Poisson equation
This demo is implemented in a single Python file,
demo_poisson.py
, which contains both the variational forms
and the solver. It illustrates how to:
Solve a linear partial differential equation
Define a
FunctionSpace
Create and apply Dirichlet boundary conditions
Equation and problem definition
For a domain \(\Omega \subset \mathbb{R}^n\) with boundary \(\partial \Omega = \Gamma_{D} \cup \Gamma_{N}\), the Poisson equation with particular boundary conditions reads:
where \(f\) and \(g\) are input data and \(n\) denotes the outward directed boundary normal. The variational problem reads: find \(u \in V\) such that
where \(V\) is a suitable function space and
The expression \(a(u, v)\) is the bilinear form and \(L(v)\) is the linear form. It is assumed that all functions in \(V\) satisfy the Dirichlet boundary conditions (\(u = 0 \ {\rm on} \ \Gamma_{D}\)).
In this demo we consider:
\(\Omega = [0,2] \times [0,1]\) (a rectangle)
\(\Gamma_{D} = \{(0, y) \cup (1, y) \subset \partial \Omega\}\)
\(\Gamma_{N} = \{(x, 0) \cup (x, 1) \subset \partial \Omega\}\)
\(g = \sin(5x)\)
\(f = 10\exp(-((x - 0.5)^2 + (y - 0.5)^2) / 0.02)\)
Implementation
The modules that will be used are imported:
import numpy as np
import ufl
from dolfinx import fem, io, mesh, plot
from ufl import ds, dx, grad, inner
from mpi4py import MPI
from petsc4py.PETSc import ScalarType
We begin by using create_rectangle
to create a rectangular
Mesh
of the domain, and creating a
finite element FunctionSpace
\(V\) on the mesh.
msh = mesh.create_rectangle(comm=MPI.COMM_WORLD,
points=((0.0, 0.0), (2.0, 1.0)), n=(32, 16),
cell_type=mesh.CellType.triangle,)
V = fem.FunctionSpace(msh, ("Lagrange", 1))
The second argument to FunctionSpace
is a tuple consisting of (family, degree)
, where family
is the finite element family, and degree
specifies the polynomial degree. in this case V
consists of
first-order, continuous Lagrange finite element functions.
Next, we locate the mesh facets that lie on the boundary \(\Gamma_D\).
We do this using using locate_entities_boundary
and providing a marker
function that returns True
for points x
on the boundary and
False
otherwise.
facets = mesh.locate_entities_boundary(msh, dim=1,
marker=lambda x: np.logical_or(np.isclose(x[0], 0.0),
np.isclose(x[0], 2.0)))
We now find the degrees-of-freedom that are associated with the
boundary facets using locate_dofs_topological
dofs = fem.locate_dofs_topological(V=V, entity_dim=1, entities=facets)
and use dirichletbc
to create a
DirichletBCMetaClass
class that represents the boundary condition
bc = fem.dirichletbc(value=ScalarType(0), dofs=dofs, V=V)
Next, we express the variational problem using UFL.
u = ufl.TrialFunction(V)
v = ufl.TestFunction(V)
x = ufl.SpatialCoordinate(msh)
f = 10 * ufl.exp(-((x[0] - 0.5) ** 2 + (x[1] - 0.5) ** 2) / 0.02)
g = ufl.sin(5 * x[0])
a = inner(grad(u), grad(v)) * dx
L = inner(f, v) * dx + inner(g, v) * ds
We create a LinearProblem
object that brings together the variational problem, the Dirichlet
boundary condition, and which specifies the linear solver. In this
case we use a direct (LU) solver. The solve
will compute a solution.
problem = fem.petsc.LinearProblem(a, L, bcs=[bc], petsc_options={"ksp_type": "preonly", "pc_type": "lu"})
uh = problem.solve()
The solution can be written to a XDMFFile
file visualization with ParaView or VisIt
with io.XDMFFile(msh.comm, "out_poisson/poisson.xdmf", "w") as file:
file.write_mesh(msh)
file.write_function(uh)
and displayed using pyvista.
try:
import pyvista
cells, types, x = plot.create_vtk_mesh(V)
grid = pyvista.UnstructuredGrid(cells, types, x)
grid.point_data["u"] = uh.x.array.real
grid.set_active_scalars("u")
plotter = pyvista.Plotter()
plotter.add_mesh(grid, show_edges=True)
warped = grid.warp_by_scalar()
plotter.add_mesh(warped)
plotter.show()
except ModuleNotFoundError:
print("'pyvista' is required to visualise the solution")
print("Install 'pyvista' with pip: 'python3 -m pip install pyvista'")