def cartesianCurl(f, F):
"""
The curl operator corresponding to ``cartesianGrad(f,F)``. For ``f`` of
rank 1, it returns a vector in 3D or a scalar in 2D. For ``f`` scalar
in 2D, it returns a vector.
"""
n = rank(f)
gradf = cartesianGrad(f, F)
if (n == 1):
m = shape(f)[0]
eps = PermutationSymbol(m)
if (m == 3):
(i, j, k) = indices(3)
return as_tensor(eps[i, j, k] * gradf[k, j], (i, ))
elif (m == 2):
(j, k) = indices(2)
return eps[j, k] * gradf[k, j]
else:
print("ERROR: Unsupported dimension of argument to curl.")
exit()
elif (n == 0):
return as_vector((-gradf[1], gradf[0]))
else:
print("ERROR: Unsupported rank of argument to curl.")
exit()
def modified_terminal(self, o):
mt = analyse_modified_terminal(o)
terminal = mt.terminal
if not isinstance(terminal, Coefficient):
# Only split coefficients
return o
if type(terminal.ufl_element()) != MixedElement:
# Only split mixed coefficients
return o
# Reference value expected
assert mt.reference_value
# Derivative indices
beta = indices(mt.local_derivatives)
components = []
for subcoeff in self._split[terminal]:
# Apply terminal modifiers onto the subcoefficient
component = construct_modified_terminal(mt, subcoeff)
# Collect components of the subcoefficient
for alpha in numpy.ndindex(subcoeff.ufl_element().reference_value_shape()):
# New modified terminal: component[alpha + beta]
components.append(component[alpha + beta])
# Repack derivative indices to shape
c, = indices(1)
return ComponentTensor(as_tensor(components)[c], MultiIndex((c,) + beta))
def c(u):
Fu = F(u)
A, B, C, D = ufl.indices(4)
i, j, k, l = ufl.indices(4)
return (1.0 / J(u)) * as_tensor(
Ctens[A, B, C, D] * Fu[i, A] * Fu[j, B] * Fu[k, C] * Fu[l, D],
(i, j, k, l))
def modified_terminal(self, o):
mt = analyse_modified_terminal(o)
terminal = mt.terminal
if not isinstance(terminal, Coefficient):
# Only split coefficients
return o
if type(terminal.ufl_element()) != MixedElement:
# Only split mixed coefficients
return o
# Reference value expected
assert mt.reference_value
# Derivative indices
beta = indices(mt.local_derivatives)
components = []
for subcoeff in self._split[terminal]:
# Apply terminal modifiers onto the subcoefficient
component = construct_modified_terminal(mt, subcoeff)
# Collect components of the subcoefficient
for alpha in numpy.ndindex(subcoeff.ufl_element().reference_value_shape()):
# New modified terminal: component[alpha + beta]
components.append(component[alpha + beta])
# Repack derivative indices to shape
c, = indices(1)
return ComponentTensor(as_tensor(components)[c], MultiIndex((c,) + beta))
def __init__(self, membrane, kwargs):
mem = membrane
# Define material - incompressible Neo-Hookean
self.nu = 0.5 # Poisson ratio, incompressible
E = kwargs.get("E", None)
if E is not None:
self.E = Constant(E, name="Elastic modulus")
mu = kwargs.get("mu", None)
if mu is not None:
self.mu = Constant(mu, name="Shear modulus")
else:
self.mu = Constant(self.E/(2*(1 + self.nu)), name="Shear modulus")
if kwargs['cylindrical']:
mem.I_C = tr(mem.C) + 1/det(mem.C) - 1
else:
C_n = mem.C_n #mem.F_n.T*mem.F_n
C_0 = mem.C_0 #mem.F_0.T*mem.F_0
C_0_sup = mem.C_0_sup
i,j = ufl.indices(2)
I1 = C_0_sup[i,j]*C_n[i,j]
mem.I_C = I1 + 1/det(mem.C)
self.psi = 0.5*self.mu*(mem.I_C - Constant(mem.nsd))
def count_flops(n):
mesh = Mesh(VectorElement('CG', interval, 1))
tfs = FunctionSpace(mesh, TensorElement('DG', interval, 1, shape=(n, n)))
vfs = FunctionSpace(mesh, VectorElement('DG', interval, 1, dim=n))
ensemble_f = Coefficient(vfs)
ensemble2_f = Coefficient(vfs)
phi = TestFunction(tfs)
i, j = indices(2)
nc = 42 # magic number
L = ((IndexSum(
IndexSum(
Product(nc * phi[i, j], Product(ensemble_f[i], ensemble_f[i])),
MultiIndex((i, ))), MultiIndex((j, ))) * dx) +
(IndexSum(
IndexSum(
Product(nc * phi[i, j], Product(
ensemble2_f[j], ensemble2_f[j])), MultiIndex(
(i, ))), MultiIndex((j, ))) * dx) -
(IndexSum(
IndexSum(
2 * nc *
Product(phi[i, j], Product(ensemble_f[i], ensemble2_f[j])),
MultiIndex((i, ))), MultiIndex((j, ))) * dx))
kernel, = compile_form(L, parameters=dict(mode='spectral'))
return EstimateFlops().visit(kernel.ast)
def LHS(q_b, C, ur, ui, vr, vi, DX):
# define the weak form with norm u v*
i, j = indices(2) # define to avoid name collisions
TT = C[i,j]*eps_T_Vgt(ur)[j]*eps_T_Vgt(vr)[i]*DX + \
C[i,j]*eps_T_Vgt(ui)[j]*eps_T_Vgt(vi)[i]*DX + \
C[i,j]*eps_T_Vgt(ui)[j]*eps_T_Vgt(vr)[i]*DX - \
C[i,j]*eps_T_Vgt(ur)[j]*eps_T_Vgt(vi)[i]*DX
TZ = q_b*(C[i,j]*eps_T_Vgt(ur)[j]*eps_Z_Vgt(vr)[i]*DX + \
C[i,j]*eps_T_Vgt(ui)[j]*eps_Z_Vgt(vi)[i]*DX - \
C[i,j]*eps_T_Vgt(ui)[j]*eps_Z_Vgt(vr)[i]*DX + \
C[i,j]*eps_T_Vgt(ur)[j]*eps_Z_Vgt(vi)[i]*DX)
ZT = q_b*(C[i,j]*eps_Z_Vgt(ur)[j]*eps_T_Vgt(vr)[i]*DX + \
C[i,j]*eps_Z_Vgt(ui)[j]*eps_T_Vgt(vi)[i]*DX - \
C[i,j]*eps_Z_Vgt(ui)[j]*eps_T_Vgt(vr)[i]*DX + \
C[i,j]*eps_Z_Vgt(ur)[j]*eps_T_Vgt(vi)[i]*DX)
ZZ = q_b*q_b*(C[i,j]*eps_Z_Vgt(ur)[j]*eps_Z_Vgt(vr)[i]*DX + \
C[i,j]*eps_Z_Vgt(ui)[j]*eps_Z_Vgt(vi)[i]*DX + \
C[i,j]*eps_Z_Vgt(ui)[j]*eps_Z_Vgt(vr)[i]*DX - \
C[i,j]*eps_Z_Vgt(ur)[j]*eps_Z_Vgt(vi)[i]*DX)
a = -TT + TZ + ZT - ZZ
return a
def test_dolfin_expression_compilation_of_dot_with_index_notation(dolfin):
# Define some PyDOLFIN coefficients
mesh = dolfin.UnitSquareMesh(3, 3)
# Using quadratic element deliberately for accuracy
V = dolfin.VectorFunctionSpace(mesh, "CG", 2)
u = dolfin.Function(V)
u.interpolate(dolfin.Expression(("x[0]", "x[1]")))
# Define ufl expression with the simplest possible index notation
uexpr = ufl.dot(u, u) # Needs expand_compounds, not testing here
uexpr = u[0]*u[0] + u[1]*u[1] # Works
i, = ufl.indices(1)
uexpr = u[i]*u[i] # Works
# Define expected output from compilation
# Oneliner version (no reuse in this expression):
xc, yc = ('v_w0[0]', 'v_w0[1]')
expected_lines = ['double s[1];',
'Array<double> v_w0(2);',
'w0->eval(v_w0, x);',
's[0] = pow(%(x)s, 2) + pow(%(y)s, 2);' % {'x':xc, 'y':yc},
'values[0] = s[0];']
# Define expected evaluation values: [(x,value), (x,value), ...]
x, y = (0.6, 0.7)
expected = (x*x+y*y)
expected_values = [((0.0, 0.0), (0.0,)),
((x, y), (expected,)),
]
# Execute all tests
check_dolfin_expression_compilation(uexpr, expected_lines, expected_values, members={'w0':u})
def grad(self, o, s):
# The representation
# o = Grad(s)
# is equivalent to
# o = as_tensor(s[ii].dx(i), ii+(i,))
# with ii a tuple of free indices and i a single free index.
# Make some unique utility indices
from ufl import indices
on = o.rank()
ind = list(indices(on))
# Get underlying expression to take gradient of
f, = o.ufl_operands
fn = f.rank()
ffc_assert(on == fn + 1, "Assuming grad always adds one axis.")
s = MonomialSum(s)
s.apply_indices(list(ind[:-1]))
s = MonomialSum(s)
s.apply_derivative([ind[-1]])
s = MonomialSum(s)
s.apply_tensor(ind)
return s
def dtheta_dC(self, u_, p_, ivar, theta_old_, thres, dt):
theta_ = ivar["theta"]
dFg_dtheta = self.F_g(theta_,tang=True)
ktheta = self.res_dtheta_growth(u_, p_, ivar, theta_old_, thres, dt, 'ktheta')
K_growth = self.res_dtheta_growth(u_, p_, ivar, theta_old_, thres, dt, 'tang')
i, j, k, l = indices(4)
if self.growth_trig == 'volstress':
Cmat = self.S(u_,p_,ivar,tang=True)
# TeX: \frac{\partial \vartheta}{\partial \boldsymbol{C}} = \frac{k(\vartheta) \Delta t}{\frac{\partial r}{\partial \vartheta}}\left(\boldsymbol{S} + \boldsymbol{C} : \frac{1}{2} \check{\mathbb{C}}\right)
tangdC = (ktheta*dt/K_growth) * (self.S(u_,p_,ivar=ivar) + 0.5*as_tensor(self.kin.C(u_)[i,j]*Cmat[i,j,k,l], (k,l)))
elif self.growth_trig == 'fibstretch':
# TeX: \frac{\partial \vartheta}{\partial \boldsymbol{C}} = \frac{k(\vartheta) \Delta t}{\frac{\partial r}{\partial \vartheta}} \frac{1}{2\lambda_{f}} \boldsymbol{f}_{0}\otimes \boldsymbol{f}_{0}
tangdC = (ktheta*dt/K_growth) * outer(self.kin.fib_funcs[0],self.kin.fib_funcs[0])/(2.*self.kin.fibstretch(u_,self.kin.fib_funcs[0]))
else:
raise NameError("Unkown growth_trig!")
return tangdC
def grad(self, o, s):
# The representation
# o = Grad(s)
# is equivalent to
# o = as_tensor(s[ii].dx(i), ii+(i,))
# with ii a tuple of free indices and i a single free index.
# Make some unique utility indices
from ufl import indices
on = len(o.ufl_shape)
ind = list(indices(on))
# Get underlying expression to take gradient of
f, = o.ufl_operands
fn = len(f.ufl_shape)
ffc_assert(on == fn + 1, "Assuming grad always adds one axis.")
s = MonomialSum(s)
s.apply_indices(list(ind[:-1]))
s = MonomialSum(s)
s.apply_derivative([ind[-1]])
s = MonomialSum(s)
s.apply_tensor(ind)
return s
def covariantDerivative(T):
"""
Returns a ``CurvilinearTensor`` that is the covariant derivative of
the ``CurvilinearTensor`` argument ``T``.
"""
n = rank(T.T)
ii = indices(n + 2)
g = T.g
gamma = getChristoffel(g)
retval = grad(T.T)
for i in range(0, n):
# use ii[n] as the new index of the covariant deriv
# use ii[n+1] as dummy index
if (T.lowered[i]):
retval -= as_tensor(T.T[ii[0:i]+(ii[n+1],)+ii[i+1:n]]\
*gamma[(ii[n+1],ii[i],ii[n])],\
ii[0:n+1])
else:
retval += as_tensor(T.T[ii[0:i]+(ii[n+1],)+ii[i+1:n]]\
*gamma[(ii[i],ii[n+1],ii[n])],\
ii[0:n+1])
newLowered = T.lowered + [
True,
]
return CurvilinearTensor(retval, g, newLowered)
def cartesianDiv(f, F):
"""
The divergence operator corresponding to ``cartesianGrad(f,F)`` that
sums on the last two indices.
"""
n = rank(f)
ii = indices(n)
return as_tensor(cartesianGrad(f, F)[ii + (ii[n - 1], )], ii[0:n - 1])
def cartesianCurl(f, F):
"""
The curl operator corresponding to ``cartesianGrad(f,F)``. Only valid
for ``f`` of rank 1, with dimension 3.
"""
eps = PermutationSymbol(3)
gradf = cartesianGrad(f, F)
(i, j, k) = indices(3)
return as_tensor(eps[i, j, k] * gradf[k, j], (i, ))
def stf3d3(rank3_3d):
r"""
Return the symmetric and trace-free part of a 3D 3-tensor.
Return the symmetric and trace-free part of a 3D 3-tensor. (dev(sym(.)))
Returns the STF part :math:`A_{\langle i j k\rangle}` of a rank-3 tensor
:math:`A \in \mathbb{R}^{3 \times 3 \times 3}` [TOR2018]_.
.. math::
A_{\langle i j k\rangle}=A_{(i j k)}-\frac{1}{5}\left[A_{(i l l)}
\delta_{j k}+A_{(l j l)} \delta_{i k}+A_{(l l k)} \delta_{i j}\right]
A gradient :math:`\frac{\partial S_{\langle i j}}{\partial x_{k \rangle}}`
of a symmetric 2-tensor :math:`S \in \mathbb{R}^{3 \times 3}` has the
deviator [STR2005]_:
.. math::
\frac{\partial S_{\langle i j}}{\partial x_{k \rangle}}
=&
\frac{1}{3}\left(
\frac{\partial S_{i j}}{\partial x_{k}}
+\frac{\partial S_{i k}}{\partial x_{j}}
+\frac{\partial S_{j k}}{\partial x_{i}}
\right)
-\frac{1}{15}
\biggl[
\left(
\frac{\partial S_{i r}}{\partial x_{r}}
+\frac{\partial S_{i r}}{\partial x_{r}}
+\frac{\partial S_{r r}}{\partial x_{i}}
\right) \delta_{j k}
\\
&+
\left(
\frac{\partial S_{j r}}{\partial x_{r}}
+\frac{\partial S_{j r}}{\partial x_{r}}
+\frac{\partial S_{r r}}{\partial x_{j}}
\right) \delta_{i k}
+\left(
\frac{\partial S_{k r}}{\partial x_{r}}
+\frac{\partial S_{k r}}{\partial x_{r}}
+\frac{\partial S_{r r}}{\partial x_{k}}
\right) \delta_{i j}
\biggr]
"""
i, j, k, L = ufl.indices(4)
delta = df.Identity(3)
sym_ijk = sym3d3(rank3_3d)[i, j, k]
traces_ijk = 1 / 5 * (+sym3d3(rank3_3d)[i, L, L] * delta[j, k] +
sym3d3(rank3_3d)[L, j, L] * delta[i, k] +
sym3d3(rank3_3d)[L, L, k] * delta[i, j])
tracefree_ijk = sym_ijk - traces_ijk
return ufl.as_tensor(tracefree_ijk, (i, j, k))
def curvilinearInner(T, S):
"""
Returns the inner product of ``CurvilinearTensor`` objects
``T`` and ``S``, inserting factors of the metric and inverse metric
as needed, depending on the co/contra-variant status of corresponding
indices.
"""
Tsharp = T.sharp()
Sflat = S.flat()
ii = indices(rank(T.T))
return as_tensor(Tsharp.T[ii] * Sflat.T[ii], ())
def getChristoffel(g):
"""
Returns Christoffel symbols associated with a metric tensor ``g``. Indices
are ordered based on the assumption that the first index is the raised one.
"""
a, b, c, d = indices(4)
return as_tensor\
(0.5*inv(g)[a,b]\
*(grad(g)[c,b,d]\
+ grad(g)[d,b,c]\
- grad(g)[d,c,b]), (a,d,c))
def Cremod(self, u_, p_, ivar, theta_old_, thres, dt):
theta_ = ivar["theta"]
i, j, k, l = indices(4)
dphi_dtheta_ = self.phi_remod(theta_,tang=True)
dtheta_dC_ = self.dtheta_dC(u_, p_, ivar, theta_old_, thres, dt)
Cremod = 2.*dphi_dtheta_ * as_tensor(dtheta_dC_[i,j]*(self.stress_remod - self.stress_base)[k,l], (i,j,k,l))
return Cremod
def strongResidual(u, p, mu, rho, u_t=None, f=None):
"""
The momenum and continuity residuals, as a tuple, of the strong PDE,
system, in terms of velocity ``u``, pressure ``p``, dynamic viscosity
``mu``, mass density ``rho``, and, optionally, the partial time derivative
of velocity, ``u_t``, and a body force per unit mass, ``f``.
"""
DuDt = materialTimeDerivative(u, u_t, f)
i, j = ufl.indices(2)
r_M = rho * DuDt - as_tensor(grad(sigma(u, p, mu))[i, j, j], (i, ))
r_C = rho * div(u)
return r_M, r_C
def shear_stress(self, velocity):
if self.geometric_dimension == 1:
return dolf.Constant(4.0 / 3.0) * velocity.dx(0) / self.dolf_constants.Re
i, j = ufl.indices(2)
shear_stress = (
velocity[i].dx(j)
+ dolf.Constant(1.0 / 3.0) * self.identity_tensor[i, j] * dolf.div(velocity)
) / self.dolf_constants.Re
# shear_stress = (
# velocity[i].dx(j)
# + velocity[j].dx(i)
# - dolf.Constant(2.0 / 3.0) * self.identity_tensor[i, j] * dolf.div(velocity)
# ) / self.dolf_constants.Re
return dolf.as_tensor(shear_stress, (i, j))
def __init__(self, angular_quad, L):
from transport_data import angular_tensors_ext_module
i,j,k1,k2,p,q = ufl.indices(6)
tensors = angular_tensors_ext_module.AngularTensors(angular_quad, L)
self.Y = ufl.as_tensor( numpy.reshape(tensors.Y(), tensors.shape_Y()) )
self.Q = ufl.as_tensor( numpy.reshape(tensors.Q(), tensors.shape_Q()) )
self.QT = ufl.transpose(self.Q)
self.Qt = ufl.as_tensor( numpy.reshape(tensors.Qt(), tensors.shape_Qt()) )
self.QtT = ufl.as_tensor( self.Qt[k1,p,i], (p,i,k1) )
self.G = ufl.as_tensor( numpy.reshape(tensors.G(), tensors.shape_G()) )
self.T = ufl.as_tensor( numpy.reshape(tensors.T(), tensors.shape_T()) )
self.Wp = ufl.as_vector( angular_quad.get_pw() )
self.W = ufl.diag(self.Wp)
def curvilinearGrad(T):
"""
Returns the gradient of ``CurvilinearTensor`` argument ``T``, i.e., the
covariant derivative with the last index raised.
"""
n = rank(T.T)
ii = indices(n + 2)
g = T.g
deriv = covariantDerivative(T)
invg = inv(g)
# raise last index
retval = as_tensor(deriv.T[ii[0:n+1]]*invg[ii[n:n+2]],\
ii[0:n]+(ii[n+1],))
return CurvilinearTensor(retval, g, T.lowered + [
False,
])
def solve(self, it=0):
if self.group_GS:
self.solve_group_GS(it)
else:
super(EllipticSNFluxModule, self).solve(it)
self.slns_mg = split(self.sln)
i,p,q,k1,k2 = ufl.indices(5)
sol_timer = Timer("-- Complete solution")
aux_timer = Timer("---- SOL: Computing angular flux + adjoint")
# TODO: Move to Discretization
V11 = FunctionSpace(self.DD.mesh, "CG", self.DD.parameters["p"])
for gto in range(self.DD.G):
self.PD.get_xs('D', self.D, gto)
form = self.D * ufl.diag_vector(ufl.as_matrix(self.DD.ordinates_matrix[i,p]*self.slns_mg[gto][q].dx(i), (p,q)))
for gfrom in range(self.DD.G):
pres_Ss = False
# TODO: Enlarge self.S and self.C to (L+1)^2 (or 1./2.*(L+1)*(L+2) in 2D) to accomodate for anisotropic
# scattering (lines below using CC, SS are correct only for L = 0, when the following inner loop runs only
# once.
for l in range(self.L+1):
for m in range(-l, l+1):
if self.DD.angular_quad.get_D() == 2 and divmod(l+m, 2)[1] == 0:
continue
pres_Ss |= self.PD.get_xs('Ss', self.S[l], gto, gfrom, l)
self.PD.get_xs('C', self.C[l], gto, gfrom, l)
if pres_Ss:
Cd = ufl.diag(self.C)
CC = self.tensors.Y[p,k1] * Cd[k1,k2] * self.tensors.Qt[k2,q,i]
form += ufl.as_vector(CC[p,q,i] * self.slns_mg[gfrom][q].dx(i), p)
# project(form, self.DD.Vpsi1, function=self.aux_slng, preconditioner_type="petsc_amg")
# FASTER, but requires form compilation for each dir.:
for pp in range(self.DD.M):
assign(self.aux_slng.sub(pp), project(form[pp], V11, preconditioner_type="petsc_amg"))
self.psi_mg[gto].assign(self.slns_mg[gto] + self.aux_slng)
self.adj_psi_mg[gto].assign(self.slns_mg[gto] - self.aux_slng)
def Cgrowth_p(self, u_, p_, ivar, theta_old_, thres, dt):
theta_ = ivar["theta"]
dFg_dtheta = self.F_g(theta_,tang=True)
i, j, k, l = indices(4)
dtheta_dp_ = self.dtheta_dp(u_, p_, ivar, theta_old_, thres, dt)
dS_dFg_ = self.dS_dFg(u_, p_, ivar, theta_old_, dt)
dS_dFg_times_dFg_dtheta = as_tensor(dS_dFg_[i,j,k,l]*dFg_dtheta[k,l], (i,j))
Cgrowth_p = dS_dFg_times_dFg_dtheta * dtheta_dp_
return Cgrowth_p
def raiseLowerIndex(self, i):
"""
Flips the raised/lowered status of the ``i``-th index.
"""
n = rank(self.T)
ii = indices(n + 1)
mat = self.g
if (self.lowered[i]):
mat = inv(self.g)
else:
mat = self.g
retval = as_tensor(self.T[ii[0:i]+(ii[i],)+ii[i+1:n]]\
*mat[ii[i],ii[n]],\
ii[0:i]+(ii[n],)+ii[i+1:n])
return CurvilinearTensor(retval,self.g,\
self.lowered[0:i]\
+[not self.lowered[i],]+self.lowered[i+1:])
def sym3d3(rank3_3d):
r"""
Return the symmetric part of a 3D 3-tensor.
Returns the symmetric part :math:`A_{(i j k)}` of a 3D rank-3 tensor
:math:`A \in \mathbb{R}^{3 \times 3 \times 3}` [STR2005]_ [TOR2018]_.
.. math::
A_{(i j k)}=\frac{1}{6}\left(
A_{i j k}+A_{i k j}+A_{j i k}+A_{j k i}+A_{k i j}+A_{k j i}
\right)
"""
i, j, k = ufl.indices(3)
symm_ijk = 1 / 6 * (
# All permutations
+rank3_3d[i, j, k] + rank3_3d[i, k, j] + rank3_3d[j, i, k] +
rank3_3d[j, k, i] + rank3_3d[k, i, j] + rank3_3d[k, j, i])
return ufl.as_tensor(symm_ijk, (i, j, k))
def div3d3(rank3_3d):
r"""
Return the 3D divergence of a 3-tensor.
Return the 3D divergence of a 3-tensor as
.. math::
{(\mathrm{div}(m))}_{ij} = \frac{\partial m_{ijk}}{\partial x_k}
Compare with [SCH2009]_ (p. 92).
.. [SCH2009] Heinz Schade, Klaus Neemann (2009). “Tensoranalysis”.
2. überarbeitete Auflage.
"""
i, j, k = ufl.indices(3)
div_ij = rank3_3d[i, j, 0].dx(0) + rank3_3d[i, j, 1].dx(1)
return ufl.as_tensor(div_ij, (i, j))
def _spatial_component(self, trial, test):
pressure, velocity, temperature = trial
test_pressure, test_velocity, test_temperature = test
i, j = ufl.indices(2)
if self.geometric_dimension == 1:
continuity_component = test_pressure * velocity.dx(0)
momentum_component = test_velocity.dx(0) * self.stress(pressure, velocity)
else:
continuity_component = test_pressure * dolf.div(velocity)
momentum_component = dolf.inner(
dolf.as_tensor(test_velocity[i].dx(j), (i, j)),
self.stress(pressure, velocity),
)
energy_component = dolf.inner(
dolf.grad(test_temperature), self.heat_flux(temperature)
)
return continuity_component + momentum_component + energy_component
def dS_dFg(self, u_, p_, ivar, theta_old_, dt):
theta_ = ivar["theta"]
Cmat = self.S(u_, p_, ivar, tang=True)
i, j, k, l, m, n = indices(6)
# elastic material tangent (living in intermediate growth configuration)
Cmat_e = dot(
self.F_g(theta_),
dot(self.F_g(theta_),
dot(Cmat, dot(self.F_g(theta_).T,
self.F_g(theta_).T))))
Fginv_outertop_S = as_tensor(
inv(self.F_g(theta_))[i, k] * self.S(u_, p_, ivar=ivar)[j, l],
(i, j, k, l))
S_outerbot_Fginv = as_tensor(
self.S(u_, p_, ivar=ivar)[i, l] * inv(self.F_g(theta_))[j, k],
(i, j, k, l))
Fginv_outertop_Fginv = as_tensor(
inv(self.F_g(theta_))[i, k] * inv(self.F_g(theta_))[j, l],
(i, j, k, l))
FginvT_outertop_Ce = as_tensor(
inv(self.F_g(theta_)).T[i, k] *
self.C_e(self.kin.C(u_), theta_)[j, l], (i, j, k, l))
Ce_outerbot_FginvT = as_tensor(
self.C_e(self.kin.C(u_), theta_)[i, l] *
inv(self.F_g(theta_)).T[j, k], (i, j, k, l))
Cmat_e_with_C_e = 0.5 * as_tensor(
Cmat_e[i, j, m, n] *
(FginvT_outertop_Ce[m, n, k, l] + Ce_outerbot_FginvT[m, n, k, l]),
(i, j, k, l))
Fginv_outertop_Fginv_with_Cmat_e_with_C_e = as_tensor(
Fginv_outertop_Fginv[i, j, m, n] * Cmat_e_with_C_e[m, n, k, l],
(i, j, k, l))
return -(Fginv_outertop_S +
S_outerbot_Fginv) - Fginv_outertop_Fginv_with_Cmat_e_with_C_e
def count_flops(n):
mesh = Mesh(VectorElement('CG', interval, 1))
tfs = FunctionSpace(mesh, TensorElement('DG', interval, 1, shape=(n, n)))
vfs = FunctionSpace(mesh, VectorElement('DG', interval, 1, dim=n))
ensemble_f = Coefficient(vfs)
ensemble2_f = Coefficient(vfs)
phi = TestFunction(tfs)
i, j = indices(2)
nc = 42 # magic number
L = ((IndexSum(IndexSum(Product(nc * phi[i, j], Product(ensemble_f[i], ensemble_f[i])),
MultiIndex((i,))), MultiIndex((j,))) * dx) +
(IndexSum(IndexSum(Product(nc * phi[i, j], Product(ensemble2_f[j], ensemble2_f[j])),
MultiIndex((i,))), MultiIndex((j,))) * dx) -
(IndexSum(IndexSum(2 * nc * Product(phi[i, j], Product(ensemble_f[i], ensemble2_f[j])),
MultiIndex((i,))), MultiIndex((j,))) * dx))
kernel, = compile_form(L, parameters=dict(mode='spectral'))
return EstimateFlops().visit(kernel.ast)
def test_dolfin_expression_compilation_of_index_notation(dolfin):
# Define some PyDOLFIN coefficients
mesh = dolfin.UnitSquareMesh(3, 3)
# Using quadratic element deliberately for accuracy
V = dolfin.VectorFunctionSpace(mesh, "CG", 2)
u = dolfin.Function(V)
u.interpolate(dolfin.Expression(("x[0]", "x[1]")))
# Define ufl expression with index notation
i, j = ufl.indices(2)
uexpr = ((u[0]*u + u[1]*u)[i]*u[j])*(u[i]*u[j])
# Define expected output from compilation
# Split version (reusing all product terms correctly!):
xc, yc = ('v_w0[0]', 'v_w0[1]')
svars = {'a':'s[2]', 'b':'s[4]', 'xx':'s[0]', 'yy':'s[3]', 'xy':'s[1]', 'x':xc, 'y':yc}
expected_lines = ['double s[6];',
'Array<double> v_w0(2);',
'w0->eval(v_w0, x);',
's[0] = pow(%(x)s, 2);' % svars,
's[1] = %(x)s * %(y)s;' % svars,
's[2] = %(xx)s + %(xy)s;' % svars,
's[3] = pow(%(y)s, 2);' % svars,
's[4] = %(yy)s + %(xy)s;' % svars,
's[5] = (%(xx)s * (%(x)s * %(a)s) + %(xy)s * (%(x)s * %(b)s)) + (%(yy)s * (%(y)s * %(b)s) + %(xy)s * (%(y)s * %(a)s));' % svars,
'values[0] = s[5];']
# Making the above line correct was a nightmare because of operator sorting in ufl... Hoping it stays stable!
# Define expected evaluation values: [(x,value), (x,value), ...]
x, y = (0.6, 0.7)
a = x*x+y*x
b = x*y+y*y
expected = (a*x)*(x*x) + (a*y)*(x*y) + (b*x)*(y*x) + (b*y)*(y*y)
expected_values = [((0.0, 0.0), (0.0,)),
((x, y), (expected,)),
]
# Execute all tests
check_dolfin_expression_compilation(uexpr, expected_lines, expected_values, members={'w0':u})
def curvilinearDiv(T):
"""
Returns the divergence of the ``CurvilinearTensor`` argument ``T``, i.e.,
the covariant derivative, but contracting over the new index and the
last raised index.
NOTE: This operation is invalid for tensors that do not
contain at least one raised index.
"""
n = rank(T.T)
ii = indices(n)
g = T.g
j = -1 # last raised index
for i in range(0, n):
if (not T.lowered[i]):
j = i
if (j == -1):
print("ERROR: Divergence operator requires at least one raised index.")
exit()
deriv = covariantDerivative(T)
retval = as_tensor(deriv.T[ii[0:n] + (ii[j], )], ii[0:j] + ii[j + 1:n])
return CurvilinearTensor(retval, g, T.lowered[0:j] + T.lowered[j + 1:n])
def assemble_algebraic_system(self):
if self.verb > 1: print0(self.print_prefix + "Assembling algebraic system.")
mat_timer = Timer("---- MTX: Complete construction")
ass_timer = Timer("---- MTX: Assembling")
add_values_A = False
add_values_B = False
add_values_Q = False
for gto in range(self.DD.G):
#=============================== LOOP OVER GROUPS AND ASSEMBLE ================================
spc = self.print_prefix + " "
if self.verb > 3 and self.DD.G > 1:
print spc + 'GROUP [', gto, ',', gto, '] :'
pres_fiss = self.PD.get_xs('chi', self.chi, gto)
self.PD.get_xs('D', self.D, gto)
self.PD.get_xs('St', self.R, gto)
i,j,p,q,k1,k2 = ufl.indices(6)
form = ( self.D*self.tensors.T[p,q,i,j]*self.u[gto][q].dx(j)*self.v[gto][p].dx(i) +
self.R*self.tensors.W[p,q]*self.u[gto][q]*self.v[gto][p] ) * dx + self.bnd_matrix_form[gto]
ass_timer.start()
assemble(form, tensor=self.A, finalize_tensor=False, add_values=add_values_A)
add_values_A = True
ass_timer.stop()
if self.fixed_source_problem:
if self.PD.isotropic_source_everywhere:
self.PD.get_Q(self.fixed_source, 0, gto)
# FIXME: This assumes that adjoint source == forward source
form = self.fixed_source * self.tensors.Wp[p] * self.v[gto][p] * dx + self.bnd_vector_form[gto]
else:
form = ufl.zero()
for n in range(self.DD.M):
self.PD.get_Q(self.fixed_source, n, gto)
# FIXME: This assumes that adjoint source == forward source
form += self.fixed_source[n,gto] * self.tensors.Wp[n] * self.v[gto][n] * dx + self.bnd_vector_form[gto]
ass_timer.start()
assemble(form, tensor=self.Q, finalize_tensor=False, add_values=add_values_Q)
add_values_Q = True
ass_timer.stop()
for gfrom in range(self.DD.G):
if self.verb > 3 and self.DD.G > 1: print spc + 'GROUP [', gto, ',', gfrom, '] :'
pres_Ss = False
# TODO: Enlarge self.S and self.C to (L+1)^2 (or 1./2.*(L+1)*(L+2) in 2D) to accomodate for anisotropic
# scattering (lines below using CC, SS are correct only for L = 0, when the following inner loop runs only
# once.
for l in range(self.L+1):
for m in range(-l, l+1):
if self.DD.angular_quad.get_D() == 2 and divmod(l+m,2)[1] == 0:
continue
pres_Ss |= self.PD.get_xs('Ss', self.S[l], gto, gfrom, l)
self.PD.get_xs('C', self.C[l], gto, gfrom, l)
if pres_Ss:
Sd = ufl.diag(self.S)
SS = self.tensors.QT[p,k1]*Sd[k1,k2]*self.tensors.Q[k2,q]
Cd = ufl.diag(self.C)
CC = self.tensors.QtT[p,i,k1]*Cd[k1,k2]*self.tensors.Qt[k2,q,j]
ass_timer.start()
form = ( CC[p,i,q,j]*self.u[gfrom][q].dx(j)*self.v[gto][p].dx(i) -
SS[q,p]*self.u[gfrom][q]*self.v[gto][p] ) * dx
assemble(form, tensor=self.A, finalize_tensor=False, add_values=add_values_A)
ass_timer.stop()
if pres_fiss:
pres_nSf = self.PD.get_xs('nSf', self.R, gfrom)
if pres_nSf:
ass_timer.start()
if self.fixed_source_problem:
form = -self.chi*self.R/(4*numpy.pi)*\
self.tensors.QT[p,0]*self.tensors.Q[0,q]*self.u[gfrom][q]*self.v[gto][p]*dx
assemble(form, tensor=self.A, finalize_tensor=False, add_values=add_values_A)
else:
form = self.chi*self.R/(4*numpy.pi)*\
self.tensors.QT[p,0]*self.tensors.Q[0,q]*self.u[gfrom][q]*self.v[gto][p]*dx
assemble(form, tensor=self.B, finalize_tensor=False, add_values=add_values_B)
add_values_B = True
ass_timer.stop()
#============================= END LOOP OVER GROUPS AND ASSEMBLE ===============================
self.A.apply("add")
if self.fixed_source_problem:
self.Q.apply("add")
elif self.eigenproblem:
self.B.apply("add")
def solve_group_GS(self, it=0, init_slns_ary=None):
if self.verb > 1: print0(self.print_prefix + "Solving..." )
if self.eigenproblem:
coupled_solver_error(__file__,
"solve using group GS",
"Group Gauss-Seidel for eigenproblem not yet supported")
sol_timer = Timer("-- Complete solution")
mat_timer = Timer("---- MTX: Complete construction")
ass_timer = Timer("---- MTX: Assembling")
sln_timer = Timer("---- SOL: Solving")
# To simplify the weak forms
u = self.u[0]
v = self.v[0]
if init_slns_ary is None:
init_slns_ary = numpy.zeros((self.DD.G, self.local_sln_size))
for g in range(self.DD.G):
self.slns_mg[g].vector()[:] = init_slns_ary[g]
err = 0.
for gsi in range(self.max_group_GS_it):
#========================================== GAUSS-SEIDEL LOOP ============================================
if self.verb > 2: print self.print_prefix + 'Gauss-Seidel iteration {}'.format(gsi)
for gto in range(self.DD.G):
#========================================= LOOP OVER GROUPS ===============================================
self.sln_vec = self.slns_mg[gto].vector()
prev_slng_vec = self.aux_slng.vector()
prev_slng_vec.zero()
prev_slng_vec.axpy(1.0, self.sln_vec)
prev_slng_vec.apply("insert")
spc = self.print_prefix + " "
if self.verb > 3 and self.DD.G > 1: print spc + 'GROUP [', gto, ',', gto, '] :'
#==================================== ASSEMBLE WITHIN-GROUP PROBLEM ========================================
mat_timer.start()
pres_fiss = self.PD.get_xs('chi', self.chi, gto)
self.PD.get_xs('D', self.D, gto)
self.PD.get_xs('St', self.R, gto)
i,j,p,q,k1,k2 = ufl.indices(6)
form = ( self.D*self.tensors.T[p,q,i,j]*u[q].dx(j)*v[p].dx(i) +
self.R*self.tensors.W[p,q]*u[q]*v[p] ) * dx + self.bnd_matrix_form[gto]
ass_timer.start()
add_values_A = False
add_values_Q = False
assemble(form, tensor=self.A, finalize_tensor=False, add_values=add_values_A)
add_values_A = True
ass_timer.stop()
if self.fixed_source_problem:
if self.PD.isotropic_source_everywhere:
self.PD.get_Q(self.fixed_source, 0, gto)
# FIXME: This assumes that adjoint source == forward source
form = self.fixed_source*self.tensors.Wp[p]*v[p]*dx + self.bnd_vector_form[gto]
else:
form = ufl.zero()
for n in range(self.DD.M):
self.PD.get_Q(self.fixed_source, n, gto)
# FIXME: This assumes that adjoint source == forward source
form += self.fixed_source[n,gto] * self.tensors.Wp[n] * v[n] * dx + self.bnd_vector_form[gto]
ass_timer.start()
assemble(form, tensor=self.Q, finalize_tensor=False, add_values=add_values_Q)
add_values_Q = True
ass_timer.stop()
for gfrom in range(self.DD.G):
if self.verb > 3 and self.DD.G > 1:
print spc + 'GROUP [', gto, ',', gfrom, '] :'
pres_Ss = False
# TODO: Enlarge self.S and self.C to (L+1)^2 (or 1./2.*(L+1)*(L+2) in 2D) to accomodate for anisotropic
# scattering (lines below using CC, SS are correct only for L = 0, when the following inner loop runs only
# once.
for l in range(self.L+1):
for m in range(-l, l+1):
if self.DD.angular_quad.get_D() == 2 and divmod(l+m,2)[1] == 0:
continue
pres_Ss |= self.PD.get_xs('Ss', self.S[l], gto, gfrom, l)
self.PD.get_xs('C', self.C[l], gto, gfrom, l)
if pres_Ss:
Sd = ufl.diag(self.S)
SS = self.tensors.QT[p,k1]*Sd[k1,k2]*self.tensors.Q[k2,q]
Cd = ufl.diag(self.C)
CC = self.tensors.QtT[p,i,k1]*Cd[k1,k2]*self.tensors.Qt[k2,q,j]
ass_timer.start()
if gfrom != gto:
form = ( SS[p,q]*self.slns_mg[gfrom][q]*v[p] - CC[p,i,q,j]*self.slns_mg[gfrom][q].dx(j)*v[p].dx(i) ) * dx
assemble(form, tensor=self.Q, finalize_tensor=False, add_values=add_values_Q)
else:
form = ( CC[p,i,q,j]*u[q].dx(j)*v[p].dx(i) - SS[q,p]*u[q]*v[p] ) * dx
assemble(form, tensor=self.A, finalize_tensor=False, add_values=add_values_A)
ass_timer.stop()
if pres_fiss:
pres_nSf = self.PD.get_xs('nSf', self.R, gfrom)
if pres_nSf:
ass_timer.start()
if gfrom != gto:
form = self.chi*self.R/(4*numpy.pi)*\
self.tensors.QT[p,0]*self.tensors.Q[0,q]*self.slns_mg[gfrom][q]*v[p]*dx
assemble(form, tensor=self.Q, finalize_tensor=False, add_values=add_values_Q)
else:
# NOTE: Fixed-source case (eigenproblems can currently be solved only by the coupled-group scheme)
if self.fixed_source_problem:
form = -self.chi*self.R/(4*numpy.pi)*self.tensors.QT[p,0]*self.tensors.Q[0,q]*u[q]*v[p]*dx
assemble(form, tensor=self.A, finalize_tensor=False, add_values=add_values_A)
ass_timer.stop()
#================================== END ASSEMBLE WITHIN-GROUP PROBLEM =======================================
self.A.apply("add")
self.Q.apply("add")
mat_timer.stop()
self.save_algebraic_system({'A':'A_{}'.format(gto), 'Q':'Q_{}'.format(gto)}, it)
#==================================== SOLVE WITHIN-GROUP PROBLEM ==========================================
sln_timer.start()
dolfin_solve(self.A, self.sln_vec, self.Q, "cg", "petsc_amg")
sln_timer.stop()
self.up_to_date["flux"] = False
err = max(err, delta(self.sln_vec.array(), prev_slng_vec.array()))
#==================================== END LOOP OVER GROUPS ====================================
if err < self.parameters["group_GS"]["tol"]:
break
def __define_boundary_terms(self):
if self.verb > 2: print0("Defining boundary terms")
self.bnd_vector_form = numpy.empty(self.DD.G, dtype=object)
self.bnd_matrix_form = numpy.empty(self.DD.G, dtype=object)
n = FacetNormal(self.DD.mesh)
i,p,q = ufl.indices(3)
natural_boundaries = self.BC.vacuum_boundaries.union(self.BC.incoming_fluxes.keys())
nonzero = lambda x: numpy.all(x > 0)
nonzero_inc_flux = any(map(nonzero, self.BC.incoming_fluxes.values()))
if nonzero_inc_flux and not self.fixed_source_problem:
coupled_solver_error(__file__,
"define boundary terms",
"Incoming flux specified for an eigenvalue problem"+\
"(Q must be given whenever phi_inc is; it may possibly be zero everywhere)")
for g in range(self.DD.G):
self.bnd_vector_form[g] = ufl.zero()
self.bnd_matrix_form[g] = ufl.zero()
if natural_boundaries:
try:
ds = Measure("ds")[self.DD.boundaries]
except TypeError:
coupled_solver_error(__file__,
"define boundary terms",
"File assigning boundary indices to facets required if vacuum or incoming flux boundaries "
"are specified.")
for bnd_idx in natural_boundaries:
# NOTE: The following doesn't work because ufl.abs requires a function
#
#for g in range(self.DD.G):
# self.bnd_matrix_form[g] += \
# abs(self.tensors.G[p,q,i]*n[i])*self.u[g][q]*self.v[g][p]*ds(bnd_idx)
#
# NOTE: Instead, the following explicit loop has to be used; this makes tensors.G unneccessary
#
for pp in range(self.DD.M):
omega_p_dot_n = self.DD.ordinates_matrix[i,pp]*n[i]
for g in range(self.DD.G):
self.bnd_matrix_form[g] += \
abs(omega_p_dot_n)*self.tensors.Wp[pp]*self.u[g][pp]*self.v[g][pp]*ds(bnd_idx)
if nonzero_inc_flux:
for pp in range(self.DD.M):
omega_p_dot_n = self.DD.ordinates_matrix[i,pp]*n[i]
for bnd_idx, psi_inc in self.BC.incoming_fluxes.iteritems():
if psi_inc.shape != (self.DD.M, self.DD.G):
coupled_solver_error(__file__,
"define boundary terms",
"Incoming flux with incorrect number of groups and directions specified: "+
"{}, expected ({}, {})".format(psi_inc.shape, self.DD.M, self.DD.G))
for g in range(self.DD.G):
self.bnd_vector_form[g] += \
ufl.conditional(omega_p_dot_n < 0,
omega_p_dot_n*self.tensors.Wp[pp]*psi_inc[pp,g]*self.v[g][pp]*ds(bnd_idx),
ufl.zero()) # FIXME: This assumes zero adjoint outgoing flux
else: # Apply vacuum b.c. everywhere
ds = Measure("ds")
for pp in range(self.DD.M):
omega_p_dot_n = self.DD.ordinates_matrix[i,pp]*n[i]
for g in range(self.DD.G):
self.bnd_matrix_form[g] += abs(omega_p_dot_n)*self.tensors.Wp[pp]*self.u[g][pp]*self.v[g][pp]*ds()
t = variable(t_const)
# Choose to have zero acceleration at $t=0$, for convenient
# imposition of initial conditions.
u_exact = Constant(1e-1) * (t**3) * as_vector(
(sin(pi * x[0] / Constant(Lx)) * sin(pi * x[1] / Constant(Ly)),
sin(pi * x[0] / Constant(Lx)) * sin(pi * x[1] / Constant(Ly))))
# Constitutive parameters for a St. Venant--Kirchhoff model:
rho = Constant(1.0)
mu = Constant(1.0)
K = Constant(1.0)
lam = K - 2.0 * mu / 3.0
I = Identity(d)
# Re-usable definitions for nonlinear elasticity:
i, j, k, l = ufl.indices(4)
Ctens = as_tensor(
lam * I[i, j] * I[k, l] + mu * (I[i, k] * I[j, l] + I[i, l] * I[j, k]),
(i, j, k, l))
def F(u):
return spline.grad(u) + I
def S(u):
E = 0.5 * (F(u).T * F(u))
i, j, k, l = ufl.indices(4)
return as_tensor(Ctens[i, j, k, l] * E[k, l], (i, j))
def S(u):
E = 0.5 * (F(u).T * F(u))
i, j, k, l = ufl.indices(4)
return as_tensor(Ctens[i, j, k, l] * E[k, l], (i, j))
import ufl
import numpy
_i, _j = ufl.indices(2)
# Heat params
#
# Volumetric expansion
beta_C = 36.0E-6 # K^{-1}
# Specific heat capacity
C_pc = 0.9E+3 # J kg^{-1} K^{-1}
# Heat conduction
lambda_c = 1.13 # W m^{-1} K^{-1}
room_temp = 293
def inv(A):
"""Matrix invariants"""
return ufl.tr(A), 1. / 2 * A[_i, _j] * A[_i, _j], ufl.det(A)
def eig(A):
"""Eigenvalues of 3x3 tensor"""
eps = 1.0e-12
q = ufl.tr(A) / 3.0
p1 = 0.5 * (A[0, 1]**2 + A[1, 0]**2 + A[0, 2]**2 + A[2, 0]**2 +
A[1, 2]**2 + A[2, 1]**2)
p2 = (A[0, 0] - q)**2 + (A[1, 1] - q)**2 + (A[2, 2] - q)**2 + 2 * p1
p = ufl.sqrt(p2 / 6)
def H(u, udot):
i, j, k, l = ufl.indices(4)
return as_tensor(c(u)[i, j, k, l] * sym(gradx(u, udot))[k, l], (i, j))
def apply_mapping(expression, mapping, domain):
"""
This applies the appropriate transformation to the
given expression for interpolation to a specific
element, according to the manner in which it maps
from the reference cell.
The following is borrowed from the UFC documentation:
Let g be a field defined on a physical domain T with physical
coordinates x. Let T_0 be a reference domain with coordinates
X. Assume that F: T_0 -> T such that
x = F(X)
Let J be the Jacobian of F, i.e J = dx/dX and let K denote the
inverse of the Jacobian K = J^{-1}. Then we (currently) have the
following four types of mappings:
'affine' mapping for g:
G(X) = g(x)
For vector fields g:
'contravariant piola' mapping for g:
G(X) = det(J) K g(x) i.e G_i(X) = det(J) K_ij g_j(x)
'covariant piola' mapping for g:
G(X) = J^T g(x) i.e G_i(X) = J^T_ij g(x) = J_ji g_j(x)
'double covariant piola' mapping for g:
G(X) = J^T g(x) J i.e. G_il(X) = J_ji g_jk(x) J_kl
'double contravariant piola' mapping for g:
G(X) = det(J)^2 K g(x) K^T i.e. G_il(X)=(detJ)^2 K_ij g_jk K_lk
If 'contravariant piola' or 'covariant piola' are applied to a
matrix-valued function, the appropriate mappings are applied row-by-row.
:arg expression: UFL expression
:arg mapping: a string indicating the mapping to apply
"""
mesh = expression.ufl_domain()
if mesh is None:
mesh = domain
if domain is not None and mesh != domain:
raise NotImplementedError("Multiple domains not supported")
rank = len(expression.ufl_shape)
if mapping == "affine":
return expression
elif mapping == "covariant piola":
J = Jacobian(mesh)
*i, j, k = indices(len(expression.ufl_shape) + 1)
expression = Indexed(expression, MultiIndex((*i, k)))
return as_tensor(J.T[j, k] * expression, (*i, j))
elif mapping == "contravariant piola":
K = JacobianInverse(mesh)
detJ = JacobianDeterminant(mesh)
*i, j, k = indices(len(expression.ufl_shape) + 1)
expression = Indexed(expression, MultiIndex((*i, k)))
return as_tensor(detJ * K[j, k] * expression, (*i, j))
elif mapping == "double covariant piola" and rank == 2:
J = Jacobian(mesh)
return J.T * expression * J
elif mapping == "double contravariant piola" and rank == 2:
K = JacobianInverse(mesh)
detJ = JacobianDeterminant(mesh)
return (detJ)**2 * K * expression * K.T
else:
raise NotImplementedError("Don't know how to handle mapping type %s for expression of rank %d" % (mapping, rank))