def op_template_struct(self, u=None):
from hedge.optemplate import Field
if u is None:
u = Field("u")
result = DecayFitDiscontinuitySensorBase\
.op_template_struct(self, u)
from pymbolic.primitives import IfPositive
from hedge.optemplate.primitives import (
CFunction, ScalarParameter)
from math import pi
from hedge.optemplate.primitives import make_common_subexpression as cse
if self.correct_for_fit_error:
decay_expt = cse(result.decay_expt_corrected, "decay_expt")
else:
decay_expt = cse(result.decay_expt, "decay_expt")
def flat_end_sin(x):
return IfPositive(-pi/2-x,
-1, IfPositive(x-pi/2, 1, sin(x)))
sin = CFunction("sin")
isnan = CFunction("isnan")
c_abs = CFunction("abs")
visc_scale = Field("viscosity_scaling")
result.sensor = IfPositive(c_abs(isnan(visc_scale)),
ScalarParameter("max_viscosity_scaling"),
0.5*visc_scale
* (1+flat_end_sin((decay_expt+2)*pi/2)))
return result
def get_mu(self, q, to_quad_op):
"""
:param to_quad_op: If not *None*, represents an operator which transforms
nodal values onto a quadrature grid on which the returned :math:`\mu`
needs to be represented. In that case, *q* is assumed to already be on the
same quadrature grid.
"""
if to_quad_op is None:
def to_quad_op(x):
return x
if self.mu == "sutherland":
# Sutherland's law: !!!not tested!!!
t_s = 110.4
mu_inf = 1.735e-5
result = cse(
mu_inf * self.cse_temperature(q)**1.5 * (1 + t_s) /
(self.cse_temperature(q) + t_s), "sutherland_mu")
else:
result = self.mu
if self.artificial_viscosity_mode == "cns":
mapped_sensor = self.sensor()
else:
mapped_sensor = None
if mapped_sensor is not None:
result = result + cse(to_quad_op(mapped_sensor), "quad_sensor")
return cse(result, "mu")
def make_flux_bc_vector(tag, bc):
if self.fixed_material:
return bc
else:
from hedge.optemplate import BoundarizeOperator
return join_fields(cse(BoundarizeOperator(tag)(epsilon)),
cse(BoundarizeOperator(tag)(mu)), bc)
def get_mu(self, q, to_quad_op):
"""
:param to_quad_op: If not *None*, represents an operator which transforms
nodal values onto a quadrature grid on which the returned :math:`\mu`
needs to be represented. In that case, *q* is assumed to already be on the
same quadrature grid.
"""
if to_quad_op is None:
def to_quad_op(x):
return x
if self.mu == "sutherland":
# Sutherland's law: !!!not tested!!!
t_s = 110.4
mu_inf = 1.735e-5
result = cse(
mu_inf * self.cse_temperature(q) ** 1.5 * (1 + t_s)
/ (self.cse_temperature(q) + t_s),
"sutherland_mu")
else:
result = self.mu
if self.artificial_viscosity_mode == "cns":
mapped_sensor = self.sensor()
else:
mapped_sensor = None
if mapped_sensor is not None:
result = result + cse(to_quad_op(mapped_sensor), "quad_sensor")
return cse(result, "mu")
def make_flux_bc_vector(tag, bc):
if self.fixed_material:
return bc
else:
from hedge.optemplate import BoundarizeOperator
return join_fields(
cse(BoundarizeOperator(tag)(epsilon)),
cse(BoundarizeOperator(tag)(mu)),
bc)
def collision_update(self, f_bar):
from hedge.optemplate.primitives import make_common_subexpression as cse
rho = cse(self.rho(f_bar), "rho")
rho_u = self.rho_u(f_bar)
u = cse(rho_u / rho, "u")
f_eq_func = self.method.f_equilibrium
f_eq = make_obj_array(
[f_eq_func(rho, alpha, u) for alpha in range(len(self.method))])
return f_bar - 1 / (self.tau + 1 / 2) * (f_bar - f_eq)
def collision_update(self, f_bar):
from hedge.optemplate.primitives import make_common_subexpression as cse
rho = cse(self.rho(f_bar), "rho")
rho_u = self.rho_u(f_bar)
u = cse(rho_u/rho, "u")
f_eq_func = self.method.f_equilibrium
f_eq = make_obj_array([
f_eq_func(rho, alpha, u) for alpha in range(len(self.method))])
return f_bar - 1/(self.tau+1/2)*(f_bar - f_eq)
def characteristic_velocity_optemplate(self, state):
from hedge.optemplate.operators import ElementwiseMaxOperator
from hedge.optemplate.primitives import CFunction
sqrt = CFunction("sqrt")
sound_speed = cse(
sqrt(self.equation_of_state.gamma * self.cse_p(state) /
self.cse_rho(state)), "sound_speed")
u = self.cse_u(state)
speed = cse(sqrt(numpy.dot(u, u)), "norm_u") + sound_speed
return ElementwiseMaxOperator()(speed)
def characteristic_velocity_optemplate(self, state):
from hedge.optemplate.operators import ElementwiseMaxOperator
from hedge.optemplate.primitives import CFunction
sqrt = CFunction("sqrt")
sound_speed = cse(sqrt(
self.equation_of_state.gamma*self.cse_p(state)/self.cse_rho(state)),
"sound_speed")
u = self.cse_u(state)
speed = cse(sqrt(numpy.dot(u, u)), "norm_u") + sound_speed
return ElementwiseMaxOperator()(speed)
def inflow_state_inner(self, normal, bc, name):
# see hedge/doc/maxima/euler.mac
return join_fields(
# bc rho
cse(bc.rho0
+ numpy.dot(normal, bc.dumvec)*bc.rho0/(2*bc.c0) + bc.dpm/(2*bc.c0*bc.c0), "bc_rho_"+name),
# bc p
cse(bc.p0
+ bc.c0*bc.rho0*numpy.dot(normal, bc.dumvec)/2 + bc.dpm/2, "bc_p_"+name),
# bc u
cse(bc.u0
+ normal*numpy.dot(normal, bc.dumvec)/2 + bc.dpm*normal/(2*bc.c0*bc.rho0), "bc_u_"+name))
def primitive_to_conservative(self, prims, use_cses=True):
if not use_cses:
from hedge.optemplate.primitives import make_common_subexpression as cse
else:
def cse(x, name):
return x
rho = prims[0]
p = prims[1]
u = prims[2:]
e = self.equation_of_state.p_to_e(p, rho, u)
return join_fields(rho, cse(e, "e"), cse(rho * u, "rho_u"))
def primitive_to_conservative(self, prims, use_cses=True):
if not use_cses:
from hedge.optemplate.primitives import make_common_subexpression as cse
else:
def cse(x, name): return x
rho = prims[0]
p = prims[1]
u = prims[2:]
e = self.equation_of_state.p_to_e(p, rho, u)
return join_fields(
rho,
cse(e, "e"),
cse(rho * u, "rho_u"))
def ic_expr(t, x, fields):
from hedge.optemplate import CFunction
from pymbolic.primitives import IfPositive
from pytools.obj_array import make_obj_array
tanh = CFunction("tanh")
sin = CFunction("sin")
rho = 1
u0 = 0.05
w = 0.05
delta = 0.05
from hedge.optemplate.primitives import make_common_subexpression as cse
u = cse(
make_obj_array([
IfPositive(x[1] - 1 / 2, u0 * tanh(4 * (3 / 4 - x[1]) / w),
u0 * tanh(4 * (x[1] - 1 / 4) / w)),
u0 * delta * sin(2 * np.pi * (x[0] + 1 / 4))
]), "u")
return make_obj_array([
op.method.f_equilibrium(rho, alpha, u)
for alpha in range(len(op.method))
])
def ic_expr(t, x, fields):
from hedge.optemplate import CFunction
from pymbolic.primitives import IfPositive
from pytools.obj_array import make_obj_array
tanh = CFunction("tanh")
sin = CFunction("sin")
rho = 1
u0 = 0.05
w = 0.05
delta = 0.05
from hedge.optemplate.primitives import make_common_subexpression as cse
u = cse(make_obj_array([
IfPositive(x[1]-1/2,
u0*tanh(4*(3/4-x[1])/w),
u0*tanh(4*(x[1]-1/4)/w)),
u0*delta*sin(2*np.pi*(x[0]+1/4))]),
"u")
return make_obj_array([
op.method.f_equilibrium(rho, alpha, u)
for alpha in range(len(op.method))
])
def grad_interior_flux(self, tgt, u):
from hedge.optemplate.primitives import make_common_subexpression as cse
n_times = tgt.normal_times_flux
v_times = tgt.vec_times
return n_times(
cse(u.avg, "u_avg")
- v_times(self.beta(tgt), n_times(u.int-u.ext)))
def inflow_state_inner(self, normal, bc, name):
# see hedge/doc/maxima/euler.mac
return join_fields(
# bc rho
cse(
bc.rho0 + numpy.dot(normal, bc.dumvec) * bc.rho0 /
(2 * bc.c0) + bc.dpm / (2 * bc.c0 * bc.c0), "bc_rho_" + name),
# bc p
cse(
bc.p0 + bc.c0 * bc.rho0 * numpy.dot(normal, bc.dumvec) / 2 +
bc.dpm / 2, "bc_p_" + name),
# bc u
cse(
bc.u0 + normal * numpy.dot(normal, bc.dumvec) / 2 +
bc.dpm * normal / (2 * bc.c0 * bc.rho0), "bc_u_" + name))
def grad_interior_flux(self, tgt, u):
from hedge.optemplate.primitives import make_common_subexpression as cse
n_times = tgt.normal_times_flux
v_times = tgt.vec_times
return n_times(
cse(u.avg, "u_avg") -
v_times(self.beta(tgt), n_times(u.int - u.ext)))
def make_bc_info(self, bc_name, tag, state, state0=None):
"""
:param state0: The boundary 'free-stream' state around which the
BC is linearized.
"""
if state0 is None:
state0 = make_sym_vector(bc_name, self.dimensions+2)
state0 = cse(to_bdry_quad(state0))
rho0 = self.rho(state0)
p0 = self.cse_p(state0)
u0 = self.cse_u(state0)
c0 = (self.equation_of_state.gamma * p0 / rho0)**0.5
from hedge.optemplate import BoundarizeOperator
bdrize_op = BoundarizeOperator(tag)
class SingleBCInfo(Record):
pass
return SingleBCInfo(
rho0=rho0, p0=p0, u0=u0, c0=c0,
# notation: suffix "m" for "minus", i.e. "interior"
drhom=cse(self.rho(cse(to_bdry_quad(bdrize_op(state))))
- rho0, "drhom"),
dumvec=cse(self.cse_u(cse(to_bdry_quad(bdrize_op(state))))
- u0, "dumvec"),
dpm=cse(self.cse_p(cse(to_bdry_quad(bdrize_op(state))))
- p0, "dpm"))
def flux(self, q):
from pytools import delta
return [ # one entry for each flux direction
cse(join_fields(
# flux rho
self.rho_u(q)[i],
# flux E
cse(self.e(q)+self.cse_p(q))*self.cse_u(q)[i],
# flux rho_u
make_obj_array([
self.rho_u(q)[i]*self.cse_u(q)[j]
+ delta(i,j) * self.cse_p(q)
for j in range(self.dimensions)
])
), "%s_flux" % AXES[i])
for i in range(self.dimensions)]
def div(self, tgt, bc_getter, dirichlet_tags, neumann_tags):
"""
:param bc_getter: a function (tag, volume_expr) -> boundary expr.
*volume_expr* will be None to query the Neumann condition.
"""
from hedge.optemplate.primitives import make_common_subexpression as cse
from hedge.flux import FluxVectorPlaceholder, PenaltyTerm
n_times = tgt.normal_times_flux
if tgt.strong_form:
def adjust_flux(f):
return n_times(flux_v.int) - f
else:
def adjust_flux(f):
return f
dim = tgt.dimensions
flux_w = FluxVectorPlaceholder(2 * tgt.dimensions)
flux_v = flux_w[:dim]
pure_diff_v = flux_w[dim:]
flux_args = (list(tgt.int_flux_operand) +
list(IPDGDerivativeGenerator()(tgt.int_flux_operand)))
stab_term_generator = StabilizationTermGenerator(flux_args)
stab_term = (self.stab_coefficient * PenaltyTerm() *
stab_term_generator(tgt.int_flux_operand))
flux = n_times(pure_diff_v.avg - stab_term)
from pytools.obj_array import make_obj_array
flux_arg_int = cse(make_obj_array(stab_term_generator.flux_args))
tgt.add_derivative(cse(tgt.operand))
tgt.add_inner_fluxes(adjust_flux(flux), flux_arg_int)
self.add_div_bcs(tgt, bc_getter, dirichlet_tags, neumann_tags,
stab_term, adjust_flux, flux_v, flux_arg_int,
2 * tgt.dimensions)
def outflow_state(self, state):
from hedge.optemplate import make_normal
normal = make_normal(self.outflow_tag, self.dimensions)
bc = self.make_bc_info("bc_q_out", self.outflow_tag, state)
# see hedge/doc/maxima/euler.mac
return join_fields(
# bc rho
cse(bc.rho0
+ bc.drhom + numpy.dot(normal, bc.dumvec)*bc.rho0/(2*bc.c0)
- bc.dpm/(2*bc.c0*bc.c0), "bc_rho_outflow"),
# bc p
cse(bc.p0
+ bc.c0*bc.rho0*numpy.dot(normal, bc.dumvec)/2 + bc.dpm/2, "bc_p_outflow"),
# bc u
cse(bc.u0
+ bc.dumvec - normal*numpy.dot(normal, bc.dumvec)/2
+ bc.dpm*normal/(2*bc.c0*bc.rho0), "bc_u_outflow"))
def flux(self, q):
from pytools import delta
return [ # one entry for each flux direction
cse(
join_fields(
# flux rho
self.rho_u(q)[i],
# flux E
cse(self.e(q) + self.cse_p(q)) * self.cse_u(q)[i],
# flux rho_u
make_obj_array([
self.rho_u(q)[i] * self.cse_u(q)[j] +
delta(i, j) * self.cse_p(q)
for j in range(self.dimensions)
])),
"%s_flux" % AXES[i]) for i in range(self.dimensions)
]
def subst_func(expr):
from pymbolic.primitives import Subscript, Variable
if isinstance(expr, Subscript):
assert (isinstance(expr.aggregate, Variable)
and expr.aggregate.name == "q")
return cbstate[expr.index]
elif isinstance(expr, Variable) and expr.name == "sensor":
from hedge.optemplate import BoundarizeOperator
result = BoundarizeOperator(tag)(self.sensor())
return cse(to_bdry_quad(result), "bdry_sensor")
def subst_func(expr):
from pymbolic.primitives import Subscript, Variable
if isinstance(expr, Subscript):
assert (isinstance(expr.aggregate, Variable)
and expr.aggregate.name == "q")
return cbstate[expr.index]
elif isinstance(expr, Variable) and expr.name =="sensor":
from hedge.optemplate import BoundarizeOperator
result = BoundarizeOperator(tag)(self.sensor())
return cse(to_bdry_quad(result), "bdry_sensor")
def outflow_state(self, state):
from hedge.optemplate import make_normal
normal = make_normal(self.outflow_tag, self.dimensions)
bc = self.make_bc_info("bc_q_out", self.outflow_tag, state)
# see hedge/doc/maxima/euler.mac
return join_fields(
# bc rho
cse(
bc.rho0 + bc.drhom + numpy.dot(normal, bc.dumvec) * bc.rho0 /
(2 * bc.c0) - bc.dpm / (2 * bc.c0 * bc.c0), "bc_rho_outflow"),
# bc p
cse(
bc.p0 + bc.c0 * bc.rho0 * numpy.dot(normal, bc.dumvec) / 2 +
bc.dpm / 2, "bc_p_outflow"),
# bc u
cse(
bc.u0 + bc.dumvec - normal * numpy.dot(normal, bc.dumvec) / 2 +
bc.dpm * normal / (2 * bc.c0 * bc.rho0), "bc_u_outflow"))
def tau(self, to_quad_op, state, mu=None):
faceq_state = self.faceq_state()
dimensions = self.dimensions
# {{{ compute gradient of u ---------------------------------------
# Use the product rule to compute the gradient of
# u from the gradient of (rho u). This ensures we don't
# compute the derivatives twice.
from pytools.obj_array import with_object_array_or_scalar
dq = with_object_array_or_scalar(to_quad_op, self.grad_of_state())
q = cse(to_quad_op(state))
du = numpy.zeros((dimensions, dimensions), dtype=object)
for i in range(dimensions):
for j in range(dimensions):
du[i, j] = cse(
(dq[i + 2, j] - self.cse_u(q)[i] * dq[0, j]) / self.rho(q),
"du%d_d%s" % (i, AXES[j]))
# }}}
# {{{ put together viscous stress tau -----------------------------
from pytools import delta
if mu is None:
mu = self.get_mu(q, to_quad_op)
tau = numpy.zeros((dimensions, dimensions), dtype=object)
for i in range(dimensions):
for j in range(dimensions):
tau[i, j] = cse(
mu *
cse(du[i, j] + du[j, i] -
2 / self.dimensions * delta(i, j) * numpy.trace(du)),
"tau_%d%d" % (i, j))
return tau
def div(self, tgt, bc_getter, dirichlet_tags, neumann_tags):
"""
:param bc_getter: a function (tag, volume_expr) -> boundary expr.
*volume_expr* will be None to query the Neumann condition.
"""
from hedge.optemplate.primitives import make_common_subexpression as cse
from hedge.flux import FluxVectorPlaceholder, PenaltyTerm
n_times = tgt.normal_times_flux
if tgt.strong_form:
def adjust_flux(f):
return n_times(flux_v.int) - f
else:
def adjust_flux(f):
return f
dim = tgt.dimensions
flux_w = FluxVectorPlaceholder(2*tgt.dimensions)
flux_v = flux_w[:dim]
pure_diff_v = flux_w[dim:]
flux_args = (
list(tgt.int_flux_operand)
+ list(IPDGDerivativeGenerator()(tgt.int_flux_operand)))
stab_term_generator = StabilizationTermGenerator(flux_args)
stab_term = (self.stab_coefficient * PenaltyTerm()
* stab_term_generator(tgt.int_flux_operand))
flux = n_times(pure_diff_v.avg - stab_term)
from pytools.obj_array import make_obj_array
flux_arg_int = cse(make_obj_array(stab_term_generator.flux_args))
tgt.add_derivative(cse(tgt.operand))
tgt.add_inner_fluxes(adjust_flux(flux), flux_arg_int)
self.add_div_bcs(tgt, bc_getter, dirichlet_tags, neumann_tags,
stab_term, adjust_flux, flux_v, flux_arg_int, 2*tgt.dimensions)
def tau(self, to_quad_op, state, mu=None):
faceq_state = self.faceq_state()
dimensions = self.dimensions
# {{{ compute gradient of u ---------------------------------------
# Use the product rule to compute the gradient of
# u from the gradient of (rho u). This ensures we don't
# compute the derivatives twice.
from pytools.obj_array import with_object_array_or_scalar
dq = with_object_array_or_scalar(
to_quad_op, self.grad_of_state())
q = cse(to_quad_op(state))
du = numpy.zeros((dimensions, dimensions), dtype=object)
for i in range(dimensions):
for j in range(dimensions):
du[i,j] = cse(
(dq[i+2,j] - self.cse_u(q)[i] * dq[0,j]) / self.rho(q),
"du%d_d%s" % (i, AXES[j]))
# }}}
# {{{ put together viscous stress tau -----------------------------
from pytools import delta
if mu is None:
mu = self.get_mu(q, to_quad_op)
tau = numpy.zeros((dimensions, dimensions), dtype=object)
for i in range(dimensions):
for j in range(dimensions):
tau[i,j] = cse(mu * cse(du[i,j] + du[j,i] -
2/self.dimensions * delta(i,j) * numpy.trace(du)),
"tau_%d%d" % (i, j))
return tau
def absorbing_bc(self, w=None):
"""Construct part of the flux operator template for 1st order
absorbing boundary conditions.
"""
from hedge.optemplate import normal
absorb_normal = normal(self.absorb_tag, self.dimensions)
from hedge.optemplate import BoundarizeOperator, Field
from hedge.tools import join_fields
e, h = self.split_eh(self.field_placeholder(w))
if self.fixed_material:
epsilon = self.epsilon
mu = self.mu
else:
epsilon = cse(
BoundarizeOperator(self.absorb_tag)(Field("epsilon")))
mu = cse(BoundarizeOperator(self.absorb_tag)(Field("mu")))
absorb_Z = (mu / epsilon)**0.5
absorb_Y = 1 / absorb_Z
absorb_e = BoundarizeOperator(self.absorb_tag)(e)
absorb_h = BoundarizeOperator(self.absorb_tag)(h)
bc = join_fields(
absorb_e + 1 / 2 *
(self.space_cross_h(absorb_normal,
self.space_cross_e(absorb_normal, absorb_e)) -
absorb_Z * self.space_cross_h(absorb_normal, absorb_h)),
absorb_h + 1 / 2 *
(self.space_cross_e(absorb_normal,
self.space_cross_h(absorb_normal, absorb_h)) +
absorb_Y * self.space_cross_e(absorb_normal, absorb_e)))
return bc
def absorbing_bc(self, w=None):
"""Construct part of the flux operator template for 1st order
absorbing boundary conditions.
"""
from hedge.optemplate import normal
absorb_normal = normal(self.absorb_tag, self.dimensions)
from hedge.optemplate import BoundarizeOperator, Field
from hedge.tools import join_fields
e, h = self.split_eh(self.field_placeholder(w))
if self.fixed_material:
epsilon = self.epsilon
mu = self.mu
else:
epsilon = cse(
BoundarizeOperator(self.absorb_tag)(Field("epsilon")))
mu = cse(
BoundarizeOperator(self.absorb_tag)(Field("mu")))
absorb_Z = (mu/epsilon)**0.5
absorb_Y = 1/absorb_Z
absorb_e = BoundarizeOperator(self.absorb_tag)(e)
absorb_h = BoundarizeOperator(self.absorb_tag)(h)
bc = join_fields(
absorb_e + 1/2*(self.space_cross_h(absorb_normal, self.space_cross_e(
absorb_normal, absorb_e))
- absorb_Z*self.space_cross_h(absorb_normal, absorb_h)),
absorb_h + 1/2*(
self.space_cross_e(absorb_normal, self.space_cross_h(
absorb_normal, absorb_h))
+ absorb_Y*self.space_cross_e(absorb_normal, absorb_e)))
return bc
def op_template(self, u=None):
from pymbolic.primitives import IfPositive, Variable
from hedge.optemplate.primitives import Field, ScalarParameter
from hedge.optemplate.primitives import make_common_subexpression as cse
from math import pi
if u is None:
u = Field("u")
from hedge.optemplate.operators import (
MassOperator, FilterOperator, OnesOperator)
mode_truncator = FilterOperator(
persson_peraire_filter_response_function)
truncated_u = mode_truncator(u)
diff = u - truncated_u
el_norm_squared_mass_diff_u = OnesOperator()(MassOperator()(diff)*diff)
el_norm_squared_mass_u = OnesOperator()(MassOperator()(u)*u)
capital_s_e = cse(el_norm_squared_mass_diff_u / el_norm_squared_mass_u,
"S_e")
sin = Variable("sin")
log10 = Variable("log10")
s_e = cse(log10(capital_s_e), "s_e")
kappa = ScalarParameter("kappa")
eps0 = ScalarParameter("eps0")
s_0 = ScalarParameter("s_0")
return IfPositive(s_0-self.kappa-s_e,
0,
IfPositive(s_e-self.kappa-s_0,
eps0,
eps0/2*(1+sin(pi*(s_e-s_0)/self.kappa))))
def test_quadrature_tri_mass_mat_monomial():
"""Check that quadrature integration on triangles is exact as designed."""
from hedge.mesh.generator import make_square_mesh
mesh = make_square_mesh(a=-1, b=1, max_area=4 * 1 / 8 + 0.001)
order = 4
discr = discr_class(
mesh, order=order, debug=discr_class.noninteractive_debug_flags(), quad_min_degrees={"quad": 3 * order}
)
m, n = 2, 1
f = Monomial((m, n))
f_vec = discr.interpolate_volume_function(lambda x, el: f(x))
from hedge.discretization import ones_on_volume
ones = ones_on_volume(discr)
if False:
from hedge.visualization import SiloVisualizer
vis = SiloVisualizer(discr)
visf = vis.make_file("test")
vis.add_data(visf, [("f", f_vec * f_vec)])
visf.close()
from hedge.optemplate import MassOperator, Field, QuadratureGridUpsampler
f_fld = Field("f")
mass_op = discr.compile(MassOperator()(f_fld * f_fld))
from hedge.optemplate.primitives import make_common_subexpression as cse
f_upsamp = cse(QuadratureGridUpsampler("quad")(f_fld))
quad_mass_op = discr.compile(MassOperator()(f_upsamp * f_upsamp))
num_integral_1 = numpy.dot(ones, mass_op(f=f_vec))
num_integral_2 = numpy.dot(ones, quad_mass_op(f=f_vec))
true_integral = 4 / ((2 * m + 1) * (2 * n + 1))
err_1 = abs(num_integral_1 - true_integral)
err_2 = abs(num_integral_2 - true_integral)
print num_integral_1, num_integral_2, true_integral
print err_1, err_2
assert err_1 > 1e-8
assert err_2 < 1e-14
def test_quadrature_tri_mass_mat_monomial():
"""Check that quadrature integration on triangles is exact as designed."""
from hedge.mesh.generator import make_square_mesh
mesh = make_square_mesh(a=-1, b=1, max_area=4 * 1 / 8 + 0.001)
order = 4
discr = discr_class(mesh,
order=order,
debug=discr_class.noninteractive_debug_flags(),
quad_min_degrees={"quad": 3 * order})
m, n = 2, 1
f = Monomial((m, n))
f_vec = discr.interpolate_volume_function(lambda x, el: f(x))
from hedge.discretization import ones_on_volume
ones = ones_on_volume(discr)
if False:
from hedge.visualization import SiloVisualizer
vis = SiloVisualizer(discr)
visf = vis.make_file("test")
vis.add_data(visf, [("f", f_vec * f_vec)])
visf.close()
from hedge.optemplate import (MassOperator, Field, QuadratureGridUpsampler)
f_fld = Field("f")
mass_op = discr.compile(MassOperator()(f_fld * f_fld))
from hedge.optemplate.primitives import make_common_subexpression as cse
f_upsamp = cse(QuadratureGridUpsampler("quad")(f_fld))
quad_mass_op = discr.compile(MassOperator()(f_upsamp * f_upsamp))
num_integral_1 = numpy.dot(ones, mass_op(f=f_vec))
num_integral_2 = numpy.dot(ones, quad_mass_op(f=f_vec))
true_integral = 4 / ((2 * m + 1) * (2 * n + 1))
err_1 = abs(num_integral_1 - true_integral)
err_2 = abs(num_integral_2 - true_integral)
print num_integral_1, num_integral_2, true_integral
print err_1, err_2
assert err_1 > 1e-8
assert err_2 < 1e-14
def incident_bc(self, w=None):
"Flux terms for incident boundary conditions"
# NOTE: Untested for inhomogeneous materials, but would usually be
# physically meaningless anyway (are there exceptions to this?)
e, h = self.split_eh(self.field_placeholder(w))
if not self.fixed_material:
from warnings import warn
if self.incident_tag != hedge.mesh.TAG_NONE:
warn("Incident boundary conditions assume homogeneous"
" background material, results may be unphysical")
from hedge.tools import count_subset
fld_cnt = count_subset(self.get_eh_subset())
from hedge.tools import is_zero
incident_bc_data = self.incident_bc_data(self, e, h)
if is_zero(incident_bc_data):
return make_obj_array([0] * fld_cnt)
else:
return cse(-incident_bc_data)
def incident_bc(self, w=None):
"Flux terms for incident boundary conditions"
# NOTE: Untested for inhomogeneous materials, but would usually be
# physically meaningless anyway (are there exceptions to this?)
e, h = self.split_eh(self.field_placeholder(w))
if not self.fixed_material:
from warnings import warn
if self.incident_tag != hedge.mesh.TAG_NONE:
warn("Incident boundary conditions assume homogeneous"
" background material, results may be unphysical")
from hedge.tools import count_subset
fld_cnt = count_subset(self.get_eh_subset())
from hedge.tools import is_zero
incident_bc_data = self.incident_bc_data(self, e, h)
if is_zero(incident_bc_data):
return make_obj_array([0]*fld_cnt)
else:
return cse(-incident_bc_data)
def make_bc_info(self, bc_name, tag, state, state0=None):
"""
:param state0: The boundary 'free-stream' state around which the
BC is linearized.
"""
if state0 is None:
state0 = make_sym_vector(bc_name, self.dimensions + 2)
state0 = cse(to_bdry_quad(state0))
rho0 = self.rho(state0)
p0 = self.cse_p(state0)
u0 = self.cse_u(state0)
c0 = (self.equation_of_state.gamma * p0 / rho0)**0.5
from hedge.optemplate import BoundarizeOperator
bdrize_op = BoundarizeOperator(tag)
class SingleBCInfo(Record):
pass
return SingleBCInfo(
rho0=rho0,
p0=p0,
u0=u0,
c0=c0,
# notation: suffix "m" for "minus", i.e. "interior"
drhom=cse(
self.rho(cse(to_bdry_quad(bdrize_op(state)))) - rho0, "drhom"),
dumvec=cse(
self.cse_u(cse(to_bdry_quad(bdrize_op(state)))) - u0,
"dumvec"),
dpm=cse(
self.cse_p(cse(to_bdry_quad(bdrize_op(state)))) - p0, "dpm"))
def grad_of_state_func(self, func, of_what_descr):
return cse(
self.grad_of(func(self.volq_state()), func(self.faceq_state())),
"grad_" + of_what_descr)
def op_template(self, sensor_scaling=None, viscosity_only=False):
u = self.cse_u
rho = self.cse_rho
rho_u = self.rho_u
p = self.p
e = self.e
# {{{ artificial diffusion
def make_artificial_diffusion():
if self.artificial_viscosity_mode not in ["diffusion"]:
return 0
dq = self.grad_of_state()
return make_obj_array([
self.div(
to_vol_quad(self.sensor()) * to_vol_quad(dq[i]),
to_int_face_quad(self.sensor()) * to_int_face_quad(dq[i]))
for i in range(dq.shape[0])
])
# }}}
# {{{ state setup
volq_flux = self.flux(self.volq_state())
faceq_flux = self.flux(self.faceq_state())
from hedge.optemplate.primitives import CFunction
sqrt = CFunction("sqrt")
speed = self.characteristic_velocity_optemplate(self.state())
has_viscosity = not is_zero(self.get_mu(self.state(), to_quad_op=None))
# }}}
# {{{ operator assembly -----------------------------------------------
from hedge.flux.tools import make_lax_friedrichs_flux
from hedge.optemplate.operators import InverseMassOperator
from hedge.optemplate.tools import make_stiffness_t
primitive_bcs_as_quad_conservative = dict(
(tag, self.primitive_to_conservative(to_bdry_quad(bc))) for tag, bc
in self.get_primitive_boundary_conditions().iteritems())
def get_bc_tuple(tag):
state = self.state()
bc = make_obj_array(
[self.get_boundary_condition_for(tag, s_i) for s_i in state])
return tag, bc, self.flux(bc)
first_order_part = InverseMassOperator()(
numpy.dot(make_stiffness_t(self.dimensions), volq_flux) -
make_lax_friedrichs_flux(
wave_speed=cse(to_int_face_quad(speed), "emax_c"),
state=self.faceq_state(),
fluxes=faceq_flux,
bdry_tags_states_and_fluxes=[
get_bc_tuple(tag) for tag in self.get_boundary_tags()
],
strong=False))
if viscosity_only:
first_order_part = 0 * first_order_part
result = join_fields(
first_order_part + self.make_second_order_part() +
make_artificial_diffusion() + self.make_extra_terms(), speed)
if self.source is not None:
result = result + join_fields(
make_sym_vector("source_vect", len(self.state())),
# extra field for speed
0)
return result
def __init__(self, **kwargs):
from hedge.optemplate.primitives import make_common_subexpression as cse
Record.__init__(self, dict((name, cse(expr, name))
for name, expr in kwargs.iteritems()))
def grad_of_state_func(self, func, of_what_descr):
return cse(self.grad_of(
func(self.volq_state()),
func(self.faceq_state())),
"grad_"+of_what_descr)
def faceq_state(self):
return cse(to_int_face_quad(self.state()), "face_quad_state")
def op_template(self, sensor_scaling=None, viscosity_only=False):
u = self.cse_u
rho = self.cse_rho
rho_u = self.rho_u
p = self.p
e = self.e
# {{{ artificial diffusion
def make_artificial_diffusion():
if self.artificial_viscosity_mode not in ["diffusion"]:
return 0
dq = self.grad_of_state()
return make_obj_array([
self.div(
to_vol_quad(self.sensor())*to_vol_quad(dq[i]),
to_int_face_quad(self.sensor())*to_int_face_quad(dq[i]))
for i in range(dq.shape[0])])
# }}}
# {{{ state setup
volq_flux = self.flux(self.volq_state())
faceq_flux = self.flux(self.faceq_state())
from hedge.optemplate.primitives import CFunction
sqrt = CFunction("sqrt")
speed = self.characteristic_velocity_optemplate(self.state())
has_viscosity = not is_zero(self.get_mu(self.state(), to_quad_op=None))
# }}}
# {{{ operator assembly -----------------------------------------------
from hedge.flux.tools import make_lax_friedrichs_flux
from hedge.optemplate.operators import InverseMassOperator
from hedge.optemplate.tools import make_stiffness_t
primitive_bcs_as_quad_conservative = dict(
(tag, self.primitive_to_conservative(to_bdry_quad(bc)))
for tag, bc in
self.get_primitive_boundary_conditions().iteritems())
def get_bc_tuple(tag):
state = self.state()
bc = make_obj_array([
self.get_boundary_condition_for(tag, s_i) for s_i in state])
return tag, bc, self.flux(bc)
first_order_part = InverseMassOperator()(
numpy.dot(make_stiffness_t(self.dimensions), volq_flux)
- make_lax_friedrichs_flux(
wave_speed=cse(to_int_face_quad(speed), "emax_c"),
state=self.faceq_state(), fluxes=faceq_flux,
bdry_tags_states_and_fluxes=[
get_bc_tuple(tag) for tag in self.get_boundary_tags()],
strong=False))
if viscosity_only:
first_order_part = 0*first_order_part
result = join_fields(
first_order_part
+ self.make_second_order_part()
+ make_artificial_diffusion()
+ self.make_extra_terms(),
speed)
if self.source is not None:
result = result + join_fields(
make_sym_vector("source_vect", len(self.state())),
# extra field for speed
0)
return result
def cse_rho_u(self, q):
return cse(self.rho_u(q), "rho_u")
def cse_p(self, q):
return cse(self.p(q), "p")
def cse_rho_u(self, q):
return cse(self.rho_u(q), "rho_u")
def cse_rho(self, q):
return cse(self.rho(q), "rho")
def cse_u(self, q):
return cse(self.u(q), "u")
def cse_temperature(self, q):
return cse(self.temperature(q), "temperature")
def minv_all(self):
from hedge.optemplate.primitives import make_common_subexpression as cse
from hedge.optemplate.operators import InverseMassOperator
return (
cse(InverseMassOperator()(self.local_derivatives), "grad_loc") +
cse(InverseMassOperator()(self.fluxes), "grad_flux"))
def cse_p(self, q):
return cse(self.p(q), "p")
def op_template_struct(self, u, with_baseline=True):
from hedge.optemplate.operators import (
MassOperator, OnesOperator, InverseVandermondeOperator,
InverseMassOperator)
from hedge.optemplate.primitives import Field
from hedge.optemplate.tools import get_flux_operator
from hedge.optemplate.primitives import make_common_subexpression as cse
from hedge.optemplate.primitives import CFunction
from pymbolic.primitives import Variable
if u is None:
u = Field("u")
from hedge.flux import (
FluxScalarPlaceholder, ElementOrder,
ElementJacobian, FaceJacobian, flux_abs)
log, exp, sqrt = CFunction("log"), CFunction("exp"), CFunction("sqrt")
if False:
# On the whole, this should scale like u.
# Columns of lift scale like 1/N^2, compensate for that.
# Further compensate for all geometric factors.
u_flux = FluxScalarPlaceholder(0)
jump_part = InverseMassOperator()(
get_flux_operator(
ElementJacobian()/(ElementOrder()**2 * FaceJacobian())
*flux_abs(u_flux.ext - u_flux.int))(u))
baseline_squared = Field("baseline_squared")
el_norm_u_squared = cse(
OnesOperator()(MassOperator()(u)*u),
"l2_norm_u")
indicator_modal_coeffs = cse(
InverseVandermondeOperator()(u),
#InverseChebyshevVandermondeOperator()(u),
"u_modes")
indicator_modal_coeffs_squared = indicator_modal_coeffs**2
if self.mode_processor is not None:
indicator_modal_coeffs_squared = \
Variable("mode_processor")(indicator_modal_coeffs_squared)
log_modal_coeffs = cse(
log(indicator_modal_coeffs_squared
+ baseline_squared*el_norm_u_squared
)/2,
"log_modal_coeffs")
if False:
modal_coeffs_jump = cse(
InverseVandermondeOperator()(jump_part),
"jump_modes")
log_modal_coeffs_jump = cse(
log(modal_coeffs_jump**2)/2,
"lmc_jump")
# fit to c * n**s
expt_op = DecayExponentOperator(
self.ignored_modes, self.weight_mode)
log_const_op = LogDecayConstantOperator(
self.ignored_modes, self.weight_mode)
mode_weights = Field("mode_weights")
weighted_log_modal_coeffs = mode_weights*log_modal_coeffs
s = cse(expt_op(weighted_log_modal_coeffs),
"first_decay_expt")
log_c = cse(log_const_op(weighted_log_modal_coeffs),
"first_decay_coeff")
c = exp(log_const_op(weighted_log_modal_coeffs))
log_mode_numbers = Field("log_mode_numbers")
estimated_log_modal_coeffs = cse(
log_c + s*log_mode_numbers,
"estimated_log_modal_coeffs")
estimate_error = cse(
sqrt((estimated_log_modal_coeffs-weighted_log_modal_coeffs)**2),
"estimate_error")
log_modal_coeffs_corrected = log_modal_coeffs + estimate_error
s_corrected = expt_op(mode_weights*log_modal_coeffs_corrected)
return DecayInformation(
indicator_modal_coeffs=indicator_modal_coeffs,
decay_expt=s, c=c,
log_modal_coeffs=log_modal_coeffs,
weighted_log_modal_coeffs=weighted_log_modal_coeffs,
estimated_log_modal_coeffs=estimated_log_modal_coeffs,
decay_expt_corrected=s_corrected,
)
def cse_temperature(self, q):
return cse(self.temperature(q), "temperature")
def volq_state(self):
return cse(to_vol_quad(self.state()), "vol_quad_state")
def op_template(self, with_sensor=False):
# {{{ operator preliminaries ------------------------------------------
from hedge.optemplate import (Field, BoundaryPair, get_flux_operator,
make_stiffness_t, InverseMassOperator,
make_sym_vector, ElementwiseMaxOperator,
BoundarizeOperator)
from hedge.optemplate.primitives import make_common_subexpression as cse
from hedge.optemplate.operators import (
QuadratureGridUpsampler, QuadratureInteriorFacesGridUpsampler)
to_quad = QuadratureGridUpsampler("quad")
to_if_quad = QuadratureInteriorFacesGridUpsampler("quad")
from hedge.tools import join_fields, \
ptwise_dot
u = Field("u")
v = make_sym_vector("v", self.dimensions)
c = ElementwiseMaxOperator()(ptwise_dot(1, 1, v, v))
quad_u = cse(to_quad(u))
quad_v = cse(to_quad(v))
w = join_fields(u, v, c)
quad_face_w = to_if_quad(w)
# }}}
# {{{ boundary conditions ---------------------------------------------
from hedge.mesh import TAG_ALL
bc_c = to_quad(BoundarizeOperator(TAG_ALL)(c))
bc_u = to_quad(Field("bc_u"))
bc_v = to_quad(BoundarizeOperator(TAG_ALL)(v))
if self.bc_u_f is "None":
bc_w = join_fields(0, bc_v, bc_c)
else:
bc_w = join_fields(bc_u, bc_v, bc_c)
minv_st = make_stiffness_t(self.dimensions)
m_inv = InverseMassOperator()
flux_op = get_flux_operator(self.flux())
# }}}
# {{{ diffusion -------------------------------------------------------
if with_sensor or (self.diffusion_coeff is not None
and self.diffusion_coeff != 0):
if self.diffusion_coeff is None:
diffusion_coeff = 0
else:
diffusion_coeff = self.diffusion_coeff
if with_sensor:
diffusion_coeff += Field("sensor")
from hedge.second_order import SecondDerivativeTarget
# strong_form here allows IPDG to reuse the value of grad u.
grad_tgt = SecondDerivativeTarget(self.dimensions,
strong_form=True,
operand=u)
self.diffusion_scheme.grad(grad_tgt,
bc_getter=None,
dirichlet_tags=[],
neumann_tags=[])
div_tgt = SecondDerivativeTarget(self.dimensions,
strong_form=False,
operand=diffusion_coeff *
grad_tgt.minv_all)
self.diffusion_scheme.div(div_tgt,
bc_getter=None,
dirichlet_tags=[],
neumann_tags=[])
diffusion_part = div_tgt.minv_all
else:
diffusion_part = 0
# }}}
to_quad = QuadratureGridUpsampler("quad")
quad_u = cse(to_quad(u))
quad_v = cse(to_quad(v))
return m_inv(numpy.dot(minv_st, cse(quad_v*quad_u))
- (flux_op(quad_face_w)
+ flux_op(BoundaryPair(quad_face_w, bc_w, TAG_ALL)))) \
+ diffusion_part
def grad_interior_flux(self, tgt, u):
from hedge.optemplate.primitives import make_common_subexpression as cse
n_times = tgt.normal_times_flux
return n_times(cse(u.avg, "u_avg"))
def op_template(self, with_sensor=False):
# {{{ operator preliminaries ------------------------------------------
from hedge.optemplate import (Field, BoundaryPair, get_flux_operator,
make_stiffness_t, InverseMassOperator, make_sym_vector,
ElementwiseMaxOperator, BoundarizeOperator)
from hedge.optemplate.primitives import make_common_subexpression as cse
from hedge.optemplate.operators import (
QuadratureGridUpsampler,
QuadratureInteriorFacesGridUpsampler)
to_quad = QuadratureGridUpsampler("quad")
to_if_quad = QuadratureInteriorFacesGridUpsampler("quad")
from hedge.tools import join_fields, \
ptwise_dot
u = Field("u")
v = make_sym_vector("v", self.dimensions)
c = ElementwiseMaxOperator()(ptwise_dot(1, 1, v, v))
quad_u = cse(to_quad(u))
quad_v = cse(to_quad(v))
w = join_fields(u, v, c)
quad_face_w = to_if_quad(w)
# }}}
# {{{ boundary conditions ---------------------------------------------
from hedge.mesh import TAG_ALL
bc_c = to_quad(BoundarizeOperator(TAG_ALL)(c))
bc_u = to_quad(Field("bc_u"))
bc_v = to_quad(BoundarizeOperator(TAG_ALL)(v))
if self.bc_u_f is "None":
bc_w = join_fields(0, bc_v, bc_c)
else:
bc_w = join_fields(bc_u, bc_v, bc_c)
minv_st = make_stiffness_t(self.dimensions)
m_inv = InverseMassOperator()
flux_op = get_flux_operator(self.flux())
# }}}
# {{{ diffusion -------------------------------------------------------
if with_sensor or (
self.diffusion_coeff is not None and self.diffusion_coeff != 0):
if self.diffusion_coeff is None:
diffusion_coeff = 0
else:
diffusion_coeff = self.diffusion_coeff
if with_sensor:
diffusion_coeff += Field("sensor")
from hedge.second_order import SecondDerivativeTarget
# strong_form here allows IPDG to reuse the value of grad u.
grad_tgt = SecondDerivativeTarget(
self.dimensions, strong_form=True,
operand=u)
self.diffusion_scheme.grad(grad_tgt, bc_getter=None,
dirichlet_tags=[], neumann_tags=[])
div_tgt = SecondDerivativeTarget(
self.dimensions, strong_form=False,
operand=diffusion_coeff*grad_tgt.minv_all)
self.diffusion_scheme.div(div_tgt,
bc_getter=None,
dirichlet_tags=[], neumann_tags=[])
diffusion_part = div_tgt.minv_all
else:
diffusion_part = 0
# }}}
to_quad = QuadratureGridUpsampler("quad")
quad_u = cse(to_quad(u))
quad_v = cse(to_quad(v))
return m_inv(numpy.dot(minv_st, cse(quad_v*quad_u))
- (flux_op(quad_face_w)
+ flux_op(BoundaryPair(quad_face_w, bc_w, TAG_ALL)))) \
+ diffusion_part
def cse_rho(self, q):
return cse(self.rho(q), "rho")
