Using super-large-scale particle image velocimetry (SLPIV), we investigate the spatial structure of the near-wall region in the fully rough atmospheric surface layer with Reynolds number$Re_{\unicode[STIX]{x1D70F}}\sim O(10^{6})$. The field site consists of relatively flat, snow-covered farmland, allowing for the development of a fully rough turbulent boundary layer under near-neutral thermal stability conditions. The imaging field of view extends from 3 m to 19 m above the ground and captures the top of the roughness sublayer and the bottom of an extensive logarithmic region. The SLPIV technique uses natural snowfall as seeding particles for the flow imaging. We demonstrate that SLPIV provides reliable measurements of first- and second-order velocity statistics in the streamwise and wall-normal directions. Our results in the logarithmic region show that the structural features identified in laboratory studies are similarly present in the atmosphere. Using instantaneous vector fields and two-point correlation analysis, we identify vortex structures sharing the signature of hairpin vortex packets. We also evaluate the zonal structure of the boundary layer by tracking uniform momentum zones (UMZs) and the shear interfaces between UMZs in space and time. Statistics of the UMZs and shear interfaces reveal the role of the zonal structure in determining the mean and variance profiles. The velocity difference across the shear interfaces scales with the friction velocity, in agreement with previous studies, and the size of the UMZs scales with wall-normal distance, in agreement with the attached eddy framework.

Since the introduction of the logarithmic law of the wall more than 80 years ago, the equation for the mean velocity profile in turbulent boundary layers has been widely applied to model near-surface processes and parameterise surface drag. Yet the hypothetical turbulent eddies proposed in the original logarithmic law derivation and mixing length theory of Prandtl have never been conclusively linked to physical features in the flow. Here, we present evidence that suggests these eddies correspond to regions of coherent streamwise momentum known as uniform momentum zones (UMZs). The arrangement of UMZs results in a step-like shape for the instantaneous velocity profile, and the smooth mean profile results from the average UMZ properties, which are shown to scale with the friction velocity and wall-normal distance in the logarithmic region. These findings are confirmed across a wide range of Reynolds number and surface roughness conditions from the laboratory scale to the atmospheric surface layer.

Field‐scale and wind tunnel experiments were conducted in the 2D to 6D turbine wake region to investigate the effect of geometric and Reynolds number scaling on wake meandering. Five field deployments took place: 4 in the wake of a single 2.5‐MW wind turbine and 1 at a wind farm with numerous 2‐MW turbines. The experiments occurred under near‐neutral thermal conditions. Ground‐based lidar was used to measure wake velocities, and a vertical array of met‐mounted sonic anemometers were used to characterize inflow conditions. Laboratory tests were conducted in an atmospheric boundary layer wind tunnel for comparison with the field results. Treatment of the low‐resolution lidar measurements is discussed, including an empirical correction to velocity spectra using colocated lidar and sonic anemometer. Spectral analysis on the laboratory‐ and utility‐scale measurements confirms a meandering frequency that scales with the Strouhal number St = fD/U based on the turbine rotor diameter D. The scaling indicates the importance of the rotor‐scaled annular shear layer to the dynamics of meandering at the field scale, which is consistent with findings of previous wind tunnel and computational studies. The field and tunnel spectra also reveal a deficit in large‐scale turbulent energy, signaling a sheltering effect of the turbine, which blocks or deflects the largest flow scales of the incoming flow. Two different mechanisms for wake meandering—large scales of the incoming flow and shear instabilities at relatively smaller scales—are discussed and inferred to be related to the turbulent kinetic energy excess and deficit observed in the wake velocity spectra.

With the expansion of hydropower, in‐stream converters, flood‐protection infrastructures, and growing concerns on deltas fragile ecosystems, there is a pressing need to evaluate and monitor bedform sediment mass flux. It is critical to estimate real‐time bedform size and migration velocity and provide a theoretical framework to convert easily accessible time histories of bed elevations into spatially evolving patterns. We collected spatiotemporally resolved bathymetries from laboratory flumes and the Colorado River in statistically steady, homogeneous, subcritical flow conditions. Wave number and frequency spectra of bed elevations show compelling evidence of scale‐dependent velocity for the hierarchy of migrating bedforms observed in the laboratory and field. New scaling laws were applied to describe the full range of migration velocities as function of two dimensionless groups based on the bed shear velocity, sediment diameter, and water depth. Further simplification resulted in a mixed length scale model estimating scale‐dependent migration velocities, without requiring bedform classification or identification.

Abstract

Abstract

Sweep and ejection events in turbulent boundary layer flows have been explored for half a century now to describe eddies impacting turbulent stresses. Yet, moving these studies from their current diagnostic phase to a prognostic form remains a formidable challenge. Here, a cumulant expansion is used to derive a link between the transport of shear stress and the balance of local sweep and ejection events. Cumulant expansion is further used to connect this transport to a metric of asymmetry in the streamwise velocity distribution. These relations are employed to develop two so-called structural models for predicting the turbulent stress transport, which is traditionally neglected in first-order closure of the shear stress budget. Several datasets collected in rough-wall conditions are used to show the importance of the transport term in the roughness sublayer and to demonstrate the predictive skill of the two structural models. The model parameters are invariant to the tested range of Reynolds number and surface roughness, indicating the structural similarity between the velocity asymmetry, sweep/ejection balance, and stress transport may be universal and independent of roughness. Finally, the implementation of the structural models for improved closure schemes of the shear stress budget in modeling applications and wall-modeling in large-eddy simulations are discussed.

Stochastic simulations are used to create synthetic one-dimensional telegraph approximation (TA) signals based on turbulent zero crossings, where the interval between crossings is governed by a power-law probability distribution with exponent α. The powerlaw exponent is determined for statistics of simulated TA signals, namely, the box-counting fractal dimension D 1 , the energy spectrum exponent β TA , and the intermittency exponent μ TA . For the binary TA signal with no variability in amplitude, the parameters are related linearly as D 1 = 2 − β TA = 1 − μ TA . The relations are unchanged if the crossing interval distribution has a finite power-law region (i.e., inertial subrange) representing a flow with a finite Reynolds number. However, the finite distribution yields statistics that are not truly scale invariant and distorts the linear relation between the statistic exponents and α. The behavior is due to finite-size effects apparent from the survival function, or the complementary cumulative distribution, which for finite Reynolds number is only approximately self-similar and has an effective exponent differing from α. An expression presented for the effective exponent recovers the expected relations between α and the TA statistics. The findings demonstrate how a finite Reynolds number can affect indicators of self-similarity, fractality, and intermittency observed from single-point measurements.