def compute_projection_error(mesh_resolution=20, p_order=1):
# Define domain and mesh
a, b = 0, 1
mesh = IntervalMesh(mesh_resolution, a, b)
# Define finite element function space
V = FunctionSpace(mesh, "CG", p_order)
# Extract vertices of the mesh
x = V.tabulate_dof_coordinates()
# Note: Not the same as x = mesh.coordinates() (apparently has been re-ordered)
# Express the analytical function
u = Expression("1 + 4 * x[0] * x[0] - 5 * x[0] * x[0] * x[0]", degree=5)
# Project u onto V and extract the values in the mesh nodes
Pu = project(u, V)
Pua = Pu.vector().array()
# Create a function in the finite element function space V
Eu = Function(V)
Eua = Eu.vector().array()
# Evaluate function in the mesh nodes
for i in range(len(x)):
Eua[i] = 1 + 4 * x[i]**2 - 5 * x[i]**3
# Compute sum of projection error in the nodes
e = Eua - Pua
error = 0
for i in range(len(e)):
error += abs(e[i])
return error
def compute_projection_error(mesh_resolution=20, p_order=1):
# Define domain and mesh
a, b = 0, 1
mesh = IntervalMesh(mesh_resolution, a, b)
# Define finite element function space
V = FunctionSpace(mesh, "CG", p_order)
# Extract vertices of the mesh
x = V.dofmap().tabulate_all_coordinates(mesh)
indices = np.argsort(x)
# Express the analytical function
u = Expression("1 + 4 * x[0] * x[0] - 5 * x[0] * x[0] * x[0]", degree=5)
# Project u onto V and extract the values in the mesh nodes
Pu = project(u, V)
Pua = Pu.vector().array()
# Create a function in the finite element function space V
Eu = Function(V)
Eua = Eu.vector().array()
# Evaluate function in the mesh nodes
for j in indices:
Eua[j] = 1 + 4 * x[j] * x[j] - 5 * x[j] * x[j] * x[j]
# Compute sum of projection error in the nodes
e = Eua - Pua
error = 0
for i in range(len(e)):
error += abs(e[i])
return error
def solve_equation():
mesh = UnitSquareMesh(32, 32)
V = FunctionSpace(mesh, "Lagrange", 1)
def boundary(x, on_boundary):
return on_boundary
u0 = Expression("cos(10 * x[0])", degree=2)
bc = DirichletBC(V, u0, boundary)
u = TrialFunction(V)
v = TestFunction(V)
f = Expression("(x[0] - 0.5)*(x[0] - 0.5)", degree=2)
a = inner(grad(u), grad(v)) * dx
L = f * v * dx
u = Function(V)
solve(a == L, u, bc)
# Plot solution at current time step
fig = plt.figure()
plot(u, fig=fig)
plt.show()
print("The norm of u is {}".format(u.vector().norm("l2")))
def simple_test():
# Declare mesh and FEM functions
mesh = UnitSquareMesh(10, 10)
V = FunctionSpace(mesh, "CG", 1)
f = Expression("1.0 + sin(10 * x[0]) * cos(7 * x[1])", degree=3)
v = TestFunction(V)
u = Function(V) # FEM solution
# Weak form of L2 projection
r = (u - f) * v * dx
# Solving the linear system generated by the L2 projection
solve(r == 0, u)
# Plot the FEM solution
fig = plt.figure()
plot(u, fig=fig)
plt.show()
print(u.vector().norm('linf'))
def plot_basis_function():
# Declare mesh and FEM functions
mesh = UnitSquareMesh(10, 10)
V = FunctionSpace(mesh, "CG", 1)
phi = Function(V)
phi.vector()[:] = 0.0
phi.vector()[10] = 1.0
# Plot the basis function
fig = plt.figure()
plot(phi, fig=fig)
plt.show()
val = assemble(phi * phi * dx)
print(val)
def solve_navier_stokes_equation(interior_circle=True, num_mesh_refinements=0):
"""
Solve the Navier-Stokes equation on a hard-coded mesh with hard-coded initial and boundary conditions
"""
mesh, om, im, nm, ymax, sub_domains = setup_geometry(
interior_circle, num_mesh_refinements)
dsi = Measure("ds", domain=mesh, subdomain_data=sub_domains)
# Setup FEM function spaces
# Function space for the velocity
V = VectorFunctionSpace(mesh, "CG", 1)
# Function space for the pressure
Q = FunctionSpace(mesh, "CG", 1)
# Mixed function space for velocity and pressure
W = V * Q
# Setup FEM functions
v, q = TestFunctions(W)
w = Function(W)
(u, p) = (as_vector((w[0], w[1])), w[2])
u0 = Function(V)
# Inlet velocity
uin = Expression(("4*(x[1]*(YMAX-x[1]))/(YMAX*YMAX)", "0."),
YMAX=ymax,
degree=1)
# Viscosity and stabilization parameters
nu = 1e-6
h = CellSize(mesh)
d = 0.2 * h**(3.0 / 2.0)
# Time parameters
time_step = 0.1
t_start, t_end = 0.0, 10.0
# Penalty parameter
gamma = 10 / h
# Time stepping
t = t_start
step = 0
while t < t_end:
# Time discretization (Crank–Nicolson method)
um = 0.5 * u + 0.5 * u0
# Navier-Stokes equations in weak residual form (stabilized FEM)
# Basic residual
r = (inner((u - u0) / time_step + grad(p) + grad(um) * um, v) +
nu * inner(grad(um), grad(v)) + div(um) * q) * dx
# Weak boundary conditions
r += gamma * (om * p * q + im * inner(u - uin, v) +
nm * inner(u, v)) * ds
# Stabilization
r += d * (inner(grad(p) + grad(um) * um,
grad(q) + grad(um) * v) + inner(div(um), div(v))) * dx
# Solve the Navier-Stokes equation (one time step)
solve(r == 0, w)
if step % 5 == 0:
# Plot norm of velocity at current time step
nov = project(sqrt(inner(u, u)), Q)
fig = plt.figure()
plot(nov, fig=fig)
plt.show()
# Compute drag force on circle
n = FacetNormal(mesh)
drag_force_measure = p * n[0] * dsi(1) # Drag (only pressure)
drag_force = assemble(drag_force_measure)
print("Drag force = " + str(drag_force))
# Shift to next time step
t += time_step
step += 1
u0 = project(u, V)
def solve_heat_equation(k, time_stepping_method):
"""
Solve the heat equation on a hard-coded mesh with a hard-coded initial and boundary conditions
:param k: Thermal conductivity
:param time_stepping_method: Time stepping method. Can be one of ["forward_euler", "backward_euler", "trapezoidal"]
"""
mesh, boundary = setup_geometry()
# Exact solution (Gauss curve)
ue = Expression("exp(-(x[0]*x[0]+x[1]*x[1])/(4*a*t))/(4*pi*a*t)",
a=k,
t=1e-7,
domain=mesh,
degree=1)
# Polynomial degree
r = 1
# Setup FEM function space
V = FunctionSpace(mesh, "CG", r)
# Create boundary condition
bc = DirichletBC(V, ue, boundary)
# Setup FEM functions
v = TestFunction(V)
u = Function(V)
# Time parameters
time_step = 0.001
t_start, t_end = 0.0, 20.0
# Time stepping
t = t_start
if time_stepping_method == "forward_euler":
theta = 0.0
if time_stepping_method == "backward_euler":
theta = 1.0
if time_stepping_method == "trapezoidal":
theta = 0.5
u0 = ue
step = 0
while t < t_end:
# Intermediate value for u (depending on the chosen time stepping method)
um = (1.0 - theta) * u0 + theta * u
# Weak form of the heat equation
a = (u - u0) / time_step * v * dx + k * inner(grad(um), grad(v)) * dx
# Solve the heat equation (one time step)
solve(a == 0, u, bc)
# Advance time in exact solution
t += time_step
ue.t = t
if step % 100 == 0:
# Compute error in L2 norm
error_L2 = errornorm(ue, u, 'L2')
# or equivalently
# sqrt(assemble((ue - u) * (ue - u) * dx))
# Compute norm of exact solution
nue = norm(ue)
# Print relative error
print("Relative error = {}".format(error_L2 / nue))
# Shift to next time step
u0 = project(u, V)
step += 1
def solve_heat_equation(k):
"""
Solve the heat equation on a hard-coded mesh with a hard-coded initial and boundary conditions
:param k: Thermal conductivity
"""
mesh, boundary = setup_geometry()
# Exact solution (Gauss curve)
ue = Expression("exp(-(x[0]*x[0]+x[1]*x[1])/(4*a*t))/(4*pi*a*t)",
a=k,
t=1e-7,
domain=mesh,
degree=2)
# Polynomial degree
r = 1
# Setup FEM function space
V = FunctionSpace(mesh, "CG", r)
# Create boundary condition
bc = DirichletBC(V, ue, boundary)
# Setup FEM functions
v = TestFunction(V)
u = Function(V)
# Time parameters
time_step = 0.5
t_start, t_end = 0.0, 20.0
# Time stepping
t = t_start
u0 = ue
step = 0
while t < t_end:
# Weak form of the heat equation
a = (u - u0) / time_step * v * dx + k * inner(grad(u), grad(v)) * dx
# Solve the heat equation (one time step)
solve(a == 0, u, bc)
# Advance time in exact solution
t += time_step
ue.t = t
if step % 5 == 0:
# Plot solution at current time step
fig = plt.figure()
plot(u, fig=fig)
plt.show()
# Compute error in L2 norm
error_L2 = errornorm(ue, u, 'L2')
# or equivalently
# sqrt(assemble((ue - u) * (ue - u) * dx))
# Print error
print(error_L2)
# Shift to next time step
u0 = project(u, V)
step += 1