cd_original = 2.0*simulation.forces[0].mean['value']
cl_original = 2.0*simulation.forces[1].mean['value']
# Computes the mean coefficients from the cuIBM simulations
# reported in Krishnan et al. (2014).
# The force coefficients are averaged between 32 and 64 time-units.
cd_krishnan, cl_krishnan = [], []
for re in ['Re1000', 'Re2000']:
for aoa in ['AoA25', 'AoA30', 'AoA35', 'AoA40']:
simulation_directory = os.path.join(os.environ['SNAKE'],
'resources',
'flyingSnake2d_cuibm_anush',
'flyingSnake2d'+re+aoa)
krishnan = CuIBMSimulation(directory=simulation_directory)
krishnan.read_forces()
krishnan.get_mean_forces(limits=[32.0, 64.0])
cd_krishnan.append(2.0*krishnan.forces[0].mean['value'])
cl_krishnan.append(2.0*krishnan.forces[1].mean['value'])
# plot figure
aoa = [25, 30, 35, 40]
pyplot.style.use(os.path.join(os.environ['SNAKE'],
'snake',
'styles',
'snakeReproducibility.mplstyle'))
fig = pyplot.figure(figsize=(6, 8))
gs = gridspec.GridSpec(3, 2,
height_ratios=[1, 1, 0.5])
ax1 = pyplot.subplot(gs[0, :])
ax2 = pyplot.subplot(gs[1, :])
published into Krishnan et al., 2014).
This script reads the forces, computes the mean forces within a given range,
computes the Strouhal number within a range, plots the force coefficients,
saves the figure, and prints a data-frame that contains the mean values.
"""
import os
from snake.cuibm.simulation import CuIBMSimulation
simulation = CuIBMSimulation(description='present')
simulation.read_forces()
time_limits = (32.0, 64.0)
simulation.get_mean_forces(limits=time_limits)
simulation.get_strouhal(limits=time_limits, order=200)
krishnan = CuIBMSimulation(description='Krishnan et al. (2014)')
filepath = os.path.join(os.environ['SNAKE'],
'resources',
'flyingSnake2d_cuibm_anush',
'flyingSnake2dRe2000AoA35',
'forces')
krishnan.read_forces(file_path=filepath)
krishnan.get_mean_forces(limits=time_limits)
krishnan.get_strouhal(limits=time_limits, order=200)
simulation.plot_forces(display_coefficients=True,
coefficient=2.0,
display_extrema=True, order=200,
if snake.__version__ != '0.1.2':
warnings.warn('The figures were originally created with snake-0.1.2, '+
'you are using snake-{}'.format(snake.__version__))
# Computes the mean force coefficients from the cuIBM simulation
# with grid-spacing h=0.006 in the uniform region and atol=1.0E-06 for the
# Poisson solver.
# The force coefficients are averaged between 32 and 64 time-units.
simulation_directory = os.path.join(os.path.dirname(__file__),
'h0.006_vatol16_patol6_dt0.0002')
simulation = CuIBMSimulation(description='atol=1.0E-06',
directory=simulation_directory)
simulation.read_forces()
simulation.get_mean_forces(limits=[32.0, 64.0])
# Computes the mean force coefficients from the cuIBM simulation
# with grid-spacing h=0.006 in the uniform region and atol=1.0E-08 for the
# Poisson solver.
# The force coefficients are averaged between 32 and 64 time-units.
simulation_directory = os.path.join(os.path.dirname(__file__),
'h0.006_vatol16_patol8_dt0.0002')
simulation2 = CuIBMSimulation(description='atol=1.0E-08',
directory=simulation_directory)
simulation2.read_forces()
simulation2.get_mean_forces(limits=[32.0, 64.0])
# Creates a table with the time-averaged force coefficients.
dataframe = simulation.create_dataframe_forces(display_coefficients=True,
coefficient=2.0)
simulation = OpenFOAMSimulation(directory=os.path.join(directory, folder))
simulation.read_forces(display_coefficients=True)
simulation.get_mean_forces(limits=[32.0, 64.0])
cd.append(simulation.forces[0].mean['value'])
cl.append(simulation.forces[1].mean['value'])
# compute mean coefficients of each simulations from Krishnan et al. (2014)
cd_krishnan, cl_krishnan = [], []
for Re in [1000, 2000]:
for a in aoa:
directory = os.path.join(os.environ['SNAKE'], 'resources',
'flyingSnake2d_cuibm_anush',
'flyingSnake2dRe{}AoA{}'.format(Re, a))
krishnan = CuIBMSimulation(directory=directory)
krishnan.read_forces()
krishnan.get_mean_forces(limits=[32.0, 64.0])
cd_krishnan.append(2.0 * krishnan.forces[0].mean['value'])
cl_krishnan.append(2.0 * krishnan.forces[1].mean['value'])
# plot mean drag coefficient versus angle of attack for Re=1000 and Re=2000
pyplot.style.use(
os.path.join(os.environ['SNAKE'], 'snake', 'styles',
'snakeReproducibility.mplstyle'))
fig = pyplot.figure(figsize=(6, 8))
gs = gridspec.GridSpec(3, 2, height_ratios=[1, 1, 0.5])
ax1 = pyplot.subplot(gs[0, :])
ax2 = pyplot.subplot(gs[1, :])
ax3 = pyplot.subplot(gs[2, 0])
ax4 = pyplot.subplot(gs[2, 1])
gs.update(wspace=0.5, hspace=0.1)
# drag coefficient versus angle-of-attack
type=str,
default=script_directory,
help='directory where to save the figures')
args = parser.parse_args()
with open(args.map, 'r') as infile:
dirs = yaml.load(infile)
# Plots the instantaneous force coefficients at Re=1000 and AoA=35deg
# using a current version of cuIBM with CUSP-0.5.1
# and compares to the results reported in Krishnan et al. (2014).
simulation_directory = dirs['cuibm-current-cusp051']['Re1000AoA35']
simulation = CuIBMSimulation(description='cuIBM (current) - cusp-0.5.1',
directory=simulation_directory)
simulation.read_forces()
simulation.get_mean_forces(limits=[32.0, 64.0])
simulation.get_strouhal(limits=[32.0, 64.0], order=200)
krishnan = CuIBMSimulation(description='Krishnan et al. (2014)')
krishnan.read_forces(file_path=os.path.join(os.environ['SNAKE'],
'resources',
'flyingSnake2d_cuibm_anush',
'flyingSnake2dRe1000AoA35',
'forces'))
krishnan.get_mean_forces(limits=[32.0, 64.0])
krishnan.get_strouhal(limits=[32.0, 64.0], order=200)
dataframe = simulation.create_dataframe_forces(display_strouhal=True,
display_coefficients=True,
coefficient=2.0)
dataframe2 = krishnan.create_dataframe_forces(display_strouhal=True,
import snake
from snake.cuibm.simulation import CuIBMSimulation
if snake.__version__ != '0.1.2':
warnings.warn('The figures were originally created with snake-0.1.2, ' +
'you are using snake-{}'.format(snake.__version__))
# Computes the mean force coefficients from the cuIBM simulation
# with grid-spacing h=0.004 in the uniform region.
# The force coefficients are averaged between 32 and 64 time-units.
simulation_directory = os.path.join(os.path.dirname(__file__), 'h0.004')
simulation = CuIBMSimulation(description='h=0.004',
directory=simulation_directory)
simulation.read_forces()
simulation.get_mean_forces(limits=[32.0, 64.0])
# Computes the mean force coefficients from the cuIBM simulation
# with grid-spacing h=0.006 in the uniform region.
# The force coefficients are averaged between 32 and 64 time-units.
simulation_directory = os.path.join(os.path.dirname(__file__),
'h0.006_vatol16_patol8_dt0.0002')
simulation2 = CuIBMSimulation(description='h=0.006',
directory=simulation_directory)
simulation2.read_forces()
simulation2.get_mean_forces(limits=[32.0, 64.0])
# Creates a table with the time-averaged force coefficients.
dataframe = simulation.create_dataframe_forces(display_coefficients=True,
coefficient=2.0)
dataframe2 = simulation2.create_dataframe_forces(display_coefficients=True,
"""
Post-processes the force coefficients from a cuIBM simulation.
This script reads the forces, computes the mean forces within a given range,
computes the Strouhal number within a range, plots the force coefficients,
saves the figure, and prints a data-frame that contains the mean values.
"""
from snake.cuibm.simulation import CuIBMSimulation
simulation = CuIBMSimulation()
simulation.read_forces()
time_limits = (60.0, 80.0)
simulation.get_mean_forces(limits=time_limits)
simulation.get_strouhal(limits=time_limits, order=200)
simulation.plot_forces(display_coefficients=True,
coefficient=2.0,
limits=(0.0, 80.0, -0.5, 1.5),
style='seaborn-dark',
save_name='forceCoefficients')
dataframe = simulation.create_dataframe_forces(display_strouhal=True,
display_coefficients=True,
coefficient=2.0)
print(dataframe)
from snake.petibm.simulation import PetIBMSimulation
from snake.cuibm.simulation import CuIBMSimulation
simulation = PetIBMSimulation(description='PetIBM')
simulation.read_forces()
time_limits = (32.0, 64.0)
simulation.get_mean_forces(limits=time_limits)
simulation.get_strouhal(limits=time_limits, order=200)
krishnan = CuIBMSimulation(description='Krishnan et al. (2014)')
filepath = os.path.join(os.environ['SNAKE'], 'resources',
'flyingSnake2d_cuibm_anush',
'flyingSnake2dRe2000AoA35', 'forces')
krishnan.read_forces(file_path=filepath)
krishnan.get_mean_forces(limits=time_limits)
krishnan.get_strouhal(limits=time_limits, order=200)
simulation.plot_forces(display_coefficients=True,
coefficient=2.0,
display_extrema=True,
order=200,
limits=(0.0, 80.0, 0.0, 3.0),
other_simulations=krishnan,
other_coefficients=2.0,
style='mesnardo',
save_name='forceCoefficientsCompareKrishnanEtAl2014')
dataframe = simulation.create_dataframe_forces(display_strouhal=True,
display_coefficients=True,
coefficient=2.0)
simulation = PetIBMSimulation(description='PetIBM')
simulation.read_forces()
time_limits = (32.0, 64.0)
simulation.get_mean_forces(limits=time_limits)
simulation.get_strouhal(limits=time_limits, order=200)
krishnan = CuIBMSimulation(description='Krishnan et al. (2014)')
filepath = os.path.join(os.environ['SNAKE'],
'resources',
'flyingSnake2d_cuibm_anush',
'flyingSnake2dRe2000AoA35',
'forces')
krishnan.read_forces(file_path=filepath)
krishnan.get_mean_forces(limits=time_limits)
krishnan.get_strouhal(limits=time_limits, order=200)
simulation.plot_forces(display_coefficients=True,
coefficient=2.0,
display_extrema=True, order=200,
limits=(0.0, 80.0, 0.0, 3.0),
other_simulations=krishnan,
other_coefficients=2.0,
style='mesnardo',
save_name='forceCoefficientsCompareKrishnanEtAl2014')
dataframe = simulation.create_dataframe_forces(display_strouhal=True,
display_coefficients=True,
coefficient=2.0)
dataframe2 = krishnan.create_dataframe_forces(display_strouhal=True,
import snake
from snake.cuibm.simulation import CuIBMSimulation
print('\nPython version:\n{}'.format(sys.version))
print('\nsnake version: {}\n'.format(snake.__version__))
simulation = CuIBMSimulation(description='cuIBM (current) - CUSP-0.4.0',
directory=os.path.join(os.environ['HOME'],
'snakeReproducibilityPackages',
'cuibm',
'current',
'cusp040',
'Re2000AoA35'))
simulation.read_forces()
simulation.get_mean_forces(limits=[32.0, 64.0])
simulation.get_strouhal(limits=[32.0, 64.0], order=200)
other = CuIBMSimulation(description='cuIBM (current) - CUSP-0.5.1',
directory=os.path.join(os.environ['HOME'],
'snakeReproducibilityPackages',
'cuibm',
'current',
'cusp051',
'Re2000AoA35'))
other.read_forces()
other.get_mean_forces(limits=[32.0, 64.0])
other.get_strouhal(limits=[32.0, 64.0], order=200)
revision86 = CuIBMSimulation(description='cuIBM (old) - CUSP-0.4.0',
directory=os.path.join(os.environ['HOME'],
import sys
from matplotlib import pyplot
import snake
from snake.cuibm.simulation import CuIBMSimulation
print('\nPython version:\n{}'.format(sys.version))
print('\nsnake version: {}\n'.format(snake.__version__))
simulation = CuIBMSimulation(
description='cuIBM (current) - CUSP-0.4.0',
directory=os.path.join(os.environ['HOME'], 'snakeReproducibilityPackages',
'cuibm', 'current', 'cusp040', 'Re2000AoA35'))
simulation.read_forces()
simulation.get_mean_forces(limits=[32.0, 64.0])
simulation.get_strouhal(limits=[32.0, 64.0], order=200)
other = CuIBMSimulation(description='cuIBM (current) - CUSP-0.5.1',
directory=os.path.join(os.environ['HOME'],
'snakeReproducibilityPackages',
'cuibm', 'current', 'cusp051',
'Re2000AoA35'))
other.read_forces()
other.get_mean_forces(limits=[32.0, 64.0])
other.get_strouhal(limits=[32.0, 64.0], order=200)
revision86 = CuIBMSimulation(
description='cuIBM (old) - CUSP-0.4.0',
directory=os.path.join(os.environ['HOME'], 'snakeReproducibilityPackages',
'cuibm', 'revision86', 'Re2000AoA35'))
if snake.__version__ != '0.1.2':
warnings.warn('The figures were originally created with snake-0.1.2, '+
'you are using snake-{}'.format(snake.__version__))
cases = []
fx, fy = [], []
resolutions = ['h=0.00267', 'h=0.004', 'h=0.006']
time_limits = (20.0, 28.0)
for resolution in resolutions:
case = CuIBMSimulation(directory=os.path.join(os.path.dirname(__file__),
resolution.replace('=', '')),
description=resolution)
case.read_forces()
case.get_mean_forces(limits=time_limits)
fx.append(case.forces[0].mean['value'])
fy.append(case.forces[1].mean['value'])
cases.append(case)
# Calculates the observed order of convergence for the time-averaged force
# coefficients.
ratio = 1.5
order = numpy.log((fx[2]-fx[1])/(fx[1]-fx[0]))/numpy.log(ratio)
print(order)
order = numpy.log((fy[2]-fy[1])/(fy[1]-fy[0]))/numpy.log(ratio)
print(order)
# Plots the instantaneous force coefficients obtained with different meshes.
fig, ax = pyplot.subplots(figsize=(6, 4))
ax.grid(True, zorder=0)
type=str,
default=script_directory,
help='directory where to save the figures')
args = parser.parse_args()
with open(args.map, 'r') as infile:
dirs = yaml.load(infile)
# Plots the instantaneous force coefficients at Re=1000 and AoA=35deg
# using a current version of cuIBM with CUSP-0.5.1
# and compares to the results reported in Krishnan et al. (2014).
simulation_directory = dirs['cuibm-current-cusp051']['Re1000AoA35']
simulation = CuIBMSimulation(description='cuIBM (current) - cusp-0.5.1',
directory=simulation_directory)
simulation.read_forces()
simulation.get_mean_forces(limits=[32.0, 64.0])
simulation.get_strouhal(limits=[32.0, 64.0], order=200)
krishnan = CuIBMSimulation(description='Krishnan et al. (2014)')
krishnan.read_forces(file_path=os.path.join(os.environ['SNAKE'],
'resources',
'flyingSnake2d_cuibm_anush',
'flyingSnake2dRe1000AoA35',
'forces'))
krishnan.get_mean_forces(limits=[32.0, 64.0])
krishnan.get_strouhal(limits=[32.0, 64.0], order=200)
dataframe = simulation.create_dataframe_forces(display_strouhal=True,
display_coefficients=True,
coefficient=2.0)
dataframe2 = krishnan.create_dataframe_forces(display_strouhal=True,