def create_demo_object():
#===========================================================================
# Control variable
#===========================================================================
e_arr = np.linspace(0, 0.012, 80)
powers = np.linspace(1, math.log(20, 10), 6)
n_int_range = np.array(np.power(10, powers), dtype=int)
#===========================================================================
# Randomization
#===========================================================================
tvars = dict(
fu=RV('weibull_min', 1200.0e6, 200.),
qf=1500.0,
# qf = RV('uniform', 1500., 100.),
L=0.02, #
# L = RV('uniform', 0.02, 0.02 / 2.),
A=RV('norm', 5.30929158457e-10, .03 * 5.30929158457e-10),
E_mod=RV('uniform', 70.e9, 250.e9),
z=RV('uniform', 0.0, 0.03),
phi=0.0, #
# phi = RV('cos_distr', 0.0, 1.0),
# phi = RV('uniform', 0.0, 1.0),
f=RV('uniform', 0.0, 0.03))
#===========================================================================
# Integrator object
#===========================================================================
s = SPIRRID(
q=ConstantFrictionFiniteFiber(),
e_arr=e_arr,
n_int=10,
tvars=tvars,
)
#===========================================================================
# Lab
#===========================================================================
slab = SPIRRIDLAB(s=s,
save_output=False,
show_output=True,
dpi=300,
qname='fiber_po_8p',
plot_mode='subplots',
n_int_range=n_int_range,
extra_compiler_args=True,
le_sampling_lst=['LHS', 'PGrid'],
le_n_int_lst=[10, 10],
plot_sampling_idx=[
0,
3,
])
return slab
def create_demo_object():
m_la, std_la = 10., 1.0
m_xi, std_xi = 1.0, 0.1
# discretize the control variable (x-axis)
e_arr = np.linspace(0, 2.0, 80)
# n_int range for sampling efficiency test
powers = np.linspace(1, math.log(500, 10), 50)
n_int_range = np.array(np.power(10, powers), dtype=int)
#===========================================================================
# Randomization
#===========================================================================
s = SPIRRID(
q=fiber_tt_2p(),
e_arr=e_arr,
n_int=10,
tvars=dict(la=RV('norm', m_la, std_la), xi=RV('norm', m_xi, std_xi)),
)
#===========================================================================
# Exact solution
#===========================================================================
def mu_q_ex(e, m_xi, std_xi, m_la):
return e * (0.5 - 0.5 * erf(0.5 * math.sqrt(2) *
(e - m_xi) / std_xi)) * m_la
#===========================================================================
# Lab
#===========================================================================
slab = SPIRRIDLAB(s=s,
save_output=False,
show_output=True,
dpi=300,
exact_arr=mu_q_ex(e_arr, m_xi, std_xi, m_la),
plot_mode='subplots',
n_int_range=n_int_range,
extra_compiler_args=True,
le_sampling_lst=['LHS', 'PGrid'],
le_n_int_lst=[440, 5000])
return slab
def setup_class(cls):
np.random.seed(2356)
#===========================================================================
# Control variable
#===========================================================================
e_arr = np.linspace(0, 0.012, 80)
cls.m_la, cls.std_la = 10., 1.0
cls.m_xi, cls.std_xi = 1.0, 0.1
#===========================================================================
# Randomization
#===========================================================================
cls.s = SPIRRID(
q=fiber_tt_2p(),
evars={'e': e_arr},
codegen_type='numpy',
n_int=10,
tvars=dict(la=RV('norm', cls.m_la, cls.std_la),
xi=RV('norm', cls.m_xi, cls.std_xi)),
)
q - T * (abs(x) - l / 2.)) * Heaviside(abs(x) - l / 2.)
q_x = q_x * Heaviside(x + Ll) * Heaviside(Lr - x)
q_x = q_x * Heaviside(q_x)
return q_x
q = CBClampedFiberSP()
s = SPIRRID(
q=q,
sampling_type='LHS',
evars=dict(w=np.linspace(0.0, 0.4, 50),
x=np.linspace(-20.1, 20.5, 100),
Lr=np.linspace(0.1, 20.0, 50)),
tvars=dict(
tau=RV('uniform', 0.7, 1.0),
l=RV('uniform', 5.0, 10.0),
D_f=26e-3,
E_f=72e3,
theta=0.0,
xi=RV('weibull_min', scale=0.017, shape=8, n_int=10),
phi=1.0,
Ll=50.0,
# Lr = 1.0
),
n_int=5)
e_arr = make_ogrid(s.evar_lst)
n_e_arr = [e / np.max(np.fabs(e)) for e in e_arr]
max_mu_q = np.max(np.fabs(s.mu_q_arr))
def create_demo_object():
D = 26 * 1.0e-6 # m
A = (D / 2.0)**2 * math.pi
# set the mean and standard deviation of the two random variables
la_mean, la_stdev = 0.0, 0.2
xi_mean, xi_stdev = 0.019027, 0.0022891
E_mean, E_stdev = 70.0e+9, 15.0e+9
th_mean, th_stdev = 0.0, 0.01
A_mean, A_stdev = A * 0.3, 0.7 * A
do = 'norm'
if do == 'general':
# set the mean and standard deviation of the two random variables
la_mean, la_stdev = 0.0, 0.2
xi_mean, xi_stdev = 0.019027, 0.0022891
E_mean, E_stdev = 70.0e+9, 15.0e+9
th_mean, th_stdev = 0.0, 0.01
A_mean, A_stdev = A * 0.3, 0.7 * A
# construct the normal distributions and get the methods
# for the evaluation of the probability density functions
g_la = RV('uniform', la_mean, la_stdev)
g_xi = RV('norm', xi_mean, xi_stdev)
g_E = RV('uniform', E_mean, E_stdev)
g_th = RV('uniform', th_mean, th_stdev)
g_A = RV('uniform', A_mean, A_stdev)
mu_ex_file = 'fiber_tt_5p_30.txt'
delimiter = ','
elif do == 'uniform':
# set the mean and standard deviation of the two random variables
la_mean, la_stdev = 0.0, 0.2
xi_mean, xi_stdev = 0.01, 0.02
E_mean, E_stdev = 70.0e+9, 15.0e+9
th_mean, th_stdev = 0.0, 0.01
A_mean, A_stdev = A * 0.3, 0.7 * A
# construct the uniform distributions and get the methods
# for the evaluation of the probability density functions
g_la = RV('uniform', la_mean, la_stdev)
g_xi = RV('uniform', xi_mean, xi_stdev)
g_E = RV('uniform', E_mean, E_stdev)
g_th = RV('uniform', th_mean, th_stdev)
g_A = RV('uniform', A_mean, A_stdev)
mu_ex_file = 'fiber_tt_5p_40_unif.txt'
delimiter = ' '
elif do == 'norm':
# set the mean and standard deviation of the two random variables
la_mean, la_stdev = 0.1, 0.02
xi_mean, xi_stdev = 0.019027, 0.0022891
E_mean, E_stdev = 70.0e+9, 15.0e+9
th_mean, th_stdev = 0.005, 0.001
A_mean, A_stdev = 5.3e-10, 1.0e-11
# construct the normal distributions and get the methods
# for the evaluation of the probability density functions
g_la = RV('norm', la_mean, la_stdev)
g_xi = RV('norm', xi_mean, xi_stdev)
g_E = RV('norm', E_mean, E_stdev)
g_th = RV('norm', th_mean, th_stdev)
g_A = RV('norm', A_mean, A_stdev)
mu_ex_file = os.path.join(file_dir,
'fiber_tt_5p_n_int_40_norm_exact.txt')
delimiter = ' '
# discretize the control variable (x-axis)
e_arr = np.linspace(0, 0.04, 40)
# n_int range for sampling efficiency test
powers = np.linspace(1, math.log(20, 10), 15)
n_int_range = np.array(np.power(10, powers), dtype=int)
#===========================================================================
# Randomization
#===========================================================================
s = SPIRRID(
q=fiber_tt_5p(),
e_arr=e_arr,
n_int=10,
tvars=dict(lambd=g_la, xi=g_xi, E_mod=g_E, theta=g_th, A=g_A),
)
# Exact solution
def mu_q_ex(e):
data = np.loadtxt(mu_ex_file, delimiter=delimiter)
x, y = data[:, 0], data[:, 1]
f = interp1d(x, y, kind='linear')
return f(e)
#===========================================================================
# Lab
#===========================================================================
slab = SPIRRIDLAB(s=s,
save_output=False,
show_output=True,
dpi=300,
exact_arr=mu_q_ex(e_arr),
plot_mode='subplots',
n_int_range=n_int_range,
extra_compiler_args=True,
le_sampling_lst=['LHS', 'PGrid'],
le_n_int_lst=[25, 30])
return slab
def _random_changed(self):
# get the default distribution
if self.random:
self.spirrid.rv_dict[ self.varname ] = RV(pd = self.pd, name = self.varname, n_int = self.n_int)
else:
del self.spirrid.rv_dict[ self.varname ]