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DAMASK_EICMD
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fix some type errors; add more comments

This commit is contained in:
Haiming Zhang 2015-04-02 19:32:48 +00:00
parent 43b665ba48
commit 0097d39873
1 changed files with 39 additions and 24 deletions
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63
processing/misc/yieldSurface.py
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@ -237,15 +237,16 @@ def Tresca(eqStress, paras, sigmas, mFix, criteria, Jac = False):
''' '''
Tresca yield criterion Tresca yield criterion
the fitted parameters is: paras(sigma0) the fitted parameters is: paras(sigma0)
eqStress, mFix, criteria are invalid input
''' '''
lambdas = principalStresses(sigmas)
r = np.amax(np.array([abs(lambdas[2,:]-lambdas[1,:]),\
abs(lambdas[1,:]-lambdas[0,:]),\
abs(lambdas[0,:]-lambdas[2,:])]),0) - paras
if not Jac: if not Jac:
lambdas = principalStresses(sigmas)
r = np.amax(np.array([abs(lambdas[2,:]-lambdas[1,:]),\
abs(lambdas[1,:]-lambdas[0,:]),\
abs(lambdas[0,:]-lambdas[2,:])]),0) - paras
return r.ravel() return r.ravel()
else: else:
return -np.ones(len(r)) return -np.ones(len(sigmas))
def Cazacu_Barlat(eqStress, paras, sigmas, mFix, criteria, Jac = False): def Cazacu_Barlat(eqStress, paras, sigmas, mFix, criteria, Jac = False):
''' '''
@ -334,7 +335,7 @@ def Hill1948(eqStress, paras, sigmas, mFix, criteria, Jac = False):
''' '''
Hill 1948 yield criterion Hill 1948 yield criterion
the fitted parameters are F, G, H, L, M, N the fitted parameters are F, G, H, L, M, N
eqStress, criteria are invalid input eqStress, mFix, criteria are invalid input
''' '''
s11,s22,s33,s12,s23,s31 = sigmas s11,s22,s33,s12,s23,s31 = sigmas
jac = np.array([(s22-s33)**2,(s33-s11)**2,(s11-s22)**2, 2.0*s23**2,2.0*s31**2,2.0*s12**2]) jac = np.array([(s22-s33)**2,(s33-s11)**2,(s11-s22)**2, 2.0*s23**2,2.0*s31**2,2.0*s12**2])
@ -347,6 +348,7 @@ def Hill1979(eqStress,paras, sigmas, mFix, criteria, Jac = False):
''' '''
Hill 1979 yield criterion Hill 1979 yield criterion
the fitted parameters are: f,g,h,a,b,c,m the fitted parameters are: f,g,h,a,b,c,m
criteria are invalid input
''' '''
if mFix[0]: m = mFix[1] if mFix[0]: m = mFix[1]
else: m = paras[-1] else: m = paras[-1]
@ -379,16 +381,16 @@ def Hosford(eqStress, paras, sigmas, mFix, criteria, Jac = False):
if criteria == 'vonmises': if criteria == 'vonmises':
coeff = np.ones(3) coeff = np.ones(3)
a = 2.0
sigma0 = paras sigma0 = paras
a = 2.0
elif criteria == 'hershey': elif criteria == 'hershey':
coeff = np.ones(3)
sigma0 = paras[0] sigma0 = paras[0]
if mFix[0]: a = mFix[1] if mFix[0]: a = mFix[1]
else: a = paras[1] else: a = paras[1]
coeff = np.ones(3)
else: else:
print '11' coeff = paras[0:3]
coeff = paras[0:3] sigma0 = eqStress
if mFix[0]: a = mFix[1] if mFix[0]: a = mFix[1]
else: a = paras[3] else: a = paras[3]
@ -421,6 +423,7 @@ def Barlat1991(eqStress, paras, sigmas, mFix, criteria, Jac=False):
the fitted parameters are: the fitted parameters are:
Isotropic: sigma0, m Isotropic: sigma0, m
Anisotropic: a, b, c, f, g, h, m Anisotropic: a, b, c, f, g, h, m
m is optional
''' '''
if criteria == 'barlat1991iso': if criteria == 'barlat1991iso':
sigma0 = paras[0] sigma0 = paras[0]
@ -451,7 +454,7 @@ def Barlat1991(eqStress, paras, sigmas, mFix, criteria, Jac=False):
jm = r*math_ln(left)/(-m**2) + dfdl*0.5*( jm = r*math_ln(left)/(-m**2) + dfdl*0.5*(
absc1**m*math_ln(absc1) + absc2**m*math_ln(absc2) + absc3**m*math_ln(absc3) ) absc1**m*math_ln(absc1) + absc2**m*math_ln(absc2) + absc3**m*math_ln(absc3) )
if criteria == 'barlat1991iso': if criteria == 'barlat1991iso':
js = -(r + 1.0)/sigma0 js = -r/sigma0
if mFix[0]: return js if mFix[0]: return js
else: return np.vstack((js,jm)).T else: return np.vstack((js,jm)).T
else: else:
@ -471,7 +474,10 @@ def Barlat1991(eqStress, paras, sigmas, mFix, criteria, Jac=False):
def BBC2003(eqStress, paras, sigmas, mFix, criteria, Jac=False): def BBC2003(eqStress, paras, sigmas, mFix, criteria, Jac=False):
''' '''
residuum of the BBC2003 yield criterion for plain stress BBC2003 yield criterion
the fitted parameters are
a, b, c, d, e, f, g, k; k is optional
criteria are invalid input
''' '''
a,b,c, d,e,f,g= paras[0:7] a,b,c, d,e,f,g= paras[0:7]
if mFix[0]: k = mFix[1] if mFix[0]: k = mFix[1]
@ -524,7 +530,10 @@ def BBC2003(eqStress, paras, sigmas, mFix, criteria, Jac=False):
def BBC2005(eqStress, paras, sigmas, mFix, criteria, Jac=False): def BBC2005(eqStress, paras, sigmas, mFix, criteria, Jac=False):
''' '''
residuum of the BBC2005 yield criterion for plain stress BBC2005 yield criterion
the fitted parameters are
a, b, L ,M, N, P, Q, R, k; k is optional
criteria are invalid input
''' '''
a,b,L, M, N, P, Q, R = paras[0:8] a,b,L, M, N, P, Q, R = paras[0:8]
if mFix[0]: k = mFix[1] if mFix[0]: k = mFix[1]
@ -571,8 +580,11 @@ def BBC2005(eqStress, paras, sigmas, mFix, criteria, Jac=False):
def Yld200418p(eqStress, paras, sigmas, mFix, criteria, Jac=False): def Yld200418p(eqStress, paras, sigmas, mFix, criteria, Jac=False):
''' '''
C: c12,c21,c23,c32,c13,c31,c44,c55,c66 Yld2004-18p yield criterion
D: d12,d21,d23,d32,d31,d13,d44,d55,d66 the fitted parameters are
C: c12,c21,c23,c32,c13,c31,c44,c55,c66; D: d12,d21,d23,d32,d31,d13,d44,d55,d66
and m, m is optional
criteria are invalid input
''' '''
C,D = paras[0:9], paras[9:18] C,D = paras[0:9], paras[9:18]
if mFix[0]: m = mFix[1] if mFix[0]: m = mFix[1]
@ -610,14 +622,21 @@ def Yld200418p(eqStress, paras, sigmas, mFix, criteria, Jac=False):
else: return np.vstack((jc,jd,jm)).T else: return np.vstack((jc,jd,jm)).T
def KarafillisBoyce(eqStress, paras, sigmas, mFix, criteria, Jac=False): def KarafillisBoyce(eqStress, paras, sigmas, mFix, criteria, Jac=False):
'''
Karafillis-Boyce yield criterion
the fitted parameters are
c11,c12,c13,c14,c15,c16,c21,c22,c23,c24,c25,c26,alpha,b1,b2,a
0<alpha<1, b1,b2,a are optional
criteria are invalid input
'''
ks = lambda (s1,s2,s3,s4,s5,s6),(c1,c2,c3,c4,c5,c6): np.array( [ ks = lambda (s1,s2,s3,s4,s5,s6),(c1,c2,c3,c4,c5,c6): np.array( [
((c2+c3)*s1-c3*s2-c2*s3)/3.0, ((c3+c1)*s2-c3*s1-c1*s3)/3.0, ((c2+c3)*s1-c3*s2-c2*s3)/3.0, ((c3+c1)*s2-c3*s1-c1*s3)/3.0,
((c1+c2)*s3-c2*s1-c1*s2)/3.0, c4*s4, c5*s5, c6*s6 ]) ((c1+c2)*s3-c2*s1-c1*s2)/3.0, c4*s4, c5*s5, c6*s6 ])
C1,C2,alpha = paras[0:6], paras[6:12], paras[12] C1,C2,alpha = paras[0:6], paras[6:12], paras[12]
if mFix[0]: b1=b2=a = mFix[1] if mFix[0]: b1=b2=a = mFix[1]
else: b1,b2,a = paras[12:15] else: b1,b2,a = paras[13:16]
print b1,b2,a
p,q = ks(sigmas, C1), ks(sigmas, C2) p,q = ks(sigmas, C1), ks(sigmas, C2)
plambdas,qlambdas = principalStress(p), principalStress(q) plambdas,qlambdas = principalStress(p), principalStress(q)
b1i,b2i,ai,rb2 = 1.0/b1, 1.0/b2, 1.0/a, 3.0**b2/(2.0**b2+2.0) b1i,b2i,ai,rb2 = 1.0/b1, 1.0/b2, 1.0/a, 3.0**b2/(2.0**b2+2.0)
@ -1109,11 +1128,11 @@ parser.add_option('-t','--threads', dest='threads', type='int',
parser.add_option('-d','--dimension', dest='dimension', type='int', parser.add_option('-d','--dimension', dest='dimension', type='int',
help='dimension of the virtual test [%default]', metavar='int') help='dimension of the virtual test [%default]', metavar='int')
parser.add_option('-v', '--vegter', dest='vegter', action='store_true', parser.add_option('-v', '--vegter', dest='vegter', action='store_true',
help='Vegter criteria [%default]') help='Vegter criteria [%default]', metavar='float')
parser.add_option('-e', '--exponent',dest='exponent', type='float', parser.add_option('-e', '--exponent',dest='exponent', type='float',
help='exponent of non-quadratic criteria') help='exponent of non-quadratic criteria')
parser.add_option('-u', '--uniaxial',dest='eqStress', type='float', parser.add_option('-u', '--uniaxial',dest='eqStress', type='float',
help='Equivalent stress') help='Equivalent stress', metavar='float')
parser.set_defaults(min = 12) parser.set_defaults(min = 12)
parser.set_defaults(max = 30) parser.set_defaults(max = 30)
parser.set_defaults(threads = 4) parser.set_defaults(threads = 4)
@ -1145,9 +1164,6 @@ if options.yieldValue[2] != int(options.yieldValue[2]):
if not os.path.isfile('numerics.config'): if not os.path.isfile('numerics.config'):
print('numerics.config file not found') print('numerics.config file not found')
if not os.path.isfile('material.config'):
print('material.config file not found')
numParas = len(fitCriteria[options.criterion]['bound']) numParas = len(fitCriteria[options.criterion]['bound'])
nExpo = fitCriteria[options.criterion]['nExpo'] nExpo = fitCriteria[options.criterion]['nExpo']
Guess = [] Guess = []
@ -1161,8 +1177,7 @@ for i in xrange(numParas):
g = (temp[0]+temp[1])/2.0 g = (temp[0]+temp[1])/2.0
if g == 0: g = temp[1]*0.5 if g == 0: g = temp[1]*0.5
Guess.append(g) Guess.append(g)
print Guess
print fitCriteria[options.criterion]['bound']
if options.vegter is True: if options.vegter is True:
options.dimension = 2 options.dimension = 2
unitGPa = 10.e8 unitGPa = 10.e8