added yield criterion of facet potential
This commit is contained in:
Fengbo Han
parent
0750f7fd01
commit
82758bd90f
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@ -6,6 +6,8 @@ import numpy as np
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from optparse import OptionParser
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import damask
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from damask.util import leastsqBound
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from scipy.optimize import nnls
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import math
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scriptName = os.path.splitext(os.path.basename(__file__))[0]
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scriptID = ' '.join([scriptName,damask.version])
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@ -15,22 +17,23 @@ def runFit(exponent, eqStress, dimension, criterion):
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global fitResidual
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global Guess, dDim
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dDim = dimension - 3
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nParas = len(fitCriteria[criterion]['bound'][dDim])
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nExpo = fitCriteria[criterion]['nExpo']
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if options.criterion!='facet':
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dDim = dimension - 3
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nParas = len(fitCriteria[criterion]['bound'][dDim])
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nExpo = fitCriteria[criterion]['nExpo']
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if exponent > 0.0: # User defined exponents
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nParas = nParas-nExpo
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fitCriteria[criterion]['bound'][dDim] = fitCriteria[criterion]['bound'][dDim][:nParas]
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if exponent > 0.0: # User defined exponents
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nParas = nParas-nExpo
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fitCriteria[criterion]['bound'][dDim] = fitCriteria[criterion]['bound'][dDim][:nParas]
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for i in range(nParas):
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temp = fitCriteria[criterion]['bound'][dDim][i]
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if fitCriteria[criterion]['bound'][dDim][i] == (None,None):
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Guess.append(1.0)
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else:
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g = (temp[0]+temp[1])/2.0
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if g == 0: g = temp[1]*0.5
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Guess.append(g)
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for i in range(nParas):
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temp = fitCriteria[criterion]['bound'][dDim][i]
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if fitCriteria[criterion]['bound'][dDim][i] == (None,None):
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Guess.append(1.0)
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else:
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g = (temp[0]+temp[1])/2.0
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if g == 0: g = temp[1]*0.5
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Guess.append(g)
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myLoad = Loadcase(options.load[0],options.load[1],options.load[2],options.flag,options.yieldValue,
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nSet = 10, dimension = dimension, vegter = options.criterion=='vegter')
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@ -43,9 +46,92 @@ def runFit(exponent, eqStress, dimension, criterion):
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for t in range(options.threads):
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threads[t].join()
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if options.criterion=='facet':
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doFacetFit()
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damask.util.croak('Residuals')
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damask.util.croak(fitResidual)
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def doFacetFit():
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n = options.order
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Data = np.zeros((options.numpoints, 10))
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for i in range(options.numpoints):
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fileName = options.geometry + '_' + str(i+1) + '.yield'
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while os.path.exists(fileName):
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break
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data_i = np.loadtxt(fileName)
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sv = (data_i[0,0] + data_i[1,1] + data_i[2,2])/3.0
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#convert stress and strain form the 6D to 5D space
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S1 = math.sqrt(2.0)*(data_i[0,0] - data_i[1,1])/2.0
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S2 = math.sqrt(6.0)*(data_i[0,0] + data_i[1,1] - 2.0*sv)/2.0
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S3 = math.sqrt(2.0)*data_i[1,2]
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S4 = math.sqrt(2.0)*data_i[2,0]
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S5 = math.sqrt(2.0)*data_i[0,1]
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E1 = math.sqrt(2.0)*(data_i[3,0]-data_i[4,1])/2.0
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E2 = math.sqrt(6.0)*(data_i[3,0]+data_i[4,1])/2.0
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E3 = math.sqrt(2.0)*data_i[4,2]
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E4 = math.sqrt(2.0)*data_i[5,0]
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E5 = math.sqrt(2.0)*data_i[3,1]
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Data[i,:] = [E1,E2,E3,E4,E5,S1,S2,S3,S4,S5]
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Data[:,5:] = Data[:,5:] / 100000000.0
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path=os.path.join(os.getcwd(),'final.mmm')
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np.savetxt(path, Data, header='', comments='', fmt='% 15.10f')
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if options.dimension == 2:
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reducedIndices = [0,1,4,5,6,9]
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strainRateIndices = [0,1,4]
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stressIndices = [5,6,9]
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elif options.dimension == 3:
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reducedIndices = [i for i in range(10)]
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strainRateIndices = [0,1,2,3,4]
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stressIndices = [5,6,7,8,9]
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numDirections = Data.shape[0]
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Indices = np.arange(numDirections)
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sdPairs = Data[:,reducedIndices][Indices,:]
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numPairs = sdPairs.shape[0]
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dimensionality = sdPairs.shape[1] / 2
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ds = sdPairs[:,0:dimensionality]
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s = sdPairs[:,dimensionality::]
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A = np.zeros((numPairs, numPairs))
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B = np.ones((numPairs,))
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for i in range(numPairs):
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for j in range(numPairs):
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lamb = 1.0
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s_i = s[i,:]
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ds_j = ds[j,:]
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A[i,j] = lamb * (np.dot(s_i.ravel(), ds_j.ravel()) ** n)
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lambdas, residuals = nnls(A, B)
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nonZeroTerms = np.logical_not(np.isclose(lambdas, 0.))
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numNonZeroTerms = np.sum(nonZeroTerms)
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dataOut = np.zeros((numNonZeroTerms, 6))
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if options.dimension == 2:
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dataOut[:,0] = lambdas[nonZeroTerms]
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dataOut[:,1] = ds[nonZeroTerms,:][:,0]
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dataOut[:,2] = ds[nonZeroTerms,:][:,1]
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dataOut[:,5] = ds[nonZeroTerms,:][:,2]
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elif options.dimension == 3:
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dataOut[:,0] = lambdas[nonZeroTerms]
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dataOut[:,1] = ds[nonZeroTerms,:][:,0]
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dataOut[:,2] = ds[nonZeroTerms,:][:,1]
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dataOut[:,3] = ds[nonZeroTerms,:][:,2]
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dataOut[:,4] = ds[nonZeroTerms,:][:,3]
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dataOut[:,5] = ds[nonZeroTerms,:][:,4]
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headerText = 'facet\n 1 \n F \n {0:<3d} \n {1:<3d} '.format(n, numNonZeroTerms)
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path=os.path.join(os.getcwd(),'facet_o{0}.fac'.format(n))
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np.savetxt(path, dataOut, header=headerText, comments='', fmt='% 15.10f')
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def principalStresses(sigmas):
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"""
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Computes principal stresses (i.e. eigenvalues) for a set of Cauchy stresses.
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@ -1015,6 +1101,12 @@ fitCriteria = {
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'labels': 'a,b,c,d,e,f,g,h',
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'dimen': [2],
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},
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'facet' :{'name': 'Facet',
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'nExpo': None,
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'bound': [(None,None)],
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'labels': 'lambdas',
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'dimen': [2,3],
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},
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}
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thresholdParameter = ['totalshear','equivalentStrain']
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@ -1225,7 +1317,10 @@ class myThread (threading.Thread):
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conv=converged()
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semaphore.release()
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while not conv:
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doSim(self.name)
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if options.criterion=='facet':
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doSimForFacet(self.name)
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else:
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doSim(self.name)
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semaphore.acquire()
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conv=converged()
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semaphore.release()
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@ -1279,6 +1374,26 @@ def doSim(thread):
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damask.util.croak('\n')
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semaphore.release()
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def doSimForFacet(thread):
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semaphore.acquire()
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global myLoad
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loadNo=loadcaseNo()
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if not os.path.isfile('%s.load'%loadNo):
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damask.util.croak('Generating load case for simulation %s (%s)'%(loadNo,thread))
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f=open('%s.load'%loadNo,'w')
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f.write(myLoad.getLoadcase(loadNo))
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f.close()
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semaphore.release()
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else: semaphore.release()
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# if spectralOut does not exist, run simulation
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semaphore.acquire()
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if not os.path.isfile('%s_%i.spectralOut'%(options.geometry,loadNo)):
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damask.util.croak('Starting simulation %i (%s)'%(loadNo,thread))
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semaphore.release()
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damask.util.execute('DAMASK_spectral -g %s -l %i'%(options.geometry,loadNo))
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else: semaphore.release()
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def loadcaseNo():
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global N_simulations
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N_simulations+=1
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@ -1287,14 +1402,20 @@ def loadcaseNo():
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def converged():
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global N_simulations; fitResidual
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if N_simulations < options.max:
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if len(fitResidual) > 5 and N_simulations >= options.min:
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residualList = np.array(fitResidual[len(fitResidual)-5:])
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if np.std(residualList)/np.max(residualList) < 0.05:
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return True
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return False
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if options.criterion=='facet':
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if N_simulations == options.numpoints:
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return True
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else:
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return False
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else:
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return True
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if N_simulations < options.max:
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if len(fitResidual) > 5 and N_simulations >= options.min:
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residualList = np.array(fitResidual[len(fitResidual)-5:])
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if np.std(residualList)/np.max(residualList) < 0.05:
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return True
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return False
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else:
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return True
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# --------------------------------------------------------------------
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# MAIN
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@ -1333,6 +1454,10 @@ parser.add_option('-u', '--uniaxial', dest='eqStress', type='float',
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help='Equivalent stress', metavar='float')
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parser.add_option('--flag', dest='flag', type='string',
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help='yield stop flag, totalStrain, plasticStrain or plasticWork', metavar='string')
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parser.add_option('--numpoints', dest='numpoints', type='int',
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help='number of yield points to fit facet potential [%default]', metavar='int')
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parser.add_option('--order', dest='order', type='int',
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help='order of facet potential [%default]', metavar='int')
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parser.set_defaults(min = 12,
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max = 20,
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@ -1346,6 +1471,8 @@ parser.set_defaults(min = 12,
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dimension = '3',
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exponent = -1.0,
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flag = 'totalStrain',
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numpoints = 100,
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order = 8
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)
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options = parser.parse_args()[0]
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@ -1385,6 +1512,9 @@ dDim = None
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myLoad = None
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myFit = None
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run = runFit(options.exponent, options.eqStress, options.dimension, options.criterion)
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if options.criterion == 'facet':
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run = runFit(options.exponent, options.eqStress, options.dimension, options.criterion)
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else:
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run = runFit(options.exponent, options.eqStress, options.dimension, options.criterion)
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damask.util.croak('Finished fitting to yield criteria')
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@ -982,7 +982,7 @@ real(pReal) pure function math_detSym33(m)
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real(pReal), dimension(3,3), intent(in) :: m
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math_detSym33 = -(m(1,1)*m(2,3)**2_pInt + m(2,2)*m(1,3)**2_pInt + m(3,3)*m(1,2)**2_pInt) &
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+ m(1,1)*m(2,2)*m(3,3) - 2.0_pReal * m(1,2)*m(1,3)*m(2,3)
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+ m(1,1)*m(2,2)*m(3,3) + 2.0_pReal * m(1,2)*m(1,3)*m(2,3)
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end function math_detSym33
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@ -1994,7 +1994,7 @@ function math_eigenvectorBasisSym(m)
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do i=1_pInt, size(m,1)
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math_eigenvectorBasisSym = math_eigenvectorBasisSym &
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+ sqrt(values(i)) * math_tensorproduct(vectors(:,i),vectors(:,i))
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+ sqrt(values(i)) * math_tensorproduct(vectors(:,i),vectors(:,i))
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enddo
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end function math_eigenvectorBasisSym
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