def _setup_solver(self):
state = self.state
H = state.parameters.H
g = state.parameters.g
beta = state.timestepping.dt*state.timestepping.alpha
# Split up the rhs vector (symbolically)
u_in, D_in = split(state.xrhs)
W = state.W
w, phi = TestFunctions(W)
u, D = TrialFunctions(W)
eqn = (
inner(w, u) - beta*g*div(w)*D
- inner(w, u_in)
+ phi*D + beta*H*phi*div(u)
- phi*D_in
)*dx
aeqn = lhs(eqn)
Leqn = rhs(eqn)
# Place to put result of u rho solver
self.uD = Function(W)
# Solver for u, D
uD_problem = LinearVariationalProblem(
aeqn, Leqn, self.state.dy)
self.uD_solver = LinearVariationalSolver(uD_problem,
solver_parameters=self.params,
options_prefix='SWimplicit')
def _setup_solver(self):
state = self.state
H = state.parameters.H
g = state.parameters.g
beta = state.timestepping.dt*state.timestepping.alpha
# Split up the rhs vector (symbolically)
u_in, D_in = split(state.xrhs)
W = state.W
w, phi = TestFunctions(W)
u, D = TrialFunctions(W)
eqn = (
inner(w, u) - beta*g*div(w)*D
- inner(w, u_in)
+ phi*D + beta*H*phi*div(u)
- phi*D_in
)*dx
aeqn = lhs(eqn)
Leqn = rhs(eqn)
# Place to put result of u rho solver
self.uD = Function(W)
# Solver for u, D
uD_problem = LinearVariationalProblem(
aeqn, Leqn, self.state.dy)
self.uD_solver = LinearVariationalSolver(uD_problem,
solver_parameters=self.solver_parameters,
options_prefix='SWimplicit')
def _setup_solver(self):
state = self.state # just cutting down line length a bit
dt = state.timestepping.dt
beta = dt*state.timestepping.alpha
mu = state.mu
# Split up the rhs vector (symbolically)
u_in, p_in, b_in = split(state.xrhs)
# Build the reduced function space for u,p
M = MixedFunctionSpace((state.V[0], state.V[1]))
w, phi = TestFunctions(M)
u, p = TrialFunctions(M)
# Get background fields
bbar = state.bbar
# Analytical (approximate) elimination of theta
k = state.k # Upward pointing unit vector
b = -dot(k,u)*dot(k,grad(bbar))*beta + b_in
# vertical projection
def V(u):
return k*inner(u,k)
eqn = (
inner(w, (u - u_in))*dx
- beta*div(w)*p*dx
- beta*inner(w,k)*b*dx
+ phi*div(u)*dx
)
if mu is not None:
eqn += dt*mu*inner(w,k)*inner(u,k)*dx
aeqn = lhs(eqn)
Leqn = rhs(eqn)
# Place to put result of u p solver
self.up = Function(M)
# Boundary conditions (assumes extruded mesh)
dim = M.sub(0).ufl_element().value_shape()[0]
bc = ("0.0",)*dim
bcs = [DirichletBC(M.sub(0), Expression(bc), "bottom"),
DirichletBC(M.sub(0), Expression(bc), "top")]
# preconditioner equation
L = self.L
Ap = (
inner(w,u) + L*L*div(w)*div(u) +
phi*p/L/L
)*dx
# Solver for u, p
up_problem = LinearVariationalProblem(
aeqn, Leqn, self.up, bcs=bcs, aP=Ap)
nullspace = MixedVectorSpaceBasis(M,
[M.sub(0),
VectorSpaceBasis(constant=True)])
self.up_solver = LinearVariationalSolver(up_problem,
solver_parameters=self.params,
nullspace=nullspace)
# Reconstruction of b
b = TrialFunction(state.V[2])
gamma = TestFunction(state.V[2])
u, p = self.up.split()
self.b = Function(state.V[2])
b_eqn = gamma*(b - b_in +
dot(k,u)*dot(k,grad(bbar))*beta)*dx
b_problem = LinearVariationalProblem(lhs(b_eqn),
rhs(b_eqn),
self.b)
self.b_solver = LinearVariationalSolver(b_problem)
bcp = [outflow]
# Create functions
u0 = Function(V)
u1 = Function(V)
p1 = Function(Q)
# Define coefficients
k = Constant(dt)
f = Constant((0, 0))
# Tentative velocity step
F1 = (1.0/k)*inner(u - u0, v)*dx + inner(grad(u0)*u0, v)*dx + \
(1.0/Re)*inner(grad(u), grad(v))*dx - inner(f, v)*dx
a1 = lhs(F1)
L1 = rhs(F1)
# Pressure update
a2 = inner(grad(p), grad(q)) * dx
L2 = -(1 / k) * div(u1) * q * dx
# Velocity update
a3 = inner(u, v) * dx
L3 = inner(u1, v) * dx - k * inner(grad(p1), v) * dx
# Assemble matrices
A1 = assemble(a1)
A2 = assemble(a2)
A3 = assemble(a3)
uw = lambda u, v: (s(u)('+') * v('+') + s(u)('-') * v('-'))
if mesh.geometric_dimension() == 2:
perp = lambda u: as_vector([-u[1], u[0]])
p_uw = lambda u, v: perp(uw(u, v))
else:
perp = lambda u: cross(CellNormal(mesh), u)
out_n = CellNormal(mesh)
p_uw = lambda u, v: (s(u)('+') * cross(out_n('+'), v('+')) + s(u)
('-') * cross(out_n('-'), v('-')))
# Initial solve for vorticity for output
eta = TestFunction(W2)
q_ = TrialFunction(W2)
q_eqn = eta * q_ * Dn * dx + inner(perp(grad(eta)), un) * dx - eta * f * dx
q_p = LinearVariationalProblem(lhs(q_eqn), rhs(q_eqn), qn)
q_solver = LinearVariationalSolver(q_p,
solver_parameters={
"ksp_type": "preonly",
"pc_type": "lu"
})
q_solver.solve()
# Build advection, forcing forms
K = inner(un, un) / 3. + inner(un, unk) / 3. + inner(unk, unk) / 3.
Prhs = g * (0.5 * (Dn + Dp) + b) + 0.5 * K
# D advection solver
v, psi = TestFunctions(M)
F_, D_ = TrialFunctions(M)
D_bar = 0.5 * (Dn + D_)
def _setup_solver(self):
state = self.state # just cutting down line length a bit
Dt = state.timestepping.dt
beta_ = Dt*state.timestepping.alpha
cp = state.parameters.cp
mu = state.mu
Vu = state.spaces("HDiv")
Vtheta = state.spaces("HDiv_v")
Vrho = state.spaces("DG")
# Store time-stepping coefficients as UFL Constants
dt = Constant(Dt)
beta = Constant(beta_)
beta_cp = Constant(beta_ * cp)
# Split up the rhs vector (symbolically)
u_in, rho_in, theta_in = split(state.xrhs)
# Build the reduced function space for u,rho
M = MixedFunctionSpace((Vu, Vrho))
w, phi = TestFunctions(M)
u, rho = TrialFunctions(M)
n = FacetNormal(state.mesh)
# Get background fields
thetabar = state.fields("thetabar")
rhobar = state.fields("rhobar")
pibar = thermodynamics.pi(state.parameters, rhobar, thetabar)
pibar_rho = thermodynamics.pi_rho(state.parameters, rhobar, thetabar)
pibar_theta = thermodynamics.pi_theta(state.parameters, rhobar, thetabar)
# Analytical (approximate) elimination of theta
k = state.k # Upward pointing unit vector
theta = -dot(k, u)*dot(k, grad(thetabar))*beta + theta_in
# Only include theta' (rather than pi') in the vertical
# component of the gradient
# the pi prime term (here, bars are for mean and no bars are
# for linear perturbations)
pi = pibar_theta*theta + pibar_rho*rho
# vertical projection
def V(u):
return k*inner(u, k)
# specify degree for some terms as estimated degree is too large
dxp = dx(degree=(self.quadrature_degree))
dS_vp = dS_v(degree=(self.quadrature_degree))
# add effect of density of water upon theta
if self.moisture is not None:
water_t = Function(Vtheta).assign(0.0)
for water in self.moisture:
water_t += self.state.fields(water)
theta_w = theta / (1 + water_t)
thetabar_w = thetabar / (1 + water_t)
else:
theta_w = theta
thetabar_w = thetabar
eqn = (
inner(w, (state.h_project(u) - u_in))*dx
- beta_cp*div(theta_w*V(w))*pibar*dxp
# following does nothing but is preserved in the comments
# to remind us why (because V(w) is purely vertical).
# + beta_cp*jump(theta*V(w), n)*avg(pibar)*dS_v
- beta_cp*div(thetabar_w*w)*pi*dxp
+ beta_cp*jump(thetabar_w*w, n)*avg(pi)*dS_vp
+ (phi*(rho - rho_in) - beta*inner(grad(phi), u)*rhobar)*dx
+ beta*jump(phi*u, n)*avg(rhobar)*(dS_v + dS_h)
)
if mu is not None:
eqn += dt*mu*inner(w, k)*inner(u, k)*dx
aeqn = lhs(eqn)
Leqn = rhs(eqn)
# Place to put result of u rho solver
self.urho = Function(M)
# Boundary conditions (assumes extruded mesh)
bcs = [DirichletBC(M.sub(0), 0.0, "bottom"),
DirichletBC(M.sub(0), 0.0, "top")]
# Solver for u, rho
urho_problem = LinearVariationalProblem(
aeqn, Leqn, self.urho, bcs=bcs)
self.urho_solver = LinearVariationalSolver(urho_problem,
solver_parameters=self.solver_parameters,
options_prefix='ImplicitSolver')
# Reconstruction of theta
theta = TrialFunction(Vtheta)
gamma = TestFunction(Vtheta)
u, rho = self.urho.split()
self.theta = Function(Vtheta)
theta_eqn = gamma*(theta - theta_in
+ dot(k, u)*dot(k, grad(thetabar))*beta)*dx
theta_problem = LinearVariationalProblem(lhs(theta_eqn),
rhs(theta_eqn),
self.theta)
self.theta_solver = LinearVariationalSolver(theta_problem,
options_prefix='thetabacksubstitution')
def _setup_solver(self):
state = self.state # just cutting down line length a bit
dt = state.dt
beta_ = dt * self.alpha
Vu = state.spaces("HDiv")
Vb = state.spaces("theta")
Vp = state.spaces("DG")
# Store time-stepping coefficients as UFL Constants
beta = Constant(beta_)
# Split up the rhs vector (symbolically)
self.xrhs = Function(self.equations.function_space)
u_in, p_in, b_in = split(self.xrhs)
# Build the reduced function space for u,p
M = MixedFunctionSpace((Vu, Vp))
w, phi = TestFunctions(M)
u, p = TrialFunctions(M)
# Get background fields
bbar = state.fields("bbar")
# Analytical (approximate) elimination of theta
k = state.k # Upward pointing unit vector
b = -dot(k, u) * dot(k, grad(bbar)) * beta + b_in
# vertical projection
def V(u):
return k * inner(u, k)
eqn = (inner(w, (u - u_in)) * dx - beta * div(w) * p * dx -
beta * inner(w, k) * b * dx + phi * div(u) * dx)
if hasattr(self.equations, "mu"):
eqn += dt * self.equations.mu * inner(w, k) * inner(u, k) * dx
aeqn = lhs(eqn)
Leqn = rhs(eqn)
# Place to put result of u p solver
self.up = Function(M)
# Boundary conditions (assumes extruded mesh)
# BCs are declared for the plain velocity space. As we need them in
# a mixed problem, we replicate the BCs but for subspace of M
bcs = [
DirichletBC(M.sub(0), bc.function_arg, bc.sub_domain)
for bc in self.equations.bcs['u']
]
# Solver for u, p
up_problem = LinearVariationalProblem(aeqn, Leqn, self.up, bcs=bcs)
# Provide callback for the nullspace of the trace system
def trace_nullsp(T):
return VectorSpaceBasis(constant=True)
appctx = {"trace_nullspace": trace_nullsp}
self.up_solver = LinearVariationalSolver(
up_problem,
solver_parameters=self.solver_parameters,
appctx=appctx)
# Reconstruction of b
b = TrialFunction(Vb)
gamma = TestFunction(Vb)
u, p = self.up.split()
self.b = Function(Vb)
b_eqn = gamma * (b - b_in + dot(k, u) * dot(k, grad(bbar)) * beta) * dx
b_problem = LinearVariationalProblem(lhs(b_eqn), rhs(b_eqn), self.b)
self.b_solver = LinearVariationalSolver(b_problem)
def _setup_solver(self):
import numpy as np
state = self.state
dt = state.dt
beta_ = dt * self.alpha
cp = state.parameters.cp
Vu = state.spaces("HDiv")
Vu_broken = FunctionSpace(state.mesh, BrokenElement(Vu.ufl_element()))
Vtheta = state.spaces("theta")
Vrho = state.spaces("DG")
# Store time-stepping coefficients as UFL Constants
beta = Constant(beta_)
beta_cp = Constant(beta_ * cp)
h_deg = Vrho.ufl_element().degree()[0]
v_deg = Vrho.ufl_element().degree()[1]
Vtrace = FunctionSpace(state.mesh, "HDiv Trace", degree=(h_deg, v_deg))
# Split up the rhs vector (symbolically)
self.xrhs = Function(self.equations.function_space)
u_in, rho_in, theta_in = split(self.xrhs)[0:3]
# Build the function space for "broken" u, rho, and pressure trace
M = MixedFunctionSpace((Vu_broken, Vrho, Vtrace))
w, phi, dl = TestFunctions(M)
u, rho, l0 = TrialFunctions(M)
n = FacetNormal(state.mesh)
# Get background fields
thetabar = state.fields("thetabar")
rhobar = state.fields("rhobar")
exnerbar = thermodynamics.exner_pressure(state.parameters, rhobar,
thetabar)
exnerbar_rho = thermodynamics.dexner_drho(state.parameters, rhobar,
thetabar)
exnerbar_theta = thermodynamics.dexner_dtheta(state.parameters, rhobar,
thetabar)
# Analytical (approximate) elimination of theta
k = state.k # Upward pointing unit vector
theta = -dot(k, u) * dot(k, grad(thetabar)) * beta + theta_in
# Only include theta' (rather than exner') in the vertical
# component of the gradient
# The exner prime term (here, bars are for mean and no bars are
# for linear perturbations)
exner = exnerbar_theta * theta + exnerbar_rho * rho
# Vertical projection
def V(u):
return k * inner(u, k)
# hydrostatic projection
h_project = lambda u: u - k * inner(u, k)
# Specify degree for some terms as estimated degree is too large
dxp = dx(degree=(self.quadrature_degree))
dS_vp = dS_v(degree=(self.quadrature_degree))
dS_hp = dS_h(degree=(self.quadrature_degree))
ds_vp = ds_v(degree=(self.quadrature_degree))
ds_tbp = (ds_t(degree=(self.quadrature_degree)) +
ds_b(degree=(self.quadrature_degree)))
# Add effect of density of water upon theta
if self.moisture is not None:
water_t = Function(Vtheta).assign(0.0)
for water in self.moisture:
water_t += self.state.fields(water)
theta_w = theta / (1 + water_t)
thetabar_w = thetabar / (1 + water_t)
else:
theta_w = theta
thetabar_w = thetabar
_l0 = TrialFunction(Vtrace)
_dl = TestFunction(Vtrace)
a_tr = _dl('+') * _l0('+') * (
dS_vp + dS_hp) + _dl * _l0 * ds_vp + _dl * _l0 * ds_tbp
def L_tr(f):
return _dl('+') * avg(f) * (
dS_vp + dS_hp) + _dl * f * ds_vp + _dl * f * ds_tbp
cg_ilu_parameters = {
'ksp_type': 'cg',
'pc_type': 'bjacobi',
'sub_pc_type': 'ilu'
}
# Project field averages into functions on the trace space
rhobar_avg = Function(Vtrace)
exnerbar_avg = Function(Vtrace)
rho_avg_prb = LinearVariationalProblem(a_tr, L_tr(rhobar), rhobar_avg)
exner_avg_prb = LinearVariationalProblem(a_tr, L_tr(exnerbar),
exnerbar_avg)
rho_avg_solver = LinearVariationalSolver(
rho_avg_prb,
solver_parameters=cg_ilu_parameters,
options_prefix='rhobar_avg_solver')
exner_avg_solver = LinearVariationalSolver(
exner_avg_prb,
solver_parameters=cg_ilu_parameters,
options_prefix='exnerbar_avg_solver')
with timed_region("Gusto:HybridProjectRhobar"):
rho_avg_solver.solve()
with timed_region("Gusto:HybridProjectExnerbar"):
exner_avg_solver.solve()
# "broken" u, rho, and trace system
# NOTE: no ds_v integrals since equations are defined on
# a periodic (or sphere) base mesh.
if any([t.has_label(hydrostatic) for t in self.equations.residual]):
u_mass = inner(w, (h_project(u) - u_in)) * dx
else:
u_mass = inner(w, (u - u_in)) * dx
eqn = (
# momentum equation
u_mass - beta_cp * div(theta_w * V(w)) * exnerbar * dxp
# following does nothing but is preserved in the comments
# to remind us why (because V(w) is purely vertical).
# + beta_cp*jump(theta_w*V(w), n=n)*exnerbar_avg('+')*dS_vp
+ beta_cp * jump(theta_w * V(w), n=n) * exnerbar_avg('+') * dS_hp +
beta_cp * dot(theta_w * V(w), n) * exnerbar_avg * ds_tbp -
beta_cp * div(thetabar_w * w) * exner * dxp
# trace terms appearing after integrating momentum equation
+ beta_cp * jump(thetabar_w * w, n=n) * l0('+') * (dS_vp + dS_hp) +
beta_cp * dot(thetabar_w * w, n) * l0 * (ds_tbp + ds_vp)
# mass continuity equation
+ (phi *
(rho - rho_in) - beta * inner(grad(phi), u) * rhobar) * dx +
beta * jump(phi * u, n=n) * rhobar_avg('+') * (dS_v + dS_h)
# term added because u.n=0 is enforced weakly via the traces
+ beta * phi * dot(u, n) * rhobar_avg * (ds_tb + ds_v)
# constraint equation to enforce continuity of the velocity
# through the interior facets and weakly impose the no-slip
# condition
+ dl('+') * jump(u, n=n) * (dS_vp + dS_hp) + dl * dot(u, n) *
(ds_tbp + ds_vp))
# contribution of the sponge term
if hasattr(self.equations, "mu"):
eqn += dt * self.equations.mu * inner(w, k) * inner(u, k) * dx
aeqn = lhs(eqn)
Leqn = rhs(eqn)
# Function for the hybridized solutions
self.urhol0 = Function(M)
hybridized_prb = LinearVariationalProblem(aeqn, Leqn, self.urhol0)
hybridized_solver = LinearVariationalSolver(
hybridized_prb,
solver_parameters=self.solver_parameters,
options_prefix='ImplicitSolver')
self.hybridized_solver = hybridized_solver
# Project broken u into the HDiv space using facet averaging.
# Weight function counting the dofs of the HDiv element:
shapes = {
"i": Vu.finat_element.space_dimension(),
"j": np.prod(Vu.shape, dtype=int)
}
weight_kernel = """
for (int i=0; i<{i}; ++i)
for (int j=0; j<{j}; ++j)
w[i*{j} + j] += 1.0;
""".format(**shapes)
self._weight = Function(Vu)
par_loop(weight_kernel, dx, {"w": (self._weight, INC)})
# Averaging kernel
self._average_kernel = """
for (int i=0; i<{i}; ++i)
for (int j=0; j<{j}; ++j)
vec_out[i*{j} + j] += vec_in[i*{j} + j]/w[i*{j} + j];
""".format(**shapes)
# HDiv-conforming velocity
self.u_hdiv = Function(Vu)
# Reconstruction of theta
theta = TrialFunction(Vtheta)
gamma = TestFunction(Vtheta)
self.theta = Function(Vtheta)
theta_eqn = gamma * (theta - theta_in + dot(k, self.u_hdiv) *
dot(k, grad(thetabar)) * beta) * dx
theta_problem = LinearVariationalProblem(lhs(theta_eqn),
rhs(theta_eqn), self.theta)
self.theta_solver = LinearVariationalSolver(
theta_problem,
solver_parameters=cg_ilu_parameters,
options_prefix='thetabacksubstitution')
# Store boundary conditions for the div-conforming velocity to apply
# post-solve
self.bcs = self.equations.bcs['u']
def _setup_solver(self):
state = self.state # just cutting down line length a bit
Dt = state.timestepping.dt
beta_ = Dt*state.timestepping.alpha
mu = state.mu
Vu = state.spaces("HDiv")
Vb = state.spaces("HDiv_v")
Vp = state.spaces("DG")
# Store time-stepping coefficients as UFL Constants
dt = Constant(Dt)
beta = Constant(beta_)
# Split up the rhs vector (symbolically)
u_in, p_in, b_in = split(state.xrhs)
# Build the reduced function space for u,p
M = MixedFunctionSpace((Vu, Vp))
w, phi = TestFunctions(M)
u, p = TrialFunctions(M)
# Get background fields
bbar = state.fields("bbar")
# Analytical (approximate) elimination of theta
k = state.k # Upward pointing unit vector
b = -dot(k, u)*dot(k, grad(bbar))*beta + b_in
# vertical projection
def V(u):
return k*inner(u, k)
eqn = (
inner(w, (u - u_in))*dx
- beta*div(w)*p*dx
- beta*inner(w, k)*b*dx
+ phi*div(u)*dx
)
if mu is not None:
eqn += dt*mu*inner(w, k)*inner(u, k)*dx
aeqn = lhs(eqn)
Leqn = rhs(eqn)
# Place to put result of u p solver
self.up = Function(M)
# Boundary conditions (assumes extruded mesh)
bcs = None if len(self.state.bcs) == 0 else self.state.bcs
# Solver for u, p
up_problem = LinearVariationalProblem(aeqn, Leqn, self.up, bcs=bcs)
# Provide callback for the nullspace of the trace system
def trace_nullsp(T):
return VectorSpaceBasis(constant=True)
appctx = {"trace_nullspace": trace_nullsp}
self.up_solver = LinearVariationalSolver(up_problem,
solver_parameters=self.solver_parameters,
appctx=appctx)
# Reconstruction of b
b = TrialFunction(Vb)
gamma = TestFunction(Vb)
u, p = self.up.split()
self.b = Function(Vb)
b_eqn = gamma*(b - b_in
+ dot(k, u)*dot(k, grad(bbar))*beta)*dx
b_problem = LinearVariationalProblem(lhs(b_eqn),
rhs(b_eqn),
self.b)
self.b_solver = LinearVariationalSolver(b_problem)
f.assign(q * f / norm)
# Plot source
# rr = np.logspace(-5, np.log(Delta_x*Nx),100)
# ff = np.exp(-rr**2/eps)/eps**0.5
# plt.loglog(rr, ff)
# plt.show()
# We can now define the bilinear and linear forms for the left and right
dx = fd.dx
TdivU = fd.as_vector(
(Tx_facet * u.dx(0), Ty_facet * u.dx(1), Tz_facet * u.dx(2)))
# F = (u - u_n)/dt*w*v*dx + (fd.dot(TdivU, fd.grad(v)))*dx + f*v*dx
F = w * u * v * dx + dt * (fd.dot(
TdivU, fd.grad(v))) * dx - (w * u_n + dt * f) * v * dx
a = fd.lhs(F)
L = fd.rhs(F)
# a = inner(u,v)*dx+ dt*(fd.dot(TdivU, fd.grad(v)))*dx
# L = (u_n + dt*f)*v*dx
# m = u * v * w * dx
u = fd.Function(V)
print('Start solver')
t = []
pwf = []
pavg = []
q = []
# t =0
def __init__(self, state, V):
super(EulerPoincareForm, self).__init__(state)
dt = state.timestepping.dt
w = TestFunction(V)
u = TrialFunction(V)
self.u0 = Function(V)
ustar = 0.5*(self.u0 + u)
n = FacetNormal(state.mesh)
Upwind = 0.5*(sign(dot(self.ubar, n))+1)
if state.mesh.geometric_dimension() == 3:
if V.extruded:
surface_measure = (dS_h + dS_v)
else:
surface_measure = dS
# <w,curl(u) cross ubar + grad( u.ubar)>
# =<curl(u),ubar cross w> - <div(w), u.ubar>
# =<u,curl(ubar cross w)> -
# <<u_upwind, [[n cross(ubar cross w)cross]]>>
both = lambda u: 2*avg(u)
Eqn = (
inner(w, u-self.u0)*dx
+ dt*inner(ustar, curl(cross(self.ubar, w)))*dx
- dt*inner(both(Upwind*ustar),
both(cross(n, cross(self.ubar, w))))*surface_measure
- dt*div(w)*inner(ustar, self.ubar)*dx
)
# define surface measure and terms involving perp differently
# for slice (i.e. if V.extruded is True) and shallow water
# (V.extruded is False)
else:
if V.extruded:
surface_measure = (dS_h + dS_v)
perp = lambda u: as_vector([-u[1], u[0]])
perp_u_upwind = Upwind('+')*perp(ustar('+')) + Upwind('-')*perp(ustar('-'))
else:
surface_measure = dS
outward_normals = CellNormal(state.mesh)
perp = lambda u: cross(outward_normals, u)
perp_u_upwind = Upwind('+')*cross(outward_normals('+'),ustar('+')) + Upwind('-')*cross(outward_normals('-'),ustar('-'))
Eqn = (
(inner(w, u-self.u0)
- dt*inner(w, div(perp(ustar))*perp(self.ubar))
- dt*div(w)*inner(ustar, self.ubar))*dx
- dt*inner(jump(inner(w, perp(self.ubar)), n),
perp_u_upwind)*surface_measure
+ dt*jump(inner(w,
perp(self.ubar))*perp(ustar), n)*surface_measure
)
a = lhs(Eqn)
L = rhs(Eqn)
self.u1 = Function(V)
uproblem = LinearVariationalProblem(a, L, self.u1)
self.usolver = LinearVariationalSolver(uproblem,
options_prefix='EPAdvection')
def solver_CG(mesh, el, space, deg, T, dt=0.001, warm_up=False):
"""Solve the scalar wave equation on a unit square/cube using a
CG FEM formulation with several different element types.
Parameters
----------
mesh: Firedrake.mesh
A utility mesh from the Firedrake package
el: string
The type of element either "tria" or "quad".
`tria` in 3d implies tetrahedra and
`quad` in 3d implies hexahedral elements.
space: string
The space of the FEM. Available options are:
"CG": Continuous Galerkin Finite Elements,
"KMV": Kong-Mulder-Veldhuzien higher-order mass lumped elements
"S" (for Serendipity) (NB: quad/hexs only)
"spectral": spectral elements using GLL quad
points (NB: quads/hexs only)
deg: int
The spatial polynomial degree.
T: float
The simulation duration in simulation seconds.
dt: float, optional
Simulation timestep
warm_up: boolean, optional
Warm up symbolics by running one timestep and shutting down.
Returns
-------
u_n: Firedrake.Function
The solution at time `T`
"""
sd = mesh.geometric_dimension()
V = _build_space(mesh, el, space, deg)
quad_rule1, quad_rule2 = _build_quad_rule(el, V, space)
params = _select_params(space)
# DEBUG
# outfile = fd.File(os.getcwd() + "/results/simple_shots.pvd")
# END DEBUG
tot_dof = COMM_WORLD.allreduce(V.dof_dset.total_size, op=MPI.SUM)
# if COMM_WORLD.rank == 0:
# print("------------------------------------------")
# print("The problem has " + str(tot_dof) + " degrees of freedom.")
# print("------------------------------------------")
nt = int(T / dt) # number of timesteps
u = fd.TrialFunction(V)
v = fd.TestFunction(V)
u_np1 = fd.Function(V) # n+1
u_n = fd.Function(V) # n
u_nm1 = fd.Function(V) # n-1
# constant speed
c = Constant(1.5)
m = (
(1.0 / (c * c))
* (u - 2.0 * u_n + u_nm1)
/ Constant(dt * dt)
* v
* dx(rule=quad_rule1)
) # mass-like matrix
a = dot(grad(u_n), grad(v)) * dx(rule=quad_rule2) # stiffness matrix
# injection of source into mesh
ricker = Constant(0.0)
source = Constant([0.5] * sd)
coords = fd.SpatialCoordinate(mesh)
F = m + a - delta_expr(source, *coords) * ricker * v * dx(rule=quad_rule2)
a, r = fd.lhs(F), fd.rhs(F)
A, R = fd.assemble(a), fd.assemble(r)
solver = fd.LinearSolver(A, solver_parameters=params, options_prefix="")
# timestepping loop
results = []
t = 0.0
for step in range(nt):
with PETSc.Log.Stage("{el}{deg}".format(el=el, deg=deg)):
ricker.assign(RickerWavelet(t, freq=6))
R = fd.assemble(r, tensor=R)
solver.solve(u_np1, R)
snes = _get_time("SNESSolve")
ksp = _get_time("KSPSolve")
pcsetup = _get_time("PCSetUp")
pcapply = _get_time("PCApply")
jac = _get_time("SNESJacobianEval")
residual = _get_time("SNESFunctionEval")
sparsity = _get_time("CreateSparsity")
results.append(
[tot_dof, snes, ksp, pcsetup, pcapply, jac, residual, sparsity]
)
if warm_up:
# Warm up symbolics/disk cache
solver.solve(u_np1, R)
sys.exit("Warming up...")
u_nm1.assign(u_n)
u_n.assign(u_np1)
t = step * float(dt)
# if step % 10 == 0:
# outfile.write(u_n)
# print("Time is " + str(t), flush=True)
results = np.asarray(results)
if mesh.comm.rank == 0:
with open(
"data/scalar_wave.{el}.{deg}.{space}.csv".format(
el=el, deg=deg, space=space
),
"w",
) as f:
np.savetxt(
f,
results,
fmt=["%d"] + ["%e"] * 7,
delimiter=",",
header="tot_dof,SNESSolve,KSPSolve,PCSetUp,PCApply,SNESJacobianEval,SNESFunctionEval,CreateSparsity",
comments="",
)
return u_n
uw = lambda u, v: (s(u)('+') * v('+') + s(u)('-') * v('-'))
if mesh.geometric_dimension() == 2:
perp = lambda u: as_vector([-u[1], u[0]])
p_uw = lambda u, v: perp(uw(u, v))
else:
perp = lambda u: cross(CellNormal(mesh), u)
out_n = CellNormal(mesh)
p_uw = lambda u, v: (s(u)('+') * cross(out_n('+'), v('+')) + s(u)
('-') * cross(out_n('-'), v('-')))
# Initial solve for vorticity for output
eta = TestFunction(W2)
q_ = TrialFunction(W2)
q_eqn = eta * q_ * Dn * dx + inner(perp(grad(eta)), un) * dx - eta * f * dx
q_p = LinearVariationalProblem(lhs(q_eqn), rhs(q_eqn), qn)
q_solver = LinearVariationalSolver(q_p,
solver_parameters={
"ksp_type": "preonly",
"pc_type": "lu"
})
q_solver.solve()
q0.assign(qn)
# Build advection, forcing forms
M_ext = MixedFunctionSpace((W1, W0, W0))
xp_ext = Function(M_ext)
u_, D_, P_ = TrialFunctions(M_ext)
v, psi, chi = TestFunctions(M_ext)
# full weak form
F = F_t + F_a + F_d
# %%
# 4) Solve problem
# -----------------
wave_speed = fd.conditional(fd.lt(np.abs(vnorm), tol), h_E / tol, h_E / vnorm)
# CFL
dt = 0.1 * fd.interpolate(wave_speed, DG1).dat.data.min()
Dt.assign(dt)
outfile = fd.File("plots/adr_dg.pvd")
limiter = fd.VertexBasedLimiter(DG1) # Kuzmin slope limiter
c_ = fd.Function(DG1, name="c")
problem = fd.LinearVariationalProblem(fd.lhs(F), fd.rhs(F), c_, bcs=bc)
solver = fd.LinearVariationalSolver(problem)
# initialize timestep
t = 0.0
it = 0
p = 0
while t < sim_time:
# check dt
dt = np.min([sim_time - t, dt])
if (t + dt > ptimes[p]):
dt = ptimes[p] - t
Dt.assign(dt)
# move next time step
def setup(self, state):
space = state.spaces("HDiv")
super(SawyerEliassenU, self).setup(state, space=space)
u = state.fields("u")
b = state.fields("b")
v = inner(u, as_vector([0., 1., 0.]))
# spaces
V0 = FunctionSpace(state.mesh, "CG", 2)
Vu = u.function_space()
# project b to V0
self.b_v0 = Function(V0)
btri = TrialFunction(V0)
btes = TestFunction(V0)
a = inner(btes, btri) * dx
L = inner(btes, b) * dx
projectbproblem = LinearVariationalProblem(a, L, self.b_v0)
self.project_b_solver = LinearVariationalSolver(
projectbproblem, solver_parameters={'ksp_type': 'cg'})
# project v to V0
self.v_v0 = Function(V0)
vtri = TrialFunction(V0)
vtes = TestFunction(V0)
a = inner(vtes, vtri) * dx
L = inner(vtes, v) * dx
projectvproblem = LinearVariationalProblem(a, L, self.v_v0)
self.project_v_solver = LinearVariationalSolver(
projectvproblem, solver_parameters={'ksp_type': 'cg'})
# stm/psi is a stream function
self.stm = Function(V0)
psi = TrialFunction(V0)
xsi = TestFunction(V0)
f = state.parameters.f
H = state.parameters.H
L = state.parameters.L
dbdy = state.parameters.dbdy
x, y, z = SpatialCoordinate(state.mesh)
bcs = [DirichletBC(V0, 0., "bottom"), DirichletBC(V0, 0., "top")]
Mat = as_matrix([[b.dx(2), 0., -f * self.v_v0.dx(2)], [0., 0., 0.],
[-self.b_v0.dx(0), 0., f**2 + f * self.v_v0.dx(0)]])
Equ = (inner(grad(xsi), dot(Mat, grad(psi))) -
dbdy * inner(grad(xsi), as_vector([-v, 0., f *
(z - H / 2)]))) * dx
# fourth-order terms
if state.parameters.fourthorder:
eps = Constant(0.0001)
brennersigma = Constant(10.0)
n = FacetNormal(state.mesh)
deltax = Constant(state.parameters.deltax)
deltaz = Constant(state.parameters.deltaz)
nn = as_matrix([[sqrt(brennersigma / Constant(deltax)), 0., 0.],
[0., 0., 0.],
[0., 0.,
sqrt(brennersigma / Constant(deltaz))]])
mu = as_matrix([[1., 0., 0.], [0., 0., 0.], [0., 0., H / L]])
# anisotropic form
Equ += eps * (
div(dot(mu, grad(psi))) * div(dot(mu, grad(xsi))) * dx -
(avg(dot(dot(grad(grad(psi)), n), n)) * jump(grad(xsi), n=n) +
avg(dot(dot(grad(grad(xsi)), n), n)) * jump(grad(psi), n=n) -
jump(nn * grad(psi), n=n) * jump(nn * grad(xsi), n=n)) *
(dS_h + dS_v))
Au = lhs(Equ)
Lu = rhs(Equ)
stmproblem = LinearVariationalProblem(Au, Lu, self.stm, bcs=bcs)
self.stream_function_solver = LinearVariationalSolver(
stmproblem, solver_parameters={'ksp_type': 'cg'})
# solve for sawyer_eliassen u
self.u = Function(Vu)
utrial = TrialFunction(Vu)
w = TestFunction(Vu)
a = inner(w, utrial) * dx
L = (w[0] * (-self.stm.dx(2)) + w[2] * (self.stm.dx(0))) * dx
ugproblem = LinearVariationalProblem(a, L, self.u)
self.sawyer_eliassen_u_solver = LinearVariationalSolver(
ugproblem, solver_parameters={'ksp_type': 'cg'})
def _setup_solver(self):
state = self.state # just cutting down line length a bit
dt = state.timestepping.dt
beta = dt*state.timestepping.alpha
cp = state.parameters.cp
mu = state.mu
# Split up the rhs vector (symbolically)
u_in, rho_in, theta_in = split(state.xrhs)
# Build the reduced function space for u,rho
M = MixedFunctionSpace((state.V[0], state.V[1]))
w, phi = TestFunctions(M)
u, rho = TrialFunctions(M)
n = FacetNormal(state.mesh)
# Get background fields
thetabar = state.thetabar
rhobar = state.rhobar
pibar = exner(thetabar, rhobar, state)
pibar_rho = exner_rho(thetabar, rhobar, state)
pibar_theta = exner_theta(thetabar, rhobar, state)
# Analytical (approximate) elimination of theta
k = state.k # Upward pointing unit vector
theta = -dot(k,u)*dot(k,grad(thetabar))*beta + theta_in
# Only include theta' (rather than pi') in the vertical
# component of the gradient
# the pi prime term (here, bars are for mean and no bars are
# for linear perturbations)
pi = pibar_theta*theta + pibar_rho*rho
# vertical projection
def V(u):
return k*inner(u,k)
eqn = (
inner(w, (u - u_in))*dx
- beta*cp*div(theta*V(w))*pibar*dx
# following does nothing but is preserved in the comments
# to remind us why (because V(w) is purely vertical.
# + beta*cp*jump(theta*V(w),n)*avg(pibar)*dS_v
- beta*cp*div(thetabar*w)*pi*dx
+ beta*cp*jump(thetabar*w,n)*avg(pi)*dS_v
+ (phi*(rho - rho_in) - beta*inner(grad(phi), u)*rhobar)*dx
+ beta*jump(phi*u, n)*avg(rhobar)*(dS_v + dS_h)
)
if mu is not None:
eqn += dt*mu*inner(w,k)*inner(u,k)*dx
aeqn = lhs(eqn)
Leqn = rhs(eqn)
# Place to put result of u rho solver
self.urho = Function(M)
# Boundary conditions (assumes extruded mesh)
dim = M.sub(0).ufl_element().value_shape()[0]
bc = ("0.0",)*dim
bcs = [DirichletBC(M.sub(0), Expression(bc), "bottom"),
DirichletBC(M.sub(0), Expression(bc), "top")]
# Solver for u, rho
urho_problem = LinearVariationalProblem(
aeqn, Leqn, self.urho, bcs=bcs)
self.urho_solver = LinearVariationalSolver(urho_problem,
solver_parameters=self.params,
options_prefix='ImplicitSolver')
# Reconstruction of theta
theta = TrialFunction(state.V[2])
gamma = TestFunction(state.V[2])
u, rho = self.urho.split()
self.theta = Function(state.V[2])
theta_eqn = gamma*(theta - theta_in +
dot(k,u)*dot(k,grad(thetabar))*beta)*dx
theta_problem = LinearVariationalProblem(lhs(theta_eqn),
rhs(theta_eqn),
self.theta)
self.theta_solver = LinearVariationalSolver(theta_problem,
options_prefix='thetabacksubstitution')
def _setup_solver(self):
from firedrake.assemble import create_assembly_callable
import numpy as np
state = self.state
dt = state.timestepping.dt
beta = dt*state.timestepping.alpha
cp = state.parameters.cp
mu = state.mu
Vu = state.spaces("HDiv")
Vu_broken = FunctionSpace(state.mesh, BrokenElement(Vu.ufl_element()))
Vtheta = state.spaces("HDiv_v")
Vrho = state.spaces("DG")
h_deg = state.horizontal_degree
v_deg = state.vertical_degree
Vtrace = FunctionSpace(state.mesh, "HDiv Trace", degree=(h_deg, v_deg))
# Split up the rhs vector (symbolically)
u_in, rho_in, theta_in = split(state.xrhs)
# Build the function space for "broken" u and rho
# and add the trace variable
M = MixedFunctionSpace((Vu_broken, Vrho))
w, phi = TestFunctions(M)
u, rho = TrialFunctions(M)
l0 = TrialFunction(Vtrace)
dl = TestFunction(Vtrace)
n = FacetNormal(state.mesh)
# Get background fields
thetabar = state.fields("thetabar")
rhobar = state.fields("rhobar")
pibar = thermodynamics.pi(state.parameters, rhobar, thetabar)
pibar_rho = thermodynamics.pi_rho(state.parameters, rhobar, thetabar)
pibar_theta = thermodynamics.pi_theta(state.parameters, rhobar, thetabar)
# Analytical (approximate) elimination of theta
k = state.k # Upward pointing unit vector
theta = -dot(k, u)*dot(k, grad(thetabar))*beta + theta_in
# Only include theta' (rather than pi') in the vertical
# component of the gradient
# The pi prime term (here, bars are for mean and no bars are
# for linear perturbations)
pi = pibar_theta*theta + pibar_rho*rho
# Vertical projection
def V(u):
return k*inner(u, k)
# Specify degree for some terms as estimated degree is too large
dxp = dx(degree=(self.quadrature_degree))
dS_vp = dS_v(degree=(self.quadrature_degree))
dS_hp = dS_h(degree=(self.quadrature_degree))
ds_vp = ds_v(degree=(self.quadrature_degree))
ds_tbp = ds_t(degree=(self.quadrature_degree)) + ds_b(degree=(self.quadrature_degree))
# Mass matrix for the trace space
tM = assemble(dl('+')*l0('+')*(dS_v + dS_h)
+ dl*l0*ds_v + dl*l0*(ds_t + ds_b))
Lrhobar = Function(Vtrace)
Lpibar = Function(Vtrace)
rhopi_solver = LinearSolver(tM, solver_parameters={'ksp_type': 'cg',
'pc_type': 'bjacobi',
'sub_pc_type': 'ilu'},
options_prefix='rhobarpibar_solver')
rhobar_avg = Function(Vtrace)
pibar_avg = Function(Vtrace)
def _traceRHS(f):
return (dl('+')*avg(f)*(dS_v + dS_h)
+ dl*f*ds_v + dl*f*(ds_t + ds_b))
assemble(_traceRHS(rhobar), tensor=Lrhobar)
assemble(_traceRHS(pibar), tensor=Lpibar)
# Project averages of coefficients into the trace space
with timed_region("Gusto:HybridProjectRhobar"):
rhopi_solver.solve(rhobar_avg, Lrhobar)
with timed_region("Gusto:HybridProjectPibar"):
rhopi_solver.solve(pibar_avg, Lpibar)
# Add effect of density of water upon theta
if self.moisture is not None:
water_t = Function(Vtheta).assign(0.0)
for water in self.moisture:
water_t += self.state.fields(water)
theta_w = theta / (1 + water_t)
thetabar_w = thetabar / (1 + water_t)
else:
theta_w = theta
thetabar_w = thetabar
# "broken" u and rho system
Aeqn = (inner(w, (state.h_project(u) - u_in))*dx
- beta*cp*div(theta_w*V(w))*pibar*dxp
# following does nothing but is preserved in the comments
# to remind us why (because V(w) is purely vertical).
# + beta*cp*dot(theta_w*V(w), n)*pibar_avg('+')*dS_vp
+ beta*cp*dot(theta_w*V(w), n)*pibar_avg('+')*dS_hp
+ beta*cp*dot(theta_w*V(w), n)*pibar_avg*ds_tbp
- beta*cp*div(thetabar_w*w)*pi*dxp
+ (phi*(rho - rho_in) - beta*inner(grad(phi), u)*rhobar)*dx
+ beta*dot(phi*u, n)*rhobar_avg('+')*(dS_v + dS_h))
if mu is not None:
Aeqn += dt*mu*inner(w, k)*inner(u, k)*dx
# Form the mixed operators using Slate
# (A K)(X) = (X_r)
# (K.T 0)(l) (0 )
# where X = ("broken" u, rho)
A = Tensor(lhs(Aeqn))
X_r = Tensor(rhs(Aeqn))
# Off-diagonal block matrices containing the contributions
# of the Lagrange multipliers (surface terms in the momentum equation)
K = Tensor(beta*cp*dot(thetabar_w*w, n)*l0('+')*(dS_vp + dS_hp)
+ beta*cp*dot(thetabar_w*w, n)*l0*ds_vp
+ beta*cp*dot(thetabar_w*w, n)*l0*ds_tbp)
# X = A.inv * (X_r - K * l),
# 0 = K.T * X = -(K.T * A.inv * K) * l + K.T * A.inv * X_r,
# so (K.T * A.inv * K) * l = K.T * A.inv * X_r
# is the reduced equation for the Lagrange multipliers.
# Right-hand side expression: (Forward substitution)
Rexp = K.T * A.inv * X_r
self.R = Function(Vtrace)
# We need to rebuild R everytime data changes
self._assemble_Rexp = create_assembly_callable(Rexp, tensor=self.R)
# Schur complement operator:
Smatexp = K.T * A.inv * K
with timed_region("Gusto:HybridAssembleTraceOp"):
S = assemble(Smatexp)
S.force_evaluation()
# Set up the Linear solver for the system of Lagrange multipliers
self.lSolver = LinearSolver(S, solver_parameters=self.solver_parameters,
options_prefix='lambda_solve')
# Result function for the multiplier solution
self.lambdar = Function(Vtrace)
# Place to put result of u rho reconstruction
self.urho = Function(M)
# Reconstruction of broken u and rho
u_, rho_ = self.urho.split()
# Split operators for two-stage reconstruction
_A = A.blocks
_K = K.blocks
_Xr = X_r.blocks
A00 = _A[0, 0]
A01 = _A[0, 1]
A10 = _A[1, 0]
A11 = _A[1, 1]
K0 = _K[0, 0]
Ru = _Xr[0]
Rrho = _Xr[1]
lambda_vec = AssembledVector(self.lambdar)
# rho reconstruction
Srho = A11 - A10 * A00.inv * A01
rho_expr = Srho.solve(Rrho - A10 * A00.inv * (Ru - K0 * lambda_vec),
decomposition="PartialPivLU")
self._assemble_rho = create_assembly_callable(rho_expr, tensor=rho_)
# "broken" u reconstruction
rho_vec = AssembledVector(rho_)
u_expr = A00.solve(Ru - A01 * rho_vec - K0 * lambda_vec,
decomposition="PartialPivLU")
self._assemble_u = create_assembly_callable(u_expr, tensor=u_)
# Project broken u into the HDiv space using facet averaging.
# Weight function counting the dofs of the HDiv element:
shapes = (Vu.finat_element.space_dimension(), np.prod(Vu.shape))
weight_kernel = """
for (int i=0; i<%d; ++i) {
for (int j=0; j<%d; ++j) {
w[i][j] += 1.0;
}}""" % shapes
self._weight = Function(Vu)
par_loop(weight_kernel, dx, {"w": (self._weight, INC)})
# Averaging kernel
self._average_kernel = """
for (int i=0; i<%d; ++i) {
for (int j=0; j<%d; ++j) {
vec_out[i][j] += vec_in[i][j]/w[i][j];
}}""" % shapes
# HDiv-conforming velocity
self.u_hdiv = Function(Vu)
# Reconstruction of theta
theta = TrialFunction(Vtheta)
gamma = TestFunction(Vtheta)
self.theta = Function(Vtheta)
theta_eqn = gamma*(theta - theta_in +
dot(k, self.u_hdiv)*dot(k, grad(thetabar))*beta)*dx
theta_problem = LinearVariationalProblem(lhs(theta_eqn), rhs(theta_eqn), self.theta)
self.theta_solver = LinearVariationalSolver(theta_problem,
solver_parameters={'ksp_type': 'cg',
'pc_type': 'bjacobi',
'pc_sub_type': 'ilu'},
options_prefix='thetabacksubstitution')
self.bcs = [DirichletBC(Vu, 0.0, "bottom"),
DirichletBC(Vu, 0.0, "top")]
