Simple method for the estimation of free-fall velocity of spherical particles in power law liquids

1991 ◽  
Vol 67 (3) ◽  
pp. 287-290 ◽  
Author(s):  
R.P. Chhabra ◽  
S.S. Peri
2018 ◽  
Vol 619 ◽  
pp. A166 ◽  
Author(s):  
M. Mattern ◽  
J. Kauffmann ◽  
T. Csengeri ◽  
J. S. Urquhart ◽  
S. Leurini ◽  
...  

Analyzing the kinematics of filamentary molecular clouds is a crucial step toward understanding their role in the star formation process. Therefore, we study the kinematics of 283 filament candidates in the inner Galaxy, that were previously identified in the ATLASGAL dust continuum data. The 13CO(2 – 1) and C18O(2 – 1) data of the SEDIGISM survey (Structure, Excitation, and Dynamics of the Inner Galactic Inter Stellar Medium) allows us to analyze the kinematics of these targets and to determine their physical properties at a resolution of 30′′ and 0.25 km s−1. To do so, we developed an automated algorithm to identify all velocity components along the line-of-sight correlated with the ATLASGAL dust emission, and derive size, mass, and kinematic properties for all velocity components. We find two-third of the filament candidates are coherent structures in position-position-velocity space. The remaining candidates appear to be the result of a superposition of two or three filamentary structures along the line-of-sight. At the resolution of the data, on average the filaments are in agreement with Plummer-like radial density profiles with a power-law exponent of p ≈ 1.5 ± 0.5, indicating that they are typically embedded in a molecular cloud and do not have a well-defined outer radius. Also, we find a correlation between the observed mass per unit length and the velocity dispersion of the filament of m ∝ σv2. We show that this relation can be explained by a virial balance between self-gravity and pressure. Another possible explanation could be radial collapse of the filament, where we can exclude infall motions close to the free-fall velocity.


2020 ◽  
Vol 32 (8) ◽  
pp. 083103
Author(s):  
Fatima Ezahra Chrit ◽  
Samuel Bowie ◽  
Alexander Alexeev

2019 ◽  
Vol 12 (2) ◽  
pp. 1409-1427 ◽  
Author(s):  
Gwo-Jong Huang ◽  
Viswanathan N. Bringi ◽  
Andrew J. Newman ◽  
Gyuwon Lee ◽  
Dmitri Moisseev ◽  
...  

Abstract. quantitative precipitation estimation (QPE) of snowfall has generally been expressed in power-law form between equivalent radar reflectivity factor (Ze) and liquid equivalent snow rate (SR). It is known that there is large variability in the prefactor of the power law due to changes in particle size distribution (PSD), density, and fall velocity, whereas the variability of the exponent is considerably smaller. The dual-wavelength radar reflectivity ratio (DWR) technique can improve SR accuracy by estimating one of the PSD parameters (characteristic diameter), thus reducing the variability due to the prefactor. The two frequencies commonly used in dual-wavelength techniques are Ku- and Ka-bands. The basic idea of DWR is that the snow particle size-to-wavelength ratio is falls in the Rayleigh region at Ku-band but in the Mie region at Ka-band. We propose a method for snow rate estimation by using NASA D3R radar DWR and Ka-band reflectivity observations collected during a long-duration synoptic snow event on 30–31 January 2012 during the GCPEx (GPM Cold-season Precipitation Experiment). Since the particle mass can be estimated using 2-D video disdrometer (2DVD) fall speed data and hydrodynamic theory, we simulate the DWR and compare it directly with D3R radar measurements. We also use the 2DVD-based mass to compute the 2DVD-based SR. Using three different mass estimation methods, we arrive at three respective sets of Z–SR and SR(Zh, DWR) relationships. We then use these relationships with D3R measurements to compute radar-based SR. Finally, we validate our method by comparing the D3R radar-retrieved SR with accumulated SR directly measured by a well-shielded Pluvio gauge for the entire synoptic event.


Particuology ◽  
2019 ◽  
Vol 46 ◽  
pp. 30-39 ◽  
Author(s):  
Zhengming Xu ◽  
Xianzhi Song ◽  
Gensheng Li ◽  
Zhaoyu Pang ◽  
Zhaopeng Zhu

1992 ◽  
Vol 114 (1) ◽  
pp. 100-106 ◽  
Author(s):  
Lian-Ping Wang ◽  
D. E. Stock

Numerical experiments can be used to study heavy particle dispersion by tracking particles through a numerically generated instantaneous turbulent flow field. In this manner, data can be generated to supplement physical experiments. To perform the numerical experiments efficiently and accurately, the time step used when tracking the particles through the fluid must be chosen correctly. After finding a suitable time step for one particular simulation, the time step must be reduced as the total integration time increases and as the free-fall velocity of the particle increases. Based on the numerical calculations, we suggest that the nonlinear drag be included in a numerical simulation if the ratio of the particle’s Stokes free-fall velocity to the fluid rms velocity is greater than two.


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