Search Results (110)

The complex jet-ground interactions of Short Take-off and Vertical Landing (STOVL) aircraft are critical to flight safety and performance, yet studying them with traditional full-scale wind tunnel tests is prohibitively expensive and time-consuming, hindering design optimization. This study addresses this challenge by developing and numerically verifying a “pressure ratio–momentum–geometry” multi-dimensional similarity framework, enabling accurate and efficient scaled-model analysis. Systematic simulations of an F-35B-like configuration demonstrate the framework’s high fidelity. For a representative curved nozzle configuration (e.g., the F-35B three-bearing swivel duct nozzle, 3BSD), across scale factors ranging from 1:1 to 1:15, the plume deflection angle remains stable at 12° ± 1°. Concurrently, axial force (F) and mass flow rate (Q) strictly follow the square scaling relationship ($F\propto 1/n^2, Q\propto 1/n^2)$, with deviations from theory remaining below 0.15% and 0.58%, respectively, even at the 1:15 scale, confirming high-fidelity momentum similarity, particularly in the near-field flow direction. Second, a 1:13.25 scale aircraft model, constructed using Froude similarity principles, exhibits critical parameter agreement (intake total pressure and total temperature) of the prototype-including vertical axial force, lift fan mass flow, and intake total temperature—all less than 1.5%, while the critical intake total pressure error is only 2.2%. Fountain flow structures and ground temperature distributions show high consistency with the full-scale aircraft, validating the reliability of the proposed “pressure ratio–momentum–geometry” multi-dimensional similarity criterion. The framework developed herein has the potential to reduce wind tunnel testing costs and shorten development cycles, offering an efficient experimental strategy for STOVL aircraft research and development. Full article

Meteorological conditions are a key factor affecting airport site selection and operational efficiency. To overcome the limitations of traditional methods in evaluating the joint impact of multiple meteorological elements, this paper aims to develop an airport site selection decision support method based on case-based reasoning (CBR) and multi-meteorological element clustering. Firstly, we propose a universal framework: utilizing K-means clustering to extract typical weather scenarios from historical meteorological data; subsequently, using Zhengzhou Xinzheng International Airport as a case study, a quantitative mapping relationship was established between these weather scenarios and flight operation efficiency (such as delay rate and cancellation rate) to calibrate and validate the model; finally, by calculating the frequency of occurrence of various weather scenarios at candidate sites, the future operational efficiency can be inferred, providing a ranking basis for site selection decisions. The results indicate that low-cloud-base weather has the greatest impact on flight takeoff performance, while good weather has a relatively small impact on flights. This method can effectively and quickly rank the advantages and disadvantages of all candidate airports, providing a reference for airport construction. Full article

This study presents an electro-thermal analysis of high-power lithium-ion battery modules for urban air mobility (UAM) applications, focusing on assessing the operational impact of installing a thermal barrier pad (TBP)—designed for thermal runaway delay—to ensure that the module maintains acceptable performance during normal operations. An integrated electro-thermal simulation model was developed and validated through single-cell experiments under step-load conditions, showing good agreement with measured voltage and temperature. In the baseline module without a TBP, higher discharge rates resulted in increased heat generation and cell temperatures, with approximately 42.5% of the electrical output dissipated as heat under the 5C condition. When the TBP was applied, the cooling performance of the heat sink decreased, leading to higher module temperatures and increased temperature differences between the cell and the heat sink, particularly as the TBP thickness increased. A simplified UAM flight scenario was simulated to evaluate temperature behavior throughout various operating phases. For the 1.5 mm TBP model, the maximum temperature (75.7 °C) remained within the design limit (80 °C). However, increasing the maximum take-off discharge rate to 6C or higher caused the module to reach its thermal limit or cut-off voltage before mission completion. These results indicate that TBP installation can be applied without unacceptable performance degradation under normal operation, provided that its thickness is optimized by considering cooling performance, thermal safety, and weight/volume constraints in UAM applications. Full article

To investigate the influence of ambient humidity on the aero-engine control plan, a twin-spool mixed-exhaust turbofan aero-engine is used as the research object. After establishing a numerical calculation model of the aero-engine using the component method and incorporating the humidity correction factor into the model, the mechanism of the influence of ambient humidity on the aero-engine’s control plan inflection point and performance is investigated. Furthermore, this paper examines the degradation factors of the performance parameters of each aero-engine component in the model, as well as the impact of the coupling effect of ambient humidity and the degradation of the performance parameters of each component on the aero-engine’s performance. The results show that the control plan inflection point shifts rightward when ambient humidity rises, increasing thrust output beyond the displacement point throughout ground tests, take-off, and cruising conditions. On the other hand, when deterioration and humidity work together, the original inflection point location is typically maintained, and very slight thrust variations occur. However, the growth rate of specific fuel consumption is far higher than when humidity effects are used alone. These results provide important information for enhancing the performance of aviation engines in different humidity and component degradation scenarios. Full article

To address the high-precision navigation requirements of urban low-altitude electric vertical take-off and landing (eVTOL) aircraft in environments where global navigation satellite systems (GNSSs) are denied and under complex urban terrain conditions, a terrain-matching optimization algorithm based on light detection and ranging (LiDAR) is proposed. Given the issues of GNSS signal susceptibility to occlusion and interference in urban low-altitude environments, as well as the error accumulation in inertial navigation systems (INSs), this algorithm leverages LiDAR point cloud data to assist in constructing a digital elevation model (DEM). A terrain-matching optimization algorithm is then designed, incorporating enhanced feature description for key regions and an adaptive random sample consensus (RANSAC)-based misalignment detection mechanism. This approach enables efficient and robust terrain feature matching and dynamic correction of INS positioning errors. The simulation results demonstrate that the proposed algorithm achieves a positioning accuracy better than 2 m in complex scenarios such as typical urban canyons, representing a significant improvement of 25.0% and 31.4% compared to the traditional SIFT-RANSAC and SURF-RANSAC methods, respectively. It also elevates the feature matching accuracy rate to 90.4%; meanwhile, at a 95% confidence level, the proposed method significantly increases the localization success rate to 96.8%, substantially enhancing the navigation and localization accuracy and robustness of eVTOLs in complex low-altitude environments. Full article

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

Background: Fenestrated stentgrafting has become a first-line treatment for juxtarenal aneurysms, and the incorporation of all renovisceral vessels with fenestrations has become common to increase the proximal sealing zone. This increases the complexity of the repair compared to using fewer fenestrations, and stenting of the celiac artery (CA), in particular, can be technically challenging. Objective: This study evaluates the mid-term outcomes of leaving the celiac artery unstented during quadruple fenestrated stentgrafting for complex aortic aneurysms. Additionally, it explores the clinical and anatomical factors that influence the decision to not stent the celiac artery. Methods: A retrospective review was conducted of patients with complex aortic aneurysms who underwent elective fenestrated endovascular aneurysm repair (FEVAR) between 2018 and 2023. Custom Cook Zenith grafts were used, and all patients underwent preoperative computed tomography angiography (CTA) as well as follow-up CTA to assess the celiac artery. This study evaluated celiac artery anatomic factors, such as proximal and distal diameter; presence of stenosis (<50% or >50%) and patency; length of any CA stenosis; CA takeoff angulation, CA tortuosity, early CA division; calcification; and presence of CA aneurysm or ectasia anatomical abnormalities. Recorded outcomes of CA instability included any stent stenosis, target vessel occlusion, reintervention, or endoleak (types 1C and 3). Results: A total of 101 patients underwent FEVAR, with 72 receiving a stent in the celiac artery and 29 not receiving it. Rates of technical success (96.5% vs. 100%), intervention times (256 min vs. 237 min), and lengths of hospital stay (5.1 vs. 4.7 days) were similar between unstented vs. stented groups. At one year, no significant difference in celiac artery instability was noted (17.2 vs. 5.5%; p = 0.06). Risk factors for CA occlusion on univariate analysis included a steep takeoff angle (≥140°), length of stenosis >6.5 mm, proximal diameter ≤6.5 mm, preoperative stenosis ≥50%, and celiac artery tortuosity. Conclusions: Anatomical features of the CA impact the ability to achieve routine CA stenting during FEVAR. Selectively not stenting the celiac artery during FEVAR might simplify the procedure without compromising patient safety and mid-term outcomes. Full article

With the global increase in maritime activities, the frequency of maritime accidents has risen, underscoring the urgent need for faster and more efficient search and rescue (SAR) solutions. This study presents an intelligent unmanned aerial vehicle (UAV)-based maritime rescue system that combines GPS-driven dynamic path planning with vision-based dual-target detection and tracking. Developed within the Gazebo simulation environment and based on modular ROS architecture, the system supports stable takeoff and smooth transitions between multi-rotor and fixed-wing flight modes. An external command module enables real-time waypoint updates. This study proposes three path-planning schemes based on the characteristics of drones. Comparative experiments have demonstrated that the triangular path is the optimal route. Compared with the other schemes, this path reduces the flight distance by 30–40%. Robust target recognition is achieved using a darknet-ROS implementation of the YOLOv4 model, enhanced with data augmentation to improve performance in complex maritime conditions. A monocular vision-based ranging algorithm ensures accurate distance estimation and continuous tracking of rescue vessels. Furthermore, a dual-target-tracking algorithm—integrating motion prediction with color-based landing zone recognition—achieves a 96% success rate in precision landings under dynamic conditions. Experimental results show a 4% increase in the overall mission success rate compared to traditional SAR methods, along with significant gains in responsiveness and reliability. This research delivers a technically innovative and cost-effective UAV solution, offering strong potential for real-world maritime emergency response applications. Full article

Unmanned tilt-rotor electric Vertical Take-Off and Landing (eVTOL) aircraft face significant control challenges during the transition from hover to forward flight, particularly when using open-source autopilot systems that rely on open-loop tilt control and static control gains. After the transition, the Total Energy Control System (TECS) becomes active in fixed-wing mode, but its default static gains often fail to correct energy imbalances, resulting in substantial altitude loss. This paper presents the Steepest Descent-based Total Energy Control System (SD-TECS), a real-time adaptive TECS framework that dynamically tunes gains using the steepest descent method to enhance post-transition altitude and airspeed regulation in unmanned tilt-rotor eVTOLs. The proposed method integrates gain adaptation directly into the TECS loop, optimizing control actions based on instantaneous flight states such as altitude and energy-rate errors. This enables improved responsiveness to nonlinear dynamics during the critical post-transition phase. Simulation results demonstrate that the SD-TECS approach significantly improves control performance compared to the default PX4 TECS, achieving a 35.5% reduction in the altitude settling time, a 57.3% improvement in the airspeed settling time, and a 66.1% decrease in the integrated altitude error. These improvements highlight the effectiveness of SD-TECS in enhancing the stability and reliability of unmanned tilt-rotor eVTOLs operating under autonomous control. Full article

Urban road networks are prone to disruptions that can result in localized congestion or even complete interruptions, thereby causing delays in conventional logistics distribution. To mitigate this issue, the present study proposes a dynamic deployment model and task planning methodology for vehicle–drone collaborative delivery in areas affected by road disruptions. Utilizing complex network theory, a framework for identifying node vulnerabilities within road networks is established. Furthermore, a dynamic model for selecting drone take-off and landing sites, as well as task planning, is developed with the dual objectives of minimizing delivery costs and time while maximizing demand coverage. An enhanced evolutionary algorithm is devised to address the model. Results from case studies indicate that when the failure rate of regional road network nodes reaches 50%, the network vulnerability value is 0.8, achieving an air–ground collaborative logistics task completion rate of 95% and a delivery time of approximately 120 min. Conversely, when node failure escalates to 70%, the vulnerability value approaches 1.0, while still achieving a 90% task completion rate and a delivery time of 150 min. The proposed air–ground collaborative dynamic logistics approach effectively addresses distribution challenges in disrupted road networks and offers technical support for the advancement of urban low-altitude logistics. Full article

The purpose of this study was to investigate the benefits of a 12-week plyometric training program intervention on lower limb joint mobility, explosive strength, advanced layup success rates, and injury rates. The study recruited 15 collegiate male basketball players as participants. They underwent basketball training five times per week, each lasting two hours, and additionally received plyometric training twice a week. The study utilized image processing software (ImageJ, version 1.54f, National Institutes of Health, Bethesda, MD, USA) to measure the lower limb joint mobility during the take-off phase of a layup and employed a force plate to assess the explosive strength of the lower limbs during the jump. Furthermore, the study examined the success rate and injury rate of advanced layups—including crossover layups, spin layups, and straight-line layups—as well as the sports injury rate. The results demonstrated that plyometric training significantly enhanced the hip, knee, and ankle joint mobility as well as lower limb explosive strength, with a strong positive correlation between these variables. Furthermore, plyometric training improved joint mobility and lower limb explosive strength. The success rate of advanced layups increased from 50% to 72%, while the sports injury rate decreased from 18% to 8%. In conclusion, plyometric training significantly improved participants’ lower limb joint mobility and explosive strength, which in turn enhanced advanced layup performance and reduced the sports injury rate. Although this study provided preliminary evidence supporting the effectiveness of plyometric training, further research is needed to examine its long-term effects and other influencing factors. Full article

Objectives: To develop and internally validate a pre-treatment score for the safe selection of the best candidates for the transradial approach when performing liver cancer intra-arterial procedures. Methods: One hundred and twenty-two patients undergoing hepatic endovascular treatments via radial access between January and December 2022 were retrospectively selected to develop a prediction model. Pre-procedural imaging data were analyzed, and variables were selected to develop the RAD-access score. Intra-procedural data were analyzed to assess effective procedural complexity (ePC). The relationship between ePC and pre-procedural variables was statistically tested, and cutoff points were defined. Results: A final score (RAD-access) was created and prospectively validated on 139 patients enrolled between June and September 2023. Aortic arch diameter and angulation, left subclavian artery angulation, suprarenal abdominal aorta diameter, celiac trunk take-off angle, and radial artery diameter were the significant variables used to build the score. In the validation cohort, based on the pre-treatment RAD-access score, 69 patients underwent a transradial approach, with a significantly lower ePC rate obtained (78.2% easy, 20.3% intermediate, 1.5% complex). No major adverse events occurred. Conclusions: Pre-treatment RAD-access score provides a good prediction for the procedural complexity of the transradial approach in patients undergoing liver cancer intra-arterial treatments, identifying the best candidates for an easy and safe transradial procedure. Full article

Due to the limitations of pure electric power endurance, turbo-electric hybrid power systems, which offer a high power-to-weight ratio, present a reliable solution for medium- and large-sized vertical take-off and landing (VTOL) aircraft. Traditional energy management strategies often fail to minimize fuel consumption across the entire flight profile while meeting power demands under varying flight conditions. To address this issue, this paper proposes a deep reinforcement learning (DRL)-based energy management strategy (EMS) specifically designed for turbo-electric hybrid propulsion systems. Firstly, the proposed strategy employs a Prior Knowledge-Guided Deep Reinforcement Learning (PKGDRL) method, which integrates domain-specific knowledge into the Deep Deterministic Policy Gradient (DDPG) algorithm to improve learning efficiency and enhance fuel economy. Then, by narrowing the exploration space, the PKGDRL method accelerates convergence and achieves superior fuel and energy efficiency. Simulation results show that PKGDRL has a strong generalization capability in all operating conditions, with a fuel economy difference of only 1.6% from the offline benchmark of the optimization algorithm, and in addition, the PKG module enables the DRL method to achieve a huge improvement in terms of fuel economy and convergence rate. In particular, the prospect theory (PT) in the PKG module improves fuel economy by 0.81%. Future research will explore the application of PKGDRL in the direction of real-time total power prediction and adaptive energy management under complex operating conditions to enhance the generalization capability of EMS. Full article

Water film depth (WFD) on runways is a key factor contributing to aircraft hydroplaning during takeoff and landing. Thus, the early measurement or prediction of WFD during rain is critical for reducing accidents. Most existing WFD prediction models are derived from experiments conducted on road surfaces. However, an accurate prediction of WFD on runways and reduced hydroplaning risk require a precise empirical prediction model. This study conducted experiments involving four parameters—rainfall intensity, pavement mean texture depth, drainage length, and transverse slope—to develop a WFD dataset specific to different runway conditions. The multiple linear regression method is employed to establish a model for WFD predictions. The proposed National Taiwan University (NTU) model’s predictability is compared with three existing empirical models using NTU and Gallaway datasets. The results clearly demonstrate the superior accuracy and robustness of the NTU model compared to the other evaluated models. The NTU model offers a precise and practical predictive formula, making it highly suitable for integration into contaminated runway warning and management systems. This study employed a laser displacement sensor and a programmable logic controller to obtain high-accuracy, high-sampling-rate WFD data. Modern automated data acquisition enables simultaneous measurement at multiple points and captures the complete WFD curve from zero to a stable depth, which was previously difficult to obtain. Full article

As an emerging strategic industry, drone delivery operation has demonstrated significant potential in urban environments due to its efficiency and adaptability to complex scenarios. However, critical bottlenecks persist during the take-off and landing phases, where accident rates account for over 52% of total flight risks, severely limiting operational safety and throughput. While existing droneport designs and sequencing strategies draw inspiration from traditional aviation methods, they inadequately address the separation of take-off/landing flows and lack tailored solutions for logistics drones’ unique characteristics. To overcome these limitations, this paper presents an integrated framework combining innovative airspace design with dynamic sequencing optimization. First, a novel terminal airspace structure is proposed to enable simultaneous multi-drone operations through spatially segregated routes and dedicated zones, fundamentally resolving collision risks between ascending and descending drones. Second, a real-time sequencing model based on the Hungarian algorithm is developed, incorporating drone-specific factors such as battery levels and task priorities to formulate a cost matrix for optimal scheduling. Experimental results demonstrate that the proposed airspace design reduces take-off/landing time by 34.8% compared to conventional funnel-shaped configurations. The sequencing algorithm prioritizes high-value missions while reducing the average waiting time for low-battery drones by 47.3%, effectively alleviating endurance pressures. Notably, the sequencing algorithm prevents low-battery drones from crashing in the experiments. In comparison, under the sequencing of the comparison method, numerous drones crash due to low battery levels. Full article