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DAMASK_EICMD
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1. Implement bbc2005 yield criterion (the residum and Jacobian); 2. bbc2005 works

This commit is contained in:
Haiming Zhang 2015-03-13 10:07:23 +00:00
parent 56cf1b53eb
commit a048b2ec15
1 changed files with 69 additions and 0 deletions
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69
processing/misc/yieldSurface.py
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@ -237,6 +237,17 @@ class BBC2003(object):
def jac(self, (sigma0, a,b,c, d,e,f,g, k), ydata, sigmas):
return BBC2003Basis(sigma0, a,b,c, d,e,f,g, k, sigmas, Jac=True)
class BBC2005(object):
'''
residuum of anisotropic Barlat 1991 yield criterion (eq. 2.37)
'''
def __init__(self, uniaxialStress):
self.stress0 = uniaxialStress
def fun(self, (a,b,L, M, N, P, Q, R, k), ydata, sigmas):
return BBC2005Basis(self.stress0, a,b,L, M, N, P, Q, R, k, sigmas)
def jac(self, (a,b,L, M, N, P, Q, R, k), ydata, sigmas):
return BBC2005Basis(self.stress0, a,b,L, M, N, P, Q, R, k, sigmas, Jac=True)
class Cazacu_Barlat2D(object):
'''
'''
@ -664,6 +675,57 @@ def BBC2003Basis(sigma0, a,b,c, d,e,f,g, k, sigmas, Jac=False):
jaco.append(jacv)
return np.array(jaco)
def BBC2005Basis(sigma0, a,b,L, M, N, P, Q, R, k, sigmas, Jac=False):
'''
residuum of the BBC2005 yield criterion for plain stress
'''
s11 = sigmas[0]; s22 = sigmas[1]; s12 = sigmas[3]
k2 = 2.0*k
Gamma = L*s11 + M*s22
Lambda = ( (N*s11 - P*s22)**2 + s12**2 )**0.5
Psi = ( (Q*s11 - R*s22)**2 + s12**2 )**0.5
l1 = Lambda + Gamma; l2 = Lambda - Gamma; l3 = Lambda + Psi; l4 = Lambda - Psi
l1s = l1**2; l2s = l2**2; l3s = l3**2; l4s = l4**2
left = a*l1s**k + a*l2s**k + b*l3s**k + b*l4s**k
sBar = left**(1.0/k2); r = sBar/sigma0 - 1.0
if not Jac:
return r.ravel()
else:
ln = lambda x : np.log(x + 1.0e-32)
jaco = []
expo = 0.5/k; k1 = k-1.0
dsBardl = expo*sBar/left/sigma0
dsBarde = sBar*ln(left); dedk = expo/(-k)
dldl1 = a*k*(l1s**k1)*(2.0*l1)
dldl2 = a*k*(l2s**k1)*(2.0*l2)
dldl3 = b*k*(l3s**k1)*(2.0*l3)
dldl4 = b*k*(l4s**k1)*(2.0*l4)
dldLambda = dldl1 + dldl2 + dldl3 + dldl4
dldGama = dldl1 - dldl2
dldPsi = dldl3 - dldl4
temp = (N*s11 - P*s22)/Lambda
dLambdadN = s11*temp; dLambdadP = -s22*temp
temp = (Q*s11 - R*s22)/Psi
dPsidQ = s11*temp; dPsidR = -s22*temp
dldk = a*ln(l1s)*l1s**k + a*ln(l2s)*l2s**k + b*ln(l3s)*l3s**k + b*ln(l4s)*l4s**k
ja = dsBardl * (l1s**k + l2s**k)
jb = dsBardl * (l3s**k + l4s**k)
jL = dsBardl * dldGama*s11
jM = dsBardl * dldGama*s22
jN = dsBardl * dldLambda*dLambdadN
jP = dsBardl * dldLambda*dLambdadP
jQ = dsBardl * dldPsi*dPsidQ
jR = dsBardl * dldPsi*dPsidR
jk = dsBardl * dldk + dsBarde * dedk
for jacv in zip(ja,jb,jL,jM, jN, jP,jQ,jR,jk):
jaco.append(jacv)
return np.array(jaco)
def principalStress(p):
sin = np.sin; cos = np.cos
s11 = p[0]; s22 = p[1]; s33 = p[2]
@ -1088,6 +1150,13 @@ fittingCriteria = {
'text' : '\nCoefficients of anisotropic Barlat 1991 criterion:\nsigma0, a, b, c, d, e, f, g, k:\n',
'error': 'The standard deviation errors are: '
},
'bbc2005' :{'func' : BBC2005,
'num' : 9,'err':np.inf,
'name' : 'Banabic-Balan-Comsa 2003',
'paras': 'a, b, L ,M, N, P, Q, R, k:',
'text' : '\nCoefficients of Banabic-Balan-Comsa 2005 criterion: a, b, L ,M, N, P, Q, R, k:\n',
'error': 'The standard deviation errors are: '
},
'Cazacu_Barlat2D':{'func' : Cazacu_Barlat2D,
'num' : 11,
'name' : 'Cazacu Barlat for plain stress',