def __init__(self,
fem,
source,
fields,
Mn=None,
Mf1=None,
Mf2=None,
abs_du_tol=1e-8,
rel_du_tol=1e-8,
abs_res_tol=1e-10,
rel_res_tol=1e-10,
max_iter=100,
criterion='residual',
norm_change='linf',
norm_res='linf'):
"""The constructor"""
Solver.__init__(self, fem, source, fields, Mn, abs_du_tol, rel_du_tol,
abs_res_tol, rel_res_tol, max_iter, criterion,
norm_change, norm_res)
# vector finite element and function space for rescaled (phi, h, y, z)
self.E = d.MixedElement(
[self.fem.Pn, self.fem.Pn, self.fem.Pn, self.fem.Pn])
self.V = d.FunctionSpace(self.fem.mesh.mesh, self.E)
# (discontinuous) vector finite element and function space for the four strong residuals
self.dE = d.MixedElement(
[self.fem.dPn, self.fem.dPn, self.fem.dPn, self.fem.dPn])
self.dV = d.FunctionSpace(self.fem.mesh.mesh, self.dE)
# fields' rescalings
if Mf1 is None:
self.Mf1 = self.source.Ms * self.Mn / self.fields.Mp
else:
self.Mf1 = Mf1
if Mf2 is None:
if (self.fields.alpha != 0.):
self.Mf2 = abs(self.fields.alpha) * self.Mf1
else:
self.Mf2 = self.Mf1
else:
self.Mf2 = Mf2
# solution and field profiles (computed by the solver)
self.u = None
self.phi, self.h, self.y, self.z = None, None, None, None
self.Phi, self.H, self.Y, self.Z = None, None, None, None
# gradients of the scalar fields
self.grad_phi = None # rescaled
self.grad_Phi = None # physical
self.grad_h = None
self.grad_H = None
# phi scalar force (physical units)
self.force = None
def __init__(self,
fem,
source,
fields,
Mn=None,
Mf1='NL',
guess_choice='NL',
abs_du_tol=1e-8,
rel_du_tol=1e-8,
abs_res_tol=1e-10,
rel_res_tol=1e-10,
max_iter=100,
criterion='residual',
norm_change='linf',
norm_res='linf'):
"""The constructor"""
Solver.__init__(self, fem, source, fields, Mn, abs_du_tol, rel_du_tol,
abs_res_tol, rel_res_tol, max_iter, criterion,
norm_change, norm_res)
# vector finite element and function space for rescaled (pi, w, y)
self.E = d.MixedElement([self.fem.Pn, self.fem.Pn, self.fem.Pn])
self.V = d.FunctionSpace(self.fem.mesh.mesh, self.E)
# (discontinuous) vector finite element and function space for the three strong residuals
self.dE = d.MixedElement([self.fem.dPn, self.fem.dPn, self.fem.dPn])
self.dV = d.FunctionSpace(self.fem.mesh.mesh, self.dE)
# field rescaling
if Mf1 == 'NL':
self.Mf1 = ( self.fields.Lambda / self.Mn )**(3.-1./self.fields.n) * \
( self.source.Ms / self.fields.Mp )**(1./self.fields.n) * self.fields.m
elif Mf1 == 'source':
self.Mf1 = self.source.Ms / self.fields.Mp * self.Mn
else:
self.Mf1 = Mf1
# choice of initial guess
self.guess_choice = guess_choice
# solution and field profiles (computed by the solver)
self.u = None
self.pi, self.w, self.y = None, None, None # rescaled
self.Pi, self.W, self.Y = None, None, None # physical
# gradient of the scalar field
self.grad_pi = None # rescaled
self.grad_Pi = None # physical
# scalar force (physical units)
self.force = None
def extension_element(elm):
'''
Produce an intermerdiate element for computing with extension of
functions in FEM space over elm
'''
# Want exact match here; otherwise VectorElement is MixedElement and while
# it works I don't find it pretty
if type(elm) == df.MixedElement:
return df.MixedElement(map(extension_element, elm.sub_elements()))
family = elm.family()
family_map = {'Lagrange': 'Lagrange'}
# This seems like a reasonable fall back option
family = family_map.get(family, 'Discontinuous Lagrange')
degree = elm.degree() # Preserve degree
cell = extension_cell(elm)
# How to construct:
# There is an issue here where e.g. Hdiv are not scalars, their
# element is FiniteElement but we want trace space from VectorElement
elmtype_map = {
0: df.FiniteElement,
1: df.VectorElement,
2: df.TensorElement
}
# So let's check first for elements where scalar = FiniteElm, vector == VectorElm
rank = len(elm.value_shape())
if elmtype_map[rank] == type(elm):
elm = type(elm) # i.e. vector element stays vector element
else:
elm = elmtype_map[rank]
return elm(family, cell, degree)
def generate_function_spaces(self, order=1, use_periodic=False):
r"""
Generates the finite-element function spaces used with topological dimension :math:`d` set by variable ``self.top_dim``.
:param order: order :math:`k` of the shape function, currently only supported are Lagrange :math:`P_1` elements.
:param use_periodic: use periodic boundaries along lateral boundary (currently not supported).
:type use_periodic: bool
The element shape-functions available from this method are :
* ``self.Q`` -- :math:`\mathcal{H}^k(\Omega)`
* ``self.Q3`` -- :math:`[\mathcal{H}^k(\Omega)]^3`
* ``self.V`` -- :math:`[\mathcal{H}^k(\Omega)]^d` formed using :func:`~dolfin.functions.functionspace.VectorFunctionSpace`
* ``self.T`` -- :math:`[\mathcal{H}^k(\Omega)]^{d \times d}` formed using :func:`~dolfin.functions.functionspace.TensorFunctionSpace`
"""
s = "::: generating fundamental function spaces of order %i :::" % order
print_text(s, cls=self.this)
if use_periodic:
self.generate_pbc()
else:
self.pBC = None
order = 1
space = 'CG'
Qe = dl.FiniteElement("CG", self.mesh.ufl_cell(), order)
self.Q3 = dl.FunctionSpace(self.mesh, dl.MixedElement([Qe] * 3))
self.Q = dl.FunctionSpace(self.mesh, space, order)
self.V = dl.VectorFunctionSpace(self.mesh, space, order)
self.T = dl.TensorFunctionSpace(self.mesh, space, order)
s = " - fundamental function spaces created - "
print_text(s, cls=self.this)
def computeVelocityField(mesh):
Xh = dl.VectorFunctionSpace(mesh,'Lagrange', 2)
Wh = dl.FunctionSpace(mesh, 'Lagrange', 1)
mixed_element = dl.MixedElement([Xh.ufl_element(), Wh.ufl_element()])
XW = dl.FunctionSpace(mesh, mixed_element)
Re = 1e2
g = dl.Expression(('0.0','(x[0] < 1e-14) - (x[0] > 1 - 1e-14)'), element=Xh.ufl_element())
bc1 = dl.DirichletBC(XW.sub(0), g, v_boundary)
bc2 = dl.DirichletBC(XW.sub(1), dl.Constant(0), q_boundary, 'pointwise')
bcs = [bc1, bc2]
vq = dl.Function(XW)
(v,q) = ufl.split(vq)
(v_test, q_test) = dl.TestFunctions (XW)
def strain(v):
return ufl.sym(ufl.grad(v))
F = ( (2./Re)*ufl.inner(strain(v),strain(v_test))+ ufl.inner (ufl.nabla_grad(v)*v, v_test)
- (q * ufl.div(v_test)) + ( ufl.div(v) * q_test) ) * ufl.dx
dl.solve(F == 0, vq, bcs, solver_parameters={"newton_solver":
{"relative_tolerance":1e-4, "maximum_iterations":100,
"linear_solver":"default"}})
return v
def __init__(self, parameters, mesh, parser):
super().__init__(parameters, mesh, parser)
V = df.FunctionSpace(
self.mesh,
df.MixedElement(
df.VectorElement('CG', self.mesh.ufl_cell(),
parameters["fe degree solid"]),
df.VectorElement('CG', self.mesh.ufl_cell(),
parameters["fe degree fluid"]),
df.FiniteElement('CG', self.mesh.ufl_cell(),
parameters["fe degree pressure"])))
self.V = V
self.two_way = True
self.three_way = False
if "3-way" in self.parameters["pc type"]:
self.two_way = False
self.three_way = True
parprint("---- Problem dofs={}, h={}, solving with {} procs".format(
V.dim(), mesh.hmin(), MPI.COMM_WORLD.size))
self.assembler = PoromechanicsAssembler(parameters, V, self.three_way)
self.index_map = IndexSet(V, self.two_way)
# Start by assembling system matrices
self.assembler.assemble()
self.sol = df.Function(V)
self.us_nm1, self.uf_nm1, self.p_nm1 = self.sol.split(True)
self.us_nm2 = self.us_nm1.copy(True)
self.first_timestep = True
def test_dirichlet_boundary(self):
frequency=50
omega = 2.*np.pi*frequency
c1,c2 = [343.4,6320]
gamma=8.4e-4
kappa = omega/c1
Nx, Ny = [21,21]
Lx,Ly=[1,1]
# create function space
mesh = dl.RectangleMesh(dl.Point(0., 0.), dl.Point(Lx, Ly), Nx, Ny)
degree=1
P1=dl.FiniteElement('Lagrange',mesh.ufl_cell(),degree)
element=dl.MixedElement([P1,P1])
function_space=dl.FunctionSpace(mesh,element)
boundary_conditions=None
kappa=dl.Constant(kappa)
# do not pass element=function_space.ufl_element()
# as want forcing to be a scalar pass degree instead
forcing=[
Forcing(kappa,'real',degree=function_space.ufl_element().degree()),
Forcing(kappa,'imag',degree=function_space.ufl_element().degree())]
p=run_model(kappa,forcing,function_space,boundary_conditions)
error = dl.errornorm(
ExactSolution(kappa,element=function_space.ufl_element()),p)
print('Error',error)
assert error<=3e-2
def test_robin_boundary(self):
frequency=50
omega = 2.*np.pi*frequency
c1,c2 = [343.4,6320]
gamma=8.4e-4
kappa = omega/c1
alpha=kappa*gamma
Nx, Ny = 21,21; Lx, Ly = 1,1
mesh = dl.RectangleMesh(dl.Point(0., 0.), dl.Point(Lx, Ly), Nx, Ny)
degree=1
P1=dl.FiniteElement('Lagrange',mesh.ufl_cell(),degree)
element=dl.MixedElement([P1,P1])
function_space=dl.FunctionSpace(mesh,element)
boundary_conditions=get_robin_bndry_conditions(
kappa,alpha,function_space)
kappa=dl.Constant(kappa)
forcing=[
Forcing(kappa,'real',degree=function_space.ufl_element().degree()),
Forcing(kappa,'imag',degree=function_space.ufl_element().degree())]
p=run_model(kappa,forcing,function_space,boundary_conditions)
error = dl.errornorm(
ExactSolution(kappa,element=function_space.ufl_element()),p,)
print('Error',error)
assert error<=3e-2
def _setup_function_spaces(self):
assert hasattr(self, "_mesh")
# element and function space definition
cell = self._mesh.ufl_cell()
# taylor-hood element
self._elemV = dlfn.VectorElement("CG", cell,
self._parameters.velocity_degree)
self._elemP = dlfn.FiniteElement("CG", cell,
self._parameters.pressure_degree)
self._elemT = dlfn.FiniteElement("CG", cell,
self._parameters.temperature_degree)
self._mixedElem = dlfn.MixedElement(
[self._elemV, self._elemP, self._elemT])
self._Wh = dlfn.FunctionSpace(self._mesh, self._mixedElem)
# print info message
ndofs_velocity = self._Wh.sub(0).dim()
ndofs_pressure = self._Wh.sub(1).dim()
ndofs_temperature = self._Wh.sub(2).dim()
print "DOFs velocity : ", ndofs_velocity, "\n" \
"DOFs pressure : ", ndofs_pressure, "\n" \
"DOFs temperature : ", ndofs_temperature
# functions
self._sol = dlfn.Function(self._Wh)
self._sol0 = dlfn.Function(self._Wh)
self._sol00 = dlfn.Function(self._Wh)
self._v0, _, self._T0 = dlfn.split(self._sol0)
self._v00, _, self._T00 = dlfn.split(self._sol00)
def main():
fsr = FunctionSubspaceRegistry()
deg = 2
mesh = dolfin.UnitSquareMesh(100, 3)
muc = mesh.ufl_cell()
el_w = dolfin.FiniteElement('DG', muc, deg - 1)
el_j = dolfin.FiniteElement('BDM', muc, deg)
el_DG0 = dolfin.FiniteElement('DG', muc, 0)
el = dolfin.MixedElement([el_w, el_j])
space = dolfin.FunctionSpace(mesh, el)
DG0 = dolfin.FunctionSpace(mesh, el_DG0)
fsr.register(space)
facet_normal = dolfin.FacetNormal(mesh)
xyz = dolfin.SpatialCoordinate(mesh)
trial = dolfin.Function(space)
test = dolfin.TestFunction(space)
w, c = dolfin.split(trial)
v, phi = dolfin.split(test)
sympy_exprs = derive_exprs()
exprs = {
k: sympy_dolfin_printer.to_ufl(sympy_exprs['R'], mesh, v)
for k, v in sympy_exprs['quantities'].items()
}
f = exprs['f']
w0 = dolfin.project(dolfin.conditional(dolfin.gt(xyz[0], 0.5), 1.0, 0.3),
DG0)
w_BC = exprs['w']
dx = dolfin.dx()
form = (+v * dolfin.div(c) * dx - v * f * dx +
dolfin.exp(w + w0) * dolfin.dot(phi, c) * dx +
dolfin.div(phi) * w * dx -
(w_BC - w0) * dolfin.dot(phi, facet_normal) * dolfin.ds() -
(w0('-') - w0('+')) * dolfin.dot(phi('+'), facet_normal('+')) *
dolfin.dS())
solver = NewtonSolver(form,
trial, [],
parameters=dict(relaxation_parameter=1.0,
maximum_iterations=15,
extra_iterations=10,
relative_tolerance=1e-6,
absolute_tolerance=1e-7))
solver.solve()
with closing(XdmfPlot("out/qflop_test.xdmf", fsr)) as X:
CG1 = dolfin.FunctionSpace(mesh, dolfin.FiniteElement('CG', muc, 1))
X.add('w0', 1, w0, CG1)
X.add('w_c', 1, w + w0, CG1)
X.add('w_e', 1, exprs['w'], CG1)
X.add('f', 1, f, CG1)
X.add('cx_c', 1, c[0], CG1)
X.add('cx_e', 1, exprs['c'][0], CG1)
def _element(s):
elements = [
s[k + '/element'] for k in s[key + '/subfunctions_keys']
]
if len(elements) == 0:
raise AssertionError()
elif len(elements) == 1:
return elements[0]
else:
return dolfin.MixedElement(elements)
def get_element(self):
''' use `element` property instead '''
descs = self.subspace_descriptors
elements = [desc['element'] for desc in descs]
n = len(elements)
if n == 0: raise AssertionError()
elif n == 1:
return elements[0]
else:
return dolfin.MixedElement(elements)
def create_functions(self):
"""
Create functions to hold solutions
"""
sim = self.simulation
# Function spaces
Vu = sim.data['Vu']
Vp = sim.data['Vp']
cd = sim.data['constrained_domain']
# Create coupled mixed function space and mixed function to hold results
func_spaces = [Vu] * sim.ndim + [Vp]
self.subspace_names = ['u%d' % d for d in range(sim.ndim)] + ['p']
# Create stress tensor space
P = Vu.ufl_element().degree()
Vs = dolfin.FunctionSpace(sim.data['mesh'],
'DG',
P,
constrained_domain=cd)
for i in range(sim.ndim**2):
stress_name = 'stress_%d' % i
sim.data[stress_name] = dolfin.Function(Vs)
func_spaces.append(Vs)
self.subspace_names.append(stress_name)
# Create mixed space
e_mixed = dolfin.MixedElement([fs.ufl_element() for fs in func_spaces])
Vcoupled = dolfin.FunctionSpace(sim.data['mesh'], e_mixed)
sim.data['Vcoupled'] = Vcoupled
# Create function assigner
Nspace = len(func_spaces)
self.subspaces = [Vcoupled.sub(i) for i in range(Nspace)]
sim.data['coupled'] = self.coupled_func = dolfin.Function(Vcoupled)
self.assigner = dolfin.FunctionAssigner(func_spaces, Vcoupled)
# Create segregated functions on component and vector form
u_list, up_list, upp_list, u_conv = [], [], [], []
for d in range(sim.ndim):
sim.data['u%d' % d] = u = dolfin.Function(Vu)
sim.data['up%d' % d] = up = dolfin.Function(Vu)
sim.data['upp%d' % d] = upp = dolfin.Function(Vu)
sim.data['u_conv%d' % d] = uc = dolfin.Function(Vu)
u_list.append(u)
up_list.append(up)
upp_list.append(upp)
u_conv.append(uc)
sim.data['u'] = dolfin.as_vector(u_list)
sim.data['up'] = dolfin.as_vector(up_list)
sim.data['upp'] = dolfin.as_vector(upp_list)
sim.data['u_conv'] = dolfin.as_vector(u_conv)
sim.data['p'] = dolfin.Function(Vp)
def _init_spaces(self):
mesh = self.geometry.mesh
P1 = dolfin.FiniteElement("Lagrange", mesh.ufl_cell(), 1)
P2 = dolfin.VectorElement("Lagrange", mesh.ufl_cell(), 2)
P3 = dolfin.VectorElement("Real", mesh.ufl_cell(), 0, 6)
self.state_space = dolfin.FunctionSpace(mesh, dolfin.MixedElement([P1, P2, P3]))
self.state = Function(self.state_space, name="state")
self.state_test = dolfin.TestFunction(self.state_space)
def computeVelocityField(self):
"""
The steady-state Navier-Stokes equation for velocity v:
-1/Re laplace v + nabla q + v dot nabla v = 0 in Omega
nabla dot v = 0 in Omega
v = g on partial Omega
"""
Xh = dl.VectorFunctionSpace(self.mesh, 'Lagrange', self.eldeg)
Wh = dl.FunctionSpace(self.mesh, 'Lagrange', 1)
mixed_element = dl.MixedElement([Xh.ufl_element(), Wh.ufl_element()])
XW = dl.FunctionSpace(self.mesh, mixed_element)
Re = dl.Constant(self.Re)
def v_boundary(x, on_boundary):
return on_boundary
def q_boundary(x, on_boundary):
return x[0] < dl.DOLFIN_EPS and x[1] < dl.DOLFIN_EPS
g = dl.Expression(('0.0', '(x[0] < 1e-14) - (x[0] > 1 - 1e-14)'),
element=Xh.ufl_element())
bc1 = dl.DirichletBC(XW.sub(0), g, v_boundary)
bc2 = dl.DirichletBC(XW.sub(1), dl.Constant(0), q_boundary,
'pointwise')
bcs = [bc1, bc2]
vq = dl.Function(XW)
(v, q) = ufl.split(vq)
(v_test, q_test) = dl.TestFunctions(XW)
def strain(v):
return ufl.sym(ufl.grad(v))
F = ((2. / Re) * ufl.inner(strain(v), strain(v_test)) +
ufl.inner(ufl.nabla_grad(v) * v, v_test) - (q * ufl.div(v_test)) +
(ufl.div(v) * q_test)) * ufl.dx
dl.solve(F == 0,
vq,
bcs,
solver_parameters={
"newton_solver": {
"relative_tolerance": 1e-4,
"maximum_iterations": 100,
"linear_solver": "default"
}
})
return v
def _generate_function_spaces(self, **kwargs):
"""
Generates a dolfin FunctionSpace of either single Finite Element (if only one Space is provided)
or Mixed Elements (if more the one Space provided).
"""
elements = [
self._generate_finite_element(space, **kwargs)
for space in self.spaces
]
if len(elements) == 1:
return dolf.FunctionSpace(self.domain.mesh, elements[0], **kwargs)
mixed_element = dolf.MixedElement(elements)
return dolf.FunctionSpace(self.domain.mesh, mixed_element, **kwargs)
def __init__(self, t_end = None, func = None, para_stiff = None, adjoint = False):
Problem_Basic.__init__(self, t_end = t_end, func = func, para_stiff = para_stiff)
self.k = dol.Constant(self.k)
self.u0_expr = dol.Constant(self.u0) # initial value
mesh = dol.UnitIntervalMesh(1)
R_elem = dol.FiniteElement("R", mesh.ufl_cell(), 0)
V_elem = dol.MixedElement([R_elem, R_elem])
self.V = dol.FunctionSpace(mesh, V_elem)
if adjoint:
self.z0_expr = dol.Constant(np.array([0., 0.])) # initial value adj
Problem_FE.__init__(self, adjoint)
(self.v1 , self.v2) = dol.split(self.v)
(self.u1trial, self.u2trial) = dol.split(self.utrial)
(self.u1old , self.u2old) = dol.split(self.uold)
(self.u1low , self.u2low) = dol.split(self.ulow)
## Crank nicolson weak formulation
F = (dol.inner(self.v1, self.u1trial - self.u1old + self.dt * (0.5 * (self.u1old + self.u1trial) - 0.5 * (self.u2old + self.u2trial)))*dol.dx
+ dol.inner(self.v2, self.u2trial - self.u2old - self.k * self.dt * 0.5 * (self.u2old + self.u2trial))*dol.dx)
prob = dol.LinearVariationalProblem(dol.lhs(F), dol.rhs(F), self.unew)
self.solver = dol.LinearVariationalSolver(prob)
## Implicit Euler weak formulation for error estimation
Flow = (dol.inner(self.v1, self.u1trial - self.u1old + self.dt * (self.u1trial - self.u2trial))*dol.dx
+ dol.inner(self.v2, self.u2trial - self.u2old - self.k * self.dt * self.u2trial)*dol.dx)
problow = dol.LinearVariationalProblem(dol.lhs(Flow), dol.rhs(Flow), self.ulow)
self.solver_low = dol.LinearVariationalSolver(problow)
if adjoint:
(self.z1old , self.z2old) = dol.split(self.zold)
(self.z1trial, self.z2trial) = dol.split(self.ztrial)
if self.func not in [1, 2]:
raise ValueError('DWR not (yet) implemented for this functional')
adj_src = dol.Function(self.V)
if self.func == 1: adj_src.interpolate(dol.Constant((1, 0)))
elif self.func == 2: adj_src.interpolate(dol.Constant((0, 1)))
src1, src2 = dol.split(adj_src)
Fadj = (dol.inner(self.z1trial - self.z1old + 0.5 * self.dt * (-self.z1trial - self.z1old + 2*src1), self.v1)*dol.dx +
dol.inner(self.z2trial - self.z2old + 0.5 * self.dt * ( self.z1trial + self.z1old + self.k*(self.z2trial + self.z2old) + 2*src2), self.v2)*dol.dx)
prob_adj = dol.LinearVariationalProblem(dol.lhs(Fadj), dol.rhs(Fadj), self.znew)
self.solver_adj = dol.LinearVariationalSolver(prob_adj)
def ode_test_form(request):
Model = eval(request.param)
model = Model()
mesh = df.UnitSquareMesh(10, 10)
V = df.FunctionSpace(mesh, "CG", 1)
S = state_space(mesh, model.num_states())
Mx = df.MixedElement((V.ufl_element(), S.ufl_element()))
VS = df.FunctionSpace(mesh, Mx)
vs = df.Function(VS)
vs.assign(df.project(model.initial_conditions(), VS))
(v, s) = df.split(vs)
(w, r) = df.TestFunctions(VS)
rhs = df.inner(model.F(v, s), r) + df.inner(- model.I(v, s), w)
form = rhs*df.dP
return form
def _init_spaces(self):
mesh = self.geometry.mesh
V = dolfin.VectorElement("P", mesh.ufl_cell(), 2)
Q = dolfin.FiniteElement("P", mesh.ufl_cell(), 1)
R = dolfin.FiniteElement("Real", mesh.ufl_cell(), 0)
el = dolfin.MixedElement([V, Q, R])
self.state_space = dolfin.FunctionSpace(mesh, el)
self.state = dolfin.Function(self.state_space)
self.state_test = dolfin.TestFunction(self.state_space)
self._Vu = get_cavity_volume_form(self.geometry.mesh,
u=dolfin.split(self.state)[0],
xshift=self.geometry.xshift)
self._V0 = dolfin.Constant(self.geometry.cavity_volume())
def create_mixed_space(mesh, k=1, augmentedTH=False, periodic_boundary=None):
Pk = df.FiniteElement("CG", mesh.ufl_cell(), k)
Pk1 = df.FiniteElement("CG", mesh.ufl_cell(), k + 1)
FE_v = df.VectorElement(Pk1, dim=mesh.geometry().dim())
FE_p = Pk
if augmentedTH:
# Use enriched element for p -> augmented TH, see Boffi et al. (2011)
P0 = df.FiniteElement("DG", mesh.ufl_cell(), 0)
gdim = mesh.geometry().dim()
assert k >= gdim - 1 # see Boffi et al. (2011, Eq. (3.1))
FE_p = df.EnrichedElement(Pk, P0)
W = df.FunctionSpace(mesh,
df.MixedElement([FE_v, FE_p]),
constrained_domain=periodic_boundary)
return W
def create_functions(self):
"""
Create functions to hold solutions
"""
sim = self.simulation
# Function spaces
Vu = sim.data['Vu']
Vp = sim.data['Vp']
cd = sim.data['constrained_domain']
# Create coupled mixed function space and mixed function to hold results
func_spaces = [Vu] * sim.ndim + [Vp]
self.subspace_names = ['u%d' % d for d in range(sim.ndim)] + ['p']
if self.use_lagrange_multiplicator:
Vl = dolfin.FunctionSpace(sim.data['mesh'],
"R",
0,
constrained_domain=cd)
sim.data['l'] = dolfin.Function(Vl)
func_spaces.append(Vl)
self.subspace_names.append('l')
e_mixed = dolfin.MixedElement([fs.ufl_element() for fs in func_spaces])
Vcoupled = dolfin.FunctionSpace(sim.data['mesh'], e_mixed)
sim.data['Vcoupled'] = Vcoupled
sim.ndofs += Vcoupled.dim()
Nspace = len(func_spaces)
self.subspaces = [Vcoupled.sub(i) for i in range(Nspace)]
sim.data['coupled'] = self.coupled_func = dolfin.Function(Vcoupled)
self.assigner = dolfin.FunctionAssigner(func_spaces, Vcoupled)
# Create segregated functions on component and vector form
create_vector_functions(sim, 'u', 'u%d', Vu)
create_vector_functions(sim, 'up', 'up%d', Vu)
create_vector_functions(sim, 'upp', 'upp%d', Vu)
create_vector_functions(sim, 'u_conv', 'u_conv%d', Vu)
create_vector_functions(sim, 'up_conv', 'up_conv%d', Vu)
create_vector_functions(sim, 'upp_conv', 'upp_conv%d', Vu)
create_vector_functions(sim, 'u_unlim', 'u_unlim%d', Vu)
sim.data['p'] = dolfin.Function(Vp)
sim.data['ui_tmp'] = dolfin.Function(Vu)
def test_variable_kappa(self):
frequency = 50
omega = 2. * np.pi * frequency
c1, c2 = [343.4, 6320 * 100]
gamma = 8.4e-4
kappa1 = omega / c1
kappa2 = omega / c2
Nx, Ny = 21, 21
Lx, Ly = 1, 1
#mesh = RectangleMesh(Point(0., 0.), Point(Lx, Ly), Nx, Ny)
mesh = generate_mesh_with_cicular_subdomain(15, 0.25, False)
degree = 1
P1 = dl.FiniteElement('Lagrange', mesh.ufl_cell(), degree)
element = dl.MixedElement([P1, P1])
function_space = dl.FunctionSpace(mesh, element)
boundary_conditions = None
forcing = [
ForcingVariableKappa(kappa1,
kappa2,
'real',
degree=function_space.ufl_element().degree()),
ForcingVariableKappa(kappa1,
kappa2,
'imag',
degree=function_space.ufl_element().degree())
]
kappa = dl.Expression('x[1] <= 0.5 + tol ? k_0 : k_1',
degree=0,
tol=1e-14,
k_0=kappa1,
k_1=kappa2)
# use mesh with circular subdomain as this should not effect result
# significantly but will test if subdomain mesh works correctly
p = run_model(kappa, forcing, function_space, boundary_conditions)
error = dl.errornorm(
ExactSolution(kappa, element=function_space.ufl_element()), p)
print('Error', error)
assert error <= 3e-2
def create_functions(self):
"""
Create functions to hold solutions
"""
sim = self.simulation
# Function spaces
Vu = sim.data['Vu']
Vp = sim.data['Vp']
# Create velocity functions on component and vector form
create_vector_functions(sim, 'u', 'u%d', Vu)
create_vector_functions(sim, 'up', 'up%d', Vu)
create_vector_functions(sim, 'upp', 'upp%d', Vu)
create_vector_functions(sim, 'u_conv', 'u_conv%d', Vu)
create_vector_functions(sim, 'up_conv', 'up_conv%d', Vu)
create_vector_functions(sim, 'upp_conv', 'upp_conv%d', Vu)
create_vector_functions(sim, 'u_unlim', 'u_unlim%d', Vu)
sim.data['ui_tmp'] = dolfin.Function(Vu)
# Create coupled vector function
ue = Vu.ufl_element()
e_mixed = dolfin.MixedElement([ue] * sim.ndim)
Vcoupled = dolfin.FunctionSpace(Vu.mesh(), e_mixed)
sim.data['uvw_star'] = dolfin.Function(Vcoupled)
sim.data['uvw_temp'] = dolfin.Function(Vcoupled)
sim.ndofs += Vcoupled.dim() + Vp.dim()
# Create assigner to extract split function from uvw and vice versa
self.assigner_split = dolfin.FunctionAssigner([Vu] * sim.ndim,
Vcoupled)
self.assigner_merge = dolfin.FunctionAssigner(Vcoupled,
[Vu] * sim.ndim)
# Create pressure function
sim.data['p'] = dolfin.Function(Vp)
sim.data['p_hat'] = dolfin.Function(Vp)
def __init__(
self,
energy,
state,
bcs,
# rayleigh=None,
Hessian=None,
nullspace=None,
parameters=None):
"""Solves second order stability problem
- computes inertia
- solves full eigenvalue problem
* Parameters:
- energy (form)
- state (tuple)
- bcs (list)
Optional arguments: rayleigh (form), Hessian (form),
nullspace, parameters
"""
OptDB = PETSc.Options()
OptDB.view()
self.i = 0
self.u = state['u']
self.alpha = state['alpha']
self._u = dolfin.Vector(self.u.vector())
self._alpha = dolfin.Vector(self.alpha.vector())
self.mesh = state['alpha'].function_space().mesh()
self.Z = dolfin.FunctionSpace(
self.mesh,
dolfin.MixedElement(
[self.u.ufl_element(),
self.alpha.ufl_element()]))
self.z = dolfin.Function(self.Z)
self.z_old = dolfin.Function(self.Z)
zeta = dolfin.TestFunction(self.Z)
v, beta = dolfin.split(zeta)
self.dm = self.Z.dofmap()
self.ownership = self.Z.dofmap().ownership_range()
Zu = self.Z.extract_sub_space([0])
Za = self.Z.extract_sub_space([1])
self.Xa = Za.collapse().tabulate_dof_coordinates()
self.Xu = Zu.collapse().tabulate_dof_coordinates()
(_, self.mapa) = Za.collapse(collapsed_dofs=True)
(_, self.mapu) = Zu.collapse(collapsed_dofs=True)
self.assigner = dolfin.FunctionAssigner(
self.Z, # receiving space
[self.u.function_space(),
self.alpha.function_space()]) # assigning spaces
self.parameters = self.setParameters(parameters)
self.ownership = self.Z.dofmap().ownership_range()
self.assigner = dolfin.FunctionAssigner(
self.Z, # receiving space
[self.u.function_space(),
self.alpha.function_space()]) # assigning space
dim = self.u.function_space().ufl_element().value_size()
self.stable = ''
self.negev = -1
self.mineig = 1.
self.Ealpha = derivative(
energy, self.alpha,
dolfin.TestFunction(self.alpha.ufl_function_space()))
self.energy = energy
(z_u, z_a) = dolfin.split(self.z)
energy = ufl.replace(energy, {self.u: z_u, self.alpha: z_a})
self.J = derivative(energy, self.z, dolfin.TestFunction(self.Z))
self.H = derivative(self.J, self.z, dolfin.TrialFunction(self.Z))
self.nullspace = nullspace
if Hessian:
self.Hessian = Hessian
self.ownership_range = self.Z.dofmap().ownership_range()
if len(bcs) > 0:
self.bcs = bcs
self.bc_dofs = self.get_bc_dofs(bcs)
else:
self.bcs = None
self.bc_dofs = set()
self.perturbation_v = dolfin.Function(self.Z.sub(0).collapse())
self.perturbation_beta = dolfin.Function(self.Z.sub(1).collapse())
self._Hessian = Hessian if Hessian.__class__ == ufl.form.Form else self.H
def comp_axisymmetric_pure_neumann(mat_obj, mesh_obj, bc, omega, save_path):
E = mat_obj.E
rho = mat_obj.rho
nu = mat_obj.nu
mesh = mesh_obj.create()
Rext = mesh_obj.Rext
Rint = mesh_obj.Rint
# rename x[0], x[1] by x, y
x, y = df.SpatialCoordinate(mesh)
dim = mesh.topology().dim()
cell_markers = df.MeshFunction("size_t", mesh, dim)
facet_markers = df.MeshFunction("size_t", mesh, dim - 1)
coord = mesh.coordinates()
class OuterRadius(df.SubDomain):
def inside(self, x, on_boundary):
return on_boundary and x[0]**2 + x[1]**2 >= Rext**2 - 1e-3
class InnerRadius(df.SubDomain):
def inside(self, x, on_boundary):
return on_boundary and x[0]**2 + x[1]**2 <= Rint**2 + 1e-3
OuterRadius().mark(facet_markers, 1)
InnerRadius().mark(facet_markers, 2)
df.File(save_path + 'cell_markers.pvd') << cell_markers
df.File(save_path + 'facet_markers.pvd') << facet_markers
# Create mesh and define function space
V = df.FunctionSpace(mesh, "CG", 1)
# =========================== Define Finite Element ===========================
degree = 1 #P1 table of periodic
# CG: Continuous Galerkin
V_ele = df.FiniteElement("CG", mesh.ufl_cell(), degree) # scalar, Ex: u
R_ele = df.FiniteElement("R", mesh.ufl_cell(), 0) # scalar, Ex: u
# Vector element # akternative : manually define vector function : better for 3D adaptation than above
total_ele = df.MixedElement([V_ele, R_ele])
W = df.FunctionSpace(mesh, total_ele)
# Define trial function
dunks = df.TrialFunction(W)
(du, dc) = df.split(dunks)
# Define variational problem
tunks = df.TestFunction(W)
(tu, tc) = df.split(tunks)
# displacement in radial direction u(x,y)
unks = df.Function(W)
#(u, c) = df.split(unks)
class THETA(df.UserExpression):
def eval(self, values, x):
values[0] = math.atan2(x[1], x[0])
def value_shape(self):
#return (1,) # vector
return () # scalar
theta = THETA(degree=1)
#theta_int = df.interpolate(theta, df.FunctionSpace(mesh, "DG", 0))
#df.File(save_path + 'theta.pvd') << theta_int
class RADIUS(df.UserExpression):
def eval(self, values, x):
values[0] = df.sqrt(x[0] * x[0] + x[1] * x[1])
def value_shape(self):
return () # scalar
r = RADIUS(degree=1)
# strain radial
def epsilon_r(du):
return 1.0 / r * (x * df.Dx(du, 0) + y * df.Dx(du, 1))
# strain circumferential
def epsilon_theta(du):
return du / df.sqrt(x**2 + y**2)
# radial stress # train-stress relation
def sigma_r(du):
return E / (1.0 - nu**2) * (epsilon_r(du) + nu * epsilon_theta(du))
# circumferential stress
def sigma_theta(du):
return E / (1.0 - nu**2) * (nu * epsilon_r(du) + epsilon_theta(du))
# Weak form
dFu = r * sigma_r(du) * epsilon_r(tu) * df.dx(0)
dFu = dFu + sigma_theta(du) * tu * df.dx(0)
dFu = dFu + dc * tu * df.dx(0)
dFu = dFu - rho * omega**2 * r**2 * tu * df.dx(0)
dFc = du * tc * df.dx(1)
dFu = r * sigma_r(du) * epsilon_r(tu) * df.dx
dFu = dFu + sigma_theta(du) * tu * df.dx
dFu = dFu + dc * tu * df.dx
dFu = dFu - rho * omega**2 * r**2 * tu * df.dx
dFc = du * tc * df.dx
dF = dFu + dFc
# residual
#F = df.action(dF, unks)
a = df.lhs(dF)
L = df.rhs(dF)
df.solve(a == L, unks)
(_u, _c) = unks.split(True)
u_pro = df.project(_u, V)
u_pro.rename('dis u [m]', 'dis u [m]')
# displacement
df.File(save_path + 'displacement.pvd') << u_pro
# compute stresses
sigma_r_pro = df.project(sigma_r(_u), V)
sigma_r_pro.rename('sigma_r [Pa]', 'sigma_r [Pa]')
df.File(save_path + 'sigma_r.pvd') << sigma_r_pro
sigma_theta_pro = df.project(sigma_theta(_u), V)
sigma_theta_pro.rename('sigma_theta [Pa]', 'sigma_theta [Pa]')
df.File(save_path + 'sigma_theta.pvd') << sigma_theta_pro
# compute von Mises stress
def von_mises_stress(sigma_r, sigma_theta):
return df.sqrt(sigma_r**2 + sigma_theta**2 - sigma_r * sigma_theta)
von_stress_pro = df.project(von_mises_stress(sigma_r(_u), sigma_theta(_u)),
V)
von_stress_pro.rename('von Mises Stress [Pa]', 'von Mises Stress [Pa]')
df.File(save_path + 'von_mises_stress.pvd') << von_stress_pro
'''
# name = 'density', function = density_function (function is the solution vector here)
density_function_space = df.FunctionSpace(mesh, 'DG', 0)
density_function = df.Function(density_function_space)
pde_problem.add_input('density', density_function)
'''
4. 2. Add states
'''
# Define mixed function space-split into temperature and displacement FS
d = mesh.geometry().dim()
cell = mesh.ufl_cell()
displacement_fe = df.VectorElement("CG", cell, 1)
temperature_fe = df.FiniteElement("CG", cell, 1)
mixed_fs = df.FunctionSpace(mesh,
df.MixedElement([displacement_fe, temperature_fe]))
mixed_fs.sub(1).dofmap().dofs()
mixed_function = df.Function(mixed_fs)
displacements_function, temperature_function = df.split(mixed_function)
# displacements_function,temperature_function = mixed_function.split()
v, T_hat = df.TestFunctions(mixed_fs)
residual_form = get_residual_form(displacements_function, v, density_function,
temperature_function, T_hat, KAPPA, K, ALPHA)
residual_form -= (df.dot(f_r, v) * dss(10) + df.dot(f_t, v) * dss(14) + \
q*T_hat*dss(5) + q_half*T_hat*dss(6) + q_quart*T_hat*dss(7))
print("get residual_form-------")
# print('ssssssss',df.assemble(T_hat*df.dx).get_local())
pde_problem.add_state('mixed_states', mixed_function, residual_form, 'density')
# Declare finite elements
elements = dict()
for name, (family, degree, is_vector) in base_elements.items():
if is_vector:
elements[name] = df.VectorElement(family, mesh.ufl_cell(), degree)
else:
elements[name] = df.FiniteElement(family, mesh.ufl_cell(), degree)
# Declare function spaces
spaces = dict()
for name, subproblem in subproblems.items():
if len(subproblem) > 1:
spaces[name] = df.FunctionSpace(
mesh,
df.MixedElement([elements[s["element"]] for s in subproblem]),
constrained_domain=constrained_domain(**vars()))
# If there is only one field in the subproblem, don't bother with
# the MixedElement.
elif len(subproblem) == 1:
spaces[name] = df.FunctionSpace(
mesh,
elements[subproblem[0]["element"]],
constrained_domain=constrained_domain(**vars()))
else:
info_on_red("Something went wrong here!")
exit("")
# dim = mesh.topology().dim() # In case the velocity fields should be
# # segregated at some point
fields = []
def __init__(self, fenics_2d_rve, **kwargs):
"""[summary]
Parameters
----------
object : [type]
[description]
fenics_2d_rve : [type]
[description]
element : tuple or dict
Type and degree of element for displacement FunctionSpace
Ex: ('CG', 2) or {'family':'Lagrange', degree:2}
solver : dict
Choose the type of the solver, its method and the preconditioner.
An up-to-date list of the available solvers and preconditioners
can be obtained with dolfin.list_linear_solver_methods() and
dolfin.list_krylov_solver_preconditioners().
"""
self.rve = fenics_2d_rve
self.topo_dim = topo_dim = fenics_2d_rve.dim
try:
bottom_left_corner = fenics_2d_rve.bottom_left_corner
except AttributeError:
logger.warning(
"For the definition of the periodicity boundary conditions,"
"the bottom left corner of the RVE is assumed to be on (0.,0.)"
)
bottom_left_corner = np.zeros(shape=(topo_dim, ))
self.pbc = periodicity.PeriodicDomain.pbc_dual_base(
fenics_2d_rve.gen_vect, "XY", bottom_left_corner, topo_dim)
solver = kwargs.pop("solver", {})
# {'type': solver_type, 'method': solver_method, 'preconditioner': preconditioner}
s_type = solver.pop("type", None)
s_method = solver.pop("method", SOLVER_METHOD)
s_precond = solver.pop("preconditioner", None)
if s_type is None:
if s_method in DOLFIN_KRYLOV_METHODS.keys():
s_type = "Krylov"
elif s_method in DOLFIN_LU_METHODS.keys():
s_type = "LU"
else:
raise RuntimeError("The indicated solver method is unknown.")
self._solver = dict(type=s_type, method=s_method)
if s_precond:
self._solver["preconditioner"] = s_precond
element = kwargs.pop("element", ("Lagrange", 2))
if isinstance(element, dict):
element = (element["family"], element["degree"])
self._element = element
# * Function spaces
cell = self.rve.mesh.ufl_cell()
self.scalar_FE = fe.FiniteElement(element[0], cell, element[1])
self.displ_FE = fe.VectorElement(element[0], cell, element[1])
strain_deg = element[1] - 1 if element[1] >= 1 else 0
strain_dim = int(topo_dim * (topo_dim + 1) / 2)
self.strain_FE = fe.VectorElement("DG",
cell,
strain_deg,
dim=strain_dim)
# Espace fonctionel scalaire
self.X = fe.FunctionSpace(self.rve.mesh,
self.scalar_FE,
constrained_domain=self.pbc)
# Espace fonctionnel 3D : deformations, notations de Voigt
self.W = fe.FunctionSpace(self.rve.mesh, self.strain_FE)
# Espace fonctionel 2D pour les champs de deplacement
# TODO : reprendre le Ve défini pour l'espace fonctionnel mixte. Par ex: V = FunctionSpace(mesh, Ve)
self.V = fe.VectorFunctionSpace(self.rve.mesh,
element[0],
element[1],
constrained_domain=self.pbc)
# * Espace fonctionel mixte pour la résolution :
# * 2D pour les champs + scalaire pour multiplicateur de Lagrange
# "R" : Real element with one global degree of freedom
self.real_FE = fe.VectorElement("R", cell, 0)
self.M = fe.FunctionSpace(
self.rve.mesh,
fe.MixedElement([self.displ_FE, self.real_FE]),
constrained_domain=self.pbc,
)
# Define variational problem
self.v, self.lamb_ = fe.TestFunctions(self.M)
self.u, self.lamb = fe.TrialFunctions(self.M)
self.w = fe.Function(self.M)
# bilinear form
self.a = (
fe.inner(sigma(self.rve.C_per, epsilon(self.u)), epsilon(self.v)) *
fe.dx + fe.dot(self.lamb_, self.u) * fe.dx +
fe.dot(self.lamb, self.v) * fe.dx)
self.K = fe.assemble(self.a)
if self._solver["type"] == "Krylov":
self.solver = fe.KrylovSolver(self.K, self._solver["method"])
elif self._solver["type"] == "LU":
self.solver = fe.LUSolver(self.K, self._solver["method"])
self.solver.parameters["symmetric"] = True
try:
self.solver.parameters.preconditioner = self._solver[
"preconditioner"]
except KeyError:
pass
# fe.info(self.solver.parameters, True)
self.localization = dict()
# dictionary of localization field objects,
# will be filled up when calling auxiliary problems (lazy evaluation)
self.ConstitutiveTensors = dict()
def __init__(self,
mesh: df.Mesh,
time: df.Constant,
M_i: tp.Union[df.Expression, tp.Dict[int, df.Expression]],
M_e: tp.Union[df.Expression, tp.Dict[int, df.Expression]],
I_s: tp.Union[df.Expression, tp.Dict[int,
df.Expression]] = None,
I_a: tp.Union[df.Expression, tp.Dict[int,
df.Expression]] = None,
ect_current: tp.Dict[int, df.Expression] = None,
v_: df.Function = None,
cell_domains: df.MeshFunction = None,
facet_domains: df.MeshFunction = None,
dirichlet_bc: tp.List[tp.Tuple[df.Expression, int]] = None,
dirichlet_bc_v: tp.List[tp.Tuple[df.Expression, int]] = None,
periodic_domain: df.SubDomain = None,
parameters: df.Parameters = None) -> None:
"""Initialise solverand check all parametersare correct.
NB! The periodic domain has to be set in the cellsolver too.
"""
self._timestep = None
comm = df.MPI.comm_world
rank = df.MPI.rank(comm)
msg = "Expecting mesh to be a Mesh instance, not {}".format(mesh)
assert isinstance(mesh, df.Mesh), msg
msg = "Expecting time to be a Constant instance (or None)."
assert isinstance(time, df.Constant) or time is None, msg
msg = "Expecting parameters to be a Parameters instance (or None)"
assert isinstance(parameters, df.Parameters) or parameters is None, msg
self._nullspace_basis = None
# Store input
self._mesh = mesh
self._time = time
# Initialize and update parameters if given
self._parameters = self.default_parameters()
if parameters is not None:
self._parameters.update(parameters)
if self._parameters["Chi"] == -1 or self._parameters["Cm"] == -1:
raise ValueError(
"Need Chi and Cm to be specified explicitly throug the parameters."
)
# Set-up function spaces
k = self._parameters["polynomial_degree"]
Ve = df.FiniteElement("CG", self._mesh.ufl_cell(), k)
V = df.FunctionSpace(self._mesh,
"CG",
k,
constrained_domain=periodic_domain)
Ue = df.FiniteElement("CG", self._mesh.ufl_cell(), k)
if self._parameters["linear_solver_type"] == "direct":
Re = df.FiniteElement("R", self._mesh.ufl_cell(), 0)
_element = df.MixedElement((Ve, Ue, Re))
self.VUR = df.FunctionSpace(mesh,
_element,
constrained_domain=periodic_domain)
else:
_element = df.MixedElement((Ve, Ue))
self.VUR = df.FunctionSpace(mesh,
_element,
constrained_domain=periodic_domain)
self.V = V
if cell_domains is None:
cell_domains = df.MeshFunction("size_t", mesh,
self._mesh.geometry().dim())
cell_domains.set_all(0)
# Chech that it is indeed a cell function.
cell_dim = cell_domains.dim()
mesh_dim = self._mesh.geometry().dim()
msg = "Got {cell_dim}, expected {mesh_dim}.".format(cell_dim=cell_dim,
mesh_dim=mesh_dim)
assert cell_dim == mesh_dim, msg
self._cell_domains = cell_domains
if facet_domains is None:
facet_domains = df.MeshFunction("size_t", mesh,
self._mesh.geometry().dim() - 1)
facet_domains.set_all(0)
# Check that it is indeed a facet function.
facet_dim = facet_domains.dim()
msg = "Got {facet_dim}, expected {mesh_dim}.".format(
facet_dim=facet_dim, mesh_dim=mesh_dim - 1)
assert facet_dim == mesh_dim - 1, msg
self._facet_domains = facet_domains
# Gather all cell keys on all processes. Greatly simplifies things
cell_keys = set(self._cell_domains.array())
all_cell_keys = comm.allgather(cell_keys)
all_cell_keys = reduce(or_, all_cell_keys)
# If Mi is not dict, make dict
if not isinstance(M_i, dict):
M_i = {int(i): M_i for i in all_cell_keys}
else: # Check that the keys match the cell function
M_i_keys = set(M_i.keys())
msg = "Got {M_i_keys}, expected {cell_keys}.".format(
M_i_keys=M_i_keys, cell_keys=all_cell_keys)
assert M_i_keys == all_cell_keys, msg
# If Me is not dict, make dict
if not isinstance(M_e, dict):
M_e = {int(i): M_e for i in all_cell_keys}
else: # Check that the keys match the cell function
M_e_keys = set(M_e.keys())
msg = "Got {M_e_keys}, expected {cell_keys}.".format(
M_e_keys=M_e_keys, cell_keys=all_cell_keys)
assert M_e_keys == all_cell_keys, msg
self._M_i = M_i
self._M_e = M_e
# Store source terms
if I_s is not None and not isinstance(I_s, dict):
I_s = {key: I_s for key in all_cell_keys}
self._I_s = I_s
if I_a is not None and not isinstance(I_a, dict):
I_a = {key: I_a for key in all_cell_keys}
self._I_a = I_a
# Set the ECT current, Note, it myst depend on `time` to be updated
if ect_current is not None:
ect_tags = set(ect_current.keys())
facet_tags = set(self._facet_domains.array())
msg = "{} not in facet domains ({}).".format(ect_tags, facet_tags)
assert ect_tags <= facet_tags, msg
self._ect_current = ect_current
# Set-up solution fields:
if v_ is None:
self.merger = df.FunctionAssigner(V, self.VUR.sub(0))
self.v_ = df.Function(V, name="v_")
else:
# df.debug("Experimental: v_ shipped from elsewhere.")
self.merger = None
self.v_ = v_
self.vur = df.Function(self.VUR, name="vur")
# Set Dirichlet bcs for the transmembrane potential
self._bcs = []
if dirichlet_bc_v is not None:
for function, marker in dirichlet_bc_v:
self._bcs.append(
df.DirichletBC(self.VUR.sub(0), function,
self._facet_domains, marker))
# Set Dirichlet bcs for the extra cellular potential
if dirichlet_bc is not None:
for function, marker in dirichlet_bc:
self._bcs.append(
df.DirichletBC(self.VUR.sub(1), function,
self._facet_domains, marker))
def traction_test(ell=0.05,
ell_e=.1,
degree=1,
n=3,
nu=0.,
load_min=0,
load_max=2,
loads=None,
nsteps=20,
Lx=1.,
Ly=0.1,
outdir="outdir",
postfix='',
savelag=1,
sigma_D0=1.,
periodic=False,
continuation=False,
checkstability=True,
configString='',
test=True):
# constants
# ell = ell
Lx = Lx
load_min = load_min
load_max = load_max
nsteps = nsteps
outdir = outdir
loads = loads
savelag = 1
nu = dolfin.Constant(nu)
ell = dolfin.Constant(ell)
ell_e = ell_e
E = dolfin.Constant(1.0)
K = E.values()[0] / ell_e**2.
sigma_D0 = E
n = n
# h = ell.values()[0]/n
h = max(ell.values()[0] / n, .005)
cell_size = h
continuation = continuation
isPeriodic = periodic
config = json.loads(configString) if configString != '' else ''
cmd_parameters = {
'material': {
"ell": ell.values()[0],
"ell_e": ell_e,
"K": K,
"E": E.values()[0],
"nu": nu.values()[0],
"sigma_D0": sigma_D0.values()[0]
},
'geometry': {
'Lx': Lx,
'Ly': Ly,
'n': n,
},
'experiment': {
'test': test,
'periodic': isPeriodic,
'signature': ''
},
'stability': {
'checkstability': checkstability,
'continuation': continuation
},
'time_stepping': {
'load_min': load_min,
'load_max': load_max,
'nsteps': nsteps,
'outdir': outdir,
'postfix': postfix,
'savelag': savelag
},
'alt_min': {},
"code": {}
}
# --------------------
for par in parameters:
parameters[par].update(cmd_parameters[par])
if config:
for par in config:
parameters[par].update(config[par])
# else:
# parameters['material']['ell_e'] =
Lx = parameters['geometry']['Lx']
Ly = parameters['geometry']['Ly']
ell = parameters['material']['ell']
ell_e = parameters['material']['ell_e']
BASE_DIR = os.path.dirname(os.path.realpath(__file__))
fname = "film"
print(BASE_DIR)
os.path.isfile(fname)
signature = hashlib.md5(str(parameters).encode('utf-8')).hexdigest()
if parameters['experiment']['test'] == True:
outdir += '-{}'.format(cmd_parameters['time_stepping']['postfix'])
else:
outdir += '-{}{}'.format(signature,
cmd_parameters['time_stepping']['postfix'])
outdir = outdir + '-cont'
parameters['time_stepping']['outdir'] = outdir
Path(outdir).mkdir(parents=True, exist_ok=True)
print('Outdir is: ' + outdir)
with open(os.path.join(outdir, 'rerun.sh'), 'w') as f:
configuration = deepcopy(parameters)
configuration['time_stepping'].pop('outdir')
str(configuration).replace("\'True\'",
"True").replace("\'False\'", "False")
rerun_cmd = 'python3 {} --config="{}"'.format(
os.path.basename(__file__), configuration)
f.write(rerun_cmd)
with open(os.path.join(outdir, 'parameters.pkl'), 'w') as f:
json.dump(parameters, f)
with open(os.path.join(outdir, 'signature.md5'), 'w') as f:
f.write(signature)
print(parameters)
# boundary_meshfunction = dolfin.MeshFunction("size_t", mesh, "meshes/%s-%s_facet_region.xml"%(fname, signature))
# cells_meshfunction = dolfin.MeshFunction("size_t", mesh, "meshes/%s-%s_physical_region.xml"%(fname, signature))
# ------------------
geometry_parameters = parameters['geometry']
geom_signature = hashlib.md5(
str(geometry_parameters).encode('utf-8')).hexdigest()
meshfile = "%s/meshes/%s-%s.xml" % (BASE_DIR, fname, geom_signature)
# cmd_parameters['experiment']['signature']=signature
if os.path.isfile(meshfile):
print("Meshfile %s exists" % meshfile)
mesh = dolfin.Mesh("meshes/%s-%s.xml" % (fname, geom_signature))
else:
print("Creating meshfile: %s" % meshfile)
print(('DEBUG: (-Lx/2. ={} , -Ly/2.={})'.format(Lx / 2., -Ly / 2.)))
geom = mshr.Rectangle(dolfin.Point(-Lx / 2., -Ly / 2.),
dolfin.Point(Lx / 2., Ly / 2.))
mesh = mshr.generate_mesh(geom, n * int(float(Lx / ell)))
print(meshfile)
mesh_xdmf = dolfin.XDMFFile("meshes/%s-%s.xdmf" % (fname, geom_signature))
mesh_xdmf.write(mesh)
if rank == 0:
meshf = dolfin.File(os.path.join(outdir, "mesh.xml"))
meshf << mesh
V_u = dolfin.VectorFunctionSpace(mesh, "CG", 1)
V_alpha = dolfin.FunctionSpace(mesh, "CG", 1)
u = dolfin.Function(V_u, name="Total displacement")
alpha = dolfin.Function(V_alpha, name="Damage")
bcs_alpha = []
bcs_u = [
DirichletBC(V_u, Constant((0., 0)),
'(near(x[0], %f) or near(x[0], %f))' % (-Lx / 2., Lx / 2.))
]
left = dolfin.CompiledSubDomain("near(x[0], -Lx/2.)", Lx=Lx)
right = dolfin.CompiledSubDomain("near(x[0], Lx/2.)", Lx=Lx)
bottom = dolfin.CompiledSubDomain("near(x[1],-Ly/2.)", Ly=Ly)
top = dolfin.CompiledSubDomain("near(x[1],Ly/2.)", Ly=Ly)
mf = dolfin.MeshFunction("size_t", mesh, 1, 0)
right.mark(mf, 1)
left.mark(mf, 2)
bottom.mark(mf, 3)
state = [u, alpha]
Z = dolfin.FunctionSpace(
mesh, dolfin.MixedElement([u.ufl_element(),
alpha.ufl_element()]))
z = dolfin.Function(Z)
v, beta = dolfin.split(z)
dx = dolfin.Measure("dx", metadata=form_compiler_parameters, domain=mesh)
ds = dolfin.Measure("ds", subdomain_data=mf)
# Files for output
file_out = dolfin.XDMFFile(os.path.join(outdir, "output.xdmf"))
file_eig = dolfin.XDMFFile(os.path.join(outdir, "perturbations.xdmf"))
file_con = dolfin.XDMFFile(os.path.join(outdir, "continuation.xdmf"))
file_bif = dolfin.XDMFFile(
os.path.join(outdir, "bifurcation_postproc.xdmf"))
for f in [file_out, file_eig, file_con, file_bif]:
f.parameters["functions_share_mesh"] = True
f.parameters["flush_output"] = True
# Problem definition
foundation_density = 1. / 2. * 1. / ell_e**2. * dot(u, u)
model = DamagePrestrainedElasticityModel(
state,
E,
nu,
ell,
sigma_D0,
user_functional=foundation_density,
eps0t=Expression([['t', 0.], [0., 0.]], t=0., degree=0))
# import pdb; .set_trace()
model.dx = dx
model.ds = ds
energy = model.total_energy_density(u, alpha) * dx
# Alternate minimization solver
solver = solvers.AlternateMinimizationSolver(
energy, [u, alpha], [bcs_u, bcs_alpha],
parameters=parameters['alt_min'])
rP = model.rP(u, alpha, v, beta) * dx + 1 / ell_e**2. * dot(v, v) * dx
rN = model.rN(u, alpha, beta) * dx
stability = StabilitySolver(mesh,
energy, [u, alpha], [bcs_u, bcs_alpha],
z,
parameters=parameters['stability'])
# stability = StabilitySolver(mesh, energy, [u, alpha], [bcs_u, bcs_alpha], z, parameters = parameters['stability'], rayleigh=[rP, rN])
# if isPeriodic:
# stability = StabilitySolver(mesh, energy, [u, alpha], [bcs_u, bcs_alpha], z,
# parameters = stability_parameters,
# constrained_domain = PeriodicBoundary(Lx))
# else:
# stability = StabilitySolver(mesh, energy, [u, alpha], [bcs_u, bcs_alpha], z, parameters = parameters['stability'])
load_steps = np.linspace(load_min, load_max,
parameters['time_stepping']['nsteps'])
if loads:
load_steps = loads
time_data = []
linesearch = LineSearch(energy, [u, alpha])
alpha_old = dolfin.Function(alpha.function_space())
lmbda_min_prev = 0.000001
bifurcated = False
bifurcation_loads = []
save_current_bifurcation = False
bifurc_count = 0
alpha_bif = dolfin.Function(V_alpha)
alpha_bif_old = dolfin.Function(V_alpha)
bifurcation_loads = []
tot_energy = model.elastic_energy_density(model.eps(u), alpha)*dx + \
1./2.*1/ell_e**2. * dot(u, u)*dx + \
model.damage_dissipation_density(alpha)*dx
cont_atol = 1e-3
for it, load in enumerate(load_steps):
model.eps0t.t = load
alpha_old.assign(alpha)
ColorPrint.print_warn('Solving load t = {:.2f}'.format(load))
# First order stability conditions
(time_data_i, am_iter) = solver.solve()
# Second order stability conditions
(stable, negev) = stability.solve(solver.problem_alpha.lb)
ColorPrint.print_pass(
'Current state is{}stable'.format(' ' if stable else ' un'))
# import pdb; pdb.set_trace()
mineig = stability.mineig if hasattr(stability, 'mineig') else 0.0
# print('DEBUG: lmbda min', lmbda_min_prev)
# print('DEBUG: mineig', mineig)
Deltav = (mineig - lmbda_min_prev) if hasattr(stability, 'eigs') else 0
if (mineig + Deltav) * (lmbda_min_prev +
dolfin.DOLFIN_EPS) < 0 and not bifurcated:
bifurcated = True
# save 3 bif modes
print('DEBUG: About to bifurcate load ', load, 'step', it)
bifurcation_loads.append(load)
bifurc_count += 1
lmbda_min_prev = mineig if hasattr(stability, 'mineig') else 0.
if stable:
solver.update()
else:
# Continuation
iteration = 1
energy_pre = dolfin.assemble(tot_energy)
alpha_bif.assign(alpha)
alpha_bif_old.assign(alpha_old)
while stable == False and iteration < 30:
# linesearch
perturbation_v = stability.perturbation_v
perturbation_beta = stability.perturbation_beta
h_opt, (hmin, hmax), energy_perturbations = linesearch.search(
[u, alpha, alpha_old], perturbation_v, perturbation_beta)
# import pdb; pdb.set_trace()
# if h_opt != 0:
if h_opt > cont_atol:
save_current_bifurcation = True
# admissible
uval = u.vector()[:] + h_opt * perturbation_v.vector()[:]
aval = alpha.vector(
)[:] + h_opt * perturbation_beta.vector()[:]
u.vector()[:] = uval
alpha.vector()[:] = aval
u.vector().vec().ghostUpdate()
alpha.vector().vec().ghostUpdate()
(time_data_i, am_iter) = solver.solve()
(stable, negev) = stability.solve(alpha_old)
ColorPrint.print_pass(
' Continuation iteration #{}, current state is{}stable'
.format(iteration, ' ' if stable else ' un'))
energy_post = dolfin.assemble(tot_energy)
ener_diff = energy_post - energy_pre
ColorPrint.print_warn(
'DEBUG: step {}, iteration {}, En_post - En_pre ={}'.
format(it, iteration, energy_post - energy_pre))
iteration += 1
if ener_diff < 0: bifurcated = False
else:
# warn
ColorPrint.print_warn(
'DEBUG: Found (almost) zero increment, we are stuck in the matrix'
)
ColorPrint.print_warn('DEBUG: h_opt = {}'.format(h_opt))
ColorPrint.print_warn('DEBUG: Continuing load program')
break
solver.update()
# stable == True
# modes = np.where(stability.eigs < 0)[0]
# with file_bif as file:
# leneigs = len(modes)
# maxmodes = min(3, leneigs)
# for n in range(maxmodes):
# mode = dolfin.project(stability.linsearch[n]['beta_n'], V_alpha)
# modename = 'beta-%d'%n
# print(modename)
# file.write_checkpoint(mode, modename, 0, append=True)
# bifurc_count += 1
time_data_i["load"] = load
time_data_i["stable"] = stable
time_data_i["dissipated_energy"] = dolfin.assemble(
model.damage_dissipation_density(alpha) * dx)
time_data_i["foundation_energy"] = dolfin.assemble(
1. / 2. * 1 / ell_e**2. * dot(u, u) * dx)
time_data_i["membrane_energy"] = dolfin.assemble(
model.elastic_energy_density(model.eps(u), alpha) * dx)
time_data_i["elastic_energy"] = time_data_i[
"membrane_energy"] + time_data_i["foundation_energy"]
time_data_i["eigs"] = stability.eigs if hasattr(stability,
'eigs') else np.inf
time_data_i["stable"] = stability.stable
time_data_i["# neg ev"] = stability.negev
# import pdb; pdb.set_trace()
_sigma = model.stress(model.eps(u), alpha)
e1 = dolfin.Constant([1, 0])
_snn = dolfin.dot(dolfin.dot(_sigma, e1), e1)
time_data_i["sigma"] = 1 / Ly * dolfin.assemble(_snn * model.ds(1))
time_data_i["S(alpha)"] = dolfin.assemble(1. / (model.a(alpha)) *
model.dx)
time_data_i["A(alpha)"] = dolfin.assemble((model.a(alpha)) * model.dx)
time_data_i["avg_alpha"] = dolfin.assemble(alpha * model.dx)
ColorPrint.print_pass(
"Time step {:.4g}: it {:3d}, err_alpha={:.4g}".format(
time_data_i["load"], time_data_i["iterations"],
time_data_i["alpha_error"]))
time_data.append(time_data_i)
time_data_pd = pd.DataFrame(time_data)
if np.mod(it, savelag) == 0:
with file_out as f:
f.write(alpha, load)
f.write(u, load)
f.write_checkpoint(alpha,
"alpha-{}".format(it),
0,
append=True)
# with file_bif as f:
print('DEBUG: written step ', it)
if save_current_bifurcation:
# modes = np.where(stability.eigs < 0)[0]
time_data_i['h_opt'] = h_opt
time_data_i['max_h'] = hmax
time_data_i['min_h'] = hmin
with file_bif as file:
beta0v = dolfin.project(stability.perturbation_beta, V_alpha)
file.write_checkpoint(beta0v,
'beta0',
bifurc_count - 1,
append=True)
file.write_checkpoint(alpha_bif_old,
'alpha-old',
bifurc_count - 1,
append=True)
file.write_checkpoint(alpha_bif,
'alpha-bif',
bifurc_count - 1,
append=True)
file.write_checkpoint(alpha,
'alpha',
bifurc_count - 1,
append=True)
np.save(os.path.join(outdir, 'energy_perturbations'),
energy_perturbations,
allow_pickle=True,
fix_imports=True)
with file_eig as file:
_v = dolfin.project(
dolfin.Constant(h_opt) * perturbation_v, V_u)
_beta = dolfin.project(
dolfin.Constant(h_opt) * perturbation_beta, V_alpha)
_v.rename('perturbation displacement',
'perturbation displacement')
_beta.rename('perturbation damage', 'perturbation damage')
# import pdb; pdb.set_trace()
f.write(_v, load)
f.write(_beta, load)
file.write_checkpoint(_v,
'perturbation_v',
bifurc_count - 1,
append=True)
file.write_checkpoint(_beta,
'perturbation_beta',
bifurc_count - 1,
append=True)
save_current_bifurcation = False
time_data_pd.to_json(os.path.join(outdir, "time_data.json"))
plt.figure()
plt.plot(time_data_pd["load"].values(),
time_data_pd["iterations"].values(),
label='its')
plt.semilogy()
ax = plt.gca()
ax2 = ax.twinx()
ax2.plot(time_data_pd["load"].values(),
time_data_pd["alpha_error"].values(),
'o',
c='C1',
label='alpha error')
plt.savefig(os.path.join(outdir, 'am.pdf'))
plt.legend()
plt.close()
# user_postprocess_timestep(alpha, parameters, load, xresol = 100)
plt.figure()
dolfin.plot(alpha)
plt.savefig(os.path.join(outdir, "alpha.png"))
plt.figure()
dolfin.plot(u, mode="displacement")
plt.savefig(os.path.join(outdir, "u.png"))
_nu = parameters['material']['nu']
_E = parameters['material']['E']
_w1 = parameters['material']['sigma_D0']**2. / parameters['material']['E']
tc = np.sqrt(2 * _w1 / (_E * (1. - 2. * _nu) * (1. + _nu)))
if parameters['stability']['checkstability'] == 'True':
pp.plot_spectrum(parameters, outdir, time_data_pd.sort_values('load'),
tc)
# plt.show()
print(time_data_pd)
print()
print('Output in: ' + outdir)
return time_data_pd