A novel volumetric velocity measurement method for small seeding tracers in large volumes

This paper presents the volumetric velocity measurement method of small seeding tracer with diameter 5µm ∼ 100µm for volumes of ≥ 500cm3. The size of seeding tracer is between helium-filled soap bubbles (HFSB) and di-ethyl-hexyl-sebacic acid ester(DEHS) droplets. The targeted measurement volume dimension is equivalent to the volume of HFSB, which will give a higher resolution of turbulence study. The relations between particle size, imaging and light intensity are formulated. The estimation of the imaging results is computed for the setup design. Finally, the methodology is demonstrated for turbulence velocity measurements in the jet flow, in which the velocities of averaged diameter 15µm air filled soap bubbles are measured in a volume of 7200cm3.

Magnetic Fields in Molecular Clouds—Observation and Interpretation

The Zeeman effect and dust grain alignment are two major methods for probing magnetic fields (B-fields) in molecular clouds, largely motivated by the study of star formation, as the B-field may regulate gravitational contraction and channel turbulence velocity. This review summarizes our observations of B-fields over the past decade, along with our interpretation. Galactic B-fields anchor molecular clouds down to cloud cores with scales around 0.1 pc and densities of 104–5 H2/cc. Within the cores, turbulence can be slightly super-Alfvénic, while the bulk volumes of parental clouds are sub-Alfvénic. The consequences of these largely ordered cloud B-fields on fragmentation and star formation are observed. The above paradigm is very different from the generally accepted theory during the first decade of the century, when cloud turbulence was assumed to be highly super-Alfvénic. Thus, turbulence anisotropy and turbulence-induced ambipolar diffusion are also revisited.

Cross-contamination effect on turbulence spectra from Doppler beam swinging wind lidar

Turbulence velocity spectra are of high importance for the estimation of loads on wind turbines and other built structures, as well as for fitting measured turbulence values to turbulence models. Spectra generated from reconstructed wind vectors of Doppler beam swinging (DBS) wind lidars differ from spectra based on one-point measurements. Profiling wind lidars have several characteristics that cause these deviations, namely cross-contamination between the three velocity components, averaging along the lines of sight and the limited sampling frequency. This study focuses on analyzing the cross-contamination effect. We sample wind data in a computer-generated turbulence box to predict lidar-derived turbulence spectra for three wind directions and four measurement heights. The data are then processed with the conventional method and with the method of squeezing that reduces the longitudinal separation distances between the measurement locations of the different lidar beams by introducing a time lag into the data processing. The results are analyzed and compared to turbulence velocity spectra from field measurements with a Windcube V2 wind lidar and ultrasonic anemometers as reference. We successfully predict lidar-derived spectra for all test cases and found that their shape is dependent on the angle between the wind direction and the lidar beams. With conventional processing, cross-contamination affects all spectra of the horizontal wind velocity components. The method of squeezing improves the spectra to an acceptable level only for the case of the longitudinal wind velocity component and when the wind blows parallel to one of the lines of sight. The analysis of the simulated spectra described here improves our understanding of the limitations of turbulence measurements with DBS profiling wind lidar.

Cross-contamination effect on turbulence spectra from Doppler beam swinging wind lidar

Turbulence velocity spectra are of high importance for the estimation of loads on wind turbines and other built structures, as well as for fitting measured turbulence values to turbulence models. Doppler beam swinging (DBS) wind lidars generate spectra that differ from spectra based on one-point measurements. Profiling wind lidars have several characteristics that cause these deviations, namely cross-contamination between the three velocity components, averaging along the lines-of-sight, and the limited sampling frequency. This study focuses on analyzing the cross-contamination effect. We sample wind data in a computer generated turbulence box to predict lidar derived turbulence spectra for three wind directions and four measurement heights. The data are then processed with the conventional method and with the method of squeezing. The results are analyzed and compared to turbulence velocity spectra from field measurements with a Windcube V2 wind lidar and ultrasonic anemometers as reference. We successfully predict lidar derived spectra for all test cases and found that their shape is dependent on the angle between the wind direction and the lidar beams. With conventional processing, cross-contamination affects all spectra of the horizontal wind velocity components. The method of squeezing improves the spectra to an acceptable level only for the case of the longitudinal wind velocity component and when the wind blows parallel to one of the lines-of-sight. The analysis of the simulated spectra described here improves our understanding of the limitations of turbulence measurements with DBS profiling wind lidar.

Turbulence velocity spectra and intensities in the inflow of a turbine rotor

The changes in spectra and intensities of the streamwise component of turbulent velocity are calculated in the inflow of a turbine rotor. The flow is initially calculated in the limit when the turbulence is of small scale compared with the rotor diameter. Rapid distortion theory (RDT), Batchelor & Proudman (Q. J. Mech. Appl. Maths, vol. 7 (1), 1954, pp. 83–103) (BP), for small-scale turbulence is combined with the effect of the fluctuating potential flow field on the turbulence caused by the direct interaction of the incident turbulence with the rotor as a sheet of resistance. A second computation is then carried out for turbulence of larger length scale. The results of the calculations are compared with velocity measurements in the inflow of both a commercial wind turbine and a tidal turbine rotor.

Mixing Coefficient in Stably Stratified Flows

Turbulent mixing in the interior of the oceans is not as well understood as mixing in the oceanic boundary layers. Mixing in the generally stably stratified interior is primarily, although not exclusively, due to intermittent shear instabilities. Part of the energy extracted by the Reynolds stresses acting on the mean shear is expended in increasing the potential energy of the fluid column through a buoyancy flux, while most of it is dissipated. The mixing coefficient χm, the ratio of the buoyancy flux to the dissipation rate of turbulence kinetic energy ε, is an important parameter, since knowledge of χm enables turbulent diffusivities to be inferred. Theory indicates that χm must be a function of the gradient Richardson number. Yet, oceanic studies suggest that a value of around 0.2 for χm gives turbulent diffusivities that are in good agreement with those inferred from tracer studies. Studies by scientists working with atmospheric radars tend to reinforce these findings but are seldom referenced in oceanographic literature. The goal of this paper is to bring together oceanographic, atmospheric, and laboratory observations related to χm and to report on the values deduced from in situ data collected in the lower troposphere by unmanned aerial vehicles, equipped with turbulence sensors and flown in the vicinity of the Middle and Upper Atmosphere (MU) radar in Japan. These observations are consistent with past studies in the oceans, in that a value of around 0.16 for χm yields good agreement between ε derived from turbulent temperature fluctuations using this value and ε obtained directly from turbulence velocity fluctuations.