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DAMASK_EICMD
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Re-write the script: 

1. replace curve_fit with leastsq, which supports the analytical Jacobian
2. specify a "class" (contains both residum and jacobian) for each criterion.
3. add the calculation of Jacobian
This commit is contained in:
Haiming Zhang 2015-02-11 16:49:40 +00:00
parent 1b1ed3bbcf
commit eed00007f9
1 changed files with 174 additions and 106 deletions
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262
processing/misc/yieldSurface.py
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@ -7,7 +7,7 @@ from scipy.optimize import curve_fit
from scipy.linalg import svd
from optparse import OptionParser
import damask
from damask.util import curve_fit_bound
from damask.util import leastsqBound
scriptID = string.replace('$Id$','\n','\\n')
scriptName = scriptID.split()[1][:-3]
@ -77,52 +77,55 @@ def get_weight(ndim):
# isotropic yield surfaces
# ---------------------------------------------------------------------------------------------
def Tresca(sigmas, sigma0):
class Tresca(object):
'''
residuum of Tresca yield criterion (eq. 2.26)
'''
def fun(self,sigma0, ydata, sigmas):
lambdas = principalStresses(sigmas)
r = np.amax(np.array([abs(lambdas[2,:]-lambdas[1,:]),\
abs(lambdas[1,:]-lambdas[0,:]),\
abs(lambdas[0,:]-lambdas[2,:])]),0) - sigma0
return r.ravel()
def jac(self,sigma0, ydata, sigmas):
return np.ones(len(ydata)) * (-1.0)
def vonMises(sigmas, sigma0):
class vonMises(object):
'''
residuum of Huber-Mises-Hencky yield criterion (eq. 2.37)
'''
def fun(self, sigma0, ydata, sigmas):
return HosfordBasis(sigma0, 1.0,1.0,1.0, 2.0, sigmas)
def jac(self, sigma0, ydata, sigmas):
return HosfordBasis(sigma0, 1.0,1.0,1.0, 2.0, sigmas, Jac=True, nParas=1)
return Hosford(sigmas, sigma0, 2.0)
def Drucker(sigmas, sigma0, C_D):
class Drucker(object):
'''
residuum of Drucker yield criterion (eq. 2.41, F = sigma0)
'''
def fun(sigma0, C_D, ydata, sigmas):
return DruckerBasis(sigma0, C_D, 1.0, ydata, sigmas)
def jac(sigma0, C_D, ydata, sigmas):
pass
return generalDrucker(sigmas, sigma0, C_D, 1.0)
def generalDrucker(sigmas, sigma0, C_D, p):
class generalDrucker(object):
'''
residuum of general Drucker yield criterion (eq. 2.42, F = sigma0)
'''
Is = stressInvariants(principalStresses(sigmas))
r = (Is[1,:]**(3.0*p)-C_D*Is[2,:]**(2.0*p)) - sigma0
return r.ravel()
def fun(sigma0, C_D, ydata, sigmas):
return DruckerBasis(sigma0, C_D, p, ydata, sigmas)
def jac(sigma0, C_D, ydata, sigmas):
pass
def Hosford(sigmas, sigma0, a):
class Hosford(object):
'''
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) **(1.0/a)\
-2.0**(1.0/a)*sigma0
return r.ravel()
def fun(self, (sigma0, a), ydata, sigmas):
return HosfordBasis(sigma0, 1.0,1.0,1.0, a, sigmas)
def jac(self, (sigma0, a), ydata, sigmas):
return HosfordBasis(sigma0, 1.0,1.0,1.0, a, sigmas, Jac=True, nParas=2)
#more to do
# KarafillisAndBoyce
@ -131,83 +134,51 @@ def Hosford(sigmas, sigma0, a):
# isotropic yield surfaces
# ---------------------------------------------------------------------------------------------
def Hill1948(sigmas, F,G,H,L,M,N):
class Hill1948(object):
'''
residuum of Hill 1948 quadratic yield criterion (eq. 2.48)
'''
r = F*(sigmas[1]-sigmas[2])**2.0\
+ G*(sigmas[2]-sigmas[0])**2.0\
+ H*(sigmas[0]-sigmas[1])**2.0\
+ 2.0*L* sigmas[4]**2.0\
+ 2.0*M* sigmas[5]**2.0\
+ 2.0*N* sigmas[3]**2.0\
- 1.0
def fun(self, (F,G,H,L,M,N), ydata, sigmas):
r = F*(sigmas[1]-sigmas[2])**2.0 + G*(sigmas[2]-sigmas[0])**2.0 + H*(sigmas[0]-sigmas[1])**2.0\
+ 2.0*L*sigmas[4]**2.0 + 2.0*M*sigmas[5]**2.0 + 2.0*N*sigmas[3]**2.0 - 1.0
return r.ravel()/2.0
def jac(self, (F,G,H,L,M,N), ydata, sigmas):
pass
#more to do
# Hill 1979
# Hill 1990,1993 need special stresses to fit
def generalHosford(sigmas, sigma0, a):
class generalHosford(object):
'''
residuum of Hershey yield criterion (eq. 2.104, sigma = sigma0)
residuum of Hershey yield criterion (eq. 2.104, sigmas = sigma0)
'''
lambdas = principalStresses(sigmas)
r = np.amax(np.array([F*(abs(lambdas[:,1]-lambdas[:,2]))**a,\
G*(abs(lambdas[:,2]-lambdas[:,0]))**a,\
H*(abs(lambdas[:,0]-lambdas[:,1]))**a]),1) - sigma0**a
return r.ravel()
def fun(self, (sigma0, F, G, H, a), ydata, sigmas, nParas=5):
return HosfordBasis(sigma0, F, G, H, a, sigmas)
def jac(self, (sigma0, F, G, H, a), ydata, sigmas):
return HosfordBasis(sigma0, F,G,H, a, sigmas, Jac=True, nParas=5)
def Barlat1991(sigmas, sigma0, order, a, b, c, f, g, h):
'''
residuum of Barlat 1997 yield criterion
'''
cos = np.cos; pi = np.pi; abs = np.abs
A = a*(sigmas[1] - sigmas[2])
B = b*(sigmas[2] - sigmas[0])
C = c*(sigmas[0] - sigmas[1])
F = f*sigmas[4]
G = g*sigmas[5]
H = h*sigmas[3]
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()
def Barlat1991iso(sigmas, sigma0, m):
class Barlat1991iso(object):
'''
residuum of isotropic Barlat 1991 yield criterion (eq. 2.37)
'''
return Barlat1991(sigmas, sigma0, m, 1.0,1.0,1.0,1.0,1.0,1.0)
def fun(self, (sigma0, m), ydata, sigmas):
return Barlat1991Basis(sigma0, 1.0,1.0,1.0,1.0,1.0,1.0, m, sigmas)
def jac(self, (sigma0, m), ydata, sigmas):
pass
def Barlat1991aniso(sigmas, sigma0, a,b,c,f,g,h, m):
class Barlat1991aniso(object):
'''
residuum of anisotropic Barlat 1991 yield criterion (eq. 2.37)
'''
return Barlat1991(sigmas, sigma0, m, a,b,c,f,g,h)
def fun(self, (sigma0, a,b,c,f,g,h, m), ydata, sigmas):
return Barlat1991Basis(sigma0, a,b,c,f,g,h, m, sigmas)
def jac(self, (sigma0, a,b,c,f,g,h, m), ydata, sigmas):
pass
def Barlat1994(sigmas, sigma0, a):
'''
residuum of Hershey yield criterion (eq. 2.104, sigma_e = sigma0)
'''
return None
def Cazacu_Barlat3D(sigmas, sigma0,
a1,a2,a3,a4,a5,a6, b1,b2,b3,b4,b5,b6,b7,b8,b9,b10,b11, c):
def Cazacu_Barlat3D(sigma0,a1,a2,a3,a4,a5,a6, b1,b2,b3,b4,b5,b6,b7,b8,b9,b10,b11, c,
ydata, sigmas):
'''
residuum of the Cazacu–Barlat (CZ) yield criterion
'''
@ -224,14 +195,13 @@ def Cazacu_Barlat3D(sigmas, sigma0,
( 2.0*b10*s33 - b5*s22 - (2*b10-b5)*s11 )*s12**2 +
( (b6+b7)*s11 - b6*s22 - b7*s33 )*s23**2
)/3.0
f0 = (J20**3 - c*J30**2)**(1.0/6.0)
k2 = (sigma0/3.0) *18.0 **(1.0/6.0)
r = f0/k2 - 1.0
return r.ravel()
def Cazacu_Barlat2D(sigmas, sigma0,
a1,a2,a3,a6, b1,b2,b3,b4,b5,b10, c):
def Cazacu_Barlat2D(sigma0,a1,a2,a3,a6, b1,b2,b3,b4,b5,b10, c,
ydata, sigmas):
'''
residuum of the Cazacu–Barlat (CZ) yield criterion for plain stress
'''
@ -242,13 +212,12 @@ def Cazacu_Barlat2D(sigmas, sigma0,
J30 = ( (b1 + b2 )*s11**3 + (b3 +b4 )*s22**3 )/27.0- \
( (b1*s11 + b4*s22)*s11*s22 )/9.0 + \
( b5*s22 + (2*b10-b5)*s11 )*s12**2/3.0
f0 = (J20**3 - c*J30**2)**(1.0/6.0)
k2 = (sigma0/3.0) *18.0 **(1.0/6.0)
r = f0/k2 - 1.0
return r.ravel()
def BBC2003(sigmas, sigma0, a,b,c, d,e,f,g, k):
def BBC2003(sigma0, a,b,c, d,e,f,g, k, ydata, sigmas):
'''
residuum of the BBC2003 yield criterion for plain stress
'''
@ -263,6 +232,84 @@ def BBC2003(sigmas, sigma0, a,b,c, d,e,f,g, k):
r = sBar/sigma0 - 1.0
return r.ravel()
def DruckerBasis(sigma0, C_D, p, ydata, sigmas):
s11 = sigmas[0]; s22 = sigmas[1]; s33 = sigmas[2]
s12 = sigmas[3]; s23 = sigmas[4]; s31 = sigmas[5]
I1 = s11 + s22 + s33
I2 = s11*s22 + s22*s33 + s11*s33 - s12**2 - s23**2 - s31**2
I3 = s11*s22*s33 + 2.0*s12*s23*s31 - s12**2*s33 - s23**2*s11 - s31**2*s22
J2 = I1**2/3.0 - I2
J3 = I1**3/13.5 - I1*I2/3.0 + I3
r = (J2**(3.0*p) - C_D*J3**(2.0*p))*27/(sigma0**6.0) - 1.0
return r.ravel()
def HosfordBasis(sigma0, F,G,H, a, sigmas, Jac=False, nParas=1):
'''
residuum of Hershey yield criterion (eq. 2.43, Y = sigma0)
'''
lambdas = principalStresses(sigmas)
diff23 = abs(lambdas[1,:] - lambdas[2,:])
diff31 = abs(lambdas[2,:] - lambdas[0,:])
diff12 = abs(lambdas[0,:] - lambdas[1,:])
base = F*diff23**a + G*diff31**a + H*diff12**a; expo = 1.0/a
left = base**expo; right = 2.0**expo*sigma0
if not Jac:
if nParas == 1: return (left - right).ravel()
else: return (left/right - 1.0).ravel()
else:
if nParas > 1:
ln = lambda x : np.log(x + 1.0e-32)
dbda = F*ln(diff23)*diff23**a + G*ln(diff31)*diff31**a + H*ln(diff12)*diff12**a
deda = -expo*expo; dldb = expo*left/base; drda = sigma0*(2.0**expo)*ln(2.0)*deda
ones = np.ones(np.shape(sigmas)[1]); jac = []
if nParas == 1: # von Mises
return ones*(-2.0**0.5)
elif nParas == 2: # isotropic Hosford
j1 = ones*(-2.0**expo) # d[]/dsigma0
j2 = dldb*dbda + left*ln(base)*deda - drda # d[]/da
for a,b in zip(j1, j2): jac.append([a,b])
return np.array(jac)
elif nParas == 5: # anisotropic Hosford
j1 = -left/right/sigma0 #ones*(-2.0**expo) # d[]/dsigma0
j2 = dldb*diff23**a/right; j3 = dldb*diff31**a/right; j4 = dldb*diff12**a/right
j5 =(dldb*dbda + left*ln(base)*deda)/right + left*(-right**(-2))*drda # d[]/da
for a,b,c,d,e in zip(j1, j2,j3,j4,j5): jac.append([a,b,c,d,e])
return np.array(jac)
def Barlat1991Basis(sigma0, a, b, c, f, g, h, order, sigmas):
'''
residuum of Barlat 1997 yield criterion
'''
cos = np.cos; pi = np.pi; abs = np.abs
A = a*(sigmas[1] - sigmas[2])
B = b*(sigmas[2] - sigmas[0])
C = c*(sigmas[0] - sigmas[1])
F = f* sigmas[4]
G = g* sigmas[5]
H = h* sigmas[3]
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
r = Phi/2.0**(1.0/order)/sigma0 - 1.0
# Phi = (3.0*I2)**(order/2.0) * (
# (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
# )
# r = (Phi - 2.0*sigma0**order)**(1.0/order)
return r.ravel()
fittingCriteria = {
'tresca' :{'func' : Tresca,
'num' : 1,'err':np.inf,
@ -278,18 +325,25 @@ fittingCriteria = {
'text' : '\nCoefficient of Huber-Mises-Hencky criterion:\nsigma0: ',
'error': 'The standard deviation error is: '
},
'hosford' :{'func' : Hosford,
'hosfordiso' :{'func' : Hosford,
'num' : 2,'err':np.inf,
'name' : 'Gerenal Hosford',
'paras': 'Initial yield stress:',
'name' : 'Gerenal isotropic Hosford',
'paras': 'Initial yield stress, a:',
'text' : '\nCoefficients of Hosford criterion:\nsigma0, a: ',
'error': 'The standard deviation errors are: '
},
'hosfordaniso' :{'func' : generalHosford,
'num' : 5,'err':np.inf,
'name' : 'Gerenal isotropic Hosford',
'paras': 'Initial yield stress, F, G, H, a:',
'text' : '\nCoefficients of Hosford criterion:\nsigma0, F, G, H, a: ',
'error': 'The standard deviation errors are: '
},
'hill1948' :{'func' : Hill1948,
'num' : 6,'err':np.inf,
'name' : 'Hill1948',
'paras': 'Normalized [F, G, H, L, M, N]',
'text' : '\nCoefficients of Hill1948 criterion:\n[F, G, H, L, M, N]:',
'paras': 'Normalized [F, G, H, L, M, N]:',
'text' : '\nCoefficients of Hill1948 criterion:\n[F, G, H, L, M, N]:'+' '*16,
'error': 'The standard deviation errors are: '
},
'drucker' :{'func' : Drucker,
@ -299,6 +353,13 @@ fittingCriteria = {
'text' : '\nCoefficients of Drucker criterion:\nsigma0, C_D: ',
'error': 'The standard deviation errors are: '
},
'gdrucker' :{'func' : generalDrucker,
'num' : 3,'err':np.inf,
'name' : 'General Drucker',
'paras': 'Initial yield stress, C_D, p:',
'text' : '\nCoefficients of Drucker criterion:\nsigma0, C_D, p: ',
'error': 'The standard deviation errors are: '
},
'barlat1991iso' :{'func' : Barlat1991iso,
'num' : 2,'err':np.inf,
'name' : 'Barlat1991iso',
@ -309,20 +370,20 @@ fittingCriteria = {
'barlat1991aniso':{'func' : Barlat1991aniso,
'num' : 8,'err':np.inf,
'name' : 'Barlat1991aniso',
'paras': 'Initial yield stress, m, a, b, c, f, g, h:',
'paras': 'Initial yield stress, a, b, c, f, g, h, m:',
'text' : '\nCoefficients of anisotropic Barlat 1991 criterion:\nsigma0, a, b, c, f, g, h, m:\n',
'error': 'The standard deviation errors are: '
},
'bbc2003' :{'func' : BBC2003,
'num' : 9,'err':np.inf,
'name' : 'Barlat1991aniso',
'name' : 'Banabic-Balan-Comsa 2003',
'paras': 'Initial yield stress, a, b, c, d, e, f, g, k:',
'text' : '\nCoefficients of anisotropic Barlat 1991 criterion:\nsigma0, a, b, c, d, e, f, g, k:\n',
'error': 'The standard deviation errors are: '
},
'Cazacu_Barlat2D':{'func' : Cazacu_Barlat2D,
'num' : 12,'err':np.inf,
'name' : 'Barlat1991aniso',
'name' : 'Cazacu Barlat for plain stress',
'paras': 'Initial yield stress, a1,a2,a3,a6; b1,b2,b3,b4,b5,b10; c:',
'text' : '\nCoefficients of Cazacu Barlat yield criterion for plane stress: \
\n Y, a1,a2,a3,a6; b1,b2,b3,b4,b5,b10; c:\n',
@ -330,7 +391,7 @@ fittingCriteria = {
},
'Cazacu_Barlat3D':{'func' : Cazacu_Barlat3D,
'num' : 19,'err':np.inf,
'name' : 'Barlat1991aniso',
'name' : 'Cazacu Barlat',
'paras': 'Initial yield stress, a1,a2,a3,a4,a5,a6; b1,b2,b3,b4,b5,b6,b7,b8,b9,b10,b11; c:',
'text' : '\nCoefficients of Cazacu Barlat yield criterion for plane stress: \
\n Y, a1,a2,a3,a4,a5,a6; b1,b2,b3,b4,b5,b6,b7,b8,b9,b10,b11; c\n',
@ -403,7 +464,7 @@ class Criterion(object):
global fitResults
nameCriterion = self.name.lower()
funResidum = fittingCriteria[nameCriterion]['func']
criteriaClass = fittingCriteria[nameCriterion]['func']; criteria = criteriaClass()
numParas = fittingCriteria[nameCriterion]['num']
textParas = fittingCriteria[nameCriterion]['text'] + formatOutput(numParas)
textError = fittingCriteria[nameCriterion]['error']+ formatOutput(numParas,'%-14.8f')+'\n'
@ -413,14 +474,21 @@ class Criterion(object):
if fitResults == [] : initialguess = guess0
else : initialguess = np.array(fitResults[-1])
weight = get_weight(np.shape(stress)[1])
ydata = np.zeros(np.shape(stress)[1])
try:
popt, pcov = \
curve_fit_bound(funResidum, stress, np.zeros(np.shape(stress)[1]),
initialguess, weight, bounds)
popt, pcov, infodict, errmsg, ierr = \
leastsqBound (criteria.fun, initialguess, args=(ydata,stress),
bounds=bounds, full_output=True)
if ierr not in [1, 2, 3, 4]: raise RuntimeError("Optimal parameters not found: " + errmsg)
if (len(ydata) > len(initialguess)) and pcov is not None:
s_sq = (criteria.fun(popt, *(ydata,stress))**2).sum()/(len(ydata)-len(initialguess))
pcov = pcov * s_sq
perr = np.sqrt(np.diag(pcov))
fitResults.append(popt.tolist())
print (textParas%array2tuple(popt))
print (textError%array2tuple(perr))
print('Number of function calls =', infodict['nfev'])
except Exception as detail:
print detail
pass