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 op_template(self, with_sensor): from hedge.optemplate import (Field, make_stiffness_t, make_nabla, InverseMassOperator, ElementwiseMaxOperator, get_flux_operator) from hedge.optemplate.operators import ( QuadratureGridUpsampler, QuadratureInteriorFacesGridUpsampler) to_quad = QuadratureGridUpsampler("quad") to_if_quad = QuadratureInteriorFacesGridUpsampler("quad") u = Field("u") u0 = Field("u0") # boundary conditions ------------------------------------------------- minv_st = make_stiffness_t(self.dimensions) nabla = make_nabla(self.dimensions) m_inv = InverseMassOperator() def flux(u): return u**2 / 2 #return u0*u emax_u = self.characteristic_velocity_optemplate(u) from hedge.flux.tools import make_lax_friedrichs_flux from pytools.obj_array import make_obj_array num_flux = make_lax_friedrichs_flux( #u0, to_if_quad(emax_u), make_obj_array([to_if_quad(u)]), [make_obj_array([flux(to_if_quad(u))])], [], strong=False)[0] from hedge.second_order import SecondDerivativeTarget if self.viscosity is not None or with_sensor: viscosity_coeff = 0 if with_sensor: viscosity_coeff += Field("sensor") if isinstance(self.viscosity, float): viscosity_coeff += self.viscosity elif self.viscosity is None: pass else: raise TypeError("unsupported type of viscosity coefficient") # strong_form here allows IPDG to reuse the value of grad u. grad_tgt = SecondDerivativeTarget(self.dimensions, strong_form=True, operand=u) self.viscosity_scheme.grad(grad_tgt, bc_getter=None, dirichlet_tags=[], neumann_tags=[]) div_tgt = SecondDerivativeTarget(self.dimensions, strong_form=False, operand=viscosity_coeff * grad_tgt.minv_all) self.viscosity_scheme.div(div_tgt, bc_getter=None, dirichlet_tags=[], neumann_tags=[]) viscosity_bit = div_tgt.minv_all else: viscosity_bit = 0 return m_inv((minv_st[0](flux(to_quad(u)))) - num_flux) \ + viscosity_bit
def op_template(self, with_sensor): from hedge.optemplate import ( Field, make_stiffness_t, make_nabla, InverseMassOperator, ElementwiseMaxOperator, get_flux_operator) from hedge.optemplate.operators import ( QuadratureGridUpsampler, QuadratureInteriorFacesGridUpsampler) to_quad = QuadratureGridUpsampler("quad") to_if_quad = QuadratureInteriorFacesGridUpsampler("quad") u = Field("u") u0 = Field("u0") # boundary conditions ------------------------------------------------- minv_st = make_stiffness_t(self.dimensions) nabla = make_nabla(self.dimensions) m_inv = InverseMassOperator() def flux(u): return u**2/2 #return u0*u emax_u = self.characteristic_velocity_optemplate(u) from hedge.flux.tools import make_lax_friedrichs_flux from pytools.obj_array import make_obj_array num_flux = make_lax_friedrichs_flux( #u0, to_if_quad(emax_u), make_obj_array([to_if_quad(u)]), [make_obj_array([flux(to_if_quad(u))])], [], strong=False)[0] from hedge.second_order import SecondDerivativeTarget if self.viscosity is not None or with_sensor: viscosity_coeff = 0 if with_sensor: viscosity_coeff += Field("sensor") if isinstance(self.viscosity, float): viscosity_coeff += self.viscosity elif self.viscosity is None: pass else: raise TypeError("unsupported type of viscosity coefficient") # strong_form here allows IPDG to reuse the value of grad u. grad_tgt = SecondDerivativeTarget( self.dimensions, strong_form=True, operand=u) self.viscosity_scheme.grad(grad_tgt, bc_getter=None, dirichlet_tags=[], neumann_tags=[]) div_tgt = SecondDerivativeTarget( self.dimensions, strong_form=False, operand=viscosity_coeff*grad_tgt.minv_all) self.viscosity_scheme.div(div_tgt, bc_getter=None, dirichlet_tags=[], neumann_tags=[]) viscosity_bit = div_tgt.minv_all else: viscosity_bit = 0 return m_inv((minv_st[0](flux(to_quad(u)))) - num_flux) \ + viscosity_bit
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