Rate of formation of caustics in heavy particles advected by turbulence
Cite this dataset
Mitra, Dhrubaditya (2021). Rate of formation of caustics in heavy particles advected by turbulence [Dataset]. Dryad. https://doi.org/10.5061/dryad.80gb5mkr0
Abstract
The rate of collision and the relative velocities of the colliding particles in turbulent flows is a crucial part of several natural phenomena, e.g., rain formation in warm clouds and planetesimal formation in a protoplanetary disks. The particles are often modeled as passive, but heavy and inertial. Within this model, large relative velocities emerge due to formation of singularities (caustics) of in the gradient matrix of the velocities of the particles. Using extensive direct numerical simulations of heavy particles in both two (direct and inverse cascade) and three dimensional turbulent flows we calculate the rate of formation of caustics, as a function of the Stokes number.
The two directories contains tar file of run directories for two different Reynolds number (90 and 180). The corresponding taueta are 0.41 and 1.54 respectively. The data can be read and analyzed with the python files included in the python directory.
Methods
The dataset is generated from direct numerical simulation of turbulent flows using the pencil-code.
Usage notes
The two directories contains tar file of run directories for two different Reynolds number (90 and 180). The corresponding taueta are 0.41 and 1.54 respectively.
A brief description of the files and directory structure:
These are raw data from direct numrical simulations using the pencil-code (http://pencil-code.nordita.org/). The code is open-source. You can also reproduce the data from the input files in these run directories and the pencil-code. The directory structure is typical of pencil-code runs. An example is given below :
Consider the directory Relam180/512kf2hiRe-5
This means that this is a run with 512^3 grid points, with a forcing wavenumber kf = 2 . The suffix 5 does not imply that they Reynolds number is 5 but is just a label.
The input files are start.in, run.in (and so on). The CVS directory contains information about our private CVS repository and is irrelevant here. The slurm-xxx-.out files contains output from job scheduler in the computing cluster. This is also not relevant.
The relevant data are in the subdirectory data/. Within the subdirectory the data are divided by the processors. It is best if you use the scripts we provide within the python directory to read these data. The scripts collate the data scattered over the directories. The python scripts assume that you have the pencil-code installed.