Search Results (496)

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

Composite pressure vessels have attracted significant attention in recent years owing to their lightweight characteristics and superior mechanical performance. However, analyzing composite layers remains challenging due to complex filament-winding (FW) pattern structures and the associated high computational costs. This study introduces a homogenization method to achieve cross-scale modeling of carbon fiber-reinforced plastic (CFRP) layers, accounting for both lay-up sequence and in-plane FW diamond-shaped form. The stacking sequence in an FW Type IV composite pressure vessel is numerically investigated through ply modeling and cross-scale homogenization. The composite tank structure, featuring a polyamide PA66 liner, is designed for a working pressure of 70 MPa and comprises 12 helical winding layers and 17 hoop winding layers. An FW cross-undulation representative volume element (RVE) is developed based on actual in-plane mesostructures, suggesting an equivalent laminate RVE effective elastic modulus. Furthermore, six different lay-up sequences are numerically compared using ply models and fully and partially homogenized models. The structural displacements in both radial and axial directions are validated across all modeling approaches. The partial homogenization method successfully captures the detailed fiber-direction stress distribution in the innermost two hoop or helical layers. By applying the Hashin tensile failure criterion, the burst pressure of the composite tank is evaluated, revealing 7.56% deviation between the partial homogenization model and the ply model. Fatigue life analysis of the Type IV composite pressure vessel is conducted using ABAQUS^® coupled with FE-SAFE, incorporating an S-N curve for polyamide PA66. The results indicate that the fatigue cycles of the liner exhibit only 0.28% variation across different stacking sequences, demonstrating that homogenization has a negligible impact on liner lifecycle predictions. The proposed cross-scale modeling framework offers an effective approach for multiscale simulation of FW composite pressure vessels, balancing computational efficiency with accuracy. Full article

In recent years, under the dual pressures of climate change and human activities, wetlands in Liaoning Province, China, are increasingly threatened, raising concerns about regional ecological security. To better understand these changes, we developed a vulnerability assessment framework integrating a 30 m wetland dataset (2000–2020) with multi-source environmental and socio-economic data. Using the XGBoost–SHAP model, we analyzed wetland spatiotemporal evolution, driving mechanisms, and ecological vulnerability. Results show the following: (1) ecosystem service functions exhibited significant spatiotemporal differentiation; carbon storage has generally increased, water conservation capacity has significantly improved in the northern region, while wind erosion control and soil retention functions have declined due to urban expansion and agricultural development; (2) driving factors had evolved dynamically, shifting from population density in the early period to increasing influences of precipitation, vegetation index, GDP, and wetland area in later years; (3) ecologically vulnerable areas demonstrated a pattern of fragmented patches coexisting with zonal distribution, forming a three-level spatial gradient of ecological vulnerability—high in the north, moderate in the central region, and low in the southeast. These findings demonstrate the cascading effects of natural and human drivers on wetland ecosystems, and provide a sound scientific basis for targeted conservation, ecological restoration, and adaptive management in Liaoning Province. Full article

This study presents the design, numerical analysis, and experimental validation of an innovative wind turbine blade incorporating an internal flow channel and tangential slots to harness the Coanda effect for enhanced aerodynamic performance. The primary objective is to improve thrust generation and lift while reducing drag, thereby increasing the efficiency of wind turbines and potential aerial propulsion systems. A three-dimensional blade model was developed in COMPAS-3D and fabricated using PET-G filament through 3D printing, enabling precise realization of the internal geometry. Computational fluid dynamics (CFD) simulations, conducted in ANSYS Fluent using a refined mesh and the k—ω SST turbulence model, revealed that the proposed blade design significantly improves pressure distribution and airflow attachment along the blade surface. Compared to a conventional blade under identical wind conditions (12 m/s), the innovative blade achieved a 12% increase in power coefficient, lift force of 33 N and drag force of 60 N, validating the efficacy of the Coanda-based flow control. Wind tunnel experiments confirmed the numerical predictions, with close agreement in thrust and lift measurements. The blade demonstrated consistent performance across varying wind velocities, highlighting its applicability in renewable energy systems and passive flow control for aerial platforms. The findings establish a practical, scalable approach to aerodynamic optimization using structural enhancements, contributing to the development of next-generation wind energy technologies and efficient propulsion systems. Full article

In our previous study, the transition behavior of a flared cone–swept fin configuration was investigated under an angle of attack (AoA) of 0°. To further explore the role of AoA in complex three-dimensional geometries with strong fin–body interactions, wind tunnel experiments were conducted at Ma = 9.3, Re = 1.36 × 10⁷/m, with AoA ranging from −6° to 6°. Global surface temperature distributions were obtained using temperature-sensitive paint (TSP), while localized heat flux and pressure fluctuations were captured using thin-film thermocouples and high-frequency pressure sensors. The results show that varying AoA shifts the location of high heat flux between the upper and lower surfaces of the flared cone and induces a switch from streamwise to separation vortices. The windward side exhibits stronger disturbance responses than the leeward side. The junction region between the flared cone and the near-horizontal surface is highly sensitive to AoA variations, consistently exhibiting pronounced second-mode instabilities. These findings provide experimental support for understanding transition mechanisms under the combined effects of shock/boundary layer interaction (SBLI), crossflow, and adverse pressure gradients, with implications for transition prediction and thermal protection system design. Full article

The increasing scale of wind turbines introduces significant aerodynamic challenges at ultra-high Reynolds numbers and under conditions of low Mach number compressibility. The stall behavior, flow separation, and boundary layer transition are all significantly changed by these characteristics. However, wind tunnel testing cannot concurrently satisfy Re-Ma similarity, and current design frameworks ignore their associated impacts, leading to a great deal of uncertainty in load prediction and power efficiency for next-generation turbines. To bridge this gap, we utilize high-fidelity CFD simulations combined with parametric scaling to develop a novel size-based decoupling technique. With Re and Ma independently controlled by changing chord length and freestream velocity, the FFA-W3-211 airfoil is used as the benchmark. Static stall prediction accuracy is confirmed by validations against the wind-tunnel experimental data of S809 and VR-7B airfoils. The results show that the influence of a high Reynolds number markedly postpones flow separation and enhances pressure distribution, delaying the onset of stall. In contrast, the effect of a high Mach number hastens flow separation and deteriorates pressure distribution due to shock-induced separation, leading to an earlier occurrence of stall. For angles of attack lower than 12°, the influence of the Reynolds number prevails, effectively counteracting the negative impacts of the Mach number. For angles of attack greater than 12°, the two effects combine to raise the risk of flow instability considerably. This study focuses on independently analyzing the effects of the Reynolds and Mach numbers on the stall behaviors of wind turbine airfoils. Full article

Tropical cyclones (TCs) rank among the most destructive natural hazards globally, with core damaging potential originating from regions of intense wind shear and steep wind speed gradients within the eyewall and spiral rainbands. Accurately characterizing these fine-scale structural features is therefore critical for understanding TC intensity evolution, wind hazard distribution, and disaster mitigation. Recently, the deep learning-based downscaling methods have shown significant advantages in efficiently obtaining high-resolution wind field distributions. However, existing methods are mainly used to downscale general wind fields, and research on downscaling extreme wind field events remains limited. There are two main difficulties in downscaling TC wind fields. The first one is that high-quality datasets for TC wind fields are scarce; the other is that general deep learning frameworks lack the ability to capture the dynamic characteristics of TCs. Consequently, this study proposes a novel deep learning framework, CycloneWind, for downscaling TC surface wind fields: (1) a high-quality dataset is constructed by integrating Cyclobs satellite observations with ERA5 reanalysis data, incorporating auxiliary variables like low cloud cover, surface pressure, and top-of-atmosphere incident solar radiation; (2) we propose CycloneWind, a dynamically constrained Transformer-based architecture incorporating three wind field dynamical operators, along with a wind dynamics-constrained loss function formulated to enforce consistency in wind divergence and vorticity; (3) an Adaptive Dynamics-Guided Block (ADGB) is designed to explicitly encode TC rotational dynamics using wind shear detection and wind vortex diffusion operators; (4) Filtering Transformer Layers (FTLs) with high-frequency filtering operators are used for modeling wind field small-scale details. Experimental results demonstrate that CycloneWind successfully achieves an 8-fold spatial resolution reconstruction in TC regions. Compared to the best-performing baseline model, CycloneWind reduces the Root Mean Square Error (RMSE) for the U and V wind components by 9.6% and 4.9%, respectively. More significantly, it achieves substantial improvements of 23.0%, 22.6%, and 20.5% in key dynamical metrics such as divergence difference, vorticity difference, and direction cosine dissimilarity. Full article

The variable camber morphing wing has the potential to achieve improved flight performance across different flight conditions by changing its geometry according to changing flight conditions. Evaluating the subtle aerodynamic benefits of variable camber technology necessitates wind tunnel testing under flight Reynolds number conditions. In high Reynolds number wind tunnels, the cryogenic environment readily damages model surface profiles through desublimation and frost, compromising test data accuracy. Consequently, cryogenic wind tunnels must enforce rigorous water vapor control standards. To address potential water vapor effects during cryogenic wind tunnel testing, high-resolution optical measurement techniques were employed to quantify the spatiotemporal evolution of desublimation frost thickness on a typical supercritical airfoil surface. Combined with numerical simulations, the mechanisms governing the frost layer’s influence on aerodynamic characteristics and flow field structures were systematically investigated. The results reveal that the influence of water vapor desublimation on the aerodynamic characteristics under diverse cryogenic working conditions has a commonality, and the difference in aerodynamic parameters shows an increasing tendency as the frost time increases; water vapor desublimation has an obvious influence on the flow structure of the airfoil and its pressure distribution on the surface, which increases flow instability and leads to the backward shift of the shock wave position; larger frost thickness gradients along the flow direction cause more drastic changes in pressure distribution and flow structure; and a larger rate of water vapor desublimation results from a lower temperature and a higher concentration of water vapor in the test environment, which causes frosting to have a more severe impact on the airfoil’s aerodynamic characteristics and flow structure. The findings establish a technical basis for cryogenic wind tunnel moisture control standards and provide a solid foundation for the refined assessment of aerodynamic benefits of the camber morphing wing. Full article

Excessive tailwind, threatening the safety of aircraft takeoff and landing, is one of the prominent research topics in the field of aviation meteorology. This paper analyzes the causes of tailwinds at Beijing Daxing International Airport (BDIA), based on coherent Doppler wind lidar (CDWL) and ERA5 reanalysis data. CDWL with high spatiotemporal resolution is utilized to detect variations in the low-level wind field in the vicinity of airport areas. ERA5 reanalysis data are employed to investigate the distribution characteristics of meteorological elements such as wind fields, pressure, and temperature in the Beijing surrounding regions. The study of two typical tailwind events reveals that frontal activity, through the combined effects of pressure gradient adjustment and topographic constraints from the Taihang Mountains, drives the development of low-level southerly jets. It serves as the key mechanism triggering excessive tailwind. By integrating CDWL and ERA5 data for local and regional analysis, this study contributes to enhancing understanding of tailwind causal mechanisms and provides critical support for aviation meteorological disaster early warning. Full article

Squall lines are some of the most common types of mesoscale cloud systems in tropical and subtropical regions. Thunderstorms associated with these systems are among the major causes of weather-related disasters and socio-economic losses in many regions across the world. This study investigates the capability of the Weather Research and Forecasting (WRF) model in simulating squall line features over the South African Highveld region. Two squall line cases were selected based on the availability of South African Weather Service (SAWS) weather radar data: 21 October 2017 (early austral summer) and 31 January–1 February 2018 (late austral summer). The European Centre for Medium-Range Weather Forecasts ERA5 datasets were used as observational proxies to analyze squall line features and compare them with WRF simulations. Mid-tropospheric perturbations were observed along westerly waves in both cases. These perturbations were coupled with surface troughs over central interior together with the high-pressure systems to the south and southeast of the country creating strong pressure gradients over the plateau, which also transports relative humidity onshore and extending to the Highveld region. The 2018 case also had a zonal structured ridging High, which was responsible for driving moisture from the southwest Indian Ocean towards the eastern parts of South Africa. Both ERA5 and WRF captured onshore near surface (800 hPa) winds and high-moisture contents over the eastern parts of the Highveld. A well-defined dryline was observed and well simulated for the 2017 event, while both ERA5 and WRF did not show any dryline for the 2018 case that was triggered by orography. While WRF successfully reproduced the synoptic-scale processes of these extreme weather events, the simulated rainfall over the area of interest exhibited a broader spatial distribution, with large-scale precipitation overestimated and convective rainfall underestimated. Our study shows that models are able to capture these systems but with some shortcomings, highlighting the need for further improvement in forecasts. Full article

This study proposes a Compressive Sensing Convolutional Long Short-Term Memory (CSCL) model that aims to improve short-term (12–24 h) forecast accuracy compared to standard ConvLSTM. It is especially useful when subtle spatiotemporal variations complicate feature extraction. CSCL uses uniform sampling to partially mask spatiotemporal wave fields. The model training strategy integrates both complete and masked samples from pre- and post-sampling. This design encourages the network to learn and amplify subtle distributional differences. Consequently, small variations in convolutional responses become more informative for feature extraction. We considered the theoretical explanations for why this sampling-augmented training enhances sensitivity to minor signals and validated the approach experimentally. For the region 120–140° E and 20–40° N, a four-layer CSCL model using the first five moments as inputs achieved the best prediction performance. Compared to ConvLSTM, the R² for significant wave height improved by 2.2–43.8% and for mean wave period by 3.7–22.3%. A wave-energy case study confirmed the model’s practicality. CSCL may be extended to the prediction of extreme events (e.g., typhoons, tsunamis) and other oceanic variables such as wind, sea-surface pressure, and temperature. Full article

Understanding the dispersion of volcanic sulphur dioxide (SO₂) plumes is crucial for assessing their environmental and climatic impacts. This study integrates satellite-based and reanalysis datasets to simulate as well as visualise the dispersion patterns of volcanic SO₂ under diverse atmospheric conditions. By incorporating data from the MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2), CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations), and OMI (Ozone Monitoring Instrument) datasets, we are able to provide comprehensive insights into the vertical and horizontal trajectories of SO₂ plumes. The methodology involves modelling SO₂ dispersion across various atmospheric pressure surfaces, incorporating wind directions, wind speeds, and vertical column mass densities. This approach allows us to trace the evolution of SO₂ plumes from their source through varying meteorological conditions, capturing detailed vertical distributions and plume paths. Combining these datasets allows for a comprehensive analysis of both natural and human-induced factors affecting SO₂ dispersion. Visual and statistical interpretations in the paper reveal overall SO₂ concentrations, first injection dates, and dissipation patterns detected across altitudes of up to ±20 km in the stratosphere. This work highlights the significance of combining satellite-based and global atmospheric reanalysis datasets to validate and enhance the accuracy of plume dispersion models while having a general agreement that OMI daily data and MERRA-2 reanalysis hourly data are capable of accurately accounting for SO₂ plume dispersion patterns under varying meteorological conditions. Full article

The northern South China Sea has abundant frontal systems near coastal and island regions, which play crucial roles in regional ocean dynamics and ecosystem. While previous studies have established preliminary understanding of their spatial distribution, seasonal variability, and dynamic characteristics, the atmospheric response to these frontal systems remains poorly understood. This study integrates observations from a moored buoy deployed on the continental shelf of the South China Sea with satellite remote sensing data to analyze oceanic and atmospheric variations during frontal passage. The results reveal that the ocean front can not only induce pronounced oceanic changes characterized by significant cooling, saltiness, and surface current acceleration, but also exert substantial influence on the overlying atmosphere, with consistent decreasing trends in air temperature, humidity, and atmospheric pressure, all of which rapidly recovered following frontal retreat. Notably, when the front directly traversed the buoy location, diurnal temperature cycles were markedly suppressed, while turbulent heat flux and downfront wind-stress curl reached peak magnitudes. These findings demonstrate that ocean fronts and associated sea surface temperature gradients can trigger intense air–sea exchange processes at the ocean–atmosphere interface. Full article

Suction caisson anchor foundations have been widely applied in oil and gas platforms but remain in the exploratory stage for floating offshore wind power applications, where research on their anchor load-bearing characteristics is insufficient. This study focuses on the influence of length-to-diameter ratio, loading angle, and loading point depth on the anchor load-bearing characteristics of suction caisson anchor foundations. Through numerical simulation, the load–displacement curves, internal force distribution along the caisson body, movement mode transitions, and soil failure characteristics were obtained. The results indicate that loading point depth and loading angle alter the movement mode of the suction caisson anchor foundation, directly affecting its bearing capacity. Smaller loading angles result in higher bearing capacity, which initially increases with loading point depth, peaks at 0.6 L, and then decreases at 0.8 L due to a transition in the foundation’s movement mode. Similarly, as the length-to-diameter ratio decreases, the bearing capacity and overall movement amplitude of the foundation decrease, leading to a shift in the optimal loading point position. The circumferential soil pressure and horizontal soil resistance distributions vary significantly with loading angle and depth. The findings of this study provide valuable reference for the design and application of suction caisson anchor foundations. Full article

This study conducts a detailed viscoelastic simulation of the side-by-side PA6/PA66 bicomponent melt spinning process to investigate the mechanisms behind reduced fiber elasticity. A two-dimensional (2D) axisymmetric finite element model was developed using ANSYS Polyflow, incorporating the Phan–Thien–Tanner (PTT) constitutive equation and a non-isothermal crystallization model. Simulation outcomes were validated with experimental and published data, showing close agreement in fiber radius, velocity, and temperature profiles (within 8% deviation). Results indicate that the dominance of the higher-viscosity PA66 phase induces uneven stress distributions and localized crystallization, leading to decreased elastic recovery. Higher winding speeds amplify this effect. This work offers a predictive framework for optimizing industrial melt spinning conditions to improve elasticity in bicomponent fibers. Key results indicate that the dominance of the PA66 component—due to its higher melt viscosity—leads to uneven stress distribution, elevated tensile stress, and localized crystallinity peaks along the spin line. These factors collectively contribute to reduced elastic recovery in the fiber. Moreover, increased winding speeds amplify axial stress and crystallinity disparities, further exacerbating the stiffness of the final product. In contrast, better elasticity was associated with lower pressure drop, balanced crystallinity, and minimized axial velocity differences between the two polymer phases. The findings offer valuable insights into optimizing industrial melt spinning processes to enhance fiber elasticity. This research not only improves fundamental understanding of viscoelastic flow behavior in bicomponent spinning but also provides a predictive framework for tailoring mechanical properties of fibers through process and material parameter adjustments. Full article