def setup():
vertices = numpy.zeros((8, 2))
vertices[0] = [0, 0]
for i in range(0, 7):
vertices[i + 1] = [
math.cos(cornerAngle / 6 * math.pi / 180 * i),
math.sin(cornerAngle / 6 * math.pi / 180 * i)
]
triangles = numpy.array([[2, 1, 0], [0, 3, 2], [4, 3, 0], [0, 5, 4],
[6, 5, 0], [0, 7, 6]])
domain = {"vertices": vertices, "simplices": triangles}
gridView = adaptiveGridView(leafGridView(domain))
gridView.hierarchicalGrid.globalRefine(2)
space = solutionSpace(gridView, order=order)
from dune.fem.scheme import galerkin as solutionScheme
u = TrialFunction(space)
v = TestFunction(space)
x = SpatialCoordinate(space.cell())
# exact solution for this angle
Phi = cornerAngle / 180 * pi
phi = atan_2(x[1], x[0]) + conditional(x[1] < 0, 2 * pi, 0)
exact = dot(x, x)**(pi / 2 / Phi) * sin(pi / Phi * phi)
a = dot(grad(u), grad(v)) * dx
# set up the scheme
laplace = solutionScheme(
[a == 0, DirichletBC(space, exact, 1)],
solver="cg",
parameters={"newton.linear.preconditioning.method": "jacobi"})
uh = space.interpolate(0, name="solution")
return uh, exact, laplace
# space form
xForm = inner(grad(u), grad(phi)) * dx
# add time discretization
form = dot(u - uh_old, phi) * dx + dt * xForm
# setup scheme
dune.fem.parameter.append({"fem.verboserank": 0})
solverParameters =\
{"newton.tolerance": 1e-9,
"newton.linear.tolerance": 1e-11,
"newton.linear.preconditioning.method": "ilu",
"newton.verbose": False,
"newton.linear.verbose": False}
scheme = solutionScheme([form == 0],
space,
solver="cg",
parameters=solverParameters)
endTime = 0.3
saveInterval = 0.01
nextSaveTime = saveInterval
vtk = gridView.sequencedVTK("mcfPaper", pointdata=[uh], subsampling=2)
vtk()
while t.value < endTime:
uh_old.assign(uh)
info = scheme.solve(target=uh)
t.value += dt.value
positions.dofVector.assign(uh.dofVector)
inner(grad(u[1]), grad(v[1])) + inner(u, v) - inner(s, v)) * dx
# <markdowncell>
# We set up the scheme with some parameters.
# <codecell>
from dune.fem.scheme import galerkin as solutionScheme
solverParameters = {
"newton.tolerance": 1e-8,
"newton.linear.tolerance": 1e-10,
"newton.linear.preconditioning.method": "sor",
"newton.verbose": True,
"newton.linear.verbose": True
}
scheme = solutionScheme(a_im == a_ex,
space,
solver="gmres",
parameters=solverParameters)
# <markdowncell>
# We set up the adaptive method. We start with a marking strategy based on the value of the gradient of the phase field variable.
# <codecell>
from dune.ufl import expression2GF
indicator = expression2GF(gridView,
dot(grad(u_h[0]), grad(u_h[0])),
0,
name="indicator")
# <markdowncell>
# We do the initial refinement of the grid.
# <codecell>
def compute(space, epsilon, weakBnd, skeleton, mol=None):
u = TrialFunction(space)
v = TestFunction(space)
n = FacetNormal(space)
he = avg(CellVolume(space)) / FacetArea(space)
hbnd = CellVolume(space) / FacetArea(space)
x = SpatialCoordinate(space)
exact = uflFunction(space.gridView,
name="exact",
order=3,
ufl=sin(x[0] * x[1]))
uh = space.interpolate(exact, name="solution")
# diffusion factor
eps = Constant(epsilon, "eps")
# transport direction and upwind flux
b = as_vector([1, 0])
hatb = (dot(b, n) + abs(dot(b, n))) / 2.0
# characteristic function for left/right boundary
dD = conditional((1 + x[0]) * (1 - x[0]) < 1e-10, 1, 0)
# penalty parameter
beta = Constant(20 * space.order**2, "beta")
rhs = -(div(eps * grad(exact) - b * exact)) * v * dx
aInternal = dot(eps * grad(u) - b * u, grad(v)) * dx
aInternal -= eps * dot(grad(exact), n) * v * (1 - dD) * ds
diffSkeleton = eps*beta/he*jump(u)*jump(v)*dS -\
eps*dot(avg(grad(u)),n('+'))*jump(v)*dS -\
eps*jump(u)*dot(avg(grad(v)),n('+'))*dS
if weakBnd:
diffSkeleton += eps*beta/hbnd*(u-exact)*v*dD*ds -\
eps*dot(grad(exact),n)*v*dD*ds
advSkeleton = jump(hatb * u) * jump(v) * dS
if weakBnd:
advSkeleton += (hatb * u + (dot(b, n) - hatb) * exact) * v * dD * ds
if skeleton:
form = aInternal + diffSkeleton + advSkeleton
else:
form = aInternal
if weakBnd and skeleton:
strongBC = None
else:
strongBC = DirichletBC(space, exact, dD)
if space.storage[0] == "numpy":
solver = {
"solver": ("suitesparse", "umfpack"),
"parameters": {
"newton.verbose": True,
"newton.linear.verbose": False,
"newton.linear.tolerance": 1e-5,
}
}
else:
solver = {
"solver": "bicgstab",
"parameters": {
"newton.linear.preconditioning.method": "ilu",
"newton.linear.tolerance": 1e-13,
"newton.verbose": True,
"newton.linear.verbose": False
}
}
if mol == 'mol':
scheme = molSolutionScheme([form == rhs, strongBC], **solver)
else:
scheme = solutionScheme([form == rhs, strongBC], **solver)
eoc = []
info = scheme.solve(target=uh)
error = dot(uh - exact, uh - exact)
error0 = math.sqrt(integrate(gridView, error, order=5))
print(error0, " # output", flush=True)
for i in range(3):
gridView.hierarchicalGrid.globalRefine(1)
uh.interpolate(exact)
scheme.solve(target=uh)
error = dot(uh - exact, uh - exact)
error1 = math.sqrt(integrate(gridView, error, order=5))
eoc += [math.log(error1 / error0) / math.log(0.5)]
print(i, error0, error1, eoc, " # output", flush=True)
error0 = error1
# print(space.order,epsilon,eoc)
if (eoc[-1] - (space.order + 1)) < -0.1:
print("ERROR:", space.order, epsilon, eoc)
return eoc
# <markdowncell>
# Finally we define the variational problem
# \begin{align*}
# \int_\Omega \sigma(u)\colon\epsilon(v) = (0,-\rho g)\cdot v
# \end{align*}
# and solve the system
# <codecell>
u = TrialFunction(space)
v = TestFunction(space)
equation = inner(sigma(u), epsilon(v)) * dx == dot(as_vector([0, -rho * g]),
v) * dx
scheme = solutionScheme(
[equation, dbc],
solver='cg',
parameters={"newton.linear.preconditioning.method": "ilu"})
info = scheme.solve(target=displacement)
# <markdowncell>
# We can directly plot the magnitude of the displacement field and the stress
# <codecell>
fig = pyplot.figure(figsize=(20, 10))
displacement.plot(gridLines=None, figure=(fig, 121), colorbar="horizontal")
s = sigma(displacement) - (1. / 3) * tr(sigma(displacement)) * Identity(2)
von_Mises = sqrt(3. / 2 * inner(s, s))
plot(von_Mises,
grid=gridView,
gridLines=None,
figure=(fig, 122),
uh = space.interpolate(x, name="uh")
uh_old = uh.copy()
# problem definition
# space form
xForm = inner(grad(u), grad(phi)) * dx
# add time discretization
form = dot(u - uh_old, phi) * dx + dt * xForm
# define dirichlet boundary conditions
bc = dune.ufl.DirichletBC(space,x)
# setup scheme
scheme = solutionScheme([form == 0,bc], space, solver="cg")
endTime = 0.1
saveInterval = 0.05
nextSaveTime = saveInterval
vtk = gridView.sequencedVTK("mcfSoap", pointdata=[uh])
vtk()
while t.value < endTime:
uh_old.assign(uh)
info = scheme.solve(target=uh)
t.value += dt.value
positions.dofVector.assign(uh.dofVector)
from dune.alugrid import aluSimplexGrid as leafGridView3d
gridView3d = leafGridView3d(domain3d)
# <markdowncell>
# As before we solve a simple Laplace problem
# <codecell>
from dune.fem.space import lagrange as solutionSpace
from dune.fem.scheme import galerkin as solutionScheme
from ufl import TrialFunction, TestFunction, SpatialCoordinate, dot, grad, dx, conditional, sqrt
space3d = solutionSpace(gridView3d, order=1)
u = TrialFunction(space3d)
v = TestFunction(space3d)
x = SpatialCoordinate(space3d)
scheme3d = solutionScheme((dot(grad(u), grad(v)) + u * v) *
dx == conditional(dot(x, x) < .01, 100, 0) * v * dx,
solver='cg')
uh3d = space3d.interpolate([0], name="solution")
info = scheme3d.solve(target=uh3d)
# <markdowncell>
# Instead of plotting this using paraview we want to only study the
# solution along a single line. This requires findings points
# $x_i = x_0+\frac{i}{N}(x1-x0)$ for $i=0,\dots,N$ within the unstructured
# grid. This would be expensive to compute on the Python so we implement
# this algorithm in C++ using the `LineSegmentSampler` class available in
# `Dune-Fem`. The resulting `algorithm` returns a pair of two lists with
# coordinates $x_i$ and the values of the grid function at these points:
#
# .. literalinclude:: utility.hh
#
# to the dual problem with right hand side $J(\varphi_i)$ for all basis
# functions $\varphi_i$. This is not directly expressible in UFL and we
# therefore implement this using a small C++ function which we then export
# to Python:
#
# .. literalinclude:: laplace-dwr.hh
#
# <codecell>
from dune.fem.scheme import galerkin as solutionScheme
spaceZ = solutionSpace(uh.space.grid, order=order + 1)
u = TrialFunction(spaceZ)
v = TestFunction(spaceZ)
x = SpatialCoordinate(spaceZ)
a = dot(grad(u), grad(v)) * dx
dual = solutionScheme([a == 0, DirichletBC(spaceZ, 0)], solver="cg")
z = spaceZ.interpolate(0, name="dual")
zh = uh.copy(name="dual_h")
point = common.FieldVector([0.4, 0.4])
pointFunctional = z.copy("dual_rhs")
eh = expression2GF(uh.space.grid, abs(exact - uh), order=order + 1)
computeFunctional = algorithm.load("pointFunctional", "laplace-dwr.hh", point,
pointFunctional, eh)
# <markdowncell>
# <codecell>
from dune.fem.space import finiteVolume as estimatorSpace
from dune.fem.operator import galerkin as estimatorOp
fvspace = estimatorSpace(uh.space.grid)
f = -div( grad(exact) )
g_N = grad(exact)
n = FacetNormal(space)
b = f*v*dx + dot(g_N,n)*conditional(x[0]>=1e-8,1,0)*v*ds
dbc = DirichletBC(space,exact,x[0]<=1e-8)
# <markdowncell>
# With the model described as a ufl form, we can construct a scheme class
# that provides the solve method which we can use to compute the solution:
# <codecell>
from dune.fem import parameter
parameter.append({"fem.verboserank": -1})
from dune.fem.scheme import galerkin as solutionScheme
scheme = solutionScheme([a == b, dbc], solver='cg')
scheme.solve(target = u_h)
# <markdowncell>
# We can compute the error between the exact and the discrete solution by
# using the `integrate` function described above:
# <codecell>
h1error = dot(grad(u_h - exact), grad(u_h - exact))
error = sqrt(integrate(gridView, h1error, order=5))
print("Number of elements:",gridView.size(0),
"number of dofs:",space.size,"H^1 error:", error)
# <markdowncell>
# To verify that the discrete scheme is converging to the exact solution
# we can compute the experimental order of convergence (EOC):
def calculate(use_cpp, gridView):
# space on Gamma_0 to describe position of Gamma(t)
space = solutionSpace(gridView, dimRange=gridView.dimWorld, order=order)
u = TrialFunction(space)
v = TestFunction(space)
x = SpatialCoordinate(space.cell())
positions = space.interpolate(x, name="position")
# space for discrete solution on Gamma(t)
surface = geoGridView(positions)
space = solutionSpace(surface, dimRange=surface.dimWorld, order=order)
solution = space.interpolate(x, name="solution")
# set up model using theta scheme
theta = 0.5 # Crank-Nicholson
I = Identity(3)
dt = dune.ufl.Constant(0, "dt")
a = (inner(u - x, v) + dt * inner(theta * grad(u) +
(1 - theta) * I, grad(v))) * dx
scheme = solutionScheme(a == 0, space, solver="cg")
Rexact = lambda t: math.sqrt(R0 * R0 - 4. * t)
radius = switchCalcRadius(use_cpp, surface)
scheme.model.dt = 0.02
numberOfLoops = 3
times = np.zeros(numberOfLoops)
errors = np.zeros(numberOfLoops)
totalIterations = np.zeros(numberOfLoops, np.dtype(np.uint32))
gridSizes = np.zeros(numberOfLoops, np.dtype(np.uint32))
for i in range(numberOfLoops):
positions.interpolate(x * (R0 / sqrt(dot(x, x))))
solution.interpolate(x)
t = 0.
error = abs(radius(surface) - Rexact(t))
iterations = 0
start = time.time()
while t < endTime:
info = scheme.solve(target=solution)
# move the surface
positions.dofVector.assign(solution.dofVector)
# store some information about the solution process
iterations += int(info["linear_iterations"])
t += scheme.model.dt
error = max(error, abs(radius(surface) - Rexact(t)))
print("time used:", time.time() - start)
times[i] = time.time() - start
errors[i] = error
totalIterations[i] = iterations
gridSizes[i] = gridView.size(2)
if i < numberOfLoops - 1:
gridView.hierarchicalGrid.globalRefine(1)
scheme.model.dt /= 2
eocs = np.log(errors[0:][:numberOfLoops - 1] / errors[1:]) / math.log(
math.sqrt(2))
try:
import pandas as pd
keys = {
'size': gridSizes,
'error': errors,
"eoc": np.insert(eocs, 0, None),
'iterations': totalIterations
}
table = pd.DataFrame(keys,
index=range(numberOfLoops),
columns=['size', 'error', 'eoc', 'iterations'])
print(table)
except ImportError:
print("pandas could not be used to show table with results")
pass
return gridSizes, times
dt = dune.ufl.Constant(0.001, "timeStep")
t = dune.ufl.Constant(0.0, "time")
# define storage for discrete solutions
uh = space.interpolate(x, name="uh")
uh_old = uh.copy()
# problem definition
# space form
xForm = inner(grad(u), grad(phi)) * dx
# add time discretization
form = dot(u - uh_old, phi) * dx + dt * xForm
# setup scheme
scheme = solutionScheme(form == 0, space, solver="cg")
nextSaveTime = saveInterval
vtk = gridView.sequencedVTK("mcfSphere", pointdata=[uh])
vtk()
while t.value < endTime:
uh_old.assign(uh)
info = scheme.solve(target=uh)
t.value += dt.value
positions.dofVector.assign(uh.dofVector)
[radius, area] = dune.fem.function.integrate(gridView,
[sqrt(dot(x, x)), 1],
def model(space, epsilon, weakBnd, skeleton, useMol):
u = TrialFunction(space)
v = TestFunction(space)
n = FacetNormal(space)
he = avg(CellVolume(space)) / FacetArea(space)
hbnd = CellVolume(space) / FacetArea(space)
x = SpatialCoordinate(space)
#exact = sin(x[0]*x[1]) # atan(1*x[1])
exact = uflFunction(space.gridView,
name="exact",
order=3,
ufl=sin(x[0] * x[1]))
# diffusion factor
eps = 1 # Constant(epsilon,"eps")
# transport direction and upwind flux
b = as_vector([1, 0])
hatb = (dot(b, n) + abs(dot(b, n))) / 2.0
# characteristic function for left/right boundary
dD = conditional((1 + x[0]) * (1 - x[0]) < 1e-10, 1, 0)
# penalty parameter
beta = Constant(10 * space.order**2 if space.order > 0 else 1, "beta")
rhs = (-div(eps * grad(exact) - b * exact) + exact) * v * dx
aInternal = (dot(eps * grad(u) - b * u, grad(v)) + dot(u, v)) * dx
diffSkeleton = eps*beta/he*jump(u)*jump(v)*dS -\
eps*dot(avg(grad(u)),n('+'))*jump(v)*dS -\
eps*jump(u)*dot(avg(grad(v)),n('+'))*dS
diffSkeleton -= eps * dot(grad(exact), n) * v * (1 - dD) * ds
if weakBnd:
diffSkeleton += eps*beta/hbnd*(u-exact)*v*dD*ds -\
eps*dot(grad(exact),n)*v*dD*ds
advSkeleton = jump(hatb * u) * jump(v) * dS
if weakBnd:
advSkeleton += (hatb * u + (dot(b, n) - hatb) * exact) * v * dD * ds
if skeleton:
form = aInternal + diffSkeleton + advSkeleton
else:
form = aInternal
if weakBnd and skeleton:
strongBC = None
else:
strongBC = None # DirichletBC(space,exact,dD)
if space.storage[0] == "fem":
solver = {"solver": ("suitesparse", "umfpack")}
else:
solver = {
"solver": "bicgstab",
"parameters": {
"newton.linear.preconditioning.method": "jacobi",
"newton.linear.tolerance": 1e-13
}
}
if useMol:
scheme = solutionMolScheme([form == rhs, strongBC], **solver)
else:
scheme = solutionScheme([form == rhs, strongBC], **solver)
uh = space.interpolate(exact, name="solution")
A = linear(scheme)
return scheme, uh, A, exact
inner(grad(u[1]), grad(v[1])) + inner(u, v) - inner(s, v)) * dx
# <markdowncell>
# We set up the scheme with some parameters.
# <codecell>
from dune.fem.scheme import galerkin as solutionScheme
solverParameters = {
"newton.tolerance": 1e-8,
"newton.linear.tolerance": 1e-10,
"newton.linear.preconditioning.method": "jacobi",
"newton.verbose": False,
"newton.linear.verbose": False
}
scheme = solutionScheme(a_im == a_ex,
space,
solver="bicgstab",
parameters=solverParameters)
scheme.model.dt = 0.0002
# <markdowncell>
# We set up the adaptive method. We start with a marking strategy based on the value of the gradient of the phase field variable.
# <codecell>
from dune.ufl import expression2GF
indicator = expression2GF(gridView,
dot(grad(u_h[0]), grad(u_h[0])),
0,
name="indicator")
# <markdowncell>
# We do the initial refinement of the grid.
