Search Results (1,236)

Under the dual challenges of global energy crisis and climate change, the building sector, as a major carbon emitter consuming 33% of global primary energy, has seen its energy efficiency optimization become a critical pathway towards achieving carbon neutrality goals. The Window-to-Wall Ratio (WWR), serving as a core parameter in building envelope design, directly influences building energy consumption, with its optimized design playing a decisive role in balancing natural daylighting, ventilation efficiency, and thermal comfort. This study focuses on the traditional One-Seal dwellings (Yikeyin) in Kunming, China, establishing a dynamic wind field-thermal environment coupled analysis framework to investigate the impact mechanism of window dimensions (WWR and aspect ratio) on indoor thermal comfort under natural wind conditions in transitional climate zones. Utilizing the Grasshopper platform integrated with Ladybug, Honeybee, and Butterfly plugins, we developed parametric models incorporating Kunming’s Energy Plus Weather meteorological data. EnergyPlus and OpenFOAM were employed, respectively, for building heat-moisture balance calculations and Computational Fluid Dynamic (CFD) simulations, with particular emphasis on analyzing the effects of varying WWR (0.05–0.20) on temperature-humidity, air velocity, and ventilation efficiency during typical winter and summer weeks. Key findings include, (1) in summer, the baseline scenario with WWR = 0.1 achieves a dynamic thermal-humidity balance (20.89–24.27 °C, 65.35–74.22%) through a “air-permeable but non-ventilative” strategy, though wing rooms show humidity-heat accumulation risks; increasing WWR to 0.15–0.2 enhances ventilation efficiency (2–3 times higher air changes) but causes a 4.5% humidity surge; (2) winter conditions with WWR ≥ 0.15 reduce wing room temperatures to 17.32 °C, approaching cold thresholds, while WWR = 0.05 mitigates heat loss but exacerbates humidity accumulation; (3) a symmetrical layout structurally constrains central ventilation, maintaining main halls air changes below one Air Change per Hour (ACH). The study proposes an optimized WWR range of 0.1–0.15 combined with asymmetric window opening strategies, providing quantitative guidance for validating the scientific value of vernacular architectural wisdom in low-energy design. Full article

In the process of plant protection for fruit trees using rotary-wing UAVs, challenges such as droplet drift, insufficient canopy penetration, and low agrochemical utilization efficiency remain prominent. Among these, the uncertainty in the spatio-temporal distribution of downwash airflow is a key factor contributing to non-uniform droplet deposition and increased drift. To address this issue, we developed a wind field numerical simulation model based on an improved hierarchical leaf density model to clarify the spatio-temporal characteristics of downwash airflow, the scale of turbulence regions, and their effects on internal canopy airflow under varying flight altitudes and different rotor speeds. Field experiments were conducted in orchards to validate the accuracy of the model. Simulation results showed that the average error between the simulated and measured wind speeds inside the canopy was 8.4%, representing a 42.11% reduction compared to the non-hierarchical model and significantly improving the prediction accuracy. The coefficient of variation (CV) was 0.26 in the middle canopy layer and 0.29 in the lower layer, indicating a decreasing trend with an increasing canopy height. We systematically analyzed the variation in turbulence region scales under different flight conditions. This study provides theoretical support for optimizing UAV operation parameters to improve droplet deposition uniformity and enhance agrochemical utilization efficiency. Full article

This paper presents a preliminary investigation into the aeroelastic behavior of a tailless flying wing equipped with a rotating wingtip. Based on the configuration of Innovative Control Effectors (ICE) aircraft, an aeroelastic model of the tailless flying wing with a rotating wingtip has been developed. Both numerical simulation and wind tunnel tests (WTTs) are employed to study the aeroelastic characteristics of this unique design. The numerical simulation involves the coupling of computational fluid dynamics (CFD) and implicit dynamic approaches (IDAs). Using the CFD/IDA coupling method, aeroelastic response results are obtained under different flow dynamic pressures. The critical flutter dynamic pressure is identified by analyzing the trend of the damping coefficient, with a focus on its transition from negative to positive values. Additionally, the critical flutter velocity and flutter frequency are obtained from the WTT results. The critical flutter parameters, including dynamic pressure, velocity, and flutter frequency, are examined under different wingtip rotation frequencies and angles. These parameters are derived using both the CFD/IDA coupling method and WTT. The results indicate that the rotating wingtip plays a significant role in influencing the flutter behavior of aircraft with such a configuration. Research has shown that the rotation characteristics of the rotating wingtip are the primary factor affecting its aeroelastic behavior, and increasing both the rotation frequency and rotation angle can raise the flutter boundary and effectively suppress flutter onset. Full article

Medium and large-sized birds exhibit remarkable agility and maneuverability in flight, with their flapping motion encompassing degrees of freedom in flapping, twist, and swing, which enables them to adapt effectively to harsh ecological environments. This study proposes a flapping–twist coupled driving mechanism for large-scale flapping-wing aircraft by mimicking the motion patterns of birds. The mechanism generates simultaneous twist and flapping motions based on the phase difference of double cranks, allowing for the adjustment of twist amplitude through modifications in crank radius and phase difference. The objective of this work is to optimize the lift and thrust of the flapping wing to enhance its flight performance. To achieve this, we first derived the kinematic model of the mechanism and conducted motion simulations. To mitigate the effects of the flapping wing’s flexibility, a rigid flapping wing was designed and manufactured. Through wind tunnel experiments, the flapping wing system was tested. The results demonstrated that, compared to the non-twist condition, there exists an optimal twist amplitude that slightly increases the lift of the flapping wing while significantly enhancing the thrust. It is hoped that this study will provide guidance for the design of multi-degree-of-freedom flapping wing mechanisms. Full article

Although artificial intelligence methods such as reinforcement learning (RL) show potential in optimizing the design of compressors, there are still two major challenges remaining: limited design variables and insufficient model explainability. For the initial design of compressors, this paper proposes a technical approach that incorporates deep reinforcement learning and decision tree distillation to enhance both the optimization capability and explainability. First, a pre-selection platform for the initial design scheme of the compressors is constructed based on the Deep Deterministic Policy Gradient (DDPG) algorithm. The optimization space is significantly enlarged by expanding the co-design of 25 key variables (e.g., the inlet airflow angle, the reaction, the load coefficient, etc.). Then, the initial design of six-stage axial compressors is successfully completed, with the axial efficiency increasing to 84.65% at the design speed and the surge margin extending to 10.75%. The design scheme is closer to the actual needs of engineering. Secondly, Shapley Additive Explanations (SHAP) analysis is utilized to reveal the influence of the mechanism of the key design parameters on the performance of the compressors in order to enhance the model explainability. Finally, the decision tree inspired by the black-winged kite (BKA) algorithm takes the interpretable design rules and transforms the data-driven intelligent optimization into explicit engineering experience. Through experimental validation, this method significantly improves the transparency of the design process while maintaining the high performance of the DDPG algorithm. The extracted design rules not only have clear physical meanings but also can effectively guide the initial design of the compressors, providing a new idea with both optimization capability and explainability for its intelligent design. Full article

Flying wing UAVs are widely used in both civil and military areas and they are vulnerable to being affected by multi-source disturbances and actuator faults due to their unique aerodynamic configuration. This paper proposes composite continuous high-order nonsingular terminal sliding mode control controllers for the longitudinal command tracking control of flying wing UAVs. The proposed method guarantees not only the finite-time convergence of command tracking errors, but also the continuity of control actions. Simulation results validate the effectiveness of the proposed method. Full article

The tandem tilt-wing UAV features an advanced aerodynamic layout design and is regarded as a solution for small-scale urban air mobility. However, the tandem wing configuration exhibits complex aerodynamic interactions between the front and rear wings during cruise flight and the wing tilt transition process. The objective of this paper is to investigate the aerodynamic coupling characteristics between the front and rear wings of the tandem tilt-wing UAV under level flight and tilt transition conditions while also assessing the influence of the propellers on the aircraft’s aerodynamic performance. Through CFD numerical analysis, the aerodynamic characteristics of various aircraft components are examined at different angles of attack and wing tilt angles, and the underlying reasons for the observed differences and variations are explored. The results indicate that, during level flight, the aerodynamic interference between the wings is primarily dominated by the detrimental influence of the front wing on the rear wing. During the tilt transition process, mutual interactions between the front and rear wings occur as wing tilt angle changes, leading to more drastic variations in lift coefficients and increased control difficulty. However, the propeller’s effect contributes to smoother changes in lift and drag, thereby enhancing aircraft stability. Full article

This study investigates the effects of key geometric parameters on the hydrodynamic and cavitation characteristics of two-element hydrofoil systems for fully submerged unmanned hydrofoil craft, aiming to solve their active stabilization problems. Using STARCCM+ software, the RANS method, and the SST k-ω turbulence model, the research analyzes the impacts of flap deflection angle (α), main wing-to-flap chord ratio (c1/c2), and spacing (g). Results show that when the spacing is fixed, increasing the chord ratio reduces the lift and drag coefficients. When the chord ratio is fixed, increasing the spacing causes the lift and drag coefficients to first rise and then fall. With increasing flap deflection angle (α), cavitation intensifies, but it can be suppressed by increasing the chord ratio, reaching a minimum at g = 2.4%c1. The optimal configuration is c1/c2 = 1.5 and g = 2.4%c1, which can balance the lift–drag performance and anti-cavitation capability. This study provides a scientific basis for solving the active stabilization problems of fully submerged unmanned hydrofoil craft and insights for enhancing their seakeeping performance. 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

Aircraft wing icing significantly threatens aviation safety, causing substantial losses to the aviation industry each year. High transparency and blurred edges of icing areas in wing images pose challenges to wing icing detection by machine vision. To address these challenges, this study proposes a detection model, Wing Icing Detection DeeplabV3+ (WID-DeeplabV3+), for efficient and precise aircraft wing leading edge icing detection under natural lighting conditions. WID-DeeplabV3+ adopts the lightweight MobileNetV3 as its backbone network to enhance the extraction of edge features in icing areas. Ghost Convolution and Atrous Spatial Pyramid Pooling modules are incorporated to reduce model parameters and computational complexity. The model is optimized using the transfer learning method, where pre-trained weights are utilized to accelerate convergence and enhance performance. Experimental results show WID-DeepLabV3+ segments the icing edge at 1920 × 1080 within 0.03 s. The model achieves the accuracy of 97.15%, an IOU of 94.16%, a precision of 97%, and a recall of 96.96%, representing respective improvements of 1.83%, 3.55%, 1.79%, and 2.04% over DeeplabV3+. The number of parameters and computational complexity are reduced by 92% and 76%, respectively. With high accuracy, superior IOU, and fast inference speed, WID-DeeplabV3+ provides an effective solution for wing-icing detection. Full article

The spotted wing drosophila, Drosophila suzukii is an injurious polyphagous pest threatening worldwide soft fruit production. Its high adaptability to new colonized environments, short life cycle, and wide host range are supporting its rapid spread. The most common techniques to reduce its significant economic damage are based on multiple insecticides applications per season, even prior to the harvest, which reduces agroecosystem biodiversity and affects human and animal health. Environmental concerns and regulatory restrictions on insecticide use are driving the need for studies on alternative biological control strategies. This study aimed to assess the effect of T. drosphilae in controlling D. suzukii infestations and its interaction with P. vindemiae, a secondary parasitoid naturally present in Apulia (South Italy). Field experiments were carried out in organic cherry orchards in Gioia del Colle (Bari, Italy) to test the efficacy and adaptability of T. drosphilae following weekly releases of artificially reared individuals. Additionally, the interaction between P. vindemiae and T. drosphilae was studied under laboratory conditions. Results from field experiments showed that D. suzukii populations were significantly lower when both parasitoids were present. However, T. drosophilae was less prone to adaptation, so its presence and parasitism were limited to the post-release period. Laboratory experiments, instead, confirmed the high reduction of D. suzukii populations when both parasitoids are present. However, the co-existence of the two parasitoids resulted in a reduced parasitism rate and offspring production, notably for T. drosophilae. This competitive disadvantage may explain its poor establishment in field conditions. These findings suggest that the field release of the two natural enemies should be carried out with reference to their natural population abundance to not generate competition effects. Full article

Flexible inflatable film wings have many functional advantages that traditional fixed rigid wings do not possess, such as foldability, small size, light weight, convenient storage, transportation, and so on. More and more scholars and engineers are paying attention to flexible inflatable wings, which have gradually become a new hot research topic. However, flexible wings rely on inflation pressure to maintain the shape and rigidity of the skin film, and the inflation pressure has a significant influence on the strain deformation and wing bearing characteristics of flexible wing skin film. Here, based on the flexible mechanics theory and balance principle of flexible inflatable film, a theoretical model of structural deformation and internal inflation pressure was constructed, and finite element simulation analysis under different internal inflation pressure conditions was carried out as well. The results demonstrate that the biaxial deformation of flexible wing skin film is closely related to internal inflation pressure, local size, configuration, and film material properties. However, strain deformation along the wingspan direction is quite distinguishing, skin films work under the condition of biaxial plane deformation, and the strain deformation of the spanning direction is obviously higher than that of the chord direction, which all increases with internal inflation pressure. Therefore, it is necessary to pay more attention to bearing strain deformation characteristics to meet the bearing stiffness requirements, which could effectively provide a theoretical reference for the structural optimization design and inflation scheme setting of flexible inflatable wings. Full article

This paper presents FWStab, a specialized video stabilization dataset tailored for flapping-wing platforms. The dataset encompasses five typical flight scenarios, featuring 48 video clips with intense dynamic jitter. The corresponding Inertial Measurement Unit (IMU) sensor data are synchronously collected, which jointly provide reliable support for multimodal modeling. Based on this, to address the issue of poor image acquisition quality due to severe vibrations in aerial vehicles, this paper proposes a multi-modal signal fusion video stabilization framework. This framework effectively integrates image features and inertial sensor features to predict smooth and stable camera poses. During the video stabilization process, the true camera motion originally estimated based on sensors is warped to the smooth trajectory predicted by the network, thereby optimizing the inter-frame stability. This approach maintains the global rigidity of scene motion, avoids visual artifacts caused by traditional dense optical flow-based spatiotemporal warping, and rectifies rolling shutter-induced distortions. Furthermore, the network is trained in an unsupervised manner by leveraging a joint loss function that integrates camera pose smoothness and optical flow residuals. When coupled with a multi-stage training strategy, this framework demonstrates remarkable stabilization adaptability across a wide range of scenarios. The entire framework employs Long Short-Term Memory (LSTM) to model the temporal characteristics of camera trajectories, enabling high-precision prediction of smooth trajectories. Full article

In this study, we used a causal analysis approach to assess the impact of deforestation on bird abundance in Mexico. Based on records in the eBird and GBIF databases, ten species were selected in 807 grids on the mainland. Relative abundances by species were estimated using a fixed-effects panel data regression for the period 2016–2018. Deforestation was used as a quasi-natural experiment, classifying treatment and control groups according to the distribution of relative abundances by quintiles of gross deforestation rates during the period 2001–2018. The treatment group was defined as relative abundances of birds present in grids in the last deforestation quintile (≥4% to 12%); the control group included relative abundances of birds present in grids of the first four quintiles (<4%). Extended regression models were used to estimate the impacts of high deforestation rates on the relative abundance of birds, finding mixed causal effects: five showed statistically significant declines in abundance (Ruddy Ground Dove (Columbina talpacoti), Black Vulture (Coragyps atratus), Melodious Blackbird (Dives dives), Bewick’s Wren (Thryomanes bewickii), and Rufous-backed Thrush (Turdus rufopalliatus)), while one specie Yellow-winged Cacique (Cassiculus melanicterus) exhibited significant increases. These findings highlight the importance of causal effect studies in contributing to empirical evidence-based conservation decision-making. Full article

In recent years, effective trajectory planning has been developed to promote the extensive application of unmanned aerial vehicles (UAVs) in various domains. However, the actual operation of UAVs in complex environments presents significant challenges to their trajectory planning, particularly in maintaining task reliability and ensuring safety. To overcome these challenges, this review presents a comprehensive summary of various trajectory planning techniques currently applied to UAVs based on the emergence of intelligent algorithms, which enhance the adaptability and learning ability of UAVs and offer innovative solutions for their application in complex environments. Firstly, the characteristics of different UAV types, including fixed-wing, multi-rotor UAV, single-rotor UAV, and tilt-rotor UAV, are introduced. Secondly, the key constraints of trajectory planning in complex environments are summarized. Thirdly, the research trend from 2010 to 2024, together with the implementation, advantages, and existing problems of machine learning, evolutionary algorithms, and swarm intelligence, are compared. Based on these algorithms, the related applications of UAVs in complex environments, including transportation, inspection, and other tasks, are summarized. Ultimately, this review provides practical guidance for developing intelligent trajectory planning methods for UAVs to achieve the minimal amount of time spent on computation, efficient dynamic collision avoidance, and superior task completion ability. Full article