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DAMASK_EICMD
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1. add __init__() for the "Class" of each Criterion; 

2. Polishing
This commit is contained in:
Haiming Zhang 2015-02-20 20:51:23 +00:00
parent a3e5da0bfd
commit 39e17fc95a
1 changed files with 41 additions and 18 deletions
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59
processing/misc/yieldSurface.py
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@ -80,6 +80,8 @@ class Tresca(object):
'''
residuum of Tresca yield criterion (eq. 2.26)
'''
def __init__(self, uniaxialStress):
self.stress0 = uniaxialStress
def fun(self,sigma0, ydata, sigmas):
lambdas = principalStresses(sigmas)
r = np.amax(np.array([abs(lambdas[2,:]-lambdas[1,:]),\
@ -93,6 +95,8 @@ class vonMises(object):
'''
residuum of Huber-Mises-Hencky yield criterion (eq. 2.37)
'''
def __init__(self, uniaxialStress):
self.stress0 = uniaxialStress
def fun(self, sigma0, ydata, sigmas):
return HosfordBasis(sigma0, 1.0,1.0,1.0, 2.0, sigmas)
def jac(self, sigma0, ydata, sigmas):
@ -102,6 +106,8 @@ class Drucker(object):
'''
residuum of Drucker yield criterion (eq. 2.41, F = sigma0)
'''
def __init__(self, uniaxialStress):
self.stress0 = uniaxialStress
def fun(self, (sigma0, C_D), ydata, sigmas):
return DruckerBasis(sigma0, C_D, 1.0, sigmas)
def jac(self, (sigma0, C_D), ydata, sigmas):
@ -111,6 +117,8 @@ class generalDrucker(object):
'''
residuum of general Drucker yield criterion (eq. 2.42, F = sigma0)
'''
def __init__(self, uniaxialStress):
self.stress0 = uniaxialStress
def fun(self, (sigma0, C_D, p), ydata, sigmas):
return DruckerBasis(sigma0, C_D, p, sigmas)
def jac(self, (sigma0, C_D, p), ydata, sigmas):
@ -120,6 +128,8 @@ class Hosford(object):
'''
residuum of Hershey yield criterion (eq. 2.43, Y = sigma0)
'''
def __init__(self, uniaxialStress):
self.stress0 = uniaxialStress
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):
@ -137,6 +147,8 @@ class Hill1948(object):
'''
residuum of Hill 1948 quadratic yield criterion (eq. 2.48)
'''
def __init__(self, uniaxialStress):
self.stress0 = uniaxialStress
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
@ -157,6 +169,8 @@ class generalHosford(object):
'''
residuum of Hershey yield criterion (eq. 2.104, sigmas = sigma0)
'''
def __init__(self, uniaxialStress):
self.stress0 = uniaxialStress
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):
@ -166,6 +180,8 @@ class Barlat1991iso(object):
'''
residuum of isotropic Barlat 1991 yield criterion (eq. 2.37)
'''
def __init__(self, uniaxialStress):
self.stress0 = uniaxialStress
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):
@ -175,6 +191,8 @@ class Barlat1991aniso(object):
'''
residuum of anisotropic Barlat 1991 yield criterion (eq. 2.37)
'''
def __init__(self, uniaxialStress):
self.stress0 = uniaxialStress
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):
@ -184,6 +202,8 @@ class Yld200418p(object):
'''
residuum of anisotropic Barlat 1991 yield criterion (eq. 2.37)
'''
def __init__(self, uniaxialStress):
self.stress0 = uniaxialStress
def fun(self, (sigma0, c12,c21,c23,c32,c31,c13,c44,c55,c66,
d12,d21,d23,d32,d31,d13,d44,d55,d66, m), ydata, sigmas):
return Yld200418pBasis(sigma0, c12,c21,c23,c32,c31,c13,c44,c55,c66,
@ -196,6 +216,8 @@ class BBC2003(object):
'''
residuum of anisotropic Barlat 1991 yield criterion (eq. 2.37)
'''
def __init__(self, uniaxialStress):
self.stress0 = uniaxialStress
def fun(self, (sigma0, a,b,c, d,e,f,g, k), ydata, sigmas):
return BBC2003Basis(sigma0, a,b,c, d,e,f,g, k, sigmas)
def jac(self, (sigma0, a,b,c, d,e,f,g, k), ydata, sigmas):
@ -269,8 +291,8 @@ def Cazacu_Barlat3DBasis(a1,a2,a3,a4,a5,a6,b1,b2,b3,b4,b5,b6,b7,b8,b9,b10,b11,c,
jb11 = dj3*s321*2.0
jaco = []
for jac in zip(ja1,ja2,ja3,ja4,ja5,ja6,jb1,jb2,jb3,jb4,jb5,jb6,jb7,jb8,jb9,jb10,jb11,jc):
jaco.append(jac)
for jacv in zip(ja1,ja2,ja3,ja4,ja5,ja6,jb1,jb2,jb3,jb4,jb5,jb6,jb7,jb8,jb9,jb10,jb11,jc):
jaco.append(jacv)
return np.array(jaco)
def Cazacu_Barlat2DBasis(a1,a2,a3,a4,b1,b2,b3,b4,b5,b10,c,
@ -304,8 +326,8 @@ def Cazacu_Barlat2DBasis(a1,a2,a3,a4,b1,b2,b3,b4,b5,b10,c,
jb5, jb10= -s12_2*(s11 - s22)/3.0, s12_2*s11*2.0/3.0
jaco = []
for jac in zip(ja1,ja2,ja3,ja4,jb1,jb2,jb3,jb4,jb5,jb10,jc):
jaco.append(jac)
for jacv in zip(ja1,ja2,ja3,ja4,jb1,jb2,jb3,jb4,jb5,jb10,jc):
jaco.append(jacv)
return np.array(jaco)
@ -328,14 +350,14 @@ def DruckerBasis(sigma0, C_D, p, sigmas, Jac=False, nParas=2):
js = -left**expo*right/sigma0
jC = -dfdl*J3**(2*p)*right
if nParas == 2:
for j1, j2 in zip(js, jC): jaco.append([j1, j2])
for jacv in zip(js, jC): jaco.append(jacv)
return np.array(jaco)
else:
ln = lambda x : np.log(x + 1.0e-32)
dldp = 3.0*J2**(3.0*p)*ln(J2) - 2.0*C_D*J3**(2.0*p)*ln(J3)
jp = dfdl*dldp*right + (left**expo)*ln(left)*expo/(-p)*right
for j1, j2, j3 in zip(js, jC, jp): jaco.append([j1, j2, j3])
for jacv in zip(js, jC, jp): jaco.append(jacv)
return np.array(jaco)
def HosfordBasis(sigma0, F,G,H, a, sigmas, Jac=False, nParas=1):
@ -368,7 +390,8 @@ def HosfordBasis(sigma0, F,G,H, a, sigmas, Jac=False, nParas=1):
elif nParas == 2: # isotropic Hosford
js = ones*(-2.0**expo) # d[]/dsigma0
ja = dldb*dbda + left*ln(base)*deda - drda # d[]/da
for j1,j2 in zip(js, ja): jaco.append([j1,j2])
for jacv in zip(js, ja):
jaco.append(jacv)
return np.array(jaco)
elif nParas == 5: # anisotropic Hosford
js = -left/right/sigma0 #ones*(-2.0**expo) # d[]/dsigma0
@ -376,7 +399,8 @@ def HosfordBasis(sigma0, F,G,H, a, sigmas, Jac=False, nParas=1):
jG = dldb*diff31**a/right
jH = dldb*diff12**a/right
ja =(dldb*dbda + left*ln(base)*deda)/right + left*(-right**(-2))*drda # d[]/da
for j1,j2,j3,j4,j5 in zip(js, jF,jG,jH,ja): jaco.append([j1,j2,j3,j4,j5])
for jacv in zip(js, jF,jG,jH,ja):
jaco.append(jacv)
return np.array(jaco)
def Barlat1991Basis(sigma0, a, b, c, f, g, h, m, sigmas, Jac=False, nParas=2):
@ -416,7 +440,7 @@ def Barlat1991Basis(sigma0, a, b, c, f, g, h, m, sigmas, Jac=False, nParas=2):
jm = (r+1.0)*ln(left)*(-expo*expo) + ratio*dfdl*0.5*(
absc1**m*ln(absc1) + absc2**m*ln(absc2) + absc3**m*ln(absc3) )
if nParas == 2:
for j1,j2 in zip(js, jm): jaco.append([j1,j2])
for jacv in zip(js, jm): jaco.append(jacv)
return np.array(jaco)
else:
dI2da = (2.0*A-B-C)*dAda/27.0
@ -451,8 +475,8 @@ def Barlat1991Basis(sigma0, a, b, c, f, g, h, m, sigmas, Jac=False, nParas=2):
jg = dfdI2*dI2dg + dfdI3*dI3dg
jh = dfdI2*dI2dh + dfdI3*dI3dh
for j1,j2,j3,j4,j5,j6,j7,j8 in zip(js,ja,jb,jc,jf,jg,jh,jm):
jaco.append([j1,j2,j3,j4,j5,j6,j7,j8])
for jacv in zip(js,ja,jb,jc,jf,jg,jh,jm):
jaco.append(jacv)
return np.array(jaco)
def BBC2003Basis(sigma0, a,b,c, d,e,f,g, k, sigmas, Jac=False):
@ -502,8 +526,8 @@ def BBC2003Basis(sigma0, a,b,c, d,e,f,g, k, sigmas, Jac=False):
jg = dsBardl * dldPsi * dPsidR * 2.0*g
jk = dsBardl * dldk + dsBarde * dedk
for j1,j2,j3,j4,j5,j6,j7,j8,j9 in zip(js,ja,jb,jc,jd, je, jf,jg,jk):
jaco.append([j1,j2,j3,j4,j5,j6,j7,j8,j9])
for jacv in zip(js,ja,jb,jc,jd, je, jf,jg,jk):
jaco.append(jacv)
return np.array(jaco)
def principalStress(p):
@ -710,10 +734,9 @@ def Yld200418pBasis(sigma0, c12,c21,c23,c32,c31,c13,c44,c55,c66,
jd66 = drdphi*( dphidQ1*dQ1dd66 + dphidQ2*dQ2dd66 + dphidQ3*dQ3dd66 )
jaco = []
for j1,j2,j3,j4,j5,j6,j7,j8,j9,j10,j11,j12,j13,j14,j15,j16,j17,j18,j19,j20 \
in zip(js,jc12,jc21,jc23,jc32,jc31,jc13,jc44,jc55,jc66,
for jacv in zip(js,jc12,jc21,jc23,jc32,jc31,jc13,jc44,jc55,jc66,
jd12,jd21,jd23,jd32,jd31,jd13,jd44,jd55,jd66, jm):
jaco.append([j1,j2,j3,j4,j5,j6,j7,j8,j9,j10,j11,j12,j13,j14,j15,j16,j17,j18,j19,j20])
jaco.append(jacv)
return np.array(jaco)
@ -879,13 +902,13 @@ class Criterion(object):
global fitResults
nameCriterion = self.name.lower()
criteriaClass = fittingCriteria[nameCriterion]['func']; criteria = criteriaClass()
criteriaClass = 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
criteria = criteriaClass(0.0)
if fitResults == [] : initialguess = guess0
else : initialguess = np.array(fitResults[-1])
weight = get_weight(np.shape(stress)[1])