import numpy as np def Cauchy(F,P): """ Return Cauchy stress calculated from 1. Piola-Kirchhoff stress and deformation gradient. Resulting tensor is symmetrized as the Cauchy stress needs to be symmetric. Parameters ---------- F : numpy.array of shape (:,3,3) or (3,3) Deformation gradient. P : numpy.array of shape (:,3,3) or (3,3) 1. Piola-Kirchhoff stress. """ if np.shape(F) == np.shape(P) == (3,3): sigma = 1.0/np.linalg.det(F) * np.dot(P,F.T) else: sigma = np.einsum('i,ijk,ilk->ijl',1.0/np.linalg.det(F),P,F) return symmetric(sigma) def strain_tensor(F,t,m): """ Return strain tensor calculated from deformation gradient. For details refer to https://en.wikipedia.org/wiki/Finite_strain_theory and https://de.wikipedia.org/wiki/Verzerrungstensor Parameters ---------- F : numpy.array of shape (:,3,3) or (3,3) Deformation gradient. t : {‘V’, ‘U’} Type of the polar decomposition, ‘V’ for left stretch tensor and ‘U’ for right stretch tensor. m : float Order of the strain. """ F_ = F.reshape((1,3,3)) if F.shape == (3,3) else F if t == 'U': B = np.matmul(F_,transpose(F_)) w,n = np.linalg.eigh(B) elif t == 'V': C = np.matmul(transpose(F_),F_) w,n = np.linalg.eigh(C) if m > 0.0: eps = 1.0/(2.0*abs(m)) * (+ np.matmul(n,np.einsum('ij,ikj->ijk',w**m,n)) - np.broadcast_to(np.ones(3),[F_.shape[0],3])) elif m < 0.0: eps = 1.0/(2.0*abs(m)) * (- np.matmul(n,np.einsum('ij,ikj->ijk',w**m,n)) + np.broadcast_to(np.ones(3),[F_.shape[0],3])) else: eps = np.matmul(n,np.einsum('ij,ikj->ijk',0.5*np.log(w),n)) return eps.reshape((3,3)) if np.shape(F) == (3,3) else \ eps def deviatoric_part(x): """ Return deviatoric part of a tensor. Parameters ---------- x : numpy.array of shape (:,3,3) or (3,3) Tensor of which the deviatoric part is computed. """ return x - np.eye(3)*spherical_part(x) if np.shape(x) == (3,3) else \ x - np.einsum('ijk,i->ijk',np.broadcast_to(np.eye(3),[x.shape[0],3,3]),spherical_part(x)) def spherical_part(x): """ Return spherical (hydrostatic) part of a tensor. A single scalar is returned, i.e. the hydrostatic part is not mapped on the 3rd order identity matrix. Parameters ---------- x : numpy.array of shape (:,3,3) or (3,3) Tensor of which the hydrostatic part is computed. """ return np.trace(x)/3.0 if np.shape(x) == (3,3) else \ np.trace(x,axis1=1,axis2=2)/3.0 def Mises_stress(sigma): """ Return the Mises equivalent of a stress tensor. Parameters ---------- sigma : numpy.array of shape (:,3,3) or (3,3) Symmetric stress tensor of which the von Mises equivalent is computed. """ s = deviatoric_part(sigma) return np.sqrt(3.0/2.0*(np.sum(s**2.0))) if np.shape(sigma) == (3,3) else \ np.sqrt(3.0/2.0*np.einsum('ijk->i',s**2.0)) def Mises_strain(epsilon): """ Return the Mises equivalent of a strain tensor. Parameters ---------- epsilon : numpy.array of shape (:,3,3) or (3,3) Symmetric strain tensor of which the von Mises equivalent is computed. """ s = deviatoric_part(epsilon) return np.sqrt(2.0/3.0*(np.sum(s**2.0))) if np.shape(epsilon) == (3,3) else \ np.sqrt(2.0/3.0*np.einsum('ijk->i',s**2.0)) def symmetric(x): """ Return the symmetrized tensor. Parameters ---------- x : numpy.array of shape (:,3,3) or (3,3) Tensor of which the symmetrized values are computed. """ return (x+transpose(x))*0.5 def maximum_shear(x): """ Return the maximum shear component of a symmetric tensor. Parameters ---------- x : numpy.array of shape (:,3,3) or (3,3) Symmetric tensor of which the maximum shear is computed. """ w = np.linalg.eigvalsh(symmetric(x)) # eigenvalues in ascending order return (w[2] - w[0])*0.5 if np.shape(x) == (3,3) else \ (w[:,2] - w[:,0])*0.5 def principal_components(x): """ Return the principal components of a symmetric tensor. The principal components (eigenvalues) are sorted in descending order, each repeated according to its multiplicity. Parameters ---------- x : numpy.array of shape (:,3,3) or (3,3) Symmetric tensor of which the principal compontents are computed. """ w = np.linalg.eigvalsh(symmetric(x)) # eigenvalues in ascending order return w[::-1] if np.shape(x) == (3,3) else \ w[:,::-1] def transpose(x): """ Return the transpose of a tensor. Parameters ---------- x : numpy.array of shape (:,3,3) or (3,3) Tensor of which the transpose is computed. """ return x.T if np.shape(x) == (3,3) else \ np.transpose(x,(0,2,1)) def rotational_part(x): """ Return the rotational part of a tensor. Parameters ---------- x : numpy.array of shape (:,3,3) or (3,3) Tensor of which the rotational part is computed. """ return __polar_decomposition(x,'R') def left_stretch(x): """ Return the left stretch of a tensor. Parameters ---------- x : numpy.array of shape (:,3,3) or (3,3) Tensor of which the left stretch is computed. """ return __polar_decomposition(x,'V') def right_stretch(x): """ Return the right stretch of a tensor. Parameters ---------- x : numpy.array of shape (:,3,3) or (3,3) Tensor of which the right stretch is computed. """ return __polar_decomposition(x,'U') def __polar_decomposition(x,requested): """ Singular value decomposition. Parameters ---------- x : numpy.array of shape (:,3,3) or (3,3) Tensor of which the singular values are computed. requested : list of str Requested outputs: ‘R’ for the rotation tensor, ‘V’ for left stretch tensor and ‘U’ for right stretch tensor. """ u, s, vh = np.linalg.svd(x) R = np.dot(u,vh) if np.shape(x) == (3,3) else \ np.einsum('ijk,ikl->ijl',u,vh) output = [] if 'R' in requested: output.append(R) if 'V' in requested: output.append(np.dot(x,R.T) if np.shape(x) == (3,3) else np.einsum('ijk,ilk->ijl',x,R)) if 'U' in requested: output.append(np.dot(R.T,x) if np.shape(x) == (3,3) else np.einsum('ikj,ikl->ijl',R,x)) return tuple(output)