Gust Magnitudes Research Articles

Aerodynamic response of a red-tailed hawk to discrete transverse gusts

A limiting factor in the design of smaller size uncrewed aerial vehicles is their inability to navigate through gust-laden environments. As a result, engineers have turned towards bio-inspired engineering approaches for gust mitigation techniques. In this study, the aerodynamics of a red-tailed hawk’s response to variable-magnitude discrete transverse gusts was investigated. The hawk was flown in an indoor flight arena instrumented by multiple high-speed cameras to quantify the 3D motion of the bird as it navigated through the gust. The hawk maintained its flapping motion across the gust in all runs; however, it encountered the gust at different points in the flapping pattern depending on the run and gust magnitude. The hawk responded with a downwards pitching motion of the wing, decreasing the wing pitch angle to between −20∘ and −5∘, and remained in this configuration until gust exit. The wing pitch data was then applied to a lower-order aerodynamic model that estimated lift coefficients across the wing. In gusts slower than the forward flight velocity (low gust ratio), the lift coefficient increases at a low-rate, to a maximum of around 2–2.5. In gusts faster than the forward flight velocity (high gust ratio), the lift coefficient initially increased rapidly, before increasing at a low-rate to a value around 4–5. In both regimes, the hawk’s observed height change due to gust interaction was similar (and small), despite larger estimated lift coefficients over the high gust regime. This suggests another mitigation factor apart from the wing response is present. One potential factor is the tail pitching response observed here, which prior work has shown serves to mitigate pitch disturbances from gusts.

Short‐Term Forecasting of Wind Gusts at Airports Across CONUS Using Machine Learning

AbstractShort‐term forecasting of wind gusts, particularly those of higher intensity, is of great societal importance but is challenging due to the presence of multiple gust generation mechanisms. Wind gust observations from eight high‐passenger‐volume airports across the continental United States (CONUS) are summarized and used to develop predictive models of wind gust occurrence and magnitude. These short‐term (same hour) forecast models are built using multiple logistic and linear regression, as well as artificial neural networks (ANNs) of varying complexity. A suite of 19 upper‐air predictors drawn from the ERA5 reanalysis and an autoregressive (AR) term are used. Stepwise procedures instruct predictor selection, and resampling is used to quantify model stability. All models are developed separately for the warm (April–September) and cold (October–March) seasons. Results show that ANNs of 3–5 hidden layers (HLs) generally exhibit higher hit rates than logistic regression models and also improve skill with respect to wind gust magnitudes. However, deeper networks with more HLs increase false alarm rates in occurrence models and mean absolute error in magnitude models due to model overfitting. For model skill, inclusion of the AR term is critical while the majority of the remaining skill derives from wind speeds and lapse rates. A predictive ceiling is also clearly demonstrated, particularly for the strong and damaging gust magnitudes, which appears to be partially due to ERA5 predictor characteristics and the presence of mixed wind climates.

Estimation of Atmospheric Gusts Using Integrated On-Board Systems of a Jet Transport Airplane—Flight Simulations

Currently, quite accurate measurements of atmospheric gusts are carried out by airport systems only in the vicinity of the runways. There is a still open issue of availability of information about real wind gusts at cruising altitudes and during approach at a considerable distance from the airfield. Standard on-board systems of a jet transport airplane provide some information which is desirable to have knowledge of how flight parameters reflect real gust parameters and their impact on the aircraft dynamics. The paper proposes an algorithm for headwind gust magnitude estimation in relation to aircraft response. The analysed estimation algorithms assume the use of data available from the existing on-board systems only without the employment of any extra sensors or ground and satellite systems. In this way, many problems caused by different structures, configurations, and ways of installation of additional sensors and structural changes are rejected. The algorithms use the classical method for estimation of wind parameters as well as a linear longitudinal model of aircraft dynamics, taking into account the influence of wind gusts. Data fusion was realised with the use of three filtration methods. Results were evaluated to select the most accurate method of the estimation. Test data were obtained from advanced flight simulation. The experimental scenario considered a flight of a passenger twin-engine jet airplane through a layer of programmed gusts. The results of the flight simulations allowed us to determine the accuracy of the proposed gust estimation algorithms in reference to the ideal wind-speed data analysis obtained directly from the simulation environment (with the accuracy of the simulation process). The use of the proposed gust estimation algorithms may provide more accurate signal for integrated on-board systems, especially for wind shear detection and sped-up response time of flight control systems, protecting aircrafts against the adverse impact of encountered wind shear or gusts, e.g., auto-thrust or auto-throttle systems. The dedicated algorithm presented in the paper may increase the safety level of take-off and approach phases in gusty conditions and also during significant changes in wind speed at cruising altitudes in the case of crossing the area of jet stream occurrence.

Do Machine Learning Approaches Offer Skill Improvement for Short-Term Forecasting of Wind Gust Occurrence and Magnitude?

Abstract Wind gusts, and in particular intense gusts, are societally relevant but extremely challenging to forecast. This study systematically assesses the skill enhancement that can be achieved using artificial neural networks (ANNs) for forecasting of wind gust occurrence and magnitude. Geophysical predictors from the ERA5 reanalysis are used in conjunction with an autoregressive term in regression and ANN models with different predictors, and varying model complexity. Models are derived and assessed for the warm (April–September) and cold (October–March) seasons for three high passenger volume airports in the United States. Model uncertainty is assessed by deriving models for 1000 different randomly selected training (70%) and testing (30%) subsets. Gust prediction fidelity in independent test samples is critically dependent on inclusion of an autoregressive term. Gust occurrence probabilities derived using five-layer ANNs exhibit consistently higher fidelity than those from regression models and shallower ANNs. Inclusion of the autoregressive term and increasing the number of hidden layers in ANNs from 1 to 5 also improve the model performance for gust magnitudes (lower RMSE, increased correlation, and model standard deviations that more closely approximate observed values). Deeper ANNs (e.g., 20 hidden layers) exhibit higher skill in forecasting strong (17–25.7 m s−1) and damaging (≥25.7 m s−1) wind gusts. However, such deep networks exhibit evidence of overfitting and still substantially underestimate (by 50%) the frequency of strong and damaging wind gusts at the three airports considered herein. Significance Statement Improved short-term forecasting of wind gusts will enhance aviation safety and logistics and may offer other societal benefits. Here we present a rigorous investigation of the relative skill of models of wind gust occurrence and magnitude that employ different statistical methods. It is shown that artificial neural networks (ANNs) offer considerable skill enhancement over regression methods, particularly for strong and damaging wind gusts. For wind gust magnitudes in particular, application of deeper learning networks (e.g., five or more hidden layers) offers tangible improvements in forecast accuracy. However, deeper networks are vulnerable to overfitting and exhibit substantial variability with the specific training and testing data subset used. Also, even deep ANNs reproduce only half of strong and damaging wind gusts. These results indicate the need for future work to elucidate the dynamical mechanisms of intense wind gusts and advance solutions to their prediction.

Characterization of Wind Gusts: A Study Based on Meteorological Tower Observations

Accurate information on wind gusts is of critical importance to various practical problems. In this study, observational wind data from high-frequency response (i.e., at a sampling rate of 10 Hz), ultrasonic anemometers instrumented at four different heights (i.e., 10 m, 40 m, 160 m, 320 m) on a weather tower were collected. The observation site featured a typical suburban condition, with no significant obstacles in the immediate proximity. The data were analyzed to identify a total of twelve descriptors of wind gusts, and to find the parent distributions that estimate these parameters well via regression analysis. The results show that the gust parameters in the context of gust magnitude and amplitude with units are best fit by the Weibull model, while non-dimensional parameters in terms of gust factor and peak factor are reasonably assessed by the log-logistic distribution. The uplift time and gust nonsymmetric factor generally exhibit a lognormal distribution, while the Gamma distribution can describe the gust length scale, uplift magnitude and passage time. It is also shown that gust factors increase linearly along with turbulence intensity. Nevertheless, empirical linear formulas given in previous studies tend to over-predict. For the vertical structure of gust descriptors, it is found that the average wind speed, gust amplitude and gust length scale in 10 min monotonically increase with height, whereas the function relationship of gust amplitude, peak factor, gust factor, turbulence intensity, rise amplitude and falling amplitude tends to decrease with height.

A Study on Directly Interconnected Offshore Wind Systems during Wind Gust Conditions

An investigation of the effects of wind gusts on the directly interconnected wind generators is reported, and techniques toward the mitigation of the wind gust negative influences have been proposed. Using a directly interconnected system approach, wind turbine generators are connected to a single synchronous bus or collection grid without the use of power converters on each turbine. This bus can then be transformed for transmission onshore using High Voltage Alternating Current, Low-Frequency Alternating Current or High Voltage Direct Current techniques with shared power conversion resources onshore connecting the farm to the grid. Analysis of the potential for instability in transient conditions on the wind farm, for example, caused by wind gusts is the subject of this paper. Gust magnitude and rise time/fall time are investigated. Using pitch control and the natural damping of the high inertial offshore system, satisfactory overall system performance and stability can be achieved during these periods of transience.

Assessing wind gust characteristics at wind turbine relevant height

Wind gust characteristics at wind turbine relevant height are closely tied with wind turbine design and wind power generation, however, they have not been detailed and documented. In this study, high-resolution wind data recorded by 3D sonic anemometers at a tall meteorological tower were analyzed to determine 12 descriptors of wind gusts and to identify the parent distributions that best fit these parameters. The main statistics were estimated using wind data from the 160 m height. It is found that the log-logistic distribution is most appropriate for a 10-min mean wind speed, gust magnitude, gust factor, and turbulence intensity; the gamma distribution appears to best fit peak factor, rise magnitude, lapse magnitude, lapse time, and gust length scale, while gust amplitude, rise time, and gust asymmetric factor are typically log-normally distributed. Gust factors tend to decrease with mean wind speed but increase as a function of turbulence intensity. The results also indicate that these wind gust descriptive parameters are height-dependent in which the 10-min mean wind speed, gust magnitude, gust length scale, rise time, and lapse time usually possess larger values at higher heights, whereas the remaining parameters exhibit negative correlation with height.

A Decline of Observed Daily Peak Wind Gusts with Distinct Seasonality in Australia, 1941–2016

AbstractWind gusts represent one of the main natural hazards due to their increasing socioeconomic and environmental impacts on, for example, human safety, maritime–terrestrial–aviation activities, engineering and insurance applications, and energy production. However, the existing scientific studies focused on observed wind gusts are relatively few compared to those on mean wind speed. In Australia, previous studies found a slowdown of near-surface mean wind speed, termed “stilling,” but a lack of knowledge on the multidecadal variability and trends in the magnitude (wind speed maxima) and frequency (exceeding the 90th percentile) of wind gusts exists. A new homogenized daily peak wind gusts (DPWG) dataset containing 548 time series across Australia for 1941–2016 is analyzed to determine long-term trends in wind gusts. Here we show that both the magnitude and frequency of DPWG declined across much of the continent, with a distinct seasonality: negative trends in summer–spring–autumn and weak negative or nontrending (even positive) trends in winter. We demonstrate that ocean–atmosphere oscillations such as the Indian Ocean dipole and the southern annular mode partly modulate decadal-scale variations of DPWG. The long-term declining trend of DPWG is consistent with the “stilling” phenomenon, suggesting that global warming may have reduced Australian wind gusts.

Wind gust quantification using seismic measurements

Wind gusts are a major cause of damage to property and the natural environment and a source of noise in seismic networks such as the USArray Transportable Array. Wind gusts cause ground motion through shear stresses, pressure fluctuations and vegetation flexing. Herein, we demonstrate the presence of a seismic response signature to wind gusts at sites across the contiguous USA and explore important geophysical factors that determine the precise nature of wind gust–seismic response relationships. There is a consistent seismic response to wind gusts that is typically manifest at relatively low frequency (0.05–0.1 Hz). However, there is also a marked seasonality in the seismic frequency of peak response, possibly due to seasonal differences in atmospheric conditions and/or vegetation and soil mediation of the atmosphere–ground interaction. The gust–seismic response functions also exhibit a clear dependence on (1) distance from the coast, (2) land cover, (3) topographic complexity and (4) lithology. We propose a generalized methodology to extract wind gust magnitude distributions from seismic networks. Although initial results from this model overestimate the spatial variability in wind gusts as measured by meteorological networks, the analyses described here highlight the potential for new methods to remove wind gust noise from seismic time series and potentially to derive quantitative wind gust estimates from seismic observations.

Application of Prescribed Velocity Methods to a Large-Amplitude Flat-Plate Gust Encounter

This paper presents a computational method to study a translating flat plate at steady velocity encountering a sudden transverse gust for different gust magnitudes and airfoil angles of incidence at a low Reynolds number (). An extended split velocity method (SVM) was used to model flat-plate wing/gust encounters in a three-dimensional Navier–Stokes (N-S) solver. The gust velocity in the flow domain was prescribed to the grid points. Source terms were derived analytically from the N-S equations and added to the solver to account for the full interaction between the airfoil and the gust. The effect of the source terms on the results is evaluated for different gust ratios (, 0.4 and 0.8) at fixed-wing incidence angles of 0 and 45 deg. The results using the SVM were compared with those from the field velocity method: a simplification of the SVM that does not include the gust source terms. Inclusion of these source terms was found to be essential to capture the full interaction, especially when the flow was initially separated. In these cases, the source terms reduced the normal force peak and delayed the response when compared to the results without gust source terms. The computational predictions of the aerodynamic forces obtained from the SVM were then compared to experimental measurements. The validation cases focused on a flat-plate wing at a 0 deg incidence encountering an intermediate transverse gust () and a flat-plate wing at 45 deg encountering a large transverse gust ().

Characterizing wind gusts in complex terrain

Abstract. Wind gusts are a key driver of aerodynamic loading, especially for tall structures such a bridges and wind turbines. However, gust characteristics in complex terrain are not well understood and common approximations used to describe wind gust behavior may not be appropriate at heights relevant to wind turbines and other structures. Data collected in the Perdigão experiment are analyzed herein to provide a foundation for improved wind gust characterization and process-level understanding of flow intermittency in complex terrain. High-resolution observations from sonic anemometers and vertically pointing Doppler lidars are used to conduct a detailed study of gust characteristics with a specific focus on the parent distributions of nine gust parameters (that describe velocity, time, and length scales), their joint distributions, height variation, and coherence in the vertical and horizontal planes. Best-fit distributional forms for varying gust properties show good agreement with those from previous experiments in moderately complex terrain but generate nonconservative estimates of the gust properties that are of key importance to structural loading. Probability distributions of gust magnitude derived from vertically pointing Doppler lidars exhibit good agreement with estimates from sonic anemometers despite differences arising from volumetric averaging and the terrain complexity. Wind speed coherence functions during gusty periods (which are important to structural wind loading) are similar to less complex sites for small vertical displacements (10 to 40 m), but do not exhibit an exponential form for larger horizontal displacements (800 to 1500 m).

Reliability and Climatological Impacts of Convective Wind Estimations

AbstractConvective surface winds in the contiguous United States are classified as severe at 50 kt (58 mi h−1, or 26 m s−1), whether measured or estimated. In 2006, NCDC (now NCEI) Storm Data, from which analyzed data are directly derived, began explicit categorization of such reports as measured gusts (MGs) or estimated gusts (EGs). Because of the documented tendency of human observers to overestimate winds, the quality and reliability of EGs (especially in comparison with MGs) has been challenged, mostly for nonconvective winds and controlled-testing situations, but only speculatively for bulk convective data. For the 10-yr period of 2006–15, 150 423 filtered convective-wind gust magnitudes are compared and analyzed, including 15 183 MGs and 135 240 EGs, both nationally and by state. Nonmeteorological artifacts include marked geographic discontinuities and pronounced “spikes” of an order of magnitude in which EG values (in both miles per hour and knots) end in the digits 0 or 5. Sources such as NWS employees, storm chasers, and the general public overestimate EGs, whereas trained spotters are relatively accurate. Analysis of the ratio of EG to MG and their sources also reveals an apparent warning-verification-influence bias in the climatological distribution of wind gusts imparted by EG reliance in the Southeast. Results from prior wind-tunnel testing of human subjects are applied to 1) illustrate the difference between measured and perceived winds for the database and 2) show the impact on the severe-wind dataset if EGs were bias-corrected for the human overestimation factor.

Observed gust wind speeds in the coterminous United States, and their relationship to local and regional drivers

Given the importance of wind gusts to structural design and the economic and ecological impacts of extreme winds, there is a need for better understanding of the spatial variability of wind gusts. Sonic anemometer wind measurements at 10-m a.g.l. from 801 US National Weather Service Automated Surface Observation System sites are used to characterize gust climates across the coterminous United States. These data indicate substantial regional and sub-regional variability across a range of gust metrics. For example, the locally-determined 95th percentile gust values exceed 16.46 ms-1 at most sites throughout the central plains, but are below 14.40 ms-1 throughout almost all of the southeastern stations. When site-specific gust metrics are conditionally sampled by the likelihood of deep convection and frequency of extra-tropical cyclones, the results indicate that gust factors tend to be lower in regions with higher convective potential, and higher in areas with a higher frequency of extra-tropical cyclones. Conversely, 1 and 10-year return period gusts are higher in the region with high convective potential, but do not exhibit consistently higher values in regions with a high frequency of extra-tropical cyclones. Terrain complexity and higher surface roughness are also found to increase gust factors but not absolute gust magnitudes.

Design and Testing of ISU-AABL Wind and Gust Tunnel

A wind tunnel with advanced capabilities will aid research efforts to understand the complex fluid structure interaction problems encountered in aerospace engineering, industrial aerodynamics and wind engineering applications since wind tunnels remain an integral component of the design process for wind sensitive structures. Whether dealing with the aerodynamics of aerospace, mechanical or civil engineering structures many issues remain to be fully resolved-including the role of non-stationary gust interactions, Reynolds number effects, and the significance of small-scale turbulence. Building the next generation of such wind tunnels will contribute to the understanding of these issues. A combination Aerodynamic/Atmospheric Boundary Layer (AABL) Wind and Gust Tunnel with a unique active gust generation capability has been developed for various applications at Iowa State University (ISU). This wind tunnel is primarily a closed-circuit tunnel that can be also operated in open-return mode. It is designed to accommodate two test sections (2.44m x 1.83m and 2.44m x 2.21m) with a maximum wind speed capability of 53 m/s. This paper describes the wind tunnel and its components and presents a comparison of the predicted and measured design parameters. It shows that the wind tunnel is capable of generating uniform flow with very low turbulence in the aerodynamic test section and produces gust magnitudes around 27% of the mean flow speed.

Gust Mitigation of Micro Air Vehicles Using Passive Articulated Wings

Birds and insects naturally use passive flexing of their wings to augment their stability in uncertain aerodynamic environments. In a similar manner, micro air vehicle designers have been investigating using wing articulation to take advantage of this phenomenon. The result is a class of articulated micro air vehicles where artificial passive joints are designed into the lifting surfaces. In order to analyze how passive articulation affects performance of micro air vehicles in gusty environments, an efficient 8 degree-of-freedom model is developed. Experimental validation of the proposed mathematical model was accomplished using flight test data of an articulated micro air vehicle obtained from a high resolution indoor tracking facility. Analytical investigation of the gust alleviation properties of the articulated micro air vehicle model was carried out using simulations with varying crosswind gust magnitudes. Simulations show that passive articulation in micro air vehicles can increase their robustness to gusts within a range of joint compliance. It is also shown that if articulation joints are made too compliant that gust mitigation performance is degraded when compared to a rigid system.

Gusts caused by high-speed trains in confined spaces and tunnels

Little is known of the behaviour of transient air velocities and dynamic pressure loads generated by high-speed trains in confined spaces, or whether current methodologies for assessing transient gust loads in open spaces can be used in confined spaces. Experiments have been carried out in which a moving-model high-speed train passed walls, a partially-enclosed tunnel, and single-track tunnels with a variety of cross-sectional areas and lengths. An open air control experiment has also been carried out. The train model was a simplified 1/25 scale four-carriage ICE2 train travelling at 32m/s. Cobra Probes measured the three-dimensional air velocity components at various positions inside the structures. The results show that the peak gust magnitudes increase in all confined cases compared to the open air. In tunnels, a ‘piston effect’ appears to have been a dominant cause of the increases in the peak gust magnitudes, as well as prolonged winds occurring before and after the train passed the probes. The tunnel length impacted considerably on the flow characteristics, and the partially-enclosed tunnel showed further increases in the gusts due to high lateral and vertical velocities.

Rational calibration of four IEC 61400‐1 extreme external conditions

AbstractBased on a set of asymptotic statistical models on closed form this paper presents a rational and consistent calibration of four extreme external conditions defined in the International Electrotechnical Commission (IEC) 61400‐1 standard: extreme operating gust, extreme wind shear, extreme coherent gust with direction change and extreme wind direction change. These four extreme external conditions are used in the definition of six of the IEC 61400‐1 ultimate load cases. The statistical models are based on simple and easily accessible mean wind speed and turbulence characteristics of the atmospheric boundary layer. Using the wind climate characteristics prescribed in the IEC 61400‐1 standard as input to the set of statistical models ensures consistency between the specified wind climate and the proposed extreme gust magnitudes. Differences and equalities between the present IEC specifications and proposed specifications of the magnitudes of the extreme external wind conditions are highlighted and discussed using an illustrative example based on two selected terrain types. Copyright © 2008 John Wiley & Sons, Ltd.

A New Look at High-Altitude Turbulence

Reconnaissance by aircraft from high altitudes remains an important priority for the Department of Defense. Aircraft at these altitudes experience both traditional mechanical turbulence as well as oscillatory fluctuations of temperature and horizontal and vertical wind speeds, which are attributed to gravity (also called buoyancy) waves. This paper investigates the effects of wavelike behavior on high-flying aircraft, finding high aircraft loads at certain frequencies. Aircraft are most susceptible to problems at the upper range of altitudes. This analysis shows that autopilots based on maintaining a constant Mach number and an indicated airspeed are both susceptible to temperature fluctuations. In addition, temperature changes shift the flight envelope, creating potentially dangerous stall or overspeeding conditions. The absence of temperature variation requirements in current aviation continuous gust load specifications along with a possible deficiency in gust magnitude design requirements at high altitude lead to a recommendation for revisiting high-altitude turbulence requirements and investigating the vulnerabilities of high-altitude unmanned aircraft.

Development of an active gust generation mechanism on a wind tunnel for wind engineering and industrial aerodynamics applications

A combination Aerodynamic/Atmospheric Boundary Layer (AABL) Wind and Gust Tunnel with a unique active gust generation capability has been developed for wind engineering and industrial aerodynamics applications. This facility is a cornerstone component of the Wind Simulation and Testing (WiST) Laboratory of the Department of Aerospace Engineering at Iowa State University (ISU). The AABL Wind and Gust tunnel is primarily a closed-circuit tunnel that can be also operated in open-return mode. It is designed to accommodate two test sections (<TEX>$2.44m{\times}1.83m$</TEX> and <TEX>$2.44m{\times}2.21m$</TEX>) with a maximum wind speed capability of 53 m/s. The gust generator is capable of producing non-stationary gust magnitudes around 27% of the mean flow speed. This paper describes the motivation for developing this gust generator and the work related to its design and testing.

ENSO Impacts on Peak Wind Gusts in the United States

Abstract Changes in the peak wind gust magnitude in association with the warm and cold phases of the El Nino–Southern Oscillation (ENSO) are identified over the contiguous United States. All calculations of the peak wind gust are differences in the extreme phases of ENSO (warm and cold) relative to neutral for all stations in the study that pass the completeness criteria. Monthly composites were created for all years in the study (1 January 1948 through 31 August 1998). The differences in the mean peak wind gust are calculated for each month. A nonparametric statistical test was invoked to determine significant shifts in the extreme phase distributions. Differences in the frequency of gale-force wind gusts were also calculated. The results show a dominant, ENSO cold phase wintertime signal. Regions most greatly affected are the Pacific Northwest, Southwest, the Great Plains, and the region extending from the Great Lakes through the Ohio River valley, and southwest into Texas. During the cold phase months ...