def returnDiff(params):
"""
Tune using the given in-plane locus
Arguments
=========
params [0] : rb
params [1:4]: y45,y90,yb
"""
## Find 4 parameters...
paramR = np.ones(4)
paramY = np.ones(4)
paramR[0:4] = rv[0], rv[1], rv[2], params[0]
paramY[1:4] = params[1:4]
k = 2
m = 8 ## the exponent is fixed?
yldFunc = wrapYLD(r=paramR, y=paramY, m=m, k=k)
if type(yldFunc).__name__ == 'int':
if yldFunc == -1:
return 1
yldLocus = locus(yldFunc, 1000) ##
## convert yldLocus in to (th,r) coordinates
r, th = xy2rt(np.array(yldLocus[0]), np.array(yldLocus[1]))
th0, r0 = YS ## reference
radIntp = rad_xy2(r, th, th0)
radIntp = np.array(radIntp)
return np.sqrt(((radIntp - r0)**2).sum()) / (len(r) - 1)
def returnDiff(params):
"""
Tune using the given in-plane locus
Arguments
=========
params [0] : rb
params [1:4]: y45,y90,yb
"""
## Find 4 parameters...
paramR = np.ones(4)
paramY = np.ones(4)
paramR[0:4] = rv[0],rv[1],rv[2],params[0]
paramY[1:4] = params[1:4]
k=2; m=8 ## the exponent is fixed?
yldFunc = wrapYLD(r=paramR,y=paramY,m=m,k=k)
if type(yldFunc).__name__=='int':
if yldFunc==-1:
return 1
yldLocus = locus(yldFunc,1000) ##
## convert yldLocus in to (th,r) coordinates
r,th=xy2rt(np.array(yldLocus[0]),np.array(yldLocus[1]))
th0,r0 = YS## reference
radIntp = rad_xy2(r,th,th0)
radIntp = np.array(radIntp)
return np.sqrt(((radIntp-r0)**2).sum())/ (len(r)-1)
def main(ax=None):
if type(ax).__name__ == 'NoneType':
fig = plt.figure()
ax = fig.add_subplot(111)
import time
t0 = time.time()
## two parameters from Jeong et al. Acta Mat 112, 2016
## for the interstitial-free steel
r0 = 2.20
r45 = 2.0
r90 = 2.9
## the rest of parameters are assumed as 1.
rb = 1.
y0 = 1
y45 = 1
y90 = 1
yb = 1.
h48g = wrapHill48R([r0, r45, r90])
yld2000 = wrapYLD(r=[r0, r45, r90, rb], y=[y0, y45, y90, yb], m=6, k=2)
funcs = vm, h48g, yld2000
labs = ['von Mises', 'Hill48R', 'yld2000']
ls = ['-', '--', '-.', ':']
xs = []
ys = []
t_indv = []
for i in xrange(len(funcs)):
t_indv0 = time.time()
x, y = locus(funcs[i])
t_indv.append(time.time() - t_indv0)
xs.append(x)
ys.append(y)
for i in xrange(len(funcs)):
print 'time %s :' % labs[i], t_indv[i]
print 'total time:', time.time() - t0
for i in xrange(len(funcs)):
ax.plot(xs[i], ys[i], label=labs[i], ls=ls[i])
ax.legend(loc='best')
fn = 'vm_check.pdf'
print '%s has been saved' % fn
ax.set_xlim(-0.25, 1.75)
ax.set_ylim(-0.25, 1.75)
ax.set_aspect('equal')
try:
fig.savefig(fn)
except:
pass
def main(ax=None):
if type(ax).__name__=='NoneType':
fig = plt.figure()
ax = fig.add_subplot(111)
import time
t0=time.time()
## two parameters from Jeong et al. Acta Mat 112, 2016
## for the interstitial-free steel
r0 = 2.20
r45 = 2.0
r90 = 2.9
## the rest of parameters are assumed as 1.
rb =1.
y0 =1
y45 =1
y90 =1
yb =1.
h48g = wrapHill48R([r0,r45,r90])
yld2000 = wrapYLD(r=[r0,r45,r90,rb],y=[y0,y45,y90,yb],m=6,k=2)
funcs = vm, h48g, yld2000
labs = ['von Mises', 'Hill48R','yld2000']
ls = ['-','--','-.',':']
xs=[];ys=[]
t_indv=[]
for i in xrange(len(funcs)):
t_indv0= time.time()
x,y = locus(funcs[i])
t_indv.append(time.time()-t_indv0)
xs.append(x)
ys.append(y)
for i in xrange(len(funcs)):
print 'time %s :'%labs[i], t_indv[i]
print 'total time:',time.time()-t0
for i in xrange(len(funcs)):
ax.plot(xs[i],ys[i],label=labs[i],ls=ls[i])
ax.legend(loc='best')
fn='vm_check.pdf'
print '%s has been saved'%fn
ax.set_xlim(-0.25,1.75)
ax.set_ylim(-0.25,1.75)
ax.set_aspect('equal')
try:
fig.savefig(fn)
except:
pass
def ex1():
"""
Excercise - Tune H48 using three r-values and calculate YS.
Using the three given r-values used in H48
find rb, y0, y45, y90, and yb by fitting YS of H48 with
that of yld2000-2d
"""
import tuneH48
import mk.yieldFunction.yf2 as yf2
rv = [2.2, 2.0, 2.9]
f, g, h, n = tuneH48.tuneGenR(r=rv)
yfunc_H48 = yf2.wrapHill48Gen(f, g, h, n)
YS_H48X, YS_H48Y = locus(yfunc_H48, 1000)
r, th = xy2rt(np.array(YS_H48X), np.array(YS_H48Y))
## popt = [rv, y45, y90, yb]
popt = case2([r, th], rv=rv)
rv.append(popt[0])
ys = np.ones(4)
ys[1:] = popt[1:]
##
print 'rv:', rv
print 'ys:', ys
yfunc_yld2000 = wrapYLD(r=rv, y=ys, m=8)
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
YS_YLDX, YS_YLDY = locus(yfunc_yld2000, 1000)
ax.plot(YS_H48X, YS_H48Y, '-', label='Hill48')
ax.plot(YS_YLDX, YS_YLDY, '-', label='YLD2000')
ax.set_xlim(0., )
ax.set_ylim(0., )
ax.set_aspect('equal')
ax.legend(loc='best')
# print YS_YLDX
# print YS_YLDY
fig.savefig('Yld2000-Hill48.pdf')
def ex1():
"""
Excercise - Tune H48 using three r-values and calculate YS.
Using the three given r-values used in H48
find rb, y0, y45, y90, and yb by fitting YS of H48 with
that of yld2000-2d
"""
import tuneH48
import mk.yieldFunction.yf2 as yf2
rv=[2.2,2.0,2.9]
f,g,h,n = tuneH48.tuneGenR(r=rv)
yfunc_H48 = yf2.wrapHill48Gen(f,g,h,n)
YS_H48X, YS_H48Y = locus(yfunc_H48,1000)
r, th = xy2rt(np.array(YS_H48X), np.array(YS_H48Y))
## popt = [rv, y45, y90, yb]
popt = case2([r,th], rv=rv)
rv.append(popt[0])
ys = np.ones(4)
ys[1:] = popt[1:]
##
print 'rv:', rv
print 'ys:', ys
yfunc_yld2000 = wrapYLD(r=rv,y=ys,m=8)
import matplotlib.pyplot as plt
fig = plt.figure(); ax=fig.add_subplot(111)
YS_YLDX, YS_YLDY = locus(yfunc_yld2000,1000)
ax.plot(YS_H48X,YS_H48Y,'-',label='Hill48')
ax.plot(YS_YLDX,YS_YLDY,'-',label='YLD2000')
ax.set_xlim(0.,)
ax.set_ylim(0.,)
ax.set_aspect('equal')
ax.legend(loc='best')
# print YS_YLDX
# print YS_YLDY
fig.savefig('Yld2000-Hill48.pdf')