This tutorial is part of multiphenicsx.

Tutorial 03: weak imposition of Dirichlet BCs by a Lagrange multiplier (linear problem)

In this tutorial we solve the problem

$$\begin{cases} -\Delta u = f, & \text{in } \Omega,\\ u = g, & \text{on } \Gamma = \partial\Omega, \end{cases}$$

where $\Omega$ is a ball in 2D.

We compare the following two cases: the corresponding weak formulation is

• strong imposition of Dirichlet BCs:

$$ \text{find } u \in V_g \text{ s.t. } \int_\Omega \nabla u \cdot \nabla v = \int_\Omega f v, \quad \forall v \in V_0\\ $$ where $$ V_g = \{v \in H^1(\Omega): v|_\Gamma = g\},\\ V_0 = \{v \in H^1(\Omega): v|_\Gamma = 0\}.\\ $$

• weak imposition of Dirichlet BCs: this requires an introduction of a multiplier $\lambda$ which is restricted to $\Gamma$, and solves $$ \text{find } w, \lambda \in V \times M \text{ s.t. }\\ \begin{cases} \int_\Omega \nabla w \cdot \nabla v + \int_\Gamma \lambda v = \int_\Omega f v, & \forall v \in V,\\ \int_\Gamma w \mu = \int_\Gamma g \mu, & \forall \mu \in M \end{cases} $$ where $$ V = H^1(\Omega),\\ M = L^{2}(\Gamma).\\ $$

This example is a prototypical case of problems containing subdomain/boundary restricted variables (the Lagrange multiplier, in this case).

import dolfinx.fem
import dolfinx.fem.petsc
import dolfinx.io
import gmsh
import mpi4py.MPI
import numpy as np
import petsc4py.PETSc
import ufl
import viskex

import multiphenicsx.fem
import multiphenicsx.fem.petsc

Geometrical parameters

r = 3
mesh_size = 1. / 4.

Mesh

gmsh.initialize()
gmsh.model.add("mesh")

p0 = gmsh.model.geo.addPoint(0.0, 0.0, 0.0, mesh_size)
p1 = gmsh.model.geo.addPoint(0.0, +r, 0.0, mesh_size)
p2 = gmsh.model.geo.addPoint(0.0, -r, 0.0, mesh_size)
c0 = gmsh.model.geo.addCircleArc(p1, p0, p2)
c1 = gmsh.model.geo.addCircleArc(p2, p0, p1)
boundary = gmsh.model.geo.addCurveLoop([c0, c1])
domain = gmsh.model.geo.addPlaneSurface([boundary])

gmsh.model.geo.synchronize()
gmsh.model.addPhysicalGroup(1, [c0, c1], 1)
gmsh.model.addPhysicalGroup(2, [boundary], 0)
gmsh.model.mesh.generate(2)

Info    : Meshing 1D...
Info    : [  0%] Meshing curve 1 (Circle)
Info    : [ 50%] Meshing curve 2 (Circle)
Info    : Done meshing 1D (Wall 0.000317883s, CPU 0.000548s)
Info    : Meshing 2D...
Info    : Meshing surface 1 (Plane, Frontal-Delaunay)
Info    : Done meshing 2D (Wall 0.0319924s, CPU 0.012821s)
Info    : 589 nodes 1177 elements

mesh, subdomains, boundaries = dolfinx.io.gmshio.model_to_mesh(
    gmsh.model, comm=mpi4py.MPI.COMM_WORLD, rank=0, gdim=2)
gmsh.finalize()

# Create connectivities required by the rest of the code
mesh.topology.create_connectivity(mesh.topology.dim - 1, mesh.topology.dim)

facets_Gamma = boundaries.indices[boundaries.values == 1]

viskex.dolfinx.plot_mesh(mesh)

viskex.dolfinx.plot_mesh_tags(mesh, boundaries, "boundaries")

Weak imposition of Dirichlet BCs

# Define a function space
V = dolfinx.fem.functionspace(mesh, ("Lagrange", 2))
M = V.clone()

# Define restrictions
dofs_V = np.arange(0, V.dofmap.index_map.size_local + V.dofmap.index_map.num_ghosts)
dofs_M_Gamma = dolfinx.fem.locate_dofs_topological(M, boundaries.dim, facets_Gamma)
restriction_V = multiphenicsx.fem.DofMapRestriction(V.dofmap, dofs_V)
restriction_M_Gamma = multiphenicsx.fem.DofMapRestriction(M.dofmap, dofs_M_Gamma)
restriction = [restriction_V, restriction_M_Gamma]

# Define trial and test functions
(u, l) = (ufl.TrialFunction(V), ufl.TrialFunction(M))
(v, m) = (ufl.TestFunction(V), ufl.TestFunction(M))

# Define problem block forms
g = dolfinx.fem.Function(V)
g.interpolate(lambda x: np.sin(3 * x[0] + 1) * np.sin(3 * x[1] + 1))
a = [[ufl.inner(ufl.grad(u), ufl.grad(v)) * ufl.dx, ufl.inner(l, v) * ufl.ds],
     [ufl.inner(u, m) * ufl.ds, None]]
f = [ufl.inner(1, v) * ufl.dx, ufl.inner(g, m) * ufl.ds]
a_cpp = dolfinx.fem.form(a)
f_cpp = dolfinx.fem.form(f)

# Assemble the block linear system
A = multiphenicsx.fem.petsc.assemble_matrix_block(a_cpp, bcs=[], restriction=(restriction, restriction))
A.assemble()
F = multiphenicsx.fem.petsc.assemble_vector_block(f_cpp, a_cpp, bcs=[], restriction=restriction)

# Solve
ul = multiphenicsx.fem.petsc.create_vector_block(f_cpp, restriction=restriction)
ksp = petsc4py.PETSc.KSP()
ksp.create(mesh.comm)
ksp.setOperators(A)
ksp.setType("preonly")
ksp.getPC().setType("lu")
ksp.getPC().setFactorSolverType("mumps")
ksp.setFromOptions()
ksp.solve(F, ul)
ul.ghostUpdate(addv=petsc4py.PETSc.InsertMode.INSERT, mode=petsc4py.PETSc.ScatterMode.FORWARD)
ksp.destroy()

<petsc4py.PETSc.KSP at 0x7fd69c62e5c0>

# Split the block solution in components
(u, l) = (dolfinx.fem.Function(V), dolfinx.fem.Function(M))
with multiphenicsx.fem.petsc.BlockVecSubVectorWrapper(ul, [V.dofmap, M.dofmap], restriction) as ul_wrapper:
    for ul_wrapper_local, component in zip(ul_wrapper, (u, l)):
        with component.x.petsc_vec.localForm() as component_local:
            component_local[:] = ul_wrapper_local
ul.destroy()

<petsc4py.PETSc.Vec at 0x7fd69c62fe70>

viskex.dolfinx.plot_scalar_field(u, "u")

viskex.dolfinx.plot_scalar_field(l, "l")

Strong imposition of Dirichlet BCs

# Define Dirichlet BC object on Gamma
dofs_V_Gamma = dolfinx.fem.locate_dofs_topological(V, boundaries.dim, facets_Gamma)
bc_ex = dolfinx.fem.dirichletbc(g, dofs_V_Gamma)

# Solve
u_ex = dolfinx.fem.Function(V)
problem_ex = dolfinx.fem.petsc.LinearProblem(
    a[0][0], f[0], bcs=[bc_ex], u=u_ex,
    petsc_options={"ksp_type": "preonly", "pc_type": "lu", "pc_factor_mat_solver_type": "mumps"})
problem_ex.solve()
u_ex.x.petsc_vec.ghostUpdate(addv=petsc4py.PETSc.InsertMode.INSERT, mode=petsc4py.PETSc.ScatterMode.FORWARD)

viskex.dolfinx.plot_scalar_field(u_ex, "u")

Comparison and error computation

u_ex_norm = np.sqrt(mesh.comm.allreduce(
    dolfinx.fem.assemble_scalar(dolfinx.fem.form(ufl.inner(ufl.grad(u_ex), ufl.grad(u_ex)) * ufl.dx)),
    op=mpi4py.MPI.SUM))
err_norm = np.sqrt(mesh.comm.allreduce(
    dolfinx.fem.assemble_scalar(dolfinx.fem.form(ufl.inner(ufl.grad(u_ex - u), ufl.grad(u_ex - u)) * ufl.dx)),
    op=mpi4py.MPI.SUM))
print("Relative error is equal to", err_norm / u_ex_norm)
assert np.isclose(err_norm / u_ex_norm, 0., atol=1.e-10)

Relative error is equal to 5.75871463647993e-15