# More General Boundary Conditions

So far we only used natural boundary conditions. Here we discuss how to impose other conditions like Neumann, Robin and especially Dirichlet. We will show how to add these to the model and how to prescribe different conditions for different components of the solution.

## Dirichlet boundary conditions

To fix Dirichlet boundary conditions \(u=g\) on part of the boundary \(\Gamma\subset\partial\Omega\) the central class is `dune.ufl.DirichletBC`

which takes three arguments: the discrete function space for \(u\), the function \(g\) given by a UFL expression, and a description of \(\Gamma\). There are different ways to do this. If it is omitted or `None`

the condition is applied to the whole domain, a integer \(s>0\) can be provided which can be set to describe a part
of the boundary during grid construction as described at the end of this section. Finally a UFL condition can be used, i.e., `x[0]<0`

.

For vector valued functions \(u\) the value function \(g\) can be a UFL vector or a list. In the later case a component of `None`

can be used to describe components which are not to be constrained by the boundary condition.

```
[1]:
```

```
import matplotlib
matplotlib.rc( 'image', cmap='jet' )
from matplotlib import ticker
from dune.fem.plotting import plotComponents
import numpy, io
from dune.grid import structuredGrid as leafGridView
from dune.fem.space import lagrange as solutionSpace
from dune.fem.scheme import galerkin as solutionScheme
from dune.fem.function import gridFunction, integrate, uflFunction
from dune.ufl import DirichletBC, Constant
from ufl import TestFunction, TrialFunction, SpatialCoordinate,\
dx, ds, grad, inner, sin, pi
gridView = leafGridView([0, 0], [1, 1], [4, 4])
vecSpace = solutionSpace(gridView, dimRange=2, order=2)
x = SpatialCoordinate(vecSpace)
vec = vecSpace.interpolate([0,0], name='u_h')
uVec,vVec = TrialFunction(vecSpace), TestFunction(vecSpace)
a = inner(grad(uVec), grad(vVec)) * dx
a += ( ( 10*(1-2*uVec[1])+uVec[0] ) * vVec[0] +\
( 10*sin(2*pi*x[1]) + (1-2*uVec[0])**2*uVec[1] ) * vVec[1]
) * dx
# Define Dirichlet boundary conditions for first component on all boundaries
bc = DirichletBC(vecSpace,[sin(2*pi*(x[0]+x[1])),None])
vecScheme = solutionScheme( [a == 0, bc],
parameters={"newton.linear.tolerance": 1e-9} )
vecScheme.solve(target=vec)
plotComponents(vec, gridLines=None, level=2,
colorbar={"orientation":"horizontal", "ticks":ticker.MaxNLocator(nbins=4)})
```

To prescribe \(u_2=1\) at the bottom boundary is also straightforward

```
[2]:
```

```
bcBottom = DirichletBC(vecSpace,[sin(2*pi*(x[0]+x[1])),1],x[1]<1e-10)
vecScheme = solutionScheme( [a == 0, bc, bcBottom],
parameters={"newton.linear.tolerance": 1e-9} )
vecScheme.solve(target=vec)
plotComponents(vec, gridLines=None, level=2,
colorbar={"orientation":"horizontal", "ticks":ticker.MaxNLocator(nbins=4)})
```

We can also use general grid functions (including discrete functions) for the boundary conditions in the same way we could use grid functions anywhere within the UFL forms. We could for example fix the boundary values of the first component using the discrete solution from # the previous computation by writing `bc = DirichletBC(vecSpace,[vec[0],None])`

Since this would just be an approximation to the actual boundary condition the overall results would not be identical to the previous version.
We therefore do not use the discrete solution to describe the boundary conditions but instead use other forms of grid functions. The following two code snippets lead to the same results as above:

```
[3]:
```

```
test = vec.copy()
test.clear()
@gridFunction(gridView ,name="bnd",order=2)
def bnd(x):
return [numpy.sin(2*numpy.pi*(x[0]+x[1])),1]
bcBottom = DirichletBC(vecSpace,bnd,x[1]<1e-10)
vecScheme = solutionScheme( [a == 0, bc, bcBottom],
parameters={"newton.linear.tolerance": 1e-9} )
vecScheme.solve(target=test)
plotComponents(test, gridLines=None, level=2,
colorbar={"orientation":"horizontal", "ticks":ticker.MaxNLocator(nbins=4)})
assert sum([ abs(td-vd) for td,vd in zip(test.dofVector, vec.dofVector)] ) /\
len(vec.dofVector) < 1e-8
```

As with other grid functions we can also use C++ code snippets. to define the Dirichlet conditions.

## Accessing the Dirichlet degrees of freedom

We provide a few method, to apply or otherwise access information about the Dirichlet constraints from an existing scheme.

The following shows how to set all degrees of freedom on the Dirichlet boundary to zero. For a more realistic example see the second approach in the section on solving Dirichlet Eigenvalue problems.

```
[4]:
```

```
dc = vecScheme.dirichletBlocks
for i,block in enumerate(dc): # a block is of size `dimRange`
for j,b in enumerate(block): # iterate over the block
# 'b' is the `id` of the boundary as described below
if b > 0: # if b>0 it's a boundary
vec.dofVector[i*len(block)+j] = 0 # set all Dirichlet dofs to 0
```

We have set all degrees of freedom on the boundary to zero. In this example the Dirichlet boundary was specified by the two `DirichletBC`

instances `bc`

and `bcBottom`

. The first specified that for the first component the whole boundary was Dirichlet while specifying nothing for the second component. The second then set Dirichlet conditions for the second component along the lower boundary. Consequently, the solution should now be zero along the whole boundary in the first component, and
zero along the lower boundary for the second:

```
[5]:
```

```
plotComponents(vec, gridLines=None, level=2,
colorbar={"orientation":"horizontal", "ticks":ticker.MaxNLocator(nbins=4)})
```

It is sometimes necessary to set all degrees of freedom on the boundary to zero, to be equal to some other function, or to as an interpolation of the boundary data provided to the scheme. To this end the operators/schemes provide methods `setConstraints`

. To cover the cases detailed above, there are three versions of this method:

the first takes a discrete function and sets the Dirichlet dofs to the boundary data provided in the

`DirichletBC`

structure.The second version takes a range vector and a discrete function; this version can used for example to set the values at the boundary to zero, e.g., :code:

`op.setConstraints(0, uh)`

or for a vector function`op.setConstraints([0,]*space.dimension, vec_h)`

.Finally there is a version taking a grid function and a discrete function

`op.setConstraints(g, uh)`

which sets the dofs at the boundary so that`uh=g`

at the Dirichlet boundary.

We use the first version to set the data at the boundary first having cleared the discrete function. As second example we interpolate the :code:`bnd`

grid function and then set all boundary dofs to 0. Recall that the second component has no Dirichlet boundary conditions set at the lower boundary.

```
[6]:
```

```
vec.clear()
vecScheme.setConstraints(vec)
plotComponents(vec, gridLines=None, level=2,
colorbar={"orientation":"horizontal", "ticks":ticker.MaxNLocator(nbins=4)})
vec.interpolate(bnd)
vecScheme.setConstraints([0,]*len(vec), vec)
plotComponents(vec, gridLines=None, level=2,
colorbar={"orientation":"horizontal", "ticks":ticker.MaxNLocator(nbins=4)})
```

## Neumann and Robin boundary conditions

Neumann boundary flux are added to the ufl form by using the measure # `ufl.ds`

. By default they will be added to all boundaries for which no Dirichlet conditions have been prescribed. In the above example we prescribed no Dirichlet conditions to the second component at both vertical and the top boundary. This leads to zero fluxes being used there. To To add some (here nonlinear) Neumann boundary conditions for the second component at these three sides we can simply add an additional term to
`a`

:

```
[7]:
```

```
a = a + uVec[0]*uVec[0] * vVec[1] * ds
vecScheme = solutionScheme( [a == 0, bc],
parameters={"newton.linear.tolerance": 1e-9} )
vecScheme.solve(target=vec)
plotComponents(vec, gridLines=None, level=2,
colorbar={"orientation":"horizontal", "ticks":ticker.MaxNLocator(nbins=4)})
```

Robin boundary conditions can be handled in the same way using the `ufl.ds`

measure.

## Using boundary ids (and some more complex examples)

Up until now we have used UFL boolean expressions to define the subset of the boundary where a Dirichlet condition should be applied. In many cases different parts of the boundary are already marked with ids (integers) during grid construction. These ids can also be used to define where boundary conditions are to be applied. At the moment to use boundary ids the grid has to be constructed using the Dune Grid Format (dgf) reader. Please consult the documentation of the DGF file format for details.

### Cartesian boundary ids

For Cartesian grids the boundary ids are rather fixed and pre-described due to implementation specifics. The numbering of the boundary edges follows the numbering of faces of the DUNE reference elements but starts from 1. For example, in 2d the boundary ids are left=1, right=2, bottom=3, top=4. For grids implementing a true Cartesian grids, like YaspGrid, this is fixed. Altering this numbering can only be done by using an unstructured grid, e.g. ALUGrid.

**Boundary ids and refinement:** Boundary ids only allow to tell apart intersections with the domain boundary on the macro level of a grid. Descendants of macro intersections on the domain boundary inherit the boundary id of their parent.

### Mixing different types of boundary conditions

These ids can be used as third argument in the `DirichletBC`

constructor and also in the boundary measure `ufl.ds`

as shown in the following examples solving a Laplace problem with a combination of **Neumann**, **Robin**, and **Dirichlet** boundary conditions.

In this example the boundary conditions are

left and right boundary (id=1 and id=2): Robin boundary conditions \(\nabla u\cdot n + u = -10\) for \(y\in[0.4,0.6]\) and natural boundary conditions (\(\nabla u\cdot n=0\)) otherwise.

bottom boundary (id=3): Neumann conditions \(\nabla u\cdot n=2\)

top boundary (id=4): Dirichlet conditions \(u=1\) for \(x\in [0.3,0.6]\) and natural boundary conditions (\(\nabla u\cdot n=0\)) otherwise.

```
[8]:
```

```
from dune.ufl import BoundaryId
from ufl import ds, grad, dot, inner, conditional, eq, And, FacetNormal, Not
gridView.hierarchicalGrid.globalRefine(3)
space = solutionSpace(gridView, order=2)
x,n = SpatialCoordinate(space), FacetNormal(space)
u,v = TrialFunction(space), TestFunction(space)
a = inner(grad(u), grad(v)) * dx
fbnd = (u-10 * v * # middle of left and right boundary
conditional(abs(x[1]-0.5)<0.1,1,0) ) * ds( (1,2) )
fbnd += 2 * v * ds(3) # bottom boundary
dbc = DirichletBC(space, 1, # middle of top boundary
And( eq(BoundaryId(space),4), abs(x[0]-0.5)<0.2 ))
scheme = solutionScheme( [a == fbnd, dbc] )
solution = space.interpolate(0, name='u_h')
scheme.solve(target=solution)
solution.plot()
```

The final example has a slightly more complex setup at the boundary:

left and right (id=1 and id=2): for \(y\in[0.4,0.6]\) we set Neumann conditions \(\nabla u\cdot n=-50\)

left and right (id=1 and id=2): for \(y\in[0.2,0.8]\setminus [0.4,0.6]\) we set Dirichlet conditions \(u=1\)

rest of boundary: 3

```
[9]:
```

```
fbnd = (-50 * v * # middle of left and right boundary
conditional(abs(x[1]-0.5)<0.1,1,0) ) * ds( (1,2) )
dbc = [ DirichletBC(space, 1, # not middle of left and right boundary
And( And( BoundaryId(space)<=2,
abs(x[1]-0.5)>0.1 ), abs(x[1]-0.5)<0.3 ) ),
DirichletBC(space, 3, # not middle of left and right boundary
Not( And(BoundaryId(space)<=2,abs(x[1]-0.5)<0.3) ) ), ]
scheme = solutionScheme( [a == fbnd, *dbc] )
solution = space.interpolate(0, name='u_h')
scheme.solve(target=solution)
solution.plot()
```

Let check if that solution makes sense…

```
[10]:
```

```
gridView.hierarchicalGrid.globalRefine(1)
scheme.solve(target=solution)
solution.plot()
```