# -*- coding: utf-8 -*-
"""
This file is part of FElupe.
FElupe is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
FElupe is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with FElupe. If not, see <http://www.gnu.org/licenses/>.
"""
import numpy as np
from ..math import cdya, dya, identity, trace, transpose
from ._base import ConstitutiveMaterial
[docs]
def lame_converter(E, nu):
r"""Convert the pair of given material parameters Young's modulus :math:`E` and
Poisson ratio :math:`\nu` to first and second Lamé - constants :math:`\lambda` and
:math:`\mu`.
Notes
-----
.. math::
\lambda &= \frac{E \nu}{(1 + \nu) (1 - 2 \nu)}
\mu &= \frac{E}{2 (1 + \nu)}
Parameters
----------
E : float
Young's modulus.
nu : float
Poisson ratio.
Returns
-------
lmbda : float
First Lamé - constant.
mu : float
Second Lamé - constant (shear modulus).
"""
lmbda = E * nu / ((1 + nu) * (1 - 2 * nu))
mu = E / (2 * (1 + nu))
return lmbda, mu
[docs]
class LinearElastic(ConstitutiveMaterial):
r"""Isotropic linear-elastic material formulation.
Parameters
----------
E : float
Young's modulus.
nu : float
Poisson ratio.
Notes
-----
.. math::
\begin{bmatrix}
\sigma_{11} \\
\sigma_{22} \\
\sigma_{33} \\
\sigma_{12} \\
\sigma_{23} \\
\sigma_{31}
\end{bmatrix} = \frac{E}{(1+\nu)(1-2\nu)}\begin{bmatrix}
1-\nu & \nu & \nu & 0 & 0 & 0\\
\nu & 1-\nu & \nu & 0 & 0 & 0\\
\nu & \nu & 1-\nu & 0 & 0 & 0\\
0 & 0 & 0 & \frac{1-2\nu}{2} & 0 & 0 \\
0 & 0 & 0 & 0 & \frac{1-2\nu}{2} & 0 \\
0 & 0 & 0 & 0 & 0 & \frac{1-2\nu}{2}
\end{bmatrix} \cdot \begin{bmatrix}
\varepsilon_{11} \\
\varepsilon_{22} \\
\varepsilon_{33} \\
2 \varepsilon_{12} \\
2 \varepsilon_{23} \\
2 \varepsilon_{31}
\end{bmatrix}
with the strain tensor
.. math::
\boldsymbol{\varepsilon} = \frac{1}{2} \left( \frac{\partial \boldsymbol{u}}
{\partial \boldsymbol{X}} + \left( \frac{\partial \boldsymbol{u}}
{\partial \boldsymbol{X}} \right)^T \right)
Examples
--------
.. pyvista-plot::
:context:
>>> import felupe as fem
>>>
>>> umat = fem.LinearElastic(E=1, nu=0.3)
>>> ax = umat.plot()
.. pyvista-plot::
:include-source: False
:context:
:force_static:
>>> import pyvista as pv
>>>
>>> fig = ax.get_figure()
>>> chart = pv.ChartMPL(fig)
>>> chart.show()
"""
def __init__(self, E=None, nu=None):
self.E = E
self.nu = nu
self.kwargs = {"E": self.E, "nu": self.nu}
# aliases for gradient and hessian
self.stress = self.gradient
self.elasticity = self.hessian
# initial variables for calling
# ``self.gradient(self.x)`` and ``self.hessian(self.x)``
self.x = [np.eye(3), np.zeros(0)]
[docs]
def gradient(self, x, E=None, nu=None):
"""Evaluate the stress tensor (as a function of the deformation gradient).
Parameters
----------
x : list of ndarray
List with Deformation gradient :math:`boldsymbol{F}` (3x3) as first item.
E : float, optional
Young's modulus (default is None).
nu : float, optional
Poisson ratio (default is None).
Returns
-------
ndarray
Stress tensor (3x3)
"""
F, statevars = x[0], x[-1]
if E is None:
E = self.E
if nu is None:
nu = self.nu
# convert the deformation gradient to strain
H = F - identity(F)
strain = (H + transpose(H)) / 2
# init stress
stress = np.zeros_like(strain)
# normal stress components
for a, b, c in zip([0, 1, 2], [1, 2, 0], [2, 0, 1]):
stress[a, a] = (1 - nu) * strain[a, a] + nu * (strain[b, b] + strain[c, c])
# shear stress components
for a, b in zip([0, 0, 1], [1, 2, 2]):
stress[a, b] = stress[b, a] = (1 - 2 * nu) / 2 * 2 * strain[a, b]
return [E / (1 + nu) / (1 - 2 * nu) * stress, statevars]
[docs]
def hessian(self, x=None, E=None, nu=None, shape=(1, 1)):
"""Evaluate the elasticity tensor. The Deformation gradient is only
used for the shape of the trailing axes.
Parameters
----------
x : list of ndarray, optional
List with Deformation gradient :math:`boldsymbol{F}` (3x3) as first item
(default is None).
E : float, optional
Young's modulus (default is None).
nu : float, optional
Poisson ratio (default is None).
shape : tuple of int, optional
Tuple with shape of the trailing axes (default is (1, 1)).
Returns
-------
ndarray
elasticity tensor (3x3x3x3)
"""
if E is None:
E = self.E
if nu is None:
nu = self.nu
elast = np.zeros((3, 3, 3, 3, *shape))
# diagonal normal components
for i in range(3):
elast[i, i, i, i] = 1 - nu
# off-diagonal normal components
for j in range(3):
if j != i:
elast[i, i, j, j] = nu
# diagonal shear components (full-symmetric)
elast[
[0, 1, 0, 1, 0, 2, 0, 2, 1, 2, 1, 2],
[1, 0, 1, 0, 2, 0, 2, 0, 2, 1, 2, 1],
[0, 0, 1, 1, 0, 0, 2, 2, 1, 1, 2, 2],
[1, 1, 0, 0, 2, 2, 0, 0, 2, 2, 1, 1],
] = (1 - 2 * nu) / 2
return [E / (1 + nu) / (1 - 2 * nu) * elast]
[docs]
class LinearElasticTensorNotation(ConstitutiveMaterial):
r"""Isotropic linear-elastic material formulation.
Parameters
----------
E : float
Young's modulus.
nu : float
Poisson ratio.
Notes
-----
.. math::
\boldsymbol{\sigma} &= 2 \mu \ \boldsymbol{\varepsilon} + \gamma \
\text{tr}(\boldsymbol{\varepsilon}) \ \boldsymbol{I}
\frac{\boldsymbol{\partial \sigma}}{\partial \boldsymbol{\varepsilon}} &=
2 \mu \ \boldsymbol{I} \odot \boldsymbol{I} + \gamma \ \boldsymbol{I} \otimes
\boldsymbol{I}
with the strain tensor
.. math::
\boldsymbol{\varepsilon} = \frac{1}{2} \left( \frac{\partial \boldsymbol{u}}
{\partial \boldsymbol{X}} + \left( \frac{\partial \boldsymbol{u}}
{\partial \boldsymbol{X}} \right)^T \right)
Examples
--------
.. pyvista-plot::
:context:
>>> import felupe as fem
>>>
>>> umat = fem.constitution.LinearElasticTensorNotation(E=1, nu=0.3)
>>> ax = umat.plot()
.. pyvista-plot::
:include-source: False
:context:
:force_static:
>>> import pyvista as pv
>>>
>>> fig = ax.get_figure()
>>> chart = pv.ChartMPL(fig)
>>> chart.show()
"""
def __init__(self, E=None, nu=None, parallel=False):
self.parallel = parallel
self.E = E
self.nu = nu
self.kwargs = {"E": self.E, "nu": self.nu}
# aliases for gradient and hessian
self.stress = self.gradient
self.elasticity = self.hessian
# initial variables for calling
# ``self.gradient(self.x)`` and ``self.hessian(self.x)``
self.x = [np.eye(3), np.zeros(0)]
[docs]
def gradient(self, x, E=None, nu=None):
"""Evaluate the stress tensor (as a function of the deformation
gradient).
Parameters
----------
x : list of ndarray
List with Deformation gradient :math:`boldsymbol{F}` (3x3) as first item.
E : float, optional
Young's modulus (default is None).
nu : float, optional
Poisson ratio (default is None).
Returns
-------
ndarray
Stress tensor (3x3)
"""
F, statevars = x[0], x[-1]
if E is None:
E = self.E
if nu is None:
nu = self.nu
# convert to lame constants
gamma, mu = lame_converter(E, nu)
# convert the deformation gradient to strain
H = F - identity(F)
strain = (H + transpose(H)) / 2
return [2 * mu * strain + gamma * trace(strain) * identity(strain), statevars]
[docs]
def hessian(self, x=None, E=None, nu=None, shape=(1, 1)):
"""Evaluate the elasticity tensor. The Deformation gradient is only
used for the shape of the trailing axes.
Parameters
----------
x : list of ndarray
List with Deformation gradient :math:`boldsymbol{F}` (3x3) as first item.
(default is None)
E : float, optional
Young's modulus (default is None).
nu : float, optional
Poisson ratio (default is None).
shape : (int, ...), optional
Tuple with shape of the trailing axes (default is (1, 1))
Returns
-------
ndarray
elasticity tensor (3x3x3x3)
"""
if E is None:
E = self.E
if nu is None:
nu = self.nu
eye = identity(dim=3, shape=shape)
# convert to lame constants
gamma, mu = lame_converter(E, nu)
elast = 2 * mu * cdya(eye, eye, parallel=self.parallel) + gamma * dya(
eye, eye, parallel=self.parallel
)
return [elast]
[docs]
class LinearElasticPlaneStrain:
"""Plane-strain isotropic linear-elastic material formulation.
Parameters
----------
E : float
Young's modulus.
nu : float
Poisson ratio.
"""
def __init__(self, E, nu):
self.E = E
self.nu = nu
self.kwargs = {"E": self.E, "nu": self.nu}
self._umat = LinearElasticPlaneStress(*self._convert(self.E, self.nu))
# initial variables for calling
# ``self.gradient(self.x)`` and ``self.hessian(self.x)``
self.x = [np.eye(2), np.zeros(0)]
self.elasticity = self.hessian
def _convert(self, E, nu):
"""Convert Lamé - constants to effective plane strain constants.
Parameters
----------
E : float
Young's modulus
nu : float
Poisson ratio
Returns
-------
float
Effective Young's modulus for plane strain formulation
float
Effective Poisson ratio for plane strain formulation
"""
if E is None or nu is None:
E_eff = None
else:
E_eff = E / (1 - nu**2)
if nu is None:
nu_eff = None
else:
nu_eff = nu / (1 - nu)
return E_eff, nu_eff
[docs]
def gradient(self, x, E=None, nu=None):
"""Evaluate the 2d-stress tensor from the deformation gradient.
Parameters
----------
x : list of ndarray
List with In-plane components (2x2) of the Deformation gradient
:math:`boldsymbol{F}` as first item.
E : float, optional
Young's modulus (default is None).
nu : float, optional
Poisson ratio (default is None).
Returns
-------
ndarray
In-plane components of stress tensor (2x2)
"""
if E is None:
E = self.E
if nu is None:
nu = self.nu
return self._umat.gradient(x, *self._convert(E, nu))
[docs]
def hessian(self, x, E=None, nu=None):
"""Evaluate the 2d-elasticity tensor from the deformation gradient.
Parameters
----------
x : list of ndarray
List with In-plane components (2x2) of the Deformation gradient
:math:`boldsymbol{F}` as first item.
E : float, optional
Young's modulus (default is None).
nu : float, optional
Poisson ratio (default is None).
Returns
-------
ndarray
In-plane components of elasticity tensor (2x2x2x2)
"""
if E is None:
E = self.E
if nu is None:
nu = self.nu
return self._umat.hessian(x, *self._convert(E, nu))
[docs]
def strain(self, x, E=None, nu=None):
"""Evaluate the strain tensor from the deformation gradient.
Parameters
----------
x : list of ndarray
List with In-plane components (2x2) of the Deformation gradient
:math:`boldsymbol{F}` as first item.
E : float, optional
Young's modulus (default is None).
nu : float, optional
Poisson ratio (default is None).
Returns
-------
e : ndarray
Strain tensor (3x3)
"""
F = x[0]
e = np.zeros((3, 3, *F.shape[-2:]))
for a in range(2):
e[a, a] = F[a, a] - 1
e[0, 1] = e[1, 0] = F[0, 1] + F[1, 0]
return [e]
[docs]
def stress(self, x, E=None, nu=None):
""" "Evaluate the 3d-stress tensor from the deformation gradient.
Parameters
----------
x : list of ndarray
List with In-plane components (2x2) of the Deformation gradient
:math:`boldsymbol{F}` as first item.
E : float, optional
Young's modulus (default is None).
nu : float, optional
Poisson ratio (default is None).
Returns
-------
ndarray
Stress tensor (3x3)
"""
F = x[0]
if E is None:
E = self.E
if nu is None:
nu = self.nu
s = np.pad(self.gradient(F, E=E, nu=nu)[0], ((0, 1), (0, 1), (0, 0), (0, 0)))
s[2, 2] = nu * (s[0, 0] + s[1, 1])
return [s]
[docs]
class LinearElasticPlaneStress:
"""Plane-stress isotropic linear-elastic material formulation.
Parameters
----------
E : float
Young's modulus.
nu : float
Poisson ratio.
"""
def __init__(self, E, nu):
self.E = E
self.nu = nu
self.kwargs = {"E": self.E, "nu": self.nu}
# initial variables for calling
# ``self.gradient(self.x)`` and ``self.hessian(self.x)``
self.x = [np.eye(2), np.zeros(0)]
self.elasticity = self.hessian
[docs]
def gradient(self, x, E=None, nu=None):
"""Evaluate the 2d-stress tensor from the deformation gradient.
Parameters
----------
x : list of ndarray
List with In-plane components (2x2) of the Deformation gradient
:math:`boldsymbol{F}` as first item.
E : float, optional
Young's modulus (default is None).
nu : float, optional
Poisson ratio (default is None).
Returns
-------
ndarray
In-plane components of stress tensor (2x2)
"""
F, statevars = x[0], x[-1]
if E is None:
E = self.E
if nu is None:
nu = self.nu
stress = np.zeros((2, 2, *F.shape[-2:]))
stress[0, 0] = E / (1 - nu**2) * ((F[0, 0] - 1) + nu * (F[1, 1] - 1))
stress[1, 1] = E / (1 - nu**2) * ((F[1, 1] - 1) + nu * (F[0, 0] - 1))
stress[0, 1] = E / (1 - nu**2) * (1 - nu) / 2 * (F[0, 1] + F[1, 0])
stress[1, 0] = stress[0, 1]
return [stress, statevars]
[docs]
def hessian(self, x=None, E=None, nu=None, shape=(1, 1)):
"""Evaluate the elasticity tensor from the deformation gradient.
Parameters
----------
x : list of ndarray, optional
List with In-plane components (2x2) of the Deformation gradient
:math:`boldsymbol{F}` as first item (default is None)-
E : float, optional
Young's modulus (default is None).
nu : float, optional
Poisson ratio (default is None).
shape : tuple of int, optional
Tuple with shape of the trailing axes (default is (1, 1)).
Returns
-------
ndarray
In-plane components of elasticity tensor (2x2x2x2).
"""
if E is None:
E = self.E
if nu is None:
nu = self.nu
elast = np.zeros((2, 2, 2, 2, *shape))
for a in range(2):
elast[a, a, a, a] = E / (1 - nu**2)
for b in range(2):
if b != a:
elast[a, a, b, b] = E / (1 - nu**2) * nu
elast[0, 1, 0, 1] = E / (1 - nu**2) * (1 - nu) / 2
elast[1, 0, 1, 0] = elast[1, 0, 0, 1] = elast[0, 1, 1, 0] = elast[0, 1, 0, 1]
return [elast]
[docs]
def strain(self, x, E=None, nu=None):
"""Evaluate the strain tensor from the deformation gradient.
Parameters
----------
x : list of ndarray
List with In-plane components (2x2) of the Deformation gradient
:math:`boldsymbol{F}` as first item.
E : float, optional
Young's modulus (default is None).
nu : float, optional
Poisson ratio (default is None).
Returns
-------
e : ndarray
Strain tensor (3x3)
"""
F = x[0]
if E is None:
E = self.E
if nu is None:
nu = self.nu
e = np.zeros((3, 3, *F.shape[-2:]))
for a in range(2):
e[a, a] = F[a, a] - 1
e[0, 1] = e[1, 0] = F[0, 1] + F[1, 0]
e[2, 2] = -nu / (1 - nu) * (F[0, 0] + F[1, 1])
return [e]
[docs]
def stress(self, x, E=None, nu=None):
""" "Evaluate the 3d-stress tensor from the deformation gradient.
Parameters
----------
x : list of ndarray
List with In-plane components (2x2) of the Deformation gradient
:math:`boldsymbol{F}` as first item.
E : float, optional
Young's modulus (default is None).
nu : float, optional
Poisson ratio (default is None).
Returns
-------
ndarray
Stress tensor (3x3)
"""
F = x[0]
return [
np.pad(self.gradient(F, E=E, nu=nu)[0], ((0, 1), (0, 1), (0, 0), (0, 0)))
]
