def setUp(self):
self.E = 92000.0
self.nu = 0.3
self.s0 = 180.0
self.Kp = 1000.0
self.H = 1000.0
self.elastic = elasticity.IsotropicLinearElasticModel(self.E, "youngs",
self.nu, "poissons")
surface = surfaces.IsoKinJ2()
iso = hardening.LinearIsotropicHardeningRule(self.s0, self.Kp)
kin = hardening.LinearKinematicHardeningRule(self.H)
hrule = hardening.CombinedHardeningRule(iso, kin)
flow = ri_flow.RateIndependentAssociativeFlow(surface, hrule)
self.bmodel = models.SmallStrainRateIndependentPlasticity(self.elastic,
flow)
self.fn = interpolate.PolynomialInterpolate([-6.653e-9,2.952e-4,-6.197e-1])
self.C = 32.06
self.lmr = larsonmiller.LarsonMillerRelation(self.fn, self.C)
self.effective = damage.VonMisesEffectiveStress()
self.model = damage.LarsonMillerCreepDamageModel_sd(
self.elastic, self.lmr, self.effective, self.bmodel)
self.stress = np.array([100,-50.0,300.0,-99,50.0,125.0])
self.T = 100.0
self.s_np1 = self.stress
self.s_n = np.array([-25,150,250,-25,-100,25])
self.d_np1 = 0.5
self.d_n = 0.4
self.e_np1 = np.array([0.1,-0.01,0.15,-0.05,-0.1,0.15])
self.e_n = np.array([-0.05,0.025,-0.1,0.2,0.11,0.13])
self.T_np1 = self.T
self.T_n = 90.0
self.t_np1 = 1.0
self.t_n = 0.0
self.dt = self.t_np1 - self.t_n
self.u_n = 0.0
self.p_n = 0.0
# This is a rather boring baseline history state to probe, but I can't
# think of a better way to get a "generic" history from a generic model
self.hist_n = np.array([self.d_n] + list(self.bmodel.init_store()))
self.x_trial = np.array([50,-25,150,-150,190,100.0] + [0.41])
self.nsteps = 10
self.etarget = np.array([0.1,-0.025,0.02,0.015,-0.02,-0.05])
self.ttarget = 10.0
def setUp(self):
self.stresses = [damage.VonMisesEffectiveStress(),
damage.MaxPrincipalEffectiveStress()]
self.weights = [0.37, 1.0-0.37]
self.es = damage.SumSeveralEffectiveStress(self.stresses, self.weights)
self.stress = np.array([100,-50.0,300.0,-99,50.0,125.0])
def setUp(self):
self.es = damage.MaxSeveralEffectiveStress([
damage.VonMisesEffectiveStress(),
damage.MaxPrincipalEffectiveStress()
])
self.stress = np.array([100, -50.0, 300.0, -99, 50.0, 125.0])
def effective_model(self):
return damage.MaxSeveralEffectiveStress([
damage.VonMisesEffectiveStress(),
damage.MaxPrincipalEffectiveStress()])
def effective_model(self): return damage.VonMisesEffectiveStress()
def setUp(self):
self.es = damage.VonMisesEffectiveStress()
self.stress = np.array([100,-50.0,300.0,-99,50.0,125.0])
S = 1.0e19
# Loading
srate = 100.0
# Setup model
emodel = elasticity.IsotropicLinearElasticModel(E, "youngs", nu,
"poissons")
bmodel = models.SmallStrainElasticity(emodel)
scmodel = creep.PowerLawCreep(A, n)
cfmodel = creep.J2CreepModel(scmodel)
cmodel = models.SmallStrainCreepPlasticity(emodel, bmodel, cfmodel)
model = damage.ClassicalCreepDamageModel_sd(emodel, S, xi, phi, cmodel)
model2 = damage.ModularCreepDamageModel_sd(
emodel, S, xi, phi, damage.VonMisesEffectiveStress(), cmodel)
# Computed life
srange = np.linspace(s0 / 2, s0, 10)
tfs = S**(xi) / (1 + phi) * srange**(-xi)
slife = []
slife2 = []
for s, tf in zip(srange, tfs):
res = drivers.creep(model,
s,
srate,
tf * 1.25,
verbose=False,
logspace=False,
check_dmg=True,
fluidity = visco_flow.ConstantFluidity(eta)
vmodel = visco_flow.ChabocheFlowRule(surface, hmodel, fluidity, n)
mu = E / (2 * (1 + nu))
K = E / (3 * (1 - 2 * nu))
elastic = elasticity.IsotropicLinearElasticModel(mu, "shear", K, "bulk")
fmodel = general_flow.TVPFlowRule(elastic, vmodel)
bmodel = models.GeneralIntegrator(elastic, fmodel, verbose=False)
A = 1e4
xi = 4.0
phi = 2.0
model = damage.ModularCreepDamageModel_sd(elastic, A, xi, phi,
damage.VonMisesEffectiveStress(),
bmodel)
emax = np.array([2.0, 2.0, 1.0, 2.0]) / 200.0
R = np.array([-1.0, -1.0, -1.0, -1.0])
erate = np.array([0.004, 0.004, 0.004, 0.004])
hold_t = np.array([0.0, 6.0, 30.0, 6.0]) * 60.0
hold_c = np.array([0.0, 0.0, 0.0, 6.0]) * 60.0
ncycles = np.array([500, 500, 200, 120])
time_act = []
time_ext = []
for r in range(len(emax)):
start = time.time()
res = drivers.strain_cyclic_extrapolated(
model,