added yield criterion of facet potential

This commit is contained in:
Fengbo Han 2017-10-24 11:15:34 +02:00
parent 0750f7fd01
commit 82758bd90f
2 changed files with 158 additions and 28 deletions

View File

@ -6,6 +6,8 @@ import numpy as np
from optparse import OptionParser from optparse import OptionParser
import damask import damask
from damask.util import leastsqBound from damask.util import leastsqBound
from scipy.optimize import nnls
import math
scriptName = os.path.splitext(os.path.basename(__file__))[0] scriptName = os.path.splitext(os.path.basename(__file__))[0]
scriptID = ' '.join([scriptName,damask.version]) scriptID = ' '.join([scriptName,damask.version])
@ -15,6 +17,7 @@ def runFit(exponent, eqStress, dimension, criterion):
global fitResidual global fitResidual
global Guess, dDim global Guess, dDim
if options.criterion!='facet':
dDim = dimension - 3 dDim = dimension - 3
nParas = len(fitCriteria[criterion]['bound'][dDim]) nParas = len(fitCriteria[criterion]['bound'][dDim])
nExpo = fitCriteria[criterion]['nExpo'] nExpo = fitCriteria[criterion]['nExpo']
@ -43,9 +46,92 @@ def runFit(exponent, eqStress, dimension, criterion):
for t in range(options.threads): for t in range(options.threads):
threads[t].join() threads[t].join()
if options.criterion=='facet':
doFacetFit()
damask.util.croak('Residuals') damask.util.croak('Residuals')
damask.util.croak(fitResidual) damask.util.croak(fitResidual)
def doFacetFit():
n = options.order
Data = np.zeros((options.numpoints, 10))
for i in range(options.numpoints):
fileName = options.geometry + '_' + str(i+1) + '.yield'
while os.path.exists(fileName):
break
data_i = np.loadtxt(fileName)
sv = (data_i[0,0] + data_i[1,1] + data_i[2,2])/3.0
#convert stress and strain form the 6D to 5D space
S1 = math.sqrt(2.0)*(data_i[0,0] - data_i[1,1])/2.0
S2 = math.sqrt(6.0)*(data_i[0,0] + data_i[1,1] - 2.0*sv)/2.0
S3 = math.sqrt(2.0)*data_i[1,2]
S4 = math.sqrt(2.0)*data_i[2,0]
S5 = math.sqrt(2.0)*data_i[0,1]
E1 = math.sqrt(2.0)*(data_i[3,0]-data_i[4,1])/2.0
E2 = math.sqrt(6.0)*(data_i[3,0]+data_i[4,1])/2.0
E3 = math.sqrt(2.0)*data_i[4,2]
E4 = math.sqrt(2.0)*data_i[5,0]
E5 = math.sqrt(2.0)*data_i[3,1]
Data[i,:] = [E1,E2,E3,E4,E5,S1,S2,S3,S4,S5]
Data[:,5:] = Data[:,5:] / 100000000.0
path=os.path.join(os.getcwd(),'final.mmm')
np.savetxt(path, Data, header='', comments='', fmt='% 15.10f')
if options.dimension == 2:
reducedIndices = [0,1,4,5,6,9]
strainRateIndices = [0,1,4]
stressIndices = [5,6,9]
elif options.dimension == 3:
reducedIndices = [i for i in range(10)]
strainRateIndices = [0,1,2,3,4]
stressIndices = [5,6,7,8,9]
numDirections = Data.shape[0]
Indices = np.arange(numDirections)
sdPairs = Data[:,reducedIndices][Indices,:]
numPairs = sdPairs.shape[0]
dimensionality = sdPairs.shape[1] / 2
ds = sdPairs[:,0:dimensionality]
s = sdPairs[:,dimensionality::]
A = np.zeros((numPairs, numPairs))
B = np.ones((numPairs,))
for i in range(numPairs):
for j in range(numPairs):
lamb = 1.0
s_i = s[i,:]
ds_j = ds[j,:]
A[i,j] = lamb * (np.dot(s_i.ravel(), ds_j.ravel()) ** n)
lambdas, residuals = nnls(A, B)
nonZeroTerms = np.logical_not(np.isclose(lambdas, 0.))
numNonZeroTerms = np.sum(nonZeroTerms)
dataOut = np.zeros((numNonZeroTerms, 6))
if options.dimension == 2:
dataOut[:,0] = lambdas[nonZeroTerms]
dataOut[:,1] = ds[nonZeroTerms,:][:,0]
dataOut[:,2] = ds[nonZeroTerms,:][:,1]
dataOut[:,5] = ds[nonZeroTerms,:][:,2]
elif options.dimension == 3:
dataOut[:,0] = lambdas[nonZeroTerms]
dataOut[:,1] = ds[nonZeroTerms,:][:,0]
dataOut[:,2] = ds[nonZeroTerms,:][:,1]
dataOut[:,3] = ds[nonZeroTerms,:][:,2]
dataOut[:,4] = ds[nonZeroTerms,:][:,3]
dataOut[:,5] = ds[nonZeroTerms,:][:,4]
headerText = 'facet\n 1 \n F \n {0:<3d} \n {1:<3d} '.format(n, numNonZeroTerms)
path=os.path.join(os.getcwd(),'facet_o{0}.fac'.format(n))
np.savetxt(path, dataOut, header=headerText, comments='', fmt='% 15.10f')
def principalStresses(sigmas): def principalStresses(sigmas):
""" """
Computes principal stresses (i.e. eigenvalues) for a set of Cauchy stresses. Computes principal stresses (i.e. eigenvalues) for a set of Cauchy stresses.
@ -1015,6 +1101,12 @@ fitCriteria = {
'labels': 'a,b,c,d,e,f,g,h', 'labels': 'a,b,c,d,e,f,g,h',
'dimen': [2], 'dimen': [2],
}, },
'facet' :{'name': 'Facet',
'nExpo': None,
'bound': [(None,None)],
'labels': 'lambdas',
'dimen': [2,3],
},
} }
thresholdParameter = ['totalshear','equivalentStrain'] thresholdParameter = ['totalshear','equivalentStrain']
@ -1225,6 +1317,9 @@ class myThread (threading.Thread):
conv=converged() conv=converged()
semaphore.release() semaphore.release()
while not conv: while not conv:
if options.criterion=='facet':
doSimForFacet(self.name)
else:
doSim(self.name) doSim(self.name)
semaphore.acquire() semaphore.acquire()
conv=converged() conv=converged()
@ -1279,6 +1374,26 @@ def doSim(thread):
damask.util.croak('\n') damask.util.croak('\n')
semaphore.release() semaphore.release()
def doSimForFacet(thread):
semaphore.acquire()
global myLoad
loadNo=loadcaseNo()
if not os.path.isfile('%s.load'%loadNo):
damask.util.croak('Generating load case for simulation %s (%s)'%(loadNo,thread))
f=open('%s.load'%loadNo,'w')
f.write(myLoad.getLoadcase(loadNo))
f.close()
semaphore.release()
else: semaphore.release()
# if spectralOut does not exist, run simulation
semaphore.acquire()
if not os.path.isfile('%s_%i.spectralOut'%(options.geometry,loadNo)):
damask.util.croak('Starting simulation %i (%s)'%(loadNo,thread))
semaphore.release()
damask.util.execute('DAMASK_spectral -g %s -l %i'%(options.geometry,loadNo))
else: semaphore.release()
def loadcaseNo(): def loadcaseNo():
global N_simulations global N_simulations
N_simulations+=1 N_simulations+=1
@ -1287,6 +1402,12 @@ def loadcaseNo():
def converged(): def converged():
global N_simulations; fitResidual global N_simulations; fitResidual
if options.criterion=='facet':
if N_simulations == options.numpoints:
return True
else:
return False
else:
if N_simulations < options.max: if N_simulations < options.max:
if len(fitResidual) > 5 and N_simulations >= options.min: if len(fitResidual) > 5 and N_simulations >= options.min:
residualList = np.array(fitResidual[len(fitResidual)-5:]) residualList = np.array(fitResidual[len(fitResidual)-5:])
@ -1333,6 +1454,10 @@ parser.add_option('-u', '--uniaxial', dest='eqStress', type='float',
help='Equivalent stress', metavar='float') help='Equivalent stress', metavar='float')
parser.add_option('--flag', dest='flag', type='string', parser.add_option('--flag', dest='flag', type='string',
help='yield stop flag, totalStrain, plasticStrain or plasticWork', metavar='string') help='yield stop flag, totalStrain, plasticStrain or plasticWork', metavar='string')
parser.add_option('--numpoints', dest='numpoints', type='int',
help='number of yield points to fit facet potential [%default]', metavar='int')
parser.add_option('--order', dest='order', type='int',
help='order of facet potential [%default]', metavar='int')
parser.set_defaults(min = 12, parser.set_defaults(min = 12,
max = 20, max = 20,
@ -1346,6 +1471,8 @@ parser.set_defaults(min = 12,
dimension = '3', dimension = '3',
exponent = -1.0, exponent = -1.0,
flag = 'totalStrain', flag = 'totalStrain',
numpoints = 100,
order = 8
) )
options = parser.parse_args()[0] options = parser.parse_args()[0]
@ -1385,6 +1512,9 @@ dDim = None
myLoad = None myLoad = None
myFit = None myFit = None
run = runFit(options.exponent, options.eqStress, options.dimension, options.criterion) if options.criterion == 'facet':
run = runFit(options.exponent, options.eqStress, options.dimension, options.criterion)
else:
run = runFit(options.exponent, options.eqStress, options.dimension, options.criterion)
damask.util.croak('Finished fitting to yield criteria') damask.util.croak('Finished fitting to yield criteria')

View File

@ -982,7 +982,7 @@ real(pReal) pure function math_detSym33(m)
real(pReal), dimension(3,3), intent(in) :: m real(pReal), dimension(3,3), intent(in) :: m
math_detSym33 = -(m(1,1)*m(2,3)**2_pInt + m(2,2)*m(1,3)**2_pInt + m(3,3)*m(1,2)**2_pInt) & math_detSym33 = -(m(1,1)*m(2,3)**2_pInt + m(2,2)*m(1,3)**2_pInt + m(3,3)*m(1,2)**2_pInt) &
+ m(1,1)*m(2,2)*m(3,3) - 2.0_pReal * m(1,2)*m(1,3)*m(2,3) + m(1,1)*m(2,2)*m(3,3) + 2.0_pReal * m(1,2)*m(1,3)*m(2,3)
end function math_detSym33 end function math_detSym33