Wind speed inference from environmental flow–structure interactions. Part 2. Leveraging unsteady kinematics

This work explores the relationship between wind speed and time-dependent structural motion response as a means of leveraging the rich information visible in flow–structure interactions for anemometry. We build on recent work by Cardona, Bouman and Dabiri (Flow, vol. 1, 2021, E4), which presented an approach using mean structural bending. Here, we present the amplitude of the dynamic structural sway as an alternative signal that can be used when mean bending is small or inconvenient to measure. A force balance relating the instantaneous loading and instantaneous deflection yields a relationship between the incident wind speed and the amplitude of structural sway. This physical model is applied to two field datasets comprising 13 trees of 4 different species exposed to ambient wind conditions. Model generalization to the diverse test structures is achieved through normalization with respect to a reference condition. The model agrees well with experimental measurements of the local wind speed, suggesting that tree sway amplitude can be used as an indirect measurement of mean wind speed, and is applicable to a broad variety of diverse trees.

Evaluation Schoolyard Wind Comfort Changes Due to Rapid Developments: Case Study of Nanjing, China

(1) Background: Evaluation of wind environments regarding pedestrian comfort may unveil potential hotspot areas, particularly in the context of the rapid urban development in China since the 1990s. (2) Method: With primary schools in Nanjing as case studies, the authors simulated the wind environment of schoolyards with the computational fluid dynamics (CFD) approach and evaluated relevant wind comfort criteria. (3) Results: The study showed that the comfortable wind environment of schoolyards generally expanded in three primary schools in summer and winter, and wind speed and the comfortable wind level decreased in some outdoor schoolyard spaces. The results also indicate that the mean wind speed of the schoolyards did not linearly correlate to the building density either within or outside the schools. An increase in the building height of the primary schools could improve the wind comfort of the schoolyard, but the increased building height in the vicinity may worsen the wind comfort in the schools. Meanwhile, a lift-up or step-shaped building design for schools can improve wind comfort in schoolyards. (4) Conclusions: This study provided simulated results and an approach for urban designers to evaluate and improve the wind environment for school children’s outdoor activities.

Comparative Study of the Near-Surface Typhoon Wind Profile Fitting between Offshore and Onshore Areas

The study of typhoon wind profiles, especially offshore typhoon wind profiles, has been constrained by the scarcity of observational data. In this study, the Doppler wind lidar was used to observe the offshore wind profiles during Super Typhoon Mangkhut and onshore wind profiles during Super Typhoon Lekima. Four wind profile models, including the power law, logarithmic law, Deaves–Harris (D-H), and Gryning, were selected in the height range of 0–300 m to fit the wind profile. The variations in the power exponent with the mean wind speed and roughness length were also analyzed. The results showed that the wind profiles fitted by the four models were generally in good agreement with the observed wind profiles with correlation coefficients greater than 0.98 and root mean square deviations less than 0.5 m s−1. For the offshore case, the fitting degree of all wind profile models improved with increasing mean wind speed. Specifically, the D-H model had the highest fitting degree when the horizontal mean wind speed at 40 m was in the range of 8–25 m s−1, while the log-law model had the highest fitting degree when the wind speed exceeded 30 m s−1. For the onshore case, the fitting degree of the four wind profile models deteriorated with increasing mean wind speed, and the log-law model had the highest fitting degree in all wind speed intervals from 8 to 30 m s−1. For both offshore and onshore cases, the power exponent was less affected by mean wind speed and increased with increasing roughness length, and the logarithmic empirical model proposed in this study could well characterize the relationship between the power exponent and roughness length.

Estimation Formula of Modal Frequency of High-Rise Buildings under Different Wind Speeds during Typhoons

On 18 October 2016, the wind-induced effects of a high-rise building with square section was measured by the monitoring system in Haikou of China during Typhoon Sarika. The wind characteristics atop the building and the time-history responses of the translational and rotational accelerations on different floors were measured by the monitoring system; the first three modal parameters were identified according to the measured acceleration. The results show that the combinations of the cross spectral density function, phase spectrum, and coherence function can clearly judge the phase of the measured floors in the frequency resonance area as well as its modal frequencies at the first three orders. The modal frequencies at the first three orders decrease linearly with the growth of mean wind speed within the range of 0~20m/s. The estimation formula of the modal frequencies of high-rise buildings considering the influences of different wind speeds is put forward, which is expected to fill the gap in the existing specification for the quantitative analysis of the influences of wind-loads on the fundamental frequencies of high-rise buildings.

Database of extreme waves generated during the passage of a cold front in Rio Grande do Sul coast, southern Brazil

This datapaper supports the use of a database generated from wavefield simulations with the WAVEWATCH III model in waters off the coast of Rio Grande do Sul in the South Atlantic Ocean. In the WAVEWATCH III simulations, three domains are generated as a part of a numerical experiment to set up the best configuration. This database includes all input and output files for the two best-fit simulations. Bathymetry and wind files at 10 m above the surface are available as input files. The period of simulation and non-stationary wind data input corresponds to March 22-28, 2016. The date was chosen because it is related to the passage of a cold front through the area of interest. The different parameterizations used and with which good results were obtained in the simulations with the model are also described. The WAVEWATCH III output files contain the spatial and temporal distribution of the wavefield in the area of interest, as well as the outputs for point locations consistent with the location of on-site records. For the two best-fit domains, the following variables were obtained: mean wind speed (m s-1), sea-air temperature difference (°C), wave height (m), mean wavelength (m), mean wave period (s), mean wave direction (degrees), mean directional propagation (degrees) and friction velocity (m s-1). All these variables are provided in NetCDF format and will serve as a reference for future wave modeling work in the region, and the results will be able to be compared with those obtained in the database.

Standardized Doppler lidar processing for operational use in a future network

<p>Fibre-optic based Doppler wind lidars (DL) are widely used for both meteorological research and in the wind energy sector. These compact systems are able to retrieve vertical profiles of kinematic quantities, such as mean wind, from the atmospheric boundary layer as well as from optically thin cloud layers in the free troposphere with high spatio-temporal resolution. It is therefore likely that especially short-term forecasting would benefit from assimilating these data. However, their potential is currently not yet employed operationally.</p> <p>As part of DWD's effort to evaluate ground-based remote sensing systems for their operational readiness, called "Pilotstation", we developed a software client (DL-client) that standardizes the processing of mean wind based on the Velocity Azimuth Display method. Results of a long-term assessment of DLs at the Meteorological Observatory Lindenberg, starting in 2012, show that the DL-client assures a high quality Level-2 product, which is compatible with the EUMETNET's E-PROFILE observation program. We verified the retrieved mean wind speed and direction with the help of independent reference data from a 482 MHz radar wind profiler and 6-hourly radiosonde ascents. Hence, the DL-client not only facilitates processing and archiving of the DL data, but also forms a basis for operational network deployment and data assimilation. Furthermore, through speeding up and standardizing the data processing, the individual users can concentrate on more advanced scientific data analyses.</p> <p>Finally, the software is freely accessible and will be continuously improved to account for different scanning strategies. Its modular build-up of processing steps offers the possibility to extend the list of products with additional retrievals, e.g. for turbulent kinetic energy and wind gusts, which are currently under development at Lindenberg.</p>

Aeroelastic and Aerodynamic Tests of Wind Turbine with Various Polygonal Towers

Traditionally, circular cross-section towers have been used as supporting systems of wind turbines, but weaknesses have become apparent with recent upsizing of wind turbines. Thus, polygonal cross-section towers have been proposed and used in Europe. In this study, the effects of polygonal cross-sections on the aeroelastic and aerodynamic characteristics of wind turbines were examined through a series of wind tunnel tests. Aeroelastic tests showed that a square cross-section tower showed instability vibrations, and polygonal cross-section towers showed limited vibrations for tower-only cases. However, for wind turbines with various polygonal cross-section towers, no instability vibrations were observed, and displacements increased proportionally to the square of mean wind speed. Furthermore, pressure measurements showed that local force coefficients changed largely depending on wind direction and azimuth angle. Local drag force coefficients decreased with increasing number of tower sides, approaching those of the tower-only case, and local lift force coefficients showed larger absolute values than those of the tower-only case. The maximum mean and fluctuating drag force and the maximum fluctuating lift coefficients at each height decreased with increasing number of tower sides.

Assessment of CMIP6 Model Performance for Wind Speed in China

As the major renewable energy, wind can greatly reduce carbon emissions. Following the “carbon neutral” strategy, wind power could help to achieve the realization of energy transformation and green development. Based on ERA5 reanalysis data and the multi-ensemble historical and scenario simulations of the Coupled Model Intercomparison Project Phase 6 (CMIP6), a variety of statistical analyses are used to evaluate the performance of CMIP6 simulating the wind speed in China. The conclusions are as follows: spatial patterns of the nine CMIP6 models are similar with ERA5, but BCC-CSM2-MR and MRI-ESM2-0 highly overestimate the wind speed in northwest China. CESM2-WACCM, NorESM2-MM, and HadGEM3-GC31-MM behave better than the other six CMIP6 models in four specific regions are chosen for detailed study. CESM2-WACCM, NorESM2-MM, and HadGEM3-GC31-MM tend to simulate a larger wind speed than ERA5 except the yearly averaged wind speed in region II and region IV. CESM2-WACCM and NorESM2-MM simulate a large monthly mean wind speed, but the value is relatively close with ERA5 in the summer. HadGEM3-GC31-MM overestimates wind speed in region I and region II from April to October, but gets closer with ERA during winter. CESM2-WACCM, NorESM2-MM, and HadGEM3-GC31-MM simulate an increasing trend in Tibetan Plateau and Xinjiang in the next 100 years, while NorESM2-MM projects rising wind speed in the eastern part of Inner Mongolia, and HadGEM3-GC31-MM simulates increasing wind speed in the northeast and central China. The future wind speed in three models is projected to decline in region I, and the value of HadGEM3-GC31-MM is much larger. In region II, wind speed simulated by three models is projected to decrease, but the wind speed from HadGEM3-GC31-MM in region III and modeled wind speed in region IV from NorESM2-MM would climb with the slope equal to 0.0001 and 0.0012, respectively. This study indicates that the CMIP6 models have certain limitations to perform realistic wind changes, but CMIP6 could provide available reference for the projection of wind in specific areas.

Measurements of natural airflow within a Stevenson screen

Climate science depends upon accurate measurements of air temperature and humidity, the majority of which are still derived from sensors exposed within passively-ventilated louvred Stevenson-type thermometer screens. It is well-documented that, under certain circumstances, air temperatures measured within such screens can differ significantly from ‘true’ air temperatures measured by other methods, such as aspirated sensors. Passively-ventilated screens depend upon wind motion to provide ventilation within the screen, and thus airflow over the sensors contained therein. Consequently, instances of anomalous temperatures occur most often during light winds when airflow through the screen is weakest, particularly when in combination with strong or low-angle incident solar radiation. Adequate ventilation is essential for reliable and consistent measurements of both air temperature and humidity, yet very few systematic comparisons to quantify relationships between external wind speed and airflow within a thermometer screen have been made. This paper addresses that gap by summarising the results of a three month field experiment in which airflow within a UK-standard Stevenson screen was measured using a sensitive sonic anemometer, and comparisons made using simultaneous wind speed and direction records from the same site. The average in-screen ventilation rate was found to be 0.2 m s−1, well below the 1 m s−1 minimum assumed in meteorological and design standard references, and only about 7 % of the scalar mean wind speed at 10 m. The implications of low in-screen ventilation on the uncertainty of air temperature and humidity measurements from Stevenson-type thermometer screens are discussed, particularly those due to the differing response times of dry- and wet-bulb temperature sensors, and ambiguity in the value of the psychrometric coefficient.

Forecasting Dengue Hotspots Associated With Variation in Meteorological Parameters Using Regression and Time Series Models

For forecasting the spread of dengue, monitoring climate change and its effects specific to the disease is necessary. Dengue is one of the most rapidly spreading vector-borne infectious diseases. This paper proposes a forecasting model for predicting dengue incidences considering climatic variability across nine cities of Maharashtra state of India over 10 years. The work involves the collection of five climatic factors such as mean minimum temperature, mean maximum temperature, relative humidity, rainfall, and mean wind speed for 10 years. Monthly incidences of dengue for the same locations are also collected. Different regression models such as random forest regression, decision trees regression, support vector regress, multiple linear regression, elastic net regression, and polynomial regression are used. Time-series forecasting models such as holt's forecasting, autoregressive, Moving average, ARIMA, SARIMA, and Facebook prophet are implemented and compared to forecast the dengue outbreak accurately. The research shows that humidity and mean maximum temperature are the major climate factors and exhibit strong positive and negative correlation, respectively, with dengue incidences for all locations of Maharashtra state. Mean minimum temperature and rainfall are moderately positively correlated with dengue incidences. Mean wind speed is a less significant factor and is weakly negatively correlated with dengue incidences. Root mean square error (RMSE), mean absolute error (MAE), and R square error (R2) evaluation metrics are used to compare the performance of the prediction model. Random Forest Regression is the best-fit regression model for five out of nine cities, while Support Vector Regression is for two cities. Facebook Prophet Model is the best fit time series forecasting model for six out of nine cities. Based on the prediction, Mumbai, Thane, Nashik, and Pune are the high-risk regions, especially in August, September, and October. The findings exhibit an effective early warning system that would predict the outbreak of other infectious diseases. It will help the relevant authorities to take accurate preventive measures.