def run_vector_test(mesh, V, degree):
"""Projection into H(div/curl) spaces"""
u, v = ufl.TrialFunction(V), ufl.TestFunction(V)
a = inner(u, v) * dx
# Source term
x = SpatialCoordinate(mesh)
u_exact = x[0]**degree
L = inner(u_exact, v[0]) * dx
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 (Note: need to use a solver that
# re-orders to handle pivots, e.g. not the PETSc built-in LU solver)
solver = PETSc.KSP().create(MPI.COMM_WORLD)
solver.setType("preonly")
solver.getPC().setType('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[0])**2 * dx
for i in range(1, mesh.topology.dim):
M += uh[i]**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
def test_context_manager_named():
"""Test that named Timer works as context manager"""
task = get_random_task_name()
# Execute task in the context manager
t = common.Timer(task)
sleep(0.05)
assert t.elapsed()[0] >= 0.05
del t
# Check timing
t = common.timing(task)
assert t[0] == 1
assert t[1] >= 0.05
def mesh_3D_dolfin(theta: float = 0,
ct: _mesh.CellType = _mesh.CellType.tetrahedron,
ext: str = "tetrahedron",
res: float = 0.1):
timer = _common.Timer("~~Contact: Create mesh")
def find_plane_function(p0, p1, p2):
"""
Find plane function given three points:
http://www.nabla.hr/CG-LinesPlanesIn3DA3.htm
"""
v1 = np.array(p1) - np.array(p0)
v2 = np.array(p2) - np.array(p0)
n = np.cross(v1, v2)
D = -(n[0] * p0[0] + n[1] * p0[1] + n[2] * p0[2])
return lambda x: np.isclose(0, np.dot(n, x) + D)
def over_plane(p0, p1, p2):
"""
Returns function that checks if a point is over a plane defined
by the points p0, p1 and p2.
"""
v1 = np.array(p1) - np.array(p0)
v2 = np.array(p2) - np.array(p0)
n = np.cross(v1, v2)
D = -(n[0] * p0[0] + n[1] * p0[1] + n[2] * p0[2])
return lambda x: n[0] * x[0] + n[1] * x[1] + D > -n[2] * x[2]
N = int(1 / res)
if MPI.COMM_WORLD.rank == 0:
mesh0 = _mesh.create_unit_cube(MPI.COMM_SELF, N, N, N, ct)
mesh1 = _mesh.create_unit_cube(MPI.COMM_SELF, 2 * N, 2 * N, 2 * N, ct)
mesh0.geometry.x[:, 2] += 1
# Stack the two meshes in one mesh
r_matrix = _utils.rotation_matrix([1 / np.sqrt(2), 1 / np.sqrt(2), 0],
-theta)
points = np.vstack([mesh0.geometry.x, mesh1.geometry.x])
points = np.dot(r_matrix, points.T).T
# Transform topology info into geometry info
tdim0 = mesh0.topology.dim
num_cells0 = mesh0.topology.index_map(tdim0).size_local
cells0 = _cpp.mesh.entities_to_geometry(
mesh0, tdim0,
np.arange(num_cells0, dtype=np.int32).reshape((-1, 1)), False)
tdim1 = mesh1.topology.dim
num_cells1 = mesh1.topology.index_map(tdim1).size_local
cells1 = _cpp.mesh.entities_to_geometry(
mesh1, tdim1,
np.arange(num_cells1, dtype=np.int32).reshape((-1, 1)), False)
cells1 += mesh0.geometry.x.shape[0]
cells = np.vstack([cells0, cells1])
cell = ufl.Cell(ext, geometric_dimension=points.shape[1])
domain = ufl.Mesh(ufl.VectorElement("Lagrange", cell, 1))
mesh = _mesh.create_mesh(MPI.COMM_SELF, cells, points, domain)
tdim = mesh.topology.dim
fdim = tdim - 1
# Find information about facets to be used in meshtags
bottom_points = np.dot(
r_matrix,
np.array([[0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0]]).T)
bottom = find_plane_function(bottom_points[:, 0], bottom_points[:, 1],
bottom_points[:, 2])
bottom_facets = _mesh.locate_entities_boundary(mesh, fdim, bottom)
top_points = np.dot(
r_matrix,
np.array([[0, 0, 2], [1, 0, 2], [0, 1, 2], [1, 1, 2]]).T)
top = find_plane_function(top_points[:, 0], top_points[:, 1],
top_points[:, 2])
top_facets = _mesh.locate_entities_boundary(mesh, fdim, top)
# left_side = find_line_function(top_points[:, 0], top_points[:, 3])
# left_facets = _mesh.locate_entities_boundary(
# mesh, fdim, left_side)
# right_side = find_line_function(top_points[:, 1], top_points[:, 2])
# right_facets = _mesh.locate_entities_boundary(
# mesh, fdim, right_side)
if_points = np.dot(
r_matrix,
np.array([[0, 0, 1], [1, 0, 1], [0, 1, 1], [1, 1, 1]]).T)
interface = find_plane_function(if_points[:, 0], if_points[:, 1],
if_points[:, 2])
i_facets = _mesh.locate_entities_boundary(mesh, fdim, interface)
mesh.topology.create_connectivity(fdim, tdim)
top_interface = []
bottom_interface = []
facet_to_cell = mesh.topology.connectivity(fdim, tdim)
num_cells = mesh.topology.index_map(tdim).size_local
cell_midpoints = _cpp.mesh.compute_midpoints(mesh, tdim,
range(num_cells))
top_cube = over_plane(if_points[:, 0], if_points[:, 1], if_points[:,
2])
for facet in i_facets:
i_cells = facet_to_cell.links(facet)
assert (len(i_cells == 1))
i_cell = i_cells[0]
if top_cube(cell_midpoints[i_cell]):
top_interface.append(facet)
else:
bottom_interface.append(facet)
num_cells = mesh.topology.index_map(tdim).size_local
cell_midpoints = _cpp.mesh.compute_midpoints(mesh, tdim,
range(num_cells))
top_cube_marker = 2
indices = []
values = []
for cell_index in range(num_cells):
if top_cube(cell_midpoints[cell_index]):
indices.append(cell_index)
values.append(top_cube_marker)
ct = _mesh.meshtags(mesh, tdim, np.array(indices, dtype=np.int32),
np.array(values, dtype=np.int32))
# Create meshtags for facet data
markers: Dict[int, np.ndarray] = {
3: top_facets,
4: np.hstack(bottom_interface),
9: np.hstack(top_interface),
5: bottom_facets
} # , 6: left_facets, 7: right_facets}
all_indices = []
all_values = []
for key in markers.keys():
all_indices.append(np.asarray(markers[key], dtype=np.int32))
all_values.append(np.full(len(markers[key]), key, dtype=np.int32))
arg_sort = np.argsort(np.hstack(all_indices))
sorted_vals = np.asarray(np.hstack(all_values)[arg_sort],
dtype=np.int32)
sorted_indices = np.asarray(np.hstack(all_indices)[arg_sort],
dtype=np.int32)
mt = _mesh.meshtags(mesh, fdim, sorted_indices, sorted_vals)
mt.name = "facet_tags" # type: ignore
fname = f"meshes/mesh_{ext}_{theta:.2f}.xdmf"
with _io.XDMFFile(MPI.COMM_SELF, fname, "w") as o_f:
o_f.write_mesh(mesh)
o_f.write_meshtags(ct)
o_f.write_meshtags(mt)
timer.stop()
def run_scalar_test(mesh, V, degree):
""" Manufactured Poisson problem, solving u = x[1]**p, where p is the
degree of the Lagrange function space.
"""
u, v = TrialFunction(V), TestFunction(V)
a = inner(grad(u), grad(v)) * dx
# Get quadrature degree for bilinear form integrand (ignores effect of non-affine map)
a = inner(grad(u), grad(v)) * dx(metadata={"quadrature_degree": -1})
a.integrals()[0].metadata(
)["quadrature_degree"] = ufl.algorithms.estimate_total_polynomial_degree(a)
# Source term
x = SpatialCoordinate(mesh)
u_exact = x[1]**degree
f = -div(grad(u_exact))
# Set quadrature degree for linear form integrand (ignores effect of non-affine map)
L = inner(f, v) * dx(metadata={"quadrature_degree": -1})
L.integrals()[0].metadata(
)["quadrature_degree"] = ufl.algorithms.estimate_total_polynomial_degree(L)
with common.Timer("Linear form compile"):
L = fem.Form(L)
with common.Timer("Function interpolation"):
u_bc = Function(V)
u_bc.interpolate(lambda x: x[1]**degree)
# Create Dirichlet boundary condition
mesh.topology.create_connectivity_all()
facetdim = mesh.topology.dim - 1
bndry_facets = np.where(
np.array(cpp.mesh.compute_boundary_facets(mesh.topology)) == 1)[0]
bdofs = locate_dofs_topological(V, facetdim, bndry_facets)
bc = DirichletBC(u_bc, bdofs)
with common.Timer("Vector assembly"):
b = assemble_vector(L)
apply_lifting(b, [a], [[bc]])
b.ghostUpdate(addv=PETSc.InsertMode.ADD,
mode=PETSc.ScatterMode.REVERSE)
set_bc(b, [bc])
with common.Timer("Bilinear form compile"):
a = fem.Form(a)
with common.Timer("Matrix assembly"):
A = assemble_matrix(a, [bc])
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)
with common.Timer("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"):
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
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
def facet_normal_approximation(V, mt: _cpp.mesh.MeshTags_int32, mt_id: int, tangent=False, jit_params: dict = {},
form_compiler_params: dict = {}):
"""
Approximate the facet normal by projecting it into the function space for a set of facets
Parameters
----------
V
The function space to project into
mt
The `dolfinx.mesh.MeshTagsMetaClass` containing facet markers
mt_id
The id for the facets in `mt` we want to represent the normal at
tangent
To approximate the tangent to the facet set this flag to `True`
jit_params
Parameters used in CFFI JIT compilation of C code generated by FFCx.
See `DOLFINx-documentation <https://github.com/FEniCS/dolfinx/blob/main/python/dolfinx/jit.py#L22-L37>`
for all available parameters. Takes priority over all other parameter values.
form_compiler_params
Parameters used in FFCx compilation of this form. Run `ffcx - -help` at
the commandline to see all available options. Takes priority over all
other parameter values, except for `scalar_type` which is determined by
DOLFINx.
"""
timer = _common.Timer("~MPC: Facet normal projection")
comm = V.mesh.comm
n = ufl.FacetNormal(V.mesh)
nh = _fem.Function(V)
u, v = ufl.TrialFunction(V), ufl.TestFunction(V)
ds = ufl.ds(domain=V.mesh, subdomain_data=mt, subdomain_id=mt_id)
if tangent:
if V.mesh.geometry.dim == 1:
raise ValueError("Tangent not defined for 1D problem")
elif V.mesh.geometry.dim == 2:
a = ufl.inner(u, v) * ds
L = ufl.inner(ufl.as_vector([-n[1], n[0]]), v) * ds
else:
def tangential_proj(u, n):
"""
See for instance:
https://link.springer.com/content/pdf/10.1023/A:1022235512626.pdf
"""
return (ufl.Identity(u.ufl_shape[0]) - ufl.outer(n, n)) * u
c = _fem.Constant(V.mesh, [1, 1, 1])
a = ufl.inner(u, v) * ds
L = ufl.inner(tangential_proj(c, n), v) * ds
else:
a = (ufl.inner(u, v) * ds)
L = ufl.inner(n, v) * ds
# Find all dofs that are not boundary dofs
imap = V.dofmap.index_map
all_blocks = np.arange(imap.size_local, dtype=np.int32)
top_blocks = _fem.locate_dofs_topological(V, V.mesh.topology.dim - 1, mt.find(mt_id))
deac_blocks = all_blocks[np.isin(all_blocks, top_blocks, invert=True)]
# Note there should be a better way to do this
# Create sparsity pattern only for constraint + bc
bilinear_form = _fem.form(a, jit_params=jit_params,
form_compiler_params=form_compiler_params)
pattern = _fem.create_sparsity_pattern(bilinear_form)
pattern.insert_diagonal(deac_blocks)
pattern.assemble()
u_0 = _fem.Function(V)
u_0.vector.set(0)
bc_deac = _fem.dirichletbc(u_0, deac_blocks)
A = _cpp.la.petsc.create_matrix(comm, pattern)
A.zeroEntries()
# Assemble the matrix with all entries
form_coeffs = _cpp.fem.pack_coefficients(bilinear_form)
form_consts = _cpp.fem.pack_constants(bilinear_form)
_cpp.fem.petsc.assemble_matrix(A, bilinear_form, form_consts, form_coeffs, [bc_deac])
if bilinear_form.function_spaces[0] is bilinear_form.function_spaces[1]:
A.assemblyBegin(PETSc.Mat.AssemblyType.FLUSH)
A.assemblyEnd(PETSc.Mat.AssemblyType.FLUSH)
_cpp.fem.petsc.insert_diagonal(A, bilinear_form.function_spaces[0], [bc_deac], 1.0)
A.assemble()
linear_form = _fem.form(L, jit_params=jit_params,
form_compiler_params=form_compiler_params)
b = _fem.petsc.assemble_vector(linear_form)
_fem.petsc.apply_lifting(b, [bilinear_form], [[bc_deac]])
b.ghostUpdate(addv=PETSc.InsertMode.ADD_VALUES, mode=PETSc.ScatterMode.REVERSE)
_fem.petsc.set_bc(b, [bc_deac])
# Solve Linear problem
solver = PETSc.KSP().create(MPI.COMM_WORLD)
solver.setType("cg")
solver.rtol = 1e-8
solver.setOperators(A)
solver.solve(b, nh.vector)
nh.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD)
timer.stop()
return nh
def test_context_manager_anonymous():
"""Test that anonymous Timer works as context manager"""
with common.Timer() as t:
sleep(0.05)
assert t.elapsed()[0] >= 0.05
def nitsche_ufl(mesh: dmesh.Mesh, mesh_data: Tuple[_cpp.mesh.MeshTags_int32, int, int],
physical_parameters: dict = {}, nitsche_parameters: Dict[str, float] = {},
plane_loc: float = 0.0, vertical_displacement: float = -0.1,
nitsche_bc: bool = True, quadrature_degree: int = 5, form_compiler_params: Dict = {},
jit_params: Dict = {}, petsc_options: Dict = {}, newton_options: Dict = {}) -> _fem.Function:
"""
Use UFL to compute the one sided contact problem with a mesh coming into contact
with a rigid surface (not meshed).
Parameters
==========
mesh
The input mesh
mesh_data
A triplet with a mesh tag for facets and values v0, v1. v0 should be the value in the mesh tags
for facets to apply a Dirichlet condition on. v1 is the value for facets which should have applied
a contact condition on
physical_parameters
Optional dictionary with information about the linear elasticity problem.
Valid (key, value) tuples are: ('E': float), ('nu', float), ('strain', bool)
nitsche_parameters
Optional dictionary with information about the Nitsche configuration.
Valid (keu, value) tuples are: ('gamma', float), ('theta', float) where theta can be -1, 0 or 1 for
skew-symmetric, penalty like or symmetric enforcement of Nitsche conditions
plane_loc
The location of the plane in y-coordinate (2D) and z-coordinate (3D)
vertical_displacement
The amount of verticial displacment enforced on Dirichlet boundary
nitsche_bc
Use Nitche's method to enforce Dirichlet boundary conditions
quadrature_degree
The quadrature degree to use for the custom contact kernels
form_compiler_params
Parameters used in FFCX compilation of this form. Run `ffcx --help` at
the commandline to see all available options. Takes priority over all
other parameter values, except for `scalar_type` which is determined by
DOLFINX.
jit_params
Parameters used in CFFI JIT compilation of C code generated by FFCX.
See https://github.com/FEniCS/dolfinx/blob/main/python/dolfinx/jit.py
for all available parameters. Takes priority over all other parameter values.
petsc_options
Parameters that is passed to the linear algebra backend
PETSc. For available choices for the 'petsc_options' kwarg,
see the `PETSc-documentation
<https://petsc4py.readthedocs.io/en/stable/manual/ksp/>`
newton_options
Dictionary with Newton-solver options. Valid (key, item) tuples are:
("atol", float), ("rtol", float), ("convergence_criterion", "str"),
("max_it", int), ("error_on_nonconvergence", bool), ("relaxation_parameter", float)
"""
# Compute lame parameters
plane_strain = physical_parameters.get("strain", False)
E = physical_parameters.get("E", 1e3)
nu = physical_parameters.get("nu", 0.1)
mu_func, lambda_func = lame_parameters(plane_strain)
mu = mu_func(E, nu)
lmbda = lambda_func(E, nu)
sigma = sigma_func(mu, lmbda)
# Nitche parameters and variables
theta = nitsche_parameters.get("theta", 1)
gamma = nitsche_parameters.get("gamma", 1)
(facet_marker, top_value, bottom_value) = mesh_data
assert(facet_marker.dim == mesh.topology.dim - 1)
# Normal vector pointing into plane (but outward of the body coming into contact)
# Similar to computing the normal by finding the gap vector between two meshes
n_vec = np.zeros(mesh.geometry.dim)
n_vec[mesh.geometry.dim - 1] = -1
n_2 = ufl.as_vector(n_vec) # Normal of plane (projection onto other body)
# Scaled Nitsche parameter
h = ufl.CellDiameter(mesh)
gamma_scaled = gamma * E / h
# Mimicking the plane y=-plane_loc
x = ufl.SpatialCoordinate(mesh)
gap = x[mesh.geometry.dim - 1] + plane_loc
g_vec = [i for i in range(mesh.geometry.dim)]
g_vec[mesh.geometry.dim - 1] = gap
V = _fem.VectorFunctionSpace(mesh, ("CG", 1))
u = _fem.Function(V)
v = ufl.TestFunction(V)
metadata = {"quadrature_degree": quadrature_degree}
dx = ufl.Measure("dx", domain=mesh)
ds = ufl.Measure("ds", domain=mesh, metadata=metadata,
subdomain_data=facet_marker)
a = ufl.inner(sigma(u), epsilon(v)) * dx
zero = np.asarray([0, ] * mesh.geometry.dim, dtype=_PETSc.ScalarType)
L = ufl.inner(_fem.Constant(mesh, zero), v) * dx
# Derivation of one sided Nitsche with gap function
n = ufl.FacetNormal(mesh)
def sigma_n(v):
# NOTE: Different normals, see summary paper
return ufl.dot(sigma(v) * n, n_2)
F = a - theta / gamma_scaled * sigma_n(u) * sigma_n(v) * ds(bottom_value) - L
F += 1 / gamma_scaled * R_minus(sigma_n(u) + gamma_scaled * (gap - ufl.dot(u, n_2))) * \
(theta * sigma_n(v) - gamma_scaled * ufl.dot(v, n_2)) * ds(bottom_value)
# Compute corresponding Jacobian
du = ufl.TrialFunction(V)
q = sigma_n(u) + gamma_scaled * (gap - ufl.dot(u, n_2))
J = ufl.inner(sigma(du), epsilon(v)) * ufl.dx - theta / gamma_scaled * sigma_n(du) * sigma_n(v) * ds(bottom_value)
J += 1 / gamma_scaled * 0.5 * (1 - ufl.sign(q)) * (sigma_n(du) - gamma_scaled * ufl.dot(du, n_2)) * \
(theta * sigma_n(v) - gamma_scaled * ufl.dot(v, n_2)) * ds(bottom_value)
# Nitsche for Dirichlet, another theta-scheme.
# https://doi.org/10.1016/j.cma.2018.05.024
if nitsche_bc:
disp_vec = np.zeros(mesh.geometry.dim)
disp_vec[mesh.geometry.dim - 1] = vertical_displacement
u_D = ufl.as_vector(disp_vec)
F += - ufl.inner(sigma(u) * n, v) * ds(top_value)\
- theta * ufl.inner(sigma(v) * n, u - u_D) * \
ds(top_value) + gamma_scaled / h * ufl.inner(u - u_D, v) * ds(top_value)
bcs = []
J += - ufl.inner(sigma(du) * n, v) * ds(top_value)\
- theta * ufl.inner(sigma(v) * n, du) * \
ds(top_value) + gamma_scaled / h * ufl.inner(du, v) * ds(top_value)
else:
# strong Dirichlet boundary conditions
def _u_D(x):
values = np.zeros((mesh.geometry.dim, x.shape[1]))
values[mesh.geometry.dim - 1] = vertical_displacement
return values
u_D = _fem.Function(V)
u_D.interpolate(_u_D)
u_D.name = "u_D"
u_D.x.scatter_forward()
tdim = mesh.topology.dim
dirichlet_dofs = _fem.locate_dofs_topological(V, tdim - 1, facet_marker.find(top_value))
bc = _fem.dirichletbc(u_D, dirichlet_dofs)
bcs = [bc]
# DEBUG: Write each step of Newton iterations
# Create nonlinear problem and Newton solver
# def form(self, x: _PETSc.Vec):
# x.ghostUpdate(addv=_PETSc.InsertMode.INSERT, mode=_PETSc.ScatterMode.FORWARD)
# self.i += 1
# xdmf.write_function(u, self.i)
# setattr(_fem.petsc.NonlinearProblem, "form", form)
problem = _fem.petsc.NonlinearProblem(F, u, bcs, J=J, jit_params=jit_params,
form_compiler_params=form_compiler_params)
# DEBUG: Write each step of Newton iterations
# problem.i = 0
# xdmf = _io.XDMFFile(mesh.comm, "results/tmp_sol.xdmf", "w")
# xdmf.write_mesh(mesh)
solver = _nls.petsc.NewtonSolver(mesh.comm, problem)
null_space = rigid_motions_nullspace(V)
solver.A.setNearNullSpace(null_space)
# Set Newton solver options
solver.atol = newton_options.get("atol", 1e-9)
solver.rtol = newton_options.get("rtol", 1e-9)
solver.convergence_criterion = newton_options.get("convergence_criterion", "incremental")
solver.max_it = newton_options.get("max_it", 50)
solver.error_on_nonconvergence = newton_options.get("error_on_nonconvergence", True)
solver.relaxation_parameter = newton_options.get("relaxation_parameter", 0.8)
def _u_initial(x):
values = np.zeros((mesh.geometry.dim, x.shape[1]))
values[-1] = -0.01 - plane_loc
return values
# Set initial_condition:
u.interpolate(_u_initial)
# Define solver and options
ksp = solver.krylov_solver
opts = _PETSc.Options()
option_prefix = ksp.getOptionsPrefix()
# Set PETSc options
opts = _PETSc.Options()
opts.prefixPush(option_prefix)
for k, v in petsc_options.items():
opts[k] = v
opts.prefixPop()
ksp.setFromOptions()
# Solve non-linear problem
_log.set_log_level(_log.LogLevel.INFO)
num_dofs_global = V.dofmap.index_map_bs * V.dofmap.index_map.size_global
with _common.Timer(f"{num_dofs_global} Solve Nitsche"):
n, converged = solver.solve(u)
u.x.scatter_forward()
if solver.error_on_nonconvergence:
assert(converged)
print(f"{num_dofs_global}, Number of interations: {n:d}")
return u
