312 lines
8.4 KiB
Python
312 lines
8.4 KiB
Python
import numpy as _np
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def Cauchy(P,F):
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"""
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Return Cauchy stress calculated from first Piola-Kirchhoff stress and deformation gradient.
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Resulting tensor is symmetrized as the Cauchy stress needs to be symmetric.
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Parameters
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----------
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F : numpy.ndarray of shape (:,3,3) or (3,3)
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Deformation gradient.
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P : numpy.ndarray of shape (:,3,3) or (3,3)
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First Piola-Kirchhoff stress.
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"""
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if _np.shape(F) == _np.shape(P) == (3,3):
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sigma = 1.0/_np.linalg.det(F) * _np.dot(P,F.T)
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else:
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#sigma = _np.einsum('i,ijk,ilk->ijl',1.0/_np.linalg.det(F),P,F)
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sigma = _np.einsum('...,...ij,...kj->...ik',1.0/_np.linalg.det(F),P,F)
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return symmetric(sigma)
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def deviatoric_part(T):
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"""
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Return deviatoric part of a tensor.
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Parameters
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----------
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T : numpy.ndarray of shape (:,3,3) or (3,3)
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Tensor of which the deviatoric part is computed.
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"""
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return T - _np.eye(3)*spherical_part(T) if _np.shape(T) == (3,3) else \
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T - _np.einsum('...ij,...->...ij',_np.eye(3),spherical_part(T))
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def eigenvalues(T_sym):
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"""
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Return the eigenvalues, i.e. principal components, of a symmetric tensor.
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The eigenvalues are sorted in ascending order, each repeated according to
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its multiplicity.
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Parameters
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----------
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T_sym : numpy.ndarray of shape (:,3,3) or (3,3)
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Symmetric tensor of which the eigenvalues are computed.
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"""
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return _np.linalg.eigvalsh(symmetric(T_sym))
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def eigenvectors(T_sym,RHS=False):
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"""
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Return eigenvectors of a symmetric tensor.
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The eigenvalues are sorted in ascending order of their associated eigenvalues.
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Parameters
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----------
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T_sym : numpy.ndarray of shape (:,3,3) or (3,3)
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Symmetric tensor of which the eigenvectors are computed.
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RHS: bool, optional
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Enforce right-handed coordinate system. Default is False.
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"""
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(u,v) = _np.linalg.eigh(symmetric(T_sym))
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if RHS:
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if _np.shape(T_sym) == (3,3):
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if _np.linalg.det(v) < 0.0: v[:,2] *= -1.0
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else:
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v[_np.linalg.det(v) < 0.0,:,2] *= -1.0
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return v
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def left_stretch(T):
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"""
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Return the left stretch of a tensor.
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Parameters
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----------
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T : numpy.ndarray of shape (:,3,3) or (3,3)
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Tensor of which the left stretch is computed.
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"""
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return _polar_decomposition(T,'V')[0]
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def maximum_shear(T_sym):
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"""
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Return the maximum shear component of a symmetric tensor.
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Parameters
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----------
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T_sym : numpy.ndarray of shape (:,3,3) or (3,3)
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Symmetric tensor of which the maximum shear is computed.
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"""
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w = eigenvalues(T_sym)
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return (w[0] - w[2])*0.5 if _np.shape(T_sym) == (3,3) else \
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(w[...,0] - w[...,2])*0.5
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def Mises_strain(epsilon):
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"""
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Return the Mises equivalent of a strain tensor.
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Parameters
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----------
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epsilon : numpy.ndarray of shape (:,3,3) or (3,3)
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Symmetric strain tensor of which the von Mises equivalent is computed.
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"""
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return _Mises(epsilon,2.0/3.0)
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def Mises_stress(sigma):
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"""
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Return the Mises equivalent of a stress tensor.
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Parameters
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----------
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sigma : numpy.ndarray of shape (:,3,3) or (3,3)
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Symmetric stress tensor of which the von Mises equivalent is computed.
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"""
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return _Mises(sigma,3.0/2.0)
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def PK2(P,F):
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"""
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Calculate second Piola-Kirchhoff stress from first Piola-Kirchhoff stress and deformation gradient.
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Parameters
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----------
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P : numpy.ndarray of shape (...,3,3) or (3,3)
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First Piola-Kirchhoff stress.
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F : numpy.ndarray of shape (...,3,3) or (3,3)
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Deformation gradient.
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"""
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if _np.shape(F) == _np.shape(P) == (3,3):
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S = _np.dot(_np.linalg.inv(F),P)
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else:
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S = _np.einsum('...jk,...kl->...jl',_np.linalg.inv(F),P)
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return symmetric(S)
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def right_stretch(T):
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"""
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Return the right stretch of a tensor.
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Parameters
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----------
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T : numpy.ndarray of shape (:,3,3) or (3,3)
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Tensor of which the right stretch is computed.
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"""
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return _polar_decomposition(T,'U')[0]
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def rotational_part(T):
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"""
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Return the rotational part of a tensor.
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Parameters
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----------
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T : numpy.ndarray of shape (:,3,3) or (3,3)
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Tensor of which the rotational part is computed.
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"""
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return _polar_decomposition(T,'R')[0]
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def spherical_part(T,tensor=False):
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"""
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Return spherical (hydrostatic) part of a tensor.
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Parameters
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----------
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T : numpy.ndarray of shape (:,3,3) or (3,3)
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Tensor of which the hydrostatic part is computed.
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tensor : bool, optional
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Map spherical part onto identity tensor. Default is false
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"""
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if T.shape == (3,3):
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sph = _np.trace(T)/3.0
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return sph if not tensor else _np.eye(3)*sph
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else:
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sph = _np.trace(T,axis2=-2,axis1=-1)/3.0
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if not tensor:
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return sph
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else:
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#return _np.einsum('ijk,i->ijk',_np.broadcast_to(_np.eye(3),(T.shape[0],3,3)),sph)
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return _np.einsum('...jk,...->...jk',_np.eye(3),sph)
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def strain_tensor(F,t,m):
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"""
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Return strain tensor calculated from deformation gradient.
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For details refer to https://en.wikipedia.org/wiki/Finite_strain_theory and
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https://de.wikipedia.org/wiki/Verzerrungstensor
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Parameters
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----------
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F : numpy.ndarray of shape (:,3,3) or (3,3)
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Deformation gradient.
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t : {‘V’, ‘U’}
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Type of the polar decomposition, ‘V’ for left stretch tensor and ‘U’ for right stretch tensor.
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m : float
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Order of the strain.
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"""
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F_ = F.reshape(1,3,3) if F.shape == (3,3) else F
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if t == 'V':
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B = _np.matmul(F_,transpose(F_))
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w,n = _np.linalg.eigh(B)
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elif t == 'U':
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C = _np.matmul(transpose(F_),F_)
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w,n = _np.linalg.eigh(C)
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if m > 0.0:
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#eps = 1.0/(2.0*abs(m)) * (+ _np.matmul(n,_np.einsum('ij,ikj->ijk',w**m,n))
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# - _np.broadcast_to(_np.eye(3),[F_.shape[0],3,3]))
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eps = 1.0/(2.0*abs(m)) * (+ _np.matmul(n,_np.einsum('...j,...kj->...jk',w**m,n))
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- _np.einsum('...jk->...jk',_np.eye(3)))
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elif m < 0.0:
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eps = 1.0/(2.0*abs(m)) * (- _np.matmul(n,_np.einsum('...j,...kj->...jk',w**m,n))
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+ _np.einsum('...jk->...jk',_np.eye(3)))
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else:
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eps = _np.matmul(n,_np.einsum('...j,...kj->....jk',0.5*_np.log(w),n))
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return eps.reshape(3,3) if _np.shape(F) == (3,3) else \
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eps
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def symmetric(T):
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"""
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Return the symmetrized tensor.
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Parameters
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----------
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T : numpy.ndarray of shape (...,3,3) or (3,3)
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Tensor of which the symmetrized values are computed.
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"""
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return (T+transpose(T))*0.5
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def transpose(T):
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"""
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Return the transpose of a tensor.
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Parameters
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----------
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T : numpy.ndarray of shape (...,3,3) or (3,3)
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Tensor of which the transpose is computed.
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"""
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return T.T if _np.shape(T) == (3,3) else \
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_np.swapaxes(T,axis2=-2,axis1=-1)
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def _polar_decomposition(T,requested):
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"""
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Singular value decomposition.
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Parameters
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----------
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T : numpy.ndarray of shape (:,3,3) or (3,3)
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Tensor of which the singular values are computed.
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requested : iterable of str
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Requested outputs: ‘R’ for the rotation tensor,
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‘V’ for left stretch tensor and ‘U’ for right stretch tensor.
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"""
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u, s, vh = _np.linalg.svd(T)
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R = _np.dot(u,vh) if _np.shape(T) == (3,3) else \
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_np.einsum('...ij,...jk->...ik',u,vh)
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output = []
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if 'R' in requested:
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output.append(R)
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if 'V' in requested:
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output.append(_np.dot(T,R.T) if _np.shape(T) == (3,3) else _np.einsum('...ij,...kj->...ik',T,R))
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if 'U' in requested:
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output.append(_np.dot(R.T,T) if _np.shape(T) == (3,3) else _np.einsum('...ji,...jk->...ik',R,T))
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return tuple(output)
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def _Mises(T_sym,s):
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"""
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Base equation for Mises equivalent of a stres or strain tensor.
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Parameters
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----------
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T_sym : numpy.ndarray of shape (:,3,3) or (3,3)
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Symmetric tensor of which the von Mises equivalent is computed.
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s : float
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Scaling factor (2/3 for strain, 3/2 for stress).
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"""
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d = deviatoric_part(T_sym)
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return _np.sqrt(s*(_np.sum(d**2.0))) if _np.shape(T_sym) == (3,3) else \
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_np.sqrt(s*_np.einsum('...jk->...',d**2.0))
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