# Source code for sfepy.terms.terms_elastic

import numpy as nm

from sfepy.linalg import dot_sequences
from sfepy.homogenization.utils import iter_sym
from sfepy.terms.terms import Term, terms
from sfepy.terms.terms_th import THTerm, ETHTerm

## expr = """
## e = 1/2 * (grad( vec( u ) ) + grad( vec( u ) ).T)
## D = map( D_sym )
## s = D * e
## div( s )
## """

## """
## e[i,j] = 1/2 * (der[j]( u[i] ) + der[i]( u[j] ))
## map =
## D[i,j,k,l]
## s[i,j] = D[i,j,k,l] * e[k,l]
## """

[docs]class LinearElasticTerm(Term):
r"""
General linear elasticity term, with :math:D_{ijkl} given in
the usual matrix form exploiting symmetry: in 3D it is :math:6\times6
with the indices ordered as :math:[11, 22, 33, 12, 13, 23], in 2D it is
:math:3\times3 with the indices ordered as :math:[11, 22, 12]. Can be
evaluated. Can use derivatives.

:Definition:

.. math::
\int_{\Omega} D_{ijkl}\ e_{ij}(\ul{v}) e_{kl}(\ul{u})

:Arguments 1:
- material : :math:D_{ijkl}
- virtual  : :math:\ul{v}
- state    : :math:\ul{u}

:Arguments 2:
- material    : :math:D_{ijkl}
- parameter_1 : :math:\ul{w}
- parameter_2 : :math:\ul{u}
"""
name = 'dw_lin_elastic'
arg_types = (('material', 'virtual', 'state'),
('material', 'parameter_1', 'parameter_2'))
arg_shapes = {'material' : 'S, S', 'virtual' : ('D', 'state'),
'state' : 'D', 'parameter_1' : 'D', 'parameter_2' : 'D'}
modes = ('weak', 'eval')
##     symbolic = {'expression': expr,
##                 'map' : {'u' : 'state', 'D_sym' : 'material'}}

[docs]    def get_fargs(self, mat, virtual, state,
mode=None, term_mode=None, diff_var=None, **kwargs):
vg, _ = self.get_mapping(state)

if mode == 'weak':
if diff_var is None:
strain = self.get(state, 'cauchy_strain')
fmode = 0

else:
strain = nm.array([0], ndmin=4, dtype=nm.float64)
fmode = 1

return 1.0, strain, mat, vg, fmode

elif mode == 'eval':
strain1 = self.get(virtual, 'cauchy_strain')
strain2 = self.get(state, 'cauchy_strain')

return 1.0, strain1, strain2, mat, vg

else:
raise ValueError('unsupported evaluation mode in %s! (%s)'
% (self.name, mode))

[docs]    def get_eval_shape(self, mat, virtual, state,
mode=None, term_mode=None, diff_var=None, **kwargs):
n_el, n_qp, dim, n_en, n_c = self.get_data_shape(state)

return (n_el, 1, 1, 1), state.dtype

[docs]    def set_arg_types(self):
if self.mode == 'weak':
self.function = terms.dw_lin_elastic

else:
self.function = terms.d_lin_elastic

[docs]class LinearElasticIsotropicTerm(LinearElasticTerm):
r"""
Isotropic linear elasticity term.

:Definition:

.. math::
\int_{\Omega} D_{ijkl}\ e_{ij}(\ul{v}) e_{kl}(\ul{u}) \mbox{ with }
D_{ijkl} = \mu (\delta_{ik} \delta_{jl}+\delta_{il} \delta_{jk}) +
\lambda \ \delta_{ij} \delta_{kl}

:Arguments:
- material_1 : :math:\lambda
- material_2 : :math:\mu
- virtual    : :math:\ul{v}
- state      : :math:\ul{u}

:Arguments 2:
- material    : :math:D_{ijkl}
- parameter_1 : :math:\ul{w}
- parameter_2 : :math:\ul{u}
"""
name = 'dw_lin_elastic_iso'
arg_types = (('material_1', 'material_2', 'virtual', 'state'),
('material_1', 'material_2', 'parameter_1', 'parameter_2'))
arg_shapes = {'material_1' : '1, 1', 'material_2' : '1, 1',
'virtual' : ('D', 'state'), 'state' : 'D',
'parameter_1' : 'D', 'parameter_2' : 'D'}
geometries = ['2_3', '2_4', '3_4', '3_8']

[docs]    def get_fargs(self, lam, mu, virtual, state,
mode=None, term_mode=None, diff_var=None, **kwargs):
from sfepy.mechanics.matcoefs import stiffness_from_lame

mat = stiffness_from_lame(self.region.dim, lam, mu)[:, :, 0, 0, :, :]
return LinearElasticTerm.get_fargs(self, mat, virtual, state,
mode=mode, term_mode=term_mode,
diff_var=diff_var, **kwargs)

[docs]    def get_eval_shape(self, mat1, mat2, virtual, state,
mode=None, term_mode=None, diff_var=None, **kwargs):
return LinearElasticTerm.get_eval_shape(self, None, None, state)

[docs]class SDLinearElasticTerm(Term):
r"""
Sensitivity analysis of the linear elastic term.

:Definition:

.. math::
\int_{\Omega} \hat{D}_{ijkl}\ e_{ij}(\ul{v}) e_{kl}(\ul{u})

.. math::
\hat{D}_{ijkl} = D_{ijkl}(\nabla \cdot \ul{\Vcal})
- D_{ijkq}{\partial \Vcal_l \over \partial x_q}
- D_{iqkl}{\partial \Vcal_j \over \partial x_q}

:Arguments:
- material    : :math:D_{ijkl}
- parameter_w : :math:\ul{w}
- parameter_u : :math:\ul{u}
- parameter_mesh_velocity : :math:\ul{\Vcal}
"""
name = 'ev_sd_lin_elastic'
arg_types = ('material', 'parameter_w', 'parameter_u',
'parameter_mesh_velocity')
arg_shapes = {'material' : 'S, S',
'parameter_w' : 'D', 'parameter_u' : 'D',
'parameter_mesh_velocity' : 'D'}
geometries = ['2_3', '2_4', '3_4', '3_8']
function = terms.d_sd_lin_elastic

[docs]    def get_fargs(self, mat, par_w, par_u, par_mv,
mode=None, term_mode=None, diff_var=None, **kwargs):
vg, _ = self.get_mapping(par_u)

[docs]    def get_eval_shape(self, mat, par_w, par_u, par_mv,
mode=None, term_mode=None, diff_var=None, **kwargs):
n_el, n_qp, dim, n_en, n_c = self.get_data_shape(par_u)

return (n_el, 1, 1, 1), par_u.dtype

[docs]class LinearElasticTHTerm(THTerm):
r"""
Fading memory linear elastic (viscous) term. Can use derivatives.

:Definition:

.. math::
\int_{\Omega} \left [\int_0^t
\Hcal_{ijkl}(t-\tau)\,e_{kl}(\ul{u}(\tau)) \difd{\tau}
\right]\,e_{ij}(\ul{v})

:Arguments:
- ts       : :class:TimeStepper instance
- material : :math:\Hcal_{ijkl}(\tau)
- virtual  : :math:\ul{v}
- state    : :math:\ul{u}
"""
name = 'dw_lin_elastic_th'
arg_types = ('ts', 'material', 'virtual', 'state')
arg_shapes = {'material' : '.: N, S, S',
'virtual' : ('D', 'state'), 'state' : 'D'}

function = staticmethod(terms.dw_lin_elastic)

[docs]    def get_fargs(self, ts, mats, virtual, state,
mode=None, term_mode=None, diff_var=None, **kwargs):
vg, _ = self.get_mapping(state)

n_el, n_qp, dim, n_en, n_c = self.get_data_shape(state)

if mode == 'weak':
if diff_var is None:
def iter_kernel():
for ii, mat in enumerate(mats):
strain = self.get(state, 'cauchy_strain',
step=-ii)
mat = nm.tile(mat, (n_el, n_qp, 1, 1))
yield ii, (ts.dt, strain, mat, vg, 0)
fargs = iter_kernel

else:
strain = nm.array([0], ndmin=4, dtype=nm.float64)
mat = nm.tile(mats[0], (n_el, n_qp, 1, 1))
fargs = ts.dt, strain, mat, vg, 1

return fargs

else:
raise ValueError('unsupported evaluation mode in %s! (%s)'
% (self.name, mode))

[docs]class LinearElasticETHTerm(ETHTerm):
r"""
This term has the same definition as dw_lin_elastic_th, but assumes an
exponential approximation of the convolution kernel resulting in much
higher efficiency. Can use derivatives.

:Definition:

.. math::
\int_{\Omega} \left [\int_0^t
\Hcal_{ijkl}(t-\tau)\,e_{kl}(\ul{u}(\tau)) \difd{\tau}
\right]\,e_{ij}(\ul{v})

:Arguments:
- ts         : :class:TimeStepper instance
- material_0 : :math:\Hcal_{ijkl}(0)
- material_1 : :math:\exp(-\lambda \Delta t) (decay at :math:t_1)
- virtual    : :math:\ul{v}
- state      : :math:\ul{u}
"""
name = 'dw_lin_elastic_eth'
arg_types = ('ts', 'material_0', 'material_1', 'virtual', 'state')
arg_shapes = {'material_0' : 'S, S', 'material_1' : '1, 1',
'virtual' : ('D', 'state'), 'state' : 'D'}

function = staticmethod(terms.dw_lin_elastic)

[docs]    def get_fargs(self, ts, mat0, mat1, virtual, state,
mode=None, term_mode=None, diff_var=None, **kwargs):
vg, _, key = self.get_mapping(state, return_key=True)

if diff_var is None:
strain = self.get(state, 'cauchy_strain')

key += tuple(self.arg_names[ii] for ii in [1, 2, 4])
data = self.get_eth_data(key, state, mat1, strain)

fargs = (ts.dt, data.history + data.values, mat0, vg, 0)

else:
aux = nm.array([0], ndmin=4, dtype=nm.float64)
fargs = (ts.dt, aux, mat0, vg, 1)

return fargs

[docs]class LinearPrestressTerm(Term):
r"""
Linear prestress term, with the prestress :math:\sigma_{ij} given either
in the usual vector form exploiting symmetry: in 3D it has 6 components
with the indices ordered as :math:[11, 22, 33, 12, 13, 23], in 2D it has
3 components with the indices ordered as :math:[11, 22, 12], or in the
matrix (possibly non-symmetric) form. Can be evaluated.

:Definition:

.. math::
\int_{\Omega} \sigma_{ij} e_{ij}(\ul{v})

:Arguments 1:
- material : :math:\sigma_{ij}
- virtual  : :math:\ul{v}

:Arguments 2:
- material : :math:\sigma_{ij}
- parameter : :math:\ul{u}
"""
name = 'dw_lin_prestress'
arg_types = (('material', 'virtual'),
('material', 'parameter'))
arg_shapes = [{'material' : 'S, 1', 'virtual' : ('D', None),
'parameter' : 'D'},
{'material' : 'D, D'}]
modes = ('weak', 'eval')

[docs]    def get_fargs(self, mat, virtual,
mode=None, term_mode=None, diff_var=None, **kwargs):
vg, _ = self.get_mapping(virtual)

sh = mat.shape
is_nonsym = sh[2] == sh[3] == vg.dim and not(vg.dim == 1)

if is_nonsym:
mat = mat.reshape(sh[:2] + (vg.dim**2, 1))

if mode == 'weak':
return mat, vg

else:
if is_nonsym:
nel, nqp, nr, nc = strain.shape
strain = strain.reshape((nel, nqp, nr*nc, 1))
else:
strain = self.get(virtual, 'cauchy_strain')

fmode = {'eval' : 0, 'el_avg' : 1, 'qp' : 2}.get(mode, 1)
return strain, mat, vg, fmode

[docs]    def get_eval_shape(self, mat, virtual,
mode=None, term_mode=None, diff_var=None, **kwargs):
n_el, n_qp, dim, n_en, n_c = self.get_data_shape(virtual)

if mode != 'qp':
n_qp = 1

return (n_el, n_qp, 1, 1), virtual.dtype

[docs]    def d_lin_prestress(self, out, strain, mat, vg, fmode):
aux = dot_sequences(mat, strain, mode='ATB')
if fmode == 2:
out[:] = aux
status = 0

else:
status = vg.integrate(out, aux, fmode)

return status

[docs]    def set_arg_types(self):
if self.mode == 'weak':
self.function = terms.dw_lin_prestress

else:
self.function = self.d_lin_prestress

[docs]class LinearStrainFiberTerm(Term):
r"""
Linear (pre)strain fiber term with the unit direction vector
:math:\ul{d}.

:Definition:

.. math::
\int_{\Omega} D_{ijkl} e_{ij}(\ul{v}) \left(d_k d_l\right)

:Arguments:
- material_1 : :math:D_{ijkl}
- material_2 : :math:\ul{d}
- virtual  : :math:\ul{v}
"""
name = 'dw_lin_strain_fib'
arg_types = ('material_1', 'material_2', 'virtual')
arg_shapes = {'material_1' : 'S, S', 'material_2' : 'D, 1',
'virtual' : ('D', None)}

function = staticmethod(terms.dw_lin_strain_fib)

[docs]    def get_fargs(self, mat1, mat2, virtual,
mode=None, term_mode=None, diff_var=None, **kwargs):
vg, _ = self.get_mapping(virtual)

omega = nm.empty(mat1.shape[:3] + (1,), dtype=nm.float64)
for ii, (ir, ic) in enumerate(iter_sym(mat2.shape[2])):
omega[..., ii, 0] = mat2[..., ir, 0] * mat2[..., ic, 0]

return mat1, omega, vg

[docs]class CauchyStrainTerm(Term):
r"""
Evaluate Cauchy strain tensor.

It is given in the usual vector form exploiting symmetry: in 3D it has 6
components with the indices ordered as :math:[11, 22, 33, 12, 13, 23], in
2D it has 3 components with the indices ordered as :math:[11, 22,
12]. The last three (non-diagonal) components are doubled so that it is
energetically conjugate to the Cauchy stress tensor with the same storage.

Supports 'eval', 'el_avg' and 'qp' evaluation modes.

:Definition:

.. math::
\int_{\cal{D}} \ull{e}(\ul{w})

.. math::
\mbox{vector for } K \from \Ical_h: \int_{T_K} \ull{e}(\ul{w}) /
\int_{T_K} 1

.. math::
\ull{e}(\ul{w})|_{qp}

:Arguments:
- parameter : :math:\ul{w}
"""
name = 'ev_cauchy_strain'
arg_types = ('parameter',)
arg_shapes = {'parameter' : 'D'}
integration = 'by_region'
surface_integration = 'surface_extra'

[docs]    @staticmethod
def function(out, strain, vg, fmode):
if fmode == 2:
out[:] = strain
status = 0

else:
status = terms.de_cauchy_strain(out, strain, vg, fmode)

return status

[docs]    def get_fargs(self, parameter,
mode=None, term_mode=None, diff_var=None, **kwargs):
vg, _ = self.get_mapping(parameter)

strain = self.get(parameter, 'cauchy_strain')

fmode = {'eval' : 0, 'el_avg' : 1, 'qp' : 2}.get(mode, 1)

return strain, vg, fmode

[docs]    def get_eval_shape(self, parameter,
mode=None, term_mode=None, diff_var=None, **kwargs):
n_el, n_qp, dim, n_en, n_c = self.get_data_shape(parameter)

if mode != 'qp':
n_qp = 1

return (n_el, n_qp, dim * (dim + 1) // 2, 1), parameter.dtype

[docs]class CauchyStressTerm(Term):
r"""
Evaluate Cauchy stress tensor.

It is given in the usual vector form exploiting symmetry: in 3D it has 6
components with the indices ordered as :math:[11, 22, 33, 12, 13, 23], in
2D it has 3 components with the indices ordered as :math:[11, 22, 12].

Supports 'eval', 'el_avg' and 'qp' evaluation modes.

:Definition:

.. math::
\int_{\cal{D}} D_{ijkl} e_{kl}(\ul{w})

.. math::
\mbox{vector for } K \from \Ical_h:
\int_{T_K} D_{ijkl} e_{kl}(\ul{w}) / \int_{T_K} 1

.. math::
D_{ijkl} e_{kl}(\ul{w})|_{qp}

:Arguments:
- material  : :math:D_{ijkl}
- parameter : :math:\ul{w}
"""
name = 'ev_cauchy_stress'
arg_types = ('material', 'parameter')
arg_shapes = {'material' : 'S, S', 'parameter' : 'D'}
integration = 'by_region'
surface_integration = 'surface_extra'

[docs]    @staticmethod
def function(out, coef, strain, mat, vg, fmode):
if fmode == 2:
out[:] = dot_sequences(mat, strain)
status = 0

else:
status = terms.de_cauchy_stress(out, strain, mat, vg, fmode)

if coef is not None:
out *= coef

return status

[docs]    def get_fargs(self, mat, parameter,
mode=None, term_mode=None, diff_var=None, **kwargs):
vg, _ = self.get_mapping(parameter)

strain = self.get(parameter, 'cauchy_strain')

fmode = {'eval' : 0, 'el_avg' : 1, 'qp' : 2}.get(mode, 1)

return None, strain, mat, vg, fmode

[docs]    def get_eval_shape(self, mat, parameter,
mode=None, term_mode=None, diff_var=None, **kwargs):
n_el, n_qp, dim, n_en, n_c = self.get_data_shape(parameter)

if mode != 'qp':
n_qp = 1

return (n_el, n_qp, dim * (dim + 1) // 2, 1), parameter.dtype

[docs]class CauchyStressTHTerm(CauchyStressTerm, THTerm):
r"""
Evaluate fading memory Cauchy stress tensor.

It is given in the usual vector form exploiting symmetry: in 3D it has 6
components with the indices ordered as :math:[11, 22, 33, 12, 13, 23], in
2D it has 3 components with the indices ordered as :math:[11, 22, 12].

Supports 'eval', 'el_avg' and 'qp' evaluation modes.

:Definition:

.. math::
\int_{\Omega} \int_0^t \Hcal_{ijkl}(t-\tau)\,e_{kl}(\ul{w}(\tau))
\difd{\tau}

.. math::
\mbox{vector for } K \from \Ical_h:
\int_{T_K} \int_0^t \Hcal_{ijkl}(t-\tau)\,e_{kl}(\ul{w}(\tau))
\difd{\tau} / \int_{T_K} 1

.. math::
\int_0^t \Hcal_{ijkl}(t-\tau)\,e_{kl}(\ul{w}(\tau)) \difd{\tau}|_{qp}

:Arguments:
- ts        : :class:TimeStepper instance
- material  : :math:\Hcal_{ijkl}(\tau)
- parameter : :math:\ul{w}
"""
name = 'ev_cauchy_stress_th'
arg_types = ('ts', 'material', 'parameter')
arg_shapes = {'material' : '.: N, S, S', 'parameter' : 'D'}

[docs]    def get_fargs(self, ts, mats, state,
mode=None, term_mode=None, diff_var=None, **kwargs):
vg, _ = self.get_mapping(state)

n_el, n_qp, dim, n_en, n_c = self.get_data_shape(state)

fmode = {'eval' : 0, 'el_avg' : 1, 'qp' : 2}.get(mode, 1)
def iter_kernel():
for ii, mat in enumerate(mats):
strain = self.get(state, 'cauchy_strain',
step=-ii)
mat = nm.tile(mat, (n_el, n_qp, 1, 1))
yield ii, (ts.dt, strain, mat, vg, fmode)

return iter_kernel

[docs]    def get_eval_shape(self, ts, mats, parameter,
mode=None, term_mode=None, diff_var=None, **kwargs):
out = CauchyStressTerm.get_eval_shape(self, mats, parameter, mode,
term_mode, diff_var, **kwargs)
return out

[docs]class CauchyStressETHTerm(CauchyStressTerm, ETHTerm):
r"""
Evaluate fading memory Cauchy stress tensor.

It is given in the usual vector form exploiting symmetry: in 3D it has 6
components with the indices ordered as :math:[11, 22, 33, 12, 13, 23], in
2D it has 3 components with the indices ordered as :math:[11, 22, 12].

Assumes an exponential approximation of the convolution kernel resulting in
much higher efficiency.

Supports 'eval', 'el_avg' and 'qp' evaluation modes.

:Definition:

.. math::
\int_{\Omega} \int_0^t \Hcal_{ijkl}(t-\tau)\,e_{kl}(\ul{w}(\tau))
\difd{\tau}

.. math::
\mbox{vector for } K \from \Ical_h:
\int_{T_K} \int_0^t \Hcal_{ijkl}(t-\tau)\,e_{kl}(\ul{w}(\tau))
\difd{\tau} / \int_{T_K} 1

.. math::
\int_0^t \Hcal_{ijkl}(t-\tau)\,e_{kl}(\ul{w}(\tau)) \difd{\tau}|_{qp}

:Arguments:
- ts         : :class:TimeStepper instance
- material_0 : :math:\Hcal_{ijkl}(0)
- material_1 : :math:\exp(-\lambda \Delta t) (decay at :math:t_1)
- parameter  : :math:\ul{w}
"""
name = 'ev_cauchy_stress_eth'
arg_types = ('ts', 'material_0', 'material_1', 'parameter')
arg_shapes = {'material_0' : 'S, S', 'material_1' : '1, 1',
'parameter' : 'D'}

[docs]    def get_fargs(self, ts, mat0, mat1, state,
mode=None, term_mode=None, diff_var=None, **kwargs):
vg, _, key = self.get_mapping(state, return_key=True)

strain = self.get(state, 'cauchy_strain')

key += tuple(self.arg_names[1:])
data = self.get_eth_data(key, state, mat1, strain)

fmode = {'eval' : 0, 'el_avg' : 1, 'qp' : 2}.get(mode, 1)

return ts.dt, data.history + data.values, mat0, vg, fmode

[docs]    def get_eval_shape(self, ts, mat0, mat1, parameter,
mode=None, term_mode=None, diff_var=None, **kwargs):
out = CauchyStressTerm.get_eval_shape(self, mat0, parameter, mode,
term_mode, diff_var, **kwargs)
return out

[docs]class NonsymElasticTerm(Term):
r"""
Elasticity term with non-symmetric gradient. The indices of matrix
:math:D_{ijkl} are ordered as
:math:[11, 12, 13, 21, 22, 23, 31, 32, 33] in 3D and as
:math:[11, 12, 21, 22] in 2D.

:Definition:

.. math::
\int_{\Omega} \ull{D} \nabla\ul{u} : \nabla\ul{v}

:Arguments 1:
- material : :math:\ull{D}
- virtual  : :math:\ul{v}
- state    : :math:\ul{u}

:Arguments 2:
- material    : :math:\ull{D}
- parameter_1 : :math:\ul{w}
- parameter_2 : :math:\ul{u}
"""
name = 'dw_nonsym_elastic'
arg_types = (('material', 'virtual', 'state'),
('material', 'parameter_1', 'parameter_2'))
arg_shapes = {'material' : 'D2, D2', 'virtual' : ('D', 'state'),
'state' : 'D', 'parameter_1' : 'D', 'parameter_2' : 'D'}
modes = ('weak', 'eval')
geometries = ['2_3', '2_4', '3_4', '3_8']

[docs]    def get_fargs(self, mat, virtual, state,
mode=None, term_mode=None, diff_var=None, **kwargs):
vg, _ = self.get_mapping(state)

if mode == 'weak':
if diff_var is None:
nel, nqp, nr, nc = grad.shape
fmode = 0

else:
fmode = 1

elif mode == 'eval':
nel, nqp, nr, nc = grad1.shape

return 1.0,\
mat, vg

else:
raise ValueError('unsupported evaluation mode in %s! (%s)'
% (self.name, mode))

[docs]    def get_eval_shape(self, mat, virtual, state,
mode=None, term_mode=None, diff_var=None, **kwargs):
n_el, n_qp, dim, n_en, n_c = self.get_data_shape(state)

return (n_el, 1, 1, 1), state.dtype

[docs]    def set_arg_types(self):
if self.mode == 'weak':
self.function = terms.dw_nonsym_elastic

else:
self.function = terms.d_lin_elastic

def _build_wave_strain_op(vec, bf):
dim = len(vec)

if dim == 2:
n0, n1 = vec
nmat = nm.array([[n0, 0],
[0, n1],
[n1, n0]], dtype=nm.float64)

else:
n0, n1, n2 = vec
nmat = nm.array([[n0, 0, 0],
[0, n1, 0],
[0, 0, n2],
[n1, n0, 0],
[n2, 0, n0],
[0, n2, n1]], dtype=nm.float64)

out = nm.einsum('ik,cqkj->cqij', nmat, bf)
return out

from sfepy.base.compat import block

def _build_cauchy_strain_op(bfg):
dim = bfg.shape[2]
if dim == 2:
g1, g2 = bfg[..., 0:1, :], bfg[..., 1:2, :]
zz = nm.zeros_like(g1)
out = block([[g1, zz],
[zz, g2],
[g2, g1]])

else:
g1, g2, g3 = bfg[..., 0:1, :], bfg[..., 1:2, :], bfg[..., 2:3, :]
zz = nm.zeros_like(g1)
out = block([[g1, zz, zz],
[zz, g2, zz],
[zz, zz, g3],
[g2, g1, zz],
[g3, zz, g1],
[zz, g3, g2]])

return out

[docs]class ElasticWaveTerm(Term):
r"""
Elastic dispersion term involving the wave strain :math:g_{ij},
:math:g_{ij}(\ul{u}) = \frac{1}{2}(u_i \kappa_j + \kappa_i u_j), with the
wave vector :math:\ul{\kappa}. :math:D_{ijkl} is given in the usual
matrix form exploiting symmetry: in 3D it is :math:6\times6 with the
indices ordered as :math:[11, 22, 33, 12, 13, 23], in 2D it is
:math:3\times3 with the indices ordered as :math:[11, 22, 12].

:Definition:

.. math::
\int_{\Omega} D_{ijkl}\ g_{ij}(\ul{v}) g_{kl}(\ul{u})

:Arguments:
- material_1 : :math:D_{ijkl}
- material_2 : :math:\ul{\kappa}
- virtual    : :math:\ul{v}
- state      : :math:\ul{u}
"""
name = 'dw_elastic_wave'
arg_types = ('material_1', 'material_2', 'virtual', 'state')
arg_shapes = {'material_1' : 'S, S', 'material_2' : '.: D',
'virtual' : ('D', 'state'), 'state' : 'D'}
geometries = ['2_3', '2_4', '3_4', '3_8']

[docs]    @staticmethod
def function(out, out_qp, geo, fmode):
status = geo.integrate(out, out_qp)
return status

[docs]    def get_fargs(self, mat, kappa, virtual, state,
mode=None, term_mode=None, diff_var=None, **kwargs):

geo, _ = self.get_mapping(state)

n_el, n_qp, dim, n_en, n_c = self.get_data_shape(virtual)

ebf = expand_basis(geo.bf, dim)

mat = Term.tile_mat(mat, n_el)
gmat = _build_wave_strain_op(kappa, ebf)

if diff_var is None:
econn = state.field.get_econn('volume', self.region)
econn, n_c, 0)
# Same as nm.einsum('qij,cj->cqi', gmat[0], vals)[..., None]
aux = dot_sequences(gmat, vals[:, None, :, None])
out_qp = dot_sequences(gmat, dot_sequences(mat, aux), 'ATB')
fmode = 0

else:
out_qp = dot_sequences(gmat, dot_sequences(mat, gmat), 'ATB')
fmode = 1

return out_qp, geo, fmode

[docs]class ElasticWaveCauchyTerm(Term):
r"""
Elastic dispersion term involving the wave strain :math:g_{ij},
:math:g_{ij}(\ul{u}) = \frac{1}{2}(u_i \kappa_j + \kappa_i u_j), with the
wave vector :math:\ul{\kappa} and the elastic strain :math:e_{ij}.
:math:D_{ijkl} is given in the usual matrix form exploiting symmetry: in
3D it is :math:6\times6 with the indices ordered as :math:[11, 22, 33,
12, 13, 23], in 2D it is :math:3\times3 with the indices ordered as
:math:[11, 22, 12].

:Definition:

.. math::
\int_{\Omega} D_{ijkl}\ g_{ij}(\ul{v}) e_{kl}(\ul{u}) \;,
\int_{\Omega} D_{ijkl}\ g_{ij}(\ul{u}) e_{kl}(\ul{v})

:Arguments 1:
- material_1 : :math:D_{ijkl}
- material_2 : :math:\ul{\kappa}
- virtual    : :math:\ul{v}
- state      : :math:\ul{u}

:Arguments 2:
- material_1 : :math:D_{ijkl}
- material_2 : :math:\ul{\kappa}
- state      : :math:\ul{u}
- virtual    : :math:\ul{v}
"""
name = 'dw_elastic_wave_cauchy'
arg_types = (('material_1', 'material_2', 'virtual', 'state'),
('material_1', 'material_2', 'state', 'virtual'))
arg_shapes = {'material_1' : 'S, S', 'material_2' : '.: D',
'virtual' : ('D', 'state'), 'state' : 'D'}
geometries = ['2_3', '2_4', '3_4', '3_8']
modes = ('ge', 'eg')

[docs]    @staticmethod
def function(out, out_qp, geo, fmode):
status = geo.integrate(out, out_qp)
return status

[docs]    def get_fargs(self, mat, kappa, gvar, evar,
mode=None, term_mode=None, diff_var=None, **kwargs):

geo, _ = self.get_mapping(evar)

n_el, n_qp, dim, n_en, n_c = self.get_data_shape(gvar)

ebf = expand_basis(geo.bf, dim)

mat = Term.tile_mat(mat, n_el)
gmat = _build_wave_strain_op(kappa, ebf)
emat = _build_cauchy_strain_op(geo.bfg)

if diff_var is None:
avar = evar if self.mode == 'ge' else gvar
econn = avar.field.get_econn('volume', self.region)
econn, n_c, 0)

if self.mode == 'ge':
# Same as aux = self.get(avar, 'cauchy_strain'),
aux = dot_sequences(emat, vals[:, None, :, None])
out_qp = dot_sequences(gmat, dot_sequences(mat, aux), 'ATB')

else:
aux = dot_sequences(gmat, vals[:, None, :, None])
out_qp = dot_sequences(emat, dot_sequences(mat, aux), 'ATB')

fmode = 0

else:
if self.mode == 'ge':
out_qp = dot_sequences(gmat, dot_sequences(mat, emat), 'ATB')

else:
out_qp = dot_sequences(emat, dot_sequences(mat, gmat), 'ATB')

fmode = 1

return out_qp, geo, fmode