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
parent
239b34e606
commit
cdb7795956
|
|
@ -118,10 +118,10 @@ def Hosford(sigmas, sigma0, a):
|
|||
residuum of Hershey yield criterion (eq. 2.43, Y = sigma0)
|
||||
'''
|
||||
lambdas = principalStresses(sigmas)
|
||||
r = (abs(lambdas[2,:]-lambdas[1,:]))**a\
|
||||
+ (abs(lambdas[1,:]-lambdas[0,:]))**a\
|
||||
+ (abs(lambdas[0,:]-lambdas[2,:]))**a\
|
||||
-2.0*(abs(sigma0))**a
|
||||
r = ((abs(lambdas[2,:]-lambdas[1,:]))**a\
|
||||
+ (abs(lambdas[1,:]-lambdas[0,:]))**a\
|
||||
+ (abs(lambdas[0,:]-lambdas[2,:]))**a) **(1.0/a)\
|
||||
-2.0**(1.0/a)*sigma0
|
||||
return r.ravel()
|
||||
|
||||
#more to do
|
||||
|
|
@ -160,8 +160,7 @@ def generalHosford(sigmas, sigma0, a):
|
|||
return r.ravel()
|
||||
|
||||
|
||||
def Barlat1991(sigmas, sigma0, order, \
|
||||
a=1.0, b=1.0, c=1.0, f=1.0, g=1.0, h=1.0):
|
||||
def Barlat1991(sigmas, sigma0, order, a, b, c, f, g, h):
|
||||
'''
|
||||
residuum of Barlat 1997 yield criterion
|
||||
'''
|
||||
|
|
@ -173,16 +172,17 @@ def Barlat1991(sigmas, sigma0, order, \
|
|||
G = g*sigmas[5]
|
||||
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
|
||||
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
|
||||
theta = np.arccos(I3/pow(I2,1.5))
|
||||
Phi = pow(3.0*I2, order/2.0)* (
|
||||
pow(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) +
|
||||
pow(abs(2.0*cos((2.0*theta + pi*5.0)/6.0)), order)
|
||||
)
|
||||
r = Phi - 2.0*pow(sigma0, order)
|
||||
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 - \
|
||||
( (C-B)*F*F + (A-C)*G*G + (B-A)*H*H )/6.0
|
||||
theta = np.arccos(I3/I2**1.5)
|
||||
Phi = np.sqrt(3.0*I2)* (
|
||||
(abs(2.0*cos((2.0*theta + pi)/6.0)))**order +
|
||||
(abs(2.0*cos((2.0*theta + pi*3.0)/6.0)))**order +
|
||||
(abs(2.0*cos(( 2.0*theta + pi*5.0)/6.0)))**order
|
||||
)**(1.0/order)
|
||||
r = Phi/2.0**(1.0/order) - sigma0
|
||||
|
||||
|
||||
return r.ravel()
|
||||
|
||||
|
|
@ -190,9 +190,9 @@ def Barlat1991iso(sigmas, sigma0, m):
|
|||
'''
|
||||
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)
|
||||
'''
|
||||
|
|
@ -209,52 +209,63 @@ def Barlat1994(sigmas, sigma0, a):
|
|||
|
||||
|
||||
fittingCriteria = {
|
||||
'Tresca' :{'num' : 1,'err':np.inf,
|
||||
'tresca' :{'func' : Tresca,
|
||||
'num' : 1,'err':np.inf,
|
||||
'name' : 'Tresca',
|
||||
'paras': 'Initial yield stress:',
|
||||
'text' : '\nCoefficient of Tresca criterion:\nsigma0: ',
|
||||
'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)',
|
||||
'paras': 'Initial yield stress:',
|
||||
'text' : '\nCoefficient of Huber-Mises-Hencky criterion:\nsigma0: ',
|
||||
'error': 'The standard deviation error is: '
|
||||
},
|
||||
'Hosford' :{'num' : 2,'err':np.inf,
|
||||
'hosford' :{'func' : Hosford,
|
||||
'num' : 2,'err':np.inf,
|
||||
'name' : 'Gerenal Hosford',
|
||||
'paras': 'Initial yield stress:',
|
||||
'text' : '\nCoefficient of Hosford criterion:\nsigma0, a: ',
|
||||
'error': 'The standard deviation errors are: '
|
||||
},
|
||||
'Hill1948' :{'num' : 6,'err':np.inf,
|
||||
'hill1948' :{'func' : Hill1948,
|
||||
'num' : 6,'err':np.inf,
|
||||
'name' : 'Hill1948',
|
||||
'paras': 'Normalized [F, G, H, L, M, N]',
|
||||
'text' : '\nCoefficient of Hill1948 criterion:\n[F, G, H, L, M, N]:',
|
||||
'error': 'The standard deviation errors are: '
|
||||
},
|
||||
'Drucker' :{'num' : 2,'err':np.inf,
|
||||
'drucker' :{'func' : Drucker,
|
||||
'num' : 2,'err':np.inf,
|
||||
'name' : 'Drucker',
|
||||
'paras': 'Initial yield stress, C_D:',
|
||||
'text' : '\nCoefficient of Drucker criterion:\nsigma0, C_D: ',
|
||||
'error': 'The standard deviation errors are: '
|
||||
},
|
||||
'Barlat1991iso' :{'num' : 2,'err':np.inf,
|
||||
'barlat1991iso' :{'func' : Barlat1991iso,
|
||||
'num' : 2,'err':np.inf,
|
||||
'name' : 'Barlat1991iso',
|
||||
'paras': 'Initial yield stress, m:',
|
||||
'text' : '\nCoefficient of isotropic Barlat 1991 criterion:\nsigma0, m:\n',
|
||||
'error': 'The standard deviation errors are: '
|
||||
},
|
||||
'Barlat1991aniso':{'num' : 7,'err':np.inf,
|
||||
'barlat1991aniso':{'func' : Barlat1991aniso,
|
||||
'num' : 8,'err':np.inf,
|
||||
'name' : 'Barlat1991aniso',
|
||||
'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: '
|
||||
},
|
||||
'worst' :{'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']
|
||||
|
||||
|
|
@ -312,21 +323,16 @@ class Criterion(object):
|
|||
|
||||
def fit(self,stress):
|
||||
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__
|
||||
numParas = fittingCriteria[nameCriterion]['num']
|
||||
textParas = fittingCriteria[nameCriterion]['text'] + formatOutput(numParas)
|
||||
textError = fittingCriteria[nameCriterion]['error']+ formatOutput(numParas,'%-14.8f')+'\n'
|
||||
bounds = [(None,None)]*numParas # Default bounds, no bound
|
||||
initialguess0 = np.ones(numParas,'d') # Default initial guess, depends on bounds
|
||||
if fitResults == [] : initialguess = initialguess0
|
||||
nameCriterion = self.name.lower()
|
||||
funResidum = fittingCriteria[nameCriterion]['func']
|
||||
numParas = fittingCriteria[nameCriterion]['num']
|
||||
textParas = fittingCriteria[nameCriterion]['text'] + formatOutput(numParas)
|
||||
textError = fittingCriteria[nameCriterion]['error']+ formatOutput(numParas,'%-14.8f')+'\n'
|
||||
bounds = fittingCriteria[nameCriterion]['bound'] # Default bounds, no bound
|
||||
guess0 = fittingCriteria[nameCriterion]['guess'] # Default initial guess, depends on bounds
|
||||
|
||||
if fitResults == [] : initialguess = guess0
|
||||
else : initialguess = np.array(fitResults[-1])
|
||||
weight = get_weight(np.shape(stress)[1])
|
||||
try:
|
||||
|
|
|
|||
Loading…
Reference in New Issue