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DAMASK_EICMD
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1. Add default boundary constraint(no constraint) and initial guess to the dictionary 

2. With the boundary constraint of exponential term, now non-quadratic yield criteria (isotropic Barlat 1991, anisotropic Barlar 1991, Hosford) take effect.
This commit is contained in:
Haiming Zhang 2015-02-07 11:07:45 +00:00
parent 239b34e606
commit cdb7795956
1 changed files with 46 additions and 40 deletions
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86
processing/misc/yieldSurface.py
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@ -118,10 +118,10 @@ def Hosford(sigmas, sigma0, a):
residuum of Hershey yield criterion (eq. 2.43, Y = sigma0) residuum of Hershey yield criterion (eq. 2.43, Y = sigma0)
''' '''
lambdas = principalStresses(sigmas) lambdas = principalStresses(sigmas)
r = (abs(lambdas[2,:]-lambdas[1,:]))**a\ r = ((abs(lambdas[2,:]-lambdas[1,:]))**a\
+ (abs(lambdas[1,:]-lambdas[0,:]))**a\ + (abs(lambdas[1,:]-lambdas[0,:]))**a\
+ (abs(lambdas[0,:]-lambdas[2,:]))**a\ + (abs(lambdas[0,:]-lambdas[2,:]))**a) **(1.0/a)\
-2.0*(abs(sigma0))**a -2.0**(1.0/a)*sigma0
return r.ravel() return r.ravel()
#more to do #more to do
@ -160,8 +160,7 @@ def generalHosford(sigmas, sigma0, a):
return r.ravel() return r.ravel()
def Barlat1991(sigmas, sigma0, order, \ def Barlat1991(sigmas, sigma0, order, a, b, c, f, g, h):
a=1.0, b=1.0, c=1.0, f=1.0, g=1.0, h=1.0):
''' '''
residuum of Barlat 1997 yield criterion residuum of Barlat 1997 yield criterion
''' '''
@ -173,16 +172,17 @@ def Barlat1991(sigmas, sigma0, order, \
G = g*sigmas[5] G = g*sigmas[5]
H = h*sigmas[3] H = h*sigmas[3]
I2 = (F*F + G*G + H*H)/3.0 + ((A-C)*(A-C)+(C-B)*(C-B)+(B-A)*(B-A))/54.0 I2 = (F*F + G*G + H*H)/3.0 + ((A-C)**2+(C-B)**2+(B-A)**2)/54.0
I3 = (C-B)*(A-C)*(B-A)/54.0 + F*G*H - \ I3 = (C-B)*(A-C) * (B-A)/54.0 + F*G*H - \
((C-B)*F*F+(A-C)*G*G+(B-A)*H*H)/6 ( (C-B)*F*F + (A-C)*G*G + (B-A)*H*H )/6.0
theta = np.arccos(I3/pow(I2,1.5)) theta = np.arccos(I3/I2**1.5)
Phi = pow(3.0*I2, order/2.0)* ( Phi = np.sqrt(3.0*I2)* (
pow(abs(2.0*cos((2.0*theta + pi)/6.0)), order) + (abs(2.0*cos((2.0*theta + pi)/6.0)))**order +
pow(abs(2.0*cos((2.0*theta + pi*3.0)/6.0)), order) + (abs(2.0*cos((2.0*theta + pi*3.0)/6.0)))**order +
pow(abs(2.0*cos((2.0*theta + pi*5.0)/6.0)), order) (abs(2.0*cos(( 2.0*theta + pi*5.0)/6.0)))**order
) )**(1.0/order)
r = Phi - 2.0*pow(sigma0, order) r = Phi/2.0**(1.0/order) - sigma0
return r.ravel() return r.ravel()
@ -190,9 +190,9 @@ def Barlat1991iso(sigmas, sigma0, m):
''' '''
residuum of isotropic Barlat 1991 yield criterion (eq. 2.37) residuum of isotropic Barlat 1991 yield criterion (eq. 2.37)
''' '''
return Barlat1991(sigmas, sigma0, m) return Barlat1991(sigmas, sigma0, m, 1.0,1.0,1.0,1.0,1.0,1.0)
def Barlat1991aniso(sigmas, sigma0, m, a,b,c,f,g,h): def Barlat1991aniso(sigmas, sigma0, a,b,c,f,g,h, m):
''' '''
residuum of anisotropic Barlat 1991 yield criterion (eq. 2.37) residuum of anisotropic Barlat 1991 yield criterion (eq. 2.37)
''' '''
@ -209,52 +209,63 @@ def Barlat1994(sigmas, sigma0, a):
fittingCriteria = { fittingCriteria = {
'Tresca' :{'num' : 1,'err':np.inf, 'tresca' :{'func' : Tresca,
'num' : 1,'err':np.inf,
'name' : 'Tresca', 'name' : 'Tresca',
'paras': 'Initial yield stress:', 'paras': 'Initial yield stress:',
'text' : '\nCoefficient of Tresca criterion:\nsigma0: ', 'text' : '\nCoefficient of Tresca criterion:\nsigma0: ',
'error': 'The standard deviation error is: ' 'error': 'The standard deviation error is: '
}, },
'vonMises' :{'num' : 1,'err':np.inf, 'vonmises' :{'func' : vonMises,
'num' : 1,'err':np.inf,
'name' : 'Huber-Mises-Hencky(von Mises)', 'name' : 'Huber-Mises-Hencky(von Mises)',
'paras': 'Initial yield stress:', 'paras': 'Initial yield stress:',
'text' : '\nCoefficient of Huber-Mises-Hencky criterion:\nsigma0: ', 'text' : '\nCoefficient of Huber-Mises-Hencky criterion:\nsigma0: ',
'error': 'The standard deviation error is: ' 'error': 'The standard deviation error is: '
}, },
'Hosford' :{'num' : 2,'err':np.inf, 'hosford' :{'func' : Hosford,
'num' : 2,'err':np.inf,
'name' : 'Gerenal Hosford', 'name' : 'Gerenal Hosford',
'paras': 'Initial yield stress:', 'paras': 'Initial yield stress:',
'text' : '\nCoefficient of Hosford criterion:\nsigma0, a: ', 'text' : '\nCoefficient of Hosford criterion:\nsigma0, a: ',
'error': 'The standard deviation errors are: ' 'error': 'The standard deviation errors are: '
}, },
'Hill1948' :{'num' : 6,'err':np.inf, 'hill1948' :{'func' : Hill1948,
'num' : 6,'err':np.inf,
'name' : 'Hill1948', 'name' : 'Hill1948',
'paras': 'Normalized [F, G, H, L, M, N]', 'paras': 'Normalized [F, G, H, L, M, N]',
'text' : '\nCoefficient of Hill1948 criterion:\n[F, G, H, L, M, N]:', 'text' : '\nCoefficient of Hill1948 criterion:\n[F, G, H, L, M, N]:',
'error': 'The standard deviation errors are: ' 'error': 'The standard deviation errors are: '
}, },
'Drucker' :{'num' : 2,'err':np.inf, 'drucker' :{'func' : Drucker,
'num' : 2,'err':np.inf,
'name' : 'Drucker', 'name' : 'Drucker',
'paras': 'Initial yield stress, C_D:', 'paras': 'Initial yield stress, C_D:',
'text' : '\nCoefficient of Drucker criterion:\nsigma0, C_D: ', 'text' : '\nCoefficient of Drucker criterion:\nsigma0, C_D: ',
'error': 'The standard deviation errors are: ' 'error': 'The standard deviation errors are: '
}, },
'Barlat1991iso' :{'num' : 2,'err':np.inf, 'barlat1991iso' :{'func' : Barlat1991iso,
'num' : 2,'err':np.inf,
'name' : 'Barlat1991iso', 'name' : 'Barlat1991iso',
'paras': 'Initial yield stress, m:', 'paras': 'Initial yield stress, m:',
'text' : '\nCoefficient of isotropic Barlat 1991 criterion:\nsigma0, m:\n', 'text' : '\nCoefficient of isotropic Barlat 1991 criterion:\nsigma0, m:\n',
'error': 'The standard deviation errors are: ' 'error': 'The standard deviation errors are: '
}, },
'Barlat1991aniso':{'num' : 7,'err':np.inf, 'barlat1991aniso':{'func' : Barlat1991aniso,
'num' : 8,'err':np.inf,
'name' : 'Barlat1991aniso', 'name' : 'Barlat1991aniso',
'paras': 'Initial yield stress, m, a, b, c, f, g, h:', 'paras': 'Initial yield stress, m, a, b, c, f, g, h:',
'text' : '\nCoefficient of anisotropic Barlat 1991 criterion:\nsigma0, \m, a, b, c, f, g, h:\n', 'text' : '\nCoefficient of anisotropic Barlat 1991 criterion:\nsigma0, a, b, c, f, g, h, m:\n',
'error': 'The standard deviation errors are: ' 'error': 'The standard deviation errors are: '
}, },
'worst' :{'err':np.inf}, 'worst' :{'err':np.inf},
'best' :{'err':np.inf} 'best' :{'err':np.inf}
} }
for key in fittingCriteria.keys():
if 'num' in fittingCriteria[key].keys():
fittingCriteria[key]['bound']=[(None,None)]*fittingCriteria[key]['num']
fittingCriteria[key]['guess']=np.ones(fittingCriteria[key]['num'],'d')
thresholdParameter = ['totalshear','equivalentStrain'] thresholdParameter = ['totalshear','equivalentStrain']
@ -312,21 +323,16 @@ class Criterion(object):
def fit(self,stress): def fit(self,stress):
global fitResults global fitResults
if self.name.lower() == 'tresca' : funResidum = Tresca
elif self.name.lower() == 'vonmises' : funResidum = vonMises
elif self.name.lower() == 'hosford' : funResidum = Hosford
elif self.name.lower() == 'drucker' : funResidum = Drucker
elif self.name.lower() == 'hill1948' : funResidum = Hill1948
elif self.name.lower() == 'barlat1991iso' : funResidum = Barlat1991iso
elif self.name.lower() == 'barlat1991aniso' : funResidum = Barlat1991aniso
nameCriterion = funResidum.__name__ nameCriterion = self.name.lower()
numParas = fittingCriteria[nameCriterion]['num'] funResidum = fittingCriteria[nameCriterion]['func']
textParas = fittingCriteria[nameCriterion]['text'] + formatOutput(numParas) numParas = fittingCriteria[nameCriterion]['num']
textError = fittingCriteria[nameCriterion]['error']+ formatOutput(numParas,'%-14.8f')+'\n' textParas = fittingCriteria[nameCriterion]['text'] + formatOutput(numParas)
bounds = [(None,None)]*numParas # Default bounds, no bound textError = fittingCriteria[nameCriterion]['error']+ formatOutput(numParas,'%-14.8f')+'\n'
initialguess0 = np.ones(numParas,'d') # Default initial guess, depends on bounds bounds = fittingCriteria[nameCriterion]['bound'] # Default bounds, no bound
if fitResults == [] : initialguess = initialguess0 guess0 = fittingCriteria[nameCriterion]['guess'] # Default initial guess, depends on bounds
if fitResults == [] : initialguess = guess0
else : initialguess = np.array(fitResults[-1]) else : initialguess = np.array(fitResults[-1])
weight = get_weight(np.shape(stress)[1]) weight = get_weight(np.shape(stress)[1])
try: try: