dutta-alankar

// Following must be put in `definitions.h` for this to work
/* #define  INTERNAL_BOUNDARY              YES */
if (side == 0) { // -- select active cells --
  RBox dom_box;
  int convert_to_cons = 0;
  DOM_LOOP(k,j,i) {
    int num_cells = 1;
    // IDIR --> x-dirction
    if ( (grid->xgc[IDIR][i] >= (grid->xl_glob[IDIR][grid->gend[IDIR]-num_cells+1])) 
      && (grid->xgc[IDIR][i] <= (grid->xr_glob[IDIR][grid->gend[IDIR]])) ){

dutta-alankar

# -*- coding: utf-8 -*-
"""
Created on Wed Apr 10 01:05:05 2024

@author: alankar
"""
from typing import List, Optional, Union

_large_distance = 10000
          

dutta-alankar

#include <stdio.h>
#include <gsl/gsl_sf_legendre.h>
#include <gsl/gsl_sf_result.h>

int main() {
    double theta = 1.2; // Example value for theta
    double phi = 0.8;   // Example value for phi
    int l = 2;          // Spherical harmonic degree
    int m = 1;          // Spherical harmonic order

dutta-alankar

Chevalier & Clegg 1985 (CC85) wind is spherically symmetric. So a coordinate system in the simulation that naturally aligns with the wind geometry allows the steady wind profiles to remain numerically stable for an arbitrarily long time. Therefore, it is worth setting cloud crushing problem in spherical coordinates to study it cloud-wind interaction in an adiabatically expanding wind, like CC85.

To retain the hierarchy that wind corresponds to hot gas and mixed gas has an intermediate temperature between cold cloud and the wind. The physically relevant portion of the domain where the general cloud crushing results are applicable is where $T_{\rm wind} \ge T_{\rm mix}$, where $T_{\rm mix} = T_{\rm wind}/\sqrt{\chi}$, and $\chi$ is the initial temperature contrast between the wind and the cloud.

Here is the entire simulation domain where the code length is the initial cloud radius. For this visualization, I have chosen $t_{\rm cool, mix}/t_{\rm cc}=0.1$. This corresponds to one of the largest clouds in the

dutta-alankar

anyio==4.4.0
argon2-cffi==23.1.0
argon2-cffi-bindings==21.2.0
arrow==1.3.0
asttokens==2.4.1
async-lru==2.0.4
attrs==23.2.0
Babel==2.15.0
beautifulsoup4==4.12.3
bleach==6.1.0

dutta-alankar

from dask_jobqueue import SLURMCluster
from dask.distributed import Client
import dask
import dask.array as da
import subprocess
import time

# Configure the SLURM cluster
cluster = SLURMCluster(
    queue='debug',           # Replace with your Slurm queue name

dutta-alankar

# -*- coding: utf-8 -*-
"""
Created on Sun Jun  16 21:57:11 2024

@author: alankar

Usage: time mpiexec -n <num_procs> python guess_pdfPass-parallel.py
Dependency: qpdf (https://github.com/qpdf/qpdf), tested with version 11.9.1
Note: Make sure qpdf and its libraries are added to PATH and LD_LIBRARY_PATH respectively
Thanks: Samriddhi Sankar Maity