Source code for run_droplet_swirl

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# This file is part of the Cortix toolkit environment
This example uses two modules instantiated many times. It be executed with MPI
(if `mpi4py` is available) or with the Python multiprocessing library. These choices
are made by variables listed below in the executable portion of this run file.

To run this case using MPI you should compute the number of
processes as follows:

    `nprocs = n_droplets + 1 vortex + 1 cortix`

then issue the MPI run command as follows (replace `nprocs` with a number):

     `mpiexec -n nprocs`

To run this case with the Python multiprocessing library, just run this file at the
command line as



import scipy.constants as const

from cortix import Cortix
from cortix import Network
from cortix.examples.droplet_swirl.droplet import Droplet
from cortix.examples.droplet_swirl.vortex import Vortex

[docs]def main(): '''Cortix run file for a `Droplet`-`Vortex` network. Attributes ---------- n_droplets: int Number of droplets to use (one per process). end_time: float End of the flow time in SI unit. time_step: float Size of the time step between port communications in SI unit. create_plots: bool Create various plots and save to files. (all data collected in the parent process; it may run out of memory). plot_vortex_profile: bool Whether to plot (to a file) the vortex function used. use_mpi: bool If set to `True` use MPI otherwise use Python multiprocessing. ''' # Configuration Parameters n_droplets = 5 end_time = 3*const.minute time_step = 0.2 create_plots = True if n_droplets >= 2000: create_plots = False plot_vortex_profile = False # True may crash the X server. use_mpi = False # True for MPI; False for Python multiprocessing swirl = Cortix(use_mpi=use_mpi, splash=True) = Network() # Vortex module (single). vortex = Vortex() vortex.show_time = (True,1*const.minute) vortex.end_time = end_time vortex.time_step = time_step if plot_vortex_profile: vortex.plot_velocity() for i in range(n_droplets): # Droplet modules (multiple). droplet = Droplet() droplet.end_time = end_time droplet.time_step = time_step droplet.bounce = False droplet.slip = False = True # Network port connectivity (connect modules through their ports) [droplet,'external-flow'], [vortex,vortex.get_port('fluid-flow:{}'.format(i))], 'bidirectional' ) # Plot all droplet trajectories if create_plots: modules = if swirl.use_multiprocessing or swirl.rank == 0: # All droplets' trajectory from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt positions = list() for m in[1:]: positions.append(m.liquid_phase.get_quantity_history('position')[0].value) fig = plt.figure(1) ax = fig.add_subplot(111,projection='3d') ax.set_title('Droplet Trajectories') ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') for pos in positions: x = [i[0] for i in pos] y = [i[1] for i in pos] z = [i[2] for i in pos] ax.plot(x, y, z) fig.savefig('trajectories.png', dpi=300) # All droplets' speed fig = plt.figure(2) plt.xlabel('Time [min]') plt.ylabel('Speed [m/s]') plt.title('All Droplets') for m in modules[1:]: speed = m.liquid_phase.get_quantity_history('speed')[0].value plt.plot(list(speed.index/60), speed.tolist()) plt.grid() fig.savefig('speeds.png', dpi=300) # All droplets' radial position fig = plt.figure(3) plt.xlabel('Time [min]') plt.ylabel('Radial Position [m]') plt.title('All Droplets') for m in modules[1:]: radial_pos = m.liquid_phase.get_quantity_history('radial-position')[0].value plt.plot(list(radial_pos.index/60)[1:], radial_pos.tolist()[1:]) plt.grid() fig.savefig('radialpos.png', dpi=300) # This properly ends the program swirl.close()
if __name__ == '__main__': main()