Wind Field Research Articles

Typhoon Early Warning and Monitoring Based on the Comprehensive Characteristics of Oceanic and Ionospheric Echoes from HFSWR: The Case of Typhoon Muifa

As devastating natural disasters, typhoons pose a tremendous threat to human society, making effective typhoon early warning and monitoring crucial. To address this challenge, High Frequency Surface Wave Radar (HFSWR), which can observe oceanic parameters such as typhoon wind fields in real time and even capture the dynamic changes in the ionosphere, has become an effective tool for typhoon monitoring. This paper investigates the interaction mechanisms about Typhoon-Acoustic Gravity Waves (AGWs)-Ionosphere, as well as Typhoon-Ocean Waves for HFSWR, and simulates these interaction processes within HFSWR. Then a typhoon early warning and monitoring scheme for HFSWR has been proposed: In the first stage, the S-shaped ionospheric disturbances observed by HFSWR are utilized as precursor signals for early typhoon warnings. In addition, the second stage involves analyzing changes in first-order oceanic echo spectral peak ratio to pinpoint when the typhoon eye enters the radar detection range, thus initiating the typhoon monitoring phase. Subsequently, the measured data from HFSWR collected during Typhoon “Muifa” (2212) in conjunction with the proposed scheme are evaluated in detail. The results indicate that AGWs generated by typhoons can propagate into non-typhoon areas within the detection range, causing S- shaped ionospheric disturbances and providing approximately 6 h of early warning. At around 8:05 (UTC+8), an increasing trend in the first-order spectral peak ratio was noted, indicating the entry of the typhoon eye into the detection range, which closely aligns with the official typhoon path and marks the transition to the monitoring phase. The proposed scheme is expected to enhance the capability for typhoon early warning and real-time monitoring in specific sea areas and mitigate the risks associated with typhoon-related disasters.

MR electrical properties mapping using vision transformers and canny edge detectors.

We developed a 3D vision transformer-based neural network to reconstruct electrical properties (EP) from magnetic resonance measurements. Our network uses the magnitude of the transmit magnetic field of a birdcage coil, the associated transceive phase, and a Canny edge mask that identifies the object boundaries as inputs to compute the EP maps. We trained our network on a dataset of 10 000 synthetic tissue-mimicking phantoms and fine-tuned it on a dataset of 11 000 realistic head models. We assessed performance in-distribution simulated data and out-of-distribution head models, with and without synthetic lesions. We further evaluated our network in experiments for an inhomogeneous phantom and a volunteer. The conductivity and permittivity maps had an average peak normalized absolute error (PNAE) of 1.3% and 1.7% for the synthetic phantoms, respectively. For the realistic heads, the average PNAE for the conductivity and permittivity was 1.8% and 2.7%, respectively. The location of synthetic lesions was accurately identified, with reconstructed conductivity and permittivity values within 15% and 25% of the ground-truth, respectively. The conductivity and permittivity for the phantom experiment yielded 2.7% and 2.1% average PNAEs with respect to probe-measured values, respectively. The in vivo EP reconstruction truthfully preserved the subject's anatomy with average values over the entire head similar to the expected literature values. We introduced a new learning-based approach for reconstructing EP from MR measurements obtained with a birdcage coil, marking an important step towards the development of clinically-usable in vivo EP reconstruction protocols.

Low-uncertainty wave tank testing and validation of numerical methods for floating offshore wind turbines

Abstract. Accurate simulation of the loads and motions of floating offshore wind turbines (FOWTs) in operation is key to the commercialisation of this technology. To improve such load predictions, a critical assessment of the capabilities and limitations of simulation methods for FOWTs is mandatory. However, uncertainties arise during the whole validation process of a numerical method. These can drastically impair the quality of the validation. In the case of FOWTs, the interaction between aerodynamic, hydrodynamic and mooring loads on the one hand and platform motions on the other hand causes a high level of uncertainty in the measurement data acquired in model tests. This also applies to comparing a numerical model to the test data, as these interactions make the distinction between cause and effect challenging. To address these challenges, several improvements to the validation process aiming to reduce the uncertainties are proposed and evaluated in this work. The major improvements are the measurement of the rotor thrust force excluding the tower top inertia loads, the wind field quality in the wave tank, a comparison of the rotor aerodynamics in the wind tunnel and wave tank, and the utilisation of hybrid simulations based on the measured platform motions. These steps are applied to wave tank tests of a FOWT utilising a single-point mooring and the subsequent validation of the numerical panel method panMARE. The improvements allowed for a considerable decrease in the random and systematic uncertainty in the model tests and made a valuable contribution to the distinction between cause and effect regarding the deviations between measurements and simulations.

Tropical Cyclone Wind Direction Retrieval Based on Wind Streaks and Rain Bands in SAR Images

Tropical cyclones (TCs) are associated with severe weather phenomena, making accurate wind field retrieval crucial for TC monitoring. SAR’s high-resolution imaging capability provides detailed information for TC observation, and wind speed calculations require wind direction as prior information. Therefore, utilizing SAR images to retrieve TC wind fields is of significant importance. This study introduces a novel approach for retrieving wind direction from SAR images of TCs through the classification of TC sub-images. The method utilizes a transfer learning-based Inception V3 model to identify wind streaks (WSs) and rain bands in SAR images under TC conditions. For sub-images containing WSs, the Mexican-hat wavelet transform is applied, while for sub-images containing rain bands, an edge detection technique is used to locate the center of the TC eye and subsequently the tangent to the spiral rain bands is employed to determine the wind direction associated with the rain bands. Wind direction retrieval from 10 SAR TC images showed an RMSD of 19.52° and a correlation coefficient of 0.96 when compared with ECMWF and HRD observation wind directions, demonstrating satisfactory consistency and providing highly accurate TC wind directions. These results confirm the method’s potential applications in TC wind direction retrieval.

Autonomous longitudinal separation control of aircraft in random wind fields based on MPC

Aiming at the problem of autonomous longitudinal separation under random disturbance of flight path, the random factor of high-altitude wind often leads to poor robustness of longitudinal separation between two aircraft. This paper proposes an autonomous longitudinal separation control method for aircraft based on model predictive control. Firstly, the nonlinear kinematic differential equation of the wind field difference between the two aircraft and the longitudinal separation is established, and the linear time-varying prediction model is derived. The longitudinal separation and path deviation distance of the two aircraft are selected as the optimization objectives, the high-altitude wind is the random disturbance, and the true airspeed and yaw angle of the leading aircraft are measurable. Terminal equality constraints are added to the air safety and aircraft performance constraints to maintain the stability of the system. To verify the effectiveness of the algorithm, within the prescribed simulation time of 120 seconds, this paper sets three groups of different expected intervals 12, 13, 14km. Through the designed MPC controller, by controlling the true airspeed and yaw angle of the trailing aircraft within the rolling time domain period, the interval curve between the two aircraft is relatively smooth and is always not less than the minimum safety interval of 10 km. It stabilizes at the expected target interval at the 74th, 90th and 118th seconds respectively, and begins to return to the route at the 58th, 74th and 95th seconds, and finally returns to the center line of the route; two groups of wind field control groups are set up, one group is the forecast wind intensity increased by 2 times, and the other group is the turbulent wind disturbance intensity increased by 8 times. Both can smoothly and stably establish the expected interval of 12 km at the 61st and 72nd seconds respectively.

Spatio-temporal averaging of jets obscures the reinforcement of baroclinicity by latent heating

Abstract. Latent heating modifies the jet stream by modifying the vertical geostrophic wind shear, thereby altering the potential for baroclinic development. Hence, correctly representing diabatic effects is important for modelling the mid-latitude atmospheric circulation and variability. However, the direct effects of diabatic heating remain poorly understood. For example, there is no consensus on the effect of latent heating on the cross-jet temperature contrast. We show that this disagreement is attributable to the choice of spatio-temporal averaging. Jet representations relying on averaged wind tend to have the strongest latent heating on the cold flank of the jet, thus weakening the cross-jet temperature contrast. In contrast, jet representations reflecting the two-dimensional instantaneous wind field have the strongest latent heating on the warm flank of the jet. Furthermore, we show that latent heating primarily occurs on the warm flank of poleward directed instantaneous jets, which is the case for all storm tracks and seasons.

A physical stochastic model of near-surface fluctuating wind fields

Modeling and simulation of atmospheric turbulence in coastal areas has emerged as a prominent area of research in recent years. This study presents a physical stochastic model to describe the horizontal coherence of fluctuating wind fields within the physical stochastic modeling frame. Based on the one-dimensional stochastic Fourier spectrum and isotropic turbulence theory, the horizontal coherence for fluctuating wind fields is expressed as a random function, with its probability information determined by the physical mechanism/background. The proposed physical stochastic model directly depicts the stochastic time series thereby enabling it to capture all probability information in detail comprehensively. The proposed model is numerically validated with the measured data obtained from an observation array constructed in Southeast China. This investigation holds significant potential for its application in wind-resistance design and reliability assessment of long-span structures.

Station-Keeping Control of Stratospheric Balloons Based on Simultaneous Optimistic Optimization in Dynamic Wind

Stratospheric balloons serve as cost-effective platforms for wireless communication. However, these platforms encounter challenges stemming from their underactuation in the horizontal plane. Consequently, controllers must continually identify favorable wind conditions to optimize station-keeping performance while managing energy consumption. This study presents a receding horizon controller based on wind and balloon models. Two neural networks, PredRNN and ResNet, are utilized for short-term wind field forecast. Additionally, an online receding horizon controller, based on simultaneous optimistic optimization (SOO), is developed for action sequence planning and adapted to accommodate various constraints, which is especially suitable due to its gradient-free nature, high efficiency, and effectiveness in black-box function optimization. A reward function is formulated to balance power consumption and station-keeping performance. Simulations conducted across diverse positions and dates demonstrate the superior performance of the proposed method compared with traditional greedy and A* algorithms.

A meso- to micro-scale coupled model under typhoon conditions considering vortex structure and coriolis effect for wind farms

Accurate simulations of typhoon-induced wind speeds on wind farms are crucial for the refined assessment of typhoon risks in wind turbines. Southeastern coastal area in China is rich in wind resources but is also severely threatened by typhoons. As an extremely destructive weather system with complex structures, the refined near-surface wind fields of typhoons are difficult to simulate by meso-scale models for risk assessment. Therefore, a coupled meso- and micro-scale model under typhoon conditions is proposed to simulate typhoon-induced winds on a wind farm. The Coriolis force is considered in the coupled model to reflect the rotating effect of typhoons caused by the vortex structure. A coupling algorithm based on objective analysis is proposed to construct boundary conditions at the coupling interface, to consider the variation of inflow variables in the vertical and cross-wind directions. Model constants of the two-equation turbulence model are calibrated based on typhoon field observations, to more reasonably simulate typhoons. A validation study is conducted, and the results show that the maximum wind speed on the wind farm simulated by the coupled model exhibits an increased accuracy compared with that of the meso-scale model, where the absolute error decreases by 34%.

Wind speed effect on infrared-image-based coal and gangue recognition with liquid intervention in LTCC

The commitment to achieving carbon neutrality by 2060 makes the clean transition of fossil energy production inevitable in China. As the primary fossil energy source, China produced approximately 4.66 billion tons of raw coal in 2023. Among them, in the fields of mining engineering and environmental science, the accurate recognition of gangue (one of the environmental pollutants produced by coal combustion is black-gray solid waste) from coal is crucial because it is a key step in reducing resource waste and environmental pollution. Hence, coal and gangue automatic recognition with high accuracy is very important for intelligent longwall top coal caving (LTCC) mining. A novel approach of “liquid intervention + infrared detection” is introduced to enhance the recognition accuracy when the exterior color of coal and gangue is similar. This study focuses on the infrared image recognition of coal and gangue after liquid intervention under varying wind speed conditions, and optimizes the image processing path. The effect of varying wind speed conditions on the recognition accuracy of coal and gangue after liquid intervention was analyzed, and the optimal wind speed range was determined. The morphological evolution characteristics of droplets under varying wind field conditions were studied by COMSOL two-phase flow interface. The results show that with the increase of wind speed, the surface contour of coal is gradually clear, and the overall recognition accuracy is also improved. When the high wind speed is 1.55 m/s, the recognition accuracy reaches 99.89%, and the recognition accuracy of other wind speed regions is also above 90% (within 7s). In addition, the numerical simulation can maintain the volume fraction of the droplet in a short time under the constant flow wind flow field, thus inhibiting the phenomenon that the recognition accuracy decreases with time. The simulation results are in good agreement with the physical experimental phenomena, which proves the reliability of the numerical simulation. This study is of great significance to the recognition of coal gangue types with low identification characteristics in LTCC, so as to ensure the primary economic benefits, ecological environment safety and sustainable development of coal mines.

Storm clouds over innovation: Typhoon shocks and corporate R&D activities

This study investigates the economic impact of typhoon disasters on corporate innovation activities, a critical driver of economic growth and resilience. Utilizing a comprehensive dataset of Chinese public companies from 2010 to 2022 and employing a wind field model to construct a city-level typhoon destruction index, we apply difference-in-differences and instrumental variable methods to analyze the effects on corporate innovation. Our findings reveal that typhoon disasters significantly reduce both patent filings and R&D investment intensity, particularly in coastal areas and technology-intensive industries. The adverse effects are transmitted through financial constraints, human capital loss, and infrastructure damage. This research contributes to understanding the microeconomic implications of extreme weather events on innovation-driven economic growth, offering insights for disaster resilience policies in vulnerable regions.

Evaluating Reduced-Order Urban Wind Models for Simulating Flight Dynamics of Advanced Aerial Mobility Aircraft

Advanced Aerial Mobility (AAM) platforms are poised to begin high-density operations in urban areas nationwide. This new category of aviation platforms spans a broad range of sizes, from small package delivery drones to passenger-carrying vehicles. Unlike traditional aircraft, AAM vehicles operate within the urban boundary layer, where large structures, such as buildings, interrupt the flow. This study examines the response of a package delivery drone, a general aviation aircraft, and a passenger-carrying urban air mobility aircraft through an urban wind field generated using Large Eddy Simulations (LES). Since it is burdensome to simulate flight dynamics in real-time using the full-order solution, reduced-order wind models are created. Comparing trajectories for each aircraft platform using full-order or reduced-order solutions reveals little difference; reduced-order wind representations appear sufficient to replicate trajectories as long as the spatiotemporal wind field is represented. However, examining control usage statistics and time histories creates a stark difference between the wind fields, especially for the lower wing-loading package delivery drone where control saturation was encountered. The control saturation occurrences were inconsistent across the full-order and reduced-order winds, advising caution when using reduced-order models for lightly wing-loaded aircraft. The results presented demonstrate the effectiveness of using a simulation environment to evaluate reduced-order models by directly comparing their trajectories and control activity metrics with the full-order model. This evaluation provides designers valuable insights for making informed decisions for disturbance rejection systems. Additionally, the results indicate that using Reynolds-averaged Navier–Stokes (RANS) solutions to represent urban wind fields is inappropriate. It was observed that the mean wind field trajectories fall outside the 95% confidence intervals, a finding consistent with the authors’ previous research.

A Time and Energy Efficient Multi-UAV Coverage Planning for the Search and Rescue Mission under Wind fields

This paper discusses how to quickly and efficiently search for victims when a disaster occurs. Since the search area is large, the search time of a single UAV will be too long to satisfy the golden window of the search and rescue mission. Therefore, it is necessary to dispatch multiple UAVs to conduct a cooperative search by proposing a multi-UAV area coverage planning under wind fields (MUCPW) algorithm and its variant MUCPW-A to achieve the shortest mission time. It first averagely divides the search area into several regions, formulates equations to calculate the minimum mission time and corresponding energy consumption among all possible area partition methods. As compared to related work, simulation results show that MUCPW and MUCPW-A achieve the shortest mission time and the least energy consumption to search all victims in the search and rescue mission. Further, MUCPW-A achieves the smallest standard deviation in energy consumption.

The –WIND RISK project: nowcast and simulation of thunderstorm outflows

–WIND RISK is a research project dealing with measurement and modelling of wind fields within thunderstorm cumulonimbus clouds and outflows at the ground (i.e., downbursts and gust fronts). The general objective of the project is to advance the knowledge about the processes occurring inside thunderstorm clouds responsible for strong outflows, and the identification of the environmental (synoptic and meso-scale) conditions favourable to their development. The area under investigation is Northwestern Italy and the Ligurian Sea, which are among the most exposed regions to strong thunderstorms in Europe. Two measurement campaigns using Doppler weather radars and remote-sensing ground-based anemometry, and ad-hoc simulations using the WRF model are carried out to build a database of thunderstorm wind fields with special focus on downdraft and downburst characterization. All measurements and simulations are shared within the scientific community after the end of the project through datasets published on open access platforms.

Synchronous measurement of wind pressure and flow field in wind tunnel: device, method and application

Accurate assessment of the relationship between flow field and surface wind pressure distribution around buildings in wind tunnel tests is crucial for understanding the interaction mechanism between wind and buildings. Despite notable technological advancements in dynamic pressure measurement and flow visualization, the acquirement of synchronous data on flow and wind pressure around buildings still faces challenges in terms of accuracy, cost, and operational complexity. Given this, a frame synchronization technique has been developed to collect flow field and wind pressure data based on particle image velocimetry and Scanivalve dynamic pressure measurement. Meanwhile, this study established a more precise correction method for the distortion effect of the synchronously measured wind pressure signal. Furthermore, the paper demonstrates the feasibility of synchronously measuring the flow field and the corrected wind pressure in time through the flow around triangular prism test. Experimental results indicate that the corrected wind pressure distribution on triangular prism surface is consistent with the temporal variations in instantaneous flow velocity and vorticity change, thereby verifying the reliability of the synchronous test system.

Realization of an Optimized Cylindrical Uniform Magnetic Field Coil via the Flexible Printed Circuit Technology

Abstract The design and fabrication method of magnetic field coils with high uniformity is essential for atomic magnetometers. In this paper, a novel design strategy for cylindrical uniform coils is first proposed, which combines the target-field method (TFM) with an optimized slime mold algorithm (SMA) to determine optimal structure parameters. Then, the realization method for the designed cylindrical coil by using the flexible printed circuit (FPC) technology is presented. Compared with traditional fabrication methods, this method has advantages in excellent flexibility and bending property, making the coils easier to be arranged in limited space. Moreover, the manufacturing process of the FPC technology via a specific cylindrical uniform magnetic field coil is discussed in detail, and the successfully realized coil is well tested in a verification system. By comparing the uniformity performance of the experimental coil with the simulation one, the effectiveness of the FPC technology in producing cylindrical coils has been well validated.

Field observation and cause investigation of low-frequency cable vibrations in a cable-stayed bridge

Considerable levels of stay cable vibration in cable-supported bridges pose significant challenges for safety, operation, and maintenance. An accurate understanding of the specific causes and types of vibration is essential for effectively addressing this problem with an optimal design for mitigation measures. This study aims to investigate the cause of an extraordinary low-frequency vibration that occurred at a stay cable by employing comprehensive methodologies. The modal properties and vibrational characteristics of cable vibrations were first obtained through finite element modeling, operational modal analysis, and vision-based displacement measurements. Subsequently, the primary cause of low-frequency vibrations was investigated using field observations and wind tunnel tests. Field-monitoring data revealed that stay cables were excited by small deck vibrations. The critical wind speeds at which deck vibrations occurred in a wind tunnel experiment were aligned with the ranges of wind speeds that produce low-frequency cable vibrations, which confirmed that these two phenomena are manifested under similar wind conditions. A calculated support excitation amplitude, determined via the use of an empirical equation, exhibited consistency with vision-based measured displacement obtained in field experiments. These findings underscore the potential risk of support excitation between minor deck vibrations and cables, which provides valuable insights that could mitigate cable vibrations.

Three-dimensional spatiotemporal wind field reconstruction based on LiDAR and multi-scale PINN

In this numerical study, we use a multi-scale version of physics-informed neural network (PINN) for wind field reconstruction from LiDAR measurements. The reference velocity field is obtained from high-fidelity large-eddy simulation of neutral atmospheric boundary layer, and the LiDAR measurement strategy is restricted to that of a real LiDAR. The multi-scale PINN reconstructs the velocity field by minimizing a combination of the L2-error with respect to the LiDAR measurements and residuals of the governing equations. Our most significant finding is that adding a multi-scale layer to PINN enables us to: (i) capture wider range of scales, (ii) reconstruct the flow field outside the LiDAR scanning area, and (iii) reach faster convergence. We also find that for volumetric flow reconstruction and to deal with uncertainty in the wind direction, the reconstruction accuracy can be greatly improved by employing multiple LiDAR devices. Overall, our results show that the normalized errors in the reconstructed wind field are lower than 5% for all the experiments conducted in this study and that the errors do not increase even in the presence of measurement noise levels of typical LiDAR. These findings show the feasibility of three-dimensional dynamic wind field reconstruction across large wind farms using LiDAR devices.

Optimization of DC Energy Storage in Tokamak Poloidal Coils

Tokamaks are a very promising option to exploit nuclear fusion as a programmable and safe energy source. A very critical issue for the practical use of tokamaks consists of the power flow required to initiate and sustain the fusion process, in particular in the poloidal field coils. This flow can be managed by introducing a DC energy storage based on supercapacitors. Because such storage may be the most expensive and largest part of the poloidal power supply system, an excessive size would cancel its potential advantages. This paper presents innovative strategies to optimize the DC storage in poloidal power supply systems. The proposed solution involves the sharing of the DC storage between different coil circuits. The study is supported by novel analytical formulas and by a circuital model developed for this application. The obtained results show that this method and the related algorithms can noticeably reduce the overall size of the storage and the power exchange with the grid, providing a practical contribution toward the feasibility and the effectiveness of nuclear fusion systems.

Numerical modeling of infrasounds radiation and propagation in hurricanes

Infrasound can be useful for monitoring natural or human-induced energetic events in the atmosphere. For high-energy sources, the infrasonic signals can propagate over large distances around the globe, given the low rate of atmospheric absorption. This study focuses on the numerical simulation of infrasound propagation in realistic large-scale three-dimensional (3D) environments. More precisely, we are interested in the simulation of infrasound, called microbaroms, which are radiated from extreme weather conditions such as hurricanes. The simulation of hurricane-induced microbaroms relies on three main components: i) a hurricane model describing the atmospheric wind and pressure fields, ii) a microbarom source model, and iii) an infrasound propagation model. 3D simulations have been performed on a rectangular mesh. Azimuthal deviation caused by the hurricane and the stratified atmosphere can be seen in 3D. Further studies are needed to improve each model, and the correlation between them. The ultimate goal is to be able to simulate radiated infrasound propagation for any meteorological conditions.