def unload_ex():
E = 92000.0
nu = 0.3
s0 = 180.0
Kp = 1000.0
H = 1000.0
elastic = elasticity.IsotropicLinearElasticModel(E, "youngs", nu,
"poissons")
surface = surfaces.IsoKinJ2()
iso = hardening.LinearIsotropicHardeningRule(s0, Kp)
kin = hardening.LinearKinematicHardeningRule(H)
hrule = hardening.CombinedHardeningRule(iso, kin)
flow = ri_flow.RateIndependentAssociativeFlow(surface, hrule)
bmodel = models.SmallStrainRateIndependentPlasticity(elastic, flow)
A = 2e-5
a = 2.2
model = damage.NEMLPowerLawDamagedModel_sd(elastic, A, a, bmodel)
driver = drivers.Driver_sd(model)
nsteps = 25
sdir = np.array([1, 0, 0, 0, 0, 0])
erate = 1.0e-5
e_inc = 0.1 / nsteps
for i in range(nsteps):
einc, ainc = driver.erate_einc_step(sdir, erate, e_inc, 0.0)
estrain = model.elastic_strains(driver.stress_int[-1], driver.T_int[-1],
driver.stored[-1])
print("Calculated elastic strain: %f" % estrain[0])
nsteps = 20
dt = 0.1
rstress = driver.stress_int[-1]
rstrain = driver.strain_int[-1][0]
for m in np.linspace(0, 1.0, nsteps, endpoint=False)[::-1]:
driver.stress_step(rstress * m, driver.t_int[-1] + dt,
driver.T_int[-1])
fstrain = driver.strain_int[-1][0]
print("Actual elastic strain: %f" % (rstrain - fstrain))
print("Calculated final elastic strain: %f" % model.elastic_strains(
driver.stress_int[-1], driver.T_int[-1], driver.stored[-1])[0])
plt.plot(driver.strain[:, 0], driver.stress[:, 0])
plt.show()
def run_compare_test(model, target_times, target_temps, target_strains):
"""
Run through a single test
"""
driver = drivers.Driver_sd(model)
for t, e, T in zip(target_times[1:], target_strains[1:], target_temps[1:]):
driver.strain_step(e, t, T)
data = np.concatenate(
(np.array(driver.t_int)[:, None], np.array(driver.T_int)[:, None],
np.array(driver.mechanical_strain_int), np.array(driver.stress_int)),
axis=1)
return data
def temp_jump_test(model,
erate,
strains,
Ts,
nsteps=50,
sdir=np.array([1, 0, 0, 0, 0, 0])):
"""
Run a temperature jump test.
"""
driver = drivers.Driver_sd(model)
strain = []
stress = []
for T, de in zip(Ts, strains):
e_inc = de / nsteps
for i in range(nsteps):
einc, ainc = driver.erate_einc_step(sdir, erate, e_inc, T)
strain.append(np.dot(driver.strain_int[-1], sdir))
stress.append(np.dot(driver.stress_int[-1], sdir))
return np.array(strain), np.array(stress)
def load_unload(model,
emax,
erate,
nsteps,
E,
ff=1.0,
verbose=False,
miter=50,
T=0):
sdir = np.array([1.0, 0, 0, 0, 0, 0])
driver = drivers.Driver_sd(model, verbose=verbose, miter=miter)
for e in np.linspace(0, emax, nsteps)[1:]:
driver.erate_step(sdir, erate, e / erate, T)
t = driver.t[-1]
my_stress = driver.stress[-1][0]
dt = my_stress / E / nsteps / erate * ff
einc_guess = np.array([-1, 0.5, 0.5, 0, 0, 0]) * 1.0e-4
ainc_guess = -0.01
for i in range(nsteps):
t += dt
if i == 0:
driver.erate_step(-sdir,
erate,
t,
T,
einc_guess=einc_guess,
ainc_guess=ainc_guess)
else:
driver.erate_step(-sdir, erate, t, T)
return driver.strain[:, 0], driver.stress[:, 0]