def variational_forms(self, dt: df.Constant) -> Tuple[Any, Any]:
"""Create the variational forms corresponding to the given
discretization of the given system of equations.
*Arguments*
kn (:py:class:`ufl.Expr` or float)
The time step
*Returns*
(lhs, rhs) (:py:class:`tuple` of :py:class:`ufl.Form`)
"""
# Extract theta parameter and conductivities
theta = self._parameters.theta
Mi = self._intracellular_conductivity
Me = self._extracellular_conductivity
# Define variational formulation
if self._parameters.linear_solver_type == "direct":
v, u, multiplier = df.TrialFunctions(self._VUR)
v_test, u_test, multiplier_test = df.TestFunctions(self._VUR)
else:
v, u = df.TrialFunctions(self._VUR)
v_test, u_test = df.TestFunctions(self._VUR)
Dt_v = (v - self._v_prev) / dt
Dt_v *= self._chi_cm # Chi is surface to volume aration. Cm is capacitance
v_mid = theta * v + (1.0 - theta) * self._v_prev
# Set-up measure and rhs from stimulus
dOmega = df.Measure("dx",
domain=self._mesh,
subdomain_data=self._cell_function)
dGamma = df.Measure("ds",
domain=self._mesh,
subdomain_data=self._interface_function)
# Loop over all domains
G = Dt_v * v_test * dOmega()
for key in self._cell_tags - self._restrict_tags:
G += df.inner(Mi[key] * df.grad(v_mid),
df.grad(v_test)) * dOmega(key)
G += df.inner(Mi[key] * df.grad(v_mid),
df.grad(u_test)) * dOmega(key)
for key in self._cell_tags:
G += df.inner(Mi[key] * df.grad(u), df.grad(v_test)) * dOmega(key)
G += df.inner((Mi[key] + Me[key]) * df.grad(u),
df.grad(u_test)) * dOmega(key)
# If Lagrangian multiplier
if self._parameters.linear_solver_type == "direct":
G += (multiplier_test * u + multiplier * u_test) * dOmega(key)
for key in set(self._interface_tags):
# Default to 0 if not defined for tag
G += self._neumann_bc.get(key,
df.Constant(0)) * u_test * dGamma(key)
a, L = df.system(G)
return a, L
def setup(self, DG3, m, Ms, unit_length=1.0):
self.DG3 = DG3
self.m = m
self.Ms = Ms
self.unit_length = unit_length
mesh = DG3.mesh()
self.mesh = mesh
DG = df.FunctionSpace(mesh, "DG", 0)
BDM = df.FunctionSpace(mesh, "BDM", 1)
#deal with three components simultaneously, each represents a vector
W1 = df.MixedFunctionSpace([BDM, BDM, BDM])
(sigma0, sigma1, sigma2) = df.TrialFunctions(W1)
(tau0, tau1, tau2) = df.TestFunctions(W1)
W2 = df.MixedFunctionSpace([DG, DG, DG])
(u0, u1, u2) = df.TrialFunctions(W2)
(v0, v1, v2) = df.TestFunction(W2)
# what we need is A x = K1 m
a0 = (df.dot(sigma0, tau0) + df.dot(sigma1, tau1) +
df.dot(sigma2, tau2)) * df.dx
self.A = df.assemble(a0)
a1 = -(df.div(tau0) * u0 + df.div(tau1) * u1 +
df.div(tau2) * u2) * df.dx
self.K1 = df.assemble(a1)
def boundary(x, on_boundary):
return on_boundary
# actually, we need to apply the Neumann boundary conditions.
# we need a tensor here
zero = df.Constant((0, 0, 0, 0, 0, 0, 0, 0, 0))
self.bc = df.DirichletBC(W1, zero, boundary)
self.bc.apply(self.A)
a2 = (df.div(sigma0) * v0 + df.div(sigma1) * v1 +
df.div(sigma2) * v2) * df.dx
self.K2 = df.assemble(a2)
self.L = df.assemble((v0 + v1 + v2) * df.dx).array()
self.mu0 = mu0
self.exchange_factor = 2.0 * self.C / (self.mu0 * Ms *
self.unit_length**2)
self.coeff = self.exchange_factor / self.L
# b = K m
self.b = df.PETScVector()
# the vector in BDM space
self.sigma_v = df.PETScVector(self.K2.size(1))
# to store the exchange fields
self.H = df.PETScVector()
def do_one_L_problem(self, i_vk, C_VK_data=0.1):
"""Solve the L equation for one (K,V) position using the "mixed" methodology,
so that we get on-grid solutions of both `f` and `df/dL`. This
requires that we use a 2nd-degree element to solve for the `df/dL`
vector, called `sigma` here.
"""
W = d.FunctionSpace(self.l_mesh, self.c12.l_element * self.c11.l_element)
D = d.Function(self.c12.l_space) # note: W.sub(0) does not work
C_VK = d.Function(self.c11.l_space)
sigma, u = d.TrialFunctions(W) # u is my 'f', sigma is df/dL
tau, v = d.TestFunctions(W)
soln = d.Function(W)
bc = [d.DirichletBC(W.sub(1), d.Constant(0), direct_boundary)]
a = (u * tau.dx(0) + d.dot(sigma, tau) + D * sigma * v.dx(0)) * d.dx
L = C_VK * v * d.dx
equation = (a == L)
# typical D value: 1e-23
ddata = np.array(self.c12.D_LL[:,i_vk])
#print('D:', ddata.min(), ddata.max(), ddata.mean(), np.sqrt((ddata**2).mean()))
D.vector()[:] = ddata
C_VK.vector()[:] = C_VK_data
C_VK.vector()[self.i_lmax] += self.c11.source_term[:,i_vk]
d.solve(equation, soln, bc)
sigma, u = soln.split(deepcopy=True)
return self.c11.l_sort(), self.c11.l_sort(u), self.c12.l_sort(), self.c12.l_sort(sigma)
def create(self):
self.metadata = {
"quadrature_degree": self.deg_q,
"quadrature_scheme": "default",
}
self.dxm = df.dx(metadata=self.metadata,
subdomain_data=self.mesh_function)
# solution field
Ed = df.VectorElement("CG", self.mesh.ufl_cell(), degree=self.deg_d)
Ee = df.FiniteElement("CG", self.mesh.ufl_cell(), degree=self.deg_d)
self.V = df.FunctionSpace(self.mesh, Ed * Ee)
self.Vd, self.Ve = self.V.split()
self.dd, self.de = df.TrialFunctions(self.V)
self.d_, self.e_ = df.TestFunctions(self.V)
self.u = df.Function(self.V, name="d-e mixed space")
self.d, self.e = df.split(self.u)
# generic quadrature function spaces
VQF, VQV, VQT = c.helper.spaces(self.mesh, self.deg_q,
c.q_dim(self.constraint))
# quadrature functions
Q = c.Q
# inputs to the model
self.q_in = OrderedDict()
self.q_in[Q.EPS] = df.Function(VQV, name="current strains")
self.q_in[Q.E] = df.Function(
VQF, name="current nonlocal equivalent strains")
self.q_in_calc = {}
self.q_in_calc[Q.EPS] = c.helper.LocalProjector(
self.eps(self.d), VQV, self.dxm)
self.q_in_calc[Q.E] = c.helper.LocalProjector(self.e, VQF, self.dxm)
# outputs of the model
self.q = {}
self.q[Q.SIGMA] = df.Function(VQV, name="current stresses")
self.q[Q.DSIGMA_DEPS] = df.Function(VQT, name="stress-strain tangent")
self.q[Q.DSIGMA_DE] = df.Function(
VQV, name="stress-nonlocal-strain tangent")
self.q[Q.EEQ] = df.Function(VQF,
name="current (local) equivalent strain")
self.q[Q.DEEQ] = df.Function(VQV,
name="equivalent-strain-strain tangent")
self.q_history = {
Q.KAPPA: df.Function(VQF, name="current history variable kappa")
}
self.n = len(self.q[Q.SIGMA].vector().get_local()) // c.q_dim(
self.constraint)
self.nq = self.n // self.mesh.num_cells()
self.ip_flags = None
if self.mesh_function is not None:
self.ip_flags = np.repeat(self.mesh_function.array(), self.nq)
def do_VK_problems(self, C_L_data, dest_f_11=None, dest_dfdV_11=None, dest_dfdK_11=None):
if dest_f_11 is None:
dest_f_11 = np.empty(self.c11.cube_shape)
else:
assert dest_f_11.shape == self.c11.cube_shape
if dest_dfdV_11 is None:
dest_dfdV_11 = np.empty(self.c11.cube_shape)
else:
assert dest_dfdV_11.shape == self.c11.cube_shape
if dest_dfdK_11 is None:
dest_dfdK_11 = np.empty(self.c11.cube_shape)
else:
assert dest_dfdK_11.shape == self.c11.cube_shape
W = d.FunctionSpace(self.vk_mesh, self.c21.vk_vector_element * self.c11.vk_scalar_element)
# We can treat the tensor data array as having shape (N, 2, 2), where
# N is the number of elements of the equivalent scalar gridding of the
# mesh. array[:,0,0] is element [0,0] of the tensor. array[:,0,1] is
# the upper right element, etc.
D = d.Function(self.c21.vk_tensor_space)
dbuf = np.empty(D.vector().size()).reshape((-1, 2, 2))
C_L = d.Function(self.c11.vk_scalar_space)
sigma, u = d.TrialFunctions(W) # u is my 'f'
tau, v = d.TestFunctions(W)
a = (u * d.div(tau) + d.dot(sigma, tau) + d.inner(D * sigma, d.grad(v))) * d.dx
L = C_L * v * d.dx
equation = (a == L)
soln = d.Function(W)
bc = d.DirichletBC(W.sub(1), d.Constant(0), direct_boundary)
for i_l in range(self.c21.l_coords.size):
dbuf[:,0,0] = self.c21.D_VV[i_l]
dbuf[:,1,0] = self.c21.D_VK[i_l]
dbuf[:,0,1] = self.c21.D_VK[i_l]
dbuf[:,1,1] = self.c21.D_KK[i_l]
D.vector()[:] = dbuf.reshape((-1,))
if i_l == self.i_lmax:
C_L.vector()[:] = C_L_data[i_l] + self.c11.source_term
else:
C_L.vector()[:] = C_L_data[i_l]
d.solve(equation, soln, bc)
s_sigma, s_u = soln.split(deepcopy=True)
dest_f_11[i_l] = s_u.vector().array()
s_sigma = s_sigma.vector().array().reshape((-1, 2))
dest_dfdV_11[i_l] = self.c21.vk_downsample(s_sigma[:,0])
dest_dfdK_11[i_l] = self.c21.vk_downsample(s_sigma[:,1])
return dest_f_11, dest_dfdV_11, dest_dfdK_11
def do_L_problems(self, C_VK_data, dest_f_11=None, dest_dfdL_11=None):
"""Solve the L equations using the "mixed" methodology, so that we get on-grid
solutions of both `f` and `df/dL`. This requires that we use a
2nd-degree element to solve for the `df/dL` vector, called `sigma`
here.
"""
if dest_f_11 is None:
dest_f_11 = np.empty(self.c11.cube_shape)
else:
assert dest_f_11.shape == self.c11.cube_shape
if dest_dfdL_11 is None:
dest_dfdL_11 = np.empty(self.c11.cube_shape)
else:
assert dest_dfdL_11.shape == self.c11.cube_shape
W = d.FunctionSpace(self.l_mesh, self.c12.l_element * self.c11.l_element)
D = d.Function(self.c12.l_space)
C_VK = d.Function(self.c11.l_space)
sigma, u = d.TrialFunctions(W) # u is my 'f'
tau, v = d.TestFunctions(W)
soln = d.Function(W)
bc = [d.DirichletBC(W.sub(1), d.Constant(0), direct_boundary)]
a = (u * tau.dx(0) + d.dot(sigma, tau) + D * sigma * v.dx(0)) * d.dx
L = C_VK * v * d.dx
equation = (a == L)
# contiguous arrays are required for input to `x.vector()[:]` so we
# need buffers. Well, we don't need them, but this should save on
# allocations.
buf11 = np.empty(self.c11.l_coords.shape)
buf12 = np.empty(self.c12.l_coords.shape)
for i_vk in range(self.c12.logv_coords.size):
buf12[:] = self.c12.D_LL[:,i_vk]
D.vector()[:] = buf12
buf11[:] = C_VK_data[:,i_vk]
buf11[self.i_lmax] += self.c11.source_term
C_VK.vector()[:] = buf11
d.solve(equation, soln, bc)
s_sigma, s_u = soln.split(deepcopy=True)
dest_f_11[:,i_vk] = s_u.vector().array()
dest_dfdL_11[:,i_vk] = self.c12.l_downsample(s_sigma)
return dest_f_11, dest_dfdL_11
def set_function_space(self):
"""set_function_space."""
self.V = df.VectorFunctionSpace(self.mesh,'P',1,dim=self.number_of_moment)
#Set test function(s)
v_list = df.TestFunctions(self.V)
#Convert to ufl form
self.v = df.as_vector(v_list)
#set trial function(s)
if self.problem_type == 'nonlinear':
u_list = df.Function(self.V)
elif self.problem_type == 'linear':
u_list = df.TrialFunctions(self.V)
#Convert to ufl form
self.u = df.as_vector(u_list)
def __init__(self, Vh_STATE, Vhs, bcs0, datafile, dx=dl.dx):
self.dx = dx
x, y, U, V, uu, vv, ww, uv, k = np.loadtxt(datafile,
skiprows=2,
unpack=True)
u_fun_mean = VelocityDNS(x=x,
y=y,
U=U,
V=V,
symmetrize=True,
coflow=0.)
u_fun_data = VelocityDNS(x=x,
y=y,
U=U,
V=V,
symmetrize=False,
coflow=0.)
k_fun_mean = KDNS(x=x, y=y, k=k, symmetrize=True)
k_fun_data = KDNS(x=x, y=y, k=k, symmetrize=False)
u_data = dl.interpolate(u_fun_data, Vhs[0])
k_data = dl.interpolate(k_fun_data, Vhs[2])
noise_var_u = dl.assemble(
dl.inner(u_data - u_fun_mean, u_data - u_fun_mean) * self.dx)
noise_var_k = dl.assemble(
dl.inner(k_data - k_fun_mean, k_data - k_fun_mean) * self.dx)
u_trial, p_trial, k_trial, e_trial = dl.TrialFunctions(Vh_STATE)
u_test, p_test, k_test, e_test = dl.TestFunctions(Vh_STATE)
Wform = dl.Constant(1./noise_var_u)*dl.inner(u_trial, u_test)*self.dx + \
dl.Constant(1./noise_var_k)*dl.inner(k_trial, k_test)*self.dx
self.W = dl.assemble(Wform)
dummy = dl.Vector()
self.W.init_vector(dummy, 0)
[bc.zero(self.W) for bc in bcs0]
Wt = Transpose(self.W)
[bc.zero(Wt) for bc in bcs0]
self.W = Transpose(Wt)
xfun = dl.Function(Vh_STATE)
assigner = dl.FunctionAssigner(Vh_STATE, Vhs)
assigner.assign(
xfun,
[u_data, dl.Function(Vhs[1]), k_data,
dl.Function(Vhs[3])])
self.d = xfun.vector()
def __init__(self, Vh_STATE, Vhs, weights, geo, bcs0, datafile, variance_u,
variance_g):
if hasattr(geo, "dx"):
self.dx = geo.dx(geo.PHYSICAL)
else:
self.dx = dl.dx
self.ds = geo.ds(geo.AXIS)
x, y, U, V, uu, vv, ww, uv, k = np.loadtxt(datafile,
skiprows=2,
unpack=True)
u_fun_data = VelocityDNS(x=x,
y=y,
U=U,
V=V,
symmetrize=True,
coflow=0.)
u_data = dl.interpolate(u_fun_data, Vhs[0])
if Vh_STATE.num_sub_spaces() == 3:
u_trial, p_trial, g_trial = dl.TrialFunctions(Vh_STATE)
u_test, p_test, g_test = dl.TestFunctions(Vh_STATE)
else:
raise InputError()
Wform = dl.Constant(1./variance_u)*dl.inner(u_trial, u_test)*self.dx +\
dl.Constant(1./variance_g)*g_trial*g_test*self.ds
self.W = dl.assemble(Wform)
dummy = dl.Vector()
self.W.init_vector(dummy, 0)
[bc.zero(self.W) for bc in bcs0]
Wt = Transpose(self.W)
[bc.zero(Wt) for bc in bcs0]
self.W = Transpose(Wt)
xfun = dl.Function(Vh_STATE)
assigner = dl.FunctionAssigner(Vh_STATE, Vhs)
assigner.assign(xfun, [
u_data,
dl.Function(Vhs[1]),
dl.interpolate(dl.Constant(1.), Vhs[2])
])
self.d = xfun.vector()
self.w = (weights * 0.5)
def create_forms(W, rho, nu, g_a, boundary_markers, gamma=0.0):
v, p = df.TrialFunctions(W)
v_t, p_t = df.TestFunctions(W)
a = (
2.0 * nu * df.inner(df.sym(df.grad(v)), df.grad(v_t)) -
p * df.div(v_t) - df.div(v) * p_t
#- nu * df.div(v) * p_t
) * df.dx
L = rho * df.inner(df.Constant((0.0, -g_a)), v_t) * df.dx
# Grad-div stabilization
a += df.Constant(gamma) * df.div(v) * df.div(v_t) * df.dx
return a, L
def do_one_VK_problem(self, i_l, C_L_data=0.1):
"""Solve the VK equations for one L position using the "mixed" methodology,
so that we get on-grid solutions for `f` and `grad_VK(f)`.
"""
W = d.FunctionSpace(self.vk_mesh, self.c21.vk_vector_element * self.c11.vk_scalar_element)
# We can treat the tensor data array as having shape (N, 2, 2), where
# N is the number of elements of the equivalent scalar gridding of the
# mesh. array[:,0,0] is element [0,0] of the tensor. array[:,0,1] is
# the upper right element, etc.
D = d.Function(self.c21.vk_tensor_space)
dbuf = np.empty(D.vector().size()).reshape((-1, 2, 2))
C_L = d.Function(self.c11.vk_scalar_space)
sigma, u = d.TrialFunctions(W) # u is my 'f'
tau, v = d.TestFunctions(W)
a = (u * d.div(tau) + d.dot(sigma, tau) + d.inner(D * sigma, d.grad(v))) * d.dx
L = C_L * v * d.dx
equation = (a == L)
soln = d.Function(W)
bc = d.DirichletBC(W.sub(1), d.Constant(0), direct_boundary)
dbuf[:,0,0] = self.c21.D_VV[i_l]
dbuf[:,1,0] = self.c21.D_VK[i_l]
dbuf[:,0,1] = self.c21.D_VK[i_l]
dbuf[:,1,1] = self.c21.D_KK[i_l]
D.vector()[:] = dbuf.reshape((-1,))
if i_l == self.i_lmax:
C_L.vector()[:] = C_L_data + self.c11.source_term
else:
C_L.vector()[:] = C_L_data
d.solve(equation, soln, bc)
ssigma, su = soln.split(deepcopy=True)
u = self.c11.vk_to_rect(su.vector().array())
sigma = ssigma.vector().array().reshape((-1, 2))
dudv = self.c21.vk_to_rect(sigma[:,0])
dudk = self.c21.vk_to_rect(sigma[:,1])
return u, dudv, dudk
def create_forms(W, rho, nu, F, g_a, p_h, boundary_markers):
v, p = df.TrialFunctions(W)
v_t, p_t = df.TestFunctions(W)
a = (
2.0 * nu * df.inner(df.sym(df.grad(v)), df.grad(v_t)) -
p * df.div(v_t) - df.div(v) * p_t
#- nu * df.div(v) * p_t
) * df.dx
L = rho * df.inner(df.Constant((0.0, -g_a)), v_t) * df.dx
n = df.FacetNormal(W.mesh())
ds = df.Measure("ds", subdomain_data=boundary_markers)
L += df.inner(df.Constant((F, 0.0)), v_t) * ds(3) # driving force
L -= p_h * df.inner(n, v_t) * (ds(2) + ds(4)) # hydrostatic balance
return a, L
def set_forms(self, unknown, geom_ord=[0]):
"""
Set up weak forms of elliptic PDE.
"""
if any(s >= 0 for s in geom_ord):
## forms for forward equation ##
# 4. Define variational problem
# functions
if not hasattr(self, 'states_fwd'):
self.states_fwd = df.Function(self.W)
# u, l = df.split(self.states_fwd)
u, l = df.TrialFunctions(self.W)
v, m = df.TestFunctions(self.W)
f = self._source_term(degree=2)
# variational forms
if 'true' in str(type(unknown)):
unknown = df.interpolate(unknown, self.V)
self.F = df.exp(unknown) * df.inner(
df.grad(u), df.grad(v)) * df.dx + (
u * m + v *
l) * self.ds - f * v * df.dx + self.nugg * l * m * df.dx
# self.dFdstates = df.derivative(self.F, self.states_fwd) # Jacobian
# self.a = unknown*df.inner(df.grad(u), df.grad(v))*df.dx + (u*m + v*l)*self.ds + self.nugg*l*m*df.dx
# self.L = f*v*df.dx
if any(s >= 1 for s in geom_ord):
## forms for adjoint equation ##
# Set up the objective functional J
# u,_,_ = df.split(self.states_fwd)
# J_form = obj.form(u)
# Compute adjoint of forward operator
F2 = df.action(self.F, self.states_fwd)
self.dFdstates = df.derivative(
F2, self.states_fwd) # linearized forward operator
args = ufl.algorithms.extract_arguments(
self.dFdstates) # arguments for bookkeeping
self.adj_dFdstates = df.adjoint(
self.dFdstates, reordered_arguments=args
) # adjoint linearized forward operator
# self.dJdstates = df.derivative(J_form, self.states_fwd, df.TestFunction(self.W)) # derivative of functional with respect to solution
# self.dirac_1 = obj.ptsrc(u,1) # dirac_1 cannot be initialized here because it involves evaluation
## forms for gradient ##
self.dFdunknown = df.derivative(F2, unknown)
self.adj_dFdunknown = df.adjoint(self.dFdunknown)
def buildFunctionSpace(self):
#-----------------------------------------------------------------------------------
""" Built the product function space """
## Deprecated code from earlier version of Fenics
#self.VReal = df.FunctionSpace(self.mesh,'CG',self.meshOpt['polynomialOrder'])
#elf.VImag = df.FunctionSpace(self.mesh,'CG',self.meshOpt['polynomialOrder'])
#self.V = df.MixedFunctionSpace([self.VReal,self.VImag])
elem = df.FiniteElement('CG', self.mesh.ufl_cell(),
self.meshOpt['polynomialOrder'])
self.VReal = df.FunctionSpace(self.mesh, elem)
self.VImag = df.FunctionSpace(self.mesh, elem)
self.V = df.FunctionSpace(self.mesh, elem * elem)
self.ur, self.ui = df.TrialFunctions(self.V)
self.wr, self.wi = df.TestFunctions(self.V)
self._buildFunctionSpaceCompleted = True
return
def define_momentum_equation(self):
"""
Setup the momentum equation weak form
"""
sim = self.simulation
Vuvw = sim.data['uvw_star'].function_space()
tests = dolfin.TestFunctions(Vuvw)
trials = dolfin.TrialFunctions(Vuvw)
# Split into components
v = dolfin.as_vector(tests[:])
u = dolfin.as_vector(trials[:])
# The pressure is explicit p* and q is zero (on a domain, to avoid warnings)
p = sim.data['p']
class MyZero(Zero):
def ufl_domains(self):
return p.ufl_domains()
q = MyZero()
lm_trial = lm_test = None
# Define the momentum equation weak form
eq = define_dg_equations(
u,
v,
p,
q,
lm_trial,
lm_test,
self.simulation,
include_hydrostatic_pressure=self.include_hydrostatic_pressure,
incompressibility_flux_type='central', # Only used with q
use_grad_q_form=False, # Only used with q
use_grad_p_form=self.use_grad_p_form,
use_stress_divergence_form=self.use_stress_divergence_form,
)
self.form_lhs, self.form_rhs = dolfin.system(eq)
def variational_forms(self, kn: df.Constant) -> tp.Tuple[tp.Any, tp.Any]:
"""Create the variational forms corresponding to the given
discretization of the given system of equations.
*Arguments*
kn (:py:class:`ufl.Expr` or float)
The time step
*Returns*
(lhs, rhs) (:py:class:`tuple` of :py:class:`ufl.Form`)
"""
# Extract theta parameter and conductivities
theta = self._parameters["theta"]
Mi = self._M_i
Me = self._M_e
# Define variational formulation
if self._parameters["linear_solver_type"] == "direct":
v, u, l = df.TrialFunctions(self.VUR)
w, q, lamda = df.TestFunctions(self.VUR)
else:
v, u = df.TrialFunctions(self.VUR)
w, q = df.TestFunctions(self.VUR)
# Get physical parameters
chi = self._parameters["Chi"]
capacitance = self._parameters["Cm"]
Dt_v = (v - self.v_) / kn
Dt_v *= chi * capacitance
v_mid = theta * v + (1.0 - theta) * self.v_
# Set-up measure and rhs from stimulus
dz = df.Measure("dx",
domain=self._mesh,
subdomain_data=self._cell_domains)
db = df.Measure("ds",
domain=self._mesh,
subdomain_data=self._facet_domains)
# Get domain tags
cell_tags = map(int, set(
self._cell_domains.array())) # np.int64 does not work
facet_tags = map(int, set(self._facet_domains.array()))
# Loop over all domains
G = Dt_v * w * dz()
for key in cell_tags:
G += df.inner(Mi[key] * df.grad(v_mid), df.grad(w)) * dz(key)
G += df.inner(Mi[key] * df.grad(u), df.grad(w)) * dz(key)
G += df.inner(Mi[key] * df.grad(v_mid), df.grad(q)) * dz(key)
G += df.inner(
(Mi[key] + Me[key]) * df.grad(u), df.grad(q)) * dz(key)
if self._I_s is None:
G -= chi * df.Constant(0) * w * dz(key)
else:
_is = self._I_s.get(key, df.Constant(0))
G -= chi * _is * w * dz(key)
# If Lagrangian multiplier
if self._parameters["linear_solver_type"] == "direct":
G += (lamda * u + l * q) * dz(key)
if self._I_a:
G -= chi * self._I_a[key] * q * dz(key)
for key in facet_tags:
if self._ect_current is not None:
# Default to 0 if not defined for tag I do not I should apply `chi` here.
G += self._ect_current.get(key, df.Constant(0)) * q * db(key)
a, L = df.system(G)
return a, L
def step(self, t0: float, t1: float) -> None:
"""Solve on the given time interval (t0, t1).
Arguments:
interval (:py:class:`tuple`)
The time interval (t0, t1) for the step
*Invariants*
Assuming that v\_ is in the correct state for t0, gives
self.vur in correct state at t1.
"""
timer = df.Timer("PDE step")
# Extract theta and conductivities
theta = self._parameters["theta"]
Mi = self._M_i
Me = self._M_e
# Extract interval and thus time-step
kn = df.Constant(t1 - t0)
# Define variational formulation
if self._parameters["linear_solver_type"] == "direct":
v, u, l = df.TrialFunctions(self.VUR)
w, q, lamda = df.TestFunctions(self.VUR)
else:
v, u = df.TrialFunctions(self.VUR)
w, q = df.TestFunctions(self.VUR)
# Get physical parameters
chi = self._parameters["Chi"]
capacitance = self._parameters["Cm"]
Dt_v = (v - self.v_) / kn
Dt_v *= chi * capacitance
v_mid = theta * v + (1.0 - theta) * self.v_
# Set time
t = t0 + theta * (t1 - t0)
self.time.assign(t)
# Define spatial integration domains:
dz = df.Measure("dx",
domain=self._mesh,
subdomain_data=self._cell_domains)
db = df.Measure("ds",
domain=self._mesh,
subdomain_data=self._facet_domains)
# Get domain labels
cell_tags = map(int, set(
self._cell_domains.array())) # np.int64 does not workv
facet_tags = map(int, set(self._facet_domains.array()))
# Loop overe all domain labels
G = Dt_v * w * dz()
for key in cell_tags:
G += df.inner(Mi[key] * df.grad(v_mid), df.grad(w)) * dz(key)
G += df.inner(Mi[key] * df.grad(u), df.grad(w)) * dz(key)
G += df.inner(Mi[key] * df.grad(v_mid), df.grad(q)) * dz(key)
G += df.inner(
(Mi[key] + Me[key]) * df.grad(u), df.grad(q)) * dz(key)
if self._I_s is None:
G -= chi * df.Constant(0) * w * dz(key)
else:
# _is = self._I_s.get(key, df.Constant(0))
# G -= chi*_is*w*dz(key)
G -= chi * self._I_s[key] * w * dz(key)
# If Lagrangian multiplier
if self._parameters["linear_solver_type"] == "direct":
G += (lamda * u + l * q) * dz(key)
# Add applied current as source in elliptic equation if applicable
if self._I_a:
G -= chi * self._I_a[key] * q * dz(key)
if self._ect_current is not None:
for key in facet_tags:
# Detfalt to 0 if not defined for that facet tag
# TODO: Should I include `chi` here? I do not think so
G += self._ect_current.get(key, df.Constant(0)) * q * db(key)
# Define variational problem
a, L = df.system(G)
pde = df.LinearVariationalProblem(a, L, self.vur, bcs=self._bcs)
# Set-up solver
solver = df.LinearVariationalSolver(pde)
solver.solve()
values[line.index()] = other
markers.set_values(values)
def top(x):
return np.abs(x[0] - width / 2) < dolfin.DOLFIN_EPS
def bottom(x):
return np.abs(x[0] + width / 2) < dolfin.DOLFIN_EPS
print('ugly stuff took {:.2f} s'.format(time.time() - t_ini))
nodal_space = dolfin.FunctionSpace(mesh, 'Lagrange', 1)
(L_i, ) = dolfin.TestFunctions(nodal_space)
(L_j, ) = dolfin.TrialFunctions(nodal_space)
bc_ground = dolfin.DirichletBC(nodal_space, dolfin.Constant(0.0), markers,
other)
bc_source = dolfin.DirichletBC(nodal_space, dolfin.Constant(1.0), markers,
metal)
rho = dolfin.Constant(0.0)
A_ij = dolfin.inner(dolfin.grad(L_i), dolfin.grad(L_j)) * dolfin.dx
b_ij = rho * L_j * dolfin.dx
A = dolfin.assemble(A_ij)
b = dolfin.assemble(b_ij)
bc_ground.apply(A, b)
bc_source.apply(A, b)
phi = dolfin.Function(nodal_space)
c = phi.vector()
def Cost(xp):
comm = nMPI.COMM_WORLD
mpi_rank = comm.Get_rank()
x1, x2 = xp #The two variables (length and feed offset)
rs = 8.0 # radiation boundary radius
l = x1 # Patch length
w = 4.5 # Patch width
s1 = x2 * x1 / 2.0 # Feed offset
h = 1.0 # Patch height
t = 0.05 # Metal thickness
lc = 1.0 # Coax length
rc = 0.25 # Coax shield radius
cc = 0.107 #Coax center conductor 50 ohm air diel
eps = 1.0e-4
tol = 1.0e-6
eta = 377.0 # vacuum intrinsic wave impedance
eps_c = 1.0 # dielectric permittivity
k0 = 2.45 * 2.0 * np.pi / 30.0 # Frequency in GHz
ls = 0.025 #Mesh density parameters for GMSH
lm = 0.8
lw = 0.06
lp = 0.3
# Run GMSH only on one MPI processor (process 0).
# We use the GMSH Python interface to generate the geometry and mesh objects
if mpi_rank == 0:
print("x[0] = {0:<f}, x[1] = {1:<f} ".format(xp[0], xp[1]))
print("length = {0:<f}, width = {1:<f}, feed offset = {2:<f}".format(l, w, s1))
gmsh.initialize()
gmsh.option.setNumber('General.Terminal', 1)
gmsh.model.add("SimplePatchOpt")
# Radiation sphere
gmsh.model.occ.addSphere(0.0, 0.0, 0.0, rs, 1)
gmsh.model.occ.addBox(0.0, -rs, 0.0, rs, 2*rs, rs, 2)
gmsh.model.occ.intersect([(3,1)],[(3,2)], 3, removeObject=True, removeTool=True)
# Patch
gmsh.model.occ.addBox(0.0, -l/2, h, w/2, l, t, 4)
# coax center
gmsh.model.occ.addCylinder(0.0, s1, -lc, 0.0, 0.0, lc+h, cc, 5, 2.0*np.pi)
# coax shield
gmsh.model.occ.addCylinder(0.0, s1, -lc, 0.0, 0.0, lc, rc, 7)
gmsh.model.occ.addBox(0.0, s1-rc, -lc, rc, 2.0*rc, lc, 8)
gmsh.model.occ.intersect([(3,7)], [(3,8)], 9, removeObject=True, removeTool=True)
gmsh.model.occ.fuse([(3,3)], [(3,9)], 10, removeObject=True, removeTool=True)
# cutout internal boundaries
gmsh.model.occ.cut([(3,10)], [(3,4),(3,5)], 11, removeObject=True, removeTool=True)
gmsh.option.setNumber('Mesh.MeshSizeMin', ls)
gmsh.option.setNumber('Mesh.MeshSizeMax', lm)
gmsh.option.setNumber('Mesh.Algorithm', 6)
gmsh.option.setNumber('Mesh.Algorithm3D', 1)
gmsh.option.setNumber('Mesh.MshFileVersion', 4.1)
gmsh.option.setNumber('Mesh.Format', 1)
gmsh.option.setNumber('Mesh.MinimumCirclePoints', 36)
gmsh.option.setNumber('Mesh.CharacteristicLengthFromCurvature', 1)
gmsh.model.occ.synchronize()
pts = gmsh.model.getEntities(0)
gmsh.model.mesh.setSize(pts, lm) #Set background mesh density
pts = gmsh.model.getEntitiesInBoundingBox(-eps, -l/2-eps, h-eps, w/2+eps, l/2+eps, h+t+eps)
gmsh.model.mesh.setSize(pts, ls)
pts = gmsh.model.getEntitiesInBoundingBox(-eps, s1-rc-eps, -lc-eps, rc+eps, s1+rc+eps, h+eps)
gmsh.model.mesh.setSize(pts, lw)
pts = gmsh.model.getEntitiesInBoundingBox(-eps, -rc-eps, -eps, rc+eps, rc+eps, eps)
gmsh.model.mesh.setSize(pts, lw)
# Embed points to reduce mesh density on patch faces
fce1 = gmsh.model.getEntitiesInBoundingBox(-eps, -l/2-eps, h+t-eps, w/2+eps, l/2+eps, h+t+eps, 2)
gmsh.model.occ.synchronize()
gmsh.model.geo.addPoint(w/4, -l/4, h+t, lp, 1000)
gmsh.model.geo.addPoint(w/4, 0.0, h+t, lp, 1001)
gmsh.model.geo.addPoint(w/4, l/4, h+t, lp, 1002)
gmsh.model.geo.synchronize()
gmsh.model.occ.synchronize()
print(fce1)
fce2 = gmsh.model.getEntitiesInBoundingBox(-eps, -l/2-eps, h-eps, w/2+eps, l/2+eps, h+eps, 2)
gmsh.model.geo.addPoint(w/4, -9*l/32, h, lp, 1003)
gmsh.model.geo.addPoint(w/4, 0.0, h, lp, 1004)
gmsh.model.geo.addPoint(w/4, 9*l/32, h, lp, 1005)
gmsh.model.geo.synchronize()
for tt in fce1:
gmsh.model.mesh.embed(0, [1000, 1001, 1002], 2, tt[1])
for tt in fce2:
gmsh.model.mesh.embed(0, [1003, 1004, 1005], 2, tt[1])
print(fce2)
gmsh.model.occ.remove(fce1)
gmsh.model.occ.remove(fce2)
gmsh.model.occ.synchronize()
gmsh.model.addPhysicalGroup(3, [11], 1)
gmsh.model.setPhysicalName(3, 1, "Air")
gmsh.model.mesh.optimize("Relocate3D", niter=5)
gmsh.model.mesh.generate(3)
gmsh.write("SimplePatch.msh")
gmsh.finalize()
# Mesh generation is finished. We now use Meshio to translate GMSH mesh to xdmf file for
# importation into Fenics FE solver
msh = meshio.read("SimplePatch.msh")
for cell in msh.cells:
if cell.type == "tetra":
tetra_cells = cell.data
for key in msh.cell_data_dict["gmsh:physical"].keys():
if key == "tetra":
tetra_data = msh.cell_data_dict["gmsh:physical"][key]
tetra_mesh = meshio.Mesh(points=msh.points, cells={"tetra": tetra_cells},
cell_data={"VolumeRegions":[tetra_data]})
meshio.write("mesh.xdmf", tetra_mesh)
# Here we import the mesh into Fenics
mesh = dolfin.Mesh()
with dolfin.XDMFFile("mesh.xdmf") as infile:
infile.read(mesh)
mvc = dolfin.MeshValueCollection("size_t", mesh, 3)
with dolfin.XDMFFile("mesh.xdmf") as infile:
infile.read(mvc, "VolumeRegions")
cf = dolfin.cpp.mesh.MeshFunctionSizet(mesh, mvc)
# The boundary classes for the FE solver
class PEC(dolfin.SubDomain):
def inside(self, x, on_boundary):
return on_boundary
class InputBC(dolfin.SubDomain):
def inside(self, x, on_boundary):
return on_boundary and dolfin.near(x[2], -lc, tol)
class OutputBC(dolfin.SubDomain):
def inside(self, x, on_boundary):
rr = np.sqrt(x[0]*x[0]+x[1]*x[1]+x[2]*x[2])
return on_boundary and dolfin.near(rr, 8.0, 1.0e-1)
class PMC(dolfin.SubDomain):
def inside(self, x, on_boundary):
return on_boundary and dolfin.near(x[0], 0.0, tol)
# Volume domains
dolfin.File("VolSubDomains.pvd").write(cf)
dolfin.File("Mesh.pvd").write(mesh)
# Mark boundaries
sub_domains = dolfin.MeshFunction("size_t", mesh, mesh.topology().dim() - 1)
sub_domains.set_all(4)
pec = PEC()
pec.mark(sub_domains, 0)
in_port = InputBC()
in_port.mark(sub_domains, 1)
out_port = OutputBC()
out_port.mark(sub_domains, 2)
pmc = PMC()
pmc.mark(sub_domains, 3)
dolfin.File("BoxSubDomains.pvd").write(sub_domains)
# Set up function spaces
cell = dolfin.tetrahedron
ele_type = dolfin.FiniteElement('N1curl', cell, 2, variant="integral") # H(curl) element for EM
V2 = dolfin.FunctionSpace(mesh, ele_type * ele_type)
V = dolfin.FunctionSpace(mesh, ele_type)
(u_r, u_i) = dolfin.TrialFunctions(V2)
(v_r, v_i) = dolfin.TestFunctions(V2)
dolfin.info(mesh)
#surface integral definitions from boundaries
ds = dolfin.Measure('ds', domain = mesh, subdomain_data = sub_domains)
#volume regions
dx_air = dolfin.Measure('dx', domain = mesh, subdomain_data = cf, subdomain_id = 1)
dx_subst = dolfin.Measure('dx', domain = mesh, subdomain_data = cf, subdomain_id = 2)
# with source and sink terms
u0 = dolfin.Constant((0.0, 0.0, 0.0)) #PEC definition
# The incident field sources (E and H-fields)
h_src = dolfin.Expression(('-(x[1] - s) / (2.0 * pi * (pow(x[0], 2.0) + pow(x[1] - s,2.0)))', 'x[0] / (2.0 * pi *(pow(x[0],2.0) + pow(x[1] - s,2.0)))', 0.0), degree = 2, s = s1)
e_src = dolfin.Expression(('x[0] / (2.0 * pi * (pow(x[0], 2.0) + pow(x[1] - s,2.0)))', 'x[1] / (2.0 * pi *(pow(x[0],2.0) + pow(x[1] - s,2.0)))', 0.0), degree = 2, s = s1)
Rrad = dolfin.Expression(('sqrt(x[0] * x[0] + x[1] * x[1] + x[2] * x[2])'), degree = 2)
#Boundary condition dictionary
boundary_conditions = {0: {'PEC' : u0},
1: {'InputBC': (h_src)},
2: {'OutputBC': Rrad}}
n = dolfin.FacetNormal(mesh)
#Build PEC boundary conditions for real and imaginary parts
bcs = []
for i in boundary_conditions:
if 'PEC' in boundary_conditions[i]:
bc = dolfin.DirichletBC(V2.sub(0), boundary_conditions[i]['PEC'], sub_domains, i)
bcs.append(bc)
bc = dolfin.DirichletBC(V2.sub(1), boundary_conditions[i]['PEC'], sub_domains, i)
bcs.append(bc)
# Build input BC source term and loading term
integral_source = []
integrals_load =[]
for i in boundary_conditions:
if 'InputBC' in boundary_conditions[i]:
r = boundary_conditions[i]['InputBC']
bb1 = 2.0 * (k0 * eta) * dolfin.inner(v_i, dolfin.cross(n, r)) * ds(i) #Factor of two from field equivalence principle
integral_source.append(bb1)
bb2 = dolfin.inner(dolfin.cross(n, v_i), dolfin.cross(n, u_r)) * k0 * np.sqrt(eps_c) * ds(i)
integrals_load.append(bb2)
bb2 = dolfin.inner(-dolfin.cross(n, v_r), dolfin.cross(n, u_i)) * k0 * np.sqrt(eps_c) * ds(i)
integrals_load.append(bb2)
for i in boundary_conditions:
if 'OutputBC' in boundary_conditions[i]:
r = boundary_conditions[i]['OutputBC']
bb2 = (dolfin.inner(dolfin.cross(n, v_i), dolfin.cross(n, u_r)) * k0 + 1.0 * dolfin.inner(dolfin.cross(n, v_i), dolfin.cross(n, u_i)) / r)* ds(i)
integrals_load.append(bb2)
bb2 = (dolfin.inner(-dolfin.cross(n, v_r), dolfin.cross(n, u_i)) * k0 + 1.0 * dolfin.inner(dolfin.cross(n, v_r), dolfin.cross(n, u_r)) / r)* ds(i)
integrals_load.append(bb2)
# for PMC, do nothing. Natural BC.
a = (dolfin.inner(dolfin.curl(v_r), dolfin.curl(u_r)) + dolfin.inner(dolfin.curl(v_i), dolfin.curl(u_i)) - eps_c * k0 * k0 * (dolfin.inner(v_r, u_r) + dolfin.inner(v_i, u_i))) * dx_subst + (dolfin.inner(dolfin.curl(v_r), dolfin.curl(u_r)) + dolfin.inner(dolfin.curl(v_i), dolfin.curl(u_i)) - k0 * k0 * (dolfin.inner(v_r, u_r) + dolfin.inner(v_i, u_i))) * dx_air + sum(integrals_load)
L = sum(integral_source)
u1 = dolfin.Function(V2)
vdim = u1.vector().size()
print("Solution vector size =", vdim)
dolfin.solve(a == L, u1, bcs, solver_parameters = {'linear_solver' : 'mumps'})
#Here we write files of the field solution for inspection
u1_r, u1_i = u1.split(True)
fp = dolfin.File("EField_r.pvd")
fp << u1_r
fp = dolfin.File("EField_i.pvd")
fp << u1_i
# Compute power relationships and reflection coefficient
H = dolfin.interpolate(h_src, V) # Get input field
P = dolfin.assemble((-dolfin.dot(u1_r,dolfin.cross(dolfin.curl(u1_i),n))+dolfin.dot(u1_i,dolfin.cross(dolfin.curl(u1_r),n))) * ds(2))
P_refl = dolfin.assemble((-dolfin.dot(u1_i,dolfin.cross(dolfin.curl(u1_r), n)) + dolfin.dot(u1_r, dolfin.cross(dolfin.curl(u1_i), n))) * ds(1))
P_inc = dolfin.assemble((dolfin.dot(H, H) * eta / (2.0 * np.sqrt(eps_c))) * ds(1))
print("Integrated power on port 2:", P/(2.0 * k0 * eta))
print("Incident power at port 1:", P_inc)
print("Integrated reflected power on port 1:", P_inc - P_refl / (2.0 * k0 * eta))
#Reflection coefficient is returned as cost function
rho_old = (P_inc - P_refl / (2.0 * k0 * eta)) / P_inc #Fraction of incident power reflected as objective function
return rho_old
def define_simple_equations(self):
"""
Setup weak forms for SIMPLE form
"""
sim = self.simulation
self.Vuvw = sim.data['uvw_star'].function_space()
Vp = sim.data['Vp']
# The trial and test functions in a coupled space (to be split)
func_spaces = [self.Vuvw, Vp]
e_mixed = dolfin.MixedElement([fs.ufl_element() for fs in func_spaces])
Vcoupled = dolfin.FunctionSpace(sim.data['mesh'], e_mixed)
tests = dolfin.TestFunctions(Vcoupled)
trials = dolfin.TrialFunctions(Vcoupled)
# Split into components
v = dolfin.as_vector(tests[0][:])
u = dolfin.as_vector(trials[0][:])
q = tests[-1]
p = trials[-1]
lm_trial = lm_test = None
# Define the full coupled form and split it into subforms depending
# on the test and trial functions
eq = define_dg_equations(
u,
v,
p,
q,
lm_trial,
lm_test,
self.simulation,
include_hydrostatic_pressure=self.include_hydrostatic_pressure,
incompressibility_flux_type=self.incompressibility_flux_type,
use_grad_q_form=self.use_grad_q_form,
use_grad_p_form=self.use_grad_p_form,
use_stress_divergence_form=self.use_stress_divergence_form,
)
mat, vec = split_form_into_matrix(eq,
Vcoupled,
Vcoupled,
check_zeros=True)
# Check matrix and vector shapes and that the matrix is a saddle point matrix
assert mat.shape == (2, 2)
assert vec.shape == (2, )
assert mat[
-1,
-1] is None, 'Found p-q coupling, this is not a saddle point system!'
# Store the forms
self.eqA = mat[0, 0]
self.eqB = mat[0, 1]
self.eqC = mat[1, 0]
self.eqD = vec[0]
self.eqE = vec[1]
if self.eqE is None:
self.eqE = dolfin.TrialFunction(Vp) * dolfin.Constant(
0) * dolfin.dx
if self.a_tilde_is_mass:
# The mass matrix. Consistent with the implementation in define_dg_equations
rho = sim.multi_phase_model.get_density(0)
c1 = sim.data['time_coeffs'][0]
dt = sim.data['dt']
eqM = rho * c1 / dt * dolfin.dot(u, v) * dolfin.dx
matM, _vecM = split_form_into_matrix(eqM,
Vcoupled,
Vcoupled,
check_zeros=True)
self.eqM = dolfin.Form(matM[0, 0])
self.M = None
H=4.1,
U0=1.5,
t=0.,
t_ref=0.1)
# inlet bc
v_bc1 = dlfn.DirichletBC(Wh.sub(0), v_inlet, facet_marker, left_id)
# wall no slip
v_bc2 = dlfn.DirichletBC(Wh.sub(0), null_vector, facet_marker, wall_id)
# circle no slip
v_bc3 = dlfn.DirichletBC(Wh.sub(0), null_vector, facet_marker, cylinder_id)
# collection of bcs
bcs = [v_bc1, v_bc2, v_bc3]
#==============================================================================
# trial and test function
(del_v, del_p) = dlfn.TestFunctions(Wh)
(dv, dp) = dlfn.TrialFunctions(Wh)
#==============================================================================
# solution functions
sol = dlfn.Function(Wh)
sol0 = dlfn.Function(Wh)
sol00 = dlfn.Function(Wh)
v0, p0 = dlfn.split(sol0)
v00, p00 = dlfn.split(sol00)
#==============================================================================
# define auxiliary operators
def a_operator(phi, psi):
return inner(grad(phi), grad(psi)) / Re * dV
field = subproblem[0]["name"]
fields.append(field)
field_to_subspace[field] = spaces[name]
field_to_subproblem[field] = (name, -1)
# Create initial folders for storing results
newfolder, tstepfiles = create_initial_folders(folder, restart_folder, fields,
tstep, parameters)
# Create overarching test and trial functions
test_functions = dict()
trial_functions = dict()
for name, subproblem in subproblems.items():
if len(subproblem) > 1:
test_functions[name] = df.TestFunctions(spaces[name])
trial_functions[name] = df.TrialFunctions(spaces[name])
else:
test_functions[name] = df.TestFunction(spaces[name])
trial_functions[name] = df.TrialFunction(spaces[name])
# Create work dictionaries for all subproblems
w_ = dict((subproblem, df.Function(space, name=subproblem))
for subproblem, space in spaces.items())
w_1 = dict((subproblem, df.Function(space, name=subproblem + "_1"))
for subproblem, space in spaces.items())
w_tmp = dict((subproblem, df.Function(space, name=subproblem + "_tmp"))
for subproblem, space in spaces.items())
# Shortcuts to the fields
x_ = dict()
for name, subproblem in subproblems.items():
print("starting the state space")
vector_order, nodal_order = 2, 2
vector_space = dolfin.FunctionSpace(mesh, 'Nedelec 1st kind H(curl)',
vector_order)
#vector_element = dolfin.VectorElement("Nedelec 1st kind H(curl)", mesh.ufl_cell(), vector_order)
nodal_space = dolfin.FunctionSpace(mesh, 'Lagrange', nodal_order)
#nodal_element = dolfin.FiniteElement("Lagrange", mesh.ufl_cell(), nodal_order)
#combined_space = vector_space * nodal_space
combined_space = dolfin.FunctionSpace(
mesh,
vector_space.ufl_element() * nodal_space.ufl_element())
#combined_element=dolfin.MixedElement([vector_element, nodal_element])
#combined_space=dolfin.FunctionSpace(mesh, combined_element)
#combined_space=dolfin.FunctionSpace(mesh, dolfin.MixedElement([vector_element, nodal_element]))
(N_i, L_i) = dolfin.TestFunctions(combined_space)
(N_j, L_j) = dolfin.TrialFunctions(combined_space)
er = 1.
ur = 1.
s_tt_ij = 1. / ur * dolfin.inner(dolfin.curl(N_i), dolfin.curl(N_j))
t_tt_ij = er * dolfin.inner(N_i, N_j)
s_zz_ij = 1. / ur * dolfin.inner(dolfin.grad(L_i), dolfin.grad(L_j))
t_zz_ij = er * L_i * L_j
s_ij = (s_tt_ij + s_zz_ij) * dolfin.dx
t_ij = (t_tt_ij + t_zz_ij) * dolfin.dx
S = dolfin.assemble(s_ij)
T = dolfin.assemble(t_ij)
print("starting the boundary conditions")
markers = dolfin.MeshFunction('size_t', mesh, 1)
markers.set_all(0)
def _setup_imex_problem(self):
assert hasattr(self, "_parameters")
assert hasattr(self, "_mesh")
assert hasattr(self, "_Wh")
assert hasattr(self, "_coefficients")
assert hasattr(self, "_one")
assert hasattr(self, "_omega")
assert hasattr(self, "_v0")
assert hasattr(self, "_v00")
assert hasattr(self, "_T0")
assert hasattr(self, "_T00")
print " setup explicit imex problem..."
#=======================================================================
# retrieve imex coefficients
a, b, c = self._imex_alpha, self._imex_beta, self._imex_gamma
#=======================================================================
# trial and test function
(del_v, del_p, del_T) = dlfn.TestFunctions(self._Wh)
(dv, dp, dT) = dlfn.TrialFunctions(self._Wh)
# volume element
dV = dlfn.Measure("dx", domain=self._mesh)
# reference to time step
timestep = self._timestep
#=======================================================================
from dolfin import dot, grad, inner
# 1) lhs momentum equation
lhs_momentum = a[0] / timestep * dot(dv, del_v) * dV \
+ c[0] * self._coefficients[1] * a_op(dv, del_v) * dV\
- b_op(del_v, dp) * dV\
- b_op(dv, del_p) * dV
# 2a) rhs momentum equation: time derivative
rhs_momentum = -dot(
a[1] / timestep * self._v0 + a[2] / timestep * self._v00,
del_v) * dV
# 2b) rhs momentum equation: nonlinear term
nonlinear_term_velocity = b[0] * dot(grad(self._v0), self._v0) \
+ b[1] * dot(grad(self._v00), self._v00)
rhs_momentum -= dot(nonlinear_term_velocity, del_v) * dV
# 2c) rhs momentum equation: linear term
rhs_momentum -= self._coefficients[1] * inner(
c[1] * grad(self._v0) + c[2] * grad(self._v00), grad(del_v)) * dV
# 2d) rhs momentum equation: coriolis term
if self._parameters.rotation is True:
assert self._coefficients[0] != 0.0
print " adding rotation to the model..."
# defining extrapolated velocity
extrapolated_velocity = (self._one + self._omega) * self._v0 \
- self._omega * self._v00
# set Coriolis term
if self._space_dim == 2:
rhs_momentum -= self._coefficients[0] * (
-extrapolated_velocity[1] * del_v[0] +
extrapolated_velocity[0] * del_v[1]) * dV
elif self._space_dim == 3:
from dolfin import cross
coriolis_term = cross(self._rotation_vector,
extrapolated_velocity)
rhs_momentum -= self._coefficients[0] * dot(
coriolis_term, del_v) * dV
print " adding rotation to the model..."
# 2e) rhs momentum equation: buoyancy term
if self._parameters.buoyancy is True:
assert self._coefficients[2] != 0.0
# defining extrapolated temperature
extrapolated_temperature = (
self._one + self._omega) * self._T0 - self._omega * self._T00
# buoyancy term
print " adding buoyancy to the model..."
rhs_momentum -= self._coefficients[
2] * extrapolated_temperature * dot(self._gravity, del_v) * dV
#=======================================================================
# 3) lhs energy equation
lhs_energy = a[0] / timestep * dot(dT, del_T) * dV \
+ self._coefficients[3] * a_op(dT, del_T) * dV
# 4a) rhs energy equation: time derivative
rhs_energy = -dot(
a[1] / timestep * self._T0 + a[2] / timestep * self._T00,
del_T) * dV
# 4b) rhs energy equation: nonlinear term
nonlinear_term_temperature = b[0] * dot(self._v0, grad(self._T0)) \
+ b[1] * dot(self._v00, grad(self._T00))
rhs_energy -= nonlinear_term_temperature * del_T * dV
# 4c) rhs energy equation: linear term
rhs_energy -= self._coefficients[3] \
* dot(c[1] * grad(self._T0) + c[2] * grad(self._T00), grad(del_T)) * dV
#=======================================================================
# full problem
self._lhs = lhs_momentum + lhs_energy
self._rhs = rhs_momentum + rhs_energy
if not hasattr(self, "_dirichlet_bcs"):
self._setup_boundary_conditions()
if self._parameters.use_assembler_method:
# system assembler
self._assembler = dlfn.SystemAssembler(self._lhs,
self._rhs,
bcs=self._dirichlet_bcs)
self._system_matrix = dlfn.Matrix()
self._system_rhs = dlfn.Vector()
self._solver = dlfn.LUSolver(self._system_matrix)
else:
# linear problem
problem = dlfn.LinearVariationalProblem(self._lhs,
self._rhs,
self._sol,
bcs=self._dirichlet_bcs)
self._solver = dlfn.LinearVariationalSolver(problem)
bcs.append(dlfn.DirichletBC(Wh.sub(0), null_vector, facet_marker, 2))
bcs.append(dlfn.DirichletBC(Wh.sub(0), null_vector, facet_marker, 3))
bcs.append(dlfn.DirichletBC(Wh.sub(0), null_vector, facet_marker, 4))
# temperature bcs on left and right boundary
bcs.append(dlfn.DirichletBC(Wh.sub(2), temp_left, facet_marker, 1))
bcs.append(dlfn.DirichletBC(Wh.sub(2), temp_right, facet_marker, 2))
#==============================================================================
# definition of volume / surface element
dA = dlfn.Measure("ds", domain = mesh, subdomain_data = facet_marker)
dV = dlfn.Measure("dx", domain = mesh)
A = dlfn.assemble(1.*dV) # "volume" or rather area of geometry
#n = dlfn.FacetNormal(mesh)
#==============================================================================
# trial and test function
(del_v, del_p, del_T) = dlfn.TestFunctions(Wh)
(dv, dp, dT) = dlfn.TrialFunctions(Wh)
#==============================================================================
# solution functions
sol = dlfn.Function(Wh)
sol0 = dlfn.Function(Wh)
sol00 = dlfn.Function(Wh)
v0, p0, T0 = dlfn.split(sol0)
v00, p00, T00 = dlfn.split(sol00)
#==============================================================================
# define auxiliary operators
def a_operator(phi, psi):
return inner(grad(phi), grad(psi)) * dV
def b_operator(phi, psi):
return div(phi) * psi * dV
def c_operator(phi, chi, psi):
return dot(dot(grad(chi), phi), psi) * dV
def test_assembly_solve_taylor_hood(mesh):
"""Assemble Stokes problem with Taylor-Hood elements and solve."""
P2 = dolfin.VectorFunctionSpace(mesh, ("Lagrange", 2))
P1 = dolfin.FunctionSpace(mesh, ("Lagrange", 1))
def boundary0(x, only_boundary):
"""Define boundary x = 0"""
return x[:, 0] < 10 * numpy.finfo(float).eps
def boundary1(x, only_boundary):
"""Define boundary x = 1"""
return x[:, 0] > (1.0 - 10 * numpy.finfo(float).eps)
u0 = dolfin.Function(P2)
u0.vector().set(1.0)
u0.vector().ghostUpdate(addv=PETSc.InsertMode.INSERT,
mode=PETSc.ScatterMode.FORWARD)
bc0 = dolfin.DirichletBC(P2, u0, boundary0)
bc1 = dolfin.DirichletBC(P2, u0, boundary1)
u, p = dolfin.TrialFunction(P2), dolfin.TrialFunction(P1)
v, q = dolfin.TestFunction(P2), dolfin.TestFunction(P1)
a00 = inner(ufl.grad(u), ufl.grad(v)) * dx
a01 = ufl.inner(p, ufl.div(v)) * dx
a10 = ufl.inner(ufl.div(u), q) * dx
a11 = None
p00 = a00
p01, p10 = None, None
p11 = inner(p, q) * dx
# FIXME
# We need zero function for the 'zero' part of L
p_zero = dolfin.Function(P1)
f = dolfin.Function(P2)
L0 = ufl.inner(f, v) * dx
L1 = ufl.inner(p_zero, q) * dx
# -- Blocked and nested
A0 = dolfin.fem.assemble_matrix_nest([[a00, a01], [a10, a11]], [bc0, bc1])
A0norm = nest_matrix_norm(A0)
P0 = dolfin.fem.assemble_matrix_nest([[p00, p01], [p10, p11]], [bc0, bc1])
P0norm = nest_matrix_norm(P0)
b0 = dolfin.fem.assemble_vector_nest([L0, L1], [[a00, a01], [a10, a11]],
[bc0, bc1])
b0norm = b0.norm()
ksp = PETSc.KSP()
ksp.create(mesh.mpi_comm())
ksp.setOperators(A0, P0)
nested_IS = P0.getNestISs()
ksp.setType("minres")
pc = ksp.getPC()
pc.setType("fieldsplit")
pc.setFieldSplitIS(["u", nested_IS[0][0]], ["p", nested_IS[1][1]])
ksp_u, ksp_p = pc.getFieldSplitSubKSP()
ksp_u.setType("preonly")
ksp_u.getPC().setType('lu')
ksp_u.getPC().setFactorSolverType('mumps')
ksp_p.setType("preonly")
def monitor(ksp, its, rnorm):
# print("Num it, rnorm:", its, rnorm)
pass
ksp.setTolerances(rtol=1.0e-8, max_it=50)
ksp.setMonitor(monitor)
ksp.setFromOptions()
x0 = b0.copy()
ksp.solve(b0, x0)
assert ksp.getConvergedReason() > 0
# -- Blocked and monolithic
A1 = dolfin.fem.assemble_matrix_block([[a00, a01], [a10, a11]], [bc0, bc1])
assert A1.norm() == pytest.approx(A0norm, 1.0e-12)
P1 = dolfin.fem.assemble_matrix_block([[p00, p01], [p10, p11]], [bc0, bc1])
assert P1.norm() == pytest.approx(P0norm, 1.0e-12)
b1 = dolfin.fem.assemble_vector_block([L0, L1], [[a00, a01], [a10, a11]],
[bc0, bc1])
assert b1.norm() == pytest.approx(b0norm, 1.0e-12)
ksp = PETSc.KSP()
ksp.create(mesh.mpi_comm())
ksp.setOperators(A1, P1)
ksp.setType("minres")
pc = ksp.getPC()
pc.setType('lu')
pc.setFactorSolverType('mumps')
ksp.setTolerances(rtol=1.0e-8, max_it=50)
ksp.setFromOptions()
x1 = A1.createVecRight()
ksp.solve(b1, x1)
assert ksp.getConvergedReason() > 0
assert x1.norm() == pytest.approx(x0.norm(), 1e-8)
# -- Monolithic
P2 = ufl.VectorElement("Lagrange", mesh.ufl_cell(), 2)
P1 = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), 1)
TH = P2 * P1
W = dolfin.FunctionSpace(mesh, TH)
(u, p) = dolfin.TrialFunctions(W)
(v, q) = dolfin.TestFunctions(W)
a00 = ufl.inner(ufl.grad(u), ufl.grad(v)) * dx
a01 = ufl.inner(p, ufl.div(v)) * dx
a10 = ufl.inner(ufl.div(u), q) * dx
a = a00 + a01 + a10
p00 = ufl.inner(ufl.grad(u), ufl.grad(v)) * dx
p11 = ufl.inner(p, q) * dx
p_form = p00 + p11
f = dolfin.Function(W.sub(0).collapse())
p_zero = dolfin.Function(W.sub(1).collapse())
L0 = inner(f, v) * dx
L1 = inner(p_zero, q) * dx
L = L0 + L1
bc0 = dolfin.DirichletBC(W.sub(0), u0, boundary0)
bc1 = dolfin.DirichletBC(W.sub(0), u0, boundary1)
A2 = dolfin.fem.assemble_matrix(a, [bc0, bc1])
A2.assemble()
assert A2.norm() == pytest.approx(A0norm, 1.0e-12)
P2 = dolfin.fem.assemble_matrix(p_form, [bc0, bc1])
P2.assemble()
assert P2.norm() == pytest.approx(P0norm, 1.0e-12)
b2 = dolfin.fem.assemble_vector(L)
dolfin.fem.apply_lifting(b2, [a], [[bc0, bc1]])
b2.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE)
dolfin.fem.set_bc(b2, [bc0, bc1])
b2norm = b2.norm()
assert b2norm == pytest.approx(b0norm, 1.0e-12)
ksp = PETSc.KSP()
ksp.create(mesh.mpi_comm())
ksp.setOperators(A2, P2)
ksp.setType("minres")
pc = ksp.getPC()
pc.setType('lu')
pc.setFactorSolverType('mumps')
def monitor(ksp, its, rnorm):
# print("Num it, rnorm:", its, rnorm)
pass
ksp.setTolerances(rtol=1.0e-8, max_it=50)
ksp.setMonitor(monitor)
ksp.setFromOptions()
x2 = A2.createVecRight()
ksp.solve(b2, x2)
assert ksp.getConvergedReason() > 0
assert x0.norm() == pytest.approx(x2.norm(), 1e-8)
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()
objects = Circle()
cell = mesh.ufl_cell() # ufl cell
V = df.FiniteElement("Lagrange", cell, 1) # CG elements of order 1
R = df.FiniteElement("Real", cell, 0) # Real elements of order 0
# Create the mixed function space
W = df.FunctionSpace(mesh, df.MixedElement([V, R]))
# Create DirichletBC of value 0 on the exterior boundaries
ext_bc = df.DirichletBC(W.sub(0), df.Constant(0), boundaries, ext_bnd_id)
int_bc = df.DirichletBC(W.sub(0), df.Constant(0), boundaries, int_bnd_id)
int_bc2 = FloatingBC(W.sub(0), boundaries, int_bnd_id)
# Create trial and test functions
u, c = df.TrialFunctions(W)
v, d = df.TestFunctions(W)
# The object charge
Q = df.Constant(10.)
# Charge density in the domain
# rho = df.Constant(0.)
rho = df.Expression("sin(x[0]-x[1])", degree=3)
# rho = df.Expression("(x[0]-pi)*(x[0]-pi)+(x[1]-pi)*(x[1]-pi)<=r*r ? 0.0 : sin(x[0]-x[1])", pi=np.pi, r=r, degree=2)
# The normal vector to the facets
n = df.FacetNormal(mesh)
# The measure on exterior boundaries
ds = df.Measure("ds", domain=mesh, subdomain_data=boundaries)
def __init__(self, Vh_STATE, Vhs, bcs0, datafile, dx=dl.dx):
self.dx = dx
x, y, U, V, uu, vv, ww, uv, k = np.loadtxt(datafile,
skiprows=2,
unpack=True)
u_fun_mean = VelocityDNS(x=x,
y=y,
U=U,
V=V,
symmetrize=True,
coflow=0.)
u_fun_data = VelocityDNS(x=x,
y=y,
U=U,
V=V,
symmetrize=False,
coflow=0.)
u_mean = dl.interpolate(u_fun_mean, Vhs[0])
u_data = dl.interpolate(u_fun_data, Vhs[0])
q_order = dl.parameters["form_compiler"]["quadrature_degree"]
dl.parameters["form_compiler"]["quadrature_degree"] = 6
noise_var_u = dl.assemble(
dl.inner(u_fun_data - u_mean, u_fun_data - u_mean) * self.dx,
form_compiler_parameters=dl.parameters["form_compiler"])
dl.parameters["form_compiler"]["quadrature_degree"] = q_order
noise_var_u = 1.e-3
mpi_comm = Vh_STATE.mesh().mpi_comm()
rank = dl.MPI.rank(mpi_comm)
if rank == 0:
print "Noise Variance = {0}".format(noise_var_u)
if Vh_STATE.num_sub_spaces() == 2:
u_trial, p_trial = dl.TrialFunctions(Vh_STATE)
u_test, p_test = dl.TestFunctions(Vh_STATE)
elif Vh_STATE.num_sub_spaces() == 3:
u_trial, p_trial, g_trial = dl.TrialFunctions(Vh_STATE)
u_test, p_test, g_test = dl.TestFunctions(Vh_STATE)
else:
raise InputError()
Wform = dl.Constant(1. / noise_var_u) * dl.inner(u_trial,
u_test) * self.dx
self.W = dl.assemble(Wform)
dummy = dl.Vector()
self.W.init_vector(dummy, 0)
[bc.zero(self.W) for bc in bcs0]
Wt = Transpose(self.W)
[bc.zero(Wt) for bc in bcs0]
self.W = Transpose(Wt)
xfun = dl.Function(Vh_STATE)
assigner = dl.FunctionAssigner(Vh_STATE, Vhs)
if Vh_STATE.num_sub_spaces() == 2:
assigner.assign(xfun, [u_data, dl.Function(Vhs[1])])
elif Vh_STATE.num_sub_spaces() == 3:
assigner.assign(
xfun,
[u_data, dl.Function(Vhs[1]),
dl.Function(Vhs[2])])
self.d = xfun.vector()
def test_get_domains_gmsh(plots=False):
""" Get subdomains and partition of the boundary from a .msh file. """
name = "test_domains"
local_dir = Path(__file__).parent
mesh_file = local_dir.joinpath(name + ".msh")
gmsh.model.add(name)
L_x, L_y = 2.0, 2.0
H = 1.0
vertices = [(0.0, 0.0), (0.0, L_y), (L_x, L_y), (L_x, 0.0)]
contour = geo.LineLoop([geo.Point(np.array(c)) for c in vertices], False)
surface = geo.PlaneSurface(contour)
inclusion_vertices = list()
for coord in [
(H / 2, -H / 2, 0.0),
(H / 2, H / 2, 0.0),
(-H / 2, H / 2, 0.0),
(-H / 2, -H / 2, 0.0),
]:
vertex = geo.translation(geo.Point((L_x / 2, L_y / 2)), coord)
inclusion_vertices.append(vertex)
inclusion = geo.PlaneSurface(geo.LineLoop(inclusion_vertices, False))
for s in [surface, inclusion]:
s.add_gmsh()
factory.synchronize()
(stiff_s, ) = geo.surface_bool_cut(surface, inclusion)
factory.synchronize()
(soft_s, ) = geo.surface_bool_cut(surface, stiff_s)
factory.synchronize()
domains = {
"stiff": geo.PhysicalGroup(stiff_s, 2),
"soft": geo.PhysicalGroup(soft_s, 2),
}
boundaries = {
"S": geo.PhysicalGroup(surface.ext_contour.sides[0], 1),
"W": geo.PhysicalGroup(surface.ext_contour.sides[1], 1),
"N": geo.PhysicalGroup(surface.ext_contour.sides[2], 1),
"E": geo.PhysicalGroup(surface.ext_contour.sides[3], 1),
}
for group in domains.values():
group.add_gmsh()
for group in boundaries.values():
group.add_gmsh()
charact_field = mesh_tools.MathEvalField("0.05")
mesh_tools.set_background_mesh(charact_field)
geo.set_gmsh_option("Mesh.SaveAll", 0)
model.mesh.generate(1)
model.mesh.generate(2)
gmsh.model.mesh.removeDuplicateNodes()
gmsh.write(str(mesh_file))
E_1, E_2, nu = 1, 3, 0.3
materials = {
domains["soft"].tag: mat.Material(E_1, nu, "cp"),
domains["stiff"].tag: mat.Material(E_1, nu, "cp"),
}
test_part = part.FenicsPart.part_from_file(mesh_file,
materials,
subdomains_import=True)
assert test_part.mat_area == approx(L_x * L_y)
elem_type = "CG"
degree = 2
V = fe.VectorFunctionSpace(test_part.mesh, elem_type, degree)
W = fe.FunctionSpace(
test_part.mesh,
fe.VectorElement(elem_type, test_part.mesh.ufl_cell(), degree, dim=3),
)
boundary_conditions = {
boundaries["N"].tag: fe.Expression(("x[0]-1", "1"), degree=1),
boundaries["S"].tag: fe.Expression(("x[0]-1", "-1"), degree=1),
boundaries["E"].tag: fe.Expression(("1", "x[1]-1"), degree=1),
boundaries["W"].tag: fe.Expression(("-1", "x[1]-1"), degree=1),
}
bcs = list()
for tag, val in boundary_conditions.items():
bcs.append(fe.DirichletBC(V, val, test_part.facet_regions, tag))
ds = fe.Measure("ds",
domain=test_part.mesh,
subdomain_data=test_part.facet_regions)
v = fe.TestFunctions(V)
u = fe.TrialFunctions(V)
F = (fe.inner(mat.sigma(test_part.elasticity_tensor, mat.epsilon(u)),
mat.epsilon(v)) * fe.dx)
a, L = fe.lhs(F), fe.rhs(F)
u_sol = fe.Function(V)
fe.solve(a == L, u_sol, bcs)
strain = fe.project(mat.epsilon(u_sol), W)
if plots:
import matplotlib.pyplot as plt
plt.figure()
plot = fe.plot(u_sol)
plt.colorbar(plot)
plt.figure()
plot = fe.plot(strain[0])
plt.colorbar(plot)
plt.figure()
plot = fe.plot(strain[1])
plt.colorbar(plot)
plt.figure()
plot = fe.plot(strain[2])
plt.colorbar(plot)
plt.show()
error = fe.errornorm(
strain,
fe.Expression(("1", "1", "0"), degree=0),
degree_rise=3,
mesh=test_part.mesh,
)
assert error == approx(0, abs=1e-12)
materials = {
domains["soft"].tag: mat.Material(E_1, nu, "cp"),
domains["stiff"].tag: mat.Material(E_2, nu, "cp"),
}
test_part = part.FenicsPart.part_from_file(mesh_file,
materials,
subdomains_import=True)
V = fe.VectorFunctionSpace(test_part.mesh, elem_type, degree)
W = fe.FunctionSpace(
test_part.mesh,
fe.VectorElement(elem_type, test_part.mesh.ufl_cell(), degree, dim=3),
)
bcs = list()
for tag, val in boundary_conditions.items():
bcs.append(fe.DirichletBC(V, val, test_part.facet_regions, tag))
v = fe.TestFunctions(V)
u = fe.TrialFunctions(V)
F = (fe.inner(mat.sigma(test_part.elasticity_tensor, mat.epsilon(u)),
mat.epsilon(v)) * fe.dx)
a, L = fe.lhs(F), fe.rhs(F)
u_sol = fe.Function(V)
fe.solve(a == L, u_sol, bcs)
strain = mat.epsilon(u_sol)
stress = mat.sigma(test_part.elasticity_tensor, strain)
energy = 0.5 * fe.assemble(
fe.inner(stress, strain) * fe.dx(test_part.mesh))
energy_abaqus = 12.8788939
assert energy == approx(energy_abaqus, rel=1e-3)
geo.reset()
