def change_thickness_ABAQUS(imperfection_file_name,
model_name,
part_name,
stack,
t_model,
t_measured,
H_model,
H_measured,
R_model,
R_best_fit=None,
number_of_sets=None,
semi_angle=0.,
stretch_H=False,
z_offset_bot=None,
scaling_factor=1.,
num_closest_points=5,
power_parameter=2,
elems_t=None,
t_set=None,
use_theta_z_format=False):
r"""Applies a given thickness imperfection to the finite element model
Assumes that a percentage variation of the laminate thickness can be
represented by the same percentage veriation of each ply, i.e., each
ply thickness is varied in order to reflect a given measured thickness
imperfection field.
Parameters
----------
imperfection_file_name : str
Full path to the imperfection file.
model_name : str
Model name.
part_name : str
Part name.
stack : list
The stacking sequence of the current model with each angle given in
degrees.
t_model : float
The nominal shell thickness of the current model.
t_measured : float
The nominal thickness of the measured specimen.
H_model : float
Total height of the model where the imperfections will be applied to,
considering also eventual resin rings.
H_measured : float
The total height of the measured test specimen, including eventual
resin rings at the edges.
R_model : float
Radius **at the bottom edge** of the model where the imperfections
will be applied to.
R_best_fit : float, optional
Best fit radius obtained with functions :func:`.best_fit_cylinder`
or :func:`.best_fit_cone`.
number_of_sets : int, optional
Defines in how many levels the thicknesses should be divided. If
``None`` it will be based on the input file, and if the threshold
of ``100`` is exceeded, ``10`` sections are used.
semi_angle : float, optional
Cone semi-vertex angle in degrees, when applicable.
stretch_H : bool, optional
If the measured imperfection data should be stretched to the current
model (which may happen when ``H_model!=H_measured``.
z_offset_bot : float, optional
It is common to have the measured data not covering the whole test
specimen, and therefore it will be centralized, if a non-centralized
position is desired this parameter can be used for the adjustment.
scaling_factor : float, optional
A scaling factor that can be used to study the imperfection
sensitivity.
num_closest_points : int, optional
See :func:`the inverse-weighted interpolation algorithm
<.inv_weighted>`.
power_parameter : float, optional
See :func:`the inverse-weighted interpolation algorithm
<.inv_weighted>`.
elems_t : np.ndarray, optional
Interpolated thickness for each element. Can be used to avoid the same
interpolation to be performed twice.
t_set : set, optional
A ``set`` object containing the unique thicknesses that will be used
to create the new properties.
use_theta_z_format : bool, optional
If the new format `\theta, Z, imp` should be used instead of the old
`X, Y, Z`.
"""
from abaqus import mdb
import desicos.abaqus.abaqus_functions as abaqus_functions
mod = mdb.models[model_name]
part = mod.parts[part_name]
part_cyl_csys = part.features['part_cyl_csys']
part_cyl_csys = part.datums[part_cyl_csys.id]
if use_theta_z_format:
if elems_t is None or t_set is None:
log('Reading coordinates for elements...')
elements = vec_calc_elem_cg(part.elements)
log('Coordinates for elements read!')
d, d, data = read_theta_z_imp(path=imperfection_file_name,
H_measured=H_measured,
stretch_H=stretch_H,
z_offset_bot=z_offset_bot)
data3D = np.zeros((data.shape[0], 4), dtype=FLOAT)
z = data[:, 1]
z *= H_model
alpharad = deg2rad(semi_angle)
tana = tan(alpharad)
def r_local(z):
return R_model - z * tana
data3D[:, 0] = r_local(z) * cos(data[:, 0])
data3D[:, 1] = r_local(z) * sin(data[:, 0])
data3D[:, 2] = z
data3D[:, 3] = data[:, 2]
dist, ans = inv_weighted(data3D,
elements[:, :3],
ncp=num_closest_points,
power_parameter=power_parameter)
t_set = set(ans)
t_set.discard(0.) #TODO why inv_weighted returns an array with 0.
elems_t = np.zeros((elements.shape[0], 2), dtype=FLOAT)
elems_t[:, 0] = elements[:, 3]
elems_t[:, 1] = ans
else:
log('Thickness differences already calculated!')
else:
if elems_t is None or t_set is None:
# reading elements data
log('Reading coordinates for elements...')
elements = vec_calc_elem_cg(part.elements)
log('Coordinates for elements read!')
# calling translate_nodes function
elems_t, t_set = calc_elems_t(
imperfection_file_name,
nodes=elements,
t_model=t_model,
t_measured=t_measured,
H_model=H_model,
H_measured=H_measured,
R_model=R_model,
R_best_fit=R_best_fit,
semi_angle=semi_angle,
stretch_H=stretch_H,
z_offset_bot=z_offset_bot,
num_closest_points=num_closest_points,
power_parameter=power_parameter)
else:
log('Thickness differences already calculated!')
# creating sets
t_list = []
max_len_t_set = 100
if len(t_set) >= max_len_t_set and number_of_sets in (None, 0):
number_of_sets = 10
log('More than {0:d} different thicknesses measured!'.format(
max_len_t_set))
log('Forcing a number_of_sets = {0:d}'.format(number_of_sets))
if number_of_sets is None or number_of_sets == 0:
number_of_sets = len(t_set)
t_list = list(t_set)
t_list.sort()
else:
t_min = min(t_set)
t_max = max(t_set)
t_list = list(np.linspace(t_min, t_max, number_of_sets + 1))
# grouping elements
sets_ids = [[] for i in range(len(t_list))]
for entry in elems_t:
elem_id, t = entry
index = index_within_linspace(t_list, t)
sets_ids[index].append(int(elem_id))
# putting elements in sets
original_layup = part.compositeLayups['CompositePlate']
for i, set_ids in enumerate(sets_ids):
if len(set_ids) == 0:
# since t_set_norm * t_model <> t_set originally measured
# there may be empty set_ids at the end
continue
elements = part.elements.sequenceFromLabels(labels=set_ids)
suffix = 'measured_imp_t_{0:03d}'.format(i)
set_name = 'Set_' + suffix
log('Creating set ({0: 7d} elements): {1}'.format(
len(set_ids), set_name))
part.Set(name=set_name, elements=elements)
region = part.sets[set_name]
layup_name = 'CLayup_' + suffix
t_diff = (float(t_list[i]) - t_model) * scaling_factor
t_scaling_factor = (t_model + t_diff) / t_model
def modify_ply(index, kwargs):
kwargs['thickness'] *= t_scaling_factor
kwargs['region'] = region
return kwargs
layup = part.CompositeLayup(name=layup_name,
objectToCopy=original_layup)
layup.resume()
abaqus_functions.modify_composite_layup(part=part,
layup_name=layup_name,
modify_func=modify_ply)
# suppress needed to put the new properties to the input file
original_layup.suppress()
return elems_t, t_set
def calc_translations_ABAQUS(imperfection_file_name,
model_name,
part_name,
H_model,
H_measured,
R_model,
R_best_fit=None,
semi_angle=0.,
stretch_H=False,
z_offset_bot=None,
rotatedeg=0.,
scaling_factor=1.,
r_TOL=1.,
num_closest_points=5,
power_parameter=2,
use_theta_z_format=True,
ignore_bot_h=None,
ignore_top_h=None,
sample_size=None,
T=None):
r"""Reads an imperfection file and calculates the nodal translations
Parameters
----------
imperfection_file_name : str
Full path to the imperfection file.
model_name : str
Model name.
part_name : str
Part name.
H_model : float
Total height of the model where the imperfections will be applied to,
considering also eventual resin rings.
H_measured : float
The total height of the measured test specimen, including eventual
resin rings at the edges.
R_model : float
Radius **at the bottom edge** of the model where the imperfections
will be applied to.
R_best_fit : float, optional
Best fit radius obtained with functions :func:`.best_fit_cylinder`
or :func:`.best_fit_cone`.
semi_angle : float, optional
Cone semi-vertex angle in degrees, when applicable.
stretch_H : bool, optional
If the measured imperfection data should be stretched to the current
model (which may happen when ``H_model!=H_measured``.
z_offset_bot : float, optional
It is common to have the measured data not covering the whole test
specimen, and therefore it will be centralized, if a non-centralized
position is desired this parameter can be used for the adjustment.
rotatedeg : float, optional
Rotation angle in degrees telling how much the imperfection pattern
should be rotated about the `X_3` (or `Z`) axis.
scaling_factor : float, optional
A scaling factor that can be used to study the imperfection
sensitivity.
r_TOL : float, optional
Percentage tolerance to ignore noisy data from the measurements.
num_closest_points : int, optional
See :func:`the inverse-weighted interpolation algorithm
<.inv_weighted>`.
power_parameter : float, optional
See :func:`the inverse-weighted interpolation algorithm
<.inv_weighted>`.
use_theta_z_format : bool, optional
If the new format `\theta, Z, imp` should be used instead of the old
`X, Y, Z`.
ignore_bot_h : None or float, optional
Used to ignore nodes from the bottom resin ring.
ignore_top_h : None or float, optional
Used to ignore nodes from the top resin ring.
sample_size : int, optional
If the input file containing the measured data is too large it may be
required to limit the sample size in order to avoid memory errors.
T : None or np.ndarray, optional
A transformation matrix (cf. :func:`.transf_matrix`) required when the
mesh is not in the :ref:`default coordinate system <figure_conecyl>`.
"""
import abaqus
import desicos.abaqus.abaqus_functions as abaqus_functions
mod = abaqus.mdb.models[model_name]
part = mod.parts[part_name]
part_nodes = np.array(part.nodes)
coords = np.array([n.coordinates for n in part_nodes], dtype=FLOAT)
if T is not None:
tmp = np.vstack((coords.T, np.ones((1, coords.shape[0]))))
coords = np.dot(T, tmp).T
del tmp
if ignore_bot_h is not None:
if ignore_bot_h <= 0:
ignore_bot_h = None
else:
mask = coords[:, 2] > ignore_bot_h
coords = coords[mask]
part_nodes = part_nodes[mask]
if ignore_top_h is not None:
if ignore_top_h <= 0:
ignore_top_h = None
else:
mask = coords[:, 2] < (H_model - ignore_top_h)
coords = coords[mask]
part_nodes = part_nodes[mask]
if use_theta_z_format:
d, d, data = read_theta_z_imp(path=imperfection_file_name,
H_measured=H_measured,
stretch_H=stretch_H,
z_offset_bot=z_offset_bot)
if sample_size:
num = data.shape[0]
if sample_size < num:
log('Using sample_size={0}'.format(sample_size), level=1)
data = data[sample(range(num), int(sample_size)), :]
if r_TOL:
max_imp = R_model * r_TOL / 100.
imp = data[:, 2]
cond = np.any(np.array((imp > max_imp, imp < (-max_imp))), axis=0)
log('Skipping {0} points'.format(len(imp[cond])))
data = data[np.logical_not(cond), :]
data3D = np.zeros((data.shape[0], 4), dtype=FLOAT)
if rotatedeg:
data[:, 0] += deg2rad(rotatedeg)
z = data[:, 1]
z *= H_model
alpharad = deg2rad(semi_angle)
tana = tan(alpharad)
def r_local(z):
return R_model - z * tana
data3D[:, 0] = r_local(z) * cos(data[:, 0])
data3D[:, 1] = r_local(z) * sin(data[:, 0])
data3D[:, 2] = z
data3D[:, 3] = data[:, 2]
dist, w0 = inv_weighted(data3D,
coords,
ncp=num_closest_points,
power_parameter=power_parameter)
thetas = arctan2(coords[:, 1], coords[:, 0])
trans = np.zeros_like(coords)
trans[:, 0] = w0 * cos(alpharad) * cos(thetas)
trans[:, 1] = w0 * cos(alpharad) * sin(thetas)
trans[:, 2] = w0 * sin(alpharad)
else:
#NOTE perhaps remove this in the future, when the imperfection files
# are stored as theta, z, amplitude only
nodes = np.array(
[[n.coordinates[0], n.coordinates[1], n.coordinates[2], n.label]
for n in part_nodes],
dtype=FLOAT)
# calling translate_nodes function
trans = calc_nodal_translations(imperfection_file_name,
nodes=nodes,
H_model=H_model,
H_measured=H_measured,
R_model=R_model,
R_best_fit=R_best_fit,
semi_angle=semi_angle,
stretch_H=stretch_H,
z_offset_bot=z_offset_bot,
rotatedeg=rotatedeg,
r_TOL=r_TOL,
num_closest_points=num_closest_points,
power_parameter=power_parameter,
sample_size=sample_size)
trans = trans[:, :3]
return trans
if __name__ == '__main__':
cc = stds['desicos_study'].ccs[1]
cc.created_model = False
cc.create_model()
part_nodes = mdb.models[cc.model_name].parts[cc.part_name_shell].nodes
coords = np.array([n.coordinates for n in part_nodes])
sf = 100
path = r'C:\clones\desicos\desicos\conecylDB\files\dlr\degenhardt_2010_z20\degenhardt_2010_z20_msi_theta_z_imp.txt'
H_measured = 510.
H_model = 510.
d, d, data = read_theta_z_imp(path=path,
H_measured=H_measured,
stretch_H=False,
z_offset_bot=None)
log('init sample')
data = np.array(sample(data, 10000))
log('end sample')
data = data[np.argsort(data[:, 0])]
data = np.vstack((data[-3000:], data[:], data[:3000]))
data[:, 0] /= data[:, 0].max()
mesh = np.zeros((coords.shape[0], 2), dtype=FLOAT)
mesh[:, 0] = arctan2(coords[:, 1], coords[:, 0])
mesh[:, 1] = coords[:, 2]
mesh_norm = mesh.copy()
mesh_norm[:, 0] /= mesh_norm[:, 0].max()
mesh_norm[:, 1] /= mesh_norm[:, 1].max()
def change_thickness_ABAQUS(imperfection_file_name,
model_name,
part_name,
stack,
t_model,
t_measured,
H_model,
H_measured,
R_model,
R_best_fit = None,
number_of_sets = None,
semi_angle = 0.,
stretch_H = False,
z_offset_bot = None,
scaling_factor = 1.,
num_closest_points = 5,
power_parameter = 2,
num_sec_z = 100,
elems_t = None,
t_set = None,
use_theta_z_format = False):
r"""Applies a given thickness imperfection to the finite element model
Assumes that a percentage variation of the laminate thickness can be
represented by the same percentage veriation of each ply, i.e., each
ply thickness is varied in order to reflect a given measured thickness
imperfection field.
Parameters
----------
imperfection_file_name : str
Full path to the imperfection file.
model_name : str
Model name.
part_name : str
Part name.
stack : list
The stacking sequence of the current model with each angle given in
degrees.
t_model : float
The nominal shell thickness of the current model.
t_measured : float
The nominal thickness of the measured specimen.
H_model : float
Total height of the model where the imperfections will be applied to,
considering also eventual resin rings.
H_measured : float
The total height of the measured test specimen, including eventual
resin rings at the edges.
R_model : float
Radius **at the bottom edge** of the model where the imperfections
will be applied to.
R_best_fit : float, optional
Best fit radius obtained with functions :func:`.best_fit_cylinder`
or :func:`.best_fit_cone`.
number_of_sets : int, optional
Defines in how many levels the thicknesses should be divided. If
``None`` it will be based on the input file, and if the threshold
of ``100`` is exceeded, ``10`` sections are used.
semi_angle : float, optional
Cone semi-vertex angle in degrees, when applicable.
stretch_H : bool, optional
If the measured imperfection data should be stretched to the current
model (which may happen when ``H_model!=H_measured``.
z_offset_bot : float, optional
It is common to have the measured data not covering the whole test
specimen, and therefore it will be centralized, if a non-centralized
position is desired this parameter can be used for the adjustment.
scaling_factor : float, optional
A scaling factor that can be used to study the imperfection
sensitivity.
num_closest_points : int, optional
See :func:`the inverse-weighted interpolation algorithm
<.inv_weighted>`.
power_parameter : float, optional
See :func:`the inverse-weighted interpolation algorithm
<.inv_weighted>`.
num_sec_z : int, optional
Number of spatial sections used to classify the measured data in order
to accelerate the searching algorithms
elems_t : np.ndarray, optional
Interpolated thickness for each element. Can be used to avoid the same
interpolation to be performed twice.
t_set : set, optional
A ``set`` object containing the unique thicknesses that will be used
to create the new properties.
use_theta_z_format : bool, optional
If the new format `\theta, Z, imp` should be used instead of the old
`X, Y, Z`.
"""
from abaqus import mdb
import desicos.abaqus.abaqus_functions as abaqus_functions
mod = mdb.models[model_name]
part = mod.parts[part_name]
part_cyl_csys = part.features['part_cyl_csys']
part_cyl_csys = part.datums[part_cyl_csys.id]
if use_theta_z_format:
if elems_t is None or t_set is None:
log('Reading coordinates for elements...')
elements = vec_calc_elem_cg(part.elements)
log('Coordinates for elements read!')
d, d, data = read_theta_z_imp(path = imperfection_file_name,
H_measured = H_measured,
stretch_H = stretch_H,
z_offset_bot = z_offset_bot)
data3D = np.zeros((data.shape[0], 4), dtype=FLOAT)
z = data[:, 1]
z *= H_model
alpharad = deg2rad(semi_angle)
tana = tan(alpharad)
def r_local(z):
return R_model - z*tana
data3D[:, 0] = r_local(z)*cos(data[:, 0])
data3D[:, 1] = r_local(z)*sin(data[:, 0])
data3D[:, 2] = z
data3D[:, 3] = data[:, 2]
ans = inv_weighted(data3D, elements[:, :3],
num_sub = num_sec_z,
col = 2,
ncp = num_closest_points,
power_parameter = power_parameter)
t_set = set(ans)
t_set.discard(0.) #TODO why inv_weighted returns an array with 0.
elems_t = np.zeros((elements.shape[0], 2), dtype=FLOAT)
elems_t[:, 0] = elements[:, 3]
elems_t[:, 1] = ans
else:
log('Thickness differences already calculated!')
else:
if elems_t is None or t_set is None:
# reading elements data
log('Reading coordinates for elements...')
elements = vec_calc_elem_cg(part.elements)
log('Coordinates for elements read!')
# calling translate_nodes function
elems_t, t_set = calc_elems_t(
imperfection_file_name,
nodes = elements,
t_model = t_model,
t_measured = t_measured,
H_model = H_model,
H_measured = H_measured,
R_model = R_model,
R_best_fit = R_best_fit,
semi_angle = semi_angle,
stretch_H = stretch_H,
z_offset_bot = z_offset_bot,
num_closest_points = num_closest_points,
power_parameter = power_parameter,
num_sec_z = num_sec_z)
else:
log('Thickness differences already calculated!')
# creating sets
t_list = []
max_len_t_set = 100
if len(t_set) >= max_len_t_set and number_of_sets in (None, 0):
number_of_sets = 10
log('More than {0:d} different thicknesses measured!'.format(
max_len_t_set))
log('Forcing a number_of_sets = {0:d}'.format(number_of_sets))
if number_of_sets is None or number_of_sets == 0:
number_of_sets = len(t_set)
t_list = list(t_set)
t_list.sort()
else:
t_min = min(t_set)
t_max = max(t_set)
t_list = list(np.linspace(t_min, t_max, number_of_sets+1))
# grouping elements
sets_ids = [[] for i in range(len(t_list))]
for entry in elems_t:
elem_id, t = entry
index = index_within_linspace(t_list, t)
sets_ids[index].append(int(elem_id))
# putting elements in sets
original_layup = part.compositeLayups['CompositePlate']
for i, set_ids in enumerate(sets_ids):
if len(set_ids) == 0:
# since t_set_norm * t_model <> t_set originally measured
# there may be empty set_ids at the end
continue
elements = part.elements.sequenceFromLabels(labels=set_ids)
suffix = 'measured_imp_t_{0:03d}'.format(i)
set_name = 'Set_' + suffix
log('Creating set ({0: 7d} elements): {1}'.format(
len(set_ids), set_name))
part.Set(name = set_name, elements = elements)
region = part.sets[set_name]
layup_name = 'CLayup_' + suffix
t_diff = (float(t_list[i]) - t_model) * scaling_factor
t_scaling_factor = (t_model + t_diff)/t_model
def modify_ply(index, kwargs):
kwargs['thickness'] *= t_scaling_factor
kwargs['region'] = region
return kwargs
layup = part.CompositeLayup(name=layup_name,
objectToCopy=original_layup)
layup.resume()
abaqus_functions.modify_composite_layup(part=part,
layup_name=layup_name, modify_func=modify_ply)
# suppress needed to put the new properties to the input file
original_layup.suppress()
return elems_t, t_set
def calc_translations_ABAQUS(imperfection_file_name,
model_name,
part_name,
H_model,
H_measured,
R_model,
R_best_fit=None,
semi_angle=0.,
stretch_H=False,
z_offset_bot=None,
rotatedeg=0.,
scaling_factor=1.,
r_TOL=1.,
num_closest_points=5,
power_parameter=2,
num_sec_z=50,
use_theta_z_format=True,
ignore_bot_h=None,
ignore_top_h=None,
sample_size=None,
T=None):
r"""Reads an imperfection file and calculates the nodal translations
Parameters
----------
imperfection_file_name : str
Full path to the imperfection file.
model_name : str
Model name.
part_name : str
Part name.
H_model : float
Total height of the model where the imperfections will be applied to,
considering also eventual resin rings.
H_measured : float
The total height of the measured test specimen, including eventual
resin rings at the edges.
R_model : float
Radius **at the bottom edge** of the model where the imperfections
will be applied to.
R_best_fit : float, optional
Best fit radius obtained with functions :func:`.best_fit_cylinder`
or :func:`.best_fit_cone`.
semi_angle : float, optional
Cone semi-vertex angle in degrees, when applicable.
stretch_H : bool, optional
If the measured imperfection data should be stretched to the current
model (which may happen when ``H_model!=H_measured``.
z_offset_bot : float, optional
It is common to have the measured data not covering the whole test
specimen, and therefore it will be centralized, if a non-centralized
position is desired this parameter can be used for the adjustment.
rotatedeg : float, optional
Rotation angle in degrees telling how much the imperfection pattern
should be rotated about the `X_3` (or `Z`) axis.
scaling_factor : float, optional
A scaling factor that can be used to study the imperfection
sensitivity.
r_TOL : float, optional
Percentage tolerance to ignore noisy data from the measurements.
num_closest_points : int, optional
See :func:`the inverse-weighted interpolation algorithm
<.inv_weighted>`.
power_parameter : float, optional
See :func:`the inverse-weighted interpolation algorithm
<.inv_weighted>`.
num_sec_z : int, optional
Number of spatial sections used to classify the measured data in order
to accelerate the searching algorithms
use_theta_z_format : bool, optional
If the new format `\theta, Z, imp` should be used instead of the old
`X, Y, Z`.
ignore_bot_h : None or float, optional
Used to ignore nodes from the bottom resin ring.
ignore_top_h : None or float, optional
Used to ignore nodes from the top resin ring.
sample_size : int, optional
If the input file containing the measured data is too large it may be
required to limit the sample size in order to avoid memory errors.
T : None or np.ndarray, optional
A transformation matrix (cf. :func:`.transf_matrix`) required when the
mesh is not in the :ref:`default coordinate system <figure_conecyl>`.
"""
import abaqus
import desicos.abaqus.abaqus_functions as abaqus_functions
mod = abaqus.mdb.models[model_name]
part = mod.parts[part_name]
part_nodes = np.array(part.nodes)
coords = np.array([n.coordinates for n in part_nodes], dtype=FLOAT)
if T is not None:
tmp = np.vstack((coords.T, np.ones((1, coords.shape[0]))))
coords = np.dot(T, tmp).T
del tmp
if ignore_bot_h is not None:
if ignore_bot_h <= 0:
ignore_bot_h = None
else:
mask = coords[:, 2] > ignore_bot_h
coords = coords[mask]
part_nodes = part_nodes[mask]
if ignore_top_h is not None:
if ignore_top_h <= 0:
ignore_top_h = None
else:
mask = coords[:, 2] < (H_model - ignore_top_h)
coords = coords[mask]
part_nodes = part_nodes[mask]
if use_theta_z_format:
d, d, data = read_theta_z_imp(path=imperfection_file_name,
H_measured=H_measured,
stretch_H=stretch_H,
z_offset_bot=z_offset_bot)
if sample_size:
num = data.shape[0]
if sample_size < num:
log('Using sample_size={0}'.format(sample_size), level=1)
data = data[sample(range(num), int(sample_size)), :]
if r_TOL:
max_imp = R_model * r_TOL / 100.
imp = data[:, 2]
cond = np.any(np.array((imp > max_imp, imp < (-max_imp))), axis=0)
log('Skipping {0} points'.format(len(imp[cond])))
data = data[np.logical_not(cond), :]
data3D = np.zeros((data.shape[0], 4), dtype=FLOAT)
if rotatedeg:
data[:, 0] += deg2rad(rotatedeg)
z = data[:, 1]
z *= H_model
alpharad = deg2rad(semi_angle)
tana = tan(alpharad)
def r_local(z):
return R_model - z*tana
data3D[:, 0] = r_local(z)*cos(data[:, 0])
data3D[:, 1] = r_local(z)*sin(data[:, 0])
data3D[:, 2] = z
data3D[:, 3] = data[:, 2]
w0 = inv_weighted(data3D, coords,
num_sub = num_sec_z,
col = 2,
ncp = num_closest_points,
power_parameter = power_parameter)
thetas = arctan2(coords[:, 1], coords[:, 0])
trans = np.zeros_like(coords)
trans[:, 0] = w0*cos(alpharad)*cos(thetas)
trans[:, 1] = w0*cos(alpharad)*sin(thetas)
trans[:, 2] = w0*sin(alpharad)
else:
#NOTE perhaps remove this in the future, when the imperfection files
# are stored as theta, z, amplitude only
nodes = np.array([[n.coordinates[0],
n.coordinates[1],
n.coordinates[2],
n.label] for n in part_nodes], dtype=FLOAT)
# calling translate_nodes function
trans = calc_nodal_translations(
imperfection_file_name,
nodes = nodes,
H_model = H_model,
H_measured = H_measured,
R_model = R_model,
R_best_fit = R_best_fit,
semi_angle = semi_angle,
stretch_H = stretch_H,
z_offset_bot = z_offset_bot,
rotatedeg = rotatedeg,
r_TOL = r_TOL,
num_closest_points = num_closest_points,
power_parameter = power_parameter,
num_sec_z = num_sec_z,
sample_size = sample_size)
trans = trans[:, :3]
return trans
if __name__ == '__main__':
cc = stds['desicos_study'].ccs[1]
cc.created_model = False
cc.create_model()
part_nodes = mdb.models[cc.model_name].parts[cc.part_name_shell].nodes
coords = np.array([n.coordinates for n in part_nodes])
sf = 100
path = r'C:\clones\desicos\desicos\conecylDB\files\dlr\degenhardt_2010_z20\degenhardt_2010_z20_msi_theta_z_imp.txt'
H_measured = 510.
H_model = 510.
d, d, data = read_theta_z_imp(path=path,
H_measured=H_measured,
stretch_H=False,
z_offset_bot=None)
log('init sample')
data = np.array(sample(data, 10000))
log('end sample')
data = data[np.argsort(data[:, 0])]
data = np.vstack((data[-3000:], data[:], data[:3000]))
data[:, 0] /= data[:, 0].max()
mesh = np.zeros((coords.shape[0], 2), dtype=FLOAT)
mesh[:, 0] = arctan2(coords[:, 1], coords[:, 0])
mesh[:, 1] = coords[:, 2]
mesh_norm = mesh.copy()
mesh_norm[:, 0] /= mesh_norm[:, 0].max()