Hubbry Logo
Wind engineeringWind engineeringMain
Open search
Wind engineering
Community hub
Wind engineering
logo
8 pages, 0 posts
0 subscribers
Be the first to start a discussion here.
Be the first to start a discussion here.
Wind engineering
Wind engineering
Wind engineering
View on Wikipedia
from Wikipedia
Flow visualization of wind speed contours around a house
Wind engineering covers the aerodynamic effects of buildings
Damaged wind turbines due to hurricane Maria

Wind engineering is a subset of mechanical engineering, structural engineering, meteorology, and applied physics that analyzes the effects of wind in the natural and the built environment and studies the possible damage, inconvenience or benefits which may result from wind. In the field of engineering it includes strong winds, which may cause discomfort, as well as extreme winds, such as in a tornado, hurricane or heavy storm, which may cause widespread destruction. In the fields of wind energy and air pollution it also includes low and moderate winds as these are relevant to electricity production and dispersion of contaminants.

Wind engineering draws upon meteorology, fluid dynamics, mechanics, geographic information systems, and a number of specialist engineering disciplines, including aerodynamics and structural dynamics.[1] The tools used include atmospheric models, atmospheric boundary layer wind tunnels, and computational fluid dynamics models.

Wind engineering involves, among other topics:

  • Wind impact on structures (buildings, bridges, towers)
  • Wind comfort near buildings
  • Effects of wind on the ventilation system in a building
  • Wind climate for wind energy
  • Air pollution near buildings

Wind engineering may be considered by structural engineers to be closely related to earthquake engineering and explosion protection.

Some sports stadiums such as Candlestick Park and Arthur Ashe Stadium are known for their strong, sometimes swirly winds, which affect the playing conditions.

History

[edit]

Wind engineering as a separate discipline can be traced to the UK in the 1960s, when informal meetings were held at the National Physical Laboratory, the Building Research Establishment, and elsewhere. The term "wind engineering" was first coined in 1970.[2] Alan Garnett Davenport was one of the most prominent contributors to the development of wind engineering.[3] He is well known for developing the Alan Davenport wind-loading chain or in short "wind-loading chain" that describes how different components contribute to the final load calculated on the structure.[4]

Wind loads on buildings

[edit]
Wind Tunnel Model of One Post Office Square, Boston

The design of buildings must account for wind loads, and these are affected by wind shear. For engineering purposes, a power law wind-speed profile may be defined as:[5][6]

where:

= speed of the wind at height
= gradient wind at gradient height
= exponential coefficient


Typically, buildings are designed to resist a strong wind with a very long return period, such as 50 years or more. The design wind speed is determined from historical records using extreme value theory to predict future extreme wind speeds. Wind speeds are generally calculated based on some regional design standard or standards. The design standards for building wind loads include:

  • AS 1170.2 for Australia
  • EN 1991-1-4 for Europe
  • NBC for Canada

Wind comfort

[edit]
Wind baffles being installed to mitigate wind danger issues at the Bridgewater Place skyscraper in Leeds, UK
Computer simulation of the airflow downwind of a hangar which caused damage to Ameristar Charters Flight 9363

The advent of high-rise tower blocks led to concerns regarding the wind nuisance caused by these buildings to pedestrians in their vicinity.

A number of wind comfort and wind danger criteria were developed from 1971, based on different pedestrian activities, such as:[7]

  • Sitting for a long period of time
  • Sitting for a short period of time
  • Strolling
  • Walking fast

Other criteria classified a wind environment as completely unacceptable or dangerous.

Building geometries consisting of one and two rectangular buildings have a number of well-known effects:[8][9]

  • Corner streams, also known as corner jets, around the corners of buildings
  • Through-flow, also known as a passage jet, in any passage through a building or small gap between two buildings due to pressure short-circuiting
  • Vortex shedding in the wake of buildings

For more complex geometries, pedestrian wind comfort studies are required. These can use an appropriately scaled model in a boundary-layer wind tunnel, or more recently, use of computational fluid dynamics techniques has increased.[10] The pedestrian level wind speeds for a given exceedance probability are calculated to allow for regional wind speeds statistics.[11]

The vertical wind profile used in these studies varies according to the terrain in the vicinity of the buildings (which may differ by wind direction), and is often grouped in categories, such as:[12]

  • Exposed open terrain with few or no obstructions and water surfaces at serviceability wind speeds
  • Water surfaces, open terrain, grassland with few, well-scattered obstructions having heights generally from 1.5 to 10 m
  • Terrain with numerous closely spaced obstructions 3 to 5 m high, such as areas of suburban housing
  • Terrain with numerous large, high (10 to 30 m high) and closely spaced obstructions, such as large city centres and well-developed industrial complexes

Wind turbines

[edit]

Wind turbines are affected by wind shear. Vertical wind-speed profiles result in different wind speeds at the blades nearest to the ground level compared to those at the top of blade travel, and this, in turn, affects the turbine operation.[13] The wind gradient can create a large bending moment in the shaft of a two bladed turbine when the blades are vertical.[14] The reduced wind gradient over water means shorter and less expensive wind turbine towers can be used in shallow seas.[15]

For wind turbine engineering, wind speed variation with height is often approximated using a power law:[13]

where:

= velocity of the wind at height [m/s]
= velocity of the wind at some reference height [m/s]
= Hellman exponent (aka power law exponent or shear exponent) (~= 1/7 in neutral flow, but can be >1)

Significance

[edit]

The knowledge of wind engineering is used to analyze and design all high-rise buildings, cable-suspension bridges and cable-stayed bridges, electricity transmission towers and telecommunication towers and all other types of towers and chimneys. The wind load is the dominant load in the analysis of many tall buildings, so wind engineering is essential for their analysis and design. Again, wind load is a dominant load in the analysis and design of all long-span cable bridges.

See also

[edit]

References

[edit]

Further reading

[edit]
[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

Wind engineering

View on Grokipedia
from Grokipedia
Wind engineering is a multidisciplinary branch of that examines the effects of on structures, environments, and activities, with the goal of designing resilient infrastructure and mitigating wind-related hazards. It is defined as the rational treatment of interactions between in the atmospheric and constructions on , drawing from , , , and . The field addresses loads on bluff bodies such as buildings, bridges, and towers, ensuring safety and efficiency in projects. The origins of wind engineering trace back to empirical observations and structural failures during the , evolving through distinct historical periods: a traditional phase up to 1750 based on localized building practices; an empirical era from 1750 to marked by innovations like in , ; an establishment period from to influenced by industrial and military advancements, including early atmospheric studies; rapid growth from to 1980 amid post-war economic expansion; and a modern phase since 1980 incorporating computational tools. Notable early catalysts included the 1879 Tay Bridge collapse in , attributed to wind-induced vibrations, and the 1940 failure, which highlighted aeroelastic phenomena like flutter. Key aspects of wind engineering include wind-resistant design using standards for load calculations, supplemented by physical modeling in wind tunnels for complex geometries where standards may overestimate loads and increase costs. (CFD), based on the Navier-Stokes equations, has become a cornerstone for simulating interactions, enabling precise predictions of distributions and structural responses. Applications extend to environmental management, such as analyzing dispersion, urban ventilation, and investigations using scales like Japan's modified , as well as broader disaster mitigation and utilization. Over 300 wind tunnel tests have been conducted at institutions like the International Center for Numerical Methods in (IDR/UPM) since the late on diverse structures including high-rises, bridges, and roofs.

Fundamentals

Definition and Scope

Wind engineering is defined as the rational treatment of the interactions between wind in the atmospheric and human activities and structures on the Earth's surface. This multidisciplinary field integrates principles from , , and to analyze wind effects on built environments, such as and bridges, and natural settings, including influences on . The term "wind engineering" emerged in the early 1970s, formalizing the study of these interactions as a distinct . The scope of wind engineering encompasses both extreme wind events, such as hurricanes and tornadoes, which demand designs for structural safety and disaster mitigation, and moderate winds that affect applications like from wind turbines, atmospheric dispersion, and comfort in urban areas. It employs to model airflow around structures, probabilistic methods for of rare high-wind events, and to evaluate broader impacts like ventilation and contaminant spread. Unlike pure , which focuses on atmospheric phenomena without engineering applications, or general unrelated to wind-structure interactions, wind engineering prioritizes practical mitigation strategies for human-built systems. Key applications include assessing wind loads on to ensure stability and optimizing placements for efficient energy production, highlighting the field's balance between hazard reduction and resource utilization.

Basic Principles of Wind-Structure Interaction

Wind-structure interaction involves the aerodynamic forces exerted by wind on structures, which can lead to static and dynamic responses depending on the structure's , , and the wind's characteristics. The primary forces include drag, which acts in the direction of the wind flow, lift, which acts perpendicular to it, and moments that induce torsion or . These forces are quantified using fundamental aerodynamic equations derived from principles. The drag force FdF_d is given by Fd=12ρV2CdAF_d = \frac{1}{2} \rho V^2 C_d A, where ρ\rho is air , VV is , CdC_d is the , and AA is the projected area perpendicular to the wind. Similarly, the lift force Fl=12ρV2ClAF_l = \frac{1}{2} \rho V^2 C_l A, with ClC_l as the lift coefficient, arises from differences across the structure. Moment components, such as pitching or yawing, follow analogous forms involving coefficients and lever arms. Coefficients CdC_d and ClC_l depend on the structure's shape, , and , typically ranging from 0.5 to 2.0 for bluff bodies like buildings or towers. Structures respond to these forces both statically and dynamically, with dynamic effects becoming prominent when wind-induced frequencies match the structure's natural frequencies, leading to amplification. A key mechanism is , where alternating vortices form in the wake of a bluff body, generating periodic pressure fluctuations and oscillatory forces. The shedding frequency ff is characterized by the St=fDVSt = \frac{f D}{V}, where DD is the characteristic dimension (e.g., for cylinders); for circular cylinders, St≈0.2St \approx 0.2 across a wide range. This periodicity can cause across-wind vibrations, with amplitude amplification if occurs, potentially increasing displacements by factors of 5–10 compared to static loads. These vibrations can cause perceptible shaking or swaying in buildings, potentially mimicking the sensation of minor tremors or earthquakes, particularly in exposed locations such as ridge tops. Resonance risks escalate through aeroelastic effects, where structural motion interacts with the airflow to modify forces. Flutter involves coupled bending-torsion s, often self-excited and leading to rapid amplitude growth, as seen in long-span bridges where low torsional allows mode coupling at critical speeds around 10–20 m/s. Galloping, a low-frequency, large-amplitude transverse , affects ice-covered or non-streamlined towers and cables, driven by negative aerodynamic when the structure's motion enhances lift in a destabilizing manner. These phenomena pose catastrophic risks to flexible structures like suspension bridges or guyed towers, necessitating aeroelastic stability analysis. Mitigating dynamic responses relies on damping mechanisms that dissipate from wind-induced vibrations. Structural damping arises from material and joint , typically 1–2% of critical damping for structures. Aerodynamic damping results from the phase lag between motion and opposing forces, providing stabilization for small amplitudes but potentially negative in galloping regimes. damping, relevant for foundation-supported structures, stems from hysteretic loss in , contributing approximately 0.2–3% of critical damping in monopile wind turbines or towers on soft soils. in atmospheric profiles introduces variability in these forces and VV, modulating shedding patterns and overall response.

Historical Development

Early Concepts and Milestones

The utilization of as a force in engineering dates back to antiquity, where empirical observations informed the design of . Ancient employed square on vessels around 4000 BC to harness prevailing northerly for navigation along the River, relying on intuitive adjustments to orientation for propulsion efficiency. These early practices represented foundational interactions between and structures, though without formal aerodynamic theory, designs evolved through to optimize lift and drag on sails. In the medieval period, wind power was systematically applied to mechanical devices, marking early milestones in wind engineering. Vertical-axis windmills, known as asbads, emerged in Persia during the CE, featuring vertical sails attached to a central shaft to and water in arid regions like . These structures demonstrated an understanding of wind capture through multi-bladed rotors, with designs optimized for consistent low-speed winds. By the , horizontal-axis post mills appeared in , particularly in and , adapting Persian concepts to local needs for milling and drainage, with pivoting towers allowing orientation into the wind. The saw advancements in wind measurement and recognition of its structural impacts, laying groundwork for modern wind engineering. Irish astronomer Thomas Romney Robinson invented the cup anemometer in 1846, a device with hemispherical cups on arms that rotated proportionally to , enabling precise quantification of wind velocities for the first time. This tool facilitated empirical studies of wind forces on structures. Concurrently, bridge failures highlighted wind's destructive potential; the 1879 Tay in , where gale-force winds contributed to the collapse of the iron structure during a storm, killing 75 people, underscored vulnerabilities in long-span designs and prompted initial considerations of aerodynamic stability. Similarly, the 1896 destruction of the Brighton Chain Pier by a violent storm revealed oscillatory responses in suspension systems, influencing cautious empirical approaches to wind-resistant framing. Empirical estimation of wind loads on buildings emerged in late 19th-century European codes, driven by observed structural failures. In the UK, the London Building Act of 1894 incorporated provisions for wall thicknesses and foundation depths scaled to building height and exposure, indirectly accounting for wind pressures based on collapses like the 1878 Haymarket building failure due to inadequate iron supports under load. These regulations prescribed conservative material strengths—such as 9-inch brick walls for structures up to 30 feet high—derived from post-failure analyses rather than theoretical models, establishing a precedent for wind-inclusive design without explicit velocity-based formulas. A pivotal pre-1960s event was the , which devastated coastal structures and catalyzed wind-resistant code reforms in the . The Category 3 storm generated s up to 120 mph, damaging or destroying approximately 57,000 homes and exposing deficiencies in framing and anchoring, leading to enhanced Northeast building codes that mandated stronger connections and elevated foundations to mitigate uplift and lateral forces. Another landmark failure was the 1940 collapse, where moderate s of 40-45 mph induced aeroelastic flutter, causing torsional oscillations that destroyed the structure. This event emphasized the need for aerodynamic analysis and testing in bridge design, shifting practices toward considering dynamic effects beyond static loads. These disasters shifted empirical practices toward region-specific wind zoning, influencing subsequent standards like those in the Uniform . These early developments paved the way for later tools, such as in the mid-20th century, to validate empirical observations.

Modern Evolution and Key Figures

The discipline of wind engineering emerged as a distinct field in the during the , marked by the organization of informal meetings and the first international on wind effects on and structures held in in 1963. This event, led by Kit Scruton, gathered researchers to address the growing need for systematic study of wind-structure interactions amid postwar construction booms and increasing designs. The term "wind engineering" itself was coined in the early by Jack E. Cermak of , formalizing the interdisciplinary approach to wind effects. Pioneering contributions came from Alan G. Davenport, whose 1961 doctoral research introduced the "wind-loading chain" concept, a probabilistic framework linking meteorological wind climate, topographic influences, aerodynamic shapes, dynamic responses, and occupancy factors to predict structural loads accurately. This model revolutionized design by emphasizing statistical variability in winds, influencing global practices for tall buildings like the World Trade Center. Complementing this, Jack E. Cermak advanced experimental methods through his establishment of the Fluid Dynamics and Diffusion Laboratory at in 1959, developing boundary-layer wind tunnels that simulated atmospheric conditions for structural testing on over 500 projects. Institutionally, the field solidified with the formation of the International Association for Wind Engineering (IAWE) in 1975 during the fourth international conference, which standardized terminology and promoted global collaboration. Post-1970s, probabilistic design evolved from Davenport's foundations into load and resistance factor methods adopted in standards like ASCE 7, accounting for wind speed uncertainties and return periods to enhance structural reliability. In the post-2000 era, wind engineering integrated climate change impacts into load predictions, influenced by IPCC assessments of intensifying extreme winds and storms, prompting updates to design codes for non-stationary climates and higher gust probabilities. Studies from the onward quantified potential increases in design wind speeds by 5-15% in vulnerable regions, urging adaptive strategies in structural resilience planning.

Atmospheric Wind Characteristics

Wind Profiles and Boundary Layer

The atmospheric boundary layer (ABL), also known as the (PBL), constitutes the lowest portion of the , typically extending from the Earth's surface to a height of approximately 1-2 km, where frictional interactions with the underlying surface significantly influence wind flow and turbulence. This layer is distinguished from the overlying free atmosphere by its responsiveness to surface forcings, such as and mechanical drag, leading to a marked vertical in near the ground. In wind engineering, understanding the ABL is essential for modeling how surface-induced effects propagate upward, affecting structure loading and environmental flows. Wind profiles within the ABL describe the variation of mean horizontal V(z)V(z) with zz above the surface, providing critical input for engineering analyses. For neutral atmospheric stability, an empirical approximation is commonly used: V(z)=Vref(zzref)α,V(z) = V_{\text{ref}} \left( \frac{z}{z_{\text{ref}}} \right)^\alpha, where VrefV_{\text{ref}} is the reference at zrefz_{\text{ref}}, and α≈1/7\alpha \approx 1/7 (or 0.143) serves as a rule-of-thumb exponent for open terrain under equilibrium conditions. This form simplifies of wind speeds from heights but assumes a constant shear exponent, which varies with site-specific factors. A more theoretically grounded description derives from Monin-Obukhov similarity theory in the surface layer (the lowest 10% of the ABL), yielding the logarithmic law: V(z)=u∗κln⁡(zz0),V(z) = \frac{u_*}{\kappa} \ln \left( \frac{z}{z_0} \right), where u∗u_* is the velocity, κ≈0.4\kappa \approx 0.4 is the (originating from Prandtl's mixing-length hypothesis), and z0z_0 is the aerodynamic representing surface drag. The logarithmic profile captures the near-surface shear driven by production, with validity limited to roughly the lowest 100 m. Several factors modulate these profiles, primarily terrain roughness, which determines z0z_0—typically 0.01-0.03 m for rural grass-covered areas but escalating to 1-5 m in urban environments due to buildings and trees that enhance drag and alter . Atmospheric stability, influenced by effects such as daytime solar heating (unstable conditions promoting vertical mixing) or nocturnal cooling ( conditions suppressing it), further distorts profiles; for instance, stability increases the wind shear exponent α\alpha from about 0.11 in unstable regimes to 0.25 or higher in ones over open . Urban-rural contrasts amplify these variations, with denser urban roughness leading to steeper near-surface gradients and reduced wind speeds aloft compared to smoother rural landscapes. Measurement of wind profiles relies on established techniques to validate models and gather site-specific data. Traditional anemometers, including and sonic types mounted on meteorological towers, provide point measurements of and direction at discrete heights, offering high but limited vertical coverage due to tower height constraints. In contrast, Doppler detection and ranging () systems enable of profiles up to several kilometers by emitting pulses and analyzing backscattered from atmospheric aerosols, yielding volume-averaged data with good agreement to results when calibrated for spatial averaging effects. These methods complement each other, with LIDAR particularly valuable for profiling in complex terrain where tower installation is impractical.

Turbulence and Gusts

In wind engineering, refers to the random, irregular fluctuations in wind velocity superimposed on the mean flow within the atmospheric (ABL), arising primarily from shear and effects that generate eddies across a range of scales. These fluctuations are in nature and essential for understanding dynamic loads on structures, as they contribute to unsteady aerodynamic forces. A key characteristic of atmospheric is the integral length scale, which represents the average size of the largest energy-containing eddies; horizontally, this scale typically ranges from approximately 100 to 1000 meters in the ABL, influencing the spatial of perturbations and the overall coherence of fields over structures. intensity, defined as the ratio of the standard deviation of longitudinal fluctuations to the mean speed, I=σuUˉI = \frac{\sigma_u}{\bar{U}}, commonly falls between 0.1 and 0.2 in the lower ABL, with higher values near the surface due to increased and shear. This measure quantifies the relative magnitude of turbulent fluctuations and is critical for assessing fatigue loads in wind-sensitive designs. Turbulence spectra describe the distribution of across frequencies or wavenumbers, with the von Kármán spectrum serving as a foundational model for fluctuations in isotropic , adapted for anisotropic ABL conditions to capture the from large-scale eddies to . The model assumes a form that peaks at low frequencies corresponding to integral scales and decays in the inertial subrange, providing a basis for simulating turbulent fields in . Gusts represent short-duration peaks in wind speed exceeding the mean, often 3 to 10 seconds long, and are to dynamic response predictions; the gust factor, defined as the of maximum gust speed to mean speed, G=Vmax⁡VˉG = \frac{V_{\max}}{\bar{V}}, typically ranges from 1.5 to 2.0 in contexts for non-thunderstorm winds, accounting for amplification due to . Gusts are classified into translational types, such as moving frontal systems that propagate across , and standing types driven by from obstacles, each affecting structures differently based on exposure and geometry. Extreme gust peaks are analyzed using extreme value statistics, with the commonly fitted to annual maxima to estimate return periods, enabling probabilistic assessments of rare events in load calculations.

Wind Loads and Structural Design

Calculation of Wind Loads on Buildings

Wind loads on buildings are typically resolved into three primary components: along-wind, across-wind, and torsional loads. The along-wind load, which is drag-dominated, induces forces parallel to the prevailing and arises from the and fluctuating components of buffeting on the structure's windward face. This component is the most straightforward to predict and is often the dominant force for low- to mid-rise buildings. In contrast, the across-wind load generates oscillations perpendicular to the direction, primarily due to lift forces and periodic from the building's edges, which can become significant for tall structures where dynamic excitation aligns with natural frequencies. Torsional moments, meanwhile, result from non-uniform pressure distributions across the building's facade, often exacerbated by geometric asymmetries or incident wind angles, leading to twisting about the vertical axis. The quasi-static method provides a foundational approach for calculating these loads, particularly suitable for rigid or low-rise where dynamic amplification is negligible. are determined using pressure coefficients CpC_p, obtained from empirical data or experiments and specified in design guidelines, multiplied by the q=12ρV2q = \frac{1}{2} \rho V^2, where ρ\rho is air and VV is the reference at building height. To account for gust effects, standards apply a gust effect factor that adjusts for ; a common approximation for the peak velocity pressure multiplier is 1+2gI1 + 2 g I, where II is the turbulence intensity and gg is the peak factor (typically around 3.5 for a 10-minute averaging period based on Gaussian assumptions for fluctuations), though exact formulations vary by . This formulation, rooted in statistical models of atmospheric , translates time-varying into equivalent static loads for . Building geometry significantly influences load magnitudes through shape factors, such as the CdC_d, which quantifies aerodynamic resistance. For rectangular prismatic buildings, CdC_d values typically range from 1.3 to 2.2 depending on aspect ratios, corner conditions, and flow environment, with values derived from measurements simulating urban boundary layers; in standards like ASCE 7, force coefficients for building components are around 1.3-1.6. Variations in plan shape, height-to-width ratios, or edge tapering can reduce CdC_d by up to 30% through flow reattachment, thereby lowering overall along-wind loads. For tall or slender buildings, where natural frequencies fall within the wind spectrum, dynamic effects must be incorporated to avoid underestimation of responses. Aeroelastic models, scaled to replicate the structure's , , and , are employed in tests to measure full dynamic interactions, including motion-induced like vortex lock-in. Root-mean-square (RMS) response calculations, drawn from random vibration theory, separate the response into background (quasi-static gusts) and resonant components, with the total peak response approximated as P^=P0+g0σp+g1σr\hat{P} = P_0 + g_0 \sigma_p + g_1 \sigma_r, where P0P_0 is the , σp\sigma_p and σr\sigma_r are RMS background and resonant responses, and g0,g1g_0, g_1 are peak factors. This approach, pioneered in early statistical frameworks for wind loading, ensures accurate prediction of across-wind and torsional excitations that can amplify displacements by factors exceeding those from along-wind alone.

Design Standards and Codes

Wind engineering design standards and codes provide the regulatory framework for determining wind loads to ensure structural safety and reliability across different regions. In the United States, the (ASCE) Standard ASCE/SEI 7-22 specifies wind provisions using a risk-targeted approach, where basic wind speeds are mapped based on mean recurrence intervals (MRIs) tailored to building risk categories; for Risk Category II structures (typical buildings and facilities), the MRI is 700 years, reflecting a low annual probability of exceedance of approximately 0.0014. In Europe, Eurocode 1 Part 1-4 (EN 1991-1-4) defines wind actions with a fundamental basic wind velocity corresponding to a 50-year return period, incorporating a directional factor cdirc_{\text{dir}} that accounts for variations in wind severity by direction, with a recommended value of 1.0 unless specified otherwise in national annexes. Similarly, in Australia and New Zealand, AS/NZS 1170.2:2021 outlines wind actions using regional basic wind speeds VRV_R derived from a 500-year return period (annual probability of exceedance 1/500) for importance level 2 structures (standard buildings), adjusted by multipliers for site-specific conditions and importance. These standards employ a probabilistic approach to characterize wind hazards, where return periods—defined as the mean recurrence interval (MRI) between events exceeding a specified intensity—guide the selection of design wind speeds. Wind speed maps are developed using statistical models of historical meteorological data, estimating exceedance probabilities (e.g., 2% annual probability for a 50-year MRI) to represent rare but severe events, ensuring a consistent reliability level across structures. For instance, in ASCE 7-22, the maps integrate Gumbel or lognormal distributions fitted to extreme records, providing contour lines of ultimate-level wind speeds that vary by risk category to target a uniform collapse probability of about 1 in 500-3000 years depending on importance. Code evolution has incorporated refinements from advanced wind climate modeling, with ASCE 7-22 featuring updated wind speed maps that show modest increases, such as up to 7-10% in some hurricane-prone coastal areas, due to improved and non-stationary trend analysis, though these do not yet project future scenarios. As of November 2025, future iterations, such as ASCE 7-28 under development, are expected to introduce provisions for non-stationary wind risks influenced by variability, including potential adjustments for rising sea levels and intensified tropical cyclones. In parallel, EN 1991-1-4 and AS/NZS 1170.2 have seen amendments for better alignment with observed wind patterns, such as refined shielding effects in urban terrains. To account for site-specific influences, standards include adjustment factors for and ; in ASCE 7-22, the velocity pressure exposure coefficient KzK_z varies with height above ground and exposure category (e.g., 0.85-1.7 for Exposure C (open with scattered obstructions) at heights from 15 ft to 700 ft). Exposure D typically results in 15–25% higher wind pressures than Exposure C, depending on building height; at low heights (e.g., 15–30 ft), KzK_z for D is approximately 21% higher (C ≈ 0.85–0.90, D ≈ 1.03), with the percentage difference decreasing for taller buildings as KzK_z values converge. Wind pressure is directly proportional to KzK_z (velocity pressure exposure coefficient), the primary factor differing between categories. Higher KzK_z in more open exposures (e.g., D compared to C) results in higher pressures due to reduced ground friction and elevated near-ground wind speeds. Other factors such as KztK_{zt} (topographic), KdK_d (directionality), and KeK_e (elevation) are generally similar across categories. The topographic factor KztK_{zt} amplifies speeds over hills or escarpments (up to 2.0 in extreme cases). These factors ensure velocity pressure exposure reflects effects, with similar provisions in EN 1991-1-4 using categories I-V and height-dependent roughness lengths. Compliance involves applying these provisions within ultimate limit state (ULS) load combinations, where is treated as a variable action with partial safety factors to achieve target reliability; for example, in Eurocode systems, the design load effect uses a factor of 1.5 on characteristic actions in fundamental ULS combinations like 1.35G + 1.5Q (where Q includes ). In ASCE 7-22, strength design combinations such as 1.2D + 1.0W + 0.5L apply a 1.0 factor to when it governs, calibrated to match the probabilistic MRI . These ensure structures resist collapse under extreme , with engineers verifying via site-specific maps and factors before computing loads for elements like walls or roofs.

Pedestrian and Environmental Wind Effects

Wind Comfort Criteria

Wind comfort criteria evaluate the suitability of wind conditions at pedestrian level—typically 1.5 to 2 meters above ground—for human activities in urban environments, focusing on speeds that avoid discomfort or safety risks around buildings. These criteria emerged from wind engineering research to guide urban design, ensuring outdoor spaces support activities like walking, standing, or sitting without excessive wind-induced disturbances. Seminal frameworks, such as those by Lawson and Davenport, relate local wind speeds to meteorological data, often using exceedance probabilities to quantify acceptability. The Lawson criteria, introduced by T.V. Lawson in 1978, categorize comfort based on activity-specific wind speed thresholds and their frequency of exceedance, drawing from field observations and human response studies. For sedentary activities like sitting at cafes or benches, wind speeds should remain below 4 m/s for at least 95% of the time to prevent discomfort from debris or chill. Standing areas, such as building entrances or bus stops, tolerate up to 6 m/s for 95% of the time, while leisurely walking paths allow up to 8 m/s, with faster walking permitting 10 m/s or more under similar exceedance limits. These thresholds use the gust equivalent mean (GEM) wind speed, defined as the mean wind plus 40% of the standard deviation, to account for gustiness, and are assessed using meteorological wind data, such as directional hourly means from nearby weather stations at 10 m height, scaled to site conditions via amplification factors. The criteria classify areas into levels like "calm" (low exceedance of low thresholds) to "unacceptable" (frequent high winds), influencing urban planning for habitable spaces. Adaptations of the Beaufort scale provide qualitative benchmarks for pedestrian effects, correlating wind speeds to observable impacts like difficulty walking or umbrella inversion. For instance, Beaufort force 4 (5.5-7.9 m/s) marks the onset of discomfort for standing or light activities, with dust and loose objects becoming problematic, while force 6 (10.8-13.8 m/s) hinders normal walking. These are integrated into comfort assessments to differentiate discomfort—such as or imbalance from sustained 8-10 m/s gusts—from safety hazards like falls, which occur above 15 m/s with exceedance probabilities below 0.02% annually. Complementary criteria, like those by Isyumov and Davenport (1975), emphasize safety by limiting exceedances of 9.8 m/s (uncomfortable for fast walking) to no more than 1.5% of the time (approximately once per week), and 15 m/s (safety) to 0.01%, based on early urban data. The Dutch NEN 8100 standard (2006) simplifies this with a uniform 5 m/s hourly mean threshold for all activities, classifying environments by exceedance classes (A-E) from 0.1% to 10%, prioritizing low-wind zones for residential or recreational use. Assessments typically involve simulations at height, reporting exceedance frequencies (e.g., ≤5% for comfort thresholds) to ensure less than 5% of annual hours feature winds above activity limits.
ActivityThreshold Wind Speed (m/s)Max Exceedance (%)Source
Sitting<4≤5Lawson (1978)
Standing<6≤5Lawson (1978)
Leisurely Walking<8≤5Lawson (1978)
Safety (General)<15≤0.02Isyumov & Davenport (1975)
All Activities<5 (hourly mean)Varies by class (0.1-10%)NEN 8100 (2006)

Mitigation Strategies in Urban Areas

Mitigation strategies in urban areas aim to counteract excessive wind speeds generated by tall buildings in pedestrian zones, enhancing safety and comfort through targeted design modifications. These approaches integrate architectural, vegetative, and engineering elements to disrupt or redirect airflow, particularly downbursts and channeling effects in dense environments. By addressing wind amplification at ground level, such strategies ensure compliance with pedestrian comfort criteria, such as those limiting mean wind speeds to below 5 m/s for prolonged exposure. Architectural solutions play a central role in wind deflection during the design phase. Podium designs, where a broader base supports a stepped-back tower, effectively redirect downward wind flows away from street level, reducing velocities in adjacent plazas. Setbacks along building heights further minimize vortex formation, while porous screens—such as perforated facades or lattice structures—allow partial airflow passage to prevent stagnation without exacerbating turbulence. These features have been shown to improve wind conditions in high-rise clusters by altering the building envelope's interaction with prevailing winds. Vegetative barriers offer a natural, low-maintenance option for wind reduction, particularly in urban canyons where street-level winds are funneled. Trees and hedges, when strategically planted along building perimeters or roadways, can decrease wind speeds by up to 50% in leeward zones by acting as porous windbreaks, with denser configurations yielding greater attenuation. Optimal layouts involve multi-row plantings with species of varying heights to create layered resistance, enhancing effectiveness in narrow streets while also providing aesthetic and ecological benefits. Studies indicate that such barriers are most impactful when porosity is balanced at 40-60%, allowing controlled dispersion without pollutant buildup. Engineering interventions provide precise control, often informed by computational fluid dynamics (CFD) simulations. Wind-permeable canopies, constructed from mesh or slatted materials, shelter pedestrian pathways by diffusing gusts while maintaining ventilation, reducing speeds by 30-60% in covered areas. Similarly, CFD-optimized shapes like twisted facades disrupt coherent wind structures, lowering pedestrian-level velocities by up to 25% compared to rectilinear forms through gradual aerodynamic tapering. These methods enable site-specific tailoring to complex urban topography. Notable case studies illustrate practical applications. The Burj Khalifa in Dubai employs setbacks and a Y-shaped plan with curved edges to deflect winds, as confirmed by extensive wind tunnel testing that measured reduced ground-level speeds and improved comfort around the base. In high-density cities like Hong Kong, post-construction retrofits—such as installing vegetative screens and permeable canopies on existing towers—have successfully mitigated wind issues, with one study on podium-level additions showing velocity reductions of 20-40% in retrofitted street canyons. These examples highlight the adaptability of mitigation techniques to both new developments and legacy urban fabrics.

Wind Energy Applications

Aerodynamics of Wind Turbines

The aerodynamics of wind turbines revolves around the principles of extracting kinetic energy from the wind using rotating blades, optimizing lift and minimizing drag to maximize power output while managing structural loads. Wind turbines operate by converting the wind's kinetic energy into mechanical rotation through airfoil-shaped blades, where the airflow over the blades generates lift forces that drive the rotor. This process is fundamentally limited by the physics of fluid dynamics, as the turbine extracts energy from the airflow, altering the downstream velocity and pressure fields. Key to performance is the power coefficient CpC_p, defined as the ratio of extracted power to the available power in the wind, which encapsulates the aerodynamic efficiency of the system. A foundational limit in wind turbine aerodynamics is the Betz limit, which establishes the theoretical maximum efficiency for an ideal horizontal-axis wind turbine operating in a uniform, steady wind. Derived by analyzing the energy conservation across an actuator disk model, the Betz limit states that no turbine can extract more than Cp,max⁡=1627≈59.3%C_{p,\max} = \frac{16}{27} \approx 59.3\% of the wind's kinetic energy, as the downstream flow must retain some velocity to allow continuous energy flow through the rotor plane. This limit, first rigorously proven by Albert Betz in 1919, assumes frictionless flow, infinite blade number, and no wake rotation, providing a benchmark against which real turbine designs are evaluated; modern large-scale turbines achieve CpC_p values around 45-50%, approaching but not exceeding this cap due to practical losses. Blade element theory (BET) forms the core analytical framework for designing and predicting turbine blade performance, dividing the blade into independent radial segments (blade elements) and summing their contributions to overall torque and thrust. For each element at radius rr, the local angle of attack α\alpha is determined by the relative wind velocity, comprising the axial wind speed VV and the tangential blade speed ωr\omega r, where ω\omega is the rotational speed and RR is the blade radius; this angle influences the lift and drag coefficients ClC_l and CdC_d of the airfoil profile, which in turn dictate the elemental forces via dL=12ρVrel2cdr CldL = \frac{1}{2} \rho V_{\text{rel}}^2 c dr \, C_l and dD=12ρVrel2cdr CddD = \frac{1}{2} \rho V_{\text{rel}}^2 c dr \, C_d, with ρ\rho as air density and cc as chord length. The theory highlights the importance of the tip speed ratio λ=ωRV\lambda = \frac{\omega R}{V}, typically optimized between 6 and 8 for three-bladed turbines to balance high lift at inboard sections with reduced drag at the tip, enabling efficient operation across varying wind speeds. Extensions like the blade element momentum (BEM) method incorporate momentum theory to account for induced velocities, improving accuracy for finite blades. Wake effects significantly influence turbine aerodynamics, particularly in wind farms where downstream turbines experience reduced wind speeds due to the velocity deficit in the turbulent wake from upstream rotors. The wake expands and slows the flow, with the centerline velocity recovering gradually over several rotor diameters, leading to power losses estimated at 10-20% for turbines spaced 5-10 diameters apart in arrays. These effects are modeled using approaches like the Jensen wake model, which assumes a linearly expanding top-hat wake profile with velocity deficit Δu/u0=2a(1+kx/d)2\Delta u / u_0 = \frac{2a}{(1 + k x / d)^2}, where aa is the axial induction factor (around 1/3 at maximum power), kk is the wake expansion constant (0.04-0.075 for onshore), dd is rotor diameter, and xx is downwind distance. Understanding and mitigating wake interactions through optimized farm layouts is crucial for overall energy yield. To regulate power and manage loads, wind turbines employ aerodynamic control mechanisms such as yaw and stall control. Yaw control orients the rotor into the wind by rotating the nacelle, maximizing energy capture and minimizing asymmetric loads, with modern systems using sensors for active tracking within ±30° misalignment tolerances. Stall control, common in fixed-speed fixed-pitch turbines, relies on increasing the angle of attack beyond the stall point at high winds to limit power by inducing flow separation and drag, thereby feathering the blades aerodynamically without mechanical pitching; this method caps power at rated levels but can lead to fatigue from unsteady loads. Modern turbines predominantly use variable-speed pitch control for smoother power regulation and reduced loads. These strategies ensure safe operation up to cut-out speeds around 25 m/s, balancing efficiency with durability. Wind shear in the atmospheric boundary layer introduces inflow variations that further modulate these aerodynamic responses.

Wind Resource Assessment

Wind resource assessment is a critical process in wind energy projects that involves evaluating the wind potential at prospective sites to estimate energy production and economic viability. This evaluation typically requires on-site measurements over extended periods, combined with statistical modeling and corrections to account for spatial and temporal variations in wind conditions. Accurate assessment helps in site selection, turbine placement, and forecasting annual energy production (AEP), minimizing uncertainties that can affect project returns. Measurement campaigns form the foundation of wind resource assessment, employing various instruments to capture wind speed, direction, and other meteorological data at relevant heights. Traditional methods use meteorological masts equipped with cup anemometers mounted at hub heights typical for modern turbines, such as 80-100 meters, to directly measure wind speeds where turbine rotors operate. These masts provide reliable, calibrated data but are limited by installation challenges in remote or complex terrains. Complementary remote sensing technologies, including SODAR (Sonic Detection and Ranging) and LIDAR (Light Detection and Ranging), enable vertical wind profiling without physical towers; SODAR uses acoustic signals to detect wind up to several hundred meters, while LIDAR employs laser pulses for higher resolution and range, often exceeding 200 meters. These tools are particularly valuable for offshore or rugged sites, allowing non-intrusive data collection over the rotor plane to assess shear and turbulence. Campaigns typically last 1-2 years to capture seasonal variations, with data quality controlled through redundancy and calibration protocols. Statistical analysis of collected wind data involves fitting probability distributions to characterize the wind regime and estimate key performance metrics. The Weibull distribution is widely adopted to model the probability density function (PDF) of s due to its flexibility in representing skewed histograms observed in nature. The PDF is expressed as f(v)=kc(vc)k−1exp⁡(−(vc)k),f(v) = \frac{k}{c} \left( \frac{v}{c} \right)^{k-1} \exp\left( -\left( \frac{v}{c} \right)^k \right), where vv is the , kk (>1) is the dimensionless indicating the distribution's peakedness (typically 1.8-2.5 for wind sites), and cc is the related to the mean (often c≈1.9vˉc \approx 1.9 \bar{v} for k=2k=2). Parameters kk and cc are estimated from site data using methods like maximum likelihood or moments, enabling computation of exceedance probabilities and energy potential. From this distribution, the —a measure of representing the ratio of actual to rated output—is estimated by convolving the Weibull PDF with the 's power curve, yielding values typically between 25-45% for viable sites depending on mean s of 6-9 m/s. This approach provides a probabilistic basis for AEP predictions, accounting for variability rather than relying on mean speeds alone. Extrapolation techniques adjust measured wind data to unmeasured heights or periods, ensuring estimates align with operating conditions. The , v(z)=vr(zzr)αv(z) = v_r \left( \frac{z}{z_r} \right)^\alpha, empirically scales vv from reference zrz_r to target zz, with shear exponent α\alpha (0.1-0.2 for neutral stability) derived from site measurements or terrain class. The logarithmic , v(z)=u∗κln⁡(zz0)v(z) = \frac{u_*}{\kappa} \ln\left( \frac{z}{z_0} \right), offers a more physically based alternative using friction velocity u∗u_*, von Kármán constant κ≈0.4\kappa \approx 0.4, and z0z_0 (e.g., 0.03 m for open terrain), suitable for flows. For long-term correction, the measure-correlate-predict (MCP) method correlates short-term on-site data (e.g., 1 year) with concurrent long-term from nearby stations or models like MERRA-2, then predicts extended-site statistics to reduce from incomplete campaigns; this can adjust mean speeds by 5-10% based on regional trends. These methods are validated against mast data to ensure accuracy within 3-5% for AEP estimates. Micrositing refines resource assessment by modeling local effects to optimize placement within a . (CFD) or linearized models simulate orographic influences, such as hill-induced speed-ups where wind accelerates over ridges, potentially increasing speeds by 20% on escarpments due to flow compression and reduced drag. These enhancements are quantified using elevation data from digital models (e.g., SRTM), identifying high-yield locations while avoiding wake interference. Such modeling integrates with statistical outputs to boost farm-wide AEP by 10-15% through precise layout optimization.

Analysis and Modeling Techniques

Experimental Methods

Experimental methods in wind engineering primarily involve physical testing in controlled environments to measure wind effects on structures, validate models, and assess aerodynamic behaviors. These techniques rely on scaled models subjected to simulated wind flows, providing empirical that informs structural , load predictions, and environmental impacts. Wind tunnels serve as the cornerstone of these methods, enabling precise replication of atmospheric conditions to study phenomena such as pressure distributions, , and around buildings and other civil structures. Boundary layer wind tunnels are specifically designed for wind engineering applications, simulating the atmospheric (ABL) profiles that develop over , including gradients and intensities characteristic of natural s. These tunnels incorporate features like roughness elements, spires, and fetch sections to generate realistic ABL flows, differing from aeronautical wind tunnels that prioritize uniform, low- streams for testing. In contrast, atmospheric-scale tunnels emphasize turbulent, shear-laden flows to mimic urban or rural exposures, ensuring relevance to full-scale structural responses. Scale modeling underpins these experiments, adhering to similarity principles to ensure that model-scale results scale accurately to prototypes. Geometric, kinematic, and dynamic similarities are targeted, with a key criterion being the , defined as Re=ρVLμRe = \frac{\rho V L}{\mu}, where ρ\rho is fluid density, VV is , LL is , and μ\mu is dynamic ; tests typically achieve Re>105Re > 10^5 to minimize viscous effects and promote independence. Blockage corrections are essential to account for the model's interference with tunnel walls, which accelerates flow and overestimates loads; methods such as those based on solid blockage ratio adjust measured pressures and forces to reflect open-terrain conditions. Instrumentation in these tests captures detailed flow characteristics, with hot-wire anemometry providing high-frequency measurements of velocity fluctuations to quantify turbulence intensity and spectra in the ABL simulation. This technique uses fine wires heated by , where cooling by airflow alters resistance to infer speed, enabling resolution of small-scale eddies relevant to structural vibrations. (PIV) complements this by offering non-intrusive flow visualization, seeding the flow with tracer particles illuminated by laser sheets to track velocity fields via double-exposure imaging, thus mapping shear layers and recirculation zones around models. Applications of these methods include force balances for quantifying wind loads, where multi-component strain-gauge systems mounted beneath the model floor measure base moments and shears under varying wind directions, aiding in the estimation of overturning and torsional effects on tall buildings. Smoke visualization techniques reveal qualitative flow patterns, such as from bluff bodies, by injecting neutrally buoyant into the airstream to highlight periodic wake instabilities that contribute to in structures. These experimental insights are often validated against real-world gust data to ensure predictive accuracy for design codes.

Computational Approaches

Computational Fluid Dynamics (CFD) serves as a cornerstone in wind engineering for simulating complex wind flows around structures and in urban environments by numerically solving the governing equations of fluid motion. At its core, involves discretizing the Navier-Stokes equations, which describe the , , and in fluid flows, using methods such as the finite volume approach to approximate solutions on computational grids. This technique enables engineers to predict wind pressures, velocities, and without physical prototypes, facilitating early-stage design optimization for buildings and infrastructure. A widely adopted approach within CFD for wind engineering is the Reynolds-Averaged Navier-Stokes (RANS) modeling, which averages turbulent fluctuations to focus on mean flow characteristics, thereby reducing computational demands. Common turbulence closures in RANS include the k-ε model, which solves transport equations for turbulent (k) and its dissipation rate (ε) to estimate eddy viscosity and capture shear stresses in atmospheric boundary layers. For instance, the standard k-ε model has been refined for urban wind simulations to better predict and reattachment around bluff bodies like high-rise buildings. However, RANS assumes steady-state conditions, which can underrepresent transient phenomena such as gusts. For more accurate representation of unsteady turbulent structures, (LES) resolves large-scale eddies directly while modeling smaller subgrid-scale ones, offering superior fidelity for urban wind flows where coherent vortices dominate dispersion and loading. LES is particularly valuable in simulating pedestrian-level winds in complex cityscapes, capturing the intermittent nature of that RANS often smooths out. Applications include predicting wind comfort around high-density developments, where LES reveals localized accelerations and downdrafts not evident in coarser models. Prominent software tools for these simulations include Fluent, a commercial platform with robust finite volume solvers for RANS and LES, and the open-source , which supports customizable models and has been extensively applied in computational wind for its flexibility in handling large-scale urban geometries. Validation of CFD results typically involves benchmarking against measurements, ensuring that simulated mean velocities and pressures align within 10-20% of experimental data for isolated structures. Despite these advances, limitations persist: high computational costs, often requiring supercomputing resources for LES over domains exceeding millions of cells, restrict routine use, while steady RANS simulations may overlook gust-induced vibrations, necessitating unsteady approaches that further escalate demands. Recent post-2020 innovations, such as learning-accelerated LES, employ neural networks to surrogate subgrid models, reducing simulation times by up to 50% for urban flows while maintaining accuracy.

Role in Engineering Practice

Wind engineering plays a pivotal role in ensuring the structural integrity of tall buildings and long-span bridges by mitigating wind-induced vibrations and loads, thereby enhancing safety in civil and projects. In , tuned mass dampers and viscous systems are integrated to control dynamic responses, as demonstrated in the in , where dampers in the structural legs increased inherent , reducing wind-induced accelerations effectively. Similarly, for bridges, aerodynamic shaping and cable configurations are optimized to prevent flutter and buffeting; the Akashi Kaikyō Bridge in employs specialized wind-resistant cable designs and separate criteria for horizontal deflections, enabling it to withstand winds up to 80 m/s while supporting a 1,991 m central span. The application of wind engineering principles leads to significant economic benefits in structural design by allowing for material optimization and reduced construction costs. Optimized aerodynamic forms can achieve 10-20% reductions in material usage, such as and , through techniques like chamfered corners and adjusted construction sequencing, as seen in high-rise projects in where base moments were lowered by up to 44%. These savings extend to , where buildings with higher wind resistance ratings qualify for lower premiums, incentivizing robust designs to minimize potential losses from . Real-world case studies underscore wind engineering's impact on coastal and hybrid hazard resilience. in 2005 revealed vulnerabilities in coastal structures, where wind pressures of 90-130 mph caused partial collapses of unreinforced walls independent of surge effects, prompting updates to design practices for better integration of wind and wave loads in low-rise buildings. The , completed in , exemplifies integrated wind-seismic design, utilizing tuned mass dampers at the apex to suppress wind-induced vibrations by up to 30% while also reducing seismic accelerations by 50% through a core system tuned to site-specific conditions. Wind engineering projects typically involve multidisciplinary teams comprising structural engineers, architects, and meteorologists to address complex interactions between , urban environments, and data. These collaborations enable early-stage refinements, such as for wind load reduction, while incorporating meteorological assessments of site-specific wind profiles and future risks. Design codes like ASCE 7 serve as key implementation tools for translating these analyses into standardized load provisions.

Emerging Challenges and Innovations

Climate change is intensifying challenges in wind engineering by altering wind regimes, particularly through increased frequencies and intensities of extreme events. The (IPCC) projects that tropical cyclone maximum surface wind speeds could increase by approximately 5% at 2°C global warming, with model ranges up to 10%, under high-emission scenarios by 2100, driven by warmer sea surface temperatures that enhance storm dynamics. This shift toward non-stationary wind statistics—where historical patterns no longer predict future extremes—demands resilient design paradigms that incorporate time-varying load factors and . For instance, studies indicate that failing to account for these changes could underestimate design wind speeds by 15-25% in vulnerable regions like coastal , potentially compromising structural integrity. Technological innovations are addressing these challenges by enhancing prediction and measurement capabilities. and algorithms enable real-time wind load prediction on structures, such as tall buildings, by analyzing data to forecast gusts with errors below 10%, allowing for dynamic load mitigation. Since 2020, drone-based anemometry has emerged as a transformative tool for field measurements, providing high-resolution vertical wind profiles in urban and offshore environments with biases as low as 2-4% compared to traditional masts, thus improving site-specific assessments in inaccessible areas. Sustainability advancements focus on expanding renewable energy capture amid evolving conditions. Offshore floating wind turbines facilitate deployment in water depths exceeding 60 meters, where fixed foundations are impractical, with the sector's market value projected to surge from $1.7 billion in 2025 to $18 billion by 2030 due to scaled prototypes achieving 15 MW capacities. In urban settings, micro-energy harvesting via building-integrated small wind turbines harnesses turbulent flows for localized power, potentially offsetting 5-10% of a building's electricity needs while minimizing grid reliance. Persistent knowledge gaps hinder comprehensive . modeling in wind engineering lacks precision in simulating near-surface wind fields within the eyewall, resulting in hazard over- or underestimations by up to 20% and complicating resilient . Similarly, interactions between urban heat islands and wind flows remain underexplored, as heat-induced can suppress wind speeds by 20-25% during heatwaves, amplifying cooling demands and reducing micro-wind harvesting .

References

  1. https://www.jawe.jp/en/about-the-society/what-is-wind-engineering.html
  2. https://www.idr.upm.es/en/wind-engineering
  3. https://www.sciencedirect.com/science/article/abs/pii/S0167610507000219
  4. https://www.iawe.org/about/purpose.html
  5. https://www.cee.ed.tum.de/en/st/research/wind-engineering/modelling-and-simulation-in-wind-engineering/
  6. https://www.jawe.or.jp/en/about-the-society/what-is-wind-engineering.html
  7. https://www.grc.nasa.gov/www/k-12/VirtualAero/BottleRocket/airplane/drageq.html
  8. https://www3.nd.edu/~nathaz/journals/%281988%29Aerodynamic_response_of_structures_with_parametric_uncertainties.pdf
  9. https://link.aps.org/doi/10.1103/PhysRevLett.93.084501
  10. https://www.easternengineeringgroup.com/vortex-shedding-in-structural-engineering-understanding-and-mitigating-wind-induced-oscillations/
  11. https://dept.math.lsa.umich.edu/~krasny/math654_irwin.pdf
  12. https://taylorandfrancis.com/knowledge/Engineering_and_technology/Aerospace_engineering/Flutter/
  13. https://www.intechopen.com/chapters/58853
  14. https://www.eng-tips.com/viewthread.cfm?qid=402433
  15. https://www.sciencedirect.com/science/article/pii/S0951833921000058
  16. https://www.open.edu/openlearn/history-the-arts/history/social-economic-history/swept-away-brightons-chain-pier-collapses-during-storm
  17. https://etheses.whiterose.ac.uk/id/eprint/10303/1/458273_vol2.pdf
  18. https://www.weather.gov/okx/1938hurricanehome
  19. https://www.weather.gov/source/zhu/ZHU_Training_Page/clouds/planetary_boundary_layer/PBL.html
  20. https://www.e-education.psu.edu/meteo300/node/711
  21. https://cas.hamptonu.edu/research/atmospheric-boundary-layer/
  22. https://faculty.lsu.edu/aly/wind-engineering-terminology.php
  23. https://www.eng.uwo.ca/people/esavory/blnotes.doc
  24. https://ep.liu.se/ecp/057/vol15/004/ecp57vol15_004.pdf
  25. https://repository.tudelft.nl/file/File_bfd447d7-4925-466f-9ef4-613924cb3d8b
  26. https://www.hou.usra.edu/meetings/planetinsitu2022/pdf/7005.pdf
  27. https://www.researchgate.net/profile/Stefan-Emeis/publication/225883255_Comparison_of_Logarithmic_Wind_Profiles_and_Power_Law_Wind_Profiles_and_their_Applicability_for_Offshore_Wind_Profiles/links/5a3b6623458515a77aa9ebe6/Comparison-of-Logarithmic-Wind-Profiles-and-Power-Law-Wind-Profiles-and-their-Applicability-for-Offshore-Wind-Profiles.pdf
  28. https://pubs.usgs.gov/pp/1099c/report.pdf
  29. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020JD033812
  30. https://www.mdpi.com/1996-1073/18/19/5211
  31. https://journals.ametsoc.org/view/journals/apme/17/8/1520-0450_1978_017_1179_acoaal_2_0_co_2.xml
  32. https://www.mdpi.com/2076-3417/13/8/5077
  33. https://orbit.dtu.dk/files/83187128/A_review_of_turbulence.pdf
  34. https://gi.copernicus.org/articles/13/205/2024/
  35. https://www.osti.gov/servlets/purl/5348174
  36. https://ntrs.nasa.gov/api/citations/19840005662/downloads/19840005662.pdf
  37. https://npg.copernicus.org/articles/23/407/2016/npg-23-407-2016.pdf
  38. https://iopscience.iop.org/article/10.1088/1742-6596/753/3/032008
  39. https://www.researchgate.net/publication/245101522_Generalized_extreme_gust_wind_speeds_distributions
  40. https://docs.nrel.gov/docs/fy23osti/86259.pdf
  41. https://www.sciencedirect.com/science/article/abs/pii/S0167610502002799
  42. https://www.frontiersin.org/journals/built-environment/articles/10.3389/fbuil.2020.00063/full
  43. https://www.sciencedirect.com/science/article/abs/pii/S0167610505000437
  44. https://www.researchgate.net/publication/268594358_Wind-Induced_Torsional_Loads_and_Responses_of_Buildings
  45. https://pdfcoffee.com/download/06-steel-chimney-code-2-pdf-free.html
  46. https://nrc-publications.canada.ca/eng/view/object/?id=00579bf8-483d-4591-95f8-6708577dba0e
  47. https://amplify.asce.org/asce-lookup/tab-link/t6.10-2/19231
  48. https://www.researchgate.net/publication/291837231_Aerodynamic_coefficients_for_a_rectangular_tall_building_under_sub-urban_terrain_using_wind_tunnel
  49. https://www.mdpi.com/2075-5309/13/7/1871
  50. https://amplify.asce.org/content/standard/9780784415788/part/provisions/back-matter/appf
  51. https://www.phd.eng.br/wp-content/uploads/2015/12/en.1991.1.4.2005.pdf
  52. https://skyciv.com/docs/tech-notes/loading/asnzs-1170-2-wind-load-calculation-example/
  53. https://ascelibrary.org/doi/10.1061/JSENDH.STENG-11899
  54. https://www.fema.gov/sites/default/files/documents/fema_asce-7-22-wind-highlights_fact-sheet_2022.pdf
  55. https://www.ncsea.com/app/uploads/2024/02/NCSEA-Hurricane-FAQ.pdf
  56. https://www.littlepeng.com/single-post/wind-load-calculation-as-per-asce-7-16
  57. https://eurocodes.jrc.ec.europa.eu/sites/default/files/2021-12/handbook3.pdf
  58. https://amplify.asce.org/content/standard/9780784415788/part/provisions/back-matter/appc
  59. https://www.simscale.com/blog/wind-comfort-criteria/
  60. https://www.simscale.com/blog/lawson-wind-comfort-criteria/
  61. https://www.sciencedirect.com/science/article/abs/pii/S0167610506000882
  62. https://docs.nablaflow.io/archiwind/guide/fields/pedestrian-wind-comfort-and-safety/
  63. https://www6.mississauga.ca/onlinemaps/planbldg/UrbanDesign/PedesterianLevelWindComfortStudies.pdf
  64. https://pmc.ncbi.nlm.nih.gov/articles/PMC7148650/
  65. https://research.fs.usda.gov/treesearch/download/45986.pdf
  66. https://www.sciencedirect.com/science/article/pii/S0160412021005080
  67. https://www.simscale.com/blog/cornering-effect-mitigation-strategies-for-pedestrian-wind-comfort/
  68. https://link.springer.com/article/10.1007/s42107-025-01269-z
  69. https://global.ctbuh.org/resources/papers/download/1016-the-wind-engineering-of-the-burj-dubai-tower.pdf
  70. https://pmc.ncbi.nlm.nih.gov/articles/PMC7127139/
  71. https://wes.copernicus.org/articles/7/2407/2022/
  72. https://www.adb.org/sites/default/files/publication/42032/guidelines-wind-resource-assessment.pdf
  73. https://www.nyserda.ny.gov/-/media/Project/Nyserda/Files/Publications/Research/Biomass-Solar-Wind/wind-resource-assessment-toolkit.pdf
  74. https://iea-wind.org/wp-content/uploads/2023/10/59_Remote-Sensing-2_v2.pdf
  75. https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2015/IRENA_Island_Wind_Measurement_2015.pdf
  76. https://www.nrel.gov/docs/fy16osti/64735.pdf
  77. https://docs.nrel.gov/docs/fy20osti/76234.pdf
  78. https://asmedigitalcollection.asme.org/solarenergyengineering/article/107/1/10/440602/Height-Extrapolation-of-Wind-Data
  79. https://doi.org/10.3390/en14144364
  80. https://www.ruzgarsempozyumu.org/wp-content/uploads/2014/08/007.pdf
  81. https://www.itl.nist.gov/div898/winds/pdf_files/NISTTN1655.pdf
  82. https://www.asce.org/about-civil-engineering/history-and-heritage/historic-landmarks/boundary-layer-wind-tunnel-laboratory
  83. https://cppwind.com/wp-content/uploads/2020/12/WindTunnelMethods-Boggs2006.pdf
  84. https://research.engineering.ucdavis.edu/wind/wp-content/uploads/sites/17/2014/03/WWEC_2004_Final_Paper2.pdf
  85. https://www.mdpi.com/2076-3417/12/14/7087
  86. https://publications.sto.nato.int/publications/AGARD/AGARD-AG-12/AGARD-AG-12.pdf
  87. https://www.sciencedirect.com/science/article/pii/S0955598697000125
  88. https://www.sciencedirect.com/science/article/abs/pii/S0167610598001743
  89. https://cdn.intechopen.com/pdfs/16669/InTech-Flow_visualization_in_wind_tunnels.pdf
  90. https://www.sciencedirect.com/science/article/pii/S2352710223010070
  91. https://link.springer.com/article/10.1007/s11012-024-01826-x
  92. https://www.sciencedirect.com/science/article/abs/pii/S016761051730020X
  93. https://www.sciencedirect.com/science/article/pii/S0360132324011272
  94. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5401831
  95. https://www.sciencedirect.com/science/article/abs/pii/S0167610524002770
  96. https://www.researchgate.net/publication/395964886_Validation_of_CFD_Simulations_Against_Wind_Tunnel_Tests_for_Wind_Pressure_on_High-Rise_Buildings_in_Baghdad
  97. https://www.tandfonline.com/doi/abs/10.1080/19401493.2010.513740
  98. https://nathaz.nd.edu/journals/(1999)Mitigation_of_motions_of_tall_buildings_with_specific_examples_of_recent_applications(WAS).pdf
  99. https://www.taylordevices.com/wp-content/uploads/104-Wind-Effects-on-Long-Span-Cable-Stayed-Bridges.pdf
  100. https://www.ce.jhu.edu/perspectives/handouts_unprotected/akashi.pdf
  101. https://windtechconsult.com/embodied-carbon-reduction-in-buildings-the-role-of-the-wind-engineer/
  102. https://www.sciencedirect.com/science/article/abs/pii/016761059290053D
  103. https://www.eng.buffalo.edu/mceer-reports/07/07-SP03.pdf
  104. https://global.ctbuh.org/resources/papers/download/876-structural-design-of-tokyo-sky-tree.pdf
  105. https://rwdi.com/services/wind-engineering/
  106. https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11/
  107. https://www.structuremag.org/article/machine-learning-model-for-wind-load-prediction-on-tall-buildings/
  108. https://pubs.aip.org/aip/pof/article/37/6/061302/3350602/Drone-anemometry-of-atmospheric-winds-A-review
  109. https://www.globenewswire.com/news-release/2025/09/30/3158360/28124/en/Floating-Offshore-Wind-Energy-Industry-Report-2025-2030-with-Aker-Solutions-ASA-BlueFloat-Energy-International-BW-Ideol-Equinor-ASA-and-GE-Vernova-Leading.html
  110. https://www.nature.com/articles/s41598-025-10540-x
  111. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023EA003099
  112. https://www.sciencedirect.com/science/article/abs/pii/S0360132325012910
Add your contribution
Related Hubs
User Avatar
No comments yet.