Wind Field Research Articles

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.

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

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

A Novel Joint Denoising Strategy for Coherent Doppler Wind Lidar Signals

Coherent Doppler Wind Lidar (CDWL) is an effective tool for measuring the atmospheric wind field. However, CDWL is affected by various noises, which can reduce the usable value of the received echo signal. This paper proposes a novel joint denoising algorithm based on SVD-ICEEMDAN-SCC-MF to remove noises in CDWL detection. The SVD-ICEEMDAN-SCC-MF consists of singular value decomposition (SVD), improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), Spearman correlation coefficient (SCC), and median filtering (MF). Specifically, the SVD first separates the signal from the noise by retaining the main feature (large singular value) and removing the remained components (small singular value) to achieve the initial signal reconstruction. Then, ICEEMDAN is used for decomposition to distinguish the intrinsic mode function (IMF) of the signal and the noise. The SCC of the retained components is calculated to determine the correlation of the reconstructed signal. Furthermore, low correlation components of the reconstructed signal are denoised again by median filtering (MF). Finally, the complete denoised signal is obtained by combining the components after MF and the high correlation components in the previous stage. The validity of the SVD-ICEEMDAN-SCC-MF is verified in simulated and real data, and the denoising effect is significantly better than other algorithms. In simulation cases, the SNRout of the proposed method is improved by 20.5117 dB at most, from −5 dB to 15.5117 dB, and the RMSE is only 0.5174. After denoising the power spectrum of the real CDWL signal, the detection range is extended from 3 km to more than 3.6 km.

Aerodynamic Optimization and Wind Field Characterization of a Quadrotor Fruit-Picking Drone Based on LBM-LES

Picking fruits from tall fruit trees manually is laborious and inefficient. Rotary-wing drones, a low-altitude carrier platform, can enhance the picking efficiency for tall fruit trees when combined with picking robotic arms. However, during the operation of rotary-wing drones, the wind field changes dramatically, and the center of gravity of the drone shifts at the moment of picking, leading to poor aerodynamic stability and making it difficult to achieve optimized attitude control. To address the aforementioned issues, this paper constructs a drone and wind field testing platform and employs the Lattice Boltzmann Method and Large Eddy Simulation (LBM-LES) algorithm to solve the high-dynamic, rapidly changing airflow field during the transient picking process of the drone. The aerodynamic structure of the drone is optimized by altering the rotor spacing and duct intake ratio of the harvesting drone. The simulation results indicate that the interaction of airflow between the drone’s rotors significantly affects the stability of the aerodynamic structure. When the rotor spacing is 2.8R and the duct ratio is 1.20, the lift coefficient is increased by 11% compared to the original structure. The test results from the drone and wind field experimental platform show that the rise time () of the drone is shortened by 0.3 s, the maximum peak time () is reduced by 0.35 s, and the adjustment time () is accelerated by 0.4 s. This paper, by studying the transient wind field of the harvesting drone, clarifies the randomness of the transient wind field and its complex vortex structures, optimizes the aerodynamic structure of the harvesting drone, and enhances its aerodynamic stability. The research findings can provide a reference for the aerodynamic optimization of other types of drones.

Safety assessment of explosion fragment projection in a wind field

Safety assessment of explosion fragment projection in a wind field

Time series model for predicting the disturbance of lychee canopy by wind field in unmanned aerial spraying system

Time series model for predicting the disturbance of lychee canopy by wind field in unmanned aerial spraying system

SAR Offshore Wind Fields in the Gulf of Lion

Abstract Offshore wind energy is rapidly growing due to high wind speeds and low visual impact at sea. In the North-Western Mediterranean, the Gulf of Lion is one area where floating offshore wind turbines will be installed. A detailed analysis of synthetic aperture radar (SAR) scenes revealed wind flow patterns induced by coastal orography, such as flow channeling and horizontal lee waves, often unresolved in model wind fields, thus quantifying the local winds of the Tramontane and the Mistral. High-accuracy wind speeds retrieved at a 10-m level from satellite-borne SAR offer significant insight for wind resource estimation. Copolarized SAR wind speed retrievals depend on wind direction input. Our motivation is to investigate the importance of reliable wind direction input in the SAR wind retrieval based on 1 year of wind direction data from the Global Forecast System (GFS), fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) (ERA5), and the New European Wind Atlas (NEWA). We validated these against buoy data to assess their accuracy. While ERA5 showed the most accurate wind direction, this did not translate into high-precision SAR wind speeds. The three derived SAR wind speeds showed correlation coefficients of R2 between 0.90 (0.81) and 0.94 (0.84) against the Lion (Begur) buoy datasets with the best agreement being with ERA5 and NEWA at the Lion and Begur buoys, respectively. These results suggest no preferred numerical model wind direction input for SAR retrievals.

Investigations of spatial-temporal distribution and regional transport in typical section of NO2 in eastern China using Mobile-DOAS.

Investigations of spatial-temporal distribution and regional transport in typical section of NO2 in eastern China using Mobile-DOAS.

Effect of contact resistivity difference on steady-state characteristics of multi-pole no-insulation HTS field coils in synchronous motors

Abstract Several superconducting motor designs with a power density exceeding 20 kW kg−1 have been proposed and are under development. However, maintaining the stable operation of superconducting coils in a rotating environment remains a partially unresolved challenge. The no-insulation (NI) high-temperature superconductor (HTS) winding technique, which deliberately removes insulation materials between turns, has emerged as a potential solution for superconducting motors. NI HTS coils have demonstrated current bypassing characteristics, making them stable under external field disturbances and robust against local critical current drops. However, contact resistivity, which largely governs the ‘NI behavior,’ has been reported to fall within a broad range in prior studies, making it challenging to consistently obtain specific values. As a result, understanding and managing contact resistivity has become a significant area of research. This is especially critical in motors where the field winding is subjected to periodic external harmonic fields, and maintaining pole-to-pole balance is crucial. In this study, we investigated the influence of contact resistivity on the performance of field windings through simulation as part of our efforts to assess the potential risks associated with NI HTS motors. Torque, voltage, and current were analyzed using the finite element method coupled with a lumped parameter circuit model, and the results were compared.

Field measurement and analysis of near-ground wind field characteristics and wind pressure on tracking photovoltaic panels

Field measurement and analysis of near-ground wind field characteristics and wind pressure on tracking photovoltaic panels

Proton Intensity Dropout in Isotropic Turbulence Regime during Gradual Solar Energetic Particle Events in Solar Cycle 23

Abstract When the Wind spacecraft started its only journey to Lagrange point L2 in 2003 October, it entered an isotropic turbulent environment with radially mean magnetic field ( B m) and slow solar wind speed (V SW). On October 26, it detected a particle intensity dropout interval following a gradual solar energetic particle event. In addition, it recorded three magnetic field rotations corresponding to one-sided increases in V SW, indicating the occurrence of magnetic switchback in the Alfvénic solar wind. Since the last switchback interval is just before the dropout interval, we studied the difference of the pitch angle distribution (PAD) of protons between them. In the switchback interval ( B m perpendicular to V sw), the PAD maximum is located at μ ∼ +1, where μ is the pitch angle cosine, while, in the dropout interval ( B m antiparallel to V sw), the maximum is at μ ∼ 0. The difference indicates that protons with greater gyroradii cannot be confined in the curved magnetic field lines and can be injected into the dropout region with μ ∼ 0. Therefore, the analysis of proton pitch angle scattering in the dropout interval can be greatly simplified due to the short duration of the interval and the sharp initial conditions. Compared with the analytical solution of the Fokker–Planck equation, we deduce that the parallel mean free path of 2.1 MeV protons is 3.7 ± 0.5 au, which means that the dropout of proton intensity in the isotropic turbulent regime is also caused by insufficient spatial diffusion of protons.