def mpi_communication_entrypoint():
cl_ctx = cl.create_some_context()
queue = cl.CommandQueue(cl_ctx)
from mpi4py import MPI
comm = MPI.COMM_WORLD
i_local_rank = comm.Get_rank()
num_parts = comm.Get_size()
from meshmode.distributed import MPIMeshDistributor, get_partition_by_pymetis
mesh_dist = MPIMeshDistributor(comm)
dim = 2
dt = 0.04
order = 4
if mesh_dist.is_mananger_rank():
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(-0.5, ) * dim,
b=(0.5, ) * dim,
n=(16, ) * dim)
part_per_element = get_partition_by_pymetis(mesh, num_parts)
local_mesh = mesh_dist.send_mesh_parts(mesh, part_per_element,
num_parts)
else:
local_mesh = mesh_dist.receive_mesh_part()
vol_discr = DGDiscretizationWithBoundaries(cl_ctx,
local_mesh,
order=order,
mpi_communicator=comm)
source_center = np.array([0.1, 0.22, 0.33])[:local_mesh.dim]
source_width = 0.05
source_omega = 3
sym_x = sym.nodes(local_mesh.dim)
sym_source_center_dist = sym_x - source_center
sym_t = sym.ScalarVariable("t")
from grudge.models.wave import StrongWaveOperator
from meshmode.mesh import BTAG_ALL, BTAG_NONE
op = StrongWaveOperator(
-0.1,
vol_discr.dim,
source_f=(
sym.sin(source_omega * sym_t) *
sym.exp(-np.dot(sym_source_center_dist, sym_source_center_dist) /
source_width**2)),
dirichlet_tag=BTAG_NONE,
neumann_tag=BTAG_NONE,
radiation_tag=BTAG_ALL,
flux_type="upwind")
from pytools.obj_array import join_fields
fields = join_fields(
vol_discr.zeros(queue),
[vol_discr.zeros(queue) for i in range(vol_discr.dim)])
# FIXME
# dt = op.estimate_rk4_timestep(vol_discr, fields=fields)
# FIXME: Should meshmode consider BTAG_PARTITION to be a boundary?
# Fails because: "found faces without boundary conditions"
# op.check_bc_coverage(local_mesh)
from pytools.log import LogManager, \
add_general_quantities, \
add_run_info, \
IntervalTimer, EventCounter
log_filename = None
# NOTE: LogManager hangs when using a file on a shared directory.
# log_filename = 'grudge_log.dat'
logmgr = LogManager(log_filename, "w", comm)
add_run_info(logmgr)
add_general_quantities(logmgr)
log_quantities =\
{"rank_data_swap_timer": IntervalTimer("rank_data_swap_timer",
"Time spent evaluating RankDataSwapAssign"),
"rank_data_swap_counter": EventCounter("rank_data_swap_counter",
"Number of RankDataSwapAssign instructions evaluated"),
"exec_timer": IntervalTimer("exec_timer",
"Total time spent executing instructions"),
"insn_eval_timer": IntervalTimer("insn_eval_timer",
"Time spend evaluating instructions"),
"future_eval_timer": IntervalTimer("future_eval_timer",
"Time spent evaluating futures"),
"busy_wait_timer": IntervalTimer("busy_wait_timer",
"Time wasted doing busy wait")}
for quantity in log_quantities.values():
logmgr.add_quantity(quantity)
# print(sym.pretty(op.sym_operator()))
bound_op = bind(vol_discr, op.sym_operator())
# print(bound_op)
# 1/0
def rhs(t, w):
val, rhs.profile_data = bound_op(queue,
profile_data=rhs.profile_data,
log_quantities=log_quantities,
t=t,
w=w)
return val
rhs.profile_data = {}
dt_stepper = set_up_rk4("w", dt, fields, rhs)
final_t = 4
nsteps = int(final_t / dt)
print("rank=%d dt=%g nsteps=%d" % (i_local_rank, dt, nsteps))
# from grudge.shortcuts import make_visualizer
# vis = make_visualizer(vol_discr, vis_order=order)
step = 0
norm = bind(vol_discr, sym.norm(2, sym.var("u")))
from time import time
t_last_step = time()
logmgr.tick_before()
for event in dt_stepper.run(t_end=final_t):
if isinstance(event, dt_stepper.StateComputed):
assert event.component_id == "w"
step += 1
print(step, event.t, norm(queue, u=event.state_component[0]),
time() - t_last_step)
# if step % 10 == 0:
# vis.write_vtk_file("rank%d-fld-%04d.vtu" % (i_local_rank, step),
# [("u", event.state_component[0]),
# ("v", event.state_component[1:])])
t_last_step = time()
logmgr.tick_after()
logmgr.tick_before()
logmgr.tick_after()
def print_profile_data(data):
print("""execute() for rank %d:
\tInstruction Evaluation: %f%%
\tFuture Evaluation: %f%%
\tBusy Wait: %f%%
\tTotal: %f seconds""" %
(i_local_rank, data['insn_eval_time'] / data['total_time'] * 100,
data['future_eval_time'] / data['total_time'] * 100,
data['busy_wait_time'] / data['total_time'] * 100,
data['total_time']))
print_profile_data(rhs.profile_data)
logmgr.close()
logger.debug("Rank %d exiting", i_local_rank)
def simple_wave_entrypoint(dim=2, num_elems=256, order=4, num_steps=30,
log_filename="grudge.dat"):
cl_ctx = cl.create_some_context()
queue = cl.CommandQueue(cl_ctx)
from mpi4py import MPI
comm = MPI.COMM_WORLD
num_parts = comm.Get_size()
n = int(num_elems ** (1./dim))
from meshmode.distributed import MPIMeshDistributor
mesh_dist = MPIMeshDistributor(comm)
if mesh_dist.is_mananger_rank():
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(-0.5,)*dim,
b=(0.5,)*dim,
n=(n,)*dim)
from pymetis import part_graph
_, p = part_graph(num_parts,
xadj=mesh.nodal_adjacency.neighbors_starts.tolist(),
adjncy=mesh.nodal_adjacency.neighbors.tolist())
part_per_element = np.array(p)
local_mesh = mesh_dist.send_mesh_parts(mesh, part_per_element, num_parts)
else:
local_mesh = mesh_dist.receive_mesh_part()
vol_discr = DGDiscretizationWithBoundaries(cl_ctx, local_mesh, order=order,
mpi_communicator=comm)
source_center = np.array([0.1, 0.22, 0.33])[:local_mesh.dim]
source_width = 0.05
source_omega = 3
sym_x = sym.nodes(local_mesh.dim)
sym_source_center_dist = sym_x - source_center
sym_t = sym.ScalarVariable("t")
from grudge.models.wave import StrongWaveOperator
from meshmode.mesh import BTAG_ALL, BTAG_NONE
op = StrongWaveOperator(-0.1, vol_discr.dim,
source_f=(
sym.sin(source_omega*sym_t)
* sym.exp(
-np.dot(sym_source_center_dist, sym_source_center_dist)
/ source_width**2)),
dirichlet_tag=BTAG_NONE,
neumann_tag=BTAG_NONE,
radiation_tag=BTAG_ALL,
flux_type="upwind")
from pytools.obj_array import join_fields
fields = join_fields(vol_discr.zeros(queue),
[vol_discr.zeros(queue) for i in range(vol_discr.dim)])
from logpyle import LogManager, \
add_general_quantities, \
add_run_info, \
IntervalTimer, EventCounter
# NOTE: LogManager hangs when using a file on a shared directory.
logmgr = LogManager(log_filename, "w", comm)
add_run_info(logmgr)
add_general_quantities(logmgr)
log_quantities =\
{"rank_data_swap_timer": IntervalTimer("rank_data_swap_timer",
"Time spent evaluating RankDataSwapAssign"),
"rank_data_swap_counter": EventCounter("rank_data_swap_counter",
"Number of RankDataSwapAssign instructions evaluated"),
"exec_timer": IntervalTimer("exec_timer",
"Total time spent executing instructions"),
"insn_eval_timer": IntervalTimer("insn_eval_timer",
"Time spend evaluating instructions"),
"future_eval_timer": IntervalTimer("future_eval_timer",
"Time spent evaluating futures"),
"busy_wait_timer": IntervalTimer("busy_wait_timer",
"Time wasted doing busy wait")}
for quantity in log_quantities.values():
logmgr.add_quantity(quantity)
bound_op = bind(vol_discr, op.sym_operator())
def rhs(t, w):
val, rhs.profile_data = bound_op(queue, profile_data=rhs.profile_data,
log_quantities=log_quantities,
t=t, w=w)
return val
rhs.profile_data = {}
dt = 0.04
dt_stepper = set_up_rk4("w", dt, fields, rhs)
logmgr.tick_before()
for event in dt_stepper.run(t_end=dt * num_steps):
if isinstance(event, dt_stepper.StateComputed):
logmgr.tick_after()
logmgr.tick_before()
logmgr.tick_after()
def print_profile_data(data):
print("""execute() for rank %d:
\tInstruction Evaluation: %f%%
\tFuture Evaluation: %f%%
\tBusy Wait: %f%%
\tTotal: %f seconds""" %
(comm.Get_rank(),
data['insn_eval_time'] / data['total_time'] * 100,
data['future_eval_time'] / data['total_time'] * 100,
data['busy_wait_time'] / data['total_time'] * 100,
data['total_time']))
print_profile_data(rhs.profile_data)
logmgr.close()
def main(ctx_factory, dim=3, order=4, visualize=False):
cl_ctx = ctx_factory()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(
queue,
allocator=cl_tools.MemoryPool(cl_tools.ImmediateAllocator(queue)),
force_device_scalars=True,
)
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(0.0, ) * dim,
b=(1.0, ) * dim,
nelements_per_axis=(4, ) * dim)
dcoll = DiscretizationCollection(actx, mesh, order=order)
if 0:
epsilon0 = 8.8541878176e-12 # C**2 / (N m**2)
mu0 = 4 * np.pi * 1e-7 # N/A**2.
epsilon = 1 * epsilon0
mu = 1 * mu0
else:
epsilon = 1
mu = 1
from grudge.models.em import MaxwellOperator
maxwell_operator = MaxwellOperator(dcoll,
epsilon,
mu,
flux_type=0.5,
dimensions=dim)
def cavity_mode(x, t=0):
if dim == 3:
return get_rectangular_cavity_mode(actx, x, t, 1, (1, 2, 2))
else:
return get_rectangular_cavity_mode(actx, x, t, 1, (2, 3))
fields = cavity_mode(thaw(dcoll.nodes(), actx), t=0)
maxwell_operator.check_bc_coverage(mesh)
def rhs(t, w):
return maxwell_operator.operator(t, w)
dt = maxwell_operator.estimate_rk4_timestep(actx, dcoll, fields=fields)
dt_stepper = set_up_rk4("w", dt, fields, rhs)
target_steps = 60
final_t = dt * target_steps
nsteps = int(final_t / dt) + 1
logger.info("dt = %g nsteps = %d", dt, nsteps)
from grudge.shortcuts import make_visualizer
vis = make_visualizer(dcoll)
step = 0
def norm(u):
return op.norm(dcoll, u, 2)
e, h = maxwell_operator.split_eh(fields)
if visualize:
vis.write_vtk_file(f"fld-cavities-{step:04d}.vtu", [
("e", e),
("h", h),
])
for event in dt_stepper.run(t_end=final_t):
if isinstance(event, dt_stepper.StateComputed):
assert event.component_id == "w"
step += 1
e, h = maxwell_operator.split_eh(event.state_component)
norm_e0 = actx.to_numpy(norm(u=e[0]))
norm_e1 = actx.to_numpy(norm(u=e[1]))
norm_h0 = actx.to_numpy(norm(u=h[0]))
norm_h1 = actx.to_numpy(norm(u=h[1]))
logger.info(
"[%04d] t = %.5f |e0| = %.5e, |e1| = %.5e, |h0| = %.5e, |h1| = %.5e",
step, event.t, norm_e0, norm_e1, norm_h0, norm_h1)
if step % 10 == 0:
if visualize:
vis.write_vtk_file(f"fld-cavities-{step:04d}.vtu", [
("e", e),
("h", h),
])
# NOTE: These are here to ensure the solution is bounded for the
# time interval specified
assert norm_e0 < 0.5
assert norm_e1 < 0.5
assert norm_h0 < 0.5
assert norm_h1 < 0.5
def main(ctx_factory, dim=2, order=4, visualize=False):
cl_ctx = ctx_factory()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(
queue,
allocator=cl_tools.MemoryPool(cl_tools.ImmediateAllocator(queue)),
force_device_scalars=True,
)
# {{{ parameters
# domain [-d/2, d/2]^dim
d = 1.0
# number of points in each dimension
npoints = 20
# final time
final_time = 1.0
# velocity field
c = np.array([0.5] * dim)
norm_c = la.norm(c)
# flux
flux_type = "central"
# }}}
# {{{ discretization
from meshmode.mesh.generation import generate_box_mesh
mesh = generate_box_mesh(
[np.linspace(-d / 2, d / 2, npoints) for _ in range(dim)], order=order)
from grudge import DiscretizationCollection
dcoll = DiscretizationCollection(actx, mesh, order=order)
# }}}
# {{{ weak advection operator
def f(x):
return actx.np.sin(3 * x)
def u_analytic(x, t=0):
return f(-np.dot(c, x) / norm_c + t * norm_c)
from grudge.models.advection import WeakAdvectionOperator
adv_operator = WeakAdvectionOperator(
dcoll,
c,
inflow_u=lambda t: u_analytic(thaw(dcoll.nodes(dd=BTAG_ALL), actx),
t=t),
flux_type=flux_type)
nodes = thaw(dcoll.nodes(), actx)
u = u_analytic(nodes, t=0)
def rhs(t, u):
return adv_operator.operator(t, u)
dt = actx.to_numpy(
adv_operator.estimate_rk4_timestep(actx, dcoll, fields=u))
logger.info("Timestep size: %g", dt)
# }}}
# {{{ time stepping
from grudge.shortcuts import set_up_rk4
dt_stepper = set_up_rk4("u", dt, u, rhs)
plot = Plotter(actx, dcoll, order, visualize=visualize, ylim=[-1.1, 1.1])
step = 0
norm_u = 0.0
for event in dt_stepper.run(t_end=final_time):
if not isinstance(event, dt_stepper.StateComputed):
continue
if step % 10 == 0:
norm_u = actx.to_numpy(op.norm(dcoll, event.state_component, 2))
plot(event, "fld-weak-%04d" % step)
step += 1
logger.info("[%04d] t = %.5f |u| = %.5e", step, event.t, norm_u)
# NOTE: These are here to ensure the solution is bounded for the
# time interval specified
assert norm_u < 1
def mpi_communication_entrypoint():
cl_ctx = cl.create_some_context()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(queue, force_device_scalars=True)
from mpi4py import MPI
comm = MPI.COMM_WORLD
i_local_rank = comm.Get_rank()
num_parts = comm.Get_size()
from meshmode.distributed import MPIMeshDistributor, get_partition_by_pymetis
mesh_dist = MPIMeshDistributor(comm)
dim = 2
order = 4
if mesh_dist.is_mananger_rank():
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(-0.5,)*dim,
b=(0.5,)*dim,
nelements_per_axis=(16,)*dim)
part_per_element = get_partition_by_pymetis(mesh, num_parts)
local_mesh = mesh_dist.send_mesh_parts(mesh, part_per_element, num_parts)
del mesh
else:
local_mesh = mesh_dist.receive_mesh_part()
dcoll = DiscretizationCollection(actx, local_mesh, order=order,
mpi_communicator=comm)
def source_f(actx, dcoll, t=0):
source_center = np.array([0.1, 0.22, 0.33])[:dcoll.dim]
source_width = 0.05
source_omega = 3
nodes = thaw(dcoll.nodes(), actx)
source_center_dist = flat_obj_array(
[nodes[i] - source_center[i] for i in range(dcoll.dim)]
)
return (
np.sin(source_omega*t)
* actx.np.exp(
-np.dot(source_center_dist, source_center_dist)
/ source_width**2
)
)
from grudge.models.wave import WeakWaveOperator
from meshmode.mesh import BTAG_ALL, BTAG_NONE
wave_op = WeakWaveOperator(
dcoll,
0.1,
source_f=source_f,
dirichlet_tag=BTAG_NONE,
neumann_tag=BTAG_NONE,
radiation_tag=BTAG_ALL,
flux_type="upwind"
)
fields = flat_obj_array(
dcoll.zeros(actx),
[dcoll.zeros(actx) for i in range(dcoll.dim)]
)
dt = actx.to_numpy(
2/3 * wave_op.estimate_rk4_timestep(actx, dcoll, fields=fields))
wave_op.check_bc_coverage(local_mesh)
from logpyle import LogManager, \
add_general_quantities, \
add_run_info
log_filename = None
# NOTE: LogManager hangs when using a file on a shared directory.
# log_filename = "grudge_log.dat"
logmgr = LogManager(log_filename, "w", comm)
add_run_info(logmgr)
add_general_quantities(logmgr)
def rhs(t, w):
return wave_op.operator(t, w)
dt_stepper = set_up_rk4("w", dt, fields, rhs)
final_t = 4
nsteps = int(final_t/dt)
logger.info("[%04d] dt %.5e nsteps %4d", i_local_rank, dt, nsteps)
step = 0
def norm(u):
return op.norm(dcoll, u, 2)
from time import time
t_last_step = time()
logmgr.tick_before()
for event in dt_stepper.run(t_end=final_t):
if isinstance(event, dt_stepper.StateComputed):
assert event.component_id == "w"
step += 1
logger.info("[%04d] t = %.5e |u| = %.5e ellapsed %.5e",
step, event.t,
norm(u=event.state_component[0]),
time() - t_last_step)
t_last_step = time()
logmgr.tick_after()
logmgr.tick_before()
logmgr.tick_after()
logmgr.close()
logger.info("Rank %d exiting", i_local_rank)
def test_convergence_maxwell(ctx_factory, order):
"""Test whether 3D Maxwell's actually converges"""
cl_ctx = ctx_factory()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(queue)
from pytools.convergence import EOCRecorder
eoc_rec = EOCRecorder()
dims = 3
ns = [4, 6, 8]
for n in ns:
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(0.0, ) * dims,
b=(1.0, ) * dims,
n=(n, ) * dims)
discr = DGDiscretizationWithBoundaries(actx, mesh, order=order)
epsilon = 1
mu = 1
from grudge.models.em import get_rectangular_cavity_mode
sym_mode = get_rectangular_cavity_mode(1, (1, 2, 2))
analytic_sol = bind(discr, sym_mode)
fields = analytic_sol(actx, t=0, epsilon=epsilon, mu=mu)
from grudge.models.em import MaxwellOperator
op = MaxwellOperator(epsilon, mu, flux_type=0.5, dimensions=dims)
op.check_bc_coverage(mesh)
bound_op = bind(discr, op.sym_operator())
def rhs(t, w):
return bound_op(t=t, w=w)
dt = 0.002
final_t = dt * 5
nsteps = int(final_t / dt)
from grudge.shortcuts import set_up_rk4
dt_stepper = set_up_rk4("w", dt, fields, rhs)
logger.info("dt %.5e nsteps %5d", dt, nsteps)
norm = bind(discr, sym.norm(2, sym.var("u")))
step = 0
for event in dt_stepper.run(t_end=final_t):
if isinstance(event, dt_stepper.StateComputed):
assert event.component_id == "w"
esc = event.state_component
step += 1
logger.debug("[%04d] t = %.5e", step, event.t)
sol = analytic_sol(actx, mu=mu, epsilon=epsilon, t=step * dt)
vals = [norm(u=(esc[i] - sol[i])) / norm(u=sol[i])
for i in range(5)] # noqa E501
total_error = sum(vals)
eoc_rec.add_data_point(1.0 / n, total_error)
logger.info(
"\n%s", eoc_rec.pretty_print(abscissa_label="h",
error_label="L2 Error"))
assert eoc_rec.order_estimate() > order
def main(dims, write_output=True, order=4):
cl_ctx = cl.create_some_context()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(queue)
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(0.0, ) * dims,
b=(1.0, ) * dims,
nelements_per_axis=(4, ) * dims)
discr = DiscretizationCollection(actx, mesh, order=order)
if 0:
epsilon0 = 8.8541878176e-12 # C**2 / (N m**2)
mu0 = 4 * np.pi * 1e-7 # N/A**2.
epsilon = 1 * epsilon0
mu = 1 * mu0
else:
epsilon = 1
mu = 1
from grudge.models.em import MaxwellOperator
op = MaxwellOperator(epsilon, mu, flux_type=0.5, dimensions=dims)
if dims == 3:
sym_mode = get_rectangular_cavity_mode(1, (1, 2, 2))
fields = bind(discr, sym_mode)(actx, t=0, epsilon=epsilon, mu=mu)
else:
sym_mode = get_rectangular_cavity_mode(1, (2, 3))
fields = bind(discr, sym_mode)(actx, t=0)
# FIXME
#dt = op.estimate_rk4_timestep(discr, fields=fields)
op.check_bc_coverage(mesh)
# print(sym.pretty(op.sym_operator()))
bound_op = bind(discr, op.sym_operator())
def rhs(t, w):
return bound_op(t=t, w=w)
if mesh.dim == 2:
dt = 0.004
elif mesh.dim == 3:
dt = 0.002
dt_stepper = set_up_rk4("w", dt, fields, rhs)
final_t = dt * STEPS
nsteps = int(final_t / dt)
print("dt=%g nsteps=%d" % (dt, nsteps))
from grudge.shortcuts import make_visualizer
vis = make_visualizer(discr)
step = 0
norm = bind(discr, sym.norm(2, sym.var("u")))
from time import time
t_last_step = time()
e, h = op.split_eh(fields)
if 1:
vis.write_vtk_file("fld-cavities-%04d.vtu" % step, [
("e", e),
("h", h),
])
for event in dt_stepper.run(t_end=final_t):
if isinstance(event, dt_stepper.StateComputed):
assert event.component_id == "w"
step += 1
print(step, event.t, norm(u=e[0]), norm(u=e[1]), norm(u=h[0]),
norm(u=h[1]),
time() - t_last_step)
if step % 10 == 0:
e, h = op.split_eh(event.state_component)
vis.write_vtk_file("fld-cavities-%04d.vtu" % step, [
("e", e),
("h", h),
])
t_last_step = time()
def main(write_output=True, order=4):
cl_ctx = cl.create_some_context()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(queue)
dims = 2
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(
a=(-0.5,)*dims,
b=(0.5,)*dims,
nelements_per_axis=(20,)*dims)
discr = DiscretizationCollection(actx, mesh, order=order)
source_center = np.array([0.1, 0.22, 0.33])[:mesh.dim]
source_width = 0.05
source_omega = 3
sym_x = sym.nodes(mesh.dim)
sym_source_center_dist = sym_x - source_center
sym_t = sym.ScalarVariable("t")
c = sym.If(sym.Comparison(
np.dot(sym_x, sym_x), "<", 0.15),
np.float32(0.1), np.float32(0.2))
from grudge.models.wave import VariableCoefficientWeakWaveOperator
from meshmode.mesh import BTAG_ALL, BTAG_NONE
op = VariableCoefficientWeakWaveOperator(c,
discr.dim,
source_f=(
sym.sin(source_omega*sym_t)
* sym.exp(
-np.dot(sym_source_center_dist, sym_source_center_dist)
/ source_width**2)),
dirichlet_tag=BTAG_NONE,
neumann_tag=BTAG_NONE,
radiation_tag=BTAG_ALL,
flux_type="upwind")
from pytools.obj_array import flat_obj_array
fields = flat_obj_array(discr.zeros(actx),
[discr.zeros(actx) for i in range(discr.dim)])
op.check_bc_coverage(mesh)
c_eval = bind(discr, c)(actx)
bound_op = bind(discr, op.sym_operator())
def rhs(t, w):
return bound_op(t=t, w=w)
if mesh.dim == 2:
dt = 0.04 * 0.3
elif mesh.dim == 3:
dt = 0.02 * 0.1
dt_stepper = set_up_rk4("w", dt, fields, rhs)
final_t = 1
nsteps = int(final_t/dt)
print("dt=%g nsteps=%d" % (dt, nsteps))
from grudge.shortcuts import make_visualizer
vis = make_visualizer(discr)
step = 0
norm = bind(discr, sym.norm(2, sym.var("u")))
from time import time
t_last_step = time()
for event in dt_stepper.run(t_end=final_t):
if isinstance(event, dt_stepper.StateComputed):
assert event.component_id == "w"
step += 1
print(step, event.t, norm(u=event.state_component[0]),
time()-t_last_step)
if step % 10 == 0:
vis.write_vtk_file("fld-var-propogation-speed-%04d.vtu" % step,
[
("u", event.state_component[0]),
("v", event.state_component[1:]),
("c", c_eval),
])
t_last_step = time()
def main(ctx_factory, dim=2, order=4, visualize=False):
cl_ctx = cl.create_some_context()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(
queue,
allocator=cl_tools.MemoryPool(cl_tools.ImmediateAllocator(queue)),
force_device_scalars=True,
)
comm = MPI.COMM_WORLD
num_parts = comm.Get_size()
from meshmode.distributed import MPIMeshDistributor, get_partition_by_pymetis
mesh_dist = MPIMeshDistributor(comm)
if mesh_dist.is_mananger_rank():
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(-0.5, ) * dim,
b=(0.5, ) * dim,
nelements_per_axis=(16, ) * dim)
logger.info("%d elements", mesh.nelements)
part_per_element = get_partition_by_pymetis(mesh, num_parts)
local_mesh = mesh_dist.send_mesh_parts(mesh, part_per_element,
num_parts)
del mesh
else:
local_mesh = mesh_dist.receive_mesh_part()
dcoll = DiscretizationCollection(actx,
local_mesh,
order=order,
mpi_communicator=comm)
def source_f(actx, dcoll, t=0):
source_center = np.array([0.1, 0.22, 0.33])[:dcoll.dim]
source_width = 0.05
source_omega = 3
nodes = thaw(dcoll.nodes(), actx)
source_center_dist = flat_obj_array(
[nodes[i] - source_center[i] for i in range(dcoll.dim)])
return (np.sin(source_omega * t) * actx.np.exp(
-np.dot(source_center_dist, source_center_dist) / source_width**2))
from grudge.models.wave import WeakWaveOperator
from meshmode.mesh import BTAG_ALL, BTAG_NONE
wave_op = WeakWaveOperator(dcoll,
0.1,
source_f=source_f,
dirichlet_tag=BTAG_NONE,
neumann_tag=BTAG_NONE,
radiation_tag=BTAG_ALL,
flux_type="upwind")
fields = flat_obj_array(dcoll.zeros(actx),
[dcoll.zeros(actx) for i in range(dcoll.dim)])
dt = 2 / 3 * wave_op.estimate_rk4_timestep(actx, dcoll, fields=fields)
wave_op.check_bc_coverage(local_mesh)
def rhs(t, w):
return wave_op.operator(t, w)
dt_stepper = set_up_rk4("w", dt, fields, rhs)
final_t = 10
nsteps = int(final_t / dt) + 1
if comm.rank == 0:
logger.info("dt=%g nsteps=%d", dt, nsteps)
from grudge.shortcuts import make_visualizer
vis = make_visualizer(dcoll)
step = 0
def norm(u):
return op.norm(dcoll, u, 2)
from time import time
t_last_step = time()
if visualize:
u = fields[0]
v = fields[1:]
vis.write_parallel_vtk_file(
comm, f"fld-wave-min-mpi-{{rank:03d}}-{step:04d}.vtu", [
("u", u),
("v", v),
])
for event in dt_stepper.run(t_end=final_t):
if isinstance(event, dt_stepper.StateComputed):
assert event.component_id == "w"
step += 1
l2norm = norm(u=event.state_component[0])
if step % 10 == 0:
if comm.rank == 0:
logger.info(f"step: {step} "
f"t: {time()-t_last_step} "
f"L2: {l2norm}")
if visualize:
vis.write_parallel_vtk_file(
comm, f"fld-wave-min-mpi-{{rank:03d}}-{step:04d}.vtu",
[
("u", event.state_component[0]),
("v", event.state_component[1:]),
])
t_last_step = time()
# NOTE: These are here to ensure the solution is bounded for the
# time interval specified
assert l2norm < 1
def test_convergence_maxwell(actx_factory, order):
"""Test whether 3D Maxwell's actually converges"""
actx = actx_factory()
from pytools.convergence import EOCRecorder
eoc_rec = EOCRecorder()
dims = 3
ns = [4, 6, 8]
for n in ns:
mesh = mgen.generate_regular_rect_mesh(a=(0.0, ) * dims,
b=(1.0, ) * dims,
nelements_per_axis=(n, ) * dims)
dcoll = DiscretizationCollection(actx, mesh, order=order)
epsilon = 1
mu = 1
from grudge.models.em import get_rectangular_cavity_mode
def analytic_sol(x, t=0):
return get_rectangular_cavity_mode(actx, x, t, 1, (1, 2, 2))
nodes = thaw(dcoll.nodes(), actx)
fields = analytic_sol(nodes, t=0)
from grudge.models.em import MaxwellOperator
maxwell_operator = MaxwellOperator(dcoll,
epsilon,
mu,
flux_type=0.5,
dimensions=dims)
maxwell_operator.check_bc_coverage(mesh)
def rhs(t, w):
return maxwell_operator.operator(t, w)
dt = maxwell_operator.estimate_rk4_timestep(actx, dcoll)
final_t = dt * 5
nsteps = int(final_t / dt)
from grudge.shortcuts import set_up_rk4
dt_stepper = set_up_rk4("w", dt, fields, rhs)
logger.info("dt %.5e nsteps %5d", dt, nsteps)
step = 0
for event in dt_stepper.run(t_end=final_t):
if isinstance(event, dt_stepper.StateComputed):
assert event.component_id == "w"
esc = event.state_component
step += 1
logger.debug("[%04d] t = %.5e", step, event.t)
sol = analytic_sol(nodes, t=step * dt)
total_error = op.norm(dcoll, esc - sol, 2)
eoc_rec.add_data_point(1.0 / n, actx.to_numpy(total_error))
logger.info(
"\n%s", eoc_rec.pretty_print(abscissa_label="h",
error_label="L2 Error"))
assert eoc_rec.order_estimate() > order
def main(write_output=True, order=4):
cl_ctx = cl.create_some_context()
queue = cl.CommandQueue(cl_ctx)
dims = 2
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(-0.5, ) * dims,
b=(0.5, ) * dims,
n=(16, ) * dims)
if mesh.dim == 2:
dt = 0.04
elif mesh.dim == 3:
dt = 0.02
print("%d elements" % mesh.nelements)
discr = DGDiscretizationWithBoundaries(cl_ctx, mesh, order=order)
source_center = np.array([0.1, 0.22, 0.33])[:mesh.dim]
source_width = 0.05
source_omega = 3
sym_x = sym.nodes(mesh.dim)
sym_source_center_dist = sym_x - source_center
sym_t = sym.ScalarVariable("t")
from grudge.models.wave import StrongWaveOperator
from meshmode.mesh import BTAG_ALL, BTAG_NONE
op = StrongWaveOperator(
-0.1,
discr.dim,
source_f=(
sym.sin(source_omega * sym_t) *
sym.exp(-np.dot(sym_source_center_dist, sym_source_center_dist) /
source_width**2)),
dirichlet_tag=BTAG_NONE,
neumann_tag=BTAG_NONE,
radiation_tag=BTAG_ALL,
flux_type="upwind")
queue = cl.CommandQueue(discr.cl_context)
from pytools.obj_array import join_fields
fields = join_fields(discr.zeros(queue),
[discr.zeros(queue) for i in range(discr.dim)])
# FIXME
#dt = op.estimate_rk4_timestep(discr, fields=fields)
op.check_bc_coverage(mesh)
# print(sym.pretty(op.sym_operator()))
bound_op = bind(discr, op.sym_operator())
def rhs(t, w):
return bound_op(queue, t=t, w=w)
dt_stepper = set_up_rk4("w", dt, fields, rhs)
final_t = 10
nsteps = int(final_t / dt)
print("dt=%g nsteps=%d" % (dt, nsteps))
from grudge.shortcuts import make_visualizer
vis = make_visualizer(discr, vis_order=order)
step = 0
norm = bind(discr, sym.norm(2, sym.var("u")))
from time import time
t_last_step = time()
for event in dt_stepper.run(t_end=final_t):
if isinstance(event, dt_stepper.StateComputed):
assert event.component_id == "w"
step += 1
print(step, event.t, norm(queue, u=event.state_component[0]),
time() - t_last_step)
if step % 10 == 0:
vis.write_vtk_file("fld-wave-min-%04d.vtu" % step, [
("u", event.state_component[0]),
("v", event.state_component[1:]),
])
t_last_step = time()
def test_convergence_advec(actx_factory,
mesh_name,
mesh_pars,
op_type,
flux_type,
order,
visualize=False):
"""Test whether 2D advection actually converges"""
actx = actx_factory()
from pytools.convergence import EOCRecorder
eoc_rec = EOCRecorder()
for mesh_par in mesh_pars:
if mesh_name == "segment":
mesh = mgen.generate_box_mesh([np.linspace(-1.0, 1.0, mesh_par)],
order=order)
dim = 1
dt_factor = 1.0
elif mesh_name == "disk":
pytest.importorskip("meshpy")
from meshpy.geometry import make_circle, GeometryBuilder
from meshpy.triangle import MeshInfo, build
geob = GeometryBuilder()
geob.add_geometry(*make_circle(1))
mesh_info = MeshInfo()
geob.set(mesh_info)
mesh_info = build(mesh_info, max_volume=mesh_par)
from meshmode.mesh.io import from_meshpy
mesh = from_meshpy(mesh_info, order=1)
dim = 2
dt_factor = 4
elif mesh_name.startswith("rect"):
dim = int(mesh_name[-1:])
mesh = mgen.generate_regular_rect_mesh(
a=(-0.5, ) * dim,
b=(0.5, ) * dim,
nelements_per_axis=(mesh_par, ) * dim,
order=4)
if dim == 2:
dt_factor = 4
elif dim == 3:
dt_factor = 2
else:
raise ValueError("dt_factor not known for %dd" % dim)
elif mesh_name.startswith("warped"):
dim = int(mesh_name[-1:])
mesh = mgen.generate_warped_rect_mesh(dim,
order=order,
nelements_side=mesh_par)
if dim == 2:
dt_factor = 4
elif dim == 3:
dt_factor = 2
else:
raise ValueError("dt_factor not known for %dd" % dim)
else:
raise ValueError("invalid mesh name: " + mesh_name)
v = np.array([0.27, 0.31, 0.1])[:dim]
norm_v = la.norm(v)
def f(x):
return actx.np.sin(10 * x)
def u_analytic(x, t=0):
return f(-v.dot(x) / norm_v + t * norm_v)
from grudge.models.advection import (StrongAdvectionOperator,
WeakAdvectionOperator)
from meshmode.mesh import BTAG_ALL
dcoll = DiscretizationCollection(actx, mesh, order=order)
op_class = {
"strong": StrongAdvectionOperator,
"weak": WeakAdvectionOperator
}[op_type]
adv_operator = op_class(dcoll,
v,
inflow_u=lambda t: u_analytic(
thaw(dcoll.nodes(dd=BTAG_ALL), actx), t=t),
flux_type=flux_type)
nodes = thaw(dcoll.nodes(), actx)
u = u_analytic(nodes, t=0)
def rhs(t, u):
return adv_operator.operator(t, u)
if dim == 3:
final_time = 0.1
else:
final_time = 0.2
from grudge.dt_utils import h_max_from_volume
h_max = h_max_from_volume(dcoll, dim=dcoll.ambient_dim)
dt = dt_factor * h_max / order**2
nsteps = (final_time // dt) + 1
dt = final_time / nsteps + 1e-15
from grudge.shortcuts import set_up_rk4
dt_stepper = set_up_rk4("u", dt, u, rhs)
last_u = None
from grudge.shortcuts import make_visualizer
vis = make_visualizer(dcoll)
step = 0
for event in dt_stepper.run(t_end=final_time):
if isinstance(event, dt_stepper.StateComputed):
step += 1
logger.debug("[%04d] t = %.5f", step, event.t)
last_t = event.t
last_u = event.state_component
if visualize:
vis.write_vtk_file("fld-%s-%04d.vtu" % (mesh_par, step),
[("u", event.state_component)])
error_l2 = op.norm(dcoll, last_u - u_analytic(nodes, t=last_t), 2)
logger.info("h_max %.5e error %.5e", h_max, error_l2)
eoc_rec.add_data_point(h_max, actx.to_numpy(error_l2))
logger.info(
"\n%s", eoc_rec.pretty_print(abscissa_label="h",
error_label="L2 Error"))
if mesh_name.startswith("warped"):
# NOTE: curvilinear meshes are hard
assert eoc_rec.order_estimate() > order - 0.5
else:
assert eoc_rec.order_estimate() > order
def main(write_output=True, order=4):
cl_ctx = cl.create_some_context()
queue = cl.CommandQueue(cl_ctx)
dim = 2
resolution = 15
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(-0.5, -0.5),
b=(0.5, 0.5),
n=(resolution, resolution),
order=order)
dt_factor = 5
h = 1 / resolution
sym_x = sym.nodes(2)
advec_v = join_fields(-1 * sym_x[1], sym_x[0]) / 2
flux_type = "upwind"
source_center = np.array([0.1, 0.1])
source_width = 0.05
sym_x = sym.nodes(2)
sym_source_center_dist = sym_x - source_center
def f_gaussian(x):
return sym.exp(
-np.dot(sym_source_center_dist, sym_source_center_dist) /
source_width**2)
def f_step(x):
return sym.If(
sym.Comparison(
np.dot(sym_source_center_dist, sym_source_center_dist), "<",
(4 * source_width)**2), 1, 0)
def u_analytic(x):
return 0
from grudge.models.advection import VariableCoefficientAdvectionOperator
from meshmode.discretization.poly_element import QuadratureSimplexGroupFactory # noqa
discr = DGDiscretizationWithBoundaries(
cl_ctx,
mesh,
order=order,
quad_tag_to_group_factory={
#"product": None,
"product": QuadratureSimplexGroupFactory(order=4 * order)
})
op = VariableCoefficientAdvectionOperator(2,
advec_v,
u_analytic(
sym.nodes(dim,
sym.BTAG_ALL)),
quad_tag="product",
flux_type=flux_type)
bound_op = bind(
discr,
op.sym_operator(),
#debug_flags=["dump_sym_operator_stages"]
)
u = bind(discr, f_gaussian(sym.nodes(dim)))(queue, t=0)
def rhs(t, u):
return bound_op(queue, t=t, u=u)
dt = dt_factor * h / order**2
nsteps = (FINAL_TIME // dt) + 1
dt = FINAL_TIME / nsteps + 1e-15
from grudge.shortcuts import set_up_rk4
dt_stepper = set_up_rk4("u", dt, u, rhs)
from grudge.shortcuts import make_visualizer
vis = make_visualizer(discr, vis_order=2 * order)
step = 0
for event in dt_stepper.run(t_end=FINAL_TIME):
if isinstance(event, dt_stepper.StateComputed):
step += 1
if step % 30 == 0:
print(step)
vis.write_vtk_file("fld-var-velocity-%04d.vtu" % step,
[("u", event.state_component)])
def main(ctx_factory, dim=2, order=4, use_quad=False, visualize=False):
cl_ctx = ctx_factory()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(
queue,
allocator=cl_tools.MemoryPool(cl_tools.ImmediateAllocator(queue)),
force_device_scalars=True,
)
# {{{ parameters
# sphere radius
radius = 1.0
# sphere resolution
resolution = 64 if dim == 2 else 1
# final time
final_time = np.pi
# flux
flux_type = "lf"
# }}}
# {{{ discretization
if dim == 2:
from meshmode.mesh.generation import make_curve_mesh, ellipse
mesh = make_curve_mesh(
lambda t: radius * ellipse(1.0, t),
np.linspace(0.0, 1.0, resolution + 1),
order)
elif dim == 3:
from meshmode.mesh.generation import generate_icosphere
mesh = generate_icosphere(radius, order=4 * order,
uniform_refinement_rounds=resolution)
else:
raise ValueError("unsupported dimension")
discr_tag_to_group_factory = {}
if use_quad:
qtag = dof_desc.DISCR_TAG_QUAD
else:
qtag = None
from meshmode.discretization.poly_element import \
default_simplex_group_factory, \
QuadratureSimplexGroupFactory
discr_tag_to_group_factory[dof_desc.DISCR_TAG_BASE] = \
default_simplex_group_factory(base_dim=dim-1, order=order)
if use_quad:
discr_tag_to_group_factory[qtag] = \
QuadratureSimplexGroupFactory(order=4*order)
from grudge import DiscretizationCollection
dcoll = DiscretizationCollection(
actx, mesh,
discr_tag_to_group_factory=discr_tag_to_group_factory
)
volume_discr = dcoll.discr_from_dd(dof_desc.DD_VOLUME)
logger.info("ndofs: %d", volume_discr.ndofs)
logger.info("nelements: %d", volume_discr.mesh.nelements)
# }}}
# {{{ Surface advection operator
# velocity field
x = thaw(dcoll.nodes(), actx)
c = make_obj_array([-x[1], x[0], 0.0])[:dim]
def f_initial_condition(x):
return x[0]
from grudge.models.advection import SurfaceAdvectionOperator
adv_operator = SurfaceAdvectionOperator(
dcoll,
c,
flux_type=flux_type,
quad_tag=qtag
)
u0 = f_initial_condition(x)
def rhs(t, u):
return adv_operator.operator(t, u)
# check velocity is tangential
from grudge.geometry import normal
surf_normal = normal(actx, dcoll, dd=dof_desc.DD_VOLUME)
error = op.norm(dcoll, c.dot(surf_normal), 2)
logger.info("u_dot_n: %.5e", error)
# }}}
# {{{ time stepping
# FIXME: dt estimate is not necessarily valid for surfaces
dt = actx.to_numpy(
0.45 * adv_operator.estimate_rk4_timestep(actx, dcoll, fields=u0))
nsteps = int(final_time // dt) + 1
logger.info("dt: %.5e", dt)
logger.info("nsteps: %d", nsteps)
from grudge.shortcuts import set_up_rk4
dt_stepper = set_up_rk4("u", dt, u0, rhs)
plot = Plotter(actx, dcoll, order, visualize=visualize)
norm_u = actx.to_numpy(op.norm(dcoll, u0, 2))
step = 0
event = dt_stepper.StateComputed(0.0, 0, 0, u0)
plot(event, "fld-surface-%04d" % 0)
if visualize and dim == 3:
from grudge.shortcuts import make_visualizer
vis = make_visualizer(dcoll)
vis.write_vtk_file(
"fld-surface-velocity.vtu",
[
("u", c),
("n", surf_normal)
],
overwrite=True
)
df = dof_desc.DOFDesc(FACE_RESTR_INTERIOR)
face_discr = dcoll.discr_from_dd(df)
face_normal = thaw(dcoll.normal(dd=df), actx)
from meshmode.discretization.visualization import make_visualizer
vis = make_visualizer(actx, face_discr)
vis.write_vtk_file("fld-surface-face-normals.vtu", [
("n", face_normal)
], overwrite=True)
for event in dt_stepper.run(t_end=final_time, max_steps=nsteps + 1):
if not isinstance(event, dt_stepper.StateComputed):
continue
step += 1
if step % 10 == 0:
norm_u = actx.to_numpy(op.norm(dcoll, event.state_component, 2))
plot(event, "fld-surface-%04d" % step)
logger.info("[%04d] t = %.5f |u| = %.5e", step, event.t, norm_u)
# NOTE: These are here to ensure the solution is bounded for the
# time interval specified
assert norm_u < 3
def main(ctx_factory, dim=2, order=4, product_tag=None, visualize=False):
cl_ctx = ctx_factory()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(queue)
# {{{ parameters
# domain [0, d]^dim
d = 1.0
# number of points in each dimension
npoints = 25
# grid spacing
h = d / npoints
# cfl
dt_factor = 1.0
# finale time
final_time = 0.5
# time steps
dt = dt_factor * h / order**2
nsteps = int(final_time // dt) + 1
dt = final_time / nsteps + 1.0e-15
# flux
flux_type = "upwind"
# velocity field
sym_x = sym.nodes(dim)
if dim == 1:
c = sym_x
else:
# solid body rotation
c = flat_obj_array(np.pi * (d / 2 - sym_x[1]),
np.pi * (sym_x[0] - d / 2), 0)[:dim]
# }}}
# {{{ discretization
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(0, ) * dim,
b=(d, ) * dim,
npoints_per_axis=(npoints, ) * dim,
order=order)
from meshmode.discretization.poly_element import \
QuadratureSimplexGroupFactory
if product_tag:
discr_tag_to_group_factory = {
product_tag: QuadratureSimplexGroupFactory(order=4 * order)
}
else:
discr_tag_to_group_factory = {}
from grudge import DiscretizationCollection
discr = DiscretizationCollection(
actx,
mesh,
order=order,
discr_tag_to_group_factory=discr_tag_to_group_factory)
# }}}
# {{{ symbolic operators
# gaussian parameters
source_center = np.array([0.5, 0.75, 0.0])[:dim]
source_width = 0.05
dist_squared = np.dot(sym_x - source_center, sym_x - source_center)
def f_gaussian(x):
return sym.exp(-dist_squared / source_width**2)
def f_step(x):
return sym.If(sym.Comparison(dist_squared, "<", (4 * source_width)**2),
1, 0)
def u_bc(x):
return 0.0
from grudge.models.advection import VariableCoefficientAdvectionOperator
op = VariableCoefficientAdvectionOperator(c,
u_bc(sym.nodes(dim, BTAG_ALL)),
quad_tag=product_tag,
flux_type=flux_type)
bound_op = bind(discr, op.sym_operator())
u = bind(discr, f_gaussian(sym.nodes(dim)))(actx, t=0)
def rhs(t, u):
return bound_op(t=t, u=u)
# }}}
# {{{ time stepping
from grudge.shortcuts import set_up_rk4
dt_stepper = set_up_rk4("u", dt, u, rhs)
plot = Plotter(actx, discr, order, visualize=visualize, ylim=[-0.1, 1.1])
step = 0
norm = bind(discr, sym.norm(2, sym.var("u")))
for event in dt_stepper.run(t_end=final_time):
if not isinstance(event, dt_stepper.StateComputed):
continue
if step % 10 == 0:
norm_u = norm(u=event.state_component)
plot(event, "fld-var-velocity-%04d" % step)
step += 1
logger.info("[%04d] t = %.5f |u| = %.5e", step, event.t, norm_u)
def main(ctx_factory, dim=2, order=4, visualize=False):
cl_ctx = ctx_factory()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(
queue,
allocator=cl_tools.MemoryPool(cl_tools.ImmediateAllocator(queue)),
force_device_scalars=True,
)
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(-0.5, ) * dim,
b=(0.5, ) * dim,
nelements_per_axis=(20, ) * dim)
dcoll = DiscretizationCollection(actx, mesh, order=order)
def source_f(actx, dcoll, t=0):
source_center = np.array([0.1, 0.22, 0.33])[:dcoll.dim]
source_width = 0.05
source_omega = 3
nodes = thaw(dcoll.nodes(), actx)
source_center_dist = flat_obj_array(
[nodes[i] - source_center[i] for i in range(dcoll.dim)])
return (np.sin(source_omega * t) * actx.np.exp(
-np.dot(source_center_dist, source_center_dist) / source_width**2))
x = thaw(dcoll.nodes(), actx)
ones = dcoll.zeros(actx) + 1
c = actx.np.where(np.dot(x, x) < 0.15, 0.1 * ones, 0.2 * ones)
from grudge.models.wave import VariableCoefficientWeakWaveOperator
from meshmode.mesh import BTAG_ALL, BTAG_NONE
wave_op = VariableCoefficientWeakWaveOperator(dcoll,
c,
source_f=source_f,
dirichlet_tag=BTAG_NONE,
neumann_tag=BTAG_NONE,
radiation_tag=BTAG_ALL,
flux_type="upwind")
fields = flat_obj_array(dcoll.zeros(actx),
[dcoll.zeros(actx) for i in range(dcoll.dim)])
wave_op.check_bc_coverage(mesh)
def rhs(t, w):
return wave_op.operator(t, w)
dt = 2 / 3 * wave_op.estimate_rk4_timestep(actx, dcoll, fields=fields)
dt_stepper = set_up_rk4("w", dt, fields, rhs)
final_t = 1
nsteps = int(final_t / dt) + 1
logger.info("dt=%g nsteps=%d", dt, nsteps)
from grudge.shortcuts import make_visualizer
vis = make_visualizer(dcoll)
step = 0
def norm(u):
return op.norm(dcoll, u, 2)
from time import time
t_last_step = time()
if visualize:
u = fields[0]
v = fields[1:]
vis.write_vtk_file(f"fld-var-propogation-speed-{step:04d}.vtu", [
("u", u),
("v", v),
("c", c),
])
for event in dt_stepper.run(t_end=final_t):
if isinstance(event, dt_stepper.StateComputed):
assert event.component_id == "w"
step += 1
if step % 10 == 0:
logger.info(f"step: {step} t: {time()-t_last_step} "
f"L2: {norm(u=event.state_component[0])}")
if visualize:
vis.write_vtk_file(
f"fld-var-propogation-speed-{step:04d}.vtu", [
("u", event.state_component[0]),
("v", event.state_component[1:]),
("c", c),
])
t_last_step = time()
# NOTE: These are here to ensure the solution is bounded for the
# time interval specified
assert norm(u=event.state_component[0]) < 1
def test_convergence_advec(ctx_factory,
mesh_name,
mesh_pars,
op_type,
flux_type,
order,
visualize=False):
"""Test whether 2D advection actually converges"""
cl_ctx = ctx_factory()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(queue)
from pytools.convergence import EOCRecorder
eoc_rec = EOCRecorder()
for mesh_par in mesh_pars:
if mesh_name == "segment":
from meshmode.mesh.generation import generate_box_mesh
mesh = generate_box_mesh([np.linspace(-1.0, 1.0, mesh_par)],
order=order)
dim = 1
dt_factor = 1.0
elif mesh_name == "disk":
pytest.importorskip("meshpy")
from meshpy.geometry import make_circle, GeometryBuilder
from meshpy.triangle import MeshInfo, build
geob = GeometryBuilder()
geob.add_geometry(*make_circle(1))
mesh_info = MeshInfo()
geob.set(mesh_info)
mesh_info = build(mesh_info, max_volume=mesh_par)
from meshmode.mesh.io import from_meshpy
mesh = from_meshpy(mesh_info, order=1)
dim = 2
dt_factor = 4
elif mesh_name.startswith("rect"):
dim = int(mesh_name[4:])
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(-0.5, ) * dim,
b=(0.5, ) * dim,
n=(mesh_par, ) * dim,
order=4)
if dim == 2:
dt_factor = 4
elif dim == 3:
dt_factor = 2
else:
raise ValueError("dt_factor not known for %dd" % dim)
else:
raise ValueError("invalid mesh name: " + mesh_name)
v = np.array([0.27, 0.31, 0.1])[:dim]
norm_v = la.norm(v)
def f(x):
return sym.sin(10 * x)
def u_analytic(x):
return f(-v.dot(x) / norm_v + sym.var("t", sym.DD_SCALAR) * norm_v)
from grudge.models.advection import (StrongAdvectionOperator,
WeakAdvectionOperator)
discr = DGDiscretizationWithBoundaries(actx, mesh, order=order)
op_class = {
"strong": StrongAdvectionOperator,
"weak": WeakAdvectionOperator,
}[op_type]
op = op_class(v,
inflow_u=u_analytic(sym.nodes(dim, sym.BTAG_ALL)),
flux_type=flux_type)
bound_op = bind(discr, op.sym_operator())
u = bind(discr, u_analytic(sym.nodes(dim)))(actx, t=0)
def rhs(t, u):
return bound_op(t=t, u=u)
if dim == 3:
final_time = 0.1
else:
final_time = 0.2
h_max = bind(discr, sym.h_max_from_volume(discr.ambient_dim))(actx)
dt = dt_factor * h_max / order**2
nsteps = (final_time // dt) + 1
dt = final_time / nsteps + 1e-15
from grudge.shortcuts import set_up_rk4
dt_stepper = set_up_rk4("u", dt, u, rhs)
last_u = None
from grudge.shortcuts import make_visualizer
vis = make_visualizer(discr, vis_order=order)
step = 0
for event in dt_stepper.run(t_end=final_time):
if isinstance(event, dt_stepper.StateComputed):
step += 1
logger.debug("[%04d] t = %.5f", step, event.t)
last_t = event.t
last_u = event.state_component
if visualize:
vis.write_vtk_file("fld-%s-%04d.vtu" % (mesh_par, step),
[("u", event.state_component)])
error_l2 = bind(discr,
sym.norm(2,
sym.var("u") - u_analytic(sym.nodes(dim))))(
t=last_t, u=last_u)
logger.info("h_max %.5e error %.5e", h_max, error_l2)
eoc_rec.add_data_point(h_max, error_l2)
logger.info(
"\n%s", eoc_rec.pretty_print(abscissa_label="h",
error_label="L2 Error"))
assert eoc_rec.order_estimate() > order
def main(ctx_factory,
dim=2,
order=4,
use_quad=False,
visualize=False,
flux_type="upwind"):
cl_ctx = ctx_factory()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(
queue,
allocator=cl_tools.MemoryPool(cl_tools.ImmediateAllocator(queue)),
force_device_scalars=True,
)
# {{{ parameters
# domain [0, d]^dim
d = 1.0
# number of points in each dimension
npoints = 25
# final time
final_time = 1
if use_quad:
qtag = dof_desc.DISCR_TAG_QUAD
else:
qtag = None
# }}}
# {{{ discretization
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(0, ) * dim,
b=(d, ) * dim,
npoints_per_axis=(npoints, ) * dim,
order=order)
from meshmode.discretization.poly_element import \
QuadratureSimplexGroupFactory
if use_quad:
discr_tag_to_group_factory = {
qtag: QuadratureSimplexGroupFactory(order=4 * order)
}
else:
discr_tag_to_group_factory = {}
from grudge import DiscretizationCollection
dcoll = DiscretizationCollection(
actx,
mesh,
order=order,
discr_tag_to_group_factory=discr_tag_to_group_factory)
# }}}
# {{{ advection operator
# gaussian parameters
def f_halfcircle(x):
source_center = np.array([d / 2, d / 2, d / 2])[:dim]
dist = x - source_center
return ((0.5 + 0.5 * actx.np.tanh(500 *
(-np.dot(dist, dist) + 0.4**2))) *
(0.5 + 0.5 * actx.np.tanh(500 * (dist[0]))))
def zero_inflow_bc(dtag, t=0):
dd = dof_desc.DOFDesc(dtag, qtag)
return dcoll.discr_from_dd(dd).zeros(actx)
from grudge.models.advection import VariableCoefficientAdvectionOperator
x = thaw(dcoll.nodes(), actx)
# velocity field
if dim == 1:
c = x
else:
# solid body rotation
c = flat_obj_array(np.pi * (d / 2 - x[1]), np.pi * (x[0] - d / 2),
0)[:dim]
adv_operator = VariableCoefficientAdvectionOperator(
dcoll,
c,
inflow_u=lambda t: zero_inflow_bc(BTAG_ALL, t),
quad_tag=qtag,
flux_type=flux_type)
u = f_halfcircle(x)
def rhs(t, u):
return adv_operator.operator(t, u)
dt = actx.to_numpy(
adv_operator.estimate_rk4_timestep(actx, dcoll, fields=u))
logger.info("Timestep size: %g", dt)
# }}}
# {{{ time stepping
from grudge.shortcuts import set_up_rk4
dt_stepper = set_up_rk4("u", dt, u, rhs)
plot = Plotter(actx, dcoll, order, visualize=visualize, ylim=[-0.1, 1.1])
step = 0
for event in dt_stepper.run(t_end=final_time):
if not isinstance(event, dt_stepper.StateComputed):
continue
if step % 10 == 0:
norm_u = actx.to_numpy(op.norm(dcoll, event.state_component, 2))
plot(event, "fld-var-velocity-%04d" % step)
step += 1
logger.info("[%04d] t = %.5f |u| = %.5e", step, event.t, norm_u)
# NOTE: These are here to ensure the solution is bounded for the
# time interval specified
assert norm_u < 1
def main(ctx_factory, dim=2, order=4, visualize=False):
cl_ctx = ctx_factory()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(queue)
# {{{ parameters
# domain [-d/2, d/2]^dim
d = 1.0
# number of points in each dimension
npoints = 20
# grid spacing
h = d / npoints
# cfl
dt_factor = 2.0
# final time
final_time = 1.0
# compute number of steps
dt = dt_factor * h / order**2
nsteps = int(final_time // dt) + 1
dt = final_time / nsteps + 1.0e-15
# velocity field
c = np.array([0.5] * dim)
norm_c = la.norm(c)
# flux
flux_type = "central"
# }}}
# {{{ discretization
from meshmode.mesh.generation import generate_box_mesh
mesh = generate_box_mesh(
[np.linspace(-d / 2, d / 2, npoints) for _ in range(dim)], order=order)
from grudge import DiscretizationCollection
discr = DiscretizationCollection(actx, mesh, order=order)
# }}}
# {{{ symbolic operators
def f(x):
return sym.sin(3 * x)
def u_analytic(x):
t = sym.var("t", dof_desc.DD_SCALAR)
return f(-np.dot(c, x) / norm_c + t * norm_c)
from grudge.models.advection import WeakAdvectionOperator
op = WeakAdvectionOperator(c,
inflow_u=u_analytic(sym.nodes(dim, BTAG_ALL)),
flux_type=flux_type)
bound_op = bind(discr, op.sym_operator())
u = bind(discr, u_analytic(sym.nodes(dim)))(actx, t=0)
def rhs(t, u):
return bound_op(t=t, u=u)
# }}}
# {{{ time stepping
from grudge.shortcuts import set_up_rk4
dt_stepper = set_up_rk4("u", dt, u, rhs)
plot = Plotter(actx, discr, order, visualize=visualize, ylim=[-1.1, 1.1])
norm = bind(discr, sym.norm(2, sym.var("u")))
step = 0
norm_u = 0.0
for event in dt_stepper.run(t_end=final_time):
if not isinstance(event, dt_stepper.StateComputed):
continue
if step % 10 == 0:
norm_u = norm(u=event.state_component)
plot(event, "fld-weak-%04d" % step)
step += 1
logger.info("[%04d] t = %.5f |u| = %.5e", step, event.t, norm_u)
def main(write_output=True, order=4):
cl_ctx = cl.create_some_context()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(queue)
comm = MPI.COMM_WORLD
num_parts = comm.Get_size()
from meshmode.distributed import MPIMeshDistributor, get_partition_by_pymetis
mesh_dist = MPIMeshDistributor(comm)
if mesh_dist.is_mananger_rank():
dims = 2
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(-0.5, ) * dims,
b=(0.5, ) * dims,
n=(16, ) * dims)
print("%d elements" % mesh.nelements)
part_per_element = get_partition_by_pymetis(mesh, num_parts)
local_mesh = mesh_dist.send_mesh_parts(mesh, part_per_element,
num_parts)
del mesh
else:
local_mesh = mesh_dist.receive_mesh_part()
discr = DGDiscretizationWithBoundaries(actx,
local_mesh,
order=order,
mpi_communicator=comm)
if local_mesh.dim == 2:
dt = 0.04
elif local_mesh.dim == 3:
dt = 0.02
source_center = np.array([0.1, 0.22, 0.33])[:local_mesh.dim]
source_width = 0.05
source_omega = 3
sym_x = sym.nodes(local_mesh.dim)
sym_source_center_dist = sym_x - source_center
sym_t = sym.ScalarVariable("t")
from grudge.models.wave import StrongWaveOperator
from meshmode.mesh import BTAG_ALL, BTAG_NONE
op = StrongWaveOperator(
-0.1,
discr.dim,
source_f=(
sym.sin(source_omega * sym_t) *
sym.exp(-np.dot(sym_source_center_dist, sym_source_center_dist) /
source_width**2)),
dirichlet_tag=BTAG_NONE,
neumann_tag=BTAG_NONE,
radiation_tag=BTAG_ALL,
flux_type="upwind")
from pytools.obj_array import flat_obj_array
fields = flat_obj_array(discr.zeros(actx),
[discr.zeros(actx) for i in range(discr.dim)])
# FIXME
#dt = op.estimate_rk4_timestep(discr, fields=fields)
op.check_bc_coverage(local_mesh)
# print(sym.pretty(op.sym_operator()))
bound_op = bind(discr, op.sym_operator())
def rhs(t, w):
return bound_op(t=t, w=w)
dt_stepper = set_up_rk4("w", dt, fields, rhs)
final_t = 10
nsteps = int(final_t / dt)
print("dt=%g nsteps=%d" % (dt, nsteps))
from grudge.shortcuts import make_visualizer
vis = make_visualizer(discr, vis_order=order)
step = 0
norm = bind(discr, sym.norm(2, sym.var("u")))
from time import time
t_last_step = time()
rank = comm.Get_rank()
for event in dt_stepper.run(t_end=final_t):
if isinstance(event, dt_stepper.StateComputed):
assert event.component_id == "w"
step += 1
print(step, event.t, norm(u=event.state_component[0]),
time() - t_last_step)
if step % 10 == 0:
vis.write_vtk_file(
"fld-wave-min-mpi-%03d-%04d.vtu" % (
rank,
step,
), [
("u", event.state_component[0]),
("v", event.state_component[1:]),
])
t_last_step = time()
def main(ctx_factory, dim=2, order=4, product_tag=None, visualize=False):
cl_ctx = ctx_factory()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(queue)
# {{{ parameters
# sphere radius
radius = 1.0
# sphere resolution
resolution = 64 if dim == 2 else 1
# cfl
dt_factor = 2.0
# final time
final_time = np.pi
# velocity field
sym_x = sym.nodes(dim)
c = make_obj_array([-sym_x[1], sym_x[0], 0.0])[:dim]
# flux
flux_type = "lf"
# }}}
# {{{ discretization
if dim == 2:
from meshmode.mesh.generation import make_curve_mesh, ellipse
mesh = make_curve_mesh(lambda t: radius * ellipse(1.0, t),
np.linspace(0.0, 1.0, resolution + 1), order)
elif dim == 3:
from meshmode.mesh.generation import generate_icosphere
mesh = generate_icosphere(radius,
order=4 * order,
uniform_refinement_rounds=resolution)
else:
raise ValueError("unsupported dimension")
discr_tag_to_group_factory = {}
if product_tag == "none":
product_tag = None
else:
product_tag = dof_desc.DISCR_TAG_QUAD
from meshmode.discretization.poly_element import \
PolynomialWarpAndBlendGroupFactory, \
QuadratureSimplexGroupFactory
discr_tag_to_group_factory[dof_desc.DISCR_TAG_BASE] = \
PolynomialWarpAndBlendGroupFactory(order)
if product_tag:
discr_tag_to_group_factory[product_tag] = \
QuadratureSimplexGroupFactory(order=4*order)
from grudge import DiscretizationCollection
discr = DiscretizationCollection(
actx, mesh, discr_tag_to_group_factory=discr_tag_to_group_factory)
volume_discr = discr.discr_from_dd(dof_desc.DD_VOLUME)
logger.info("ndofs: %d", volume_discr.ndofs)
logger.info("nelements: %d", volume_discr.mesh.nelements)
# }}}
# {{{ symbolic operators
def f_initial_condition(x):
return x[0]
from grudge.models.advection import SurfaceAdvectionOperator
op = SurfaceAdvectionOperator(c, flux_type=flux_type, quad_tag=product_tag)
bound_op = bind(discr, op.sym_operator())
u0 = bind(discr, f_initial_condition(sym_x))(actx, t=0)
def rhs(t, u):
return bound_op(actx, t=t, u=u)
# check velocity is tangential
sym_normal = sym.surface_normal(dim, dim=dim - 1,
dd=dof_desc.DD_VOLUME).as_vector()
error = bind(discr, sym.norm(2, c.dot(sym_normal)))(actx)
logger.info("u_dot_n: %.5e", error)
# }}}
# {{{ time stepping
# compute time step
h_min = bind(discr, sym.h_max_from_volume(discr.ambient_dim,
dim=discr.dim))(actx)
dt = dt_factor * h_min / order**2
nsteps = int(final_time // dt) + 1
dt = final_time / nsteps + 1.0e-15
logger.info("dt: %.5e", dt)
logger.info("nsteps: %d", nsteps)
from grudge.shortcuts import set_up_rk4
dt_stepper = set_up_rk4("u", dt, u0, rhs)
plot = Plotter(actx, discr, order, visualize=visualize)
norm = bind(discr, sym.norm(2, sym.var("u")))
norm_u = norm(actx, u=u0)
step = 0
event = dt_stepper.StateComputed(0.0, 0, 0, u0)
plot(event, "fld-surface-%04d" % 0)
if visualize and dim == 3:
from grudge.shortcuts import make_visualizer
vis = make_visualizer(discr)
vis.write_vtk_file("fld-surface-velocity.vtu",
[("u", bind(discr, c)(actx)),
("n", bind(discr, sym_normal)(actx))],
overwrite=True)
df = dof_desc.DOFDesc(FACE_RESTR_INTERIOR)
face_discr = discr.connection_from_dds(dof_desc.DD_VOLUME, df).to_discr
face_normal = bind(
discr, sym.normal(df, face_discr.ambient_dim,
dim=face_discr.dim))(actx)
from meshmode.discretization.visualization import make_visualizer
vis = make_visualizer(actx, face_discr)
vis.write_vtk_file("fld-surface-face-normals.vtu",
[("n", face_normal)],
overwrite=True)
for event in dt_stepper.run(t_end=final_time, max_steps=nsteps + 1):
if not isinstance(event, dt_stepper.StateComputed):
continue
step += 1
if step % 10 == 0:
norm_u = norm(actx, u=event.state_component)
plot(event, "fld-surface-%04d" % step)
logger.info("[%04d] t = %.5f |u| = %.5e", step, event.t, norm_u)
plot(event, "fld-surface-%04d" % step)
def main(write_output=True, order=4):
cl_ctx = cl.create_some_context()
queue = cl.CommandQueue(cl_ctx)
dim = 2
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(-0.5, -0.5),
b=(0.5, 0.5),
n=(20, 20),
order=order)
dt_factor = 4
h = 1 / 20
discr = DGDiscretizationWithBoundaries(cl_ctx, mesh, order=order)
c = np.array([0.1, 0.1])
norm_c = la.norm(c)
flux_type = "central"
def f(x):
return sym.sin(3 * x)
def u_analytic(x):
return f(-np.dot(c, x) / norm_c + sym.var("t", sym.DD_SCALAR) * norm_c)
from grudge.models.advection import WeakAdvectionOperator
discr = DGDiscretizationWithBoundaries(cl_ctx, mesh, order=order)
op = WeakAdvectionOperator(c,
inflow_u=u_analytic(sym.nodes(
dim, sym.BTAG_ALL)),
flux_type=flux_type)
bound_op = bind(discr, op.sym_operator())
u = bind(discr, u_analytic(sym.nodes(dim)))(queue, t=0)
def rhs(t, u):
return bound_op(queue, t=t, u=u)
final_time = 0.3
dt = dt_factor * h / order**2
nsteps = (final_time // dt) + 1
dt = final_time / nsteps + 1e-15
from grudge.shortcuts import set_up_rk4
dt_stepper = set_up_rk4("u", dt, u, rhs)
from grudge.shortcuts import make_visualizer
vis = make_visualizer(discr, vis_order=order)
step = 0
for event in dt_stepper.run(t_end=final_time):
if isinstance(event, dt_stepper.StateComputed):
step += 1
#print(step, event.t, norm(queue, u=event.state_component[0]))
vis.write_vtk_file("fld-weak-%04d.vtu" % step,
[("u", event.state_component)])
