Commit 70f486ac authored by Jeremy BLEYER's avatar Jeremy BLEYER

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parent 237835dc
#
# .. _demo_poisson_equation:
#
# Poisson equation
# ================
#
# This demo is implemented in a single Python file,
# :download:`demo_poisson.py`, which contains both the variational forms
# and the solver.
#
# This demo illustrates how to:
#
# * Solve a linear partial differential equation
# * Create and apply Dirichlet boundary conditions
# * Define Expressions
# * Define a FunctionSpace
# * Create a SubDomain
#
# The solution for :math:`u` in this demo will look as follows:
#
# .. image:: poisson_u.png
# :scale: 75 %
#
#
# Equation and problem definition
# -------------------------------
#
# The Poisson equation is the canonical elliptic partial differential
# equation. For a domain :math:`\Omega \subset \mathbb{R}^n` with
# boundary :math:`\partial \Omega = \Gamma_{D} \cup \Gamma_{N}`, the
# Poisson equation with particular boundary conditions reads:
#
# .. math::
# - \nabla^{2} u &= f \quad {\rm in} \ \Omega, \\
# u &= 0 \quad {\rm on} \ \Gamma_{D}, \\
# \nabla u \cdot n &= g \quad {\rm on} \ \Gamma_{N}. \\
#
# Here, :math:`f` and :math:`g` are input data and :math:`n` denotes the
# outward directed boundary normal. The most standard variational form
# of Poisson equation reads: find :math:`u \in V` such that
#
# .. math::
# a(u, v) = L(v) \quad \forall \ v \in V,
#
# where :math:`V` is a suitable function space and
#
# .. math::
# a(u, v) &= \int_{\Omega} \nabla u \cdot \nabla v \, {\rm d} x, \\
# L(v) &= \int_{\Omega} f v \, {\rm d} x
# + \int_{\Gamma_{N}} g v \, {\rm d} s.
#
# The expression :math:`a(u, v)` is the bilinear form and :math:`L(v)`
# is the linear form. It is assumed that all functions in :math:`V`
# satisfy the Dirichlet boundary conditions (:math:`u = 0 \ {\rm on} \
# \Gamma_{D}`).
#
# In this demo, we shall consider the following definitions of the input
# functions, the domain, and the boundaries:
#
# * :math:`\Omega = [0,1] \times [0,1]` (a unit square)
# * :math:`\Gamma_{D} = \{(0, y) \cup (1, y) \subset \partial \Omega\}`
# (Dirichlet boundary)
# * :math:`\Gamma_{N} = \{(x, 0) \cup (x, 1) \subset \partial \Omega\}`
# (Neumann boundary)
# * :math:`g = \sin(5x)` (normal derivative)
# * :math:`f = 10\exp(-((x - 0.5)^2 + (y - 0.5)^2) / 0.02)` (source
# term)
#
#
# Implementation
# --------------
#
# This description goes through the implementation (in
# :download:`demo_poisson.py`) of a solver for the above described
# Poisson equation step-by-step.
#
# First, the :py:mod:`dolfin` module is imported: ::
from dolfin import *
# We begin by defining a mesh of the domain and a finite element
# function space :math:`V` relative to this mesh. As the unit square is
# a very standard domain, we can use a built-in mesh provided by the
# class :py:class:`UnitSquareMesh <dolfin.cpp.UnitSquareMesh>`. In order
# to create a mesh consisting of 32 x 32 squares with each square
# divided into two triangles, we do as follows ::
# Create mesh and define function space
mesh = UnitSquareMesh(32, 32)
V = FunctionSpace(mesh, "Lagrange", 1)
# The second argument to :py:class:`FunctionSpace
# <dolfin.functions.functionspace.FunctionSpace>` is the finite element
# family, while the third argument specifies the polynomial
# degree. Thus, in this case, our space ``V`` consists of first-order,
# continuous Lagrange finite element functions (or in order words,
# continuous piecewise linear polynomials).
#
# Next, we want to consider the Dirichlet boundary condition. A simple
# Python function, returning a boolean, can be used to define the
# subdomain for the Dirichlet boundary condition (:math:`\Gamma_D`). The
# function should return ``True`` for those points inside the subdomain
# and ``False`` for the points outside. In our case, we want to say that
# the points :math:`(x, y)` such that :math:`x = 0` or :math:`x = 1` are
# inside on the inside of :math:`\Gamma_D`. (Note that because of
# rounding-off errors, it is often wise to instead specify :math:`x <
# \epsilon` or :math:`x > 1 - \epsilon` where :math:`\epsilon` is a
# small number (such as machine precision).) ::
# Define Dirichlet boundary (x = 0 or x = 1)
def boundary(x):
return x[0] < DOLFIN_EPS or x[0] > 1.0 - DOLFIN_EPS
# Now, the Dirichlet boundary condition can be created using the class
# :py:class:`DirichletBC <dolfin.fem.bcs.DirichletBC>`. A
# :py:class:`DirichletBC <dolfin.fem.bcs.DirichletBC>` takes three
# arguments: the function space the boundary condition applies to, the
# value of the boundary condition, and the part of the boundary on which
# the condition applies. In our example, the function space is ``V``,
# the value of the boundary condition (0.0) can represented using a
# :py:class:`Constant <dolfin.functions.constant.Constant>` and the
# Dirichlet boundary is defined immediately above. The definition of the
# Dirichlet boundary condition then looks as follows: ::
# Define boundary condition
u0 = Constant(0.0)
bc = DirichletBC(V, u0, boundary)
# Next, we want to express the variational problem. First, we need to
# specify the trial function :math:`u` and the test function :math:`v`,
# both living in the function space :math:`V`. We do this by defining a
# :py:class:`TrialFunction <dolfin.functions.function.TrialFunction>`
# and a :py:class:`TestFunction
# <dolfin.functions.function.TrialFunction>` on the previously defined
# :py:class:`FunctionSpace
# <dolfin.functions.functionspace.FunctionSpace>` ``V``.
#
# Further, the source :math:`f` and the boundary normal derivative
# :math:`g` are involved in the variational forms, and hence we must
# specify these. Both :math:`f` and :math:`g` are given by simple
# mathematical formulas, and can be easily declared using the
# :py:class:`Expression <dolfin.functions.expression.Expression>` class.
# Note that the strings defining ``f`` and ``g`` use C++ syntax since,
# for efficiency, DOLFIN will generate and compile C++ code for these
# expressions at run-time.
#
# With these ingredients, we can write down the bilinear form ``a`` and
# the linear form ``L`` (using UFL operators). In summary, this reads ::
# Define variational problem
u = TrialFunction(V)
v = TestFunction(V)
f = Expression("10*exp(-(pow(x[0] - 0.5, 2) + pow(x[1] - 0.5, 2)) / 0.02)", degree=2)
g = Expression("sin(5*x[0])", degree=2)
a = inner(grad(u), grad(v))*dx
L = f*v*dx + g*v*ds
# Now, we have specified the variational forms and can consider the
# solution of the variational problem. First, we need to define a
# :py:class:`Function <dolfin.functions.function.Function>` ``u`` to
# represent the solution. (Upon initialization, it is simply set to the
# zero function.) A :py:class:`Function
# <dolfin.functions.function.Function>` represents a function living in
# a finite element function space. Next, we can call the :py:func:`solve
# <dolfin.fem.solving.solve>` function with the arguments ``a == L``,
# ``u`` and ``bc`` as follows: ::
# Compute solution
u = Function(V)
solve(a == L, u, bc)
# The function ``u`` will be modified during the call to solve. The
# default settings for solving a variational problem have been
# used. However, the solution process can be controlled in much more
# detail if desired.
#
# A :py:class:`Function <dolfin.functions.function.Function>` can be
# manipulated in various ways, in particular, it can be plotted and
# saved to file. Here, we output the solution to a ``VTK`` file (using
# the suffix ``.pvd``) for later visualization and also plot it using
# the :py:func:`plot <dolfin.common.plot.plot>` command: ::
# Save solution in VTK format
file = File("poisson.pvd")
file << u
# Plot solution
import matplotlib.pyplot as plt
plot(u)
plt.show()
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