def run_convergence_test_advec(dtype,
flux_type,
random_partition,
mesh_gen,
debug_output=False):
"""Test whether 2/3D advection actually converges"""
from hedge.timestep import RK4TimeStepper
from hedge.tools import EOCRecorder
from math import sin
from hedge.data import TimeDependentGivenFunction
from hedge.visualization import SiloVisualizer
from hedge.backends import guess_run_context
rcon = guess_run_context(["mpi"])
# note: x component must remain zero because x-periodicity is used
v = np.array([0.0, 0.9, 0.3])
def f(x):
return sin(x)
def u_analytic(x, el, t):
return f(
(np.dot(-v[:dims], x) / la.norm(v[:dims]) + t * la.norm(v[:dims])))
def boundary_tagger(vertices, el, face_nr, points):
face_normal = el.face_normals[face_nr]
if np.dot(face_normal, v[:len(face_normal)]) < 0:
return ["inflow"]
else:
return ["outflow"]
mesh = mesh_gen(boundary_tagger)
eoc_rec = EOCRecorder()
if random_partition:
# Distribute elements randomly across nodes.
# This is bad, efficiency-wise, but it puts stress
# on the parallel implementation, which is desired here.
# Another main point of this is to force the code to split
# a periodic face pair across nodes.
from random import choice
partition = [choice(rcon.ranks) for el in mesh.elements]
else:
partition = None
for order in [1, 2, 3, 4]:
if rcon.is_head_rank:
mesh_data = rcon.distribute_mesh(mesh, partition)
else:
mesh_data = rcon.receive_mesh()
dims = mesh.points.shape[1]
discr = rcon.make_discretization(mesh_data,
order=order,
default_scalar_type=dtype)
op = StrongAdvectionOperator(
v[:dims],
inflow_u=TimeDependentGivenFunction(u_analytic),
flux_type=flux_type)
if debug_output:
vis = SiloVisualizer(discr, rcon)
u = discr.interpolate_volume_function(
lambda x, el: u_analytic(x, el, 0))
ic = u.copy()
if debug_output and rcon.is_head_rank:
print "#elements=%d" % len(mesh.elements)
test_name = "test-%s-o%d-m%s-r%s" % (
flux_type, order, mesh_gen.__name__, random_partition)
rhs = op.bind(discr)
stepper = RK4TimeStepper(dtype=dtype)
from hedge.timestep import times_and_steps
final_time = 1
step_it = times_and_steps(final_time=final_time,
max_dt_getter=lambda t: op.estimate_timestep(
discr, stepper=stepper, t=t, fields=u))
for step, t, dt in step_it:
u = stepper(u, t, dt, rhs)
assert u.dtype == dtype
u_true = discr.interpolate_volume_function(
lambda x, el: u_analytic(x, el, final_time))
error = u - u_true
l2_error = discr.norm(error)
if debug_output:
visf = vis.make_file(test_name + "-final")
vis.add_data(visf, [("u", u), ("u_true", u_true), ("ic", ic)])
visf.close()
eoc_rec.add_data_point(order, l2_error)
if debug_output and rcon.is_head_rank:
print "%s\n%s\n" % (flux_type.upper(), "-" * len(flux_type))
print eoc_rec.pretty_print(abscissa_label="Poly. Order",
error_label="L2 Error")
assert eoc_rec.estimate_order_of_convergence()[0, 1] > 3
assert eoc_rec.estimate_order_of_convergence(2)[-1, 1] > 7
def test_convergence_advec_2d():
"""Test whether 2D advection actually converges"""
import pyublas # noqa
from hedge.mesh.generator import make_disk_mesh, make_regular_rect_mesh
from hedge.discretization.local import TriangleDiscretization
from hedge.timestep import RK4TimeStepper
from hedge.tools import EOCRecorder
from math import sin, pi
from hedge.models.advection import StrongAdvectionOperator
from hedge.data import TimeDependentGivenFunction
v = numpy.array([0.27, 0])
norm_a = la.norm(v)
from numpy import dot
def f(x):
return sin(x)
def u_analytic(x, el, t):
return f((-dot(v, x) / norm_a + t * norm_a))
def boundary_tagger(vertices, el, face_nr, all_v):
if dot(el.face_normals[face_nr], v) < 0:
return ["inflow"]
else:
return ["outflow"]
for mesh in [
# non-periodic
make_disk_mesh(r=pi, boundary_tagger=boundary_tagger,
max_area=0.5),
# periodic
make_regular_rect_mesh(
a=(0, 0),
b=(2 * pi, 1),
n=(8, 4),
periodicity=(True, False),
boundary_tagger=boundary_tagger,
)
]:
for flux_type in StrongAdvectionOperator.flux_types:
eoc_rec = EOCRecorder()
for order in [1, 2, 3, 4, 5, 6]:
discr = discr_class(
mesh,
TriangleDiscretization(order),
debug=discr_class.noninteractive_debug_flags())
op = StrongAdvectionOperator(
v,
inflow_u=TimeDependentGivenFunction(u_analytic),
flux_type=flux_type)
u = discr.interpolate_volume_function(
lambda x, el: u_analytic(x, el, 0))
stepper = RK4TimeStepper()
dt = op.estimate_timestep(discr, stepper=stepper)
nsteps = int(1 / dt)
rhs = op.bind(discr)
for step in range(nsteps):
u = stepper(u, step * dt, dt, rhs)
u_true = discr.interpolate_volume_function(
lambda x, el: u_analytic(x, el, nsteps * dt))
error = u - u_true
error_l2 = discr.norm(error)
eoc_rec.add_data_point(order, error_l2)
if False:
print "%s\n%s\n" % (flux_type.upper(), "-" * len(flux_type))
print eoc_rec.pretty_print(abscissa_label="Poly. Order",
error_label="L2 Error")
assert eoc_rec.estimate_order_of_convergence()[0, 1] > 4
assert eoc_rec.estimate_order_of_convergence(2)[-1, 1] > 10
def test_symmetry_preservation_2d():
"""Test whether we preserve symmetry in a symmetric 2D advection problem"""
from numpy import dot
def make_mesh():
array = numpy.array
#
# 1---8---2
# |7 /|\ 1|
# | / | \ |
# |/ 6|0 \|
# 5---4---7
# |\ 5|3 /|
# | \ | / |
# |4 \|/ 2|
# 0---6---3
#
points = [
array([-0.5, -0.5]),
array([-0.5, 0.5]),
array([0.5, 0.5]),
array([0.5, -0.5]),
array([0.0, 0.0]),
array([-0.5, 0.0]),
array([0.0, -0.5]),
array([0.5, 0.0]),
array([0.0, 0.5])
]
elements = [
[8, 7, 4],
[8, 7, 2],
[6, 7, 3],
[7, 4, 6],
[5, 6, 0],
[5, 6, 4],
[5, 8, 4],
[1, 5, 8],
]
def boundary_tagger(vertices, el, face_nr, all_v):
if dot(el.face_normals[face_nr], v) < 0:
return ["inflow"]
else:
return ["outflow"]
from hedge.mesh import make_conformal_mesh
return make_conformal_mesh(points, elements, boundary_tagger)
from hedge.discretization import SymmetryMap
from hedge.timestep import RK4TimeStepper
from hedge.models.advection import StrongAdvectionOperator
from hedge.data import TimeDependentGivenFunction
v = numpy.array([-1, 0])
mesh = make_mesh()
discr = discr_class(mesh,
order=4,
debug=discr_class.noninteractive_debug_flags())
#ref_discr = DynamicDiscretization(mesh, order=4)
def f(x):
if x < 0.5:
return 0
else:
return (x - 0.5)
#def f(x):
#return sin(5*x)
def u_analytic(x, el, t):
return f(-dot(v, x) + t)
u = discr.interpolate_volume_function(lambda x, el: u_analytic(x, el, 0))
sym_map = SymmetryMap(discr, lambda x: numpy.array([x[0], -x[1]]), {
0: 3,
2: 1,
5: 6,
7: 4
})
for flux_type in StrongAdvectionOperator.flux_types:
stepper = RK4TimeStepper()
op = StrongAdvectionOperator(
v,
inflow_u=TimeDependentGivenFunction(u_analytic),
flux_type=flux_type)
dt = op.estimate_timestep(discr, stepper=stepper)
nsteps = int(1 / dt)
rhs = op.bind(discr)
#test_comp = [ "bflux"]
#test_rhs = op.bind(discr, test_comp)
#ref_rhs = op.bind(ref_discr, test_comp)
for step in xrange(nsteps):
u = stepper(u, step * dt, dt, rhs)
sym_error_u = u - sym_map(u)
sym_error_u_l2 = discr.norm(sym_error_u)
if False:
from hedge.visualization import SiloVisualizer
vis = SiloVisualizer(discr)
visf = vis.make_file("test-%s-%04d" % (flux_type, step))
vis.add_data(
visf,
[
("u", u),
("sym_u", sym_map(u)),
("sym_diff", u - sym_map(u)),
("rhs", rhs(step * dt, u)),
#("rhs_test", test_rhs(step*dt, u)),
#("rhs_ref", ref_rhs(step*dt, u)),
#("rhs_diff", test_rhs(step*dt, u)-ref_rhs(step*dt, u)),
])
print sym_error_u_l2
assert sym_error_u_l2 < 4e-15
def run_convergence_test_advec(dtype, debug_output=False):
"""Test whether 2/3D advection actually converges"""
from hedge.mesh.generator import make_ball_mesh, make_box_mesh, make_rect_mesh
from hedge.timestep import RK4TimeStepper
from hedge.tools import EOCRecorder
from math import sin, pi, sqrt
from hedge.models.advection import StrongAdvectionOperator
from hedge.data import TimeDependentGivenFunction
from hedge.visualization import SiloVisualizer
from hedge.backends import guess_run_context
rcon = guess_run_context(["mpi"])
# note: x component must remain zero because x-periodicity is used
v = numpy.array([0.0,0.9,0.3])
def f(x):
return sin(x)
def u_analytic(x, el, t):
return f((numpy.dot(-v[:dims],x)/la.norm(v[:dims])+t*la.norm(v[:dims])))
def boundary_tagger(vertices, el, face_nr, points):
face_normal = el.face_normals[face_nr]
if numpy.dot(face_normal, v[:len(face_normal)]) < 0:
return ["inflow"]
else:
return ["outflow"]
for i_mesh, mesh in enumerate([
# 2D semiperiodic
make_rect_mesh(b=(2*pi,3), max_area=0.4,
periodicity=(True, False),
subdivisions=(5,10),
boundary_tagger=boundary_tagger,
),
# 3D x-periodic
make_box_mesh((0,0,0), (2*pi, 2, 2), max_volume=0.4,
periodicity=(True, False, False),
boundary_tagger=boundary_tagger,
),
# non-periodic
make_ball_mesh(r=pi,
boundary_tagger=boundary_tagger, max_volume=0.7),
]):
for flux_type in StrongAdvectionOperator.flux_types:
for random_partition in [True, False]:
eoc_rec = EOCRecorder()
if random_partition:
# Distribute elements randomly across nodes.
# This is bad, efficiency-wise, but it puts stress
# on the parallel implementation, which is desired here.
# Another main point of this is to force the code to split
# a periodic face pair across nodes.
from random import choice
partition = [choice(rcon.ranks) for el in mesh.elements]
else:
partition = None
for order in [1,2,3,4]:
if rcon.is_head_rank:
mesh_data = rcon.distribute_mesh(mesh, partition)
else:
mesh_data = rcon.receive_mesh()
dims = mesh.points.shape[1]
discr = rcon.make_discretization(mesh_data, order=order,
default_scalar_type=dtype)
op = StrongAdvectionOperator(v[:dims],
inflow_u=TimeDependentGivenFunction(u_analytic),
flux_type=flux_type)
if debug_output:
vis = SiloVisualizer(discr, rcon)
u = discr.interpolate_volume_function(
lambda x, el: u_analytic(x, el, 0))
ic = u.copy()
if debug_output and rcon.is_head_rank:
print "#elements=%d" % len(mesh.elements)
test_name = "test-%s-o%d-m%d-r%s" % (
flux_type, order, i_mesh, random_partition)
rhs = op.bind(discr)
stepper = RK4TimeStepper(dtype=dtype)
from hedge.timestep import times_and_steps
final_time = 1
step_it = times_and_steps(
final_time=final_time,
max_dt_getter=lambda t: op.estimate_timestep(discr,
stepper=stepper, t=t, fields=u))
for step, t, dt in step_it:
u = stepper(u, t, dt, rhs)
assert u.dtype == dtype
u_true = discr.interpolate_volume_function(
lambda x, el: u_analytic(x, el, final_time))
error = u-u_true
l2_error = discr.norm(error)
if debug_output:
visf = vis.make_file(test_name+"-final")
vis.add_data(visf, [
("u", u),
("u_true", u_true),
("ic", ic)])
visf.close()
eoc_rec.add_data_point(order, l2_error)
if debug_output and rcon.is_head_rank:
print "%s\n%s\n" % (flux_type.upper(), "-" * len(flux_type))
print eoc_rec.pretty_print(abscissa_label="Poly. Order",
error_label="L2 Error")
assert eoc_rec.estimate_order_of_convergence()[0,1] > 3
assert eoc_rec.estimate_order_of_convergence(2)[-1,1] > 7
def test_convergence_advec_2d():
"""Test whether 2D advection actually converges"""
import pyublas
from hedge.mesh.generator import make_disk_mesh, make_regular_rect_mesh
from hedge.discretization.local import TriangleDiscretization
from hedge.timestep import RK4TimeStepper
from hedge.tools import EOCRecorder
from math import sin, pi, sqrt
from hedge.models.advection import StrongAdvectionOperator
from hedge.data import TimeDependentGivenFunction
v = numpy.array([0.27, 0])
norm_a = la.norm(v)
from numpy import dot
def f(x):
return sin(x)
def u_analytic(x, el, t):
return f((-dot(v, x) / norm_a + t * norm_a))
def boundary_tagger(vertices, el, face_nr, all_v):
if dot(el.face_normals[face_nr], v) < 0:
return ["inflow"]
else:
return ["outflow"]
for mesh in [
# non-periodic
make_disk_mesh(r=pi, boundary_tagger=boundary_tagger, max_area=0.5),
# periodic
make_regular_rect_mesh(
a=(0, 0), b=(2 * pi, 1), n=(8, 4), periodicity=(True, False), boundary_tagger=boundary_tagger
),
]:
for flux_type in StrongAdvectionOperator.flux_types:
eoc_rec = EOCRecorder()
for order in [1, 2, 3, 4, 5, 6]:
discr = discr_class(mesh, TriangleDiscretization(order), debug=discr_class.noninteractive_debug_flags())
op = StrongAdvectionOperator(v, inflow_u=TimeDependentGivenFunction(u_analytic), flux_type=flux_type)
u = discr.interpolate_volume_function(lambda x, el: u_analytic(x, el, 0))
stepper = RK4TimeStepper()
dt = op.estimate_timestep(discr, stepper=stepper)
nsteps = int(1 / dt)
rhs = op.bind(discr)
for step in range(nsteps):
u = stepper(u, step * dt, dt, rhs)
u_true = discr.interpolate_volume_function(lambda x, el: u_analytic(x, el, nsteps * dt))
error = u - u_true
error_l2 = discr.norm(error)
eoc_rec.add_data_point(order, error_l2)
if False:
print "%s\n%s\n" % (flux_type.upper(), "-" * len(flux_type))
print eoc_rec.pretty_print(abscissa_label="Poly. Order", error_label="L2 Error")
assert eoc_rec.estimate_order_of_convergence()[0, 1] > 4
assert eoc_rec.estimate_order_of_convergence(2)[-1, 1] > 10
def test_symmetry_preservation_2d():
"""Test whether we preserve symmetry in a symmetric 2D advection problem"""
from numpy import dot
def make_mesh():
array = numpy.array
#
# 1---8---2
# |7 /|\ 1|
# | / | \ |
# |/ 6|0 \|
# 5---4---7
# |\ 5|3 /|
# | \ | / |
# |4 \|/ 2|
# 0---6---3
#
points = [
array([-0.5, -0.5]),
array([-0.5, 0.5]),
array([0.5, 0.5]),
array([0.5, -0.5]),
array([0.0, 0.0]),
array([-0.5, 0.0]),
array([0.0, -0.5]),
array([0.5, 0.0]),
array([0.0, 0.5]),
]
elements = [[8, 7, 4], [8, 7, 2], [6, 7, 3], [7, 4, 6], [5, 6, 0], [5, 6, 4], [5, 8, 4], [1, 5, 8]]
def boundary_tagger(vertices, el, face_nr, all_v):
if dot(el.face_normals[face_nr], v) < 0:
return ["inflow"]
else:
return ["outflow"]
from hedge.mesh import make_conformal_mesh
return make_conformal_mesh(points, elements, boundary_tagger)
from hedge.discretization import SymmetryMap
from hedge.discretization.local import TriangleDiscretization
from hedge.timestep import RK4TimeStepper
from math import sqrt, sin
from hedge.models.advection import StrongAdvectionOperator
from hedge.data import TimeDependentGivenFunction
v = numpy.array([-1, 0])
mesh = make_mesh()
discr = discr_class(mesh, order=4, debug=discr_class.noninteractive_debug_flags())
# ref_discr = DynamicDiscretization(mesh, order=4)
def f(x):
if x < 0.5:
return 0
else:
return x - 0.5
# def f(x):
# return sin(5*x)
def u_analytic(x, el, t):
return f(-dot(v, x) + t)
u = discr.interpolate_volume_function(lambda x, el: u_analytic(x, el, 0))
sym_map = SymmetryMap(discr, lambda x: numpy.array([x[0], -x[1]]), {0: 3, 2: 1, 5: 6, 7: 4})
for flux_type in StrongAdvectionOperator.flux_types:
stepper = RK4TimeStepper()
op = StrongAdvectionOperator(v, inflow_u=TimeDependentGivenFunction(u_analytic), flux_type=flux_type)
dt = op.estimate_timestep(discr, stepper=stepper)
nsteps = int(1 / dt)
rhs = op.bind(discr)
# test_comp = [ "bflux"]
# test_rhs = op.bind(discr, test_comp)
# ref_rhs = op.bind(ref_discr, test_comp)
for step in xrange(nsteps):
u = stepper(u, step * dt, dt, rhs)
sym_error_u = u - sym_map(u)
sym_error_u_l2 = discr.norm(sym_error_u)
if False:
from hedge.visualization import SiloVisualizer
vis = SiloVisualizer(discr)
visf = vis.make_file("test-%s-%04d" % (flux_type, step))
vis.add_data(
visf,
[
("u", u),
("sym_u", sym_map(u)),
("sym_diff", u - sym_map(u)),
("rhs", rhs(step * dt, u)),
("rhs_test", test_rhs(step * dt, u)),
("rhs_ref", ref_rhs(step * dt, u)),
("rhs_diff", test_rhs(step * dt, u) - ref_rhs(step * dt, u)),
],
)
print sym_error_u_l2
assert sym_error_u_l2 < 4e-15
