# Setting multiple Dirichlet, Neumann, and Robin conditions#

Author: Hans Petter Langtangen and Anders Logg

We consider the variable coefficient example from the previous section. In this section we will cover how to apply a mixture of Dirichlet, Neumann and Robin type boundary conditions for this type of problem.

We divide our boundary into three distinct sections:

• $$\Gamma_D$$ for Dirichlet conditions: $$u=u_D^i \text{ on } \Gamma_D^i, \dots$$ where $$\Gamma_D=\Gamma_D^0\cup \Gamma_D^1 \cup \dots$$.

• $$\Gamma_N$$ for Neumann conditions: $$-\kappa \frac{\partial u}{\partial n}=g_j \text{ on } \Gamma_N^j$$ where $$\Gamma_N=\Gamma_N^0\cup \Gamma_N^1 \cup \dots$$.

• $$\Gamma_R$$ for Robin conditions: $$-\kappa \frac{\partial u}{\partial n}=r(u-s)$$

where $$r$$ and $$s$$ are specified functions. The Robin condition is most often used to model heat transfer to the surroundings and arise naturally from Newton’s cooling law. In that case, $$r$$ is a heat transfer coefficient, and $$s$$ is the temperature of the surroundings. Both can be space and time-dependent. The Robin conditions apply at some parts $$\Gamma_R^0,\Gamma_R^1,\dots$$, of the boundary:

$-\kappa \frac{\partial u}{\partial n}=r_k(u-s_k) \text{ on } \Gamma_R^k$

## The PDE problem and variational formulation#

We can summarize the PDE problem as

$-\nabla (\kappa \nabla u) = f \qquad \text{in } \Omega,$
$u=u_D^i \qquad \text{on } \Gamma_D^i,$
$-\kappa \frac{\partial u}{\partial n}=g_j \quad\text{on } \Gamma_N^j,$
$-\kappa \frac{\partial u}{\partial n}=r_k(u-s_k)\quad \text{ on } \Gamma_R^k,$

As usual, we multiply by a test function and integrate by parts.

$-\int_{\Omega}\nabla \cdot (\kappa \nabla u)v ~\mathrm{d} x = \int_{\Omega}\kappa \nabla u\cdot \nabla v~\mathrm{d} x - \int_{\partial\Omega}\kappa \frac{\partial u}{\partial n} v ~\mathrm{d} s.$

On the Dirichlet part ($$\Gamma_D^i$$), the boundary integral vanishes since $$v=0$$. On the remaining part of the boundary, we split the boundary into contributions from the Neumann parts ($$\Gamma_N^i$$) and Robin parts ($$\Gamma_R^i$$). Inserting the boundary conditions, we obtain

$-\int_{\Omega}\kappa\frac{\partial u }{\partial n }v~\mathrm{d} s=\sum_i \int_{\Gamma_N^i} g_i~\mathrm{d} s + \sum_i\int_{\Gamma_R^i}r_i(u-s_i)~\mathrm{d}s.$

Thus we have the following variational problem

$F(u, v)=\int_\Omega \kappa \nabla u \cdot \nabla v~\mathrm{d} x + \sum_i\int_{\Gamma_N^i}g_i v~\mathrm{d}s +\sum_i\int_{\Gamma_R^i}r_i(u-s_i)~\mathrm{d}s - \int_\Omega fv~\mathrm{d} x = 0.$

We have been used to writing the variational formulation as $$a(u,v)=L(v)$$, which requires that we identify the integrals dependent on the trial function $$u$$ and collect these in $$a(u,v)$$, while the remaining terms form $$L(v)$$. We note that the Robin condition has a contribution to both $$a(u,v)$$ and $$L(v)$$. We then have

$a(u,v)= \int_{\Omega} \kappa \nabla u \cdot \nabla v ~\mathrm{d} x + \sum_i \int_{\Gamma_R^i}r_i u v~\mathrm{d} s,$
$L(v) = \int_{\Omega} fv~\mathrm{d} x - \sum_i \int_{\Gamma_N^i}g_i v~\mathrm{d} s + \sum_i \int_{\Gamma_R^i}r_i s_i v ~\mathrm{d}s.$

## Implementation#

Author: Jørgen S. Dokken

We start by defining the domain $$\Omega$$ as the unit square $$[0,1]\times[0,1]$$.

import numpy as np
import pyvista

from dolfinx.fem import (Constant,  Function, FunctionSpace, assemble_scalar,
dirichletbc, form, locate_dofs_topological)
from dolfinx.fem.petsc import LinearProblem
from dolfinx.mesh import create_unit_square, locate_entities, meshtags
from mpi4py import MPI
from petsc4py.PETSc import ScalarType
from ufl import (FacetNormal, Measure, SpatialCoordinate, TestFunction, TrialFunction,
div, dot, dx, grad, inner, lhs, rhs)
from dolfinx.io import XDMFFile
from dolfinx.plot import create_vtk_mesh

mesh = create_unit_square(MPI.COMM_WORLD, 10, 10)


In this section, we will solve the Poisson problem for the manufactured solution $$u_{ex} = 1+x^2+2y^2$$, which yields $$\kappa=1$$, $$f=-6$$. The next step is to define the parameters of the boundary condition, and where we should apply them. In this example, we will apply the following

$u = u_D \qquad \text{for } x=0,1$
$-\kappa \frac{\partial u}{\partial n} = r(u-s) \quad \text{for } y=0$
$-\kappa \frac{\partial u}{\partial n} =g_0 \quad\text{for } y = 1$

To reproduce the analytical solution, we have that

$u_D=u_{ex}=1+x^2+2y^2$
$g_0=\left.\frac{\partial u_{ex}}{y}\right\vert_{y=1}=4y\vert_{y=1}=-4$

The Robin condition can be specified in many ways. As $$-\left.\frac{\partial u_{ex}}{n}\right\vert_{x=0}=\left.\frac{\partial u_{ex}}{\partial x}\right\vert_{x=0}=2x=0,$$ we can specify $$r\neq 0$$ arbitrarly and $$s=u_{ex}$$. We choose $$r=1000$$. We can now create all the necessary variable definitions and the traditional part of the variational form.

u_ex = lambda x: 1 + x**2 + 2*x**2
x = SpatialCoordinate(mesh)
# Define physical parameters and boundary condtions
s = u_ex(x)
n = FacetNormal(mesh)
kappa = Constant(mesh, ScalarType(1))
r = Constant(mesh, ScalarType(1000))
# Define function space and standard part of variational form
V = FunctionSpace(mesh, ("CG", 1))
u, v = TrialFunction(V), TestFunction(V)
F = kappa * inner(grad(u), grad(v)) * dx - inner(f, v) * dx


We start by identifying the facets contained in each boundary and create a custom integration measure ds.

boundaries = [(1, lambda x: np.isclose(x, 0)),
(2, lambda x: np.isclose(x, 1)),
(3, lambda x: np.isclose(x, 0)),
(4, lambda x: np.isclose(x, 1))]


We now loop through all the boundary conditions and create MeshTags identifying the facets for each boundary condition.

facet_indices, facet_markers = [], []
fdim = mesh.topology.dim - 1
for (marker, locator) in boundaries:
facets = locate_entities(mesh, fdim, locator)
facet_indices.append(facets)
facet_markers.append(np.full_like(facets, marker))
facet_indices = np.hstack(facet_indices).astype(np.int32)
facet_markers = np.hstack(facet_markers).astype(np.int32)
sorted_facets = np.argsort(facet_indices)
facet_tag = meshtags(mesh, fdim, facet_indices[sorted_facets], facet_markers[sorted_facets])


## Debugging boundary condition#

To debug boundary conditions, the easiest thing to do is to visualize the boundary in Paraview by writing the MeshTags to file. We can then inspect individual boundaries using the Threshold-filter.

mesh.topology.create_connectivity(mesh.topology.dim-1, mesh.topology.dim)
with XDMFFile(mesh.comm, "facet_tags.xdmf", "w") as xdmf:
xdmf.write_mesh(mesh)
xdmf.write_meshtags(facet_tag)


Now we can create a custom integration measure ds, which can be used to restrict integration. If we integrate over ds(1), we only integrate over facets marked with value 1 in the corresponding facet_tag.

ds = Measure("ds", domain=mesh, subdomain_data=facet_tag)


We can now create a general boundary condition class.

class BoundaryCondition():
def __init__(self, type, marker, values):
self._type = type
if type == "Dirichlet":
u_D = Function(V)
u_D.interpolate(values)
facets = facet_tag.find(marker)
dofs = locate_dofs_topological(V, fdim, facets)
self._bc = dirichletbc(u_D, dofs)
elif type == "Neumann":
self._bc = inner(values, v) * ds(marker)
elif type == "Robin":
self._bc = values * inner(u-values, v)* ds(marker)
else:
raise TypeError("Unknown boundary condition: {0:s}".format(type))
@property
def bc(self):
return self._bc

@property
def type(self):
return self._type

# Define the Dirichlet condition
boundary_conditions = [BoundaryCondition("Dirichlet", 1, u_ex),
BoundaryCondition("Dirichlet", 2, u_ex),
BoundaryCondition("Robin", 3, (r, s)),
BoundaryCondition("Neumann", 4, g)]


We can now loop through the boundary condition and append them to L(v) or the list of Dirichlet boundary conditions

bcs = []
for condition in boundary_conditions:
if condition.type == "Dirichlet":
bcs.append(condition.bc)
else:
F += condition.bc


We can now create the bilinear form $$a$$ and linear form $$L$$ by using the ufl-functions lhs and rhs

# Solve linear variational problem
a = lhs(F)
L = rhs(F)
problem = LinearProblem(a, L, bcs=bcs, petsc_options={"ksp_type": "preonly", "pc_type": "lu"})
uh = problem.solve()

# Visualize solution
pyvista.set_jupyter_backend("pythreejs")
pyvista_cells, cell_types, geometry = create_vtk_mesh(V)
grid = pyvista.UnstructuredGrid(pyvista_cells, cell_types, geometry)
grid.point_data["u"] = uh.x.array
grid.set_active_scalars("u")

plotter = pyvista.Plotter()
plotter.view_xy()
if not pyvista.OFF_SCREEN:
plotter.show()
else:
pyvista.start_xvfb()
figure = plotter.screenshot("robin_neumann_dirichlet.png")

2022-07-04 08:29:50.108 (   0.651s) [        8F80E480]    vtkExtractEdges.cxx:435   INFO| Executing edge extractor: points are renumbered
2022-07-04 08:29:50.109 (   0.652s) [        8F80E480]    vtkExtractEdges.cxx:551   INFO| Created 320 edges


## Verification#

As for the previous problems, we compute the error of our computed solution and compare it to the analytical solution.

# Compute L2 error and error at nodes
V_ex = FunctionSpace(mesh, ("CG", 2))
u_exact = Function(V_ex)
u_exact.interpolate(u_ex)
error_L2 = np.sqrt(mesh.comm.allreduce(assemble_scalar(form((uh - u_exact)**2 * dx)), op=MPI.SUM))

u_vertex_values = uh.x.array
uex_1 = Function(V)
uex_1.interpolate(u_ex)
u_ex_vertex_values = uex_1.x.array
error_max = np.max(np.abs(u_vertex_values - u_ex_vertex_values))
error_max = mesh.comm.allreduce(error_max, op=MPI.MAX)
print(f"Error_L2 : {error_L2:.2e}")
print(f"Error_max : {error_max:.2e}")

Error_L2 : 4.86e-03
Error_max : 2.07e-03