class EmbeddedDGAdvection(DGAdvection):
def __init__(self, state, V, Vdg=None, continuity=False):
if Vdg is None:
Vdg_elt = BrokenElement(V.ufl_element())
Vdg = FunctionSpace(state.mesh, Vdg_elt)
super(EmbeddedDGAdvection, self).__init__(state, Vdg, continuity)
self.xdg_in = Function(Vdg)
self.xdg_out = Function(Vdg)
self.x_projected = Function(V)
pparameters = {'ksp_type':'cg',
'pc_type':'bjacobi',
'sub_pc_type':'ilu'}
self.Projector = Projector(self.xdg_out, self.x_projected,
solver_parameters=pparameters)
def apply(self, x_in, x_out):
self.xdg_in.interpolate(x_in)
super(EmbeddedDGAdvection, self).apply(self.xdg_in, self.xdg_out)
self.Projector.project()
x_out.assign(self.x_projected)
def _setup(self, state, field, options):
if options.name in ["embedded_dg", "recovered"]:
self.fs = options.embedding_space
self.xdg_in = Function(self.fs)
self.xdg_out = Function(self.fs)
self.x_projected = Function(field.function_space())
parameters = {'ksp_type': 'cg',
'pc_type': 'bjacobi',
'sub_pc_type': 'ilu'}
self.Projector = Projector(self.xdg_out, self.x_projected,
solver_parameters=parameters)
if options.name == "recovered":
# set up the necessary functions
self.x_in = Function(field.function_space())
x_rec = Function(options.recovered_space)
x_brok = Function(options.broken_space)
# set up interpolators and projectors
self.x_rec_projector = Recoverer(self.x_in, x_rec, VDG=self.fs, boundary_method=options.boundary_method) # recovered function
self.x_brok_projector = Projector(x_rec, x_brok) # function projected back
self.xdg_interpolator = Interpolator(self.x_in + x_rec - x_brok, self.xdg_in)
if self.limiter is not None:
self.x_brok_interpolator = Interpolator(self.xdg_out, x_brok)
self.x_out_projector = Recoverer(x_brok, self.x_projected)
def _prepare_output(self, function, cg):
from firedrake import FunctionSpace, VectorFunctionSpace, \
TensorFunctionSpace, Function, Projector, Interpolator
name = function.name()
# Need to project/interpolate?
# If space is linear and continuity of output space matches
# continuity of current space, then we can just use the
# input function.
if is_linear(function.function_space()) and \
is_dg(function.function_space()) == (not cg) and \
is_cg(function.function_space()) == cg:
return OFunction(array=get_array(function),
name=name, function=function)
# OK, let's go and do it.
if cg:
family = "Lagrange"
else:
family = "Discontinuous Lagrange"
output = self._output_functions.get(function)
if output is None:
# Build appropriate space for output function.
shape = function.ufl_shape
if len(shape) == 0:
V = FunctionSpace(function.ufl_domain(), family, 1)
elif len(shape) == 1:
if numpy.prod(shape) > 3:
raise ValueError("Can't write vectors with more than 3 components")
V = VectorFunctionSpace(function.ufl_domain(), family, 1,
dim=shape[0])
elif len(shape) == 2:
if numpy.prod(shape) > 9:
raise ValueError("Can't write tensors with more than 9 components")
V = TensorFunctionSpace(function.ufl_domain(), family, 1,
shape=shape)
else:
raise ValueError("Unsupported shape %s" % (shape, ))
output = Function(V)
self._output_functions[function] = output
if self.project:
projector = self._mappers.get(function)
if projector is None:
projector = Projector(function, output)
self._mappers[function] = projector
projector.project()
else:
interpolator = self._mappers.get(function)
if interpolator is None:
interpolator = Interpolator(function, output)
self._mappers[function] = interpolator
interpolator.interpolate()
return OFunction(array=get_array(output), name=name, function=output)
def _prepare_output(self, function, cg):
from firedrake import FunctionSpace, VectorFunctionSpace, \
TensorFunctionSpace, Function, Projector, Interpolator
name = function.name()
# Need to project/interpolate?
# If space is linear and continuity of output space matches
# continuity of current space, then we can just use the
# input function.
if is_linear(function.function_space()) and \
is_dg(function.function_space()) == (not cg) and \
is_cg(function.function_space()) == cg:
return OFunction(array=get_array(function),
name=name, function=function)
# OK, let's go and do it.
if cg:
family = "Lagrange"
else:
family = "Discontinuous Lagrange"
output = self._output_functions.get(function)
if output is None:
# Build appropriate space for output function.
shape = function.ufl_shape
if len(shape) == 0:
V = FunctionSpace(function.ufl_domain(), family, 1)
elif len(shape) == 1:
if numpy.prod(shape) > 3:
raise ValueError("Can't write vectors with more than 3 components")
V = VectorFunctionSpace(function.ufl_domain(), family, 1,
dim=shape[0])
elif len(shape) == 2:
if numpy.prod(shape) > 9:
raise ValueError("Can't write tensors with more than 9 components")
V = TensorFunctionSpace(function.ufl_domain(), family, 1,
shape=shape)
else:
raise ValueError("Unsupported shape %s" % (shape, ))
output = Function(V)
self._output_functions[function] = output
if self.project:
projector = self._mappers.get(function)
if projector is None:
projector = Projector(function, output)
self._mappers[function] = projector
projector.project()
else:
interpolator = self._mappers.get(function)
if interpolator is None:
interpolator = Interpolator(function, output)
self._mappers[function] = interpolator
interpolator.interpolate()
return OFunction(array=get_array(output), name=name, function=output)
def __init__(self, state, problem, physics_list=None,
prescribed_transporting_velocity=None):
super().__init__(state, problem,
physics_list=physics_list)
if prescribed_transporting_velocity is not None:
self.velocity_projection = Projector(prescribed_transporting_velocity(self.state.t),
self.state.fields('u'))
else:
self.velocity_projection = None
def _prepare_output(self, function, max_elem):
from firedrake import FunctionSpace, VectorFunctionSpace, \
TensorFunctionSpace, Function, Projector, Interpolator
name = function.name()
# Need to project/interpolate?
# If space is not the max element, we can do so.
if function.ufl_element == max_elem:
return OFunction(array=get_array(function),
name=name,
function=function)
# OK, let's go and do it.
shape = function.ufl_shape
output = self._output_functions.get(function)
if output is None:
# Build appropriate space for output function.
shape = function.ufl_shape
if len(shape) == 0:
V = FunctionSpace(function.ufl_domain(), max_elem)
elif len(shape) == 1:
if numpy.prod(shape) > 3:
raise ValueError(
"Can't write vectors with more than 3 components")
V = VectorFunctionSpace(function.ufl_domain(),
max_elem,
dim=shape[0])
elif len(shape) == 2:
if numpy.prod(shape) > 9:
raise ValueError(
"Can't write tensors with more than 9 components")
V = TensorFunctionSpace(function.ufl_domain(),
max_elem,
shape=shape)
else:
raise ValueError("Unsupported shape %s" % (shape, ))
output = Function(V)
self._output_functions[function] = output
if self.project:
projector = self._mappers.get(function)
if projector is None:
projector = Projector(function, output)
self._mappers[function] = projector
projector.project()
else:
interpolator = self._mappers.get(function)
if interpolator is None:
interpolator = Interpolator(function, output)
self._mappers[function] = interpolator
interpolator.interpolate()
return OFunction(array=get_array(output), name=name, function=output)
class PrescribedTransport(Timestepper):
def __init__(self, state, problem, physics_list=None,
prescribed_transporting_velocity=None):
super().__init__(state, problem,
physics_list=physics_list)
if prescribed_transporting_velocity is not None:
self.velocity_projection = Projector(prescribed_transporting_velocity(self.state.t),
self.state.fields('u'))
else:
self.velocity_projection = None
@property
def transporting_velocity(self):
return self.state.fields('u')
def timestep(self):
if self.velocity_projection is not None:
self.velocity_projection.project()
super().timestep()
def initialize(self, pc):
"""Set up the problem context. Take the original
mixed problem and reformulate the problem as a
hybridized mixed system.
A KSP is created for the Lagrange multiplier system.
"""
from ufl.algorithms.map_integrands import map_integrand_dags
from firedrake import (FunctionSpace, TrialFunction, TrialFunctions,
TestFunction, Function, BrokenElement,
MixedElement, FacetNormal, Constant,
DirichletBC, Projector)
from firedrake.assemble import (allocate_matrix,
create_assembly_callable)
from firedrake.formmanipulation import ArgumentReplacer, split_form
# Extract the problem context
prefix = pc.getOptionsPrefix()
_, P = pc.getOperators()
context = P.getPythonContext()
test, trial = context.a.arguments()
V = test.function_space()
if V.mesh().cell_set._extruded:
# TODO: Merge FIAT branch to support TPC trace elements
raise NotImplementedError("Not implemented on extruded meshes.")
# Break the function spaces and define fully discontinuous spaces
broken_elements = [BrokenElement(Vi.ufl_element()) for Vi in V]
elem = MixedElement(broken_elements)
V_d = FunctionSpace(V.mesh(), elem)
arg_map = {test: TestFunction(V_d), trial: TrialFunction(V_d)}
# Replace the problems arguments with arguments defined
# on the new discontinuous spaces
replacer = ArgumentReplacer(arg_map)
new_form = map_integrand_dags(replacer, context.a)
# Create the space of approximate traces.
# The vector function space will have a non-empty value_shape
W = next(v for v in V if bool(v.ufl_element().value_shape()))
if W.ufl_element().family() in ["Raviart-Thomas", "RTCF"]:
tdegree = W.ufl_element().degree() - 1
else:
tdegree = W.ufl_element().degree()
# NOTE: Once extruded is ready, we will need to be aware of this
# and construct the appropriate trace space for the HDiv element
TraceSpace = FunctionSpace(V.mesh(), "HDiv Trace", tdegree)
# NOTE: For extruded, we will need to add "on_top" and "on_bottom"
trace_conditions = [
DirichletBC(TraceSpace, Constant(0.0), "on_boundary")
]
# Set up the functions for the original, hybridized
# and schur complement systems
self.broken_solution = Function(V_d)
self.broken_rhs = Function(V_d)
self.trace_solution = Function(TraceSpace)
self.unbroken_solution = Function(V)
self.unbroken_rhs = Function(V)
# Create the symbolic Schur-reduction
Atilde = Tensor(new_form)
gammar = TestFunction(TraceSpace)
n = FacetNormal(V.mesh())
# Vector trial function will have a non-empty ufl_shape
sigma = next(f for f in TrialFunctions(V_d) if bool(f.ufl_shape))
# NOTE: Once extruded is ready, this will change slightly
# to include both horizontal and vertical interior facets
K = Tensor(gammar('+') * ufl.dot(sigma, n) * ufl.dS)
# Assemble the Schur complement operator and right-hand side
self.schur_rhs = Function(TraceSpace)
self._assemble_Srhs = create_assembly_callable(
K * Atilde.inv * self.broken_rhs,
tensor=self.schur_rhs,
form_compiler_parameters=context.fc_params)
schur_comp = K * Atilde.inv * K.T
self.S = allocate_matrix(schur_comp,
bcs=trace_conditions,
form_compiler_parameters=context.fc_params)
self._assemble_S = create_assembly_callable(
schur_comp,
tensor=self.S,
bcs=trace_conditions,
form_compiler_parameters=context.fc_params)
self._assemble_S()
self.S.force_evaluation()
Smat = self.S.petscmat
# Nullspace for the multiplier problem
nullsp = P.getNullSpace()
if nullsp.handle != 0:
new_vecs = get_trace_nullspace_vecs(K * Atilde.inv, nullsp, V, V_d,
TraceSpace)
tr_nullsp = PETSc.NullSpace().create(vectors=new_vecs,
comm=pc.comm)
Smat.setNullSpace(tr_nullsp)
# Set up the KSP for the system of Lagrange multipliers
ksp = PETSc.KSP().create(comm=pc.comm)
ksp.setOptionsPrefix(prefix + "trace_")
ksp.setTolerances(rtol=1e-13)
ksp.setOperators(Smat)
ksp.setUp()
ksp.setFromOptions()
self.ksp = ksp
# Now we construct the local tensors for the reconstruction stage
# TODO: Add support for mixed tensors and these variables
# become unnecessary
split_forms = split_form(new_form)
A = Tensor(next(sf.form for sf in split_forms if sf.indices == (0, 0)))
B = Tensor(next(sf.form for sf in split_forms if sf.indices == (1, 0)))
C = Tensor(next(sf.form for sf in split_forms if sf.indices == (1, 1)))
trial = TrialFunction(
FunctionSpace(V.mesh(), BrokenElement(W.ufl_element())))
K_local = Tensor(gammar('+') * ufl.dot(trial, n) * ufl.dS)
# Split functions and reconstruct each bit separately
sigma_h, u_h = self.broken_solution.split()
g, f = self.broken_rhs.split()
# Pressure reconstruction
M = B * A.inv * B.T + C
u_sol = M.inv * f + M.inv * (
B * A.inv * K_local.T * self.trace_solution - B * A.inv * g)
self._assemble_pressure = create_assembly_callable(
u_sol, tensor=u_h, form_compiler_parameters=context.fc_params)
# Velocity reconstruction
sigma_sol = A.inv * g + A.inv * (B.T * u_h -
K_local.T * self.trace_solution)
self._assemble_velocity = create_assembly_callable(
sigma_sol,
tensor=sigma_h,
form_compiler_parameters=context.fc_params)
# Set up the projector for projecting the broken solution
# into the unbroken finite element spaces
# NOTE: Tolerance here matters!
sigma_b, _ = self.broken_solution.split()
sigma_u, _ = self.unbroken_solution.split()
self.projector = Projector(sigma_b,
sigma_u,
solver_parameters={
"ksp_type": "cg",
"ksp_rtol": 1e-13
})
def __init__(self, v_in, v_out, VDG=None, boundary_method=None):
# check if v_in is valid
if isinstance(v_in, expression.Expression) or not isinstance(
v_in, (ufl.core.expr.Expr, function.Function)):
raise ValueError(
"Can only recover UFL expression or Functions not '%s'" %
type(v_in))
self.v_in = v_in
self.v_out = v_out
self.V = v_out.function_space()
if VDG is not None:
self.v = Function(VDG)
self.interpolator = Interpolator(v_in, self.v)
else:
self.v = v_in
self.interpolator = None
self.VDG = VDG
self.boundary_method = boundary_method
self.averager = Averager(self.v, self.v_out)
# check boundary method options are valid
if boundary_method is not None:
if boundary_method != Boundary_Method.dynamics and boundary_method != Boundary_Method.physics:
raise ValueError(
"Boundary method must be a Boundary_Method Enum object.")
if VDG is None:
raise ValueError(
"If boundary_method is specified, VDG also needs specifying."
)
# now specify things that we'll need if we are doing boundary recovery
if boundary_method == Boundary_Method.physics:
# check dimensions
if self.V.value_size != 1:
raise ValueError(
'This method only works for scalar functions.')
self.boundary_recoverer = Boundary_Recoverer(
self.v_out, self.v, method=Boundary_Method.physics)
else:
mesh = self.V.mesh()
# this ensures we get the pure function space, not an indexed function space
V0 = FunctionSpace(mesh,
self.v_in.function_space().ufl_element())
VCG1 = FunctionSpace(mesh, "CG", 1)
if V0.extruded:
cell = mesh._base_mesh.ufl_cell().cellname()
DG1_hori_elt = FiniteElement("DG",
cell,
1,
variant="equispaced")
DG1_vert_elt = FiniteElement("DG",
interval,
1,
variant="equispaced")
DG1_element = TensorProductElement(DG1_hori_elt,
DG1_vert_elt)
else:
cell = mesh.ufl_cell().cellname()
DG1_element = FiniteElement("DG",
cell,
1,
variant="equispaced")
VDG1 = FunctionSpace(mesh, DG1_element)
if self.V.value_size == 1:
coords_to_adjust = find_coords_to_adjust(V0, VDG1)
self.boundary_recoverer = Boundary_Recoverer(
self.v_out,
self.v,
coords_to_adjust=coords_to_adjust,
method=Boundary_Method.dynamics)
else:
VuDG1 = VectorFunctionSpace(mesh, DG1_element)
coords_to_adjust = find_coords_to_adjust(V0, VuDG1)
# now, break the problem down into components
v_scalars = []
v_out_scalars = []
self.boundary_recoverers = []
self.project_to_scalars_CG = []
self.extra_averagers = []
coords_to_adjust_list = []
for i in range(self.V.value_size):
v_scalars.append(Function(VDG1))
v_out_scalars.append(Function(VCG1))
coords_to_adjust_list.append(
Function(VDG1).project(coords_to_adjust[i]))
self.project_to_scalars_CG.append(
Projector(self.v_out[i], v_out_scalars[i]))
self.boundary_recoverers.append(
Boundary_Recoverer(
v_out_scalars[i],
v_scalars[i],
method=Boundary_Method.dynamics,
coords_to_adjust=coords_to_adjust_list[i]))
# need an extra averager that works on the scalar fields rather than the vector one
self.extra_averagers.append(
Averager(v_scalars[i], v_out_scalars[i]))
# the boundary recoverer needs to be done on a scalar fields
# so need to extract component and restore it after the boundary recovery is done
self.interpolate_to_vector = Interpolator(
as_vector(v_out_scalars), self.v_out)
def __init__(self, state, field, equation=None, *, solver_parameters=None,
limiter=None):
if equation is not None:
self.state = state
self.field = field
self.equation = equation
# get ubar from the equation class
self.ubar = self.equation.ubar
self.dt = self.state.timestepping.dt
# get default solver options if none passed in
if solver_parameters is None:
self.solver_parameters = equation.solver_parameters
else:
self.solver_parameters = solver_parameters
if state.output.log_level == DEBUG:
self.solver_parameters["ksp_monitor_true_residual"] = True
self.limiter = limiter
# check to see if we are using an embedded DG method - if we are then
# the projector and output function will have been set up in the
# equation class and we can get the correct function space from
# the output function.
if isinstance(equation, EmbeddedDGAdvection):
# check that the field and the equation are compatible
if equation.V0 != field.function_space():
raise ValueError('The field to be advected is not compatible with the equation used.')
self.embedded_dg = True
fs = equation.space
self.xdg_in = Function(fs)
self.xdg_out = Function(fs)
self.x_projected = Function(field.function_space())
parameters = {'ksp_type': 'cg',
'pc_type': 'bjacobi',
'sub_pc_type': 'ilu'}
self.Projector = Projector(self.xdg_out, self.x_projected,
solver_parameters=parameters)
self.recovered = equation.recovered
if self.recovered:
# set up the necessary functions
self.x_in = Function(field.function_space())
x_adv = Function(fs)
x_rec = Function(equation.V_rec)
x_brok = Function(equation.V_brok)
# set up interpolators and projectors
self.x_adv_interpolator = Interpolator(self.x_in, x_adv) # interpolate before recovery
self.x_rec_projector = Recoverer(x_adv, x_rec) # recovered function
# when the "average" method comes into firedrake master, this will be
# self.x_rec_projector = Projector(self.x_in, equation.Vrec, method="average")
self.x_brok_projector = Projector(x_rec, x_brok) # function projected back
self.xdg_interpolator = Interpolator(self.x_in + x_rec - x_brok, self.xdg_in)
if self.limiter is not None:
self.x_brok_interpolator = Interpolator(self.xdg_out, x_brok)
self.x_out_projector = Recoverer(x_brok, self.x_projected)
# when the "average" method comes into firedrake master, this will be
# self.x_out_projector = Projector(x_brok, self.x_projected, method="average")
else:
self.embedded_dg = False
fs = field.function_space()
# setup required functions
self.fs = fs
self.dq = Function(fs)
self.q1 = Function(fs)
class Advection(object, metaclass=ABCMeta):
"""
Base class for advection schemes.
:arg state: :class:`.State` object.
:arg field: field to be advected
:arg equation: :class:`.Equation` object, specifying the equation
that field satisfies
:arg solver_parameters: solver_parameters
:arg limiter: :class:`.Limiter` object.
:arg options: :class:`.AdvectionOptions` object
"""
def __init__(self, state, field, equation=None, *, solver_parameters=None,
limiter=None):
if equation is not None:
self.state = state
self.field = field
self.equation = equation
# get ubar from the equation class
self.ubar = self.equation.ubar
self.dt = self.state.timestepping.dt
# get default solver options if none passed in
if solver_parameters is None:
self.solver_parameters = equation.solver_parameters
else:
self.solver_parameters = solver_parameters
if logger.isEnabledFor(DEBUG):
self.solver_parameters["ksp_monitor_true_residual"] = True
self.limiter = limiter
if hasattr(equation, "options"):
self.discretisation_option = equation.options.name
self._setup(state, field, equation.options)
else:
self.discretisation_option = None
self.fs = field.function_space()
# setup required functions
self.dq = Function(self.fs)
self.q1 = Function(self.fs)
def _setup(self, state, field, options):
if options.name in ["embedded_dg", "recovered"]:
self.fs = options.embedding_space
self.xdg_in = Function(self.fs)
self.xdg_out = Function(self.fs)
self.x_projected = Function(field.function_space())
parameters = {'ksp_type': 'cg',
'pc_type': 'bjacobi',
'sub_pc_type': 'ilu'}
self.Projector = Projector(self.xdg_out, self.x_projected,
solver_parameters=parameters)
if options.name == "recovered":
# set up the necessary functions
self.x_in = Function(field.function_space())
x_rec = Function(options.recovered_space)
x_brok = Function(options.broken_space)
# set up interpolators and projectors
self.x_rec_projector = Recoverer(self.x_in, x_rec, VDG=self.fs, boundary_method=options.boundary_method) # recovered function
self.x_brok_projector = Projector(x_rec, x_brok) # function projected back
self.xdg_interpolator = Interpolator(self.x_in + x_rec - x_brok, self.xdg_in)
if self.limiter is not None:
self.x_brok_interpolator = Interpolator(self.xdg_out, x_brok)
self.x_out_projector = Recoverer(x_brok, self.x_projected)
def pre_apply(self, x_in, discretisation_option):
"""
Extra steps to advection if using an embedded method,
which might be either the plain embedded method or the
recovered space advection scheme.
:arg x_in: the input set of prognostic fields.
:arg discretisation option: string specifying which scheme to use.
"""
if discretisation_option == "embedded_dg":
try:
self.xdg_in.interpolate(x_in)
except NotImplementedError:
self.xdg_in.project(x_in)
elif discretisation_option == "recovered":
self.x_in.assign(x_in)
self.x_rec_projector.project()
self.x_brok_projector.project()
self.xdg_interpolator.interpolate()
def post_apply(self, x_out, discretisation_option):
"""
The projection steps, returning a field to its original space
for an embedded DG advection scheme. For the case of the
recovered scheme, there are two options dependent on whether
the scheme is limited or not.
:arg x_out: the outgoing field.
:arg discretisation_option: string specifying which option to use.
"""
if discretisation_option == "embedded_dg":
self.Projector.project()
elif discretisation_option == "recovered":
if self.limiter is not None:
self.x_brok_interpolator.interpolate()
self.x_out_projector.project()
else:
self.Projector.project()
x_out.assign(self.x_projected)
@abstractproperty
def lhs(self):
return self.equation.mass_term(self.equation.trial)
@abstractproperty
def rhs(self):
return self.equation.mass_term(self.q1) - self.dt*self.equation.advection_term(self.q1)
def update_ubar(self, xn, xnp1, alpha):
un = xn.split()[0]
unp1 = xnp1.split()[0]
self.ubar.assign(un + alpha*(unp1-un))
@cached_property
def solver(self):
# setup solver using lhs and rhs defined in derived class
problem = LinearVariationalProblem(self.lhs, self.rhs, self.dq)
solver_name = self.field.name()+self.equation.__class__.__name__+self.__class__.__name__
return LinearVariationalSolver(problem, solver_parameters=self.solver_parameters, options_prefix=solver_name)
@abstractmethod
def apply(self, x_in, x_out):
"""
Function takes x as input, computes L(x) as defined by the equation,
and returns x_out as output.
:arg x: :class:`.Function` object, the input Function.
:arg x_out: :class:`.Function` object, the output Function.
"""
pass
def __init__(self, state, moments=AdvectedMoments.M3, limit=True):
super().__init__(state)
# function spaces
Vt = state.fields('rain').function_space()
Vu = state.fields('u').function_space()
# declare properties of class
self.state = state
self.moments = moments
self.rain = state.fields('rain')
self.v = state.fields('rainfall_velocity', Vu)
self.limit = limit
if moments == AdvectedMoments.M0:
# all rain falls at terminal velocity
terminal_velocity = Constant(5) # in m/s
if state.mesh.geometric_dimension() == 2:
self.v.project(as_vector([0, -terminal_velocity]))
elif state.mesh.geometric_dimension() == 3:
self.v.project(as_vector([0, 0, -terminal_velocity]))
elif moments == AdvectedMoments.M3:
# this advects the third moment M3 of the raindrop
# distribution, which corresponds to the mean mass
rho = state.fields('rho')
rho_w = Constant(1000.0) # density of liquid water
# assume n(D) = n_0 * D^mu * exp(-Lambda*D)
# n_0 = N_r * Lambda^(1+mu) / gamma(1 + mu)
N_r = Constant(10**5) # number of rain droplets per m^3
mu = 0.0 # shape constant of droplet gamma distribution
# assume V(D) = a * D^b * exp(-f*D) * (rho_0 / rho)^g
# take f = 0
a = Constant(362.) # intercept for velocity distr. in log space
b = 0.65 # inverse scale parameter for velocity distr.
rho0 = Constant(1.22) # reference density in kg/m^3
g = Constant(0.5) # scaling of density correction
# we keep mu in the expressions even though mu = 0
threshold = Constant(10**-10) # only do rainfall for r > threshold
Lambda = (N_r * pi * rho_w * gamma(4 + mu) /
(6 * gamma(1 + mu) * rho * self.rain))**(1. / 3)
Lambda0 = (N_r * pi * rho_w * gamma(4 + mu) /
(6 * gamma(1 + mu) * rho * threshold))**(1. / 3)
v_expression = conditional(
self.rain > threshold,
(a * gamma(4 + b + mu) / (gamma(4 + mu) * Lambda**b) *
(rho0 / rho)**g),
(a * gamma(4 + b + mu) / (gamma(4 + mu) * Lambda0**b) *
(rho0 / rho)**g))
else:
raise NotImplementedError(
'Currently we only have implementations for zero and one moment schemes for rainfall. Valid options are AdvectedMoments.M0 and AdvectedMoments.M3'
)
if moments != AdvectedMoments.M0:
if state.mesh.geometric_dimension() == 2:
self.determine_v = Projector(as_vector([0, -v_expression]),
self.v)
elif state.mesh.geometric_dimension() == 3:
self.determine_v = Projector(as_vector([0, 0, -v_expression]),
self.v)
# determine whether to do recovered space advection scheme
# if horizontal and vertical degrees are 0 do recovered space
if state.horizontal_degree == 0 and state.vertical_degree == 0:
VDG1 = FunctionSpace(Vt.mesh(), "DG", 1)
VCG1 = FunctionSpace(Vt.mesh(), "CG", 1)
Vbrok = FunctionSpace(Vt.mesh(), BrokenElement(Vt.ufl_element()))
boundary_method = Boundary_Method.dynamics
advect_options = RecoveredOptions(embedding_space=VDG1,
recovered_space=VCG1,
broken_space=Vbrok,
boundary_method=boundary_method)
else:
advect_options = EmbeddedDGOptions()
# need to define advection equation before limiter (as it is needed for the ThetaLimiter)
advection_equation = EmbeddedDGAdvection(state,
Vt,
equation_form="advective",
outflow=True,
options=advect_options)
# decide which limiter to use
if self.limit:
if state.horizontal_degree == 0 and state.vertical_degree == 0:
limiter = VertexBasedLimiter(VDG1)
elif state.horizontal_degree == 1 and state.vertical_degree == 1:
limiter = ThetaLimiter(Vt)
else:
logger.warning(
"There is no limiter yet implemented for the spaces used. NoLimiter() is being used for the rainfall in this case."
)
limiter = NoLimiter()
else:
limiter = None
# sedimentation will happen using a full advection method
self.advection_method = SSPRK3(state,
self.rain,
advection_equation,
limiter=limiter)
def setup(self, equation, uadv=None, apply_bcs=True, *active_labels):
self.residual = equation.residual
if self.field_name is not None:
self.idx = equation.field_names.index(self.field_name)
self.fs = self.state.fields(self.field_name).function_space()
self.residual = self.residual.label_map(
lambda t: t.get(prognostic) == self.field_name,
lambda t: Term(
split_form(t.form)[self.idx].form,
t.labels),
drop)
bcs = equation.bcs[self.field_name]
else:
self.field_name = equation.field_name
self.fs = equation.function_space
self.idx = None
if type(self.fs.ufl_element()) is MixedElement:
bcs = [bc for _, bcs in equation.bcs.items() for bc in bcs]
else:
bcs = equation.bcs[self.field_name]
if len(active_labels) > 0:
self.residual = self.residual.label_map(
lambda t: any(t.has_label(time_derivative, *active_labels)),
map_if_false=drop)
options = self.options
# -------------------------------------------------------------------- #
# Routines relating to transport
# -------------------------------------------------------------------- #
if hasattr(self.options, 'ibp'):
self.replace_transport_term()
self.replace_transporting_velocity(uadv)
# -------------------------------------------------------------------- #
# Wrappers for embedded / recovery methods
# -------------------------------------------------------------------- #
if self.discretisation_option in ["embedded_dg", "recovered"]:
# construct the embedding space if not specified
if options.embedding_space is None:
V_elt = BrokenElement(self.fs.ufl_element())
self.fs = FunctionSpace(self.state.mesh, V_elt)
else:
self.fs = options.embedding_space
self.xdg_in = Function(self.fs)
self.xdg_out = Function(self.fs)
if self.idx is None:
self.x_projected = Function(equation.function_space)
else:
self.x_projected = Function(self.state.fields(self.field_name).function_space())
new_test = TestFunction(self.fs)
parameters = {'ksp_type': 'cg',
'pc_type': 'bjacobi',
'sub_pc_type': 'ilu'}
# -------------------------------------------------------------------- #
# Make boundary conditions
# -------------------------------------------------------------------- #
if not apply_bcs:
self.bcs = None
elif self.discretisation_option in ["embedded_dg", "recovered"]:
# Transfer boundary conditions onto test function space
self.bcs = [DirichletBC(self.fs, bc.function_arg, bc.sub_domain) for bc in bcs]
else:
self.bcs = bcs
# -------------------------------------------------------------------- #
# Modify test function for SUPG methods
# -------------------------------------------------------------------- #
if self.discretisation_option == "supg":
# construct tau, if it is not specified
dim = self.state.mesh.topological_dimension()
if options.tau is not None:
# if tau is provided, check that is has the right size
tau = options.tau
assert as_ufl(tau).ufl_shape == (dim, dim), "Provided tau has incorrect shape!"
else:
# create tuple of default values of size dim
default_vals = [options.default*self.dt]*dim
# check for directions is which the space is discontinuous
# so that we don't apply supg in that direction
if is_cg(self.fs):
vals = default_vals
else:
space = self.fs.ufl_element().sobolev_space()
if space.name in ["HDiv", "DirectionalH"]:
vals = [default_vals[i] if space[i].name == "H1"
else 0. for i in range(dim)]
else:
raise ValueError("I don't know what to do with space %s" % space)
tau = Constant(tuple([
tuple(
[vals[j] if i == j else 0. for i, v in enumerate(vals)]
) for j in range(dim)])
)
self.solver_parameters = {'ksp_type': 'gmres',
'pc_type': 'bjacobi',
'sub_pc_type': 'ilu'}
test = TestFunction(self.fs)
new_test = test + dot(dot(uadv, tau), grad(test))
if self.discretisation_option is not None:
# replace the original test function with one defined on
# the embedding space, as this is the space where the
# the problem will be solved
self.residual = self.residual.label_map(
all_terms,
map_if_true=replace_test_function(new_test))
if self.discretisation_option == "embedded_dg":
if self.limiter is None:
self.x_out_projector = Projector(self.xdg_out, self.x_projected,
solver_parameters=parameters)
else:
self.x_out_projector = Recoverer(self.xdg_out, self.x_projected)
if self.discretisation_option == "recovered":
# set up the necessary functions
self.x_in = Function(self.state.fields(self.field_name).function_space())
x_rec = Function(options.recovered_space)
x_brok = Function(options.broken_space)
# set up interpolators and projectors
self.x_rec_projector = Recoverer(self.x_in, x_rec, VDG=self.fs, boundary_method=options.boundary_method) # recovered function
self.x_brok_projector = Projector(x_rec, x_brok) # function projected back
self.xdg_interpolator = Interpolator(self.x_in + x_rec - x_brok, self.xdg_in)
if self.limiter is not None:
self.x_brok_interpolator = Interpolator(self.xdg_out, x_brok)
self.x_out_projector = Recoverer(x_brok, self.x_projected)
else:
self.x_out_projector = Projector(self.xdg_out, self.x_projected)
# setup required functions
self.dq = Function(self.fs)
self.q1 = Function(self.fs)
class TimeDiscretisation(object, metaclass=ABCMeta):
"""
Base class for time discretisation schemes.
:arg state: :class:`.State` object.
:arg field: field to be evolved
:arg equation: :class:`.Equation` object, specifying the equation
that field satisfies
:arg solver_parameters: solver_parameters
:arg limiter: :class:`.Limiter` object.
:arg options: :class:`.DiscretisationOptions` object
"""
def __init__(self, state, field_name=None, solver_parameters=None,
limiter=None, options=None):
self.state = state
self.field_name = field_name
self.dt = self.state.dt
self.limiter = limiter
self.options = options
if options is not None:
self.discretisation_option = options.name
else:
self.discretisation_option = None
# get default solver options if none passed in
if solver_parameters is None:
self.solver_parameters = {'ksp_type': 'cg',
'pc_type': 'bjacobi',
'sub_pc_type': 'ilu'}
else:
self.solver_parameters = solver_parameters
if logger.isEnabledFor(DEBUG):
self.solver_parameters["ksp_monitor_true_residual"] = None
def setup(self, equation, uadv=None, apply_bcs=True, *active_labels):
self.residual = equation.residual
if self.field_name is not None:
self.idx = equation.field_names.index(self.field_name)
self.fs = self.state.fields(self.field_name).function_space()
self.residual = self.residual.label_map(
lambda t: t.get(prognostic) == self.field_name,
lambda t: Term(
split_form(t.form)[self.idx].form,
t.labels),
drop)
bcs = equation.bcs[self.field_name]
else:
self.field_name = equation.field_name
self.fs = equation.function_space
self.idx = None
if type(self.fs.ufl_element()) is MixedElement:
bcs = [bc for _, bcs in equation.bcs.items() for bc in bcs]
else:
bcs = equation.bcs[self.field_name]
if len(active_labels) > 0:
self.residual = self.residual.label_map(
lambda t: any(t.has_label(time_derivative, *active_labels)),
map_if_false=drop)
options = self.options
# -------------------------------------------------------------------- #
# Routines relating to transport
# -------------------------------------------------------------------- #
if hasattr(self.options, 'ibp'):
self.replace_transport_term()
self.replace_transporting_velocity(uadv)
# -------------------------------------------------------------------- #
# Wrappers for embedded / recovery methods
# -------------------------------------------------------------------- #
if self.discretisation_option in ["embedded_dg", "recovered"]:
# construct the embedding space if not specified
if options.embedding_space is None:
V_elt = BrokenElement(self.fs.ufl_element())
self.fs = FunctionSpace(self.state.mesh, V_elt)
else:
self.fs = options.embedding_space
self.xdg_in = Function(self.fs)
self.xdg_out = Function(self.fs)
if self.idx is None:
self.x_projected = Function(equation.function_space)
else:
self.x_projected = Function(self.state.fields(self.field_name).function_space())
new_test = TestFunction(self.fs)
parameters = {'ksp_type': 'cg',
'pc_type': 'bjacobi',
'sub_pc_type': 'ilu'}
# -------------------------------------------------------------------- #
# Make boundary conditions
# -------------------------------------------------------------------- #
if not apply_bcs:
self.bcs = None
elif self.discretisation_option in ["embedded_dg", "recovered"]:
# Transfer boundary conditions onto test function space
self.bcs = [DirichletBC(self.fs, bc.function_arg, bc.sub_domain) for bc in bcs]
else:
self.bcs = bcs
# -------------------------------------------------------------------- #
# Modify test function for SUPG methods
# -------------------------------------------------------------------- #
if self.discretisation_option == "supg":
# construct tau, if it is not specified
dim = self.state.mesh.topological_dimension()
if options.tau is not None:
# if tau is provided, check that is has the right size
tau = options.tau
assert as_ufl(tau).ufl_shape == (dim, dim), "Provided tau has incorrect shape!"
else:
# create tuple of default values of size dim
default_vals = [options.default*self.dt]*dim
# check for directions is which the space is discontinuous
# so that we don't apply supg in that direction
if is_cg(self.fs):
vals = default_vals
else:
space = self.fs.ufl_element().sobolev_space()
if space.name in ["HDiv", "DirectionalH"]:
vals = [default_vals[i] if space[i].name == "H1"
else 0. for i in range(dim)]
else:
raise ValueError("I don't know what to do with space %s" % space)
tau = Constant(tuple([
tuple(
[vals[j] if i == j else 0. for i, v in enumerate(vals)]
) for j in range(dim)])
)
self.solver_parameters = {'ksp_type': 'gmres',
'pc_type': 'bjacobi',
'sub_pc_type': 'ilu'}
test = TestFunction(self.fs)
new_test = test + dot(dot(uadv, tau), grad(test))
if self.discretisation_option is not None:
# replace the original test function with one defined on
# the embedding space, as this is the space where the
# the problem will be solved
self.residual = self.residual.label_map(
all_terms,
map_if_true=replace_test_function(new_test))
if self.discretisation_option == "embedded_dg":
if self.limiter is None:
self.x_out_projector = Projector(self.xdg_out, self.x_projected,
solver_parameters=parameters)
else:
self.x_out_projector = Recoverer(self.xdg_out, self.x_projected)
if self.discretisation_option == "recovered":
# set up the necessary functions
self.x_in = Function(self.state.fields(self.field_name).function_space())
x_rec = Function(options.recovered_space)
x_brok = Function(options.broken_space)
# set up interpolators and projectors
self.x_rec_projector = Recoverer(self.x_in, x_rec, VDG=self.fs, boundary_method=options.boundary_method) # recovered function
self.x_brok_projector = Projector(x_rec, x_brok) # function projected back
self.xdg_interpolator = Interpolator(self.x_in + x_rec - x_brok, self.xdg_in)
if self.limiter is not None:
self.x_brok_interpolator = Interpolator(self.xdg_out, x_brok)
self.x_out_projector = Recoverer(x_brok, self.x_projected)
else:
self.x_out_projector = Projector(self.xdg_out, self.x_projected)
# setup required functions
self.dq = Function(self.fs)
self.q1 = Function(self.fs)
def pre_apply(self, x_in, discretisation_option):
"""
Extra steps to discretisation if using an embedded method,
which might be either the plain embedded method or the
recovered space scheme.
:arg x_in: the input set of prognostic fields.
:arg discretisation option: string specifying which scheme to use.
"""
if discretisation_option == "embedded_dg":
try:
self.xdg_in.interpolate(x_in)
except NotImplementedError:
self.xdg_in.project(x_in)
elif discretisation_option == "recovered":
self.x_in.assign(x_in)
self.x_rec_projector.project()
self.x_brok_projector.project()
self.xdg_interpolator.interpolate()
def post_apply(self, x_out, discretisation_option):
"""
The projection steps, returning a field to its original space
for an embedded DG scheme. For the case of the
recovered scheme, there are two options dependent on whether
the scheme is limited or not.
:arg x_out: the outgoing field.
:arg discretisation_option: string specifying which option to use.
"""
if discretisation_option == "recovered" and self.limiter is not None:
self.x_brok_interpolator.interpolate()
self.x_out_projector.project()
x_out.assign(self.x_projected)
@abstractproperty
def lhs(self):
l = self.residual.label_map(
lambda t: t.has_label(time_derivative),
map_if_true=replace_subject(self.dq, self.idx),
map_if_false=drop)
return l.form
@abstractproperty
def rhs(self):
r = self.residual.label_map(
all_terms,
map_if_true=replace_subject(self.q1, self.idx))
r = r.label_map(
lambda t: t.has_label(time_derivative),
map_if_false=lambda t: -self.dt*t)
return r.form
def replace_transport_term(self):
"""
This routine allows the default transport term to be replaced with a
different one, specified through the transport options.
This is necessary because when the prognostic equations are declared,
the whole transport
"""
# Extract transport term of equation
old_transport_term_list = self.residual.label_map(
lambda t: t.has_label(transport), map_if_false=drop)
# If there are more transport terms, extract only the one for this variable
if len(old_transport_term_list.terms) > 1:
raise NotImplementedError('Cannot replace transport terms when there are more than one')
# Then we should only have one transport term
old_transport_term = old_transport_term_list.terms[0]
# If the transport term has an ibp label, then it could be replaced
if old_transport_term.has_label(ibp_label) and hasattr(self.options, 'ibp'):
# Do the options specify a different ibp to the old transport term?
if old_transport_term.labels['ibp'] != self.options.ibp:
# Set up a new transport term
field = self.state.fields(self.field_name)
test = TestFunction(self.fs)
# Set up new transport term (depending on the type of transport equation)
if old_transport_term.labels['transport'] == TransportEquationType.advective:
new_transport_term = advection_form(self.state, test, field, ibp=self.options.ibp)
elif old_transport_term.labels['transport'] == TransportEquationType.conservative:
new_transport_term = continuity_form(self.state, test, field, ibp=self.options.ibp)
else:
raise NotImplementedError(f'Replacement of transport term not implemented yet for {old_transport_term.labels["transport"]}')
# Finally, drop the old transport term and add the new one
self.residual = self.residual.label_map(
lambda t: t.has_label(transport), map_if_true=drop)
self.residual += subject(new_transport_term, field)
def replace_transporting_velocity(self, uadv):
# replace the transporting velocity in any terms that contain it
if any([t.has_label(transporting_velocity) for t in self.residual]):
assert uadv is not None
if uadv == "prognostic":
self.residual = self.residual.label_map(
lambda t: t.has_label(transporting_velocity),
map_if_true=lambda t: Term(ufl.replace(
t.form, {t.get(transporting_velocity): split(t.get(subject))[0]}), t.labels)
)
else:
self.residual = self.residual.label_map(
lambda t: t.has_label(transporting_velocity),
map_if_true=lambda t: Term(ufl.replace(
t.form, {t.get(transporting_velocity): uadv}), t.labels)
)
self.residual = transporting_velocity.update_value(self.residual, uadv)
@cached_property
def solver(self):
# setup solver using lhs and rhs defined in derived class
problem = NonlinearVariationalProblem(self.lhs-self.rhs, self.dq, bcs=self.bcs)
solver_name = self.field_name+self.__class__.__name__
return NonlinearVariationalSolver(problem, solver_parameters=self.solver_parameters, options_prefix=solver_name)
@abstractmethod
def apply(self, x_in, x_out):
"""
Function takes x as input, computes L(x) as defined by the equation,
and returns x_out as output.
:arg x: :class:`.Function` object, the input Function.
:arg x_out: :class:`.Function` object, the output Function.
"""
pass
class Recoverer(object):
"""
An object that 'recovers' a field from a low order space
(e.g. DG0) into a higher order space (e.g. CG1). This encompasses
the process of interpolating first to a the right space before
using the :class:`Averager` object, and also automates the
boundary recovery process. If no boundary method is specified,
this simply performs the action of the :class: `Averager`.
:arg v_in: the :class:`ufl.Expr` or
:class:`.Function` to project. (e.g. a VDG0 function)
:arg v_out: :class:`.Function` to put the result in. (e.g. a CG1 function)
:arg VDG: optional :class:`.FunctionSpace`. If not None, v_in is interpolated
to this space first before recovery happens.
:arg boundary_method: an Enum object, .
"""
def __init__(self, v_in, v_out, VDG=None, boundary_method=None):
# check if v_in is valid
if isinstance(v_in, expression.Expression) or not isinstance(
v_in, (ufl.core.expr.Expr, function.Function)):
raise ValueError(
"Can only recover UFL expression or Functions not '%s'" %
type(v_in))
self.v_in = v_in
self.v_out = v_out
self.V = v_out.function_space()
if VDG is not None:
self.v = Function(VDG)
self.interpolator = Interpolator(v_in, self.v)
else:
self.v = v_in
self.interpolator = None
self.VDG = VDG
self.boundary_method = boundary_method
self.averager = Averager(self.v, self.v_out)
# check boundary method options are valid
if boundary_method is not None:
if boundary_method != Boundary_Method.dynamics and boundary_method != Boundary_Method.physics:
raise ValueError(
"Boundary method must be a Boundary_Method Enum object.")
if VDG is None:
raise ValueError(
"If boundary_method is specified, VDG also needs specifying."
)
# now specify things that we'll need if we are doing boundary recovery
if boundary_method == Boundary_Method.physics:
# check dimensions
if self.V.value_size != 1:
raise ValueError(
'This method only works for scalar functions.')
self.boundary_recoverer = Boundary_Recoverer(
self.v_out, self.v, method=Boundary_Method.physics)
else:
mesh = self.V.mesh()
# this ensures we get the pure function space, not an indexed function space
V0 = FunctionSpace(mesh,
self.v_in.function_space().ufl_element())
VDG1 = FunctionSpace(mesh, "DG", 1)
VCG1 = FunctionSpace(mesh, "CG", 1)
if self.V.value_size == 1:
coords_to_adjust = find_coords_to_adjust(V0, VDG1)
self.boundary_recoverer = Boundary_Recoverer(
self.v_out,
self.v,
coords_to_adjust=coords_to_adjust,
method=Boundary_Method.dynamics)
else:
VuDG1 = VectorFunctionSpace(mesh, "DG", 1)
coords_to_adjust = find_coords_to_adjust(V0, VuDG1)
# now, break the problem down into components
v_scalars = []
v_out_scalars = []
self.boundary_recoverers = []
self.project_to_scalars_CG = []
self.extra_averagers = []
coords_to_adjust_list = []
for i in range(self.V.value_size):
v_scalars.append(Function(VDG1))
v_out_scalars.append(Function(VCG1))
coords_to_adjust_list.append(
Function(VDG1).project(coords_to_adjust[i]))
self.project_to_scalars_CG.append(
Projector(self.v_out[i], v_out_scalars[i]))
self.boundary_recoverers.append(
Boundary_Recoverer(
v_out_scalars[i],
v_scalars[i],
method=Boundary_Method.dynamics,
coords_to_adjust=coords_to_adjust_list[i]))
# need an extra averager that works on the scalar fields rather than the vector one
self.extra_averagers.append(
Averager(v_scalars[i], v_out_scalars[i]))
# the boundary recoverer needs to be done on a scalar fields
# so need to extract component and restore it after the boundary recovery is done
self.project_to_vector = Projector(
as_vector(v_out_scalars), self.v_out)
def project(self):
"""
Perform the fully specified recovery.
"""
if self.interpolator is not None:
self.interpolator.interpolate()
self.averager.project()
if self.boundary_method is not None:
if self.V.value_size > 1:
for i in range(self.V.value_size):
self.project_to_scalars_CG[i].project()
self.boundary_recoverers[i].apply()
self.extra_averagers[i].project()
self.project_to_vector.project()
else:
self.boundary_recoverer.apply()
self.averager.project()
return self.v_out
def __init__(self, diagnostic_variables, prognostic_variables, outputting,
simulation_parameters):
self.diagnostic_variables = diagnostic_variables
self.prognostic_variables = prognostic_variables
self.outputting = outputting
self.simulation_parameters = simulation_parameters
Dt = Constant(simulation_parameters['dt'][-1])
Ld = simulation_parameters['Ld'][-1]
u = self.prognostic_variables.u
Xi = self.prognostic_variables.dXi
Vu = u.function_space()
vector_u = True if Vu.ufl_element() == VectorElement else False
ones = Function(
VectorFunctionSpace(self.prognostic_variables.mesh, "CG",
1)).project(as_vector([Constant(1.0)]))
self.to_update_constants = False
self.interpolators = []
self.projectors = []
self.solvers = []
mesh = u.function_space().mesh()
x, = SpatialCoordinate(mesh)
alphasq = simulation_parameters['alphasq'][-1]
periodic = simulation_parameters['periodic'][-1]
# do peakon data checks here
true_peakon_data = simulation_parameters['true_peakon_data'][-1]
if true_peakon_data is not None:
self.true_peakon_file = Dataset(
'results/' + true_peakon_data + '/data.nc', 'r')
# check length of file is correct
ndump = simulation_parameters['ndump'][-1]
tmax = simulation_parameters['tmax'][-1]
dt = simulation_parameters['dt'][-1]
if len(self.true_peakon_file['time'][:]) != int(tmax /
(ndump * dt)) + 1:
raise ValueError(
'If reading in true peakon data, the dump frequency must be the same as that used for the true peakon data.'
+
' Length of true peakon data as %i, but proposed length is %i'
% (len(self.true_peakon_file['time'][:]),
int(tmax / (ndump * dt)) + 1))
if self.true_peakon_file['p'][:].shape != (int(tmax /
(ndump * dt)) +
1, ):
raise ValueError(
'True peakon data shape %i must be the same shape as proposed data %i'
% ((int(tmax / (ndump * dt)) + 1, ),
self.true_peakon_file['p'][:].shape))
# do peakon data checks here
true_mean_peakon_data = simulation_parameters['true_mean_peakon_data'][
-1]
if true_mean_peakon_data is not None:
self.true_mean_peakon_file = Dataset(
'results/' + true_mean_peakon_data + '/data.nc', 'r')
# check length of file is correct
ndump = simulation_parameters['ndump'][-1]
tmax = simulation_parameters['tmax'][-1]
dt = simulation_parameters['dt'][-1]
if len(self.true_mean_peakon_file['time'][:]) != int(tmax /
(ndump * dt)):
raise ValueError(
'If reading in true peakon data, the dump frequency must be the same as that used for the true peakon data.'
)
if self.true_mean_peakon_file['p'][:].shape != (int(
tmax / (ndump * dt)), ):
raise ValueError(
'True peakon data must have same shape as proposed data!')
for key, value in self.diagnostic_variables.fields.items():
if key == 'uscalar':
uscalar = self.diagnostic_variables.fields['uscalar']
u_interpolator = Interpolator(dot(ones, u), uscalar)
self.interpolators.append(u_interpolator)
elif key == 'Euscalar':
Eu = self.prognostic_variables.Eu
Euscalar = self.diagnostic_variables.fields['Euscalar']
Eu_interpolator = Interpolator(dot(ones, Eu), Euscalar)
self.interpolators.append(Eu_interpolator)
elif key == 'Xiscalar':
Xi = self.prognostic_variables.dXi
Xiscalar = self.diagnostic_variables.fields['Xiscalar']
Xi_interpolator = Interpolator(dot(ones, Xi), Xiscalar)
self.interpolators.append(Xi_interpolator)
elif key == 'du':
if type(u.function_space().ufl_element()) == VectorElement:
u_to_project = self.diagnostic_variables.fields['uscalar']
else:
u_to_project = u
du = self.diagnostic_variables.fields['du']
du_projector = Projector(u_to_project.dx(0), du)
self.projectors.append(du_projector)
elif key == 'jump_du':
du = self.diagnostic_variables.fields['du']
jump_du = self.diagnostic_variables.fields['jump_du']
V = jump_du.function_space()
jtrial = TrialFunction(V)
psi = TestFunction(V)
Lj = psi('+') * abs(jump(du)) * dS
aj = psi('+') * jtrial('+') * dS
jprob = LinearVariationalProblem(aj, Lj, jump_du)
jsolver = LinearVariationalSolver(jprob)
self.solvers.append(jsolver)
elif key == 'du_smooth':
du = self.diagnostic_variables.fields['du']
du_smooth = self.diagnostic_variables.fields['du_smooth']
projector = Projector(du, du_smooth)
self.projectors.append(projector)
elif key == 'u2_flux':
gamma = simulation_parameters['gamma'][-1]
u2_flux = self.diagnostic_variables.fields['u2_flux']
xis = self.prognostic_variables.pure_xi_list
xis_x = []
xis_xxx = []
CG1 = FunctionSpace(mesh, "CG", 1)
psi = TestFunction(CG1)
for xi in xis:
xis_x.append(Function(CG1).project(xi.dx(0)))
for xi_x in xis_x:
xi_xxx = Function(CG1)
form = (psi * xi_xxx + psi.dx(0) * xi_x.dx(0)) * dx
prob = NonlinearVariationalProblem(form, xi_xxx)
solver = NonlinearVariationalSolver(prob)
solver.solve()
xis_xxx.append(xi_xxx)
flux_expr = 0.0 * x
for xi, xi_x, xi_xxx in zip(xis, xis_x, xis_xxx):
flux_expr += (6 * u.dx(0) * xi + 12 * u * xi_x + gamma *
xi_xxx) * (6 * u.dx(0) * xi + 24 * u * xi_x +
gamma * xi_xxx)
projector = Projector(flux_expr, u2_flux)
self.projectors.append(projector)
elif key == 'a':
# find 6 * u_x * Xi + gamma * Xi_xxx
mesh = u.function_space().mesh()
gamma = simulation_parameters['gamma'][-1]
a_flux = self.diagnostic_variables.fields['a']
xis = self.prognostic_variables.pure_xis
xis_x = []
xis_xxx = []
CG1 = FunctionSpace(mesh, "CG", 1)
psi = TestFunction(CG1)
for xi in xis:
xis_x.append(Function(CG1).project(xi.dx(0)))
for xi_x in xis_x:
xi_xxx = Function(CG1)
form = (psi * xi_xxx + psi.dx(0) * xi_x.dx(0)) * dx
prob = NonlinearVariationalProblem(form, xi_xxx)
solver = NonlinearVariationalSolver(prob)
solver.solve()
xis_xxx.append(xi_xxx)
x, = SpatialCoordinate(mesh)
a_expr = 0.0 * x
for xi, xi_x, xi_xxx in zip(xis, xis_x, xis_xxx):
a_expr += 6 * u.dx(0) * xi + gamma * xi_xxx
projector = Projector(a_expr, a_flux)
self.projectors.append(projector)
elif key == 'b':
# find 12 * u * Xi_x
mesh = u.function_space().mesh()
gamma = simulation_parameters['gamma'][-1]
b_flux = self.diagnostic_variables.fields['b']
xis = self.prognostic_variables.pure_xis
x, = SpatialCoordinate(mesh)
b_expr = 0.0 * x
for xi, xi_x, xi_xxx in zip(xis, xis_x, xis_xxx):
b_expr += 12 * u * xi.dx(0)
projector = Projector(b_expr, b_flux)
self.projectors.append(projector)
elif key == 'kdv_1':
# find the first part of the kdv form
u0 = prognostic_variables.u0
uh = (u + u0) / 2
us = Dt * uh + sqrt(Dt) * Xi
psi = TestFunction(Vu)
du_1 = self.diagnostic_variables.fields['kdv_1']
eqn = psi * du_1 * dx - 6 * psi.dx(0) * uh * us * dx
prob = NonlinearVariationalProblem(eqn, du_1)
solver = NonlinearVariationalSolver(prob)
self.solvers.append(solver)
elif key == 'kdv_2':
# find the second part of the kdv form
u0 = prognostic_variables.u0
uh = (u + u0) / 2
us = Dt * uh + sqrt(Dt) * Xi
psi = TestFunction(Vu)
du_2 = self.diagnostic_variables.fields['kdv_2']
eqn = psi * du_2 * dx + 6 * psi * uh * us.dx(0) * dx
prob = NonlinearVariationalProblem(eqn, du_2)
solver = NonlinearVariationalSolver(prob)
self.solvers.append(solver)
elif key == 'kdv_3':
# find the third part of the kdv form
u0 = prognostic_variables.u0
uh = (u + u0) / 2
us = Dt * uh + sqrt(Dt) * Xi
du_3 = self.diagnostic_variables.fields['kdv_3']
gamma = simulation_parameters['gamma'][-1]
phi = TestFunction(Vu)
F = Function(Vu)
eqn = (phi * F * dx + phi.dx(0) * us.dx(0) * dx)
prob = NonlinearVariationalProblem(eqn, F)
solver = NonlinearVariationalSolver(prob)
self.solvers.append(solver)
self.projectors.append(Projector(-gamma * F.dx(0), du_3))
# nu = TestFunction(Vu)
# back_eqn = nu * du_3 * dx - gamma * nu.dx(0) * F * dx
# back_prob = NonlinearVariationalProblem(back_eqn, du_3)
# back_solver = NonlinearVariationalSolver(back_prob)
# self.solvers.append(solver)
elif key == 'm':
m = self.diagnostic_variables.fields['m']
phi = TestFunction(Vu)
eqn = phi * m * dx - phi * u * dx - alphasq * phi.dx(0) * u.dx(
0) * dx
prob = NonlinearVariationalProblem(eqn, m)
solver = NonlinearVariationalSolver(prob)
self.solvers.append(solver)
elif key == 'u_xx':
u_xx = self.diagnostic_variables.fields['u_xx']
phi = TestFunction(Vu)
eqn = phi * u_xx * dx + phi.dx(0) * u_xx.dx(0) * dx
prob = NonlinearVariationalProblem(eqn, u_xx)
solver = NonlinearVariationalSolver(prob)
self.solvers.append(solver)
elif key == 'u_sde':
self.to_update_constants = True
self.Ld = Ld
self.alphasq = alphasq
self.p = Constant(1.0 * 0.5 * (1 + exp(-Ld / sqrt(alphasq))) /
(1 - exp(-Ld / sqrt(alphasq))))
self.q = Constant(Ld / 2)
u_sde = self.diagnostic_variables.fields['u_sde']
if periodic:
expr = conditional(
x < self.q - Ld / 2,
self.p * ((exp(-(x - self.q + Ld) / sqrt(alphasq)) +
exp(-Ld / sqrt(alphasq)) * exp(
(x - self.q + Ld) / sqrt(alphasq))) /
(1 - exp(-Ld / sqrt(alphasq)))),
conditional(
x < self.q + Ld / 2,
self.p * ((exp(-sqrt((self.q - x)**2 / alphasq)) +
exp(-Ld / sqrt(alphasq)) *
exp(sqrt((self.q - x)**2 / alphasq))) /
(1 - exp(-Ld / sqrt(alphasq)))),
self.p *
((exp(-(self.q + Ld - x) / sqrt(alphasq)) +
exp(-Ld / sqrt(alphasq) * exp(
(self.q + Ld - x) / sqrt(alphasq)))) /
(1 - exp(-Ld / sqrt(alphasq))))))
else:
expr = conditional(
x < self.q - Ld / 2,
self.p * exp(-(x - self.q + Ld) / sqrt(alphasq)),
conditional(
x < self.q + Ld / 2,
self.p * exp(-sqrt((self.q - x)**2 / alphasq)),
self.p * exp(-(self.q + Ld - x) / sqrt(alphasq))))
self.interpolators.append(Interpolator(expr, u_sde))
elif key == 'u_sde_weak':
u_sde = self.diagnostic_variables.fields['u_sde']
u_sde_weak = self.diagnostic_variables.fields['u_sde_weak']
psi = TestFunction(Vu)
eqn = psi * u_sde_weak * dx - psi * (u - u_sde) * dx
prob = NonlinearVariationalProblem(eqn, u_sde_weak)
solver = NonlinearVariationalSolver(prob)
self.solvers.append(solver)
elif key == 'u_sde_mean':
self.to_update_constants = True
self.p = Constant(1.0)
self.q = Constant(Ld / 2)
if periodic:
raise NotImplementedError(
'u_sde_mean not yet implemented for periodic peakon')
u_sde = self.diagnostic_variables.fields['u_sde_mean']
expr = conditional(
x < self.q - Ld / 2,
self.p * exp(-(x - self.q + Ld) / sqrt(alphasq)),
conditional(
x < self.q + Ld / 2,
self.p * exp(-sqrt((self.q - x)**2 / alphasq)),
self.p * exp(-(self.q + Ld - x) / sqrt(alphasq))))
self.interpolators.append(Interpolator(expr, u_sde))
elif key == 'u_sde_weak_mean':
u_sde = self.diagnostic_variables.fields['u_sde_mean']
u_sde_weak = self.diagnostic_variables.fields[
'u_sde_weak_mean']
psi = TestFunction(Vu)
eqn = psi * u_sde_weak * dx - psi * (u - u_sde) * dx
prob = NonlinearVariationalProblem(eqn, u_sde_weak)
solver = NonlinearVariationalSolver(prob)
self.solvers.append(solver)
elif key == 'pure_xi':
pure_xi = 0.0 * x
for xi in self.prognostic_variables.pure_xi_list:
if vector_u:
pure_xi += dot(ones, xi)
else:
pure_xi += xi
Xiscalar = self.diagnostic_variables.fields['pure_xi']
Xi_interpolator = Interpolator(pure_xi, Xiscalar)
self.interpolators.append(Xi_interpolator)
elif key == 'pure_xi_x':
pure_xi_x = 0.0 * x
for xix in self.prognostic_variables.pure_xi_x_list:
if vector_u:
pure_xi_x += dot(ones, xix)
else:
pure_xi_x += xix
Xiscalar = self.diagnostic_variables.fields['pure_xi_x']
Xi_interpolator = Interpolator(pure_xi_x, Xiscalar)
self.interpolators.append(Xi_interpolator)
elif key == 'pure_xi_xx':
pure_xi_xx = 0.0 * x
for xixx in self.prognostic_variables.pure_xi_xx_list:
if vector_u:
pure_xi_xx += dot(ones, xixx)
else:
pure_xi_xx += xixx
Xiscalar = self.diagnostic_variables.fields['pure_xi_xx']
Xi_interpolator = Interpolator(pure_xi_xx, Xiscalar)
self.interpolators.append(Xi_interpolator)
elif key == 'pure_xi_xxx':
pure_xi_xxx = 0.0 * x
for xixxx in self.prognostic_variables.pure_xi_xxx_list:
if vector_u:
pure_xi_xxx += dot(ones, xixxx)
else:
pure_xi_xxx += xixxx
Xiscalar = self.diagnostic_variables.fields['pure_xi_xxx']
Xi_interpolator = Interpolator(pure_xi_xxx, Xiscalar)
self.interpolators.append(Xi_interpolator)
elif key == 'pure_xi_xxxx':
pure_xi_xxxx = 0.0 * x
for xixxxx in self.prognostic_variables.pure_xi_xx_list:
if vector_u:
pure_xi_xxxx += dot(ones, xixxxx)
else:
pure_xi_xxxx += xixxxx
Xiscalar = self.diagnostic_variables.fields['pure_xi_xxxx']
Xi_interpolator = Interpolator(pure_xi_xxxx, Xiscalar)
self.interpolators.append(Xi_interpolator)
else:
raise NotImplementedError('Diagnostic %s not yet implemented' %
key)
