Turbulent Friction Velocity Calculated from the Reynolds Stress Tensor

Klipp, Cheryl

doi:10.1175/jas-d-16-0282.1

Cited by 17 publications

(10 citation statements)

References 18 publications

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“…The encouraging results presented above make us confident that anisotropy is the dominant process causing the departure from scaling for unstable stratification and that including information on it would improve scaling relations.We therefore hypothesize that these deviations from scaling exist due to the fact that scaling relations are evaluated in the physical, streamwise Cartesian coordinate system, whereas anisotropy is defined in the eigenvector reference frame. Similarly, Klipp () suggested calculating the friction velocity in the eigenvector space as a means of improving scaling relations. Here we apply a different approach and instead use anisotropy as one of the explanatory variables that causes deviations from scaling curves.…”

Section: Resultsmentioning

confidence: 99%

Scaling, Anisotropy, and Complexity in Near‐Surface Atmospheric Turbulence

2019

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The development of a unified similarity scaling has so far failed over complex surfaces, as scaling studies show large deviations from the empirical formulations developed over flat and horizontally homogeneous terrain as well as large deviations between the different complex terrain data sets. However, a recent study of turbulence anisotropy for flat and horizontally homogeneous terrain has shown that separating the data according to the limiting states of anisotropy (isotropic, two‐component axisymmetric and one‐component turbulence) improves near‐surface scaling. In this paper we explore whether this finding can be extended to turbulence over inclined and horizontally heterogeneous surfaces by examining near‐surface scaling for 12 different data sets obtained over terrain ranging from flat to mountainous. Although these data sets show large deviations in scaling when all anisotropy types are examined together, the separation according to the limiting states of anisotropy significantly improves the collapse of data onto common scaling relations, indicating the possibility of a unified framework for turbulence scaling. A measure of turbulence complexity is developed, and the causes for the breakdown of scaling and the physical mechanisms behind the turbulence complexity encountered over complex terrain are identified and shown to be related to the distance to the isotropic state, prevalence of directional shear with height in mountainous terrain, and the deviations from isotropy in the inertial subrange.

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Section: Resultsmentioning

confidence: 99%

Scaling, Anisotropy, and Complexity in Near‐Surface Atmospheric Turbulence

2019

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“…4 Schematic showing the variation of the inclination of the eigenvector corresponding to the smallest eigenvalue of the Reynolds stress tensor with the vertical direction. Literature Klipp (2018) shows that this tilt should be nearly 15 − 20 degrees near the surface.…”

Section: Modeling and Simulation Infrastructurementioning

confidence: 99%

On the Trajectory Dependence of Atmospheric Boundary Layer Turbulence Sensing using Small Unmanned Vehicles

Jayaraman,

Allison,

Bai

2018

Preprint

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Atmospheric turbulence, especially in the near-surface boundary layer is known to be under-sampled due to the need to capture a wide separation in length and time-scales and limitation in the number of sensors. Over the past decade, the use if Unmanned Aircraft Systems (UAS) technology is approaching ubiquitous proportions for a wide variety of applications, especially with the recent FAA relaxation of flying restrictions. From a geophysical sciences perspective, such technology would allow for sensing of large-scale atmospheric flows, particularly, atmospheric boundary layer (ABL) turbulence, air quality monitoring in urban settings where multitude of small, minimally-invasive and mobile sensors can drastically alter our ability to study such complex phenomena. Currently available observational data of atmospheric boundary layer physics is so sparse and infrequent which significantly limits analysis. With the quantity and resolution of the data that can be measured using a swarm of UAS, three-dimensional reconstruction and deduction of coherent structures in ABL turbulence may be feasible. However, key challenges remain in the form of identifying optimal trajectories to fly the UAS to obtain the relevant quantifications of the turbulence, interpretation of the sensor data from mobile sensors and understanding how representative are the sparse measurements of the overall turbulent boundary layer. This leads to many fundamentally interesting questions that are itemized here: (a) How does UAS trajectory influence sensing and measurements of turbulence? (b) How does ABL turbulence impact UAS trajectory? (c) How to design optimal sensing strategy for canonical turbulence? The key to answering these questions requires the study and understanding of the coupled system of sUAS flight dynamics, controller and ABL turbulence. It is also worth mentioning that some of the above are relevant issues only for small UAS such as quadcopters whose trajectory can be modulated to ABL gusts as against medium-scale fixed wing UAS. In this paper, we leverage a unique sUAS-in-ABL simulation infrastructure that couples high fidelity Large-eddy Simulation (LES) of the ABL with 6-DOF model for the

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“…The choice of the algorithm relied on a comparison between the friction velocity u * estimated using Eq. ( 2) and the method by Klipp (2018), which does not require any tilt correction. The latter method provides an estimate u * R of the friction velocity using the eigenvalues of the Reynolds stress tensor.…”

Section: Data Processingmentioning

confidence: 99%

Turbulence in a coastal environment: the case of Vindeby

Putri¹,

Cheynet²,

Obhrai³

et al. 2022

Wind Energ. Sci.

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Abstract. The one-point and two-point power spectral densities of the wind velocity fluctuations are studied using the observations from an offshore mast at Vindeby Offshore Wind Farm, for a wide range of thermal stratifications of the atmosphere. A comparison with estimates from the FINO1 platform (North Sea) is made to identify shared spectral characteristics of turbulence between different offshore sites. The sonic anemometer measurement data at 6, 18, and 45 m a.m.s.l. (above mean sea level) are considered. These heights are lower than at the FINO1 platform, where the measurements were collected at heights between 40 and 80 m. Although the sonic anemometers are affected by transducer-flow distortion, the spectra of the along-wind velocity component are consistent with those from FINO1 when surface-layer scaling is used, for near-neutral and moderately diabatic conditions. The co-coherence of the along-wind component, estimated for vertical separations under near-neutral conditions, matches remarkably well with the results from the dataset at the FINO1 platform. These findings mark an important step toward more comprehensive coherence models for wind load calculation. The turbulence characteristics estimated from the present dataset are valuable for better understanding the structure of turbulence in the marine atmospheric boundary layer and are relevant for load estimations of offshore wind turbines. Yet, the datasets recorded at Vindeby and FINO1 cover only the lower part of the rotor of state-of-the-art offshore wind turbines. Further improvements in the characterisation of atmospheric turbulence for wind turbine design will require measurements at heights above 100 m a.m.s.l.

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Turbulent Friction Velocity Calculated from the Reynolds Stress Tensor

Cited by 17 publications

References 18 publications

Scaling, Anisotropy, and Complexity in Near‐Surface Atmospheric Turbulence

Scaling, Anisotropy, and Complexity in Near‐Surface Atmospheric Turbulence

On the Trajectory Dependence of Atmospheric Boundary Layer Turbulence Sensing using Small Unmanned Vehicles

Turbulence in a coastal environment: the case of Vindeby

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