1. Implement Yld2004-18p yield criterion(the residum and Jacobian);
2. Yld2004-18p yield criterion takes effect
This commit is contained in:
Haiming Zhang
parent
3670aef27e
commit
4a63aff1ed
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@ -107,7 +107,6 @@ class Drucker(object):
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def jac(self, (sigma0, C_D), ydata, sigmas):
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def jac(self, (sigma0, C_D), ydata, sigmas):
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return DruckerBasis(sigma0, C_D, 1.0, sigmas, Jac=True, nParas=2)
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return DruckerBasis(sigma0, C_D, 1.0, sigmas, Jac=True, nParas=2)
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class generalDrucker(object):
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class generalDrucker(object):
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'''
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'''
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residuum of general Drucker yield criterion (eq. 2.42, F = sigma0)
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residuum of general Drucker yield criterion (eq. 2.42, F = sigma0)
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@ -117,7 +116,6 @@ class generalDrucker(object):
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def jac(self, (sigma0, C_D, p), ydata, sigmas):
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def jac(self, (sigma0, C_D, p), ydata, sigmas):
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return DruckerBasis(sigma0, C_D, p, sigmas, Jac=True, nParas=3)
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return DruckerBasis(sigma0, C_D, p, sigmas, Jac=True, nParas=3)
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class Hosford(object):
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class Hosford(object):
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'''
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'''
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residuum of Hershey yield criterion (eq. 2.43, Y = sigma0)
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residuum of Hershey yield criterion (eq. 2.43, Y = sigma0)
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@ -182,14 +180,26 @@ class Barlat1991aniso(object):
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def jac(self, (sigma0, a,b,c,f,g,h, m), ydata, sigmas):
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def jac(self, (sigma0, a,b,c,f,g,h, m), ydata, sigmas):
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return Barlat1991Basis(sigma0, a,b,c,f,g,h, m, sigmas, Jac=True, nParas=8)
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return Barlat1991Basis(sigma0, a,b,c,f,g,h, m, sigmas, Jac=True, nParas=8)
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class Yld200418p(object):
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'''
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residuum of anisotropic Barlat 1991 yield criterion (eq. 2.37)
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'''
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def fun(self, (sigma0, c12,c21,c23,c32,c31,c13,c44,c55,c66,
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d12,d21,d23,d32,d31,d13,d44,d55,d66, m), ydata, sigmas):
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return Yld200418pBasis(sigma0, c12,c21,c23,c32,c31,c13,c44,c55,c66,
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d12,d21,d23,d32,d31,d13,d44,d55,d66, m, sigmas)
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def jac(self, (sigma0, c12,c21,c23,c32,c31,c13,c44,c55,c66,
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d12,d21,d23,d32,d31,d13,d44,d55,d66, m), ydata, sigmas):
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return Yld200418pBasis(sigma0, c12,c21,c23,c32,c31,c13,c44,c55,c66,
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d12,d21,d23,d32,d31,d13,d44,d55,d66, m, sigmas, Jac=True)
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class BBC2003(object):
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class BBC2003(object):
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'''
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'''
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residuum of anisotropic Barlat 1991 yield criterion (eq. 2.37)
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residuum of anisotropic Barlat 1991 yield criterion (eq. 2.37)
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'''
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'''
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def fun(self, (sigma0, a,b,c, d,e,f,g, k), ydata, sigmas):
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def fun(self, (sigma0, a,b,c, d,e,f,g, k), ydata, sigmas):
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return BBC2003Basis(sigma0, a,b,c, d,e,f,g, k, ydata, sigmas)
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return BBC2003Basis(sigma0, a,b,c, d,e,f,g, k, sigmas)
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def jac(self, (sigma0, a,b,c, d,e,f,g, k), ydata, sigmas):
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def jac(self, (sigma0, a,b,c, d,e,f,g, k), ydata, sigmas):
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return BBC2003Basis(sigma0, a,b,c, d,e,f,g, k, ydata, sigmas, Jac=True)
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return BBC2003Basis(sigma0, a,b,c, d,e,f,g, k, sigmas, Jac=True)
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def Cazacu_Barlat3D(sigma0,a1,a2,a3,a4,a5,a6, b1,b2,b3,b4,b5,b6,b7,b8,b9,b10,b11, c,
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def Cazacu_Barlat3D(sigma0,a1,a2,a3,a4,a5,a6, b1,b2,b3,b4,b5,b6,b7,b8,b9,b10,b11, c,
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ydata, sigmas):
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ydata, sigmas):
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@ -247,17 +257,17 @@ def DruckerBasis(sigma0, C_D, p, sigmas, Jac=False, nParas=2):
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else:
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else:
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jaco = []
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jaco = []
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dfdl = expo*left**(expo-1.0)
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dfdl = expo*left**(expo-1.0)
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j1 = -left**expo*right/sigma0
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js = -left**expo*right/sigma0
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j2 = -dfdl*J3**(2*p)*right
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jC = -dfdl*J3**(2*p)*right
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if nParas == 2:
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if nParas == 2:
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for a,b in zip(j1, j2): jaco.append([a,b])
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for j1, j2 in zip(js, jC): jaco.append([j1, j2])
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return np.array(jaco)
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return np.array(jaco)
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else:
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else:
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ln = lambda x : np.log(x + 1.0e-32)
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ln = lambda x : np.log(x + 1.0e-32)
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dldp = 3.0*J2**(3.0*p)*ln(J2) - 2.0*C_D*J3**(2.0*p)*ln(J3)
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dldp = 3.0*J2**(3.0*p)*ln(J2) - 2.0*C_D*J3**(2.0*p)*ln(J3)
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j3 = dfdl*dldp*right + (left**expo)*ln(left)*expo/(-p)*right
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jp = dfdl*dldp*right + (left**expo)*ln(left)*expo/(-p)*right
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for a,b,c in zip(j1, j2, j3): jaco.append([a,b,c])
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for j1, j2, j3 in zip(js, jC, jp): jaco.append([j1, j2, j3])
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return np.array(jaco)
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return np.array(jaco)
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def HosfordBasis(sigma0, F,G,H, a, sigmas, Jac=False, nParas=1):
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def HosfordBasis(sigma0, F,G,H, a, sigmas, Jac=False, nParas=1):
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@ -269,7 +279,8 @@ def HosfordBasis(sigma0, F,G,H, a, sigmas, Jac=False, nParas=1):
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diff31 = abs(lambdas[2,:] - lambdas[0,:])
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diff31 = abs(lambdas[2,:] - lambdas[0,:])
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diff12 = abs(lambdas[0,:] - lambdas[1,:])
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diff12 = abs(lambdas[0,:] - lambdas[1,:])
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base = F*diff23**a + G*diff31**a + H*diff12**a; expo = 1.0/a
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base = F*diff23**a + G*diff31**a + H*diff12**a; expo = 1.0/a
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left = base**expo; right = 2.0**expo*sigma0
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left = base**expo
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right = 2.0**expo*sigma0
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if not Jac:
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if not Jac:
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if nParas == 1: return (left - right).ravel()
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if nParas == 1: return (left - right).ravel()
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@ -279,21 +290,25 @@ def HosfordBasis(sigma0, F,G,H, a, sigmas, Jac=False, nParas=1):
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if nParas > 1:
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if nParas > 1:
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ln = lambda x : np.log(x + 1.0e-32)
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ln = lambda x : np.log(x + 1.0e-32)
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dbda = F*ln(diff23)*diff23**a + G*ln(diff31)*diff31**a + H*ln(diff12)*diff12**a
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dbda = F*ln(diff23)*diff23**a + G*ln(diff31)*diff31**a + H*ln(diff12)*diff12**a
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deda = -expo*expo; dldb = expo*left/base; drda = sigma0*(2.0**expo)*ln(2.0)*deda
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deda = -expo*expo
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drda = sigma0*(2.0**expo)*ln(2.0)*deda
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dldb = expo*left/base
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jaco = []
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jaco = []
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if nParas == 1: # von Mises
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if nParas == 1: # von Mises
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return ones*(-2.0**0.5)
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return ones*(-2.0**0.5)
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elif nParas == 2: # isotropic Hosford
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elif nParas == 2: # isotropic Hosford
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j1 = ones*(-2.0**expo) # d[]/dsigma0
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js = ones*(-2.0**expo) # d[]/dsigma0
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j2 = dldb*dbda + left*ln(base)*deda - drda # d[]/da
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ja = dldb*dbda + left*ln(base)*deda - drda # d[]/da
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for a,b in zip(j1, j2): jaco.append([a,b])
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for j1,j2 in zip(js, ja): jaco.append([j1,j2])
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return np.array(jaco)
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return np.array(jaco)
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elif nParas == 5: # anisotropic Hosford
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elif nParas == 5: # anisotropic Hosford
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j1 = -left/right/sigma0 #ones*(-2.0**expo) # d[]/dsigma0
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js = -left/right/sigma0 #ones*(-2.0**expo) # d[]/dsigma0
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j2 = dldb*diff23**a/right; j3 = dldb*diff31**a/right; j4 = dldb*diff12**a/right
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jF = dldb*diff23**a/right
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j5 =(dldb*dbda + left*ln(base)*deda)/right + left*(-right**(-2))*drda # d[]/da
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jG = dldb*diff31**a/right
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for a,b,c,d,e in zip(j1, j2,j3,j4,j5): jaco.append([a,b,c,d,e])
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jH = dldb*diff12**a/right
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ja =(dldb*dbda + left*ln(base)*deda)/right + left*(-right**(-2))*drda # d[]/da
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for j1,j2,j3,j4,j5 in zip(js, jF,jG,jH,ja): jaco.append([j1,j2,j3,j4,j5])
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return np.array(jaco)
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return np.array(jaco)
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def Barlat1991Basis(sigma0, a, b, c, f, g, h, m, sigmas, Jac=False, nParas=2):
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def Barlat1991Basis(sigma0, a, b, c, f, g, h, m, sigmas, Jac=False, nParas=2):
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@ -312,9 +327,12 @@ def Barlat1991Basis(sigma0, a, b, c, f, g, h, m, sigmas, Jac=False, nParas=2):
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I3 = (C-B)*(A-C)*(B-A)/54.0 + F*G*H - \
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I3 = (C-B)*(A-C)*(B-A)/54.0 + F*G*H - \
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( (C-B)*F*F + (A-C)*G*G + (B-A)*H*H )/6.0
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( (C-B)*F*F + (A-C)*G*G + (B-A)*H*H )/6.0
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theta = np.arccos(I3/I2**1.5)
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theta = np.arccos(I3/I2**1.5)
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phi1 = (2.0*theta + pi)/6.0; cos1 = 2.0*cos(phi1); absc1 = abs(cos1)
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phi1 = (2.0*theta + pi)/6.0
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phi2 = (2.0*theta + pi*3.0)/6.0; cos2 = 2.0*cos(phi2); absc2 = abs(cos2)
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phi2 = (2.0*theta + pi*3.0)/6.0
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phi3 = (2.0*theta + pi*5.0)/6.0; cos3 = 2.0*cos(phi3); absc3 = abs(cos3)
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phi3 = (2.0*theta + pi*5.0)/6.0
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cos1 = 2.0*cos(phi1); absc1 = abs(cos1)
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cos2 = 2.0*cos(phi2); absc2 = abs(cos2)
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cos3 = 2.0*cos(phi3); absc3 = abs(cos3)
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ratio= np.sqrt(3.0*I2)/sigma0; expo = 1.0/m
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ratio= np.sqrt(3.0*I2)/sigma0; expo = 1.0/m
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left = ( absc1**m + absc2**m + absc3**m )/2.0
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left = ( absc1**m + absc2**m + absc3**m )/2.0
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leftNorm = left**expo
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leftNorm = left**expo
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@ -333,9 +351,12 @@ def Barlat1991Basis(sigma0, a, b, c, f, g, h, m, sigmas, Jac=False, nParas=2):
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for j1,j2 in zip(js, jm): jaco.append([j1,j2])
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for j1,j2 in zip(js, jm): jaco.append([j1,j2])
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return np.array(jaco)
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return np.array(jaco)
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else:
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else:
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dI2da = (2.0*A-B-C)*dAda/27.0; dI2df = 2.0*F*dFdf/3.0
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dI2da = (2.0*A-B-C)*dAda/27.0
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dI2db = (2.0*B-C-A)*dBdb/27.0; dI2dg = 2.0*G*dGdg/3.0
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dI2db = (2.0*B-C-A)*dBdb/27.0
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dI2dc = (2.0*C-A-B)*dCdc/27.0; dI2dh = 2.0*H*dHdh/3.0
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dI2dc = (2.0*C-A-B)*dCdc/27.0
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dI2df = 2.0*F*dFdf/3.0
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dI2dg = 2.0*G*dGdg/3.0
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dI2dh = 2.0*H*dHdh/3.0
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dI3da = dI2da*(B-C)/2.0 + (H**2 - G**2)*dAda/6.0
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dI3da = dI2da*(B-C)/2.0 + (H**2 - G**2)*dAda/6.0
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dI3db = dI2db*(C-A)/2.0 + (F**2 - H**2)*dBdb/6.0
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dI3db = dI2db*(C-A)/2.0 + (F**2 - H**2)*dBdb/6.0
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dI3dc = dI2dc*(A-B)/2.0 + (G**2 - F**2)*dCdc/6.0
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dI3dc = dI2dc*(A-B)/2.0 + (G**2 - F**2)*dCdc/6.0
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@ -346,22 +367,27 @@ def Barlat1991Basis(sigma0, a, b, c, f, g, h, m, sigmas, Jac=False, nParas=2):
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darccos = -(1.0 - I3**2/I2**3)**(-0.5)
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darccos = -(1.0 - I3**2/I2**3)**(-0.5)
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dthedI2 = darccos*I3*(-1.5)*I2**(-2.5)
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dthedI2 = darccos*I3*(-1.5)*I2**(-2.5)
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dthedI3 = darccos*I2**(-1.5)
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dthedI3 = darccos*I2**(-1.5)
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dc1dthe = -sin(phi1)/3.0; dc2dthe = -sin(phi2)/3.0; dc3dthe = -sin(phi3)/3.0
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dc1dthe = -sin(phi1)/3.0
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dc2dthe = -sin(phi2)/3.0
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dc3dthe = -sin(phi3)/3.0
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dfdc = ratio * dfdl * 0.5 * m
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dfdc = ratio * dfdl * 0.5 * m
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dfdc1 = dfdc* absc1**(expo-1.0)*np.sign(cos1)
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dfdc1 = dfdc* absc1**(expo-1.0)*np.sign(cos1)
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dfdc2 = dfdc* absc2**(expo-1.0)*np.sign(cos2)
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dfdc2 = dfdc* absc2**(expo-1.0)*np.sign(cos2)
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dfdc3 = dfdc* absc3**(expo-1.0)*np.sign(cos3)
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dfdc3 = dfdc* absc3**(expo-1.0)*np.sign(cos3)
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dfdthe= (dfdc1*dc1dthe + dfdc2*dc2dthe + dfdc2*dc2dthe)*2.0
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dfdthe= (dfdc1*dc1dthe + dfdc2*dc2dthe + dfdc2*dc2dthe)*2.0
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dfdI2 = dfdthe*dthedI2; dfdI3 = dfdthe*dthedI3
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dfdI2 = dfdthe*dthedI2; dfdI3 = dfdthe*dthedI3
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ja = dfdI2*dI2da + dfdI3*dI3da; jf = dfdI2*dI2df + dfdI3*dI3df
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ja = dfdI2*dI2da + dfdI3*dI3da
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jb = dfdI2*dI2db + dfdI3*dI3db; jg = dfdI2*dI2dg + dfdI3*dI3dg
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jb = dfdI2*dI2db + dfdI3*dI3db
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jc = dfdI2*dI2dc + dfdI3*dI3dc; jh = dfdI2*dI2dh + dfdI3*dI3dh
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jc = dfdI2*dI2dc + dfdI3*dI3dc
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jf = dfdI2*dI2df + dfdI3*dI3df
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jg = dfdI2*dI2dg + dfdI3*dI3dg
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jh = dfdI2*dI2dh + dfdI3*dI3dh
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for j1,j2,j3,j4,j5,j6,j7,j8 in zip(js,ja,jb,jc,jf,jg,jh,jm):
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for j1,j2,j3,j4,j5,j6,j7,j8 in zip(js,ja,jb,jc,jf,jg,jh,jm):
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jaco.append([j1,j2,j3,j4,j5,j6,j7,j8])
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jaco.append([j1,j2,j3,j4,j5,j6,j7,j8])
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return np.array(jaco)
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return np.array(jaco)
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def BBC2003Basis(sigma0, a,b,c, d,e,f,g, k, ydata, sigmas, Jac=False):
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def BBC2003Basis(sigma0, a,b,c, d,e,f,g, k, sigmas, Jac=False):
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'''
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'''
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residuum of the BBC2003 yield criterion for plain stress
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residuum of the BBC2003 yield criterion for plain stress
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'''
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'''
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@ -412,6 +438,216 @@ def BBC2003Basis(sigma0, a,b,c, d,e,f,g, k, ydata, sigmas, Jac=False):
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jaco.append([j1,j2,j3,j4,j5,j6,j7,j8,j9])
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jaco.append([j1,j2,j3,j4,j5,j6,j7,j8,j9])
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return np.array(jaco)
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return np.array(jaco)
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def principalStress(p):
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sin = np.sin; cos = np.cos
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s11 = p[0]; s22 = p[1]; s33 = p[2]
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s12 = p[3]; s23 = p[4]; s31 = p[5]
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I1 = s11 + s22 + s33
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I2 = s11*s22 + s22*s33 + s33*s11 - s12**2 - s23**2 - s31**2
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I3 = s11*s22*s33 + 2.0*s12*s23*s31 - s12**2*s33 - s23**2*s11 - s31**2*s22
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third = 1.0/3.0
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I1s3I2= (I1**2 - 3.0*I2)**0.5
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numer = 2.0*I1**3 - 9.0*I1*I2 + 27.0*I3
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denom = I1s3I2**(-3.0)
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cs = 0.5*numer*denom
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phi = np.arccos(cs)/3.0
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t1 = I1/3.0; t2 = 2.0/3.0*I1s3I2
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S1 = t1 + t2*cos(phi)
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S2 = t1 + t2*cos(phi+np.pi*2.0/3.0)
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S3 = t1 + t2*cos(phi+np.pi*4.0/3.0)
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return np.array([S1,S2,S3]), np.array([I1,I2,I3])
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def principalStrs_Der(p, Invariant, s1, s2, s3, s4, s5, s6):
|
||||||
|
sin = np.sin; cos = np.cos
|
||||||
|
I1 = Invariant[0,:]; I2 = Invariant[1,:]; I3 = Invariant[2,:]
|
||||||
|
|
||||||
|
third = 1.0/3.0
|
||||||
|
I1s3I2= (I1**2 - 3.0*I2)**0.5
|
||||||
|
numer = 2.0*I1**3 - 9.0*I1*I2 + 27.0*I3
|
||||||
|
denom = I1s3I2**(-3.0)
|
||||||
|
cs = 0.5*numer*denom
|
||||||
|
phi = np.arccos(cs)*third
|
||||||
|
|
||||||
|
dphidcs = -third/np.sqrt(1.0 - cs**2)
|
||||||
|
dcsddenom = 0.5*numer*(-1.5)*I1s3I2**(-5.0)
|
||||||
|
dcsdI1 = 0.5*(6.0*I1**2 - 9*I2)*denom + dcsddenom*(2.0*I1)
|
||||||
|
dcsdI2 = 0.5*(2.0*I1**3 - 9*I1)*denom + dcsddenom*(-3.0)
|
||||||
|
dcsdI3 = 13.5*denom
|
||||||
|
dphidI1 = dphidcs*dcsdI1
|
||||||
|
dphidI2 = dphidcs*dcsdI2
|
||||||
|
dphidI3 = dphidcs*dcsdI3
|
||||||
|
|
||||||
|
dI1s3I2dI1= I1/I1s3I2; dI1s3I2dI2 = -1.5/I1s3I2
|
||||||
|
third2 = 2.0*third; tcoeff = third2*I1s3I2
|
||||||
|
|
||||||
|
theta = phi
|
||||||
|
dS1dI1 = third + tcoeff*(-sin(theta))*dphidI1 + third2*dI1s3I2dI1*cos(theta)
|
||||||
|
dS1dI2 = + tcoeff*(-sin(theta))*dphidI2 + third2*dI1s3I2dI2*cos(theta)
|
||||||
|
dS1dI3 = tcoeff*(-sin(theta))*dphidI3
|
||||||
|
|
||||||
|
theta = phi + np.pi*2.0/3.0
|
||||||
|
dS2dI1 = third + tcoeff*(-sin(theta))*dphidI1 + third2*dI1s3I2dI1*cos(theta)
|
||||||
|
dS2dI2 = + tcoeff*(-sin(theta))*dphidI2 + third2*dI1s3I2dI2*cos(theta)
|
||||||
|
dS2dI3 = tcoeff*(-sin(theta))*dphidI3
|
||||||
|
|
||||||
|
theta = phi + np.pi*4.0/3.0
|
||||||
|
dS3dI1 = third + tcoeff*(-sin(theta))*dphidI1 + third2*dI1s3I2dI1*cos(theta)
|
||||||
|
dS3dI2 = + tcoeff*(-sin(theta))*dphidI2 + third2*dI1s3I2dI2*cos(theta)
|
||||||
|
dS3dI3 = tcoeff*(-sin(theta))*dphidI3
|
||||||
|
|
||||||
|
# calculate the derivation of principal stress with regards to the anisotropic coefficients
|
||||||
|
dI1dp0 = dI1dp1 = dI1dp2 = 1.0
|
||||||
|
dI1dp3 = dI1dp4 = dI1dp5 = 0.0
|
||||||
|
dI2dp0 = p[1] + p[2]; dI2dp4 = -2.0*p[4]
|
||||||
|
dI2dp1 = p[2] + p[0]; dI2dp5 = -2.0*p[5]
|
||||||
|
dI2dp2 = p[0] + p[1]; dI2dp3 = -2.0*p[3]
|
||||||
|
|
||||||
|
dI3dp0 = p[1]*p[2] - p[4]**2; dI3dp4 = -2.0*p[4]*p[0] + 2.0*p[5]*p[3]
|
||||||
|
dI3dp1 = p[2]*p[0] - p[5]**2; dI3dp5 = -2.0*p[5]*p[1] + 2.0*p[3]*p[4]
|
||||||
|
dI3dp2 = p[0]*p[1] - p[3]**2; dI3dp3 = -2.0*p[3]*p[2] + 2.0*p[4]*p[5]
|
||||||
|
|
||||||
|
dI1dc12 = dI1dp0*(-s2); dI2dc12 = dI2dp0*(-s2); dI3dc12 = dI3dp0*(-s2) # c12
|
||||||
|
dI1dc21 = dI1dp1*(-s1); dI2dc21 = dI2dp1*(-s1); dI3dc21 = dI3dp1*(-s1) # c21
|
||||||
|
dI1dc23 = dI1dp1*(-s3); dI2dc23 = dI2dp1*(-s3); dI3dc23 = dI3dp1*(-s3) # c23
|
||||||
|
dI1dc32 = dI1dp2*(-s2); dI2dc32 = dI2dp2*(-s2); dI3dc32 = dI3dp2*(-s2) # c32
|
||||||
|
dI1dc31 = dI1dp2*(-s1); dI2dc31 = dI2dp2*(-s1); dI3dc31 = dI3dp2*(-s1) # c31
|
||||||
|
dI1dc13 = dI1dp0*(-s3); dI2dc13 = dI2dp0*(-s3); dI3dc13 = dI3dp0*(-s3) # c13
|
||||||
|
dI1dc44 = dI1dp3* s4 ; dI2dc44 = dI2dp3* s4 ; dI3dc44 = dI3dp3* s4 # c44
|
||||||
|
dI1dc55 = dI1dp4* s5 ; dI2dc55 = dI2dp4* s5 ; dI3dc55 = dI3dp4* s5 # c55
|
||||||
|
dI1dc66 = dI1dp5* s6 ; dI2dc66 = dI2dp5* s6 ; dI3dc66 = dI3dp5* s6 # c66
|
||||||
|
|
||||||
|
dS1dc12 = dS1dI1 * dI1dc12 + dS1dI2 * dI2dc12 + dS1dI3 * dI3dc12
|
||||||
|
dS1dc21 = dS1dI1 * dI1dc21 + dS1dI2 * dI2dc21 + dS1dI3 * dI3dc21
|
||||||
|
dS1dc23 = dS1dI1 * dI1dc23 + dS1dI2 * dI2dc23 + dS1dI3 * dI3dc23
|
||||||
|
dS1dc32 = dS1dI1 * dI1dc32 + dS1dI2 * dI2dc32 + dS1dI3 * dI3dc32
|
||||||
|
dS1dc31 = dS1dI1 * dI1dc31 + dS1dI2 * dI2dc31 + dS1dI3 * dI3dc31
|
||||||
|
dS1dc13 = dS1dI1 * dI1dc13 + dS1dI2 * dI2dc13 + dS1dI3 * dI3dc13
|
||||||
|
dS1dc44 = dS1dI1 * dI1dc44 + dS1dI2 * dI2dc44 + dS1dI3 * dI3dc44
|
||||||
|
dS1dc55 = dS1dI1 * dI1dc55 + dS1dI2 * dI2dc55 + dS1dI3 * dI3dc55
|
||||||
|
dS1dc66 = dS1dI1 * dI1dc66 + dS1dI2 * dI2dc66 + dS1dI3 * dI3dc66
|
||||||
|
|
||||||
|
dS2dc12 = dS2dI1 * dI1dc12 + dS2dI2 * dI2dc12 + dS2dI3 * dI3dc12
|
||||||
|
dS2dc21 = dS2dI1 * dI1dc21 + dS2dI2 * dI2dc21 + dS2dI3 * dI3dc21
|
||||||
|
dS2dc23 = dS2dI1 * dI1dc23 + dS2dI2 * dI2dc23 + dS2dI3 * dI3dc23
|
||||||
|
dS2dc32 = dS2dI1 * dI1dc32 + dS2dI2 * dI2dc32 + dS2dI3 * dI3dc32
|
||||||
|
dS2dc31 = dS2dI1 * dI1dc31 + dS2dI2 * dI2dc31 + dS2dI3 * dI3dc31
|
||||||
|
dS2dc13 = dS2dI1 * dI1dc13 + dS2dI2 * dI2dc13 + dS2dI3 * dI3dc13
|
||||||
|
dS2dc44 = dS2dI1 * dI1dc44 + dS2dI2 * dI2dc44 + dS2dI3 * dI3dc44
|
||||||
|
dS2dc55 = dS2dI1 * dI1dc55 + dS2dI2 * dI2dc55 + dS2dI3 * dI3dc55
|
||||||
|
dS2dc66 = dS2dI1 * dI1dc66 + dS2dI2 * dI2dc66 + dS2dI3 * dI3dc66
|
||||||
|
|
||||||
|
dS3dc12 = dS3dI1 * dI1dc12 + dS3dI2 * dI2dc12 + dS3dI3 * dI3dc12
|
||||||
|
dS3dc21 = dS3dI1 * dI1dc21 + dS3dI2 * dI2dc21 + dS3dI3 * dI3dc21
|
||||||
|
dS3dc23 = dS3dI1 * dI1dc23 + dS3dI2 * dI2dc23 + dS3dI3 * dI3dc23
|
||||||
|
dS3dc32 = dS3dI1 * dI1dc32 + dS3dI2 * dI2dc32 + dS3dI3 * dI3dc32
|
||||||
|
dS3dc31 = dS3dI1 * dI1dc31 + dS3dI2 * dI2dc31 + dS3dI3 * dI3dc31
|
||||||
|
dS3dc13 = dS3dI1 * dI1dc13 + dS3dI2 * dI2dc13 + dS3dI3 * dI3dc13
|
||||||
|
dS3dc44 = dS3dI1 * dI1dc44 + dS3dI2 * dI2dc44 + dS3dI3 * dI3dc44
|
||||||
|
dS3dc55 = dS3dI1 * dI1dc55 + dS3dI2 * dI2dc55 + dS3dI3 * dI3dc55
|
||||||
|
dS3dc66 = dS3dI1 * dI1dc66 + dS3dI2 * dI2dc66 + dS3dI3 * dI3dc66
|
||||||
|
|
||||||
|
return dS1dc12, dS1dc21, dS1dc23, dS1dc32, dS1dc31, dS1dc13, dS1dc44, dS1dc55, dS1dc66, \
|
||||||
|
dS2dc12, dS2dc21, dS2dc23, dS2dc32, dS2dc31, dS2dc13, dS2dc44, dS2dc55, dS2dc66, \
|
||||||
|
dS3dc12, dS3dc21, dS3dc23, dS3dc32, dS3dc31, dS3dc13, dS3dc44, dS3dc55, dS3dc66
|
||||||
|
|
||||||
|
def Yld200418pBasis(sigma0, c12,c21,c23,c32,c31,c13,c44,c55,c66,
|
||||||
|
d12,d21,d23,d32,d31,d13,d44,d55,d66, m, sigmas, Jac=False):
|
||||||
|
|
||||||
|
sv = (sigmas[0] + sigmas[1] + sigmas[2])/3.0
|
||||||
|
s1 = sigmas[0]-sv; s2 = sigmas[1]-sv; s3 = sigmas[2]-sv
|
||||||
|
s4 = sigmas[3]; s5 = sigmas[4]; s6 = sigmas[5]
|
||||||
|
|
||||||
|
p = np.empty_like(sigmas); q = np.empty_like(sigmas)
|
||||||
|
p[0] = -c12*s2 - c13*s3
|
||||||
|
p[1] = -c21*s1 - c23*s3
|
||||||
|
p[2] = -c31*s1 - c32*s2
|
||||||
|
p[3] = c44*s4
|
||||||
|
p[4] = c55*s5
|
||||||
|
p[5] = c66*s6
|
||||||
|
|
||||||
|
q[0] = -d12*s2 - d13*s3
|
||||||
|
q[1] = -d21*s1 - d23*s3
|
||||||
|
q[2] = -d31*s1 - d32*s2
|
||||||
|
q[3] = d44*s4
|
||||||
|
q[4] = d55*s5
|
||||||
|
q[5] = d66*s6
|
||||||
|
|
||||||
|
plambdas, pInvariant = principalStress(p) # no sort
|
||||||
|
qlambdas, qInvariant = principalStress(q) # no sort
|
||||||
|
|
||||||
|
P1 = plambdas[0,:]; P2 = plambdas[1,:]; P3 = plambdas[2,:]
|
||||||
|
Q1 = qlambdas[0,:]; Q2 = qlambdas[1,:]; Q3 = qlambdas[2,:]
|
||||||
|
|
||||||
|
m2 = m/2.0; m1 = 1.0/m; m21 = m2-1.0
|
||||||
|
P1Q1s = (P1-Q1)**2; P1Q2s = (P1-Q2)**2; P1Q3s = (P1-Q3)**2
|
||||||
|
P2Q1s = (P2-Q1)**2; P2Q2s = (P2-Q2)**2; P2Q3s = (P2-Q3)**2
|
||||||
|
P3Q1s = (P3-Q1)**2; P3Q2s = (P3-Q2)**2; P3Q3s = (P3-Q3)**2
|
||||||
|
|
||||||
|
phi= P1Q1s**m2 + P1Q2s**m2 + P1Q3s**m2 + \
|
||||||
|
P2Q1s**m2 + P2Q2s**m2 + P2Q3s**m2 + \
|
||||||
|
P3Q1s**m2 + P3Q2s**m2 + P3Q3s**m2
|
||||||
|
r = (0.25*phi)**m1/sigma0 - 1.0
|
||||||
|
|
||||||
|
if not Jac:
|
||||||
|
return r.ravel()
|
||||||
|
else:
|
||||||
|
ln = lambda x : np.log(x + 1.0e-32)
|
||||||
|
|
||||||
|
drdphi = (r+1.0)*m1/phi
|
||||||
|
dphidm =( (P1Q1s**m2)*ln(P1Q1s) + (P1Q2s**m2)*ln(P1Q2s) + (P1Q3s**m2)*ln(P1Q3s) +
|
||||||
|
(P2Q1s**m2)*ln(P2Q1s) + (P2Q2s**m2)*ln(P2Q2s) + (P2Q3s**m2)*ln(P2Q3s) +
|
||||||
|
(P3Q1s**m2)*ln(P3Q1s) + (P3Q2s**m2)*ln(P3Q2s) + (P3Q3s**m2)*ln(P3Q3s)
|
||||||
|
)*0.5
|
||||||
|
js = -(r+1.0)/sigma0
|
||||||
|
jm = drdphi*dphidm + (r+1.0)*ln(0.25*phi)*(-m1*m1)
|
||||||
|
|
||||||
|
dP1dc12, dP1dc21, dP1dc23, dP1dc32, dP1dc31, dP1dc13, dP1dc44, dP1dc55, dP1dc66, \
|
||||||
|
dP2dc12, dP2dc21, dP2dc23, dP2dc32, dP2dc31, dP2dc13, dP2dc44, dP2dc55, dP2dc66, \
|
||||||
|
dP3dc12, dP3dc21, dP3dc23, dP3dc32, dP3dc31, dP3dc13, dP3dc44, dP3dc55, dP3dc66= \
|
||||||
|
principalStrs_Der(p, pInvariant, s1,s2,s3,s4,s5,s6)
|
||||||
|
|
||||||
|
dQ1dd12, dQ1dd21, dQ1dd23, dQ1dd32, dQ1dd31, dQ1dd13, dQ1dd44, dQ1dd55, dQ1dd66, \
|
||||||
|
dQ2dd12, dQ2dd21, dQ2dd23, dQ2dd32, dQ2dd31, dQ2dd13, dQ2dd44, dQ2dd55, dQ2dd66, \
|
||||||
|
dQ3dd12, dQ3dd21, dQ3dd23, dQ3dd32, dQ3dd31, dQ3dd13, dQ3dd44, dQ3dd55, dQ3dd66= \
|
||||||
|
principalStrs_Der(q, qInvariant, s1,s2,s3,s4,s5,s6)
|
||||||
|
|
||||||
|
dphidP1 = m*( P1Q1s**m21*(P1-Q1) + P1Q2s**m21*(P1-Q2) + P1Q3s**m21*(P1-Q3) )
|
||||||
|
dphidP2 = m*( P2Q1s**m21*(P2-Q1) + P2Q2s**m21*(P2-Q2) + P2Q3s**m21*(P2-Q3) )
|
||||||
|
dphidP3 = m*( P3Q1s**m21*(P3-Q1) + P3Q2s**m21*(P3-Q2) + P3Q3s**m21*(P3-Q3) )
|
||||||
|
|
||||||
|
dphidQ1 = m*( P1Q1s**m21*(Q1-P1) + P2Q1s**m21*(Q1-P2) + P3Q1s**m21*(Q1-P3) )
|
||||||
|
dphidQ2 = m*( P1Q2s**m21*(Q2-P1) + P2Q2s**m21*(Q2-P2) + P3Q2s**m21*(Q2-P3) )
|
||||||
|
dphidQ3 = m*( P1Q3s**m21*(Q3-P1) + P2Q3s**m21*(Q3-P2) + P3Q3s**m21*(Q3-P3) )
|
||||||
|
|
||||||
|
jc12 = drdphi*( dphidP1*dP1dc12 + dphidP2*dP2dc12 + dphidP3*dP3dc12 )
|
||||||
|
jc21 = drdphi*( dphidP1*dP1dc21 + dphidP2*dP2dc21 + dphidP3*dP3dc21 )
|
||||||
|
jc23 = drdphi*( dphidP1*dP1dc23 + dphidP2*dP2dc23 + dphidP3*dP3dc23 )
|
||||||
|
jc32 = drdphi*( dphidP1*dP1dc32 + dphidP2*dP2dc32 + dphidP3*dP3dc32 )
|
||||||
|
jc31 = drdphi*( dphidP1*dP1dc31 + dphidP2*dP2dc31 + dphidP3*dP3dc31 )
|
||||||
|
jc13 = drdphi*( dphidP1*dP1dc13 + dphidP2*dP2dc13 + dphidP3*dP3dc13 )
|
||||||
|
jc44 = drdphi*( dphidP1*dP1dc44 + dphidP2*dP2dc44 + dphidP3*dP3dc44 )
|
||||||
|
jc55 = drdphi*( dphidP1*dP1dc55 + dphidP2*dP2dc55 + dphidP3*dP3dc55 )
|
||||||
|
jc66 = drdphi*( dphidP1*dP1dc66 + dphidP2*dP2dc66 + dphidP3*dP3dc66 )
|
||||||
|
|
||||||
|
jd12 = drdphi*( dphidQ1*dQ1dd12 + dphidQ2*dQ2dd12 + dphidQ3*dQ3dd12 )
|
||||||
|
jd21 = drdphi*( dphidQ1*dQ1dd21 + dphidQ2*dQ2dd21 + dphidQ3*dQ3dd21 )
|
||||||
|
jd23 = drdphi*( dphidQ1*dQ1dd23 + dphidQ2*dQ2dd23 + dphidQ3*dQ3dd23 )
|
||||||
|
jd32 = drdphi*( dphidQ1*dQ1dd32 + dphidQ2*dQ2dd32 + dphidQ3*dQ3dd32 )
|
||||||
|
jd31 = drdphi*( dphidQ1*dQ1dd31 + dphidQ2*dQ2dd31 + dphidQ3*dQ3dd31 )
|
||||||
|
jd13 = drdphi*( dphidQ1*dQ1dd13 + dphidQ2*dQ2dd13 + dphidQ3*dQ3dd13 )
|
||||||
|
jd44 = drdphi*( dphidQ1*dQ1dd44 + dphidQ2*dQ2dd44 + dphidQ3*dQ3dd44 )
|
||||||
|
jd55 = drdphi*( dphidQ1*dQ1dd55 + dphidQ2*dQ2dd55 + dphidQ3*dQ3dd55 )
|
||||||
|
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,
|
||||||
|
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])
|
||||||
|
return np.array(jaco)
|
||||||
|
|
||||||
|
|
||||||
fittingCriteria = {
|
fittingCriteria = {
|
||||||
'tresca' :{'func' : Tresca,
|
'tresca' :{'func' : Tresca,
|
||||||
|
|
@ -500,6 +736,14 @@ fittingCriteria = {
|
||||||
\n Y, a1,a2,a3,a4,a5,a6; b1,b2,b3,b4,b5,b6,b7,b8,b9,b10,b11; c\n',
|
\n Y, a1,a2,a3,a4,a5,a6; b1,b2,b3,b4,b5,b6,b7,b8,b9,b10,b11; c\n',
|
||||||
'error': 'The standard deviation errors are: '
|
'error': 'The standard deviation errors are: '
|
||||||
},
|
},
|
||||||
|
'yld200418p' :{'func' : Yld200418p,
|
||||||
|
'num' : 20,'err':np.inf,
|
||||||
|
'name' : 'Yld200418p',
|
||||||
|
'paras': 'Equivalent stress,c12,c21,c23,c32,c31,c13,c44,c55,c66,d12,d21,d23,d32,d31,d13,d44,d55,d66,m:',
|
||||||
|
'text' : '\nCoefficients of Yld2004-18p yield criterion: \
|
||||||
|
\n Y, c12,c21,c23,c32,c31,c13,c44,c55,c66,d12,d21,d23,d32,d31,d13,d44,d55,d66,m\n',
|
||||||
|
'error': 'The standard deviation errors are: '
|
||||||
|
},
|
||||||
'worst' :{'err':np.inf},
|
'worst' :{'err':np.inf},
|
||||||
'best' :{'err':np.inf}
|
'best' :{'err':np.inf}
|
||||||
}
|
}
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue