def continuity_form(state, test, q, ibp=IntegrateByParts.ONCE, outflow=False):
if outflow and ibp == IntegrateByParts.NEVER:
raise ValueError(
"outflow is True and ibp is None are incompatible options")
Vu = state.spaces("HDiv")
dS_ = (dS_v + dS_h) if Vu.extruded else dS
ubar = Function(Vu)
if ibp == IntegrateByParts.ONCE:
L = -inner(grad(test), outer(q, ubar)) * dx
else:
L = inner(test, div(outer(q, ubar))) * dx
if ibp != IntegrateByParts.NEVER:
n = FacetNormal(state.mesh)
un = 0.5 * (dot(ubar, n) + abs(dot(ubar, n)))
L += dot(jump(test), (un('+') * q('+') - un('-') * q('-'))) * dS_
if ibp == IntegrateByParts.TWICE:
L -= (inner(test('+'),
dot(ubar('+'), n('+')) * q('+')) +
inner(test('-'),
dot(ubar('-'), n('-')) * q('-'))) * dS_
if outflow:
n = FacetNormal(state.mesh)
un = 0.5 * (dot(ubar, n) + abs(dot(ubar, n)))
L += test * un * q * (ds_v + ds_t + ds_b)
form = transporting_velocity(L, ubar)
return ibp_label(transport(form, TransportEquationType.conservative), ibp)
def get_flux_form(dS, M):
fluxes = (-inner(2 * avg(outer(phi, n)), avg(grad(gamma) * M)) -
inner(avg(grad(phi) * M), 2 * avg(outer(gamma, n))) +
mu * inner(2 * avg(outer(phi, n)),
2 * avg(outer(gamma, n) * kappa))) * dS
return fluxes
def get_flux_form(dS, M):
fluxes = (
-inner(2*avg(outer(q, n)), avg(grad(test)*M))
- inner(avg(grad(q)*M), 2*avg(outer(test, n)))
+ mu*inner(2*avg(outer(q, n)), 2*avg(outer(test, n)*kappa))
)*dS
return fluxes
def horizontal_strain(u, s, h):
r"""Calculate the horizontal strain rate with corrections for terrain-
following coordinates"""
ζ = firedrake.SpatialCoordinate(u.ufl_domain())[2]
b = s - h
v = -((1 - ζ) * grad_2(b) + ζ * grad_2(s)) / h
du_dζ = u.dx(2)
return sym(grad_2(u)) + 0.5 * (outer(du_dζ, v) + outer(v, du_dζ))
def horizontal_strain_rate(**kwargs):
r"""Calculate the horizontal strain rate with corrections for terrain-
following coordinates"""
u, h, s = itemgetter("velocity", "surface", "thickness")(kwargs)
mesh = u.ufl_domain()
dim = mesh.geometric_dimension()
ζ = firedrake.SpatialCoordinate(mesh)[dim - 1]
b = s - h
v = -((1 - ζ) * grad(b) + ζ * grad(s)) / h
du_dζ = u.dx(dim - 1)
return sym_grad(u) + 0.5 * (outer(du_dζ, v) + outer(v, du_dζ))
def _boundary_flux(z, h_ext, q_ext, g, boundary_ids):
Z = z.function_space()
n = firedrake.FacetNormal(Z.mesh())
φ, v = firedrake.TestFunctions(Z)
F_hx, F_qx = _fluxes(h_ext, q_ext, g)
h, q = firedrake.split(z)
F_h, F_q = _fluxes(h, q, g)
return 0.5 * (inner(F_hx, φ * n) + inner(F_qx, outer(v, n)) + inner(
F_h, φ * n) + inner(F_q, outer(v, n))) * ds(boundary_ids)
def tensor_jump(v, n):
r"""
Jump term for vector functions based on the tensor product
.. math::
\text{jump}(\mathbf{u}, \mathbf{n}) = (\mathbf{u}^+ \mathbf{n}^+) +
(\mathbf{u}^- \mathbf{n}^-)
This is the discrete equivalent of grad(u) as opposed to the
vectorial UFL jump operator :meth:`ufl.jump` which represents div(u).
The equivalent of nabla_grad(u) is given by tensor_jump(n, u).
"""
return outer(v('+'), n('+')) + outer(v('-'), n('-'))
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 _fluxes(h, q, g):
r"""Calculate the flux of mass and momentum for the shallow water
equations"""
I = firedrake.Identity(2)
F_h = q
F_q = outer(q, q) / h + 0.5 * g * h**2 * I
return F_h, F_q
def advection_term(self, q):
if self.continuity:
if self.ibp == IntegrateByParts.ONCE:
L = -inner(grad(self.test), outer(q, self.ubar)) * dx
else:
L = inner(self.test, div(outer(q, self.ubar))) * dx
else:
if self.ibp == IntegrateByParts.ONCE:
L = -inner(div(outer(self.test, self.ubar)), q) * dx
else:
L = inner(outer(self.test, self.ubar), grad(q)) * dx
if self.dS is not None and self.ibp != IntegrateByParts.NEVER:
n = FacetNormal(self.state.mesh)
un = 0.5 * (dot(self.ubar, n) + abs(dot(self.ubar, n)))
L += dot(jump(self.test),
(un('+') * q('+') - un('-') * q('-'))) * self.dS
if self.ibp == IntegrateByParts.TWICE:
L -= (inner(self.test('+'),
dot(self.ubar('+'), n('+')) * q('+')) +
inner(self.test('-'),
dot(self.ubar('-'), n('-')) * q('-'))) * self.dS
if self.outflow:
n = FacetNormal(self.state.mesh)
un = 0.5 * (dot(self.ubar, n) + abs(dot(self.ubar, n)))
L += self.test * un * q * (ds_v + ds_t + ds_b)
if self.vector_manifold:
n = FacetNormal(self.state.mesh)
w = self.test
dS = self.dS
u = q
L += un('+') * inner(w('-'),
n('+') + n('-')) * inner(u('+'), n('+')) * dS
L += un('-') * inner(w('+'),
n('+') + n('-')) * inner(u('-'), n('-')) * dS
return L
def central_facet_flux(F, v):
r"""Create the weak form of the central numerical flux through cell facets
This numerical flux, by itself, is unstable. The full right-hand side of
the problem requires an additional facet flux term for stability.
Parameters
----------
F : ufl.Expr
A symbolic expression for the flux
v : firedrake.TestFunction
A test function from the state space
Returns
-------
f : firedrake.Form
A 1-form that discretizes the residual of the flux
"""
mesh = v.ufl_domain()
n = FacetNormal(mesh)
return inner(avg(F), outer(v('+'), n('+')) + outer(v('-'), n('-'))) * dS
def __init__(self, state, V, continuity=False):
super(DGAdvection, self).__init__(state)
element = V.fiat_element
assert element.entity_dofs() == element.entity_closure_dofs(), "Provided space is not discontinuous"
dt = state.timestepping.dt
if V.extruded:
surface_measure = (dS_h + dS_v)
else:
surface_measure = dS
phi = TestFunction(V)
D = TrialFunction(V)
self.D1 = Function(V)
self.dD = Function(V)
n = FacetNormal(state.mesh)
# ( dot(v, n) + |dot(v, n)| )/2.0
un = 0.5*(dot(self.ubar, n) + abs(dot(self.ubar, n)))
a_mass = inner(phi,D)*dx
if continuity:
a_int = -inner(grad(phi), outer(D, self.ubar))*dx
else:
a_int = -inner(div(outer(phi,self.ubar)),D)*dx
a_flux = (dot(jump(phi), un('+')*D('+') - un('-')*D('-')))*surface_measure
arhs = a_mass - dt*(a_int + a_flux)
DGproblem = LinearVariationalProblem(a_mass, action(arhs,self.D1),
self.dD)
self.DGsolver = LinearVariationalSolver(DGproblem,
solver_parameters={
'ksp_type':'preonly',
'pc_type':'bjacobi',
'sub_pc_type': 'ilu'},
options_prefix='DGAdvection')
def advection_term(self, q):
if self.continuity:
if self.ibp == "once":
L = -inner(grad(self.test), outer(q, self.ubar)) * dx
else:
L = inner(self.test, div(outer(q, self.ubar))) * dx
else:
if self.ibp == "once":
L = -inner(div(outer(self.test, self.ubar)), q) * dx
else:
L = inner(outer(self.test, self.ubar), grad(q)) * dx
if self.dS is not None and self.ibp is not None:
L += dot(jump(self.test),
(self.un('+') * q('+') - self.un('-') * q('-'))) * self.dS
if self.ibp == "twice":
L -= (
inner(self.test('+'),
dot(self.ubar('+'), self.n('+')) * q('+')) +
inner(self.test('-'),
dot(self.ubar('-'), self.n('-')) * q('-'))) * self.dS
if self.outflow:
L += self.test * self.un * q * self.ds
if self.vector_manifold:
un = self.un
w = self.test
u = q
n = self.n
dS = self.dS
L += un('+') * inner(w('-'),
n('+') + n('-')) * inner(u('+'), n('+')) * dS
L += un('-') * inner(w('+'),
n('+') + n('-')) * inner(u('-'), n('-')) * dS
return L
def get_flux_form(dS, M):
fluxes = (-inner(2*avg(outer(phi, n)), avg(grad(gamma)*M))
- inner(avg(grad(phi)*M), 2*avg(outer(gamma, n)))
+ mu*inner(2*avg(outer(phi, n)), 2*avg(outer(gamma, n)*kappa)))*dS
return fluxes
def surf_grad(u):
return fd.sym(fd.grad(u) - fd.outer(fd.grad(u) * n, n))
def central_inflow_flux(F_in, v, boundary_ids):
r"""Create the weak form of the central numerical flux through the domain
boundary"""
mesh = v.ufl_domain()
n = FacetNormal(mesh)
return inner(F_in, outer(v, n)) * ds(boundary_ids)
def residual(self, test, trial, trial_lagged, fields, bcs):
if 'background_viscosity' in fields:
assert('grid_resolution' in fields)
mu_background = fields['background_viscosity']
grid_dx = fields['grid_resolution'][0]
grid_dz = fields['grid_resolution'][1]
mu_h = 0.5*abs(trial[0]) * grid_dx + mu_background
mu_v = 0.5*abs(trial[1]) * grid_dz + mu_background
print("use redx viscosity")
diff_tensor = as_tensor([[mu_h, 0], [0, mu_v]])
else:
mu = fields['viscosity']
if len(mu.ufl_shape) == 2:
diff_tensor = mu
else:
diff_tensor = mu * Identity(self.dim)
phi = test
n = self.n
u = trial
u_lagged = trial_lagged
grad_test = nabla_grad(phi)
stress = dot(diff_tensor, nabla_grad(u))
if self.symmetric_stress:
stress += dot(diff_tensor, grad(u))
F = 0
F += inner(grad_test, stress)*self.dx
# Interior Penalty method
#
# see https://www.researchgate.net/publication/260085826 for details
# on the choice of sigma
degree = self.trial_space.ufl_element().degree()
if not isinstance(degree, int):
degree = max(degree[0], degree[1])
# safety factor: 1.0 is theoretical minimum
alpha = fields.get('interior_penalty', 2.0)
if degree == 0:
# probably only works for orthog. quads and hexes
sigma = 1.0
else:
nf = self.mesh.ufl_cell().num_facets()
family = self.trial_space.ufl_element().family()
if family in ['DQ', 'TensorProductElement', 'EnrichedElement']:
degree_gradient = degree
else:
degree_gradient = degree - 1
sigma = alpha * cell_edge_integral_ratio(self.mesh, degree_gradient) * nf
# we use (3.23) + (3.20) from https://www.researchgate.net/publication/260085826
# instead of maximum over two adjacent cells + and -, we just sum (which is 2*avg())
# and the for internal facets we have an extra 0.5:
# WEIRDNESS: avg(1/CellVolume(mesh)) crashes TSFC - whereas it works in scalar diffusion! - instead just writing out explicitly
sigma *= FacetArea(self.mesh)*(1/CellVolume(self.mesh)('-') + 1/CellVolume(self.mesh)('+'))/2
if not is_continuous(self.trial_space):
u_tensor_jump = tensor_jump(n, u)
if self.symmetric_stress:
u_tensor_jump += transpose(u_tensor_jump)
F += sigma*inner(tensor_jump(n, phi), dot(avg(diff_tensor), u_tensor_jump))*self.dS
F += -inner(avg(dot(diff_tensor, nabla_grad(phi))), u_tensor_jump)*self.dS
F += -inner(tensor_jump(n, phi), avg(stress))*self.dS
for id, bc in bcs.items():
if 'u' in bc or 'un' in bc:
if 'u' in bc:
u_tensor_jump = outer(n, u-bc['u'])
else:
u_tensor_jump = outer(n, n)*(dot(n, u)-bc['un'])
if self.symmetric_stress:
u_tensor_jump += transpose(u_tensor_jump)
# this corresponds to the same 3 terms as the dS integrals for DG above:
F += 2*sigma*inner(outer(n, phi), dot(diff_tensor, u_tensor_jump))*self.ds(id)
F += -inner(dot(diff_tensor, nabla_grad(phi)), u_tensor_jump)*self.ds(id)
if 'u' in bc:
F += -inner(outer(n, phi), stress) * self.ds(id)
elif 'un' in bc:
# we only keep, the normal part of stress, the tangential
# part is assumed to be zero stress (i.e. free slip), or prescribed via 'stress'
F += -dot(n, phi)*dot(n, dot(stress, n)) * self.ds(id)
if 'stress' in bc: # a momentum flux, a.k.a. "force"
# here we need only the third term, because we assume jump_u=0 (u_ext=u)
# the provided stress = n.(mu.stress_tensor)
F += dot(-phi, bc['stress']) * self.ds(id)
if 'drag' in bc: # (bottom) drag of the form tau = -C_D u |u|
C_D = bc['drag']
if 'coriolis_frequency' in fields and self.dim == 2:
assert 'u_velocity' in fields
u_vel_component = fields['u_velocity']
unorm = pow(dot(u_lagged, u_lagged) + pow(u_vel_component, 2) + 1e-6, 0.5)
else:
unorm = pow(dot(u_lagged, u_lagged) + 1e-6, 0.5)
F += dot(-phi, -C_D*unorm*u) * self.ds(id)
# NOTE 1: unspecified boundaries are equivalent to free stress (i.e. free in all directions)
# NOTE 2: 'un' can be combined with 'stress' provided the stress force is tangential (e.g. no-normal flow with wind)
if 'u' in bc and 'stress' in bc:
raise ValueError("Cannot apply both 'u' and 'stress' bc on same boundary")
if 'u' in bc and 'drag' in bc:
raise ValueError("Cannot apply both 'u' and 'drag' bc on same boundary")
if 'u' in bc and 'un' in bc:
raise ValueError("Cannot apply both 'u' and 'un' bc on same boundary")
return -F
def __init__(self,
mesh: object,
kappa: float,
comm_space: MPI.Comm,
mu: float = 5.,
*args,
**kwargs):
"""
Constructor
:param mesh: spatial domain
:param kappa: diffusion coefficient
:param mu: penalty weighting function
"""
super(Diffusion2D, self).__init__(*args, **kwargs)
# Spatial domain and function space
self.mesh = mesh
V = FunctionSpace(self.mesh, "DG", 1)
self.function_space = V
self.comm_space = comm_space
# Placeholder for time step - will be updated in the update method
self.dt = Constant(0.)
# Things we need for the form
gamma = TestFunction(V)
phi = TrialFunction(V)
self.f = Function(V)
n = FacetNormal(mesh)
# Set up the rhs and bilinear form of the equation
a = (inner(gamma, phi) * dx + self.dt *
(inner(grad(gamma),
grad(phi) * kappa) * dx -
inner(2 * avg(outer(phi, n)), avg(grad(gamma) * kappa)) * dS -
inner(avg(grad(phi) * kappa), 2 * avg(outer(gamma, n))) * dS +
mu * inner(2 * avg(outer(phi, n)),
2 * avg(outer(gamma, n) * kappa)) * dS))
rhs = inner(gamma, self.f) * dx
# Function to hold the solution
self.soln = Function(V)
# Setup problem and solver
prob = LinearVariationalProblem(a, rhs, self.soln)
self.solver = NonlinearVariationalSolver(prob)
# Set the data structure for any user-defined time point
self.vector_template = VectorDiffusion2D(size=len(self.function_space),
comm_space=self.comm_space)
# Set initial condition:
# Setting up a Gaussian blob in the centre of the domain.
self.vector_t_start = VectorDiffusion2D(size=len(self.function_space),
comm_space=self.comm_space)
x = SpatialCoordinate(self.mesh)
initial_tracer = exp(-((x[0] - 5)**2 + (x[1] - 5)**2))
tmp = Function(self.function_space)
tmp.interpolate(initial_tracer)
self.vector_t_start.set_values(np.copy(tmp.dat.data))
fd.inner(f, v)*fd.dx + idt*c_t*p0*q*fd.dx
# -------------------------------------------------------------------------
# transport
# coefficients
K = fd.Constant(K)
Dt = fd.Constant(dt)
c_mid = 0.5 * (c + c0) # Crank-Nicolson timestepping
# advective velocity
vel = fd.Function(V, name='velocity')
vnorm = fd.sqrt(fd.dot(vel, vel))
# Diffusion tensor
Diff = fd.Identity(mesh.geometric_dimension())*(Dm + alphaT*vnorm) + \
(alphaL-alphaT)*fd.outer(vel, vel)/vnorm
# source term
fc = fd.Constant(s)
# weak form (transport)
F1 = w * (c - c0) * fd.dx + Dt * (w * fd.dot(vel, fd.grad(c_mid)) + fd.dot(
fd.grad(w), Diff * fd.grad(c_mid)) + w * K * c_mid -
fc * w) * fd.dx # - Dt*h_n*w*fd.ds(outlet)
# strong form
R = (c - c0) + Dt * (fd.dot(vel, fd.grad(c_mid)) -
fd.div(Diff * fd.grad(c_mid)) + K * c_mid - fc)
# *** Adding SUPG stabilizing and shock cap. terms ***
# SUPG stabilisation parameters
dt = np.sqrt(tol)
dtc = fd.Constant(dt)
n = fd.FacetNormal(mesh)
h = fd.sqrt(2) * fd.CellVolume(mesh) / fd.CellDiameter(mesh)
# advective velocity
velocity = fd.Function(vDG1)
velocity.interpolate(fd.Constant(v_inlet))
vnorm = fd.sqrt(fd.dot(velocity, velocity))
# Diffusion tensor
if d_t < 0:
Diff = fd.Identity(mesh.geometric_dimension()) * Dm
else:
Diff = fd.Identity(mesh.geometric_dimension())*(Dm + d_t*vnorm) + \
fd.Constant(d_l-d_t)*fd.outer(velocity, velocity)/vnorm
# upwind ter
# vn = 0.5*(fd.dot(velocity, n) + abs(fd.dot(velocity, n)))
# cupw = fd.conditional(vn < 0, ch, lmbd_h)
# stability
tau_d = fd.Constant(max([Dm, tau_e])) / h
tau_a = abs(fd.dot(velocity, n))
# tau_a = vn
# numerical flux
chat = lmbd_h
###########################################
qhat = qh + tau_d * (ch - chat) * n + velocity * chat + tau_a * (ch - chat) * n
# qhat = qh + tau*(ch - chat)*n + velocity * chat + tau_a*(ch - chat)*n
# 3.3) set boundary conditions
t_bc = fd.DirichletBC(X, cIn, inlet)
# ======================
# 3.4) Variational Form
# coefficients
# advective velocity
vel = fd.Function(V, name='velocity')
vel.interpolate(v_inlet)
vnorm = fd.sqrt(fd.dot(vel, vel))
# Diffusion tensor
Diff = fd.Identity(mesh.geometric_dimension())*(Dm + d_t*vnorm) + \
fd.Constant(d_l-d_t)*fd.outer(vel, vel)/vnorm
Diff = fd.Identity(mesh.geometric_dimension())*Dm
Dt = fd.Constant(dt)
K = fd.Constant(K)
c_mid = 0.5 * (c + c0) # Crank-Nicolson timestepping
fc = fd.Constant(s)
# weak form (transport)
F = w*(c - c0)*fd.dx + Dt*(w*fd.dot(vel, fd.grad(c_mid))
+ fd.dot(fd.grad(w),
Diff*fd.grad(c_mid)) + w*K*c_mid
- fc*w)*fd.dx # - Dt*h_n*w*fd.ds(outlet)
# strong form
R = (c - c0) + Dt*(fd.dot(vel, fd.grad(c_mid)) -