def test_mixed_constant_bc(mesh_factory):
"""Test that setting a dirichletbc with on a component of a mixed
function yields the same result as setting it with a function"""
func, args = mesh_factory
mesh = func(*args)
tdim, gdim = mesh.topology.dim, mesh.geometry.dim
boundary_facets = locate_entities_boundary(
mesh, tdim - 1, lambda x: np.ones(x.shape[1], dtype=bool))
TH = ufl.MixedElement([
ufl.VectorElement("Lagrange", mesh.ufl_cell(), 2),
ufl.FiniteElement("Lagrange", mesh.ufl_cell(), 1)
])
W = FunctionSpace(mesh, TH)
U = Function(W)
# Apply BC to component of a mixed space using a Constant
c = Constant(mesh, (PETSc.ScalarType(2), PETSc.ScalarType(2)))
dofs0 = locate_dofs_topological(W.sub(0), tdim - 1, boundary_facets)
bc0 = dirichletbc(c, dofs0, W.sub(0))
u = U.sub(0)
set_bc(u.vector, [bc0])
# Apply BC to component of a mixed space using a Function
ubc1 = u.collapse()
ubc1.interpolate(lambda x: np.full((gdim, x.shape[1]), 2.0))
dofs1 = locate_dofs_topological((W.sub(0), ubc1.function_space), tdim - 1,
boundary_facets)
bc1 = dirichletbc(ubc1, dofs1, W.sub(0))
U1 = Function(W)
u1 = U1.sub(0)
set_bc(u1.vector, [bc1])
# Check that both approaches yield the same vector
assert np.allclose(u.x.array, u1.x.array)
def monolithic_solve():
"""Monolithic (interleaved) solver"""
P2_el = ufl.VectorElement("Lagrange", mesh.ufl_cell(), 2)
P1_el = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), 1)
TH = P2_el * P1_el
W = FunctionSpace(mesh, TH)
(u, p) = ufl.TrialFunctions(W)
(v, q) = ufl.TestFunctions(W)
a00 = ufl.inner(ufl.grad(u), ufl.grad(v)) * dx
a01 = ufl.inner(p, ufl.div(v)) * dx
a10 = ufl.inner(ufl.div(u), q) * dx
a = a00 + a01 + a10
p00 = ufl.inner(ufl.grad(u), ufl.grad(v)) * dx
p11 = ufl.inner(p, q) * dx
p_form = p00 + p11
f = Function(W.sub(0).collapse()[0])
p_zero = Function(W.sub(1).collapse()[0])
L0 = inner(f, v) * dx
L1 = inner(p_zero, q) * dx
L = L0 + L1
a, p_form, L = form(a), form(p_form), form(L)
bdofsW0_P2_0 = locate_dofs_topological(W.sub(0), facetdim, bndry_facets0)
bdofsW0_P2_1 = locate_dofs_topological(W.sub(0), facetdim, bndry_facets1)
bc0 = dirichletbc(bc_value, bdofsW0_P2_0, W.sub(0))
bc1 = dirichletbc(bc_value, bdofsW0_P2_1, W.sub(0))
A = assemble_matrix(a, bcs=[bc0, bc1])
A.assemble()
P = assemble_matrix(p_form, bcs=[bc0, bc1])
P.assemble()
b = assemble_vector(L)
apply_lifting(b, [a], bcs=[[bc0, bc1]])
b.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE)
set_bc(b, [bc0, bc1])
ksp = PETSc.KSP()
ksp.create(mesh.comm)
ksp.setOperators(A, P)
ksp.setType("minres")
pc = ksp.getPC()
pc.setType('lu')
def monitor(ksp, its, rnorm):
# print("Num it, rnorm:", its, rnorm)
pass
ksp.setTolerances(rtol=1.0e-8, max_it=50)
ksp.setMonitor(monitor)
ksp.setFromOptions()
x = A.createVecRight()
ksp.solve(b, x)
assert ksp.getConvergedReason() > 0
return b.norm(), x.norm(), A.norm(), P.norm()
def monolithic_solve():
"""Monolithic version"""
E = P * P
W = FunctionSpace(mesh, E)
U = Function(W)
dU = ufl.TrialFunction(W)
u0, u1 = ufl.split(U)
v0, v1 = ufl.TestFunctions(W)
F = inner((u0**2 + 1) * ufl.grad(u0), ufl.grad(v0)) * dx \
+ inner((u1**2 + 1) * ufl.grad(u1), ufl.grad(v1)) * dx \
- inner(f, v0) * ufl.dx - inner(g, v1) * dx
J = derivative(F, U, dU)
F, J = form(F), form(J)
u0_bc = Function(V0)
u0_bc.interpolate(bc_val_0)
u1_bc = Function(V1)
u1_bc.interpolate(bc_val_1)
bdofsW0_V0 = locate_dofs_topological((W.sub(0), V0), facetdim,
bndry_facets)
bdofsW1_V1 = locate_dofs_topological((W.sub(1), V1), facetdim,
bndry_facets)
bcs = [
dirichletbc(u0_bc, bdofsW0_V0, W.sub(0)),
dirichletbc(u1_bc, bdofsW1_V1, W.sub(1))
]
Jmat = create_matrix(J)
Fvec = create_vector(F)
snes = PETSc.SNES().create(MPI.COMM_WORLD)
snes.setTolerances(rtol=1.0e-15, max_it=10)
snes.getKSP().setType("preonly")
snes.getKSP().getPC().setType("lu")
problem = NonlinearPDE_SNESProblem(F, J, U, bcs)
snes.setFunction(problem.F_mono, Fvec)
snes.setJacobian(problem.J_mono, J=Jmat, P=None)
U.sub(0).interpolate(initial_guess_u)
U.sub(1).interpolate(initial_guess_p)
x = create_vector(F)
x.array = U.vector.array_r
snes.solve(None, x)
assert snes.getKSP().getConvergedReason() > 0
assert snes.getConvergedReason() > 0
return x.norm()
def rigid_motions_nullspace(V: _fem.FunctionSpace):
"""
Function to build nullspace for 2D/3D elasticity.
Parameters:
===========
V
The function space
"""
_x = _fem.Function(V)
# Get geometric dim
gdim = V.mesh.geometry.dim
assert gdim == 2 or gdim == 3
# Set dimension of nullspace
dim = 3 if gdim == 2 else 6
# Create list of vectors for null space
nullspace_basis = [_x.vector.copy() for _ in range(dim)]
with ExitStack() as stack:
vec_local = [stack.enter_context(x.localForm()) for x in nullspace_basis]
basis = [np.asarray(x) for x in vec_local]
dofs = [V.sub(i).dofmap.list.array for i in range(gdim)]
# Build translational null space basis
for i in range(gdim):
basis[i][dofs[i]] = 1.0
# Build rotational null space basis
x = V.tabulate_dof_coordinates()
dofs_block = V.dofmap.list.array
x0, x1, x2 = x[dofs_block, 0], x[dofs_block, 1], x[dofs_block, 2]
if gdim == 2:
basis[2][dofs[0]] = -x1
basis[2][dofs[1]] = x0
elif gdim == 3:
basis[3][dofs[0]] = -x1
basis[3][dofs[1]] = x0
basis[4][dofs[0]] = x2
basis[4][dofs[2]] = -x0
basis[5][dofs[2]] = x1
basis[5][dofs[1]] = -x2
_la.orthonormalize(nullspace_basis)
assert _la.is_orthonormal(nullspace_basis)
return PETSc.NullSpace().create(vectors=nullspace_basis)
def test_locate_dofs_geometrical():
"""Test that locate_dofs_geometrical, when passed two function
spaces, returns the correct degrees of freedom in each space"""
mesh = create_unit_square(MPI.COMM_WORLD, 4, 8)
p0, p1 = 1, 2
P0 = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), p0)
P1 = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), p1)
W = FunctionSpace(mesh, P0 * P1)
V = W.sub(0).collapse()[0]
with pytest.raises(RuntimeError):
locate_dofs_geometrical(
W, lambda x: np.isclose(x.T, [0, 0, 0]).all(axis=1))
dofs = locate_dofs_geometrical(
(W.sub(0), V), lambda x: np.isclose(x.T, [0, 0, 0]).all(axis=1))
# Collect dofs (global indices) from all processes
dofs0_global = W.sub(0).dofmap.index_map.local_to_global(dofs[0])
dofs1_global = V.dofmap.index_map.local_to_global(dofs[1])
all_dofs0 = set(np.concatenate(MPI.COMM_WORLD.allgather(dofs0_global)))
all_dofs1 = set(np.concatenate(MPI.COMM_WORLD.allgather(dofs1_global)))
# Check only one dof pair is found globally
assert len(all_dofs0) == 1
assert len(all_dofs1) == 1
# On process with the dof pair
if len(dofs) == 1:
# Check correct dof returned in W
coords_W = W.tabulate_dof_coordinates()
assert np.isclose(coords_W[dofs[0][0]], [0, 0, 0]).all()
# Check correct dof returned in V
coords_V = V.tabulate_dof_coordinates()
assert np.isclose(coords_V[dofs[0][1]], [0, 0, 0]).all()
def test_tabulate_dofs(mesh_factory):
func, args = mesh_factory
mesh = func(*args)
W0 = FiniteElement("Lagrange", mesh.ufl_cell(), 1)
W1 = VectorElement("Lagrange", mesh.ufl_cell(), 1)
W = FunctionSpace(mesh, W0 * W1)
L0 = W.sub(0)
L1 = W.sub(1)
L01 = L1.sub(0)
L11 = L1.sub(1)
map = mesh.topology.index_map(mesh.topology.dim)
num_cells = map.size_local + map.num_ghosts
for c in range(num_cells):
dofs0 = L0.dofmap.cell_dofs(c)
dofs1 = L01.dofmap.cell_dofs(c)
dofs2 = L11.dofmap.cell_dofs(c)
dofs3 = L1.dofmap.cell_dofs(c)
assert len(np.intersect1d(dofs0, dofs1)) == 0
assert len(np.intersect1d(dofs0, dofs2)) == 0
assert len(np.intersect1d(dofs1, dofs2)) == 0
assert np.array_equal(np.append(dofs1, dofs2), dofs3)
def test_assembly_solve_taylor_hood_nl(mesh):
"""Assemble Stokes problem with Taylor-Hood elements and solve."""
gdim = mesh.geometry.dim
P2 = VectorFunctionSpace(mesh, ("Lagrange", 2))
P1 = FunctionSpace(mesh, ("Lagrange", 1))
def boundary0(x):
"""Define boundary x = 0"""
return np.isclose(x[0], 0.0)
def boundary1(x):
"""Define boundary x = 1"""
return np.isclose(x[0], 1.0)
def initial_guess_u(x):
u_init = np.row_stack(
(np.sin(x[0]) * np.sin(x[1]), np.cos(x[0]) * np.cos(x[1])))
if gdim == 3:
u_init = np.row_stack((u_init, np.cos(x[2])))
return u_init
def initial_guess_p(x):
return -x[0]**2 - x[1]**3
u_bc_0 = Function(P2)
u_bc_0.interpolate(
lambda x: np.row_stack(tuple(x[j] + float(j) for j in range(gdim))))
u_bc_1 = Function(P2)
u_bc_1.interpolate(
lambda x: np.row_stack(tuple(np.sin(x[j]) for j in range(gdim))))
facetdim = mesh.topology.dim - 1
bndry_facets0 = locate_entities_boundary(mesh, facetdim, boundary0)
bndry_facets1 = locate_entities_boundary(mesh, facetdim, boundary1)
bdofs0 = locate_dofs_topological(P2, facetdim, bndry_facets0)
bdofs1 = locate_dofs_topological(P2, facetdim, bndry_facets1)
bcs = [dirichletbc(u_bc_0, bdofs0), dirichletbc(u_bc_1, bdofs1)]
u, p = Function(P2), Function(P1)
du, dp = ufl.TrialFunction(P2), ufl.TrialFunction(P1)
v, q = ufl.TestFunction(P2), ufl.TestFunction(P1)
F = [
inner(ufl.grad(u), ufl.grad(v)) * dx + inner(p, ufl.div(v)) * dx,
inner(ufl.div(u), q) * dx
]
J = [[derivative(F[0], u, du),
derivative(F[0], p, dp)],
[derivative(F[1], u, du),
derivative(F[1], p, dp)]]
P = [[J[0][0], None], [None, inner(dp, q) * dx]]
F, J, P = form(F), form(J), form(P)
# -- Blocked and monolithic
Jmat0 = create_matrix_block(J)
Pmat0 = create_matrix_block(P)
Fvec0 = create_vector_block(F)
snes = PETSc.SNES().create(MPI.COMM_WORLD)
snes.setTolerances(rtol=1.0e-15, max_it=10)
snes.getKSP().setType("minres")
snes.getKSP().getPC().setType("lu")
problem = NonlinearPDE_SNESProblem(F, J, [u, p], bcs, P=P)
snes.setFunction(problem.F_block, Fvec0)
snes.setJacobian(problem.J_block, J=Jmat0, P=Pmat0)
u.interpolate(initial_guess_u)
p.interpolate(initial_guess_p)
x0 = create_vector_block(F)
with u.vector.localForm() as _u, p.vector.localForm() as _p:
scatter_local_vectors(x0, [_u.array_r, _p.array_r],
[(u.function_space.dofmap.index_map,
u.function_space.dofmap.index_map_bs),
(p.function_space.dofmap.index_map,
p.function_space.dofmap.index_map_bs)])
x0.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
snes.solve(None, x0)
assert snes.getConvergedReason() > 0
# -- Blocked and nested
Jmat1 = create_matrix_nest(J)
Pmat1 = create_matrix_nest(P)
Fvec1 = create_vector_nest(F)
snes = PETSc.SNES().create(MPI.COMM_WORLD)
snes.setTolerances(rtol=1.0e-15, max_it=10)
nested_IS = Jmat1.getNestISs()
snes.getKSP().setType("minres")
snes.getKSP().setTolerances(rtol=1e-12)
snes.getKSP().getPC().setType("fieldsplit")
snes.getKSP().getPC().setFieldSplitIS(["u", nested_IS[0][0]],
["p", nested_IS[1][1]])
ksp_u, ksp_p = snes.getKSP().getPC().getFieldSplitSubKSP()
ksp_u.setType("preonly")
ksp_u.getPC().setType('lu')
ksp_p.setType("preonly")
ksp_p.getPC().setType('lu')
problem = NonlinearPDE_SNESProblem(F, J, [u, p], bcs, P=P)
snes.setFunction(problem.F_nest, Fvec1)
snes.setJacobian(problem.J_nest, J=Jmat1, P=Pmat1)
u.interpolate(initial_guess_u)
p.interpolate(initial_guess_p)
x1 = create_vector_nest(F)
for x1_soln_pair in zip(x1.getNestSubVecs(), (u, p)):
x1_sub, soln_sub = x1_soln_pair
soln_sub.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
soln_sub.vector.copy(result=x1_sub)
x1_sub.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
x1.set(0.0)
snes.solve(None, x1)
assert snes.getConvergedReason() > 0
assert nest_matrix_norm(Jmat1) == pytest.approx(Jmat0.norm(), 1.0e-12)
assert Fvec1.norm() == pytest.approx(Fvec0.norm(), 1.0e-12)
assert x1.norm() == pytest.approx(x0.norm(), 1.0e-12)
# -- Monolithic
P2_el = ufl.VectorElement("Lagrange", mesh.ufl_cell(), 2)
P1_el = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), 1)
TH = P2_el * P1_el
W = FunctionSpace(mesh, TH)
U = Function(W)
dU = ufl.TrialFunction(W)
u, p = ufl.split(U)
du, dp = ufl.split(dU)
v, q = ufl.TestFunctions(W)
F = inner(ufl.grad(u), ufl.grad(v)) * dx + inner(p, ufl.div(v)) * dx \
+ inner(ufl.div(u), q) * dx
J = derivative(F, U, dU)
P = inner(ufl.grad(du), ufl.grad(v)) * dx + inner(dp, q) * dx
F, J, P = form(F), form(J), form(P)
bdofsW0_P2_0 = locate_dofs_topological((W.sub(0), P2), facetdim,
bndry_facets0)
bdofsW0_P2_1 = locate_dofs_topological((W.sub(0), P2), facetdim,
bndry_facets1)
bcs = [
dirichletbc(u_bc_0, bdofsW0_P2_0, W.sub(0)),
dirichletbc(u_bc_1, bdofsW0_P2_1, W.sub(0))
]
Jmat2 = create_matrix(J)
Pmat2 = create_matrix(P)
Fvec2 = create_vector(F)
snes = PETSc.SNES().create(MPI.COMM_WORLD)
snes.setTolerances(rtol=1.0e-15, max_it=10)
snes.getKSP().setType("minres")
snes.getKSP().getPC().setType("lu")
problem = NonlinearPDE_SNESProblem(F, J, U, bcs, P=P)
snes.setFunction(problem.F_mono, Fvec2)
snes.setJacobian(problem.J_mono, J=Jmat2, P=Pmat2)
U.sub(0).interpolate(initial_guess_u)
U.sub(1).interpolate(initial_guess_p)
x2 = create_vector(F)
x2.array = U.vector.array_r
snes.solve(None, x2)
assert snes.getConvergedReason() > 0
assert Jmat2.norm() == pytest.approx(Jmat0.norm(), 1.0e-12)
assert Fvec2.norm() == pytest.approx(Fvec0.norm(), 1.0e-12)
assert x2.norm() == pytest.approx(x0.norm(), 1.0e-12)
def test_matrix_assembly_block_nl():
"""Test assembly of block matrices and vectors into (a) monolithic
blocked structures, PETSc Nest structures, and monolithic structures
in the nonlinear setting
"""
mesh = create_unit_square(MPI.COMM_WORLD, 4, 8)
p0, p1 = 1, 2
P0 = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), p0)
P1 = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), p1)
V0 = FunctionSpace(mesh, P0)
V1 = FunctionSpace(mesh, P1)
def initial_guess_u(x):
return np.sin(x[0]) * np.sin(x[1])
def initial_guess_p(x):
return -x[0]**2 - x[1]**3
def bc_value(x):
return np.cos(x[0]) * np.cos(x[1])
facetdim = mesh.topology.dim - 1
bndry_facets = locate_entities_boundary(
mesh, facetdim,
lambda x: np.logical_or(np.isclose(x[0], 0.0), np.isclose(x[0], 1.0)))
u_bc = Function(V1)
u_bc.interpolate(bc_value)
bdofs = locate_dofs_topological(V1, facetdim, bndry_facets)
bc = dirichletbc(u_bc, bdofs)
# Define variational problem
du, dp = ufl.TrialFunction(V0), ufl.TrialFunction(V1)
u, p = Function(V0), Function(V1)
v, q = ufl.TestFunction(V0), ufl.TestFunction(V1)
u.interpolate(initial_guess_u)
p.interpolate(initial_guess_p)
f = 1.0
g = -3.0
F0 = inner(u, v) * dx + inner(p, v) * dx - inner(f, v) * dx
F1 = inner(u, q) * dx + inner(p, q) * dx - inner(g, q) * dx
a_block = form([[derivative(F0, u, du),
derivative(F0, p, dp)],
[derivative(F1, u, du),
derivative(F1, p, dp)]])
L_block = form([F0, F1])
# Monolithic blocked
x0 = create_vector_block(L_block)
scatter_local_vectors(x0, [u.vector.array_r, p.vector.array_r],
[(u.function_space.dofmap.index_map,
u.function_space.dofmap.index_map_bs),
(p.function_space.dofmap.index_map,
p.function_space.dofmap.index_map_bs)])
x0.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
# Ghosts are updated inside assemble_vector_block
A0 = assemble_matrix_block(a_block, bcs=[bc])
b0 = assemble_vector_block(L_block, a_block, bcs=[bc], x0=x0, scale=-1.0)
A0.assemble()
assert A0.getType() != "nest"
Anorm0 = A0.norm()
bnorm0 = b0.norm()
# Nested (MatNest)
x1 = create_vector_nest(L_block)
for x1_soln_pair in zip(x1.getNestSubVecs(), (u, p)):
x1_sub, soln_sub = x1_soln_pair
soln_sub.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
soln_sub.vector.copy(result=x1_sub)
x1_sub.ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
A1 = assemble_matrix_nest(a_block, bcs=[bc])
b1 = assemble_vector_nest(L_block)
apply_lifting_nest(b1, a_block, bcs=[bc], x0=x1, scale=-1.0)
for b_sub in b1.getNestSubVecs():
b_sub.ghostUpdate(addv=PETSc.InsertMode.ADD,
mode=PETSc.ScatterMode.REVERSE)
bcs0 = bcs_by_block([L.function_spaces[0] for L in L_block], [bc])
set_bc_nest(b1, bcs0, x1, scale=-1.0)
A1.assemble()
assert A1.getType() == "nest"
assert nest_matrix_norm(A1) == pytest.approx(Anorm0, 1.0e-12)
assert b1.norm() == pytest.approx(bnorm0, 1.0e-12)
# Monolithic version
E = P0 * P1
W = FunctionSpace(mesh, E)
dU = ufl.TrialFunction(W)
U = Function(W)
u0, u1 = ufl.split(U)
v0, v1 = ufl.TestFunctions(W)
U.sub(0).interpolate(initial_guess_u)
U.sub(1).interpolate(initial_guess_p)
F = inner(u0, v0) * dx + inner(u1, v0) * dx + inner(u0, v1) * dx + inner(u1, v1) * dx \
- inner(f, v0) * ufl.dx - inner(g, v1) * dx
J = derivative(F, U, dU)
F, J = form(F), form(J)
bdofsW_V1 = locate_dofs_topological((W.sub(1), V1), facetdim, bndry_facets)
bc = dirichletbc(u_bc, bdofsW_V1, W.sub(1))
A2 = assemble_matrix(J, bcs=[bc])
A2.assemble()
b2 = assemble_vector(F)
apply_lifting(b2, [J], bcs=[[bc]], x0=[U.vector], scale=-1.0)
b2.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE)
set_bc(b2, [bc], x0=U.vector, scale=-1.0)
assert A2.getType() != "nest"
assert A2.norm() == pytest.approx(Anorm0, 1.0e-12)
assert b2.norm() == pytest.approx(bnorm0, 1.0e-12)
def test_biharmonic():
"""Manufactured biharmonic problem.
Solved using rotated Regge mixed finite element method. This is equivalent
to the Hellan-Herrmann-Johnson (HHJ) finite element method in
two-dimensions."""
mesh = create_rectangle(
MPI.COMM_WORLD,
[np.array([0.0, 0.0]), np.array([1.0, 1.0])], [32, 32],
CellType.triangle)
element = ufl.MixedElement([
ufl.FiniteElement("Regge", ufl.triangle, 1),
ufl.FiniteElement("Lagrange", ufl.triangle, 2)
])
V = FunctionSpace(mesh, element)
sigma, u = ufl.TrialFunctions(V)
tau, v = ufl.TestFunctions(V)
x = ufl.SpatialCoordinate(mesh)
u_exact = ufl.sin(ufl.pi * x[0]) * ufl.sin(ufl.pi * x[0]) * ufl.sin(
ufl.pi * x[1]) * ufl.sin(ufl.pi * x[1])
f_exact = div(grad(div(grad(u_exact))))
sigma_exact = grad(grad(u_exact))
# sigma and tau are tangential-tangential continuous according to the
# H(curl curl) continuity of the Regge space. However, for the biharmonic
# problem we require normal-normal continuity H (div div). Theorem 4.2 of
# Lizao Li's PhD thesis shows that the latter space can be constructed by
# the former through the action of the operator S:
def S(tau):
return tau - ufl.Identity(2) * ufl.tr(tau)
sigma_S = S(sigma)
tau_S = S(tau)
# Discrete duality inner product eq. 4.5 Lizao Li's PhD thesis
def b(tau_S, v):
n = FacetNormal(mesh)
return inner(tau_S, grad(grad(v))) * dx \
- ufl.dot(ufl.dot(tau_S('+'), n('+')), n('+')) * jump(grad(v), n) * dS \
- ufl.dot(ufl.dot(tau_S, n), n) * ufl.dot(grad(v), n) * ds
# Non-symmetric formulation
a = form(inner(sigma_S, tau_S) * dx - b(tau_S, u) + b(sigma_S, v))
L = form(inner(f_exact, v) * dx)
V_1 = V.sub(1).collapse()[0]
zero_u = Function(V_1)
zero_u.x.array[:] = 0.0
# Strong (Dirichlet) boundary condition
boundary_facets = locate_entities_boundary(
mesh, mesh.topology.dim - 1,
lambda x: np.full(x.shape[1], True, dtype=bool))
boundary_dofs = locate_dofs_topological(
(V.sub(1), V_1), mesh.topology.dim - 1, boundary_facets)
bcs = [dirichletbc(zero_u, boundary_dofs, V.sub(1))]
A = assemble_matrix(a, bcs=bcs)
A.assemble()
b = assemble_vector(L)
apply_lifting(b, [a], bcs=[bcs])
b.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE)
set_bc(b, bcs)
# Solve
solver = PETSc.KSP().create(MPI.COMM_WORLD)
PETSc.Options()["ksp_type"] = "preonly"
PETSc.Options()["pc_type"] = "lu"
# PETSc.Options()["pc_factor_mat_solver_type"] = "mumps"
solver.setFromOptions()
solver.setOperators(A)
x_h = Function(V)
solver.solve(b, x_h.vector)
x_h.x.scatter_forward()
# Recall that x_h has flattened indices.
u_error_numerator = np.sqrt(
mesh.comm.allreduce(assemble_scalar(
form(
inner(u_exact - x_h[4], u_exact - x_h[4]) *
dx(mesh, metadata={"quadrature_degree": 5}))),
op=MPI.SUM))
u_error_denominator = np.sqrt(
mesh.comm.allreduce(assemble_scalar(
form(
inner(u_exact, u_exact) *
dx(mesh, metadata={"quadrature_degree": 5}))),
op=MPI.SUM))
assert np.absolute(u_error_numerator / u_error_denominator) < 0.05
# Reconstruct tensor from flattened indices.
# Apply inverse transform. In 2D we have S^{-1} = S.
sigma_h = S(ufl.as_tensor([[x_h[0], x_h[1]], [x_h[2], x_h[3]]]))
sigma_error_numerator = np.sqrt(
mesh.comm.allreduce(assemble_scalar(
form(
inner(sigma_exact - sigma_h, sigma_exact - sigma_h) *
dx(mesh, metadata={"quadrature_degree": 5}))),
op=MPI.SUM))
sigma_error_denominator = np.sqrt(
mesh.comm.allreduce(assemble_scalar(
form(
inner(sigma_exact, sigma_exact) *
dx(mesh, metadata={"quadrature_degree": 5}))),
op=MPI.SUM))
assert np.absolute(sigma_error_numerator / sigma_error_denominator) < 0.005
print(
"(C) Norm of pressure coefficient vector (blocked, direct): {}".format(
norm_p_2))
assert np.isclose(norm_u_2, norm_u_0)
assert np.isclose(norm_p_2, norm_p_0)
# ### Non-blocked direct solver
#
# Again, solve the same problem but this time with a non-blocked direct
# solver approach
# +
# Create the function space
TH = P2 * P1
W = FunctionSpace(msh, TH)
W0, _ = W.sub(0).collapse()
# No slip boundary condition
noslip = Function(V)
facets = locate_entities_boundary(msh, 1, noslip_boundary)
dofs = locate_dofs_topological((W.sub(0), V), 1, facets)
bc0 = dirichletbc(noslip, dofs, W.sub(0))
# Driving velocity condition u = (1, 0) on top boundary (y = 1)
lid_velocity = Function(W0)
lid_velocity.interpolate(lid_velocity_expression)
facets = locate_entities_boundary(msh, 1, lid)
dofs = locate_dofs_topological((W.sub(0), V), 1, facets)
bc1 = dirichletbc(lid_velocity, dofs, W.sub(0))
# Since for this problem the pressure is only determined up to a
def test_assembly_solve_block(mode):
"""Solve a two-field mass-matrix like problem with block matrix approaches
and test that solution is the same"""
mesh = create_unit_square(MPI.COMM_WORLD, 32, 31, ghost_mode=mode)
P = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), 1)
V0 = FunctionSpace(mesh, P)
V1 = V0.clone()
# Locate facets on boundary
facetdim = mesh.topology.dim - 1
bndry_facets = locate_entities_boundary(mesh, facetdim, lambda x: np.logical_or(np.isclose(x[0], 0.0),
np.isclose(x[0], 1.0)))
bdofsV0 = locate_dofs_topological(V0, facetdim, bndry_facets)
bdofsV1 = locate_dofs_topological(V1, facetdim, bndry_facets)
u0_bc = PETSc.ScalarType(50.0)
u1_bc = PETSc.ScalarType(20.0)
bcs = [dirichletbc(u0_bc, bdofsV0, V0), dirichletbc(u1_bc, bdofsV1, V1)]
# Variational problem
u, p = ufl.TrialFunction(V0), ufl.TrialFunction(V1)
v, q = ufl.TestFunction(V0), ufl.TestFunction(V1)
f = 1.0
g = -3.0
zero = Function(V0)
a00 = form(inner(u, v) * dx)
a01 = form(zero * inner(p, v) * dx)
a10 = form(zero * inner(u, q) * dx)
a11 = form(inner(p, q) * dx)
L0 = form(inner(f, v) * dx)
L1 = form(inner(g, q) * dx)
def monitor(ksp, its, rnorm):
pass
# print("Norm:", its, rnorm)
A0 = assemble_matrix_block([[a00, a01], [a10, a11]], bcs=bcs)
b0 = assemble_vector_block([L0, L1], [[a00, a01], [a10, a11]], bcs=bcs)
A0.assemble()
A0norm = A0.norm()
b0norm = b0.norm()
x0 = A0.createVecLeft()
ksp = PETSc.KSP()
ksp.create(mesh.comm)
ksp.setOperators(A0)
ksp.setMonitor(monitor)
ksp.setType('cg')
ksp.setTolerances(rtol=1.0e-14)
ksp.setFromOptions()
ksp.solve(b0, x0)
x0norm = x0.norm()
# Nested (MatNest)
A1 = assemble_matrix_nest([[a00, a01], [a10, a11]], bcs=bcs, diagonal=1.0)
A1.assemble()
b1 = assemble_vector_nest([L0, L1])
apply_lifting_nest(b1, [[a00, a01], [a10, a11]], bcs=bcs)
for b_sub in b1.getNestSubVecs():
b_sub.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE)
bcs0 = bcs_by_block([L0.function_spaces[0], L1.function_spaces[0]], bcs)
set_bc_nest(b1, bcs0)
b1.assemble()
b1norm = b1.norm()
assert b1norm == pytest.approx(b0norm, 1.0e-12)
A1norm = nest_matrix_norm(A1)
assert A0norm == pytest.approx(A1norm, 1.0e-12)
x1 = b1.copy()
ksp = PETSc.KSP()
ksp.create(mesh.comm)
ksp.setMonitor(monitor)
ksp.setOperators(A1)
ksp.setType('cg')
ksp.setTolerances(rtol=1.0e-12)
ksp.setFromOptions()
ksp.solve(b1, x1)
x1norm = x1.norm()
assert x1norm == pytest.approx(x0norm, rel=1.0e-12)
# Monolithic version
E = P * P
W = FunctionSpace(mesh, E)
u0, u1 = ufl.TrialFunctions(W)
v0, v1 = ufl.TestFunctions(W)
a = inner(u0, v0) * dx + inner(u1, v1) * dx
L = inner(f, v0) * ufl.dx + inner(g, v1) * dx
a, L = form(a), form(L)
bdofsW0_V0 = locate_dofs_topological(W.sub(0), facetdim, bndry_facets)
bdofsW1_V1 = locate_dofs_topological(W.sub(1), facetdim, bndry_facets)
bcs = [dirichletbc(u0_bc, bdofsW0_V0, W.sub(0)), dirichletbc(u1_bc, bdofsW1_V1, W.sub(1))]
A2 = assemble_matrix(a, bcs=bcs)
A2.assemble()
b2 = assemble_vector(L)
apply_lifting(b2, [a], [bcs])
b2.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE)
set_bc(b2, bcs)
A2norm = A2.norm()
b2norm = b2.norm()
assert A2norm == pytest.approx(A0norm, 1.0e-12)
assert b2norm == pytest.approx(b0norm, 1.0e-12)
x2 = b2.copy()
ksp = PETSc.KSP()
ksp.create(mesh.comm)
ksp.setMonitor(monitor)
ksp.setOperators(A2)
ksp.setType('cg')
ksp.getPC().setType('jacobi')
ksp.setTolerances(rtol=1.0e-12)
ksp.setFromOptions()
ksp.solve(b2, x2)
x2norm = x2.norm()
assert x2norm == pytest.approx(x0norm, 1.0e-10)
def test_matrix_assembly_block(mode):
"""Test assembly of block matrices and vectors into (a) monolithic
blocked structures, PETSc Nest structures, and monolithic
structures"""
mesh = create_unit_square(MPI.COMM_WORLD, 4, 8, ghost_mode=mode)
p0, p1 = 1, 2
P0 = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), p0)
P1 = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), p1)
V0 = FunctionSpace(mesh, P0)
V1 = FunctionSpace(mesh, P1)
# Locate facets on boundary
facetdim = mesh.topology.dim - 1
bndry_facets = locate_entities_boundary(mesh, facetdim, lambda x: np.logical_or(np.isclose(x[0], 0.0),
np.isclose(x[0], 1.0)))
bdofsV1 = locate_dofs_topological(V1, facetdim, bndry_facets)
u_bc = PETSc.ScalarType(50.0)
bc = dirichletbc(u_bc, bdofsV1, V1)
# Define variational problem
u, p = ufl.TrialFunction(V0), ufl.TrialFunction(V1)
v, q = ufl.TestFunction(V0), ufl.TestFunction(V1)
f = 1.0
g = -3.0
zero = Function(V0)
a00 = inner(u, v) * dx
a01 = inner(p, v) * dx
a10 = inner(u, q) * dx
a11 = inner(p, q) * dx
L0 = zero * inner(f, v) * dx
L1 = inner(g, q) * dx
a_block = form([[a00, a01], [a10, a11]])
L_block = form([L0, L1])
# Monolithic blocked
A0 = assemble_matrix_block(a_block, bcs=[bc])
A0.assemble()
b0 = assemble_vector_block(L_block, a_block, bcs=[bc])
assert A0.getType() != "nest"
Anorm0 = A0.norm()
bnorm0 = b0.norm()
# Nested (MatNest)
A1 = assemble_matrix_nest(a_block, bcs=[bc], mat_types=[["baij", "aij"], ["aij", ""]])
A1.assemble()
Anorm1 = nest_matrix_norm(A1)
assert Anorm0 == pytest.approx(Anorm1, 1.0e-12)
b1 = assemble_vector_nest(L_block)
apply_lifting_nest(b1, a_block, bcs=[bc])
for b_sub in b1.getNestSubVecs():
b_sub.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE)
bcs0 = bcs_by_block([L.function_spaces[0] for L in L_block], [bc])
set_bc_nest(b1, bcs0)
b1.assemble()
bnorm1 = math.sqrt(sum([x.norm()**2 for x in b1.getNestSubVecs()]))
assert bnorm0 == pytest.approx(bnorm1, 1.0e-12)
# Monolithic version
E = P0 * P1
W = FunctionSpace(mesh, E)
u0, u1 = ufl.TrialFunctions(W)
v0, v1 = ufl.TestFunctions(W)
a = inner(u0, v0) * dx + inner(u1, v1) * dx + inner(u0, v1) * dx + inner(
u1, v0) * dx
L = zero * inner(f, v0) * ufl.dx + inner(g, v1) * dx
a, L = form(a), form(L)
bdofsW_V1 = locate_dofs_topological(W.sub(1), mesh.topology.dim - 1, bndry_facets)
bc = dirichletbc(u_bc, bdofsW_V1, W.sub(1))
A2 = assemble_matrix(a, bcs=[bc])
A2.assemble()
b2 = assemble_vector(L)
apply_lifting(b2, [a], bcs=[[bc]])
b2.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE)
set_bc(b2, [bc])
assert A2.getType() != "nest"
assert A2.norm() == pytest.approx(Anorm0, 1.0e-9)
assert b2.norm() == pytest.approx(bnorm0, 1.0e-9)
# Output file
file = XDMFFile(MPI.COMM_WORLD, "output.xdmf", "w")
file.write_mesh(mesh)
# Step in time
t = 0.0
# Reduce run time if on test (CI) server
if "CI" in os.environ.keys() or "GITHUB_ACTIONS" in os.environ.keys():
T = 3 * dt
else:
T = 50 * dt
# Get the sub-space for c and the corresponding dofs in the mixed space
# vector
V0, dofs = ME.sub(0).collapse()
# Prepare viewer for plotting the solution during the computation
if have_pyvista:
# Create a VTK 'mesh' with 'nodes' at the function dofs
topology, cell_types, x = plot.create_vtk_mesh(V0)
grid = pv.UnstructuredGrid(topology, cell_types, x)
# Set output data
grid.point_data["c"] = u.x.array[dofs].real
grid.set_active_scalars("c")
p = pvqt.BackgroundPlotter(title="concentration", auto_update=True)
p.add_mesh(grid, clim=[0, 1])
p.view_xy(True)
p.add_text(f"time: {t}", font_size=12, name="timelabel")
r=1
# r=2
# r=3
# x_extents = mesh_bounding_box(mesh, 0)
# y_extents = mesh_bounding_box(mesh, 1)
# Measures
dx = ufl.Measure("dx", domain=mesh)
ds = ufl.Measure("ds", domain=mesh)
element = ufl.MixedElement([ufl.FiniteElement("Regge", ufl.triangle, r),
ufl.FiniteElement("Lagrange", ufl.triangle, r+1)])
V = FunctionSpace(mesh, element)
V_1 = V.sub(1).collapse()
sigma, u = ufl.TrialFunctions(V)
tau, v = ufl.TestFunctions(V)
def S(tau):
return tau - ufl.Identity(2) * ufl.tr(tau)
# Discrete duality inner product
# cf. eq. 4.5 Lizao Li's PhD thesis
def b(tau_S, v):
n = FacetNormal(mesh)
return inner(tau_S, grad(grad(v))) * dx \
- ufl.dot(ufl.dot(tau_S('+'), n('+')), n('+')) * jump(grad(v), n) * dS \
- ufl.dot(ufl.dot(tau_S, n), n) * ufl.dot(grad(v), n) * ds
def create_dictionary_constraint(
V: fem.FunctionSpace,
slave_master_dict: typing.Dict[bytes, typing.Dict[bytes, float]],
subspace_slave: int = None,
subspace_master: int = None):
"""
Returns a multi point constraint for a given function space
and dictionary constraint.
Parameters
----------
V
The function space
slave_master_dict
The dictionary.
subspace_slave
If using mixed or vector space, and only want to use dofs from
a sub space as slave add index here.
subspace_master
Subspace index for mixed or vector spaces
Example
-------
If the dof D located at [d0,d1] should be constrained to the dofs E and
F at [e0,e1] and [f0,f1] as
D = alpha E + beta F
the dictionary should be:
{np.array([d0, d1], dtype=numpy.float64).tobytes():
{numpy.array([e0, e1], dtype=numpy.float64).tobytes(): alpha,
numpy.array([f0, f1], dtype=numpy.float64).tobytes(): beta}}
"""
dfloat = np.float64
comm = V.mesh.comm
bs = V.dofmap.index_map_bs
local_size = V.dofmap.index_map.size_local * bs
index_map = V.dofmap.index_map
owned_entities = {}
ghosted_entities = {}
non_local_entities = {}
slaves_local = {}
slaves_ghost = {}
slave_point_nd = np.zeros((3, 1), dtype=dfloat)
for i, slave_point in enumerate(slave_master_dict.keys()):
num_masters = len(list(slave_master_dict[slave_point].keys()))
# Status for current slave, -1 if not on proc, 0 if ghost, 1 if owned
slave_status = -1
# Wrap slave point as numpy array
sp = np.frombuffer(slave_point, dtype=dfloat)
for j, coord in enumerate(sp):
slave_point_nd[j] = coord
slave_point_nd[len(sp):] = 0
if subspace_slave is None:
slave_dofs = fem.locate_dofs_geometrical(V,
close_to(slave_point_nd))
else:
Vsub = V.sub(subspace_slave).collapse()[0]
slave_dofs = fem.locate_dofs_geometrical(
(V.sub(subspace_slave), Vsub), close_to(slave_point_nd))[0]
if len(slave_dofs) == 1:
# Decide if slave is ghost or not
if slave_dofs[0] < local_size:
slaves_local[i] = slave_dofs[0]
owned_entities[i] = {
"masters": np.full(num_masters, -1, dtype=np.int64),
"coeffs": np.full(num_masters, -1, dtype=np.float64),
"owners": np.full(num_masters, -1, dtype=np.int32),
"master_count": 0,
"local_index": []
}
slave_status = 1
else:
slaves_ghost[i] = slave_dofs[0]
ghosted_entities[i] = {
"masters": np.full(num_masters, -1, dtype=np.int64),
"coeffs": np.full(num_masters, -1, dtype=np.float64),
"owners": np.full(num_masters, -1, dtype=np.int32),
"master_count": 0,
"local_index": []
}
slave_status = 0
elif len(slave_dofs) > 1:
raise RuntimeError("Multiple slaves found at same point. " +
"You should use sub-space locators.")
# Wrap as list to ensure order later
master_points = list(slave_master_dict[slave_point].keys())
master_points_nd = np.zeros((3, len(master_points)), dtype=dfloat)
for (j, master_point) in enumerate(master_points):
# Wrap bytes as numpy array
for k, coord in enumerate(np.frombuffer(master_point,
dtype=dfloat)):
master_points_nd[k, j] = coord
if subspace_master is None:
master_dofs = fem.locate_dofs_geometrical(
V, close_to(master_points_nd[:, j:j + 1]))
else:
Vsub = V.sub(subspace_master).collapse()[0]
master_dofs = fem.locate_dofs_geometrical(
(V.sub(subspace_master), Vsub),
close_to(master_points_nd[:, j:j + 1]))[0]
# Only add masters owned by this processor
master_dofs = master_dofs[master_dofs < local_size]
if len(master_dofs) == 1:
master_block = master_dofs[0] // bs
master_rem = master_dofs % bs
glob_master = index_map.local_to_global([master_block])[0]
if slave_status == -1:
if i in non_local_entities.keys():
non_local_entities[i]["masters"].append(glob_master *
bs +
master_rem)
non_local_entities[i]["coeffs"].append(
slave_master_dict[slave_point][master_point])
non_local_entities[i]["owners"].append(comm.rank),
non_local_entities[i]["local_index"].append(j)
else:
non_local_entities[i] = {
"masters": [glob_master * bs + master_rem],
"coeffs":
[slave_master_dict[slave_point][master_point]],
"owners": [comm.rank],
"local_index": [j]
}
elif slave_status == 0:
ghosted_entities[i]["masters"][
j] = glob_master * bs + master_rem
ghosted_entities[i]["owners"][j] = comm.rank
ghosted_entities[i]["coeffs"][j] = slave_master_dict[
slave_point][master_point]
ghosted_entities[i]["local_index"].append(j)
elif slave_status == 1:
owned_entities[i]["masters"][
j] = glob_master * bs + master_rem
owned_entities[i]["owners"][j] = comm.rank
owned_entities[i]["coeffs"][j] = slave_master_dict[
slave_point][master_point]
owned_entities[i]["local_index"].append(j)
else:
raise RuntimeError(
"Invalid slave status: {0:d} (-1,0,1 are valid options)"
.format(slave_status))
elif len(master_dofs) > 1:
raise RuntimeError(
"Multiple masters found at same point. You should use sub-space locators."
)
# Send the ghost and owned entities to processor 0 to gather them
data_to_send = [owned_entities, ghosted_entities, non_local_entities]
if comm.rank != 0:
comm.send(data_to_send, dest=0, tag=1)
del owned_entities, ghosted_entities, non_local_entities
# Gather all info on proc 0 and sort data
owned_slaves, ghosted_slaves = None, None
if comm.rank == 0:
recv = {0: data_to_send}
for proc in range(1, comm.size):
recv[proc] = comm.recv(source=proc, tag=1)
for proc in range(comm.size):
# Loop through all masters
other_procs = np.arange(comm.size)
other_procs = other_procs[other_procs != proc]
# Loop through all owned slaves and ghosts, and update
# the master entries
for pair in [[0, 1], [1, 0]]:
i, j = pair
for slave in recv[proc][i].keys():
for o_proc in other_procs:
# If slave is ghost on other proc add local masters
if slave in recv[o_proc][j].keys():
# Update master with possible entries from ghost
o_masters = recv[o_proc][j][slave]["local_index"]
for o_master in o_masters:
recv[proc][i][slave]["masters"][
o_master] = recv[o_proc][j][slave][
"masters"][o_master]
recv[proc][i][slave]["coeffs"][
o_master] = recv[o_proc][j][slave][
"coeffs"][o_master]
recv[proc][i][slave]["owners"][
o_master] = recv[o_proc][j][slave][
"owners"][o_master]
# If proc only has master, but not the slave
if slave in recv[o_proc][2].keys():
o_masters = recv[o_proc][2][slave]["local_index"]
# As non owned indices only store non-zero entries
for k, o_master in enumerate(o_masters):
recv[proc][i][slave]["masters"][
o_master] = recv[o_proc][2][slave][
"masters"][k]
recv[proc][i][slave]["coeffs"][
o_master] = recv[o_proc][2][slave][
"coeffs"][k]
recv[proc][i][slave]["owners"][
o_master] = recv[o_proc][2][slave][
"owners"][k]
if proc == comm.rank:
owned_slaves = recv[proc][0]
ghosted_slaves = recv[proc][1]
else:
# If no owned masters, do not send masters
if len(recv[proc][0].keys()) > 0:
comm.send(recv[proc][0], dest=proc, tag=55)
if len(recv[proc][1].keys()) > 0:
comm.send(recv[proc][1], dest=proc, tag=66)
else:
if len(slaves_local.keys()) > 0:
owned_slaves = comm.recv(source=0, tag=55)
if len(slaves_ghost.keys()) > 0:
ghosted_slaves = comm.recv(source=0, tag=66)
# Flatten slaves (local)
slaves, masters, coeffs, owners, offsets = [], [], [], [], [0]
for slave_index in slaves_local.keys():
slaves.append(slaves_local[slave_index])
masters.extend(owned_slaves[slave_index]["masters"]) # type: ignore
owners.extend(owned_slaves[slave_index]["owners"]) # type: ignore
coeffs.extend(owned_slaves[slave_index]["coeffs"]) # type: ignore
offsets.append(len(masters))
for slave_index in slaves_ghost.keys():
slaves.append(slaves_ghost[slave_index])
masters.extend(ghosted_slaves[slave_index]["masters"]) # type: ignore
owners.extend(ghosted_slaves[slave_index]["owners"]) # type: ignore
coeffs.extend(ghosted_slaves[slave_index]["coeffs"]) # type: ignore
offsets.append(len(masters))
return (np.asarray(slaves,
dtype=np.int32), np.asarray(masters, dtype=np.int64),
np.asarray(coeffs, dtype=PETSc.ScalarType),
np.asarray(owners,
dtype=np.int32), np.asarray(offsets, dtype=np.int32))
