class POClampedFiber(RF):
'''
Pullout of fiber from a stiff matrix;
stress criterion for debonding, fixed fiber end
'''
implements(IRF)
title = Str('pullout - clamped fiber with constant friction')
image = Image('pics/cb_short_fiber.jpg', size=2)
xi = Float(0.0179, auto_set=False, enter_set=True, input=True,
distr=['weibull_min', 'uniform'])
tau = Float(2.5, auto_set=False, enter_set=True, input=True,
distr=['uniform', 'norm'])
l = Float(0.0, auto_set=False, enter_set=True, input=True,
distr=['uniform'], desc='free length')
D_f = Float(26e-3, auto_set=False, input=True,
enter_set=True, distr=['uniform', 'weibull_min'])
E_f = Float(72.0e3, auto_set=False, enter_set=True, input=True,
distr=['uniform'])
theta = Float(0.01, auto_set=False, enter_set=True, input=True,
distr=['uniform', 'norm'], desc='slack')
phi = Float(1., auto_set=False, enter_set=True, input=True,
distr=['uniform', 'norm'], desc='bond quality')
L = Float(1., auto_set=False, enter_set=True, input=True,
distr=['uniform'], desc='embedded length')
u = Float(auto_set=False, enter_set=True,
ctrl_range=(0.0, 0.02, 100))
x_label = Str('displacement [mm]')
y_label = Str('force [N]')
C_code = Str('')
def __call__(self, u, tau, l, D_f, E_f, theta, xi, phi, L):
A = pi * D_f ** 2 / 4.
l = l * (1 + theta)
u = u - theta * l
T = tau * phi * D_f * pi
q = (-l * T + sqrt(l ** 2 * T ** 2 + 2 * u * H(u) * E_f * A * T))
# displacement at which the debonding is finished
u0 = L * T * (L + 2 * l) / 2 / E_f / A
q = q * H(T * L - q) + (T * L + (u - u0) / (l + L) * A * E_f) * H(q - T * L)
# ----------------------------------
q = q * H(A * E_f * xi - q)
return q
class RVModelView(ModelView):
'''
ModelView class for displaying the table of parameters and
set the distribution parameters of random variables
'''
title = Str('randomization setup')
model = Instance(SPIRRID)
rv_list = List(RIDVariable)
@on_trait_change('model.rf')
def get_rv_list(self):
self.rv_list = [
RIDVariable(s=self.model,
rf=self.model.rf,
varname=nm,
trait_value=st) for nm, st in
zip(self.model.rf.param_keys, self.model.rf.param_values)
]
selected_var = Instance(RIDVariable)
def _selected_var_default(self):
return self.rv_list[0]
title = Str('random variable editor')
selected_var = Instance(RIDVariable)
traits_view = View(VSplit(
HGroup(
Item('rv_list', editor=rv_list_editor, show_label=False),
id='rid.tview.randomization.rv',
label='Model variables',
),
HGroup(
Item('selected_var@', show_label=False, resizable=True),
id='rid.tview.randomization.distr',
label='Distribution',
),
scrollable=True,
id='rid.tview.tabs',
dock='tab',
),
title='RANDOM VARIABLES',
id='rid.ridview',
dock='tab',
resizable=True,
height=1.0,
width=1.0)
class ConstantFrictionAndFreeLength(RF):
'''
'''
implements(IRF)
title = Str('pull-out with constant friction and free length ')
tau = Float(8, auto_set=False, enter_set=True, distr=['uniform'])
# free length
l = Float(1, auto_set=False, enter_set=True, distr=['uniform', 'norm'])
E = Float(70e9, auto_set=False, enter_set=True, distr=['uniform'])
A = Float(5.30929158457e-10,
auto_set=False,
enter_set=True,
distr=['uniform', 'weibull_min'])
# waviness in strains
slack = Float(0.1, auto_set=False, enter_set=True, distr=['uniform'])
u = Float(auto_set=False, enter_set=True, ctrl_range=(0.0, 1.0, 10))
def __call__(self, u, tau, l, E, A, slack):
return -l * (1 + slack) * tau * Heaviside(u - l * (slack)) + \
+ sqrt((l * (1 + slack) * tau) ** 2 \
+ 2 * E * A * (u - (l * slack)) * Heaviside(u - l * (slack)))
class S(YMBSource):
yarn_type = Enum('__TEST__',
changed_source=True,
editor=EnumEditor(values=['__TEST__']))
root_dir = Str('')
view = View(Item('yarn_type'))
class Preprocessor(HasTraits):
title = Str('preprocessor')
prep_components = Property(List)
def _get_prep_components(self):
rf_view = RFModelView(model=default_rf, child=spirrid)
rf_view._redraw()
rv_view = RVModelView(model=spirrid)
return [rf_view, rv_view]
class ConstantFrictionFreeLengthFiniteFiber(RF):
'''
'''
implements(IRF)
title = Str('pull-out with constant friction and free length ')
tau = Float(0.5e6,
auto_set=False,
enter_set=True,
distr=['uniform', 'weibull_min', 'norm'])
# free length
l = Float(0.01, auto_set=False, enter_set=True, distr=['uniform', 'norm'])
E = Float(70e9, auto_set=False, enter_set=True, distr=['uniform'])
A = Float(5.30929158457e-10,
auto_set=False,
enter_set=True,
distr=['uniform', 'weibull_min'])
# waviness in strains
slack = Float(0.0, auto_set=False, enter_set=True, distr=['uniform'])
# embedded length
L = Float(0.03, auto_set=False, enter_set=True, distr=['uniform'])
# breking stress
sigma_fu = Float(1200.e6,
auto_set=False,
enter_set=True,
distr=['uniform'])
u = Float(auto_set=False, enter_set=True, ctrl_range=(0.0, 1.0, 10))
#
def __call__(self, u, tau, l, E, A, slack, L, sigma_fu):
''' method for evaluating the u-F diagram for
pullout with constant friction at the interface and
free fiber length sticking out of the matrix '''
# defining tau as length dependent
tau = tau * sqrt(4 * A * pi)
# constitutive law for a pullout without free length
q = ( -l * ( 1 + slack ) * tau * Heaviside( u / 2 - l * ( slack ) ) + \
+ sqrt( ( l * ( 1 + slack ) * tau ) ** 2 * Heaviside( u / 2 - l * ( slack ) ) \
+ 2 * E * A * ( u / 2 - ( l * slack ) ) * Heaviside( u / 2 - l * ( slack ) ) ) )
# deformation of the free length added
continuous = q * Heaviside( L - l - q / tau )\
+ ( L - l ) * tau * Heaviside( l + q / tau - L )
# a check whether the fiber has reached the breaking stress
return continuous * Heaviside(sigma_fu - continuous / A)
class fiber_tt_5p_np(RF):
''' Response function of a single fiber '''
implements(IRF)
title = Str('brittle filament')
def __call__(self, eps, lambd, xi, E_mod, theta, A):
'''
Implements the response function with arrays as variables.
first extract the variable discretizations from the orthogonal grid.
'''
eps_ = (eps - theta * (1 + lambd)) / ((1 + theta) * (1 + lambd))
eps_ *= Heaviside(eps_)
eps_grid = eps_ * Heaviside(xi - eps_)
q_grid = E_mod * A * eps_grid
return q_grid
class fiber_tt_5p_ne(RF):
''' Response function of a single fiber '''
implements(IRF)
title = Str('brittle filament')
def __call__(self, eps, lambd, xi, E_mod, theta, A):
'''
Implements the response function with arrays as variables.
first extract the variable discretizations from the orthogonal grid.
'''
eps_ = ne.evaluate(
"((eps - theta * (1 + lambd)) / ((1 + theta) * (1 + lambd)))")
tmp = Heaviside_ne(eps_)
eps_ = ne.evaluate('eps_*tmp')
tmp = Heaviside_ne(ne.evaluate("(xi - eps_)"))
eps_grid = ne.evaluate("eps_ * tmp")
q_grid = ne.evaluate("E_mod * A * eps_grid")
return q_grid