def get_N_form(self):
try:
return self.N_form
except AttributeError:
pass
# Set up magnetic field and equivalent electric current forms
H_r = -curl(self.E_i)/(self.k0*Z0)
H_i = curl(self.E_r)/(self.k0*Z0)
J_r = cross(self.n, H_r)
J_i = cross(self.n, H_i)
#------------------------------
# Set up form for far field potential N
theta_hat = self.theta_hat
phi_hat = self.phi_hat
phase = self.phase
N_r = J_r*dolfin.cos(phase) - J_i*dolfin.sin(phase)
N_i = J_r*dolfin.sin(phase) + J_i*dolfin.cos(phase)
# UFL does not seem to like vector valued functionals, so we split the
# final functional from into theta and phi components
self.N_form = dict(
r_theta=dot(theta_hat, N_r)*ds,
r_phi=dot(phi_hat, N_r)*ds,
i_theta=dot(theta_hat, N_i)*ds,
i_phi=dot(phi_hat, N_i)*ds)
return self.N_form
def _get_form(self):
n = self.function_space.cell().n
k0 = self.k0
E_i = self.E_i
E_r = self.E_r
mu_r = self._get_mur_function()
return (1/k0/Z0)*dolfin.dot(n, (dolfin.cross(E_r, -dolfin.curl(E_i)/mu_r) +
dolfin.cross(E_i, dolfin.curl(E_r)/mu_r)))*dolfin.ds
def _get_form(self):
n = self.function_space.cell().n
k0 = self.k0
E_i = self.E_i
E_r = self.E_r
mu_r = self._get_mur_function()
return (1 / k0 / Z0) * dolfin.dot(
n, (dolfin.cross(E_r, -dolfin.curl(E_i) / mu_r) +
dolfin.cross(E_i,
dolfin.curl(E_r) / mu_r))) * dolfin.ds
def rigid_motion_term(mesh, u, r):
position = dolfin.SpatialCoordinate(mesh)
RM = [
Constant((1, 0, 0)),
Constant((0, 1, 0)),
Constant((0, 0, 1)),
dolfin.cross(position, Constant((1, 0, 0))),
dolfin.cross(position, Constant((0, 1, 0))),
dolfin.cross(position, Constant((0, 0, 1))),
]
return sum(dolfin.dot(u, zi) * r[i] * dolfin.dx for i, zi in enumerate(RM))
def __init__(self, mesh):
self.mesh = mesh
self.S1 = df.FunctionSpace(mesh, 'CG', 1)
self.S3 = df.VectorFunctionSpace(mesh, 'CG', 1, dim=3)
#zero = df.Expression('0.3')
m_init = df.Constant([1, 1, 1.0])
self.m = df.interpolate(m_init, self.S3)
self.field = df.interpolate(m_init, self.S3)
self.dmdt = df.interpolate(m_init, self.S3)
self.spin = self.m.vector().array()
self.t = 0
#It seems it's not safe to specify rank in df.interpolate???
self._alpha = df.interpolate(df.Constant("0.001"), self.S1)
self._alpha.vector().set_local(self._alpha.vector().array())
print 'dolfin', self._alpha.vector().array()
self.llg = LLG(self.S1, self.S3, unit_length=1e-9)
#normalise doesn't work due to the wrong order
self.llg.set_m(m_init, normalise=True)
parameters = {
'absolute_tolerance': 1e-10,
'relative_tolerance': 1e-10,
'maximum_iterations': int(1e5)
}
demag = Demag()
demag.parameters['phi_1'] = parameters
demag.parameters['phi_2'] = parameters
self.exchange = Exchange(13e-12)
self.zeeman = Zeeman([0, 0, 1e5])
self.llg.effective_field.add(self.exchange)
#self.llg.effective_field.add(demag)
self.llg.effective_field.add(self.zeeman)
self.m_petsc = df.as_backend_type(self.llg.m_field.f.vector()).vec()
self.h_petsc = df.as_backend_type(self.field.vector()).vec()
self.alpha_petsc = df.as_backend_type(self._alpha.vector()).vec()
self.dmdt_petsc = df.as_backend_type(self.dmdt.vector()).vec()
alpha = 0.001
gamma = 2.21e5
LLG1 = -gamma / (1 + alpha * alpha) * df.cross(
self.m,
self.field) - alpha * gamma / (1 + alpha * alpha) * df.cross(
self.m, df.cross(self.m, self.field))
self.L = df.dot(LLG1, df.TestFunction(self.S3)) * df.dP
def skyrmion_number_density_function(self):
"""
This function returns the skyrmion number density function calculated
from the spin texture in this simulation instance. This function can be
probed to determine the local nature of the skyrmion number.
"""
integrand = -0.25 / np.pi * df.dot(
self.m_field.f,
df.cross(df.Dx(self.m_field.f, 0), df.Dx(self.m_field.f, 1)))
# Build the function space of the mesh nodes.
dofmap = self.S3.dofmap()
S1 = df.FunctionSpace(self.S3.mesh(),
"Lagrange",
1,
constrained_domain=dofmap.constrained_domain)
# Find the skyrmion density at the mesh nodes using the above function
# space.
nodalSkx = df.dot(integrand, df.TestFunction(S1)) * df.dx
nodalVolumeS1 = nodal_volume(S1, self.unit_length)
skDensity = df.assemble(nodalSkx).array() * self.unit_length\
** self.S3.mesh().topology().dim() / nodalVolumeS1
# Build the skyrmion number density dolfin function from the skDensity
# array.
skDensityFunc = df.Function(S1)
skDensityFunc.vector()[:] = skDensity
return skDensityFunc
def get_L_form(self):
try:
return self.L_form
except AttributeError:
pass
# Set up equivalent magnetic current forms
M_r = -cross(self.n, self.E_r)
M_i = -cross(self.n, self.E_i)
#------------------------------
# Set up form for far field potential L
theta_hat = self.theta_hat
phi_hat = self.phi_hat
phase = self.phase
L_r = M_r * dolfin.cos(phase) - M_i * dolfin.sin(phase)
L_i = M_r * dolfin.sin(phase) + M_i * dolfin.cos(phase)
self.L_form = dict(r_theta=dot(theta_hat, L_r) * ds,
r_phi=dot(phi_hat, L_r) * ds,
i_theta=dot(theta_hat, L_i) * ds,
i_phi=dot(phi_hat, L_i) * ds)
return self.L_form
def get_L_form(self):
try:
return self.L_form
except AttributeError:
pass
# Set up equivalent magnetic current forms
M_r = -cross(self.n, self.E_r)
M_i = -cross(self.n, self.E_i)
#------------------------------
# Set up form for far field potential L
theta_hat = self.theta_hat
phi_hat = self.phi_hat
phase = self.phase
L_r = M_r*dolfin.cos(phase) - M_i*dolfin.sin(phase)
L_i = M_r*dolfin.sin(phase) + M_i*dolfin.cos(phase)
self.L_form = dict(
r_theta=dot(theta_hat, L_r)*ds,
r_phi=dot(phi_hat, L_r)*ds,
i_theta=dot(theta_hat, L_i)*ds,
i_phi=dot(phi_hat, L_i)*ds)
return self.L_form
def skyrmion_number(self):
"""
This function returns the skyrmion number calculated from the spin
texture in this simulation instance.
If the sim object is 3D, the skyrmion number is calculated from the
magnetisation of the top surface (since the skyrmion number formula
is only defined for 2D).
Details about computing functionals over subsets of the mesh (the
method used in this function to calculate the skyrmion number) can be
found at:
https://bitbucket.org/fenics-project/dolfin/src/master/demo/undocumented/lift-drag/python/demo_lift-drag.py
"""
mesh = self.mesh
# Get m field and define skyrmion number integrand
m = self.m_field.f
integrand = -0.25 / np.pi * df.dot(m, df.cross(df.Dx(m, 0), df.Dx(m, 1)))
# determine if 3D system or not. Act accordingly.
if self.mesh.topology().dim() == 3:
mesh_coords = mesh.coordinates()
z_max = max(mesh_coords[:, 2])
# Define a subdomain which consists of the top surface of the geometry
class Top(df.SubDomain):
def inside(self, pt, on_boundary):
x, y, z = pt
return z >= z_max - df.DOLFIN_EPS and on_boundary
# Define a set of Markers from a MeshFunction (which is a function that
# can be evaluated at a set of mesh entities). Set all the marks to Zero.
markers = df.MeshFunction("size_t", mesh, mesh.topology().dim() - 1)
markers.set_all(0)
top = Top()
# Redefine the marks on the top surface to 1.
top.mark(markers, 1)
# Define ds so that it only computes where the marker=1 (top surface)
ds = df.ds[markers]
# Assemble the integrand on the the original domain, but only over the
# marked region (top surface).
S = df.assemble(form=integrand * ds(1, domain=mesh))
else:
S = df.assemble(integrand * df.dx)
return S
def cross(self, other):
"""
Return vector field representing the cross product of this field with `other`.
"""
if not isinstance(other, Field):
raise TypeError("Argument must be a Field. Got: {} ({})".format(
other, type(other)))
if not (self.value_dim() == 3 and other.value_dim() == 3):
raise ValueError(
"The cross product is only defined for 3d vector fields.")
# We use Claas Abert's 'point measure hack' for the vertex-wise cross product.
w = df.TestFunction(self.functionspace)
v_res = df.assemble(df.dot(df.cross(self.f, other.f), w) * df.dP)
return Field(self.functionspace, value=v_res)
def get_bilinear_form(self, test_function=None, trial_function=None):
"""Calculate and return the bilinear form associated with the boundary condition.
The form is calculated as: M{<n S{times} v, n S{times} u>}, where M{v} and M{u} are the
test and trial functions, respectively.
@keyword test_function: override the stored test function space.
(default: None.)
@keyword trial_function: override the stored trial function space.
(default: None.)
"""
V = self.function_space
if test_function is None:
v = dolfin.TestFunction(self.function_space)
else:
v = test_function
if trial_function is None:
u = dolfin.TrialFunction(self.function_space)
else:
u = trial_function
n = V.cell().n
s_0 = inner(cross(n, v), cross(n, u)) * ds
return s_0
def get_bilinear_form(self, test_function=None, trial_function=None):
"""Calculate and return the bilinear form associated with the boundary condition.
The form is calculated as: M{<n S{times} v, n S{times} u>}, where M{v} and M{u} are the
test and trial functions, respectively.
@keyword test_function: override the stored test function space.
(default: None.)
@keyword trial_function: override the stored trial function space.
(default: None.)
"""
V = self.function_space
if test_function is None:
v = dolfin.TestFunction(self.function_space)
else:
v = test_function
if trial_function is None:
u = dolfin.TrialFunction(self.function_space)
else:
u = trial_function
n = V.cell().n
s_0 = inner(cross(n, v), cross(n, u))*ds
return s_0
def get_N_form(self):
try:
return self.N_form
except AttributeError:
pass
# Set up magnetic field and equivalent electric current forms
H_r = -curl(self.E_i) / (self.k0 * Z0)
H_i = curl(self.E_r) / (self.k0 * Z0)
J_r = cross(self.n, H_r)
J_i = cross(self.n, H_i)
#------------------------------
# Set up form for far field potential N
theta_hat = self.theta_hat
phi_hat = self.phi_hat
phase = self.phase
N_r = J_r * dolfin.cos(phase) - J_i * dolfin.sin(phase)
N_i = J_r * dolfin.sin(phase) + J_i * dolfin.cos(phase)
# UFL does not seem to like vector valued functionals, so we split the
# final functional from into theta and phi components
self.N_form = dict(r_theta=dot(theta_hat, N_r) * ds,
r_phi=dot(phi_hat, N_r) * ds,
i_theta=dot(theta_hat, N_i) * ds,
i_phi=dot(phi_hat, N_i) * ds)
return self.N_form
beta_endo_lv=0,
beta_epi_lv=0,
)
circ, _, _ = ldrb.dolfin_ldrb(
mesh=mesh,
fiber_space=space,
ffun=ffun,
markers=markers,
alpha_endo_lv=0,
alpha_epi_lv=0,
beta_endo_lv=0,
beta_epi_lv=0,
)
rad = dolfin.project(dolfin.cross(circ, long), circ.function_space())
#
# For the single ventricle it is much simpler
#
else:
long, circ, rad = ldrb.dolfin_ldrb(
mesh=mesh,
fiber_space=space,
ffun=ffun,
markers=markers,
alpha_endo_lv=-90,
alpha_epi_lv=-90,
beta_endo_lv=0,
for i in subIds:
dA[i] = dlfn.Measure("ds",
subMeshes[i],
subdomain_data=facetSubMeshFuns[i])
dV[i] = dlfn.Measure("dx", subMeshes[i])
normals[i] = dlfn.FacetNormal(subMeshes[i])
#------------------------------------------------------------------------------#
# weak forms and assembly in interior
#------------------------------------------------------------------------------#
# linear forms in interior domain
from dolfin import curl, inner, dot, cross, grad
jumpM = dlfn.Constant((0.0, 0.0, -1.0))
a_int = inner(curl(A), curl(B)) * dV[intId]
b_int = dot(A, grad(v)) * dV[intId]
c_int = u * dot(normals[intId], curl(B)) * dA[intId](intrfcId)
l_int = dot(cross(normals[intId], jumpM), B) * dA[intId](intrfcId)
# assemble (0,0)-block
matA = dlfn.assemble(a_int)
rhs_int = dlfn.assemble(l_int)
# assemble (0,1)-block
matB = dlfn.assemble(b_int)
# assemble (0,2)-block
matC = dlfn.assemble(c_int)
#------------------------------------------------------------------------------#
# weak forms and assembly in exterior
#------------------------------------------------------------------------------#
# linear forms in exterior domain
a_ext = inner(grad(phi), grad(psi)) * dV[extId]
l_ext = dlfn.Constant(0.) * psi * dV[extId]
# apply boundary condition
bc = dlfn.DirichletBC(extH1, dlfn.Constant(0.), facetSubMeshFuns[extId],
A, phi = dlfn.TrialFunctions(Vh)
# test functions
delA, psi = dlfn.TestFunctions(Vh)
# geometric objects
n = dlfn.FacetNormal(mesh)
P = dlfn.Identity(dim) - dlfn.outer(n, n)
# Dirichlet boundary condition on exterior surface
bcA = dlfn.DirichletBC(Vh.sub(0), dlfn.Constant((0.0, 0.0, 0.0)), facetIds,
bndryId)
bcPhi = dlfn.DirichletBC(Vh.sub(1), dlfn.Constant(0.0), facetIds, bndryId)
# bilinear form
a = dlfn.dot(dlfn.curl(A), dlfn.curl(delA)) * dV() \
+ dlfn.dot(A, dlfn.grad(psi)) * dV() \
+ dlfn.dot(dlfn.grad(phi), delA) * dV()
# rhs form
l = dlfn.dot(dlfn.cross(n("+"), jumpM("+")), delA("+")) * dA(intrfcId)
# compute solution
sol = dlfn.Function(Vh)
lin_problem = dlfn.LinearVariationalProblem(a, l, sol, bcs=[bcA, bcPhi])
lin_solver = dlfn.LinearVariationalSolver(lin_problem)
lin_solver_parameters = lin_solver.parameters
lin_solver.solve()
# sub solutions
solA = sol.sub(0)
solPhi = sol.sub(1)
# output to pvd
pvd_A = dlfn.File("solution-A.pvd")
pvd_A << solA
# solving for magnetic field
Wh = dlfn.VectorFunctionSpace(mesh, "CG", 1)
curlA = dlfn.project(dlfn.curl(solA), Wh)
# Define function space
order = 1
V = dol.FunctionSpace(mesh, "Nedelec 1st kind H(curl)", order)
# Define basis and bilinear form
k_0 = 2*N.pi*freq/c0
u = dol.TrialFunction(V)
v = dol.TestFunction(V)
m = eps_r*inner(v, u)*dx # Mass form
s = (1/mu_r)*dot(curl(v), curl(u))*dx # Stiffness form
n = V.cell().n
s_0 = inner ( cross ( n, v), cross ( n, u ) )*ds
def boundary(x, on_boundary):
return on_boundary
# Assemble forms
M = dol.uBLASSparseMatrix()
S = dol.uBLASSparseMatrix()
S_0 = dol.uBLASSparseMatrix()
dol.assemble(m, tensor=M, mesh=mesh)
dol.assemble(s, tensor=S, mesh=mesh)
dol.assemble(s_0, tensor=S_0, mesh=mesh)
from scipy.sparse import csr_matrix
from pyamg import smoothed_aggregation_solver
def main():
# Define mesh
domain_subdivisions = N.array(
N.ceil(N.sqrt(2) * domain_size / max_edge_len), N.uint)
print 'Numer of domain subdomain_subdivisions: ', domain_subdivisions
mesh = dolfin.UnitCube(*domain_subdivisions)
# Transform mesh to correct dimensions
mesh.coordinates()[:] *= domain_size
# Centred around [0,0,0]
mesh.coordinates()[:] -= domain_size / 2
## Translate mesh slightly so that source coordinate lies at
## centroid of an element
io = mesh.intersection_operator()
source_elnos = io.all_intersected_entities(source_point)
closest_elno = source_elnos[(N.argmin([
source_point.distance(dolfin.Cell(mesh, i).midpoint())
for i in source_elnos
]))]
centre_pt = dolfin.Cell(mesh, closest_elno).midpoint()
centre_coord = N.array([centre_pt.x(), centre_pt.y(), centre_pt.z()])
# There seems to be an issue with the intersect operator if the
# mesh coordinates are changed after calling it for the first
# time. Since we called it to find the centroid, we should init a
# new mesh
mesh_coords = mesh.coordinates().copy()
mesh = dolfin.UnitCube(*domain_subdivisions)
mesh.coordinates()[:] = mesh_coords
mesh.coordinates()[:] -= centre_coord
##
# Define function space
V = dolfin.FunctionSpace(mesh, "Nedelec 1st kind H(curl)", order)
k_0 = 2 * N.pi * freq / c0 # Freespace wave number
# Definite test- and trial functions
u = dolfin.TrialFunction(V)
v = dolfin.TestFunction(V)
# Define the bilinear forms
m = eps_r * inner(v, u) * dx # Mass form
s = (1 / mu_r) * dot(curl(v), curl(u)) * dx # Stiffness form
n = V.cell().n # Get the surface normal
s_0 = inner(cross(n, v), cross(n, u)) * ds # ABC boundary condition form
# Assemble forms using uBLASS matrices so that we can easily export to scipy
print 'Assembling forms'
M = dolfin.uBLASSparseMatrix()
S = dolfin.uBLASSparseMatrix()
S_0 = dolfin.uBLASSparseMatrix()
dolfin.assemble(m, tensor=M, mesh=mesh)
dolfin.assemble(s, tensor=S, mesh=mesh)
dolfin.assemble(s_0, tensor=S_0, mesh=mesh)
print 'Number of degrees of freedom: ', M.size(0)
# Set up RHS
b = N.zeros(M.size(0), dtype=N.complex128)
dofnos, rhs_contrib = calc_pointsource_contrib(V, source_coord,
source_value)
rhs_contrib = 1j * k_0 * Z0 * rhs_contrib
b[dofnos] += rhs_contrib
Msp = dolfin_ublassparse_to_scipy_csr(M)
Ssp = dolfin_ublassparse_to_scipy_csr(S)
S_0sp = dolfin_ublassparse_to_scipy_csr(S_0)
# A is the system matrix that must be solved
A = Ssp - k_0**2 * Msp + 1j * k_0 * S_0sp
solved = False
if solver == 'iterative':
# solve using scipy bicgstab
print 'solve using scipy bicgstab'
x = solve_sparse_system(A, b)
elif solver == 'direct':
import scipy.sparse.linalg
A_lu = scipy.sparse.linalg.factorized(A.T)
x = A_lu(b)
else:
raise ValueError("solver must have value 'iterative' or 'direct'")
dolfin.set_log_active(False) # evaluation seems to make a lot of noise
u_re = dolfin.Function(V)
u_im = dolfin.Function(V)
# N.require is important, since dolfin seems to expect a contiguous array
u_re.vector()[:] = N.require(N.real(x), requirements='C')
u_im.vector()[:] = N.require(N.imag(x), requirements='C')
E_field = N.zeros((len(field_pts), 3), dtype=N.complex128)
for i, fp in enumerate(field_pts):
try:
E_field[i, :] = u_re(fp) + 1j * u_im(fp)
except (RuntimeError, StandardError):
E_field[i, :] = N.nan + 1j * N.nan
import pylab as P
r1 = field_pts[:] / lam
x1 = r1[:, 0]
E_ana = N.abs(analytical_result)
E_num = E_field
P.figure()
P.plot(x1, N.abs(E_num[:, 0]), '-g', label='x_num')
P.plot(x1, N.abs(E_num[:, 1]), '-b', label='y_num')
P.plot(x1, N.abs(E_num[:, 2]), '-r', label='z_num')
P.plot(analytical_pts, E_ana, '--r', label='z_ana')
P.ylabel('E-field Magnitude')
P.xlabel('Distance (wavelengths)')
P.legend(loc='best')
P.grid(True)
P.show()
def as_real_imag(self, deep=True, **hints):
u_re, u_im = self.args[0].as_real_imag()
v_re, v_im = self.args[1].as_real_imag()
return (cross(u_re, v_re) - cross(u_im, v_im),
cross(u_re, v_im) + cross(v_re, u_im))
def _setup_implicit_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 implicit problem..."
#=======================================================================
# retrieve imex coefficients
a, c = self._imex_alpha, self._imex_gamma
#=======================================================================
# trial and test function
(del_v, del_p, del_T) = dlfn.TestFunctions(self._Wh)
v, p, T = dlfn.split(self._sol)
# volume element
dV = dlfn.Measure("dx", domain=self._mesh)
# reference to time step
timestep = self._timestep
#=======================================================================
from dolfin import dot, grad
# 1) momentum equation
momentum_eqn = (
dot((a[0] * v + a[1] * self._v0 + a[2] * self._v00) / timestep,
del_v) + dot(dot(grad(v), v), del_v) + self._coefficients[1] *
a_op(c[0] * v + c[1] * self._v0 + c[2] * self._v00, del_v) -
b_op(del_v, p) - b_op(v, del_p)) * dV
# 2) momentum equation: coriolis term
if self._parameters.rotation is True:
assert self._coefficients[0] != 0.0
print " adding rotation to the model..."
# add Coriolis term
if self._space_dim == 2:
momentum_eqn += self._coefficients[0] * (-v[1] * del_v[0] +
v[0] * del_v[1]) * dV
elif self._space_dim == 3:
assert hasattr(self, "_rotation_vector")
assert isinstance(self._rotation_vector,
(dlfn.Constant, dlfn.Expression))
from dolfin import cross
momentum_eqn += self._coefficients[0] * dot(
cross(self._rotation_vector, v), del_v) * dV
# 3) momentum equation: coriolis term
if self._parameters.buoyancy is True:
assert self._coefficients[2] != 0.0
assert hasattr(self, "_gravity") and isinstance(
self._gravity, (dlfn.Expression, dlfn.Constant))
print " adding buoyancy to the model..."
# add buoyancy term
momentum_eqn += self._coefficients[2] * T * dot(
self._gravity, del_v) * dV
#=======================================================================
# 4) energy equation
energy_eqn = (
(a[0] * T + a[1] * self._T0 + a[2] * self._T00) / timestep * del_T
+ dot(v, grad(T)) * del_T + self._coefficients[3] *
a_op(c[0] * T + c[1] * self._T0 + c[2] * self._T00, del_T)) * dV
#=======================================================================
# full problem
self._eqn = momentum_eqn + energy_eqn
if not hasattr(self, "_dirichlet_bcs"):
self._setup_boundary_conditons()
self._jacobian = dlfn.derivative(self._eqn, self._sol)
problem = dlfn.NonlinearVariationalProblem(self._eqn,
self._sol,
J=self._jacobian,
bcs=self._dirichlet_bcs)
self._solver = dlfn.NonlinearVariationalSolver(problem)
prm = self._solver.parameters
prm["newton_solver"]["absolute_tolerance"] = 1e-6
prm["newton_solver"]["relative_tolerance"] = 1e-9
prm["newton_solver"]["maximum_iterations"] = 10
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)
def main():
# Define mesh
domain_subdivisions = N.array(N.ceil(N.sqrt(2)*domain_size/max_edge_len), N.uint)
print 'Numer of domain subdomain_subdivisions: ', domain_subdivisions
mesh = dolfin.UnitCube(*domain_subdivisions)
# Transform mesh to correct dimensions
mesh.coordinates()[:] *= domain_size
# Centred around [0,0,0]
mesh.coordinates()[:] -= domain_size/2
## Translate mesh slightly so that source coordinate lies at
## centroid of an element
source_elnos = mesh.all_intersected_entities(source_point)
closest_elno = source_elnos[(N.argmin([source_point.distance(dolfin.Cell(mesh, i).midpoint())
for i in source_elnos]))]
centre_pt = dolfin.Cell(mesh, closest_elno).midpoint()
centre_coord = N.array([centre_pt.x(), centre_pt.y(), centre_pt.z()])
# There seems to be an issue with the intersect operator if the
# mesh coordinates are changed after calling it for the first
# time. Since we called it to find the centroid, we should init a
# new mesh
mesh_coords = mesh.coordinates().copy()
mesh = dolfin.UnitCube(*domain_subdivisions)
mesh.coordinates()[:] = mesh_coords
mesh.coordinates()[:] -= centre_coord
##
# Define function space
V = dolfin.FunctionSpace(mesh, "Nedelec 1st kind H(curl)", order)
k_0 = 2*N.pi*freq/c0 # Freespace wave number
# Definite test- and trial functions
u = dolfin.TrialFunction(V)
v = dolfin.TestFunction(V)
# Define the bilinear forms
m = eps_r*inner(v, u)*dx # Mass form
s = (1/mu_r)*dot(curl(v), curl(u))*dx # Stiffness form
n = V.cell().n # Get the surface normal
s_0 = inner(cross(n, v), cross(n, u))*ds # ABC boundary condition form
# Assemble forms using uBLASS matrices so that we can easily export to scipy
print 'Assembling forms'
M = dolfin.uBLASSparseMatrix()
S = dolfin.uBLASSparseMatrix()
S_0 = dolfin.uBLASSparseMatrix()
dolfin.assemble(m, tensor=M, mesh=mesh)
dolfin.assemble(s, tensor=S, mesh=mesh)
dolfin.assemble(s_0, tensor=S_0, mesh=mesh)
print 'Number of degrees of freedom: ', M.size(0)
# Set up RHS
b = N.zeros(M.size(0), dtype=N.complex128)
dofnos, rhs_contrib = calc_pointsource_contrib(V, source_coord, source_value)
rhs_contrib = 1j*k_0*Z0*rhs_contrib
b[dofnos] += rhs_contrib
Msp = dolfin_ublassparse_to_scipy_csr(M)
Ssp = dolfin_ublassparse_to_scipy_csr(S)
S_0sp = dolfin_ublassparse_to_scipy_csr(S_0)
# A is the system matrix that must be solved
A = Ssp - k_0**2*Msp + 1j*k_0*S_0sp
solved = False;
if solver == 'iterative':
# solve using scipy bicgstab
print 'solve using scipy bicgstab'
x = solve_sparse_system ( A, b )
elif solver == 'direct':
import scipy.sparse.linalg
A_lu = scipy.sparse.linalg.factorized(A.T)
x = A_lu(b)
else: raise ValueError("solver must have value 'iterative' or 'direct'")
dolfin.set_log_active(False) # evaluation seems to make a lot of noise
u_re = dolfin.Function(V)
u_im = dolfin.Function(V)
# N.require is important, since dolfin seems to expect a contiguous array
u_re.vector()[:] = N.require(N.real(x), requirements='C')
u_im.vector()[:] = N.require(N.imag(x), requirements='C')
E_field = N.zeros((len(field_pts), 3), dtype=N.complex128)
for i, fp in enumerate(field_pts):
try: E_field[i,:] = u_re(fp) + 1j*u_im(fp)
except (RuntimeError, StandardError): E_field[i,:] = N.nan + 1j*N.nan
import pylab as P
r1 = field_pts[:]/lam
x1 = r1[:,0]
E_ana = N.abs(analytical_result)
E_num = E_field
P.figure()
P.plot(x1, N.abs(E_num[:,0]), '-g', label='x_num')
P.plot(x1, N.abs(E_num[:,1]), '-b', label='y_num')
P.plot(x1, N.abs(E_num[:,2]), '-r', label='z_num')
P.plot(analytical_pts, E_ana, '--r', label='z_ana')
P.ylabel('E-field Magnitude')
P.xlabel('Distance (wavelengths)')
P.legend(loc='best')
P.grid(True)
P.show()
def cross_product(a, b, S3):
"""
Function computing the cross product of two vector functions.
The result is a dolfin vector.
"""
return df.assemble(df.dot(df.cross(a, b), df.TestFunction(S3)) * df.dP)
def generate_matrices(mesh_file, order):
"""
Generate the matrices for a 1\lambda spherical domain with an infintesimal dipole at the origin.
@param mesh_file: the full path and filename to the spherical mesh to be used (this is a femmesh file).
@todo: change the mesh type to a gmsh mesh.
"""
problem_data = {
'l': 2.9979245800000002e-4,
'I': 1.0,
'f': 1.0e9,
'source_coord': np.array([0, 0, 0.]),
'order': order,
}
lam = c0 / problem_data['f']
# load the mesh and scale coordinates accordingly
mesh = femmesh_2_dolfin_mesh(mesh_file)
mesh.init()
mesh.coordinates()[:] *= lam
eps_r = 1
mu_r = 1
freq = problem_data['f']
source_coord = problem_data['source_coord']
source_point = dolfin.Point(*source_coord)
source_value = np.array([0, 0, 1.]) * problem_data['I'] * problem_data['l']
V = dolfin.FunctionSpace(mesh, "Nedelec 1st kind H(curl)",
problem_data['order'])
print 'DOFs: ', V.dim()
# Define basis and bilinear form
k_0 = 2 * np.pi * freq / c0
u = dolfin.TrialFunction(V)
v = dolfin.TestFunction(V)
m = eps_r * inner(v, u) * dx # Mass form
s = (1 / mu_r) * dot(curl(v), curl(u)) * dx # Stiffness form
n = V.cell().n
s_0 = inner(cross(n, v), cross(n, u)) * ds
def boundary(x, on_boundary):
return on_boundary
# Assemble forms
print 'assembling forms'
M = dolfin.uBLASSparseMatrix()
S = dolfin.uBLASSparseMatrix()
S_0 = dolfin.uBLASSparseMatrix()
dolfin.assemble(m, tensor=M, mesh=mesh)
dolfin.assemble(s, tensor=S, mesh=mesh)
dolfin.assemble(s_0, tensor=S_0, mesh=mesh)
# Set up RHS
b = np.zeros(M.size(0), dtype=np.complex128)
dofnos, rhs_contrib = point_source.calc_pointsource_contrib(
V, source_coord, source_value)
rhs_contrib = 1j * k_0 * Z0 * rhs_contrib
b[dofnos] += rhs_contrib
Msp = dolfin_ublassparse_to_scipy_csr(M)
Ssp = dolfin_ublassparse_to_scipy_csr(S)
S_0sp = dolfin_ublassparse_to_scipy_csr(S_0)
A = Ssp - k_0**2 * Msp + 1j * k_0 * S_0sp
return A, b
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 setup_kinematics(self):
from dolfin import cross, sqrt, dot
if self.nsd == 2:
# Get the dual basis
self.Gsup1 = self.Gsub1 / dot(self.Gsub1, self.Gsub1)
R = as_tensor([[0, -1], [1, 0]])
self.gsub1 = self.Gsub1 + self.u.dx(0)
gradu = outer(self.u.dx(0), self.Gsup1)
I = Identity(self.nsd)
self.F = I + gradu
self.C = dot(self.F.T, self.F)
self.Gsub3 = dot(R, self.Gsub1)
self.gsub3 = dot(R, self.gsub1)
lmbda = sqrt(
dot(self.gsub1, self.gsub1) / dot(self.Gsub1, self.Gsub1))
self.lambda1, self.lambda2 = lmbda, lmbda
self.lambda3 = 1 / self.lambda1
elif self.nsd == 3:
from dolfin import cross, sqrt, dot
from fenicsmembranes.calculus_utils import contravariant_base_vector
# Get the contravariant tangent basis
self.Gsup1 = contravariant_base_vector(self.Gsub1, self.Gsub2)
self.Gsup2 = contravariant_base_vector(self.Gsub2, self.Gsub1)
# Reference normal
self.Gsub3 = cross(self.Gsub1, self.Gsub2)
self.Gsup3 = self.Gsub3 / dot(self.Gsub3, self.Gsub3)
# Construct the covariant convective basis
self.gsub1 = self.Gsub1 + self.u.dx(0)
self.gsub2 = self.Gsub2 + self.u.dx(1)
# Construct the contravariant convective basis
self.gsup1 = contravariant_base_vector(self.gsub1, self.gsub2)
self.gsup2 = contravariant_base_vector(self.gsub2, self.gsub1)
# Deformed normal
self.gsub3 = cross(self.gsub1, self.gsub2)
self.gsup3 = self.gsub3 / dot(self.gsub3, self.gsub3)
# Deformation gradient
gradu = outer(self.u.dx(0), self.Gsup1) + outer(
self.u.dx(1), self.Gsup2)
I = Identity(self.nsd)
self.F = I + gradu
self.C = self.F.T * self.F # from initial to current
# 3x2 deformation tensors
# TODO: check/test
self.F_0 = as_tensor([self.Gsub1, self.Gsub2]).T
self.F_n = as_tensor([self.gsub1, self.gsub2]).T
# 2x2 surface metrics
self.C_0 = self.get_metric(self.Gsub1, self.Gsub2)
self.C_0_sup = self.get_metric(self.Gsup1, self.Gsup2)
self.C_n = self.get_metric(self.gsub1, self.gsub2)
# TODO: not tested. do we need these?
self.det_C_0 = dot(self.Gsub1, self.Gsub1) * dot(
self.Gsub2, self.Gsub2) - dot(self.Gsub1, self.Gsub2)**2
self.det_C_n = dot(self.gsub1, self.gsub1) * dot(
self.gsub2, self.gsub2) - dot(self.gsub1, self.gsub2)**2
self.lambda1, self.lambda2, self.lambda3 = self.get_lambdas()
self.I1 = inner(inv(self.C_0), self.C_n)
self.I2 = det(self.C_n) / det(self.C_0)
else:
raise Exception("Could not infer spatial dimension")
# Unit normals
self.J_A = sqrt(dot(self.Gsub3, self.Gsub3))
self.N = self.Gsub3 / self.J_A
self.j_a = sqrt(dot(self.gsub3, self.gsub3))
self.n = self.gsub3 / self.j_a
def calcs(fname):
data = pickle.load(open(fname+'.pickle'))
mesh = dolfin.Mesh(data['meshfile'])
elen = np.array([e.length() for e in dolfin.edges(mesh)])
ave_elen = np.average(elen)
material_meshfn = dolfin.MeshFunction('uint', mesh, data['materialsfile'])
V = dolfin.FunctionSpace(mesh, "Nedelec 1st kind H(curl)", data['order'])
x = data['x']
x_r = as_dolfin_vector(x.real)
x_i = as_dolfin_vector(x.imag)
E_r = dolfin.Function(V, x_r)
E_i = dolfin.Function(V, x_i)
k0 = 2*np.pi*data['freq']/c0
n = V.cell().n
ReS = (1/k0/Z0)*dolfin.dot(n, (dolfin.cross(E_r, -dolfin.curl(E_i)) +
dolfin.cross(E_i, dolfin.curl(E_r))))*dolfin.ds
energy_flux = dolfin.assemble(ReS)
surface_flux = SurfaceFlux(V)
surface_flux.set_dofs(x)
surface_flux.set_k0(k0)
energy_flux2 = surface_flux.calc_flux()
assert(np.allclose(energy_flux, energy_flux2, rtol=1e-8, atol=1e-8))
def boundary(x, on_boundary):
return on_boundary
E_r_dirich = dolfin.DirichletBC(V, E_r, boundary)
x_r_dirich = as_dolfin_vector(np.zeros(len(x)))
E_r_dirich.apply(x_r_dirich)
E_i_dirich = dolfin.DirichletBC(V, E_i, boundary)
x_i_dirich = as_dolfin_vector(np.zeros(len(x)))
E_i_dirich.apply(x_i_dirich)
x_dirich = x_r_dirich.array() + 1j*x_i_dirich.array()
emfunc = CalcEMFunctional(V)
emfunc.set_k0(k0)
cell_domains = dolfin.CellFunction('uint', mesh)
cell_domains.set_all(0)
cell_region = 1
boundary_cells = Geometry.BoundaryEdgeCells(mesh)
boundary_cells.mark(cell_domains, cell_region)
emfunc.set_cell_domains(cell_domains, cell_region)
emfunc.set_E_dofs(x)
emfunc.set_g_dofs(1j*x_dirich.conjugate()/k0/Z0)
var_energy_flux = emfunc.calc_functional().conjugate()
var_surf_flux = VariationalSurfaceFlux(V)
var_surf_flux.set_dofs(x)
var_surf_flux.set_k0(k0)
var_energy_flux2 = var_surf_flux.calc_flux()
assert(np.allclose(var_energy_flux, var_energy_flux2, rtol=1e-8, atol=1e-8))
complex_voltage = ComplexVoltageAlongLine(V)
complex_voltage.set_dofs(x)
volts = complex_voltage.calculate_voltage(*data['source_endpoints'])
result = dict(h=ave_elen, order=order, volts=volts,
sflux=energy_flux, vflux=var_energy_flux)
print 'source power: ', volts*data['I']
print 'energy flux: ', energy_flux
print 'var energy flux: ', var_energy_flux
# print '|'.join(str(s) for s in ('', volts*data['I'], energy_flux,
# var_energy_flux, ''))
return result
def s_number(m, dx):
s_density = df.dot(m, df.cross(df.Dx(m, 0), df.Dx(m, 1)))
return 1/(4*np.pi) * df.assemble(s_density*dx)
+ time_step * c[0] * equation_coefficients[1] * a_operator(dv, del_v) \
- time_step * b_operator(del_v, dp) \
- time_step * b_operator(dv, del_p)
rhs_momentum = a[1] * dot(v0, del_v) * dV \
- a[2] * dot(v00, del_v) * dV \
- time_step * b[1] * c_operator(v0, v0, del_v) \
+ time_step * b[2] * c_operator(v00, v00, del_v) \
+ time_step * equation_coefficients[2] * extrapolated_temperature * dot(gravity_vector, del_v) * dV
if rotation == True:
print "\nAdding rotation to the model...\n"
if space_dim == 2:
rhs_momentum += - time_step * equation_coefficients[0] * dot(cross_2D(extrapolated_velocity), del_v) * dV
elif space_dim == 3:
rotation_vector = dlfn.Constant((0., 0., 1.))
rhs_momentum += - time_step * equation_coefficients[0] * dot(
dlfn.cross(rotation_vector, extrapolated_velocity),
del_v) * dV
# energy equation
lhs_energy = a[0] * dot(dT, del_T) * dV \
+ time_step * equation_coefficients[3] * a_operator(dT, del_T)
rhs_energy = a[1] * dot(T0, del_T) * dV \
- a[2] * dot(T00, del_T) * dV \
- time_step * b[1] * c_operator(v0, T0, del_T) \
+ time_step * b[2] * c_operator(v00, T00, del_T)
lhs = lhs_momentum + lhs_energy
#===============================================================================
# full problem
lhs = lhs_momentum + lhs_energy
rhs = rhs_momentum + rhs_energy
if not use_assembler_method:
# linear problem
This should be replaced in the parts of the code where dmdt is computed.
"""
import dolfin as df
import time
mesh = df.IntervalMesh(1000, 0, 1)
S3 = df.VectorFunctionSpace(mesh, 'CG', 1, 3)
a = df.Function(S3)
b = df.Function(S3)
a.assign(df.Constant((1, 0, 0))) # unit x vector
b.assign(df.Constant((0, 1, 0))) # unit y vector
alpha = 01
gamma = 2.11e5
m = df.Function(S3)
m.assign(df.Constant((0, 0, 1)))
Heff = df.Function(S3)
Heff.assign(df.Constant((0, 0.3, 0.2)))
dmdt = df.Function(S3)
start = time.time()
for i in range(1000):
L = df.dot(
-gamma / (1 + alpha * alpha) * df.cross(m, Heff) - alpha * gamma /
(1 + alpha * alpha) * df.cross(m, df.cross(m, Heff)),
df.TestFunction(S3)) * df.dP
df.assemble(L, tensor=dmdt.vector())
stop = time.time()
print 'Time:', stop - start
# define dmdt, test and trial functions
dmdt = df.Function(V)
v = df.TrialFunction(V)
w = df.TestFunction(V)
# define the exchange field
# f_ex = df.Constant(2*A/(mu0*Ms))
# results for m_x at times t_array in
mx_simulation = np.zeros(t_array.shape)
for i, t in enumerate(t_array):
mx_simulation[i] = m((0, 0, 0))[0]
a = df.inner(df.Constant(alpha) * v + df.cross(m, v), w) * df.dx
L = df.inner(gamma * Heff, w) * df.dx
df.solve(a == L, dmdt)
m += dmdt * df.Constant(dt)
m = df.project(m / df.sqrt(df.dot(m, m)), V)
###################
# Analytic solution
###################
mx_analytic = macrospin_analytic_solution(alpha, gamma, H, t_array)
###################
# Plot comparison.
###################
plt.figure(figsize=(8, 5))
V = df.VectorFunctionSpace(mesh, "CG", 1, dim=3)
u = df.TrialFunction(V)
v = df.TestFunction(V)
f = df.Function(V)
g = df.Function(V)
fxg = df.Function(V)
f.assign(df.Constant((1, 0, 0)))
g.assign(df.Constant((0, 1, 0)))
# FINITE ELEMENT METHOD
# but dP instead of dx (both work)
a = df.dot(u, v) * df.dP
L = df.dot(df.cross(f, g), v) * df.dP
u = df.Function(V)
start = time.time()
for _ in xrange(1000):
df.solve(a == L, u)
stop = time.time()
print "fem, delta = {} s.".format(stop - start)
print u.vector().array()
# LINEAR ALGEBRA FORMULATION
A = df.assemble(a)
b = df.Function(V)
u = df.Function(V)