Wind Field Research Articles

Predicting Convectively Induced Turbulence With Regionally Convection-Permitting Simulations

Abstract Convectively induced turbulence (CIT) is a severe aviation hazard. It is challenging to forecast CIT because low-resolution models cannot explicitly resolve convective motions at kilometer scales. In this study, we used the Model for Prediction Across Scales (MPAS) to simulate CIT cases with convection-permitting resolution ( $$\sim $$ ∼ 1 km) in the region of the CIT events and coarse resolution in other parts of the globe. We developed a method to estimate the eddy dissipation rate (EDR) using the resolved wind field of the MPAS simulations. The method is based on explicit filtering and reconstruction in the turbulence modeling for large-eddy simulations (LES). It estimates turbulence kinetic energy (TKE), which is then used to derive EDR. The new method produces different turbulence distribution and intensity than previous methods based on second-order structure functions and convective gravity wave drag, with higher accuracy and better correlation with observations for CIT cases tested in this study. The 1-km resolution simulation generates more accurate EDR and improves spatial patterns, but it is computationally demanding. The 3-km resolution can get benefits from reasonable accuracy and affordable computational cost. Because convection-permitting resolutions are in the gray zone for simulating convection, we evaluated the sensitivity of the prediction to the variations in physical and numerical schemes. Varying cumulus convection parameterization and monotonicity of numerical schemes are identified as practical approaches to generate beneficial ensemble spread. However, the physical perturbation-based ensemble has limitations, and initial condition perturbations are still necessary to encompass uncertainties in the development of convection.

Voronoi-based distributed formation control method of stratospheric aerostat for area coverage

Abstract Station-keeping control is a critical technology for stratospheric aerostats. For those aerostats that utilise wind field environments to achieve trajectory control, the station-keeping capability of a single aerostat is inherently limited. This limitation can lead to instances of the aerostat flying outside the designated task area, thereby diminishing the effectiveness of station-keeping control. To ensure continuous monitoring of the restricted area for long endurance, dynamic adjustments and cooperative coverage among multiple aerostats are necessary. This paper introduces an optimal coverage algorithm based on Voronoi diagrams and presents a formation control method for stratospheric aerostats that employs the virtual force method and the ${A^{\rm{*}}}$ algorithm, respectively. In a real wind field environment, ten aerostats are deployed to optimally cover the restricted area. Simulation results indicate that the coverage rate of the stratospheric aerostats within the restricted area can exceed 70%, while the network connectivity rate among the aerostats can reach 80% following guidance control during return flights. Furthermore, the stratospheric aerostats that flying out of the restricted area can return through path planning and optimal coverage algorithm, and the networking connectivity rate between aerostats is higher than that using the virtual force method.

An analysis of the seeing conditions at the Platform C of the Lenghu site

Abstract This paper statistically analyzes seeing data at the Lenghu site Platform C from 2018 to 2024, during which extensive construction modified the original landscape. The study focuses on the impacts of meteorological factors and building obstructions. Results reveal a
progressive degradation in seeing as the monitoring setup passively changed: the median values were 0.76 arcsec (original location), 0.83 arcsec during the Terrace, and 0.99 arcsec at the new Dome (temporarily considered the permanent monitoring location). Once the instruments are fully deployed, wind speed and wind direction critically affect seeing quality, with optimal conditions occurring when wind speed is 2–6 m/s and wind direction is between 180° and 270°. However, in 2023 and 2024, the wind speeds decreased and the
prevailing wind direction shifted from southwest to northwest, correlating with poorer seeing. Computational Fluid Dynamics (CFD) simulations reveal the construction of the Wide Field Survey Telescope (WFST) altered the local wind field, increasing turbulence around the Dome, especially when the winds blow from 225° to 255°. In contrast, Platform A, located in a higher, more open area, consistently maintained better seeing, particularly after midnight, likely due to fewer obstructions and lower nocturnal heat release.

Analytical Methods for Wind-Driven Dynamic Behavior of Pear Leaves (Pyrus pyrifolia)

The fluttering of leaves under wind fields significantly impacts the efficiency and precision of agricultural spraying. However, existing spraying technologies often overlook the complex mechanisms of wind–leaf interactions. This study integrates the fine-tuned Segment Anything Model 2 with multi-dimensional dynamic behavior analysis to provide a systematic approach for investigating leaf fluttering under wind fields. First, a segmentation algorithm based on Principal Component Analysis was employed to eliminate background interference in leaf fluttering data. The results showed that the segmentation algorithm achieved an Intersection over Union (IoU) ranging from 98.2% to 98.7%, with Precision reaching 99.0% to 99.5%, demonstrating high segmentation accuracy and reliability. Building on this, experiments on leaf segmentation and tracking in dynamic scenarios were conducted using the SAM2-FT model. The results indicated that SAM2-FT effectively captured the dynamic behavior of leaves by integrating spatiotemporal information, achieving Precision and AP50/% values exceeding 97%. Its overall performance significantly outperformed mainstream YOLO-series models. In the analysis of dynamic response patterns, the Hilbert transform and time-series quantification methods were introduced to reveal the amplitude, frequency, and trajectory characteristics of a leaf fluttering under wind fields across three dimensions: area, inclination angle, and centroid. This comprehensive analysis highlights the dynamic response characteristics of leaves to wind field perturbations.

TLP-Supported NREL 5MW Floating Offshore Wind Turbine Tower Vibration Reduction Under Aligned and Misaligned Wind-Wave Excitations

This paper presents a numerical study on the structural vibrations of a TLP-supported NREL 5MW wind turbine equipped with a tuned vibration absorber (TVA) in the nacelle. The analysis was focused on tower bending deflections and was conducted using a reference OpenFAST V3.5.3 dedicated wind turbine modelling software and a finite element simulation framework based on Comsol Multiphysics V6.3 which was newly developed for this study. The obtained four-degree-of-freedom (4-DOF) tower bending model was transferred using modal decomposition to the MATLAB/Simulink R2020b environment, where a 2-DOF TLP surge/sway model and a bidirectional (2-DOF) TVA model were embedded. The wind field was approximated by a Weibull distribution of velocities (8.86 m/s mean, 4.63 m/s standard deviation). It was combined with the wave actions simulated using a Bretschneider spectrum with a significant height of 2.5 m and a peak period of 8.1 s. The TVA model used was either the standard NREL reference 20-ton passive TVA, a 10-ton passive, or a 10-ton controlled TVA (the latter two tuned to the tower’s first bending mode). The controlled TVA utilised a magnetorheological (MR) damper, either operating independently (forming a semi-active MR-TVA) or simultaneously with a force actuator, forming, in this case, a hybrid H-MR-TVA. Both aligned and 45°/90° misaligned wind–wave excitations were examined to investigate the performance of a 10-ton real-time controlled (H-)MR-TVA operating with less working space. In aligned conditions, the semi-active and hybrid MR-TVA solutions demonstrated superior tower vibration mitigation, reducing maximum tower deflections by 11.2% compared to the reference TVA and by 14.9% with regard to the structure without TVA. The reduction in root-mean-square deflection reached up to 4.2%/2.9%, respectively, for the critical along-the-waves direction, while the TVA stroke reduction reached 18.6%. For misaligned excitations, the tower deflection was reduced by 4.3%/4.8% concerning the reference 20-ton TVA, while the stroke was reduced by 22.2%/34.4% (for 45°/90° misalignment, respectively). It is concluded that the implementation of the 10-ton real-time controlled (H-)MR-TVA is a promising alternative to the reference 20-ton passive TVA regarding tower deflection minimisation and TVA stroke reduction for the critical along-the-waves direction. The current research results may be used to design a full-scale semi-active or hybrid TVA system serving a TLP-supported floating offshore wind turbine structure.

Phycocyanin-based multifunctional hydrogel with self-healing, hemostatic, antioxidative, and antibacterial activity for wound healing.

Phycocyanin-based multifunctional hydrogel with self-healing, hemostatic, antioxidative, and antibacterial activity for wound healing.

Design and test of HTS rotor magnets for high energy density motor

Abstract High-temperature superconducting (HTS) technology enables the realization of compact and efficient designs, with the potential to break through the bottleneck of high-power-density motors. In HTS motors, field windings are crucial components that significantly influence overall performance. The motor’s power capacity is generally proportional to the air gap magnetic field (Bg), which is further limited by the critical current of the HTS coils. In this paper, we designed and tested a single pole magnet for the HTS rotor that generated Bg of 3.39 T for an air gap of 3.5 mm and 3.04 T for an air gap of 15 mm at 30 K. These results are promising for high-energy-density motors, especially for HTS motors intended for aircraft. In addition, we established a 2D axisymmetric numerical model accommodating the Jc ( B , θ ) dependence of the critical current of HTS tapes to yield a more accurate estimation of the critical current of the racetrack coils. This methodology has been validated with various HTS tapes possessing different intrinsic flux pinning properties, demonstrating high accuracy and effective capability in simulating HTS coil performance. This work provided valuable guidance for the design and optimization of future high-power-density superconducting motors, while also validating a universal numerical simulation method.

Improved deep learning method and high-resolution reanalysis model-based intelligent marine navigation

Large-scale weather forecasting is critical for ensuring maritime safety and optimizing transoceanic voyages. However, sparse meteorological data, incomplete forecasts, and unreliable communication hinder accurate, high-resolution wind system predictions. This study addresses these challenges to enhance dynamic voyage planning and intelligent ship navigation. We propose IPCA-MHA-DSRU-Net, a novel deep learning model integrating incremental principal component analysis (IPCA) with a spatial-temporal depthwise separable U-Net. Key components include: (1) IPCA preprocessing to reduce dimensionality and noise in 2D wind field data; (2) depthwise-separable convolution (DSC) blocks to minimize parameters and computational costs; (3) multi-head attention (MHA) and residual mechanisms to improve spatial-temporal feature extraction and prediction accuracy. The framework is optimized for real-time onboard deployment under communication constraints. The model achieves high accuracy in high-resolution wind predictions, validated through reanalysis datasets. Experiments demonstrated enhanced path planning efficiency and robustness in dynamic oceanic conditions. The IPCA-MHA-DSRU-Net balances computational efficiency and accuracy, making it viable for resource-limited ships. This novel IPCA application provides a promising alternative for preprocessing large-scale meteorological data.

Numerical Simulation of Urban Impacts on Tropical Cyclone Wind Field Structures Over the Yangtze River Delta Region

AbstractHow the complex urban surface heterogeneity influences wind field structures induced by tropical cyclones remains poorly understood despite its importance for disaster mitigation. Here high‐resolution numerical simulations using the Weather Research and Forecasting model coupled with the multi‐layer urban canopy model (WRF/BEP, Building Effect Parameterization) are conducted to address this issue with the case of landfalling Typhoon Lekima (2019) over the Yangtze River Delta (YRD) urban agglomeration. Results show that the WRF/BEP reproduces the typhoon track and intensity reasonably well and the YRD urban agglomeration only slightly influences the typhoon track and intensity. The BEP significantly improves the overestimation and probability distribution of the 10 m wind speed attributed to the explicit representation of building surface drag effect. Urbanization enhances boundary layer vertical ascending and descending motions, aligning with cross‐sectional analyses along urban clusters. During the typhoon primarily influenced period, the attenuation rate of the daytime wind speed due to urbanization at the lowest level reaches 56.6%, which is about 15.5% larger than that during pre‐typhoon or post‐typhoon periods, leading to a more pronounced vertical gradient in near‐surface wind speeds. This is attributed to the almost vanished urban heat island (UHI) effect during the typhoon influenced period, which stabilizes the daytime boundary layer conditions. As a result, the vertical mixing is reduced and consequently the vertical downward transport of momentum to the surface is weakened. This study provides new insights into the importance of the urban land surface processes on regulating the wind structures under tropical cyclone backgrounds.

A Lightweight Terrain‐Constraint Model for Wind Spatial Downscaling

AbstractHigh‐resolution wind fields has always been the goal of refined meteorological forecasting. Using advanced deep learning algorithms for wind downscaling is an effective approach to achieve this goal. However, the lack of physical process understanding in deep learning algorithms results in the inability to accurately reconstruct fine‐scale structures after downscaling. In this study, we propose a Terrain‐Constraint Wind Downscaling Model (TCWDM), a lightweight deep learning model consisting of a downscaling module and a terrain‐constraint module. By combining low‐resolution wind with high‐resolution terrain data, the model achieves a tenfold downscaling of spatial wind fields and reconstructs the detailed structure of the wind. Due to the incorporation of an attention mechanism, multi‐feature inputs, and the terrain‐constraint module, TCWDM demonstrates superior downscaling performance. Compared to traditional interpolation methods and other deep learning models, the mean absolute error is reduced by up to 49%. The terrain‐constraint module, in particular, contributes most significantly to the model's performance, especially in complex terrains, where it enables greater optimization of downscaling results. Furthermore, due to the lightweight model structure and a specific fine‐tuning strategy, TCWDM can deliver significantly better downscaling results at a lower cost across different regions, offering potential for broader applications.

On the influence of cross-sectional deformations on the aerodynamic performance of wind turbine rotor blades

Abstract. The aerodynamic performance of a wind turbine rotor blade depends on the geometry of the airfoils used. The airfoil shape can be affected by elastic deformations of the blade during operation due to structural loads. This paper provides an initial estimation of the extent to which cross-sectional deformations influence the aerodynamic loads on the rotor. The IEA 15 MW reference wind turbine model is used for this study. A constant wind field at rated wind speed is applied as an operational load test case. The resulting loads are calculated by an aero-servo-elastic simulation of the turbine. The loads are applied to a three-dimensional (3D) finite shell element model of the rotor blade, which serves to calculate the cross-sectional deformations. For the individual cross-sections in the deformed configuration, the new lift and drag coefficients are calculated. These are then included in the aero-servo-elastic simulation, and the obtained results are compared with those of the initial simulation that is based on the undeformed cross-sections. The cross-sectional deformations consist of a change in the chord length and the geometry of the trailing edge panels and depend on the azimuth position of the blade. The change in the airfoil geometries results in altered aerodynamic characteristics and therefore a deviation of the blade root bending moments, the maximum change of which is −1.4 % in the in-plane direction and +0.71 % in the out-of-plane direction. These results show that cross-sectional deformations have a minor influence on the internal loads of rotor blades in normal operation.

Numerical analysis of dry heat transfer in textiles and clothing microclimate

The computational fluid dynamics approach has been widely applied to explore the comfort level of human clothing. In many studies, the human limbs and trunk are treated as a uniform cylinder, yet there is a failure to consider the uniformity of airflow velocity and temperature on the surface of clothing. This poses challenges for the testing of cylindrical fabric thermal and moisture resistance to assess clothing comfort. In this study, a 2D heat transfer model for porous textiles under different airflow and temperature conditions was established. Then, a comparative analysis was conducted of three air supply methods. The results show that the wind speed difference rates generated on the surface of the clothing by these three air supply methods were 83.3%, 65.1%, and 1.9%, respectively. To ensure that the wind speed on the surface of the clothing is as uniform as possible, method 3 was adopted to establish the wind field. In this study, the combined influences of thermal conduction, natural convection, and forced convection were incorporated to investigate the distribution of temperature and wind fields within the model, as well as the changes in clothing performance under various environmental conditions. Additionally, the microclimate between the skin and clothing directly affects human comfort. Therefore, in this study, heat transfer phenomena within the microclimate were also explored under different environmental conditions. The model in this paper is intended to provide an effective and accurate method for cylindrical fabric thermal and moisture resistance testing, to study the comfort of clothing.

Development of mean wind retrieval methodology for the Generalized Velocity Track Display

Since 1990s, the Ground-Based Velocity Track Display (GBVTD) – family of techniques, including the Generalized Velocity Track Display (GVTD), have been used to retrieve the kinematic structure of Tropical cyclones (TCs) using data from a single Doppler radar. This study builds on the GVTD formulation and proposes a single Doppler TC inner core mean wind retrieval technique, called GVTD-Mean Wind (GVTD-MW), that can improve the retrieved TC inner core structures and the understanding of TC intensity change. Results from analytical wind field experiments demonstrate that GVTD-MW can estimate mean wind speed and direction, with errors below 10% and 6°, respectively. GVTD-MW estimated mean winds are not sensitive to magnitude and direction of the along-beam mean wind component, vortex size, and uncertainty of the TC center. GVTD-MW was applied to Typhoon Nock-Ten (2004) and Typhoon Koinu (2023). For Nock-Ten, the significant cross-beam mean wind component enabled GVTD-MW to reduce the retrieval errors compared to GVTD. Consequently, the root mean square error (RMSE) for the entire volume decreased from 8.8 m s−1 (GVTD) to 7.6 m s−1 (GVTD-MW) when compared with radar observations. For Koinu, where the cross-beam mean winds were much smaller than those in Nock-Ten, GVTD-MW also improved the accuracy of storm structure retrieval. These findings demonstrate the characteristics and impacts of GVTD-MW in improving the TC structures from single-Doppler radar observations.

Machine Learning Techniques for Predicting Typhoon‐Induced Storm Surge Using a Hybrid Wind Field

Machine Learning Techniques for Predicting Typhoon‐Induced Storm Surge Using a Hybrid Wind Field

Chemically active wetting

Wetting of liquid droplets on passive surfaces is ubiquitous in our daily lives, and the governing physical laws are well understood. When surfaces become active, however, the governing laws of wetting remain elusive. Here, we propose chemically active wetting as a class of active systems where the surface is active due to a binding process that is maintained away from equilibrium. We derive the corresponding nonequilibrium thermodynamic theory and show that active binding fundamentally changes the wetting behavior, leading to steady, nonequilibrium states with droplet shapes reminiscent of a pancake or a mushroom. The origin of such anomalous shapes can be explained by mapping to electrostatics, where pairs of binding sinks and sources correspond to electrostatic dipoles along the triple line. This is an example of a more general analogy, where localized chemical activity gives rise to a multipole field of the chemical potential. The underlying physics is relevant for cells, where droplet-forming proteins can bind to membranes accompanied by the turnover of biological fuels.

Simulation and Analysis of Coherent Wind Lidar Based on Range Resolution.

The wind field, a critical atmospheric parameter, significantly influences climate, weather forecasting, aviation safety, and wind energy applications. The precise observation of wind fields is essential for improving weather predictions, studying climate change, ensuring aviation safety, and optimizing wind energy systems. Among the various wind field detection methods, coherent wind lidar technology stands out due to its superior detection range, accuracy, and robustness. However, the high-range resolution required for applications such as aircraft takeoff and landing or wind turbine region monitoring presents unique challenges in wind detection. To address the aforementioned challenges, this study established a modular coherent Doppler wind lidar simulation system. Unlike traditional single-module simulation approaches, this system achieves multi-parameter coupling analysis of laser emission under pulse modulation, atmospheric transmission, and wind speed inversion through integrated hardware-transmission-processing collaborative modeling. Subsequently, by adjusting key parameters of the system model, an in-depth analysis of wind speed inversion within a 1.2 km detection range was conducted, investigating the dual impacts of reducing pulse duration on both range resolution and wind speed measurement accuracy. Furthermore, a Mach-Zehnder modulator module was implemented in the radar hardware section to generate odd-even pulse pairs, while a differential correlation algorithm was introduced in the data processing module to enhance range resolution. Ultimately, wind speed measurements with a 4.5 m range resolution along the laser emission direction were achieved in simulations. Comparative analysis shows that pulse modulation techniques effectively reduce wind speed measurement errors caused by short-pulse methods, offering a reliable framework for practical wind field measurements.

Physics‐Guided Deep Learning for Modeling Single‐Point Wave Spectra Using Wind Inputs of Two Resolutions

AbstractDirectional Wave Spectra (DWSs) are essential for various applications such as seafaring and ocean engineering. Traditionally, DWSs are modeled with numerical wave models, which, despite their solid physical basis, are often computationally expensive. For any given point in the ocean, once the recent prior wind fields that could affect this point are known, the current DWS at this location can be largely determined. This is because local wave energy is generated by either local winds or recent prior remote winds. This fundamental physical principle can be leveraged for the statistical modeling of single‐point DWSs. This study presents a deep learning approach to directly model single‐point DWS using wind inputs. We utilize wind fields with two different spatial resolutions: specifically, a high‐resolution, small‐area, short‐time local wind field for local wind‐sea spectra, and a low‐resolution, large‐area, long‐time wind field for remotely generated swell spectra. A tailored network architecture and loss function are developed to capture the relevant information from both wind scales. The performance of the proposed model was evaluated using both open ocean spectra from ERA5 and coastal spectra from numerical wave hindcasts. Results indicate that the deep learning model can effectively and efficiently simulate DWSs in both open oceans and coastal regions without relying on predefined spectral shapes, demonstrating its potential for applications in wave forecasting and wave climate studies.

Flight guidance concept for the launching and landing phase of a flying wing used in an airborne wind energy system

Abstract. Airborne wind energy (AWE) is an emerging technology that harvests energy by utilizing tethered airborne systems in wind fields. Given their favorable aerodynamic characteristics, employing flying wings as airborne systems holds considerable promise for system performance. Moreover, when designed as motorized tail sitters, they can provide vertical takeoff and landing capabilities. However, the processes of launching and landing present considerable challenges for these specialized flying wing airborne wind energy systems (AWESs). It is essential to consider the controllability at varying wind speeds and the limitations imposed by the tether. This work reviews existing industry approaches to launching and landing AWESs, highlighting their limitations, before introducing a novel guidance concept for flying wing AWESs. The proposed concept incorporates tethered multi-axial motion to address controllability constraints and enhance operational reliability. A comprehensive trim analysis examines the system's behavior during these phases under various wind conditions and guidance parameters, identifying operational limits. This novel guidance concept is integrated into the top level of a cascaded flight controller. The lower levels of this flight controller comprise a translational controller and a rotational controller. The performance of the overall controller is demonstrated through a simulation of a representative wind field. The results show that the control concept enables the desired launch and landing in simulations. It forms a base for future research covering the control of flying wing AWESs and the process of launching and landing within AWE.

Optimizing radiation monitoring networks to improve emergency response strategies during nuclear power plant accidents

This paper presents a new strategy to optimize radiation monitoring networks for effectively predicting contaminated areas and radiation levels during nuclear power plant accidents in order to improve emergency response efforts. Our strategy addresses variable metrological fields by generating ensemble simulations of wind fields and radionuclide migration in the atmosphere using the WSPEEDI (Worldwide version of System for Prediction of Environmental Emergency Dose Information) simulator. GPCAM (Gaussian Process for Continuous-time Acquisition of Measurements) is then used to capture the heterogeneity of radiation levels by sparse monitoring points, and to optimize their locations. We consider three different scenarios: (a) a single static spatial distribution of the radiation levels, (b) the temporal evolution of the distribution within a single release scenario for mobile sensor deployment, and (c) ensemble optimization with variable metrological conditions for assessing risks and emergency responses at a particular site a priori. The results are compared with the homogeneously-distributed network. Our results show that GPCAM is able to identify effective monitoring locations for each of these scenarios, except that a prevailing wind direction is required for the ensemble case. In addition, we compare the effect of different acquisition functions, kernel functions, and hyperparameters in GPCAM on the sensor locations.

Geospatially Enhanced Convolutional Neural Network for HY-2B Scatterometer-Based Marine Wind Field Retrieval

Geospatially Enhanced Convolutional Neural Network for HY-2B Scatterometer-Based Marine Wind Field Retrieval