skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Evidence of preferential sweeping during snow settling in atmospheric turbulence
We present a field study of snow settling dynamics based on simultaneous measurements of the atmospheric flow field and snow particle trajectories. Specifically, a super-large-scale particle image velocimetry (SLPIV) system using natural snow particles as tracers is deployed to quantify the velocity field and identify vortex structures in a 22 m  $$\times$$  39 m field of view centred 18 m above the ground. Simultaneously, we track individual snow particles in a 3 m  $$\times$$  5 m sample area within the SLPIV using particle tracking velocimetry. The results reveal the direct linkage among vortex structures in atmospheric turbulence, the spatial distribution of snow particle concentration and their settling dynamics. In particular, with snow turbulence interaction at near-critical Stokes number, the settling velocity enhancement of snow particles is multifold, and larger than what has been observed in previous field studies. Super-large-scale particle image velocimetry measurements show a higher concentration of snow particles preferentially located on the downward side of the vortices identified in the atmospheric flow field. Particle tracking velocimetry, performed on high resolution images around the reconstructed vortices, confirms the latter trend and provides statistical evidence of the acceleration of snow particles, as they move toward the downward side of vortices. Overall, the simultaneous multi-scale particle imaging presented here enables us to directly quantify the salient features of preferential sweeping, supporting it as an underlying mechanism of snow settling enhancement in the atmospheric surface layer.  more » « less
Award ID(s):
1822192
PAR ID:
10340694
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Journal of Fluid Mechanics
Volume:
928
ISSN:
0022-1120
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Research on the settling dynamics of snow particles, considering their complex morphologies and real atmospheric conditions, remains scarce despite extensive simulations and laboratory studies. Our study bridges this gap through a comprehensive field investigation into the three-dimensional (3-D) snow settling dynamics under weak atmospheric turbulence, enabled by a 3-D particle tracking velocimetry (PTV) system to record over a million trajectories, coupled with a snow particle analyser for simultaneous aerodynamic property characterization of four distinct snow types (aggregates, graupels, dendrites, needles). Our findings indicate that while the terminal velocity predicted by the aerodynamic model aligns well with the PTV-measured settling velocity for graupels, significant discrepancies arise for non-spherical particles, particularly dendrites, which exhibit higher drag coefficients than predicted. Qualitative observations of the 3-D settling trajectories highlight pronounced meandering in aggregates and dendrites, in contrast to the subtler meandering observed in needles and graupels, attributable to their smaller frontal areas. This meandering in aggregates and dendrites occurs at lower frequencies compared with that of graupels. Further quantification of trajectory acceleration and curvature suggests that the meandering frequencies in aggregates and dendrites are smaller than that of morphology-induced vortex shedding of disks, likely due to their rotational inertia, and those of graupels align with the small-scale atmospheric turbulence. Moreover, our analysis of vertical acceleration along trajectories elucidates that the orientation changes in dendrites and aggregates enhance their settling velocity. Such insights into settling dynamics refine models of snow settling velocity under weak atmospheric turbulence, with broader implications for more accurately predicting ground snow accumulation. 
    more » « less
  2. null (Ed.)
    The effect of turbulence on snow precipitation is not incorporated into present weather forecasting models. Here we show evidence that turbulence is in fact a key influence on both fall speed and spatial distribution of settling snow. We consider three snowfall events under vastly different levels of atmospheric turbulence. We characterize the size and morphology of the snow particles, and we simultaneously image their velocity, acceleration and relative concentration over vertical planes approximately $$30\ \textrm {m}^2$$ in area. We find that turbulence-driven settling enhancement explains otherwise contradictory trends between the particle size and velocity. The estimates of the Stokes number and the correlation between vertical velocity and local concentration are consistent with the view that the enhanced settling is rooted in the preferential sweeping mechanism. When the snow vertical velocity is large compared to the characteristic turbulence velocity, the crossing trajectories effect results in strong accelerations. When the conditions of preferential sweeping are met, the concentration field is highly non-uniform and clustering appears over a wide range of scales. These clusters, identified for the first time in a naturally occurring flow, display the signature features seen in canonical settings: power-law size distribution, fractal-like shape, vertical elongation and large fall speed that increases with the cluster size. These findings demonstrate that the fundamental phenomenology of particle-laden turbulence can be leveraged towards a better predictive understanding of snow precipitation and ground snow accumulation. They also demonstrate how environmental flows can be used to investigate dispersed multiphase flows at Reynolds numbers not accessible in laboratory experiments or numerical simulations. 
    more » « less
  3. Controlling the downhole pressure is an important parameter for successful and safe drilling operations. Several types of weighting agents (i.e., high-density particles), traditionally barite particles, are added to maintain the desired density of the drilling fluid (DF). The DF density is an important design parameter for preventing multiple drilling complications. These issues are caused by the settling of the dense particles, an undesired phenomenon also referred to as sagging. Therefore, there is a need to understand the settling characteristics of heavy particles in such scenarios. To this end, simultaneous measurements of liquid phase flow patterns and particle settling velocities have been conducted in a Taylor-Couette (TC) cell with a rotating inner cylinder and stationary outer cylinder separated by an annular gap of 9.0 mm. Liquid flow patterns and particle settling velocities have been measured using particle image velocimetry (PIV) and particle tracking velocimetry (PTV) techniques, respectively. Experiments have been performed by varying the rotational speed of the inner cylinder up to 200 rev/min, which is used in normal drilling operations. Spherical particles with diameters of 3.0 mm or 4.0 mm and densities between 1.2 g/cm3 and 3.95 g/cm3 were used. The liquid phases studied included deionized (DI) water and mineral oil, which are the basic components of a non-Newtonian DF with a shear-thinning viscosity. The DF is a mud-like emulsion of opaque appearance, which impedes the ability to observe the liquid flow field and particle settling in the TC cell. To address this issue, a solution of carboxymethyl cellulose (CMC) with a 6% weight concentration in DI water was used. This non-Newtonian solution displays shear-thinning rheological behavior and was used as a transparent alternative to the opaque DF. For water, PIV results have shown wavy vortex flow (WVF) to turbulent Taylor vortex flow (TTVF), which agrees with the flow patterns reported in the literature. For mineral oil, circular Couette flow (CCF) was observed at up to 100 rev/min and vortex formation at 200 rev/min. For CMC, no vortex formation was observed up to 200 rev/min, only CCF. The settling velocities for all particles in water matched with the particle settling velocities predicted using the Basset-Boussinesq-Oseen (BBO) equation of motion. For mineral oil and CMC, the results did not match well with the predicted settling velocities, especially for heavy particles due possibly to the radial particle migration and interactions with the outer cylinder wall. 
    more » « less
  4. The preferential organisation of coherent vortices in a turbulent boundary layer in relation to local large-scale streamwise velocity features was investigated. Coherent vortices were identified in the wake region using the Triple Decomposition Method (originally proposed by Kolář) from 2D particle image velocimetry (PIV) data of a canonical turbulent boundary layer. Two different approaches, based on conditional averaging and quantitative statistical analysis, were used to analyze the data. The large-scale streamwise velocity field was first conditionally averaged on the height of the detected coherent vortices and a change in the sign of the average large scale streamwise fluctuating velocity was seen depending on the height of the vortex core. A correlation coefficient was then defined to quantify this relationship between the height of coherent vortices and local large-scale streamwise fluctuating velocity. Both of these results indicated a strong negative correlation in the wake region of the boundary layer between vortex height and large-scale velocity. The relationship between vortex height and full large-scale velocity isocontours was also studied and a conceptual model based on the findings of the study was proposed. The results served to relate the hairpin vortex model of Adrian et al. to the scale interaction results reported by Mathis et al., and Chung and McKeon. 
    more » « less
  5. Abstract Understanding the organization and dynamics of turbulence structures in the atmospheric surface layer (ASL) is important for fundamental and applied research in different fields, including weather prediction, snow settling, particle and pollutant transport, and wind energy. The main challenges associated with probing and modeling turbulence in the ASL are: i) the broad range of turbulent scales associated with the different eddies present in high Reynolds-number boundary layers ranging from the viscous scale (𝒪(mm)) up to large energy-containing structures (𝒪(km)); ii) the non-stationarity of the wind conditions and the variability associated with the daily cycle of the atmospheric stability; iii) the interactions among eddies of different sizes populating different layers of the ASL, which contribute to momentum, energy, and scalar turbulent fluxes. Creative and innovative measurement techniques are required to probe near-surface turbulence by generating spatio-temporally-resolved data in the proximity of the ground and, at the same time, covering the entire ASL height with large enough streamwise extent to characterize the dynamics of larger eddies evolving aloft. To this aim, the U.S. National Science Foundation sponsored the development of the Grand-scale Atmospheric Imaging Apparatus (GAIA) enabling super-large snow particle image velocimetry (SLPIV) in the near-surface region of the ASL. This inaugural version of GAIA provides a comprehensive measuring system by coupling SLPIV and two scanning Doppler LiDARs to probe the ASL at an unprecedented resolution. A field campaign performed in 2021–2022 and its preliminary results are presented herein elucidating new research opportunities enabled by the GAIA measuring system. 
    more » « less