def __init__(self, args, tc, metadata):
self.has_analytic_solution = False
self.problem_code = 'FACB'
super(Problem, self).__init__(args, tc, metadata)
self.name = 'test on real mesh'
self.status_functional_str = 'not selected'
# input parameters
self.factor = args.factor
self.scale_factor.append(self.factor)
self.nu = 0.001 * args.nufactor # kinematic viscosity
# Import gmsh mesh
self.compatible_meshes = ['bench3D_1', 'bench3D_2', 'bench3D_3']
if args.mesh not in self.compatible_meshes:
exit('Bad mesh, should be some from %s' %
str(self.compatible_meshes))
self.mesh = Mesh("meshes/" + args.mesh + ".xml")
self.cell_function = MeshFunction(
"size_t", self.mesh,
"meshes/" + args.mesh + "_physical_region.xml")
self.facet_function = MeshFunction(
"size_t", self.mesh, "meshes/" + args.mesh + "_facet_region.xml")
self.dsIn = Measure("ds",
subdomain_id=2,
subdomain_data=self.facet_function)
self.dsOut = Measure("ds",
subdomain_id=3,
subdomain_data=self.facet_function)
self.dsWall = Measure("ds",
subdomain_id=1,
subdomain_data=self.facet_function)
self.dsCyl = Measure("ds",
subdomain_id=5,
subdomain_data=self.facet_function)
self.normal = FacetNormal(self.mesh)
print("Mesh name: ", args.mesh, " ", self.mesh)
print("Mesh norm max: ", self.mesh.hmax())
print("Mesh norm min: ", self.mesh.hmin())
self.actual_time = None
self.v_in = None
def facetarea():
from ufl import Measure
assert integral_type != 'cell'
integrand, degree = ufl_utils.one_times(Measure(integral_type, domain=domain))
integrand = ufl_utils.replace_coordinates(integrand, coordinate_coefficient)
config = kernel_config.copy()
config.update(quadrature_degree=degree)
expr, = fem.compile_ufl(integrand, point_sum=True, **config)
return expr
def test_ufl_id():
"""Test that UFL can process MeshTags (tests ufl_id attribute)"""
comm = MPI.COMM_WORLD
mesh = create_unit_cube(comm, 6, 6, 6)
tdim = mesh.topology.dim
marked_facets = locate_entities(mesh, tdim - 1,
lambda x: np.isclose(x[1], 1))
f_v = mesh.topology.connectivity(tdim - 1, 0).array.reshape(-1, 3)
entities = create_adjacencylist(f_v[marked_facets])
values = np.full(marked_facets.shape[0], 2, dtype=np.int32)
ft = meshtags_from_entities(mesh, tdim - 1, entities, values)
ds = Measure("ds", domain=mesh, subdomain_data=ft, subdomain_id=(2, 3))
a = 1 * ds
assert isinstance(a.subdomain_data(), dict)
def solve_navier_stokes_equation(interior_circle=True, num_mesh_refinements=0):
"""
Solve the Navier-Stokes equation on a hard-coded mesh with hard-coded initial and boundary conditions
"""
mesh, om, im, nm, ymax, sub_domains = setup_geometry(
interior_circle, num_mesh_refinements)
dsi = Measure("ds", domain=mesh, subdomain_data=sub_domains)
# Setup FEM function spaces
# Function space for the velocity
V = VectorFunctionSpace(mesh, "CG", 1)
# Function space for the pressure
Q = FunctionSpace(mesh, "CG", 1)
# Mixed function space for velocity and pressure
W = V * Q
# Setup FEM functions
v, q = TestFunctions(W)
w = Function(W)
(u, p) = (as_vector((w[0], w[1])), w[2])
u0 = Function(V)
# Inlet velocity
uin = Expression(("4*(x[1]*(YMAX-x[1]))/(YMAX*YMAX)", "0."),
YMAX=ymax,
degree=1)
# Viscosity and stabilization parameters
nu = 1e-6
h = CellSize(mesh)
d = 0.2 * h**(3.0 / 2.0)
# Time parameters
time_step = 0.1
t_start, t_end = 0.0, 10.0
# Penalty parameter
gamma = 10 / h
# Time stepping
t = t_start
step = 0
while t < t_end:
# Time discretization (Crank–Nicolson method)
um = 0.5 * u + 0.5 * u0
# Navier-Stokes equations in weak residual form (stabilized FEM)
# Basic residual
r = (inner((u - u0) / time_step + grad(p) + grad(um) * um, v) +
nu * inner(grad(um), grad(v)) + div(um) * q) * dx
# Weak boundary conditions
r += gamma * (om * p * q + im * inner(u - uin, v) +
nm * inner(u, v)) * ds
# Stabilization
r += d * (inner(grad(p) + grad(um) * um,
grad(q) + grad(um) * v) + inner(div(um), div(v))) * dx
# Solve the Navier-Stokes equation (one time step)
solve(r == 0, w)
if step % 5 == 0:
# Plot norm of velocity at current time step
nov = project(sqrt(inner(u, u)), Q)
fig = plt.figure()
plot(nov, fig=fig)
plt.show()
# Compute drag force on circle
n = FacetNormal(mesh)
drag_force_measure = p * n[0] * dsi(1) # Drag (only pressure)
drag_force = assemble(drag_force_measure)
print("Drag force = " + str(drag_force))
# Shift to next time step
t += time_step
step += 1
u0 = project(u, V)
gmsh.model.mesh.setOrder(2) # Command required for quadratic elements
# Use a gmsh helper function to create the mesh. This requires Jorgen's
# file http://jsdokken.com/converted_files/gmsh_helpers.py
mesh, cell_tags = gmsh_model_to_mesh(gmsh.model, cell_data=True, gdim=2)
n = FacetNormal(mesh)
def boundary(x):
return (x[0]**2 + x[1]**2) < (radius + 1.0e-2)**2
circle_facets = locate_entities_boundary(mesh, 1, boundary)
mt = dolfinx.mesh.MeshTags(mesh, 1, circle_facets, 1)
ds = Measure("ds", subdomain_data=mt)
''' Incident field, wavenumber and adiabatic absorber functions '''
def incident(x):
# Plane wave travelling in positive x-direction
return np.exp(1.0j * k0 * x[0])
def adiabatic_layer(x):
''' Contribution to wavenumber k in absorbing layers '''
# In absorbing layer, have k = k0 + 1j * sigma
# => k^2 = (k0 + 1j*sigma)^2 = k0^2 + 2j*sigma - sigma^2
# Therefore, the 2j*sigma - sigma^2 piece must be included in the layer.
# Find borders of width d_absorb in x- and y-directions
def forward(mu_expression,
lmbda_expression,
rho,
Lx=10,
Ly=10,
t_end=1,
omega_p=5,
amplitude=5000,
center=0,
target=False):
Lpml = Lx / 10
#c_p = cp(mu.vector(), lmbda.vector(), rho)
max_velocity = 200 #c_p.max()
stable_hx = stable_dx(max_velocity, omega_p)
nx = int(Lx / stable_hx) + 1
#nx = max(nx, 60)
ny = int(Ly * nx / Lx) + 1
mesh = mesh_generator(Lx, Ly, Lpml, nx, ny)
used_hx = Lx / nx
dt = stable_dt(used_hx, max_velocity)
cfl_ct = cfl_constant(max_velocity, dt, used_hx)
print(used_hx, stable_hx)
print(cfl_ct)
#time.sleep(10)
PE = FunctionSpace(mesh, "DG", 0)
mu = interpolate(mu_expression, PE)
lmbda = interpolate(lmbda_expression, PE)
m = 2
R = 10e-8
t = 0.0
gamma = 0.50
beta = 0.25
ff = MeshFunction("size_t", mesh, mesh.geometry().dim() - 1)
Dirichlet(Lx, Ly, Lpml).mark(ff, 1)
# Create function spaces
VE = VectorElement("CG", mesh.ufl_cell(), 1, dim=2)
TE = TensorElement("DG", mesh.ufl_cell(), 0, shape=(2, 2), symmetry=True)
W = FunctionSpace(mesh, MixedElement([VE, TE]))
F = FunctionSpace(mesh, "CG", 2)
V = W.sub(0).collapse()
M = W.sub(1).collapse()
alpha_0 = Alpha_0(m, stable_hx, R, Lpml)
alpha_1 = Alpha_1(alpha_0, Lx, Lpml, degree=2)
alpha_2 = Alpha_2(alpha_0, Ly, Lpml, degree=2)
beta_0 = Beta_0(m, max_velocity, R, Lpml)
beta_1 = Beta_1(beta_0, Lx, Lpml, degree=2)
beta_2 = Beta_2(beta_0, Ly, Lpml, degree=2)
alpha_1 = interpolate(alpha_1, F)
alpha_2 = interpolate(alpha_2, F)
beta_1 = interpolate(beta_1, F)
beta_2 = interpolate(beta_2, F)
a_ = alpha_1 * alpha_2
b_ = alpha_1 * beta_2 + alpha_2 * beta_1
c_ = beta_1 * beta_2
Lambda_e = as_tensor([[alpha_2, 0], [0, alpha_1]])
Lambda_p = as_tensor([[beta_2, 0], [0, beta_1]])
a_ = alpha_1 * alpha_2
b_ = alpha_1 * beta_2 + alpha_2 * beta_1
c_ = beta_1 * beta_2
Lambda_e = as_tensor([[alpha_2, 0], [0, alpha_1]])
Lambda_p = as_tensor([[beta_2, 0], [0, beta_1]])
# Set up boundary condition
bc = DirichletBC(W.sub(0), Constant(("0.0", "0.0")), ff, 1)
# Create measure for the source term
dx = Measure("dx", domain=mesh)
ds = Measure("ds", domain=mesh, subdomain_data=ff)
# Set up initial values
u0 = Function(V)
u0.set_allow_extrapolation(True)
v0 = Function(V)
a0 = Function(V)
U0 = Function(M)
V0 = Function(M)
A0 = Function(M)
# Test and trial functions
(u, S) = TrialFunctions(W)
(w, T) = TestFunctions(W)
g = ModifiedRickerPulse(0, omega_p, amplitude, center)
F = rho * inner(a_ * N_ddot(u, u0, a0, v0, dt, beta) \
+ b_ * N_dot(u, u0, v0, a0, dt, beta, gamma) + c_ * u, w) * dx \
+ inner(N_dot(S, U0, V0, A0, dt, beta, gamma).T * Lambda_e + S.T * Lambda_p, grad(w)) * dx \
- inner(g, w) * ds \
+ inner(compliance(a_ * N_ddot(S, U0, A0, V0, dt, beta) + b_ * N_dot(S, U0, V0, A0, dt, beta, gamma) + c_ * S, u, mu, lmbda), T) * dx \
- 0.5 * inner(grad(u) * Lambda_p + Lambda_p * grad(u).T + grad(N_dot(u, u0, v0, a0, dt, beta, gamma)) * Lambda_e \
+ Lambda_e * grad(N_dot(u, u0, v0, a0, dt, beta, gamma)).T, T) * dx \
a, L = lhs(F), rhs(F)
# Assemble rhs (once)
A = assemble(a)
# Create GMRES Krylov solver
solver = KrylovSolver(A, "gmres")
# Create solution function
S = Function(W)
if target:
xdmffile_u = XDMFFile("inversion_temporal_file/target/u.xdmf")
pvd = File("inversion_temporal_file/target/u.pvd")
xdmffile_u.write(u0, t)
timeseries_u = TimeSeries(
"inversion_temporal_file/target/u_timeseries")
else:
xdmffile_u = XDMFFile("inversion_temporal_file/obs/u.xdmf")
xdmffile_u.write(u0, t)
timeseries_u = TimeSeries("inversion_temporal_file/obs/u_timeseries")
rec_counter = 0
while t < t_end - 0.5 * dt:
t += float(dt)
if rec_counter % 10 == 0:
print(
'\n\rtime: {:.3f} (Progress: {:.2f}%)'.format(
t, 100 * t / t_end), )
g.t = t
# Assemble rhs and apply boundary condition
b = assemble(L)
bc.apply(A, b)
# Compute solution
solver.solve(S.vector(), b)
(u, U) = S.split(True)
# Update previous time step
update(u, u0, v0, a0, beta, gamma, dt)
update(U, U0, V0, A0, beta, gamma, dt)
xdmffile_u.write(u, t)
pvd << (u, t)
timeseries_u.store(u.vector(), t)
energy = inner(u, u) * dx
E = assemble(energy)
print("E = ", E)
print(u.vector().max())
mu_pr = kap_by_mu * (mu1 + mu2) # make this value very high
alph1 = fem.Constant(mesh, 1.0)
alph2 = fem.Constant(mesh, -2.474)
m1 = fem.Constant(mesh, 5.42 * 10**3)
m2 = fem.Constant(mesh, 20.78 * 10**3)
a1 = fem.Constant(mesh, -10.0)
a2 = fem.Constant(mesh, 1.948)
K1 = fem.Constant(mesh, 3507.0 * 10**3)
K2 = fem.Constant(mesh, 10**(-6))
bta1 = fem.Constant(mesh, 1.852)
bta2 = fem.Constant(mesh, 0.26)
eta0 = fem.Constant(mesh, 7014.0 * 10**3)
etaInf = fem.Constant(mesh, 0.1 * 10**3) # 0.1
# integration measures
dx = Measure("dx", metadata=metadata)
dS = Measure("dS", metadata=metadata)
# stabilization constant
qvals = 5.0 * (mu1 + mu2 + m1 + m2)
# loading rate
ldot = 0.05
# array of `time` like variable for stepping in loading
timeVals = np.linspace(0, 4.0 / ldot, 203)
# delta t values (will change when doing adaptive computation)
dt = timeVals[1] - timeVals[0]
# array of displacement values to apply at right boundary
def dS_from_measure(mesh):
boundaries = MeshFunction("size_t", mesh, mesh.topology.dim - 1, 1)
dS = Measure("dS")(subdomain_data=boundaries, domain=mesh)
return dS
def separate(self):
class _SeparatedParametrizedForm_Replacer(Transformer):
def __init__(self, mapping):
Transformer.__init__(self)
self.mapping = mapping
def operator(self, e, *ops):
if e in self.mapping:
return self.mapping[e]
else:
return e._ufl_expr_reconstruct_(*ops)
def terminal(self, e):
return self.mapping.get(e, e)
logger.log(DEBUG,
"*** SEPARATE FORM COEFFICIENTS ***")
logger.log(DEBUG, "1. Extract coefficients")
integral_to_coefficients = dict()
for integral in self._form.integrals():
logger.log(
DEBUG,
"\t Currently on integrand " + str(integral.integrand()))
self._coefficients.append(list()) # of ParametrizedExpression
for e in iter_expressions(integral):
logger.log(DEBUG, "\t\t Expression " + str(e))
pre_traversal_e = [n for n in pre_traversal(e)]
tree_nodes_skip = [False for _ in pre_traversal_e]
for (n_i, n) in enumerate(pre_traversal_e):
if not tree_nodes_skip[n_i]:
# Skip expressions which are trivially non parametrized
if isinstance(n, Argument):
logger.log(
DEBUG, "\t\t Node " + str(n) +
" is skipped because it is an Argument")
continue
elif isinstance(n, Constant):
logger.log(
DEBUG, "\t\t Node " + str(n) +
" is skipped because it is a Constant")
continue
elif isinstance(n, MultiIndex):
logger.log(
DEBUG, "\t\t Node " + str(n) +
" is skipped because it is a MultiIndex")
continue
# Skip all expressions with at least one leaf which is an Argument
for t in traverse_terminals(n):
if isinstance(t, Argument):
logger.log(
DEBUG, "\t\t Node " + str(n) +
" is skipped because it contains an Argument"
)
break
else: # not broken
logger.log(
DEBUG, "\t\t Node " + str(n) +
" and its descendants are being analyzed for non-parametrized check"
)
# Make sure to skip all descendants of this node in the outer loop
# Note that a map with key set to the expression is not enough to
# mark the node as visited, since the same expression may appear
# on different sides of the tree
pre_traversal_n = [d for d in pre_traversal(n)]
for (d_i, d) in enumerate(pre_traversal_n):
assert d == pre_traversal_e[
n_i +
d_i] # make sure that we are marking the right node
tree_nodes_skip[n_i + d_i] = True
# We might be able to strip any (non-parametrized) expression out
all_candidates = list()
internal_tree_nodes_skip = [
False for _ in pre_traversal_n
]
for (d_i, d) in enumerate(pre_traversal_n):
if not internal_tree_nodes_skip[d_i]:
# Skip all expressions where at least one leaf is not parametrized
for t in traverse_terminals(d):
if isinstance(t, BaseExpression):
if wrapping.is_pull_back_expression(
t
) and not wrapping.is_pull_back_expression_parametrized(
t):
logger.log(
DEBUG,
"\t\t\t Descendant node "
+ str(d) +
" causes the non-parametrized check to break because it contains a non-parametrized pulled back expression"
)
break
else:
parameters = t._parameters
if "mu_0" not in parameters:
logger.log(
DEBUG,
"\t\t\t Descendant node "
+ str(d) +
" causes the non-parametrized check to break because it contains a non-parametrized expression"
)
break
elif isinstance(t, Constant):
logger.log(
DEBUG,
"\t\t\t Descendant node " +
str(d) +
" causes the non-parametrized check to break because it contains a constant"
)
break
elif isinstance(
t, GeometricQuantity
) and not isinstance(
t, FacetNormal
) and self._strict:
logger.log(
DEBUG,
"\t\t\t Descendant node " +
str(d) +
" causes the non-parametrized check to break because it contains a geometric quantity and strict mode is on"
)
break
elif wrapping.is_problem_solution_type(
t):
if not wrapping.is_problem_solution(
t
) and not wrapping.is_problem_solution_dot(
t):
logger.log(
DEBUG,
"\t\t\t Descendant node "
+ str(d) +
" causes the non-parametrized check to break because it contains a non-parametrized function"
)
break
elif self._strict: # solutions are not allowed, break
if wrapping.is_problem_solution(
t):
(
_, component,
solution
) = wrapping.solution_identify_component(
t)
problem = get_problem_from_solution(
solution)
logger.log(
DEBUG,
"\t\t\t Descendant node "
+ str(d) +
" causes the non-parametrized check to break because it contains the solution of "
+ problem.name() +
" (exact problem decorator: "
+ str(
hasattr(
problem,
"__is_exact__"
)) +
", component: " +
str(component) +
") and strict mode is on"
)
break
elif wrapping.is_problem_solution_dot(
t):
(
_, component,
solution_dot
) = wrapping.solution_dot_identify_component(
t)
problem = get_problem_from_solution_dot(
solution_dot)
logger.log(
DEBUG,
"\t\t\t Descendant node "
+ str(d) +
" causes the non-parametrized check to break because it contains the solution_dot of "
+ problem.name() +
" (exact problem decorator: "
+ str(
hasattr(
problem,
"__is_exact__"
)) +
", component: " +
str(component) +
") and strict mode is on"
)
else:
raise RuntimeError(
"Unidentified solution found"
)
else:
at_least_one_expression_or_solution = False
for t in traverse_terminals(d):
if isinstance(
t, BaseExpression
): # which is parametrized, because previous for loop was not broken
at_least_one_expression_or_solution = True
logger.log(
DEBUG,
"\t\t\t Descendant node "
+ str(d) +
" is a candidate after non-parametrized check because it contains the parametrized expression "
+ str(t))
break
elif wrapping.is_problem_solution_type(
t):
if wrapping.is_problem_solution(
t):
at_least_one_expression_or_solution = True
(
_, component,
solution
) = wrapping.solution_identify_component(
t)
problem = get_problem_from_solution(
solution)
logger.log(
DEBUG,
"\t\t\t Descendant node "
+ str(d) +
" is a candidate after non-parametrized check because it contains the solution of "
+ problem.name() +
" (exact problem decorator: "
+ str(
hasattr(
problem,
"__is_exact__"
)) +
", component: " +
str(component) +
")")
break
elif wrapping.is_problem_solution_dot(
t):
at_least_one_expression_or_solution = True
(
_, component,
solution_dot
) = wrapping.solution_dot_identify_component(
t)
problem = get_problem_from_solution_dot(
solution_dot)
logger.log(
DEBUG,
"\t\t\t Descendant node "
+ str(d) +
" is a candidate after non-parametrized check because it contains the solution_dot of "
+ problem.name() +
" (exact problem decorator: "
+ str(
hasattr(
problem,
"__is_exact__"
)) +
", component: " +
str(component) +
")")
break
if at_least_one_expression_or_solution:
all_candidates.append(d)
pre_traversal_d = [
q for q in pre_traversal(d)
]
for (q_i, q) in enumerate(
pre_traversal_d):
assert q == pre_traversal_n[
d_i +
q_i] # make sure that we are marking the right node
internal_tree_nodes_skip[
d_i + q_i] = True
else:
logger.log(
DEBUG,
"\t\t\t Descendant node " +
str(d) +
" has not passed the non-parametrized because it is not a parametrized expression or a solution"
)
# Evaluate candidates
if len(
all_candidates
) == 0: # the whole expression was actually non-parametrized
logger.log(
DEBUG, "\t\t Node " + str(n) +
" is skipped because it is a non-parametrized coefficient"
)
continue
elif len(
all_candidates
) == 1: # the whole expression was actually parametrized
logger.log(
DEBUG, "\t\t Node " + str(n) +
" will be accepted because it is a non-parametrized coefficient"
)
pass
else: # part of the expression was not parametrized, and separating the non parametrized part may result in more than one coefficient
if self._strict: # non parametrized coefficients are not allowed, so split the expression
logger.log(
DEBUG, "\t\t\t Node " + str(n) +
" will be accepted because it is a non-parametrized coefficient with more than one candidate. It will be split because strict mode is on. Its split coefficients are "
+ ", ".join([
str(c) for c in all_candidates
]))
else: # non parametrized coefficients are allowed, so go on with the whole expression
logger.log(
DEBUG, "\t\t\t Node " + str(n) +
" will be accepted because it is a non-parametrized coefficient with more than one candidate. It will not be split because strict mode is off. Splitting it would have resulted in more than one coefficient, namely "
+ ", ".join([
str(c) for c in all_candidates
]))
all_candidates = [n]
# Add the coefficient(s)
for candidate in all_candidates:
def preprocess_candidate(candidate):
if isinstance(candidate, Indexed):
assert len(
candidate.ufl_operands) == 2
assert isinstance(
candidate.ufl_operands[1],
MultiIndex)
if all([
isinstance(
index, FixedIndex)
for index in candidate.
ufl_operands[1].indices()
]):
logger.log(
DEBUG,
"\t\t\t Preprocessed descendant node "
+ str(candidate) +
" as an Indexed expression with fixed indices, resulting in a candidate "
+ str(candidate) +
" of type " +
str(type(candidate)))
return candidate # no further preprocessing needed
else:
logger.log(
DEBUG,
"\t\t\t Preprocessed descendant node "
+ str(candidate) +
" as an Indexed expression with at least one mute index, resulting in a candidate "
+ str(candidate.
ufl_operands[0]) +
" of type " + str(
type(candidate.
ufl_operands[0])))
return preprocess_candidate(
candidate.ufl_operands[0])
elif isinstance(candidate, IndexSum):
assert len(
candidate.ufl_operands) == 2
assert isinstance(
candidate.ufl_operands[1],
MultiIndex)
assert all([
isinstance(index, MuteIndex)
for index in candidate.
ufl_operands[1].indices()
])
logger.log(
DEBUG,
"\t\t\t Preprocessed descendant node "
+ str(candidate) +
" as an IndexSum expression, resulting in a candidate "
+
str(candidate.ufl_operands[0])
+ " of type " + str(
type(candidate.
ufl_operands[0])))
return preprocess_candidate(
candidate.ufl_operands[0])
elif isinstance(candidate, ListTensor):
candidates = set([
preprocess_candidate(component)
for component in
candidate.ufl_operands
])
if len(candidates) == 1:
preprocessed_candidate = candidates.pop(
)
logger.log(
DEBUG,
"\t\t\t Preprocessed descendant node "
+ str(candidate) +
" as an ListTensor expression with a unique preprocessed component, resulting in a candidate "
+
str(preprocessed_candidate)
+ " of type " + str(
type(
preprocessed_candidate
)))
return preprocess_candidate(
preprocessed_candidate)
else:
at_least_one_mute_index = False
candidates_from_components = list(
)
for component in candidates:
assert isinstance(
component,
(ComponentTensor,
Indexed))
assert len(
component.ufl_operands
) == 2
assert isinstance(
component.
ufl_operands[1],
MultiIndex)
if not all([
isinstance(
index,
FixedIndex) for
index in component.
ufl_operands[1].
indices()
]):
at_least_one_mute_index = True
candidates_from_components.append(
preprocess_candidate(
component.
ufl_operands[0]))
if at_least_one_mute_index:
candidates_from_components = set(
candidates_from_components
)
assert len(
candidates_from_components
) == 1
preprocessed_candidate = candidates_from_components.pop(
)
logger.log(
DEBUG,
"\t\t\t Preprocessed descendant node "
+ str(candidate) +
" as an ListTensor expression with multiple preprocessed components with at least one mute index, resulting in a candidate "
+
str(preprocessed_candidate
) + " of type " +
str(
type(
preprocessed_candidate
)))
return preprocess_candidate(
preprocessed_candidate)
else:
logger.log(
DEBUG,
"\t\t\t Preprocessed descendant node "
+ str(candidate) +
" as an ListTensor expression with multiple preprocessed components with fixed indices, resulting in a candidate "
+ str(candidate) +
" of type " +
str(type(candidate)))
return candidate # no further preprocessing needed
else:
logger.log(
DEBUG,
"\t\t\t No preprocessing required for descendant node "
+ str(candidate) +
" as a coefficient of type " +
str(type(candidate)))
return candidate
preprocessed_candidate = preprocess_candidate(
candidate)
if preprocessed_candidate not in self._coefficients[
-1]:
self._coefficients[-1].append(
preprocessed_candidate)
logger.log(
DEBUG,
"\t\t\t Accepting descendant node " +
str(preprocessed_candidate) +
" as a coefficient of type " +
str(type(preprocessed_candidate)))
else:
logger.log(
DEBUG, "\t\t Node " + str(n) +
" to be skipped because it is a descendant of a coefficient which has already been detected"
)
if len(self._coefficients[-1]
) == 0: # then there were no coefficients to extract
logger.log(DEBUG,
"\t There were no coefficients to extract")
self._coefficients.pop(
) # remove the (empty) element that was added to possibly store coefficients
else:
logger.log(DEBUG, "\t Extracted coefficients are:")
for c in self._coefficients[-1]:
logger.log(DEBUG, "\t\t" + str(c))
integral_to_coefficients[integral] = self._coefficients[-1]
logger.log(DEBUG,
"2. Prepare placeholders and forms with placeholders")
for integral in self._form.integrals():
# Prepare measure for the new form (from firedrake/mg/ufl_utils.py)
measure = Measure(integral.integral_type(),
domain=integral.ufl_domain(),
subdomain_id=integral.subdomain_id(),
subdomain_data=integral.subdomain_data(),
metadata=integral.metadata())
if integral not in integral_to_coefficients:
logger.log(
DEBUG, "\t Adding form for integrand " +
str(integral.integrand()) + " to unchanged forms")
self._form_unchanged.append(integral.integrand() * measure)
else:
logger.log(
DEBUG,
"\t Preparing form with placeholders for integrand " +
str(integral.integrand()))
self._placeholders.append(list()) # of Constants
placeholders_dict = dict()
for c in integral_to_coefficients[integral]:
self._placeholders[-1].append(
Constant(self._NaN * ones(c.ufl_shape)))
placeholders_dict[c] = self._placeholders[-1][-1]
logger.log(
DEBUG, "\t\t " + str(placeholders_dict[c]) +
" is the placeholder for " + str(c))
replacer = _SeparatedParametrizedForm_Replacer(
placeholders_dict)
new_integrand = apply_transformer(integral.integrand(),
replacer)
self._form_with_placeholders.append(new_integrand *
measure)
logger.log(
DEBUG,
"3. Assert that there are no parametrized expressions left")
for form in self._form_with_placeholders:
for integral in form.integrals():
for e in pre_traversal(integral.integrand()):
if isinstance(e, BaseExpression):
assert not (
wrapping.is_pull_back_expression(e)
and wrapping.
is_pull_back_expression_parametrized(e)
), "Form " + str(
integral
) + " still contains a parametrized pull back expression"
parameters = e._parameters
assert "mu_0" not in parameters, "Form " + str(
integral
) + " still contains a parametrized expression"
logger.log(DEBUG, "4. Prepare coefficients hash codes")
for addend in self._coefficients:
self._placeholder_names.append(list()) # of string
for factor in addend:
self._placeholder_names[-1].append(
wrapping.expression_name(factor))
logger.log(DEBUG, "5. Assert list length consistency")
assert len(self._coefficients) == len(self._placeholders)
assert len(self._coefficients) == len(self._placeholder_names)
for (c, p, pn) in zip(self._coefficients, self._placeholders,
self._placeholder_names):
assert len(c) == len(p)
assert len(c) == len(pn)
assert len(self._coefficients) == len(self._form_with_placeholders)
logger.log(DEBUG,
"*** DONE - SEPARATE FORM COEFFICIENTS - DONE ***")
logger.log(DEBUG, "")
def demo_stacked_cubes(outfile: XDMFFile,
theta: float,
gmsh: bool = True,
quad: bool = False,
compare: bool = False,
res: float = 0.1):
log_info(
f"Run theta:{theta:.2f}, Quad: {quad}, Gmsh {gmsh}, Res {res:.2e}")
celltype = "quadrilateral" if quad else "triangle"
if gmsh:
mesh, mt = gmsh_2D_stacked(celltype, theta)
mesh.name = f"mesh_{celltype}_{theta:.2f}_gmsh"
else:
mesh_name = "mesh"
filename = f"meshes/mesh_{celltype}_{theta:.2f}.xdmf"
mesh_2D_dolfin(celltype, theta)
with XDMFFile(MPI.COMM_WORLD, filename, "r") as xdmf:
mesh = xdmf.read_mesh(name=mesh_name)
mesh.name = f"mesh_{celltype}_{theta:.2f}"
tdim = mesh.topology.dim
fdim = tdim - 1
mesh.topology.create_connectivity(tdim, tdim)
mesh.topology.create_connectivity(fdim, tdim)
mt = xdmf.read_meshtags(mesh, name="facet_tags")
# Helper until meshtags can be read in from xdmf
V = VectorFunctionSpace(mesh, ("Lagrange", 1))
r_matrix = rotation_matrix([0, 0, 1], theta)
g_vec = np.dot(r_matrix, [0, -1.25e2, 0])
g = Constant(mesh, PETSc.ScalarType(g_vec[:2]))
def bottom_corner(x):
return np.isclose(x, [[0], [0], [0]]).all(axis=0)
# Fix bottom corner
bc_value = np.array((0, ) * mesh.geometry.dim, dtype=PETSc.ScalarType)
bottom_dofs = locate_dofs_geometrical(V, bottom_corner)
bc_bottom = dirichletbc(bc_value, bottom_dofs, V)
bcs = [bc_bottom]
# Elasticity parameters
E = PETSc.ScalarType(1.0e3)
nu = 0
mu = Constant(mesh, E / (2.0 * (1.0 + nu)))
lmbda = Constant(mesh, E * nu / ((1.0 + nu) * (1.0 - 2.0 * nu)))
# Stress computation
def sigma(v):
return (2.0 * mu * sym(grad(v)) +
lmbda * tr(sym(grad(v))) * Identity(len(v)))
# Define variational problem
u = TrialFunction(V)
v = TestFunction(V)
a = inner(sigma(u), grad(v)) * dx
ds = Measure("ds", domain=mesh, subdomain_data=mt, subdomain_id=3)
rhs = inner(Constant(mesh, PETSc.ScalarType(
(0, 0))), v) * dx + inner(g, v) * ds
def left_corner(x):
return np.isclose(x.T, np.dot(r_matrix, [0, 2, 0])).all(axis=1)
# Create multi point constraint
mpc = MultiPointConstraint(V)
with Timer("~Contact: Create contact constraint"):
nh = create_normal_approximation(V, mt, 4)
mpc.create_contact_slip_condition(mt, 4, 9, nh)
with Timer("~Contact: Add non-slip condition at bottom interface"):
bottom_normal = facet_normal_approximation(V, mt, 5)
mpc.create_slip_constraint(V, (mt, 5), bottom_normal, bcs=bcs)
with Timer("~Contact: Add tangential constraint at one point"):
vertex = locate_entities_boundary(mesh, 0, left_corner)
tangent = facet_normal_approximation(V, mt, 3, tangent=True)
mtv = meshtags(mesh, 0, vertex, np.full(len(vertex), 6,
dtype=np.int32))
mpc.create_slip_constraint(V, (mtv, 6), tangent, bcs=bcs)
mpc.finalize()
rtol = 1e-9
petsc_options = {
"ksp_rtol": 1e-9,
"pc_type": "gamg",
"pc_gamg_type": "agg",
"pc_gamg_square_graph": 2,
"pc_gamg_threshold": 0.02,
"pc_gamg_coarse_eq_limit": 1000,
"pc_gamg_sym_graph": True,
"mg_levels_ksp_type": "chebyshev",
"mg_levels_pc_type": "jacobi",
"mg_levels_esteig_ksp_type": "cg"
# , "help": None, "ksp_view": None
}
# Solve Linear problem
problem = LinearProblem(a, rhs, mpc, bcs=bcs, petsc_options=petsc_options)
# Build near nullspace
null_space = rigid_motions_nullspace(mpc.function_space)
problem.A.setNearNullSpace(null_space)
u_h = problem.solve()
it = problem.solver.getIterationNumber()
if MPI.COMM_WORLD.rank == 0:
print("Number of iterations: {0:d}".format(it))
unorm = u_h.vector.norm()
if MPI.COMM_WORLD.rank == 0:
print(f"Norm of u: {unorm}")
# Write solution to file
ext = "_gmsh" if gmsh else ""
u_h.name = "u_mpc_{0:s}_{1:.2f}{2:s}".format(celltype, theta, ext)
outfile.write_mesh(mesh)
outfile.write_function(u_h, 0.0,
f"Xdmf/Domain/Grid[@Name='{mesh.name}'][1]")
# Solve the MPC problem using a global transformation matrix
# and numpy solvers to get reference values
if not compare:
return
log_info("Solving reference problem with global matrix (using numpy)")
with Timer("~MPC: Reference problem"):
# Generate reference matrices and unconstrained solution
A_org = assemble_matrix(form(a), bcs)
A_org.assemble()
L_org = assemble_vector(form(rhs))
apply_lifting(L_org, [form(a)], [bcs])
L_org.ghostUpdate(addv=PETSc.InsertMode.ADD_VALUES,
mode=PETSc.ScatterMode.REVERSE)
set_bc(L_org, bcs)
root = 0
with Timer("~MPC: Verification"):
compare_mpc_lhs(A_org, problem.A, mpc, root=root)
compare_mpc_rhs(L_org, problem.b, mpc, root=root)
# Gather LHS, RHS and solution on one process
A_csr = gather_PETScMatrix(A_org, root=root)
K = gather_transformation_matrix(mpc, root=root)
L_np = gather_PETScVector(L_org, root=root)
u_mpc = gather_PETScVector(u_h.vector, root=root)
if MPI.COMM_WORLD.rank == root:
KTAK = K.T * A_csr * K
reduced_L = K.T @ L_np
# Solve linear system
d = scipy.sparse.linalg.spsolve(KTAK, reduced_L)
# Back substitution to full solution vector
uh_numpy = K @ d
assert np.allclose(uh_numpy, u_mpc, rtol=rtol)
W_element = BlockElement(V_element, Q_element)
elif discretization == "EG":
Q_element = FiniteElement("CG", mesh.ufl_cell(), 1)
D_element = FiniteElement("DG", mesh.ufl_cell(), 0)
EG_element = Q_element + D_element
W_element = BlockElement(V_element, EG_element)
else:
raise RuntimeError("Invalid discretization")
W = BlockFunctionSpace(mesh, W_element)
PM = FunctionSpace(mesh, "DG", 0)
TM = TensorFunctionSpace(mesh, "DG", 0)
I = Identity(mesh.topology().dim())
dx = Measure("dx", domain=mesh, subdomain_data=subdomains)
ds = Measure("ds", domain=mesh, subdomain_data=boundaries)
dS = Measure("dS", domain=mesh, subdomain_data=boundaries)
# Test and trial functions
vq = BlockTestFunction(W)
(v, q) = block_split(vq)
up = BlockTrialFunction(W)
(u, p) = block_split(up)
w = BlockFunction(W)
w0 = BlockFunction(W)
(u0, p0) = block_split(w0)
n = FacetNormal(mesh)
vc = CellVolume(mesh)
local.array[left_dofs] = E_left * nu_left / ((1 + nu_left) *
(1 - 2 * nu_left))
local.array[right_dofs] = E_right * nu_right / ((1 + nu_right) *
(1 - 2 * nu_right))
# Stress computation
def sigma(v):
return (2.0 * mu * sym(grad(v)) +
lmbda * tr(sym(grad(v))) * Identity(len(v)))
# Define variational problem
u = TrialFunction(V)
v = TestFunction(V)
dx = Measure("dx", domain=mesh, subdomain_data=ct)
a = inner(sigma(u), grad(v)) * dx
x = SpatialCoordinate(mesh)
rhs = inner(Constant(mesh, PETSc.ScalarType((0, 0))), v) * dx
# Set boundary conditions
u_push = np.array([0.1, 0], dtype=PETSc.ScalarType)
dofs = locate_dofs_geometrical(V, lambda x: np.isclose(x[0], 0))
bc_push = dirichletbc(u_push, dofs, V)
u_fix = np.array([0, 0], dtype=PETSc.ScalarType)
bc_fix = dirichletbc(
u_fix, locate_dofs_geometrical(V, lambda x: np.isclose(x[0], 2.1)), V)
bcs = [bc_push, bc_fix]
def gather_dof_coordinates(V: FunctionSpace, dofs: np.ndarray):
def _basic_form_on_reduced_function_space(form_wrapper, at):
form = form_wrapper._form
form_name = form_wrapper.name()
mu = get_problem_from_parametrized_operator(form_wrapper).mu
reduced_V = at.get_reduced_function_spaces()
reduced_subdomain_data = at.get_reduced_subdomain_data()
if (form_name,
reduced_V) not in form_on_reduced_function_space__form_cache:
visited = set()
replacements = dict()
truth_problems = list()
truth_problem_to_components = dict()
truth_problem_to_exact_truth_problem = dict()
truth_problem_to_reduced_mesh_solution = dict()
truth_problem_to_reduced_mesh_interpolator = dict()
reduced_problem_to_components = dict()
reduced_problem_to_reduced_mesh_solution = dict()
reduced_problem_to_reduced_basis_functions = dict()
# Look for terminals on truth mesh
for node in wrapping.form_iterator(form, "nodes"):
if node in visited:
continue
# ... test and trial functions
elif isinstance(node, Argument):
replacements[node] = wrapping.form_argument_replace(
node, reduced_V)
visited.add(node)
# ... problem solutions related to nonlinear terms
elif wrapping.is_problem_solution_or_problem_solution_component_type(
node):
if wrapping.is_problem_solution_or_problem_solution_component(
node):
(preprocessed_node, component, truth_solution
) = wrapping.solution_identify_component(node)
truth_problem = get_problem_from_solution(
truth_solution)
truth_problems.append(truth_problem)
# Store the component
if truth_problem not in truth_problem_to_components:
truth_problem_to_components[truth_problem] = list()
truth_problem_to_components[truth_problem].append(
component)
# Get the function space corresponding to preprocessed_node on the reduced mesh
auxiliary_reduced_V = at.get_auxiliary_reduced_function_space(
truth_problem, component)
# Define and store the replacement
if truth_problem not in truth_problem_to_reduced_mesh_solution:
truth_problem_to_reduced_mesh_solution[
truth_problem] = list()
replacements[preprocessed_node] = backend.Function(
auxiliary_reduced_V)
truth_problem_to_reduced_mesh_solution[
truth_problem].append(
replacements[preprocessed_node])
# Get interpolator on reduced mesh
if truth_problem not in truth_problem_to_reduced_mesh_interpolator:
truth_problem_to_reduced_mesh_interpolator[
truth_problem] = list()
truth_problem_to_reduced_mesh_interpolator[
truth_problem].append(
at.get_auxiliary_function_interpolator(
truth_problem, component))
else:
(
auxiliary_problem, component
) = wrapping.get_auxiliary_problem_for_non_parametrized_function(
node)
preprocessed_node = node
# Get the function space corresponding to preprocessed_node on the reduced mesh
auxiliary_reduced_V = at.get_auxiliary_reduced_function_space(
auxiliary_problem, component)
# Get interpolator on reduced mesh
auxiliary_truth_problem_to_reduced_mesh_interpolator = at.get_auxiliary_function_interpolator(
auxiliary_problem, component)
# Define and store the replacement
replacements[
preprocessed_node] = auxiliary_truth_problem_to_reduced_mesh_interpolator(
preprocessed_node)
# Make sure to skip any parent solution related to this one
visited.add(node)
visited.add(preprocessed_node)
for parent_node in wrapping.solution_iterator(
preprocessed_node):
visited.add(parent_node)
# ... geometric quantities
elif isinstance(node, GeometricQuantity):
if len(reduced_V) == 2:
assert reduced_V[0].mesh().ufl_domain(
) == reduced_V[1].mesh().ufl_domain()
replacements[node] = type(node)(reduced_V[0].mesh())
visited.add(node)
# ... and replace them
replaced_form = wrapping.form_replace(form, replacements, "nodes")
# Look for measures ...
if len(reduced_V) == 2:
assert reduced_V[0].mesh().ufl_domain() == reduced_V[1].mesh(
).ufl_domain()
measure_reduced_domain = reduced_V[0].mesh().ufl_domain()
replacements_measures = dict()
for integral in wrapping.form_iterator(replaced_form, "integrals"):
# Prepare measure for the new form (from firedrake/mg/ufl_utils.py)
integral_subdomain_data = integral.subdomain_data()
if integral_subdomain_data is not None:
integral_reduced_subdomain_data = reduced_subdomain_data[
integral_subdomain_data]
else:
integral_reduced_subdomain_data = None
measure = Measure(
integral.integral_type(),
domain=measure_reduced_domain,
subdomain_id=integral.subdomain_id(),
subdomain_data=integral_reduced_subdomain_data,
metadata=integral.metadata())
replacements_measures[integral.integrand(),
integral.integral_type(),
integral.subdomain_id()] = measure
# ... and replace them
replaced_form_with_replaced_measures = wrapping.form_replace(
replaced_form, replacements_measures, "measures")
# Cache the resulting dicts
form_on_reduced_function_space__form_cache[(
form_name, reduced_V)] = replaced_form_with_replaced_measures
form_on_reduced_function_space__truth_problems_cache[(
form_name, reduced_V)] = truth_problems
form_on_reduced_function_space__truth_problem_to_components_cache[(
form_name, reduced_V)] = truth_problem_to_components
form_on_reduced_function_space__truth_problem_to_exact_truth_problem_cache[
(form_name, reduced_V)] = truth_problem_to_exact_truth_problem
form_on_reduced_function_space__truth_problem_to_reduced_mesh_solution_cache[
(form_name,
reduced_V)] = truth_problem_to_reduced_mesh_solution
form_on_reduced_function_space__truth_problem_to_reduced_mesh_interpolator_cache[
(form_name,
reduced_V)] = truth_problem_to_reduced_mesh_interpolator
form_on_reduced_function_space__reduced_problem_to_components_cache[
(form_name, reduced_V)] = reduced_problem_to_components
form_on_reduced_function_space__reduced_problem_to_reduced_mesh_solution_cache[
(form_name,
reduced_V)] = reduced_problem_to_reduced_mesh_solution
form_on_reduced_function_space__reduced_problem_to_reduced_basis_functions_cache[
(form_name,
reduced_V)] = reduced_problem_to_reduced_basis_functions
# Extract from cache
replaced_form_with_replaced_measures = form_on_reduced_function_space__form_cache[
(form_name, reduced_V)]
truth_problems = form_on_reduced_function_space__truth_problems_cache[(
form_name, reduced_V)]
truth_problem_to_components = form_on_reduced_function_space__truth_problem_to_components_cache[
(form_name, reduced_V)]
truth_problem_to_exact_truth_problem = form_on_reduced_function_space__truth_problem_to_exact_truth_problem_cache[
(form_name, reduced_V)]
truth_problem_to_reduced_mesh_solution = form_on_reduced_function_space__truth_problem_to_reduced_mesh_solution_cache[
(form_name, reduced_V)]
truth_problem_to_reduced_mesh_interpolator = form_on_reduced_function_space__truth_problem_to_reduced_mesh_interpolator_cache[
(form_name, reduced_V)]
reduced_problem_to_components = form_on_reduced_function_space__reduced_problem_to_components_cache[
(form_name, reduced_V)]
reduced_problem_to_reduced_mesh_solution = form_on_reduced_function_space__reduced_problem_to_reduced_mesh_solution_cache[
(form_name, reduced_V)]
reduced_problem_to_reduced_basis_functions = form_on_reduced_function_space__reduced_problem_to_reduced_basis_functions_cache[
(form_name, reduced_V)]
# Get list of truth and reduced problems that need to be solved, possibly updating cache
required_truth_problems = list()
required_reduced_problems = list()
for truth_problem in truth_problems:
truth_problem_is_solving = hasattr(truth_problem, "_is_solving")
if is_training_started(truth_problem):
reduced_problem = get_reduced_problem_from_problem(
truth_problem)
reduced_problem_is_solving = hasattr(reduced_problem,
"_is_solving")
else:
reduced_problem = None
reduced_problem_is_solving = False
if not truth_problem_is_solving:
if is_training_finished(truth_problem):
# Store the component
if reduced_problem not in reduced_problem_to_components:
reduced_problem_to_components[
reduced_problem] = truth_problem_to_components[
truth_problem]
# Store the replacement
if reduced_problem not in reduced_problem_to_reduced_mesh_solution:
reduced_problem_to_reduced_mesh_solution[
reduced_problem] = truth_problem_to_reduced_mesh_solution[
truth_problem]
# Get reduced problem basis functions on reduced mesh
if reduced_problem not in reduced_problem_to_reduced_basis_functions:
reduced_problem_to_reduced_basis_functions[
reduced_problem] = list()
for component in reduced_problem_to_components[
reduced_problem]:
reduced_problem_to_reduced_basis_functions[
reduced_problem].append(
at.get_auxiliary_basis_functions_matrix(
truth_problem, reduced_problem,
component))
# Append to list of required reduced problems
required_reduced_problems.append(
(reduced_problem, reduced_problem_is_solving))
else:
if (hasattr(truth_problem,
"_apply_exact_evaluation_at_stages") and
not hasattr(truth_problem, "_apply_EIM_at_stages")
and not hasattr(truth_problem,
"_apply_DEIM_at_stages")):
# Init truth problem (if required), as it may not have been initialized
truth_problem.init()
# Append to list of required truth problems which are not currently solving
required_truth_problems.append(
(truth_problem, False, reduced_problem_is_solving))
else:
# Store the corresponding exact truth problem
if truth_problem not in truth_problem_to_exact_truth_problem:
exact_truth_problem = exact_problem(truth_problem)
truth_problem_to_exact_truth_problem[
truth_problem] = exact_truth_problem
# Init exact truth problem (if required), as it may not have been initialized
exact_truth_problem.init()
else:
exact_truth_problem = truth_problem_to_exact_truth_problem[
truth_problem]
# Store the component
if exact_truth_problem not in truth_problem_to_components:
truth_problem_to_components[
exact_truth_problem] = truth_problem_to_components[
truth_problem]
# Store the replacement
if exact_truth_problem not in truth_problem_to_reduced_mesh_solution:
truth_problem_to_reduced_mesh_solution[
exact_truth_problem] = truth_problem_to_reduced_mesh_solution[
truth_problem]
# Get interpolator on reduced mesh
if exact_truth_problem not in truth_problem_to_reduced_mesh_interpolator:
truth_problem_to_reduced_mesh_interpolator[
exact_truth_problem] = list()
for component in truth_problem_to_components[
exact_truth_problem]:
truth_problem_to_reduced_mesh_interpolator[
exact_truth_problem].append(
at.get_auxiliary_function_interpolator(
exact_truth_problem, component))
# Append to list of required truth problems which are not currently solving
required_truth_problems.append(
(exact_truth_problem, False,
reduced_problem_is_solving))
else:
assert not reduced_problem_is_solving
# Append to list of required truth problems which are currently solving
required_truth_problems.append((truth_problem, True, False))
# Solve truth problems (which have not been reduced yet) associated to nonlinear terms
for (truth_problem, truth_problem_is_solving,
reduced_problem_is_solving) in required_truth_problems:
if not reduced_problem_is_solving:
# Solve (if necessary) ...
truth_problem.set_mu(mu)
if not truth_problem_is_solving:
log(
PROGRESS,
"In form_on_reduced_function_space, requiring truth problem solve for problem "
+ truth_problem.name())
truth_problem.solve()
else:
log(
PROGRESS,
"In form_on_reduced_function_space, loading current truth problem solution for problem "
+ truth_problem.name())
else:
reduced_problem = get_reduced_problem_from_problem(
truth_problem)
log(
PROGRESS,
"In form_on_reduced_function_space, replacing current truth problem solution with reduced solution for problem "
+ reduced_problem.truth_problem.name())
# ... and assign to reduced_mesh_solution
for (reduced_mesh_solution, reduced_mesh_interpolator) in zip(
truth_problem_to_reduced_mesh_solution[truth_problem],
truth_problem_to_reduced_mesh_interpolator[truth_problem]):
solution_to = reduced_mesh_solution
if not reduced_problem_is_solving:
solution_from = reduced_mesh_interpolator(
truth_problem._solution)
else:
solution_from = reduced_mesh_interpolator(
reduced_problem.basis_functions[:reduced_problem.
_solution.N] *
reduced_problem._solution)
backend.assign(solution_to, solution_from)
# Solve reduced problems associated to nonlinear terms
for (reduced_problem, is_solving) in required_reduced_problems:
# Solve (if necessary) ...
reduced_problem.set_mu(mu)
if not is_solving:
log(
PROGRESS,
"In form_on_reduced_function_space, requiring reduced problem solve for problem "
+ reduced_problem.truth_problem.name())
reduced_problem.solve()
else:
log(
PROGRESS,
"In form_on_reduced_function_space, loading current reduced problem solution for problem "
+ reduced_problem.truth_problem.name())
# ... and assign to reduced_mesh_solution
for (reduced_mesh_solution, reduced_basis_functions) in zip(
reduced_problem_to_reduced_mesh_solution[reduced_problem],
reduced_problem_to_reduced_basis_functions[reduced_problem]
):
solution_to = reduced_mesh_solution
solution_from_N = OnlineSizeDict()
for c, v in reduced_problem._solution.N.items():
if c in reduced_basis_functions._components_name:
solution_from_N[c] = v
solution_from = online_backend.OnlineFunction(solution_from_N)
online_backend.online_assign(solution_from,
reduced_problem._solution)
solution_from = reduced_basis_functions[:solution_from_N] * solution_from
backend.assign(solution_to, solution_from)
# Assemble and return
assembled_replaced_form = wrapping.assemble(
replaced_form_with_replaced_measures)
form_rank = assembled_replaced_form.rank()
return (assembled_replaced_form, form_rank)
# --------------------------Variational problem---------------------------
# Traditional terms
mu = 1
f = fem.Constant(mesh, PETSc.ScalarType((0, 0)))
(u, p) = TrialFunctions(W)
(v, q) = TestFunctions(W)
a = (2 * mu * inner(sym_grad(u), sym_grad(v)) - inner(p, div(v)) -
inner(div(u), q)) * dx
L = inner(f, v) * dx
# No prescribed shear stress
n = FacetNormal(mesh)
g_tau = tangential_proj(
fem.Constant(mesh, PETSc.ScalarType(((0, 0), (0, 0)))) * n, n)
ds = Measure("ds", domain=mesh, subdomain_data=mt, subdomain_id=1)
# Terms due to slip condition
# Explained in for instance: https://arxiv.org/pdf/2001.10639.pdf
a -= inner(outer(n, n) * dot(T(u, p, mu), n), v) * ds
L += inner(g_tau, v) * ds
# Solve linear problem
petsc_options = {
"ksp_type": "preonly",
"pc_type": "lu",
"pc_factor_solver_type": "mumps"
}
problem = LinearProblem(a, L, mpc, bcs=bcs, petsc_options=petsc_options)
U = problem.solve()
def __init__(self, args, tc, metadata):
self.has_analytic_solution = True
self.problem_code = 'WCYL'
super(Problem, self).__init__(args, tc, metadata)
self.tc.init_watch('assembleSol', 'Assembled analytic solution', True)
self.tc.init_watch('analyticP', 'Analytic pressure', True)
self.tc.init_watch('analyticVnorms',
'Computed analytic velocity norms', True)
self.tc.init_watch('errorP', 'Computed pressure error', True)
self.tc.init_watch('errorForce', 'Computed force error', True)
self.tc.init_watch('computePG', 'Computed pressure gradient', True)
self.name = 'womersley_cylinder'
self.status_functional_str = 'last H1 velocity error'
# input parameters
self.ic = args.ic
self.factor = args.factor
self.metadata['factor'] = self.factor
self.scale_factor.append(self.factor)
# fixed parameters (used in analytic solution and in BC)
self.nu = 3.71 * self.args.nufactor # kinematic viscosity
self.R = 5.0 # cylinder radius
self.mesh_volume = pi * 25. * 20.
# Import gmsh mesh
self.tc.start('mesh')
self.mesh, self.facet_function = super(Problem,
self).loadMesh(args.mesh)
self.dsIn = Measure("ds",
subdomain_id=2,
subdomain_data=self.facet_function)
self.dsOut = Measure("ds",
subdomain_id=3,
subdomain_data=self.facet_function)
self.dsWall = Measure("ds",
subdomain_id=1,
subdomain_data=self.facet_function)
self.normal = FacetNormal(self.mesh)
print("Mesh name: ", args.mesh, " ", self.mesh)
print("Mesh norm max: ", self.mesh.hmax())
print("Mesh norm min: ", self.mesh.hmin())
self.tc.end('mesh')
self.sol_p = None
self.last_analytic_pressure_norm = None
self.v_in = None
self.area = None
choose_note = {1.0: '', 0.1: 'nuL10', 0.01: 'nuL100', 10.0: 'nuH10'}
self.precomputed_filename = args.mesh + choose_note[self.args.nufactor]
print('chosen filename for precomputed solution',
self.precomputed_filename)
# partial Bessel functions and coefficients
self.bessel_parabolic = None
self.bessel_real = []
self.bessel_complex = []
self.coefs_exp = [-8, -6, -4, -2, 2, 4, 6, 8]
self.listDict.update({
'u_H1w': {
'list': [],
'name': 'corrected velocity H1 error on wall',
'abrev': 'CE_H1w',
'scale': self.scale_factor,
'relative': 'av_norm_H1w',
'slist': []
},
'u2H1w': {
'list': [],
'name': 'tentative velocity H1 error on wall',
'abrev': 'TE_H1w',
'scale': self.scale_factor,
'relative': 'av_norm_H1w',
'slist': []
},
'av_norm_H1w': {
'list': [],
'name': 'analytic velocity H1 norm on wall',
'abrev': 'AVN_H1w'
},
'a_force_wall': {
'list': [],
'name': 'analytic force on wall',
'abrev': 'AF'
},
'a_force_wall_normal': {
'list': [],
'name': 'analytic force on wall',
'abrev': 'AFN'
},
'a_force_wall_shear': {
'list': [],
'name': 'analytic force on wall',
'abrev': 'AFS'
},
'force_wall': {
'list': [],
'name': 'force error on wall',
'abrev': 'FE',
'relative': 'a_force_wall',
'slist': []
},
'force_wall_normal': {
'list': [],
'name': 'normal force error on wall',
'abrev': 'FNE',
'relative': 'a_force_wall',
'slist': []
},
'force_wall_shear': {
'list': [],
'name': 'shear force error on wall',
'abrev': 'FSE',
'relative': 'a_force_wall',
'slist': []
},
})
def fwi_si(gt_data, i_guess, n_receivers, noise_lv, path):
"""
This is the main function of the project.
Entries
gt_data: string path to the ground truth image data
i_guess: integer pointing the algorithm initialization guess
n_shots: integer, number of strikes for the FWI
n_receivers: integer, number of receivers for the FWI
noise_lv: float type variable that we use to compute noise level
path: string type variable, path to local results directory
"""
# Implementing parallel processing at shots level """
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
n_shots = comm.Get_size()
seism_vel = [4.12, 1.95]
image_phi = mpimg.imread(gt_data)
chi0 = np.int64(image_phi == 0)
chi1 = 1.0 - chi0
synth_model = seism_vel[0] * chi1 + seism_vel[1] * chi0
#scale in meter
xMin = 0.0
xMax = 1.0
zMin = 0.0
zMax = 0.650
#scale in seconds
tMin = 0.0
tMax = 1.0
# Damping layer width and damping limits
damp_layer = 0.1 * xMax
dmp_xMin = xMin + damp_layer
dmp_xMax = xMax - damp_layer
dmp_zMax = zMax - damp_layer
# Number of grid points are determined by the loaded image size
# Nz, Nx are (#) of grid point
Nz, Nx = synth_model.shape
delta_x = xMax / Nx
delta_z = zMax / Nz
CFL = 0.4
delta_t = (CFL * min(delta_x, delta_z)) / max(seism_vel)
gc_t = np.arange(tMin, tMax, delta_t)
Nt = len(gc_t)
# Level set parameters
MainItMax = 5000
gamma = 0.8
gamma2 = 0.8
stop_coeff = 1.0e-8
add_weight = True
ls_max = 3
ls = 0
beta0_init = 1.5 # 1.2 #0.8 #0.5 #0.3
beta0 = beta0_init
beta = beta0
stop_decision_limit = 150
stop_decision = 0
alpha1 = 0.01
alpha2 = 0.97
# wave Parameters
PlotFields = True
add_noise = False if noise_lv == 0 else True
src_Zpos = 5.0
source_peak_frequency = 5.0 # (kilo hertz)
# Grid coordinates
gc_x = np.arange(xMin, xMax, delta_x)
gc_z = np.arange(zMin, zMax, delta_z)
# Compute receivers
id_dmp_xMin = np.where(gc_x == dmp_xMin)[0][0]
id_dmp_xMax = np.where(gc_x == dmp_xMax)[0][0]
id_dmp_zMax = np.where(gc_z == dmp_zMax)[0][0]
rec_index = np.linspace(id_dmp_xMin,
id_dmp_xMax,
n_receivers + 1,
dtype='int')
try:
assert (len(rec_index) < id_dmp_xMax - id_dmp_xMin)
except AssertionError:
"receivers in different positions"
# Build the HUGE parameter dictionary
parameters = {
"gamma": gamma,
"gamma2": gamma2,
"ls_max": ls_max,
"stop_coeff": stop_coeff,
"add_noise": add_noise,
"add_weight": add_weight,
"beta0_init": beta0_init,
"stop_decision_limit": stop_decision_limit,
"alpha1": alpha1,
"alpha2": alpha2,
"CFL": CFL,
"source_peak_frequency": source_peak_frequency,
"src_Zpos": src_Zpos,
"i_guess": i_guess,
"n_shots": n_shots,
"n_receivers": n_receivers,
"add_weight": add_weight,
"nz": Nz,
"nx": Nx,
"nt": Nt,
"gc_t": gc_t,
"gc_x": gc_x,
"gc_z": gc_z,
"xMin": xMin,
"xMax": xMax,
"zMin": zMin,
"zMax": zMax,
"tMin": tMin,
"tMax": tMax,
"hz": delta_z,
"hx": delta_x,
"ht": delta_t,
"dmp_xMin": dmp_xMin,
"dmp_xMax": dmp_xMax,
"dmp_zMax": dmp_zMax,
"dmp_layer": damp_layer,
"id_dmp_xMin": id_dmp_xMin,
"id_dmp_xMax": id_dmp_xMax,
"id_dmp_zMax": id_dmp_zMax,
"rec": gc_x[rec_index],
"rec_index": rec_index,
'noise_lv': noise_lv,
"path": path,
"path_misfit": path + 'misfit/',
"path_phi": path + 'phi/'
}
# Compute initial guess matrix
if rank == 0:
outputs_and_paths(parameters)
gnu_data(image_phi, 'ground_truth.dat', parameters)
mkDirectory(parameters["path_phi"])
comm.Barrier()
phi_mat = initial_guess(parameters)
ind = inside_shape(phi_mat)
ind_c = np.ones_like(phi_mat) - ind
vel_field = seism_vel[0] * ind + seism_vel[1] * ind_c
# Initialization of Fenics-Dolfin functions
# ----------------------------------------
# Define mesh for the entire domain Omega
# ----------------------------------------
mesh = fc.RectangleMesh(comm, fc.Point(xMin, zMin), fc.Point(xMax, zMax),
Nx - 1, Nz - 1)
# ----------------------------------------
# Function spaces
# ----------------------------------------
V = fc.FunctionSpace(mesh, "Lagrange", 1)
VF = fc.VectorFunctionSpace(mesh, "Lagrange", 1)
theta = fc.TrialFunction(VF)
csi = fc.TestFunction(VF)
# ----------------------------------------
# Define boundaries of the domain
# ----------------------------------------
tol = fc.DOLFIN_EPS # tolerance for coordinate comparisons
class Left(fc.SubDomain):
def inside(self, x, on_boundary):
return on_boundary and abs(x[0] - xMin) < tol
class Right(fc.SubDomain):
def inside(self, x, on_boundary):
return on_boundary and abs(x[0] - xMax) < tol
class Bottom(fc.SubDomain):
def inside(self, x, on_boundary):
return on_boundary and abs(x[1] - zMin) < tol
class Top(fc.SubDomain):
def inside(self, x, on_boundary):
return on_boundary and abs(x[1] - zMax) < tol
# --------------------------------------
# Initialize sub-domain instances
# --------------------------------------
left = Left()
top = Top()
right = Right()
bottom = Bottom()
# ----------------------------------------------
# Initialize mesh function for boundary domains
# ----------------------------------------------
boundaries = fc.MeshFunction("size_t", mesh, mesh.topology().dim() - 1)
domains = fc.MeshFunction("size_t", mesh, mesh.topology().dim())
left.mark(boundaries, 3)
top.mark(boundaries, 4)
right.mark(boundaries, 5)
bottom.mark(boundaries, 6)
# ---------------------------------------
# Define operator for speed vector theta
# ---------------------------------------
dtotal = Measure("dx")
dircond = 1
# ---------------------------------------
# setting shape derivative weights
# re-balancing sensibility to be greater at the bottom
# ---------------------------------------
wei_equation = '1.0e8*(pow(x[0] - 0.5, 16) + pow(x[1] - 0.325, 10))+100'
wei = fc.Expression(str(wei_equation), degree=1)
# Building the left hand side of the bi-linear system
# to obtain the descendant direction from shape derivative
if dircond < 4:
bcF = [
fc.DirichletBC(VF, (0, 0), boundaries, 3),
fc.DirichletBC(VF, (0, 0), boundaries, 4),
fc.DirichletBC(VF, (0, 0), boundaries, 5),
fc.DirichletBC(VF, (0, 0), boundaries, 6)
]
if dircond == 1:
lhs = wei * alpha1 * inner(grad(theta), grad(csi)) * dtotal \
+ wei * alpha2 * inner(theta, csi) * dtotal
#
elif dircond == 2:
lhs = alpha1 * inner(grad(theta), grad(csi)) * \
dtotal + alpha2 * inner(theta, csi) * dtotal
elif dircond == 3:
lhs = inner(grad(theta), grad(csi)) * dtotal
elif dircond == 5:
lhs = inner(grad(theta), grad(csi)) * \
dtotal + inner(theta, csi) * dtotal
aV = fc.assemble(lhs)
#
if dircond < 4:
for bc in bcF:
bc.apply(aV)
#
# solver_V = fc.LUSolver(aV, "mumps")
solver_V = fc.LUSolver(aV)
# ------------------------------
# Initialize Level set function
# ------------------------------
phi = fc.Function(V)
phivec = phi.vector()
phivalues = phivec.get_local() # empty values
my_first, my_last = V.dofmap().ownership_range()
tabcoord = V.tabulate_dof_coordinates().reshape((-1, 2))
unowned = V.dofmap().local_to_global_unowned()
dofs = list(
filter(
lambda dof: V.dofmap().local_to_global_index(dof) not in unowned,
[i for i in range(my_last - my_first)]))
tabcoord = tabcoord[dofs]
phivalues[:] = phi_mat.reshape(Nz * Nx)[dofs] # assign values
phivec.set_local(phivalues)
phivec.apply('insert')
cont = 0
boundaries = fc.MeshFunction("size_t", mesh, mesh.topology().dim() - 1)
domains = fc.MeshFunction("size_t", mesh, mesh.topology().dim())
# -----------------------------
# Define measures
# -----------------------------
dx = Measure('dx')(subdomain_data=domains)
# -------------------------------
# Define function Omega1
# -------------------------------
class Omega1(fc.SubDomain):
def __init__(self) -> None:
super(Omega1, self).__init__()
def inside(self, x, on_boundary):
return True if phi(x) <= 0 and x[0] >= xMin and x[0] <= xMax and x[
1] >= zMin and x[1] <= zMax else False
# instantiate variables
eta = dmp(parameters)
source = Source(parameters)
FT = source.inject()
phi_mat_old = np.zeros_like(phi_mat)
vel_field_new = np.zeros_like(vel_field)
theta1_mat = np.zeros((Nz * Nx))
theta2_mat = np.zeros_like(theta1_mat)
MainItEff = 0
MainIt = 0
stop_decision = 0
st_mem_usage = 0.0
adj_mem_usage = 0.0
Jevaltotal = np.zeros((MainItMax))
norm_theta = np.zeros((MainItMax))
# path to recording phi function
# path to recording misfit function
if rank == 0:
plot_mat(parameters, 'Damping', 'Damping function', eta)
mkDirectory(parameters["path_phi"])
mkDirectory(parameters["path_misfit"])
comm.Barrier()
# -------------------------------
# Seismograms
# -------------------------------
wavesolver = WaveSolver(parameters, eta)
start = time.time()
d_send = np.empty((Nz, Nx, Nt), np.dtype('float'))
d = wavesolver.measurements(d_send[0:Nz, 0:Nx, 0:Nt], synth_model,
FT[rank, 0:Nz, 0:Nx, 0:Nt], add_noise)
seismograms = d[0, rec_index, 0:Nt].copy(order='C')
end = time.time()
# Plot Seismograms
if PlotFields:
print("{:.1f}s to build synthetic seismograms".format(end - start))
plotMeasurements(parameters, seismograms, rank)
if rank == 0:
plot_displacement_field(parameters, d)
sys.stdout.flush()
del (d, d_send)
###################################################
# Main Loop
###################################################
gradshape = ShapeDerivative(parameters, csi, V, dtotal, seism_vel)
while MainIt < MainItMax:
# ----------------------------------------------
# Initialize mesh function for boundary domains
# ----------------------------------------------
if MainIt > 0:
vel_field = vel_field_new
domains.set_all(0)
omega1 = Omega1()
omega1.mark(domains, 1)
dx = Measure('dx')(subdomain_data=domains)
u = np.empty((Nz, Nx, Nt), np.dtype('float'))
P = np.empty((Nz, Nx, Nt), np.dtype('float'))
if MainIt > 0:
vel_field = vel_field_new
# ------------------------------------
# Compute STATE. u stands for displacement field
# ------------------------------------
start = time.time()
u[0:Nz, 0:Nx, 0:Nt] = wavesolver.state(u[0:Nz, 0:Nx, 0:Nt], vel_field,
FT[rank, 0:Nz, 0:Nx, 0:Nt])
end = time.time()
# ------------------------------------
# Compute ADJOINT. P stands for the adjoint variable
# ------------------------------------
start1 = time.time()
tr_u = u[0, rec_index, 0:Nt].copy(order='C')
misfit = tr_u - seismograms
P[0:Nz, 0:Nx, 0:Nt] = wavesolver.adjoint(P[0:Nz, 0:Nx, 0:Nt],
vel_field, misfit)
end1 = time.time()
comm.Barrier()
print(
'{:.1f}s to compute state and {:.1f}s to compute adjoint with {:d} shots. '
.format(end - start, end1 - start1, n_shots))
del (start, end, start1, end1)
# Plot state/adjoint in 1st-iteration only
if MainIt == 0 and PlotFields:
if rank == 0:
mkDirectory(path + 'initial_state_%03d/' % (n_shots))
plotadjoint(parameters, P[0:Nz, 0:Nx, 0:Nt])
folder_name = 'initial_state_%03d/' % (n_shots)
plotstate(parameters, u[0:Nz, 0:Nx, 0:Nt], folder_name, rank)
# plot_displacement_field(parameters, u[1, 0:Nz, 0:Nx, 0:Nt])
st_mem_usage = (u.size * u.itemsize) / 1_073_741_824 # 1GB
adj_mem_usage = (P.size * P.itemsize) / 1_073_741_824 # 1GB
# Plotting reconstructions
if rank == 0 and (MainItEff % 10 == 0
or stop_decision == stop_decision_limit - 1):
plottype1(parameters, synth_model, phi_mat, cont)
plottype2(parameters, synth_model, phi_mat, cont)
plottype3(parameters, synth_model, phi_mat, MainIt, cont)
plotcostfunction(parameters, Jevaltotal, MainItEff)
plotnormtheta(parameters, norm_theta, MainItEff)
np.save(path + 'last_phi_mat.npy', phi_mat)
gnu_data(phi_mat, 'reconstruction.dat', parameters)
plot_misfit(parameters, 'misfit', 'Misfit', misfit,
rank) if (MainItEff % 50 == 0 and PlotFields) else None
# -------------------------
# Compute Cost Function
# -------------------------
J_omega = np.zeros((1))
l2_residual = np.sum(np.power(misfit, 2), axis=0)
if MainIt == 0 and add_weight:
weights = 1.0e-5
comm.Reduce(simpson_rule(l2_residual[0:Nt], gc_t), J_omega, op=MPI.SUM)
Jevaltotal[MainItEff] = 0.5 * (J_omega / weights)
del (J_omega)
# -------------------------
# Evaluate shape derivative
# -------------------------
start = time.time()
shapeder = (1.0 / weights) * gradshape.compute(u[0:Nz, 0:Nx, 0:Nt],
P[0:Nz, 0:Nx, 0:Nt], dx)
# Build the rhs of bi-linear system
shapeder = fc.assemble(shapeder)
end = time.time()
print('{}s to compute shape derivative.'.format(end - start))
del (start, end)
del (u, P)
with open(path + "cost_function.txt", "a") as file_costfunction:
file_costfunction.write('{:d} - {:.4e} \n'.format(
MainItEff, Jevaltotal[MainItEff]))
# ====================================
# ---------- Line search -------------
# ====================================
if MainIt > 0 and Jevaltotal[MainItEff] > Jevaltotal[
MainItEff - 1] and ls < ls_max:
ls = ls + 1
beta = beta * gamma
phi_mat = phi_mat_old
# ------------------------------------------------------------
# Update level set function using the descent direction theta
# ------------------------------------------------------------
hj_input = [
theta1_mat, theta2_mat, phi_mat, parameters, beta, MainItEff
]
phi_mat = hamiltonjacobi(*hj_input)
del (hj_input)
ind = inside_shape(phi_mat)
ind_c = np.ones_like(phi_mat) - ind
vel_field_new = seism_vel[0] * ind + seism_vel[1] * ind_c
phivec = phi.vector()
phivalues = phivec.get_local() # empty values
my_first, my_last = V.dofmap().ownership_range()
tabcoord = V.tabulate_dof_coordinates().reshape((-1, 2))
set_trace()
unowned = V.dofmap().local_to_global_unowned()
dofs = list(
filter(
lambda dof: V.dofmap().local_to_global_index(dof) not in
unowned, [i for i in range(my_last - my_first)]))
tabcoord = tabcoord[dofs]
phivalues[:] = phi_mat.reshape(Nz * Nx)[dofs] # assign values
phivec.set_local(phivalues)
phivec.apply('insert')
else:
print("----------------------------------------------")
print("Record in: {}".format(path))
print("----------------------------------------------")
print("ITERATION NUMBER (MainItEff) : {:d}".format(MainItEff))
print("ITERATION NUMBER (MainIt) : {:d}".format(MainIt))
print("----------------------------------------------")
print("Grid Size : {:d} x {:d}".format(Nx, Nz))
print("State memory usage : {:.4f} GB".format(st_mem_usage))
print("Adjoint memory usage : {:.4f} GB".format(adj_mem_usage))
print("----------------------------------------------")
print("Line search iterations : {:d}".format(ls))
print("Step length beta : {:.4e}".format(beta))
if ls == ls_max:
beta0 = max(beta0 * gamma2, 0.1 * beta0_init)
if ls == 0:
beta0 = min(beta0 / gamma2, 1.0)
ls = 0
MainItEff = MainItEff + 1
beta = beta0 # /(0.999**MainIt)
theta = fc.Function(VF)
solver_V.solve(theta.vector(), -1.0 * shapeder)
# ------------------------------------
# Compute norm theta and grad(phi)
# ------------------------------------
mpi_comm = theta.function_space().mesh().mpi_comm()
arraytheta = theta.vector().get_local()
theta_gathered = mpi_comm.gather(arraytheta, root=0)
# parei aqui !!!!!
comm.Barrier()
if rank == 0:
set_trace()
theta_vec = theta.vector()[fc.vertex_to_dof_map(VF)]
theta1_mat = theta_vec[0:len(theta_vec):2].reshape(Nz, Nx)
theta2_mat = theta_vec[1:len(theta_vec):2].reshape(Nz, Nx)
norm_theta[MainItEff - 1] = np.sqrt(
theta1_mat.reshape(Nz * Nx).dot(theta1_mat.reshape(Nx * Nz)) +
theta2_mat.reshape(Nz * Nx).dot(theta2_mat.reshape(Nx * Nz)))
max_gnp = np.sqrt(fc.assemble(dot(grad(phi), grad(phi)) * dtotal))
print("Norm(grad(phi)) : {:.4e}".format(max_gnp))
print("L2-norm of theta : {:.4e}".format(
norm_theta[MainItEff - 1]))
print("Cost functional : {:.4e}".format(
Jevaltotal[MainItEff - 1]))
# ------------------------------------------------------------
# Update level set function using the descent direction theta
# ------------------------------------------------------------
phi_mat_old = phi_mat
hj_input = [
theta1_mat, theta2_mat, phi_mat, parameters, beta,
MainItEff - 1
]
phi_mat = hamiltonjacobi(*hj_input)
del (hj_input)
phi.vector()[:] = phi_mat.reshape(
(Nz) * (Nx))[fc.dof_to_vertex_map(V)]
ind = inside_shape(phi_mat)
ind_c = np.ones_like(phi_mat) - ind
vel_field_new = seism_vel[0] * ind + seism_vel[1] * ind_c
# ----------------
# Computing error
# ----------------
error_area = np.abs(chi1 - ind)
relative_error = np.sum(error_area) / np.sum(chi0)
print('relative error : {:.3f}%'.format(100 *
relative_error))
with open(path + "error.txt", "a") as text_file:
text_file.write(f'{MainIt} {np.round(relative_error,3):>3}\n')
# Plot actual phi function
if MainIt % 50 == 0:
plot_mat3D(parameters, 'phi_3D', phi_mat, MainIt)
plot_countour(parameters, 'phi_contour', phi_mat, MainIt)
phi_ind = '%03d_' % (MainIt)
np.save(parameters["path_phi"] + phi_ind + 'phi.npy', phi_mat)
# --------------------------------
# Reinitialize level set function
# --------------------------------
if np.mod(MainItEff, 10) == 0:
phi_mat = reinit(Nz, Nx, phi_mat)
# ====================================
# -------- Stopping criterion --------
# ====================================
if MainItEff > 5:
stop0 = stop_coeff * (Jevaltotal[1] - Jevaltotal[2])
stop1 = Jevaltotal[MainItEff - 2] - Jevaltotal[MainItEff - 1]
if stop1 < stop0:
stop_decision = stop_decision + 1
if stop_decision == stop_decision_limit:
MainIt = MainItMax + 1
print("stop0 : {:.4e}".format(stop0))
print("stop1 : {:.4e}".format(stop1))
print("Stopping step : {:d} of {:d}".format(
stop_decision, stop_decision_limit))
print("----------------------------------------------\n")
cont += 1
MainIt += 1
return None
def _basic_form_on_reduced_function_space(form_wrapper, at):
form = form_wrapper._form
form_name = form_wrapper.name()
form_problem = get_problem_from_parametrized_operator(form_wrapper)
reduced_V = at.get_reduced_function_spaces()
reduced_subdomain_data = at.get_reduced_subdomain_data()
mu = form_problem.mu
if hasattr(form_problem, "set_time"):
t = form_problem.t
else:
t = None
if (form_name, reduced_V) not in form_cache:
visited = set()
replacements = dict()
truth_problems = list()
truth_problem_to_components = { # outer dict index over time derivative
0: dict(),
1: dict()
}
truth_problem_to_exact_truth_problem = dict()
truth_problem_to_reduced_mesh_solution = dict()
truth_problem_to_reduced_mesh_solution_dot = dict()
truth_problem_to_reduced_mesh_interpolator = { # outer dict index over time derivative
0: dict(),
1: dict()
}
reduced_problem_to_components = { # outer dict index over time derivative
0: dict(),
1: dict()
}
reduced_problem_to_reduced_mesh_solution = dict()
reduced_problem_to_reduced_mesh_solution_dot = dict()
reduced_problem_to_reduced_basis_functions = { # outer dict index over time derivative
0: dict(),
1: dict()
}
# Look for terminals on truth mesh
logger.log(DEBUG, "Traversing terminals of form " + form_name)
for node in wrapping.form_iterator(form, "nodes"):
if node in visited:
continue
# ... test and trial functions
elif isinstance(node, Argument):
logger.log(
DEBUG, "\tFound argument, number: " +
str(node.number()) + ", part: " + str(node.part()))
replacements[node] = wrapping.form_argument_replace(
node, reduced_V)
visited.add(node)
# ... problem solutions related to nonlinear terms
elif wrapping.is_problem_solution_type(node):
node_is_problem_solution = wrapping.is_problem_solution(
node)
node_is_problem_solution_dot = wrapping.is_problem_solution_dot(
node)
if node_is_problem_solution or node_is_problem_solution_dot:
if node_is_problem_solution:
(preprocessed_node, component, truth_solution
) = wrapping.solution_identify_component(node)
truth_problem = get_problem_from_solution(
truth_solution)
logger.log(
DEBUG,
"\tFound problem solution of truth problem " +
truth_problem.name() +
" (exact problem decorator: " +
str(hasattr(truth_problem, "__is_exact__")) +
", component: " + str(component) + ")")
# Time derivative key for components and interpolator dicts
time_derivative = 0
elif node_is_problem_solution_dot:
(preprocessed_node, component, truth_solution_dot
) = wrapping.solution_dot_identify_component(node)
truth_problem = get_problem_from_solution_dot(
truth_solution_dot)
logger.log(
DEBUG,
"\tFound problem solution dot of truth problem "
+ truth_problem.name() +
" (exact problem decorator: " +
str(hasattr(truth_problem, "__is_exact__")) +
", component: " + str(component) + ")")
# Time derivative key for components and interpolator dicts
time_derivative = 1
# Store truth problem
if truth_problem not in truth_problems:
truth_problems.append(truth_problem)
# Store the component
if truth_problem not in truth_problem_to_components[
time_derivative]:
truth_problem_to_components[time_derivative][
truth_problem] = list()
if component not in truth_problem_to_components[
time_derivative][truth_problem]:
truth_problem_to_components[time_derivative][
truth_problem].append(component)
# Get the function space corresponding to preprocessed_node on the reduced mesh
auxiliary_reduced_V = at.get_auxiliary_reduced_function_space(
truth_problem, component)
# Define and store the replacement
assert preprocessed_node not in replacements # as it is related to a new truth solution component
replacements[preprocessed_node] = backend.Function(
auxiliary_reduced_V)
if time_derivative == 0:
if truth_problem not in truth_problem_to_reduced_mesh_solution:
truth_problem_to_reduced_mesh_solution[
truth_problem] = list()
truth_problem_to_reduced_mesh_solution[
truth_problem].append(
replacements[preprocessed_node])
elif time_derivative == 1:
if truth_problem not in truth_problem_to_reduced_mesh_solution_dot:
truth_problem_to_reduced_mesh_solution_dot[
truth_problem] = list()
truth_problem_to_reduced_mesh_solution_dot[
truth_problem].append(
replacements[preprocessed_node])
# Get interpolator on reduced mesh
if truth_problem not in truth_problem_to_reduced_mesh_interpolator[
time_derivative]:
truth_problem_to_reduced_mesh_interpolator[
time_derivative][truth_problem] = list()
truth_problem_to_reduced_mesh_interpolator[
time_derivative][truth_problem].append(
at.get_auxiliary_function_interpolator(
truth_problem, component))
else:
(
preprocessed_node, component, auxiliary_problem
) = wrapping.get_auxiliary_problem_for_non_parametrized_function(
node)
logger.log(
DEBUG, "\tFound non parametrized function " +
str(preprocessed_node) +
" associated to auxiliary problem " +
str(auxiliary_problem.name()) + ", component: " +
str(component))
if preprocessed_node not in replacements:
# Get interpolator on reduced mesh
auxiliary_truth_problem_to_reduced_mesh_interpolator = at.get_auxiliary_function_interpolator(
auxiliary_problem, component)
# Define and store the replacement
replacements[
preprocessed_node] = auxiliary_truth_problem_to_reduced_mesh_interpolator(
preprocessed_node)
# Make sure to skip any parent solution related to this one
visited.add(node)
visited.add(preprocessed_node)
for parent_node in wrapping.solution_iterator(
preprocessed_node):
visited.add(parent_node)
# ... geometric quantities
elif isinstance(node, GeometricQuantity):
logger.log(DEBUG,
"\tFound geometric quantity " + str(node))
if len(reduced_V) == 2:
assert reduced_V[0].mesh().ufl_domain(
) == reduced_V[1].mesh().ufl_domain()
replacements[node] = type(node)(reduced_V[0].mesh())
visited.add(node)
else:
visited.add(node)
# ... and replace them
replaced_form = wrapping.form_replace(form, replacements, "nodes")
# Look for measures ...
if len(reduced_V) == 2:
assert reduced_V[0].mesh().ufl_domain() == reduced_V[1].mesh(
).ufl_domain()
measure_reduced_domain = reduced_V[0].mesh().ufl_domain()
replacements_measures = dict()
for integral in wrapping.form_iterator(replaced_form, "integrals"):
# Prepare measure for the new form (from firedrake/mg/ufl_utils.py)
integral_subdomain_data = integral.subdomain_data()
if integral_subdomain_data is not None:
integral_reduced_subdomain_data = reduced_subdomain_data[
integral_subdomain_data]
else:
integral_reduced_subdomain_data = None
measure = Measure(
integral.integral_type(),
domain=measure_reduced_domain,
subdomain_id=integral.subdomain_id(),
subdomain_data=integral_reduced_subdomain_data,
metadata=integral.metadata())
replacements_measures[integral.integrand(),
integral.integral_type(),
integral.subdomain_id()] = measure
# ... and replace them
replaced_form_with_replaced_measures = wrapping.form_replace(
replaced_form, replacements_measures, "measures")
# Cache the resulting dicts
form_cache[(form_name,
reduced_V)] = replaced_form_with_replaced_measures
truth_problems_cache[(form_name, reduced_V)] = truth_problems
truth_problem_to_components_cache[(
form_name, reduced_V)] = truth_problem_to_components
truth_problem_to_exact_truth_problem_cache[(
form_name, reduced_V)] = truth_problem_to_exact_truth_problem
truth_problem_to_reduced_mesh_solution_cache[(
form_name, reduced_V)] = truth_problem_to_reduced_mesh_solution
truth_problem_to_reduced_mesh_solution_dot_cache[(
form_name,
reduced_V)] = truth_problem_to_reduced_mesh_solution_dot
truth_problem_to_reduced_mesh_interpolator_cache[(
form_name,
reduced_V)] = truth_problem_to_reduced_mesh_interpolator
reduced_problem_to_components_cache[(
form_name, reduced_V)] = reduced_problem_to_components
reduced_problem_to_reduced_mesh_solution_cache[(
form_name,
reduced_V)] = reduced_problem_to_reduced_mesh_solution
reduced_problem_to_reduced_mesh_solution_dot_cache[(
form_name,
reduced_V)] = reduced_problem_to_reduced_mesh_solution_dot
reduced_problem_to_reduced_basis_functions_cache[(
form_name,
reduced_V)] = reduced_problem_to_reduced_basis_functions
# Extract from cache
replaced_form_with_replaced_measures = form_cache[(form_name,
reduced_V)]
truth_problems = truth_problems_cache[(form_name, reduced_V)]
truth_problem_to_components = truth_problem_to_components_cache[(
form_name, reduced_V)]
truth_problem_to_exact_truth_problem = truth_problem_to_exact_truth_problem_cache[
(form_name, reduced_V)]
truth_problem_to_reduced_mesh_solution = truth_problem_to_reduced_mesh_solution_cache[
(form_name, reduced_V)]
truth_problem_to_reduced_mesh_solution_dot = truth_problem_to_reduced_mesh_solution_dot_cache[
(form_name, reduced_V)]
truth_problem_to_reduced_mesh_interpolator = truth_problem_to_reduced_mesh_interpolator_cache[
(form_name, reduced_V)]
reduced_problem_to_components = reduced_problem_to_components_cache[(
form_name, reduced_V)]
reduced_problem_to_reduced_mesh_solution = reduced_problem_to_reduced_mesh_solution_cache[
(form_name, reduced_V)]
reduced_problem_to_reduced_mesh_solution_dot = reduced_problem_to_reduced_mesh_solution_dot_cache[
(form_name, reduced_V)]
reduced_problem_to_reduced_basis_functions = reduced_problem_to_reduced_basis_functions_cache[
(form_name, reduced_V)]
# Get list of truth and reduced problems that need to be solved, possibly updating cache
required_truth_problems = list()
required_reduced_problems = list()
for truth_problem in truth_problems:
truth_problem_is_solving = hasattr(truth_problem, "_is_solving")
if is_training_started(truth_problem):
reduced_problem = get_reduced_problem_from_problem(
truth_problem)
reduced_problem_is_solving = hasattr(reduced_problem,
"_is_solving")
else:
reduced_problem = None
reduced_problem_is_solving = False
if not truth_problem_is_solving:
if is_training_finished(truth_problem):
logger.log(
DEBUG, "Truth problem " + truth_problem.name() +
" (exact problem decorator: " +
str(hasattr(truth_problem, "__is_exact__")) +
") is not currently solving, and its offline stage has finished: truth problem will be replaced by reduced problem"
)
# Store the replacement for solution
if (reduced_problem
not in reduced_problem_to_reduced_mesh_solution
and truth_problem
in truth_problem_to_reduced_mesh_solution):
reduced_problem_to_reduced_mesh_solution[
reduced_problem] = truth_problem_to_reduced_mesh_solution[
truth_problem]
# Store the component
assert reduced_problem not in reduced_problem_to_components[
0]
assert truth_problem in truth_problem_to_components[0]
reduced_problem_to_components[0][
reduced_problem] = truth_problem_to_components[0][
truth_problem]
# Get reduced problem basis functions on reduced mesh
assert reduced_problem not in reduced_problem_to_reduced_basis_functions[
0]
reduced_problem_to_reduced_basis_functions[0][
reduced_problem] = [
at.get_auxiliary_basis_functions_matrix(
truth_problem, component) for component in
reduced_problem_to_components[0]
[reduced_problem]
]
# Store the replacement for solution_dot
if (reduced_problem
not in reduced_problem_to_reduced_mesh_solution_dot
and truth_problem
in truth_problem_to_reduced_mesh_solution_dot):
reduced_problem_to_reduced_mesh_solution_dot[
reduced_problem] = truth_problem_to_reduced_mesh_solution_dot[
truth_problem]
# Store the component
assert reduced_problem not in reduced_problem_to_components[
1]
assert truth_problem in truth_problem_to_components[1]
reduced_problem_to_components[1][
reduced_problem] = truth_problem_to_components[1][
truth_problem]
# Get reduced problem basis functions on reduced mesh
assert reduced_problem not in reduced_problem_to_reduced_basis_functions[
1]
reduced_problem_to_reduced_basis_functions[1][
reduced_problem] = [
at.get_auxiliary_basis_functions_matrix(
truth_problem, component) for component in
reduced_problem_to_components[1]
[reduced_problem]
]
# Append to list of required reduced problems
required_reduced_problems.append(
(reduced_problem, reduced_problem_is_solving))
else:
if (hasattr(truth_problem,
"_apply_exact_evaluation_at_stages") and
not hasattr(truth_problem, "_apply_EIM_at_stages")
and not hasattr(truth_problem,
"_apply_DEIM_at_stages")):
logger.log(
DEBUG, "Truth problem " + truth_problem.name() +
" (exact problem decorator: " +
str(hasattr(truth_problem, "__is_exact__")) +
") is not currently solving, its offline stage has not finished, and only @ExactParametrizedFunctions has been used: truth solve of this truth problem instance will be called"
)
# Init truth problem (if required), as it may not have been initialized
truth_problem.init()
# Append to list of required truth problems which are not currently solving
required_truth_problems.append(
(truth_problem, False, reduced_problem_is_solving))
else:
logger.log(
DEBUG, "Truth problem " + truth_problem.name() +
" (exact problem decorator: " +
str(hasattr(truth_problem, "__is_exact__")) +
") is not currently solving, its offline stage has not finished, and either @ExactParametrizedFunctions has not been used or it has been used in combination with @DEIM or @EIM: truth solve on an auxiliary instance (with exact problem decorator) will be called, to prevent early initialization of DEIM/EIM data structures"
)
# Store the corresponding exact truth problem
if truth_problem not in truth_problem_to_exact_truth_problem:
exact_truth_problem = exact_problem(truth_problem)
truth_problem_to_exact_truth_problem[
truth_problem] = exact_truth_problem
# Init exact truth problem (if required), as it may not have been initialized
exact_truth_problem.init()
else:
exact_truth_problem = truth_problem_to_exact_truth_problem[
truth_problem]
# Store the replacement for solution
if (exact_truth_problem
not in truth_problem_to_reduced_mesh_solution
and truth_problem
in truth_problem_to_reduced_mesh_solution):
truth_problem_to_reduced_mesh_solution[
exact_truth_problem] = truth_problem_to_reduced_mesh_solution[
truth_problem]
# Store the component
assert exact_truth_problem not in truth_problem_to_components[
0]
assert truth_problem in truth_problem_to_components[
0]
truth_problem_to_components[0][
exact_truth_problem] = truth_problem_to_components[
0][truth_problem]
# Get interpolator on reduced mesh
assert exact_truth_problem not in truth_problem_to_reduced_mesh_interpolator[
0]
assert truth_problem in truth_problem_to_reduced_mesh_interpolator[
0]
truth_problem_to_reduced_mesh_interpolator[0][
exact_truth_problem] = truth_problem_to_reduced_mesh_interpolator[
0][truth_problem]
# Store the replacement for solution_dot
if (exact_truth_problem not in
truth_problem_to_reduced_mesh_solution_dot
and truth_problem
in truth_problem_to_reduced_mesh_solution_dot):
truth_problem_to_reduced_mesh_solution_dot[
exact_truth_problem] = truth_problem_to_reduced_mesh_solution_dot[
truth_problem]
# Store the component
assert exact_truth_problem not in truth_problem_to_components[
1]
assert truth_problem in truth_problem_to_components[
1]
truth_problem_to_components[1][
exact_truth_problem] = truth_problem_to_components[
1][truth_problem]
# Get interpolator on reduced mesh
assert exact_truth_problem not in truth_problem_to_reduced_mesh_interpolator[
1]
assert truth_problem in truth_problem_to_reduced_mesh_interpolator[
1]
truth_problem_to_reduced_mesh_interpolator[1][
exact_truth_problem] = truth_problem_to_reduced_mesh_interpolator[
1][truth_problem]
# Append to list of required truth problems which are not currently solving
required_truth_problems.append(
(exact_truth_problem, False,
reduced_problem_is_solving))
else:
logger.log(
DEBUG, "Truth problem " + truth_problem.name() +
" (exact problem decorator: " +
str(hasattr(truth_problem, "__is_exact__")) +
") is currently solving: current truth solution will be loaded"
)
assert not reduced_problem_is_solving
# Append to list of required truth problems which are currently solving
required_truth_problems.append((truth_problem, True, False))
# Solve truth problems (which have not been reduced yet) associated to nonlinear terms
for (truth_problem, truth_problem_is_solving,
reduced_problem_is_solving) in required_truth_problems:
if not reduced_problem_is_solving:
# Solve (if necessary)
truth_problem.set_mu(mu)
if not truth_problem_is_solving:
logger.log(
DEBUG, "Requiring truth problem solve for problem " +
truth_problem.name() + " (exact problem decorator: " +
str(hasattr(truth_problem, "__is_exact__")) + ")")
truth_problem.solve()
else:
logger.log(
DEBUG,
"Loading current truth problem solution for problem " +
truth_problem.name() + " (exact problem decorator: " +
str(hasattr(truth_problem, "__is_exact__")) + ")")
else:
reduced_problem = get_reduced_problem_from_problem(
truth_problem)
logger.log(
DEBUG,
"Replacing current truth problem solution with reduced solution for problem "
+ reduced_problem.truth_problem.name())
# Assign to reduced_mesh_solution
if truth_problem in truth_problem_to_reduced_mesh_solution:
for (reduced_mesh_solution, reduced_mesh_interpolator) in zip(
truth_problem_to_reduced_mesh_solution[truth_problem],
truth_problem_to_reduced_mesh_interpolator[0]
[truth_problem]):
solution_to = reduced_mesh_solution
if t is None:
if not reduced_problem_is_solving:
solution_from = reduced_mesh_interpolator(
truth_problem._solution)
else:
solution_from = reduced_mesh_interpolator(
reduced_problem.
basis_functions[:reduced_problem._solution.N] *
reduced_problem._solution)
else:
if not reduced_problem_is_solving:
if not truth_problem_is_solving:
solution_from = reduced_mesh_interpolator(
truth_problem._solution_over_time.at(t))
else:
solution_from = reduced_mesh_interpolator(
truth_problem._solution)
else:
solution_from = reduced_mesh_interpolator(
reduced_problem.
basis_functions[:reduced_problem._solution.N] *
reduced_problem._solution)
backend.assign(solution_to, solution_from)
# Assign to reduced_mesh_solution_dot
if truth_problem in truth_problem_to_reduced_mesh_solution_dot:
for (reduced_mesh_solution_dot,
reduced_mesh_interpolator) in zip(
truth_problem_to_reduced_mesh_solution_dot[
truth_problem],
truth_problem_to_reduced_mesh_interpolator[1]
[truth_problem]):
solution_dot_to = reduced_mesh_solution_dot
assert t is not None
if not reduced_problem_is_solving:
if not truth_problem_is_solving:
solution_dot_from = reduced_mesh_interpolator(
truth_problem._solution_dot_over_time.at(t))
else:
solution_dot_from = reduced_mesh_interpolator(
truth_problem._solution_dot)
else:
solution_dot_from = reduced_mesh_interpolator(
reduced_problem.basis_functions[:reduced_problem.
_solution_dot.N] *
reduced_problem._solution_dot)
backend.assign(solution_dot_to, solution_dot_from)
# Solve reduced problems associated to nonlinear terms
for (reduced_problem, is_solving) in required_reduced_problems:
# Solve (if necessary)
reduced_problem.set_mu(mu)
if not is_solving:
logger.log(
DEBUG, "Requiring reduced problem solve for problem " +
reduced_problem.truth_problem.name())
reduced_problem.solve()
else:
logger.log(
DEBUG,
"Loading current reduced problem solution for problem " +
reduced_problem.truth_problem.name())
# Assign to reduced_mesh_solution
if reduced_problem in reduced_problem_to_reduced_mesh_solution:
for (reduced_mesh_solution, reduced_basis_functions) in zip(
reduced_problem_to_reduced_mesh_solution[
reduced_problem],
reduced_problem_to_reduced_basis_functions[0]
[reduced_problem]):
solution_to = reduced_mesh_solution
solution_from_N = OnlineSizeDict()
for c, v in reduced_problem._solution.N.items():
if c in reduced_basis_functions._components_name:
solution_from_N[c] = v
solution_from = online_backend.OnlineFunction(
solution_from_N)
if t is None or is_solving:
online_backend.online_assign(solution_from,
reduced_problem._solution)
else:
online_backend.online_assign(
solution_from,
reduced_problem._solution_over_time.at(t))
solution_from = reduced_basis_functions[:solution_from_N] * solution_from
backend.assign(solution_to, solution_from)
# Assign to reduced_mesh_solution_dot
if reduced_problem in reduced_problem_to_reduced_mesh_solution_dot:
for (reduced_mesh_solution_dot,
reduced_basis_functions) in zip(
reduced_problem_to_reduced_mesh_solution_dot[
reduced_problem],
reduced_problem_to_reduced_basis_functions[1]
[reduced_problem]):
solution_dot_to = reduced_mesh_solution_dot
solution_dot_from_N = OnlineSizeDict()
for c, v in reduced_problem._solution_dot.N.items():
if c in reduced_basis_functions._components_name:
solution_dot_from_N[c] = v
solution_dot_from = online_backend.OnlineFunction(
solution_dot_from_N)
assert t is not None
if is_solving:
online_backend.online_assign(
solution_dot_from, reduced_problem._solution_dot)
else:
online_backend.online_assign(
solution_dot_from,
reduced_problem._solution_dot_over_time.at(t))
solution_dot_from = reduced_basis_functions[:
solution_dot_from_N] * solution_dot_from
backend.assign(solution_dot_to, solution_dot_from)
# Assemble and return
assembled_replaced_form = wrapping.assemble(
replaced_form_with_replaced_measures)
if not isinstance(assembled_replaced_form, Number):
form_rank = assembled_replaced_form.rank()
else:
form_rank = 0
return (assembled_replaced_form, form_rank)
def __init__(self, args, tc, metadata):
self.has_analytic_solution = False
self.problem_code = 'REAL'
super(Problem, self).__init__(args, tc, metadata)
self.name = 'test on real mesh'
self.status_functional_str = 'outflow/inflow'
self.last_inflow = 0
# time settings
self.itp_lengths = {
1: 1.0,
2: 0.9375,
}
self.cycle_length = self.itp_lengths[self.args.itp]
# input parameters
self.nu = self.args.nu # kinematic viscosity
self.factor = args.factor
self.metadata['factor'] = self.factor
self.scale_factor.append(self.factor)
self.tc.start('mesh')
# Import mesh
try:
self.mesh, self.facet_function = super(Problem,
self).loadMesh(args.mesh)
info("Mesh name: " + args.mesh + " " + str(self.mesh))
f_ini = open('meshes/' + args.mesh + '.ini', 'r')
reader = csv.reader(f_ini, delimiter=' ', escapechar='\\')
except (EnvironmentError, RuntimeError):
print(
'Unable to open mesh.hdf5 or mesh.ini file. Check if the mesh was prepared to be used '
'with \"real\" problem.')
exit(1)
# load inflows and outflows (interfaces) from mesh.ini file
obj = None
self.interfaces = []
for row in reader:
if not row:
pass
elif row[0] == 'volume':
self.mesh_volume = float(row[1])
elif row[0] == 'in':
if obj is not None:
self.interfaces.append(obj)
obj = {'inflow': True, 'number': row[1]}
elif row[0] == 'out':
if obj is not None:
self.interfaces.append(obj)
obj = {'inflow': False, 'number': row[1]}
else:
if len(row) == 2: # scalar values
obj[row[0]] = row[1]
else: # vector values
obj[row[0]] = [float(f) for f in row[1:]]
self.interfaces.append(obj)
f_ini.close()
self.tc.end('mesh')
# collect inflows and outflows into separate lists
self.outflow_area = 0
self.inflows = []
self.outflows = []
for obj in self.interfaces:
if not obj['inflow']:
self.outflow_area += float(obj['S'])
self.outflows.append(obj)
else:
self.inflows.append(obj)
info('Outflow area: %f' % self.outflow_area)
# self.dsWall = Measure("ds", subdomain_id=1, subdomain_data=self.facet_function)
self.normal = FacetNormal(self.mesh)
# generate measures, collect measure lists
self.inflow_measures = []
for obj in self.interfaces:
obj['measure'] = Measure("ds",
subdomain_id=int(obj['number']),
subdomain_data=self.facet_function)
if obj['inflow']:
self.inflow_measures.append(obj['measure'])
else:
self.outflow_measures.append(obj['measure'])
self.listDict.update({
'outflow': {
'list': [],
'name': 'outflow rate',
'abrev': 'OUT',
'slist': []
},
'inflow': {
'list': [],
'name': 'inflow rate',
'abrev': 'IN',
'slist': []
},
'oiratio': {
'list': [],
'name': 'outflow/inflow ratio (mass conservation)',
'abrev': 'O/I',
'slist': []
},
})
for obj in self.outflows:
n = obj['number']
self.listDict.update({
'outflow' + n: {
'list': [],
'name': 'outflow rate ' + n,
'abrev': 'OUT' + n,
'slist': []
}
})
self.can_force_outflow = True
def dx_from_measure(mesh):
subdomains = MeshFunction("size_t", mesh, mesh.topology.dim, 1)
dx = Measure("dx")(subdomain_data=subdomains, domain=mesh)
dx = dx(1)
return dx
plot(facet_function)
f_mesh = HDF5File(mpi_comm_world(), 'meshes/' + meshName + '.hdf5', 'w')
f_mesh.write(mesh, 'mesh')
f_mesh.write(facet_function, 'facet_function')
f_mesh.close()
# compute volume of mesh
V = FunctionSpace(mesh, 'Lagrange', 1)
one = interpolate(Expression('1.'), V)
volume = assemble(one * dx)
# compute real areas of boudary parts
for obj in itertools.chain(inflows, outflows):
dS = Measure("ds",
subdomain_id=obj['number'],
subdomain_data=facet_function)
obj['S'] = assemble(one * dS)
# compute reference coefs
for inf in inflows:
inf['reference_coef'] = inf['reference_radius'] * inf[
'reference_radius'] / (inf['radius'] * inf['radius'])
# create .ini file ====================
f_ini = open('meshes/' + meshName + '.ini', 'w')
w = csv.writer(f_ini, delimiter=' ', escapechar='\\', quoting=csv.QUOTE_NONE)
w.writerow(['volume', volume])
for inf in inflows:
w.writerow(['in', inf['number']])
w.writerow(['normal'] + inf['normal'])
def __init__(self, args, tc, metadata):
self.has_analytic_solution = True
self.problem_code = 'SCYL'
super(Problem, self).__init__(args, tc, metadata)
self.tc.init_watch('errorP', 'Computed pressure error', True)
self.tc.init_watch('errorV', 'Computed velocity error', True)
self.tc.init_watch('errorForce', 'Computed force error', True)
self.tc.init_watch('errorVtest', 'Computed velocity error test', True)
self.tc.init_watch('computePG', 'Computed pressure gradient', True)
self.name = 'steady_cylinder'
self.status_functional_str = 'last H1 velocity error'
# input parameters
self.ic = args.ic
self.factor = args.factor
self.metadata['factor'] = self.factor
self.scale_factor.append(self.factor)
# fixed parameters (used in analytic solution and in BC)
self.nu = 3.71 # kinematic viscosity
self.R = 5.0 # cylinder radius
self.mesh_volume = pi * 25. * 20.
# Import gmsh mesh
self.tc.start('mesh')
self.mesh, self.facet_function = super(Problem,
self).loadMesh(args.mesh)
self.dsIn = Measure("ds",
subdomain_id=2,
subdomain_data=self.facet_function)
self.dsOut = Measure("ds",
subdomain_id=3,
subdomain_data=self.facet_function)
self.dsWall = Measure("ds",
subdomain_id=1,
subdomain_data=self.facet_function)
self.normal = FacetNormal(self.mesh)
print("Mesh name: ", args.mesh, " ", self.mesh)
print("Mesh norm max: ", self.mesh.hmax())
print("Mesh norm min: ", self.mesh.hmin())
self.tc.end('mesh')
self.sol_p = None
self.analytic_gradient = None
self.analytic_pressure_norm = None
self.v_in = None
self.area = None
self.analytic_v_norm_L2 = None
self.analytic_v_norm_H1 = None
self.analytic_v_norm_H1w = None
self.listDict.update({
'u_H1w': {
'list': [],
'name': 'corrected velocity H1 error on wall',
'abrev': 'CE_H1w',
'scale': self.scale_factor,
'relative': 'av_norm_H1w',
'slist': []
},
'u2H1w': {
'list': [],
'name': 'tentative velocity H1 error on wall',
'abrev': 'TE_H1w',
'scale': self.scale_factor,
'relative': 'av_norm_H1w',
'slist': []
},
'av_norm_H1w': {
'list': [],
'name': 'analytic velocity H1 norm on wall',
'abrev': 'AVN_H1w'
},
'a_force_wall': {
'list': [],
'name': 'analytic force on wall',
'abrev': 'AF'
},
'a_force_wall_normal': {
'list': [],
'name': 'analytic force on wall',
'abrev': 'AFN'
},
'a_force_wall_shear': {
'list': [],
'name': 'analytic force on wall',
'abrev': 'AFS'
},
'force_wall': {
'list': [],
'name': 'force error on wall',
'abrev': 'FE',
'relative': 'a_force_wall',
'slist': []
},
'force_wall_normal': {
'list': [],
'name': 'normal force error on wall',
'abrev': 'FNE',
'relative': 'a_force_wall',
'slist': []
},
'force_wall_shear': {
'list': [],
'name': 'shear force error on wall',
'abrev': 'FSE',
'relative': 'a_force_wall',
'slist': []
},
})
alpha_2 = interpolate(alpha_2(degree=2), F)
beta_1 = interpolate(beta_1(degree=2), F)
beta_2 = interpolate(beta_2(degree=2), F)
a_ = alpha_1*alpha_2
b_ = alpha_1*beta_2 + alpha_2*beta_1
c_ = beta_1*beta_2
Lambda_e = as_tensor([[alpha_2, 0],[0, alpha_1]])
Lambda_p = as_tensor([[beta_2, 0],[0, beta_1]])
# Set up boundary condition
bc = DirichletBC(W.sub(0), Constant(("0.0", "0.0")), ff, 1)
# Create measure for the source term
dx = Measure("dx", domain=mesh, subdomain_data=mf)
ds = Measure("ds", subdomain_data=ff, domain=mesh)
# Source term
#f = ricker_pulse(t=0.0, omega=omega_p, degree=1)
# Set up initial values
u0 = Function(V)
u0.set_allow_extrapolation(True)
v0 = Function(V)
a0 = Function(V)
U0 = Function(M)
V0 = Function(M)
A0 = Function(M)
# Test and trial functions
(u, S) = TrialFunctions(W)
subdomains = MeshFunction("size_t", mesh, 2, 0)
subdomain_0.mark(subdomains, 0)
subdomain_1.mark(subdomains, 1)
lmbda = MaterialProperty(lambdas[0],
lambdas[1],
subdomains=subdomains,
degree=0)
rho = MaterialProperty(rhos[0],
rhos[1],
subdomains=subdomains,
degree=0)
mu = MaterialProperty(mus[0], mus[1], subdomains=subdomains, degree=0)
# MEASURE
dx = Measure("dx", domain=mesh, subdomain_data=subdomains)
elif type_of_medium == "oblique":
layer_start_0 = 0
layer_start_1 = layer_end_0 = Ly / 2
layer_end_1 = Ly + Lpml
layer_start_slope_0 = 0
layer_start_slope_1 = layer_end_slope_0 = 1 / 3
layer_end_slope_1 = 0
subdomain_0 = ObliqueLayer(Lx, Ly, Lpml, layer_start_0, layer_end_0,
layer_start_slope_0, layer_end_slope_0)
subdomain_1 = ObliqueLayer(Lx, Ly, Lpml, layer_start_1, layer_end_1,
layer_start_slope_1, layer_end_slope_1)
subdomains = MeshFunction("size_t", mesh, 2, 0)
range_max=2 * v_in,
window_width=width,
window_height=height)
plt.write_png('%s/correct' % dir)
# v_in_expr = Expression('(t<1.0)?t*v:v', v=Constant(v_in), t=0.0)
# v_in_expr = Expression('(t<1.0)?(1-cos(pi*t))*v*0.5:v', v=Constant(v_in), t=0.0)
bcp = DirichletBC(Q, Constant(0.0), boundary_parts, 2)
bcu = DirichletBC(V, v_in_expr, boundary_parts, 1)
foo = Function(Q)
null_vec = Vector(foo.vector())
Q.dofmap().set(null_vec, 1.0)
null_vec *= 1.0 / null_vec.norm('l2')
# print(null_vec.array())
null_space = VectorSpaceBasis([null_vec])
ds = Measure("ds", subdomain_data=boundary_parts)
# Define forms (dont redefine functions used here)
# step 1
u0 = Function(V)
u1 = Function(V)
p0 = Function(Q)
u_tent = TrialFunction(V)
v = TestFunction(V)
# U_ = 1.5*u0 - 0.5*u1
# nonlinearity = inner(dot(0.5 * (u_tent.dx(0) + u0.dx(0)), U_), v) * dx
# F_tent = (1./dt)*inner(u_tent - u0, v) * dx + nonlinearity\
# + nu*inner(0.5 * (u_tent.dx(0) + u0.dx(0)), v.dx(0)) * dx + inner(p0.dx(0), v) * dx\
# - inner(f, v)*dx # solve to u_
# using explicite scheme: so LHS has interpretation as heat equation, RHS are sources
F_tent = (1./dt)*inner(u_tent - u0, v)*dx + inner(dot(u0.dx(0), u0), v)*dx + nu*inner((u_tent.dx(0) + u0.dx(0)), v.dx(0)) * dx + inner(p0.dx(0), v) * dx\