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Commit d6c79077 authored by Klaus Böhnlein's avatar Klaus Böhnlein
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Correct Wrong prestrain Formulas

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src/ClassifyMin.py
+ 18
− 3
View file @ d6c79077
...@@ -32,7 +32,7 @@ def determinant(q1,q2,q3,q12): # TODO General:M ...@@ -32,7 +32,7 @@ def determinant(q1,q2,q3,q12): # TODO General:M
def harmonicMean(mu_1, beta, theta): def harmonicMean(mu_1, beta, theta):
return mu_1*(beta/(theta+(1-theta)*beta)) return mu_1*(beta/(theta+((1-theta)*beta)))
def arithmeticMean(mu_1, beta, theta): def arithmeticMean(mu_1, beta, theta):
...@@ -40,11 +40,13 @@ def arithmeticMean(mu_1, beta, theta): ...@@ -40,11 +40,13 @@ def arithmeticMean(mu_1, beta, theta):
def prestrain_b1(rho_1, beta, alpha, theta): def prestrain_b1(rho_1, beta, alpha, theta):
return (3.0*rho_1/2.0)*beta*(1-(theta*(1+alpha))) return (3.0*rho_1/2.0)*(1-(theta*(1+alpha)))
# return (3.0*rho_1/2.0)*beta*(1-(theta*(1+alpha)))
def prestrain_b2(rho_1, beta, alpha, theta): def prestrain_b2(rho_1, beta, alpha, theta):
return (3.0*rho_1/(4.0*((1.0-theta) + theta*beta)))*(1-theta*(1+beta*alpha)) return (3.0*rho_1/(2.0*((1.0-theta) + theta*beta)))*(1-theta*(1+beta*alpha))
# return (3.0*rho_1/(4.0*((1.0-theta) + theta*beta)))*(1-theta*(1+beta*alpha))
# Define function to be minimized # Define function to be minimized
...@@ -72,6 +74,11 @@ def classifyMin_ana(alpha,beta,theta,q3,mu_1,rho_1,print_Cases=False, print_Outp ...@@ -72,6 +74,11 @@ def classifyMin_ana(alpha,beta,theta,q3,mu_1,rho_1,print_Cases=False, print_Outp
# print('q2: ', q2) # print('q2: ', q2)
b1 = prestrain_b1(rho_1, beta, alpha,theta) b1 = prestrain_b1(rho_1, beta, alpha,theta)
b2 = prestrain_b2(rho_1, beta, alpha,theta) b2 = prestrain_b2(rho_1, beta, alpha,theta)
# print('alpha:',alpha)
# print('beta:',beta)
# print('theta:',theta)
return classifyMin(q1, q2, q3, q12, b1, b2, print_Cases, print_Output) return classifyMin(q1, q2, q3, q12, b1, b2, print_Cases, print_Output)
...@@ -147,6 +154,7 @@ def classifyMin(q1, q2, q3, q12, b1, b2, print_Cases=False, print_Output=False) ...@@ -147,6 +154,7 @@ def classifyMin(q1, q2, q3, q12, b1, b2, print_Cases=False, print_Output=False)
# ------------------------------------ Parabolic Case ----------------------------------- # ------------------------------------ Parabolic Case -----------------------------------
if abs(determinant) < epsilon: if abs(determinant) < epsilon:
if print_Cases: print('P : parabolic case (determinant equal zero)') if print_Cases: print('P : parabolic case (determinant equal zero)')
print('P : parabolic case (determinant equal zero)')
# if print_Cases: print('P : parabolic case (determinant equal zero)') # if print_Cases: print('P : parabolic case (determinant equal zero)')
# check if B is in range of A # check if B is in range of A
...@@ -283,6 +291,9 @@ def classifyMin(q1, q2, q3, q12, b1, b2, print_Cases=False, print_Output=False) ...@@ -283,6 +291,9 @@ def classifyMin(q1, q2, q3, q12, b1, b2, print_Cases=False, print_Output=False)
# compute a3 # compute a3
# a3 = math.sqrt(2.0*a1*a2) # never needed? # a3 = math.sqrt(2.0*a1*a2) # never needed?
# print('a1:', a1)
# print('a2:', a2)
# compute the angle <(e,e_1) where Minimizer = kappa* (e (x) e) # compute the angle <(e,e_1) where Minimizer = kappa* (e (x) e)
e = [math.sqrt((a1/(a1+a2))), math.sqrt((a2/(a1+a2)))] e = [math.sqrt((a1/(a1+a2))), math.sqrt((a2/(a1+a2)))]
angle = math.atan2(e[1], e[0]) angle = math.atan2(e[1], e[0])
...@@ -294,6 +305,9 @@ def classifyMin(q1, q2, q3, q12, b1, b2, print_Cases=False, print_Output=False) ...@@ -294,6 +305,9 @@ def classifyMin(q1, q2, q3, q12, b1, b2, print_Cases=False, print_Output=False)
Minimizer = np.array([[a1, math.sqrt(a1*a2)], [math.sqrt(a1*a2), a2]],dtype=object) Minimizer = np.array([[a1, math.sqrt(a1*a2)], [math.sqrt(a1*a2), a2]],dtype=object)
# Minimizer = np.array([[a1, math.sqrt(a1*a2)], [math.sqrt(a1*a2), a2]]) # Minimizer = np.array([[a1, math.sqrt(a1*a2)], [math.sqrt(a1*a2), a2]])
# MinimizerVec = np.array([a1, a2],dtype=object)
MinimizerVec = np.array([a1, a2])
if print_Output: if print_Output:
print('--- Output ClassifyMin ---') print('--- Output ClassifyMin ---')
print("Minimizing Matrix G:") print("Minimizing Matrix G:")
...@@ -303,6 +317,7 @@ def classifyMin(q1, q2, q3, q12, b1, b2, print_Cases=False, print_Output=False) ...@@ -303,6 +317,7 @@ def classifyMin(q1, q2, q3, q12, b1, b2, print_Cases=False, print_Output=False)
print("kappa = ", kappa) print("kappa = ", kappa)
return Minimizer, angle, type, kappa return Minimizer, angle, type, kappa
# return MinimizerVec, angle, type, kappa #return Minimizer Vector instead
# ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
......
src/ClassifyMinVec.py
+ 4
− 2
View file @ d6c79077
...@@ -40,11 +40,13 @@ def arithmeticMean(mu_1, beta, theta): ...@@ -40,11 +40,13 @@ def arithmeticMean(mu_1, beta, theta):
def prestrain_b1(rho_1, beta, alpha, theta): def prestrain_b1(rho_1, beta, alpha, theta):
return (3.0*rho_1/2.0)*beta*(1-(theta*(1+alpha))) return (3.0*rho_1/2.0)*(1-(theta*(1+alpha)))
# return (3.0*rho_1/2.0)*beta*(1-(theta*(1+alpha)))
def prestrain_b2(rho_1, beta, alpha, theta): def prestrain_b2(rho_1, beta, alpha, theta):
return (3.0*rho_1/(4.0*((1.0-theta) + theta*beta)))*(1-theta*(1+beta*alpha)) return (3.0*rho_1/(2.0*((1.0-theta) + theta*beta)))*(1-theta*(1+beta*alpha))
# return (3.0*rho_1/(4.0*((1.0-theta) + theta*beta)))*(1-theta*(1+beta*alpha))
# Define function to be minimized # Define function to be minimized
......
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