def velocity_update(self):
"""
Update the velocity predictions with the updated pressure
field from the pressure correction equation
"""
if self.use_local_solver_for_update:
# Element-wise projection
if self.u_upd_solver is None:
self.u_upd_solver = dolfin.LocalSolver(
self.eqs_vel_upd[0].form_lhs)
self.u_upd_solver.factorize()
Vu = self.simulation.data['Vu']
for d in range(self.simulation.ndim):
eq = self.eqs_vel_upd[d]
b = eq.assemble_rhs()
u_new = self.simulation.data['u%d' % d]
self.u_upd_solver.solve_local(u_new.vector(), b, Vu.dofmap())
self.niters_u_upd[d] = 0
else:
# Global projection
for d in range(self.simulation.ndim):
eq = self.eqs_vel_upd[d]
if self.Au_upd is None:
self.Au_upd = eq.assemble_lhs()
A = self.Au_upd
b = eq.assemble_rhs()
u_new = self.simulation.data['u%d' % d]
self.niters_u_upd[d] = self.u_upd_solver.solve(
A, u_new.vector(), b)
def _setup_divergence(self, vel, method, name='u'):
"""
Calculate divergence and element to element velocity
flux differences on the same edges
"""
V = df.FunctionSpace(self.mesh, 'DG', 0)
n = df.FacetNormal(self.mesh)
u, v = df.TrialFunction(V), df.TestFunction(V)
# The difference between the flux on the same facet between two different cells
a1 = u * v * dx
w = dot(vel('+') - vel('-'), n('+'))
if method == 'div0':
L1 = w * avg(v) * dS
else:
L1 = abs(w) * avg(v) * dS
# The divergence internally in the cell
a2 = u * v * dx
if method in ('div', 'div0'):
L2 = abs(df.div(vel)) * v * dx
elif method == 'gradq_avg':
L2 = dot(avg(vel), n('+')) * jump(v) * dS - dot(vel, grad(v)) * dx
else:
raise ValueError('Divergence type %r not supported' % method)
# Store for usage in projection
storage = self._div[name] = {}
storage['dS_solver'] = df.LocalSolver(a1, L1)
storage['dx_solver'] = df.LocalSolver(a2, L2)
storage['div_dS'] = df.Function(V)
storage['div_dx'] = df.Function(V)
# Pre-factorize matrices
storage['dS_solver'].factorize()
storage['dx_solver'].factorize()
storage['div_dS'].rename('Divergence_%s_dS' % name,
'Divergence_%s_dS' % name)
storage['div_dx'].rename('Divergence_%s_dx' % name,
'Divergence_%s_dx' % name)
def _setup_peclet(self, vel, nu):
"""
Pe = a*h/(2*nu) where a = mag(vel)
"""
V = df.FunctionSpace(self.mesh, 'DG', 0)
h = self.simulation.data['h']
u, v = df.TrialFunction(V), df.TestFunction(V)
a = u * v * dx
L = dot(vel, vel)**0.5 * h / (2 * nu) * v * dx
# Pre-factorize matrices and store for usage in projection
self._peclet_solver = df.LocalSolver(a, L)
self._peclet_solver.factorize()
self._peclet = df.Function(V)
def __init__(self, expr, V, dxm):
"""
expr:
expression to project
V:
quadrature function space
dxm:
dolfin.Measure("dx") that matches V
"""
dv = df.TrialFunction(V)
v_ = df.TestFunction(V)
a_proj = df.inner(dv, v_) * dxm
b_proj = df.inner(expr, v_) * dxm
self.solver = df.LocalSolver(a_proj, b_proj)
self.solver.factorize()
def _setup_courant(self, vel, dt):
"""
Co = a*dt/h where a = mag(vel)
"""
V = df.FunctionSpace(self.mesh, 'DG', 0)
h = self.simulation.data['h']
u, v = df.TrialFunction(V), df.TestFunction(V)
a = u * v * dx
vmag = sqrt(dot(vel, vel))
L = vmag * dt / h * v * dx
# Pre-factorize matrices and store for usage in projection
self._courant_solver = df.LocalSolver(a, L)
self._courant_solver.factorize()
self._courant = df.Function(V)
def solve(self, var, b):
x = dolfin.Function(self.test_function().function_space())
# b is a libadjoint object (form or function)
(a, L) = (self.data, b.data)
# First: check if L is None (meaning zero)
if L is None:
x_vec = adjlinalg.Vector(x)
return x_vec
if isinstance(L, dolfin.Function):
b_vec = L.vector()
L = None
else:
b_vec = dolfin.assemble(L)
# Next: if necessary, create a new solver and add to dictionary
idx = a.arguments()
if idx not in caching.localsolvers:
if dolfin.parameters["adjoint"]["debug_cache"]:
dolfin.info_red("Creating new LocalSolver")
newsolver = dolfin.LocalSolver(
a, None, solver_type=self.solver_parameters["solver_type"])
if self.solver_parameters["factorize"]: newsolver.factorize()
caching.localsolvers[idx] = newsolver
else:
if dolfin.parameters["adjoint"]["debug_cache"]:
dolfin.info_green("Reusing LocalSolver")
# Get the right solver from the solver dictionary
solver = caching.localsolvers[idx]
solver.solve_local(x.vector(), b_vec, b.fn_space.dofmap())
x_vec = adjlinalg.Vector(x)
return x_vec
def __init__(self, simulation, field_inp):
"""
A scalar field
"""
self.simulation = simulation
self.read_input(field_inp)
# Show the input data
simulation.log.info('Creating a sharp field %r' % self.name)
simulation.log.info(' Variable: %r' % self.var_name)
simulation.log.info(' Local proj: %r' % self.local_projection)
simulation.log.info(' Proj. degree: %r' % self.projection_degree)
simulation.log.info(' Poly. degree: %r' % self.polynomial_degree)
mesh = simulation.data['mesh']
self.V = dolfin.FunctionSpace(mesh, 'DG', self.polynomial_degree)
self.func = dolfin.Function(self.V)
if self.local_projection:
# Represent the jump using a quadrature element
quad_elem = dolfin.FiniteElement(
'Quadrature',
mesh.ufl_cell(),
self.projection_degree,
quad_scheme="default",
)
xpos, ypos, zpos = self.xpos, self.ypos, self.zpos
if simulation.ndim == 2:
cpp0 = 'x[0] < %r and x[1] < %r' % (xpos, ypos)
else:
cpp0 = 'x[0] < %r and x[1] < %r and x[2] < %r' % (xpos, ypos, zpos)
cpp = '(%s) ? %r : %r' % (cpp0, self.val_below, self.val_above)
e = dolfin.Expression(cpp, element=quad_elem)
dx = dolfin.dx(metadata={'quadrature_degree': self.projection_degree})
# Perform the local projection
u, v = dolfin.TrialFunction(self.V), dolfin.TestFunction(self.V)
a = u * v * dolfin.dx
L = e * v * dx
ls = dolfin.LocalSolver(a, L)
ls.solve_local_rhs(self.func)
# Clip values to allowable bounds
arr = self.func.vector().get_local()
val_min = min(self.val_below, self.val_above)
val_max = max(self.val_below, self.val_above)
clipped_arr = numpy.clip(arr, val_min, val_max)
self.func.vector().set_local(clipped_arr)
self.func.vector().apply('insert')
else:
# Initialise the sharp static field
dm = self.V.dofmap()
arr = self.func.vector().get_local()
above, below = self.val_above, self.val_below
xpos, ypos, zpos = self.xpos, self.ypos, self.zpos
for cell in dolfin.cells(simulation.data['mesh']):
mp = cell.midpoint()[:]
dof, = dm.cell_dofs(cell.index())
if (
mp[0] < xpos
and mp[1] < ypos
and (simulation.ndim == 2 or mp[2] < zpos)
):
arr[dof] = below
else:
arr[dof] = above
self.func.vector().set_local(arr)
self.func.vector().apply('insert')
def local_project(v, fspace, solver_method: str = "", **kwargs):
"""
Parameters
----------
v : [type]
input field
fspace : [type]
[description]
solver_method : str, optional
"LU" or "Cholesky" factorization. LU method used by default.
keyword arguments
----------
metadata : dict, optional
This can be used to define different quadrature degrees for different
terms in a form, and to override other form compiler specific options
separately for different terms. By default {}
See UFL user manual for more information
** WARNING** : May be over ignored if dx argument is also used. (Non étudié)
dx : Measure
Impose the dx measure for the projection.
This is for example useful to do some kind of interpolation on DG functionspaces.
Returns
-------
Function
Notes
-----
Source for this code:
https://comet-fenics.readthedocs.io/en/latest/tips_and_tricks.html#efficient-projection-on-dg-or-quadrature-spaces
"""
metadata = kwargs.get("metadata", {})
dx = kwargs.get("dx", fe.dx(metadata=metadata))
reuse_solver = kwargs.get(
"reuse_solver", True
) # Reuse a LocalSolver that already exists
id_function_space = (
fspace.id()
) # The id is unique. https://fenicsproject.org/olddocs/dolfin/latest/cpp/d8/df0/classdolfin_1_1Variable.html#details
dv = fe.TrialFunction(fspace)
v_ = fe.TestFunction(fspace)
a_proj = fe.inner(dv, v_) * dx
b_proj = fe.inner(v, v_) * dx
if solver_method == "LU":
solver_type = fe.cpp.fem.LocalSolver.SolverType.LU
solver = fe.LocalSolver(a_proj, b_proj, solver_type=solver_type)
elif solver_method == "Cholesky":
solver_type = fe.cpp.fem.LocalSolver.SolverType.Cholesky
solver = fe.LocalSolver(a_proj, b_proj, solver_type=solver_type)
else:
solver = fe.LocalSolver(a_proj, b_proj)
if not (reuse_solver and ((id_function_space, solver_method) in _local_solvers)):
solver.factorize()
# You can optionally call the factorize() method to pre-calculate the local left-hand side factorizations to speed up repeated applications of the LocalSolver with the same LHS
# This class can be used for post-processing solutions, e.g.computing stress fields for visualisation, far more cheaply that using global projections
# https://fenicsproject.org/olddocs/dolfin/latest/cpp/de/d86/classdolfin_1_1LocalSolver.html#details
_local_solvers.append((id_function_space, solver_method))
u = fe.Function(fspace)
solver.solve_local_rhs(u)
return u
