The Detection, Genesis, and Modeling of Turbulence Intermittency in the Stable Atmospheric Surface Layer

Intermittent transitions between turbulent and non-turbulent states are ubiquitous in the stable atmospheric surface layer (ASL). Data from two field experiments in Utqiagvik, Alaska, and from direct numerical simulations are used to probe these state transitions so as to (i) identify statistical metrics for the detection of intermittency, (ii) probe the physical origin of turbulent bursts, and (iii) quantify intermittency effects on overall fluxes and their representation in closure models. The analyses reveal three turbulence regimes, two of which correspond to weakly turbulent periods accompanied by intermittent behavior (regime 1: intermittent, regime 2: transitional), while the third is associated with a fully turbulent flow. Based on time series of the turbulence kinetic energy (TKE), two non-dimensional parameters are proposed to diagnostically categorize the ASL state into these regimes; the first characterizes the weakest turbulence state, while the second describes the range of turbulence variability. The origins of intermittent turbulence activity are then investigated based on the TKE budget over the identified bursts. While the quantitative results depend on the height, the analyses indicate that these bursts are predominantly advected by the mean flow, produced locally by mechanical shear, or lofted from lower levels by turbulent ejections. Finally, a new flux model is proposed using the vertical velocity variance in combination with different mixing length scales. The model provides improved representation (correlation coefficients with observations of 0.61 for momentum and 0.94 for sensible heat) compared to Monin–Obukhov similarity (correlation coefficients of 0.0047 for momentum and 0.49 for sensible heat), thus opening new pathways for improved parametrizations in coarse atmospheric models.

Vortex clustering, polarisation and circulation intermittency in classical and quantum turbulence

The understanding of turbulent flows is one of the biggest current challenges in physics, as no first-principles theory exists to explain their observed spatio-temporal intermittency. Turbulent flows may be regarded as an intricate collection of mutually-interacting vortices. This picture becomes accurate in quantum turbulence, which is built on tangles of discrete vortex filaments. Here, we study the statistics of velocity circulation in quantum and classical turbulence. We show that, in quantum flows, Kolmogorov turbulence emerges from the correlation of vortex orientations, while deviations—associated with intermittency—originate from their non-trivial spatial arrangement. We then link the spatial distribution of vortices in quantum turbulence to the coarse-grained energy dissipation in classical turbulence, enabling the application of existent models of classical turbulence intermittency to the quantum case. Our results provide a connection between the intermittency of quantum and classical turbulence and initiate a promising path to a better understanding of the latter.

Wind Speed Parameterization and Turbulence Intermittency in the Roughness Sublayers over Urban Areas: a Wind Tunnel Study

<p>The flow in inertial sublayer (ISL) is horizontally homogeneous where the Monin–Obukhov similarity theory (MOST) well describes the flux-gradient relationship.  In contrast, roughness sublayer (RSL) flow is highly inhomogeneous. Its dynamics is influenced by the length scale of individual roughness elements. This study presents an analytical solution to the mean wind profile for both ISL and RSL by adding a new function in the flux-gradient relationship to handle the RSL dynamics. The mean wind speeds measured in the wind tunnel experiments over a range of idealized and real urban geometries are well predicted by the new analytical solution. The root-mean-square errors (RMSE) are reduced over an order of magnitude compared with the conventional logarithmic law of the wall (log-law). Its key parameter, the RSL constant converges asymptotically to μ = 1.7 for urban setting which is different from that (μ = 2.6) for vegetation canopy. The RSL turbulence intermittency is revealed by higher-order moments of velocities, probability density function (PDF), quadrant analysis, and conditional sampling. Ejection Q2 (-u’’, +w”) and sweep Q4 (+u’’, -w”) dominate in both RSL and ISL but with different share. Unlike the ISL, Q2 occurs more frequently (but contributes less to momentum flux) than Q4 in the RSL. It is thus suggested that RSL turbulent transport is driven by occasional, fast motions of accelerating downward flow (Q4) and bulk, slow decelerating upward flow (Q2).</p>

Wind turbine load dynamics in the context of turbulence intermittency

The importance of a high-order statistical feature of wind, which is neglected in common wind models, is investigated: non-Gaussian distributed wind velocity increments related to the intermittency of turbulence and their impact on wind turbine dynamics and fatigue loads are the focus. Gaussian and non-Gaussian synthetic wind fields obtained from a continuous-time random walk model are compared and fed to a common aero-servo-elastic model of a wind turbine employing blade element momentum (BEM) aerodynamics. It is discussed why and how the effect of the non-Gaussian increment statistics has to be isolated. This is achieved by assuring that both types feature equivalent probability density functions, spectral properties and coherence, which makes them indistinguishable based on wind characterizations of common design guidelines. Due to limitations in the wind field genesis, idealized spatial correlations are considered. Three examples with idealized; differently sized wind structures are presented. A comparison between the resulting wind turbine loads is made. For the largest wind structure sizes, differences in the fatigue loads between intermittent and Gaussian are observed. These are potentially relevant in a wind turbine certification context. Subsequently, the dependency of this intermittency effect on the field's spatial variation is discussed. Towards very small structured fields, the effect vanishes.

Wind turbine load dynamics in the context of turbulence intermittency

The importance of a high order statistical feature of wind, which is neglected in common wind models, is investigated: Non-Gaussian distributed wind velocity increments related to the intermittency of turbulence and their impact on wind turbines dynamics and fatigue loads are in the focus. Two types of synthetic wind fields obtained from a Continuous-Time-Random Walk model are compared and fed to a common Blade-Element/Momentum theory based aero-servo-elastic wind turbine model. It is discussed why and how the effect of the non-Gaussian increment statistics has to be isolated. This is achieved by assuring that both types feature equivalent probability density functions, spectral properties and coherence, which makes them indistinguishable based on wind characterizations of common design guidelines. Due to limitations in the wind field genesis idealized spatial correlations are considered. Three examples with idealized, differently sized wind structures are presented. A comparison between the resulting wind turbine loads is made. For the largest wind structure sizes differences in the fatigue loads between intermittent and Gaussian are observed. These are potentially relevant in a wind turbine certification context. Subsequently, the dependency of this intermittency effect on the field's spatial variation is discussed. Towards very small structured fields the effect vanishes.

Turbulent Intermittency in a Random Fiber Laser

In fluid turbulence, intermittency is the emergence of non-Gaussian tails in the distribution of velocity increments in small space and/or time scales. Intermittence is thus expected to gradually disappear as one moves from small to large scales. Here we study the turbulent-like intermittency effect experimentally observed in the distribution of intensity fluctuations in a disordered continuous-wave-pumped erbium-doped-based random fiber laser with specially-designed random fiber Bragg gratings. The intermittency effect is investigated as a crossover in the distribution of intensity increments from a heavy-tailed distribution (for short time scales), to a Gaussian distribution (for large time scales). The results are theoretically supported by a hierarchical stochastic model that incorporates Kolmogorov’s theory of turbulence. In particular, the discrete version of the hierachical model allows a general direct interpretation of the number of relevant scales in the photonic hierarchy as the order of the transitions induced by the non-linearities in the medium. Our results thus provide further statistical evidence for the interpretation of the turbulence-like emission previously observed in this system.