Search Results (129)

This study investigates the connection between coherent structures in the flow around a vehicle and the aerodynamic forces acting on its body. Dynamic Mode Decomposition (DMD) was applied to analyze the flow field of a squareback Ahmed body at $R{e}_H=7.7\times {10}^5$. DMD enabled the identification of coherent structures in the near and far wake by isolating their individual oscillation frequencies and spatial energy distributions. These structures were classified into three regimes based on their underlying mechanisms: symmetry breaking, bubble pumping, and large-scale vortex shedding in range of $St\le 0.2$. The energy contributions of these flow regimes were quantified across different regions of the flow field and compared to the aerodynamic forces on the body. Additionally, the linear correlation between pressure and velocity components was examined using Pearson correlation coefficients of DMD spectral amplitudes. The locations of maximum and minimum correlation values, as well as their relationship to energy contributions, were identified and analyzed in detail. Full article

During the spraying operation, it is important to consider the environmental conditions, particularly the wind velocity. Droplets carried by the wind out of the spray zone may be carried onto nearby plants, soil, water bodies, residential areas, etc. Various measures have been developed and used to reduce droplet drift to address this problem. Robotic spraying systems, such as unmanned aerial vehicles and spraying robots, are now increasingly being used. The influence of lateral winds on the spraying processes of these systems has not yet been extensively investigated. In this study, spray coverage and spray drift of manufactured artificial plants were investigated. Spraying was carried out with an XAG R150 spraying robot and a lateral wind from 2 m s⁻¹ to 8 m s⁻¹ was generated with an air flow generation stand by varying the air flow velocity every 2 m s⁻¹. Spray coverage on the artificial plants was measured at two heights (0.5 and 1 m). The droplet coverage measurements were significantly influenced by the lateral wind velocity and the height of the plant coverage measurement site. The results showed that even in the presence of a high lateral wind velocity (v = 6–8 m s⁻¹), the droplet spray had better coverage of the middle part of the artificial plant (0.5 m from the ground) than the upper part (1 m from the ground). For the spray drift studies, three solutions with low concentrations (0.1%) of chemical drift reduction agents (DRAs) were sprayed, with water as control. It was found that the proportion of drifting droplets also increased with increasing lateral wind velocity. The spray coverage at 3 m from the spray zone (spray drift) was 1.6% at a lateral wind velocity of v = 2 m s⁻¹, 4.2% at v = 4 m s⁻¹, 5.3% at v = 6 m s⁻¹, and 8.1% at v = 8 m s⁻¹. The use of DRAs was able to reduce spray drift in strong (v = 8 m s⁻¹) lateral winds. It was found that at 3 m from the spray zone, at a spray height of 1 m, the spray coverage was about 40.7% lower than that of water for DRA1, 44.4% for DRA2, and 43.2% for DRA3. Full article

In the perception of unmanned systems, small object detection faces numerous challenges, including small size, low resolution, dense distribution, and occlusion, leading to suboptimal perception performance. To address these issues, we propose a specialized algorithm named Unmanned-system Small-object Detection-You Only Look Once (USD-YOLO). First, we designed an innovative module called the Anchor-Free Precision Enhancer to achieve more accurate bounding box overlap measurements and provide a smarter processing mechanism, thereby improving the localization accuracy of candidate boxes for small and densely distributed objects. Second, we introduced the Spatial and Channel Reconstruction Convolution module to reduce redundancy in spatial and channel features while extracting key features of small objects. Additionally, we designed a novel C2f-Global Attention Mechanism module to expand the receptive field and capture more contextual information, optimizing the detection head’s ability to handle small and low-resolution objects. We conducted extensive experimental comparisons with state-of-the-art models on three mainstream unmanned system datasets and a real unmanned ground vehicle. The experimental results demonstrate that USD-YOLO achieves higher detection precision and faster speed. On the Citypersons dataset, compared with the baseline, USD-YOLO improves mAP50-95, mAP50, and Recall by 8.5%, 5.9%, and 2.3%, respectively. Additionally, on the Flow-Img and DOTA-v1.0 datasets, USD-YOLO improves mAP50-95 by 2.5% and 2.5%, respectively. Full article

Unmanned Aerial Vehicle (UAV) high-resolution remote sensing imagery has been used for unprecedented coastal environment monitoring with ground sampling distance and time intervals of a few centimeters and seconds, respectively. However, high spatial-time resolutions of UAV remote sensing data consist of unexpected signals from water surface level changes induced by wind-driven currents and waves. This leads to non-linear and non-stationary forms of sun and sky glints in the UAV sea surface image. Consequently, these surface glints interfere with the detection of water body reflections and objects, reducing the accuracy and usability of the measurements. This study employed Fast and Adaptive Multidimensional Empirical Mode Decomposition (FA-MEMD) to separate the spatial periodicity of time-continuous multispectral images of the sea surface from the original data and retain non-oscillatory signals called residual images. The residual images effectively represented the spatial-temporal radiance and flow variations in the water body by correcting the regions of surface glint. This study presents three key findings: First, homogeneous surface radiance data with surface glint removed from the raw image sequence was acquired using FA-MEMD. Second, the continuous surface glint removal effect is validated through water-leaving radiance (L_w-SBA) measurements obtained via the Skylight-Blocked Approach (SBA) method. Comparisons showed that R² values for the data obtained from clear water before and after surface glint removal were 0.02 and 0.56 with RMSE values of 8.37 × 10⁻⁵ and 5.51 × 10⁻⁵ W·m⁻²·sr⁻¹, respectively, indicating an improvement rate of 34.19%. Third, a comparative analysis with previous study methods demonstrated that our approach yielded spatially and temporally uniform homogeneous surface radiance data with less variability than traditional methods. The spatially and temporally synchronized residual images and the L_w-SBA data showed high similarity, confirming that the FA-MEMD technique effectively removed the surface glint from wave-induced roughness, enhancing the reliability of high-resolution UAV sea color observations. Full article

Cross-media vehicles, which combine the advantages of airplanes and submarines, are capable of performing complex tasks in different media and have attracted significant interest in recent years. In practice, however, cross-media rotorcrafts face numerous challenges during the cross-media transition, one of which is the complex mixed air–water flows induced by their rotors operating in close proximity to the water surface. These flows can result in aerodynamic penalties and structural damage to the rotors. The interactions between a water surface and a rotor wake bring about potential risks of cross-media locomotion, which is known as the near-water effect of rotors. Given that the distinctions between the near-water effect and the ground effect of rotors are not yet widely understood, this study details the discovery of the near-water effect and provides a comprehensive review of the evolutionary development of the near-water effect, tracing its understanding from the ground effect to the influence of droplets through aerodynamic modeling, numerical simulations, and near-water experimental studies. Furthermore, open problems and challenges associated with the near-water effect are discussed, including flow field measurements and numerical simulation approaches. Additionally, potential applications of the near-water effect for the development of cross-media rotorcraft are also described, which are valuable for aerodynamic design and cross-media control. Full article

The continuous assessment of cover crop growth throughout the season is a crucial baseline observation for making informed crop management decisions and sustainable farming operation. Precision agriculture techniques involving applications of sensors and unmanned aerial vehicles provide precise and prompt spectral and structural data, which allows for effective evaluation of cover crop biomass. Vegetation indices are widely used to quantify crop growth and biomass metrics. The objective of this study was to evaluate the accuracy of biomass estimation using a machine learning approach leveraging spectral and canopy height data acquired from unmanned aerial vehicles (UAVs), comparing different neural network architectures, optimizers, and activation functions. Field trials were carried out at two sites in Louisiana involving winter cover crops. The canopy height was estimated by subtracting the digital surface model taken at the time of peak growth of the cover crop from the data captured during a bare ground condition. When evaluated against the validation dataset, the neural network model facilitated with a Keras TensorFlow library with Adam optimizers and a sigmoid activation function performed the best, predicting cover crop biomass with an average of 96 g m⁻² root mean squared error (RMSE). Other statistical metrics including the Pearson correlation and R² also showed satisfactory conditions with this combination of hyperparameters. The observed cover crop biomass ranged from 290 to 1217 g m⁻². The present study findings highlight the merit of comprehensive analysis of cover crop traits using UAV remote sensing and machine learning involving realistic underpinning biophysical mechanisms, as our approach captured both horizontal (vegetation indices) and vertical (canopy height) aspects of plant growth. Full article

This study provides a detailed analysis of the convergence criteria for dynamic mode decomposition (DMD) parameters, with a focus on sampling frequency and period in high-Reynolds-number flows. The analysis is based on flow over an idealized road vehicle, the Ahmed body ($Re=7.7\times {10}^5$), using computational fluid dynamics (CFD) data from improved delayed detached eddy simulation (IDDES). The pressure and velocity spectrum analysis validated IDDES’s ability to capture system dynamics, consistent with existing studies. For a comprehensive understanding of the contributions of different components of the circle, the Ahmed body was divided into three regions: (a) front; (b) side, lower, and upper surfaces; and (c) rear fascia. Both pressure and skin-friction drag were analyzed in terms of frequency spectra and cumulative energy. Key findings show that a 90% contribution to the pressure drag comes from modes with a frequency of less than 26 Hz ($St$ = 0.187), while the friction drag requires 84 Hz ($St$ = 0.604) for similar energy capture. This study highlights the significance of accounting for intermittency and non-stationary behavior in turbulent flows for DMD convergence. A minimum of 3000 snapshots is necessary for the convergence of DMD eigenvalues, and sampling frequency ratios between 5 and 10 are needed to achieve a reconstruction error of less than 1%. The sampling period’s convergence showed that $T^{*}=250$ (equivalent to 20 cycles of the slowest coherent structures) stabilizes coherent mode shapes and energy levels. Beyond this, DMD may become unstable. Additionally, mean subtraction was found to improve DMD stability. These results offer critical insights into the effective application of DMD in analyzing complex vehicle flow fields. Full article

Underground coal mining induces surface subsidence, which in turn impacts the stability of slopes in mountainous regions. However, research that investigates the coupling relationship between surface subsidence in mountainous regions and the occurrence of multiple surface hazards is scarce. Taking a coal mine in southwestern China as a case study, a detailed catalog of the surface hazards in the study area was created based on multi-temporal satellite imagery interpretation and Unmanned aerial vehicle (UAV) surveys. Using interferometric synthetic aperture radar (InSAR) technology and the logistic subsidence prediction method, this study investigated the evolution of surface subsidence induced by underground mining activities and its impact on the triggering of multiple surface hazards. We found that the study area experienced various types of surface hazards, including subsidence, landslides, debris flows, sinkholes, and ground fissures, due to the effects of underground mining activities. The InSAR monitoring results showed that the maximum subsidence at the back edge of the slope terrace was 98.2 mm, with the most severe deformation occurring at the mid-slope of the mountain, where the maximum subsidence reached 139.8 mm. The surface subsidence process followed an S-shaped curve, comprising the stages of initial subsidence, accelerated subsidence, and residual subsidence. Additionally, the subsidence continued even after coal mining operations concluded. Predictions derived from the logistic model indicate that the duration of residual surface subsidence in the study area is approximately 1 to 2 years. This study aimed to provide a scientific foundation for elucidating the temporal and spatial variation patterns of subsidence induced by underground coal mining in mountainous regions and its impact on the formation of multiple surface hazards. Full article

Hydraulic motors have been widely used in large-scale machinery such as ground heavy equipment and heavy-duty vehicles, ships, and so on because of their high-power drive capability. However, the driving device is confronted with constraints related to its size and weight. Typically, the hydraulic axial piston motor is preferred for its simplicity and efficiency. However, the oil distributor in traditional hydraulic motors faces significant challenges, such as evident oil leakage and power loss from the mating surfaces of the fixed oil distributor and rotating cylinder block. To enhance the reliability and performance of hydraulic motors employed in paper driving applications, this paper introduces a digital radial hydraulic motor used for heavy-duty vehicle traction. The motor is powered by an on-board pump station from which several on/off valves can distribute the hydraulic oil. This design effectively mitigates the performance degradation issues associated with friction and wear in traditional hydraulic motor oil distributors. The drive characteristics of the motor can be flexibly adjusted through the combination of valves. Our investigation into the motor’s design principles and parameter analysis is poised to make an indirect yet significant contribution to the optimization of heavy-duty vehicle traction systems. This paper delineates the application conditions and operational principles of the digital hydraulic motor, thoroughly analyzes the intricate topological interrelationships of its parameters, and meticulously develops a detailed component-level model. Through comprehensive calculations, it reveals the impact of configuration and flow valve parameters on motor efficiency. A simulation model is established for the purpose of verification. Furthermore, the influence of the flow allocation method on efficiency and pressure pulsation is examined, leading to the proposal of a novel flow allocation strategy, the efficacy of which is substantiated through simulation. In conclusion, this paper formulates critical insights to inform the design and selection of components for digital hydraulic motors. These findings may provide a feasible solution for heavy-duty vehicle traction application scenarios. Full article

This paper presents the design and development project of an engine test stand specifically constructed for ground testing of miniature turbine jet engines (MTJEs) along with conclusive results of the conducted investigations. The tested engines serve as the propulsion system for an unmanned aerial vehicle (UAV) platform. The engine test stand was used to determine various operating parameters of the engine, with a particular focus on recording variations and changes in temperature and pressure at the engine control cross-sections: behind the compressor, the combustion chamber, and at the final cross-section of the nozzle. The analysis of the direct test results allowed the evaluation of the engine’s behavior under hydration conditions and documents the quantitative and qualitative response of the control system of the engine. Of particular interest are the results showing an increase in exhaust system temperature with a decrease in the temperature in combustion chamber under hydrated conditions. The test program assumed and considered the acting loads and forces in both standard and specific flight conditions, including scenarios for a heavy rain. The preliminary evaluation of the investigation results provided data and insights required for further analysis. Quantitatively, the measured temperature value in the exhaust system does not exceed 700 °C and the temperature increase resulting from the introduction of water and the engine’s response to the out-of-operation event is approximately 50 °C for the JetCat 140. Qualitatively different effects were observed in the combustion moment, consisting in a drop in temperature values during the introduction of water into the engine flow channel. The introduction of water into the GTM 140 inlet revealed no significant changes in the variations of pressure and temperature measured in selected engine design sections. Based on the knowledge and experience gained, a fully operational test stand to monitor the parameters and performance of the MTJEs, which are used for aerial target propulsion, was developed. Full article

This study explores the use of a passive control technique to mitigate aeroelastic effects on a wing operating near the ground. An aeroelastic model, based on a typical airfoil section, equipped with a nonlinear tuned vibration absorber (NLTVA), is established to study the interactions between the airfoil’s dynamics, aerodynamics, and the nonlinear energy dissipation mechanisms. Geometric nonlinearity is incorporated into the airfoil’s dynamics to account for possible large wing deflection and rotation. The flow is modeled based on the nonlinear unsteady discrete vortex method with the ground effect simulated using the mirror image method. Stability analyses are conducted to study the influence of NLTVA parameters on flutter mitigation and the bifurcation behavior of the airfoil near the ground. The numerical results demonstrate that the NLTVA effectively delays the onset of flutter and promotes a supercritical bifurcation in the presence of ground effect. Optimally tuning the NLTVA’s linear parameters significantly increases flutter speed, while selecting the optimal nonlinear parameter is key to preventing subcritical behavior near the ground and reducing the amplitude of post-flutter limit cycle oscillations. Overall, this study highlights the potential of the NLTVA in enhancing the aeroelastic stability of flying vehicles with highly flexible wings, especially under the influence of ground effects during takeoff and landing. Full article

Hybrid power systems are now widely utilized in a variety of vehicle platforms due to their efficacy in reducing pollution and enhancing energy utilization efficiency. Nevertheless, the existing vehicle hybrid systems are of a considerable size and weight, rendering them unsuitable for integration into 25 kg compound-wing UAVs. This study presents a design solution for a compound-wing vertical takeoff and landing unmanned aerial vehicle (VTOL) equipped with an improved series hybrid power system. The system comprises a 48 V lithium polymer battery(Li-Po battery), a 60cc internal combustion engine (ICE), a converter, and a dedicated permanent magnet synchronous machine (PMSM) with four motors, which collectively facilitate dual-directional energy flow. The four motors serve as a load and lift assembly, providing the requisite lift during the take-off, landing, and hovering phases, and in the event of the ICE thrust insufficiency, as well as forward thrust during the level cruise phase by mounting the variable pitch propeller directly on the ICE. The entire hybrid power system of the UAV undergoes numerical modeling and experimental simulation to validate the feasibility of the complete hybrid power configuration. The validation is achieved by comparing and analyzing the results of the numerical simulations with ground tests. Moreover, the effectiveness of this hybrid power system is validated through the successful completion of flight test experiments. The hybrid power system has been demonstrated to significantly enhance the endurance of vertical flight for a compound-wing VTOL by more than 25 min, thereby establishing a solid foundation for future compound-wing VTOLs to enable multi-destination flights and multiple takeoffs and landings. Full article

With the global increase in air transport demand, the shortage of ground handling personnel to support ground operations at airports has become a major challenge, impacting airport services and causing considerable flight delays. This study presents a novel method to generate trip data that specify the origin and destination locations as the purpose of travel for each ground support equipment (GSE) vehicle. The proposed method uses data obtained from comprehensive observations of 2234 GSE vehicles over a 24 h × 7 d time interval at Tokyo International Airport. From these observations and trip data, the characteristics of the driving conditions for each GSE vehicle type, the locations where GSE traffic volume increases in the airport, and changes in the time interval are identified. The primary results show that the GSE traffic volume is the highest mainly around passenger terminals and in the vehicle corridors connecting these terminals, which aligns with the airport’s operational status. Investigating GSE driving conditions, such as the traffic flow throughout an airport, can provide valuable data to improve the efficiency of GSE scheduling and facilitate the introduction of automated driving technology. Full article

Real-Time/Post-Processing Kinematic (RTK/PPK) technology has been widely applied in Unmanned Aerial Vehicle (UAV) photogrammetry in glaciological research. Considering that ground control points (GCPs) cannot be set on glaciers, evaluating the impacts of one-sided distribution is essential. In this study, 8571 images were captured at Sermeq Avannarleq glacier in western Greenland from 4 August 2021 to the 6th, covering approximately 85 km², with the furthest distance being 13.22 km away from the coastline. Benefited by the meandering coastline, 11 roving stations roughly uniformly distributed on bare rock were surveyed with the RTK technique. PPK-geotagged images were processed in Agisoft Metashape Professional to derive the DSMs, utilizing eight different configurations of GCP distributions that gradually extended longitudinally (along the glacier flow direction) to the upper part of the glacier. The accuracy of DSMs was evaluated by referring to the validation points (VPs) that were not employed in the Bundle Block Adjustment (BBA). The results indicated that the RMSE values of the easting, northing, and height of the reconstruction model georeferenced by only PPK geotagging (no GCPs applied) were 0.038 m, 0.031 m, and 0.146 m, respectively. Applying four GCPs located at one side of the region but with both longitudinal and lateral distribution improved the RMSE values in easting, northing, and vertical to 0.037 m, 0.031 m, and 0.081 m, respectively, and these values were stable even when distributing four GCPs evenly or when increasing the number of GCPs to eleven. Moreover, the cross-validation with ICESat-2 and ArcticDEM performed only at an off-glacier region also suggested that vertical accuracy shows significant improvements for every configuration of GCPs compared to the reconstruction model optimized only by PPK, but such improvements were not obvious if the number of GCPs exceeded four. Moreover, no elevation ramps appeared in the UAV DSM, even for the GCP configuration with only two GCPs distributed at the terminus. Therefore, combining PPK with only a few GCPs but distributing in both directions of the surveying region can offer a viable solution for obtaining glacier DSMs at the coastline with decimeter-level accuracy. Full article

Traditionally, traffic characteristics such as speed, volume, and travel time are obtained from a range of sensors and systems such as inductive loop detectors (ILDs), automatic number plate recognition cameras (ANPR), and GPS-equipped floating cars. However, many issues associated with these data have been identified in the existing literature. Although roadside surveillance cameras cover most road segments, especially on freeways, existing techniques to extract traffic data (e.g., speed measurements of individual vehicles) from video are not accurate enough to be employed in a proactive traffic management system. Therefore, this paper aims to develop a technique for estimating traffic data from video captured by surveillance cameras. This paper then develops a deep learning-based video processing algorithm for detecting, tracking, and predicting highly disaggregated vehicle-based data, such as trajectories and speed, and transforms such data into aggregated traffic characteristics such as speed variance, average speed, and flow. By taking traffic observations from a high-quality LiDAR sensor as ‘ground truth’, the results indicate that the developed technique estimates lane-based traffic volume with an accuracy of 97%. With the application of the deep learning model, the computer vision technique can estimate individual vehicle-based speed calculations with an accuracy of 90–95% for different angles when the objects are within 50 m of the camera. The developed algorithm was then utilised to obtain dynamic traffic characteristics from a freeway in southern China and employed in a statistical model to predict monthly crashes. Full article