def test_additivity(mode):
mesh = dolfinx.UnitSquareMesh(MPI.COMM_WORLD, 12, 12, ghost_mode=mode)
V = dolfinx.FunctionSpace(mesh, ("CG", 1))
f1 = dolfinx.Function(V)
f2 = dolfinx.Function(V)
f3 = dolfinx.Function(V)
with f1.vector.localForm() as f1_local:
f1_local.set(1.0)
with f2.vector.localForm() as f2_local:
f2_local.set(2.0)
with f3.vector.localForm() as f3_local:
f3_local.set(3.0)
j1 = ufl.inner(f1, f1) * ufl.dx(mesh)
j2 = ufl.inner(f2, f2) * ufl.ds(mesh)
j3 = ufl.inner(ufl.avg(f3), ufl.avg(f3)) * ufl.dS(mesh)
# Assemble each scalar form separately
J1 = mesh.mpi_comm().allreduce(dolfinx.fem.assemble_scalar(j1), op=MPI.SUM)
J2 = mesh.mpi_comm().allreduce(dolfinx.fem.assemble_scalar(j2), op=MPI.SUM)
J3 = mesh.mpi_comm().allreduce(dolfinx.fem.assemble_scalar(j3), op=MPI.SUM)
# Sum forms and assemble the result
J12 = mesh.mpi_comm().allreduce(dolfinx.fem.assemble_scalar(j1 + j2), op=MPI.SUM)
J13 = mesh.mpi_comm().allreduce(dolfinx.fem.assemble_scalar(j1 + j3), op=MPI.SUM)
J23 = mesh.mpi_comm().allreduce(dolfinx.fem.assemble_scalar(j2 + j3), op=MPI.SUM)
J123 = mesh.mpi_comm().allreduce(dolfinx.fem.assemble_scalar(j1 + j2 + j3), op=MPI.SUM)
# Compare assembled values
assert (J1 + J2) == pytest.approx(J12)
assert (J1 + J3) == pytest.approx(J13)
assert (J2 + J3) == pytest.approx(J23)
assert (J1 + J2 + J3) == pytest.approx(J123)
def test_additivity(mode):
mesh = create_unit_square(MPI.COMM_WORLD, 12, 12, ghost_mode=mode)
V = FunctionSpace(mesh, ("Lagrange", 1))
f1 = Function(V)
f2 = Function(V)
f3 = Function(V)
f1.x.array[:] = 1.0
f2.x.array[:] = 2.0
f3.x.array[:] = 3.0
j1 = ufl.inner(f1, f1) * ufl.dx(mesh)
j2 = ufl.inner(f2, f2) * ufl.ds(mesh)
j3 = ufl.inner(ufl.avg(f3), ufl.avg(f3)) * ufl.dS(mesh)
# Assemble each scalar form separately
J1 = mesh.comm.allreduce(assemble_scalar(form(j1)), op=MPI.SUM)
J2 = mesh.comm.allreduce(assemble_scalar(form(j2)), op=MPI.SUM)
J3 = mesh.comm.allreduce(assemble_scalar(form(j3)), op=MPI.SUM)
# Sum forms and assemble the result
J12 = mesh.comm.allreduce(assemble_scalar(form(j1 + j2)), op=MPI.SUM)
J13 = mesh.comm.allreduce(assemble_scalar(form(j1 + j3)), op=MPI.SUM)
J23 = mesh.comm.allreduce(assemble_scalar(form(j2 + j3)), op=MPI.SUM)
J123 = mesh.comm.allreduce(assemble_scalar(form(j1 + j2 + j3)), op=MPI.SUM)
# Compare assembled values
assert (J1 + J2) == pytest.approx(J12)
assert (J1 + J3) == pytest.approx(J13)
assert (J2 + J3) == pytest.approx(J23)
assert (J1 + J2 + J3) == pytest.approx(J123)
def error_indicators(self):
"""
Generate and return linear form defining error indicators
"""
# Extract these to increase readability
R_T = self._R_T
R_dT = self._R_dT
z = self._Ez_h
z_h = self._z_h
# Define linear form for computing error indicators
v = self.module.TestFunction(self._DG0)
eta_T = (v * inner(R_T, z - z_h) * dx(self.domain) +
avg(v)*(inner(R_dT('+'), (z - z_h)('+')) +
inner(R_dT('-'), (z - z_h)('-'))) * dS(self.domain) +
v * inner(R_dT, z - z_h) * ds(self.domain))
return eta_T
def facet_residual(self):
"""
Generate and return (bilinear, linear) forms defining linear
variational problem for the strong facet residual(s)
"""
# Define trial and test functions for the facet residuals on
# discontinuous version of primal trial space
R_e = self.module.TrialFunction(self._dV)
v = self.module.TestFunction(self._dV)
# Extract original test function in the weak residual
v_h = self.weak_residual.arguments()[0]
# Define forms defining linear variational problem for facet
# residual
v_e = self._b_e * v
a_R_dT = ((inner(v_e('+'), R_e('+')) + inner(v_e('-'), R_e('-'))) *
dS(self.domain) + inner(v_e, R_e) * ds(self.domain))
L_R_dT = (replace(self.weak_residual, {v_h: v_e}) -
inner(v_e, self._R_T) * dx(self.domain))
return (a_R_dT, L_R_dT)
def facet_residual(self):
"""
Generate and return (bilinear, linear) forms defining linear
variational problem for the strong facet residual(s)
"""
# Define trial and test functions for the facet residuals on
# discontinuous version of primal trial space
R_e = self.module.TrialFunction(self._dV)
v = self.module.TestFunction(self._dV)
# Extract original test function in the weak residual
v_h = self.weak_residual.arguments()[0]
# Define forms defining linear variational problem for facet
# residual
v_e = self._b_e*v
a_R_dT = ((inner(v_e('+'), R_e('+')) + inner(v_e('-'), R_e('-')))*dS(self.domain)
+ inner(v_e, R_e)*ds(self.domain))
L_R_dT = (replace(self.weak_residual, {v_h: v_e})
- inner(v_e, self._R_T)*dx(self.domain))
return (a_R_dT, L_R_dT)
def test_manufactured_poisson_dg(degree, filename, datadir):
""" Manufactured Poisson problem, solving u = x[component]**n, where n is the
degree of the Lagrange function space.
"""
with XDMFFile(MPI.COMM_WORLD,
os.path.join(datadir, filename),
"r",
encoding=XDMFFile.Encoding.ASCII) as xdmf:
mesh = xdmf.read_mesh(name="Grid")
V = FunctionSpace(mesh, ("DG", degree))
u, v = TrialFunction(V), TestFunction(V)
# Exact solution
x = SpatialCoordinate(mesh)
u_exact = x[1]**degree
# Coefficient
k = Function(V)
k.vector.set(2.0)
k.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
# Source term
f = -div(k * grad(u_exact))
# Mesh normals and element size
n = FacetNormal(mesh)
h = CellDiameter(mesh)
h_avg = (h("+") + h("-")) / 2.0
# Penalty parameter
alpha = 32
dx_ = dx(metadata={"quadrature_degree": -1})
ds_ = ds(metadata={"quadrature_degree": -1})
dS_ = dS(metadata={"quadrature_degree": -1})
a = inner(k * grad(u), grad(v)) * dx_ \
- k("+") * inner(avg(grad(u)), jump(v, n)) * dS_ \
- k("+") * inner(jump(u, n), avg(grad(v))) * dS_ \
+ k("+") * (alpha / h_avg) * inner(jump(u, n), jump(v, n)) * dS_ \
- inner(k * grad(u), v * n) * ds_ \
- inner(u * n, k * grad(v)) * ds_ \
+ (alpha / h) * inner(k * u, v) * ds_
L = inner(f, v) * dx_ - inner(k * u_exact * n, grad(v)) * ds_ \
+ (alpha / h) * inner(k * u_exact, v) * ds_
for integral in a.integrals():
integral.metadata(
)["quadrature_degree"] = ufl.algorithms.estimate_total_polynomial_degree(
a)
for integral in L.integrals():
integral.metadata(
)["quadrature_degree"] = ufl.algorithms.estimate_total_polynomial_degree(
L)
b = assemble_vector(L)
b.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE)
A = assemble_matrix(a, [])
A.assemble()
# Create LU linear solver
solver = PETSc.KSP().create(MPI.COMM_WORLD)
solver.setType(PETSc.KSP.Type.PREONLY)
solver.getPC().setType(PETSc.PC.Type.LU)
solver.setOperators(A)
# Solve
uh = Function(V)
solver.solve(b, uh.vector)
uh.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
error = mesh.mpi_comm().allreduce(assemble_scalar((u_exact - uh)**2 * dx),
op=MPI.SUM)
assert np.absolute(error) < 1.0e-14
def run_dg_test(mesh, V, degree):
""" Manufactured Poisson problem, solving u = x[component]**n, where n is the
degree of the Lagrange function space.
"""
u, v = TrialFunction(V), TestFunction(V)
# Exact solution
x = SpatialCoordinate(mesh)
u_exact = x[1]**degree
# Coefficient
k = Function(V)
k.vector.set(2.0)
k.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
# Source term
f = -div(k * grad(u_exact))
# Mesh normals and element size
n = FacetNormal(mesh)
h = CellDiameter(mesh)
h_avg = (h("+") + h("-")) / 2.0
# Penalty parameter
alpha = 32
dx_ = dx(metadata={"quadrature_degree": -1})
ds_ = ds(metadata={"quadrature_degree": -1})
dS_ = dS(metadata={"quadrature_degree": -1})
with common.Timer("Compile forms"):
a = inner(k * grad(u), grad(v)) * dx_ \
- k("+") * inner(avg(grad(u)), jump(v, n)) * dS_ \
- k("+") * inner(jump(u, n), avg(grad(v))) * dS_ \
+ k("+") * (alpha / h_avg) * inner(jump(u, n), jump(v, n)) * dS_ \
- inner(k * grad(u), v * n) * ds_ \
- inner(u * n, k * grad(v)) * ds_ \
+ (alpha / h) * inner(k * u, v) * ds_
L = inner(f, v) * dx_ - inner(k * u_exact * n, grad(v)) * ds_ \
+ (alpha / h) * inner(k * u_exact, v) * ds_
for integral in a.integrals():
integral.metadata(
)["quadrature_degree"] = ufl.algorithms.estimate_total_polynomial_degree(
a)
for integral in L.integrals():
integral.metadata(
)["quadrature_degree"] = ufl.algorithms.estimate_total_polynomial_degree(
L)
with common.Timer("Assemble vector"):
b = assemble_vector(L)
b.ghostUpdate(addv=PETSc.InsertMode.ADD,
mode=PETSc.ScatterMode.REVERSE)
with common.Timer("Assemble matrix"):
A = assemble_matrix(a, [])
A.assemble()
with common.Timer("Solve"):
# Create LU linear solver
solver = PETSc.KSP().create(MPI.COMM_WORLD)
solver.setType(PETSc.KSP.Type.PREONLY)
solver.getPC().setType(PETSc.PC.Type.LU)
solver.setOperators(A)
# Solve
uh = Function(V)
solver.solve(b, uh.vector)
uh.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
with common.Timer("Error functional compile"):
# Calculate error
M = (u_exact - uh)**2 * dx
M = fem.Form(M)
with common.Timer("Error assembly"):
error = mesh.mpi_comm().allreduce(assemble_scalar(M), op=MPI.SUM)
common.list_timings(MPI.COMM_WORLD, [common.TimingType.wall])
assert np.absolute(error) < 1.0e-14
# Copyright (C) 2008 Anders Logg and Kristian B. Oelgaard
#
# This file is part of UFL.
#
# UFL is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# UFL is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with UFL. If not, see <http://www.gnu.org/licenses/>.
#
# This simple example illustrates how forms can be defined on different sub domains.
# It is supported for all three integral types.
from ufl import (FiniteElement, TestFunction, TrialFunction, ds, dS, dx,
tetrahedron)
element = FiniteElement("CG", tetrahedron, 1)
v = TestFunction(element)
u = TrialFunction(element)
a = v * u * dx(0) + 10.0 * v * u * dx(1) + v * u * ds(0) + 2.0 * v * u * ds(1)\
+ v('+') * u('+') * dS(0) + 4.3 * v('+') * u('+') * dS(1)
