def residual(self, test, trial, trial_lagged, fields, bcs):
u_adv = trial_lagged
phi = test
n = self.n
u = trial
F = -dot(u, div(outer(phi, u_adv)))*self.dx
for id, bc in bcs.items():
if 'u' in bc:
u_in = bc['u']
elif 'un' in bc:
u_in = bc['un'] * n # this implies u_t=0 on the inflow
else:
u_in = zero(self.dim)
F += conditional(dot(u_adv, n) < 0,
dot(phi, u_in)*dot(u_adv, n),
dot(phi, u)*dot(u_adv, n)) * self.ds(id)
if not (is_continuous(self.trial_space) and normal_is_continuous(u_adv)):
# s=0: u.n(-)<0 => flow goes from '+' to '-' => '+' is upwind
# s=1: u.n(-)>0 => flow goes from '-' to '+' => '-' is upwind
s = 0.5*(sign(dot(avg(u), n('-'))) + 1.0)
u_up = u('-')*s + u('+')*(1-s)
F += dot(u_up, (dot(u_adv('+'), n('+'))*phi('+') + dot(u_adv('-'), n('-'))*phi('-'))) * self.dS
return -F
def incompressible_hydrostatic_balance(state, b0, p0, top=False, params=None):
# get F
Vu = state.spaces("HDiv")
Vv = FunctionSpace(state.mesh, Vu.ufl_element()._elements[-1])
v = TrialFunction(Vv)
w = TestFunction(Vv)
if top:
bstring = "top"
else:
bstring = "bottom"
bcs = [DirichletBC(Vv, 0.0, bstring)]
a = inner(w, v) * dx
L = inner(state.k, w) * b0 * dx
F = Function(Vv)
solve(a == L, F, bcs=bcs)
# define mixed function space
VDG = state.spaces("DG")
WV = (Vv) * (VDG)
# get pprime
v, pprime = TrialFunctions(WV)
w, phi = TestFunctions(WV)
bcs = [DirichletBC(WV[0], zero(), bstring)]
a = (inner(w, v) + div(w) * pprime + div(v) * phi) * dx
L = phi * div(F) * dx
w1 = Function(WV)
if params is None:
params = {
'ksp_type': 'gmres',
'pc_type': 'fieldsplit',
'pc_fieldsplit_type': 'schur',
'pc_fieldsplit_schur_fact_type': 'full',
'pc_fieldsplit_schur_precondition': 'selfp',
'fieldsplit_1_ksp_type': 'preonly',
'fieldsplit_1_pc_type': 'gamg',
'fieldsplit_1_mg_levels_pc_type': 'bjacobi',
'fieldsplit_1_mg_levels_sub_pc_type': 'ilu',
'fieldsplit_0_ksp_type': 'richardson',
'fieldsplit_0_ksp_max_it': 4,
'ksp_atol': 1.e-08,
'ksp_rtol': 1.e-08
}
solve(a == L, w1, bcs=bcs, solver_parameters=params)
v, pprime = w1.split()
p0.project(pprime)
def initialize(self, pc):
_, P = pc.getOperators()
assert P.type == "python"
context = P.getPythonContext()
(self.J, self.bcs) = (context.a, context.row_bcs)
test, trial = self.J.arguments()
if test.function_space() != trial.function_space():
raise NotImplementedError("test and trial spaces must be the same")
Pk = test.function_space()
element = Pk.ufl_element()
shape = element.value_shape()
mesh = Pk.ufl_domain()
if len(shape) == 0:
P1 = firedrake.FunctionSpace(mesh, "CG", 1)
elif len(shape) == 2:
P1 = firedrake.VectorFunctionSpace(mesh, "CG", 1, dim=shape[0])
else:
P1 = firedrake.TensorFunctionSpace(mesh,
"CG",
1,
shape=shape,
symmetry=element.symmetry())
# TODO: A smarter low-order operator would also interpolate
# any coefficients to the coarse space.
mapper = ArgumentReplacer({
test: firedrake.TestFunction(P1),
trial: firedrake.TrialFunction(P1)
})
self.lo_J = map_integrands.map_integrand_dags(mapper, self.J)
lo_bcs = []
for bc in self.bcs:
# Don't actually need the value, since it's only used for
# killing parts of the restriction matrix.
lo_bcs.append(
firedrake.DirichletBC(P1,
firedrake.zero(P1.shape),
bc.sub_domain,
method=bc.method))
self.lo_bcs = tuple(lo_bcs)
mat_type = PETSc.Options().getString(
pc.getOptionsPrefix() + "lo_mat_type",
firedrake.parameters["default_matrix_type"])
self.lo_op = firedrake.assemble(self.lo_J,
bcs=self.lo_bcs,
mat_type=mat_type)
self.lo_op.force_evaluation()
A, P = pc.getOperators()
nearnullsp = P.getNearNullSpace()
if nearnullsp.handle != 0:
# Actually have a near nullspace
tmp = firedrake.Function(Pk)
low = firedrake.Function(P1)
vecs = []
for vec in nearnullsp.getVecs():
with tmp.dat.vec as v:
vec.copy(v)
low.interpolate(tmp)
with low.dat.vec_ro as v:
vecs.append(v.copy())
nullsp = PETSc.NullSpace().create(vectors=vecs, comm=pc.comm)
self.lo_op.petscmat.setNearNullSpace(nullsp)
lo = PETSc.PC().create(comm=pc.comm)
lo.incrementTabLevel(1, parent=pc)
lo.setOperators(self.lo_op.petscmat, self.lo_op.petscmat)
lo.setOptionsPrefix(pc.getOptionsPrefix() + "lo_")
lo.setFromOptions()
self.lo = lo
self.restriction = restriction_matrix(Pk, P1, self.bcs, self.lo_bcs)
self.work = self.lo_op.petscmat.createVecs()
if len(self.bcs) > 0:
bc_nodes = numpy.unique(
numpy.concatenate([bc.nodes for bc in self.bcs]))
bc_nodes = bc_nodes[bc_nodes < Pk.dof_dset.size]
bc_iset = PETSc.IS().createBlock(numpy.prod(shape),
bc_nodes,
comm=PETSc.COMM_SELF)
self.bc_indices = bc_iset.getIndices()
bc_iset.destroy()
else:
self.bc_indices = numpy.empty(0, dtype=numpy.int32)
def coarsen(self, fdm, comm):
fctx = get_appctx(fdm)
test, trial = fctx.J.arguments()
fV = test.function_space()
fu = fctx._problem.u
cele = self.coarsen_element(fV.ufl_element())
cV = firedrake.FunctionSpace(fV.mesh(), cele)
cdm = cV.dm
cu = firedrake.Function(cV)
interpolators = tuple(
firedrake.Interpolator(fus, cus)
for fus, cus in zip(fu.split(), cu.split()))
def inject_state(interpolators):
for interpolator in interpolators:
interpolator.interpolate()
parent = get_parent(fdm)
assert parent is not None
add_hook(parent,
setup=partial(push_parent, cdm, parent),
teardown=partial(pop_parent, cdm, parent),
call_setup=True)
replace_d = {
fu: cu,
test: firedrake.TestFunction(cV),
trial: firedrake.TrialFunction(cV)
}
cJ = replace(fctx.J, replace_d)
cF = replace(fctx.F, replace_d)
if fctx.Jp is not None:
cJp = replace(fctx.Jp, replace_d)
else:
cJp = None
cbcs = []
for bc in fctx._problem.bcs:
# Don't actually need the value, since it's only used for
# killing parts of the matrix. This should be generalised
# for p-FAS, if anyone ever wants to do that
cV_ = cV
for index in bc._indices:
cV_ = cV_.sub(index)
cbcs.append(
firedrake.DirichletBC(cV_,
firedrake.zero(cV_.shape),
bc.sub_domain,
method=bc.method))
fcp = fctx._problem.form_compiler_parameters
cproblem = firedrake.NonlinearVariationalProblem(
cF,
cu,
cbcs,
cJ,
Jp=cJp,
form_compiler_parameters=fcp,
is_linear=fctx._problem.is_linear)
cctx = _SNESContext(
cproblem,
fctx.mat_type,
fctx.pmat_type,
appctx=fctx.appctx,
pre_jacobian_callback=fctx._pre_jacobian_callback,
pre_function_callback=fctx._pre_function_callback,
post_jacobian_callback=fctx._post_jacobian_callback,
post_function_callback=fctx._post_function_callback,
options_prefix=fctx.options_prefix,
transfer_manager=fctx.transfer_manager)
add_hook(parent,
setup=partial(push_appctx, cdm, cctx),
teardown=partial(pop_appctx, cdm, cctx),
call_setup=True)
add_hook(parent,
setup=partial(inject_state, interpolators),
call_setup=True)
cdm.setKSPComputeOperators(_SNESContext.compute_operators)
cdm.setCreateInterpolation(self.create_interpolation)
cdm.setOptionsPrefix(fdm.getOptionsPrefix())
# If we're the coarsest grid of the p-hierarchy, don't
# overwrite the coarsen routine; this is so that you can
# use geometric multigrid for the p-coarse problem
try:
self.coarsen_element(cele)
cdm.setCoarsen(self.coarsen)
except ValueError:
pass
return cdm
def compressible_hydrostatic_balance(state,
theta0,
rho0,
exner0=None,
top=False,
exner_boundary=Constant(1.0),
mr_t=None,
solve_for_rho=False,
params=None):
"""
Compute a hydrostatically balanced density given a potential temperature
profile. By default, this uses a vertically-oriented hybridization
procedure for solving the resulting discrete systems.
:arg state: The :class:`State` object.
:arg theta0: :class:`.Function`containing the potential temperature.
:arg rho0: :class:`.Function` to write the initial density into.
:arg top: If True, set a boundary condition at the top. Otherwise, set
it at the bottom.
:arg exner_boundary: a field or expression to use as boundary data for exner
on the top or bottom as specified.
:arg mr_t: the initial total water mixing ratio field.
"""
# Calculate hydrostatic Pi
VDG = state.spaces("DG")
Vu = state.spaces("HDiv")
Vv = FunctionSpace(state.mesh, Vu.ufl_element()._elements[-1])
W = MixedFunctionSpace((Vv, VDG))
v, exner = TrialFunctions(W)
dv, dexner = TestFunctions(W)
n = FacetNormal(state.mesh)
cp = state.parameters.cp
# add effect of density of water upon theta
theta = theta0
if mr_t is not None:
theta = theta0 / (1 + mr_t)
alhs = ((cp * inner(v, dv) - cp * div(dv * theta) * exner) * dx +
dexner * div(theta * v) * dx)
if top:
bmeasure = ds_t
bstring = "bottom"
else:
bmeasure = ds_b
bstring = "top"
arhs = -cp * inner(dv, n) * theta * exner_boundary * bmeasure
# Possibly make g vary with spatial coordinates?
g = state.parameters.g
arhs -= g * inner(dv, state.k) * dx
bcs = [DirichletBC(W.sub(0), zero(), bstring)]
w = Function(W)
exner_problem = LinearVariationalProblem(alhs, arhs, w, bcs=bcs)
if params is None:
params = {
'ksp_type': 'preonly',
'pc_type': 'python',
'mat_type': 'matfree',
'pc_python_type': 'gusto.VerticalHybridizationPC',
# Vertical trace system is only coupled vertically in columns
# block ILU is a direct solver!
'vert_hybridization': {
'ksp_type': 'preonly',
'pc_type': 'bjacobi',
'sub_pc_type': 'ilu'
}
}
exner_solver = LinearVariationalSolver(exner_problem,
solver_parameters=params,
options_prefix="exner_solver")
exner_solver.solve()
v, exner = w.split()
if exner0 is not None:
exner0.assign(exner)
if solve_for_rho:
w1 = Function(W)
v, rho = w1.split()
rho.interpolate(thermodynamics.rho(state.parameters, theta0, exner))
v, rho = split(w1)
dv, dexner = TestFunctions(W)
exner = thermodynamics.exner_pressure(state.parameters, rho, theta0)
F = ((cp * inner(v, dv) - cp * div(dv * theta) * exner) * dx +
dexner * div(theta0 * v) * dx +
cp * inner(dv, n) * theta * exner_boundary * bmeasure)
F += g * inner(dv, state.k) * dx
rhoproblem = NonlinearVariationalProblem(F, w1, bcs=bcs)
rhosolver = NonlinearVariationalSolver(rhoproblem,
solver_parameters=params,
options_prefix="rhosolver")
rhosolver.solve()
v, rho_ = w1.split()
rho0.assign(rho_)
else:
rho0.interpolate(thermodynamics.rho(state.parameters, theta0, exner))
def coarsen_bc_value(self, bc, cV):
return firedrake.zero(cV.shape)
def initialize(self, pc):
_, P = pc.getOperators()
assert P.type == "python"
context = P.getPythonContext()
(self.J, self.bcs) = (context.a, context.row_bcs)
test, trial = self.J.arguments()
if test.function_space() != trial.function_space():
raise NotImplementedError("test and trial spaces must be the same")
Pk = test.function_space()
element = Pk.ufl_element()
shape = element.value_shape()
mesh = Pk.ufl_domain()
if len(shape) == 0:
P1 = firedrake.FunctionSpace(mesh, "CG", 1)
elif len(shape) == 1:
P1 = firedrake.VectorFunctionSpace(mesh, "CG", 1, dim=shape[0])
else:
P1 = firedrake.TensorFunctionSpace(mesh, "CG", 1, shape=shape,
symmetry=element.symmetry())
# TODO: A smarter low-order operator would also interpolate
# any coefficients to the coarse space.
mapper = ArgumentReplacer({test: firedrake.TestFunction(P1),
trial: firedrake.TrialFunction(P1)})
self.lo_J = map_integrands.map_integrand_dags(mapper, self.J)
lo_bcs = []
for bc in self.bcs:
# Don't actually need the value, since it's only used for
# killing parts of the restriction matrix.
lo_bcs.append(firedrake.DirichletBC(P1, firedrake.zero(P1.shape),
bc.sub_domain,
method=bc.method))
self.lo_bcs = tuple(lo_bcs)
mat_type = PETSc.Options().getString(pc.getOptionsPrefix() + "lo_mat_type",
firedrake.parameters["default_matrix_type"])
self.lo_op = firedrake.assemble(self.lo_J, bcs=self.lo_bcs,
mat_type=mat_type)
self.lo_op.force_evaluation()
A, P = pc.getOperators()
nearnullsp = P.getNearNullSpace()
if nearnullsp.handle != 0:
# Actually have a near nullspace
tmp = firedrake.Function(Pk)
low = firedrake.Function(P1)
vecs = []
for vec in nearnullsp.getVecs():
with tmp.dat.vec as v:
vec.copy(v)
low.interpolate(tmp)
with low.dat.vec_ro as v:
vecs.append(v.copy())
nullsp = PETSc.NullSpace().create(vectors=vecs, comm=pc.comm)
self.lo_op.petscmat.setNearNullSpace(nullsp)
lo = PETSc.PC().create(comm=pc.comm)
lo.incrementTabLevel(1, parent=pc)
lo.setOperators(self.lo_op.petscmat, self.lo_op.petscmat)
lo.setOptionsPrefix(pc.getOptionsPrefix() + "lo_")
lo.setFromOptions()
self.lo = lo
self.restriction = restriction_matrix(Pk, P1, self.bcs, self.lo_bcs)
self.work = self.lo_op.petscmat.createVecs()
if len(self.bcs) > 0:
bc_nodes = numpy.unique(numpy.concatenate([bc.nodes for bc in self.bcs]))
bc_nodes = bc_nodes[bc_nodes < Pk.dof_dset.size]
bc_iset = PETSc.IS().createBlock(numpy.prod(shape), bc_nodes,
comm=PETSc.COMM_SELF)
self.bc_indices = bc_iset.getIndices()
bc_iset.destroy()
else:
self.bc_indices = numpy.empty(0, dtype=numpy.int32)