan tilt, nozzle angle, tail inclination, and tip shape—were varied in a comprehensive parametric study to maximize lift-to-drag ratio and maintain constant mass flow. The optimization reveals that a nearly horizontal fan axis maximizes cruise efficiency (${\displaystyle \frac{L}{D}}\approx 2.98$), a nozzle angle of about $22°$ offers the best lift-vs-drag compromise during transition, and refining the tip geometry yields a $10$–$20\%$ performance boost. To validate the numerical predictions, a 1:1.05 scale VTOL nacelle model (fan diameter $D = 0.42 \mathrm{m}$) was fabricated and tested in a low-speed wind tunnel at $52 {\displaystyle \frac{\mathrm{m}}{\mathrm{s}}}$ ($Re \approx 5 \times {10}^6$, turbulence intensity ≈ 2%). Total-pressure probes at the intake exit plane and static taps along the inner cowl wall provided detailed pressure distributions, from which exit Mach number, velocity and the equivalent flow coefficient φ (≈0.68 under test conditions) were derived. Oil-flow visualization on the external cowl surface confirmed smooth, attached streamlines with no large separation bubbles. This dual validation combining surface-flow visualization and pressure-recovery mapping demonstrates the accuracy and reliability of the proposed simulation methodology. By successfully bridging detailed CFD with genetic-algorithm-driven design and validating against comprehensive wind-tunnel measurements, this integrated approach paves the way for next-generation VTOL configurations with longer range and lower fuel consumption. Full article

Urban Air Mobility (UAM) promises to reduce ground traffic and journey times by using electric vertical take-off and landing (eVTOL) aircraft for short, low-altitude flights, especially in urban environments. However, low-flying aircraft are at particularly high risk of collisions with wildlife, such as birds. This study builds on previous research into UAM collision avoidance systems (UAM-CAS) by implementing one such system in the BlueSky open-source air traffic simulator and evaluating its efficacy in reducing bird strikes. Several modifications were made to the original UAM-CAS framework to improve performance. Realistic UAM flight plans were developed and combined with real-world bird movement datasets representing typical birds in sustained flight from all seasons, recorded by an avian radar at Leeuwarden Air Base. Fast-time simulations were conducted in the BlueSky Open Air Traffic Simulator using the UAM flight plan, the bird datasets, and the UAM-CAS algorithm. Results demonstrated that, under modelling assumptions, the UAM-CAS reduced bird strikes by 62%, with an average delay per flight of 15 s, whereas 27% of the remaining strikes occurred with birds outside the system’s design scope. A small number of flights faced substantially longer delays, indicating some operational impacts. Based on the findings, specific avenues for future research to improve UAM-CAS performance are suggested. Full article

Urban air mobility operations, such as flying Uncrewed Aerial Vehicles (UAVs) and small passenger aircraft in and around cities, will be inherently susceptible to the turbulent wind conditions in urban environments. Therefore, understanding UAM aircraft performance under microscale wind disturbances is critical. Gaining such insight is non-trivial due to the lack of sufficient UAM aircraft operational data and the complexities involved in flight testing UAM aircraft. A viable solution to overcome this hindrance is through simulation-based flight testing, data collection, and performance assessment. To support this effort, the present paper establishes a custom Stochastic microscale Wind Model (SWM) capable of efficiently generating high-resolution, spatio-temporally varying urban wind fields. The SWM is validated against wind tunnel test data, and subsequently, the findings are employed to guide targeted refinements of urban wake simulation. Furthermore, to incorporate realistic atmospheric conditions and demonstrate the ability to generate location-specific wind fields, the SWM is coupled with the mesoscale Weather Research and Forecasting (WRF) model. This integrated approach is demonstrated through a case study focused on a potential vertiport site in Milan, Italy, illustrating its utility for assessing operational area-specific UAM aircraft performance and vertiport emplacement. Full article

High-Frequency (HF) geolocation is crucial for emergency search and rescue operations and for re-geolocation of missing targets. This paper proposes a two-stage (Receiver selection then geolocation with Random Spatial Spectrum (RSS)) framework with HF skywave propagation as the main link, which is suitable for scenarios where the electric Vertical Take-off and Landing (eVTOL) aircraft loses contact, crashes, or has communication interruption after a malfunction. First, stage A employs two receiver selection paths. One is selection with unknown biases, which combines geometric observability to determine receiver selection. The other is selection with bias priors, which introduces non-line-of-sight bias priors and robust weighting to improve availability. Secondly, stage B constructs RSS-based geolocation using grid objective function matching to alleviate the sensitivity of explicit time difference estimation to noise and model mismatch, thereby maintaining robustness under non-line-of-sight (NLOS) conditions. Finally, simulation and actual measurements demonstrate that the “select first, geolocation later” approach achieves superior overall performance compared to direct geolocation without receiver selection. This study provides a methodological basis and initial field evidence for HF skywave-based emergency eVTOL geolocation. Full article

Advances in distributed electric propulsion and urban air mobility technologies have spurred a surge of research on electric Vertical Take-Off and Landing (eVTOL) aircraft. Distributed Multi-Propeller Tilting-Wing (DMT) eVTOL configurations offer higher forward flight speed and efficiency. However, aerodynamic challenges during the transition phase have limited their practical application. This study develops a high-fidelity body-fitted mesh CFD numerical simulation method for flow field calculations of DMT aircraft. Using the reverse overset assembly method and CPU-GPU collaborative acceleration technology, the accuracy and efficiency of flow field simulations are enhanced. Using the established method, the influence of dynamic tilting speeds on the flow field of this configuration is investigated. This paper presents the variations in the aerodynamic characteristics of the tandem propellers and tilt-wings throughout the full tilt process under different tilting speeds, analyzes the mechanisms behind reductions in the propeller’s aerodynamic performance and tilt-wing lift overshoot, and conducts a detailed comparison of flow field distribution characteristics under fixed-angle tilting, slow tilting, and fast tilting conditions. The study explores the influence mechanism of tilting speed on blade tip vortex-lifting surface interactions and interference between tandem propellers and tilt-wings, providing valuable conclusions for the aerodynamic design and safe transition implementation of DMT aircraft. Full article

Modern aircraft usually employ control allocation to distribute virtual control commands among redundant effectors. Infeasible virtual command can occur frequently due to aggressive maneuvers and limited control authority. This paper proposes a lightweight command scaling law for incremental flight control allocation. The method scales the raw incremental virtual command by a direction-preserving gain $K\in$ [0,1]. It is updated via gradient descent on a Lyapunov function that balances allocation error against deviation from unity gain. The proposed adaptive update law ensures the convergence of K to a value that corresponds to the attainable portion of infeasible commands, independent of the specific allocator used. At the same time, feasible virtual commands will be preserved. Its performance was evaluated through open-loop ray sweeps of the attainable moment set and closed-loop INDI simulations for a yaw-limited eVTOL. The results demonstrate that the adaptive scaling gain closely approximates the linear programming ground truth while offering significantly higher computational efficiency. Furthermore, it effectively mitigates cross-axis coupling, reduces peak excursions, and alleviates rotor saturation. These findings highlight the method’s effectiveness, modularity, and suitability for real-time implementation in aerospace applications. Full article

Unmanned vertical takeoff and landing (VTOL) aircraft are increasingly deployed for logistics, surveillance, and urban air mobility (UAM) applications. However, the limitations of full-electric (FE) and internal combustion engine (ICE) systems in meeting diverse mission requirements have motivated the development of hybrid-electric (HE) propulsion systems. The design of HE powertrains remains challenging due to configuration flexibility and the lack of unified criteria for performance trade-offs among FE, ICE-powered, and HE configurations. This study proposes an integrated propulsion co-design framework coupling power allocation, energy management, and component capacity constraints through parametric system modeling. These interdependencies are represented by three key matching parameters: the power ratio (Φ), energy ratio (Ω), and maximum continuous discharge rate (rc). Through Pareto-optimal design space exploration, trade-off analysis results and optimization principles are derived for diverse mission scenarios such as UAM, remote sensing, and military surveillance. Different technological conditions are considered to guide component-level technological advancements. The method was applied to the power system retrofit of the Great White eVTOL. Subsystem steady-state tests provided accurate design inputs, and a simulation model was developed to reproduce the full flight mission. By comparing the simulation with flight-test measurements, mean absolute percentage errors of 8.91% for instantaneous fuel consumption and 0.26% for battery voltage were obtained. Based on these error magnitudes, a dynamic design margin was defined and then incorporated into a subsequent re-optimization, which achieved the 1.5 h endurance target with a 10.49% increase in cost per ton-kilometer relative to the initial design. These results demonstrate that the proposed co-design methodology offers a scalable, data-driven foundation for early-stage hybrid-electric VTOL powertrain design, enabling iterative performance correction and supporting system optimization in subsequent design stages. Full article

Advanced and Urban Air Mobility (AAM and UAM) represent emerging transportation concepts that involve the use of novel aircraft technologies to transport passengers and cargo within urban, regional, and intra-regional environments. These systems may include Electric Vertical Take-off and Landing (eVTOL) aircraft, Short Take-off and Landing (STOL) aircraft, and unmanned aerial vehicles (UAVs), which are being considered for a range of applications including passenger transport, cargo delivery, and other specialized operations. This study introduced a state-specific analytical framework that integrates different methodologies and data to enable a more precise evaluation of AAM viability in the State of Illinois, compared to generic national or global assessments, capturing the state’s unique mobility patterns, infrastructure constraints, and demographic distributions. One of the main goals is to provide a comprehensive evaluation of the potential implications—both challenges and opportunities—associated with AAM and UAM operations. The analysis examines potential impacts on mobility, infrastructure, economic development, and public services, with particular emphasis on identifying key considerations for policy development. The research framework categorizes use cases into two broad types: AAM for the transportation of people and cargo, and AAM for functional applications such as emergency response, agriculture, and infrastructure monitoring. The study provides a detailed quantitative assessment of passenger air taxi services, including demand estimation, business model feasibility analysis, integration effects on existing transportation systems, and infrastructure requirements. For other AAM applications, the analysis identifies operational considerations, regulatory implications, and potential barriers to implementation, establishing a foundation for future detailed evaluation. Full article

Advances in motor technology and the application of distributed electric propulsion systems have greatly promoted the development of electric vertical take-off and landing aircraft. As a critical safety component of eVTOL aircraft, the motor system design must satisfy both performance requirements and stringent airworthiness standards. This paper studies the lift–thrust unit drive motor system of an eVTOL aircraft and proposes an architecture that utilizes analytical redundancy to enhance system-level reliability. This paper focuses on threshold optimization in analytical redundancy systems. Through simulations and reliability analyses, the performance of the analytical redundancy system is quantified, with false alarm and missed detection probabilities evaluated, fault detection thresholds optimized, and overall system reliability enhanced analytical redundancy systems is improved. Simulation and calculation results demonstrate that the proposed fault detection method can effectively meet the requirements for rapid detection and achieve optimal reliability at the given optimal threshold. Full article

Unmanned aircraft are increasingly recognized for their potential to enhance healthcare logistics, offering rapid and reliable transport solutions. Among the many envisioned use cases, emergency medical deliveries stand out as particularly promising due to their immediate societal value. This study investigates the potential of drones operating under U-space to support hospital-to-hospital emergency deliveries in Madrid. Using the GEMMA tool, we modeled and simulated operations with two drone types along direct routes between four hospitals, resulting in six hospital pairs. Drone travel times were estimated and compared against road transport times obtained from the Google Routes API, incorporating one week of traffic data to capture daily and weekend variability. The results show substantial advantages of aerial transport, with time savings ranging from 2 to 26 min, equivalent to 35–58% compared to road transport. Drones consistently ensured deliveries within 15 min, outperforming regular cars (39%) and ambulances or motorcycles in highly congested periods. Sensitivity analysis confirms their reliability in scenarios with strict time constraints, especially under 15 min. These findings demonstrate that drones reduce travel times and improve predictability, providing a robust evidence base for policymakers and regulators to advance U-space integration in healthcare logistics. Full article