Search Results (68)

Polymer electrolyte membrane fuel cells (PEMFCs) are emerging as the next-generation powertrain for unmanned aerial vehicles (UAVs) due to their high energy density and long operating duration. PEMFCs are subject to icing and performance degradation problems at sub-zero temperatures, especially at high altitudes. Therefore, an effective preheating system is required to ensure stable PEMFC operation in high-altitude environments. This study aimed to mathematically model a shell-and-tube heat exchanger that utilizes waste heat recovery to prevent internal and external PEMFC damage in cold, high-altitude conditions. The waste heat from the PEMFC is estimated based on the thrust of the MQ-9 Reaper, and the proposed heat exchanger must be capable of heating air to −5 °C. As the heat exchanger utilizes only waste heat, the primary energy consumption arises from the coolant pumping process. Calculation results indicated that the proposed heat exchanger design improved the overall system efficiency by up to 15.7%, demonstrating its effectiveness in utilizing waste heat under aviation conditions. Full article

This paper presents an adaptive fault-tolerant control strategy tailored for fixed-wing unmanned aerial vehicles (UAV) operating under adverse conditions such as icing. Using radial basis function neural networks and nonlinear dynamic inversion, the proposed framework effectively handles simultaneous actuator and sensor faults with arbitrary nonlinear dynamics caused by environmental effects, model uncertainties and external disturbances. A nonlinear disturbance observer is incorporated for accurate sensor fault detection and estimation, thereby enhancing the robustness of the control system. The integration of the radial basis function neural network enables an adaptive estimation of the faults, ensuring accurate fault compensation and system stability under challenging conditions. The observer is optimised to minimise the deviation of the closed-loop dynamics eigenvalues from the assigned eigenvalues and to approach unity observer steady-state gain. The stability of the control architecture is mathematically proven using Lyapunov analysis, and the performance of the approach is validated through numerical simulations on a six Degrees of Freedom fixed-wing unmanned aerial vehicles model. The results show superior performance and robustness to challenging fault scenarios. This research provides a comprehensive fault management solution that enhances the safety and reliability of unmanned aircraft operations in extreme environments. Full article

Icing on an unmanned aerial vehicle (UAV) can degrade aerodynamic performance, reduce flight capabilities, impair maneuverability and stability, and significantly impact flight safety. At present, most flight control methods for icing-affected aircraft adopt a conservative control strategy, in which small control inputs are used to keep the aircraft’s angle of attack and other state variables within a limited range. However, this approach restricts the flight performance of icing aircraft. To address this issue, this paper innovatively proposes a design method of an ice tolerance flight envelope protection control system for a UAV on the base of icing severity detection using a long short-term memory (LSTM) neural network. First, the icing severity is detected using an LSTM neural network without requiring control surface excitation. It relies solely on the aircraft’s historical flight data to detect the icing severity. Second, by modifying the fuzzy risk level boundaries of the icing aircraft flight parameters, a nonlinear mapping relationship is established between the tracking command risk level, the UAV flight control command magnitude, and the icing severity. This provides a safe range of tracking commands for guiding the aircraft out of the icing region. Finally, the ice tolerance flight envelope protection control law is developed, using a nonlinear dynamic inverse controller (NDIC) as the inner loop and a nonlinear model predictive controller (NMPC) as the outer loop. This approach ensures boundary protection for state variables such as the angle of attack and roll angle while simultaneously enhancing the robustness of the flight control system. The effectiveness and superiority of the method proposed in this paper are verified for the example aircraft through mathematical simulation. Full article

In order to contribute to the operation of unmanned aerial vehicles (UAVs) according to visual flight rules (VFR), this article proposes a monocular approach for cloud detection using an electro-optical sensor. Cloud avoidance is motivated by several factors, including improving visibility for collision prevention and reducing the risks of icing and turbulence. The described workflow is based on parallelized detection, tracking and triangulation of features with prior segmentation of clouds in the image. As output, the system generates a cloud occupancy grid of the aircraft’s vicinity, which can be used for cloud avoidance calculations afterwards. The proposed methodology was tested in simulation and flight experiments. With the aim of developing cloud segmentation methods, datasets were created, one of which was made publicly available and features 5488 labeled, augmented cloud images from a real flight experiment. The trained segmentation models based on the YOLOv8 framework are able to separate clouds from the background even under challenging environmental conditions. For a performance analysis of the subsequent cloud position estimation stage, calculated and actual cloud positions are compared and feature evaluation metrics are applied. The investigations demonstrate the functionality of the approach, even if challenges become apparent under real flight conditions. Full article

In cold environments, ice formation poses significant risks to infrastructure such as transportation systems and power transmission. Yet, traditional de-icing methods are often time-consuming, hazardous, and inefficient. In this regard, unmanned aerial vehicles (UAVs) have shown great potential in environmental ice detection and de-icing applications. This study comprehensively reviews the application of UAVs in ice detection and de-icing operations in external environments, emphasizing their potential to replace traditional manual methods. Firstly, the latest developments in UAV-based external ice detection technology are examined, with a focus on the unique capabilities of sensors such as multispectral cameras, infrared imagers, and LiDAR in capturing specific ice features. Subsequently, the implementation and effectiveness of chemical, mechanical, and thermal de-icing methods delivered via UAV platforms are evaluated, focusing on their operational efficiency and adaptability. In addition, key operational requirements are reviewed, including environmental adaptability, mission planning and execution, and command transmission, as well as system design and manufacturing. Finally, the practical challenges involved in deploying UAVs under complex weather conditions are examined and solutions are proposed. These are aimed at promoting future research and ultimately driving the adoption of UAV technology in de-icing applications. Full article

Electrothermal de-icing systems are a popular approach to protect unmanned aerial vehicles (UAVs) from the performance degradation caused by in-cloud icing. However, their power and energy requirements must be minimized to make these systems viable for small and medium-sized fixed-wing UAVs. Thermal de-icing systems allow intercycle ice accretions and can result in runback icing. Intercycle and runback ice increase the aircraft’s drag, requiring more engine thrust and energy. This study investigates the aerodynamic influence of intercycle and runback ice on a typical UAV wing. Lift and drag coefficients from a wind tunnel campaign and Ansys FENSAP-ICE simulations are compared. Intercycle ice shapes result in a drag increase of approx. 50% for a realistic cruise angle of attack. While dispersed runback ice increases the drag by 30% compared to the clean wing, a spanwise ice ridge can increase the drag by more than 170%. The results highlight that runback ice can significantly influence the drag coefficient. Therefore, it is important to design the de-icing system and its operation sequence to minimize runback ice. Understanding the need to minimize runback ice helps in designing viable de-icing systems for UAVs. Full article

Accurate estimation of building height is crucial for urban aesthetics and urban planning as it enables an accurate calculation of the shadow period, the effective management of urban energy consumption, and thorough investigation of regional climatic patterns and human-environment interactions. Although three-dimensional (3D) cadastral data, ground measurements (total station, Global Positioning System (GPS), ground laser scanning) and air-based (such as Unmanned Aerial Vehicle—UAV) measurement methods are used to determine building heights, more comprehensive and advanced techniques need to be used in large-scale studies, such as in cities or countries. Although satellite-based altimetry data, such as Ice, Cloud and land Elevation Satellite (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI), provide important information on building heights due to their high vertical accuracy, it is often difficult to distinguish between building photons and other objects. To overcome this challenge, a self-adaptive method with minimal data is proposed. Using building photons from ICESat-2 and GEDI data and building footprints from the New York City (NYC) and Los Angeles (LA) open data platform, the heights of 50,654 buildings in NYC and 84,045 buildings in LA were estimated. As a result of the study, root mean square error (RMSE) 8.28 m and mean absolute error (MAE) 6.24 m were obtained for NYC. In addition, 46% of the buildings had an RMSE of less than 5 m and 7% less than 1 m. In LA data, the RMSE and MAE were 6.42 m and 4.66 m, respectively. It was less than 5 m in 67% of the buildings and less than 1 m in 7%. However, ICESat-2 data had a better RMSE than GEDI data. Nevertheless, combining the two data provided the advantage of detecting more building heights. This study highlights the importance of using minimum data for determining urban-scale building heights. Moreover, continuous monitoring of urban alterations using satellite altimetry data would provide more effective energy consumption assessment and management. 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

The black soil region of Northeast China is the largest commercial grain production base in China, accounting for about 25% of the total in China. In this region, the water erosion is prominent, which seriously threatens China’s food security. It is of great significance to effectively identify the erosion-prone points for the prevention and control of soil erosion on the slope of the black soil region in Northeast China. This article takes the Tongshuang small watershed (Heilongjiang Province in China) as an example, which is dominated by hilly landforms with mainly black soil and terraces planted with corn and soybeans. Based on the 2.5 cm resolution Digital Elevation Model (DEM) reconstructed by unmanned aerial vehicles (UAVs), we explore the optimal resolution for hydrological simulation research on sloping farmland in the black soil region of Northeast China and explore the critical water depth at which erosion damage occurs in ridges on this basis. The results show that the following: (1) Compared with the 2 m resolution DEM, the interpretation accuracy of field roads, wasteland, damaged points, ridges and cultivated land at the 0.2 m resolution is increased by 4.55–27.94%, which is the best resolution in the study region. (2) When the water depth is between 0.335 and 0.359 m, there is a potential erosion risk of ridges. When the average water depth per unit length is between 0.0040 and 0.0045, the ridge is in the critical range for its breaking, and when the average water depth per unit length is less than the critical range, ridge erosion damage occurs. (3) When local erosion damage occurs, the connectivity will change abruptly, and the remarkable change in the index of connectivity (IC) can provide a reference for predicting erosion damage. Full article

The glacier surface is composed not only of ice or snow but also of a heterogeneous mixture of various materials. The presence of light-absorbing impurities darkens the glacier surface, reducing local reflectance and thereby accelerating the glacier melting process. However, our understanding of the spatial distribution of these impurities remains limited, and there is a lack of studies on quantifying the dirty degree of glacier surfaces. During the Sentinel satellite overpass on 21 August 2023, we used an ASD FieldSpec3 spectrometer to measure the reflectance spectra of glacier surfaces with varying degrees of dirtiness on the Qiyi glacier, Qinghai–Tibet Plateau. Using Multiple Endmember Spectral Mixture Analysis (MESMA), the Sentinel imagery was decomposed to generate fraction images of five primary ice surface materials as follows: coarse-grained snow, slightly dirty ice, moderately dirty ice, extremely dirty ice, and debris. Using unmanned aerial vehicle (UAV) imagery with a 0.05 m resolution, the primary ice surface was delineated and utilized as reference data to validate the fraction images. The findings revealed a strong correlation between the fraction images and the reference data (R² ≥ 0.66, RMSE ≤ 0.21). Based on pixel-based classification from the UAV imagery, approximately 80% of the glacier surface is covered by slightly dirty ice (19.2%), moderately dirty ice (33.3%), extremely dirty ice (26.3%), and debris (1.2%), which significantly contributes to its darkening. Our study demonstrates the effectiveness of using Sentinel imagery in conjunction with MESMA to map the degree of glacier surface dirtiness accurately. Full article

Low-altitude unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), which boast high-resolution imaging and agile maneuvering capabilities, are widely utilized in military scenarios and generate a vast amount of image data that can be leveraged for textual intelligence generation to support military decision making. Military image captioning (MilitIC), as a visual-language learning task, provides innovative solutions for military image understanding and intelligence generation. However, the scarcity of military image datasets hinders the advancement of MilitIC methods, especially those based on deep learning. To overcome this limitation, we introduce an open-access benchmark dataset, which was termed the Military Objects in Real Combat (MOCO) dataset. It features real combat images captured from the perspective of low-altitude UAVs or UGVs, along with a comprehensive set of captions. Furthermore, we propose a novel encoder–augmentation–decoder image-captioning architecture with a map augmentation embedding (MAE) mechanism, MAE-MilitIC, which leverages both image and text modalities as a guiding prefix for caption generation and bridges the semantic gap between visual and textual data. The MAE mechanism maps both image and text embeddings onto a semantic subspace constructed by relevant military prompts, and augments the military semantics of the image embeddings with attribute-explicit text embeddings. Finally, we demonstrate through extensive experiments that MAE-MilitIC surpasses existing models in performance on two challenging datasets, which provides strong support for intelligence warfare based on military UAVs and UGVs. Full article

Ice accretion on aircrafts due to atmospheric conditions is still a relevant research topic, especially in the case of Unmanned Aerial Vehicles (UAVs), due to their smaller size and the relative underdevelopment of ice protection systems (anti-icing and de-icing) for these aircraft. For the research and development of these systems, it is necessary to assess their performance in icing wind tunnels (IWTs), which are generally high-cost facilities. This article describes the design and building process of a new IWT for testing fixed-wing UAVs, aimed at cost reduction and restricted to an existing cold climate chamber of limited size. The designed IWT is an open-circuit type with two corners, a test section size of 0.40 m × 0.27 m and speed up to 70 m/s. The design process employs widely used and proven semi-empirical formulas, supported by detailed calculations using Computational Fluid Dynamics (CFD) tools, to achieve a test section core of useful quality and avoid flow separation. Theoretical limits with respect to a usable droplet size and Liquid Water Content (LWC) are calculated, and the test section core is estimated. The design process followed proves to be a very good approach to the design and aerodynamic optimisation of a low-cost IWT. Full article

Rocky coastlines are characterised by steep cliffs, which frequently experience a variety of natural processes that often exhibit intricate interdependencies, such as rainfall, ice and water run-off, and marine actions. The advent of high temporal and spatial resolution data, that can be acquired through remote sensing and geomatics techniques, has facilitated the safe exploration of otherwise inaccessible areas. The datasets that can be gathered from these techniques, typically combined with data from fieldwork, can subsequently undergo analyses employing/applying machine learning algorithms and/or numerical modeling, in order to identify/discern the predominant influencing factors affecting cliff top erosion. This study focuses on a specific case situated at the Conero promontory of the Adriatic Sea in the Marche region. The research methodology entails several steps. Initially, the morphological, geological and geomechanical characteristics of the areas were determined through unmanned aerial vehicle (UAV) and conventional geological/geomechanical surveys. Subsequently, cliff top retreat was determined within a GIS environment by comparing orthophotos taken in 1978 and 2022 using the DSAS tool (Digital Shoreline Analysis System), highlighting cliff top retreat up to 50 m in some sectors. Further analysis was conducted via the use of two Machine Learning (ML) algorithms, namely Random Forest (RF) and eXtreme Gradient Boosting (XGB). The Mean Decrease in Impurity (MDI) methodology was employed to assess the significance of each factor. Both algorithms yielded congruent results, emphasising that cliff top erosion rates are primarily influenced by slope height. Finally, a validation of the ML algorithm results was conducted using 2D Limit Equilibrium Method (LEM) codes. Ten sections extracted from the sector experiencing the most substantial cliff top retreat, as identified by DSAS, were utilised for 2D LEM analysis. Factor of Safety (FS) values were identified and compared with the cliff height of each section. The results from the 2D LEM analyses corroborated the outputs of the ML algorithms, showing a strong correlation between the slope instability and slope height (R² of 0.84), with FS decreasing with slope height. Full article

The Vanj River Basin contains a dynamic glacier, the Medvezhiy glacier, which occasionally poses a danger to local residents due to its surging, flooding, and frequent blockages of the Abdukahor River, leading to intense glacial lake outburst floods (GLOF). This study offers a new perspective on the quantitative assessment of glacier surface velocities and associated lake changes during six surges from 1968 to 2023 by using time-series imagery (Corona, Hexagon, Landsat), SRTM elevation maps, ITS_LIVE, unmanned aerial vehicles, local climate, and glacier surface elevation changes. Six turbulent periods (1968, 1973, 1977, 1989–1990, 2001, and 2011) were investigated, each lasting three years within a 10–11-year cycle. During inactive phases, a reduction in the thickness of the glacier tongue in the ablation zone occurred. During a surge in 2011, the flow accelerated, creating an ice dam and conditions for GLOF. Using these datasets, we reconstructed the process of the Medvezhiy glacier surge with high detail and identified a clear signal of uplift in the surface above the lower glacier tongue as well as a uniform increase in velocities associated with the onset of the surge. The increased activity of the Medvezhiy glacier and seasonal fluctuations in surface runoff are closely linked to climatic factors throughout the surge phase, and recent UAV observations indicate the absence of GLOFs in the glacier’s channel. Comprehending the processes of glacier movements and related changes at a regional level is crucial for implementing more proactive measures and identifying appropriate strategies for mitigation. Full article

The operational regime of Unmanned Aerial Vehicles (UAVs) is distinguished by the dominance of laminar flow and the flow field is characterized by the appearance of Laminar Separation Bubbles (LSBs). Ice accretion on the leading side of the airfoil leads to the formation of an Ice-induced Separation Bubble (ISB). These separation bubbles have a considerable influence on the pressure, heat flux, and shear stress distribution on the surface of airfoils and can affect the prediction of aerodynamic coefficients. Therefore, it is necessary to capture these separation bubbles in the numerical simulations. Previous studies have shown that these bubbles can be modeled successfully using the Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS) but are computationally costly. Also, for numerical modeling of ice accretion, the flow field needs to be recomputed at specific intervals, thus making LES and DNS unsuitable for ice accretion simulations. Thus, it is necessary to come up with a Reynolds-Averaged Navier–Stokes (RANS) equation-based model that can predict the LSBs and ISBs as accurately as possible. Numerical studies were performed to assess the fidelity of various RANS turbulence models in predicting LSBs and ISBs. The findings are compared with the experimental and LES data available in the literature. The structure of these bubbles is only studied from a pressure coefficient perspective, so an attempt is made in these studies to explain it using the skin friction coefficient distribution. The results indicate the importance of the use of transition-based models when dealing with low-Reynolds-number applications that involve LSB. ISB can be predicted by conventional RANS models but are subjected to high levels of uncertainty. Possible recommendations were made with respect to turbulence models when dealing with flows involving LSBs and ISBs, especially for ice accretion simulations. Full article