def test_inviscid_flux_components(actx_factory, dim):
"""Test uniform pressure case.
Checks that the Euler-internal inviscid flux routine
:func:`mirgecom.inviscid.inviscid_flux` returns exactly the expected result
with a constant pressure and no flow.
Expected inviscid flux is:
F(q) = <rhoV, (E+p)V, rho(V.x.V) + pI>
Checks that only diagonal terms of the momentum flux:
[ rho(V.x.V) + pI ] are non-zero and return the correctly calculated p.
"""
actx = actx_factory()
eos = IdealSingleGas()
p0 = 1.0
nel_1d = 4
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=(nel_1d, ) * dim)
order = 3
discr = EagerDGDiscretization(actx, mesh, order=order)
eos = IdealSingleGas()
logger.info(f"Number of {dim}d elems: {mesh.nelements}")
# === this next block tests 1,2,3 dimensions,
# with single and multiple nodes/states. The
# purpose of this block is to ensure that when
# all components of V = 0, the flux recovers
# the expected values (and p0 within tolerance)
# === with V = 0, fixed P = p0
tolerance = 1e-15
nodes = thaw(actx, discr.nodes())
mass = discr.zeros(actx) + np.dot(nodes, nodes) + 1.0
mom = make_obj_array([discr.zeros(actx) for _ in range(dim)])
p_exact = discr.zeros(actx) + p0
energy = p_exact / 0.4 + 0.5 * np.dot(mom, mom) / mass
cv = make_conserved(dim, mass=mass, energy=energy, momentum=mom)
p = eos.pressure(cv)
flux = inviscid_flux(discr, eos, cv)
assert discr.norm(p - p_exact, np.inf) < tolerance
logger.info(f"{dim}d flux = {flux}")
# for velocity zero, these components should be == zero
assert discr.norm(flux.mass, 2) == 0.0
assert discr.norm(flux.energy, 2) == 0.0
# The momentum diagonal should be p
# Off-diagonal should be identically 0
assert discr.norm(flux.momentum - p0 * np.identity(dim),
np.inf) < tolerance
def test_inviscid_mom_flux_components(actx_factory, dim, livedim):
r"""Test components of the momentum flux with constant pressure, V != 0.
Checks that the flux terms are returned in the proper order by running
only 1 non-zero velocity component at-a-time.
"""
actx = actx_factory()
eos = IdealSingleGas()
p0 = 1.0
nel_1d = 4
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=(nel_1d, ) * dim)
order = 3
discr = EagerDGDiscretization(actx, mesh, order=order)
nodes = thaw(actx, discr.nodes())
tolerance = 1e-15
for livedim in range(dim):
mass = discr.zeros(actx) + 1.0 + np.dot(nodes, nodes)
mom = make_obj_array([discr.zeros(actx) for _ in range(dim)])
mom[livedim] = mass
p_exact = discr.zeros(actx) + p0
energy = (p_exact / (eos.gamma() - 1.0) +
0.5 * np.dot(mom, mom) / mass)
cv = make_conserved(dim, mass=mass, energy=energy, momentum=mom)
p = eos.pressure(cv)
from mirgecom.gas_model import GasModel, make_fluid_state
state = make_fluid_state(cv, GasModel(eos=eos))
def inf_norm(x):
return actx.to_numpy(discr.norm(x, np.inf))
assert inf_norm(p - p_exact) < tolerance
flux = inviscid_flux(state)
logger.info(f"{dim}d flux = {flux}")
vel_exact = mom / mass
# first two components should be nonzero in livedim only
assert inf_norm(flux.mass - mom) == 0
eflux_exact = (energy + p_exact) * vel_exact
assert inf_norm(flux.energy - eflux_exact) == 0
logger.info("Testing momentum")
xpmomflux = mass * np.outer(vel_exact,
vel_exact) + p_exact * np.identity(dim)
assert inf_norm(flux.momentum - xpmomflux) < tolerance
def test_viscous_timestep(actx_factory, dim, mu, vel):
"""Test timestep size."""
actx = actx_factory()
nel_1d = 4
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(1.0, ) * dim,
b=(2.0, ) * dim,
nelements_per_axis=(nel_1d, ) * dim)
order = 1
discr = EagerDGDiscretization(actx, mesh, order=order)
zeros = discr.zeros(actx)
ones = zeros + 1.0
velocity = make_obj_array([zeros + vel for _ in range(dim)])
massval = 1
mass = massval * ones
# I *think* this energy should yield c=1.0
energy = zeros + 1.0 / (1.4 * .4)
mom = mass * velocity
species_mass = None
cv = make_conserved(dim,
mass=mass,
energy=energy,
momentum=mom,
species_mass=species_mass)
from grudge.dt_utils import characteristic_lengthscales
chlen = characteristic_lengthscales(actx, discr)
from grudge.op import nodal_min
chlen_min = nodal_min(discr, "vol", chlen)
mu = mu * chlen_min
if mu < 0:
mu = 0
tv_model = None
else:
tv_model = SimpleTransport(viscosity=mu)
eos = IdealSingleGas()
gas_model = GasModel(eos=eos, transport=tv_model)
fluid_state = make_fluid_state(cv, gas_model)
from mirgecom.viscous import get_viscous_timestep
dt_field = get_viscous_timestep(discr, fluid_state)
speed_total = fluid_state.wavespeed
dt_expected = chlen / (speed_total + (mu / chlen))
error = (dt_expected - dt_field) / dt_expected
assert actx.to_numpy(discr.norm(error, np.inf)) == 0
def main():
cl_ctx = cl.create_some_context()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(queue)
dim = 2
nel_1d = 16
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(
a=(-0.5,)*dim,
b=(0.5,)*dim,
n=(nel_1d,)*dim)
order = 3
if dim == 2:
# no deep meaning here, just a fudge factor
dt = 0.75/(nel_1d*order**2)
elif dim == 3:
# no deep meaning here, just a fudge factor
dt = 0.45/(nel_1d*order**2)
else:
raise ValueError("don't have a stable time step guesstimate")
print("%d elements" % mesh.nelements)
discr = EagerDGDiscretization(actx, mesh, order=order)
fields = flat_obj_array(
bump(discr, actx),
[discr.zeros(actx) for i in range(discr.dim)]
)
vis = make_visualizer(discr, order+3 if dim == 2 else order)
def rhs(t, w):
return wave_operator(discr, c=1, w=w)
t = 0
t_final = 3
istep = 0
while t < t_final:
fields = rk4_step(fields, t, dt, rhs)
if istep % 10 == 0:
print(f"step: {istep} t: {t} L2: {discr.norm(fields[0])} "
f"sol max: {discr.nodal_max('vol', fields[0])}")
vis.write_vtk_file("fld-wave-eager-%04d.vtu" % istep,
[
("u", fields[0]),
("v", fields[1:]),
])
t += dt
istep += 1
def test_velocity_gradient_eoc(actx_factory, dim):
"""Test that the velocity gradient converges at the proper rate."""
from mirgecom.fluid import velocity_gradient
actx = actx_factory()
order = 3
from pytools.convergence import EOCRecorder
eoc = EOCRecorder()
nel_1d_0 = 4
for hn1 in [1, 2, 3, 4]:
nel_1d = hn1 * nel_1d_0
h = 1/nel_1d
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(
a=(1.0,) * dim, b=(2.0,) * dim, nelements_per_axis=(nel_1d,) * dim
)
discr = EagerDGDiscretization(actx, mesh, order=order)
nodes = thaw(actx, discr.nodes())
zeros = discr.zeros(actx)
energy = zeros + 2.5
mass = nodes[dim-1]*nodes[dim-1]
velocity = make_obj_array([actx.np.cos(nodes[i]) for i in range(dim)])
mom = mass*velocity
q = join_conserved(dim, mass=mass, energy=energy, momentum=mom)
cv = split_conserved(dim, q)
grad_q = obj_array_vectorize(discr.grad, q)
grad_cv = split_conserved(dim, grad_q)
grad_v = velocity_gradient(discr, cv, grad_cv)
def exact_grad_row(xdata, gdim, dim):
exact_grad_row = make_obj_array([zeros for _ in range(dim)])
exact_grad_row[gdim] = -actx.np.sin(xdata)
return exact_grad_row
comp_err = make_obj_array([
discr.norm(grad_v[i] - exact_grad_row(nodes[i], i, dim), np.inf)
for i in range(dim)])
err_max = comp_err.max()
eoc.add_data_point(h, err_max)
logger.info(eoc)
assert (
eoc.order_estimate() >= order - 0.5
or eoc.max_error() < 1e-9
)
def test_viscous_stress_tensor(actx_factory, transport_model):
"""Test tau data structure and values against exact."""
actx = actx_factory()
dim = 3
nel_1d = 4
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(1.0, ) * dim,
b=(2.0, ) * dim,
nelements_per_axis=(nel_1d, ) * dim)
order = 1
discr = EagerDGDiscretization(actx, mesh, order=order)
nodes = thaw(actx, discr.nodes())
zeros = discr.zeros(actx)
ones = zeros + 1.0
# assemble velocities for simple, unique grad components
velocity_x = nodes[0] + 2 * nodes[1] + 3 * nodes[2]
velocity_y = 4 * nodes[0] + 5 * nodes[1] + 6 * nodes[2]
velocity_z = 7 * nodes[0] + 8 * nodes[1] + 9 * nodes[2]
velocity = make_obj_array([velocity_x, velocity_y, velocity_z])
mass = 2 * ones
energy = zeros + 2.5
mom = mass * velocity
cv = make_conserved(dim, mass=mass, energy=energy, momentum=mom)
grad_cv = op.local_grad(discr, cv)
if transport_model:
tv_model = SimpleTransport(bulk_viscosity=1.0, viscosity=0.5)
else:
tv_model = PowerLawTransport()
eos = IdealSingleGas()
gas_model = GasModel(eos=eos, transport=tv_model)
fluid_state = make_fluid_state(cv, gas_model)
mu = tv_model.viscosity(eos, cv)
lam = tv_model.volume_viscosity(eos, cv)
# Exact answer for tau
exp_grad_v = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
exp_grad_v_t = np.array([[1, 4, 7], [2, 5, 8], [3, 6, 9]])
exp_div_v = 15
exp_tau = (mu * (exp_grad_v + exp_grad_v_t) + lam * exp_div_v * np.eye(3))
from mirgecom.viscous import viscous_stress_tensor
tau = viscous_stress_tensor(fluid_state, grad_cv)
# The errors come from grad_v
assert actx.to_numpy(discr.norm(tau - exp_tau, np.inf)) < 1e-12
def test_species_mass_gradient(actx_factory, dim):
"""Test gradY structure and values against exact."""
actx = actx_factory()
nel_1d = 4
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(
a=(1.0,) * dim, b=(2.0,) * dim, nelements_per_axis=(nel_1d,) * dim
)
order = 1
discr = EagerDGDiscretization(actx, mesh, order=order)
nodes = thaw(actx, discr.nodes())
zeros = discr.zeros(actx)
ones = zeros + 1
nspecies = 2*dim
mass = 2*ones # make mass != 1
energy = zeros + 2.5
velocity = make_obj_array([ones for _ in range(dim)])
mom = mass * velocity
# assemble y so that each one has simple, but unique grad components
y = make_obj_array([ones for _ in range(nspecies)])
for idim in range(dim):
ispec = 2*idim
y[ispec] = ispec*(idim*dim+1)*sum([(iidim+1)*nodes[iidim]
for iidim in range(dim)])
y[ispec+1] = -y[ispec]
species_mass = mass*y
cv = make_conserved(dim, mass=mass, energy=energy, momentum=mom,
species_mass=species_mass)
from grudge.op import local_grad
grad_cv = local_grad(discr, cv)
from mirgecom.fluid import species_mass_fraction_gradient
grad_y = species_mass_fraction_gradient(cv, grad_cv)
assert grad_y.shape == (nspecies, dim)
from meshmode.dof_array import DOFArray
assert type(grad_y[0, 0]) == DOFArray
def inf_norm(x):
return actx.to_numpy(discr.norm(x, np.inf))
tol = 1e-11
for idim in range(dim):
ispec = 2*idim
exact_grad = np.array([(ispec*(idim*dim+1))*(iidim+1)
for iidim in range(dim)])
assert inf_norm(grad_y[ispec] - exact_grad) < tol
assert inf_norm(grad_y[ispec+1] + exact_grad) < tol
def test_local_max_species_diffusivity(actx_factory, dim, array_valued):
"""Test the local maximum species diffusivity."""
actx = actx_factory()
nel_1d = 4
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(1.0, ) * dim,
b=(2.0, ) * dim,
nelements_per_axis=(nel_1d, ) * dim)
order = 1
discr = EagerDGDiscretization(actx, mesh, order=order)
nodes = thaw(actx, discr.nodes())
zeros = discr.zeros(actx)
ones = zeros + 1.0
vel = .32
velocity = make_obj_array([zeros + vel for _ in range(dim)])
massval = 1
mass = massval * ones
energy = zeros + 1.0 / (1.4 * .4)
mom = mass * velocity
species_mass = np.array([1., 2., 3.], dtype=object)
cv = make_conserved(dim,
mass=mass,
energy=energy,
momentum=mom,
species_mass=species_mass)
d_alpha_input = np.array([.1, .2, .3])
if array_valued:
f = 1 + 0.1 * actx.np.sin(nodes[0])
d_alpha_input *= f
tv_model = SimpleTransport(species_diffusivity=d_alpha_input)
eos = IdealSingleGas()
d_alpha = tv_model.species_diffusivity(eos, cv)
from mirgecom.viscous import get_local_max_species_diffusivity
expected = .3 * ones
if array_valued:
expected *= f
calculated = get_local_max_species_diffusivity(actx, d_alpha)
assert actx.to_numpy(discr.norm(calculated - expected, np.inf)) == 0
def test_velocity_gradient_structure(actx_factory):
"""Test gradv data structure, verifying usability with other helper routines."""
from mirgecom.fluid import velocity_gradient
actx = actx_factory()
dim = 3
nel_1d = 4
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(
a=(1.0,) * dim, b=(2.0,) * dim, nelements_per_axis=(nel_1d,) * dim
)
order = 1
discr = EagerDGDiscretization(actx, mesh, order=order)
nodes = thaw(actx, discr.nodes())
zeros = discr.zeros(actx)
ones = zeros + 1.0
mass = 2*ones
energy = zeros + 2.5
velocity_x = nodes[0] + 2*nodes[1] + 3*nodes[2]
velocity_y = 4*nodes[0] + 5*nodes[1] + 6*nodes[2]
velocity_z = 7*nodes[0] + 8*nodes[1] + 9*nodes[2]
velocity = make_obj_array([velocity_x, velocity_y, velocity_z])
mom = mass * velocity
cv = make_conserved(dim, mass=mass, energy=energy, momentum=mom)
from grudge.op import local_grad
grad_cv = local_grad(discr, cv)
grad_v = velocity_gradient(cv, grad_cv)
tol = 1e-11
exp_result = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
exp_trans = [[1, 4, 7], [2, 5, 8], [3, 6, 9]]
exp_trace = 15
assert grad_v.shape == (dim, dim)
from meshmode.dof_array import DOFArray
assert type(grad_v[0, 0]) == DOFArray
def inf_norm(x):
return actx.to_numpy(discr.norm(x, np.inf))
assert inf_norm(grad_v - exp_result) < tol
assert inf_norm(grad_v.T - exp_trans) < tol
assert inf_norm(np.trace(grad_v) - exp_trace) < tol
def test_velocity_gradient_sanity(actx_factory, dim, mass_exp, vel_fac):
"""Test that the grad(v) returns {0, I} for v={constant, r_xyz}."""
from mirgecom.fluid import velocity_gradient
actx = actx_factory()
nel_1d = 16
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(1.0, ) * dim,
b=(2.0, ) * dim,
nelements_per_axis=(nel_1d, ) * dim)
order = 3
discr = EagerDGDiscretization(actx, mesh, order=order)
nodes = thaw(actx, discr.nodes())
zeros = discr.zeros(actx)
ones = zeros + 1.0
mass = 1 * ones
for i in range(mass_exp):
mass *= (mass + i)
energy = zeros + 2.5
velocity = vel_fac * nodes
mom = mass * velocity
cv = make_conserved(dim, mass=mass, energy=energy, momentum=mom)
from grudge.op import local_grad
grad_cv = make_conserved(dim, q=local_grad(discr, cv.join()))
grad_v = velocity_gradient(discr, cv, grad_cv)
tol = 1e-11
exp_result = vel_fac * np.eye(dim) * ones
grad_v_err = [
discr.norm(grad_v[i] - exp_result[i], np.inf) for i in range(dim)
]
assert max(grad_v_err) < tol
def test_analytic_comparison(actx_factory):
"""Quick test of state comparison routine."""
from mirgecom.initializers import Vortex2D
from mirgecom.simutil import compare_fluid_solutions, componentwise_norms
actx = actx_factory()
nel_1d = 4
dim = 2
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(1.0, ) * dim,
b=(2.0, ) * dim,
nelements_per_axis=(nel_1d, ) * dim)
order = 2
discr = EagerDGDiscretization(actx, mesh, order=order)
nodes = thaw(discr.nodes(), actx)
zeros = discr.zeros(actx)
ones = zeros + 1.0
mass = ones
energy = ones
velocity = 2 * nodes
mom = mass * velocity
vortex_init = Vortex2D()
vortex_soln = vortex_init(x_vec=nodes, eos=IdealSingleGas())
cv = make_conserved(dim, mass=mass, energy=energy, momentum=mom)
resid = vortex_soln - cv
expected_errors = actx.to_numpy(
flatten(componentwise_norms(discr, resid, order=np.inf),
actx)).tolist()
errors = compare_fluid_solutions(discr, cv, cv)
assert max(errors) == 0
errors = compare_fluid_solutions(discr, cv, vortex_soln)
assert errors == expected_errors
def test_facial_flux(actx_factory, nspecies, order, dim):
"""Check the flux across element faces.
The flux is checked by prescribing states (q) with known fluxes. Only uniform
states are tested currently - ensuring that the Lax-Friedrichs flux terms which
are proportional to jumps in state data vanish.
Since the returned fluxes use state data which has been interpolated
to-and-from the element faces, this test is grid-dependent.
"""
actx = actx_factory()
tolerance = 1e-14
p0 = 1.0
from meshmode.mesh.generation import generate_regular_rect_mesh
from pytools.convergence import EOCRecorder
eoc_rec0 = EOCRecorder()
eoc_rec1 = EOCRecorder()
for nel_1d in [4, 8, 12]:
mesh = generate_regular_rect_mesh(a=(-0.5, ) * dim,
b=(0.5, ) * dim,
nelements_per_axis=(nel_1d, ) * dim)
logger.info(f"Number of elements: {mesh.nelements}")
discr = EagerDGDiscretization(actx, mesh, order=order)
zeros = discr.zeros(actx)
ones = zeros + 1.0
mass_input = discr.zeros(actx) + 1.0
energy_input = discr.zeros(actx) + 2.5
mom_input = flat_obj_array(
[discr.zeros(actx) for i in range(discr.dim)])
mass_frac_input = flat_obj_array(
[ones / ((i + 1) * 10) for i in range(nspecies)])
species_mass_input = mass_input * mass_frac_input
cv = make_conserved(dim,
mass=mass_input,
energy=energy_input,
momentum=mom_input,
species_mass=species_mass_input)
from grudge.trace_pair import interior_trace_pairs
cv_interior_pairs = interior_trace_pairs(discr, cv)
# Check the boundary facial fluxes as called on an interior boundary
# eos = IdealSingleGas()
from mirgecom.gas_model import (GasModel, make_fluid_state)
gas_model = GasModel(eos=IdealSingleGas())
from mirgecom.gas_model import make_fluid_state_trace_pairs
state_tpairs = make_fluid_state_trace_pairs(cv_interior_pairs,
gas_model)
interior_state_pair = state_tpairs[0]
from mirgecom.inviscid import inviscid_facial_flux
interior_face_flux = \
inviscid_facial_flux(discr, state_tpair=interior_state_pair)
def inf_norm(data):
if len(data) > 0:
return actx.to_numpy(discr.norm(data, np.inf, dd="all_faces"))
else:
return 0.0
assert inf_norm(interior_face_flux.mass) < tolerance
assert inf_norm(interior_face_flux.energy) < tolerance
assert inf_norm(interior_face_flux.species_mass) < tolerance
# The expected pressure is 1.0 (by design). And the flux diagonal is
# [rhov_x*v_x + p] (etc) since we have zero velocities it's just p.
#
# The off-diagonals are zero. We get a {ndim}-vector for each
# dimension, the flux for the x-component of momentum (for example) is:
# f_momx = < 1.0, 0 , 0> , then we return f_momx .dot. normal, which
# can introduce negative values.
#
# (Explanation courtesy of Mike Campbell,
# https://github.com/illinois-ceesd/mirgecom/pull/44#discussion_r463304292)
nhat = thaw(actx, discr.normal("int_faces"))
mom_flux_exact = discr.project("int_faces", "all_faces", p0 * nhat)
print(f"{mom_flux_exact=}")
print(f"{interior_face_flux.momentum=}")
momerr = inf_norm(interior_face_flux.momentum - mom_flux_exact)
assert momerr < tolerance
eoc_rec0.add_data_point(1.0 / nel_1d, momerr)
# Check the boundary facial fluxes as called on a domain boundary
dir_mass = discr.project("vol", BTAG_ALL, mass_input)
dir_e = discr.project("vol", BTAG_ALL, energy_input)
dir_mom = discr.project("vol", BTAG_ALL, mom_input)
dir_mf = discr.project("vol", BTAG_ALL, species_mass_input)
dir_bc = make_conserved(dim,
mass=dir_mass,
energy=dir_e,
momentum=dir_mom,
species_mass=dir_mf)
dir_bval = make_conserved(dim,
mass=dir_mass,
energy=dir_e,
momentum=dir_mom,
species_mass=dir_mf)
state_tpair = TracePair(BTAG_ALL,
interior=make_fluid_state(dir_bval, gas_model),
exterior=make_fluid_state(dir_bc, gas_model))
boundary_flux = inviscid_facial_flux(discr, state_tpair=state_tpair)
assert inf_norm(boundary_flux.mass) < tolerance
assert inf_norm(boundary_flux.energy) < tolerance
assert inf_norm(boundary_flux.species_mass) < tolerance
nhat = thaw(actx, discr.normal(BTAG_ALL))
mom_flux_exact = discr.project(BTAG_ALL, "all_faces", p0 * nhat)
momerr = inf_norm(boundary_flux.momentum - mom_flux_exact)
assert momerr < tolerance
eoc_rec1.add_data_point(1.0 / nel_1d, momerr)
logger.info(f"standalone Errors:\n{eoc_rec0}"
f"boundary Errors:\n{eoc_rec1}")
assert (eoc_rec0.order_estimate() >= order - 0.5
or eoc_rec0.max_error() < 1e-9)
assert (eoc_rec1.order_estimate() >= order - 0.5
or eoc_rec1.max_error() < 1e-9)
def main():
cl_ctx = cl.create_some_context()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(queue,
allocator=cl_tools.MemoryPool(
cl_tools.ImmediateAllocator(queue)))
from mpi4py import MPI
comm = MPI.COMM_WORLD
num_parts = comm.Get_size()
from meshmode.distributed import MPIMeshDistributor, get_partition_by_pymetis
mesh_dist = MPIMeshDistributor(comm)
dim = 2
nel_1d = 16
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=(nel_1d, ) * dim,
boundary_tag_to_face={
"dirichlet": ["+x", "-x"],
"neumann": ["+y", "-y"]
})
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()
order = 3
discr = EagerDGDiscretization(actx,
local_mesh,
order=order,
mpi_communicator=comm)
if dim == 2:
# no deep meaning here, just a fudge factor
dt = 0.0025 / (nel_1d * order**2)
else:
raise ValueError("don't have a stable time step guesstimate")
source_width = 0.2
nodes = thaw(actx, discr.nodes())
u = discr.zeros(actx)
vis = make_visualizer(discr, order + 3 if dim == 2 else order)
boundaries = {
grudge_sym.DTAG_BOUNDARY("dirichlet"): DirichletDiffusionBoundary(0.),
grudge_sym.DTAG_BOUNDARY("neumann"): NeumannDiffusionBoundary(0.)
}
def rhs(t, u):
return (
diffusion_operator(discr, alpha=1, boundaries=boundaries, u=u) +
actx.np.exp(-np.dot(nodes, nodes) / source_width**2))
rank = comm.Get_rank()
t = 0
t_final = 0.01
istep = 0
while True:
if istep % 10 == 0:
print(istep, t, discr.norm(u))
vis.write_vtk_file(
"fld-heat-source-mpi-%03d-%04d.vtu" % (rank, istep),
[("u", u)])
if t >= t_final:
break
u = rk4_step(u, t, dt, rhs)
t += dt
istep += 1
def test_uniform_rhs(actx_factory, dim, order):
"""Tests the inviscid rhs using a trivial
constant/uniform state which should
yield rhs = 0 to FP. The test is performed
for 1, 2, and 3 dimensions.
"""
actx = actx_factory()
tolerance = 1e-9
maxxerr = 0.0
from pytools.convergence import EOCRecorder
eoc_rec0 = EOCRecorder()
eoc_rec1 = EOCRecorder()
# for nel_1d in [4, 8, 12]:
for nel_1d in [4, 8]:
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(-0.5, ) * dim,
b=(0.5, ) * dim,
n=(nel_1d, ) * dim)
logger.info(f"Number of {dim}d elements: {mesh.nelements}")
discr = EagerDGDiscretization(actx, mesh, order=order)
mass_input = discr.zeros(actx) + 1
energy_input = discr.zeros(actx) + 2.5
mom_input = make_obj_array(
[discr.zeros(actx) for i in range(discr.dim)])
fields = join_conserved(dim,
mass=mass_input,
energy=energy_input,
momentum=mom_input)
expected_rhs = make_obj_array(
[discr.zeros(actx) for i in range(len(fields))])
boundaries = {BTAG_ALL: DummyBoundary()}
inviscid_rhs = inviscid_operator(discr,
eos=IdealSingleGas(),
boundaries=boundaries,
q=fields,
t=0.0)
rhs_resid = inviscid_rhs - expected_rhs
resid_split = split_conserved(dim, rhs_resid)
rho_resid = resid_split.mass
rhoe_resid = resid_split.energy
mom_resid = resid_split.momentum
rhs_split = split_conserved(dim, inviscid_rhs)
rho_rhs = rhs_split.mass
rhoe_rhs = rhs_split.energy
rhov_rhs = rhs_split.momentum
message = (f"rho_rhs = {rho_rhs}\n"
f"rhoe_rhs = {rhoe_rhs}\n"
f"rhov_rhs = {rhov_rhs}")
logger.info(message)
assert discr.norm(rho_resid, np.inf) < tolerance
assert discr.norm(rhoe_resid, np.inf) < tolerance
for i in range(dim):
assert discr.norm(mom_resid[i], np.inf) < tolerance
err_max = discr.norm(rhs_resid[i], np.inf)
eoc_rec0.add_data_point(1.0 / nel_1d, err_max)
assert (err_max < tolerance)
if err_max > maxxerr:
maxxerr = err_max
# set a non-zero, but uniform velocity component
for i in range(len(mom_input)):
mom_input[i] = discr.zeros(actx) + (-1.0)**i
boundaries = {BTAG_ALL: DummyBoundary()}
inviscid_rhs = inviscid_operator(discr,
eos=IdealSingleGas(),
boundaries=boundaries,
q=fields,
t=0.0)
rhs_resid = inviscid_rhs - expected_rhs
resid_split = split_conserved(dim, rhs_resid)
rho_resid = resid_split.mass
rhoe_resid = resid_split.energy
mom_resid = resid_split.momentum
assert discr.norm(rho_resid, np.inf) < tolerance
assert discr.norm(rhoe_resid, np.inf) < tolerance
for i in range(dim):
assert discr.norm(mom_resid[i], np.inf) < tolerance
err_max = discr.norm(rhs_resid[i], np.inf)
eoc_rec1.add_data_point(1.0 / nel_1d, err_max)
assert (err_max < tolerance)
if err_max > maxxerr:
maxxerr = err_max
message = (f"V == 0 Errors:\n{eoc_rec0}" f"V != 0 Errors:\n{eoc_rec1}")
print(message)
assert (eoc_rec0.order_estimate() >= order - 0.5
or eoc_rec0.max_error() < 1e-9)
assert (eoc_rec1.order_estimate() >= order - 0.5
or eoc_rec1.max_error() < 1e-9)
def test_basic_cfd_healthcheck(actx_factory):
"""Quick test of some health checking utilities."""
actx = actx_factory()
nel_1d = 4
dim = 2
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(
a=(1.0,) * dim, b=(2.0,) * dim, nelements_per_axis=(nel_1d,) * dim
)
order = 3
discr = EagerDGDiscretization(actx, mesh, order=order)
nodes = thaw(discr.nodes(), actx)
zeros = discr.zeros(actx)
ones = zeros + 1.0
# Let's make a very bad state (negative mass)
mass = -1*ones
velocity = 2 * nodes
mom = mass * velocity
energy = zeros + .5*np.dot(mom, mom)/mass
eos = IdealSingleGas()
cv = make_conserved(dim, mass=mass, energy=energy, momentum=mom)
pressure = eos.pressure(cv)
from mirgecom.simutil import check_range_local
assert check_range_local(discr, "vol", mass, min_value=0, max_value=np.inf)
assert check_range_local(discr, "vol", pressure, min_value=1e-6,
max_value=np.inf)
# Let's make another very bad state (nans)
mass = 1*ones
energy = zeros + 2.5
velocity = np.nan * nodes
mom = mass * velocity
cv = make_conserved(dim, mass=mass, energy=energy, momentum=mom)
pressure = eos.pressure(cv)
from mirgecom.simutil import check_naninf_local
assert check_naninf_local(discr, "vol", pressure)
# Let's make one last very bad state (inf)
mass = 1*ones
energy = np.inf * ones
velocity = 2 * nodes
mom = mass * velocity
cv = make_conserved(dim, mass=mass, energy=energy, momentum=mom)
pressure = eos.pressure(cv)
assert check_naninf_local(discr, "vol", pressure)
# What the hey, test a good one
energy = 2.5 + .5*np.dot(mom, mom)
cv = make_conserved(dim, mass=mass, energy=energy, momentum=mom)
pressure = eos.pressure(cv)
assert not check_naninf_local(discr, "vol", pressure)
assert not check_range_local(discr, "vol", pressure, min_value=0,
max_value=np.inf)
def main(snapshot_pattern="wave-eager-{step:04d}-{rank:04d}.pkl",
restart_step=None):
"""Drive the example."""
cl_ctx = cl.create_some_context()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(queue,
allocator=cl_tools.MemoryPool(
cl_tools.ImmediateAllocator(queue)))
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
num_parts = comm.Get_size()
if restart_step is None:
from meshmode.distributed import MPIMeshDistributor, get_partition_by_pymetis
mesh_dist = MPIMeshDistributor(comm)
dim = 2
nel_1d = 16
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=(nel_1d, ) *
dim)
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()
fields = None
else:
from mirgecom.restart import read_restart_data
restart_data = read_restart_data(
actx, snapshot_pattern.format(step=restart_step, rank=rank))
local_mesh = restart_data["local_mesh"]
nel_1d = restart_data["nel_1d"]
assert comm.Get_size() == restart_data["num_parts"]
order = 3
discr = EagerDGDiscretization(actx,
local_mesh,
order=order,
mpi_communicator=comm)
if local_mesh.dim == 2:
# no deep meaning here, just a fudge factor
dt = 0.7 / (nel_1d * order**2)
elif dim == 3:
# no deep meaning here, just a fudge factor
dt = 0.4 / (nel_1d * order**2)
else:
raise ValueError("don't have a stable time step guesstimate")
t_final = 3
if restart_step is None:
t = 0
istep = 0
fields = flat_obj_array(bump(actx, discr),
[discr.zeros(actx) for i in range(discr.dim)])
else:
t = restart_data["t"]
istep = restart_step
assert istep == restart_step
restart_fields = restart_data["fields"]
old_order = restart_data["order"]
if old_order != order:
old_discr = EagerDGDiscretization(actx,
local_mesh,
order=old_order,
mpi_communicator=comm)
from meshmode.discretization.connection import make_same_mesh_connection
connection = make_same_mesh_connection(
actx, discr.discr_from_dd("vol"),
old_discr.discr_from_dd("vol"))
fields = connection(restart_fields)
else:
fields = restart_fields
vis = make_visualizer(discr)
def rhs(t, w):
return wave_operator(discr, c=1, w=w)
while t < t_final:
# restart must happen at beginning of step
if istep % 100 == 0 and (
# Do not overwrite the restart file that we just read.
istep != restart_step):
from mirgecom.restart import write_restart_file
write_restart_file(actx,
restart_data={
"local_mesh": local_mesh,
"order": order,
"fields": fields,
"t": t,
"step": istep,
"nel_1d": nel_1d,
"num_parts": num_parts
},
filename=snapshot_pattern.format(step=istep,
rank=rank),
comm=comm)
if istep % 10 == 0:
print(istep, t, discr.norm(fields[0]))
vis.write_parallel_vtk_file(
comm, "fld-wave-eager-mpi-%03d-%04d.vtu" % (rank, istep), [
("u", fields[0]),
("v", fields[1:]),
])
fields = rk4_step(fields, t, dt, rhs)
t += dt
istep += 1
def test_uniform_rhs(actx_factory, nspecies, dim, order, use_overintegration):
"""Test the inviscid rhs using a trivial constant/uniform state.
This state should yield rhs = 0 to FP. The test is performed for 1, 2,
and 3 dimensions, with orders 1, 2, and 3, with and without passive species.
"""
actx = actx_factory()
tolerance = 1e-9
from pytools.convergence import EOCRecorder
eoc_rec0 = EOCRecorder()
eoc_rec1 = EOCRecorder()
# for nel_1d in [4, 8, 12]:
for nel_1d in [4, 8]:
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=(nel_1d,) * dim
)
logger.info(
f"Number of {dim}d elements: {mesh.nelements}"
)
from grudge.dof_desc import DISCR_TAG_BASE, DISCR_TAG_QUAD
from meshmode.discretization.poly_element import \
default_simplex_group_factory, QuadratureSimplexGroupFactory
discr = EagerDGDiscretization(
actx, mesh,
discr_tag_to_group_factory={
DISCR_TAG_BASE: default_simplex_group_factory(
base_dim=dim, order=order),
DISCR_TAG_QUAD: QuadratureSimplexGroupFactory(2*order + 1)
}
)
if use_overintegration:
quadrature_tag = DISCR_TAG_QUAD
else:
quadrature_tag = None
zeros = discr.zeros(actx)
ones = zeros + 1.0
mass_input = discr.zeros(actx) + 1
energy_input = discr.zeros(actx) + 2.5
mom_input = make_obj_array(
[discr.zeros(actx) for i in range(discr.dim)]
)
mass_frac_input = flat_obj_array(
[ones / ((i + 1) * 10) for i in range(nspecies)]
)
species_mass_input = mass_input * mass_frac_input
num_equations = dim + 2 + len(species_mass_input)
cv = make_conserved(
dim, mass=mass_input, energy=energy_input, momentum=mom_input,
species_mass=species_mass_input)
gas_model = GasModel(eos=IdealSingleGas())
fluid_state = make_fluid_state(cv, gas_model)
expected_rhs = make_conserved(
dim, q=make_obj_array([discr.zeros(actx)
for i in range(num_equations)])
)
boundaries = {BTAG_ALL: DummyBoundary()}
inviscid_rhs = euler_operator(discr, state=fluid_state, gas_model=gas_model,
boundaries=boundaries, time=0.0,
quadrature_tag=quadrature_tag)
rhs_resid = inviscid_rhs - expected_rhs
rho_resid = rhs_resid.mass
rhoe_resid = rhs_resid.energy
mom_resid = rhs_resid.momentum
rhoy_resid = rhs_resid.species_mass
rho_rhs = inviscid_rhs.mass
rhoe_rhs = inviscid_rhs.energy
rhov_rhs = inviscid_rhs.momentum
rhoy_rhs = inviscid_rhs.species_mass
logger.info(
f"rho_rhs = {rho_rhs}\n"
f"rhoe_rhs = {rhoe_rhs}\n"
f"rhov_rhs = {rhov_rhs}\n"
f"rhoy_rhs = {rhoy_rhs}\n"
)
def inf_norm(x):
return actx.to_numpy(discr.norm(x, np.inf))
assert inf_norm(rho_resid) < tolerance
assert inf_norm(rhoe_resid) < tolerance
for i in range(dim):
assert inf_norm(mom_resid[i]) < tolerance
for i in range(nspecies):
assert inf_norm(rhoy_resid[i]) < tolerance
err_max = inf_norm(rho_resid)
eoc_rec0.add_data_point(1.0 / nel_1d, err_max)
# set a non-zero, but uniform velocity component
for i in range(len(mom_input)):
mom_input[i] = discr.zeros(actx) + (-1.0) ** i
cv = make_conserved(
dim, mass=mass_input, energy=energy_input, momentum=mom_input,
species_mass=species_mass_input)
gas_model = GasModel(eos=IdealSingleGas())
fluid_state = make_fluid_state(cv, gas_model)
boundaries = {BTAG_ALL: DummyBoundary()}
inviscid_rhs = euler_operator(discr, state=fluid_state, gas_model=gas_model,
boundaries=boundaries, time=0.0)
rhs_resid = inviscid_rhs - expected_rhs
rho_resid = rhs_resid.mass
rhoe_resid = rhs_resid.energy
mom_resid = rhs_resid.momentum
rhoy_resid = rhs_resid.species_mass
assert inf_norm(rho_resid) < tolerance
assert inf_norm(rhoe_resid) < tolerance
for i in range(dim):
assert inf_norm(mom_resid[i]) < tolerance
for i in range(nspecies):
assert inf_norm(rhoy_resid[i]) < tolerance
err_max = inf_norm(rho_resid)
eoc_rec1.add_data_point(1.0 / nel_1d, err_max)
logger.info(
f"V == 0 Errors:\n{eoc_rec0}"
f"V != 0 Errors:\n{eoc_rec1}"
)
assert (
eoc_rec0.order_estimate() >= order - 0.5
or eoc_rec0.max_error() < 1e-9
)
assert (
eoc_rec1.order_estimate() >= order - 0.5
or eoc_rec1.max_error() < 1e-9
)
def main(use_profiling=False, use_logmgr=False, lazy_eval: bool = False):
"""Drive the example."""
cl_ctx = cl.create_some_context()
logmgr = initialize_logmgr(use_logmgr, filename="wave.sqlite", mode="wu")
if use_profiling:
if lazy_eval:
raise RuntimeError("Cannot run lazy with profiling.")
queue = cl.CommandQueue(
cl_ctx, properties=cl.command_queue_properties.PROFILING_ENABLE)
actx = PyOpenCLProfilingArrayContext(
queue,
allocator=cl_tools.MemoryPool(cl_tools.ImmediateAllocator(queue)))
else:
queue = cl.CommandQueue(cl_ctx)
if lazy_eval:
actx = PytatoPyOpenCLArrayContext(queue)
else:
actx = PyOpenCLArrayContext(
queue,
allocator=cl_tools.MemoryPool(
cl_tools.ImmediateAllocator(queue)))
dim = 2
nel_1d = 16
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=(nel_1d, ) * dim)
order = 3
discr = EagerDGDiscretization(actx, mesh, order=order)
current_cfl = 0.485
wave_speed = 1.0
from grudge.dt_utils import characteristic_lengthscales
nodal_dt = characteristic_lengthscales(actx, discr) / wave_speed
from grudge.op import nodal_min
dt = actx.to_numpy(current_cfl * nodal_min(discr, "vol", nodal_dt))[()]
print("%d elements" % mesh.nelements)
fields = flat_obj_array(bump(actx, discr),
[discr.zeros(actx) for i in range(discr.dim)])
if logmgr:
logmgr_add_cl_device_info(logmgr, queue)
logmgr_add_device_memory_usage(logmgr, queue)
logmgr.add_watches(["step.max", "t_step.max", "t_log.max"])
try:
logmgr.add_watches(
["memory_usage_python.max", "memory_usage_gpu.max"])
except KeyError:
pass
if use_profiling:
logmgr.add_watches(["multiply_time.max"])
vis_timer = IntervalTimer("t_vis", "Time spent visualizing")
logmgr.add_quantity(vis_timer)
vis = make_visualizer(discr)
def rhs(t, w):
return wave_operator(discr, c=wave_speed, w=w)
compiled_rhs = actx.compile(rhs)
t = 0
t_final = 1
istep = 0
while t < t_final:
if logmgr:
logmgr.tick_before()
fields = thaw(freeze(fields, actx), actx)
fields = rk4_step(fields, t, dt, compiled_rhs)
if istep % 10 == 0:
if use_profiling:
print(actx.tabulate_profiling_data())
print(istep, t, actx.to_numpy(discr.norm(fields[0], np.inf)))
vis.write_vtk_file("fld-wave-%04d.vtu" % istep, [
("u", fields[0]),
("v", fields[1:]),
],
overwrite=True)
t += dt
istep += 1
if logmgr:
set_dt(logmgr, dt)
logmgr.tick_after()
def main(snapshot_pattern="wave-mpi-{step:04d}-{rank:04d}.pkl", restart_step=None,
use_profiling=False, use_logmgr=False, actx_class=PyOpenCLArrayContext):
"""Drive the example."""
cl_ctx = cl.create_some_context()
queue = cl.CommandQueue(cl_ctx)
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
num_parts = comm.Get_size()
logmgr = initialize_logmgr(use_logmgr,
filename="wave-mpi.sqlite", mode="wu", mpi_comm=comm)
if use_profiling:
queue = cl.CommandQueue(cl_ctx,
properties=cl.command_queue_properties.PROFILING_ENABLE)
actx = actx_class(queue,
allocator=cl_tools.MemoryPool(cl_tools.ImmediateAllocator(queue)),
logmgr=logmgr)
else:
queue = cl.CommandQueue(cl_ctx)
actx = actx_class(queue,
allocator=cl_tools.MemoryPool(cl_tools.ImmediateAllocator(queue)))
if restart_step is None:
from meshmode.distributed import MPIMeshDistributor, get_partition_by_pymetis
mesh_dist = MPIMeshDistributor(comm)
dim = 2
nel_1d = 16
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=(nel_1d,)*dim)
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()
fields = None
else:
from mirgecom.restart import read_restart_data
restart_data = read_restart_data(
actx, snapshot_pattern.format(step=restart_step, rank=rank)
)
local_mesh = restart_data["local_mesh"]
nel_1d = restart_data["nel_1d"]
assert comm.Get_size() == restart_data["num_parts"]
order = 3
discr = EagerDGDiscretization(actx, local_mesh, order=order,
mpi_communicator=comm)
current_cfl = 0.485
wave_speed = 1.0
from grudge.dt_utils import characteristic_lengthscales
dt = current_cfl * characteristic_lengthscales(actx, discr) / wave_speed
from grudge.op import nodal_min
dt = nodal_min(discr, "vol", dt)
t_final = 1
if restart_step is None:
t = 0
istep = 0
fields = flat_obj_array(
bump(actx, discr),
[discr.zeros(actx) for i in range(discr.dim)]
)
else:
t = restart_data["t"]
istep = restart_step
assert istep == restart_step
restart_fields = restart_data["fields"]
old_order = restart_data["order"]
if old_order != order:
old_discr = EagerDGDiscretization(actx, local_mesh, order=old_order,
mpi_communicator=comm)
from meshmode.discretization.connection import make_same_mesh_connection
connection = make_same_mesh_connection(actx, discr.discr_from_dd("vol"),
old_discr.discr_from_dd("vol"))
fields = connection(restart_fields)
else:
fields = restart_fields
if logmgr:
logmgr_add_cl_device_info(logmgr, queue)
logmgr_add_device_memory_usage(logmgr, queue)
logmgr.add_watches(["step.max", "t_step.max", "t_log.max"])
try:
logmgr.add_watches(["memory_usage_python.max", "memory_usage_gpu.max"])
except KeyError:
pass
if use_profiling:
logmgr.add_watches(["multiply_time.max"])
vis_timer = IntervalTimer("t_vis", "Time spent visualizing")
logmgr.add_quantity(vis_timer)
vis = make_visualizer(discr)
def rhs(t, w):
return wave_operator(discr, c=wave_speed, w=w)
compiled_rhs = actx.compile(rhs)
while t < t_final:
if logmgr:
logmgr.tick_before()
# restart must happen at beginning of step
if istep % 100 == 0 and (
# Do not overwrite the restart file that we just read.
istep != restart_step):
from mirgecom.restart import write_restart_file
write_restart_file(
actx, restart_data={
"local_mesh": local_mesh,
"order": order,
"fields": fields,
"t": t,
"step": istep,
"nel_1d": nel_1d,
"num_parts": num_parts},
filename=snapshot_pattern.format(step=istep, rank=rank),
comm=comm
)
if istep % 10 == 0:
print(istep, t, discr.norm(fields[0]))
vis.write_parallel_vtk_file(
comm,
"fld-wave-mpi-%03d-%04d.vtu" % (rank, istep),
[
("u", fields[0]),
("v", fields[1:]),
], overwrite=True
)
fields = thaw(freeze(fields, actx), actx)
fields = rk4_step(fields, t, dt, compiled_rhs)
t += dt
istep += 1
if logmgr:
set_dt(logmgr, dt)
logmgr.tick_after()
final_soln = discr.norm(fields[0])
assert np.abs(final_soln - 0.04409852463947439) < 1e-14
def main(use_profiling=False):
"""Drive the example."""
cl_ctx = cl.create_some_context()
if use_profiling:
queue = cl.CommandQueue(
cl_ctx, properties=cl.command_queue_properties.PROFILING_ENABLE)
actx = PyOpenCLProfilingArrayContext(
queue,
allocator=cl_tools.MemoryPool(cl_tools.ImmediateAllocator(queue)))
else:
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(queue,
allocator=cl_tools.MemoryPool(
cl_tools.ImmediateAllocator(queue)))
dim = 2
nel_1d = 16
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=(nel_1d, ) * dim)
order = 3
if dim == 2:
# no deep meaning here, just a fudge factor
dt = 0.7 / (nel_1d * order**2)
elif dim == 3:
# no deep meaning here, just a fudge factor
dt = 0.4 / (nel_1d * order**2)
else:
raise ValueError("don't have a stable time step guesstimate")
print("%d elements" % mesh.nelements)
discr = EagerDGDiscretization(actx, mesh, order=order)
fields = flat_obj_array(bump(actx, discr),
[discr.zeros(actx) for i in range(discr.dim)])
vis = make_visualizer(discr)
def rhs(t, w):
return wave_operator(discr, c=1, w=w)
t = 0
t_final = 3
istep = 0
while t < t_final:
fields = rk4_step(fields, t, dt, rhs)
if istep % 10 == 0:
if use_profiling:
print(actx.tabulate_profiling_data())
print(istep, t, discr.norm(fields[0], np.inf))
vis.write_vtk_file("fld-wave-eager-%04d.vtu" % istep, [
("u", fields[0]),
("v", fields[1:]),
])
t += dt
istep += 1
def test_diffusion_compare_to_nodal_dg(actx_factory,
problem,
order,
print_err=False):
"""Compares diffusion operator to Hesthaven/Warburton Nodal-DG code."""
pytest.importorskip("oct2py")
actx = actx_factory()
p = problem
assert p.dim == 1
assert p.sym_alpha == 1.
from meshmode.interop.nodal_dg import download_nodal_dg_if_not_present
download_nodal_dg_if_not_present()
sym_diffusion_u = sym_diffusion(p.dim, p.sym_alpha, p.sym_u)
for n in [4, 8, 16, 32, 64]:
mesh = p.get_mesh(n)
from meshmode.interop.nodal_dg import NodalDGContext
with NodalDGContext("./nodal-dg/Codes1.1") as ndgctx:
ndgctx.set_mesh(mesh, order=order)
t = 1.23456789
from grudge.eager import EagerDGDiscretization
discr_mirgecom = EagerDGDiscretization(actx, mesh, order=order)
nodes_mirgecom = thaw(actx, discr_mirgecom.nodes())
def sym_eval_mirgecom(expr):
return sym.EvaluationMapper({
"x": nodes_mirgecom,
"t": t
})(expr)
u_mirgecom = sym_eval_mirgecom(p.sym_u)
diffusion_u_mirgecom = diffusion_operator(
discr_mirgecom,
quad_tag=DISCR_TAG_BASE,
alpha=discr_mirgecom.zeros(actx) + 1.,
boundaries=p.get_boundaries(discr_mirgecom, actx, t),
u=u_mirgecom)
discr_ndg = ndgctx.get_discr(actx)
nodes_ndg = thaw(actx, discr_ndg.nodes())
def sym_eval_ndg(expr):
return sym.EvaluationMapper({"x": nodes_ndg, "t": t})(expr)
u_ndg = sym_eval_ndg(p.sym_u)
ndgctx.push_dof_array("u", u_ndg)
ndgctx.octave.push("t", t)
ndgctx.octave.eval("[rhs] = HeatCRHS1D(u,t)", verbose=False)
diffusion_u_ndg = ndgctx.pull_dof_array(actx, "rhs")
def inf_norm(f):
return actx.np.linalg.norm(f, np.inf)
err = (inf_norm(diffusion_u_mirgecom - diffusion_u_ndg) /
inf_norm(diffusion_u_ndg))
if print_err:
diffusion_u_exact = sym_eval_mirgecom(sym_diffusion_u)
err_exact = (
inf_norm(diffusion_u_mirgecom - diffusion_u_exact) /
inf_norm(diffusion_u_exact))
print(err, err_exact)
assert err < 1e-9
def test_uniform_rhs(actx_factory, nspecies, dim, order):
"""Test the inviscid rhs using a trivial constant/uniform state.
This state should yield rhs = 0 to FP. The test is performed for 1, 2,
and 3 dimensions, with orders 1, 2, and 3, with and without passive species.
"""
actx = actx_factory()
tolerance = 1e-9
from pytools.convergence import EOCRecorder
eoc_rec0 = EOCRecorder()
eoc_rec1 = EOCRecorder()
# for nel_1d in [4, 8, 12]:
for nel_1d in [4, 8]:
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=(nel_1d, ) * dim)
logger.info(f"Number of {dim}d elements: {mesh.nelements}")
discr = EagerDGDiscretization(actx, mesh, order=order)
zeros = discr.zeros(actx)
ones = zeros + 1.0
mass_input = discr.zeros(actx) + 1
energy_input = discr.zeros(actx) + 2.5
mom_input = make_obj_array(
[discr.zeros(actx) for i in range(discr.dim)])
mass_frac_input = flat_obj_array(
[ones / ((i + 1) * 10) for i in range(nspecies)])
species_mass_input = mass_input * mass_frac_input
num_equations = dim + 2 + len(species_mass_input)
cv = make_conserved(dim,
mass=mass_input,
energy=energy_input,
momentum=mom_input,
species_mass=species_mass_input)
expected_rhs = make_conserved(
dim,
q=make_obj_array([discr.zeros(actx)
for i in range(num_equations)]))
boundaries = {BTAG_ALL: DummyBoundary()}
inviscid_rhs = euler_operator(discr,
eos=IdealSingleGas(),
boundaries=boundaries,
cv=cv,
time=0.0)
rhs_resid = inviscid_rhs - expected_rhs
rho_resid = rhs_resid.mass
rhoe_resid = rhs_resid.energy
mom_resid = rhs_resid.momentum
rhoy_resid = rhs_resid.species_mass
rho_rhs = inviscid_rhs.mass
rhoe_rhs = inviscid_rhs.energy
rhov_rhs = inviscid_rhs.momentum
rhoy_rhs = inviscid_rhs.species_mass
logger.info(f"rho_rhs = {rho_rhs}\n"
f"rhoe_rhs = {rhoe_rhs}\n"
f"rhov_rhs = {rhov_rhs}\n"
f"rhoy_rhs = {rhoy_rhs}\n")
assert discr.norm(rho_resid, np.inf) < tolerance
assert discr.norm(rhoe_resid, np.inf) < tolerance
for i in range(dim):
assert discr.norm(mom_resid[i], np.inf) < tolerance
for i in range(nspecies):
assert discr.norm(rhoy_resid[i], np.inf) < tolerance
err_max = discr.norm(rho_resid, np.inf)
eoc_rec0.add_data_point(1.0 / nel_1d, err_max)
# set a non-zero, but uniform velocity component
for i in range(len(mom_input)):
mom_input[i] = discr.zeros(actx) + (-1.0)**i
cv = make_conserved(dim,
mass=mass_input,
energy=energy_input,
momentum=mom_input,
species_mass=species_mass_input)
boundaries = {BTAG_ALL: DummyBoundary()}
inviscid_rhs = euler_operator(discr,
eos=IdealSingleGas(),
boundaries=boundaries,
cv=cv,
time=0.0)
rhs_resid = inviscid_rhs - expected_rhs
rho_resid = rhs_resid.mass
rhoe_resid = rhs_resid.energy
mom_resid = rhs_resid.momentum
rhoy_resid = rhs_resid.species_mass
assert discr.norm(rho_resid, np.inf) < tolerance
assert discr.norm(rhoe_resid, np.inf) < tolerance
for i in range(dim):
assert discr.norm(mom_resid[i], np.inf) < tolerance
for i in range(nspecies):
assert discr.norm(rhoy_resid[i], np.inf) < tolerance
err_max = discr.norm(rho_resid, np.inf)
eoc_rec1.add_data_point(1.0 / nel_1d, err_max)
logger.info(f"V == 0 Errors:\n{eoc_rec0}" f"V != 0 Errors:\n{eoc_rec1}")
assert (eoc_rec0.order_estimate() >= order - 0.5
or eoc_rec0.max_error() < 1e-9)
assert (eoc_rec1.order_estimate() >= order - 0.5
or eoc_rec1.max_error() < 1e-9)
def main(ctx_factory=cl.create_some_context,
use_logmgr=True,
use_leap=False,
use_overintegration=False,
use_profiling=False,
casename=None,
rst_filename=None,
actx_class=PyOpenCLArrayContext,
log_dependent=True):
"""Drive example."""
cl_ctx = ctx_factory()
if casename is None:
casename = "mirgecom"
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
nproc = comm.Get_size()
from mirgecom.simutil import global_reduce as _global_reduce
global_reduce = partial(_global_reduce, comm=comm)
logmgr = initialize_logmgr(use_logmgr,
filename=f"{casename}.sqlite",
mode="wu",
mpi_comm=comm)
if use_profiling:
queue = cl.CommandQueue(
cl_ctx, properties=cl.command_queue_properties.PROFILING_ENABLE)
else:
queue = cl.CommandQueue(cl_ctx)
actx = actx_class(queue,
allocator=cl_tools.MemoryPool(
cl_tools.ImmediateAllocator(queue)))
# Some discretization parameters
dim = 2
nel_1d = 8
order = 1
# {{{ Time stepping control
# This example runs only 3 steps by default (to keep CI ~short)
# With the mixture defined below, equilibrium is achieved at ~40ms
# To run to equilibrium, set t_final >= 40ms.
# Time stepper selection
if use_leap:
from leap.rk import RK4MethodBuilder
timestepper = RK4MethodBuilder("state")
else:
timestepper = rk4_step
# Time loop control parameters
current_step = 0
t_final = 1e-8
current_cfl = 1.0
current_dt = 1e-9
current_t = 0
constant_cfl = False
# i.o frequencies
nstatus = 1
nviz = 5
nhealth = 1
nrestart = 5
# }}} Time stepping control
debug = False
rst_path = "restart_data/"
rst_pattern = (rst_path + "{cname}-{step:04d}-{rank:04d}.pkl")
if rst_filename: # read the grid from restart data
rst_filename = f"{rst_filename}-{rank:04d}.pkl"
from mirgecom.restart import read_restart_data
restart_data = read_restart_data(actx, rst_filename)
local_mesh = restart_data["local_mesh"]
local_nelements = local_mesh.nelements
global_nelements = restart_data["global_nelements"]
assert restart_data["num_parts"] == nproc
rst_time = restart_data["t"]
rst_step = restart_data["step"]
rst_order = restart_data["order"]
else: # generate the grid from scratch
from meshmode.mesh.generation import generate_regular_rect_mesh
box_ll = -0.005
box_ur = 0.005
generate_mesh = partial(generate_regular_rect_mesh,
a=(box_ll, ) * dim,
b=(box_ur, ) * dim,
nelements_per_axis=(nel_1d, ) * dim)
local_mesh, global_nelements = generate_and_distribute_mesh(
comm, generate_mesh)
local_nelements = local_mesh.nelements
from grudge.dof_desc import DISCR_TAG_BASE, DISCR_TAG_QUAD
from meshmode.discretization.poly_element import \
default_simplex_group_factory, QuadratureSimplexGroupFactory
discr = EagerDGDiscretization(
actx,
local_mesh,
discr_tag_to_group_factory={
DISCR_TAG_BASE:
default_simplex_group_factory(base_dim=local_mesh.dim,
order=order),
DISCR_TAG_QUAD:
QuadratureSimplexGroupFactory(2 * order + 1)
},
mpi_communicator=comm)
nodes = thaw(discr.nodes(), actx)
ones = discr.zeros(actx) + 1.0
if use_overintegration:
quadrature_tag = DISCR_TAG_QUAD
else:
quadrature_tag = None
ones = discr.zeros(actx) + 1.0
vis_timer = None
if logmgr:
logmgr_add_cl_device_info(logmgr, queue)
logmgr_add_device_memory_usage(logmgr, queue)
vis_timer = IntervalTimer("t_vis", "Time spent visualizing")
logmgr.add_quantity(vis_timer)
logmgr.add_watches([("step.max", "step = {value}, "),
("t_sim.max", "sim time: {value:1.6e} s\n"),
("t_step.max",
"------- step walltime: {value:6g} s, "),
("t_log.max", "log walltime: {value:6g} s")])
if log_dependent:
logmgr_add_many_discretization_quantities(
logmgr, discr, dim, extract_vars_for_logging,
units_for_logging)
logmgr.add_watches([
("min_pressure",
"\n------- P (min, max) (Pa) = ({value:1.9e}, "),
("max_pressure", "{value:1.9e})\n"),
("min_temperature",
"------- T (min, max) (K) = ({value:7g}, "),
("max_temperature", "{value:7g})\n")
])
# {{{ Set up initial state using Cantera
# Use Cantera for initialization
# -- Pick up a CTI for the thermochemistry config
# --- Note: Users may add their own CTI file by dropping it into
# --- mirgecom/mechanisms alongside the other CTI files.
from mirgecom.mechanisms import get_mechanism_cti
mech_cti = get_mechanism_cti("uiuc")
cantera_soln = cantera.Solution(phase_id="gas", source=mech_cti)
nspecies = cantera_soln.n_species
# Initial temperature, pressure, and mixutre mole fractions are needed to
# set up the initial state in Cantera.
temperature_seed = 1500.0 # Initial temperature hot enough to burn
# Parameters for calculating the amounts of fuel, oxidizer, and inert species
equiv_ratio = 1.0
ox_di_ratio = 0.21
stoich_ratio = 3.0
# Grab the array indices for the specific species, ethylene, oxygen, and nitrogen
i_fu = cantera_soln.species_index("C2H4")
i_ox = cantera_soln.species_index("O2")
i_di = cantera_soln.species_index("N2")
x = np.zeros(nspecies)
# Set the species mole fractions according to our desired fuel/air mixture
x[i_fu] = (ox_di_ratio * equiv_ratio) / (stoich_ratio +
ox_di_ratio * equiv_ratio)
x[i_ox] = stoich_ratio * x[i_fu] / equiv_ratio
x[i_di] = (1.0 - ox_di_ratio) * x[i_ox] / ox_di_ratio
# Uncomment next line to make pylint fail when it can't find cantera.one_atm
one_atm = cantera.one_atm # pylint: disable=no-member
# one_atm = 101325.0
# Let the user know about how Cantera is being initilized
print(f"Input state (T,P,X) = ({temperature_seed}, {one_atm}, {x}")
# Set Cantera internal gas temperature, pressure, and mole fractios
cantera_soln.TPX = temperature_seed, one_atm, x
# Pull temperature, total density, mass fractions, and pressure from Cantera
# We need total density, and mass fractions to initialize the fluid/gas state.
can_t, can_rho, can_y = cantera_soln.TDY
can_p = cantera_soln.P
# *can_t*, *can_p* should not differ (significantly) from user's initial data,
# but we want to ensure that we use exactly the same starting point as Cantera,
# so we use Cantera's version of these data.
# }}}
# {{{ Create Pyrometheus thermochemistry object & EOS
# Create a Pyrometheus EOS with the Cantera soln. Pyrometheus uses Cantera and
# generates a set of methods to calculate chemothermomechanical properties and
# states for this particular mechanism.
from mirgecom.thermochemistry import make_pyrometheus_mechanism_class
pyro_mechanism = make_pyrometheus_mechanism_class(cantera_soln)(actx.np)
eos = PyrometheusMixture(pyro_mechanism,
temperature_guess=temperature_seed)
gas_model = GasModel(eos=eos)
from pytools.obj_array import make_obj_array
def get_temperature_update(cv, temperature):
y = cv.species_mass_fractions
e = gas_model.eos.internal_energy(cv) / cv.mass
return pyro_mechanism.get_temperature_update_energy(e, temperature, y)
from mirgecom.gas_model import make_fluid_state
def get_fluid_state(cv, tseed):
return make_fluid_state(cv=cv,
gas_model=gas_model,
temperature_seed=tseed)
compute_temperature_update = actx.compile(get_temperature_update)
construct_fluid_state = actx.compile(get_fluid_state)
# }}}
# {{{ MIRGE-Com state initialization
# Initialize the fluid/gas state with Cantera-consistent data:
# (density, pressure, temperature, mass_fractions)
print(f"Cantera state (rho,T,P,Y) = ({can_rho}, {can_t}, {can_p}, {can_y}")
velocity = np.zeros(shape=(dim, ))
initializer = MixtureInitializer(dim=dim,
nspecies=nspecies,
pressure=can_p,
temperature=can_t,
massfractions=can_y,
velocity=velocity)
my_boundary = AdiabaticSlipBoundary()
boundaries = {BTAG_ALL: my_boundary}
if rst_filename:
current_step = rst_step
current_t = rst_time
if logmgr:
from mirgecom.logging_quantities import logmgr_set_time
logmgr_set_time(logmgr, current_step, current_t)
if order == rst_order:
current_cv = restart_data["cv"]
temperature_seed = restart_data["temperature_seed"]
else:
rst_cv = restart_data["cv"]
old_discr = EagerDGDiscretization(actx,
local_mesh,
order=rst_order,
mpi_communicator=comm)
from meshmode.discretization.connection import make_same_mesh_connection
connection = make_same_mesh_connection(
actx, discr.discr_from_dd("vol"),
old_discr.discr_from_dd("vol"))
current_cv = connection(rst_cv)
temperature_seed = connection(restart_data["temperature_seed"])
else:
# Set the current state from time 0
current_cv = initializer(eos=gas_model.eos, x_vec=nodes)
temperature_seed = temperature_seed * ones
# The temperature_seed going into this function is:
# - At time 0: the initial temperature input data (maybe from Cantera)
# - On restart: the restarted temperature seed from restart file (saving
# the *seed* allows restarts to be deterministic
current_fluid_state = construct_fluid_state(current_cv, temperature_seed)
current_dv = current_fluid_state.dv
temperature_seed = current_dv.temperature
# Inspection at physics debugging time
if debug:
print("Initial MIRGE-Com state:")
print(f"Initial DV pressure: {current_fluid_state.pressure}")
print(f"Initial DV temperature: {current_fluid_state.temperature}")
# }}}
visualizer = make_visualizer(discr)
initname = initializer.__class__.__name__
eosname = gas_model.eos.__class__.__name__
init_message = make_init_message(dim=dim,
order=order,
nelements=local_nelements,
global_nelements=global_nelements,
dt=current_dt,
t_final=t_final,
nstatus=nstatus,
nviz=nviz,
cfl=current_cfl,
constant_cfl=constant_cfl,
initname=initname,
eosname=eosname,
casename=casename)
# Cantera equilibrate calculates the expected end state @ chemical equilibrium
# i.e. the expected state after all reactions
cantera_soln.equilibrate("UV")
eq_temperature, eq_density, eq_mass_fractions = cantera_soln.TDY
eq_pressure = cantera_soln.P
# Report the expected final state to the user
if rank == 0:
logger.info(init_message)
logger.info(f"Expected equilibrium state:"
f" {eq_pressure=}, {eq_temperature=},"
f" {eq_density=}, {eq_mass_fractions=}")
def my_write_status(dt, cfl, dv=None):
status_msg = f"------ {dt=}" if constant_cfl else f"----- {cfl=}"
if ((dv is not None) and (not log_dependent)):
temp = dv.temperature
press = dv.pressure
from grudge.op import nodal_min_loc, nodal_max_loc
tmin = allsync(actx.to_numpy(nodal_min_loc(discr, "vol", temp)),
comm=comm,
op=MPI.MIN)
tmax = allsync(actx.to_numpy(nodal_max_loc(discr, "vol", temp)),
comm=comm,
op=MPI.MAX)
pmin = allsync(actx.to_numpy(nodal_min_loc(discr, "vol", press)),
comm=comm,
op=MPI.MIN)
pmax = allsync(actx.to_numpy(nodal_max_loc(discr, "vol", press)),
comm=comm,
op=MPI.MAX)
dv_status_msg = f"\nP({pmin}, {pmax}), T({tmin}, {tmax})"
status_msg = status_msg + dv_status_msg
if rank == 0:
logger.info(status_msg)
def my_write_viz(step, t, dt, state, ts_field, dv, production_rates, cfl):
viz_fields = [("cv", state), ("dv", dv),
("production_rates", production_rates),
("dt" if constant_cfl else "cfl", ts_field)]
write_visfile(discr,
viz_fields,
visualizer,
vizname=casename,
step=step,
t=t,
overwrite=True,
vis_timer=vis_timer)
def my_write_restart(step, t, state, temperature_seed):
rst_fname = rst_pattern.format(cname=casename, step=step, rank=rank)
if rst_fname == rst_filename:
if rank == 0:
logger.info("Skipping overwrite of restart file.")
else:
rst_data = {
"local_mesh": local_mesh,
"cv": state.cv,
"temperature_seed": temperature_seed,
"t": t,
"step": step,
"order": order,
"global_nelements": global_nelements,
"num_parts": nproc
}
from mirgecom.restart import write_restart_file
write_restart_file(actx, rst_data, rst_fname, comm)
def my_health_check(cv, dv):
import grudge.op as op
health_error = False
pressure = dv.pressure
temperature = dv.temperature
from mirgecom.simutil import check_naninf_local, check_range_local
if check_naninf_local(discr, "vol", pressure):
health_error = True
logger.info(f"{rank=}: Invalid pressure data found.")
if check_range_local(discr, "vol", pressure, 1e5, 2.6e5):
health_error = True
logger.info(f"{rank=}: Pressure range violation.")
if check_naninf_local(discr, "vol", temperature):
health_error = True
logger.info(f"{rank=}: Invalid temperature data found.")
if check_range_local(discr, "vol", temperature, 1.498e3, 1.6e3):
health_error = True
logger.info(f"{rank=}: Temperature range violation.")
# This check is the temperature convergence check
# The current *temperature* is what Pyrometheus gets
# after a fixed number of Newton iterations, *n_iter*.
# Calling `compute_temperature` here with *temperature*
# input as the guess returns the calculated gas temperature after
# yet another *n_iter*.
# The difference between those two temperatures is the
# temperature residual, which can be used as an indicator of
# convergence in Pyrometheus `get_temperature`.
# Note: The local max jig below works around a very long compile
# in lazy mode.
temp_resid = compute_temperature_update(cv, temperature) / temperature
temp_err = (actx.to_numpy(op.nodal_max_loc(discr, "vol", temp_resid)))
if temp_err > 1e-8:
health_error = True
logger.info(
f"{rank=}: Temperature is not converged {temp_resid=}.")
return health_error
from mirgecom.inviscid import get_inviscid_timestep
def get_dt(state):
return get_inviscid_timestep(discr, state=state)
compute_dt = actx.compile(get_dt)
from mirgecom.inviscid import get_inviscid_cfl
def get_cfl(state, dt):
return get_inviscid_cfl(discr, dt=dt, state=state)
compute_cfl = actx.compile(get_cfl)
def get_production_rates(cv, temperature):
return eos.get_production_rates(cv, temperature)
compute_production_rates = actx.compile(get_production_rates)
def my_get_timestep(t, dt, state):
# richer interface to calculate {dt,cfl} returns node-local estimates
t_remaining = max(0, t_final - t)
if constant_cfl:
ts_field = current_cfl * compute_dt(state)
from grudge.op import nodal_min_loc
dt = allsync(actx.to_numpy(nodal_min_loc(discr, "vol", ts_field)),
comm=comm,
op=MPI.MIN)
cfl = current_cfl
else:
ts_field = compute_cfl(state, current_dt)
from grudge.op import nodal_max_loc
cfl = allsync(actx.to_numpy(nodal_max_loc(discr, "vol", ts_field)),
comm=comm,
op=MPI.MAX)
return ts_field, cfl, min(t_remaining, dt)
def my_pre_step(step, t, dt, state):
cv, tseed = state
fluid_state = construct_fluid_state(cv, tseed)
dv = fluid_state.dv
try:
if logmgr:
logmgr.tick_before()
from mirgecom.simutil import check_step
do_viz = check_step(step=step, interval=nviz)
do_restart = check_step(step=step, interval=nrestart)
do_health = check_step(step=step, interval=nhealth)
do_status = check_step(step=step, interval=nstatus)
if do_health:
health_errors = global_reduce(my_health_check(cv, dv),
op="lor")
if health_errors:
if rank == 0:
logger.info("Fluid solution failed health check.")
raise MyRuntimeError("Failed simulation health check.")
ts_field, cfl, dt = my_get_timestep(t=t, dt=dt, state=fluid_state)
if do_status:
my_write_status(dt=dt, cfl=cfl, dv=dv)
if do_restart:
my_write_restart(step=step,
t=t,
state=fluid_state,
temperature_seed=tseed)
if do_viz:
production_rates = compute_production_rates(
fluid_state.cv, fluid_state.temperature)
my_write_viz(step=step,
t=t,
dt=dt,
state=cv,
dv=dv,
production_rates=production_rates,
ts_field=ts_field,
cfl=cfl)
except MyRuntimeError:
if rank == 0:
logger.info("Errors detected; attempting graceful exit.")
# my_write_viz(step=step, t=t, dt=dt, state=cv)
# my_write_restart(step=step, t=t, state=fluid_state)
raise
return state, dt
def my_post_step(step, t, dt, state):
cv, tseed = state
fluid_state = construct_fluid_state(cv, tseed)
# Logmgr needs to know about EOS, dt, dim?
# imo this is a design/scope flaw
if logmgr:
set_dt(logmgr, dt)
set_sim_state(logmgr, dim, cv, gas_model.eos)
logmgr.tick_after()
return make_obj_array([cv, fluid_state.temperature]), dt
def my_rhs(t, state):
cv, tseed = state
from mirgecom.gas_model import make_fluid_state
fluid_state = make_fluid_state(cv=cv,
gas_model=gas_model,
temperature_seed=tseed)
return make_obj_array([
euler_operator(discr,
state=fluid_state,
time=t,
boundaries=boundaries,
gas_model=gas_model,
quadrature_tag=quadrature_tag) +
eos.get_species_source_terms(cv, fluid_state.temperature),
0 * tseed
])
current_dt = get_sim_timestep(discr, current_fluid_state, current_t,
current_dt, current_cfl, t_final,
constant_cfl)
current_step, current_t, current_state = \
advance_state(rhs=my_rhs, timestepper=timestepper,
pre_step_callback=my_pre_step,
post_step_callback=my_post_step, dt=current_dt,
state=make_obj_array([current_cv, temperature_seed]),
t=current_t, t_final=t_final)
# Dump the final data
if rank == 0:
logger.info("Checkpointing final state ...")
final_cv, tseed = current_state
final_fluid_state = construct_fluid_state(final_cv, tseed)
final_dv = final_fluid_state.dv
final_dm = compute_production_rates(final_cv, final_dv.temperature)
ts_field, cfl, dt = my_get_timestep(t=current_t,
dt=current_dt,
state=final_fluid_state)
my_write_viz(step=current_step,
t=current_t,
dt=dt,
state=final_cv,
dv=final_dv,
production_rates=final_dm,
ts_field=ts_field,
cfl=cfl)
my_write_status(dt=dt, cfl=cfl, dv=final_dv)
my_write_restart(step=current_step,
t=current_t,
state=final_fluid_state,
temperature_seed=tseed)
if logmgr:
logmgr.close()
elif use_profiling:
print(actx.tabulate_profiling_data())
finish_tol = 1e-16
assert np.abs(current_t - t_final) < finish_tol
def main():
cl_ctx = cl.create_some_context()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(queue)
dim = 2
nel_1d = 16
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(-0.5, ) * dim,
b=(0.5, ) * dim,
n=(nel_1d, ) * dim)
order = 3
if dim == 2:
# no deep meaning here, just a fudge factor
dt = 0.75 / (nel_1d * order**2)
elif dim == 3:
# no deep meaning here, just a fudge factor
dt = 0.45 / (nel_1d * order**2)
else:
raise ValueError("don't have a stable time step guesstimate")
print("%d elements" % mesh.nelements)
from meshmode.discretization.poly_element import \
QuadratureSimplexGroupFactory, \
PolynomialWarpAndBlendGroupFactory
discr = EagerDGDiscretization(
actx,
mesh,
quad_tag_to_group_factory={
QTAG_NONE: PolynomialWarpAndBlendGroupFactory(order),
"vel_prod": QuadratureSimplexGroupFactory(3 * order),
})
# bounded above by 1
c = 0.2 + 0.8 * bump(actx, discr, center=np.zeros(3), width=0.5)
fields = flat_obj_array(bump(
actx,
discr,
), [discr.zeros(actx) for i in range(discr.dim)])
vis = make_visualizer(discr, order + 3 if dim == 2 else order)
def rhs(t, w):
return wave_operator(discr, c=c, w=w)
t = 0
t_final = 3
istep = 0
while t < t_final:
fields = rk4_step(fields, t, dt, rhs)
if istep % 10 == 0:
print(istep, t, discr.norm(fields[0]))
vis.write_vtk_file("fld-wave-eager-var-velocity-%04d.vtu" % istep,
[
("c", c),
("u", fields[0]),
("v", fields[1:]),
])
t += dt
istep += 1
def test_facial_flux(actx_factory, order, dim):
"""Check the flux across element faces by
prescribing states (q) with known fluxes. Only
uniform states are tested currently - ensuring
that the Lax-Friedrichs flux terms which are
proportional to jumps in state data vanish.
Since the returned fluxes use state data which
has been interpolated to-and-from the element
faces, this test is grid-dependent.
"""
actx = actx_factory()
tolerance = 1e-14
p0 = 1.0
from meshmode.mesh.generation import generate_regular_rect_mesh
from pytools.convergence import EOCRecorder
eoc_rec0 = EOCRecorder()
eoc_rec1 = EOCRecorder()
for nel_1d in [4, 8, 12]:
mesh = generate_regular_rect_mesh(a=(-0.5, ) * dim,
b=(0.5, ) * dim,
n=(nel_1d, ) * dim)
logger.info(f"Number of elements: {mesh.nelements}")
discr = EagerDGDiscretization(actx, mesh, order=order)
mass_input = discr.zeros(actx) + 1.0
energy_input = discr.zeros(actx) + 2.5
mom_input = flat_obj_array(
[discr.zeros(actx) for i in range(discr.dim)])
fields = join_conserved(dim,
mass=mass_input,
energy=energy_input,
momentum=mom_input)
from mirgecom.euler import _facial_flux
interior_face_flux = _facial_flux(discr,
eos=IdealSingleGas(),
q_tpair=interior_trace_pair(
discr, fields))
from functools import partial
fnorm = partial(discr.norm, p=np.inf, dd="all_faces")
iff_split = split_conserved(dim, interior_face_flux)
assert fnorm(iff_split.mass) < tolerance
assert fnorm(iff_split.energy) < tolerance
# The expected pressure 1.0 (by design). And the flux diagonal is
# [rhov_x*v_x + p] (etc) since we have zero velocities it's just p.
#
# The off-diagonals are zero. We get a {ndim}-vector for each
# dimension, the flux for the x-component of momentum (for example) is:
# f_momx = < 1.0, 0 , 0> , then we return f_momx .dot. normal, which
# can introduce negative values.
#
# (Explanation courtesy of Mike Campbell,
# https://github.com/illinois-ceesd/mirgecom/pull/44#discussion_r463304292)
momerr = fnorm(iff_split.momentum) - p0
assert momerr < tolerance
eoc_rec0.add_data_point(1.0 / nel_1d, momerr)
# Check the boundary facial fluxes as called on a boundary
dir_mass = discr.project("vol", BTAG_ALL, mass_input)
dir_e = discr.project("vol", BTAG_ALL, energy_input)
dir_mom = discr.project("vol", BTAG_ALL, mom_input)
dir_bval = join_conserved(dim,
mass=dir_mass,
energy=dir_e,
momentum=dir_mom)
dir_bc = join_conserved(dim,
mass=dir_mass,
energy=dir_e,
momentum=dir_mom)
boundary_flux = _facial_flux(discr,
eos=IdealSingleGas(),
q_tpair=TracePair(BTAG_ALL,
interior=dir_bval,
exterior=dir_bc))
bf_split = split_conserved(dim, boundary_flux)
assert fnorm(bf_split.mass) < tolerance
assert fnorm(bf_split.energy) < tolerance
momerr = fnorm(bf_split.momentum) - p0
assert momerr < tolerance
eoc_rec1.add_data_point(1.0 / nel_1d, momerr)
message = (f"standalone Errors:\n{eoc_rec0}"
f"boundary Errors:\n{eoc_rec1}")
logger.info(message)
assert (eoc_rec0.order_estimate() >= order - 0.5
or eoc_rec0.max_error() < 1e-9)
assert (eoc_rec1.order_estimate() >= order - 0.5
or eoc_rec1.max_error() < 1e-9)
def test_pyrometheus_eos(ctx_factory, mechname, dim, y0, vel):
"""Test PyrometheusMixture EOS for all available mechanisms.
Tests that the PyrometheusMixture EOS gets the same thermo properties
(p, T, e) as the Pyrometheus-native mechanism code.
"""
cl_ctx = ctx_factory()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(queue)
nel_1d = 4
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=(nel_1d, ) * dim)
order = 4
logger.info(f"Number of elements {mesh.nelements}")
discr = EagerDGDiscretization(actx, mesh, order=order)
nodes = thaw(actx, discr.nodes())
# Pyrometheus initialization
mech_cti = get_mechanism_cti(mechname)
sol = cantera.Solution(phase_id="gas", source=mech_cti)
prometheus_mechanism = pyro.get_thermochem_class(sol)(actx.np)
nspecies = prometheus_mechanism.num_species
print(f"PrometheusMixture::Mechanism = {mechname}")
print(f"PrometheusMixture::NumSpecies = {nspecies}")
press0 = 101500.0
temp0 = 300.0
y0s = np.zeros(shape=(nspecies, ))
for i in range(1, nspecies):
y0s[i] = y0 / (10.0**i)
y0s[0] = 1.0 - np.sum(y0s[1:])
velocity = vel * np.ones(shape=(dim, ))
for fac in range(1, 11):
tempin = fac * temp0
pressin = fac * press0
print(f"Testing {mechname}(t,P) = ({tempin}, {pressin})")
ones = discr.zeros(actx) + 1.0
tin = tempin * ones
pin = pressin * ones
yin = y0s * ones
tguess = 300.0
pyro_rho = prometheus_mechanism.get_density(pin, tin, yin)
pyro_e = prometheus_mechanism.get_mixture_internal_energy_mass(
tin, yin)
pyro_t = prometheus_mechanism.get_temperature(pyro_e, tguess, yin,
True)
pyro_p = prometheus_mechanism.get_pressure(pyro_rho, pyro_t, yin)
print(f"prom(rho, y, p, t, e) = ({pyro_rho}, {y0s}, "
f"{pyro_p}, {pyro_t}, {pyro_e})")
eos = PyrometheusMixture(prometheus_mechanism)
initializer = MixtureInitializer(dim=dim,
nspecies=nspecies,
pressure=pyro_p,
temperature=pyro_t,
massfractions=y0s,
velocity=velocity)
cv = initializer(eos=eos, t=0, x_vec=nodes)
p = eos.pressure(cv)
temperature = eos.temperature(cv)
internal_energy = eos.get_internal_energy(tin, yin)
y = eos.species_fractions(cv)
print(f"pyro_y = {y}")
print(f"pyro_eos.p = {p}")
print(f"pyro_eos.temp = {temperature}")
print(f"pyro_eos.e = {internal_energy}")
tol = 1e-14
assert discr.norm((cv.mass - pyro_rho) / pyro_rho, np.inf) < tol
assert discr.norm((temperature - pyro_t) / pyro_t, np.inf) < tol
assert discr.norm((internal_energy - pyro_e) / pyro_e, np.inf) < tol
assert discr.norm((p - pyro_p) / pyro_p, np.inf) < tol
def main():
cl_ctx = cl.create_some_context()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(queue,
allocator=cl_tools.MemoryPool(
cl_tools.ImmediateAllocator(queue)))
comm = MPI.COMM_WORLD
num_parts = comm.Get_size()
from meshmode.distributed import MPIMeshDistributor, get_partition_by_pymetis
mesh_dist = MPIMeshDistributor(comm)
dim = 2
nel_1d = 16
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=(nel_1d, ) * dim)
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()
order = 3
discr = EagerDGDiscretization(actx,
local_mesh,
order=order,
mpi_communicator=comm)
if dim == 2:
# no deep meaning here, just a fudge factor
dt = 0.75 / (nel_1d * order**2)
elif dim == 3:
# no deep meaning here, just a fudge factor
dt = 0.45 / (nel_1d * order**2)
else:
raise ValueError("don't have a stable time step guesstimate")
fields = flat_obj_array(bump(actx, discr),
[discr.zeros(actx) for i in range(discr.dim)])
vis = make_visualizer(discr, order + 3 if dim == 2 else order)
def rhs(t, w):
return wave_operator(discr, c=1, w=w)
rank = comm.Get_rank()
t = 0
t_final = 3
istep = 0
while t < t_final:
fields = rk4_step(fields, t, dt, rhs)
if istep % 10 == 0:
print(istep, t, discr.norm(fields[0]))
vis.write_vtk_file(
"fld-wave-eager-mpi-%03d-%04d.vtu" % (rank, istep), [
("u", fields[0]),
("v", fields[1:]),
])
t += dt
istep += 1
def test_pyrometheus_kinetics(ctx_factory, mechname, rate_tol, y0):
"""Test known pyrometheus reaction mechanisms.
This test reproduces a pyrometheus-native test in the MIRGE context.
Tests that the Pyrometheus mechanism code gets the same chemical properties
and reaction rates as the corresponding mechanism in Cantera. The reactions
are integrated in time and verified against a homogeneous reactor in
Cantera.
"""
cl_ctx = ctx_factory()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(queue)
dim = 1
nel_1d = 4
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=(nel_1d, ) * dim)
order = 4
logger.info(f"Number of elements {mesh.nelements}")
discr = EagerDGDiscretization(actx, mesh, order=order)
ones = discr.zeros(actx) + 1.0
# Pyrometheus initialization
mech_cti = get_mechanism_cti(mechname)
cantera_soln = cantera.Solution(phase_id="gas", source=mech_cti)
pyro_obj = pyro.get_thermochem_class(cantera_soln)(actx.np)
nspecies = pyro_obj.num_species
print(f"PrometheusMixture::NumSpecies = {nspecies}")
tempin = 1500.0
pressin = cantera.one_atm
print(f"Testing (t,P) = ({tempin}, {pressin})")
# Homogeneous reactor to get test data
equiv_ratio = 1.0
ox_di_ratio = 0.21
stoich_ratio = 0.5
i_fu = cantera_soln.species_index("H2")
i_ox = cantera_soln.species_index("O2")
i_di = cantera_soln.species_index("N2")
x = np.zeros(shape=(nspecies, ))
x[i_fu] = (ox_di_ratio * equiv_ratio) / (stoich_ratio +
ox_di_ratio * equiv_ratio)
x[i_ox] = stoich_ratio * x[i_fu] / equiv_ratio
x[i_di] = (1.0 - ox_di_ratio) * x[i_ox] / ox_di_ratio
cantera_soln.TPX = tempin, pressin, x
# cantera_soln.equilibrate("UV")
can_t, can_rho, can_y = cantera_soln.TDY
# can_p = cantera_soln.P
reactor = cantera.IdealGasConstPressureReactor(cantera_soln)
sim = cantera.ReactorNet([reactor])
time = 0.0
for _ in range(50):
time += 1.0e-6
sim.advance(time)
# Cantera kinetics
can_r = reactor.kinetics.net_rates_of_progress
can_omega = reactor.kinetics.net_production_rates
# Get state from Cantera
can_t = reactor.T
can_rho = reactor.density
can_y = reactor.Y
print(f"can_y = {can_y}")
tin = can_t * ones
rhoin = can_rho * ones
yin = can_y * ones
# Prometheus kinetics
pyro_c = pyro_obj.get_concentrations(rhoin, yin)
print(f"pyro_conc = {pyro_c}")
pyro_r = pyro_obj.get_net_rates_of_progress(tin, pyro_c)
pyro_omega = pyro_obj.get_net_production_rates(rhoin, tin, yin)
# Print
print(f"can_r = {can_r}")
print(f"pyro_r = {pyro_r}")
abs_diff = discr.norm(pyro_r - can_r, np.inf)
if abs_diff > 1e-14:
min_r = (np.abs(can_r)).min()
if min_r > 0:
assert discr.norm((pyro_r - can_r) / can_r, np.inf) < rate_tol
else:
assert discr.norm(pyro_r, np.inf) < rate_tol
print(f"can_omega = {can_omega}")
print(f"pyro_omega = {pyro_omega}")
for i, omega in enumerate(can_omega):
omin = np.abs(omega).min()
if omin > 1e-12:
assert discr.norm(
(pyro_omega[i] - omega) / omega, np.inf) < 1e-8
else:
assert discr.norm(pyro_omega[i], np.inf) < 1e-12
def main(ctx_factory=cl.create_some_context, use_logmgr=True,
use_leap=False, use_profiling=False, casename=None,
rst_filename=None, actx_class=PyOpenCLArrayContext):
"""Run the example."""
cl_ctx = cl.create_some_context()
queue = cl.CommandQueue(cl_ctx)
from mpi4py import MPI
comm = MPI.COMM_WORLD
num_parts = comm.Get_size()
logmgr = initialize_logmgr(use_logmgr,
filename="heat-source.sqlite", mode="wu", mpi_comm=comm)
if use_profiling:
queue = cl.CommandQueue(
cl_ctx, properties=cl.command_queue_properties.PROFILING_ENABLE)
else:
queue = cl.CommandQueue(cl_ctx)
actx = actx_class(
queue,
allocator=cl_tools.MemoryPool(cl_tools.ImmediateAllocator(queue)))
from meshmode.distributed import MPIMeshDistributor, get_partition_by_pymetis
mesh_dist = MPIMeshDistributor(comm)
dim = 2
nel_1d = 16
t = 0
t_final = 0.0002
istep = 0
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=(nel_1d,)*dim,
boundary_tag_to_face={
"dirichlet": ["+x", "-x"],
"neumann": ["+y", "-y"]
}
)
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()
order = 3
discr = EagerDGDiscretization(actx, local_mesh, order=order,
mpi_communicator=comm)
if dim == 2:
# no deep meaning here, just a fudge factor
dt = 0.0025/(nel_1d*order**2)
else:
raise ValueError("don't have a stable time step guesstimate")
source_width = 0.2
from arraycontext import thaw
nodes = thaw(discr.nodes(), actx)
boundaries = {
DTAG_BOUNDARY("dirichlet"): DirichletDiffusionBoundary(0.),
DTAG_BOUNDARY("neumann"): NeumannDiffusionBoundary(0.)
}
u = discr.zeros(actx)
if logmgr:
logmgr_add_device_name(logmgr, queue)
logmgr_add_device_memory_usage(logmgr, queue)
logmgr.add_watches(["step.max", "t_step.max", "t_log.max"])
try:
logmgr.add_watches(["memory_usage_python.max", "memory_usage_gpu.max"])
except KeyError:
pass
if use_profiling:
logmgr.add_watches(["multiply_time.max"])
vis_timer = IntervalTimer("t_vis", "Time spent visualizing")
logmgr.add_quantity(vis_timer)
vis = make_visualizer(discr)
def rhs(t, u):
return (
diffusion_operator(
discr, quad_tag=DISCR_TAG_BASE,
alpha=1, boundaries=boundaries, u=u)
+ actx.np.exp(-np.dot(nodes, nodes)/source_width**2))
compiled_rhs = actx.compile(rhs)
rank = comm.Get_rank()
while t < t_final:
if logmgr:
logmgr.tick_before()
if istep % 10 == 0:
print(istep, t, actx.to_numpy(actx.np.linalg.norm(u[0])))
vis.write_vtk_file("fld-heat-source-mpi-%03d-%04d.vtu" % (rank, istep),
[
("u", u)
], overwrite=True)
u = rk4_step(u, t, dt, compiled_rhs)
t += dt
istep += 1
if logmgr:
set_dt(logmgr, dt)
logmgr.tick_after()
final_answer = discr.norm(u, np.inf)
resid = abs(final_answer - 0.00020620711665201585)
if resid > 1e-15:
raise ValueError(f"Run did not produce the expected result {resid=}")
def test_pyrometheus_mechanisms(ctx_factory, mechname, rate_tol, y0):
"""Test known pyrometheus mechanisms.
This test reproduces a pyrometheus-native test in the MIRGE context.
Tests that the Pyrometheus mechanism code gets the same thermo properties as the
corresponding mechanism in Cantera.
"""
cl_ctx = ctx_factory()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(queue)
dim = 1
nel_1d = 2
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=(nel_1d, ) * dim)
order = 4
logger.info(f"Number of elements {mesh.nelements}")
discr = EagerDGDiscretization(actx, mesh, order=order)
# Pyrometheus initialization
mech_cti = get_mechanism_cti(mechname)
sol = cantera.Solution(phase_id="gas", source=mech_cti)
prometheus_mechanism = pyro.get_thermochem_class(sol)(actx.np)
nspecies = prometheus_mechanism.num_species
print(f"PyrometheusMixture::NumSpecies = {nspecies}")
press0 = 101500.0
temp0 = 300.0
y0s = np.zeros(shape=(nspecies, ))
for i in range(nspecies - 1):
y0s[i] = y0 / (10.0**(i + 1))
y0s[-1] = 1.0 - np.sum(y0s[:-1])
for fac in range(1, 11):
pressin = fac * press0
tempin = fac * temp0
print(f"Testing (t,P) = ({tempin}, {pressin})")
cantera_soln = cantera.Solution(phase_id="gas", source=mech_cti)
cantera_soln.TPY = tempin, pressin, y0s
cantera_soln.equilibrate("UV")
can_t, can_rho, can_y = cantera_soln.TDY
can_p = cantera_soln.P
can_e = cantera_soln.int_energy_mass
can_k = cantera_soln.forward_rate_constants
can_c = cantera_soln.concentrations
# Chemistry functions for testing pyro chem
can_r = cantera_soln.net_rates_of_progress
can_omega = cantera_soln.net_production_rates
ones = discr.zeros(actx) + 1.0
tin = can_t * ones
pin = can_p * ones
yin = make_obj_array([can_y[i] * ones for i in range(nspecies)])
prom_rho = prometheus_mechanism.get_density(pin, tin, yin)
prom_e = prometheus_mechanism.get_mixture_internal_energy_mass(
tin, yin)
prom_t = prometheus_mechanism.get_temperature(prom_e, tin, yin, True)
prom_p = prometheus_mechanism.get_pressure(prom_rho, tin, yin)
prom_c = prometheus_mechanism.get_concentrations(prom_rho, yin)
prom_k = prometheus_mechanism.get_fwd_rate_coefficients(prom_t, prom_c)
# Pyro chemistry functions
prom_r = prometheus_mechanism.get_net_rates_of_progress(prom_t, prom_c)
prom_omega = prometheus_mechanism.get_net_production_rates(
prom_rho, prom_t, yin)
print(f"can(rho, y, p, t, e, k) = ({can_rho}, {can_y}, "
f"{can_p}, {can_t}, {can_e}, {can_k})")
print(f"prom(rho, y, p, t, e, k) = ({prom_rho}, {y0s}, "
f"{prom_p}, {prom_t}, {prom_e}, {prom_k})")
# For pyro chem testing
print(f"can_r = {can_r}")
print(f"prom_r = {prom_r}")
print(f"can_omega = {can_omega}")
print(f"prom_omega = {prom_omega}")
assert discr.norm((prom_c - can_c) / can_c, np.inf) < 1e-14
assert discr.norm((prom_t - can_t) / can_t, np.inf) < 1e-14
assert discr.norm((prom_rho - can_rho) / can_rho, np.inf) < 1e-14
assert discr.norm((prom_p - can_p) / can_p, np.inf) < 1e-14
assert discr.norm((prom_e - can_e) / can_e, np.inf) < 1e-6
assert discr.norm((prom_k - can_k) / can_k, np.inf) < 1e-10
# Pyro chem test comparisons
for i, rate in enumerate(can_r):
assert discr.norm((prom_r[i] - rate), np.inf) < rate_tol
for i, rate in enumerate(can_omega):
assert discr.norm((prom_omega[i] - rate), np.inf) < rate_tol