498 lines
17 KiB
Python
Executable File
498 lines
17 KiB
Python
Executable File
#!/usr/bin/python
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# -*- coding: UTF-8 no BOM -*-
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import threading,time,os,subprocess,shlex,string
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import numpy as np
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from scipy.optimize import curve_fit
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from scipy.linalg import svd
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from optparse import OptionParser
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import damask
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scriptID = string.replace('$Id$','\n','\\n')
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scriptName = scriptID.split()[1][:-3]
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def execute(cmd,streamIn=None,wd='./'):
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'''
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executes a command in given directory and returns stdout and stderr for optional stdin
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'''
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initialPath=os.getcwd()
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os.chdir(wd)
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process = subprocess.Popen(shlex.split(cmd),stdout=subprocess.PIPE,stderr = subprocess.PIPE,stdin=subprocess.PIPE)
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if streamIn != None:
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out,error = process.communicate(streamIn.read())
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else:
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out,error = process.communicate()
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os.chdir(initialPath)
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return out,error
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def principalStresses(sigmas):
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'''
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computes principal stresses (i.e. eigenvalues) for a set of Cauchy stresses.
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sorted in descending order.
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'''
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lambdas=np.zeros(0,'d')
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for i in xrange(np.shape(sigmas)[1]):
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eigenvalues = np.linalg.eigvalsh(np.array(sigmas[:,i]).reshape(3,3))
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lambdas = np.append(lambdas,np.sort(eigenvalues)[::-1]) #append eigenvalues in descending order
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lambdas = np.transpose(lambdas.reshape(np.shape(sigmas)[1],3))
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return lambdas
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def stressInvariants(lambdas):
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'''
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computes stress invariants (i.e. eigenvalues) for a set of principal Cauchy stresses.
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'''
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Is=np.zeros(0,'d')
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for i in xrange(np.shape(lambdas)[1]):
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I = np.array([lambdas[0,i]+lambdas[1,i]+lambdas[2,i],\
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lambdas[0,i]*lambdas[1,i]+lambdas[1,i]*lambdas[2,i]+lambdas[2,i]*lambdas[0,i],\
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lambdas[0,i]*lambdas[1,i]*lambdas[2,i]])
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Is = np.append(Is,I)
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Is = Is.reshape(3,np.shape(lambdas)[1])
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return Is
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def formatOutput(n, type='%14.6f'):
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return ''.join([type for i in xrange(n)])
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def get_weight(ndim):
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#more to do
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return np.ones(ndim)
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# ---------------------------------------------------------------------------------------------
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# isotropic yield surfaces
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# ---------------------------------------------------------------------------------------------
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def Tresca(sigmas, sigma0):
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'''
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residuum of Tresca yield criterion (eq. 2.26)
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'''
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lambdas = principalStresses(sigmas)
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r = np.amax(np.array([abs(lambdas[2,:]-lambdas[1,:]),\
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abs(lambdas[1,:]-lambdas[0,:]),\
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abs(lambdas[0,:]-lambdas[2,:])]),0) - sigma0
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return r.ravel()
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def vonMises(sigmas, sigma0):
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'''
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residuum of Huber-Mises-Hencky yield criterion (eq. 2.37)
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'''
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return Hosford(sigmas, sigma0, 2.0)
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def Drucker(sigmas, sigma0, C_D):
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'''
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residuum of Drucker yield criterion (eq. 2.41, F = sigma0)
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'''
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return generalDrucker(sigmas, sigma0, C_D, 1.0)
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def generalDrucker(sigmas, sigma0, C_D, p):
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'''
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residuum of general Drucker yield criterion (eq. 2.42, F = sigma0)
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'''
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Is = stressInvariants(principalStresses(sigmas))
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r = (Is[1,:]**(3.0*p)-C_D*Is[2,:]**(2.0*p)) - sigma0
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return r.ravel()
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def Hosford(sigmas, sigma0, a):
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'''
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residuum of Hershey yield criterion (eq. 2.43, Y = sigma0)
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'''
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lambdas = principalStresses(sigmas)
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r = (lambdas[2,:]-lambdas[1,:])**a\
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+ (lambdas[1,:]-lambdas[0,:])**a\
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+ (lambdas[0,:]-lambdas[2,:])**a\
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-2.0*sigma0**a
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return r.ravel()
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#more to do
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# KarafillisAndBoyce
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# ---------------------------------------------------------------------------------------------
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# isotropic yield surfaces
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# ---------------------------------------------------------------------------------------------
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def Hill1948(sigmas, F,G,H,L,M,N):
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'''
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residuum of Hill 1948 quadratic yield criterion (eq. 2.48)
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'''
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r = F*(sigmas[4]-sigmas[8])**2.0\
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+ G*(sigmas[8]-sigmas[0])**2.0\
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+ H*(sigmas[0]-sigmas[4])**2.0\
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+ 2.0*L* sigmas[1]**2.0\
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+ 2.0*M* sigmas[2]**2.0\
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+ 2.0*N* sigmas[5]**2.0\
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- 1.0
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return r.ravel()/2.0
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#more to do
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# Hill 1979
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# Hill 1990,1993 need special stresses to fit
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def generalHosford(sigmas, sigma0, a):
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'''
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residuum of Hershey yield criterion (eq. 2.104, sigma = sigma0)
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'''
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lambdas = principalStresses(sigmas)
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r = np.amax(np.array([F*(abs(lambdas[:,1]-lambdas[:,2]))**a,\
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G*(abs(lambdas[:,2]-lambdas[:,0]))**a,\
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H*(abs(lambdas[:,0]-lambdas[:,1]))**a]),1) - sigma0**a
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return r.ravel()
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def Barlat1991(sigmas, sigma0, order, \
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a=1.0, b=1.0, c=1.0, f=1.0, g=1.0, h=1.0):
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'''
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residuum of Barlat 1997 yield criterion
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'''
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cos = np.cos; pi = np.pi; abs = np.abs
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A = a*(sigmas[4] - sigmas[8])
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B = b*(sigmas[8] - sigmas[0])
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C = c*(sigmas[0] - sigmas[4])
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F = f*sigmas[5]
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G = g*sigmas[2]
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H = h*sigmas[1]
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I2 = (F*F + G*G + H*H)/3.0 + ((A-C)*(A-C)+(C-B)*(C-B)+(B-A)*(B-A))/54.0
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I3 = (C-B)*(A-C)*(B-A)/54.0 + F*G*H - \
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((C-B)*F*F+(A-C)*G*G+(B-A)*H*H)/6
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theta = np.arccos(I3/pow(I2,1.5))
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Phi = pow(3.0*I2, order/2.0)* (
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pow(abs(2.0*cos((2.0*theta + pi)/6.0)), order) +
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pow(abs(2.0*cos((2.0*theta + pi*3.0)/6.0)), order) +
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pow(abs(2.0*cos((2.0*theta + pi*5.0)/6.0)), order)
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)
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r = Phi - 2.0*pow(sigma0, order)
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return r.ravel()
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def Barlat1991iso(sigmas, sigma0, m):
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'''
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residuum of isotropic Barlat 1991 yield criterion (eq. 2.37)
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'''
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return Barlat1991(sigmas, sigma0, m)
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def Barlat1991aniso(sigmas, sigma0, m, a,b,c,f,g,h):
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'''
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residuum of anisotropic Barlat 1991 yield criterion (eq. 2.37)
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'''
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return Barlat1991(sigmas, sigma0, m, a,b,c,f,g,h)
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def Barlat1994(sigmas, sigma0, a):
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'''
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residuum of Hershey yield criterion (eq. 2.104, sigma_e = sigma0)
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'''
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return None
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fittingCriteria = {
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'Tresca' :{'fit' :np.ones(1,'d'),'err':np.inf,
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'name' :'Tresca',
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'paras':'Initial yield stress:'},
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'vonMises' :{'fit' :np.ones(1,'d'),'err':np.inf,
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'name' :'Huber-Mises-Hencky(von Mises)',
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'paras':'Initial yield stress:'},
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'Hill48' :{'fit' :np.ones(6,'d'),'err':np.inf,
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'name' :'Hill1948',
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'paras':'Normalized [F, G, H, L, M, N]'},
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'Drucker' :{'fit' :np.ones(2,'d'),'err':np.inf,
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'name' :'Drucker',
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'paras':'Initial yield stress, C_D:'},
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'Barlat1991iso' :{'fit' :np.ones(2,'d'),'err':np.inf,
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'name' :'Barlat1991iso',
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'paras':'Initial yield stress, m:'},
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'Barlat1991aniso':{'fit' :np.ones(8,'d'),'err':np.inf,
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'name' :'Barlat1991aniso',
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'paras':'Initial yield stress, m, a, b, c, f, g, h:'},
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'worst' :{'err':np.inf},
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'best' :{'err':np.inf}
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}
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thresholdParameter = ['totalshear','equivalentStrain']
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#---------------------------------------------------------------------------------------------------
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class Loadcase():
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#---------------------------------------------------------------------------------------------------
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'''
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Class for generating load cases for the spectral solver
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'''
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# ------------------------------------------------------------------
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def __init__(self,finalStrain,incs,time):
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print('using the random load case generator')
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self.finalStrain = finalStrain
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self.incs = incs
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self.time = time
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def getLoadcase(self,N=0):
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defgrad=['*']*9
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stress =[0]*9
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values=(np.random.random_sample(9)-.5)*self.finalStrain*2
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main=np.array([0,4,8])
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np.random.shuffle(main)
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for i in main[:2]: # fill 2 out of 3 main entries
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defgrad[i]=1.+values[i]
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stress[i]='*'
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for off in [[1,3,0],[2,6,0],[5,7,0]]: # fill 3 off-diagonal pairs of defgrad (1 or 2 entries)
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off=np.array(off)
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np.random.shuffle(off)
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for i in off[0:2]:
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if i != 0:
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defgrad[i]=values[i]
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stress[i]='*'
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return 'f '+' '.join(str(c) for c in defgrad)+\
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' p '+' '.join(str(c) for c in stress)+\
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' incs %s'%self.incs+\
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' time %s'%self.time
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#---------------------------------------------------------------------------------------------------
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class Criterion(object):
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#---------------------------------------------------------------------------------------------------
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'''
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Fitting to certain criterion
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'''
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def __init__(self,name='worst'):
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self.name = name
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self.results = fittingCriteria
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if self.name.lower() not in map(str.lower, self.results.keys()):
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raise Exception('no suitable fitting criterion selected')
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else:
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print('fitting to the %s criterion'%name)
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def fit(self,stress):
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global fitResults
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if self.name.lower() == 'tresca':
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funResidum = Tresca
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text = '\nCoefficient of Tresca criterion:\nsigma0: '+formatOutput(1)
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elif self.name.lower() == 'vonmises':
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funResidum = vonMises
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text = '\nCoefficient of Huber-Mises-Hencky criterion:\nsigma0: '+formatOutput(1)
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elif self.name.lower() == 'drucker':
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funResidum = Drucker
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text = '\nCoefficient of Drucker criterion:\nsigma0, C_D: '+formatOutput(2)
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elif self.name.lower() == 'hill48':
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funResidum = Hill1948
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text = '\nCoefficient of Hill1948 criterion:\n[F, G, H, L, M, N]:\n'+formatOutput(6)
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elif self.name.lower() == 'barlat91iso':
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funResidum = Barlat1991iso
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text = '\nCoefficient of isotropic Barlat 1991 criterion:\nsigma0, m:\n'+formatOutput(2)
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elif self.name.lower() == 'barlat91aniso':
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funResidum = Barlat1991iso
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text = '\nCoefficient of anisotropic Barlat 1991 criterion:\nsigma0, \m, a, b, c, f, g, h:\n' \
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+formatOutput(8)
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if fitResults == []:
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initialguess = fittingCriteria[funResidum.__name__]['fit']
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else:
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initialguess = np.array(fitResults[-1])
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weight = get_weight(np.shape(stress)[1])
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try:
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popt, pcov = \
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curve_fit(funResidum, stress, np.zeros(np.shape(stress)[1]),
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initialguess, weight)
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print (text%popt)
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fitResults.append(popt.tolist())
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except Exception as detail:
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print detail
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pass
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#---------------------------------------------------------------------------------------------------
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class myThread (threading.Thread):
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#---------------------------------------------------------------------------------------------------
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'''
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Runner class
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'''
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def __init__(self, threadID):
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threading.Thread.__init__(self)
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self.threadID = threadID
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def run(self):
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s.acquire()
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conv=converged()
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s.release()
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while not conv:
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doSim(4.,self.name)
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s.acquire()
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conv=converged()
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s.release()
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def doSim(delay,thread):
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s.acquire()
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me=getLoadcase()
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if not os.path.isfile('%s.load'%me):
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print('generating loadcase for sim %s from %s'%(me,thread))
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f=open('%s.load'%me,'w')
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f.write(myLoad.getLoadcase(me))
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f.close()
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s.release()
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else: s.release()
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s.acquire()
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if not os.path.isfile('%s_%i.spectralOut'%(options.geometry,me)):
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print('starting simulation %s from %s'%(me,thread))
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s.release()
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execute('DAMASK_spectral -g %s -l %i'%(options.geometry,me))
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else: s.release()
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s.acquire()
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if not os.path.isfile('./postProc/%s_%i.txt'%(options.geometry,me)):
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print('starting post processing for sim %i from %s'%(me,thread))
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s.release()
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try:
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execute('postResults --cr f,p --co totalshear %s_%i.spectralOut'%(options.geometry,me))
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except:
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execute('postResults --cr f,p %s_%i.spectralOut'%(options.geometry,me))
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execute('addCauchy ./postProc/%s_%i.txt'%(options.geometry,me))
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execute('addStrainTensors -l -v ./postProc/%s_%i.txt'%(options.geometry,me))
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execute('addMises -s Cauchy -e ln(V) ./postProc/%s_%i.txt'%(options.geometry,me))
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else: s.release()
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s.acquire()
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print('reading values for sim %i from %s'%(me,thread))
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s.release()
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refFile = open('./postProc/%s_%i.txt'%(options.geometry,me))
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table = damask.ASCIItable(refFile)
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table.head_read()
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if options.fitting =='equivalentStrain':
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thresholdKey = 'Mises(ln(V))'
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elif options.fitting =='totalshear':
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thresholdKey = 'totalshear'
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s.acquire()
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for l in [thresholdKey,'1_Cauchy']:
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if l not in table.labels: print '%s not found'%l
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s.release()
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table.data_readArray(['%i_Cauchy'%(i+1) for i in xrange(9)]+[thresholdKey])
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line = 0
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lines = np.shape(table.data)[0]
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yieldStress=[None for i in xrange(int(options.yieldValue[2]))]
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for i,threshold in enumerate(np.linspace(options.yieldValue[0],options.yieldValue[1],options.yieldValue[2])):
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while line < lines:
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if table.data[line,9]>= threshold:
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upper,lower = table.data[line,9],table.data[line-1,9] # values for linear interpolation
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yieldStress[i] = table.data[line-1,0:9] * (upper-threshold)/(upper-lower) \
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+ table.data[line ,0:9] * (threshold-lower)/(upper-lower) # linear interpolation of stress values
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yieldStress[i][1] = (yieldStress[i][1] + yieldStress[i][3])/2.0
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yieldStress[i][2] = (yieldStress[i][2] + yieldStress[i][6])/2.0
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yieldStress[i][5] = (yieldStress[i][5] + yieldStress[i][7])/2.0
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yieldStress[i][3] = yieldStress[i][1]
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yieldStress[i][6] = yieldStress[i][2]
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yieldStress[i][7] = yieldStress[i][5]
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break
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else:
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line+=1
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s.acquire()
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global stressAll
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print('number of yield points of sim %i: %i'%(me,len(yieldStress)))
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print('starting fitting for sim %i from %s'%(me,thread))
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try:
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for i in xrange(int(options.yieldValue[2])):
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stressAll[i]=np.append(yieldStress[i]/unitGPa,stressAll[i])
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myFit.fit(stressAll[i].reshape(len(stressAll[i])//9,9).transpose())
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except Exception:
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print('could not fit for sim %i from %s'%(me,thread))
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s.release()
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return
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s.release()
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def getLoadcase():
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global N_simulations
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N_simulations+=1
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return N_simulations
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def converged():
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global N_simulations
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if N_simulations < options.max:
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return False
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else:
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return True
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# --------------------------------------------------------------------
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# MAIN
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# --------------------------------------------------------------------
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parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
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Performs calculations with various loads on given geometry file and fits yield surface.
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""", version=string.replace(scriptID,'\n','\\n')
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)
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parser.add_option('-l','--load' , dest='load', type='float', nargs=3,
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help='load: final strain; increments; time %default', metavar='float int float')
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parser.add_option('-g','--geometry', dest='geometry', type='string',
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help='name of the geometry file [%default]', metavar='string')
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parser.add_option('-c','--criterion', dest='criterion', choices=fittingCriteria.keys(),
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help='criterion for stopping simulations [%default]', metavar='string')
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parser.add_option('-f','--fitting', dest='fitting', choices=thresholdParameter,
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help='yield criterion [%default]', metavar='string')
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parser.add_option('-y','--yieldvalue', dest='yieldValue', type='float', nargs=3,
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help='yield points: start; end; count %default', metavar='float float int')
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parser.add_option('--min', dest='min', type='int',
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help='minimum number of simulations [%default]', metavar='int')
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parser.add_option('--max', dest='max', type='int',
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help='maximum number of iterations [%default]', metavar='int')
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parser.add_option('-t','--threads', dest='threads', type='int',
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help='number of parallel executions [%default]', metavar='int')
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parser.set_defaults(min = 12)
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parser.set_defaults(max = 30)
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parser.set_defaults(threads = 4)
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parser.set_defaults(yieldValue = (0.002,0.004,2))
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parser.set_defaults(load = (0.010,100,100.0))
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parser.set_defaults(criterion = 'worst')
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parser.set_defaults(fitting = 'totalshear')
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parser.set_defaults(geometry = '20grains16x16x16')
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options = parser.parse_args()[0]
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|
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if not os.path.isfile(options.geometry+'.geom'):
|
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parser.error('geometry file %s.geom not found'%options.geometry)
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if not os.path.isfile('material.config'):
|
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parser.error('material.config file not found')
|
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if options.threads<1:
|
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parser.error('invalid number of threads %i'%options.threads)
|
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if options.min<0:
|
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parser.error('invalid minimum number of simulations %i'%options.min)
|
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if options.max<options.min:
|
|
parser.error('invalid maximum number of simulations (below minimum)')
|
|
if options.yieldValue[0]>options.yieldValue[1]:
|
|
parser.error('invalid yield start (below yield end)')
|
|
if options.yieldValue[2] != int(options.yieldValue[2]):
|
|
parser.error('count must be an integer')
|
|
if not os.path.isfile('numerics.config'):
|
|
print('numerics.config file not found')
|
|
|
|
if not os.path.isfile('material.config'):
|
|
print('material.config file not found')
|
|
|
|
unitGPa = 10.e8
|
|
N_simulations=0
|
|
fitResults = []
|
|
s=threading.Semaphore(1)
|
|
|
|
stressAll=[np.zeros(0,'d').reshape(0,0) for i in xrange(int(options.yieldValue[2]))]
|
|
myLoad = Loadcase(options.load[0],options.load[1],options.load[2])
|
|
myFit = Criterion(options.criterion)
|
|
|
|
threads=[]
|
|
|
|
for i in range(options.threads):
|
|
threads.append(myThread(i))
|
|
threads[i].start()
|
|
|
|
for i in range(options.threads):
|
|
threads[i].join()
|
|
|
|
print 'finished fitting to yield criteria'
|