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DAMASK_EICMD
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1. add initial guess and weight to the fitting (nonlinear least square regression); 

2. extend the dictionary:fittingCriteria
This commit is contained in:
Haiming Zhang 2015-02-03 12:18:53 +00:00
parent c24aa71e3c
commit 55445af9bc
1 changed files with 32 additions and 15 deletions
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43
processing/misc/yieldSurface.py
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@ -35,7 +35,7 @@ def principalStresses(sigmas):
for i in xrange(np.shape(sigmas)[1]):
eigenvalues = np.linalg.eigvalsh(np.array(sigmas[:,i]).reshape(3,3))
lambdas = np.append(lambdas,np.sort(eigenvalues)[::-1]) #append eigenvalues in descending order
lambdas = np.transpose( lambdas.reshape(np.shape(sigmas)[1],3) )
lambdas = np.transpose(lambdas.reshape(np.shape(sigmas)[1],3))
return lambdas
def stressInvariants(lambdas):
@ -54,6 +54,9 @@ def stressInvariants(lambdas):
def formatOutput(n, type='%14.6f'):
return ''.join([type for i in xrange(n)])
def get_weight(ndim):
#more to do
return np.ones(ndim)
# ---------------------------------------------------------------------------------------------
# isotropic yield surfaces
# ---------------------------------------------------------------------------------------------
@ -69,7 +72,7 @@ def Tresca(sigmas, sigma0):
return r.ravel()
def HuberHenckyMises(sigmas, sigma0):
def vonMises(sigmas, sigma0):
'''
residuum of Huber-Mises-Hencky yield criterion (eq. 2.37)
'''
@ -112,13 +115,6 @@ def Hosford(sigmas, sigma0, a):
# isotropic yield surfaces
# ---------------------------------------------------------------------------------------------
def vonMises():
'''
residuum of von Mises quadratic yield criterion (eq. 2.47, theta = sigma0)
'''
return None
def Hill1948(sigmas, F,G,H,L,M,N):
'''
residuum of Hill 1948 quadratic yield criterion (eq. 2.48)
@ -166,11 +162,22 @@ def Barlat1994(sigmas, sigma0, a):
fittingCriteria = {
'vonMises':{'fit':np.ones(1,'d'),'err':np.inf},
'hill48' :{'fit':np.ones(6,'d'),'err':np.inf},
'Tresca': {'fit' :np.ones(1,'d'),'err':np.inf,
'name' :'Tresca',
'paras':'Initial yield stress:'},
'vonMises':{'fit' :np.ones(1,'d'),'err':np.inf,
'name' :'Huber-Mises-Hencky(von Mises)',
'paras':'Initial yield stress:'},
'Hill48' :{'fit' :np.ones(6,'d'),'err':np.inf,
'name' :'Hill1948',
'paras':'Normalized [F, G, H, L, M, N]'},
'Drucker' :{'fit' :np.ones(2,'d'),'err':np.inf,
'name' :'Drucker',
'paras':'Initial yield stress, C_D:'},
'worst' :{'err':np.inf},
'best' :{'err':np.inf}
}
thresholdParameter = ['totalshear','equivalentStrain']
#---------------------------------------------------------------------------------------------------
@ -226,11 +233,12 @@ class Criterion(object):
print('fitting to the %s criterion'%name)
def fit(self,stress):
global fitResults
if self.name.lower() == 'tresca':
funResidum = Tresca
text = '\nCoefficient of Tresca criterion:\nsigma0: '+formatOutput(1)
elif self.name.lower() == 'vonmises':
funResidum = HuberHenckyMises
funResidum = vonMises
text = '\nCoefficient of Huber-Mises-Hencky criterion:\nsigma0: '+formatOutput(1)
elif self.name.lower() == 'drucker':
funResidum = Drucker
@ -238,10 +246,18 @@ class Criterion(object):
elif self.name.lower() == 'hill48':
funResidum = Hill1948
text = '\nCoefficient of Hill1948 criterion:\n[F, G, H, L, M, N]:\n'+formatOutput(6)
if fitResults == []:
initialguess = fittingCriteria[funResidum.__name__]['fit']
else:
initialguess = np.array(fitResults[-1])
weight = get_weight(np.shape(stress)[1])
try:
popt, pcov = \
curve_fit(funResidum, stress, np.zeros(np.shape(stress)[1]))
curve_fit(funResidum, stress, np.zeros(np.shape(stress)[1]),
initialguess, weight)
print (text%popt)
fitResults.append(popt.tolist())
except Exception as detail:
print detail
pass
@ -419,6 +435,7 @@ if not os.path.isfile('material.config'):
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]))]