def test_damage(self):
s0 = 180.0
Kp = 1000.0
H = 1000.0
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(
self.elastic1, flow)
W0 = 10.0
k0 = 0.0001
a0 = 2.0
model1 = damage.NEMLExponentialWorkDamagedModel_sd(
self.elastic1, W0, k0, a0, bmodel)
W02 = 10.0
k02 = 0.001
a02 = 1.5
model2 = damage.NEMLExponentialWorkDamagedModel_sd(
self.elastic1, W02, k02, a02, bmodel)
model = damage.CombinedDamageModel_sd(self.elastic1, [model1, model2],
bmodel)
self.very_close(model, self.emodel1)
model.set_elastic_model(self.elastic2)
self.very_close(model, self.emodel2)
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.W0 = 10.0
self.k0 = 0.0001
self.a0 = 2.0
self.model1 = damage.NEMLExponentialWorkDamagedModel_sd(
self.elastic, self.W0, self.k0,
self.a0, self.bmodel)
self.W02 = 10.0
self.k02 = 0.001
self.a02 = 1.5
self.model2 = damage.NEMLExponentialWorkDamagedModel_sd(
self.elastic, self.W02, self.k02,
self.a02, self.bmodel)
self.model = damage.CombinedDamageModel_sd(self.elastic,
[self.model1, self.model2], self.bmodel)
self.stress = np.array([100,-50.0,300.0,-99,50.0,125.0])
self.T = 100.0
self.d = 0.45
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.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