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DAMASK_EICMD
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fixed bug (wrong index in stress tensor for Hill48) and possible deadlock

This commit is contained in:
Martin Diehl 2014-08-10 21:22:22 +00:00
parent a16c5b807b
commit ad7fe1ecbc
1 changed files with 16 additions and 13 deletions
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27
processing/misc/yieldSurface.py
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@ -23,15 +23,10 @@ def execute(cmd,dir='./'):
out += line out += line
os.chdir(initialPath) os.chdir(initialPath)
def asFullTensor(voigt):
return np.array([[voigt[0],voigt[3],voigt[5]],\
[voigt[3],voigt[1],voigt[4]],\
[voigt[5],voigt[4],voigt[2]]])
def Hill48(x, F,G,H,L,M,N): def Hill48(x, F,G,H,L,M,N):
a = F*(x[1]-x[2])**2.0 + G*(x[2]-x[0])**2.0 + H*(x[0]-x[1])**2.0 + \ a = F*(x[4]-x[8])**2.0 + G*(x[8]-x[0])**2.0 + H*(x[0]-x[4])**2.0 + \
2.0*L*x[4]**2.0 + 2.0*M*x[5]**2.0 + 2.0*N*x[3]**2.0 -1.0 2.0*L*x[1]**2.0 + 2.0*M*x[2]**2.0 + 2.0*N*x[5]**2.0 -1.0
return a.ravel() return a.ravel()
@ -107,18 +102,18 @@ class Criterion(object):
def fit(self,stress): def fit(self,stress):
try: try:
#popt1[0], pcov = curve_fit(self.vonMises, stress, np.zeros(np.shape(stress)[1]),p0=popt1[0]) #popt1[0], pcov = curve_fit(self.vonMises, stress, np.zeros(np.shape(stress)[1]),p0=popt1[0]) #use guessing?
popt, pcov = curve_fit(vonMises, stress, np.zeros(np.shape(stress)[1])) popt, pcov = curve_fit(vonMises, stress, np.zeros(np.shape(stress)[1]))
perr = np.sqrt(np.diag(pcov)) perr = np.sqrt(np.diag(pcov))
print 'Mises', popt, perr print 'Mises', popt, 'normalized Standard deviation', np.array(perr)/np.array(popt) #Variationskoeffizient
except Exception as detail: except Exception as detail:
print detail print detail
pass pass
try: try:
popt, pcov = curve_fit(Hill48, stress, np.zeros(np.shape(stress)[1])) popt, pcov = curve_fit(Hill48, stress, np.zeros(np.shape(stress)[1]))
#popt1[1], pcov = curve_fit(self.Hill48, stress, np.zeros(np.shape(stress)[1]),p0=popt1[1]) #popt1[1], pcov = curve_fit(self.Hill48, stress, np.zeros(np.shape(stress)[1]),p0=popt1[1]) #use guessing?
perr = np.sqrt(np.diag(pcov)) perr = np.sqrt(np.diag(pcov))
print 'Hill48', popt, perr print 'Hill48', popt, 'normalized Standard deviation', np.array(perr)/np.array(popt) #Variationskoeffizient
except Exception as detail: except Exception as detail:
print detail print detail
pass pass
@ -188,14 +183,22 @@ def doSim(delay,thread):
while table.data_read(): while table.data_read():
if float(table.data[table.labels.index('Mises(ln(V))')]) > options.yieldValue: if float(table.data[table.labels.index('Mises(ln(V))')]) > options.yieldValue:
yieldStress = np.array(table.data[table.labels.index('1_Cauchy'):table.labels.index('9_Cauchy')+1],'d')/10.e8 yieldStress = np.array(table.data[table.labels.index('1_Cauchy'):table.labels.index('9_Cauchy')+1],'d')/10.e8
break
elif options.fitting =='totalshear' : elif options.fitting =='totalshear' :
for l in ['totalshear','1_Cauchy']: for l in ['totalshear','1_Cauchy']:
if l not in table.labels: print '%s not found'%l if l not in table.labels: print '%s not found'%l
while table.data_read(): while table.data_read():
if float(table.data[table.labels.index('totalshear')]) > options.yieldValue: if float(table.data[table.labels.index('totalshear')]) > options.yieldValue:
yieldStress = np.array(table.data[table.labels.index('1_Cauchy'):table.labels.index('9_Cauchy')+1],'d')/10.e8 yieldStress = np.array(table.data[table.labels.index('1_Cauchy'):table.labels.index('9_Cauchy')+1],'d')/10.e8
break
s.acquire() s.acquire()
try:
yieldStress
except NameError:
print('could not fit for sim %i from %s'%(me,thread))
s.release()
return
global stressAll global stressAll
stressAll=np.append(yieldStress,stressAll) stressAll=np.append(yieldStress,stressAll)
@ -245,7 +248,7 @@ parser.set_defaults(min = 12)
parser.set_defaults(max = 30) parser.set_defaults(max = 30)
parser.set_defaults(threads = 4) parser.set_defaults(threads = 4)
parser.set_defaults(yieldValue = 0.002) parser.set_defaults(yieldValue = 0.002)
parser.set_defaults(load = [0.008,80,80.0]) parser.set_defaults(load = [0.010,100,100.0])
parser.set_defaults(criterion = 'worst') parser.set_defaults(criterion = 'worst')
parser.set_defaults(fitting = 'totalshear') parser.set_defaults(fitting = 'totalshear')
parser.set_defaults(geometry = '20grains16x16x16') parser.set_defaults(geometry = '20grains16x16x16')