Biharmonic equation
===================
This demo is implemented in a single Python file,
:download:`demo_biharmonic.py`, which contains both the variational
forms and the solver.
This demo illustrates how to:
* Solve a linear partial differential equation
* Use a discontinuous Galerkin method
* Solve a fourth-order differential equation
The solution for :math:`u` in this demo will look as follows:
.. image:: biharmonic_u.png
:scale: 75 %
Equation and problem definition
-------------------------------
The biharmonic equation is a fourth-order elliptic equation. On the
domain :math:`\Omega \subset \mathbb{R}^{d}`, :math:`1 \le d \le 3`,
it reads
.. math::
\nabla^{4} u = f \quad {\rm in} \ \Omega,
where :math:`\nabla^{4} \equiv \nabla^{2} \nabla^{2}` is the
biharmonic operator and :math:`f` is a prescribed source term. To
formulate a complete boundary value problem, the biharmonic equation
must be complemented by suitable boundary conditions.
Multiplying the biharmonic equation by a test function and integrating
by parts twice leads to a problem second-order derivatives, which
would requires :math:`H^{2}` conforming (roughly :math:`C^{1}`
continuous) basis functions. To solve the biharmonic equation using
Lagrange finite element basis functions, the biharmonic equation can
be split into two second-order equations (see the Mixed Poisson demo
for a mixed method for the Poisson equation), or a variational
formulation can be constructed that imposes weak continuity of normal
derivatives between finite element cells. The demo uses a
discontinuous Galerkin approach to impose continuity of the normal
derivative weakly.
Consider a triangulation :math:`\mathcal{T}` of the domain
:math:`\Omega`, where the set of interior facets is denoted by
:math:`\mathcal{E}_h^{\rm int}`. Functions evaluated on opposite
sides of a facet are indicated by the subscripts ':math:`+`' and
':math:`-`'. Using the standard continuous Lagrange finite element
space
.. math::
V = \left\{v \in H^{1}_{0}(\Omega)\,:\, v \in P_{k}(K) \ \forall \ K \in \mathcal{T} \right\}
and considering the boundary conditions
.. math::
u &= 0 \quad {\rm on} \ \partial\Omega \\
\nabla^{2} u &= 0 \quad {\rm on} \ \partial\Omega
a weak formulation of the biharmonic problem reads: find :math:`u \in
V` such that
.. math::
a(u,v)=L(v) \quad \forall \ v \in V,
where the bilinear form is
.. math::
a(u, v) = \sum_{K \in \mathcal{T}} \int_{K} \nabla^{2} u \nabla^{2} v \, {\rm d}x \
+\sum_{E \in \mathcal{E}_h^{\rm int}}\left(\int_{E} \frac{\alpha}{h_E} [\!\![ \nabla u ]\!\!] [\!\![ \nabla v ]\!\!] \, {\rm d}s
- \int_{E} \left<\nabla^{2} u \right>[\!\![ \nabla v ]\!\!] \, {\rm d}s
- \int_{E} [\!\![ \nabla u ]\!\!] \left<\nabla^{2} v \right> \, {\rm d}s\right)
and the linear form is
.. math::
L(v) = \int_{\Omega} fv \, {\rm d}x
Furthermore, :math:`\left< u \right> = \frac{1}{2} (u_{+} + u_{-})`,
:math:`[\!\![ w ]\!\!] = w_{+} \cdot n_{+} + w_{-} \cdot n_{-}`,
:math:`\alpha \ge 0` is a penalty parameter and :math:`h_E` is a
measure of the cell size.
The input parameters for this demo are defined as follows:
* :math:`\Omega = [0,1] \times [0,1]` (a unit square)
* :math:`\alpha = 8.0` (penalty parameter)
* :math:`f = 4.0 \pi^4\sin(\pi x)\sin(\pi y)` (source term)
Implementation
--------------
This demo is implemented in the :download:`demo_biharmonic.py` file.
First, the necessary modules are imported::
import matplotlib.pyplot as plt
from dolfin import *
Next, some parameters for the form compiler are set::
# Optimization options for the form compiler
parameters["form_compiler"]["cpp_optimize"] = True
parameters["form_compiler"]["optimize"] = True
A mesh is created, and a quadratic finite element function space::
# Make mesh ghosted for evaluation of DG terms
parameters["ghost_mode"] = "shared_facet"
# Create mesh and define function space
mesh = UnitSquareMesh(32, 32)
V = FunctionSpace(mesh, "CG", 2)
A subclass of :py:class:`SubDomain <dolfin.cpp.SubDomain>`,
``DirichletBoundary`` is created for later defining the boundary of
the domain::
# Define Dirichlet boundary
class DirichletBoundary(SubDomain):
def inside(self, x, on_boundary):
return on_boundary
A subclass of :py:class:`Expression
<dolfin.functions.expression.Expression>`, ``Source`` is created for
the source term :math:`f`::
class Source(UserExpression):
def eval(self, values, x):
values[0] = 4.0*pi**4*sin(pi*x[0])*sin(pi*x[1])
The Dirichlet boundary condition is created::
# Define boundary condition
u0 = Constant(0.0)
bc = DirichletBC(V, u0, DirichletBoundary())
On the finite element space ``V``, trial and test functions are
created::
# Define trial and test functions
u = TrialFunction(V)
v = TestFunction(V)
A function for the cell size :math:`h` is created, as is a function
for the average size of cells that share a facet (``h_avg``). The UFL
syntax ``('+')`` and ``('-')`` restricts a function to the ``('+')``
and ``('-')`` sides of a facet, respectively. The unit outward normal
to cell boundaries (``n``) is created, as is the source term ``f`` and
the penalty parameter ``alpha``. The penalty parameters is made a
:py:class:`Constant <dolfin.functions.constant.Constant>` so that it
can be changed without needing to regenerate code. ::
# Define normal component, mesh size and right-hand side
h = CellDiameter(mesh)
h_avg = (h('+') + h('-'))/2.0
n = FacetNormal(mesh)
f = Source(degree=2)
# Penalty parameter
alpha = Constant(8.0)
The bilinear and linear forms are defined::
# Define bilinear form
a = inner(div(grad(u)), div(grad(v)))*dx \
- inner(avg(div(grad(u))), jump(grad(v), n))*dS \
- inner(jump(grad(u), n), avg(div(grad(v))))*dS \
+ alpha/h_avg*inner(jump(grad(u),n), jump(grad(v),n))*dS
# Define linear form
L = f*v*dx
A :py:class:`Function <dolfin.functions.function.Function>` is created
to store the solution and the variational problem is solved::
# Solve variational problem
u = Function(V)
solve(a == L, u, bc)
The computed solution is written to a file in VTK format and plotted to
the screen. ::
# Save solution to file
file = File("biharmonic.pvd")
file << u
# Plot solution
plot(u)
plt.show()