def compute_stress_strain_u( pb, integral, region, material, vu, data ):
var = pb.create_variables([vu])[vu]
var.data_from_any(data)
stress = pb.evaluate('de_cauchy_stress.%s.%s( %s, %s )'
% (integral, region, material, vu), verbose=False,
mode='el_avg', **{vu : var})
strain = pb.evaluate('de_cauchy_strain.%s.%s( %s )'
% (integral, region, vu), verbose=False,
mode='el_avg', **{vu : var})
return extend_cell_data( stress, pb.domain, region ), \
extend_cell_data( strain, pb.domain, region )
def compute_mac_stress_part(pb, integral, region, material, vu, mac_strain):
avgmat = pb.evaluate('ev_integrate_mat.%s.%s( %s, %s )' \
% (integral, region, material, vu), verbose=False,
mode='el_avg')
return extend_cell_data(nm.dot(avgmat, mac_strain), pb.domain, region)
def compute_mac_stress_part( pb, integral, region, material, vu, mac_strain ):
avgmat = pb.evaluate('di_integrate_mat.%s.%s( %s, %s )' \
% (integral, region, material, vu), verbose=False,
mode='el_avg')
return extend_cell_data( nm.dot( avgmat, mac_strain ), pb.domain, region )
def compute_stress_strain_u(pb, integral, region, material, vu, data):
var = pb.create_variables([vu])[vu]
var.set_data(data)
stress = pb.evaluate('ev_cauchy_stress.%s.%s( %s, %s )' %
(integral, region, material, vu),
verbose=False,
mode='el_avg',
**{vu: var})
strain = pb.evaluate('ev_cauchy_strain.%s.%s( %s )' %
(integral, region, vu),
verbose=False,
mode='el_avg',
**{vu: var})
return extend_cell_data( stress, pb.domain, region ), \
extend_cell_data( strain, pb.domain, region )
def add_stress_p( out, pb, integral, region, vp, data ):
var = pb.create_variables([vp])[vp]
var.data_from_any(data)
press0 = pb.evaluate('de_average_variable.%s.%s( %s )' \
% (integral, region, vp), verbose=False, **{vp : var})
press = extend_cell_data( press0, pb.domain, region )
dim = pb.domain.mesh.dim
nn = out.shape[0]
for ii in range( nn ):
for j in range( dim ):
out[ii,0,j,0] += press[ii,0,0,0]
def add_stress_p(out, pb, integral, region, vp, data):
var = pb.create_variables([vp])[vp]
var.set_data(data)
press0 = pb.evaluate('ev_volume_integrate.%s.%s( %s )' \
% (integral, region, vp), verbose=False,
mode='el_avg', **{vp : var})
press = extend_cell_data(press0, pb.domain, region)
dim = pb.domain.mesh.dim
nn = out.shape[0]
for ii in range(nn):
for j in range(dim):
out[ii, 0, j, 0] += press[ii, 0, 0, 0]
def post_process(out, problem, mtx_phi):
var = problem.get_variables()['u']
for key in out.keys():
ii = int(key[1:])
vec = mtx_phi[:,ii].copy()
var.set_data(vec)
strain = problem.evaluate('ev_cauchy_strain.i.Y_c(u)', u=var,
verbose=False, mode='el_avg')
strain = extend_cell_data(strain, problem.domain, 'Y_c')
out['strain%03d' % ii] = Struct(name='output_data',
mode='cell', data=strain,
dofs=None)
return out
def post_process(out, problem, mtx_phi):
var = problem.get_variables()['u']
for key in out.keys():
ii = int(key[1:])
vec = mtx_phi[:, ii].copy()
var.data_from_any(vec)
strain = problem.evaluate('ev_cauchy_strain.i1.Y_c(u)',
u=var,
verbose=False,
mode='el_avg')
strain = extend_cell_data(strain, problem.domain, 'Y_c')
out['strain%03d' % ii] = Struct(name='output_data',
mode='cell',
data=strain,
dofs=None)
return out
def recover_bones(problem,
micro_problem,
region,
eps0,
ts,
strain,
dstrains,
p_grad,
pressures,
corrs_permeability,
corrs_rs,
corrs_time_rs,
corrs_pressure,
corrs_time_pressure,
var_names,
naming_scheme='step_iel'):
r"""
Notes
-----
- note that
.. math::
\widetilde{\pi}^P
is in corrs_pressure -> from time correctors only 'u', 'dp' are needed.
"""
dim = problem.domain.mesh.dim
vu, vp, vn, vpp1, vppp1 = var_names
vdp = 'd' + vp
variables = micro_problem.create_variables()
to_output = variables.state_to_output
micro_u, micro_p = variables[vu], variables[vp]
micro_coor = micro_u.field.get_coor()
nodes_yc = micro_problem.domain.regions['Yc'].all_vertices
join = os.path.join
aux = max(problem.domain.shape.n_gr, 2)
format = get_print_info( aux, fill = '0' )[1] \
+ '_' + get_print_info( problem.domain.mesh.n_el, fill = '0' )[1]
for ig, ii, iel in region.iter_cells():
print 'ig: %d, ii: %d, iel: %d' % (ig, ii, iel)
pressure = pressures[-1][ii, 0, 0, 0]
us = corrs_pressure[vu].data * pressure
add_strain_rs(corrs_rs, strain, vu, dim, ii, out=us)
ut = convolve_field_scalar(corrs_time_pressure[vu], pressures, ii, ts)
ut += convolve_field_sym_tensor(corrs_time_rs, dstrains, vu, dim, ii,
ts)
u1 = us + ut
u_mic = compute_u_from_macro(strain, micro_coor, ii) + u1
ps = corrs_pressure[vp].data * pressure
pt = convolve_field_scalar(corrs_time_pressure[vdp], pressures, ii, ts)
pt += convolve_field_sym_tensor(corrs_time_rs, dstrains, vdp, dim, ii,
ts)
## print us
## print ut
## print ps
## print pt
p_hat = ps + pt
# \eta_k \partial_k^x p
p1 = combine_scalar_grad(corrs_permeability, p_grad, vn, ii)
p_hat_e = micro_p.field.extend_dofs(p_hat[:, None], fill_value=0.0)
p_mic = compute_p_from_macro(p_grad, micro_coor, ii)[:,None] \
+ p_hat_e / eps0
p_mic[nodes_yc] = p1[:, None]
## print u_mic
## print p_mic
# (y_k + \eta_k) \partial_k^x p
p_aux = combine_scalar_grad(corrs_permeability,
p_grad,
vn,
ii,
shift_coors=micro_coor[nodes_yc])
meval = micro_problem.evaluate
var_p = variables[vppp1]
var_p.set_data(p_aux)
dvel_m1 = meval('ev_diffusion_velocity.i1.Yc( m.K, %s )' % vppp1,
verbose=False,
mode='el_avg',
**{vppp1: var_p})
var_p = variables[vpp1]
var_p.set_data(p_hat)
dvel_m2 = meval('ev_diffusion_velocity.i1.Ym( m.K, %s )' % vpp1,
verbose=False,
mode='el_avg',
**{vpp1: var_p}) * eps0
out = {}
out.update(to_output(u_mic, var_info={vu: (True, vu)}, extend=True))
out[vp] = Struct(name='output_data',
mode='vertex',
data=p_mic,
var_name=vp,
dofs=micro_p.dofs)
aux = extend_cell_data(dvel_m1, micro_problem.domain, 'Yc')
out['dvel_m1'] = Struct(name='output_data',
mode='cell',
data=aux,
dofs=None)
aux = extend_cell_data(dvel_m2, micro_problem.domain, 'Ym')
out['dvel_m2'] = Struct(name='output_data',
mode='cell',
data=aux,
dofs=None)
suffix = get_output_suffix(ig, iel, ts, naming_scheme, format,
micro_problem.output_format)
micro_name = micro_problem.get_output_name(extra=suffix)
filename = join(problem.output_dir,
'recovered_' + os.path.basename(micro_name))
micro_problem.save_state(filename, out=out, ts=ts)
def recover_bones( problem, micro_problem, region, eps0,
ts, strain, dstrains, p_grad, pressures,
corrs_permeability, corrs_rs, corrs_time_rs,
corrs_pressure, corrs_time_pressure,
var_names, naming_scheme = 'step_iel' ):
r"""
Notes
-----
- note that
.. math::
\widetilde{\pi}^P
is in corrs_pressure -> from time correctors only 'u', 'dp' are needed.
"""
dim = problem.domain.mesh.dim
vu, vp, vn, vpp1, vppp1 = var_names
vdp = 'd' + vp
variables = micro_problem.create_variables()
to_output = variables.state_to_output
micro_u, micro_p = variables[vu], variables[vp]
micro_coor = micro_u.field.get_coor()
nodes_yc = micro_problem.domain.regions['Yc'].all_vertices
join = os.path.join
aux = max(problem.domain.shape.n_gr, 2)
format = get_print_info( aux, fill = '0' )[1] \
+ '_' + get_print_info( problem.domain.mesh.n_el, fill = '0' )[1]
for ig, ii, iel in region.iter_cells():
print 'ig: %d, ii: %d, iel: %d' % (ig, ii, iel)
pressure = pressures[-1][ii,0,0,0]
us = corrs_pressure[vu].data * pressure
add_strain_rs(corrs_rs, strain, vu, dim, ii, out=us)
ut = convolve_field_scalar(corrs_time_pressure[vu], pressures, ii, ts)
ut += convolve_field_sym_tensor(corrs_time_rs, dstrains, vu,
dim, ii, ts)
u1 = us + ut
u_mic = compute_u_from_macro(strain, micro_coor, ii ) + u1
ps = corrs_pressure[vp].data * pressure
pt = convolve_field_scalar(corrs_time_pressure[vdp], pressures, ii, ts)
pt += convolve_field_sym_tensor(corrs_time_rs, dstrains, vdp,
dim, ii, ts)
## print us
## print ut
## print ps
## print pt
p_hat = ps + pt
# \eta_k \partial_k^x p
p1 = combine_scalar_grad(corrs_permeability, p_grad, vn, ii)
p_hat_e = micro_p.field.extend_dofs(p_hat[:,None], fill_value=0.0)
p_mic = compute_p_from_macro(p_grad, micro_coor, ii)[:,None] \
+ p_hat_e / eps0
p_mic[nodes_yc] = p1[:,None]
## print u_mic
## print p_mic
# (y_k + \eta_k) \partial_k^x p
p_aux = combine_scalar_grad(corrs_permeability, p_grad, vn, ii,
shift_coors=micro_coor[nodes_yc])
meval = micro_problem.evaluate
var_p = variables[vppp1]
var_p.data_from_any(p_aux)
dvel_m1 = meval('de_diffusion_velocity.i1.Yc( m.K, %s )' % vppp1,
verbose=False, mode='el_avg', **{vppp1 : var_p})
var_p = variables[vpp1]
var_p.data_from_any(p_hat)
dvel_m2 = meval('de_diffusion_velocity.i1.Ym( m.K, %s )' % vpp1,
verbose=False, mode='el_avg',
**{vpp1 : var_p}) * eps0
out = {}
out.update( to_output( u_mic, var_info = {vu : (True, vu)},
extend = True ) )
out[vp] = Struct(name = 'output_data',
mode = 'vertex', data = p_mic,
var_name = vp, dofs = micro_p.dofs)
aux = extend_cell_data(dvel_m1, micro_problem.domain, 'Yc')
out['dvel_m1'] = Struct(name = 'output_data',
mode = 'cell', data = aux,
dofs = None)
aux = extend_cell_data(dvel_m2, micro_problem.domain, 'Ym')
out['dvel_m2'] = Struct(name = 'output_data',
mode = 'cell', data = aux,
dofs = None)
suffix = get_output_suffix(ig, iel, ts, naming_scheme, format,
micro_problem.output_format)
micro_name = micro_problem.get_output_name(extra=suffix)
filename = join(problem.output_dir,
'recovered_' + os.path.basename(micro_name))
micro_problem.save_state(filename, out=out, ts=ts)
