def _apply_inverse(matrix, r, v, options=None):
options = options or _solver_options()
solver = options.get('solver')
preconditioner = options.get('preconditioner')
# preconditioner argument may only be specified for iterative solvers:
options = (solver, preconditioner) if preconditioner else (solver,)
df.solve(matrix, r, v, *options)
def computeVelocityField(mesh):
Xh = dl.VectorFunctionSpace(mesh,'Lagrange', 2)
Wh = dl.FunctionSpace(mesh, 'Lagrange', 1)
if dlversion() <= (1,6,0):
XW = dl.MixedFunctionSpace([Xh, Wh])
else:
mixed_element = dl.MixedElement([Xh.ufl_element(), Wh.ufl_element()])
XW = dl.FunctionSpace(mesh, mixed_element)
Re = 1e2
g = dl.Expression(('0.0','(x[0] < 1e-14) - (x[0] > 1 - 1e-14)'), element=Xh.ufl_element())
bc1 = dl.DirichletBC(XW.sub(0), g, v_boundary)
bc2 = dl.DirichletBC(XW.sub(1), dl.Constant(0), q_boundary, 'pointwise')
bcs = [bc1, bc2]
vq = dl.Function(XW)
(v,q) = dl.split(vq)
(v_test, q_test) = dl.TestFunctions (XW)
def strain(v):
return dl.sym(dl.nabla_grad(v))
F = ( (2./Re)*dl.inner(strain(v),strain(v_test))+ dl.inner (dl.nabla_grad(v)*v, v_test)
- (q * dl.div(v_test)) + ( dl.div(v) * q_test) ) * dl.dx
dl.solve(F == 0, vq, bcs, solver_parameters={"newton_solver":
{"relative_tolerance":1e-4, "maximum_iterations":100,
"linear_solver":"default"}})
return v
def get_distance_function(config, domains):
V = dolfin.FunctionSpace(config.domain.mesh, "CG", 1)
v = dolfin.TestFunction(V)
d = dolfin.TrialFunction(V)
sol = dolfin.Function(V)
s = dolfin.interpolate(Constant(1.0), V)
domains_func = dolfin.Function(dolfin.FunctionSpace(config.domain.mesh, "DG", 0))
domains_func.vector().set_local(domains.array().astype(numpy.float))
def boundary(x):
eps_x = config.params["turbine_x"]
eps_y = config.params["turbine_y"]
min_val = 1
for e_x, e_y in [(-eps_x, 0), (eps_x, 0), (0, -eps_y), (0, eps_y)]:
try:
min_val = min(min_val, domains_func((x[0] + e_x, x[1] + e_y)))
except RuntimeError:
pass
return min_val == 1.0
bc = dolfin.DirichletBC(V, 0.0, boundary)
# Solve the diffusion problem with a constant source term
log(INFO, "Solving diffusion problem to identify feasible area ...")
a = dolfin.inner(dolfin.grad(d), dolfin.grad(v)) * dolfin.dx
L = dolfin.inner(s, v) * dolfin.dx
dolfin.solve(a == L, sol, bc)
return sol
def __init__(self, config, feasible_area, attraction_center):
'''
Generates the inequality constraints to enforce the turbines in the feasible area.
If the turbine is outside the domain, the constraints is equal to the distance between the turbine and the attraction center.
'''
self.config = config
self.feasible_area = feasible_area
# Compute the gradient of the feasible area
fs = dolfin.FunctionSpace(feasible_area.function_space().mesh(),
"DG",
feasible_area.function_space().ufl_element().degree() - 1)
feasible_area_grad = (dolfin.Function(fs),
dolfin.Function(fs))
t = dolfin.TestFunction(fs)
log(INFO, "Solving for gradient of feasible area")
for i in range(2):
form = dolfin.inner(feasible_area_grad[i], t) * dolfin.dx - dolfin.inner(feasible_area.dx(i), t) * dolfin.dx
if dolfin.NonlinearVariationalSolver.default_parameters().has_parameter("linear_solver"):
dolfin.solve(form == 0, feasible_area_grad[i], solver_parameters={"linear_solver": "cg", "preconditioner": "amg"})
else:
dolfin.solve(form == 0, feasible_area_grad[i], solver_parameters={"newton_solver": {"linear_solver": "cg", "preconditioner": "amg"}})
self.feasible_area_grad = feasible_area_grad
self.attraction_center = attraction_center
def time_step(self, rhs):
dfn.begin("Computing velocity correction")
if params['all_steps_adaptive']:
self.adaptive_step(rhs)
else:
[bc.apply(self.A, rhs) for bc in self.bcs]
dfn.solve(self.A, self.cur_vel.vector(), rhs, "cg", "amg")
dfn.end()
def apply_inverse(self, V, ind=None, mu=None, options=None):
assert V in self.range
assert options is None # for now, simply use the default solver options set in FEniCS.
vectors = V._list if ind is None else [V._list[ind]] if isinstance(ind, Number) else [V._list[i] for i in ind]
R = self.source.zeros(len(vectors))
for r, v in zip(R._list, vectors):
df.solve(self.matrix, r.impl, v.impl)
return R
def apply_inverse(self, V, ind=None, mu=None, least_squares=False):
assert V in self.range
if least_squares:
raise NotImplementedError
vectors = V._list if ind is None else [V._list[ind]] if isinstance(ind, Number) else [V._list[i] for i in ind]
R = self.source.zeros(len(vectors))
for r, v in zip(R._list, vectors):
df.solve(self.matrix, r.impl, v.impl)
return R
def compute_direct_sample_solution_old(pde, RV_samples, coeff_field, A, maxm, proj_basis):
a = coeff_field.mean_func
for m in range(maxm):
a_m = RV_samples[m] * coeff_field[m][0]
a = a + a_m
A = pde.assemble_lhs(basis=proj_basis, coeff=a)
b = pde.assemble_rhs(basis=proj_basis, coeff=a)
X = 0 * b
solve(A, X, b)
return FEniCSVector(Function(proj_basis._fefs, X)), a
def eval_poisson(vec=None):
if vec == None:
# set default vector for new indices
# mesh0 = refine(Mesh(lshape_xml))
mesh0 = UnitSquare(4, 4)
fs = FunctionSpace(mesh0, "CG", 1)
vec = FEniCSVector(Function(fs))
pde = FEMPoisson()
fem_A = pde.assemble_lhs(diffcoeff, vec.basis)
fem_b = pde.assemble_rhs(f, vec.basis)
solve(fem_A, vec.coeffs, fem_b)
return vec
def solve(self):
"""
Solve the equation. This calls the DOLFIN solve function.
"""
if self.is_linear() and form_rank(self.__eq.lhs) == 1:
raise NotImplementedException("Solve for linear variational problem with rank 1 LHS not implemented")
if self.__J is None:
dolfin.solve(self.__eq, self.__x, self.__bcs, solver_parameters = self.__solver_parameters)
else:
dolfin.solve(self.__eq, self.__x, self.__bcs, J = self.__J, solver_parameters = self.__solver_parameters)
return
def compute_Nij(Nij,
G_matr,
G_under,
tensdim,
Sijmats,
Sijfcomps,
delta_CG1_sq,
alphaval=None,
u_f=None,
**NS_namespace):
"""
Function for computing Nij in ScaleDepLagrangian
"""
Sijf = Sijfcomps
alpha = alphaval
deltasq = 2 * delta_CG1_sq.vector().array()
# Need to compute F(F(Sij)), set up right hand sides
if tensdim == 3:
Ax, Ay = Sijmats
uf = u_f[0].vector()
vf = u_f[1].vector()
# Filtered rhs
buf = [Ax * uf, 0.5 * (Ay * uf + Ax * vf), Ay * vf]
else:
Ax, Ay, Az = Sijmats
uf = u_f[0].vector()
vf = u_f[1].vector()
wf = u_f[2].vector()
buf = [
Ax * uf, 0.5 * (Ay * uf + Ax * vf), 0.5 * (Az * uf + Ax * wf),
Ay * vf, 0.5 * (Az * vf + Ay * wf), Az * wf
]
for i in xrange(tensdim):
# Solve for the diff. components of F(F(Sij)))
solve(G_matr, Sijf[i].vector(), buf[i], "cg", "default")
# Compute magSf
magSf = mag(Sijf, tensdim)
for i in xrange(tensdim):
# Filter Nij = F(|S|Sij) --> F(F(|S|Sij))
tophatfilter(unfiltered=Nij[i], filtered=Nij[i], weight=1, **vars())
# Compute 2*delta**2*(F(F(|S|Sij)) - alpha**2*F(F(|S))F(F(Sij)))
Nij[i].vector().set_local(
deltasq * (Nij[i].vector().array() -
(alpha**2) * magSf * Sijf[i].vector().array()))
Nij[i].vector().apply("insert")
def advance_one_timestep(self, f, u_1):
"""
Solve the PDE for one time step.
f: the source term in the PDE.
u_1: solution at the previous time step.
"""
from dolfin import TestFunction, dx, solve
V, a, dt = self.V, self.a, self.dt # strip off self prefix
v = TestFunction(V)
L = (u_1 + dt * f) * v * dx
solve(self.a == L, self.U)
return self.U
def computeObservation(self, u_o):
"""
Compute the synthetic observation
"""
mt = dl.interpolate(self.mtrue, Vh[PARAMETER])
x = [self.generate_vector(STATE), mt.vector(), None]
A, b = self.assembleA(x, assemble_rhs = True)
A.init_vector(u_o, 1)
dl.solve(A, u_o, b, "cg", amg_method())
# Create noisy data, ud
MAX = u_o.norm("linf")
parRandom.normal_perturb(.01 * MAX, u_o)
def compute_velocity_correction(
ui, p0, p1, u_bcs, rho, mu, dt, rotational_form, my_dx, tol, verbose
):
"""Compute the velocity correction according to
.. math::
U = u_0 - \\frac{dt}{\\rho} \\nabla (p_1-p_0).
"""
W = ui.function_space()
P = p1.function_space()
u = TrialFunction(W)
v = TestFunction(W)
a3 = dot(u, v) * my_dx
phi = Function(P)
phi.assign(p1)
if p0:
phi -= p0
if rotational_form:
r = SpatialCoordinate(W.mesh())[0]
div_ui = 1 / r * (r * ui[0]).dx(0) + ui[1].dx(1)
phi += mu * div_ui
L3 = dot(ui, v) * my_dx - dt / rho * (phi.dx(0) * v[0] + phi.dx(1) * v[1]) * my_dx
u1 = Function(W)
solve(
a3 == L3,
u1,
bcs=u_bcs,
solver_parameters={
"linear_solver": "iterative",
"symmetric": True,
"preconditioner": "hypre_amg",
"krylov_solver": {
"relative_tolerance": tol,
"absolute_tolerance": 0.0,
"maximum_iterations": 100,
"monitor_convergence": verbose,
},
},
)
# u = project(ui - k/rho * grad(phi), V)
# div_u = 1/r * div(r*u)
r = SpatialCoordinate(W.mesh())[0]
div_u1 = 1.0 / r * (r * u1[0]).dx(0) + u1[1].dx(1)
info("||u||_div = {:e}".format(sqrt(assemble(div_u1 * div_u1 * my_dx))))
return u1
def field(self, state, x = None, lump = True, rhs_func = None):
"""
Returns the effective-field contribution for a given state.
This method uses a projection method to retrieve the field from the
RHS-form given by the :code:`form_rhs` method. It should be overriden
for better performance.
*Arguments*
state (:class:`State`)
the simulation state
x (:class:`dolfin.Vector`)
the vector to store the result or :code:`None`
*Returns*
:class:`dolfin.Function`
the effective-field contribution
"""
# TODO set particular solver
# TODO use caching for mass matrix
if rhs_func is None:
w = TestFunction(state.VectorFunctionSpace())
b = assemble(self.form_rhs(state, w) / Constant(Constants.gamma) * state.dx('magnetic'))
else:
b = rhs_func(state)
# Optional mass lumping
if x is None:
result = Function(state.VectorFunctionSpace())
else:
result = Function(state.VectorFunctionSpace(), x)
if lump:
A = state.M_inv_diag('magnetic')
A.mult(b, result.vector())
else:
w = TestFunction(state.VectorFunctionSpace())
h = TrialFunction(state.VectorFunctionSpace())
A = assemble(inner(w, h) * state.dx('magnetic'))
solve(A, result.vector(), b)
return result
def __invert_mass_matrix(self,u):
"""
Helper routine to invert mass matrix
Args:
u: current values
Returns:
inv(M)*u
"""
me = fenics_mesh(self.V)
A = 1.0*self.M
b = fenics_mesh(u)
df.solve(A,me.values.vector(),b.values.vector())
return me
def _error_estimator(dx, phi, mu, sigma, omega, conv, voltages):
'''Simple error estimator from
A posteriori error estimation and adaptive mesh-refinement techniques;
R. Verfürth;
Journal of Computational and Applied Mathematics;
Volume 50, Issues 1-3, 20 May 1994, Pages 67-83;
<https://www.sciencedirect.com/science/article/pii/0377042794902909>.
The strong PDE is
- div(1/(mu r) grad(rphi)) + <u, 1/r grad(rphi)> + i sigma omega phi
= sigma v_k / (2 pi r).
'''
from dolfin import cells
mesh = phi.function_space().mesh()
# Assemble the cell-wise residual in DG space
DG = FunctionSpace(mesh, 'DG', 0)
# get residual in DG
v = TestFunction(DG)
R = _residual_strong(dx, v, phi, mu, sigma, omega, conv, voltages)
r_r = assemble(R[0])
r_i = assemble(R[1])
r = r_r * r_r + r_i * r_i
visualize = True
if visualize:
# Plot the cell-wise residual
u = TrialFunction(DG)
a = zero() * dx(0)
subdomain_indices = mu.keys()
for i in subdomain_indices:
a += u * v * dx(i)
A = assemble(a)
R2 = Function(DG)
solve(A, R2.vector(), r)
plot(R2, title='||R||^2')
interactive()
K = r.array()
info('%r' % K)
h = numpy.array([c.diameter() for c in cells(mesh)])
eta = h * numpy.sqrt(K)
return eta
def solve_system(self,rhs,factor,u0,t):
"""
Dolfin's linear solver for (M-dtA)u = rhs
Args:
rhs: right-hand side for the nonlinear system
factor: abbrev. for the node-to-node stepsize (or any other factor required)
u0: initial guess for the iterative solver (not used here so far)
Returns:
solution as mesh
"""
A = self.M - factor*self.K
b = fenics_mesh(rhs)
u = fenics_mesh(u0)
df.solve(A,u.values.vector(),b.values.vector())
return u
def amr(mesh, m, DirichletBoundary, g, mesh2d, s0=1, alpha=1):
"""Function for computing the Anisotropic MagnetoResistance (AMR),
using given magnetisation configuration."""
# Scalar and vector function spaces.
V = df.FunctionSpace(mesh, "CG", 1)
VV = df.VectorFunctionSpace(mesh, 'CG', 1, 3)
# Define boundary conditions.
bcs = df.DirichletBC(V, g, DirichletBoundary())
# Nonlinear conductivity.
def sigma(u):
E = -df.grad(u)
costheta = df.dot(m, E)/(df.sqrt(df.dot(E, E))*df.sqrt(df.dot(m, m)))
return s0/(1 + alpha*costheta**2)
# Define variational problem for Picard iteration.
u = df.TrialFunction(V) # electric potential
v = df.TestFunction(V)
u_k = df.interpolate(df.Expression('x[0]'), V) # previous (known) u
a = df.inner(sigma(u_k)*df.grad(u), df.grad(v))*df.dx
# RHS to mimic linear problem.
f = df.Constant(0.0) # set to 0 -> nonlinear Poisson equation.
L = f*v*df.dx
u = df.Function(V) # new unknown function
eps = 1.0 # error measure ||u-u_k||
tol = 1.0e-20 # tolerance
iter = 0 # iteration counter
maxiter = 50 # maximum number of iterations allowed
while eps > tol and iter < maxiter:
iter += 1
df.solve(a == L, u, bcs)
diff = u.vector().array() - u_k.vector().array()
eps = np.linalg.norm(diff, ord=np.Inf)
print 'iter=%d: norm=%g' % (iter, eps)
u_k.assign(u) # update for next iteration
j = df.project(-sigma(u)*df.grad(u), VV)
return u, j, compute_flux(j, mesh2d)
def __eval_fexpl(self,u,t):
"""
Helper routine to evaluate the explicit part of the RHS
Args:
u: current values (not used here)
t: current time
Returns:
explicit part of RHS
"""
A = 1.0*self.K
b = self.apply_mass_matrix(u)
psi = fenics_mesh(self.V)
df.solve(A,psi.values.vector(),b.values.vector())
fexpl = fenics_mesh(self.V)
fexpl.values = df.project(df.Dx(psi.values,1)*df.Dx(u.values,0) - df.Dx(psi.values,0)*df.Dx(u.values,1),self.V)
return fexpl
def __init__(self, config, feasible_area, attraction_center):
'''
Generates the inequality constraints to enforce the turbines in the feasible area.
If the turbine is outside the domain, the constraints is equal to the distance between the turbine and the attraction center.
'''
self.config = config
self.feasible_area = feasible_area
# Compute the gradient of the feasible area
fs = dolfin.FunctionSpace(feasible_area.function_space().mesh(),
"DG",
feasible_area.function_space().ufl_element().degree() - 1)
feasible_area_grad = (dolfin.Function(fs),
dolfin.Function(fs))
t = dolfin.TestFunction(fs)
info_blue("Solving for gradient of feasible area")
for i in range(2):
form = dolfin.inner(feasible_area_grad[i], t) * dolfin.dx - dolfin.inner(feasible_area.dx(i), t) * dolfin.dx
dolfin.solve(form == 0, feasible_area_grad[i])
self.feasible_area_grad = feasible_area_grad
self.attraction_center = attraction_center
def compute_direct_sample_solution(pde, RV_samples, coeff_field, A, maxm, proj_basis, cache=None):
try:
A0 = cache.A
A_m = cache.A_m
b = cache.b
logger.debug("compute_direct_sample_solution: CACHE USED")
print "CACHE USED"
except AttributeError:
with timing(msg="direct_sample_sol: compute A_0, b", logfunc=logger.info):
a = coeff_field.mean_func
A0 = pde.assemble_lhs(basis=proj_basis, coeff=a, withDirichletBC=False)
b = pde.assemble_rhs(basis=proj_basis, coeff=a, withDirichletBC=False)
A_m = [None] * maxm
logger.debug("compute_direct_sample_solution: CACHE NOT USED")
print "CACHE NOT USED"
if cache is not None:
cache.A = A0
cache.A_m = A_m
cache.b = b
with timing(msg="direct_sample_sol: compute A_m", logfunc=logger.info):
A = A0.copy()
for m in range(maxm):
if A_m[m] is None:
a_m = coeff_field[m][0]
A_m[m] = pde.assemble_lhs(basis=proj_basis, coeff=a_m, withDirichletBC=False)
A += RV_samples[m] * A_m[m]
with timing(msg="direct_sample_sol: apply BCs", logfunc=logger.info):
A, b = pde.apply_dirichlet_bc(proj_basis._fefs, A, b)
with timing(msg="direct_sample_sol: solve linear system", logfunc=logger.info):
X = 0 * b
logger.info("compute_direct_sample_solution with %i dofs" % b.size())
solve(A, X, b)
return FEniCSVector(Function(proj_basis._fefs, X))
F = u * v * dx + dt * dot(grad(u), grad(v)) * dx - (u_n + dt * f) * v * dx
a, L = lhs(F), rhs(F)
# Time-stepping
u = Function(V)
t = 0
for n in range(num_steps):
# Update current time
t += dt
u_D.t = t
# Compute solution
solve(a == L, u, bc)
# Plot solution
# plot(u)
# axi.triplot(u)
sol_file << u
# Compute error at vertices
# u_e = interpolate(u_D, V)
# error = np.abs(u_e.vector().array() - u.vector().array()).max()
# print('t = %.2f: error = %.3g' % (t, error))
# Update previous solution
u_n.assign(u)
# Compute error in L2 norm
try: # If the solvers don't converge, reduce the time step and try again.
bmelt = -20.0
bdot = df.conditional(df.gt(H, np.abs(bmelt)), bmelt,
-H) * (1 - grounded)
P = None
if climate in "ltop":
P = get_adot_from_orog_precip(ltop_constants)
adot.vector().set_local(P)
print(t, dt_float, H0.vector().max(), df.assemble(h_s0 * df.dx))
assigner_s.assign(T, [B0, Qs0, h_s0, h_s_0, h_eff0])
assigner_g.assign(U, [ubar0, udef0, H0, H0_])
# Solve for water flux
df.solve(A_Qw == b_Qw, Qw)
# Solve for sediment variables
print("solving sed")
sed_solver = df.NonlinearVariationalSolver(sed_problem)
sed_solver.parameters["nonlinear_solver"] = "newton"
sed_solver.parameters["newton_solver"]["relative_tolerance"] = 1e-2
sed_solver.parameters["newton_solver"]["absolute_tolerance"] = 1e-2
sed_solver.parameters["newton_solver"][
"error_on_nonconvergence"] = True
sed_solver.parameters["newton_solver"]["linear_solver"] = "gmres"
sed_solver.parameters["newton_solver"]["maximum_iterations"] = 10
sed_solver.parameters["newton_solver"]["report"] = True
sed_solver.parameters["newton_solver"]["relaxation_parameter"] = 0.7
sed_solver.parameters["newton_solver"]["krylov_solver"][
def solve(tstep, w_, w_1, w_tmp, solvers, enable_PF, enable_EC, enable_NS,
**namespace):
""" Solve equations. """
timer_outer = df.Timer("Solve system")
for subproblem, enable in zip(["PF", "EC"], [enable_PF, enable_EC]):
timer_inner = df.Timer("Solve subproblem " + subproblem)
df.mpi_comm_world().barrier()
solvers[subproblem].solve()
timer_inner.stop()
if enable_NS:
# timer = df.Timer("NS: Assemble matrices")
# A1 = df.assemble(solvers["NSu"]["a1"])
# A2 = df.assemble(solvers["NSp"]["a2"])
# A3 = df.assemble(solvers["NSu"]["a3"])
# timer.stop()
# timer = df.Timer("NS: Apply BCs 1")
# [bc.apply(A1) for bc in solvers["NSu"]["bcs"]]
# [bc.apply(A2) for bc in solvers["NSp"]["bcs"]]
# timer.stop()
du = np.array([1e9])
tol = 1e-6
max_num_iterations = 1
i_iter = 0
Fu = solvers["NSu"]["Fu"]
bcs_u = solvers["NSu"]["bcs"]
while du > tol and i_iter < max_num_iterations:
print du[0]
i_iter += 1
du[0] = 0.
# Step 1: Tentative velocity
timer = df.Timer("NS: Tentative velocity")
# b1 = df.assemble(solvers["NSu"]["L1"])
# [bc.apply(b1) for bc in solvers["NSu"]["bcs"]]
# df.solve(A1, w_["NSu"].vector(), b1)
w_tmp["NSu"].vector()[:] = w_["NSu"].vector()
# A, L = df.system(Fu)
# df.solve(A == L, w_["NSu"], bcs_u)
df.solve(df.lhs(Fu) == df.rhs(Fu), w_["NSu"], bcs_u)
du[0] += df.norm(w_["NSu"].vector() - w_tmp["NSu"].vector())
timer.stop()
# Step 2: Pressure correction
timer = df.Timer("NS: Pressure correction")
# b2 = df.assemble(solvers["NSp"]["L2"])
# [bc.apply(b2) for bc in solvers["NSp"]["bcs"]]
# df.solve(A2, w_["NSp"].vector(), b2)
Fp = solvers["NSp"]["Fp"]
bcs_p = solvers["NSp"]["bcs"]
df.solve(df.lhs(Fp) == df.rhs(Fp), w_["NSp"], bcs_p)
w_1["NSp"].assign(w_["NSp"])
timer.stop()
# Step 3: Velocity correction
timer = df.Timer("NS: Velocity correction")
# b3 = df.assemble(solvers["NSu"]["L3"])
# df.solve(A3, w_["NSu"].vector(), b3)
Fu_corr = solvers["NSu"]["Fu_corr"]
df.solve(df.lhs(Fu_corr) == df.rhs(Fu_corr), w_["NSu"], bcs_u)
timer.stop()
timer_outer.stop()
deltaH*rob*Kinetic_oxy(u_Tn)*u_A*u_B*(pow(R*u_T, 2)/Uc1)*v_T*dx + \
(rof*cpf*Vz)*(u_T - T_in)*v_T*ds_in + \
hw*(u_T - Twall)*v_T*ds_wall
F = F_A + F_B + F_C + F_T
for n in range(num_steps):
print('{} out of {}'.format(n, num_steps))
t += delta_t # Update current time
# Solve variational problem for time step
solve(F == 0,
u,
solver_parameters={
"newton_solver": {
"relative_tolerance": 1e-6
},
"newton_solver": {
"maximum_iterations": 60
}
})
print('solver done')
# Save solution to files for visualization and postprocessing(HDF5)
_u_A, _u_B, _u_C, _u_T = u_n.split()
_u_D = _u_C * 3
if Writting_xdmf:
xdmffile_A.write(_u_A, t)
xdmffile_B.write(_u_B, t)
xdmffile_C.write(_u_C, t)
def compute_pressure(
P,
p0,
mu,
ui,
u,
my_dx,
p_bcs=None,
rotational_form=False,
tol=1.0e-10,
verbose=True,
):
"""Solve the pressure Poisson equation
.. math::
\\begin{align}
-\\frac{1}{r} \\div(r \\nabla (p_1-p_0)) =
-\\frac{1}{r} \\div(r u),\\\\
\\text{(with boundary conditions)},
\\end{align}
for :math:`\\nabla p = u`.
The pressure correction is based on the update formula
.. math::
\\frac{\\rho}{dt} (u_{n+1}-u^*)
+ \\begin{pmatrix}
\\text{d}\\phi/\\text{d}r\\\\
\\text{d}\\phi/\\text{d}z\\\\
\\frac{1}{r} \\text{d}\\phi/\\text{d}\\theta
\\end{pmatrix}
= 0
with :math:`\\phi = p_{n+1} - p^*` and
.. math::
\\frac{1}{r} \\frac{\\text{d}}{\\text{d}r} (r u_r^{(n+1)})
+ \\frac{\\text{d}}{\\text{d}z} (u_z^{(n+1)})
+ \\frac{1}{r} \\frac{\\text{d}}{\\text{d}\\theta} (u_{\\theta}^{(n+1)})
= 0
With the assumption that u does not change in the direction
:math:`\\theta`, one derives
.. math::
- \\frac{1}{r} \\div(r \\nabla \\phi) =
\\frac{1}{r} \\frac{\\rho}{dt} \\div(r (u_{n+1} - u^*))\\\\
- \\frac{1}{r} \\langle n, r \\nabla \\phi\\rangle =
\\frac{1}{r} \\frac{\\rho}{dt} \\langle n, r (u_{n+1} - u^*)\\rangle
In its weak form, this is
.. math::
\\int r \\langle\\nabla\\phi, \\nabla q\\rangle \\,2 \\pi =
- \\frac{\\rho}{dt} \\int \\div(r u^*) q \\, 2 \\pi
- \\frac{\\rho}{dt} \\int_{\\Gamma}
\\langle n, r (u_{n+1}-u^*)\\rangle q \\, 2\\pi.
(The terms :math:`1/r` cancel with the volume elements :math:`2\\pi r`.)
If the Dirichlet boundary conditions are applied to both :math:`u^*` and
:math:`u_n` (the latter in the velocity correction step), the boundary
integral vanishes.
If no Dirichlet conditions are given (which is the default case), the
system has no unique solution; one eigenvalue is 0. This however, does not
hurt CG convergence if the system is consistent, cf. :cite:`vdV03`. And
indeed it is consistent if and only if
.. math::
\\int_\\Gamma r \\langle n, u\\rangle = 0.
This condition makes clear that for incompressible Navier-Stokes, one
either needs to make sure that inflow and outflow always add up to 0, or
one has to specify pressure boundary conditions.
Note that, when using a multigrid preconditioner as is done here, the
coarse solver must be chosen such that it preserves the nullspace of the
problem.
"""
W = ui.function_space()
r = SpatialCoordinate(W.mesh())[0]
p = TrialFunction(P)
q = TestFunction(P)
a2 = dot(r * grad(p), grad(q)) * 2 * pi * my_dx
# The boundary conditions
# n.(p1-p0) = 0
# are implicitly included.
#
# L2 = -div(r*u) * q * 2*pi*my_dx
div_u = 1 / r * (r * u[0]).dx(0) + u[1].dx(1)
L2 = -div_u * q * 2 * pi * r * my_dx
if p0:
L2 += r * dot(grad(p0), grad(q)) * 2 * pi * my_dx
# In the Cartesian variant of the rotational form, one makes use of the
# fact that
#
# curl(curl(u)) = grad(div(u)) - div(grad(u)).
#
# The same equation holds true in cylindrical form. Hence, to get the
# rotational form of the splitting scheme, we need to
#
# rotational form
if rotational_form:
# If there is no dependence of the angular coordinate, what is
# div(grad(div(u))) in Cartesian coordinates becomes
#
# 1/r div(r * grad(1/r div(r*u)))
#
# in cylindrical coordinates (div and grad are in cylindrical
# coordinates). Unfortunately, we cannot write it down that
# compactly since u_phi is in the game.
# When using P2 elements, this value will be 0 anyways.
div_ui = 1 / r * (r * ui[0]).dx(0) + ui[1].dx(1)
grad_div_ui = as_vector((div_ui.dx(0), div_ui.dx(1)))
L2 -= r * mu * dot(grad_div_ui, grad(q)) * 2 * pi * my_dx
# div_grad_div_ui = 1/r * (r * grad_div_ui[0]).dx(0) \
# + (grad_div_ui[1]).dx(1)
# L2 += mu * div_grad_div_ui * q * 2*pi*r*dx
# n = FacetNormal(Q.mesh())
# L2 -= mu * (n[0] * grad_div_ui[0] + n[1] * grad_div_ui[1]) \
# * q * 2*pi*r*ds
p1 = Function(P)
if p_bcs:
solve(
a2 == L2,
p1,
bcs=p_bcs,
solver_parameters={
"linear_solver": "iterative",
"symmetric": True,
"preconditioner": "hypre_amg",
"krylov_solver": {
"relative_tolerance": tol,
"absolute_tolerance": 0.0,
"maximum_iterations": 100,
"monitor_convergence": verbose,
},
},
)
else:
# If we're dealing with a pure Neumann problem here (which is the
# default case), this doesn't hurt CG if the system is consistent,
# cf. :cite:`vdV03`. And indeed it is consistent if and only if
#
# \int_\Gamma r n.u = 0.
#
# This makes clear that for incompressible Navier-Stokes, one
# either needs to make sure that inflow and outflow always add up
# to 0, or one has to specify pressure boundary conditions.
#
# If the right-hand side is very small, round-off errors may impair
# the consistency of the system. Make sure the system we are
# solving remains consistent.
A = assemble(a2)
b = assemble(L2)
# Assert that the system is indeed consistent.
e = Function(P)
e.interpolate(Constant(1.0))
evec = e.vector()
evec /= norm(evec)
alpha = b.inner(evec)
normB = norm(b)
# Assume that in every component of the vector, a round-off error
# of the magnitude DOLFIN_EPS is present. This leads to the
# criterion
# |<b,e>| / (||b||*||e||) < DOLFIN_EPS
# as a check whether to consider the system consistent up to
# round-off error.
#
# TODO think about condition here
# if abs(alpha) > normB * DOLFIN_EPS:
if abs(alpha) > normB * 1.0e-12:
# divu = 1 / r * (r * u[0]).dx(0) + u[1].dx(1)
adivu = assemble(((r * u[0]).dx(0) + u[1].dx(1)) * 2 * pi * my_dx)
info("\\int 1/r * div(r*u) * 2*pi*r = {:e}".format(adivu))
n = FacetNormal(P.mesh())
boundary_integral = assemble((n[0] * u[0] + n[1] * u[1]) * 2 * pi * r * ds)
info("\\int_Gamma n.u * 2*pi*r = {:e}".format(boundary_integral))
message = (
"System not consistent! "
"<b,e> = {:g}, ||b|| = {:g}, <b,e>/||b|| = {:e}.".format(
alpha, normB, alpha / normB
)
)
info(message)
# # Plot the stuff, and project it to a finer mesh with linear
# # elements for the purpose.
# plot(divu, title='div(u_tentative)')
# # Vp = FunctionSpace(Q.mesh(), 'CG', 2)
# # Wp = MixedFunctionSpace([Vp, Vp])
# # up = project(u, Wp)
# fine_mesh = Q.mesh()
# for k in range(1):
# fine_mesh = refine(fine_mesh)
# V = FunctionSpace(fine_mesh, 'CG', 1)
# W = V * V
# # uplot = Function(W)
# # uplot.interpolate(u)
# uplot = project(u, W)
# plot(uplot[0], title='u_tentative[0]')
# plot(uplot[1], title='u_tentative[1]')
# # plot(u, title='u_tentative')
# interactive()
# exit()
raise RuntimeError(message)
# Project out the roundoff error.
b -= alpha * evec
#
# In principle, the ILU preconditioner isn't advised here since it
# might destroy the semidefiniteness needed for CG.
#
# The system is consistent, but the matrix has an eigenvalue 0.
# This does not harm the convergence of CG, but when
# preconditioning one has to make sure that the preconditioner
# preserves the kernel. ILU might destroy this (and the
# semidefiniteness). With AMG, the coarse grid solves cannot be LU
# then, so try Jacobi here.
# <http://lists.mcs.anl.gov/pipermail/petsc-users/2012-February/012139.html>
#
prec = PETScPreconditioner("hypre_amg")
from dolfin import PETScOptions
PETScOptions.set("pc_hypre_boomeramg_relax_type_coarse", "jacobi")
solver = PETScKrylovSolver("cg", prec)
solver.parameters["absolute_tolerance"] = 0.0
solver.parameters["relative_tolerance"] = tol
solver.parameters["maximum_iterations"] = 100
solver.parameters["monitor_convergence"] = verbose
# Create solver and solve system
A_petsc = as_backend_type(A)
b_petsc = as_backend_type(b)
p1_petsc = as_backend_type(p1.vector())
solver.set_operator(A_petsc)
solver.solve(p1_petsc, b_petsc)
return p1
bval = lambda lv, rv: df.Expression("({0}*(1-x[0])+{1}*(1+x[0]))/2".format(
lv, rv))
bcs = df.DirichletBC(V, bval(1, -1), u0_boundary)
dx = df.Measure("dx")
# Define variational problem
u = df.Function(V)
u_x = u.dx(0)
v = df.TestFunction(V)
v_x = v.dx(0)
mu = df.Constant(0.1)
F = (mu * u_x * v_x + v * u * u_x) * dx
df.solve(F == 0,
u,
bcs,
solver_parameters={"newton_solver": {
"relative_tolerance": 1e-6
}})
# plot solution
df.plot(u, title="Velocity")
# hold plot
df.interactive()
def run_with_params(Tb, mu_value, k_s, path):
run_time_init = clock()
mesh = BoxMesh(Point(0.0, 0.0, 0.0),
Point(mesh_width, mesh_width, mesh_height), nx, ny, nz)
pbc = PeriodicBoundary()
WE = VectorElement('CG', mesh.ufl_cell(), 2)
SE = FiniteElement('CG', mesh.ufl_cell(), 1)
WSSS = FunctionSpace(mesh,
MixedElement(WE, SE, SE, SE),
constrained_domain=pbc)
# W = FunctionSpace(mesh, WE, constrained_domain=pbc)
# S = FunctionSpace(mesh, SE, constrained_domain=pbc)
W = WSSS.sub(0).collapse()
S = WSSS.sub(1).collapse()
temperature_vals = [27.0 + 273, Tb + 273, 1300.0 + 273, 1305.0 + 273]
temp_prof = TemperatureProfile(temperature_vals, element=S.ufl_element())
mu_a = mu_value # this was taken from the Blankenbach paper, can change
Ep = b / temp_prof.delta
mu_bot = exp(-Ep *
(temp_prof.bottom * temp_prof.delta - 1573.0) + cc) * mu_a
# TODO: verify exponentiation
Ra = rho_0 * alpha * g * temp_prof.delta * h**3 / (kappa_0 * mu_a)
w0 = rho_0 * alpha * g * temp_prof.delta * h**2 / mu_a
tau = h / w0
p0 = mu_a * w0 / h
log(mu_a, mu_bot, Ra, w0, p0)
slip_vx = 1.6E-09 / w0 # Non-dimensional
slip_velocity = Constant((slip_vx, 0.0, 0.0))
zero_slip = Constant((0.0, 0.0, 0.0))
time_step = 3.0E11 / tau * 2
dt = Constant(time_step)
t_end = 3.0E15 / tau / 5.0 # Non-dimensional times
u = Function(WSSS)
# Instead of TrialFunctions, we use split(u) for our non-linear problem
v, p, T, Tf = split(u)
v_t, p_t, T_t, Tf_t = TestFunctions(WSSS)
T0 = interpolate(temp_prof, S)
mu_exp = Expression(
'exp(-Ep * (T_val * dTemp - 1573.0) + cc * x[2] / mesh_height)',
Ep=Ep,
dTemp=temp_prof.delta,
cc=cc,
mesh_height=mesh_height,
T_val=T0,
element=S.ufl_element())
Tf0 = interpolate(temp_prof, S)
mu = Function(S)
v0 = Function(W)
v_theta = (1.0 - theta) * v0 + theta * v
T_theta = (1.0 - theta) * T0 + theta * T
Tf_theta = (1.0 - theta) * Tf0 + theta * Tf
# TODO: Verify forms
r_v = (inner(sym(grad(v_t)), 2.0 * mu * sym(grad(v))) - div(v_t) * p -
T * v_t[2]) * dx
r_p = p_t * div(v) * dx
heat_transfer = Constant(k_s) * (Tf_theta - T_theta) * dt
r_T = (
T_t *
((T - T0) + dt * inner(v_theta, grad(T_theta))) # TODO: Inner vs dot
+
(dt / Ra) * inner(grad(T_t), grad(T_theta)) - T_t * heat_transfer) * dx
v_melt = Function(W)
z_hat = Constant((0.0, 0.0, 1.0))
# TODO: inner -> dot, take out Tf_t
r_Tf = (Tf_t * ((Tf - Tf0) + dt * inner(v_melt, grad(Tf_theta))) +
Tf_t * heat_transfer) * dx
r = r_v + r_p + r_T + r_Tf
bcv0 = DirichletBC(WSSS.sub(0), zero_slip, top)
bcv1 = DirichletBC(WSSS.sub(0), slip_velocity, bottom)
bcv2 = DirichletBC(WSSS.sub(0).sub(1), Constant(0.0), back)
bcv3 = DirichletBC(WSSS.sub(0).sub(1), Constant(0.0), front)
bcp0 = DirichletBC(WSSS.sub(1), Constant(0.0), bottom)
bct0 = DirichletBC(WSSS.sub(2), Constant(temp_prof.surface), top)
bct1 = DirichletBC(WSSS.sub(2), Constant(temp_prof.bottom), bottom)
bctf1 = DirichletBC(WSSS.sub(3), Constant(temp_prof.bottom), bottom)
bcs = [bcv0, bcv1, bcv2, bcv3, bcp0, bct0, bct1, bctf1]
t = 0
count = 0
files = DefaultDictByKey(partial(create_xdmf, path))
while t < t_end:
mu.interpolate(mu_exp)
rhosolid = rho_0 * (1.0 - alpha * (T0 * temp_prof.delta - 1573.0))
deltarho = rhosolid - rho_melt
# TODO: project (accuracy) vs interpolate
assign(
v_melt,
project(
v0 - darcy * (grad(p) * p0 / h - deltarho * z_hat * g) / w0,
W))
# TODO: Written out one step later?
# v_melt.assign(v0 - darcy * (grad(p) * p0 / h - deltarho * yvec * g) / w0)
# TODO: use nP after to avoid projection?
solve(r == 0, u, bcs)
nV, nP, nT, nTf = u.split() # TODO: write with Tf, ... etc
if count % output_every == 0:
time_left(count, t_end / time_step,
run_time_init) # TODO: timestep vs dt
# TODO: Make sure all writes are to the same function for each time step
files['T_fluid'].write(nTf, t)
files['p'].write(nP, t)
files['v_solid'].write(nV, t)
files['T_solid'].write(nT, t)
files['mu'].write(mu, t)
files['v_melt'].write(v_melt, t)
files['gradp'].write(project(grad(nP), W), t)
files['rho'].write(project(rhosolid, S), t)
files['Tf_grad'].write(project(grad(Tf), W), t)
files['advect'].write(project(dt * dot(v_melt, grad(nTf))), t)
files['ht'].write(project(heat_transfer, S), t)
assign(T0, nT)
assign(v0, nV)
assign(Tf0, nTf)
t += time_step
count += 1
log('Case mu={}, Tb={}, k={} complete. Run time = {:.2f} minutes'.format(
mu_a, Tb, k_s, (clock() - run_time_init) / 60.0))
# Loop over all layers in y-direction
for j in range(ny + 1):
y_.assign(ydofs[j])
L = assemble(L_)
# This is ordinary matrix multiplication
# np.max(np.abs(M*Vtilde[3,:] - M.array() @ Vtilde[3,:]))
Mvtmp.vector().set_local(M * Vtilde[j, :])
Lfull = Mvtmp.vector() - dt[i] * L
bc_Vx.apply(Lfull)
# Solve problem in j'th layer
solve(Sfull, vsol.vector(), Lfull)
# Store solution for j'th layer
vold[j, :] = vsol.vector().get_local()
if storeV:
Vsol[i + 1, :, :] = vold
if plotSol:
Z = np.abs(vold - u_numpy(trange[i], X, Y))
if plotMode == 'contour':
plotViContour(
X[:, ix],
Y[:, ix],
Z[:, ix],
def stokes_solve(
up_out,
mu,
u_bcs, p_bcs,
f,
dx=dx,
verbose=True,
tol=1.0e-10,
maxiter=1000
):
# Some initial sanity checks.
assert mu > 0.0
WP = up_out.function_space()
# Translate the boundary conditions into the product space.
new_bcs = []
for k, bcs in enumerate([u_bcs, p_bcs]):
for bc in bcs:
space = bc.function_space()
C = space.component()
if len(C) == 0:
new_bcs.append(DirichletBC(WP.sub(k),
bc.value(),
bc.domain_args[0]))
elif len(C) == 1:
new_bcs.append(DirichletBC(WP.sub(k).sub(int(C[0])),
bc.value(),
bc.domain_args[0]))
else:
raise RuntimeError('Illegal number of subspace components.')
# TODO define p*=-1 and reverse sign in the end to get symmetric system?
# Define variational problem
(u, p) = TrialFunctions(WP)
(v, q) = TestFunctions(WP)
r = Expression('x[0]', degree=1, domain=WP.mesh())
print("mu = %e" % mu)
# build system
a = mu * inner(r * grad(u), grad(v)) * 2 * pi * dx \
- ((r * v[0]).dx(0) + (r * v[1]).dx(1)) * p * 2 * pi * dx \
+ ((r * u[0]).dx(0) + (r * u[1]).dx(1)) * q * 2 * pi * dx
#- div(r*v)*p* 2*pi*dx \
#+ q*div(r*u)* 2*pi*dx
L = inner(f, v) * 2 * pi * r * dx
A, b = assemble_system(a, L, new_bcs)
mode = 'lu'
if mode == 'lu':
solve(A, up_out.vector(), b, 'lu')
elif mode == 'gmres':
# For preconditioners for the Stokes system, see
#
# Fast iterative solvers for discrete Stokes equations;
# J. Peters, V. Reichelt, A. Reusken.
#
prec = mu * inner(r * grad(u), grad(v)) * 2 * pi * dx \
- p * q * 2 * pi * r * dx
P, btmp = assemble_system(prec, L, new_bcs)
solver = KrylovSolver('tfqmr', 'amg')
#solver = KrylovSolver('gmres', 'amg')
solver.set_operators(A, P)
solver.parameters['monitor_convergence'] = verbose
solver.parameters['report'] = verbose
solver.parameters['absolute_tolerance'] = 0.0
solver.parameters['relative_tolerance'] = tol
solver.parameters['maximum_iterations'] = maxiter
# Solve
solver.solve(up_out.vector(), b)
elif mode == 'fieldsplit':
raise NotImplementedError('Fieldsplit solver not yet implemented.')
# For an assortment of preconditioners, see
#
# Performance and analysis of saddle point preconditioners
# for the discrete steady-state Navier-Stokes equations;
# H.C. Elman, D.J. Silvester, A.J. Wathen;
# Numer. Math. (2002) 90: 665-688;
# <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.145.3554>.
#
# Set up field split.
W = SubSpace(WP, 0)
P = SubSpace(WP, 1)
u_dofs = W.dofmap().dofs()
p_dofs = P.dofmap().dofs()
prec = PETScPreconditioner()
prec.set_fieldsplit([u_dofs, p_dofs], ['u', 'p'])
PETScOptions.set('pc_type', 'fieldsplit')
PETScOptions.set('pc_fieldsplit_type', 'additive')
PETScOptions.set('fieldsplit_u_pc_type', 'lu')
PETScOptions.set('fieldsplit_p_pc_type', 'jacobi')
# Create Krylov solver with custom preconditioner.
solver = PETScKrylovSolver('gmres', prec)
solver.set_operator(A)
return
def solve_regular(a, L, bcs, u, solver_parameters):
if solver_parameters is None:
solver_parameters = {"linear_solver": "gmres"}
df.solve(a == L, u, bcs, solver_parameters=solver_parameters)
def _evaluateGlobalMixedEstimator(cls, mu, w, coeff_field, pde, f, quadrature_degree, vectorspace_type='BDM'):
"""Evaluation of global mixed equilibrated estimator."""
# set quadrature degree
# quadrature_degree_old = parameters["form_compiler"]["quadrature_degree"]
# parameters["form_compiler"]["quadrature_degree"] = quadrature_degree
# logger.debug("residual quadrature order = " + str(quadrature_degree))
# prepare numerical flux and f
sigma_mu, f_mu = evaluate_numerical_flux(w, mu, coeff_field, f)
# ###################
# ## MIXED PROBLEM ##
# ###################
# get setup data for mixed problem
V = w[mu]._fefunc.function_space()
mesh = V.mesh()
degree = element_degree(w[mu]._fefunc)
# create function spaces
DG0 = FunctionSpace(mesh, 'DG', 0)
DG0_dofs = [DG0.dofmap().cell_dofs(c.index())[0] for c in cells(mesh)]
RT = FunctionSpace(mesh, vectorspace_type, degree)
W = RT * DG0
# setup boundary conditions
# bcs = pde.create_dirichlet_bcs(W.sub(1))
# debug ===
# from dolfin import DOLFIN_EPS, DirichletBC
# def boundary(x):
# return x[0] < DOLFIN_EPS or x[0] > 1.0 + DOLFIN_EPS or x[1] < DOLFIN_EPS or x[1] > 1.0 + DOLFIN_EPS
# bcs = [DirichletBC(W.sub(1), Constant(0.0), boundary)]
# === debug
# create trial and test functions
(sigma, u) = TrialFunctions(W)
(tau, v) = TestFunctions(W)
# define variational form
a_eq = (dot(sigma, tau) + div(tau) * u + div(sigma) * v) * dx
L_eq = (- f_mu * v + dot(sigma_mu, tau)) * dx
# compute solution
w_eq = Function(W)
solve(a_eq == L_eq, w_eq)
(sigma_mixed, u_mixed) = w_eq.split()
# #############################
# ## EQUILIBRATION ESTIMATOR ##
# #############################
# evaluate error estimator
dg0 = TestFunction(DG0)
eta_mu = inner(sigma_mu, sigma_mu) * dg0 * dx
eta_T = assemble(eta_mu, form_compiler_parameters={'quadrature_degree': quadrature_degree})
eta_T = np.array([sqrt(e) for e in eta_T])
# evaluate global error
eta = sqrt(sum(i**2 for i in eta_T))
# reorder array entries for local estimators
eta_T = eta_T[DG0_dofs]
# restore quadrature degree
# parameters["form_compiler"]["quadrature_degree"] = quadrature_degree_old
return eta, FlatVector(eta_T)
# We also need to create a :py:class:`Function
# <dolfin.cpp.function.Function>` to store the solution(s). The (full)
# solution will be stored in ``w``, which we initialize using the mixed
# function space ``W``. The actual
# computation is performed by calling solve with the arguments ``a``,
# ``L``, ``w`` and ``bcs``. The separate components ``u`` and ``p`` of
# the solution can be extracted by calling the :py:meth:`split
# <dolfin.functions.function.Function.split>` function. Here we use an
# optional argument True in the split function to specify that we want a
# deep copy. If no argument is given we will get a shallow copy. We want
# a deep copy for further computations on the coefficient vectors::
# Compute solution
w = Function(W)
solve(a == L, w, bcs, petsc_options={"ksp_type": "preonly",
"pc_type": "lu", "pc_factor_mat_solver_type": "mumps"})
# Split the mixed solution and collapse
u = w.sub(0).collapse()
p = w.sub(1).collapse()
# We can calculate the :math:`L^2` norms of u and p as follows::
print("Norm of velocity coefficient vector: %.15g" % u.vector.norm())
print("Norm of pressure coefficient vector: %.15g" % p.vector.norm())
# Check pressure norm
assert np.isclose(p.vector.norm(), 4147.69457577)
# Finally, we can save and plot the solutions::
def test_heat_equation_fenics():
# Define problem
class Heat(object):
'''
u' = \\Delta u + f
'''
def __init__(self, V):
self.V = V
u = TrialFunction(V)
v = TestFunction(V)
self.M = assemble(u * v * dx)
self.A = assemble(-dot(grad(u), grad(v)) * dx)
self.b = assemble(1.0 * v * dx)
self.bcs = DirichletBC(self.V, 0.0, 'on_boundary')
return
# pylint: disable=unused-argument
def eval_alpha_M_beta_F(self, alpha, beta, u, t):
# Evaluate alpha * M * u + beta * F(u, t).
uvec = u.vector()
return alpha * (self.M * uvec) + beta * (self.A * uvec + self.b)
def solve_alpha_M_beta_F(self, alpha, beta, b, t):
# Solve alpha * M * u + beta * F(u, t) = b for u.
A = alpha * self.M + beta * self.A
rhs = b - beta * self.b
self.bcs.apply(A, rhs)
solver = KrylovSolver('gmres', 'ilu')
solver.parameters['relative_tolerance'] = 1.0e-13
solver.parameters['absolute_tolerance'] = 0.0
solver.parameters['maximum_iterations'] = 100
solver.parameters['monitor_convergence'] = True
solver.set_operator(A)
u = Function(self.V)
solver.solve(u.vector(), rhs)
return u
# create initial guess
mesh = UnitSquareMesh(20, 20, 'crossed')
V = FunctionSpace(mesh, 'CG', 1)
u = TrialFunction(V)
v = TestFunction(V)
u0 = Function(V)
solve(u * v * dx == Constant(0.0) * v * dx, u0)
u1 = Function(V)
u1.assign(u0)
# create time stepper
# stepper = parabolic.Dummy(Heat(V))
# stepper = parabolic.ExplicitEuler(Heat(V))
stepper = parabolic.ImplicitEuler(Heat(V))
# stepper = parabolic.Trapezoidal(Heat(V))
# step
t = 0.0
dt = 1.0e-3
with XDMFFile('heat.xdmf') as xf:
xf.write(u1, t)
for _ in range(10):
u1.assign(stepper.step(u0, t, dt))
u0.assign(u1)
t += dt
xf.write(u1, t)
return
def comp_full_model_heat_dirichlet(mat_obj, mesh_obj, bc, omega, save_path):
# ======
# Parameters
# ======
E = mat_obj.E
rho = mat_obj.rho
nu = mat_obj.nu
mesh = mesh_obj.create()
Rext = mesh_obj.Rext
Rint = mesh_obj.Rint
G = 1 # FAKE
# ======
# Thermal load
# ======
alpha = 1
T = 1
# ======
# Thickness profile
# ======
h = 1
omega_velo = 1 #Fake
# ======
# markers
# ======
cell_markers, facet_markers = define_markers(mesh, Rext, Rint)
# rename x[0], x[1] by x, y
x, y = df.SpatialCoordinate(mesh)
dim = mesh.topology().dim()
coord = mesh.coordinates()
# ======
# Create function space
# ======
V = df.FunctionSpace(mesh, "CG", 1)
degree = 1
fi_ele = FiniteElement("CG", mesh.ufl_cell(),
degree) # CG: Continuous Galerkin
vec_ele = VectorElement("CG", mesh.ufl_cell(), degree)
total_ele = MixedElement([fi_ele, fi_ele])
W = df.FunctionSpace(mesh, total_ele)
# ======
# Define boundary condition
# ======
if bc == 'CC':
u_Dbc = [
df.DirichletBC(W.sub(0), df.Constant(0.0), facet_markers, bc)
for bc in [1, 2]
]
v_Dbc = [
df.DirichletBC(W.sub(1), df.Constant(0.0), facet_markers, bc)
for bc in [1, 2]
]
elif bc == 'CF':
u_Dbc = [
df.DirichletBC(W.sub(0), df.Constant(0.0), facet_markers, bc)
for bc in [2]
]
v_Dbc = [
df.DirichletBC(W.sub(1), df.Constant(0.0), facet_markers, bc)
for bc in [2]
]
Dbc = u_Dbc + v_Dbc
# ======
# Define functions
# ======
dunks = df.TrialFunction(W)
tunks = df.TestFunction(W)
unks = df.Function(W, name='displacement')
# u(x,y): displacement in radial direction
# v(x,y): displacement in tangential direction
(du, dv) = df.split(dunks)
(tu, tv) = df.split(tunks)
(u, v) = df.split(unks)
# ======
# Define variable
# ======
class THETA(df.UserExpression):
def eval(self, values, x):
values[0] = math.atan2(x[1], x[0])
def value_shape(self):
#return (1,) # vector
return () # scalar
theta = THETA(degree=1)
#theta_int = df.interpolate(theta, df.FunctionSpace(mesh, "DG", 0))
#df.File(save_path + 'theta.pvd') << theta_int
class RADIUS(df.UserExpression):
def eval(self, values, x):
values[0] = df.sqrt(x[0] * x[0] + x[1] * x[1])
def value_shape(self):
return () # scalar
r = RADIUS(degree=1)
# ======
# Define week form
# ======
def d_dr(du):
return 1.0 / r * (x * df.Dx(du, 0) + y * df.Dx(du, 1))
def d_dtheta(du):
return -y * df.Dx(du, 0) + x * df.Dx(du, 1)
# strain radial
def epsilon_r(du):
return d_dr(du)
# strain circumferential
def epsilon_theta(du, dv):
return du / r + 1.0 / r * d_dtheta(dv)
# shear srain component
def gamma_rtheta(du, dv):
return d_dr(dv) - dv / r + 1.0 / r * d_dtheta(du)
epsilon_r(du)
epsilon_theta(du, dv)
gamma_rtheta(du, dv)
'''
S = [[1.0/E, -nu/E, 0.0],
[-nu/E, 1.0/E, 0.0],
[0.0, 0.0, 1.0/G]]
C = df.inv(df.as_matrix(S))
eps_vector = df.as_vector([epsilon_r(du), epsilon_theta(du, dv), gamma_rtheta(du, dv)])
sig_vector = dot(C, eps_vector)
'''
def sigma_r(du, dv):
return E / (1.0 - nu**2) * ((epsilon_r(du) - alpha * T) + nu *
(epsilon_theta(du, dv) - alpha * T))
def sigma_theta(du, dv):
return E / (1.0 - nu**2) * ((epsilon_theta(du, dv) - alpha * T) + nu *
(epsilon_r(du) - alpha * T))
def tau_rtheta(du, dv):
return G * gamma_rtheta(du, dv)
# week form
dF_sigma = -sigma_r(du, dv) * h * r * d_dr(tu) * dx
dF_sigma = dF_sigma - tau_rtheta(du, dv) * h * d_dtheta(tu) * dx
dF_sigma = dF_sigma - sigma_theta(du, dv) * h * tu * dx
dF_sigma = dF_sigma + rho * omega**2 * r**2 * h * tu * dx
dF_tau = -tau_rtheta(du, dv) * h * r * d_dr(tv) * dx
dF_tau = dF_tau - sigma_theta(du, dv) * h * d_dtheta(tv) * dx
dF_tau = dF_tau + tau_rtheta(du, dv) * h * tv * dx
dF_tau = dF_tau + rho * omega_velo * r**2 * h * tv * dx
dF = dF_sigma + dF_tau
# residual
F = df.action(dF, unks)
# solve
df.solve(F == 0, unks, Dbc)
# splits solution
_u, _v = unks.split(True)
# displacement
df.File(save_path + 'u.pvd') << _u
df.File(save_path + 'v.pvd') << _v
# ======
# Analyze
# ======
# compute stresses
sigma_r_pro = df.project(sigma_r(_u, _v), V)
sigma_r_pro.rename('sigma_r [Pa]', 'sigma_r [Pa]')
df.File(save_path + 'sigma_r.pvd') << sigma_r_pro
sigma_theta_pro = df.project(sigma_theta(_u, _v), V)
sigma_theta_pro.rename('sigma_theta [Pa]', 'sigma_theta [Pa]')
df.File(save_path + 'sigma_theta.pvd') << sigma_theta_pro
# compute von Mises stress
def von_mises_stress(sigma_r, sigma_theta):
return df.sqrt(sigma_r**2 + sigma_theta**2 - sigma_r * sigma_theta)
von_stress_pro = df.project(
von_mises_stress(sigma_r(_u, _v), sigma_theta(_u, _v)), V)
von_stress_pro.rename('von Mises Stress [Pa]', 'von Mises Stress [Pa]')
df.File(save_path + 'von_mises_stress.pvd') << von_stress_pro
tau_rtheta_pro = df.project(tau_rtheta(_u, _v), V)
tau_rtheta_pro.rename('tau_rtheta [Pa]', 'tau_rtheta [Pa]')
df.File(save_path + 'tau_rtheta.pvd') << tau_rtheta_pro
# save results to h5
rfile = df.HDF5File(mesh.mpi_comm(), save_path + 'results.h5', "w")
rfile.write(_u, "u")
rfile.write(_v, "v")
rfile.write(sigma_r_pro, "sigma_r")
rfile.write(sigma_theta_pro, "sigma_theta")
rfile.write(von_stress_pro, "von_mises_stress")
rfile.write(tau_rtheta_pro, "tau_rtheta_pro")
rfile.close()
def solve_linear(self, d_outputs, d_residuals, mode):
linear_solver_ = self.options['linear_solver_']
pde_problem = self.options['pde_problem']
state_name = self.options['state_name']
state_function = pde_problem.states_dict[state_name]['function']
for argument_name, argument_function in iteritems(self.argument_functions_dict):
density_func = argument_function
mesh = state_function.function_space().mesh()
sub_domains = df.MeshFunction('size_t', mesh, mesh.topology().dim() - 1)
upper_edge = TractionBoundary()
upper_edge.mark(sub_domains, 6)
dss = df.Measure('ds')(subdomain_data=sub_domains)
tractionBC = dss(6)
residual_form = get_residual_form(
state_function,
df.TestFunction(state_function.function_space()),
density_func,
density_func.function_space(),
tractionBC,
# df.Constant((0.0, -9.e-1))
df.Constant((0.0, -9.e-1)),
int(self.itr)
)
A, _ = df.assemble_system(self.derivative_form, - residual_form, pde_problem.bcs_list)
if linear_solver_=='fenics_direct':
rhs_ = df.Function(state_function.function_space())
dR = df.Function(state_function.function_space())
rhs_.vector().set_local(d_outputs[state_name])
for bc in pde_problem.bcs_list:
bc.apply(A)
Am = df.as_backend_type(A).mat()
ATm = Am.transpose()
AT = df.PETScMatrix(ATm)
df.solve(AT,dR.vector(),rhs_.vector())
d_residuals[state_name] = dR.vector().get_local()
elif linear_solver_=='scipy_splu':
for bc in pde_problem.bcs_list:
bc.apply(A)
Am = df.as_backend_type(A).mat()
ATm = Am.transpose()
ATm_csr = csr_matrix(ATm.getValuesCSR()[::-1], shape=Am.size)
lu = splu(ATm_csr.tocsc())
d_residuals[state_name] = lu.solve(d_outputs[state_name],trans='T')
elif linear_solver_=='fenics_Krylov':
rhs_ = df.Function(state_function.function_space())
dR = df.Function(state_function.function_space())
rhs_.vector().set_local(d_outputs[state_name])
for bc in pde_problem.bcs_list:
bc.apply(A)
Am = df.as_backend_type(A).mat()
ATm = Am.transpose()
AT = df.PETScMatrix(ATm)
solver = df.KrylovSolver('gmres', 'ilu')
prm = solver.parameters
prm["maximum_iterations"]=1000000
prm["divergence_limit"] = 1e2
solver.solve(AT,dR.vector(),rhs_.vector())
d_residuals[state_name] = dR.vector().get_local()
elif linear_solver_=='petsc_gmres_ilu':
ksp = PETSc.KSP().create()
ksp.setType(PETSc.KSP.Type.GMRES)
ksp.setTolerances(rtol=5e-11)
for bc in pde_problem.bcs_list:
bc.apply(A)
Am = df.as_backend_type(A).mat()
ksp.setOperators(Am)
ksp.setFromOptions()
pc = ksp.getPC()
pc.setType("ilu")
size = state_function.function_space().dim()
dR = PETSc.Vec().create()
dR.setSizes(size)
dR.setType('seq')
dR.setValues(range(size), d_residuals[state_name])
dR.setUp()
du = PETSc.Vec().create()
du.setSizes(size)
du.setType('seq')
du.setValues(range(size), d_outputs[state_name])
du.setUp()
if mode == 'fwd':
ksp.solve(dR,du)
d_outputs[state_name] = du.getValues(range(size))
else:
ksp.solveTranspose(du,dR)
d_residuals[state_name] = dR.getValues(range(size))
def discretize(DIM, N, ORDER):
# ### problem definition
import dolfin as df
if DIM == 2:
mesh = df.UnitSquareMesh(N, N)
elif DIM == 3:
mesh = df.UnitCubeMesh(N, N, N)
else:
raise NotImplementedError
V = df.FunctionSpace(mesh, "CG", ORDER)
g = df.Constant(1.0)
c = df.Constant(1.)
class DirichletBoundary(df.SubDomain):
def inside(self, x, on_boundary):
return abs(x[0] - 1.0) < df.DOLFIN_EPS and on_boundary
db = DirichletBoundary()
bc = df.DirichletBC(V, g, db)
u = df.Function(V)
v = df.TestFunction(V)
f = df.Expression("x[0]*sin(x[1])", degree=2)
F = df.inner(
(1 + c * u**2) * df.grad(u), df.grad(v)) * df.dx - f * v * df.dx
df.solve(F == 0,
u,
bc,
solver_parameters={"newton_solver": {
"relative_tolerance": 1e-6
}})
# ### pyMOR wrapping
from pymor.bindings.fenics import FenicsVectorSpace, FenicsOperator, FenicsVisualizer
from pymor.models.basic import StationaryModel
from pymor.operators.constructions import VectorOperator
from pymor.parameters.spaces import CubicParameterSpace
space = FenicsVectorSpace(V)
op = FenicsOperator(
F,
space,
space,
u, (bc, ),
parameter_setter=lambda mu: c.assign(float(mu['c'])),
parameter_type={'c': ()},
solver_options={'inverse': {
'type': 'newton',
'rtol': 1e-6
}})
rhs = VectorOperator(op.range.zeros())
fom = StationaryModel(op,
rhs,
visualizer=FenicsVisualizer(space),
parameter_space=CubicParameterSpace({'c': ()}, 0.,
1000.))
return fom
def solve_nonlinear(self, inputs, outputs):
pde_problem = self.options['pde_problem']
state_name = self.options['state_name']
problem_type = self.options['problem_type']
visualization = self.options['visualization']
state_function = pde_problem.states_dict[state_name]['function']
for argument_name, argument_function in iteritems(self.argument_functions_dict):
density_func = argument_function
mesh = state_function.function_space().mesh()
sub_domains = df.MeshFunction('size_t', mesh, mesh.topology().dim() - 1)
upper_edge = TractionBoundary()
upper_edge.mark(sub_domains, 6)
dss = df.Measure('ds')(subdomain_data=sub_domains)
tractionBC = dss(6)
self.itr = self.itr + 1
state_function = pde_problem.states_dict[state_name]['function']
residual_form = get_residual_form(
state_function,
df.TestFunction(state_function.function_space()),
density_func,
density_func.function_space(),
tractionBC,
# df.Constant((0.0, -9.e-1))
df.Constant((0.0, -9.e-1)),
int(self.itr)
)
self._set_values(inputs, outputs)
self.derivative_form = df.derivative(residual_form, state_function)
df.set_log_level(df.LogLevel.ERROR)
df.set_log_active(True)
# df.solve(residual_form==0, state_function, bcs=pde_problem.bcs_list, J=self.derivative_form)
if problem_type == 'linear_problem':
df.solve(residual_form==0, state_function, bcs=pde_problem.bcs_list, J=self.derivative_form,
solver_parameters={"newton_solver":{"maximum_iterations":60, "error_on_nonconvergence":False}})
elif problem_type == 'nonlinear_problem':
problem = df.NonlinearVariationalProblem(residual_form, state_function, pde_problem.bcs_list, self.derivative_form)
solver = df.NonlinearVariationalSolver(problem)
solver.parameters['nonlinear_solver_']='snes'
solver.parameters["snes_solver"]["line_search"] = 'bt'
solver.parameters["snes_solver"]["linear_solver_"]='mumps' # "cg" "gmres"
solver.parameters["snes_solver"]["maximum_iterations"]=500
solver.parameters["snes_solver"]["relative_tolerance"]=5e-13
solver.parameters["snes_solver"]["absolute_tolerance"]=5e-13
# solver.parameters["snes_solver"]["linear_solver_"]["maximum_iterations"]=1000
solver.parameters["snes_solver"]["error_on_nonconvergence"] = False
solver.solve()
elif problem_type == 'nonlinear_problem_load_stepping':
num_steps = 3
state_function.vector().set_local(np.zeros((state_function.function_space().dim())))
for i in range(num_steps):
v = df.TestFunction(state_function.function_space())
if i < (num_steps-1):
residual_form = get_residual_form(
state_function,
v,
density_func,
density_func.function_space(),
tractionBC,
# df.Constant((0.0, -9.e-1))
df.Constant((0.0, -9.e-1/num_steps*(i+1))),
int(self.itr)
)
else:
residual_form = get_residual_form(
state_function,
v,
density_func,
density_func.function_space(),
tractionBC,
# df.Constant((0.0, -9.e-1))
df.Constant((0.0, -9.e-1/num_steps*(i+1))),
int(self.itr)
)
problem = df.NonlinearVariationalProblem(residual_form, state_function, pde_problem.bcs_list, self.derivative_form)
solver = df.NonlinearVariationalSolver(problem)
solver.parameters['nonlinear_solver_']='snes'
solver.parameters["snes_solver"]["line_search"] = 'bt'
solver.parameters["snes_solver"]["linear_solver_"]='mumps' # "cg" "gmres"
solver.parameters["snes_solver"]["maximum_iterations"]=500
solver.parameters["snes_solver"]["relative_tolerance"]=1e-15
solver.parameters["snes_solver"]["absolute_tolerance"]=1e-15
# solver.parameters["snes_solver"]["linear_solver_"]["maximum_iterations"]=1000
solver.parameters["snes_solver"]["error_on_nonconvergence"] = False
solver.solve()
# option to store the visualization results
if visualization == 'True':
for argument_name, argument_function in iteritems(self.argument_functions_dict):
df.File('solutions_iterations_3d/{}_{}.pvd'.format(argument_name, self.itr)) << argument_function
self.L = -residual_form
self.itr = self.itr+1
outputs[state_name] = state_function.vector().get_local()
mu = fac_avg * 0.5
# Define single scale constitutive law
def sigma(u):
return lamb * ufl.nabla_div(u) * df.Identity(2) + 2 * mu * symgrad(u)
# Define single scale variational problem
a_single_scale = df.inner(sigma(uh), df.grad(vh)) * dx
f_single_scale = df.inner(traction, vh) * ds(2)
# Compute single scale solution
uh_single_scale = df.Function(Uh)
df.solve(a_single_scale == f_single_scale,
uh_single_scale,
bcs=bcL,
solver_parameters={"linear_solver": "mumps"})
# Save single scale solution in XDMF format
file_results = df.XDMFFile("bar_single_scale.xdmf")
file_results.write(uh_single_scale)
# ~~~ PART II: multiscale constitutive law ~~~ #
# Define the mesh of the micro model. Note that such mesh is associated to the current processor only
# and not partitioned across multiple processors.
Nx_micro = Ny_micro = 50
Lx_micro = Ly_micro = 1.0
mesh_micro = df.RectangleMesh(MPI.COMM_SELF, df.Point(0.0, 0.0),
df.Point(Lx_micro, Ly_micro), Nx_micro, Ny_micro,
"right/left")
def run_model(function_space,
kappa,
forcing,
init_condition,
dt,
final_time,
boundary_conditions=None,
second_order_timestepping=False,
exact_sol=None,
velocity=None,
point_sources=None):
"""
Use implicit euler to solve transient advection diffusion equation
du/dt = grad (k* grad u) - vel*grad u + f
WARNINGarningW: when point sources solution changes significantly when mesh is varied
"""
mesh = function_space.mesh()
time_independent_boundaries = False
if boundary_conditions == None:
bndry_obj = dl.CompiledSubDomain("on_boundary")
boundary_conditions = [['dirichlet', bndry_obj, dl.Constant(0)]]
time_independent_boundaries = True
num_bndrys = len(boundary_conditions)
boundaries = mark_boundaries(mesh, boundary_conditions)
dirichlet_bcs = collect_dirichlet_boundaries(function_space,
boundary_conditions,
boundaries)
# To express integrals over the boundary parts using ds(i), we must first
# redefine the measure ds in terms of our boundary markers:
ds = dl.Measure('ds', domain=mesh, subdomain_data=boundaries)
dx = dl.Measure('dx', domain=mesh)
# Variational problem at each time
u = dl.TrialFunction(function_space)
v = dl.TestFunction(function_space)
# Previous solution
if hasattr(init_condition, 't'):
assert init_condition.t == 0
u_1 = dl.interpolate(init_condition, function_space)
if not second_order_timestepping:
theta = 1
else:
theta = 0.5
if hasattr(forcing, 't'):
forcing_1 = copy_expression(forcing)
else:
forcing_1 = forcing
def steady_state_form(u, v, f):
F = kappa * dl.inner(dl.grad(u), dl.grad(v)) * dx
F -= f * v * dx
if velocity is not None:
F += dl.dot(velocity, dl.grad(u)) * v * dx
return F
F = u*v*dx-u_1*v*dx + dt*theta*steady_state_form(u,v,forcing) + \
dt*(1.-theta)*steady_state_form(u_1,v,forcing_1)
a, L = dl.lhs(F), dl.rhs(F)
# a = u*v*dx + theta*dt*kappa*dl.inner(dl.grad(u), dl.grad(v))*dx
# L = (u_1 + dt*theta*forcing)*v*dx
# if velocity is not None:
# a += theta*dt*v*dl.dot(velocity,dl.grad(u))*dx
# if second_order_timestepping:
# L -= (1-theta)*dt*dl.inner(kappa*dl.grad(u_1), dl.grad(v))*dx
# L += (1-theta)*dt*forcing_1*v*dx
# if velocity is not None:
# L -= (1-theta)*dt*(v*dl.dot(velocity,dl.grad(u_1)))*dx
beta_1_list = []
alpha_1_list = []
for ii in range(num_bndrys):
if (boundary_conditions[ii][0] == 'robin'):
alpha = boundary_conditions[ii][3]
a += theta * dt * alpha * u * v * ds(ii)
if second_order_timestepping:
if hasattr(alpha, 't'):
alpha_1 = copy_expression(alpha)
alpha_1_list.append(alpha_1)
else:
alpha_1 = alpha
L -= (1 - theta) * dt * alpha_1 * u_1 * v * ds(ii)
if ((boundary_conditions[ii][0] == 'robin')
or (boundary_conditions[ii][0] == 'neumann')):
beta = boundary_conditions[ii][2]
L -= theta * dt * beta * v * ds(ii)
if second_order_timestepping:
if hasattr(beta, 't'):
beta_1 = copy_expression(beta)
beta_1_list.append(beta_1)
else:
# boundary condition is constant in time
beta_1 = beta
L -= (1 - theta) * dt * beta_1 * v * ds(ii)
if time_independent_boundaries:
# TODO this can be used if dirichlet and robin conditions are not
# time dependent.
A = dl.assemble(a)
for bc in dirichlet_bcs:
bc.apply(A)
solver = dl.LUSolver(A)
#solver.parameters["reuse_factorization"] = True
else:
solver = None
u_2 = dl.Function(function_space)
u_2.assign(u_1)
t = 0.0
dt_tol = 1e-12
n_time_steps = 0
while t < final_time - dt_tol:
# Update current time
t += dt
forcing.t = t
forcing_1.t = t - dt
# set current time for time varying boundary conditions
for ii in range(num_bndrys):
if hasattr(boundary_conditions[ii][2], 't'):
boundary_conditions[ii][2].t = t
# set previous time for time varying boundary conditions when
# using second order timestepping. lists will be empty if using
# first order timestepping
for jj in range(len(beta_1_list)):
beta_1_list[jj].t = t - dt
for jj in range(len(alpha_1_list)):
alpha_1_list[jj].t = t - dt
#A, b = dl.assemble_system(a, L, dirichlet_bcs)
#for bc in dirichlet_bcs:
# bc.apply(A,b)
if boundary_conditions is not None:
A = dl.assemble(a)
for bc in dirichlet_bcs:
bc.apply(A)
b = dl.assemble(L)
for bc in dirichlet_bcs:
bc.apply(b)
if point_sources is not None:
ps_list = []
for ii in range(len(point_sources)):
point, expr = point_sources[ii]
ps_list.append((dl.Point(point[0], point[1]), expr(t)))
ps = dl.PointSource(function_space, ps_list)
ps.apply(b)
if solver is None:
dl.solve(A, u_2.vector(), b)
else:
solver.solve(u_2.vector(), b)
#print ("t =", t, "end t=", final_time)
# Update previous solution
u_1.assign(u_2)
# import matplotlib.pyplot as plt
# plt.subplot(131)
# pp=dl.plot(u_1)
# plt.subplot(132)
# dl.plot(forcing,mesh=mesh)
# plt.subplot(133)
# dl.plot(forcing_1,mesh=mesh)
# plt.colorbar(pp)
# plt.show()
# compute error
if exact_sol is not None:
exact_sol.t = t
error = dl.errornorm(exact_sol, u_2)
print('t = %.2f: error = %.3g' % (t, error))
#dl.plot(exact_sol,mesh=mesh)
#plt.show()
t = min(t, final_time)
n_time_steps += 1
#print ("t =", t, "end t=", final_time,"# time steps", n_time_steps)
return u_2
# Galerkin Least Square stabilization term
stb_gls = L(tu) * tau * Res(du) * df.dx
# Weak form
dres = r * sigma_r(du) * epsilon_r(tu) * df.dx
dres = dres + sigma_theta(du) * tu * df.dx
dres = dres - Fc * tu * df.dx
dres = dres + 1e9 * stb_gls
# residual
res = df.action(dres, u)
# solve
df.solve(res == 0, u, bc)
# displacement
df.File(save_path + 'displacement.pvd') << u
# compute stresses
sigma_r_pro = df.project(sigma_r(u), V)
sigma_r_pro.rename('sigma_r [Pa]', 'sigma_r [Pa]')
df.File(save_path + 'sigma_r.pvd') << sigma_r_pro
sigma_theta_pro = df.project(sigma_theta(u), V)
sigma_theta_pro.rename('sigma_theta [Pa]', 'sigma_theta [Pa]')
df.File(save_path + 'sigma_theta.pvd') << sigma_theta_pro
# compute von Mises stress
def burgers2d(run, nu, ngx, ngy, dt, T, ngx_out, ngy_out, save_dir,
save_every, save_pvd=False, save_vector=False, plot=False, order=4):
"""simulate 2D Burgers' equation
https://www.firedrakeproject.org/demos/burgers.py.html
Args:
run (int): # run
nu (float): viscosity
ngx (int): # grid in x axis
ngy (int):
dt (float): time step for simulation
T (float): simulation time from 0 to T
ngx_out (int): output # grid in x axis
ngy_out (int): output # grid in y axis
save_dir (str): runs folder
order (int): order for sampling initial U
save_every (int): save frequency in terms of # dt
save_pvd (bool): save the field as vtk file for paraview
save_vector (bool): save fenics field vector for later operation
plot (bool): plot fields
"""
assert not (save_pvd and save_vector), 'wasting memory to save pvd & vector'
save_dir = save_dir + f'/run{run}'
mkdirs(save_dir)
mesh = df.UnitSquareMesh(ngx-1, ngy-1)
mesh_out = df.UnitSquareMesh(ngx_out-1, ngy_out-1)
V = df.VectorFunctionSpace(mesh, 'CG', 2, constrained_domain=PeriodicBoundary())
Vout = df.VectorFunctionSpace(mesh_out, 'CG', 1, constrained_domain=PeriodicBoundary())
# initial vector field
u0, lam, c = init_field_fenics(mesh, V, order=order, seed=run)
np.savez(save_dir + '/init_lam_c.npz', lam=lam, c=c)
u = df.Function(V)
u_old = df.Function(V)
v = df.TestFunction(V)
u = df.project(u0, V)
u_old.assign(u)
# backward Euler
F = (df.inner((u - u_old)/dt, v) \
+ df.inner(df.dot(u, df.nabla_grad(u)), v) \
+ nu*df.inner(df.grad(u), df.grad(v)))*df.dx
t = 0
k = 0
vtkfile = df.File(save_dir + f'/soln{ngx_out}x{ngy_out}_.pvd')
u_out = df.project(u, Vout)
u_out.rename('u', 'u')
# (2, ngy_out, ngx_out) ?
u_out_vertex = u_out.compute_vertex_values(mesh_out).reshape(2, ngx_out, ngy_out)
np.save(save_dir + f'/u{k}.npy', u_out_vertex)
# if plot:
# plot_row([u_out_vertex[0], u_out_vertex[1]], save_dir, f'u{k}',
# same_range=False, plot_fn='imshow', cmap='jet')
if save_pvd:
vtkfile << (u_out, t)
elif save_vector:
u_out_vector = u_out.vector().get_local()
np.save(save_dir + f'/u{k}_fenics_vec.npy', u_out_vector)
# u_vec_load = np.load(save_dir + f'/u{k}.npy')
# u_load = Function(Vout)
# u_load.vector().set_local(u_vec_load)
# not much log
df.set_log_level(30)
tic = time.time()
while t < T:
t += dt
k += 1
df.solve(F == 0, u)
u_old.assign(u)
u_out = df.project(u, Vout)
u_out.rename('u', 'u')
if k % save_every == 0:
u_out_vertex = u_out.compute_vertex_values(mesh_out).reshape(2, ngx_out, ngy_out)
np.save(save_dir + f'/u{k}.npy', u_out_vertex)
# if k % (10 * save_every) == 0 and plot:
# plot_row([u_out_vertex[0], u_out_vertex[1]], save_dir, f'u{k}',
# same_range=False, plot_fn='imshow', cmap='jet')
if save_pvd:
vtkfile << (u_out, t)
elif save_vector:
u_out_vector = u_out.vector().get_local()
np.save(save_dir + f'/u{k}_fenics_vec.npy', u_out_vector)
print(f'Run {run}: solved {k} steps with total {time.time()-tic:.3f} seconds')
return time.time() - tic
def comp_axisymmetric_heat_dirichlet(mat_obj, mesh_obj, bc, omega, save_path):
E = mat_obj.E
rho = mat_obj.rho
nu = mat_obj.nu
mesh = mesh_obj.create()
Rext = mesh_obj.Rext
Rint = mesh_obj.Rint
G = 1 # FAKE
# ======
# Thermal load
# ======
alpha = 1
T = 1
# ======
# Thickness profile
# ======
h = 1
# ======
# markers
# ======
cell_markers, facet_markers = define_markers(mesh, Rext, Rint)
# rename x[0], x[1] by x, y
x, y = df.SpatialCoordinate(mesh)
dim = mesh.topology().dim()
coord = mesh.coordinates()
# ======
# Create function space
# ======
# Create mesh and define function space
V = df.FunctionSpace(mesh, "CG", 1)
# Define boundary condition (homogeneous BC)
u0 = df.Constant(0.0)
if bc == 'CC':
bc = [df.DirichletBC(V, u0, facet_markers, i) for i in [1, 2]]
elif bc == 'CF':
bc = df.DirichletBC(V, u0, facet_markers, 2)
# Define variational problem
du = df.TrialFunction(V)
tu = df.TestFunction(V)
# displacement in radial direction u(x,y)
u = df.Function(V, name='displacement')
class THETA(df.UserExpression):
def eval(self, values, x):
values[0] = math.atan2(x[1], x[0])
def value_shape(self):
#return (1,) # vector
return () # scalar
theta = THETA(degree=1)
#theta_int = df.interpolate(theta, df.FunctionSpace(mesh, "DG", 0))
#df.File(save_path + 'theta.pvd') << theta_int
class RADIUS(df.UserExpression):
def eval(self, values, x):
values[0] = df.sqrt(x[0] * x[0] + x[1] * x[1])
def value_shape(self):
return () # scalar
r = RADIUS(degree=1)
# ======
# Define week form
# ======
def d_dr(du):
return 1.0 / r * (x * df.Dx(du, 0) + y * df.Dx(du, 1))
def d_dtheta(du):
return -y * df.Dx(du, 0) + x * df.Dx(du, 1)
# strain radial
def epsilon_r(du):
return d_dr(du)
# strain circumferential
def epsilon_theta(du):
return du / r
def sigma_r(du):
return E / (1.0 - nu**2) * ((epsilon_r(du) - alpha * T) + nu *
(epsilon_theta(du) - alpha * T))
def sigma_theta(du):
return E / (1.0 - nu**2) * ((epsilon_theta(du) - alpha * T) + nu *
(epsilon_r(du) - alpha * T))
# week form
dF = -sigma_r(du) * r * h * epsilon_r(tu) * df.dx
dF = -dF + sigma_theta(du) * h * tu * df.dx
dF = dF + rho * omega**2 * r**2 * h * tu * df.dx
# residual
F = df.action(dF, u)
# solve
df.solve(F == 0, u, bc)
# displacement
df.File(save_path + 'u.pvd') << _u
# ======
# Analyze
# ======
# compute stresses
sigma_r_pro = df.project(sigma_r(u), V)
sigma_r_pro.rename('sigma_r [Pa]', 'sigma_r [Pa]')
df.File(save_path + 'sigma_r.pvd') << sigma_r_pro
sigma_theta_pro = df.project(sigma_theta(u), V)
sigma_theta_pro.rename('sigma_theta [Pa]', 'sigma_theta [Pa]')
df.File(save_path + 'sigma_theta.pvd') << sigma_theta_pro
# compute von Mises stress
def von_mises_stress(sigma_r, sigma_theta):
return df.sqrt(sigma_r**2 + sigma_theta**2 - sigma_r * sigma_theta)
von_stress_pro = df.project(von_mises_stress(sigma_r(u), sigma_theta(u)),
V)
von_stress_pro.rename('von Mises Stress [Pa]', 'von Mises Stress [Pa]')
df.File(save_path + 'von_mises_stress.pvd') << von_stress_pro
# save results to h5
rfile = df.HDF5File(mesh.mpi_comm(), save_path + 'results.h5', "w")
rfile.write(u, "u")
rfile.write(sigma_r_pro, "sigma_r")
rfile.write(sigma_theta_pro, "sigma_theta")
rfile.write(von_stress_pro, "von_mises_stress")
rfile.close()
def run_model(kappa, forcing, function_space, boundary_conditions=None):
"""
Solve complex valued Helmholtz equation by solving coupled system, one
for the real part of the solution one for the imaginary part.
"""
mesh = function_space.mesh()
kappa_sq = kappa**2
if boundary_conditions == None:
bndry_obj = dl.CompiledSubDomain("on_boundary")
boundary_conditions = [['dirichlet', bndry_obj, [0, 0]]]
num_bndrys = len(boundary_conditions)
boundaries = mark_boundaries(mesh, boundary_conditions)
dirichlet_bcs = collect_dirichlet_boundaries(function_space,
boundary_conditions,
boundaries)
# To express integrals over the boundary parts using ds(i), we must first
# redefine the measure ds in terms of our boundary markers:
ds = dl.Measure('ds', domain=mesh, subdomain_data=boundaries)
#dx = dl.Measure('dx', domain=mesh)
dx = dl.dx
(pr, pi) = dl.TrialFunction(function_space)
(vr, vi) = dl.TestFunction(function_space)
# real part
bilinear_form = kappa_sq * (pr * vr - pi * vi) * dx
bilinear_form += (-dl.inner(dl.nabla_grad(pr), dl.nabla_grad(vr)) +
dl.inner(dl.nabla_grad(pi), dl.nabla_grad(vi))) * dx
# imaginary part
bilinear_form += kappa_sq * (pr * vi + pi * vr) * dx
bilinear_form += -(dl.inner(dl.nabla_grad(pr), dl.nabla_grad(vi)) +
dl.inner(dl.nabla_grad(pi), dl.nabla_grad(vr))) * dx
for ii in range(num_bndrys):
if (boundary_conditions[ii][0] == 'robin'):
alpha_real, alpha_imag = boundary_conditions[ii][3]
bilinear_form -= alpha_real * (pr * vr - pi * vi) * ds(ii)
bilinear_form -= alpha_imag * (pr * vi + pi * vr) * ds(ii)
forcing_real, forcing_imag = forcing
rhs = (forcing_real * vr + forcing_real * vi + forcing_imag * vr -
forcing_imag * vi) * dx
for ii in range(num_bndrys):
if ((boundary_conditions[ii][0] == 'robin')
or (boundary_conditions[ii][0] == 'neumann')):
beta_real, beta_imag = boundary_conditions[ii][2]
# real part of robin boundary conditions
rhs += (beta_real * vr - beta_imag * vi) * ds(ii)
# imag part of robin boundary conditions
rhs += (beta_real * vi + beta_imag * vr) * ds(ii)
# compute solution
p = dl.Function(function_space)
#solve(a == L, p)
dl.solve(bilinear_form == rhs, p, bcs=dirichlet_bcs)
return p
def main(traction, outfile='displacement.json'):
# Create the Beam geometry
# Length
L = 10
# Width
W = 1
print('Got traction of {} kN'.format(traction))
# Create mesh
mesh = dolfin.BoxMesh(dolfin.Point(0, 0, 0), dolfin.Point(L, W, W), 30, 3,
3)
# Mark boundary subdomians
left = dolfin.CompiledSubDomain("near(x[0], side) && on_boundary", side=0)
bottom = dolfin.CompiledSubDomain("near(x[2], side) && on_boundary",
side=0)
boundary_markers = dolfin.MeshFunction("size_t", mesh,
mesh.topology().dim() - 1)
boundary_markers.set_all(0)
left_marker = 1
bottom_marker = 2
left.mark(boundary_markers, left_marker)
bottom.mark(boundary_markers, bottom_marker)
f = dolfin.File('boundary_markers.pvd')
f << boundary_markers
P2 = dolfin.VectorElement("Lagrange", mesh.ufl_cell(), 2)
P1 = dolfin.FiniteElement("Lagrange", mesh.ufl_cell(), 1)
state_space = dolfin.FunctionSpace(mesh, P2 * P1)
state = dolfin.Function(state_space)
state_test = dolfin.TestFunction(state_space)
u, p = dolfin.split(state)
v, q = dolfin.split(state_test)
# Some mechanical quantities
I = dolfin.Identity(3)
gradu = dolfin.grad(u)
F = dolfin.variable(I + gradu)
J = dolfin.det(F)
# Material properites
mu = dolfin.Constant(100.0)
lmbda = dolfin.Constant(1.0)
epsilon = 0.5 * (gradu + gradu.T)
# Strain energy
W = lmbda / 2 * (dolfin.tr(epsilon)**2) \
+ mu * dolfin.tr(epsilon * epsilon)
internal_energy = W - p * (J - 1)
# Neumann BC
N = dolfin.FacetNormal(mesh)
p_bottom = dolfin.Constant(traction)
external_work = dolfin.inner(v, p_bottom * dolfin.cofac(F) * N) \
* dolfin.ds(bottom_marker, subdomain_data=boundary_markers)
# Virtual work
G = dolfin.derivative(internal_energy * dolfin.dx, state,
state_test) + external_work
# Anchor the left side
bcs = dolfin.DirichletBC(state_space.sub(0),
dolfin.Constant((0.0, 0.0, 0.0)), left)
# Traction at the bottom of the beam
dolfin.solve(G == 0, state, [bcs])
# Get displacement and hydrostatic pressure
u, p = state.split(deepcopy=True)
point = np.array([10.0, 0.5, 1.0])
disp = np.zeros(3)
u.eval(disp, point)
print(('Get z-position of point ({}): {:.4f} mm'
'').format(', '.join(['{:.1f}'.format(p) for p in point]),
point[2] + disp[2]))
with open(outfile, 'w') as f:
json.dump({
'point': point.tolist(),
'displacement': disp.tolist()
},
f,
indent=4)
print('Output saved to {}'.format(outfile))
V = dolfin.VectorFunctionSpace(mesh, "CG", 1)
u_int = dolfin.interpolate(u, V)
moved_mesh = dolfin.Mesh(mesh)
dolfin.ALE.move(mesh, u_int)
f = dolfin.File('mesh.pvd')
f << mesh
f = dolfin.File('bending_beam.pvd')
f << moved_mesh
def femsolve():
''' Bilineaarinen muoto:
a(u,v) = L(v)
a(u,v) = (inner(grad(u), grad(v)) + u*v)*dx
L(v) = f*v*dx - g*v*ds
g(x) = -du/dx = -u1, x = x1
u(x0) = u0
Omega = {xeR|x0<=x<=x1}
'''
from dolfin import UnitInterval, FunctionSpace, DirichletBC, TrialFunction
from dolfin import TestFunction, grad, Constant, Function, solve, inner, dx, ds
from dolfin import MeshFunction, assemble
import dolfin
# from dolfin import set_log_level, PROCESS
# Create mesh and define function space
mesh = UnitInterval(30)
V = FunctionSpace(mesh, 'Lagrange', 2)
boundaries = MeshFunction('uint', mesh, mesh.topology().dim()-1)
boundaries.set_all(0)
class Left(dolfin.SubDomain):
def inside(self, x, on_boundary):
tol = 1E-14 # tolerance for coordinate comparisons
return on_boundary and abs(x[0]) < tol
class Right(dolfin.SubDomain):
def inside(self, x, on_boundary):
return dolfin.near(x[0], 1.0)
left = Left()
right = Right()
left.mark(boundaries, 1)
right.mark(boundaries, 2)
# def u0_boundary(x):
# return abs(x[0]) < tol
#
# bc = DirichletBC(V, Constant(u0), lambda x: abs(x[0]) < tol)
bcs = [DirichletBC(V, Constant(u0), boundaries, 1)]
# Define variational problem
u = TrialFunction(V)
v = TestFunction(V)
a = (inner(grad(u), grad(v)) + u*v)*dx
g = Constant(-u1)
L = Constant(f)*v*dx - g*v*ds(2)
# set_log_level(PROCESS)
# Compute solution
A = assemble(a, exterior_facet_domains=boundaries)
b = assemble(L, exterior_facet_domains=boundaries)
for bc in bcs:
bc.apply(A, b)
u = Function(V)
solve(A, u.vector(), b, 'lu')
coor = mesh.coordinates()
u_array = u.vector().array()
a = []
b = []
for i in range(mesh.num_vertices()):
a.append(coor[i])
b.append(u_array[i])
print('u(%3.2f) = %0.14E'%(coor[i],u_array[i]))
import numpy as np
np.savez('fem',a,b)
def plot_bulk_electrostriction(E, direction, q_b, materials, mesh, power_opt):
""" plots bulk electrostriction in 2D for the whole simulation space
the solution is first projected into Lagrange basis """
from dolfin import (VectorElement, FunctionSpace, TrialFunction, Function,
TestFunction, split, dot, inner, lhs, rhs, solve, plot)
import matplotlib.pyplot as plt
# bulk electrostriction
V = VectorElement("Lagrange", mesh.ufl_cell(), 1, dim = 3)
VComplex = FunctionSpace( mesh, V*V)
u = TrialFunction(VComplex)
(ur, ui) = split(u)
v = TestFunction(VComplex)
(vr, vi) = split(v)
f_bulk = Function(VComplex)
if not isinstance(materials, collections.Iterable):
(fr_bulk, fi_bulk) = bulk_electrostriction(E, materials.em.e_r, materials.em.p, direction, q_b)
F = dot(vr,fr_bulk)*materials.domain + dot(vi,fi_bulk)*materials.domain
F -= inner(vr,ur)*materials.domain + inner(vi,ui)*materials.domain
else:
for idx, material in enumerate(materials):
if idx == 0:
(fr_bulk, fi_bulk) = bulk_electrostriction(E, material.em.e_r, material.em.p, direction, q_b)
F = dot(vr,fr_bulk)*material.domain + dot(vi,fi_bulk)*material.domain
F -= inner(vr,ur)*material.domain + inner(vi,ui)*material.domain
else:
(fr_bulk, fi_bulk) = bulk_electrostriction(E, material.em.e_r, material.em.p, direction, q_b)
F += dot(vr,fr_bulk)*material.domain + dot(vi,fi_bulk)*material.domain
F -= inner(vr,ur)*material.domain + inner(vi,ui)*material.domain
scaling = 1.0/(power_opt*1e3)*1e12 ##pN/(um^2mW)
a = lhs(F)
L = rhs(F)
solve(a==L, f_bulk)
w0 = f_bulk.compute_vertex_values(mesh)
nv = mesh.num_vertices()
w0 = [w0[i * nv: (i + 1) * nv] for i in range(3)]
U = w0[0]*scaling
V = w0[1]*scaling
#W = w0[2]
XY = mesh.coordinates()
X = XY[:,0]
Y = XY[:,1]
#Z = np.zeros(nv)
# make a pretty plot
fig = plt.figure()
ax = fig.gca()
Q1 = ax.quiver(X,Y, U,V, scale=4000, scale_units='inches')
plt.quiverkey(Q1, 0.4, 0.9, 1000.0, r'$ 1000 \frac{pN}{\mu m^3 mW} Re(f_{x,y})$', labelpos='E',
coordinates='figure', fontproperties={'size': 24})
plt.xlabel('x [$\mu$m]', fontsize=24, rotation = 0)
plt.ylabel('y [$\mu$m]', fontsize=24)
plt.tick_params(labelsize=24)
plt.show()
return fig
def compute_Mij(Mij, G_matr, G_under, Sijmats, Sijcomps, Sijfcomps, delta_CG1_sq,
tensdim, alphaval=None, u_nf=None, u_f=None, Nij=None, **NS_namespace):
"""
Manually compute the tensor Mij = 2*delta**2*(F(|S|Sij)-alpha**2*F(|S|)F(Sij)
"""
Sij = Sijcomps
Sijf = Sijfcomps
alpha = alphaval
deltasq = 2*delta_CG1_sq.vector().array()
# Apply pre-assembled matrices and compute right hand sides
if tensdim == 3:
Ax, Ay = Sijmats
u = u_nf[0].vector()
v = u_nf[1].vector()
uf = u_f[0].vector()
vf = u_f[1].vector()
# Unfiltered rhs
bu = [Ax*u, 0.5*(Ay*u + Ax*v), Ay*v]
# Filtered rhs
buf = [Ax*uf, 0.5*(Ay*uf + Ax*vf), Ay*vf]
else:
Ax, Ay, Az = Sijmats
u = u_nf[0].vector()
v = u_nf[1].vector()
w = u_nf[2].vector()
uf = u_f[0].vector()
vf = u_f[1].vector()
wf = u_f[2].vector()
bu = [Ax*u, 0.5*(Ay*u + Ax*v), 0.5*(Az*u + Ax*w), Ay*v, 0.5*(Az*v + Ay*w), Az*w]
buf = [Ax*uf, 0.5*(Ay*uf + Ax*vf), 0.5*(Az*uf + Ax*wf), Ay*vf, 0.5*(Az*vf + Ay*wf), Az*wf]
for i in xrange(tensdim):
# Solve for the different components of Sij
solve(G_matr, Sij[i].vector(), bu[i], "cg", "default")
# Solve for the different components of F(Sij)
solve(G_matr, Sijf[i].vector(), buf[i], "cg", "default")
# Compute magnitudes of Sij and Sijf
magS = mag(Sij, tensdim)
magSf = mag(Sijf, tensdim)
# Loop over components and add to Mij
for i in xrange(tensdim):
# Compute |S|*Sij
Mij[i].vector().set_local(magS*Sij[i].vector().array())
Mij[i].vector().apply("insert")
# Compute F(|S|*Sij)
tophatfilter(unfiltered=Mij[i], filtered=Mij[i], **vars())
# Check if Nij, assign F(|S|Sij) if not None
if Nij != None:
Nij[i].vector().zero()
Nij[i].vector().axpy(1.0, Mij[i].vector())
# Compute 2*delta**2*(F(|S|Sij) - alpha**2*F(|S|)F(Sij)) and add to Mij[i]
Mij[i].vector().set_local(deltasq*(Mij[i].vector().array()-(alpha**2)*magSf*Sijf[i].vector().array()))
Mij[i].vector().apply("insert")
# Return magS for use when updating nut_
return magS
# Define Dirichlet boundary conditions at top and bottom boundaries
bcs = [
DirichletBC(V, u5, boundaries.where_equal(boundary["TOP"][0])),
DirichletBC(V, u0, boundaries.where_equal(boundary["BOTTOM"][0])),
]
dx = dx(subdomain_data=domains)
ds = ds(subdomain_data=boundaries)
# Define variational form
F = (inner(a0 * grad(u), grad(v)) * dx(boundary["DOMAIN"][0]) +
inner(a1 * grad(u), grad(v)) * dx(boundary["OBSTACLE"][0]) -
g_L * v * ds(boundary["LEFT"][0]) - g_R * v * ds(boundary["RIGHT"][0]) -
f * v * dx(boundary["DOMAIN"][0]) - f * v * dx(boundary["OBSTACLE"][0]))
# Separate left and right hand sides of equation
a, L = lhs(F), rhs(F)
# Solve problem
u = Function(V)
solve(a == L, u, bcs)
bb_tree = cpp.geometry.BoundingBoxTree(mesh, 2)
print(u([0.5, 0.5], bb_tree)[0])
# print((u.vector().array))
file = XDMFFile(MPI.comm_world, "input/saved_function.xdmf")
file.write(u)
pass
def teXXXst_fenics_vector():
# quad_degree = 13
# dolfin.parameters["form_compiler"]["quadrature_degree"] = quad_degree
pi = 3.14159265358979323
k1, k2 = 2, 3
EV = pi * pi * (k1 * k1 + k2 * k2)
N = 11
degree = 1
mesh = UnitSquare(N, N)
fs = FunctionSpace(mesh, "CG", degree)
ex = Expression("A*sin(k1*pi*x[0])*sin(k2*pi*x[1])", k1=k1, k2=k2, A=1.0)
x = FEniCSVector(interpolate(ex, fs))
# print "x.coeff", x.coeffs.array()
ex.A = EV
b_ex = assemble_rhs(ex, fs)
bexg = interpolate(ex, fs)
# print b_ex.array()
# print b_ex.array() / (2 * pi * pi * x.coeffs.array())
Afe = assemble_lhs(Expression('1'), fs)
# apply discrete operator on (interpolated) x
A = FEniCSOperator(Afe, x.basis)
b = A * x
# evaluate solution for eigenfunction rhs
if False:
b_num = Function(fs)
solve(A, b_num.vector(), b_ex)
bnv = A * b_num.vector()
b3 = Function(fs, bnv / EV)
np.set_printoptions(threshold='nan', suppress=True)
print b.coeffs.array()
print np.abs((b_ex.array() - b.coeffs.array()) / np.max(b_ex.array()))
print np.max(np.abs((b_ex.array() - b.coeffs.array()) / np.max(b_ex.array())))
#print b_ex.array() / (M * interpolate(ex1, fs).vector()).array()
# #assert_array_almost_equal(b.coeffs, b_ex.coeffs)
b2 = Function(fs, b_ex.copy())
bg = Function(fs, b_ex.copy())
b2g = Function(fs, b_ex.copy())
G = assemble_gramian(x.basis)
dolfin.solve(G, bg.vector(), b.coeffs)
dolfin.solve(G, b2g.vector(), b2.vector())
# # compute eigenpairs numerically
# eigensolver = evaluate_evp(FEniCSBasis(fs))
# # Extract largest (first) eigenpair
# r, c, rx, cx = eigensolver.get_eigenpair(0)
# print "Largest eigenvalue: ", r
# # Initialize function and assign eigenvector
# ef0 = Function(fs)
# ef0.vector()[:] = rx
if False:
# export
out_b = dolfin.File(__name__ + "_b.pvd", "compressed")
out_b << b._fefunc
out_b_ex = dolfin.File(__name__ + "_b_ex.pvd", "compressed")
out_b_ex << b2
out_b_num = dolfin.File(__name__ + "_b_num.pvd", "compressed")
out_b_num << b_num
#dolfin.plot(x._fefunc, title="interpolant x", rescale=False, axes=True, legend=True)
dolfin.plot(bg, title="b", rescale=False, axes=True, legend=True)
dolfin.plot(b2g, title="b_ex (ass/G)", rescale=False, axes=True, legend=True)
dolfin.plot(bexg, title="b_ex (dir)", rescale=False, axes=True, legend=True)
#dolfin.plot(b_num, title="b_num", rescale=False, axes=True, legend=True)
# dolfin.plot(b3, title="M*b_num", rescale=False, axes=True, legend=True)
#dolfin.plot(ef0, title="ef0", rescale=False, axes=True, legend=True)
print dolfin.errornorm(u=b._fefunc, uh=b2) #, norm_type, degree, mesh)
dolfin.interactive()
def calculate_fiber_strain(fib, e_circ, e_rad, e_long, strain_markers, mesh,
strains):
import dolfin
from dolfin import (
Measure,
Function,
TensorFunctionSpace,
VectorFunctionSpace,
TrialFunction,
TestFunction,
inner,
assemble_system,
solve,
)
dX = dolfin.Measure("dx", subdomain_data=strain_markers, domain=mesh)
fiber_space = fib.function_space()
strain_space = dolfin.VectorFunctionSpace(mesh, "R", 0, dim=3)
full_strain_space = dolfin.TensorFunctionSpace(mesh, "R", 0)
fib1 = dolfin.Function(strain_space)
e_c1 = dolfin.Function(strain_space)
e_r1 = dolfin.Function(strain_space)
e_l1 = dolfin.Function(strain_space)
mean_coords, coords = get_regional_midpoints(strain_markers, mesh)
# ax = plt.subplot(111, projection='3d')
region = 1
fiber_strain = []
for region in range(1, 18):
# For each region
# Find the average unit normal in the fiber direction
u = dolfin.TrialFunction(strain_space)
v = TestFunction(strain_space)
a = inner(u, v) * dX(region)
L_fib = inner(fib, v) * dX(region)
A, b = assemble_system(a, L_fib)
solve(A, fib1.vector(), b)
fib1_norm = np.linalg.norm(fib1.vector().array())
# Unit normal
fib1_arr = fib1.vector().array() / fib1_norm
# Find the average unit normal in Circumferential direction
u = TrialFunction(strain_space)
v = TestFunction(strain_space)
a = inner(u, v) * dX(region)
L_c = inner(e_circ, v) * dX(region)
A, b = assemble_system(a, L_c)
solve(A, e_c1.vector(), b)
e_c1_norm = np.linalg.norm(e_c1.vector().array())
# Unit normal
e_c1_arr = e_c1.vector().array() / e_c1_norm
# Find the averag unit normal in Radial direction
u = TrialFunction(strain_space)
v = TestFunction(strain_space)
a = inner(u, v) * dX(region)
L_r = inner(e_rad, v) * dX(region)
A, b = assemble_system(a, L_r)
solve(A, e_r1.vector(), b)
e_r1_norm = np.linalg.norm(e_r1.vector().array())
# Unit normal
e_r1_arr = e_r1.vector().array() / e_r1_norm
# Find the average unit normal in Longitudinal direction
u = TrialFunction(strain_space)
v = TestFunction(strain_space)
a = inner(u, v) * dX(region)
L_l = inner(e_long, v) * dX(region)
A, b = assemble_system(a, L_l)
solve(A, e_l1.vector(), b)
e_l1_norm = np.linalg.norm(e_l1.vector().array())
# Unit normal
e_l1_arr = e_l1.vector().array() / e_l1_norm
# ax.plot([mean_coords[region][0], mean_coords[region][0]+e_c1_arr[0]],[mean_coords[region][1], mean_coords[region][1]+e_c1_arr[1]], [mean_coords[region][2],mean_coords[region][2]+e_c1_arr[2]], 'b', label = "circ")
# ax.plot([mean_coords[region][0],mean_coords[region][0]+e_r1_arr[0]],[mean_coords[region][1], mean_coords[region][1]+e_r1_arr[1]], [mean_coords[region][2],mean_coords[region][2]+e_r1_arr[2]] , 'r',label = "rad")
# ax.plot([mean_coords[region][0],mean_coords[region][0]+e_l1_arr[0]],[mean_coords[region][1], mean_coords[region][1]+e_l1_arr[1]], [mean_coords[region][2],mean_coords[region][2]+e_l1_arr[2]] , 'g',label = "long")
# ax.plot([mean_coords[region][0],mean_coords[region][0]+fib1_arr[0]],[mean_coords[region][1], mean_coords[region][1]+fib1_arr[1]], [mean_coords[region][2],mean_coords[region][2]+fib1_arr[2]] , 'y', label = "fib")
fiber_strain_region = []
for strain in strains[region]:
mat = np.array([
strain[0] * e_c1_arr, strain[1] * e_r1_arr,
strain[2] * e_l1_arr
]).T
fiber_strain_region.append(np.linalg.norm(np.dot(mat, fib1_arr)))
fiber_strain.append(fiber_strain_region)
# for i in range(18):
# ax.scatter3D(coords[i][0], coords[i][1], coords[i][2], s = 0.1)
# plt.show()
return fiber_strain
def solve_subdomains(self, **kwargs):
# FIXME: do it on parallel
for s in self.subdomains:
solve(s.a == s.L, s.solution, s.bcs, solver_parameters=s.solver_params)
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()
print('before solve took {:.2f} s'.format(time.time() - t_ini))
t_ini = time.time()
dolfin.solve(A, c, b)
file = dolfin.File('test.pvd')
file << phi
print('solve and write took {:.2f} s'.format(time.time() - t_ini))
fname_vel=vel_file,
fname_pressure=p_file,
fname_hdf5=hdf5_file,
fname_xdmf=xdmf_file)
solver.solve(save_freq=save_freq)
else:
problem = fm.SolidMechanicsProblem(config)
solver = fm.SolidMechanicsSolver(problem,
fname_disp=disp_file,
fname_pressure=p_file,
fname_hdf5=hdf5_file,
fname_xdmf=xdmf_file)
solver.full_solve(save_freq=save_freq)
# Compute the final volume
if args.compute_volume:
W1 = dlf.VectorFunctionSpace(problem.mesh, 'CG', 1)
xi1 = dlf.TestFunction(W1)
du1 = dlf.TrialFunction(W1)
u_move = dlf.Function(W1)
move_bcs = dlf.DirichletBC(W1, dlf.Constant([0.0] * args.dim),
problem.boundaries, CLIP)
a = dlf.dot(xi1, du1) * dlf.dx
L = dlf.dot(xi1, problem.displacement) * dlf.dx
dlf.solve(a == L, u_move, move_bcs)
ale = dlf.ALE()
ale.move(problem.mesh, u_move)
print ("Total volume after: ", \
dlf.assemble(dlf.Constant(1.0)*dlf.dx(domain=problem.mesh)))
if SUPG_stabilization:
alpha = 2
h = 1 / N
magnitude = 1
Pe = magnitude * h / (2.0 * mu)
tau = h / (2.0 * magnitude) * (1.0 / np.tanh(Pe) - 1.0 / Pe)
beta = df.Constant(tau * alpha)
v = v + beta * h * v.dx(0)
F = df.Constant(mu) * inner(grad(u), grad(v)) * dx + inner(u.dx(0),
v) * dx
bc1 = df.DirichletBC(V, df.Constant(0), left)
bc2 = df.DirichletBC(V, df.Constant(1), right)
# Neumann on y = 0 and y = 1 enforced implicitly
df.solve(df.lhs(F) == df.rhs(F), u_, bcs=[bc1, bc2])
# interpolate(u_e, VV)
# u_numerical = u_.vector().vec().array
# X = V.tabulate_dof_coordinates()
X = mesh.coordinates()
u_numerical = u_.compute_vertex_values(mesh)
# u_analytical = A*np.exp(B*X[:, 0]) + C
# u_analytical.shape = (N+1, N+1)
# u_numerical.shape = (N+1, N+1)
# e = (u_analytical-u_numerical)
# e.shape = (N+1, N+1)
# e = e[:, 1:-1] # exclude Dirichtlet BC
L1[i, j] = df.errornorm(u_, df.project(u_e, V), norm_type='H1')
L2[i, j] = df.errornorm(u_, df.project(u_e, V), norm_type='l2')
if N == 8:
def compute_dw(self, dm):
""" Compute dw """
setfct(self.dm, dm)
b = assemble(self.rhswwk)
solve(self.Mw, self.dw.vector(), b)
Ms.assign(df.Constant(1))
# just assembling it
LLG = -gamma / (1 + alpha * alpha) * df.cross(m, Heff) - alpha * gamma / (
1 + alpha * alpha) * df.cross(m, df.cross(m, Heff))
L = df.dot(LLG, df.TestFunction(S3)) * df.dP
dmdt = df.Function(S3)
start = time.time()
for i in xrange(1000):
df.assemble(L, tensor=dmdt.vector())
stop = time.time()
print "delta = ", stop - start
print dmdt.vector().array()
# more linear algebra, same problem... still need to assemble the cross product
# we're doing even more work than before
a = df.dot(u, v) * df.dP
A = df.assemble(a)
b = df.Function(S3)
dmdt = df.Function(S3)
start = time.time()
for i in xrange(1000):
df.assemble(L, tensor=b.vector()) # this is what should go out of the loop
df.solve(A, dmdt.vector(),
b.vector()) # some variation of this could stay in
stop = time.time()
print "delta = ", stop - start
print dmdt.vector().array()
def solve_cycle(state):
print('Solving state:', state['name'])
# u1.assign(state['u_prev'])
u0.assign(state['u_last'])
p0.assign(state['pressure'])
rhs.assign(project(dt*(- u0.dx(0)*u0 - nu*u0.dx(0).dx(0)/2.0 - p0.dx(0)), Q))
plot(rhs, title='RHS')
# rhs_nonlinear.assign(project(dt*(- u0.dx(0)*u0), Q))
# rhs_visc.assign(project(dt*(-nu*u0.dx(0).dx(0)/2.0), Q))
# rhs_pressure.assign(project(dt*(-p0.dx(0)), Q))
# plot(rhs_nonlinear, title='RHS nonlin')
# plot(rhs_visc, title='RHS visc')
# plot(rhs_pressure, title='RHS pressure')
solve(a_tent == L_tent, u_tent_computed, bcu)
if state['rot']:
if state['null']:
b = assemble(L_p_rot)
null_space.orthogonalize(b)
solve(A_p_rot, p_computed.vector(), b, 'cg')
else:
solve(a_p_rot == L_p_rot, p_computed, bcp)
solve(a_cor_rot == L_cor_rot, u_cor_computed)
div_u_tent.assign(project(-nu*u_tent_computed.dx(0), Vplot))
plot(div_u_tent, title=state['name']+'_div u_tent (pressure correction), t = ' +str(t))
# div_u_tent.assign(project(p0+p_computed-nu*state['p_tent'].dx(0), Q))
# plot(div_u_tent, title=state['name']+'_RHS (pressure correction), t = ' +str(t))
solve(a_rot == L_rot, p_cor_computed)
p_correction.assign(p_cor_computed-p_computed-p0)
plot(p_correction, title=state['name']+'_(computed pressure correction), t = ' +str(t))
print(' updating state')
state['u_prev'].assign(state['u_last'])
state['u_tent'].assign(u_tent_computed)
state['u_last'].assign(u_cor_computed)
state['pressure'].assign(p_cor_computed)
state['p_tent'].assign(p_computed+p0)
else:
if state['null']:
b = assemble(L_p)
null_space.orthogonalize(b)
print('new:', assemble((v_in_expr-u_tent_computed)*ds(2)))
# plot(interpolate((v_in_expr-u_tent_computed)*ds(2), Q), title='new')
# print(A_p.array())
# print(b.array())
solve(A_p, p_computed.vector(), b, 'gmres')
else:
solve(a_p == L_p, p_computed, bcp)
solve(a_cor == L_cor, u_cor_computed)
print(' updating state')
# state['u_prev'].assign(state['u_last'])
state['u_tent'].assign(u_tent_computed)
state['u_last'].assign(u_cor_computed)
state['pressure'].assign(p_computed)
def test_DoGIP_vs_FEniCS(self):
print(
'\n== testing DoGIP vs. FEniCS for problem of weighted projection ===='
)
for dim, pol_order in itertools.product([2, 3], [1, 2]):
print('dim={}; pol_order={}'.format(dim, pol_order))
N = 2 # no. of elements
# creating MESH, defining MATERIAL and SOURCE
if dim == 2:
mesh = UnitSquareMesh(N, N)
m = Expression("1+10*16*x[0]*(1-x[0])*x[1]*(1-x[1])", degree=4)
f = Expression("80*x[0]*(0.5-x[0])*(1.-x[0])*x[1]*(1.-x[1])",
degree=5)
elif dim == 3:
mesh = UnitCubeMesh(N, N, N)
m = Expression("1+10*16*x[0]*(1-x[0])*(1-x[1])*x[2]", degree=4)
f = Expression("80*x[0]*(0.5-x[0])*(1.-x[0])*x[1]*(1.-x[1])",
degree=5)
mesh.coordinates()[:] += 0.1 * np.random.random(
mesh.coordinates().shape) # mesh perturbation
## standard approach with FEniCS #############################################
V = FunctionSpace(mesh, "CG", pol_order) # original FEM space
bc = DirichletBC(V, Constant(0.0),
lambda x, on_boundary: on_boundary)
u, v = TrialFunction(V), TestFunction(V)
u_fenics = Function(V) # the vector for storing the solution
solve(m * inner(grad(u), grad(v)) * dx == f * v * dx, u_fenics,
bc) # solution by FEniCS
## DoGIP - double-grid integration with interpolation-projection #############
W = FunctionSpace(mesh, "DG",
2 * (pol_order - 1)) # double-grid space
Wvector = VectorFunctionSpace(
mesh, "DG",
2 * (pol_order - 1)) # vector variant of double-grid space
w = TestFunction(W)
A_dogip = assemble(
m * w *
dx).get_local() # diagonal matrix of material coefficients
A_dogip_full = np.einsum(
'i,jk->ijk', A_dogip,
np.eye(dim)) # block-diagonal mat. for non-isotropic mat.
bv = assemble(f * v * dx)
bc.apply(bv)
b = bv.get_local() # vector of right-hand side
# assembling global interpolation-projection matrix B
B = get_B(V, Wvector, problem=1)
# solution to DoGIP problem
def Afun(x):
Axd = np.einsum('...jk,...j', A_dogip_full,
B.dot(x).reshape((-1, dim)))
Afunx = B.T.dot(Axd.ravel())
Afunx[list(bc.get_boundary_values()
)] = 0 # application of Dirichlet BC
return Afunx
Alinoper = linalg.LinearOperator((b.size, b.size),
matvec=Afun,
dtype=np.float) # system matrix
x, info = linalg.cg(Alinoper,
b,
x0=np.zeros_like(b),
tol=1e-8,
maxiter=1e2) # conjugate gradients
# testing the difference between DoGIP and FEniCS
self.assertAlmostEqual(
0, np.linalg.norm(u_fenics.vector().get_local() - x))
print('...ok')