def setUp(self):
self.model1 = parse.parse_xml(localize("examples.xml"), "test_perfect")
E = [-100, 100000]
nu = 0.3
youngs = interpolate.PolynomialInterpolate(E)
poissons = interpolate.ConstantInterpolate(nu)
elastic = elasticity.IsotropicLinearElasticModel(
youngs, "youngs", poissons, "poissons")
surface = surfaces.IsoJ2()
Ts = [100.0, 300.0, 500.0, 700.0]
Sys = [1000.0, 120.0, 60.0, 30.0]
yields = interpolate.PiecewiseLinearInterpolate(Ts, Sys)
self.model2 = models.SmallStrainPerfectPlasticity(
elastic, surface, yields)
self.T = 550.0
self.tmax = 10.0
self.nsteps = 50
self.emax = np.array([0.1, 0.05, 0, -0.025, 0, 0])
def setUp(self):
self.model1 = parse.parse_xml("test/examples.xml", "test_pcreep")
E = [-100, 100000]
nu = 0.3
youngs = interpolate.PolynomialInterpolate(E)
poissons = interpolate.ConstantInterpolate(nu)
elastic = elasticity.IsotropicLinearElasticModel(
youngs, "youngs", poissons, "poissons")
surface = surfaces.IsoJ2()
Ts = [100.0, 300.0, 500.0, 700.0]
Sys = [1000.0, 120.0, 60.0, 30.0]
yields = interpolate.PiecewiseLinearInterpolate(Ts, Sys)
pmodel = models.SmallStrainPerfectPlasticity(elastic, surface, yields)
self.T = 550.0
self.tmax = 10.0
self.nsteps = 50.0
self.emax = np.array([0.1, 0.05, 0, -0.025, 0, 0])
A = 1.85e-10
n = 2.5
smodel = creep.PowerLawCreep(A, n)
cmodel = creep.J2CreepModel(smodel)
self.model2 = models.SmallStrainCreepPlasticity(
elastic, pmodel, cmodel)
def setUp(self):
self.xs = [1.0, 5.0]
self.cvs = [-1.0, 5.0, -2.0]
self.fns = [interpolate.ConstantInterpolate(cv) for cv in self.cvs]
self.interpolate = interpolate.GenericPiecewiseInterpolate(
self.xs, self.fns)
self.x = 4.0
def setUp(self):
# Fully-defined perfectly plastic model
Epoly = [-78.2759, 236951.0]
nu = 0.3
A = -9.6187
B = -1.4819
C = -5.0486
g0 = 0.3708
b = 0.248 * 1.0e-6
kboltz = 1.38064e-23 * 1000.0
eps0 = 1.0e10
# Temperature range over which to consider (K)
Tmin = 550.0
Tmax = 950.0
Trange = np.linspace(Tmin, Tmax)
# Elastic
E_m = interpolate.PolynomialInterpolate(Epoly)
nu_m = interpolate.ConstantInterpolate(nu)
elastic_m = elasticity.IsotropicLinearElasticModel(
E_m, "youngs", nu_m, "poissons")
self.elastic = elastic_m
# Rate sensitivity interpolates values
mu_values = np.array([elastic_m.G(T) for T in Trange])
n_values = -mu_values * b**3.0 / (kboltz * Trange * A)
eta_values = np.exp(B) * eps0**(kboltz * Trange * A /
(mu_values * b**3.0)) * mu_values
# Rate independent interpolate values
flow_stress = mu_values * np.exp(C)
# Common objects
surface = surfaces.IsoKinJ2()
hmodulus = interpolate.PolynomialInterpolate([-10.0, 12000.0])
# Setup visco model
n_interp = interpolate.PiecewiseLinearInterpolate(
list(Trange), list(n_values))
eta_interp = interpolate.PiecewiseLinearInterpolate(
list(Trange), list(eta_values))
eta_m = visco_flow.ConstantFluidity(eta_interp)
iso_rd = hardening.LinearIsotropicHardeningRule(
interpolate.ConstantInterpolate(0.0), hmodulus)
hard_rd = hardening.Chaboche(
iso_rd, [interpolate.ConstantInterpolate(0.0)],
[hardening.ConstantGamma(interpolate.ConstantInterpolate(0.0))],
[interpolate.ConstantInterpolate(0.0)],
[interpolate.ConstantInterpolate(1.0)])
visco_rd = visco_flow.ChabocheFlowRule(surface, hard_rd, eta_m,
n_interp)
general_rd = general_flow.TVPFlowRule(elastic_m, visco_rd)
rate_dependent = models.GeneralIntegrator(elastic_m, general_rd)
# Setup rate independent
sy_interp = interpolate.PiecewiseLinearInterpolate(
list(Trange), list(flow_stress))
iso_ri = hardening.LinearIsotropicHardeningRule(sy_interp, hmodulus)
hard_ri = hardening.Chaboche(
iso_ri, [interpolate.ConstantInterpolate(0.0)],
[hardening.ConstantGamma(interpolate.ConstantInterpolate(0.0))],
[interpolate.ConstantInterpolate(0.0)],
[interpolate.ConstantInterpolate(1.0)])
flow_ri = ri_flow.RateIndependentNonAssociativeHardening(
surface, hard_ri)
rate_independent = models.SmallStrainRateIndependentPlasticity(
elastic_m, flow_ri)
# Combined model
self.model = models.KMRegimeModel(elastic_m,
[rate_independent, rate_dependent],
[g0], kboltz, b, eps0)
self.efinal = np.array([0.05, 0, 0, 0.02, 0, -0.01])
self.tfinal = 10.0
self.T = 700.0
self.nsteps = 100
def setUp(self):
self.v = ra.random((1, ))[0]
self.interpolate = interpolate.ConstantInterpolate(self.v)
self.x = ra.random((1, ))[0]
b = 0.248 * 1.0e-6
kboltz = 1.38064e-23 * 1000.0
eps0 = 1.0e10
A = -9.774577
B = -1.407274
C = -5.060187
g0 = 0.373716
K = 150000.0 / 50.0
h = 1.0e-2
l = 1.0
# Elastic
E_m = interpolate.PolynomialInterpolate(E_poly)
nu_m = interpolate.ConstantInterpolate(nu)
elastic_m = elasticity.IsotropicLinearElasticModel(E_m, "youngs", nu_m,
"poissons")
# Rate sensitivity interpolates
Ts_rate = np.linspace(Tmin, Tmax, num=nrate_sens)
n_values = []
eta_values = []
flow_stress_values = []
for T in Ts_rate:
mu = elastic_m.G(T)
n_values.append(-mu * b**3.0 / (kboltz * T * A))
eta_values.append(
np.exp(B) * eps0**(kboltz * T * A / (mu * b**3.0)) * mu)
flow_stress_values.append(mu * np.exp(C))
B = -1.4819
C = -5.0486
g0 = 0.3708
b = 0.248 * 1.0e-6
kboltz = 1.38064e-23 * 1000.0
eps0 = 1.0e10
# Temperature range over which to consider (K)
Tmin = 550.0
Tmax = 950.0
Trange = np.linspace(Tmin, Tmax)
# Elastic
E_m = interpolate.PolynomialInterpolate(Epoly)
nu_m = interpolate.ConstantInterpolate(nu)
elastic_m = elasticity.IsotropicLinearElasticModel(E_m, "youngs", nu_m,
"poissons")
# Rate sensitivity interpolates values
mu_values = np.array([elastic_m.G(T) for T in Trange])
n_values = -mu_values * b**3.0 / (kboltz * Trange * A)
eta_values = np.exp(B) * eps0**(kboltz * Trange * A /
(mu_values * b**3.0)) * mu_values
# Rate independent interpolate values
flow_stress = mu_values * np.exp(C)
# Common objects
surface = surfaces.IsoKinJ2()
hmodulus = interpolate.PolynomialInterpolate([-10.0, 12000.0])