polished
strain calculation agrees up to 1e-4 with results from addStrainTensors. Not too exciting, but ok
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38f6609ad7
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@ -81,7 +81,7 @@ class DADF5():
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self.active[t] = list(existing.add(valid))
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def __on_air_del(self,output):
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def __on_air_del(self,output,t):
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choice = [output] if isinstance(output,str) else output
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existing = set(self.active[t])
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self.active[t] = list(existing.remove(choice))
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@ -275,7 +275,7 @@ class DADF5():
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"""
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def Cauchy(F,P):
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sigma = np.einsum('i,ijk,ilk->ijl',1.0/np.linalg.det(F['data']),P['data'],F['data'])
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sigma = (sigma + np.einsum('ijk->ikj',sigma))*0.5 # enforce symmetry
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sigma = (sigma + np.einsum('ikj',sigma))*0.5 # enforce symmetry
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return {
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'data' : sigma,
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'label' : 'sigma',
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@ -294,10 +294,10 @@ class DADF5():
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def add_Mises(self,x):
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"""Adds the equivalent Mises stres of a tensor."""
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"""Adds the equivalent Mises stress or strain of a tensor."""
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def deviator(x):
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if x['meta']['Unit'] == 'Pa':
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if x['meta']['Unit'] == 'Pa': #ToDo: Should we use this? Then add_Cauchy and add_strain_tensors also should perform sanity checks
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factor = 3.0/2.0
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elif x['meta']['Unit'] == '-':
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factor = 2.0/3.0
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@ -306,10 +306,10 @@ class DADF5():
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d = x['data']
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dev = d - np.einsum('ijk,i->ijk',np.broadcast_to(np.eye(3),[d.shape[0],3,3]),np.trace(d,axis1=1,axis2=2)/3.0)
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dev_sym = (dev + np.einsum('ikj',dev))*0.5
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#dev_sym = (dev + np.einsum('ikj',dev))*0.5 # ToDo: this is not needed (only if the input is not symmetric, but then the whole concept breaks down)
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return {
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'data' : np.sqrt(np.einsum('ijk->i',dev_sym**2)*factor),
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'data' : np.sqrt(np.einsum('ijk->i',dev**2)*factor),
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'label' : 'dev({})'.format(x['label']),
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'meta' : {
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'Unit' : x['meta']['Unit'],
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@ -348,7 +348,7 @@ class DADF5():
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'label' : 'norm({})'.format(x['label']),
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'meta' : {
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'Unit' : x['meta']['Unit'],
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'Description' : 'Norm of {} {} ({})'.format(t,x['label'],x['meta']['Description']),
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'Description' : 'Norm of {} {} ({})'.format(t,x['label'],x['meta']['Description']), # ToDo: add details about norm used
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'Creator' : 'dadf5.py:add_norm vXXXXX'
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}
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}
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@ -403,6 +403,10 @@ class DADF5():
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"""Adds the deviator of a tensor."""
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def deviator(x):
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d = x['data']
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if not np.all(np.array(d.shape[1:]) == np.array([3,3])):
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raise ValueError
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return {
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'data' : d - np.einsum('ijk,i->ijk',np.broadcast_to(np.eye(3),[d.shape[0],3,3]),np.trace(d,axis1=1,axis2=2)/3.0),
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'label' : 'dev({})'.format(x['label']),
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@ -418,11 +422,11 @@ class DADF5():
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self.__add_generic_pointwise(deviator,requested)
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def add_strain_tensor(self,t,ord,defgrad='F'):
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def add_strain_tensor(self,t,ord,defgrad='F'): #ToDo: Use t and ord
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"""Adds the a strain tensor."""
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def strain_tensor(defgrad,t,ord):
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(U,S,Vh) = np.linalg.svd(defgrad['data']) # singular value decomposition
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R_inv = np.einsum('ijk->ikj',np.matmul(U,Vh)) # inverse rotation of polar decomposition
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R_inv = np.einsum('ikj',np.matmul(U,Vh)) # inverse rotation of polar decomposition
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U = np.matmul(R_inv,defgrad['data']) # F = RU
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(D,V) = np.linalg.eigh((U+np.einsum('ikj',U))*.5) # eigen decomposition (of symmetric(ed) matrix)
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@ -431,17 +435,14 @@ class DADF5():
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V[neg[0],:,neg[1]] = V[neg[0],:,neg[1]]* -1 # ... and vector
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d = np.log(D)
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a = np.matmul(V,np.einsum('ij,ikj->ijk',d,V)) # this is wrong ...
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for j in range(V.shape[0]): # but this is slow ...
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a[j,:,:] = np.dot(V[j,:,:],np.dot(np.diag(d[j,:]),V[j,:,:].T))
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print(np.max(a))
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a = np.matmul(V,np.einsum('ij,ikj->ijk',d,V))
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return {
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'data' : a,
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'label' : 'lnV({})'.format(defgrad['label']),
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'label' : 'ln(V)({})'.format(defgrad['label']),
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'meta' : {
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'Unit' : defgrad['meta']['Unit'],
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'Description' : 'Strain tensor {} ({})'.format(defgrad['label'],defgrad['meta']['Description']),
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'Description' : 'Strain tensor ln(V){} ({})'.format(defgrad['label'],defgrad['meta']['Description']),
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'Creator' : 'dadf5.py:add_deviator vXXXXX'
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}
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}
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