class TLine(BMCSLeafNode):
'''
Time line for the control parameter.
This class sets the time-range of the computation - the start and stop time.
val is the value of the current time.
TODO - the info page including the number of load steps
and estimated computation time.
TODO - the slide bar is not read-only. How to include a real progress bar?
'''
node_name = 'time range'
min = bu.Float(0.0, TIME=True)
max = bu.Float(1.0, TIME=True)
step = bu.Float(0.1, TIME=True)
val = bu.Float(0.0)
def _val_changed(self):
if self.time_change_notifier:
self.time_change_notifier(self.val)
@on_trait_change('min,max')
def _time_range_changed(self):
if self.time_range_change_notifier:
self.time_range_change_notifier(self.max)
time_change_notifier = Callable
time_range_change_notifier = Callable
ipw_view = View(Item('min', full_size=True), Item('max'), Item('step'),
Item('val', style='readonly'))
class BarLayer(ReinfLayer):
""""Layer consisting of discrete bar reinforcement"""
name = 'Bar layer'
ds = Float(16, CS=True)
count = Int(1, CS=True)
A = tr.Property(Float, depends_on='+CS')
"""cross section area of reinforcement layers"""
@tr.cached_property
def _get_A(self):
return self.count * np.pi * (self.ds / 2.)**2
P = tr.Property(Float, depends_on='+CS')
"""permeter of reinforcement layers"""
@tr.cached_property
def _get_P(self):
return self.count * np.pi * (self.ds)
def _matmod_default(self):
return 'steel'
ipw_view = View(
Item('matmod', latex=r'\mathrm{behavior}'),
Item('z', latex=r'z \mathrm{[mm]}'),
Item('ds', latex=r'ds \mathrm{[mm]}'),
Item('count', latex='count'),
Item('A', latex=r'A [mm^2]'),
)
class CustomShape(CrossSectionShapeBase):
cs_points_str = Str(
'100, 0\n100, 140\n25, 150\n25, 750\n100, 760\n100, 900\n-100, 900\n-100, 760\n-25, 750\n-25, 150\n-100, 140\n-100, 0'
)
apply_points_btn = Button()
@tr.observe("apply_points_btn")
def apply_points_btn_clicked(self, event):
print(
'This should update the plot with the new points, but maybe it\'s not needed'
)
ipw_view = View(
Item('cs_points_str',
latex=r'\mathrm{Points}',
editor=TextAreaEditor()),
Item('apply_points_btn',
editor=ButtonEditor(label='Apply points', icon='refresh')),
)
cs_points = tr.Property()
def _get_cs_points(self):
return self._parse_2d_points_str_into_array(self.cs_points_str)
@staticmethod
def _parse_2d_points_str_into_array(points_str):
""" This will parse str of points written like this '0, 0\n 1, 1' to ([0, 0], [1, 1]) """
points_str = points_str.replace('\n', ', ').replace('\r', ', ')
points_array = np.fromstring(points_str, dtype=int, sep=',')
if points_array.size % 2 != 0:
points_array = np.append(points_array, 0)
points_array = points_array.reshape((int(points_array.size / 2), 2))
return points_array.reshape((int(points_array.size / 2), 2))
def get_cs_area(self):
points_xy = self.cs_points
x = points_xy[:, 0]
y = points_xy[:, 1]
# See https://stackoverflow.com/a/30408825 for following Implementation of Shoelace formula
return 0.5 * np.abs(
np.dot(x, np.roll(y, 1)) - np.dot(y, np.roll(x, 1)))
def get_cs_i(self):
pass
def get_b(self, z_positions_array):
# TODO get b for polygon given its points coordinates
pass
def update_plot(self, ax):
cs = Polygon(self.cs_points)
patch_collection = PatchCollection([cs],
facecolor=(.5, .5, .5, 0.2),
edgecolors=(0, 0, 0, 1))
ax.add_collection(patch_collection)
# ax.scatter(0, CustomShape.get_cs_i(self)[0], color='white', s=self.B_w, marker="+")
ax.autoscale_view()
ax.set_aspect('equal')
class FabricLayer(ReinfLayer):
"""Reinforcement with a grid structure
"""
name = 'Fabric layer'
width = Float(100, CS=True)
spacing = Float(14, CS=True)
A_roving = Float(1, CS=True)
A = tr.Property(Float, depends_on='+CS')
"""cross section area of reinforcement layers"""
@tr.cached_property
def _get_A(self):
return int(self.width / self.spacing) * self.A_roving
P = tr.Property(Float, depends_on='+CS')
"""cross section area of reinforcement layers"""
@tr.cached_property
def _get_P(self):
raise NotImplementedError
def _matmod_default(self):
return 'carbon'
ipw_view = View(
Item('matmod', latex=r'\mathrm{behavior}'),
Item('z', latex='z \mathrm{[mm]}'),
Item('width', latex='\mathrm{fabric~width} \mathrm{[mm]}'),
Item('spacing', latex='\mathrm{rov~spacing} \mathrm{[mm]}'),
Item('A_roving', latex='A_r \mathrm{[mm^2]}'),
Item('A', latex=r'A [mm^2]', readonly=True),
)
class CrossSectionShapeBase(InteractiveModel):
""""This class describes the geometry of the cross section."""
name = 'Cross Section Shape'
H = Float(400, GEO=True)
ipw_view = View(Item('H', minmax=(10, 3000), latex='H [mm]'))
class TFBilinear(TimeFunction):
loading_ratio = Float(0.5, TIME=True)
time_ratio = Float(0.5, TIME=True)
ipw_view = View(
Item('t_max'),
Item('loading_ratio', editor=FloatRangeEditor(low=0, high=1)),
Item('time_ratio', editor=FloatRangeEditor(low=0, high=1)),
Item('range_factor'))
range_factor = Float(1)
def _generate_time_function(self):
# Define the monotonic range beyond the unit range
# allow for interactive extensions. The fact
d_history = np.array([
0, 1 * self.loading_ratio * self.range_factor,
1 * self.range_factor
])
t_arr = np.array([
0, self.t_max * self.time_ratio * self.range_factor,
self.t_max * self.range_factor
])
return interp1d(t_arr,
d_history,
bounds_error=False,
fill_value=self.t_max)
class CrossSectionDesign(Model):
name = 'Cross Section Design'
matrix = EitherType(
options=[
('piecewise linear', PWLConcreteMatMod),
('EC2', EC2ConcreteMatMod),
('EC2 with plateau', EC2PlateauConcreteMatMod),
# ('EC2 softening tension', ConcreteMaterialModelAdv),
],
MAT=True)
cross_section_layout = Instance(CrossSectionLayout)
def _cross_section_layout_default(self):
return CrossSectionLayout(cs_design=self)
tree = ['matrix', 'cross_section_layout', 'cross_section_shape']
csl = tr.Property
def _get_csl(self):
return self.cross_section_layout
H = tr.Property(Float)
def _get_H(self):
return self.cross_section_shape_.H
def _set_H(self, value):
self.cross_section_shape_.H = value
cross_section_shape = EitherType(
options=[
('rectangle', Rectangle),
# ('circle', Circle),
('I-shape', IShape),
('T-shape', TShape),
('custom', CustomShape)
],
CS=True,
tree=True)
ipw_view = View(
Item('matrix',
latex=r'\mathrm{concrete behavior}',
editor=EitherTypeEditor(show_properties=False)),
Item('cross_section_shape',
latex=r'\mathrm{shape}',
editor=EitherTypeEditor(show_properties=False)),
Item('cross_section_layout', latex=r'\mathrm{layout}'),
)
def subplots(self, fig):
return fig.subplots(1, 1)
def update_plot(self, ax):
self.cross_section_shape_.update_plot(ax)
self.cross_section_layout.update_plot(ax)
class CrossSectionLayout(ModelList):
name = 'Cross Section Layout'
items = List(ReinfLayer, [])
cs_design = tr.WeakRef
def add_layer(self, rl):
rl.cs_layout = self
self.items.append(rl)
z_j = tr.Property
def _get_z_j(self):
return np.array([r.z for r in self.items], dtype=np.float_)
p_j = tr.Property
'''
Get the perimeter
'''
def _get_p_j(self):
return np.array([r.p for r in self.items], dtype=np.float_)
A_j = tr.Property
def _get_A_j(self):
return np.array([r.A for r in self.items], dtype=np.float_)
def get_N_tj(self, eps_tj):
return np.array(
[r.get_N(eps_t) for r, eps_t in zip(self.items, eps_tj.T)],
dtype=np.float_).T
ipw_view = View()
def subplots(self, fig):
return fig.subplots(1, 1)
def update_plot(self, ax):
self.cs_design.cross_section_shape_.update_plot(ax)
H = int(self.cs_design.cross_section_shape_.H)
maxA = 0
for layer in self.items:
A = layer.A
maxA = max(A, maxA)
for layer in self.items:
z = layer.z
A = layer.A
b = self.cs_design.cross_section_shape_.get_b([z])
ax.plot([-0.9 * b / 2, 0.9 * b / 2], [z, z],
color='r',
linewidth=5 * A / maxA)
class TFMonotonic(TimeFunction):
ipw_view = View(Item('t_max'), Item('range_factor'))
range_factor = Float(10000)
def _generate_time_function(self):
# Define the monotonic range beyond the unit range
# allow for interactive extensions. The fact
d_history = np.array([0, 1 * self.range_factor])
t_arr = np.array([0, self.t_max * self.range_factor])
return interp1d(t_arr, d_history)
class TFCyclicSymmetricConstant(TimeFunction):
number_of_cycles = Int(10, TIME=True)
ipw_view = View(Item('number_of_cycles'), Item('t_max'))
def _generate_time_function(self):
d_levels = np.zeros((self.number_of_cycles * 2, ))
d_levels.reshape(-1, 2)[:, 0] = -1
d_levels.reshape(-1, 2)[:, 1] = 1
d_history = d_levels.flatten()
t_arr = np.linspace(0, self.t_max, len(d_history))
return interp1d(t_arr, d_history)
class TFCyclicNonsymmetricIncreasing(TimeFunction):
number_of_cycles = Int(10, TIME=True)
ipw_view = View(Item('number_of_cycles'), Item('t_max'))
def _generate_time_function(self):
d_levels = np.linspace(0, 1, self.number_of_cycles * 2)
d_levels.reshape(-1, 2)[:, 0] *= 0
d_history = d_levels.flatten()
t_arr = np.linspace(0, self.t_max, len(d_history))
return interp1d(t_arr,
d_history,
bounds_error=False,
fill_value=self.t_max)
class TFCyclicSin(TimeFunction):
number_of_cycles = Int(10, TIME=True)
phase_shift = Float(0, TIME=True)
ipw_view = View(Item('number_of_cycles'), Item('phase_shift'),
Item('t_max'))
def _generate_time_function(self):
t = sp.symbols(r't')
p = self.phase_shift
tf = sp.sin(2 * self.number_of_cycles * sp.pi * t / self.t_max)
if p > 0:
T = self.t_max / self.number_of_cycles
pT = p * T
tf = sp.Piecewise((0, t < pT), (tf.subs(t, t - pT), True))
return sp.lambdify(t, tf, 'numpy')
class Rectangle(CrossSectionShapeBase):
B = Float(200, GEO=True)
ipw_view = View(
*CrossSectionShapeBase.ipw_view.
content, # this will add View Items of the base class CrossSectionShapeBase
Item('B', minmax=(10, 500), latex='B \mathrm{[mm]}'))
def get_cs_area(self):
return self.B * self.H
def get_cs_i(self):
return self.B * self.H**3 / 12
def get_b(self, z_positions_array):
return np.full_like(z_positions_array, self.B)
def update_plot(self, ax):
ax.fill(
[-self.B / 2, self.B / 2, self.B / 2, -self.B / 2, -self.B / 2],
[0, 0, self.H, self.H, 0],
color='gray',
alpha=0.2)
ax.plot(
[-self.B / 2, self.B / 2, self.B / 2, -self.B / 2, -self.B / 2],
[0, 0, self.H, self.H, 0],
color='black')
ax.autoscale_view()
ax.set_aspect('equal')
ax.annotate('Area = {} $mm^2$'.format(int(Rectangle.get_cs_area(self)),
0),
xy=(-self.H / 2 * 0.8, self.H / 2),
color='blue')
ax.annotate('I = {} $mm^4$'.format(int(Rectangle.get_cs_i(self)), 0),
xy=(-self.H / 2 * 0.8, self.H / 2 * 0.8),
color='blue')
class ReinfLayer(InteractiveModel):
# TODO: changes in the ipw interactive window doesn't reflect on mkappa
# (maybe because these are lists and changing the elements doesn't notify)
name = 'Reinf layer'
cs_layout = tr.WeakRef
z = Float(50, CS=True)
"""z positions of reinforcement layers"""
P = Float(100, CS=True)
"""Perimeter"""
A = Float(100, CS=True)
"""Cross sectional area"""
matmod = EitherType(
options=[('steel', SteelReinfMatMod), ('carbon', CarbonReinfMatMod)])
ipw_view = View(
Item('matmod',
latex=r'\mathrm{behavior}',
editor=EitherTypeEditor(show_properties=False)),
Item('z', latex='z \mathrm{[mm]}'),
Item('A', latex='A \mathrm{[mm^2]}'),
)
tree = ['matmod']
def get_N(self, eps):
return self.A * self.matmod_.get_sig(eps)
def update_plot(self, ax):
eps_range = self.matmod_.get_eps_plot_range()
N_range = self.get_N(eps_range)
ax.plot(eps_range, N_range, color='red')
ax.fill_between(eps_range, N_range, 0, color='red', alpha=0.1)
ax.set_xlabel(r'$\varepsilon$ [-]')
ax.set_ylabel(r'$F$ [N]')
class TFCyclicNonsymmetricConstant(TimeFunction):
number_of_cycles = Int(10, TIME=True)
unloading_ratio = Float(0.5, TIME=True)
shift_cycles = Int(0, TIME=True)
ipw_view = View(
Item('number_of_cycles'), Item('shift_cycles'),
Item('unloading_ratio', editor=FloatRangeEditor(low=0, high=1)),
Item('t_max'))
def _generate_time_function(self):
d_1 = np.zeros(1)
d_2 = np.zeros(((self.number_of_cycles + self.shift_cycles) * 2, ))
d_2.reshape(-1, 2)[self.shift_cycles:, 0] = 1
d_2.reshape(-1, 2)[self.shift_cycles:, 1] = self.unloading_ratio
d_history = d_2.flatten()
d_arr = np.hstack((d_1, d_history))
t_arr = np.linspace(0, self.t_max, len(d_arr))
return interp1d(t_arr,
d_arr,
bounds_error=False,
fill_value=self.t_max)
class TFSelector(TimeFunction):
name = 'time function'
profile = EitherType(options=[
('monotonic', TFMonotonic), ('bilinear', TFBilinear),
('cyclic-sym-incr', TFCyclicSymmetricConstant),
('cyclic-sym-const', TFCyclicSymmetricIncreasing),
('cyclic-nonsym-incr', TFCyclicNonsymmetricConstant),
('cyclic-nonsym-const', TFCyclicNonsymmetricIncreasing)
],
TIME=True)
t_max = tr.Property(Float)
def _get_t_max(self):
return self.profile_.t_max
ipw_view = View(Item('profile'))
def __call__(self, arg):
return self.profile_(arg)
def update_plot(self, axes):
self.profile_.update_plot(axes)
class Circle(CrossSectionShapeBase):
# TODO->Rostia: provide input field instead minmax range
# H from the base class is used as the D, for the diameter of the circular section
H = Float(250, GEO=True)
ipw_view = View(Item('H', minmax=(10, 3000), latex='D [mm]'), )
def get_cs_area(self):
return np.pi * self.H * self.H
def get_cs_i(self):
return np.pi * self.H**4 / 4
def get_b(self, z_positions_array):
return np.full_like(z_positions_array, self.H)
# TODO->Saeed: complete this
def update_plot(self, ax):
# TODO->Saeed: fix this
circle = MPL_Circle((0, self.H / 2),
self.H / 2,
facecolor=(.5, .5, .5, 0.2),
edgecolor=(0, 0, 0, 1))
ax.add_patch(circle)
ax.autoscale_view()
ax.set_aspect('equal')
ax.annotate('Area = {} $mm^2$'.format(int(self.get_cs_area()), 0),
xy=(-self.H / 2 * 0.8, self.H / 2),
color='blue')
ax.annotate('I = {} $mm^4$'.format(int(self.get_cs_i()), 0),
xy=(-self.H / 2 * 0.8, self.H / 2 * 0.8),
color='blue')
class MATS3DIfcElastic(MATSEval):
node_name = "Elastic interface"
E_n = Float(1e+6, tooltip='Normal stiffness of the interface [MPa]',
MAT=True, unit='MPa/m', symbol='E_\mathrm{n}',
desc='Normal stiffness of the interface',
auto_set=False, enter_set=True)
E_s = Float(1.0, tooltip='Shear stiffness of the interface [MPa]',
MAT=True, unit='MPa/m', symbol='E_\mathrm{s}',
desc='Shear-modulus of the interface',
auto_set=True, enter_set=True)
state_var_shapes = {}
D_rs = Property(depends_on='E_n,E_s')
@cached_property
def _get_D_rs(self):
return np.array([[self.E_n, 0, 0],
[0, self.E_s, 0],
[0, 0, self.E_s]], dtype=np.float_)
def get_corr_pred(self, u_r, tn1):
tau = np.einsum(
'rs,...s->...r',
self.D_rs, u_r)
grid_shape = tuple([1 for _ in range(len(u_r.shape[:-1]))])
D = self.D_rs.reshape(grid_shape + self.D_rs.shape)
return tau, D
ipw_view = View(
Item('E_s'),
Item('E_n')
)
class MS1EEQ(MATS3DEval, InteractiveModel):
gamma_T = tr.Float(1000000.,
label="gamma_T",
desc=" Tangential Kinematic hardening modulus",
enter_set=True,
auto_set=False)
K_T = tr.Float(10000.,
label="K_T",
desc="Tangential Isotropic harening",
enter_set=True,
auto_set=False)
S_T = tr.Float(0.005,
label="S_T",
desc="Damage strength",
enter_set=True,
auto_set=False)
r_T = tr.Float(9.,
label="r",
desc="Damage cumulation parameter",
enter_set=True,
auto_set=False)
p_T = tr.Float(2.,
label="p_T",
desc="Damage cumulation parameter",
enter_set=True,
auto_set=False)
c_T = tr.Float(3,
label="c_T",
desc="Damage cumulation parameter",
enter_set=True,
auto_set=False)
sigma_T_0 = tr.Float(1.7,
label="sigma_T_0",
desc="Reversibility limit",
enter_set=True,
auto_set=False)
m_T = tr.Float(0.1,
label="m_T",
desc="Lateral pressure coefficient",
enter_set=True,
auto_set=False)
# -------------------------------------------
# Normal_Tension constitutive law parameters (without cumulative normal strain)
# -------------------------------------------
Ad = tr.Float(10.0,
label="A_d",
desc="brittleness coefficient",
enter_set=True,
auto_set=False)
eps_0 = tr.Float(.0001,
label="eps_N_0",
desc="threshold strain",
enter_set=True,
auto_set=False)
# -----------------------------------------------
# Normal_Compression constitutive law parameters
# -----------------------------------------------
K_N = tr.Float(10000.,
label="K_N",
desc=" Normal isotropic harening",
enter_set=True,
auto_set=False)
gamma_N = tr.Float(5000.,
label="gamma_N",
desc="Normal kinematic hardening",
enter_set=True,
auto_set=False)
sigma_N_0 = tr.Float(10.,
label="sigma_N_0",
desc="Yielding stress",
enter_set=True,
auto_set=False)
# -------------------------------------------------------------------------
# Cached elasticity tensors
# -------------------------------------------------------------------------
E = tr.Float(35e+3,
label="E",
desc="Young's Modulus",
auto_set=False,
input=True)
nu = tr.Float(0.2,
label='nu',
desc="Poison ratio",
auto_set=False,
input=True)
ipw_view = View(
Item('gamma_T', latex=r'\gamma_\mathrm{T}', minmax=(10, 100000)),
Item('K_T', latex=r'K_\mathrm{T}', minmax=(10, 10000)),
Item('S_T', latex=r'S_\mathrm{T}', minmax=(0.001, 0.01)),
Item('r_T', latex=r'r_\mathrm{T}', minmax=(1, 3)),
Item('p_T', latex=r'e_\mathrm{T}', minmax=(1, 40)),
Item('c_T', latex=r'c_\mathrm{T}', minmax=(1, 10)),
Item('sigma_T_0', latex=r'\bar{sigma}^\pi_{T}', minmax=(1, 10)),
Item('m_T', latex=r'm_\mathrm{T}', minmax=(0.001, 3)),
)
n_D = 3
state_var_shapes = tr.Property
@tr.cached_property
def _get_state_var_shapes(self):
dictionay = {
name: (self.n_mp, ) + shape
for name, shape in self.mic_state_var_shapes.items()
}
dictionay_macro = {
name: shape
for name, shape in self.mac_state_var_shapes.items()
}
dictionay.update(dictionay_macro)
return dictionay
mic_state_var_shapes = dict(
omega_N_Emn=(), # damage N
z_N_Emn=(),
alpha_N_Emn=(),
r_N_Emn=(),
eps_N_p_Emn=(),
sigma_N_Emn=(),
omega_T_Emn=(),
z_T_Emn=(),
alpha_T_Emna=(n_D, ),
eps_T_pi_Emna=(n_D, ),
sigma_T_Emna=(n_D, ),
plastic_dissip_T_Emn=(),
damage_dissip_T_Emn=(),
plastic_dissip_N_Emn=(),
damage_dissip_N_Emn=(),
)
mac_state_var_shapes = dict(
total_work_microplane=(),
total_work_macro=(),
eps_aux=(
n_D,
n_D,
),
)
'''
State variables
1) damage N,
2) iso N,
3) kin N,
4) consolidation N,
5) eps p N,
6) sigma N,
7) iso F N,
8) kin F N,
9) energy release N,
10) damage T,
11) iso T,
12-13) kin T,
14-15) eps p T,
16-17) sigma T, 18) iso F T, 19-20) kin F T, 21) energy release T
'''
# --------------------------------------------------------------
# microplane constitutive law (normal behavior CP + TD)
# (without cumulative normal strain for fatigue under tension)
# --------------------------------------------------------------
def get_normal_law(self, eps_N_Emn, omega_N_Emn, z_N_Emn, alpha_N_Emn,
r_N_Emn, eps_N_p_Emn, sigma_N_Emn, omega_T_Emn, z_T_Emn,
alpha_T_Emna, eps_T_pi_Emna, sigma_T_Emna,
plastic_dissip_T_Emn, damage_dissip_T_Emn,
plastic_dissip_N_Emn, damage_dissip_N_Emn,
total_work_microplane, total_work_macro, eps_aux):
E_N = self.E / (1.0 - 2.0 * self.nu)
sigma_N_Emn_tilde = E_N * (eps_N_Emn - eps_N_p_Emn)
r_N_Emn_aux = copy.deepcopy(r_N_Emn)
omega_N_Emn_aux = copy.deepcopy(omega_N_Emn)
pos = sigma_N_Emn_tilde > 1e-6 # microplanes under tension
pos2 = sigma_N_Emn_tilde < -1e-6 # microplanes under compression
tension = 1.0 * pos
compression = 1.0 * pos2
# thermo forces
Z = self.K_N * z_N_Emn * compression
X = self.gamma_N * alpha_N_Emn * compression
h = (self.sigma_N_0 + Z) * compression
f_trial = (abs(sigma_N_Emn_tilde - X) - h) * compression
# threshold plasticity
thres_1 = f_trial > 1e-10
delta_lamda = f_trial / \
(E_N / (1 - omega_N_Emn) + abs(self.K_N) + self.gamma_N) * thres_1
eps_N_p_Emn += delta_lamda * \
np.sign(sigma_N_Emn_tilde - X)
z_N_Emn += delta_lamda
alpha_N_Emn += delta_lamda * \
np.sign(sigma_N_Emn_tilde - X)
def R_N(r_N_Emn):
return (1.0 / self.Ad) * (-r_N_Emn / (1.0 + r_N_Emn))
Y_N = 0.5 * tension * E_N * (eps_N_Emn - eps_N_p_Emn)**2.0
Y_0 = 0.5 * E_N * self.eps_0**2.0
f = (Y_N - (Y_0 + R_N(r_N_Emn))) * tension
# threshold damage
thres_2 = f > 1e-6
def f_w(Y):
return 1.0 - 1.0 / (1.0 + self.Ad * (Y - Y_0))
omega_N_Emn[f > 1e-6] = f_w(Y_N)[f > 1e-6]
omega_N_Emn[...] = np.clip(omega_N_Emn, 0, 1.0)
r_N_Emn[f > 1e-6] = -omega_N_Emn[f > 1e-6]
sigma_N_Emn[...] = (1.0 - tension * omega_N_Emn) * E_N * (eps_N_Emn -
eps_N_p_Emn)
Z = self.K_N * z_N_Emn * compression
X = self.gamma_N * alpha_N_Emn * compression
# sigma_N_Emn = E_N * (eps_N_Emn - eps_N_p_Emn)
# pos1 = [(eps_N_Emn < -1e-6) & (sigma_trial > 1e-6)] # looking for microplanes violating strain boundary
# sigma_N_Emn[pos1[0]] = 0
delta_eps_N_p_Emn = np.zeros_like(eps_N_p_Emn)
delta_alpha_N_Emn = np.zeros_like(alpha_N_Emn)
delta_z_N_Emn = np.zeros_like(z_N_Emn)
delta_omega_N_Emn = np.zeros_like(omega_N_Emn)
delta_r_N_Emn = np.zeros_like(r_N_Emn)
delta_eps_N_p_Emn = delta_lamda * \
np.sign(sigma_N_Emn_tilde - X)
delta_alpha_N_Emn = delta_lamda * \
np.sign(sigma_N_Emn_tilde - X)
delta_z_N_Emn = delta_lamda
delta_omega_N_Emn = omega_N_Emn - omega_N_Emn_aux
delta_r_N_Emn = r_N_Emn - r_N_Emn_aux
plastic_dissip_N_Emn[...] = np.einsum('...n,...n->...n', sigma_N_Emn, delta_eps_N_p_Emn) - \
np.einsum('...n,...n->...n', X, delta_alpha_N_Emn) - np.einsum('...n,...n->...n',
Z, delta_z_N_Emn)
damage_dissip_N_Emn[...] = np.einsum('...n,...n->...n', Y_N, delta_omega_N_Emn) - \
np.einsum('...n,...n->...n', R_N(r_N_Emn), delta_r_N_Emn)
return sigma_N_Emn, Z, X, Y_N
# -------------------------------------------------------------------------
# microplane constitutive law (Tangential CSD)-(Pressure sensitive cumulative damage)
# -------------------------------------------------------------------------
def get_tangential_law(self, eps_T_Emna, eps_Emab, omega_N_Emn, z_N_Emn,
alpha_N_Emn, r_N_Emn, eps_N_p_Emn, sigma_N_Emn,
omega_T_Emn, z_T_Emn, alpha_T_Emna, eps_T_pi_Emna,
sigma_T_Emna, plastic_dissip_T_Emn,
damage_dissip_T_Emn, plastic_dissip_N_Emn,
damage_dissip_N_Emn, total_work_microplane,
total_work_macro, eps_aux):
E_T = self.E * (1.0 - 4 * self.nu) / \
((1.0 + self.nu) * (1.0 - 2 * self.nu))
# thermo forces
sig_pi_trial = E_T * (eps_T_Emna - eps_T_pi_Emna)
Z = self.K_T * z_T_Emn
X = self.gamma_T * alpha_T_Emna
norm_1 = np.sqrt(
np.einsum('...na,...na->...n', (sig_pi_trial - X),
(sig_pi_trial - X)))
Y = 0.5 * E_T * \
np.einsum(
'...na,...na->...n',
(eps_T_Emna - eps_T_pi_Emna),
(eps_T_Emna - eps_T_pi_Emna))
E_N = self.E / (1.0 - 2.0 * self.nu)
eps_N_Emn = self._get_e_N_Emn(eps_Emab)
sigma_N_Emn = (1.0 - omega_N_Emn) * E_N * (eps_N_Emn - eps_N_p_Emn)
f = norm_1 - self.sigma_T_0 - Z + self.m_T * sigma_N_Emn
plas_1 = f > 1e-15
elas_1 = f < 1e-15
delta_lamda = f / \
(E_T / (1.0 - omega_T_Emn) + self.gamma_T + self.K_T) * plas_1
norm_2 = 1.0 * elas_1 + np.sqrt(
np.einsum('...na,...na->...n', (sig_pi_trial - X),
(sig_pi_trial - X))) * plas_1
eps_T_pi_Emna[..., 0] += plas_1 * delta_lamda * \
((sig_pi_trial[..., 0] - X[..., 0]) /
(1.0 - omega_T_Emn)) / norm_2
eps_T_pi_Emna[..., 1] += plas_1 * delta_lamda * \
((sig_pi_trial[..., 1] - X[..., 1]) /
(1.0 - omega_T_Emn)) / norm_2
eps_T_pi_Emna[..., 2] += plas_1 * delta_lamda * \
((sig_pi_trial[..., 2] - X[..., 2]) /
(1.0 - omega_T_Emn)) / norm_2
omega_T_Emn += plas_1 * ((1 - omega_T_Emn) ** self.c_T) * \
(delta_lamda * (Y / self.S_T) ** self.r_T) * \
(self.sigma_T_0 / (self.sigma_T_0 - self.m_T * sigma_N_Emn)) ** self.p_T
omega_T_Emn[...] = np.clip(omega_T_Emn, 0, 1.0)
alpha_T_Emna[..., 0] += plas_1 * delta_lamda * \
(sig_pi_trial[..., 0] - X[..., 0]) / norm_2
alpha_T_Emna[..., 1] += plas_1 * delta_lamda * \
(sig_pi_trial[..., 1] - X[..., 1]) / norm_2
alpha_T_Emna[..., 2] += plas_1 * delta_lamda * \
(sig_pi_trial[..., 2] - X[..., 2]) / norm_2
z_T_Emn += plas_1 * delta_lamda
sigma_T_Emna[...] = np.einsum('...n,...na->...na', (1 - omega_T_Emn),
E_T * (eps_T_Emna - eps_T_pi_Emna))
Z = self.K_T * z_T_Emn
X = self.gamma_T * alpha_T_Emna
Y = 0.5 * E_T * \
np.einsum(
'...na,...na->...n',
(eps_T_Emna - eps_T_pi_Emna),
(eps_T_Emna - eps_T_pi_Emna))
# Energy dissipation evaluation
delta_eps_T_pi_Emna = np.zeros_like(eps_T_pi_Emna)
delta_alpha_T_Emna = np.zeros_like(alpha_T_Emna)
delta_z_T_Emn = np.zeros_like(z_T_Emn)
delta_omega_T_Emn = np.zeros_like(omega_T_Emn)
delta_eps_T_pi_Emna[..., 0] = plas_1 * delta_lamda * \
((sig_pi_trial[..., 0] - X[..., 0]) /
(1.0 - omega_T_Emn)) / norm_2
delta_eps_T_pi_Emna[..., 1] = plas_1 * delta_lamda * \
((sig_pi_trial[..., 1] - X[..., 1]) /
(1.0 - omega_T_Emn)) / norm_2
delta_eps_T_pi_Emna[..., 2] = plas_1 * delta_lamda * \
((sig_pi_trial[..., 2] - X[..., 2]) /
(1.0 - omega_T_Emn)) / norm_2
delta_alpha_T_Emna[..., 0] = plas_1 * delta_lamda * \
(sig_pi_trial[..., 0] - X[..., 0]) / norm_2
delta_alpha_T_Emna[..., 1] = plas_1 * delta_lamda * \
(sig_pi_trial[..., 1] - X[..., 1]) / norm_2
delta_alpha_T_Emna[..., 2] = plas_1 * delta_lamda * \
(sig_pi_trial[..., 2] - X[..., 2]) / norm_2
delta_z_T_Emn = plas_1 * delta_lamda
delta_omega_T_Emn = plas_1 * ((1 - omega_T_Emn) ** self.c_T) * \
(delta_lamda * (Y / self.S_T) ** self.r_T) * \
(self.sigma_T_0 / (self.sigma_T_0 - self.m_T * sigma_N_Emn)) ** self.p_T
plastic_dissip_T_Emn[...] = np.einsum('...na,...na->...n',
sigma_T_Emna,
delta_eps_T_pi_Emna) #- \
#np.einsum('...na,...na->...n', X, delta_alpha_T_Emna) - np.einsum('...n,...n->...n', Z, delta_z_T_Emn)
damage_dissip_T_Emn[...] = np.einsum('...n,...n->...n', Y,
delta_omega_T_Emn)
# if plastic_dissip_T_Emn.any() < -1e-15:
# print(sigma_T_Emna, delta_eps_T_pi_Emna)
return sigma_T_Emna, Z, X, Y, plastic_dissip_T_Emn
# #-------------------------------------------------------------------------
# # MICROPLANE-Kinematic constraints
# #-------------------------------------------------------------------------
# -------------------------------------------------
# get the operator of the microplane normals
_MPNN = tr.Property(depends_on='n_mp')
@tr.cached_property
def _get__MPNN(self):
MPNN_nij = np.einsum('ni,nj->nij', self._MPN, self._MPN)
return MPNN_nij
# get the third order tangential tensor (operator) for each microplane
_MPTT = tr.Property(depends_on='n_mp')
@tr.cached_property
def _get__MPTT(self):
delta = self.DELTA
MPTT_nijr = 0.5 * (
np.einsum('ni,jr -> nijr', self._MPN, delta) +
np.einsum('nj,ir -> njir', self._MPN, delta) -
2 * np.einsum('ni,nj,nr -> nijr', self._MPN, self._MPN, self._MPN))
return MPTT_nijr
def _get_e_N_Emn(self, eps_Emab):
# get the normal strain array for each microplane
return np.einsum('nij,...ij->...n', self._MPNN, eps_Emab)
def _get_e_T_Emna(self, eps_Emab):
# get the tangential strain vector array for each microplane
MPTT_ijr = self._get__MPTT()
return np.einsum('nija,...ij->...na', MPTT_ijr, eps_Emab)
# ---------------------------------------------------------------------
# Extra homogenization of damage tensor in case of two damage parameters
# Returns the 4th order damage tensor 'beta4' using (ref. [Baz99], Eq.(63))
# ---------------------------------------------------------------------
def _get_beta_Emabcd(self, eps_Emab, omega_N_Emn, z_N_Emn, alpha_N_Emn,
r_N_Emn, eps_N_p_Emn, sigma_N_Emn, omega_T_Emn,
z_T_Emn, alpha_T_Emna, eps_T_pi_Emna, sigma_T_Emna,
plastic_dissip_T_Emn, damage_dissip_T_Emn,
plastic_dissip_N_Emn, damage_dissip_N_Emn,
total_work_microplane, total_work_macro, eps_aux):
# Returns the 4th order damage tensor 'beta4' using
# (cf. [Baz99], Eq.(63))
eps_N_Emn = self._get_e_N_Emn(eps_Emab)
eps_T_Emna = self._get_e_T_Emna(eps_Emab)
sigma_N_Emn, Z, X, Y_N = self.get_normal_law(
eps_N_Emn, omega_N_Emn, z_N_Emn, alpha_N_Emn, r_N_Emn, eps_N_p_Emn,
sigma_N_Emn, omega_T_Emn, z_T_Emn, alpha_T_Emna, eps_T_pi_Emna,
sigma_T_Emna, plastic_dissip_T_Emn, damage_dissip_T_Emn,
plastic_dissip_N_Emn, damage_dissip_N_Emn, total_work_microplane,
total_work_macro, eps_aux)
# sliding tangential strains
sigma_T_Emna, Z_T, X_T, Y_T, plastic_dissip_T_Emn = self.get_tangential_law(
eps_T_Emna, eps_Emab, omega_N_Emn, z_N_Emn, alpha_N_Emn, r_N_Emn,
eps_N_p_Emn, sigma_N_Emn, omega_T_Emn, z_T_Emn, alpha_T_Emna,
eps_T_pi_Emna, sigma_T_Emna, plastic_dissip_T_Emn,
damage_dissip_T_Emn, plastic_dissip_N_Emn, damage_dissip_N_Emn,
total_work_microplane, total_work_macro, eps_aux)
delta = self.DELTA
beta_N = np.sqrt(1. - omega_N_Emn)
beta_T = np.sqrt(1. - omega_T_Emn)
beta_ijkl = np.einsum('n, ...n,ni, nj, nk, nl -> ...ijkl', self._MPW, beta_N, self._MPN, self._MPN, self._MPN,
self._MPN) + \
0.25 * (np.einsum('n, ...n,ni, nk, jl -> ...ijkl', self._MPW, beta_T, self._MPN, self._MPN, delta) +
np.einsum('n, ...n,ni, nl, jk -> ...ijkl', self._MPW, beta_T, self._MPN, self._MPN, delta) +
np.einsum('n, ...n,nj, nk, il -> ...ijkl', self._MPW, beta_T, self._MPN, self._MPN, delta) +
np.einsum('n, ...n,nj, nl, ik -> ...ijkl', self._MPW, beta_T, self._MPN, self._MPN, delta) -
4.0 * np.einsum('n, ...n, ni, nj, nk, nl -> ...ijkl', self._MPW, beta_T, self._MPN,
self._MPN, self._MPN, self._MPN))
return beta_ijkl
def _get_phi(self, eps_Emab, omega_N_Emn, z_N_Emn, alpha_N_Emn, r_N_Emn,
eps_N_p_Emn, sigma_N_Emn, omega_T_Emn, z_T_Emn, alpha_T_Emna,
eps_T_pi_Emna, plastic_dissip_T_Emn):
phi_n = np.sqrt(1.0 - omega_N_Emn) * np.sqrt(1.0 - omega_T_Emn)
phi_ab = np.einsum('...n,n,nab->...ab', phi_n, self._MPW, self._MPNN)
return phi_ab
DELTA = np.identity(n_D)
# -----------------------------------------------------------
# Integration of the (inelastic) strains for each microplane
# -----------------------------------------------------------
def _get_eps_p_Emab(self, eps_Emab, omega_N_Emn, z_N_Emn, alpha_N_Emn,
r_N_Emn, eps_N_p_Emn, sigma_N_Emn, omega_T_Emn,
z_T_Emn, alpha_T_Emna, eps_T_pi_Emna, sigma_T_Emna,
plastic_dissip_T_Emn, damage_dissip_T_Emn,
plastic_dissip_N_Emn, damage_dissip_N_Emn,
total_work_microplane, total_work_macro, eps_aux):
eps_N_Emn = self._get_e_N_Emn(eps_Emab)
eps_T_Emna = self._get_e_T_Emna(eps_Emab)
sigma_N_Emn, Z, X, Y_N = self.get_normal_law(
eps_N_Emn, omega_N_Emn, z_N_Emn, alpha_N_Emn, r_N_Emn, eps_N_p_Emn,
sigma_N_Emn, omega_T_Emn, z_T_Emn, alpha_T_Emna, eps_T_pi_Emna,
sigma_T_Emna, plastic_dissip_T_Emn, damage_dissip_T_Emn,
plastic_dissip_N_Emn, damage_dissip_N_Emn, total_work_microplane,
total_work_macro, eps_aux)
# sliding tangential strains
sigma_T_Emna, Z_T, X_T, Y_T, plastic_dissip_T_Emn = self.get_tangential_law(
eps_T_Emna, eps_Emab, omega_N_Emn, z_N_Emn, alpha_N_Emn, r_N_Emn,
eps_N_p_Emn, sigma_N_Emn, omega_T_Emn, z_T_Emn, alpha_T_Emna,
eps_T_pi_Emna, sigma_T_Emna, plastic_dissip_T_Emn,
damage_dissip_T_Emn, plastic_dissip_N_Emn, damage_dissip_N_Emn,
total_work_microplane, total_work_macro, eps_aux)
# 2-nd order plastic (inelastic) tensor
eps_p_Emab = (np.einsum('n,...n,na,nb->...ab', self._MPW, eps_N_p_Emn,
self._MPN, self._MPN) + 0.5 *
(np.einsum('n,...nf,na,fb->...ab', self._MPW,
eps_T_pi_Emna, self._MPN, self.DELTA) +
np.einsum('n,...nf,nb,fa->...ab', self._MPW,
eps_T_pi_Emna, self._MPN, self.DELTA)))
return eps_p_Emab
def get_total_work(self, eps_Emab, sig_Emab, omega_N_Emn, z_N_Emn,
alpha_N_Emn, r_N_Emn, eps_N_p_Emn, sigma_N_Emn,
omega_T_Emn, z_T_Emn, alpha_T_Emna, eps_T_pi_Emna,
sigma_T_Emna, plastic_dissip_T_Emn, damage_dissip_T_Emn,
plastic_dissip_N_Emn, damage_dissip_N_Emn,
total_work_microplane, total_work_macro, eps_aux):
delta_eps_Emab = eps_Emab - eps_aux
delta_eps_N_Emn = self._get_e_N_Emn(eps_Emab) - self._get_e_N_Emn(
eps_aux)
delta_eps_T_Emna = self._get_e_T_Emna(eps_Emab) - self._get_e_T_Emna(
eps_aux)
# work_microplane = np.einsum('...n,...n->...n', sigma_N_Emn, delta_eps_N_Emn) + np.einsum('...na,...na->...n',
# sigma_T_Emna,
# delta_eps_T_Emna)
delta_eps_vector = np.einsum('...na,...n->...na', self._MPN,
delta_eps_N_Emn) + delta_eps_T_Emna
sigma_vector = np.einsum('...na,...n->...na', self._MPN,
sigma_N_Emn) + sigma_T_Emna
work_microplane = np.einsum('...na,...na->...n', sigma_vector,
delta_eps_vector)
total_work_microplane[...] = np.einsum('...n,...n->...', self._MPW,
work_microplane)
total_work_macro[...] = np.einsum('...ij,...ij->...', sig_Emab,
delta_eps_Emab)
eps_aux[...] = eps_Emab
return total_work_microplane, total_work_macro
def get_corr_pred(self, eps_Emab, t_n1, omega_N_Emn, z_N_Emn, alpha_N_Emn,
r_N_Emn, eps_N_p_Emn, sigma_N_Emn, omega_T_Emn, z_T_Emn,
alpha_T_Emna, eps_T_pi_Emna, sigma_T_Emna,
plastic_dissip_T_Emn, damage_dissip_T_Emn,
plastic_dissip_N_Emn, damage_dissip_N_Emn,
total_work_microplane, total_work_macro, eps_aux):
"""Evaluation - get the corrector and predictor
"""
# Corrector predictor computation.
eps_N_Emn = self._get_e_N_Emn(eps_Emab)
eps_T_Emna = self._get_e_T_Emna(eps_Emab)
sigma_N_Emn, Z, X, Y_N = self.get_normal_law(
eps_N_Emn, omega_N_Emn, z_N_Emn, alpha_N_Emn, r_N_Emn, eps_N_p_Emn,
sigma_N_Emn, omega_T_Emn, z_T_Emn, alpha_T_Emna, eps_T_pi_Emna,
sigma_T_Emna, plastic_dissip_T_Emn, damage_dissip_T_Emn,
plastic_dissip_N_Emn, damage_dissip_N_Emn, total_work_microplane,
total_work_macro, eps_aux)
# sliding tangential strains
sigma_T_Emna, Z_T, X_T, Y_T, plastic_dissip_T_Emn = self.get_tangential_law(
eps_T_Emna, eps_Emab, omega_N_Emn, z_N_Emn, alpha_N_Emn, r_N_Emn,
eps_N_p_Emn, sigma_N_Emn, omega_T_Emn, z_T_Emn, alpha_T_Emna,
eps_T_pi_Emna, sigma_T_Emna, plastic_dissip_T_Emn,
damage_dissip_T_Emn, plastic_dissip_N_Emn, damage_dissip_N_Emn,
total_work_microplane, total_work_macro, eps_aux)
eps_N_Emn_effective = np.einsum('...n,...n->...n', eps_N_Emn,
(1 - omega_N_Emn))
eps_T_Emna_effective = np.einsum('...na,...n->...na', eps_T_Emna,
(1 - omega_T_Emn))
delta = np.identity(3)
eps_Emab_effective = (
np.einsum('n,...n,na,nb->...ab', self._MPW, eps_N_Emn_effective,
self._MPN, self._MPN) + 0.5 *
(np.einsum('n,...nf,na,fb->...ab', self._MPW, eps_T_Emna_effective,
self._MPN, delta) +
np.einsum('n,...nf,nb,fa->...ab', self._MPW, eps_T_Emna_effective,
self._MPN, delta)))
sigma_Emab_effective = np.einsum('...abcd,...cd->...ab', self.D_abef,
eps_Emab_effective)
sigma_N_Emn_effective = self._get_e_N_Emn(sigma_Emab_effective)
sigma_T_Emna_effective = self._get_e_T_Emna(sigma_Emab_effective)
sigma_N_Emn[...] = np.einsum('...n,...n->...n', sigma_N_Emn_effective,
(1 - omega_N_Emn))
sigma_T_Emna[...] = np.einsum('...na,...n->...na',
sigma_T_Emna_effective,
(1 - omega_T_Emn))
sig_Emab = (np.einsum('n,...n,na,nb->...ab', self._MPW, sigma_N_Emn,
self._MPN, self._MPN) + 0.5 *
(np.einsum('n,...nf,na,fb->...ab', self._MPW, sigma_T_Emna,
self._MPN, delta) +
np.einsum('n,...nf,nb,fa->...ab', self._MPW, sigma_T_Emna,
self._MPN, delta)))
# ------------------------------------------------------------------
# Damage tensor (4th order) using product- or sum-type symmetrization:
# ------------------------------------------------------------------
beta_Emabcd = self._get_beta_Emabcd(
eps_Emab, omega_N_Emn, z_N_Emn, alpha_N_Emn, r_N_Emn, eps_N_p_Emn,
sigma_N_Emn, omega_T_Emn, z_T_Emn, alpha_T_Emna, eps_T_pi_Emna,
sigma_T_Emna, plastic_dissip_T_Emn, damage_dissip_T_Emn,
plastic_dissip_N_Emn, damage_dissip_N_Emn, total_work_microplane,
total_work_macro, eps_aux)
# ------------------------------------------------------------------
# Damaged stiffness tensor calculated based on the damage tensor beta4:
# ------------------------------------------------------------------
D_Emabcd = np.einsum('...ijab, abef, ...cdef->...ijcd', beta_Emabcd,
self.D_abef, beta_Emabcd)
total_work_micro, total_work_macro = self.get_total_work(
eps_Emab, sig_Emab, omega_N_Emn, z_N_Emn, alpha_N_Emn, r_N_Emn,
eps_N_p_Emn, sigma_N_Emn, omega_T_Emn, z_T_Emn, alpha_T_Emna,
eps_T_pi_Emna, sigma_T_Emna, plastic_dissip_T_Emn,
damage_dissip_T_Emn, plastic_dissip_N_Emn, damage_dissip_N_Emn,
total_work_microplane, total_work_macro, eps_aux)
return sig_Emab, D_Emabcd
class MSX(MATS3DEval):
name = 'MSX'
double_pvw = Bool(True, MAT=True)
ipw_view = View(Item('E'), Item('nu'), Item('double_pvw'), Item('eps_max'),
Item('n_eps'))
mic = EitherType(options=[('contim', VCoNTIM), ('untim', VUNTIM),
('dntim', VDNTIM)],
on_option_change='reset_mic')
integ_scheme = EitherType(options=[('3DM28', MSIS3DM28)])
@tr.on_trait_change('E, nu')
def _set_E(self, event):
self.reset_mic()
def reset_mic(self):
self.mic_.E_N = self.E / (1.0 - 2.0 * self.nu)
self.mic_.E_T = self.E * (1.0 - 4 * self.nu) / \
((1.0 + self.nu) * (1.0 - 2 * self.nu))
tree = ['mic', 'integ_scheme']
state_var_shapes = tr.Property(depends_on='mic, integ_scheme')
@tr.cached_property
def _get_state_var_shapes(self):
sv_shapes = {
name: (self.integ_scheme_.n_mp, ) + shape
for name, shape in self.mic_.state_var_shapes.items()
}
return sv_shapes
def _get_e_a(self, eps_ab):
"""
Get the microplane projected strains
"""
# get the normal strain array for each microplane
e_N = np.einsum('nij,...ij->...n', self.integ_scheme_.MPNN, eps_ab)
# get the tangential strain vector array for each microplane
MPTT_ijr = self.integ_scheme_.MPTT
e_T_a = np.einsum('nija,...ij->...na', MPTT_ijr, eps_ab)
return np.concatenate([e_N[..., np.newaxis], e_T_a], axis=-1)
def _get_beta_abcd(self, eps_ab, omega_N, omega_T, **Eps):
"""
Returns the 4th order damage tensor 'beta4' using
(cf. [Baz99], Eq.(63))
"""
MPW = self.integ_scheme_.MPW
MPN = self.integ_scheme_.MPN
delta = np.identity(3)
beta_N = np.sqrt(1. - omega_N)
beta_T = np.sqrt(1. - omega_T)
beta_ijkl = (
np.einsum('n,...n,ni,nj,nk,nl->...ijkl', MPW, beta_N, MPN, MPN,
MPN, MPN) + 0.25 *
(np.einsum('n,...n,ni,nk,jl->...ijkl', MPW, beta_T, MPN, MPN,
delta) + np.einsum('n,...n,ni,nl,jk->...ijkl', MPW,
beta_T, MPN, MPN, delta) +
np.einsum('n,...n,nj,nk,il->...ijkl', MPW, beta_T, MPN, MPN,
delta) + np.einsum('n,...n,nj,nl,ik->...ijkl', MPW,
beta_T, MPN, MPN, delta) -
4.0 * np.einsum('n,...n,ni,nj,nk,nl->...ijkl', MPW, beta_T, MPN,
MPN, MPN, MPN)))
return beta_ijkl
def NT_to_ab(self, v_N, v_T_a):
"""
Integration of the (inelastic) strains for each microplane
"""
MPW = self.integ_scheme_.MPW
MPN = self.integ_scheme_.MPN
delta = np.identity(3)
# 2-nd order plastic (inelastic) tensor
tns_ab = (np.einsum('n,...n,na,nb->...ab', MPW, v_N, MPN, MPN) + 0.5 *
(np.einsum('n,...nf,na,fb->...ab', MPW, v_T_a, MPN, delta) +
np.einsum('n,...nf,nb,fa->...ab', MPW, v_T_a, MPN, delta)))
return tns_ab
def get_corr_pred(self, eps_ab, t_n1, **Eps):
"""
Corrector predictor computation.
"""
# ------------------------------------------------------------------
# Damage tensor (4th order) using product- or sum-type symmetrization:
# ------------------------------------------------------------------
eps_a = self._get_e_a(eps_ab)
sig_a, D_ab = self.mic_.get_corr_pred(eps_a, t_n1, **Eps)
beta_abcd = self._get_beta_abcd(eps_ab, **Eps)
# ------------------------------------------------------------------
# Damaged stiffness tensor calculated based on the damage tensor beta4:
# ------------------------------------------------------------------
D_abcd = np.einsum('...ijab, abef, ...cdef->...ijcd', beta_abcd,
self.D_abef, beta_abcd)
if self.double_pvw:
# ----------------------------------------------------------------------
# Return stresses (corrector) and damaged secant stiffness matrix (predictor)
# ----------------------------------------------------------------------
eps_p_a = self.mic_.get_eps_NT_p(**Eps)
if eps_p_a:
eps_N_p, eps_T_p_a = eps_p_a
eps_p_ab = self.NT_to_ab(eps_N_p, eps_T_p_a)
eps_e_ab = eps_ab - eps_p_ab
else:
eps_e_ab = eps_ab
sig_ab = np.einsum('...abcd,...cd->...ab', D_abcd, eps_e_ab)
else:
sig_N, sig_T_a = sig_a[..., 0], sig_a[..., 1:]
sig_ab = self.NT_to_ab(sig_N, sig_T_a)
return sig_ab, D_abcd
def update_plot(self, axes):
ax_sig, ax_d_sig = axes
eps_max = self.eps_max
n_eps = self.n_eps
eps11_range = np.linspace(1e-9, eps_max, n_eps)
eps_range = np.zeros((n_eps, 3, 3))
eps_range[:, 0, 0] = eps11_range
state_vars = {
var: np.zeros((1, ) + shape)
for var, shape in self.state_var_shapes.items()
}
sig11_range, d_sig1111_range = [], []
for eps_ab in eps_range:
try:
sig_ab, D_range = self.get_corr_pred(eps_ab[np.newaxis, ...],
1, **state_vars)
except ReturnMappingError:
break
sig11_range.append(sig_ab[0, 0, 0])
d_sig1111_range.append(D_range[0, 0, 0, 0, 0])
sig11_range = np.array(sig11_range, dtype=np.float_)
eps11_range = eps11_range[:len(sig11_range)]
ax_sig.plot(eps11_range, sig11_range, color='blue')
d_sig1111_range = np.array(d_sig1111_range, dtype=np.float_)
ax_d_sig.plot(eps11_range,
d_sig1111_range,
linestyle='dashed',
color='gray')
ax_sig.set_xlabel(r'$\varepsilon_{11}$ [-]')
ax_sig.set_ylabel(r'$\sigma_{11}$ [MPa]')
ax_d_sig.set_ylabel(
r'$\mathrm{d} \sigma_{11} / \mathrm{d} \varepsilon_{11}$ [MPa]')
ax_d_sig.plot(eps11_range[:-1], (sig11_range[:-1] - sig11_range[1:]) /
(eps11_range[:-1] - eps11_range[1:]),
color='orange',
linestyle='dashed')
class MS1(MATS3DEval, InteractiveModel):
gamma_T = tr.Float(100000.,
label="gamma_T",
desc=" Tangential Kinematic hardening modulus",
enter_set=True,
auto_set=False)
K_T = tr.Float(10000.,
label="K_T",
desc="Tangential Isotropic harening",
enter_set=True,
auto_set=False)
S_T = tr.Float(0.005,
label="S_T",
desc="Damage strength",
enter_set=True,
auto_set=False)
r_T = tr.Float(9.,
label="r",
desc="Damage cumulation parameter",
enter_set=True,
auto_set=False)
p_T = tr.Float(12.,
label="p_T",
desc="Damage cumulation parameter",
enter_set=True,
auto_set=False)
c_T = tr.Float(4.6,
label="c_T",
desc="Damage cumulation parameter",
enter_set=True,
auto_set=False)
sigma_T_0 = tr.Float(1.7,
label="sigma_T_0",
desc="Reversibility limit",
enter_set=True,
auto_set=False)
m_T = tr.Float(0.003,
label="m_T",
desc="Lateral pressure coefficient",
enter_set=True,
auto_set=False)
# -------------------------------------------
# Normal_Tension constitutive law parameters (without cumulative normal strain)
# -------------------------------------------
Ad = tr.Float(100.0,
label="A_d",
desc="brittleness coefficient",
enter_set=True,
auto_set=False)
eps_0 = tr.Float(0.00008,
label="eps_N_0",
desc="threshold strain",
enter_set=True,
auto_set=False)
# -----------------------------------------------
# Normal_Compression constitutive law parameters
# -----------------------------------------------
K_N = tr.Float(10000.,
label="K_N",
desc=" Normal isotropic harening",
enter_set=True,
auto_set=False)
gamma_N = tr.Float(5000.,
label="gamma_N",
desc="Normal kinematic hardening",
enter_set=True,
auto_set=False)
sigma_N_0 = tr.Float(30.,
label="sigma_N_0",
desc="Yielding stress",
enter_set=True,
auto_set=False)
# -------------------------------------------------------------------------
# Cached elasticity tensors
# -------------------------------------------------------------------------
E = tr.Float(35e+3,
label="E",
desc="Young's Modulus",
auto_set=False,
input=True)
nu = tr.Float(0.2,
label='nu',
desc="Poison ratio",
auto_set=False,
input=True)
ipw_view = View(
Item('gamma_T', latex=r'\gamma_\mathrm{T}', minmax=(10, 100000)),
Item('K_T', latex=r'K_\mathrm{T}', minmax=(10, 10000)),
Item('S_T', latex=r'S_\mathrm{T}', minmax=(0.001, 0.01)),
Item('r_T', latex=r'r_\mathrm{T}', minmax=(1, 3)),
Item('p_T', latex=r'e_\mathrm{T}', minmax=(1, 40)),
Item('c_T', latex=r'c_\mathrm{T}', minmax=(1, 10)),
Item('sigma_T_0', latex=r'\bar{sigma}^\pi_{T}', minmax=(1, 10)),
Item('m_T', latex=r'm_\mathrm{T}', minmax=(0.001, 3)),
)
n_D = 3
state_var_shapes = tr.Property
@tr.cached_property
def _get_state_var_shapes(self):
return {
name: (self.n_mp, ) + shape
for name, shape in self.mic_state_var_shapes.items()
}
mic_state_var_shapes = dict(
omega_N_Emn=(), # damage N
z_N_Emn=(),
alpha_N_Emn=(),
r_N_Emn=(),
eps_N_p_Emn=(),
sigma_N_Emn=(),
omega_T_Emn=(),
z_T_Emn=(),
alpha_T_Emna=(n_D, ),
eps_T_pi_Emna=(n_D, ),
)
'''
State variables
1) damage N,
2) iso N,
3) kin N,
4) consolidation N,
5) eps p N,
6) sigma N,
7) iso F N,
8) kin F N,
9) energy release N,
10) damage T,
11) iso T,
12-13) kin T,
14-15) eps p T,
16-17) sigma T, 18) iso F T, 19-20) kin F T, 21) energy release T
'''
# --------------------------------------------------------------
# microplane constitutive law (normal behavior CP + TD)
# (without cumulative normal strain for fatigue under tension)
# --------------------------------------------------------------
def get_normal_law(self, eps_N_Emn, omega_N_Emn, z_N_Emn, alpha_N_Emn,
r_N_Emn, eps_N_p_Emn):
E_N = self.E / (1.0 - 2.0 * self.nu)
# When deciding if a microplane is in tensile or compression,
# we define a strain boundary such that
# sigmaN <= 0 if eps_N < 0, avoiding entering in the quadrant
# of compressive strains and traction
sigma_trial = E_N * (eps_N_Emn - eps_N_p_Emn)
# looking for microplanes violating strain boundary
pos1 = [(eps_N_Emn < -1e-6) & (sigma_trial > 1e-6)]
sigma_trial[pos1[0]] = 0
pos = eps_N_Emn > 1e-6 # microplanes under traction
pos2 = eps_N_Emn < -1e-6 # microplanes under compression
H = 1.0 * pos
H2 = 1.0 * pos2
# thermo forces
sigma_N_Emn_tilde = E_N * (eps_N_Emn - eps_N_p_Emn)
sigma_N_Emn_tilde[pos1[0]] = 0 # imposing strain boundary
Z = self.K_N * z_N_Emn
X = self.gamma_N * alpha_N_Emn * H2
h = (self.sigma_N_0 + Z) * H2
f_trial = (abs(sigma_N_Emn_tilde - X) - h) * H2
# threshold plasticity
thres_1 = f_trial > 1e-6
delta_lamda = f_trial / \
(E_N / (1 - omega_N_Emn) + abs(self.K_N) + self.gamma_N) * thres_1
eps_N_p_Emn += delta_lamda * \
np.sign(sigma_N_Emn_tilde - X)
z_N_Emn += delta_lamda
alpha_N_Emn += delta_lamda * \
np.sign(sigma_N_Emn_tilde - X)
def R_N(r_N_Emn):
return (1.0 / self.Ad) * (-r_N_Emn / (1.0 + r_N_Emn))
Y_N = 0.5 * H * E_N * (eps_N_Emn - eps_N_p_Emn)**2.0
Y_0 = 0.5 * E_N * self.eps_0**2.0
f = (Y_N - (Y_0 + R_N(r_N_Emn)))
# threshold damage
thres_2 = f > 1e-6
def f_w(Y):
return 1.0 - 1.0 / (1.0 + self.Ad * (Y - Y_0))
omega_N_Emn[f > 1e-6] = f_w(Y_N)[f > 1e-6]
omega_N_Emn[...] = np.clip(omega_N_Emn, 0, 0.9999)
r_N_Emn[f > 1e-6] = -omega_N_Emn[f > 1e-6]
sigma_N_Emn = (1.0 - H * omega_N_Emn) * E_N * (eps_N_Emn - eps_N_p_Emn)
pos1 = [(eps_N_Emn < -1e-6) & (sigma_trial > 1e-6)
] # looking for microplanes violating strain boundary
sigma_N_Emn[pos1[0]] = 0
Z = self.K_N * z_N_Emn
X = self.gamma_N * alpha_N_Emn * H2
return sigma_N_Emn, Z, X, Y_N
# -------------------------------------------------------------------------
# microplane constitutive law (Tangential CSD)-(Pressure sensitive cumulative damage)
# -------------------------------------------------------------------------
def get_tangential_law(self, eps_T_Emna, omega_T_Emn, z_T_Emn,
alpha_T_Emna, eps_T_pi_Emna, sigma_N_Emn):
E_T = self.E * (1.0 - 4 * self.nu) / \
((1.0 + self.nu) * (1.0 - 2 * self.nu))
# thermo forces
sig_pi_trial = E_T * (eps_T_Emna - eps_T_pi_Emna)
Z = self.K_T * z_T_Emn
X = self.gamma_T * alpha_T_Emna
norm_1 = np.sqrt(
np.einsum('...na,...na->...n', (sig_pi_trial - X),
(sig_pi_trial - X)))
Y = 0.5 * E_T * \
np.einsum(
'...na,...na->...n',
(eps_T_Emna - eps_T_pi_Emna),
(eps_T_Emna - eps_T_pi_Emna))
# threshold
f = norm_1 - self.sigma_T_0 - Z + self.m_T * sigma_N_Emn
plas_1 = f > 1e-6
elas_1 = f < 1e-6
delta_lamda = f / \
(E_T / (1.0 - omega_T_Emn) + self.gamma_T + self.K_T) * plas_1
norm_2 = 1.0 * elas_1 + np.sqrt(
np.einsum('...na,...na->...n', (sig_pi_trial - X),
(sig_pi_trial - X))) * plas_1
eps_T_pi_Emna[..., 0] += plas_1 * delta_lamda * \
((sig_pi_trial[..., 0] - X[..., 0]) /
(1.0 - omega_T_Emn)) / norm_2
eps_T_pi_Emna[..., 1] += plas_1 * delta_lamda * \
((sig_pi_trial[..., 1] - X[..., 1]) /
(1.0 - omega_T_Emn)) / norm_2
eps_T_pi_Emna[..., 2] += plas_1 * delta_lamda * \
((sig_pi_trial[..., 2] - X[..., 2]) /
(1.0 - omega_T_Emn)) / norm_2
omega_T_Emn += ((1 - omega_T_Emn) ** self.c_T) * \
(delta_lamda * (Y / self.S_T) ** self.r_T) * \
(self.sigma_T_0 / (self.sigma_T_0 + self.m_T * sigma_N_Emn)) ** self.p_T
omega_T_Emn[...] = np.clip(omega_T_Emn, 0, 0.9999)
alpha_T_Emna[..., 0] += plas_1 * delta_lamda * \
(sig_pi_trial[..., 0] - X[..., 0]) / norm_2
alpha_T_Emna[..., 1] += plas_1 * delta_lamda * \
(sig_pi_trial[..., 1] - X[..., 1]) / norm_2
alpha_T_Emna[..., 2] += plas_1 * delta_lamda * \
(sig_pi_trial[..., 2] - X[..., 2]) / norm_2
z_T_Emn += delta_lamda
sigma_T_Emna = np.einsum('...n,...na->...na', (1 - omega_T_Emn),
E_T * (eps_T_Emna - eps_T_pi_Emna))
Z = self.K_T * z_T_Emn
X = self.gamma_T * alpha_T_Emna
Y = 0.5 * E_T * \
np.einsum(
'...na,...na->...n',
(eps_T_Emna - eps_T_pi_Emna),
(eps_T_Emna - eps_T_pi_Emna))
return sigma_T_Emna, Z, X, Y
# #-------------------------------------------------------------------------
# # MICROPLANE-Kinematic constraints
# #-------------------------------------------------------------------------
# -------------------------------------------------
# get the operator of the microplane normals
_MPNN = tr.Property(depends_on='n_mp')
@tr.cached_property
def _get__MPNN(self):
MPNN_nij = np.einsum('ni,nj->nij', self._MPN, self._MPN)
return MPNN_nij
# get the third order tangential tensor (operator) for each microplane
_MPTT = tr.Property(depends_on='n_mp')
@tr.cached_property
def _get__MPTT(self):
delta = self.DELTA
MPTT_nijr = 0.5 * (
np.einsum('ni,jr -> nijr', self._MPN, delta) +
np.einsum('nj,ir -> njir', self._MPN, delta) -
2 * np.einsum('ni,nj,nr -> nijr', self._MPN, self._MPN, self._MPN))
return MPTT_nijr
def _get_e_N_Emn_2(self, eps_Emab):
# get the normal strain array for each microplane
return np.einsum('nij,...ij->...n', self._MPNN, eps_Emab)
def _get_e_T_Emnar_2(self, eps_Emab):
# get the tangential strain vector array for each microplane
MPTT_ijr = self._get__MPTT()
return np.einsum('nija,...ij->...na', MPTT_ijr, eps_Emab)
# ---------------------------------------------------------------------
# Extra homogenization of damage tensor in case of two damage parameters
# Returns the 4th order damage tensor 'beta4' using (ref. [Baz99], Eq.(63))
# ---------------------------------------------------------------------
def _get_beta_Emabcd_2(self, eps_Emab, omega_N_Emn, z_N_Emn, alpha_N_Emn,
r_N_Emn, eps_N_p_Emn, sigma_N_Emn, omega_T_Emn,
z_T_Emn, alpha_T_Emna, eps_T_pi_Emna):
# Returns the 4th order damage tensor 'beta4' using
# (cf. [Baz99], Eq.(63))
eps_N_Emn = self._get_e_N_Emn_2(eps_Emab)
eps_T_Emna = self._get_e_T_Emnar_2(eps_Emab)
sigma_N_Emn, Z_n, X_n, Y_n = self.get_normal_law(
eps_N_Emn, omega_N_Emn, z_N_Emn, alpha_N_Emn, r_N_Emn, eps_N_p_Emn)
sigma_T_Emna, Z_T, X_T, Y_T = self.get_tangential_law(
eps_T_Emna, omega_T_Emn, z_T_Emn, alpha_T_Emna, eps_T_pi_Emna,
sigma_N_Emn)
delta = self.DELTA
beta_N = np.sqrt(1. - omega_N_Emn)
beta_T = np.sqrt(1. - omega_T_Emn)
beta_ijkl = np.einsum('n, ...n,ni, nj, nk, nl -> ...ijkl', self._MPW, beta_N, self._MPN, self._MPN, self._MPN,
self._MPN) + \
0.25 * (np.einsum('n, ...n,ni, nk, jl -> ...ijkl', self._MPW, beta_T, self._MPN, self._MPN, delta) +
np.einsum('n, ...n,ni, nl, jk -> ...ijkl', self._MPW, beta_T, self._MPN, self._MPN, delta) +
np.einsum('n, ...n,nj, nk, il -> ...ijkl', self._MPW, beta_T, self._MPN, self._MPN, delta) +
np.einsum('n, ...n,nj, nl, ik -> ...ijkl', self._MPW, beta_T, self._MPN, self._MPN, delta) -
4.0 * np.einsum('n, ...n, ni, nj, nk, nl -> ...ijkl', self._MPW, beta_T, self._MPN,
self._MPN, self._MPN, self._MPN))
return beta_ijkl
DELTA = np.identity(n_D)
# -----------------------------------------------------------
# Integration of the (inelastic) strains for each microplane
# -----------------------------------------------------------
def _get_eps_p_Emab(self, eps_Emab, omega_N_Emn, z_N_Emn, alpha_N_Emn,
r_N_Emn, eps_N_p_Emn, omega_T_Emn, z_T_Emn,
alpha_T_Emna, eps_T_pi_Emna, sigma_N_Emn):
eps_N_Emn = self._get_e_N_Emn_2(eps_Emab)
eps_T_Emna = self._get_e_T_Emnar_2(eps_Emab)
# plastic normal strains
sigma_N_Emn, Z_n, X_n, Y_n = self.get_normal_law(
eps_N_Emn, omega_N_Emn, z_N_Emn, alpha_N_Emn, r_N_Emn, eps_N_p_Emn)
# sliding tangential strains
sigma_T_Emna, Z_T, X_T, Y_T = self.get_tangential_law(
eps_T_Emna, omega_T_Emn, z_T_Emn, alpha_T_Emna, eps_T_pi_Emna,
sigma_N_Emn)
# 2-nd order plastic (inelastic) tensor
eps_p_Emab = (np.einsum('n,...n,na,nb->...ab', self._MPW, eps_N_p_Emn,
self._MPN, self._MPN) + 0.5 *
(np.einsum('n,...nf,na,fb->...ab', self._MPW,
eps_T_pi_Emna, self._MPN, self.DELTA) +
np.einsum('n,...nf,nb,fa->...ab', self._MPW,
eps_T_pi_Emna, self._MPN, self.DELTA)))
return eps_p_Emab
def get_corr_pred(self, eps_Emab, t_n1, **Eps_k):
"""Evaluation - get the corrector and predictor
"""
# Corrector predictor computation.
# ------------------------------------------------------------------
# Damage tensor (4th order) using product- or sum-type symmetrization:
# ------------------------------------------------------------------
beta_Emabcd = self._get_beta_Emabcd_2(eps_Emab, **Eps_k)
# ------------------------------------------------------------------
# Damaged stiffness tensor calculated based on the damage tensor beta4:
# ------------------------------------------------------------------
D_Emabcd = np.einsum('...ijab, abef, ...cdef->...ijcd', beta_Emabcd,
self.D_abef, beta_Emabcd)
# ----------------------------------------------------------------------
# Return stresses (corrector) and damaged secant stiffness matrix (predictor)
# ----------------------------------------------------------------------
# plastic strain tensor
eps_p_Emab = self._get_eps_p_Emab(eps_Emab, **Eps_k)
# elastic strain tensor
eps_e_Emab = eps_Emab - eps_p_Emab
# calculation of the stress tensor
sig_Emab = np.einsum('...abcd,...cd->...ab', D_Emabcd, eps_e_Emab)
return sig_Emab, D_Emabcd
class MKappa(InteractiveModel, InjectSymbExpr):
"""Class returning the moment curvature relationship."""
name = 'Moment-Curvature'
symb_class = MKappaSymbolic
cs_design = Instance(CrossSectionDesign, ())
tree = ['cs_design']
# Use PrototypedFrom only when the prototyped object is a class
# (The prototyped attribute behaves similarly
# to a delegated attribute, until it is explicitly
# changed; from that point forward, the prototyped attribute
# changes independently from its prototype.)
# (it's kind of like tr.DelegatesTo('cs_design.cross_section_shape'))
cross_section_shape = tr.DelegatesTo('cs_design')
cross_section_shape_ = tr.DelegatesTo('cs_design')
cross_section_layout = tr.DelegatesTo('cs_design')
matrix_ = tr.DelegatesTo('cs_design')
# Geometry
H = tr.DelegatesTo('cross_section_shape_')
DEPSTR = 'state_changed'
n_m = Int(
100,
DSC=True,
desc=
'Number of discretization points along the height of the cross-section'
)
# @todo: fix the dependency - `H` should be replaced by _GEO
z_m = tr.Property(depends_on=DEPSTR)
@tr.cached_property
def _get_z_m(self):
return np.linspace(0, self.H, self.n_m)
low_kappa = Float(0.0, BC=True, GEO=True)
high_kappa = Float(0.00002, BC=True, GEO=True)
n_kappa = Int(100, BC=True)
step_kappa = tr.Property(Float, depends_on='low_kappa, high_kappa')
@tr.cached_property
def _get_step_kappa(self):
return float((self.high_kappa - self.low_kappa) / self.n_kappa)
kappa_slider = Float(0.0000001)
ipw_view = View(
Item(
'low_kappa',
latex=r'\text{Low}~\kappa'), #, editor=FloatEditor(step=0.00001)),
Item('high_kappa', latex=r'\text{High}~\kappa'
), # , editor=FloatEditor(step=0.00001)),
Item('n_kappa', latex='n_{\kappa}'),
Item('plot_strain'),
Item('solve_for_eps_bot_pointwise'),
Item('n_m', latex='n_m'),
Item('kappa_slider', latex='\kappa', readonly=True),
# editor=FloatRangeEditor(low_name='low_kappa',
# high_name='high_kappa',
# n_steps_name='n_kappa')
# ),
time_editor=HistoryEditor(
var='kappa_slider',
min_var='low_kappa',
max_var='high_kappa',
),
)
idx = tr.Property(depends_on='kappa_slider')
apply_material_safety_factors = tr.Bool(False)
@tr.cached_property
def _get_idx(self):
ks = self.kappa_slider
idx = np.argmax(ks <= self.kappa_t)
return idx
kappa_t = tr.Property(tr.Array(np.float_), depends_on=DEPSTR)
'''Curvature values for which the bending moment must be found
'''
@tr.cached_property
def _get_kappa_t(self):
return np.linspace(self.low_kappa, self.high_kappa, self.n_kappa)
z_j = tr.Property
def _get_z_j(self):
return self.cross_section_layout.z_j
A_j = tr.Property
def _get_A_j(self):
return self.cross_section_layout.A_j
# Normal force in steel (tension and compression)
def get_N_s_tj(self, kappa_t, eps_bot_t):
# get the strain at the height of the reinforcement
eps_z_tj = self.symb.get_eps_z(kappa_t[:, np.newaxis],
eps_bot_t[:, np.newaxis],
self.z_j[np.newaxis, :])
# Get the crack bridging force in each reinforcement layer
# given the corresponding crack-bridge law.
N_s_tj = self.cross_section_layout.get_N_tj(eps_z_tj)
return N_s_tj
# TODO - [RC] avoid repeated evaluations of stress profile in
# N and M calculations for the same inputs as it
# is the case now.
def get_sig_c_z(self, kappa_t, eps_bot_t, z_tm):
"""Get the stress profile over the height"""
eps_z = self.symb.get_eps_z(kappa_t[:, np.newaxis],
eps_bot_t[:, np.newaxis], z_tm)
sig_c_z = self.matrix_.get_sig(eps_z)
return sig_c_z
# Normal force in concrete (tension and compression)
def get_N_c_t(self, kappa_t, eps_bot_t):
z_tm = self.z_m[np.newaxis, :]
b_z_m = self.cross_section_shape_.get_b(z_tm)
N_z_tm2 = b_z_m * self.get_sig_c_z(kappa_t, eps_bot_t, z_tm)
return np.trapz(N_z_tm2, x=z_tm, axis=-1)
def get_N_t(self, kappa_t, eps_bot_t):
N_s_t = np.sum(self.get_N_s_tj(kappa_t, eps_bot_t), axis=-1)
N_c_t = self.get_N_c_t(kappa_t, eps_bot_t)
return N_c_t + N_s_t
# SOLVER: Get eps_bot to render zero force
# num_of_trials = tr.Int(30)
eps_bot_t = tr.Property(depends_on=DEPSTR)
r'''Resolve the tensile strain to get zero normal force for the prescribed curvature'''
# @tr.cached_property
# def _get_eps_bot_t(self):
# initial_step = (self.high_kappa - self.low_kappa) / self.num_of_trials
# for i in range(self.num_of_trials):
# print('Solution started...')
# res = root(lambda eps_bot_t: self.get_N_t(self.kappa_t, eps_bot_t),
# 0.0000001 + np.zeros_like(self.kappa_t), tol=1e-6)
# if res.success:
# print('success high_kappa: ', self.high_kappa)
# if i == 0:
# print('Note: high_kappa success from 1st try! selecting a higher value for high_kappa may produce '
# 'a more reliable result!')
# return res.x
# else:
# print('failed high_kappa: ', self.high_kappa)
# self.high_kappa -= initial_step
# self.kappa_t = np.linspace(self.low_kappa, self.high_kappa, self.n_kappa)
#
# print('No solution', res.message)
# return res.x
# @tr.cached_property
# def _get_eps_bot_t(self):
# res = root(lambda eps_bot_t: self.get_N_t(self.kappa_t, eps_bot_t),
# 0.0000001 + np.zeros_like(self.kappa_t), tol=1e-6)
# if not res.success:
# raise SolutionNotFoundError('No solution', res.message)
# return res.x
solve_for_eps_bot_pointwise = Bool(True, BC=True, GEO=True)
@tr.cached_property
def _get_eps_bot_t(self):
if self.solve_for_eps_bot_pointwise:
""" INFO: Instability in eps_bot solutions was caused by unsuitable init_guess value causing a convergence
to non-desired solutions. Solving the whole kappa_t array improved the init_guess after each
calculated value, however, instability still there. The best results were obtained by taking the last
solution as the init_guess for the next solution like in the following.. """
# One by one solution for kappa values
eps_bot_sol_for_pos_kappa = self._get_eps_bot_piecewise_sol(
kappa_pos=True)
eps_bot_sol_for_neg_kappa = self._get_eps_bot_piecewise_sol(
kappa_pos=False)
res = np.concatenate(
[eps_bot_sol_for_neg_kappa, eps_bot_sol_for_pos_kappa])
return res
else:
# Array solution for the whole kappa_t
res = root(lambda eps_bot_t: self.get_N_t(self.kappa_t, eps_bot_t),
0.0000001 + np.zeros_like(self.kappa_t),
tol=1e-6)
if not res.success:
print('No solution', res.message)
return res.x
def _get_eps_bot_piecewise_sol(self, kappa_pos=True):
if kappa_pos:
kappas = self.kappa_t[np.where(self.kappa_t >= 0)]
else:
kappas = self.kappa_t[np.where(self.kappa_t < 0)]
res = []
if kappa_pos:
init_guess = 0.00001
kappa_loop_list = kappas
else:
init_guess = -0.00001
kappa_loop_list = reversed(kappas)
for kappa in kappa_loop_list:
sol = root(
lambda eps_bot: self.get_N_t(np.array([kappa]), eps_bot),
np.array([init_guess]),
tol=1e-6).x[0]
# This condition is to avoid having init_guess~0 which causes non-convergence
if abs(sol) > 1e-5:
init_guess = sol
res.append(sol)
if kappa_pos:
return res
else:
return list(reversed(res))
# POSTPROCESSING
kappa_cr = tr.Property(depends_on=DEPSTR)
'''Curvature at which a critical strain is attained at the eps_bot'''
@tr.cached_property
def _get_kappa_cr(self):
res = root(lambda kappa: self.get_N_t(kappa, self.eps_cr),
0.0000001 + np.zeros_like(self.eps_cr),
tol=1e-10)
if not res.success:
print('No kappa_cr solution (for plot_norm() function)',
res.message)
return res.x
M_s_t = tr.Property(depends_on=DEPSTR)
'''Bending moment (steel)
'''
@tr.cached_property
def _get_M_s_t(self):
if len(self.z_j) == 0:
return np.zeros_like(self.kappa_t)
eps_z_tj = self.symb.get_eps_z(self.kappa_t[:, np.newaxis],
self.eps_bot_t[:, np.newaxis],
self.z_j[np.newaxis, :])
# Get the crack bridging force in each reinforcement layer
# given the corresponding crack-bridge law.
N_tj = self.cross_section_layout.get_N_tj(eps_z_tj)
return -np.einsum('tj,j->t', N_tj, self.z_j)
M_c_t = tr.Property(depends_on=DEPSTR)
'''Bending moment (concrete)
'''
@tr.cached_property
def _get_M_c_t(self):
z_tm = self.z_m[np.newaxis, :]
b_z_m = self.cross_section_shape_.get_b(z_tm)
N_z_tm2 = b_z_m * self.get_sig_c_z(self.kappa_t, self.eps_bot_t, z_tm)
return -np.trapz(N_z_tm2 * z_tm, x=z_tm, axis=-1)
M_t = tr.Property(depends_on=DEPSTR)
'''Bending moment
'''
@tr.cached_property
def _get_M_t(self):
# print('M - k recalculated')
eta_factor = 1.
return eta_factor * (self.M_c_t + self.M_s_t)
# @tr.cached_property
# def _get_M_t(self):
# initial_step = (self.high_kappa - self.low_kappa) / self.num_of_trials
# for i in range(self.num_of_trials):
# try:
# M_t = self.M_c_t + self.M_s_t
# except SolutionNotFoundError:
# print('failed high_kappa: ', self.high_kappa)
# self.high_kappa -= initial_step
# else:
# # This will run when no exception has been received
# print('success high_kappa: ', self.high_kappa)
# if i == 0:
# print('Note: high_kappa success from 1st try! selecting a higher value for high_kappa may produce '
# 'a more reliable result!')
# return M_t
# print('No solution has been found!')
# return M_t
N_s_tj = tr.Property(depends_on=DEPSTR)
'''Normal forces (steel)
'''
@tr.cached_property
def _get_N_s_tj(self):
return self.get_N_s_tj(self.kappa_t, self.eps_bot_t)
eps_tm = tr.Property(depends_on=DEPSTR)
'''strain profiles
'''
@tr.cached_property
def _get_eps_tm(self):
return self.symb.get_eps_z(self.kappa_t[:, np.newaxis],
self.eps_bot_t[:, np.newaxis],
self.z_m[np.newaxis, :])
sig_tm = tr.Property(depends_on=DEPSTR)
'''strain profiles
'''
@tr.cached_property
def _get_sig_tm(self):
return self.get_sig_c_z(self.kappa_t, self.eps_bot_t,
self.z_m[np.newaxis, :])
M_norm = tr.Property(depends_on=DEPSTR)
'''
'''
@tr.cached_property
def _get_M_norm(self):
# Section modulus @TODO optimize W for var b
W = (self.b * self.H**2) / 6
sig_cr = self.E_ct * self.eps_cr
return W * sig_cr
kappa_norm = tr.Property()
def _get_kappa_norm(self):
return self.kappa_cr
inv_M_kappa = tr.Property(depends_on=DEPSTR)
'''Return the inverted data points
'''
@tr.cached_property
def _get_inv_M_kappa(self):
try:
"""cut off the descending tails"""
M_t = self.M_t
I_max = np.argmax(M_t)
I_min = np.argmin(M_t)
M_I = np.copy(M_t[I_min:I_max + 1])
kappa_I = np.copy(self.kappa_t[I_min:I_max + 1])
# find the index corresponding to zero kappa
idx = np.argmax(0 <= kappa_I)
# and modify the values such that the
# Values of moment are non-descending
M_plus = M_I[idx:]
M_diff = M_plus[:, np.newaxis] - M_plus[np.newaxis, :]
n_ij = len(M_plus)
ij = np.mgrid[0:n_ij:1, 0:n_ij:1]
M_diff[np.where(ij[1] >= ij[0])] = 0
i_x = np.argmin(M_diff, axis=1)
M_I[idx:] = M_plus[i_x]
return M_I, kappa_I
except ValueError:
print(
'M inverse has not succeeded, the M-Kappa solution may have failed due to '
'a wrong kappa range or not suitable material law!')
return np.array([0]), np.array([0])
def get_kappa_M(self, M):
M_I, kappa_I = self.inv_M_kappa
return np.interp(M, M_I, kappa_I)
def plot_norm(self, ax1, ax2):
idx = self.idx
ax1.plot(self.kappa_t / self.kappa_norm, self.M_t / self.M_norm)
ax1.plot(self.kappa_t[idx] / self.kappa_norm,
self.M_t[idx] / self.M_norm,
marker='o')
ax2.barh(self.z_j,
self.N_s_tj[idx, :],
height=2,
color='red',
align='center')
# ax2.fill_between(eps_z_arr[idx,:], z_arr, 0, alpha=0.1);
ax3 = ax2.twiny()
# ax3.plot(self.eps_tm[idx, :], self.z_m, color='k', linewidth=0.8)
ax3.plot(self.sig_tm[idx, :], self.z_m)
ax3.axvline(0, linewidth=0.8, color='k')
ax3.fill_betweenx(self.z_m, self.sig_tm[idx, :], 0, alpha=0.1)
mpl_align_xaxis(ax2, ax3)
M_scale = Float(1e+6)
plot_strain = Bool(False)
def plot(self, ax1, ax2, ax3):
self.plot_mk_and_stress_profile(ax1, ax2)
if self.plot_strain:
self.plot_strain_profile(ax3)
else:
self.plot_mk_inv(ax3)
@staticmethod
def subplots(fig):
ax1, ax2, ax3 = fig.subplots(1, 3)
return ax1, ax2, ax3
def update_plot(self, axes):
self.plot(*axes)
def plot_mk_inv(self, ax3):
try:
M, kappa = self.inv_M_kappa
ax3.plot(M / self.M_scale, kappa)
except ValueError:
print(
'M inverse has not succeeded, the M-Kappa solution may have failed due to a wrong kappa range!'
)
ax3.set_xlabel('Moment [kNm]')
ax3.set_ylabel('Curvature[mm$^{-1}$]')
def plot_mk_and_stress_profile(self, ax1, ax2):
self.plot_mk(ax1)
idx = self.idx
ax1.plot(self.kappa_t[idx],
self.M_t[idx] / self.M_scale,
color='orange',
marker='o')
if len(self.z_j):
ax2.barh(self.z_j,
self.N_s_tj[idx, :] / self.A_j,
height=4,
color='red',
align='center')
ax2.set_ylabel('z [mm]')
ax2.set_xlabel('$\sigma_r$ [MPa]')
ax22 = ax2.twiny()
ax22.set_xlabel('$\sigma_c$ [MPa]')
ax22.plot(self.sig_tm[idx, :], self.z_m)
ax22.axvline(0, linewidth=0.8, color='k')
ax22.fill_betweenx(self.z_m, self.sig_tm[idx, :], 0, alpha=0.1)
mpl_align_xaxis(ax2, ax22)
def plot_mk(self, ax1):
ax1.plot(self.kappa_t, self.M_t / self.M_scale, label='M-K')
ax1.set_ylabel('Moment [kNm]')
ax1.set_xlabel('Curvature [mm$^{-1}$]')
ax1.legend()
def plot_strain_profile(self, ax):
ax.set_ylabel('z [mm]')
ax.set_xlabel(r'$\varepsilon$ [-]')
ax.plot(self.eps_tm[self.idx, :], self.z_m)
ax.axvline(0, linewidth=0.8, color='k')
ax.fill_betweenx(self.z_m, self.eps_tm[self.idx, :], 0, alpha=0.1)
def get_mk(self):
return self.M_t / self.M_scale, self.kappa_t
class VUNTIM(MATS3DEval):
"""
Vectorized uncoupled normal tngential interface model
"""
# -------------------------------------------------------------------------
# Elasticity
# -------------------------------------------------------------------------
E_N = Float(10000, MAT=True)
E_T = Float(1000, MAT=True)
gamma_T = Float(100000., MAT=True)
K_T = Float(10000., MAT=True)
S_T = Float(0.005, MAT=True)
r_T = Float(9., MAT=True)
e_T = Float(12., MAT=True)
c_T = Float(4.6, MAT=True)
tau_pi_bar = Float(1.7, MAT=True)
a = Float(0.003, MAT=True)
# ------------------------------------------------------------------------------
# Normal_Tension constitutive law parameters (without cumulative normal strain)
# ------------------------------------------------------------------------------
Ad = Float(100.0, MAT=True)
eps_0 = Float(0.00008, MAT=True)
# -----------------------------------------------
# Normal_Compression constitutive law parameters
# -----------------------------------------------
K_N = Float(10000., MAT=True)
gamma_N = Float(5000., MAT=True)
sig_0 = Float(30., MAT=True)
ipw_view = View(
Item('E_N'),
Item('E_T'),
Item('Ad'),
Item('eps_0'),
Item('K_N'),
Item('gamma_N'),
Item('sig_0'),
Item('gamma_T', latex=r'\gamma_\mathrm{T}', minmax=(10, 100000)),
Item('K_T', latex=r'K_\mathrm{T}', minmax=(10, 10000)),
Item('S_T', latex=r'S_\mathrm{T}', minmax=(0.001, 0.01)),
Item('r_T', latex=r'r_\mathrm{T}', minmax=(1, 3)),
Item('e_T', latex=r'e_\mathrm{T}', minmax=(1, 40)),
Item('c_T', latex=r'c_\mathrm{T}', minmax=(1, 10)),
Item('tau_pi_bar', latex=r'\bar{\sigma}^\pi_{T}', minmax=(1, 10)),
Item('a', latex=r'a_\mathrm{T}', minmax=(0.001, 3)),
)
n_D = 3
state_var_shapes = dict(
omega_N=(), # damage N
z_N=(),
alpha_N=(),
r_N=(),
eps_N_p=(),
sig_N=(),
omega_T=(),
z_T=(),
alpha_T_a=(n_D, ),
eps_T_p_a=(n_D, ),
sig_T_a=(n_D, ),
)
# --------------------------------------------------------------
# microplane constitutive law (normal behavior CP + TD)
# --------------------------------------------------------------
def get_normal_law(self, eps_N, **Eps):
omega_N, z_N, alpha_N, r_N, eps_N_p = [
Eps[key]
for key in ['omega_N', 'z_N', 'alpha_N', 'r_N', 'eps_N_p']
]
E_N = self.E_N
# When deciding if a microplane is in tensile or compression, we define a strain boundary such that that
# sigN <= 0 if eps_N < 0, avoiding entering in the quadrant of compressive strains and traction
sig_trial = E_N * (eps_N - eps_N_p)
pos1 = [(eps_N < -1e-6) & (sig_trial > 1e-6)
] # looking for microplanes violating strain boundary
sig_trial[pos1[0]] = 0
pos = sig_trial > 1e-6 # microplanes under traction
pos2 = sig_trial < -1e-6 # microplanes under compression
H = 1.0 * pos
H2 = 1.0 * pos2
# thermo forces
sig_N_tilde = E_N * (eps_N - eps_N_p)
sig_N_tilde[pos1[0]] = 0 # imposing strain boundary
Z = self.K_N * z_N
X = self.gamma_N * alpha_N * H2
h = (self.sig_0 + Z) * H2
f_trial = (abs(sig_N_tilde - X) - h) * H2
# threshold plasticity
thres_1 = f_trial > 1e-6
delta_lamda = f_trial / \
(E_N / (1 - omega_N) + abs(self.K_N) + self.gamma_N) * thres_1
eps_N_p = eps_N_p + delta_lamda * \
np.sign(sig_N_tilde - X)
z_N = z_N + delta_lamda
alpha_N = alpha_N + delta_lamda * \
np.sign(sig_N_tilde - X)
def R_N(r_N):
return (1.0 / self.Ad) * (-r_N / (1.0 + r_N))
Y_N = 0.5 * H * E_N * (eps_N - eps_N_p)**2.0
Y_0 = 0.5 * E_N * self.eps_0**2.0
f = (Y_N - (Y_0 + R_N(r_N)))
# threshold damage
thres_2 = f > 1e-6
def f_w(Y):
return 1.0 - 1.0 / (1.0 + self.Ad * (Y - Y_0))
omega_N[f > 1e-6] = f_w(Y_N)[f > 1e-6]
r_N[f > 1e-6] = -omega_N[f > 1e-6]
sig_N = (1.0 - H * omega_N) * E_N * (eps_N - eps_N_p)
pos1 = [(eps_N < -1e-6) & (sig_trial > 1e-6)
] # looking for microplanes violating strain boundary
sig_N[pos1[0]] = 0
return sig_N
# -------------------------------------------------------------------------
# microplane constitutive law (Tangential CSD)-(Pressure sensitive cumulative damage)
# -------------------------------------------------------------------------
def get_tangential_law(self, eps_T_a, **Eps):
omega_T, z_T, alpha_T_a, eps_T_p_a, sig_N, = [
Eps[key] for key in [
'omega_T',
'z_T',
'alpha_T_a',
'eps_T_p_a',
'sig_N',
]
]
E_T = self.E_T
# thermodynamic forces
sig_pi_trial = E_T * (eps_T_a - eps_T_p_a)
Z = self.K_T * z_T
X = self.gamma_T * alpha_T_a
norm_1 = np.sqrt(
np.einsum('...na,...na->...n', (sig_pi_trial - X),
(sig_pi_trial - X)))
Y = 0.5 * E_T * np.einsum('...na,...na->...n', (eps_T_a - eps_T_p_a),
(eps_T_a - eps_T_p_a))
# threshold
f = norm_1 - self.tau_pi_bar - Z + self.a * sig_N
plas_1 = f > 1e-6
elas_1 = f < 1e-6
delta_lamda = f / (E_T /
(1.0 - omega_T) + self.gamma_T + self.K_T) * plas_1
norm_2 = 1.0 * elas_1 + np.sqrt(
np.einsum('...na,...na->...n', (sig_pi_trial - X),
(sig_pi_trial - X))) * plas_1
eps_T_p_a[..., 0] += plas_1 * delta_lamda * \
((sig_pi_trial[..., 0] - X[..., 0]) /
(1.0 - omega_T)) / norm_2
eps_T_p_a[..., 1] += plas_1 * delta_lamda * \
((sig_pi_trial[..., 1] - X[..., 1]) /
(1.0 - omega_T)) / norm_2
eps_T_p_a[..., 2] += plas_1 * delta_lamda * \
((sig_pi_trial[..., 2] - X[..., 2]) /
(1.0 - omega_T)) / norm_2
omega_T[...] += ((1.0 - omega_T) ** self.c_T) * \
(delta_lamda * (Y / self.S_T) ** self.r_T) * \
(self.tau_pi_bar / (self.tau_pi_bar - self.a * sig_N)) ** self.e_T
alpha_T_a[..., 0] += plas_1 * delta_lamda * \
(sig_pi_trial[..., 0] - X[..., 0]) / norm_2
alpha_T_a[..., 1] += plas_1 * delta_lamda * \
(sig_pi_trial[..., 1] - X[..., 1]) / norm_2
alpha_T_a[..., 2] += plas_1 * delta_lamda * \
(sig_pi_trial[..., 2] - X[..., 2]) / norm_2
z_T[...] += delta_lamda
sig_T_a = np.einsum('...n,...na->...na', (1 - omega_T),
E_T * (eps_T_a - eps_T_p_a))
return sig_T_a
def get_corr_pred(self, eps_a, t_n1, **Eps):
eps_a_ = np.einsum('...a->a...', eps_a)
eps_N_n1 = eps_a_[0, ...]
eps_T_a_n1 = np.einsum('a...->...a', eps_a_[1:, ...])
sig_N = self.get_normal_law(eps_N_n1, **Eps)
sig_T_a = self.get_tangential_law(eps_T_a_n1, **Eps)
D_ = np.zeros(eps_a.shape + (eps_a.shape[-1], ))
D_[..., 0, 0] = self.E_N # * (1 - omega_N)
D_[..., 1, 1] = self.E_T # * (1 - omega_T)
D_[..., 2, 2] = self.E_T # * (1 - omega_T)
D_[..., 3, 3] = self.E_T # * (1 - omega_T)
sig_a = np.concatenate([sig_N[..., np.newaxis], sig_T_a], axis=-1)
return sig_a, D_
def get_eps_NT_p(self, **Eps):
"""Plastic strain tensor
"""
return Eps['eps_N_p'], Eps['eps_T_p_a']
def plot_idx(self, ax_sig, ax_d_sig, idx=0):
eps_max = self.eps_max
n_eps = self.n_eps
eps1_range = np.linspace(1e-9, eps_max, n_eps)
Eps = {
var: np.zeros((1, ) + shape)
for var, shape in self.state_var_shapes.items()
}
eps_range = np.zeros((n_eps, 4))
eps_range[:, idx] = eps1_range
# monotonic load in the normal direction
sig1_range, d_sig11_range = [], []
for eps_a in eps_range:
sig_a, D_range = self.get_corr_pred(eps_a[np.newaxis, ...], 1,
**Eps)
sig1_range.append(sig_a[0, idx])
d_sig11_range.append(D_range[0, idx, idx])
sig1_range = np.array(sig1_range, dtype=np.float_)
eps1_range = eps1_range[:len(sig1_range)]
ax_sig.plot(eps1_range, sig1_range, color='blue')
d_sig11_range = np.array(d_sig11_range, dtype=np.float_)
ax_d_sig.plot(eps1_range,
d_sig11_range,
linestyle='dashed',
color='gray')
ax_sig.set_xlabel(r'$\varepsilon_{11}$ [-]')
ax_sig.set_ylabel(r'$\sigma_{11}$ [MPa]')
ax_d_sig.set_ylabel(
r'$\mathrm{d} \sigma_{11} / \mathrm{d} \varepsilon_{11}$ [MPa]')
ax_d_sig.plot(eps1_range[:-1], (sig1_range[:-1] - sig1_range[1:]) /
(eps1_range[:-1] - eps1_range[1:]),
color='orange',
linestyle='dashed')
def subplots(self, fig):
ax_sig_N, ax_sig_T = fig.subplots(1, 2)
ax_d_sig_N = ax_sig_N.twinx()
ax_d_sig_T = ax_sig_T.twinx()
return ax_sig_N, ax_d_sig_N, ax_sig_T, ax_d_sig_T
def update_plot(self, axes):
ax_sig_N, ax_d_sig_N, ax_sig_T, ax_d_sig_T = axes
self.plot_idx(ax_sig_N, ax_d_sig_N, 0)
self.plot_idx(ax_sig_T, ax_d_sig_T, 1)
class TShape(CrossSectionShapeBase):
B_f = Float(400, GEO=True)
B_w = Float(100, GEO=True)
H_w = Float(300, GEO=True)
ipw_view = View(
*CrossSectionShapeBase.ipw_view.content,
Item('B_f', minmax=(10, 3000), latex=r'B_f \mathrm{[mm]}'),
Item('B_w', minmax=(10, 3000), latex=r'B_w \mathrm{[mm]}'),
Item('H_w', minmax=(10, 3000), latex=r'H_w \mathrm{[mm]}'),
)
def get_cs_area(self):
return self.B_w * self.H_w + self.B_f * (self.H - self.H_w)
def get_cs_i(self):
A_f = self.B_f * (self.H - self.H_w)
Y_f = (self.H - self.H_w) / 2 + self.H_w
I_f = self.B_f * (self.H - self.H_w)**3 / 12
A_w = self.B_w * self.H_w
Y_w = self.H_w / 2
I_w = self.B_w * self.H_w**3 / 12
Y_c = (Y_f * A_f + Y_w * A_w) / (A_f + A_w)
I_c = I_f + A_f * (Y_c - Y_f)**2 + I_w + A_w * (Y_c - Y_w)**2
return Y_c, I_c
get_b = tr.Property(tr.Callable, depends_on='+input')
@tr.cached_property
def _get_get_b(self):
z_ = sp.Symbol('z')
b_p = sp.Piecewise((self.B_w, z_ < self.H_w), (self.B_f, True))
return sp.lambdify(z_, b_p, 'numpy')
def update_plot(self, ax):
# Start drawing from bottom center of the cross section
cs_points = np.array([[self.B_w / 2, 0], [self.B_w / 2, self.H_w],
[self.B_f / 2, self.H_w], [self.B_f / 2, self.H],
[-self.B_f / 2, self.H],
[-self.B_f / 2, self.H_w],
[-self.B_w / 2, self.H_w], [-self.B_w / 2, 0]])
cs = Polygon(cs_points)
patch_collection = PatchCollection([cs],
facecolor=(.5, .5, .5, 0.2),
edgecolors=(0, 0, 0, 1))
ax.add_collection(patch_collection)
ax.scatter(0,
TShape.get_cs_i(self)[0],
color='white',
s=self.B_w,
marker="+")
ax.autoscale_view()
ax.set_aspect('equal')
ax.annotate('Area = {} $mm^2$'.format(int(TShape.get_cs_area(self)),
0),
xy=(-self.H / 2 * 0.8, (self.H / 2 + self.H_w / 2)),
color='blue')
ax.annotate('I = {} $mm^4$'.format(int(TShape.get_cs_i(self)[1]), 0),
xy=(-self.H / 2 * 0.8, (self.H / 2 + self.H_w / 2) * 0.9),
color='blue')
ax.annotate('$Y_c$',
xy=(0, TShape.get_cs_i(self)[0] * 0.85),
color='purple')
class IShape(CrossSectionShapeBase):
H = Float(900, GEO=True)
B_w = Float(50, GEO=True)
B_f_bot = Float(200, GEO=True)
B_f_top = Float(200, GEO=True)
H_f_bot = Float(145, GEO=True)
H_f_top = Float(145, GEO=True)
ipw_view = View(
*CrossSectionShapeBase.ipw_view.content,
Item('B_w', minmax=(10, 3000), latex=r'B_w \mathrm{[mm]}'),
Item('B_f_bot', minmax=(10, 3000), latex=r'B_{f_{bot}} \mathrm{[mm]}'),
Item('B_f_top', minmax=(10, 3000), latex=r'B_{f_{top}} \mathrm{[mm]}'),
Item('H_f_bot', minmax=(10, 3000), latex=r'H_{f_{bot}} \mathrm{[mm]}'),
Item('H_f_top', minmax=(10, 3000), latex=r'H_{f_{top}} \mathrm{[mm]}'),
)
def get_cs_area(self):
return self.B_f_bot * self.H_f_bot + self.B_f_top * self.H_f_top + self.B_w * (
self.H - self.H_f_top - self.H_f_bot)
def get_cs_i(self):
pass
get_b = tr.Property(tr.Callable, depends_on='+input')
@tr.cached_property
def _get_get_b(self):
z_ = sp.Symbol('z')
b_p = sp.Piecewise((self.B_f_bot, z_ < self.H_f_bot),
(self.B_w, z_ < self.H - self.H_f_top),
(self.B_f_top, True))
return sp.lambdify(z_, b_p, 'numpy')
def update_plot(self, ax):
# Start drawing from bottom center of the cross section
cs_points = np.array([[self.B_f_bot / 2, 0],
[self.B_f_bot / 2, self.H_f_bot],
[self.B_w / 2, self.H_f_bot],
[self.B_w / 2, self.H - self.H_f_top],
[self.B_f_top / 2, self.H - self.H_f_top],
[self.B_f_top / 2, self.H],
[-self.B_f_top / 2, self.H],
[-self.B_f_top / 2, self.H - self.H_f_top],
[-self.B_w / 2, self.H - self.H_f_top],
[-self.B_w / 2, self.H_f_bot],
[-self.B_f_bot / 2, self.H_f_bot],
[-self.B_f_bot / 2, 0]])
cs = Polygon(cs_points)
patch_collection = PatchCollection([cs],
facecolor=(.5, .5, .5, 0.2),
edgecolors=(0, 0, 0, 1))
ax.add_collection(patch_collection)
# ax.scatter(0, TShape.get_cs_i(self)[0], color='white', s=self.B_w, marker="+")
ax.autoscale_view()
ax.set_aspect('equal')
ax.annotate('Area = {} $mm^2$'.format(int(IShape.get_cs_area(self)),
0),
xy=(0, self.H / 2),
color='blue')
class VDNTIM(MATS3DEval):
"""
Vectorized uncoupled normal tngential interface model
"""
# -------------------------------------------------------------------------
# Elasticity
# -------------------------------------------------------------------------
name = 'equiv damage NTIM'
E_N = Float(10000, MAT=True)
E_T = Float(1000, MAT=True)
epsilon_0 = Float(59.0e-6, MAT=True,
label="a",
desc="Lateral pressure coefficient",
enter_set=True,
auto_set=False)
epsilon_f = Float(250.0e-6, MAT=True,
label="a",
desc="Lateral pressure coefficient",
enter_set=True,
auto_set=False)
c_T = Float(0.01, MAT=True,
label="a",
desc="Lateral pressure coefficient",
enter_set=True,
auto_set=False)
ipw_view = View(
Item('E_T', readonly=True),
Item('E_N', readonly=True),
Item('epsilon_0'),
Item('epsilon_f'),
Item('c_T'),
Item('eps_max'),
Item('n_eps')
)
n_D = 3
state_var_shapes = dict(
kappa=(),
omega_N=(),
omega_T=()
)
def get_e_equiv(self, eps_N, eps_T):
r"""
Returns a list of the microplane equivalent strains
based on the list of microplane strain vectors
"""
# positive part of the normal strain magnitude for each microplane
e_N_pos = (np.abs(eps_N) + eps_N) / 2.0
# tangent strain ratio
c_T = self.c_T
# equivalent strain for each microplane
eps_equiv = np.sqrt(e_N_pos * e_N_pos + c_T * eps_T)
return eps_equiv
def get_normal_law(self, eps_N, eps_T_a, kappa, omega_N, omega_T):
E_N = self.E_N
E_T = self.E_T
eps_T = np.sqrt(np.einsum('...a,...a->...', eps_T_a, eps_T_a))
eps_equiv = self.get_e_equiv(eps_N, eps_T)
kappa[...] = np.max(np.array([kappa, eps_equiv]), axis=0)
epsilon_0 = self.epsilon_0
epsilon_f = self.epsilon_f
I = np.where(kappa >= epsilon_0)
omega_N[I] = (
1.0 - (epsilon_0 / kappa[I] *
np.exp(-1.0 * (kappa[I] - epsilon_0) / (epsilon_f - epsilon_0))
)
)
sig_N = (1 - omega_N) * E_N * eps_N
sig_T_a = (1 - omega_N[..., np.newaxis]) * E_T * eps_T_a
sig_a = np.concatenate([sig_N[...,np.newaxis], sig_T_a], axis=-1)
return sig_a
def get_corr_pred(self, eps_a, t_n1, **Eps):
eps_a_ = np.einsum('...a->a...',eps_a)
eps_N = eps_a_[0,...]
eps_T_a = np.einsum('a...->...a', eps_a_[1:,...])
sig_a = self.get_normal_law(eps_N, eps_T_a, **Eps)
D_ = np.zeros(eps_a.shape + (eps_a.shape[-1],))
D_[..., 0, 0] = self.E_N# * (1 - omega_N)
D_[..., 1, 1] = self.E_T# * (1 - omega_T)
D_[..., 2, 2] = self.E_T# * (1 - omega_T)
D_[..., 3, 3] = self.E_T# * (1 - omega_T)
return sig_a, D_
def get_eps_NT_p(self, **Eps):
"""Plastic strain tensor
"""
return None
def plot_idx(self, ax_sig, ax_d_sig, idx=0):
eps_max = self.eps_max
n_eps = self.n_eps
eps1_range = np.linspace(1e-9,eps_max,n_eps)
Eps = { var : np.zeros( (1,) + shape )
for var, shape in self.state_var_shapes.items()
}
eps_range = np.zeros((n_eps, 4))
eps_range[:,idx] = eps1_range
# monotonic load in the normal direction
sig1_range, d_sig11_range = [], []
for eps_a in eps_range:
sig_a, D_range = self.get_corr_pred(eps_a[np.newaxis, ...], 1, **Eps)
sig1_range.append(sig_a[0, idx])
d_sig11_range.append(D_range[0, idx, idx])
sig1_range = np.array(sig1_range, dtype=np.float_)
eps1_range = eps1_range[:len(sig1_range)]
ax_sig.plot(eps1_range, sig1_range,color='blue')
d_sig11_range = np.array(d_sig11_range, dtype=np.float_)
ax_d_sig.plot(eps1_range, d_sig11_range, linestyle='dashed', color='gray')
ax_sig.set_xlabel(r'$\varepsilon_{11}$ [-]')
ax_sig.set_ylabel(r'$\sigma_{11}$ [MPa]')
ax_d_sig.set_ylabel(r'$\mathrm{d} \sigma_{11} / \mathrm{d} \varepsilon_{11}$ [MPa]')
ax_d_sig.plot(eps1_range[:-1],
(sig1_range[:-1]-sig1_range[1:])/(eps1_range[:-1]-eps1_range[1:]),
color='orange', linestyle='dashed')
def subplots(self, fig):
ax_sig_N, ax_sig_T = fig.subplots(1,2)
ax_d_sig_N = ax_sig_N.twinx()
ax_d_sig_T = ax_sig_T.twinx()
return ax_sig_N, ax_d_sig_N, ax_sig_T, ax_d_sig_T
def update_plot(self, axes):
ax_sig_N, ax_d_sig_N, ax_sig_T, ax_d_sig_T = axes
self.plot_idx(ax_sig_N, ax_d_sig_N, 0)
self.plot_idx(ax_sig_T, ax_d_sig_T, 1)
class MATS2DMplCSDEEQ(MATSBase, InteractiveModel):
concrete_type = tr.Int
gamma_T = tr.Float(100000.,
label="Gamma",
desc=" Tangential Kinematic hardening modulus",
enter_set=True,
auto_set=False)
K_T = tr.Float(10000.,
label="K",
desc="Tangential Isotropic harening",
enter_set=True,
auto_set=False)
S_T = tr.Float(0.005,
label="S",
desc="Damage strength",
enter_set=True,
auto_set=False)
r_T = tr.Float(9.,
label="r",
desc="Damage cumulation parameter",
enter_set=True,
auto_set=False)
e_T = tr.Float(12.,
label="e",
desc="Damage cumulation parameter",
enter_set=True,
auto_set=False)
c_T = tr.Float(4.6,
label="c",
desc="Damage cumulation parameter",
enter_set=True,
auto_set=False)
tau_pi_bar = tr.Float(1.7,
label="Tau_bar",
desc="Reversibility limit",
enter_set=True,
auto_set=False)
a = tr.Float(0.003,
label="a",
desc="Lateral pressure coefficient",
enter_set=True,
auto_set=False)
ipw_view = View(
Item('gamma_T', latex=r'\gamma_\mathrm{T}', minmax=(10,100000)),
Item('K_T', latex=r'K_\mathrm{T}', minmax=(10, 10000)),
Item('S_T', latex=r'S_\mathrm{T}', minmax=(0.001, 0.01)),
Item('r_T', latex=r'r_\mathrm{T}', minmax=(1, 3)),
Item('e_T', latex=r'e_\mathrm{T}', minmax=(1, 40)),
Item('c_T', latex=r'c_\mathrm{T}', minmax=(1, 10)),
Item('tau_pi_bar', latex=r'\bar{\tau}', minmax=(1, 10)),
Item('a', latex=r'a', minmax=(0.001, 3)),
)
# -------------------------------------------
# Normal_Tension constitutive law parameters (without cumulative normal strain)
# -------------------------------------------
Ad = tr.Float(100.0,
label="a",
desc="brittleness coefficient",
enter_set=True,
auto_set=False)
eps_0 = tr.Float(0.00008,
label="a",
desc="threshold strain",
enter_set=True,
auto_set=False)
# -----------------------------------------------
# Normal_Compression constitutive law parameters
# -----------------------------------------------
K_N = tr.Float(10000.,
label="K_N",
desc=" Normal isotropic harening",
enter_set=True,
auto_set=False)
gamma_N = tr.Float(5000.,
label="gamma_N",
desc="Normal kinematic hardening",
enter_set=True,
auto_set=False)
sigma_0 = tr.Float(30.,
label="sigma_0",
desc="Yielding stress",
enter_set=True,
auto_set=False)
# -------------------------------------------------------------------------
# Cached elasticity tensors
# -------------------------------------------------------------------------
E = tr.Float(35e+3,
label="E",
desc="Young's Modulus",
auto_set=False,
input=True)
nu = tr.Float(0.2,
label='nu',
desc="Poison ratio",
auto_set=False,
input=True)
def _get_lame_params(self):
# la = self.E * self.nu / ((1. + self.nu) * (1. - 2. * self.nu))
# # second Lame parameter (shear modulus)
# mu = self.E / (2. + 2. * self.nu)
la = self.E * self.nu / ((1. + self.nu) * (1. - self.nu))
mu = self.E / (2. + 2. * self.nu)
return la, mu
D_abef = tr.Property(tr.Array, depends_on='+input')
@tr.cached_property
def _get_D_abef(self):
la = self._get_lame_params()[0]
mu = self._get_lame_params()[1]
delta = np.identity(2)
D_abef = (np.einsum(',ij,kl->ijkl', la, delta, delta) +
np.einsum(',ik,jl->ijkl', mu, delta, delta) +
np.einsum(',il,jk->ijkl', mu, delta, delta))
return D_abef
@tr.cached_property
def _get_state_var_shapes(self):
return {'w_N_Emn': (self.n_mp,),
'z_N_Emn': (self.n_mp,),
'alpha_N_Emn': (self.n_mp,),
'r_N_Emn': (self.n_mp,),
'eps_N_p_Emn': (self.n_mp,),
'sigma_N_Emn': (self.n_mp,),
'w_T_Emn': (self.n_mp,),
'z_T_Emn': (self.n_mp,),
'alpha_T_Emna': (self.n_mp, 2),
'eps_T_pi_Emna': (self.n_mp, 2),
}
#--------------------------------------------------------------
# microplane constitutive law (normal behavior CP + TD)
# (without cumulative normal strain for fatigue under tension)
#--------------------------------------------------------------
def get_normal_law(self, eps_N_Emn, omega_N_Emn, z_N_Emn,
alpha_N_Emn, r_N_Emn, eps_N_p_Emn, eps_aux):
eps_N_Aux = self._get_e_N_Emn_2(eps_aux)
E_N = self.E / (1.0 - 2.0 * self.nu)
# E_N = self.E * (1.0 + 2.0 * self.nu) / (1.0 - self.nu**2)
# When deciding if a microplane is in tensile or compression, we define a strain boundary such that that
# sigmaN <= 0 if eps_N < 0, avoiding entering in the quadrant of compressive strains and traction
sigma_trial = E_N * (eps_N_Emn - eps_N_p_Emn)
pos1 = [(eps_N_Emn < -1e-6) & (sigma_trial > 1e-6)] # looking for microplanes violating strain boundary
sigma_trial [pos1[0]] = 0
pos = eps_N_Emn > 1e-6 # microplanes under traction
pos2 = eps_N_Emn < -1e-6 # microplanes under compression
H = 1.0 * pos
H2 = 1.0 * pos2
# thermo forces
sigma_N_Emn_tilde = E_N * (eps_N_Emn - eps_N_p_Emn)
sigma_N_Emn_tilde[pos1[0]] = 0 # imposing strain boundary
Z = self.K_N * z_N_Emn
X = self.gamma_N * alpha_N_Emn * H2
h = (self.sigma_0 + Z) * H2
f_trial = (abs(sigma_N_Emn_tilde - X) - h) * H2
# threshold plasticity
thres_1 = f_trial > 1e-6
delta_lamda = f_trial / \
(E_N / (1 - omega_N_Emn) + abs(self.K_N) + self.gamma_N) * thres_1
eps_N_p_Emn = eps_N_p_Emn + delta_lamda * \
np.sign(sigma_N_Emn_tilde - X)
z_N_Emn = z_N_Emn + delta_lamda
alpha_N_Emn = alpha_N_Emn + delta_lamda * \
np.sign(sigma_N_Emn_tilde - X)
def R_N(r_N_Emn): return (1.0 / self.Ad) * (-r_N_Emn / (1.0 + r_N_Emn))
Y_N = 0.5 * H * E_N * (eps_N_Emn - eps_N_p_Emn) ** 2.0
Y_0 = 0.5 * E_N * self.eps_0 ** 2.0
f = (Y_N - (Y_0 + R_N(r_N_Emn)))
# threshold damage
thres_2 = f > 1e-6
def f_w(Y): return 1.0 - 1.0 / (1.0 + self.Ad * (Y - Y_0))
omega_N_Emn[f > 1e-6] = f_w(Y_N)[f > 1e-6]
r_N_Emn[f > 1e-6] = -omega_N_Emn[f > 1e-6]
sigma_N_Emn = (1.0 - H * omega_N_Emn) * E_N * (eps_N_Emn - eps_N_p_Emn)
pos1 = [(eps_N_Emn < -1e-6) & (sigma_trial > 1e-6)] # looking for microplanes violating strain boundary
sigma_N_Emn[pos1[0]] = 0
Z = self.K_N * z_N_Emn
X = self.gamma_N * alpha_N_Emn * H2
return omega_N_Emn, z_N_Emn, alpha_N_Emn, r_N_Emn, eps_N_p_Emn, sigma_N_Emn, Z, X, Y_N
#-------------------------------------------------------------------------
# microplane constitutive law (Tangential CSD)-(Pressure sensitive cumulative damage)
#-------------------------------------------------------------------------
def get_tangential_law(self, eps_T_Emna, omega_T_Emn, z_T_Emn,
alpha_T_Emna, eps_T_pi_Emna, sigma_N_Emn):
E_T = self.E / (1.0 + self.nu)
# E_T = self.E * (1.0 - 4 * self.nu) / \
# ((1.0 + self.nu) * (1.0 - 2 * self.nu))
# E_T = self.E * (1.0 - 3.0 * self.nu) / (1.0 - self.nu**2)
# thermo forces
sig_pi_trial = E_T * (eps_T_Emna - eps_T_pi_Emna)
Z = self.K_T * z_T_Emn
X = self.gamma_T * alpha_T_Emna
norm_1 = np.sqrt(
np.einsum(
'...na,...na->...n',
(sig_pi_trial - X), (sig_pi_trial - X))
)
Y = 0.5 * E_T * \
np.einsum(
'...na,...na->...n',
(eps_T_Emna - eps_T_pi_Emna),
(eps_T_Emna - eps_T_pi_Emna))
# threshold
f = norm_1 - self.tau_pi_bar - \
Z + self.a * sigma_N_Emn
plas_1 = f > 1e-6
elas_1 = f < 1e-6
delta_lamda = f / \
(E_T / (1.0 - omega_T_Emn) + self.gamma_T + self.K_T) * plas_1
norm_2 = 1.0 * elas_1 + np.sqrt(
np.einsum(
'...na,...na->...n',
(sig_pi_trial - X), (sig_pi_trial - X))) * plas_1
eps_T_pi_Emna[..., 0] = eps_T_pi_Emna[..., 0] + plas_1 * delta_lamda * \
((sig_pi_trial[..., 0] - X[..., 0]) /
(1.0 - omega_T_Emn)) / norm_2
eps_T_pi_Emna[..., 1] = eps_T_pi_Emna[..., 1] + plas_1 * delta_lamda * \
((sig_pi_trial[..., 1] - X[..., 1]) /
(1.0 - omega_T_Emn)) / norm_2
omega_T_Emn += ((1 - omega_T_Emn) ** self.c_T) * \
(delta_lamda * (Y / self.S_T) ** self.r_T) * \
(self.tau_pi_bar / (self.tau_pi_bar + self.a * sigma_N_Emn)) ** self.e_T
alpha_T_Emna[..., 0] = alpha_T_Emna[..., 0] + plas_1 * delta_lamda * \
(sig_pi_trial[..., 0] - X[..., 0]) / norm_2
alpha_T_Emna[..., 1] = alpha_T_Emna[..., 1] + plas_1 * delta_lamda * \
(sig_pi_trial[..., 1] - X[..., 1]) / norm_2
z_T_Emn = z_T_Emn + delta_lamda
sigma_T_Emna = np.einsum(
'...n,...na->...na', (1 - omega_T_Emn), E_T * (eps_T_Emna - eps_T_pi_Emna))
Z = self.K_T * z_T_Emn
X = self.gamma_T * alpha_T_Emna
Y = 0.5 * E_T * \
np.einsum(
'...na,...na->...n',
(eps_T_Emna - eps_T_pi_Emna),
(eps_T_Emna - eps_T_pi_Emna))
return omega_T_Emn, z_T_Emn, alpha_T_Emna, eps_T_pi_Emna, sigma_T_Emna, Z, X, Y
# #-------------------------------------------------------------------------
# # MICROPLANE-Kinematic constraints
# #-------------------------------------------------------------------------
#-------------------------------------------------
# get the operator of the microplane normals
_MPNN = tr.Property(depends_on='n_mp')
@tr.cached_property
def _get__MPNN(self):
print(self._MPN)
MPNN_nij = np.einsum('ni,nj->nij', self._MPN, self._MPN)
return MPNN_nij
# get the third order tangential tensor (operator) for each microplane
_MPTT = tr.Property(depends_on='n_mp')
@tr.cached_property
def _get__MPTT(self):
delta = np.identity(2)
MPTT_nijr = 0.5 * (
np.einsum('ni,jr -> nijr', self._MPN, delta) +
np.einsum('nj,ir -> njir', self._MPN, delta) - 2 *
np.einsum('ni,nj,nr -> nijr', self._MPN, self._MPN, self._MPN)
)
return MPTT_nijr
def _get_e_N_Emn_2(self, eps_Emab):
# get the normal strain array for each microplane
return np.einsum('nij,...ij->...n', self._MPNN, eps_Emab)
def _get_e_T_Emnar_2(self, eps_Emab):
# get the tangential strain vector array for each microplane
MPTT_ijr = self._get__MPTT()
return np.einsum('nija,...ij->...na', MPTT_ijr, eps_Emab)
#--------------------------------------------------------
# return the state variables (Damage , inelastic strains)
#--------------------------------------------------------
def _get_state_variables(self, eps_Emab,
int_var, eps_aux):
e_N_arr = self._get_e_N_Emn_2(eps_Emab)
e_T_vct_arr = self._get_e_T_Emnar_2(eps_Emab)
omega_N_Emn = int_var[:, 0]
z_N_Emn = int_var[:, 1]
alpha_N_Emn = int_var[:, 2]
r_N_Emn = int_var[:, 3]
eps_N_p_Emn = int_var[:, 4]
sigma_N_Emn = int_var[:, 5]
omega_T_Emn = int_var[:, 9]
z_T_Emn = int_var[:, 10]
alpha_T_Emna = int_var[:, 11:13]
eps_T_pi_Emna = int_var[:, 13:15]
omega_N_Emn, z_N_Emn, alpha_N_Emn, r_N_Emn, eps_N_p_Emn, sigma_N_Emn, Z_n, X_n, Y_n = self.get_normal_law(e_N_arr, omega_N_Emn, z_N_Emn,
alpha_N_Emn, r_N_Emn, eps_N_p_Emn, eps_aux)
omega_T_Emn, z_T_Emn, alpha_T_Emna, eps_T_pi_Emna, sigma_T_Emna, Z_T, X_T, Y_T = self.get_tangential_law(e_T_vct_arr, omega_T_Emn, z_T_Emn, alpha_T_Emna, eps_T_pi_Emna, sigma_N_Emn)
# Definition internal variables / forces per column: 1) damage N, 2)iso N, 3)kin N, 4)consolidation N, 5) eps p N,
# 6) sigma N, 7) iso F N, 8) kin F N, 9) energy release N, 10) damage T, 11) iso T, 12-13) kin T, 14-15) eps p T,
# 16-17) sigma T, 18) iso F T, 19-20) kin F T, 21) energy release T
int_var[:, 0] = omega_N_Emn
int_var[:, 1] = z_N_Emn
int_var[:, 2] = alpha_N_Emn
int_var[:, 3] = r_N_Emn
int_var[:, 4] = eps_N_p_Emn
int_var[:, 5] = sigma_N_Emn
int_var[:, 6] = Z_n
int_var[:, 7] = X_n
int_var[:, 8] = Y_n
int_var[:, 9] = omega_T_Emn
int_var[:, 10] = z_T_Emn
int_var[:, 11:13] = alpha_T_Emna
int_var[:, 13:15] = eps_T_pi_Emna
int_var[:, 15:17] = sigma_T_Emna
int_var[:, 17] = Z_T
int_var[:, 18:20] = X_T
int_var[:, 20] = Y_T
return int_var
#---------------------------------------------------------------------
# Extra homogenization of damage tensor in case of two damage parameters
# Returns the 4th order damage tensor 'beta4' using (ref. [Baz99], Eq.(63))
#---------------------------------------------------------------------
def _get_beta_Emabcd_2(self, eps_Emab, omega_N_Emn, z_N_Emn,
alpha_N_Emn, r_N_Emn, eps_N_p_Emn, omega_T_Emn, z_T_Emn,
alpha_T_Emna, eps_T_pi_Emna, eps_aux):
# Returns the 4th order damage tensor 'beta4' using
#(cf. [Baz99], Eq.(63))
eps_N_Emn = self._get_e_N_Emn_2(eps_Emab)
eps_T_Emna = self._get_e_T_Emnar_2(eps_Emab)
omega_N_Emn, z_N_Emn, alpha_N_Emn, r_N_Emn, eps_N_p_Emn, sigma_N_Emn, Z_n, X_n, Y_n = self.get_normal_law(
eps_N_Emn, omega_N_Emn, z_N_Emn, alpha_N_Emn, r_N_Emn, eps_N_p_Emn, eps_aux)
omega_T_Emn, z_T_Emn, alpha_T_Emna, eps_T_pi_Emna, sigma_T_Emna, Z_T, X_T, Y_T = self.get_tangential_law(
eps_T_Emna, omega_T_Emn, z_T_Emn, alpha_T_Emna, eps_T_pi_Emna, sigma_N_Emn)
delta = np.identity(2)
beta_N = np.sqrt(1. - omega_N_Emn)
beta_T = np.sqrt(1. - omega_T_Emn)
beta_ijkl = np.einsum('n, ...n,ni, nj, nk, nl -> ...ijkl', self._MPW, beta_N, self._MPN, self._MPN, self._MPN, self._MPN) + \
0.25 * (np.einsum('n, ...n,ni, nk, jl -> ...ijkl', self._MPW, beta_T, self._MPN, self._MPN, delta) +
np.einsum('n, ...n,ni, nl, jk -> ...ijkl', self._MPW, beta_T, self._MPN, self._MPN, delta) +
np.einsum('n, ...n,nj, nk, il -> ...ijkl', self._MPW, beta_T, self._MPN, self._MPN, delta) +
np.einsum('n, ...n,nj, nl, ik -> ...ijkl', self._MPW, beta_T, self._MPN, self._MPN, delta) -
4.0 * np.einsum('n, ...n, ni, nj, nk, nl -> ...ijkl', self._MPW, beta_T, self._MPN, self._MPN, self._MPN, self._MPN))
return beta_ijkl
#-----------------------------------------------------------
# Integration of the (inelastic) strains for each microplane
#-----------------------------------------------------------
def _get_eps_p_Emab(self, eps_Emab, omega_N_Emn, z_N_Emn,
alpha_N_Emn, r_N_Emn, eps_N_p_Emn,
omega_T_Emn, z_T_Emn, alpha_T_Emna, eps_T_pi_Emna, sigma_N_Emn, eps_aux):
eps_N_Emn = self._get_e_N_Emn_2(eps_Emab)
eps_T_Emna = self._get_e_T_Emnar_2(eps_Emab)
# plastic normal strains
omegaN, z_N_Emn, alpha_N_Emn, r_N_Emn, eps_N_p_Emn, sigma_N_Emn, Z_n, X_n, Y_n = self.get_normal_law(
eps_N_Emn, omega_N_Emn, z_N_Emn, alpha_N_Emn, r_N_Emn, eps_N_p_Emn, eps_aux)
# sliding tangential strains
omega_T_Emn, z_T_Emn, alpha_T_Emna, eps_T_pi_Emna, sigma_T_Emna, Z_T, X_T, Y_T = self.get_tangential_law(
eps_T_Emna, omega_T_Emn, z_T_Emn, alpha_T_Emna, eps_T_pi_Emna, sigma_N_Emn)
delta = np.identity(2)
# 2-nd order plastic (inelastic) tensor
eps_p_Emab = (
np.einsum('n,...n,na,nb->...ab',
self._MPW, eps_N_p_Emn, self._MPN, self._MPN) +
0.5 * (
np.einsum('n,...nf,na,fb->...ab',
self._MPW, eps_T_pi_Emna, self._MPN, delta) +
np.einsum('n,...nf,nb,fa->...ab', self._MPW,
eps_T_pi_Emna, self._MPN, delta)
)
)
return eps_p_Emab
#-------------------------------------------------------------------------
# Evaluation - get the corrector and predictor
#-------------------------------------------------------------------------
def get_corr_pred(self, eps_Emab, t_n1, int_var, eps_aux, F):
# Definition internal variables / forces per column: 1) damage N, 2)iso N, 3)kin N, 4)consolidation N, 5) eps p N,
# 6) sigma N, 7) iso F N, 8) kin F N, 9) energy release N, 10) damage T, 11) iso T, 12-13) kin T, 14-15) eps p T,
# 16-17) sigma T, 18) iso F T, 19-20) kin F T, 21) energy release T
# Corrector predictor computation.
#------------------------------------------------------------------
# Damage tensor (4th order) using product- or sum-type symmetrization:
#------------------------------------------------------------------
eps_N_Emn = self._get_e_N_Emn_2(eps_Emab)
eps_T_Emna = self._get_e_T_Emnar_2(eps_Emab)
omega_N_Emn = int_var[:, 0]
z_N_Emn = int_var[:, 1]
alpha_N_Emn = int_var[:, 2]
r_N_Emn = int_var[:, 3]
eps_N_p_Emn = int_var[:, 4]
sigma_N_Emn = int_var[:, 5]
omega_T_Emn = int_var[:, 9]
z_T_Emn = int_var[:, 10]
alpha_T_Emna = int_var[:, 11:13]
eps_T_pi_Emna = int_var[:, 13:15]
beta_Emabcd = self._get_beta_Emabcd_2(
eps_Emab, omega_N_Emn, z_N_Emn, alpha_N_Emn, r_N_Emn, eps_N_p_Emn, omega_T_Emn, z_T_Emn,
alpha_T_Emna, eps_T_pi_Emna, eps_aux
)
#------------------------------------------------------------------
# Damaged stiffness tensor calculated based on the damage tensor beta4:
#------------------------------------------------------------------
D_Emabcd = np.einsum(
'...ijab, abef, ...cdef->...ijcd', beta_Emabcd, self.D_abef, beta_Emabcd)
#----------------------------------------------------------------------
# Return stresses (corrector) and damaged secant stiffness matrix (predictor)
#----------------------------------------------------------------------
# plastic strain tensor
eps_p_Emab = self._get_eps_p_Emab(
eps_Emab, omega_N_Emn, z_N_Emn,
alpha_N_Emn, r_N_Emn, eps_N_p_Emn,
omega_T_Emn, z_T_Emn, alpha_T_Emna, eps_T_pi_Emna, sigma_N_Emn, eps_aux)
# elastic strain tensor
eps_e_Emab = eps_Emab - eps_p_Emab
# calculation of the stress tensor
sig_Emab = np.einsum('...abcd,...cd->...ab', D_Emabcd, eps_e_Emab)
return D_Emabcd, sig_Emab, eps_p_Emab
#-----------------------------------------------
# number of microplanes
#-----------------------------------------------
n_mp = tr.Constant(360)
#-----------------------------------------------
# get the normal vectors of the microplanes
#-----------------------------------------------
_MPN = tr.Property(depends_on='n_mp')
@tr.cached_property
def _get__MPN(self):
# microplane normals:
alpha_list = np.linspace(0, 2 * np.pi, self.n_mp)
MPN = np.array([[np.cos(alpha), np.sin(alpha)]
for alpha in alpha_list])
return MPN
#-------------------------------------
# get the weights of the microplanes
#-------------------------------------
_MPW = tr.Property(depends_on='n_mp')
@tr.cached_property
def _get__MPW(self):
MPW = np.ones(self.n_mp) / self.n_mp * 2
return MPW
class ConcreteMaterialModelAdv(ConcreteMatMod, bu.InjectSymbExpr):
name = 'Concrete behavior'
node_name = 'material model'
symb_class = ConcreteMaterialModelAdvExpr
d_a = Float(16, MAT=True) ## dia of steel mm
E_c = Float(28000, MAT=True) ## tensile strength of Concrete in MPa
f_t = Float(3, MAT=True) ## Fracture Energy in N/m
c_1 = Float(3, MAT=True)
c_2 = Float(6.93, MAT=True)
f_c = Float(33.3, MAT=True)
L_fps = Float(50, MAT=True)
a = Float(1.038, MAT=True)
b = Float(0.245, MAT=True)
interlock_factor = Float(1, MAT=True)
ipw_view = View(
Item('d_a', latex=r'd_a'), Item('E_c', latex=r'E_c'),
Item('f_t', latex=r'f_t'), Item('f_c', latex=r'f_c'),
Item('c_1', latex=r'c_1'), Item('c_2', latex=r'c_2'),
Item('L_fps', latex=r'L_{fps}'), Item('a', latex=r'a'),
Item('b', latex=r'b'),
Item('interlock_factor',
latex=r'\gamma_\mathrm{ag}',
editor=FloatRangeEditor(low=0, high=1)),
Item('w_cr', latex=r'w_\mathrm{cr}', readonly=True),
Item('L_c', latex=r'L_\mathrm{c}', readonly=True),
Item('G_f', latex=r'G_\mathrm{f}', readonly=True))
# G_f_baz = tr.Property(depends_on='_ITR, _INC, _GEO, _MAT, _DSC')
# @tr.cached_property
# def _get_G_f_baz(self):
# xi = self.sz_crack_tip_orientation.
# return self.symb.G_f_baz
L_c = tr.Property(Float, depends_on='state_changed')
@tr.cached_property
def _get_L_c(self):
return self.E_c * self.G_f / self.f_t**2
w_cr = tr.Property(Float, depends_on='state_changed')
@tr.cached_property
def _get_w_cr(self):
return (self.f_t / self.E_c) * self._get_L_c()
G_f = tr.Property(Float, depends_on='state_changed')
@tr.cached_property
def _get_G_f(self):
'''Calculating fracture energy
'''
return (0.028 * self.f_c**0.18 * self.d_a**0.32)
def get_w_tc(self):
'''Calculating point of softening curve resulting in 0 stress
'''
return (5.14 * self.G_f_baz / self.f_t)
def get_sig_a(self, u_a): #w, s
'''Calculating stresses
'''
sig_w = self.get_sig_w(u_a[..., 0], u_a[..., 1])
tau_s = self.get_tau_s(u_a[..., 0], u_a[..., 1])
return np.einsum('b...->...b', np.array([sig_w, tau_s],
dtype=np.float_)) #, tau_s
def get_sig_w(self, w, s):
return self.symb.get_sig_w(w, s)
def get_tau_s(self, w, s):
return self.symb.get_tau_s(w, s) * self.interlock_factor
w_min_factor = Float(1.2)
w_max_factor = Float(15)
def plot3d_sig_w(self, ax3d, vot=1.0):
w_min = -(self.f_c / self.E_c * self._get_L_c()) * self.w_min_factor
w_max = self.w_cr * self.w_max_factor
w_range = np.linspace(w_min, w_max, 100)
s_max = 3
s_data = np.linspace(-1.1 * s_max, 1.1 * s_max, 100)
s_, w_ = np.meshgrid(s_data, w_range)
sig_w = self.get_sig_w(w_, s_)
ax3d.plot_surface(w_, s_, sig_w, cmap='viridis', edgecolor='none')
ax3d.set_xlabel(r'$w\;\;\mathrm{[mm]}$', fontsize=12)
ax3d.set_ylabel(r'$s\;\;\mathrm{[mm]}$', fontsize=12)
ax3d.set_zlabel(r'$\sigma_w\;\;\mathrm{[MPa]}$', fontsize=12)
ax3d.set_title('crack opening law', fontsize=12)
# def plot_sig_w(self, ax, vot=1.0):
# w_min = -(self.f_c / self.E_c * self._get_L_c()) * self.w_min_factor
# w_max = self.w_cr * self.w_max_factor
# w_range = np.linspace(w_min, w_max, 100)
# s_max = 3
# s_data = np.linspace(-1.1 * s_max, 1.1 * s_max, 100)
# sig_w = self.get_sig_w(w_range, s_data)
# ax.plot(w_range, sig_w, lw=2, color='red')
# ax.fill_between(w_range, sig_w,
# color='red', alpha=0.2)
# ax.set_xlabel(r'$w\;\;\mathrm{[mm]}$', fontsize=12)
# ax.set_ylabel(r'$\sigma\;\;\mathrm{[MPa]}$', fontsize=12)
# ax.set_title('crack opening law', fontsize=12)
def plot3d_tau_s(self, ax3d, vot=1.0):
w_min = 1e-9 #-1
w_max = 3
w_data = np.linspace(w_min, w_max, 100)
s_max = 3
s_data = np.linspace(-1.1 * s_max, 1.1 * s_max, 100) #-1.1
s_, w_ = np.meshgrid(s_data, w_data)
tau_s = self.get_tau_s(w_, s_)
ax3d.plot_surface(w_, s_, tau_s, cmap='viridis', edgecolor='none')
ax3d.set_xlabel(r'$w\;\;\mathrm{[mm]}$', fontsize=12)
ax3d.set_ylabel(r'$s\;\;\mathrm{[mm]}$', fontsize=12)
ax3d.set_zlabel(r'$\tau\;\;\mathrm{[MPa]}$', fontsize=12)
ax3d.set_title('aggregate interlock law', fontsize=12)
# def subplots(self, fig):
# #ax_2d = fig.add_subplot(1, 2, 2)
# ax_3d_1 = fig.add_subplot(1, 2, 2, projection='3d')
# ax_3d_2 = fig.add_subplot(1, 2, 1, projection='3d')
# return ax_3d_1, ax_3d_2
#
# def update_plot(self, axes):
# '''Plotting function
# '''
# ax_3d_1, ax_3d_2 = axes
# self.plot3d_sig_w(ax_3d_1)
# self.plot3d_tau_s(ax_3d_2)
def get_eps_plot_range(self):
return np.linspace(1.1 * self.eps_cu, 1.1 * self.eps_tu, 300)
def get_sig(self, eps):
return self.factor * self.symb.get_sig_w(eps)
class MATS2DScalarDamage(MATS2DEval):
r'''Isotropic damage model.
'''
name = 'isotropic damage model'
node_name = 'isotropic damage model'
tree = ['omega_fn','strain_norm']
omega_fn = EitherType(
options=[('exp-slope', ExpSlopeDamageFn),
('linear', LinearDamageFn),
('abaqus', AbaqusDamageFn),
('fracture-energy', GfDamageFn),
('weibull-CDF', WeibullDamageFn),
],
MAT=True,
on_option_change='link_omega_to_mats'
)
D_alg = Float(0)
r'''Selector of the stiffness calculation.
'''
eps_max = Float(0.03, ALG=True)
# upon change of the type attribute set the link to the material model
def link_omega_to_mats(self):
self.omega_fn_.trait_set(mats=self,
E_name='E',
x_max_name='eps_max')
#=========================================================================
# Material model
#=========================================================================
strain_norm = EitherType(
options=[('Rankine', SN2DRankine),
('Masars', SN2DMasars),
('Energy', SN2DEnergy)],
on_option_change='link_strain_norm_to_mats'
)
def link_strain_norm_to_mats(self):
self.strain_norm_.trait_set(mats=self)
state_var_shapes = {'kappa': (),
'omega': ()}
r'''
Shapes of the state variables
to be stored in the global array at the level
of the domain.
'''
def get_corr_pred(self, eps_ab_n1, tn1, kappa, omega):
r'''
Corrector predictor computation.
@param eps_app_eng input variable - engineering strain
'''
eps_eq = self.strain_norm_.get_eps_eq(eps_ab_n1, kappa)
I = self.omega_fn_.get_f_trial(eps_eq, kappa)
eps_eq_I = eps_eq[I]
kappa[I] = eps_eq_I
omega[I] = self._omega(eps_eq_I)
phi = (1.0 - omega)
D_abcd = np.einsum(
'...,abcd->...abcd',
phi, self.D_abcd
)
sig_ab = np.einsum(
'...abcd,...cd->...ab',
D_abcd, eps_ab_n1
)
if self.D_alg > 0:
domega_ds_I = self._omega_derivative(eps_eq_I)
deps_eq_I = self.strain_norm_.get_deps_eq(eps_ab_n1[I])
D_red_I = np.einsum('...,...ef,cdef,...ef->...cdef', domega_ds_I,
deps_eq_I, self.D_abcd, eps_ab_n1[I]) * self.D_alg
D_abcd[I] -= D_red_I
return sig_ab, D_abcd
def _omega(self, kappa):
return self.omega_fn_(kappa)
def _omega_derivative(self, kappa):
return self.omega_fn_.diff(kappa)
ipw_view = View(
Item('E'),
Item('nu'),
Item('strain_norm'),
Item('omega_fn'),
Item('stress_state'),
Item('D_alg', latex=r'\theta_\mathrm{alg. stiff.}',
editor=FloatRangeEditor(low=0,high=1)),
Item('eps_max'),
Item('G_f', latex=r'G_\mathrm{f}^{\mathrm{estimate}}', readonly=True),
)
G_f = tr.Property(Float, depends_on='state_changed')
@tr.cached_property
def _get_G_f(self):
eps_max = self.eps_max
n_eps = 1000
eps11_range = np.linspace(1e-9,eps_max,n_eps)
eps_range = np.zeros((len(eps11_range), 2, 2))
eps_range[:,1,1] = eps11_range
state_vars = { var : np.zeros( (len(eps11_range),) + shape )
for var, shape in self.state_var_shapes.items()
}
sig_range, D = self.get_corr_pred(eps_range, 1, **state_vars)
sig11_range = sig_range[:,1,1]
return np.trapz(sig11_range, eps11_range)
def subplots(self, fig):
ax_sig = fig.subplots(1,1)
ax_d_sig = ax_sig.twinx()
return ax_sig, ax_d_sig
def update_plot(self, axes):
ax_sig, ax_d_sig = axes
eps_max = self.eps_max
n_eps = 100
eps11_range = np.linspace(1e-9,eps_max,n_eps)
eps_range = np.zeros((n_eps, 2, 2))
eps_range[:,0,0] = eps11_range
state_vars = { var : np.zeros( (n_eps,) + shape )
for var, shape in self.state_var_shapes.items()
}
sig_range, D_range = self.get_corr_pred(eps_range, 1, **state_vars)
sig11_range = sig_range[:,0,0]
ax_sig.plot(eps11_range, sig11_range,color='blue')
d_sig1111_range = D_range[...,0,0,0,0]
ax_d_sig.plot(eps11_range, d_sig1111_range,
linestyle='dashed', color='gray')
ax_sig.set_xlabel(r'$\varepsilon_{11}$ [-]')
ax_sig.set_ylabel(r'$\sigma_{11}$ [MPa]')
ax_d_sig.set_ylabel(r'$\mathrm{d} \sigma_{11} / \mathrm{d} \varepsilon_{11}$ [MPa]')
ax_d_sig.plot(eps11_range[:-1],
(sig11_range[:-1]-sig11_range[1:])/(eps11_range[:-1]-eps11_range[1:]),
color='orange', linestyle='dashed')
def get_omega(self, eps_ab, tn1, **Eps):
return Eps['omega']
var_dict = tr.Property(tr.Dict(tr.Str, tr.Callable))
'''Dictionary of response variables
'''
@tr.cached_property
def _get_var_dict(self):
var_dict = dict(omega=self.get_omega)
var_dict.update(super()._get_var_dict())
return var_dict