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4月22日周三
4月17日周五
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4月15日周三
4月14日周二
  1. arXiv eVTOL预印本历史资料

    Design Optimization of eVTOL Propellers using a Viscous-Extension Discrete Vortex Method

    Potential flow theory remains a cornerstone of unsteady aerodynamics due to its computational efficiency in modeling complex flow phenomena. This study presents a significant advancement by integrating a viscous unsteady theory with established numerical vortex methods, creating a hybrid computational tool for low-to-moderate Reynolds number flows. We develop a Viscous Discrete Vortex Method (VDVM) by replacing the classical inviscid Kutta condition with a closure derived from triple-deck boundary layer theory, allowing the model to account for Reynolds number dependencies and unsteady viscous effects. The framework utilizes a three-dimensional vortex ring scheme and an unsteady Bernoulli formulation for load calculation. The model is validated against experimental and high-fidelity CFD data, showing excellent agreement in thrust and torque across a wide operational envelope. Using this validated framework, we conduct a systematic parametric investigation into rotor blade design for electric vertical take-off and landing (eVTOL). A sophisticated optimization of the spanwise geometry was performed: twist distributions were calculated by iteratively solving for axial and tangential induction factors to maintain optimal local angles of attack, while chord distributions were derived using the Adkins and Liebeck framework to satisfy the Betz condition for maximum efficiency. Results demonstrate that this tapered chord and nonlinear twist profile significantly mitigate tip losses and manage spanwise loading. The optimized geometry achieved an 8.99% increase in the efficiency comp（预印本；同行评审状态请核对原文。）

4月13日周一
4月10日周五
4月8日周三
  1. arXiv先进空中交通研究历史资料

    eVTOL Aircraft Energy Overhead Estimation under Conflict Resolution in High-Density Airspaces

    Electric vertical takeoff and landing (eVTOL) aircraft operating in high-density urban airspace must maintain safe separation through tactical conflict resolution, yet the energy cost of such maneuvers has not been systematically quantified. This paper investigates how conflict-resolution maneuvers under the Modified Voltage Potential (MVP) algorithm affect eVTOL energy consumption. Using a physics-based power model integrated within a traffic simulation, we analyze approximately 71,767 en route sections within a sector, across traffic densities of 10-60 simultaneous aircraft. The main finding is that MVP-based deconfliction is energy-efficient: median energy overhead remains below 1.5% across all density levels, and the majority of en route flights within the sector incur negligible penalty. However, the distribution exhibits pronounced right-skewness, with tail cases reaching 44% overhead at the highest densities due to sustained multi-aircraft conflicts. The 95th percentile ranges from 3.84% to 5.3%, suggesting that a 4-5% reserve margin accommodates the vast majority of tactical deconfliction scenarios. To support operational planning, we develop a machine learning model that estimates energy overhead at mission initiation. Because conflict outcomes depend on future traffic interactions that cannot be known in advance, the model provides both point estimates and uncertainty bounds. These bounds are conservative; actual outcomes fall within the predicted range more often than the stated confidence level, making them suitable for safety-critical reserve planning. Together（预印本；同行评审状态请核对原文。）

4月7日周二
  1. 低空街企业动态历史资料

    中国通号旗下低空智能科技公司增资至8亿

    工商信息显示，近日，通号低空智能科技有限公司发生工商变更，注册资本由3亿人民币增至8亿人民币，增幅约167%。该公司成立于2016年5月，法定代表人为蒋灵...（披露日：2026-04-07）

4月3日周五
  1. arXiv先进空中交通研究历史资料

    Cooperative Detour Planning for Dual-Task Drone Fleets

    As Urban air mobility scales, commercial drone fleets offer a compelling, yet underexplored opportunity to function as mobile sensor networks for real-time urban traffic monitoring. In this paper, we propose a decentralized framework that enables drone fleets to simultaneously execute delivery tasks and observe network traffic conditions. We model the urban environment with dynamic information values associated with road segments, which accumulate traffic condition uncertainty over time and are reset upon drone visitation. This problem is formulated as a mixed-integer linear programming problem where drones maximize the traffic information reward while respecting the maximum detour for each delivery and the battery budget of each drone. Unlike centralized approaches that are computationally heavy for large fleets, our method focuses on dynamic local clustering. When drones enter communication range, they exchange their belief in traffic status and transition from isolated path planning to a local joint optimization mode, resolving coupled constraints to obtain replanned paths for each drone, respectively. Simulation results built on the real city network of Barcelona, Spain, demonstrate that, compared to a shortest-path policy that ignores the traffic monitoring task, our proposed method better utilizes the battery and detour budget to explore the city area and obtain adequate traffic information; and, thanks to its decentralized manner, this ``meet-and-merge" strategy achieves near-global optimality in network coverage with significantly reduced computation overhead compar（预印本；同行评审状态请核对原文。）

4月2日周四
4月1日周三
3月31日周二
3月27日周五
3月26日周四
3月25日周三
3月24日周二
3月23日周一
  1. arXiv先进空中交通研究历史资料

    DQN Based Joint UAV Trajectory and Association Planning in NTN Assisted Networks

    Advanced Air Mobility (AAM) has emerged as a key pillar of next-generation transportation systems, encompassing a wide range of uncrewed aerial vehicle (UAV) applications. To enable AAM, maintaining reliable and efficient communication links between UAVs and control centers is essential. At the same time, the highly dynamic nature of wireless networks, combined with the limited onboard energy of UAVs, makes efficient trajectory planning and network association crucial. Existing terrestrial networks often fail to provide ubiquitous coverage due to frequent handovers and coverage gaps. To address these challenges, geostationary Earth orbit (GEO) satellites offer a promising complementary solution for extending UAV connectivity beyond terrestrial boundaries. This work proposes an integrated GEO terrestrial network architecture to ensure seamless UAV connectivity. Leveraging artificial intelligence (AI), a deep Q network (DQN) based algorithm is developed for joint UAV trajectory and association planning (JUTAP), aiming to minimize energy consumption, handover frequency, and disconnectivity. Simulation results validate the effectiveness of the proposed algorithm within the integrated GEO terrestrial framework.（预印本；同行评审状态请核对原文。）

3月20日周五
  2. arXiv先进空中交通研究历史资料

    String stable platoons of all-electric aircraft with operating costs and airspace complexity trade-off

    This paper formulates an optimal control framework for computing cruise airspeeds in predecessor-follower platoons of all-electric aircraft that balance operational cost and airspace complexity. To quantify controller workload and coordination effort, a novel pairwise dynamic workload (PDW) function is developed. Within this framework, the optimal airspeed solution is derived for all-electric aircraft under longitudinal wind disturbances. Moreover, an analytical suboptimal solution for heterogeneous platoons with nonlinear aircraft dynamics is determined, for which a general sufficient condition for string stability is formally established. The methodology is validated through case studies of all-electric aircraft operating in air corridors that are suitable for low-altitude advanced/urban air mobility (AAM/UAM) applications. Results show that the suboptimal solution closely approximates the optimal, while ensuring safe separations, maintaining string stability, and reducing operational cost and airspace complexity. These findings support the development of sustainable and more autonomous air traffic procedures that will enable the implementation of emerging air transportation technologies, such as AAM/UAM, and their integration to the air traffic system environment.（预印本；同行评审状态请核对原文。）

3月19日周四
  1. arXiv先进空中交通研究历史资料

    Minimum Energy Cruise of All-Electric Aircraft with Applications to Advanced Air Mobility

    Electrified propulsion is expected to play an important role in the sustainable development of Advanced Air Mobility (AAM). However, the limited energy density of batteries motivates the need to minimize energy consumption during flight. This paper studies the minimum total energy problem for an all-electric aircraft in steady cruise flight. The problem is formulated as an optimal control problem in which the cruise airspeed and final cruise time are optimization variables. The battery supply voltage is modeled as an affine function of the battery charge. Pontryagin's Minimum Principle is used to derive the necessary and sufficient conditions for optimality, from which closed-form expressions for the optimal cruise airspeed and optimal final cruise time are obtained. Additional analytical conditions are derived that determine when all-electric operation is feasible, one of which is that sufficient electric charge must be available. Numerical simulations based on the BETA Technologies CX300 all-electric aircraft and a representative AAM scenario illustrate how the aircraft weight, cruising altitude, electrical system efficiency, and initial battery charge influence the optimal airspeed and the feasibility of all-electric cruise.（预印本；同行评审状态请核对原文。）

3月18日周三
3月17日周二
3月14日周六
3月13日周五
  1. arXiv先进空中交通研究历史资料

    Rooftop Wind Field Reconstruction Using Sparse Sensors: From Deterministic to Generative Learning Methods

    Real-time rooftop wind-speed distribution is important for the safe operation of drones and urban air mobility systems, wind control systems, and rooftop utilization. However, rooftop flows show strong nonlinearity, separation, and cross-direction variability, which make flow field reconstruction from sparse sensors difficult. This study develops a learning-from-observation framework using wind-tunnel experimental data obtained by Particle Image Velocimetry (PIV) and compares Kriging interpolation with three deep learning models: UNet, Vision Transformer Autoencoder (ViTAE), and Conditional Wasserstein GAN (CWGAN). We evaluate two training strategies, single wind-direction training (SDT) and mixed wind-direction training (MDT), across sensor densities from 5 to 30, test robustness under sensor position perturbations of plus or minus 1 grid, and optimize sensor placement via Proper Orthogonal Decomposition with QR decomposition. Results show that deep learning methods can reconstruct rooftop wind fields from sparse sensor data effectively. Compared with Kriging interpolation, the deep learning models improved SSIM by up to 32.7%, FAC2 by 24.2%, and NMSE by 27.8%. Mixed wind-direction training further improved performance, with gains of up to 173.7% in SSIM, 16.7% in FAC2, and 98.3% in MG compared with single-direction training. The results also show that sensor configuration, optimization, and training strategy should be considered jointly for reliable deployment. QR-based optimization improved robustness by up to 27.8% under sensor perturbations, although with metric-depend（预印本；同行评审状态请核对原文。）

3月12日周四