def _solve(self, z, x=None):
# problem variables
du = TrialFunction(self.V) # incremental displacement
v = TestFunction(self.V) # test function
u = Function(self.V) # displacement from previous iteration
# kinematics
ii = Identity(3) # identity tensor dimension 3
f = ii + grad(u) # deformation gradient
c = f.T * f # right Cauchy-Green tensor
# invariants of deformation tensors
ic = tr(c)
j = det(f)
# elasticity parameters
if type(z) in [list, np.ndarray]:
param = self.param_remapper(z[0]) if self.param_remapper is not None else z[0]
else:
param = self.param_remapper(z) if self.param_remapper is not None else z
e_var = variable(Constant(param)) # Young's modulus
nu = Constant(.3) # Shear modulus (Lamè's second parameter)
mu, lmbda = e_var / (2 * (1 + nu)), e_var * nu / ((1 + nu) * (1 - 2 * nu))
# strain energy density, total potential energy
psi = (mu / 2) * (ic - 3) - mu * ln(j) + (lmbda / 2) * (ln(j)) ** 2
pi = psi * dx - self.time * dot(self.f, u) * self.ds(3)
ff = derivative(pi, u, v) # compute first variation of pi
jj = derivative(ff, u, du) # compute jacobian of f
# solving
if x is not None:
numeric_evals = np.zeros(shape=(x.shape[1], len(self.times)))
evals = np.zeros(shape=(x.shape[1], len(self.eval_times)))
else:
numeric_evals = None
evals = None
for it, t in enumerate(self.times):
self.time.t = t
self.solver(ff == 0, u, self.bcs, J=jj, bcs=self.bcs, solver_parameters=self.solver_parameters)
if x is not None:
numeric_evals[:, it] = np.log(np.array([-u(x_)[2] for x_ in x.T]).T)
# time-interpolation
if x is not None:
for i in range(evals.shape[0]):
evals[i, :] = np.interp(self.eval_times, self.times, numeric_evals[i, :])
return (evals, u) if x is not None else u
def psi_aux_Borden(F, mu, kappa):
J = fe.det(F)
C = F.T * F
Jinv = J**(-2 / 3)
U = 0.5 * kappa * (0.5 * (J**2 - 1) - fe.ln(J))
Wbar = 0.5 * mu * (Jinv * (fe.tr(C) + 1) - 3)
return U, Wbar
def CompressibleNeoHookean(Mu, Lambda, Ic, J):
Psi = (Mu/2)*(Ic - 3) - Mu*fe.ln(J) + (Lambda/2)*(fe.ln(J))**2
return Psi
def compute_static_deformation(self):
assert self.mesh is not None
# now we define subdomains on the mesh
bottom = fe.CompiledSubDomain('near(x[2], 0) && on_boundary')
top = fe.CompiledSubDomain('near(x[2], 1) && on_boundary')
# middle = fe.CompiledSubDomain('x[2] > 0.3 && x[2] < 0.7')
# Initialize mesh function for interior domains
self.domains = fe.MeshFunction('size_t', self.mesh, 3)
self.domains.set_all(0)
# middle.mark(self.domains, 1)
# Initialize mesh function for boundary domains
self.boundaries = fe.MeshFunction('size_t', self.mesh, 2)
self.boundaries.set_all(0)
bottom.mark(self.boundaries, 1)
top.mark(self.boundaries, 2)
# Define new measures associated with the interior domains and
# exterior boundaries
self.dx = fe.Measure('dx',
domain=self.mesh,
subdomain_data=self.domains)
self.ds = fe.Measure('ds',
domain=self.mesh,
subdomain_data=self.boundaries)
# define function spaces
V = fe.VectorFunctionSpace(self.mesh, "Lagrange", 1)
# now we define subdomains on the mesh
bottom = fe.CompiledSubDomain('near(x[2], 0) && on_boundary')
top = fe.CompiledSubDomain('near(x[2], 1) && on_boundary')
# middle = fe.CompiledSubDomain('x[2] > 0.3 && x[2] < 0.7')
d = self.mesh.geometry().dim()
# Initialize mesh function for interior domains
self.domains = fe.MeshFunction('size_t', self.mesh, d)
self.domains.set_all(0)
# middle.mark(self.domains, 1)
# Initialize mesh function for boundary domains
self.boundaries = fe.MeshFunction('size_t', self.mesh, d - 1)
self.boundaries.set_all(0)
bottom.mark(self.boundaries, 1)
top.mark(self.boundaries, 2)
# Define new measures associated with the interior domains and
# exterior boundaries
self.dx = fe.Measure('dx',
domain=self.mesh,
subdomain_data=self.domains)
self.ds = fe.Measure('ds',
domain=self.mesh,
subdomain_data=self.boundaries)
c_zero = fe.Constant((0, 0, 0))
# define boundary conditions
bc_bottom = fe.DirichletBC(V, c_zero, bottom)
bc_top = fe.DirichletBC(V, c_zero, top)
bcs = [bc_bottom] # , bc_top]
# define functions
du = TrialFunction(V)
v = TestFunction(V)
u = Function(V)
B = fe.Constant((0., 2.0, 0.))
T = fe.Constant((0.0, 0.0, 0.0))
d = u.geometric_dimension()
I = fe.Identity(d)
F = I + grad(u)
C = F.T * F
I_1 = tr(C)
J = det(F)
E, mu = 10., 0.3
mu, lmbda = fe.Constant(E / (2 * (1 + mu))), fe.Constant(
E * mu / ((1 + mu) * (1 - 2 * mu)))
# stored energy (comp. neo-hookean model)
psi = (mu / 2.) * (I_1 - 3) - mu * fe.ln(J) + (lmbda /
2.) * (fe.ln(J))**2
dx = self.dx
ds = self.ds
Pi = psi * fe.dx - dot(B, u) * fe.dx - dot(T, u) * fe.ds
F = fe.derivative(Pi, u, v)
J = fe.derivative(F, u, du)
fe.solve(F == 0, u, bcs, J=J)
# save results
self.u = u
# write to disk
output = fe.File("/tmp/static.pvd")
output << u
def Vfunc(U):
return -params['beta']*fe.ln(U + params['alpha'])