#!/usr/bin/python # -*- coding: UTF-8 no BOM -*- import threading,time,os,subprocess,shlex,string import numpy as np from scipy.linalg import svd from optparse import OptionParser import damask from damask.util import leastsqBound scriptID = string.replace('$Id$','\n','\\n') scriptName = scriptID.split()[1][:-3] def execute(cmd,streamIn=None,wd='./'): ''' executes a command in given directory and returns stdout and stderr for optional stdin ''' initialPath=os.getcwd() os.chdir(wd) process = subprocess.Popen(shlex.split(cmd),stdout=subprocess.PIPE,stderr = subprocess.PIPE,stdin=subprocess.PIPE) if streamIn != None: out,error = process.communicate(streamIn.read()) else: out,error = process.communicate() os.chdir(initialPath) return out,error def principalStresses(sigmas): ''' computes principal stresses (i.e. eigenvalues) for a set of Cauchy stresses. sorted in descending order. ''' lambdas=np.zeros(0,'d') for i in xrange(np.shape(sigmas)[1]): eigenvalues = np.linalg.eigvalsh(sym6to33(sigmas[:,i])) lambdas = np.append(lambdas,np.sort(eigenvalues)[::-1]) #append eigenvalues in descending order lambdas = np.transpose(lambdas.reshape(np.shape(sigmas)[1],3)) return lambdas def stressInvariants(lambdas): ''' computes stress invariants (i.e. eigenvalues) for a set of principal Cauchy stresses. ''' Is=np.zeros(0,'d') for i in xrange(np.shape(lambdas)[1]): I = np.array([lambdas[0,i]+lambdas[1,i]+lambdas[2,i],\ lambdas[0,i]*lambdas[1,i]+lambdas[1,i]*lambdas[2,i]+lambdas[2,i]*lambdas[0,i],\ lambdas[0,i]*lambdas[1,i]*lambdas[2,i]]) Is = np.append(Is,I) Is = Is.reshape(3,np.shape(lambdas)[1]) return Is def formatOutput(n, type='%-14.6f'): return ''.join([type for i in xrange(n)]) def sym6to33(sigma6): ''' Shape the symmetric stress tensor(6,1) into (3,3) ''' sigma33 = np.empty((3,3)) sigma33[0,0] = sigma6[0]; sigma33[1,1] = sigma6[1]; sigma33[2,2] = sigma6[2]; sigma33[0,1] = sigma6[3]; sigma33[1,0] = sigma6[3] sigma33[1,2] = sigma6[4]; sigma33[2,1] = sigma6[4] sigma33[2,0] = sigma6[5]; sigma33[0,2] = sigma6[5] return sigma33 def array2tuple(array): '''transform numpy.array into tuple''' try: return tuple(array2tuple(i) for i in array) except TypeError: return array def get_weight(ndim): #more to do return np.ones(ndim) # --------------------------------------------------------------------------------------------- # isotropic yield surfaces # --------------------------------------------------------------------------------------------- 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,:]),\ 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) 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): return HosfordBasis(sigma0, 1.0,1.0,1.0, 2.0, sigmas, Jac=True, nParas=1) 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): return DruckerBasis(sigma0, C_D, 1.0, sigmas, Jac=True, nParas=2) 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): return DruckerBasis(sigma0, C_D, p, sigmas, Jac=True, nParas=3) 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): return HosfordBasis(sigma0, 1.0,1.0,1.0, a, sigmas, Jac=True, nParas=2) 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 return r.ravel()/2.0 def jac(self, (F,G,H,L,M,N), ydata, sigmas): jF=(sigmas[1]-sigmas[2])**2.0; jG=(sigmas[2]-sigmas[0])**2.0; jH=(sigmas[0]-sigmas[1])**2.0 jL=2.0*sigmas[4]**2.0; jM=2.0*sigmas[5]**2.0; jN=2.0*sigmas[3]**2.0 jaco = [] for jacv in zip(jF, jG, jH, jL, jM, jN): jaco.append(jacv) return np.array(jaco) class Hill1979(object): ''' residuum of Hill 1979 quadratic yield criterion (eq. 2.48) ''' def __init__(self, uniaxialStress): self.stress0 = uniaxialStress def fun(self, (f,g,h,a,b,c,m), ydata, sigmas): return Hill1979Basis(self.stress0, f,g,h,a,b,c,m, sigmas) def jac(self, (f,g,h,a,b,c,m), ydata, sigmas): return Hill1979Basis(self.stress0, f,g,h,a,b,c,m, sigmas, Jac=True) 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): return HosfordBasis(sigma0, F,G,H, a, sigmas, Jac=True, nParas=5) 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): return Barlat1991Basis(sigma0, 1.0,1.0,1.0,1.0,1.0,1.0, m, sigmas, Jac=True, nParas=2) 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): return Barlat1991Basis(sigma0, a,b,c,f,g,h, m, sigmas, Jac=True, nParas=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, d12,d21,d23,d32,d31,d13,d44,d55,d66, m, sigmas) def jac(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, d12,d21,d23,d32,d31,d13,d44,d55,d66, m, sigmas, Jac=True) class KarafillisBoyce(object): ''' residuum of Karafillis-Boyce yield criterion ''' def __init__(self, uniaxialStress): self.stress0 = uniaxialStress def fun(self, (c11,c12,c13,c14,c15,c16,c21,c22,c23,c24,c25,c26, b1, b2, a, alpha), ydata, sigmas): return KarafillisBoyceBasis(self.stress0, c11,c12,c13,c14,c15,c16,c21,c22,c23,c24,c25,c26, b1, b2, a, alpha, sigmas) def jac(self, (c11,c12,c13,c14,c15,c16,c21,c22,c23,c24,c25,c26, b1, b2, a, alpha), ydata, sigmas): return KarafillisBoyceBasis(self.stress0, c11,c12,c13,c14,c15,c16,c21,c22,c23,c24,c25,c26, b1, b2, a, alpha, sigmas, Jac=True) 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): return BBC2003Basis(sigma0, a,b,c, d,e,f,g, k, sigmas, Jac=True) class Cazacu_Barlat2D(object): ''' ''' def __init__(self, uniaxialStress): self.stress0 = uniaxialStress def fun(self, (a1,a2,a3,a4,b1,b2,b3,b4,b5,b10,c), ydata, sigmas): return Cazacu_Barlat2DBasis(a1,a2,a3,a4,b1,b2,b3,b4,b5,b10,c, self.stress0, sigmas) def jac(self, (a1,a2,a3,a4,b1,b2,b3,b4,b5,b10,c), ydata, sigmas): return Cazacu_Barlat2DBasis(a1,a2,a3,a4,b1,b2,b3,b4,b5,b10,c, self.stress0, sigmas,Jac=True) class Cazacu_Barlat3D(object): ''' ''' def __init__(self, uniaxialStress): self.stress0 = uniaxialStress def fun(self, (a1,a2,a3,a4,a5,a6,b1,b2,b3,b4,b5,b6,b7,b8,b9,b10,b11,c),ydata, sigmas): return Cazacu_Barlat3DBasis(a1,a2,a3,a4,a5,a6,b1,b2,b3,b4,b5,b6,b7,b8,b9,b10,b11,c, self.stress0, sigmas) def jac(self, (a1,a2,a3,a4,a5,a6,b1,b2,b3,b4,b5,b6,b7,b8,b9,b10,b11,c),ydata, sigmas): return Cazacu_Barlat3DBasis(a1,a2,a3,a4,a5,a6,b1,b2,b3,b4,b5,b6,b7,b8,b9,b10,b11,c, self.stress0, sigmas,Jac=True) def Cazacu_Barlat3DBasis(a1,a2,a3,a4,a5,a6,b1,b2,b3,b4,b5,b6,b7,b8,b9,b10,b11,c, sigma0,sigmas, Jac = False): ''' residuum of the 3D Cazacu–Barlat (CZ) yield criterion ''' s11 = sigmas[0]; s22 = sigmas[1]; s33 = sigmas[2] s12 = sigmas[3]; s23 = sigmas[4]; s31 = sigmas[5] s123, s321 = s11*s22*s33, s12*s23*s31 s1_2, s2_2, s3_2 = s11**2, s22**2, s33**2 s1_3, s2_3, s3_3 = s11*s1_2, s22*s2_2, s33*s3_2 s12_2, s23_2, s31_2 = s12**2, s23**2, s31**2 d12_2, d23_2, d31_2 = (s11-s22)**2, (s22-s33)**2, (s33-s11)**2 J20 = ( a1*d12_2 + a2*d23_2 + a3*d31_2 )/6.0 + a4*s12_2 + a5*s23_2 + a6*s31_2 J30 = ( (b1 +b2 )*s1_3 + (b3 +b4 )*s2_3 + ( b1+b4-b2 + b1+b4-b3 )*s3_3 )/27.0- \ ( (b1*s22+b2*s33)*s1_2 + (b3*s33+b4*s11)*s2_2 + ((b1+b4-b2)*s11 + (b1+b4-b3)*s22)*s3_2 )/9.0 + \ ( (b1+b4)*s123/9.0 + b11*s321 )*2.0 - \ ( ( 2.0*b9 *s22 - b8*s33 - (2.0*b9 -b8)*s11 )*s31_2 + ( 2.0*b10*s33 - b5*s22 - (2.0*b10-b5)*s11 )*s12_2 + ( (b6+b7)*s11 - b6*s22 - b7*s33 )*s23_2 )/3.0 f0 = (J20**3 - c*J30**2)/18.0 r = f0**(1.0/6.0)*(3.0/sigma0) if not Jac: return (r - 1.0).ravel() else: drdf = r/f0/108.0 dj2 = drdf*3.0*J20**2.0 dj3 = -drdf*2.0*J30*c jc = -drdf*J30**2 ja1,ja2,ja3 = dj2*d12_2/6.0, dj2*d23_2/6.0, dj2*d31_2/6.0 ja4,ja5,ja6 = dj2*s12_2, dj2*s23_2, dj2*s31_2 jb1 = dj3*( (s1_3 + 2.0*s3_3)/27.0 - s22*s1_2/9.0 - (s11+s22)*s3_2/9.0 + s123/4.5 ) jb2 = dj3*( (s1_3 - s3_3)/27.0 - s33*s1_2/9.0 + s11 *s3_2/9.0 ) jb3 = dj3*( (s2_3 - s3_3)/27.0 - s33*s2_2/9.0 + s22 *s3_2/9.0 ) jb4 = dj3*( (s2_3 + 2.0*s3_3)/27.0 - s11*s2_2/9.0 - (s11+s22)*s3_2/9.0 + s123/4.5 ) jb5, jb10 = dj3*(s22 - s11)*s12_2/3.0, dj3*(s11 - s33)*s12_2/3.0*2.0 jb6, jb7 = dj3*(s22 - s11)*s23_2/3.0, dj3*(s33 - s11)*s23_2/3.0 jb8, jb9 = dj3*(s33 - s11)*s31_2/3.0, dj3*(s11 - s22)*s31_2/3.0*2.0 jb11 = dj3*s321*2.0 jaco = [] 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, sigma0,sigmas, Jac = False): ''' residuum of the 2D Cazacu–Barlat (CZ) yield criterion for plain stress ''' s11 = sigmas[0]; s22 = sigmas[1]; s12 = sigmas[3] s1_2, s2_2 = s11**2, s22**2 s1_3, s2_3 = s11*s1_2, s22*s2_2 s12_2 = s12**2 J20 = ( a1*(s11-s22)**2 + a2*s2_2 + a3*s1_2 )/6.0 + a4*s12_2 J30 = ( (b1+b2)*s1_3 + (b3+b4)*s2_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)/18.0 r = f0**(1.0/6.0)*(3.0/sigma0) if not Jac: return (r - 1.0).ravel() else: drdf = r/f0/108.0 dj2 = drdf*3.0*J20**2.0 dj3 = -drdf*2.0*J30*c jc = -drdf*J30**2 ja1,ja2,ja3,ja4 = dj2*(s11-s22)**2/6.0, dj2*s2_2/6.0, dj2*s1_2/6.0, dj2*s12_2 jb1, jb2 = s1_3/27.0 - s1_2*s22/9.0, s1_3/27.0 jb4, jb3 = s2_3/27.0 - s2_2*s11/9.0, s2_3/27.0 jb5, jb10= -s12_2*(s11 - s22)/3.0, s12_2*s11*2.0/3.0 jaco = [] for jacv in zip(ja1,ja2,ja3,ja4,jb1,jb2,jb3,jb4,jb5,jb10,jc): jaco.append(jacv) return np.array(jaco) def DruckerBasis(sigma0, C_D, p, sigmas, Jac=False, nParas=2): 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 + s33*s11 - 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 left= J2**(3.0*p) - C_D*J3**(2.0*p); right = 3.0**(0.5)/sigma0 expo= 1.0/(6.0*p) if not Jac: return (left**expo*right - 1.0).ravel() else: jaco = [] dfdl = expo*left**(expo-1.0) js = -left**expo*right/sigma0 jC = -dfdl*J3**(2*p)*right if nParas == 2: 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 jacv in zip(js, jC, jp): jaco.append(jacv) return np.array(jaco) def Hill1979Basis(sigma0, f,g,h,a,b,c,m, sigmas, Jac=False): s1,s2,s3 = principalStresses(sigmas) d23 = s2-s3; d123 = 2.0*s1 - s2 - s3 d31 = s3-s1; d231 = 2.0*s2 - s3 - s1 d12 = s1-s2; d312 = 2.0*s3 - s1 - s2 d23s = d23**2; d123s = d123**2 d31s = d31**2; d231s = d231**2 d12s = d12**2; d312s = d312**2 m2 = m/2.0; mi = 1.0/m base = f* d23s**m2 + g* d31s**m2 + h* d12s**m2 + \ a*d123s**m2 + b*d231s**m2 + c*d312s**m2 left = base**mi r = left/sigma0 if not Jac: return (r-1.0).ravel() else: ln = lambda x : np.log(x + 1.0e-32) drdb = r/base*mi dbdm = ( f* d23s**m2*ln( d23s) + g* d31s**m2*ln( d31s) + h*d12s**m2*ln( d12s) + a*d123s**m2*ln(d123s) + b*d231s**m2*ln(d231s) + c*d312s**m2*ln(d312s) )*0.5 jf = drdb*d23s**m2; ja = drdb*d123s**m2 jg = drdb*d31s**m2; jb = drdb*d231s**m2 jh = drdb*d12s**m2; jc = drdb*d312s**m2 jm = drdb*dbdm + r*ln(base)*(-mi*mi) jaco = [] for jacv in zip(jf,jg,jh,ja,jb,jc,jm): jaco.append(jacv) return np.array(jaco) 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: ones = np.ones(np.shape(sigmas)[1]) 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 drda = sigma0*(2.0**expo)*ln(2.0)*deda dldb = expo*left/base jaco = [] if nParas == 1: # von Mises return ones*(-2.0**0.5) elif nParas == 2: # isotropic Hosford js = ones*(-2.0**expo) # d[]/dsigma0 ja = dldb*dbda + left*ln(base)*deda - drda # d[]/da 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 jF = dldb*diff23**a/right 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 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): ''' residuum of Barlat 1997 yield criterion ''' cos = np.cos; sin = np.sin; pi = np.pi; abs = np.abs dAda = sigmas[1] - sigmas[2]; A = a*dAda dBdb = sigmas[2] - sigmas[0]; B = b*dBdb dCdc = sigmas[0] - sigmas[1]; C = c*dCdc dFdf = sigmas[4]; F = f*dFdf dGdg = sigmas[5]; G = g*dGdg dHdh = sigmas[3]; H = h*dHdh 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) phi1 = (2.0*theta + pi)/6.0 phi2 = (2.0*theta + pi*3.0)/6.0 phi3 = (2.0*theta + pi*5.0)/6.0 cos1 = 2.0*cos(phi1); absc1 = abs(cos1) cos2 = 2.0*cos(phi2); absc2 = abs(cos2) cos3 = 2.0*cos(phi3); absc3 = abs(cos3) ratio= np.sqrt(3.0*I2)/sigma0; expo = 1.0/m left = ( absc1**m + absc2**m + absc3**m )/2.0 leftNorm = left**expo r = ratio*leftNorm - 1.0 if not Jac: return r.ravel() else: ln = lambda x : np.log(x + 1.0e-32) jaco = [] dfdl = expo*leftNorm/left js = -(r + 1.0)/sigma0 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 jacv in zip(js, jm): jaco.append(jacv) return np.array(jaco) else: dI2da = (2.0*A-B-C)*dAda/27.0 dI2db = (2.0*B-C-A)*dBdb/27.0 dI2dc = (2.0*C-A-B)*dCdc/27.0 dI2df = 2.0*F*dFdf/3.0 dI2dg = 2.0*G*dGdg/3.0 dI2dh = 2.0*H*dHdh/3.0 dI3da = dI2da*(B-C)/2.0 + (H**2 - G**2)*dAda/6.0 dI3db = dI2db*(C-A)/2.0 + (F**2 - H**2)*dBdb/6.0 dI3dc = dI2dc*(A-B)/2.0 + (G**2 - F**2)*dCdc/6.0 dI3df = ( (H*G + (B-C)) * F/3.0 )*dFdf dI3dg = ( (F*H + (C-A)) * G/3.0 )*dGdg dI3dh = ( (G*F + (A-B)) * H/3.0 )*dHdh darccos = -(1.0 - I3**2/I2**3)**(-0.5) dthedI2 = darccos*I3*(-1.5)*I2**(-2.5) dthedI3 = darccos*I2**(-1.5) dc1dthe = -sin(phi1)/3.0 dc2dthe = -sin(phi2)/3.0 dc3dthe = -sin(phi3)/3.0 dfdc = ratio * dfdl * 0.5 * m dfdc1 = dfdc* absc1**(expo-1.0)*np.sign(cos1) dfdc2 = dfdc* absc2**(expo-1.0)*np.sign(cos2) dfdc3 = dfdc* absc3**(expo-1.0)*np.sign(cos3) dfdthe= (dfdc1*dc1dthe + dfdc2*dc2dthe + dfdc2*dc2dthe)*2.0 dfdI2 = dfdthe*dthedI2; dfdI3 = dfdthe*dthedI3 ja = dfdI2*dI2da + dfdI3*dI3da jb = dfdI2*dI2db + dfdI3*dI3db jc = dfdI2*dI2dc + dfdI3*dI3dc jf = dfdI2*dI2df + dfdI3*dI3df jg = dfdI2*dI2dg + dfdI3*dI3dg jh = dfdI2*dI2dh + dfdI3*dI3dh 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): ''' residuum of the BBC2003 yield criterion for plain stress ''' s11 = sigmas[0]; s22 = sigmas[1]; s12 = sigmas[3] k2 = 2.0*k M = d+e; N = e+f; P = (d-e)/2.0; Q = (e-f)/2.0; R = g**2 Gamma = M*s11 + N*s22 Psi = ( (P*s11 + Q*s22)**2 + s12**2*R )**0.5 l1 = b*Gamma + c*Psi; l2 = b*Gamma - c*Psi; l3 = 2.0*c*Psi l1s = l1**2; l2s = l2**2; l3s = l3**2 left = a*l1s**k + a*l2s**k + (1-a)*l3s**k sBar = left**(1.0/k2); r = sBar/sigma0 - 1.0 if not Jac: return r.ravel() else: temp = (P*s11 + Q*s22)/Psi dPsidP = temp*s11; dPsidQ = temp*s22; dPsidR = 0.5*s12**2/Psi 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 = (1-a)*k*(l3s**k1)*(2.0*l3) dldGama = (dldl1 + dldl2)*b dldPsi = (dldl1 - dldl2 + 2.0*dldl3)*c dlda = l1s**k + l2s**k - l3s**k dldb = dldl1*Gamma + dldl2*Gamma dldc = dldl1*Psi - dldl2*Psi + dldl3*2.0*Psi dldk = a*ln(l1s)*l1s**k + a*ln(l2s)*l2s**k + (1-a)*ln(l3s)*l3s**k js = -(r + 1.0)/sigma0 ja = dsBardl * dlda jb = dsBardl * dldb jc = dsBardl * dldc jd = dsBardl *(dldGama*s11 + dldPsi*dPsidP*0.5) je = dsBardl *(dldGama*(s11+s22) + dldPsi*(dPsidP*(-0.5) + dPsidQ*0.5) ) jf = dsBardl *(dldGama*s22 + dldPsi*dPsidQ*(-0.5)) jg = dsBardl * dldPsi * dPsidR * 2.0*g jk = dsBardl * dldk + dsBarde * dedk for jacv in zip(js,ja,jb,jc,jd, je, jf,jg,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] s12 = p[3]; s23 = p[4]; s31 = p[5] I1 = s11 + s22 + s33 I2 = s11*s22 + s22*s33 + s33*s11 - s12**2 - s23**2 - s31**2 I3 = s11*s22*s33 + 2.0*s12*s23*s31 - s12**2*s33 - s23**2*s11 - s31**2*s22 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)/3.0 t1 = I1/3.0; t2 = 2.0/3.0*I1s3I2 S1 = t1 + t2*cos(phi) S2 = t1 + t2*cos(phi+np.pi*2.0/3.0) S3 = t1 + t2*cos(phi+np.pi*4.0/3.0) return np.array([S1,S2,S3]), np.array([I1,I2,I3]) def principalStrs_Der(p, Invariant, s1, s2, s3, s4, s5, s6, Karafillis=False): 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 - 9.0*I2)*denom + dcsddenom*(2.0*I1) dcsdI2 = 0.5*( - 9.0*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] if Karafillis: dS1dp0 = dS1dI1*dI1dp0 + dS1dI2*dI2dp0 + dS1dI3*dI3dp0 dS1dp1 = dS1dI1*dI1dp1 + dS1dI2*dI2dp1 + dS1dI3*dI3dp1 dS1dp2 = dS1dI1*dI1dp2 + dS1dI2*dI2dp2 + dS1dI3*dI3dp2 dS1dp3 = dS1dI1*dI1dp3 + dS1dI2*dI2dp3 + dS1dI3*dI3dp3 dS1dp4 = dS1dI1*dI1dp4 + dS1dI2*dI2dp4 + dS1dI3*dI3dp4 dS1dp5 = dS1dI1*dI1dp5 + dS1dI2*dI2dp5 + dS1dI3*dI3dp5 dS2dp0 = dS2dI1*dI1dp0 + dS2dI2*dI2dp0 + dS2dI3*dI3dp0 dS2dp1 = dS2dI1*dI1dp1 + dS2dI2*dI2dp1 + dS2dI3*dI3dp1 dS2dp2 = dS2dI1*dI1dp2 + dS2dI2*dI2dp2 + dS2dI3*dI3dp2 dS2dp3 = dS2dI1*dI1dp3 + dS2dI2*dI2dp3 + dS2dI3*dI3dp3 dS2dp4 = dS2dI1*dI1dp4 + dS2dI2*dI2dp4 + dS2dI3*dI3dp4 dS2dp5 = dS2dI1*dI1dp5 + dS2dI2*dI2dp5 + dS2dI3*dI3dp5 dS3dp0 = dS3dI1*dI1dp0 + dS3dI2*dI2dp0 + dS3dI3*dI3dp0 dS3dp1 = dS3dI1*dI1dp1 + dS3dI2*dI2dp1 + dS3dI3*dI3dp1 dS3dp2 = dS3dI1*dI1dp2 + dS3dI2*dI2dp2 + dS3dI3*dI3dp2 dS3dp3 = dS3dI1*dI1dp3 + dS3dI2*dI2dp3 + dS3dI3*dI3dp3 dS3dp4 = dS3dI1*dI1dp4 + dS3dI2*dI2dp4 + dS3dI3*dI3dp4 dS3dp5 = dS3dI1*dI1dp5 + dS3dI2*dI2dp5 + dS3dI3*dI3dp5 dS1dc1 = ( dS1dp0*0.0 + dS1dp1*(s2-s3) + dS1dp2*(s3-s2) )/3.0 dS1dc2 = ( dS1dp0*(s1-s3) + dS1dp1*0.0 + dS1dp2*(s3-s1) )/3.0 dS1dc3 = ( dS1dp0*(s1-s2) + dS1dp1*(s2-s1) + dS1dp2*0.0 )/3.0 dS2dc1 = ( dS2dp0*0.0 + dS2dp1*(s2-s3) + dS2dp2*(s3-s2) )/3.0 dS2dc2 = ( dS2dp0*(s1-s3) + dS2dp1*0.0 + dS2dp2*(s3-s1) )/3.0 dS2dc3 = ( dS2dp0*(s1-s2) + dS2dp1*(s2-s1) + dS2dp2*0.0 )/3.0 dS3dc1 = ( dS3dp0*0.0 + dS3dp1*(s2-s3) + dS3dp2*(s3-s2) )/3.0 dS3dc2 = ( dS3dp0*(s1-s3) + dS3dp1*0.0 + dS3dp2*(s3-s1) )/3.0 dS3dc3 = ( dS3dp0*(s1-s2) + dS3dp1*(s2-s1) + dS3dp2*0.0 )/3.0 dS1dc4 = dS1dp3*s4; dS1dc5 = dS1dp4*s5; dS1dc6 = dS1dp5*s6 dS2dc4 = dS2dp3*s4; dS2dc5 = dS2dp4*s5; dS2dc6 = dS2dp5*s6 dS3dc4 = dS3dp3*s4; dS3dc5 = dS3dp4*s5; dS3dc6 = dS3dp5*s6 return dS1dc1,dS1dc2,dS1dc3,dS1dc4,dS1dc5,dS1dc6,\ dS2dc1,dS2dc2,dS2dc3,dS2dc4,dS2dc5,dS2dc6,\ dS3dc1,dS3dc2,dS3dc3,dS3dc4,dS3dc5,dS3dc6 else: 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 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(jacv) return np.array(jaco) def KarafillisBoyceBasis(sigma0, c11,c12,c13,c14,c15,c16,c21,c22,c23,c24,c25,c26, b1, b2, a, alpha , sigmas, Jac=False): s1 = sigmas[0]; s2 = sigmas[1]; s3 = sigmas[2] s4 = sigmas[3]; s5 = sigmas[4]; s6 = sigmas[5] p = np.empty_like(sigmas); q = np.empty_like(sigmas) p[0] = ( (c12+c13)*s1 - c13*s2 - c12*s3 )/3.0 p[1] = ( (c13+c11)*s2 - c13*s1 - c11*s3 )/3.0 p[2] = ( (c11+c12)*s3 - c12*s1 - c11*s2 )/3.0 p[3] = c14*s4 p[4] = c15*s5 p[5] = c16*s6 q[0] = ( (c22+c23)*s1 - c23*s2 - c22*s3 )/3.0 q[1] = ( (c23+c21)*s2 - c23*s1 - c21*s3 )/3.0 q[2] = ( (c21+c22)*s3 - c22*s1 - c21*s2 )/3.0 q[3] = c24*s4 q[4] = c25*s5 q[5] = c26*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,:] b1h = b1/2.0; b1h1 = b1h-1.0; b2h = b2/2.0; b2h1 = b2h-1.0 b1i = 1.0/b1; b2i = 1.0/b2 ai = 1.0/a P2P3s = (P2-P3)**2; Q1s = Q1**2 P3P1s = (P3-P1)**2; Q2s = Q2**2 P1P2s = (P1-P2)**2; Q3s = Q3**2 phi10 = P2P3s**b1h + P3P1s**b1h + P1P2s**b1h phi20 = Q1s**b2h+Q2s**b2h+Q3s**b2h; rb2 = 3.0**b2/(2.0**b2+2.0) phi1 = (0.5*phi10)**b1i phi2 = (rb2*phi20)**b2i Stress = alpha*phi1**a + (1.0-alpha)*phi2**a; EqStress = Stress**ai r = EqStress/sigma0 - 1.0 if not Jac: return r.ravel() else: ln = lambda x : np.log(x + 1.0e-32) drds = (r+1.0)*ai/Stress drdphi1 = drds* alpha *a*phi1**(a-1.0) drdphi2 = drds*(1.0-alpha)*a*phi2**(a-1.0) dsda = alpha*phi1**a*ln(phi1) + (1.0-alpha)*phi2**a*ln(phi2) dphi1dphi10 = phi1/phi10/b1; dphi2dphi20 = phi2/phi20/b2 dphi1dP1 = dphi1dphi10*b1*( P3P1s**b1h1*(P1-P3) + P1P2s**b1h1*(P1-P2)) dphi1dP2 = dphi1dphi10*b1*( P2P3s**b1h1*(P2-P3) + P1P2s**b1h1*(P2-P1)) dphi1dP3 = dphi1dphi10*b1*( P3P1s**b1h1*(P3-P1) + P2P3s**b1h1*(P3-P2)) dphi2dQ1 = dphi2dphi20*b2*Q1s*b2h1*Q1 dphi2dQ2 = dphi2dphi20*b2*Q2s*b2h1*Q2 dphi2dQ3 = dphi2dphi20*b2*Q3s*b2h1*Q3 dP1dc1,dP1dc2,dP1dc3,dP1dc4,dP1dc5,dP1dc6, \ dP2dc1,dP2dc2,dP2dc3,dP2dc4,dP2dc5,dP2dc6, \ dP3dc1,dP3dc2,dP3dc3,dP3dc4,dP3dc5,dP3dc6= \ principalStrs_Der(p, pInvariant, s1,s2,s3,s4,s5,s6, Karafillis=True) dQ1dc1,dQ1dc2,dQ1dc3,dQ1dc4,dQ1dc5,dQ1dc6, \ dQ2dc1,dQ2dc2,dQ2dc3,dQ2dc4,dQ2dc5,dQ2dc6, \ dQ3dc1,dQ3dc2,dQ3dc3,dQ3dc4,dQ3dc5,dQ3dc6= \ principalStrs_Der(q, qInvariant, s1,s2,s3,s4,s5,s6, Karafillis=True) dphi10db1 = ( (P2P3s**b1h)*ln(P2P3s)+(P3P1s**b1h)*ln(P3P1s)+(P1P2s**b1h)*ln(P1P2s) )*0.5 dphi20db2 = ( (P2P3s**b1h)*ln(P2P3s)+(P3P1s**b1h)*ln(P3P1s)+(P1P2s**b1h)*ln(P1P2s) )*0.5 drb2db2 = rb2*ln(3.0) - rb2*ln(2.0)/(1.0+2.0**(1.0-b2)) dphi1db1 = phi1*ln(phi10)*(-b1i*b1i) + b1i*phi1/(0.5*phi10)* 0.5*dphi10db1 dphi2db2 = phi2*ln(phi20)*(-b2i*b2i) + b2i*phi2/(rb2*phi20)*(rb2*dphi20db2 + drb2db2*phi20) ja = drds*dsda - (r+1.0)*ln(Stress)/a/a #drda jb1 = drds * ( alpha *a*phi1**(a-1)) * dphi1db1 jb2 = drds * ((1.0-alpha)*a*phi2**(a-1)) * dphi2db2 jalpha = drds * (phi1**a - phi2**a) jc11 = drdphi1*( dphi1dP1*dP1dc1 + dphi1dP2*dP2dc1 + dphi1dP3*dP3dc1 ) jc12 = drdphi1*( dphi1dP1*dP1dc2 + dphi1dP2*dP2dc2 + dphi1dP3*dP3dc2 ) jc13 = drdphi1*( dphi1dP1*dP1dc3 + dphi1dP2*dP2dc3 + dphi1dP3*dP3dc3 ) jc14 = drdphi1*( dphi1dP1*dP1dc4 + dphi1dP2*dP2dc4 + dphi1dP3*dP3dc4 ) jc15 = drdphi1*( dphi1dP1*dP1dc5 + dphi1dP2*dP2dc5 + dphi1dP3*dP3dc5 ) jc16 = drdphi1*( dphi1dP1*dP1dc6 + dphi1dP2*dP2dc6 + dphi1dP3*dP3dc6 ) jc21 = drdphi2*( dphi2dQ1*dQ1dc1 + dphi2dQ2*dQ2dc1 + dphi2dQ3*dQ3dc1 ) jc22 = drdphi2*( dphi2dQ1*dQ1dc2 + dphi2dQ2*dQ2dc2 + dphi2dQ3*dQ3dc2 ) jc23 = drdphi2*( dphi2dQ1*dQ1dc3 + dphi2dQ2*dQ2dc3 + dphi2dQ3*dQ3dc3 ) jc24 = drdphi2*( dphi2dQ1*dQ1dc4 + dphi2dQ2*dQ2dc4 + dphi2dQ3*dQ3dc4 ) jc25 = drdphi2*( dphi2dQ1*dQ1dc5 + dphi2dQ2*dQ2dc5 + dphi2dQ3*dQ3dc5 ) jc26 = drdphi2*( dphi2dQ1*dQ1dc6 + dphi2dQ2*dQ2dc6 + dphi2dQ3*dQ3dc6 ) jaco = [] for jacv in zip(jc11,jc12,jc13,jc14,jc15,jc16,jc21,jc22,jc23,jc24,jc25,jc26, jb1,jb2,ja,jalpha): jaco.append(jacv) return np.array(jaco) fittingCriteria = { 'tresca' :{'func' : Tresca, 'num' : 1,'err':np.inf, 'name' : 'Tresca', 'paras': 'Initial yield stress:', 'text' : '\nCoefficient of Tresca criterion:\nsigma0: ', 'error': 'The standard deviation error is: ' }, 'vonmises' :{'func' : vonMises, 'num' : 1,'err':np.inf, 'name' : 'Huber-Mises-Hencky(von Mises)', 'paras': 'Initial yield stress:', 'text' : '\nCoefficient of Huber-Mises-Hencky criterion:\nsigma0: ', 'error': 'The standard deviation error is: ' }, 'hosfordiso' :{'func' : Hosford, 'num' : 2,'err':np.inf, '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]:'+' '*16, 'error': 'The standard deviation errors are: ' }, 'hill1979' :{'func' : Hill1979, 'num' : 7,'err':np.inf, 'name' : 'Hill1979', 'paras': 'f,g,h,a,b,c,m:', 'text' : '\nCoefficients of Hill1979 criterion:\n f,g,h,a,b,c,m:\n', 'error': 'The standard deviation errors are: ' }, 'drucker' :{'func' : Drucker, 'num' : 2,'err':np.inf, 'name' : 'Drucker', 'paras': 'Initial yield stress, C_D:', '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', 'paras': 'Initial yield stress, m:', 'text' : '\nCoefficients of isotropic Barlat 1991 criterion:\nsigma0, m:\n', 'error': 'The standard deviation errors are: ' }, 'barlat1991aniso':{'func' : Barlat1991aniso, 'num' : 8,'err':np.inf, 'name' : 'Barlat1991aniso', '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' : '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' : 11,'err':np.inf, 'name' : 'Cazacu Barlat for plain stress', 'paras': 'a1,a2,a3,a6; b1,b2,b3,b4,b5,b10; c:', 'text' : '\nCoefficients of Cazacu Barlat yield criterion for plane stress: \ \n a1,a2,a3,a6; b1,b2,b3,b4,b5,b10; c:\n', 'error': 'The standard deviation errors are: ' }, 'Cazacu_Barlat3D':{'func' : Cazacu_Barlat3D, 'num' : 18,'err':np.inf, 'name' : 'Cazacu Barlat', 'paras': '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 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: ' }, '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: ' }, 'karafillis' :{'func' : KarafillisBoyce, 'num' : 16,'err':np.inf, 'name' : 'Yld200418p', 'paras': 'c11,c12,c13,c14,c15,c16,c21,c22,c23,c24,c25,c26,b1,b2,a,alpha', 'text' : '\nCoefficients of Karafillis-Boyce yield criterion: \ \n c11,c12,c13,c14,c15,c16,c21,c22,c23,c24,c25,c26,b1,b2,a,alpha\n', 'error': 'The standard deviation errors are: ' }, 'worst' :{'err':np.inf}, 'best' :{'err':np.inf} } for key in fittingCriteria.keys(): if 'num' in fittingCriteria[key].keys(): fittingCriteria[key]['bound']=[(None,None)]*fittingCriteria[key]['num'] fittingCriteria[key]['guess']=np.ones(fittingCriteria[key]['num'],'d') thresholdParameter = ['totalshear','equivalentStrain'] #--------------------------------------------------------------------------------------------------- class Loadcase(): #--------------------------------------------------------------------------------------------------- ''' Class for generating load cases for the spectral solver ''' # ------------------------------------------------------------------ def __init__(self,finalStrain,incs,time): print('using the random load case generator') self.finalStrain = finalStrain self.incs = incs self.time = time def getLoadcase(self,N=0): defgrad=['*']*9 stress =[0]*9 values=(np.random.random_sample(9)-.5)*self.finalStrain*2 main=np.array([0,4,8]) np.random.shuffle(main) for i in main[:2]: # fill 2 out of 3 main entries defgrad[i]=1.+values[i] stress[i]='*' for off in [[1,3,0],[2,6,0],[5,7,0]]: # fill 3 off-diagonal pairs of defgrad (1 or 2 entries) off=np.array(off) np.random.shuffle(off) for i in off[0:2]: if i != 0: defgrad[i]=values[i] stress[i]='*' return 'f '+' '.join(str(c) for c in defgrad)+\ ' p '+' '.join(str(c) for c in stress)+\ ' incs %s'%self.incs+\ ' time %s'%self.time #--------------------------------------------------------------------------------------------------- class Criterion(object): #--------------------------------------------------------------------------------------------------- ''' Fitting to certain criterion ''' def __init__(self,name='worst'): self.name = name self.results = fittingCriteria if self.name.lower() not in map(str.lower, self.results.keys()): raise Exception('no suitable fitting criterion selected') else: print('fitting to the %s criterion'%name) def fit(self,stress): global fitResults nameCriterion = self.name.lower() 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]) ydata = np.zeros(np.shape(stress)[1]) try: popt, pcov, infodict, errmsg, ierr = \ leastsqBound (criteria.fun, initialguess, args=(ydata,stress), bounds=bounds, Dfun=criteria.jac, 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 #--------------------------------------------------------------------------------------------------- class myThread (threading.Thread): #--------------------------------------------------------------------------------------------------- ''' Runner class ''' def __init__(self, threadID): threading.Thread.__init__(self) self.threadID = threadID def run(self): s.acquire() conv=converged() s.release() while not conv: doSim(4.,self.name) s.acquire() conv=converged() s.release() def doSim(delay,thread): s.acquire() me=getLoadcase() if not os.path.isfile('%s.load'%me): print('generating loadcase for sim %s from %s'%(me,thread)) f=open('%s.load'%me,'w') f.write(myLoad.getLoadcase(me)) f.close() s.release() else: s.release() s.acquire() if not os.path.isfile('%s_%i.spectralOut'%(options.geometry,me)): print('starting simulation %s from %s'%(me,thread)) s.release() execute('DAMASK_spectral -g %s -l %i'%(options.geometry,me)) else: s.release() s.acquire() if not os.path.isfile('./postProc/%s_%i.txt'%(options.geometry,me)): print('starting post processing for sim %i from %s'%(me,thread)) s.release() try: execute('postResults --cr f,p --co totalshear %s_%i.spectralOut'%(options.geometry,me)) except: execute('postResults --cr f,p %s_%i.spectralOut'%(options.geometry,me)) execute('addCauchy ./postProc/%s_%i.txt'%(options.geometry,me)) execute('addStrainTensors -l -v ./postProc/%s_%i.txt'%(options.geometry,me)) execute('addMises -s Cauchy -e ln(V) ./postProc/%s_%i.txt'%(options.geometry,me)) else: s.release() s.acquire() print('-'*10) print('reading values for sim %i from %s'%(me,thread)) s.release() refFile = open('./postProc/%s_%i.txt'%(options.geometry,me)) table = damask.ASCIItable(refFile) table.head_read() if options.fitting =='equivalentStrain': thresholdKey = 'Mises(ln(V))' elif options.fitting =='totalshear': thresholdKey = 'totalshear' s.acquire() for l in [thresholdKey,'1_Cauchy']: if l not in table.labels: print '%s not found'%l s.release() table.data_readArray(['%i_Cauchy'%(i+1) for i in xrange(9)]+[thresholdKey]+['%i_ln(V)'%(i+1) for i in xrange(9)]) line = 0 lines = np.shape(table.data)[0] yieldStress = np.empty((int(options.yieldValue[2]),6),'d') deformationRate = np.empty((int(options.yieldValue[2]),6),'d') for i,threshold in enumerate(np.linspace(options.yieldValue[0],options.yieldValue[1],options.yieldValue[2])): while line < lines: if table.data[line,9]>= threshold: upper,lower = table.data[line,9],table.data[line-1,9] # values for linear interpolation stress = np.array(table.data[line-1,0:9] * (upper-threshold)/(upper-lower) + \ table.data[line ,0:9] * (threshold-lower)/(upper-lower)).reshape(3,3) # linear interpolation of stress values dstrain= np.array(table.data[line,10:] - table.data[line-1,10:]).reshape(3,3) yieldStress[i,0]= stress[0,0]; yieldStress[i,1]=stress[1,1]; yieldStress[i,2]=stress[2,2] yieldStress[i,3]=(stress[0,1] + stress[1,0])/2.0 # 0 3 5 yieldStress[i,4]=(stress[1,2] + stress[2,1])/2.0 # * 1 4 yieldStress yieldStress[i,5]=(stress[2,0] + stress[0,2])/2.0 # * * 2 # D*dt = 0.5(L+L^T)*dt = 0.5*d(lnF + lnF^T) = dlnV deformationRate[i,0]= dstrain[0,0]; deformationRate[i,1]=dstrain[1,1]; deformationRate[i,2]=dstrain[2,2] deformationRate[i,3]=(dstrain[0,1] + dstrain[1,0])/2.0 # 0 3 5 deformationRate[i,4]=(dstrain[1,2] + dstrain[2,1])/2.0 # * 1 4 deformationRate[i,5]=(dstrain[2,0] + dstrain[0,2])/2.0 # * * 2 break else: line+=1 s.acquire() global stressAll, strainAll print('number of yield points of sim %i: %i'%(me,len(yieldStress))) print('starting fitting for sim %i from %s'%(me,thread)) try: for i in xrange(int(options.yieldValue[2])): stressAll[i]=np.append(stressAll[i], yieldStress[i]/unitGPa) strainAll[i]=np.append(strainAll[i], deformationRate[i]) myFit.fit(stressAll[i].reshape(len(stressAll[i])//6,6).transpose()) except Exception as detail: print('could not fit for sim %i from %s'%(me,thread)) print detail s.release() return s.release() def getLoadcase(): global N_simulations N_simulations+=1 return N_simulations def converged(): global N_simulations if N_simulations < options.max: return False else: return True # -------------------------------------------------------------------- # MAIN # -------------------------------------------------------------------- parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """ Performs calculations with various loads on given geometry file and fits yield surface. """, version=string.replace(scriptID,'\n','\\n') ) parser.add_option('-l','--load' , dest='load', type='float', nargs=3, help='load: final strain; increments; time %default', metavar='float int float') parser.add_option('-g','--geometry', dest='geometry', type='string', help='name of the geometry file [%default]', metavar='string') parser.add_option('-c','--criterion', dest='criterion', choices=fittingCriteria.keys(), help='criterion for stopping simulations [%default]', metavar='string') parser.add_option('-f','--fitting', dest='fitting', choices=thresholdParameter, help='yield criterion [%default]', metavar='string') parser.add_option('-y','--yieldvalue', dest='yieldValue', type='float', nargs=3, help='yield points: start; end; count %default', metavar='float float int') parser.add_option('--min', dest='min', type='int', help='minimum number of simulations [%default]', metavar='int') parser.add_option('--max', dest='max', type='int', help='maximum number of iterations [%default]', metavar='int') parser.add_option('-t','--threads', dest='threads', type='int', help='number of parallel executions [%default]', metavar='int') parser.set_defaults(min = 12) parser.set_defaults(max = 30) parser.set_defaults(threads = 4) parser.set_defaults(yieldValue = (0.002,0.004,2)) parser.set_defaults(load = (0.010,100,100.0)) parser.set_defaults(criterion = 'worst') parser.set_defaults(fitting = 'totalshear') parser.set_defaults(geometry = '20grains16x16x16') options = parser.parse_args()[0] if not os.path.isfile(options.geometry+'.geom'): parser.error('geometry file %s.geom not found'%options.geometry) if not os.path.isfile('material.config'): parser.error('material.config file not found') if options.threads<1: parser.error('invalid number of threads %i'%options.threads) if options.min<0: parser.error('invalid minimum number of simulations %i'%options.min) if options.maxoptions.yieldValue[1]: parser.error('invalid yield start (below yield end)') if options.yieldValue[2] != int(options.yieldValue[2]): parser.error('count must be an integer') if not os.path.isfile('numerics.config'): print('numerics.config file not found') if not os.path.isfile('material.config'): print('material.config file not found') unitGPa = 10.e8 N_simulations=0 fitResults = [] s=threading.Semaphore(1) stressAll=[np.zeros(0,'d').reshape(0,0) for i in xrange(int(options.yieldValue[2]))] strainAll=[np.zeros(0,'d').reshape(0,0) for i in xrange(int(options.yieldValue[2]))] myLoad = Loadcase(options.load[0],options.load[1],options.load[2]) myFit = Criterion(options.criterion) threads=[] for i in range(options.threads): threads.append(myThread(i)) threads[i].start() for i in range(options.threads): threads[i].join() print 'finished fitting to yield criteria'