def test(self):
labels = ['L2u', 'L2v', 'L2p']
df1 = run_spatial('plane-poiseuille-flow.i',
7,
"velocity_interp_method='average'",
'two_term_boundary_expansion=false',
"--error",
"--error-unused",
y_pp=labels,
mpi=16)
fig = mms.ConvergencePlot(xlabel='Element Size ($h$)',
ylabel='$L_2$ Error')
fig.plot(df1,
label=labels,
marker='o',
markersize=8,
num_fitted_points=3,
slope_precision=1)
fig.save('plane-poiseuille-average-first.png')
for key, value in fig.label_to_slope.items():
if key == 'L2p':
self.assertTrue(fuzzyAbsoluteEqual(value, 1., .1))
else:
self.assertTrue(fuzzyAbsoluteEqual(value, 2., .1))
def secondary_and_primary_plots(self,
fine,
num_refinements,
additional_cli_args,
name,
plots_to_do=None,
mpi=1):
if not plots_to_do:
if (fine == 2):
plots_to_do = ['same']
else:
plots_to_do = ['coarse_secondary', 'coarse_primary']
fig = mms.ConvergencePlot('Element Size ($h$)', ylabel='$L_2$ Error')
second_order = False
for cli_arg in additional_cli_args:
if "SECOND" in cli_arg:
second_order = True
if second_order:
deltas = [0, 0.1]
else:
deltas = [0, 0.4]
for delta in deltas:
if 'coarse_primary' in plots_to_do:
self.do_plot('continuity.i',
num_refinements,
["Mesh/left_block/ny="+str(fine),
"Mesh/left_block/nx="+str(fine),
"Constraints/mortar/delta="+str(delta)] + \
additional_cli_args,
fig,
"coarse_primary_"+str(delta),
mpi)
if 'coarse_secondary' in plots_to_do:
self.do_plot('continuity.i',
num_refinements,
["Mesh/right_block/ny="+str(fine),
"Mesh/right_block/nx="+str(fine),
"Constraints/mortar/delta="+str(delta)] + \
additional_cli_args,
fig,
"coarse_secondary_"+str(delta),
mpi)
if 'same' in plots_to_do:
self.do_plot('continuity.i',
num_refinements,
["Constraints/mortar/delta="+str(delta)] + additional_cli_args,
fig,
"same_"+str(delta),
mpi)
fig.set_title(name)
fig.save(name+'.png')
return fig
def test(self):
df1 = run_spatial('2d-rc.i', 6, y_pp=['L2u', 'L2v', 'L2p'], mpi=16)
fig = mms.ConvergencePlot(xlabel='Element Size ($h$)', ylabel='$L_2$ Error')
fig.plot(df1, label=['L2u', 'L2v', 'L2p'], marker='o', markersize=8, num_fitted_points=3, slope_precision=1)
fig.save('rc-2d.png')
for key,value in fig.label_to_slope.items():
print("%s, %f" % (key, value))
self.assertTrue(fuzzyAbsoluteEqual(value, 2., .15))
def test(self):
labels = ['L2u', 'L2v', 'L2p', 'L2t']
df1 = mms.run_spatial('2d-average-with-temp.i', 7, "velocity_interp_method='rc'", y_pp=labels, mpi=16)
fig = mms.ConvergencePlot(xlabel='Element Size ($h$)', ylabel='$L_2$ Error')
fig.plot(df1, label=labels, marker='o', markersize=8, num_fitted_points=3, slope_precision=1)
fig.save('2d-rc-with-temp.png')
for key,value in fig.label_to_slope.items():
print("%s, %f" % (key, value))
self.assertTrue(fuzzyAbsoluteEqual(value, 2., .1))
def test(self):
labels = ['L2u', 'L2v', 'L2p']
df1 = run_spatial('rotated-pp-flow.i', 6, "velocity_interp_method='rc'", "--error", y_pp=labels, mpi=8)
fig = mms.ConvergencePlot(xlabel='Element Size ($h$)', ylabel='$L_2$ Error')
fig.plot(df1, label=labels, marker='o', markersize=8, num_fitted_points=3, slope_precision=1)
fig.save('plane-poiseuille-rc.png')
for key,value in fig.label_to_slope.items():
print("%s, %f" % (key, value))
self.assertTrue(fuzzyAbsoluteEqual(value, 2., .1))
def test(self):
labels = ['L2rho', 'L2rho_u', 'L2rho_et']
df1 = run_spatial('hllc-mms.i', list(range(6,12)), "--allow-test-objects", "--error", y_pp=labels, mpi=6)
fig = mms.ConvergencePlot(xlabel='Element Size ($h$)', ylabel='$L_2$ Error')
fig.plot(df1, label=labels, marker='o', markersize=8, num_fitted_points=3, slope_precision=1)
fig.save('hllc.png')
for key,value in fig.label_to_slope.items():
print("%s, %f" % (key, value))
self.assertTrue(fuzzyAbsoluteEqual(value, 1., .05))
def test(self):
labels = ['L2u', 'L2v', 'L2p']
df1 = mms.run_spatial('2d-average.i', 6, y_pp=labels)
fig = mms.ConvergencePlot(xlabel='Element Size ($h$)', ylabel='$L_2$ Error')
fig.plot(df1, label=labels, marker='o', markersize=8, num_fitted_points=3, slope_precision=1)
fig.save('2d-average.png')
for key,value in fig.label_to_slope.items():
print("%s, %f" % (key, value))
if key == 'L2p':
self.assertTrue(fuzzyAbsoluteEqual(value, 1., .1))
else:
self.assertTrue(fuzzyAbsoluteEqual(value, 2., .1))
def test(self):
df1 = mms.run_spatial('advection-diffusion-reaction.i', 7, mpi=2)
fig = mms.ConvergencePlot(xlabel='Element Size ($h$)', ylabel='$L_2$ Error')
fig.plot(df1,
label='compact-adr',
marker='o',
markersize=8,
num_fitted_points=3,
slope_precision=1)
fig.save('compact-adr.png')
for _,value in fig.label_to_slope.items():
self.assertTrue(fuzzyEqual(value, 2., .05))
def test(self):
df1 = mms.run_spatial('steady-adapt.i', 7, mpi=8)
fig = mms.ConvergencePlot(xlabel='Element Size ($h$)', ylabel='$L_2$ Error')
fig.plot(df1,
label='steady-adapt',
marker='o',
markersize=8,
num_fitted_points=3,
slope_precision=1)
fig.save('steady-adapt.png')
for _,value in fig.label_to_slope.items():
self.assertTrue(fuzzyEqual(value, 1., .05))
def test(self):
df1 = mms.run_spatial('advection-outflow.i', 7, y_pp=['L2u', 'L2v'])
fig = mms.ConvergencePlot(xlabel='Element Size ($h$)',
ylabel='$L_2$ Error')
fig.plot(df1,
label=['L2u', 'L2v'],
marker='o',
markersize=8,
num_fitted_points=3,
slope_precision=1)
fig.save('outflow.png')
for label, value in fig.label_to_slope.items():
if label == 'L2u':
self.assertTrue(fuzzyAbsoluteEqual(value, 1., .05))
else:
self.assertTrue(fuzzyAbsoluteEqual(value, 2., .05))
def test(self):
df1 = mms.run_spatial('limited-advection.i',
9,
"FVKernels/advection_u/limiter='vanLeer'",
y_pp=['L2u'])
fig = mms.ConvergencePlot(xlabel='Element Size ($h$)',
ylabel='$L_2$ Error')
fig.plot(df1,
label=['L2u'],
marker='o',
markersize=8,
num_fitted_points=3,
slope_precision=1)
fig.save('vanLeer-limiter.png')
for label, value in fig.label_to_slope.items():
self.assertTrue(fuzzyAbsoluteEqual(value, 2., .05))
def test(self):
df1 = mms.run_spatial('skewed.i',
5,
'Variables/v/face_interp_method=average',
mpi=1)
fig = mms.ConvergencePlot(xlabel='Element Size ($h$)',
ylabel='$L_2$ Error')
fig.plot(df1,
label='average',
marker='o',
markersize=8,
num_fitted_points=3,
slope_precision=1)
fig.save('average.png')
for _, value in fig.label_to_slope.items():
self.assertTrue(fuzzyEqual(value, 1., .15))
def test(self):
df1 = mms.run_spatial(
'kt-limited-advection.i',
11,
"FVKernels/advection_u/limiter='central_difference'",
y_pp=['L2u'])
fig = mms.ConvergencePlot(xlabel='Element Size ($h$)',
ylabel='$L_2$ Error')
fig.plot(df1,
label=['L2u'],
marker='o',
markersize=8,
num_fitted_points=3,
slope_precision=1)
fig.save('kt-cd-limiter.png')
for key, value in fig.label_to_slope.items():
self.assertTrue(fuzzyAbsoluteEqual(value, 2., .05))
print("%s slope, %f" % (key, value))
def test(self):
df1 = mms.run_spatial(
'skewed.i',
5,
'Variables/v/face_interp_method=skewness-corrected FVKernels/advection/advected_interp_method=skewness-corrected',
mpi=1)
fig = mms.ConvergencePlot(xlabel='Element Size ($h$)',
ylabel='$L_2$ Error')
fig.plot(df1,
label='corrected-advection',
marker='o',
markersize=8,
num_fitted_points=3,
slope_precision=1)
fig.save('corrected-advection.png')
for _, value in fig.label_to_slope.items():
self.assertTrue(fuzzyEqual(value, 2.0, .1))
def secondary_and_primary_plots(self,
fine,
num_refinements,
additional_cli_args,
name,
plots_to_do=None,
mpi=1):
if not plots_to_do:
if (fine == 2):
plots_to_do = ['same']
else:
plots_to_do = ['coarse_secondary', 'coarse_primary']
fig = mms.ConvergencePlot('Element Size ($h$)', ylabel='$L_2$ Error')
if 'coarse_primary' in plots_to_do:
self.do_plot('gap-conductance.i',
num_refinements,
["Mesh/left_block/ny="+str(fine),
"Mesh/left_block/nx="+str(fine)] + \
additional_cli_args,
fig,
"coarse_primary",
mpi)
if 'coarse_secondary' in plots_to_do:
self.do_plot('gap-conductance.i',
num_refinements,
["Mesh/right_block/ny="+str(fine),
"Mesh/right_block/nx="+str(fine)] + \
additional_cli_args,
fig,
"coarse_secondary",
mpi)
if 'same' in plots_to_do:
self.do_plot('gap-conductance.i', num_refinements,
additional_cli_args, fig, "same", mpi)
fig.set_title(name)
fig.save(name + '.png')
return fig
def test(self):
labels = ['L2u', 'L2v', 'L2p']
df1 = run_spatial('2d-rc-rz-symmetry.i',
list(range(1, 6)),
"--error",
y_pp=labels,
mpi=8)
fig = mms.ConvergencePlot(xlabel='Element Size ($h$)',
ylabel='$L_2$ Error')
fig.plot(df1,
label=labels,
marker='o',
markersize=8,
num_fitted_points=3,
slope_precision=1)
fig.save('2d-rc-rz-symmetry.png')
for key, value in fig.label_to_slope.items():
print("%s, %f" % (key, value))
self.assertTrue(fuzzyAbsoluteEqual(value, 2., .1))
def test(self):
df1 = run_spatial('pressure-interpolation-corrected.i',
7,
"--error",
"--error-unused",
"--error-deprecated",
y_pp=['L2u', 'L2v', 'L2p'],
mpi=8)
fig = mms.ConvergencePlot(xlabel='Element Size ($h$)',
ylabel='$L_2$ Error')
fig.plot(df1,
label=['L2u', 'L2v', 'L2p'],
marker='o',
markersize=8,
num_fitted_points=3,
slope_precision=1)
fig.save('pressure-interpolation-corrected.png')
for key, value in fig.label_to_slope.items():
print("%s, %f" % (key, value))
self.assertTrue(fuzzyAbsoluteEqual(value, 2., .1))
def test(self):
velocity_labels = ['L2u', 'L2v']
pressure_labels = ['L2p']
labels = velocity_labels + pressure_labels
df1 = run_spatial('skewed-vortex.i', 5, y_pp=labels, mpi=2)
fig = mms.ConvergencePlot(xlabel='Element Size ($h$)',
ylabel='$L_2$ Error')
fig.plot(df1,
label=labels,
marker='o',
markersize=8,
num_fitted_points=3,
slope_precision=1)
fig.save('skewed.png')
for key, value in fig.label_to_slope.items():
print("%s, %f" % (key, value))
if key in velocity_labels:
self.assertTrue(fuzzyAbsoluteEqual(value, 2., .2))
else:
self.assertTrue(fuzzyAbsoluteEqual(value, 1., .2))
def secondary_and_primary_plots(self,
num_refinements,
additional_cli_args,
name,
plots_to_do=None,
mpi=1):
fig = mms.ConvergencePlot('Element Size ($h$)', ylabel='$L_2$ Error')
has_tets = True
for cli_arg in additional_cli_args:
if "hex" in cli_arg:
has_tets = False
if has_tets:
deltas = [0.1]
else:
deltas = [0.1]
for delta in deltas:
self.do_plot('continuity.i', num_refinements,
["Constraints/mortar/delta=" + str(delta)] +
additional_cli_args, fig, str(delta), mpi)
fig.set_title(name)
fig.save(name + '.png')
return fig
def test(self):
df1 = run_spatial('1d-rc-no-diffusion.i',
5,
"--error",
"--error-unused",
"--error-deprecated",
y_pp=['L2u', 'L2p'])
fig = mms.ConvergencePlot(xlabel='Element Size ($h$)',
ylabel='$L_2$ Error')
fig.plot(df1,
label=['L2u', 'L2p'],
marker='o',
markersize=8,
num_fitted_points=3,
slope_precision=1)
fig.save('1d-rc-no-diffusion.png')
for key, value in fig.label_to_slope.items():
print("%s, %f" % (key, value))
if (key == 'L2u'):
self.assertTrue(fuzzyAbsoluteEqual(value, 1.5, .1))
else:
self.assertTrue(fuzzyAbsoluteEqual(value, 1., .1))
def run_each_test(testName, hyperConvergent=False, refinementNo=5):
df1 = mms.run_spatial(testName + '.i',
refinementNo,
console=False,
executable='../../../heat_conduction-opt')
if (not hyperConvergent):
df2 = mms.run_spatial(testName + '.i',
refinementNo,
'Mesh/second_order=true',
'Variables/u/order=SECOND',
console=False,
executable='../../../heat_conduction-opt')
fig = mms.ConvergencePlot(xlabel='Element Size ($h$)',
ylabel='$L_2$ Error')
fig.plot(df1, label=r'EDGE2 (1$^{st}$-order) $\hat{p}_O$', marker='o')
if (not hyperConvergent):
fig.plot(df2, label='EDGE3 (2$^{nd}$-order) $\hat{p}_O$', marker='D')
fig.save(testName + '_spatial.png')
df1.to_csv(testName + '_first_order.csv')
if (not hyperConvergent): df2.to_csv(testName + '_second_order.csv')
#!/usr/bin/env python3
import mms
df1 = mms.run_spatial('advection-diffusion.i', 7,
"GlobalParams/advected_interp_method='upwind'")
fig = mms.ConvergencePlot(xlabel='Element Size ($h$)', ylabel='$L_2$ Error')
fig.plot(df1,
label='upwind-adv-diff',
marker='o',
markersize=8,
num_fitted_points=3,
slope_precision=1)
fig.save('upwind-advection-diffusion.png')
def runConvergenceTest(input_file, additional_cli_args, dt, image_filename):
df1 = mms.run_temporal(input_file,
N,
'Executioner/TimeIntegrator/type=ExplicitEuler ' +
additional_cli_args,
file_base='ee_{}',
console=False,
dt=dt,
y_pp='l2_err',
executable=EXE)
df2 = mms.run_temporal(
input_file,
N,
'Executioner/TimeIntegrator/type=ActuallyExplicitEuler ' +
additional_cli_args,
file_base='aee_{}',
console=False,
dt=dt,
y_pp='l2_err',
executable=EXE)
df3 = mms.run_temporal(
input_file,
N,
'Executioner/TimeIntegrator/type=ExplicitSSPRungeKutta Executioner/TimeIntegrator/order=1 '
+ additional_cli_args,
file_base='ssprk1_{}',
console=False,
dt=dt,
y_pp='l2_err',
executable=EXE)
df4 = mms.run_temporal(
input_file,
N,
'Executioner/TimeIntegrator/type=ExplicitSSPRungeKutta Executioner/TimeIntegrator/order=2 '
+ additional_cli_args,
file_base='ssprk2_{}',
console=False,
dt=dt,
y_pp='l2_err',
executable=EXE)
df5 = mms.run_temporal(
input_file,
N,
'Executioner/TimeIntegrator/type=ExplicitSSPRungeKutta Executioner/TimeIntegrator/order=3 '
+ additional_cli_args,
file_base='ssprk3_{}',
console=False,
dt=dt,
y_pp='l2_err',
executable=EXE)
fig = mms.ConvergencePlot(xlabel='$\Delta t$', ylabel='$L_2$ Error')
fig.plot(df1, label='ExplicitEuler', marker='+', markersize=8)
fig.plot(df2, label='ActuallyExplicitEuler', marker='x', markersize=8)
fig.plot(df3,
label='SSP Runge-Kutta 1',
marker='o',
markersize=8,
markerfacecolor='none')
fig.plot(df4, label='SSP Runge-Kutta 2', marker='v', markersize=8)
fig.plot(df5, label='SSP Runge-Kutta 3', marker='s', markersize=8)
fig.save(image_filename)
#!/usr/bin/env python3
#* This file is part of the MOOSE framework
#* https://www.mooseframework.org
#*
#* All rights reserved, see COPYRIGHT for full restrictions
#* https://github.com/idaholab/moose/blob/master/COPYRIGHT
#*
#* Licensed under LGPL 2.1, please see LICENSE for details
#* https://www.gnu.org/licenses/lgpl-2.1.html
import mms
df1 = mms.run_temporal('mms_temporal.i', 4, console=False, executable='../../../test')
df2 = mms.run_temporal('mms_temporal.i', 4, 'Executioner/scheme=bdf2', console=False,
executable='../../../test')
fig = mms.ConvergencePlot(xlabel='$\Delta t$', ylabel='$L_2$ Error')
fig.plot(df1, label='1st Order (Implicit Euler)', marker='o', markersize=8)
fig.plot(df2, label='2nd Order (Backward Difference)', marker='o', markersize=8)
fig.save('mms_temporal.png')
#TESTING (leave this comment, it is used in doco to remove the following from a demo)
df1.to_csv('mms_temporal_first.csv')
df2.to_csv('mms_temporal_second.csv')
