def disturb_my_model(imperfection_file_name,
nodes_file_name,
output_file_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=25,
sample_size=None):
# reading nodes data
log('Reading nodes data from {0} ...'.format(nodes_file_name))
nodes = get_nodes_from_txt_file(nodes_file_name)
# calling translate_nodes function
nodal_translations = 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)
# writing output file
log('Writing output file "{0}" ...'.format(output_file_name))
outfile = open(output_file_name, 'w')
for i, node in enumerate(nodal_translations):
original_coords = node[0:3]
translations = nodal_translations[k]
new_coords = original_coords + translations * scaling_factor
outfile.write('{0:d} {1:f} {2:f} {3:f}\n'.format(k, coords[0],
coords[1], coords[2]))
outfile.close()
return True
def disturb_my_model(imperfection_file_name,
nodes_file_name,
output_file_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=25,
sample_size=None):
# reading nodes data
log('Reading nodes data from {0} ...'.format(nodes_file_name))
nodes = get_nodes_from_txt_file(nodes_file_name)
# calling translate_nodes function
nodal_translations = 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)
# writing output file
log('Writing output file "{0}" ...'.format(output_file_name))
outfile = open(output_file_name, 'w')
for i, node in enumerate(nodal_translations):
original_coords = node[0:3]
translations = nodal_translations[k]
new_coords = original_coords + translations * scaling_factor
outfile.write('{0:d} {1:f} {2:f} {3:f}\n'.format(
k, coords[0], coords[1], coords[2]))
outfile.close()
return True
def check_completed(self, wait=False, print_found=False):
if not self.rebuilt:
self.rebuild()
tmp = os.path.join(self.output_dir, self.model_name + '.log')
if wait == True:
log('Waiting for job completion...')
#TODO a general function to check the log file
while True:
if os.path.isfile(tmp):
tmpfile = open(tmp, 'r')
lines = tmpfile.readlines()
tmpfile.close()
if len(lines) == 0:
continue
if len(lines) < 2:
continue
if lines[-2].find('End Abaqus/Standard Analysis') > -1:
if print_found:
log('RUN COMPLETED for model {0}'.format(
self.model_name))
return True
elif lines[-1].find('Abaqus/Analysis exited with errors') > -1:
if print_found:
log('RUN COMPLETED WITH ERRORS for model {0}'.format(
self.model_name))
return True
else:
if not wait:
warn('RUN NOT COMPLETED for model {0}'.format(
self.model_name))
return False
else:
if not wait:
warn('RUN NOT STARTED for model {0}'.format(
self.model_name))
return False
if wait:
import time
time.sleep(5)
def check_completed(self, wait=False, print_found=False):
if not self.rebuilt:
self.rebuild()
tmp = os.path.join(self.output_dir, self.model_name + '.log')
if wait == True:
log('Waiting for job completion...')
#TODO a general function to check the log file
while True:
if os.path.isfile(tmp):
tmpfile = open(tmp, 'r')
lines = tmpfile.readlines()
tmpfile.close()
if len(lines) == 0:
continue
if len(lines) < 2:
continue
if lines[-2].find('End Abaqus/Standard Analysis') > -1:
if print_found:
log('RUN COMPLETED for model {0}'.format(
self.model_name))
return True
elif lines[-1].find('Abaqus/Analysis exited with errors') > -1:
if print_found:
log('RUN COMPLETED WITH ERRORS for model {0}'.format(
self.model_name))
return True
else:
if not wait:
warn('RUN NOT COMPLETED for model {0}'.format(
self.model_name))
return False
else:
if not wait:
warn('RUN NOT STARTED for model {0}'.format(
self.model_name))
return False
if wait:
import time
time.sleep(5)
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()
import desicos.conecylDB.interpolate
reload(desicos.conecylDB.interpolate)
def read_file(file_name,
frequency=1,
forced_average_radius=None,
H_measured=None,
R_best_fit=None,
stretch_H=False,
z_offset_bot=None,
r_TOL=1.):
log('Reading imperfection file: {0} ...'.format(file_name))
# user warnings
if stretch_H:
if z_offset_bot:
warn('Because of the stretch_H option, ' +
'consider setting z_offset_bot to None')
# reading the imperfection file
ignore = False
mps = np.loadtxt(file_name, dtype=FLOAT)
r = np.sqrt(mps[:, 0]**2 + mps[:, 1]**2)
# measuring model dimensions
if R_best_fit is None:
R_best_fit = np.average(r)
warn('The cylinder average radius of the measured points ' +
'assumed to be {0:1.2f}'.format(R_best_fit))
z_min = mps[:, 2].min()
z_max = mps[:, 2].max()
z_center = (z_max + z_min) / 2.
H_points = (z_max - z_min)
log('R_best_fit : {0}'.format(R_best_fit))
# applying user inputs
R = R_best_fit
if forced_average_radius:
R = forced_average_radius
log('Forced measured radius: {0}'.format(forced_average_radius))
H_model = H_points
if not H_measured:
H_measured = H_points
warn('The cylinder height of the measured points assumed ' +
'to be {0:1.2f}'.format(H_measured))
# calculating default z_offset_bot
if not z_offset_bot:
if stretch_H:
z_offset_bot = 0.
else:
z_offset_bot = (H_measured - H_points) / 2.
offset_z = z_offset_bot - z_min
log('H_points : {0}'.format(H_points))
log('H_measured : {0}'.format(H_measured))
log('z_min : {0}'.format(z_min))
log('z_max : {0}'.format(z_max))
log('offset_z : {0}'.format(offset_z))
r_TOL_min = (R * (1 - r_TOL / 100.))
r_TOL_max = (R * (1 + r_TOL / 100.))
cond = np.all(np.array((r > r_TOL_max, r < r_TOL_min)), axis=0)
skept = mps[cond]
log('Skipping {0} points'.format(len(skept)))
mps = mps[np.logical_not(cond)]
offset_mps = mps.copy()
offset_mps[:, 2] += offset_z
norm_mps = offset_mps.copy()
norm_mps[:, 0] /= R
norm_mps[:, 1] /= R
if stretch_H:
norm_mps[:, 2] /= H_points
else:
norm_mps[:, 2] /= H_measured
return mps, offset_mps, norm_mps
def calc_nodal_translations(imperfection_file_name, nodes, H_model, H_measured,
R_model, R_best_fit, semi_angle, stretch_H,
z_offset_bot, rotatedeg, r_TOL, num_closest_points,
power_parameter, num_sec_z, sample_size):
# reading imperfection file
m, o, mps = read_file(file_name=imperfection_file_name,
H_measured=H_measured,
R_best_fit=R_best_fit,
forced_average_radius=R_best_fit,
stretch_H=stretch_H,
z_offset_bot=z_offset_bot,
r_TOL=r_TOL)
log('Calculating nodal translations!')
if sample_size:
num = mps.shape[0]
if sample_size < num:
log('Using sample_size={0}'.format(sample_size), level=1)
mps = mps[sample(range(num), int(sample_size)), :]
num_nodes = nodes.shape[0]
R_top = R_model - np.tan(np.deg2rad(semi_angle)) * H_model
semi_angle = abs(semi_angle)
def local_radius(z):
return R_model + (R_top - R_model) * z / H_model
mps[:, 2] *= H_model
if semi_angle < 1.e-6:
R_local = R_model
else:
R_local = local_radius(mps[:, 2])
thetarads = np.arctan2(mps[:, 1], mps[:, 0])
if rotatedeg:
thetarads += np.deg2rad(rotatedeg)
mps[:, 0] = R_local * np.cos(thetarads)
mps[:, 1] = R_local * np.sin(thetarads)
num_sec_z = int(num_sec_z)
mem_limit = 1024 * 1024 * 1024 * 8 * 2 # 2 GB
mem_entries = int(mem_limit / 64) # if float64 is used
sec_size = int(num_nodes / num_sec_z)
#TODO better memory control...
if sec_size**2 * 10 > mem_entries:
while True:
num_sec_z += 1
sec_size = int(num_nodes / num_sec_z)
if sec_size**2 * 10 <= mem_entries:
warn('New sec_size {0}'.format(sec_size))
break
ncp = num_closest_points
nodes = nodes[np.argsort(nodes[:, 2])]
mps = mps[np.argsort(mps[:, 2])]
nodal_t = np.zeros(nodes.shape, dtype=nodes.dtype)
limit = int(num_sec_z / 5)
for i in xrange(num_sec_z + 1):
i_inf = sec_size * i
i_sup = sec_size * (i + 1)
if i % limit == 0:
log('processed {0:7d} out of {1:7d} entries'.format(
min(i_sup, num_nodes), num_nodes),
level=1)
sub_nodes = nodes[i_inf:i_sup]
if not np.any(sub_nodes):
continue
inf_z = sub_nodes[:, 2].min()
sup_z = sub_nodes[:, 2].max()
tol = 0.01
if i == 0 or i == num_sec_z:
tol = 0.05
while True:
cond1 = mps[:, 2] >= inf_z - tol * H_model
cond2 = mps[:, 2] <= sup_z + tol * H_model
cond = np.all(np.array((cond1, cond2)), axis=0)
sub_mps = mps[cond]
if not np.any(sub_mps):
tol += 0.01
else:
break
dist = np.subtract.outer(sub_nodes[:, 0], sub_mps[:, 0])**2
dist += np.subtract.outer(sub_nodes[:, 1], sub_mps[:, 1])**2
dist += np.subtract.outer(sub_nodes[:, 2], sub_mps[:, 2])**2
asort = np.argsort(dist, axis=1)
lenn = sub_nodes.shape[0]
lenp = sub_mps.shape[0]
asort_mesh = asort + np.meshgrid(
np.arange(lenn) * lenp, np.arange(lenp))[0].transpose()
# getting the z coordinate of the closest points
sub_mps_z = np.take(sub_mps[:, 2], asort[:, :ncp])
# getting the distance of the closest points
dist_ncp = np.take(dist, asort_mesh[:, :ncp])
# avoiding division by zero
dist_ncp[(dist_ncp == 0)] == 1.e-12
# calculating the radius of the sub-group of measured points
radius = np.sqrt(sub_mps[:, 0]**2 + sub_mps[:, 1]**2)
# taking only the radius of the closest points
radius_ncp = np.take(radius, asort[:, :ncp])
# weight calculation
total_weight = np.sum(1. / (dist_ncp**power_parameter), axis=1)
weight = 1. / (dist_ncp**power_parameter)
# calculating the local radius for the closest points
r_local_ncp = local_radius(sub_mps_z)
# computing the new radius
r_new = np.sum(radius_ncp * weight / r_local_ncp,
axis=1) / total_weight
r_new *= local_radius(sub_nodes[:, 2])
#NOTE modified after Regina, Mariano and Saullo decided to use
# the imperfection amplitude constant along the whole cone
# surface, which represents better the real manufacturing
# conditions. In that case the amplitude will be re-scaled
# using only the bottom radius
# calculating the local radius for the nodes for the new assumption
r_local_nodes = np.sqrt(sub_nodes[:, 0]**2 + sub_nodes[:, 1]**2)
# calculating the scaling factor required for the new assumption
sf = R_model / r_local_nodes
theta = np.arctan2(sub_nodes[:, 1], sub_nodes[:, 0])
nodal_t[i_inf : i_sup][:, 0] = \
(r_new*np.cos(theta) - sub_nodes[:, 0])*sf
nodal_t[i_inf : i_sup][:, 1] = \
(r_new*np.sin(theta) - sub_nodes[:, 1])*sf
nodal_t[i_inf:i_sup][:, 3] = sub_nodes[:, 3]
nodal_t = nodal_t[np.argsort(nodal_t[:, 3])]
log('Nodal translations calculated!')
return nodal_t
def translate_nodes_ABAQUS_c0(m0,
n0,
c0,
funcnum,
model_name,
part_name,
H_model,
semi_angle=0.,
scaling_factor=1.,
fem_meridian_bot2top=True,
ignore_bot_h=None,
ignore_top_h=None,
T=None):
r"""Translates the nodes in Abaqus based on a Fourier series
The Fourier Series can be a half-sine, half-cosine or a complete Fourier
Series as detailed in :func:`desicos.conecylDB.fit_data.calc_c0`.
Parameters
----------
m0 : int
Number of terms along the `x` coordinate.
n0 : int
Number of terms along the `\theta` coordinate.
c0 : numpy.ndarray
The coefficients that will give the imperfection pattern.
funcnum : int
The function type, as detailed in
:func:`desicos.conecylDB.fit_data.calc_c0`.
model_name : str
Must be a valid key in the dictionary ``mdb.models``, in the
interactive Python inside Abaqus.
part_name : str
Must be a valid key in the dictionary
``mdb.models[model_name].parts``, in the interactive Python inside
Abaqus.
H_model : float
Total height of the model where the imperfections will be applied to,
considering also eventual resin rings.
semi_angle : float, optional
The cone semi-vertex angle (a null value indicates that a cylinder is
beeing analyzed).
scaling_factor : float, optional
The scaling factor that will multiply ``c0`` when applying the
imperfections.
fem_meridian_bot2top : bool, optional
A boolean indicating if the finite element has the `x` axis starting
at the bottom or at the top.
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.
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>`.
Returns
-------
nodal_translations : numpy.ndarray
A 2-D array containing the translations ``x, y, z`` for each column.
Notes
-----
Despite the nodal traslations are returned all the nodes belonging to this
model will be already translated.
"""
from abaqus import mdb, session
from desicos.conecylDB import fit_data
log('Calculating imperfection amplitudes for model {0} ...\n\t'.format(
model_name) + '(using a scaling factor of {0})'.format(scaling_factor))
c0 = c0 * scaling_factor
mod = 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])
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:
log('Applying ignore_bot_h: ignoring nodes with z <= {0}'.format(
ignore_bot_h))
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:
log('Applying ignore_top_h: ignoring nodes with z >= {0}'.format(
H_model - ignore_top_h))
mask = coords[:, 2] < (H_model - ignore_top_h)
coords = coords[mask]
part_nodes = part_nodes[mask]
if ignore_bot_h is None:
ignore_bot_h = 0
if ignore_top_h is None:
ignore_top_h = 0
H_eff = H_model - (ignore_bot_h + ignore_top_h)
if fem_meridian_bot2top:
xs_norm = (coords[:, 2] - ignore_bot_h) / H_eff
else:
xs_norm = (H_eff - (coords[:, 2] - ignore_bot_h)) / H_eff
thetas = arctan2(coords[:, 1], coords[:, 0])
alpharad = deg2rad(semi_angle)
w0 = fit_data.fw0(m0, n0, c0, xs_norm, thetas, funcnum)
nodal_translations = np.zeros_like(coords)
nodal_translations[:, 0] = w0 * cos(alpharad) * cos(thetas)
nodal_translations[:, 1] = w0 * cos(alpharad) * sin(thetas)
nodal_translations[:, 2] = w0 * sin(alpharad)
log('Calculation of imperfection amplitudes finished!')
# applying translations
viewport = session.viewports[session.currentViewportName]
log('Applying new nodal positions in ABAQUS CAE to model {0} ...'.format(
model_name))
log(' (using a scaling factor of {0})'.format(scaling_factor))
new_coords = coords + nodal_translations
if T is not None:
Tinv = np.zeros_like(T)
Tinv[:3, :3] = T[:3, :3].T
Tinv[:, 3] = -T[:, 3]
tmp = np.vstack((new_coords.T, np.ones((1, new_coords.shape[0]))))
new_coords = np.dot(Tinv, tmp).T
del tmp
meshNodeArray = part.nodes.sequenceFromLabels(
[n.label for n in part_nodes])
new_coords = np.ascontiguousarray(new_coords)
part.editNode(nodes=meshNodeArray, coordinates=new_coords)
log('Application of new nodal positions finished!')
ra = mod.rootAssembly
viewport.setValues(displayedObject=ra)
ra.regenerate()
return nodal_translations
def read_file(file_name,
frequency = 1,
forced_average_radius = None,
H_measured = None,
R_best_fit = None,
stretch_H = False,
z_offset_bot = None,
r_TOL = 1.):
log('Reading imperfection file: {0} ...'.format(file_name))
# user warnings
if stretch_H:
if z_offset_bot:
warn('Because of the stretch_H option, '+
'consider setting z_offset_bot to None')
# reading the imperfection file
ignore = False
mps = np.loadtxt(file_name, dtype=FLOAT)
r = np.sqrt(mps[:, 0]**2 + mps[:, 1]**2)
# measuring model dimensions
if R_best_fit is None:
R_best_fit = np.average(r)
warn('The cylinder average radius of the measured points ' +
'assumed to be {0:1.2f}'.format(R_best_fit))
z_min = mps[:, 2].min()
z_max = mps[:, 2].max()
z_center = (z_max + z_min)/2.
H_points = (z_max - z_min)
log('R_best_fit : {0}'.format(R_best_fit))
# applying user inputs
R = R_best_fit
if forced_average_radius:
R = forced_average_radius
log('Forced measured radius: {0}'.format(forced_average_radius))
H_model = H_points
if not H_measured:
H_measured = H_points
warn('The cylinder height of the measured points assumed ' +
'to be {0:1.2f}'.format(H_measured))
# calculating default z_offset_bot
if not z_offset_bot:
if stretch_H:
z_offset_bot = 0.
else:
z_offset_bot = (H_measured - H_points) / 2.
offset_z = z_offset_bot - z_min
log('H_points : {0}'.format(H_points))
log('H_measured : {0}'.format(H_measured))
log('z_min : {0}'.format(z_min))
log('z_max : {0}'.format(z_max))
log('offset_z : {0}'.format(offset_z))
r_TOL_min = (R * (1-r_TOL/100.))
r_TOL_max = (R * (1+r_TOL/100.))
cond = np.all(np.array((r > r_TOL_max,
r < r_TOL_min)), axis=0)
skept = mps[cond]
log('Skipping {0} points'.format(len(skept)))
mps = mps[np.logical_not(cond)]
offset_mps = mps.copy()
offset_mps[:, 2] += offset_z
norm_mps = offset_mps.copy()
norm_mps[:, 0] /= R
norm_mps[:, 1] /= R
if stretch_H:
norm_mps[:, 2] /= H_points
else:
norm_mps[:, 2] /= H_measured
return mps, offset_mps, norm_mps
def calc_nodal_translations(imperfection_file_name,
nodes,
H_model,
H_measured,
R_model,
R_best_fit,
semi_angle,
stretch_H,
z_offset_bot,
rotatedeg,
r_TOL,
num_closest_points,
power_parameter,
num_sec_z,
sample_size):
# reading imperfection file
m, o, mps = read_file(file_name = imperfection_file_name,
H_measured = H_measured,
R_best_fit = R_best_fit,
forced_average_radius = R_best_fit,
stretch_H = stretch_H,
z_offset_bot = z_offset_bot,
r_TOL = r_TOL)
log('Calculating nodal translations!')
if sample_size:
num = mps.shape[0]
if sample_size < num:
log('Using sample_size={0}'.format(sample_size), level=1)
mps = mps[sample(range(num), int(sample_size)), :]
num_nodes = nodes.shape[0]
R_top = R_model - np.tan(np.deg2rad(semi_angle)) * H_model
semi_angle = abs(semi_angle)
def local_radius(z):
return R_model + (R_top - R_model) * z / H_model
mps[:, 2] *= H_model
if semi_angle < 1.e-6:
R_local = R_model
else:
R_local = local_radius(mps[:, 2])
thetarads = np.arctan2(mps[:, 1], mps[:, 0])
if rotatedeg:
thetarads += np.deg2rad(rotatedeg)
mps[:, 0] = R_local*np.cos(thetarads)
mps[:, 1] = R_local*np.sin(thetarads)
num_sec_z = int(num_sec_z)
mem_limit = 1024*1024*1024*8*2 # 2 GB
mem_entries = int(mem_limit / 64) # if float64 is used
sec_size = int(num_nodes/num_sec_z)
#TODO better memory control...
if sec_size**2*10 > mem_entries:
while True:
num_sec_z +=1
sec_size = int(num_nodes/num_sec_z)
if sec_size**2*10 <= mem_entries:
warn('New sec_size {0}'.format(sec_size))
break
ncp = num_closest_points
nodes = nodes[np.argsort(nodes[:, 2])]
mps = mps[np.argsort(mps[:, 2])]
nodal_t = np.zeros(nodes.shape, dtype=nodes.dtype)
limit = int(num_sec_z/5)
for i in xrange(num_sec_z+1):
i_inf = sec_size*i
i_sup = sec_size*(i+1)
if i % limit == 0:
log('processed {0:7d} out of {1:7d} entries'.format(
min(i_sup, num_nodes), num_nodes), level=1)
sub_nodes = nodes[i_inf : i_sup]
if not np.any(sub_nodes):
continue
inf_z = sub_nodes[:, 2].min()
sup_z = sub_nodes[:, 2].max()
tol = 0.01
if i == 0 or i == num_sec_z:
tol = 0.05
while True:
cond1 = mps[:, 2] >= inf_z - tol*H_model
cond2 = mps[:, 2] <= sup_z + tol*H_model
cond = np.all(np.array((cond1, cond2)), axis=0)
sub_mps = mps[cond]
if not np.any(sub_mps):
tol += 0.01
else:
break
dist = np.subtract.outer(sub_nodes[:, 0], sub_mps[:, 0])**2
dist += np.subtract.outer(sub_nodes[:, 1], sub_mps[:, 1])**2
dist += np.subtract.outer(sub_nodes[:, 2], sub_mps[:, 2])**2
asort = np.argsort(dist, axis=1)
lenn = sub_nodes.shape[0]
lenp = sub_mps.shape[0]
asort_mesh = asort + np.meshgrid(np.arange(lenn)*lenp,
np.arange(lenp))[0].transpose()
# getting the z coordinate of the closest points
sub_mps_z = np.take(sub_mps[:, 2], asort[:, :ncp])
# getting the distance of the closest points
dist_ncp = np.take(dist, asort_mesh[:, :ncp])
# avoiding division by zero
dist_ncp[(dist_ncp==0)] == 1.e-12
# calculating the radius of the sub-group of measured points
radius = np.sqrt(sub_mps[:, 0]**2 + sub_mps[:, 1]**2)
# taking only the radius of the closest points
radius_ncp = np.take(radius, asort[:, :ncp])
# weight calculation
total_weight = np.sum(1./(dist_ncp**power_parameter), axis=1)
weight = 1./(dist_ncp**power_parameter)
# calculating the local radius for the closest points
r_local_ncp = local_radius(sub_mps_z)
# computing the new radius
r_new = np.sum(radius_ncp*weight/r_local_ncp, axis=1)/total_weight
r_new *= local_radius(sub_nodes[:, 2])
#NOTE modified after Regina, Mariano and Saullo decided to use
# the imperfection amplitude constant along the whole cone
# surface, which represents better the real manufacturing
# conditions. In that case the amplitude will be re-scaled
# using only the bottom radius
# calculating the local radius for the nodes for the new assumption
r_local_nodes = np.sqrt(sub_nodes[:,0]**2 + sub_nodes[:, 1]**2)
# calculating the scaling factor required for the new assumption
sf = R_model/r_local_nodes
theta = np.arctan2(sub_nodes[:, 1], sub_nodes[:, 0])
nodal_t[i_inf : i_sup][:, 0] = \
(r_new*np.cos(theta) - sub_nodes[:, 0])*sf
nodal_t[i_inf : i_sup][:, 1] = \
(r_new*np.sin(theta) - sub_nodes[:, 1])*sf
nodal_t[i_inf : i_sup][:, 3] = sub_nodes[:, 3]
nodal_t = nodal_t[np.argsort(nodal_t[:, 3])]
log('Nodal translations calculated!')
return nodal_t
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 translate_nodes_ABAQUS_c0(m0, n0, c0, funcnum,
model_name,
part_name,
H_model,
semi_angle=0.,
scaling_factor=1.,
fem_meridian_bot2top=True,
ignore_bot_h=None,
ignore_top_h=None,
T=None):
r"""Translates the nodes in Abaqus based on a Fourier series
The Fourier Series can be a half-sine, half-cosine or a complete Fourier
Series as detailed in :func:`desicos.conecylDB.fit_data.calc_c0`.
Parameters
----------
m0 : int
Number of terms along the `x` coordinate.
n0 : int
Number of terms along the `\theta` coordinate.
c0 : numpy.ndarray
The coefficients that will give the imperfection pattern.
funcnum : int
The function type, as detailed in
:func:`desicos.conecylDB.fit_data.calc_c0`.
model_name : str
Must be a valid key in the dictionary ``mdb.models``, in the
interactive Python inside Abaqus.
part_name : str
Must be a valid key in the dictionary
``mdb.models[model_name].parts``, in the interactive Python inside
Abaqus.
H_model : float
Total height of the model where the imperfections will be applied to,
considering also eventual resin rings.
semi_angle : float, optional
The cone semi-vertex angle (a null value indicates that a cylinder is
beeing analyzed).
scaling_factor : float, optional
The scaling factor that will multiply ``c0`` when applying the
imperfections.
fem_meridian_bot2top : bool, optional
A boolean indicating if the finite element has the `x` axis starting
at the bottom or at the top.
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.
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>`.
Returns
-------
nodal_translations : numpy.ndarray
A 2-D array containing the translations ``x, y, z`` for each column.
Notes
-----
Despite the nodal traslations are returned all the nodes belonging to this
model will be already translated.
"""
from abaqus import mdb, session
from desicos.conecylDB import fit_data
log('Calculating imperfection amplitudes for model {0} ...\n\t'.
format(model_name) +
'(using a scaling factor of {0})'.format(scaling_factor))
c0 = c0*scaling_factor
mod = 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])
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:
log('Applying ignore_bot_h: ignoring nodes with z <= {0}'.format(
ignore_bot_h))
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:
log('Applying ignore_top_h: ignoring nodes with z >= {0}'.format(
H_model - ignore_top_h))
mask = coords[:, 2] < (H_model - ignore_top_h)
coords = coords[mask]
part_nodes = part_nodes[mask]
if ignore_bot_h is None:
ignore_bot_h = 0
if ignore_top_h is None:
ignore_top_h = 0
H_eff = H_model - (ignore_bot_h + ignore_top_h)
if fem_meridian_bot2top:
xs_norm = (coords[:, 2]-ignore_bot_h)/H_eff
else:
xs_norm = (H_eff-(coords[:, 2]-ignore_bot_h))/H_eff
thetas = arctan2(coords[:, 1], coords[:, 0])
alpharad = deg2rad(semi_angle)
w0 = fit_data.fw0(m0, n0, c0, xs_norm, thetas, funcnum)
nodal_translations = np.zeros_like(coords)
nodal_translations[:, 0] = w0*cos(alpharad)*cos(thetas)
nodal_translations[:, 1] = w0*cos(alpharad)*sin(thetas)
nodal_translations[:, 2] = w0*sin(alpharad)
log('Calculation of imperfection amplitudes finished!')
# applying translations
viewport = session.viewports[session.currentViewportName]
log('Applying new nodal positions in ABAQUS CAE to model {0} ...'.
format(model_name))
log(' (using a scaling factor of {0})'.format(scaling_factor))
new_coords = coords + nodal_translations
if T is not None:
Tinv = np.zeros_like(T)
Tinv[:3, :3] = T[:3, :3].T
Tinv[:, 3] = -T[:, 3]
tmp = np.vstack((new_coords.T, np.ones((1, new_coords.shape[0]))))
new_coords = np.dot(Tinv, tmp).T
del tmp
meshNodeArray = part.nodes.sequenceFromLabels(
[n.label for n in part_nodes])
new_coords = np.ascontiguousarray(new_coords)
part.editNode(nodes=meshNodeArray, coordinates=new_coords)
log('Application of new nodal positions finished!')
ra = mod.rootAssembly
viewport.setValues(displayedObject=ra)
ra.regenerate()
return nodal_translations
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
def translate_nodes_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,
nodal_translations=None,
use_theta_z_format=False,
ignore_bot_h=None,
ignore_top_h=None,
sample_size=None,
T=None):
r"""Translates the nodes in Abaqus based on imperfection data
The imperfection amplitude for each node is calculated using an inversed
weight function (see :func:`desicos.conecylDB.interpolate.inv_weighted`).
Parameters
----------
imperfection_file_name : str
The full path to the imperfection file, which must be a file with
three columns containing the ``x, y, z`` coordinates when
``use_theta_z_format=False`` or containing ``x, theta, amplitude``
when ``use_theta_z_format=True``.
model_name : str
Must be a valid key in the dictionary ``mdb.models``, in the
interactive Python inside Abaqus.
part_name : str
Must be a valid key in the dictionary
``mdb.models[model_name].parts``, in the interactive Python inside
Abaqus.
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
The radius of the current model. In case of cones this should be the
bottom radius.
R_best_fit : float, optional
Best fit radius obtained with functions :func:`.best_fit_cylinder`
or :func:`.best_fit_cone`.
semi_angle : float, optional
The cone semi-vertex angle (a null value indicates that a cylinder is
beeing analyzed).
stretch_H : float, optional
A boolean indicating if the imperfection pattern should be stretched
when applied to the model. The measurement systems usually cannot
obtain data for the whole surface, making it an option to stretch the
data to fit the whole surface. In case ``stretch_H=False`` the
measured data of the extremities will be extruded up to the end of the
domain.
z_offset_bot : float, optional
This parameter allows the analyst to adjust the height of the measured
data about the model, when the measured data is not available for the
whole domain.
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
The scaling factor that will multiply the calculated imperfection
amplitude.
r_TOL : float, optional
Parameter to ignore noisy data in the imperfection file, the points
with a radius higher than `r=r_{model} \times (1 + r_{TOL})` will not
be considered in the interpolation.
num_closest_points : int, optional
See :func:`the inverse-weighted interpolation algorithm
<.inv_weighted>`.
power_parameter : int, optional
See :func:`the inverse-weighted interpolation algorithm
<.inv_weighted>`.
num_sec_z : int, optional
Number of cross sections that will be used to classify the points
spatially in the inverse-weighted algorithm.
nodal_translations : None or numpy.ndarray, optional
An array containing the interpolated traslations, which is passed to
avoid repeated calls to the interpolation functions.
use_theta_z_format : bool, optional
A boolean to indicate whether the imperfection file contains ``x, y,
z`` positions or ``theta, z, amplitude``.
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>`.
Returns
-------
nodal_translations : numpy.ndarray
A 2-D array containing the translations ``x, y, z`` for each column.
Notes
-----
Despite the nodal traslations are returned all the nodes belonging to this
model will already be translated.
"""
from abaqus import mdb, session
mod = 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])
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:
log('Applying ignore_bot_h: ignoring nodes with z <= {0}'.format(
ignore_bot_h))
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:
log('Applying ignore_top_h: ignoring nodes with z >= {0}'.format(
H_model - ignore_top_h))
mask = coords[:, 2] < (H_model - ignore_top_h)
coords = coords[mask]
part_nodes = part_nodes[mask]
if use_theta_z_format:
if nodal_translations is None:
trans = calc_translations_ABAQUS(
imperfection_file_name = imperfection_file_name,
model_name = model_name,
part_name = part_name,
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,
scaling_factor = scaling_factor,
r_TOL = r_TOL,
num_closest_points = num_closest_points,
power_parameter = power_parameter,
num_sec_z = num_sec_z,
use_theta_z_format = use_theta_z_format,
ignore_bot_h = ignore_bot_h,
ignore_top_h = ignore_top_h,
sample_size = sample_size,
T = T)
else:
trans = nodal_translations
# applying translations
viewport = session.viewports[session.currentViewportName]
log('Applying new nodal positions in ABAQUS CAE to model {0} ...'.
format(model_name))
log(' (using a scaling factor of {0})'.format(scaling_factor))
new_coords = coords + trans*scaling_factor
if T is not None:
Tinv = np.zeros_like(T)
Tinv[:3, :3] = T[:3, :3].T
Tinv[:, 3] = -T[:, 3]
tmp = np.vstack((new_coords.T, np.ones((1, new_coords.shape[0]))))
new_coords = np.dot(Tinv, tmp).T
del tmp
meshNodeArray = part.nodes.sequenceFromLabels(
[n.label for n in part_nodes])
new_coords = np.ascontiguousarray(new_coords)
part.editNode(nodes=part_nodes.tolist(), coordinates=new_coords)
log('Application of new nodal positions finished!')
ra = mod.rootAssembly
viewport.setValues(displayedObject=ra)
ra.regenerate()
return trans
else:
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
if nodal_translations is None:
nodal_translations = calc_translations_ABAQUS(
imperfection_file_name = imperfection_file_name,
model_name = model_name,
part_name = part_name,
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,
scaling_factor = scaling_factor,
r_TOL = r_TOL,
num_closest_points = num_closest_points,
power_parameter = power_parameter,
num_sec_z = num_sec_z,
use_theta_z_format = use_theta_z_format,
ignore_bot_h = ignore_bot_h,
ignore_top_h = ignore_top_h,
sample_size = sample_size,
T = T)
# applying translations
viewport = session.viewports[session.currentViewportName]
log('Applying new nodal positions in ABAQUS CAE for model {0} ...'.
format(model_name))
log(' (using a scaling factor of {0})'.format(scaling_factor))
trans = nodal_translations
new_coords = coords + trans*scaling_factor
if T is not None:
Tinv = np.zeros_like(T)
Tinv[:3, :3] = T[:3, :3].T
Tinv[:, 3] = -T[:, 3]
tmp = np.vstack((new_coords.T, np.ones((1, new_coords.shape[0]))))
new_coords = np.dot(Tinv, tmp).T
del tmp
meshNodeArray = part.nodes.sequenceFromLabels(
[n.label for n in part_nodes])
new_coords = np.ascontiguousarray(new_coords)
part.editNode(nodes=meshNodeArray, coordinates=new_coords)
log('Application of new nodal positions finished!')
# regenerating ra
ra = mod.rootAssembly
viewport.setValues(displayedObject=ra)
ra.regenerate()
return nodal_translations
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()
import desicos.conecylDB.interpolate
def translate_nodes_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,
nodal_translations=None,
use_theta_z_format=False,
ignore_bot_h=None,
ignore_top_h=None,
sample_size=None,
T=None):
r"""Translates the nodes in Abaqus based on imperfection data
The imperfection amplitude for each node is calculated using an inversed
weight function (see :func:`desicos.conecylDB.interpolate.inv_weighted`).
Parameters
----------
imperfection_file_name : str
The full path to the imperfection file, which must be a file with
three columns containing the ``x, y, z`` coordinates when
``use_theta_z_format=False`` or containing ``x, theta, amplitude``
when ``use_theta_z_format=True``.
model_name : str
Must be a valid key in the dictionary ``mdb.models``, in the
interactive Python inside Abaqus.
part_name : str
Must be a valid key in the dictionary
``mdb.models[model_name].parts``, in the interactive Python inside
Abaqus.
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
The radius of the current model. In case of cones this should be the
bottom radius.
R_best_fit : float, optional
Best fit radius obtained with functions :func:`.best_fit_cylinder`
or :func:`.best_fit_cone`.
semi_angle : float, optional
The cone semi-vertex angle (a null value indicates that a cylinder is
beeing analyzed).
stretch_H : float, optional
A boolean indicating if the imperfection pattern should be stretched
when applied to the model. The measurement systems usually cannot
obtain data for the whole surface, making it an option to stretch the
data to fit the whole surface. In case ``stretch_H=False`` the
measured data of the extremities will be extruded up to the end of the
domain.
z_offset_bot : float, optional
This parameter allows the analyst to adjust the height of the measured
data about the model, when the measured data is not available for the
whole domain.
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
The scaling factor that will multiply the calculated imperfection
amplitude.
r_TOL : float, optional
Parameter to ignore noisy data in the imperfection file, the points
with a radius higher than `r=r_{model} \times (1 + r_{TOL})` will not
be considered in the interpolation.
num_closest_points : int, optional
See :func:`the inverse-weighted interpolation algorithm
<.inv_weighted>`.
power_parameter : int, optional
See :func:`the inverse-weighted interpolation algorithm
<.inv_weighted>`.
nodal_translations : None or numpy.ndarray, optional
An array containing the interpolated traslations, which is passed to
avoid repeated calls to the interpolation functions.
use_theta_z_format : bool, optional
A boolean to indicate whether the imperfection file contains ``x, y,
z`` positions or ``theta, z, amplitude``.
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>`.
Returns
-------
nodal_translations : numpy.ndarray
A 2-D array containing the translations ``x, y, z`` for each column.
Notes
-----
Despite the nodal traslations are returned all the nodes belonging to this
model will already be translated.
"""
from abaqus import mdb, session
mod = 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])
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:
log('Applying ignore_bot_h: ignoring nodes with z <= {0}'.format(
ignore_bot_h))
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:
log('Applying ignore_top_h: ignoring nodes with z >= {0}'.format(
H_model - ignore_top_h))
mask = coords[:, 2] < (H_model - ignore_top_h)
coords = coords[mask]
part_nodes = part_nodes[mask]
if use_theta_z_format:
if nodal_translations is None:
trans = calc_translations_ABAQUS(
imperfection_file_name=imperfection_file_name,
model_name=model_name,
part_name=part_name,
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,
scaling_factor=scaling_factor,
r_TOL=r_TOL,
num_closest_points=num_closest_points,
power_parameter=power_parameter,
use_theta_z_format=use_theta_z_format,
ignore_bot_h=ignore_bot_h,
ignore_top_h=ignore_top_h,
sample_size=sample_size,
T=T)
else:
trans = nodal_translations
# applying translations
viewport = session.viewports[session.currentViewportName]
log('Applying new nodal positions in ABAQUS CAE to model {0} ...'.
format(model_name))
log(' (using a scaling factor of {0})'.format(scaling_factor))
new_coords = coords + trans * scaling_factor
if T is not None:
Tinv = np.zeros_like(T)
Tinv[:3, :3] = T[:3, :3].T
Tinv[:, 3] = -T[:, 3]
tmp = np.vstack((new_coords.T, np.ones((1, new_coords.shape[0]))))
new_coords = np.dot(Tinv, tmp).T
del tmp
meshNodeArray = part.nodes.sequenceFromLabels(
[n.label for n in part_nodes])
new_coords = np.ascontiguousarray(new_coords)
part.editNode(nodes=part_nodes.tolist(), coordinates=new_coords)
log('Application of new nodal positions finished!')
ra = mod.rootAssembly
viewport.setValues(displayedObject=ra)
ra.regenerate()
return trans
else:
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
if nodal_translations is None:
nodal_translations = calc_translations_ABAQUS(
imperfection_file_name=imperfection_file_name,
model_name=model_name,
part_name=part_name,
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,
scaling_factor=scaling_factor,
r_TOL=r_TOL,
num_closest_points=num_closest_points,
power_parameter=power_parameter,
use_theta_z_format=use_theta_z_format,
ignore_bot_h=ignore_bot_h,
ignore_top_h=ignore_top_h,
sample_size=sample_size,
T=T)
# applying translations
viewport = session.viewports[session.currentViewportName]
log('Applying new nodal positions in ABAQUS CAE for model {0} ...'.
format(model_name))
log(' (using a scaling factor of {0})'.format(scaling_factor))
trans = nodal_translations
new_coords = coords + trans * scaling_factor
if T is not None:
Tinv = np.zeros_like(T)
Tinv[:3, :3] = T[:3, :3].T
Tinv[:, 3] = -T[:, 3]
tmp = np.vstack((new_coords.T, np.ones((1, new_coords.shape[0]))))
new_coords = np.dot(Tinv, tmp).T
del tmp
meshNodeArray = part.nodes.sequenceFromLabels(
[n.label for n in part_nodes])
new_coords = np.ascontiguousarray(new_coords)
part.editNode(nodes=meshNodeArray, coordinates=new_coords)
log('Application of new nodal positions finished!')
# regenerating ra
ra = mod.rootAssembly
viewport.setValues(displayedObject=ra)
ra.regenerate()
return nodal_translations
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
def plot_field_data(self, x, y, field, create_npz_only, ax, figsize, save_png,
aspect, clean, outpath, pngname, npzname, pyname,
num_levels, show_colorbar, lines):
npzname = npzname.split('.npz')[0] + '.npz'
pyname = pyname.split('.py')[0] + '.py'
pngname = pngname.split('.png')[0] + '.png'
npzname = os.path.join(outpath, npzname)
pyname = os.path.join(outpath, pyname)
pngname = os.path.join(outpath, pngname)
if lines: # not empty or None
# Concatenate all lines into a single matrix, separated by 'sep'
sep = np.array([[float('NaN')], [float('NaN')]])
all_lines = (2 * len(lines) - 1) * [sep]
all_lines[::2] = [np.array(l, copy=False) for l in lines]
line = np.hstack(all_lines)
else:
line = np.empty((2, 0))
if not create_npz_only:
try:
import matplotlib.pyplot as plt
import matplotlib
except:
create_npz_only = True
if not create_npz_only:
levels = np.linspace(field.min(), field.max(), num_levels)
if ax is None:
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
else:
if isinstance(ax, matplotlib.axes.Axes):
ax = ax
fig = ax.figure
save_png = False
else:
raise ValueError('"ax" must be an Axes object')
contours = ax.contourf(x, y, field, levels=levels)
if show_colorbar:
plt.colorbar(contours,
ax=ax,
shrink=0.5,
format='%.4g',
use_gridspec=True)
ax.plot(line[0, :], line[1, :], 'k-', linewidth=0.5)
ax.grid(False)
ax.set_aspect(aspect)
#ax.set_title(
#r'$PL=20 N$, $F_{{C}}=50 kN$, $w_{{PL}}=\beta$, $mm$')
if clean:
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
ax.set_frame_on(False)
else:
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks([-self.rbot * np.pi, 0, self.rbot * np.pi])
ax.xaxis.set_ticklabels([r'$-\pi$', '$0$', r'$+\pi$'])
if save_png:
log('')
log('Plot saved at: {0}'.format(pngname))
plt.tight_layout()
plt.savefig(pngname,
transparent=True,
bbox_inches='tight',
pad_inches=0.05,
dpi=400)
else:
log('Matplotlib cannot be imported from Abaqus')
np.savez(npzname, x=x, y=y, field=field, line=line)
with open(pyname, 'w') as f:
f.write("import os\n")
f.write("\n")
f.write("import numpy as np\n")
f.write("import matplotlib\n")
f.write("matplotlib.use('TkAgg')\n")
f.write("from matplotlib import cm\n")
f.write("import matplotlib.pyplot as plt\n")
f.write("from mpl_toolkits.axes_grid1 import make_axes_locatable\n")
f.write("\n")
f.write("add_title = False\n")
f.write("tmp = np.load(r'{0}')\n".format(basename(npzname)))
f.write("pngname = r'{0}'\n".format(basename(pngname)))
f.write("x = tmp['x']\n")
f.write("y = tmp['y']\n")
f.write("field = tmp['field']\n")
f.write("line = tmp['line']\n")
f.write("clean = {0}\n".format(clean))
f.write("show_colorbar = {0}\n".format(show_colorbar))
f.write("plt.figure(figsize={0})\n".format(figsize))
f.write("ax = plt.gca()\n")
f.write("levels = np.linspace(field.min(), field.max(), {0})\n".format(
num_levels))
f.write(
"contours = ax.contourf(x, y, field, levels=levels, cmap=cm.jet)\n"
)
f.write("if show_colorbar:\n")
f.write(" divider = make_axes_locatable(ax)\n")
f.write(
" cax = divider.append_axes('right', size='5%', pad=0.05)\n")
f.write(
" cbar = plt.colorbar(contours, cax=cax, format='%.2f', use_gridspec=True,\n"
)
f.write(" ticks=[field.min(), field.max()])\n")
f.write(" cbar.outline.remove()\n")
f.write(
" cbar.ax.tick_params(labelsize=14, pad=0., tick2On=False)\n")
f.write("ax.plot(line[0,:], line[1,:], 'k-', linewidth=0.5)\n")
f.write("ax.grid(False)\n")
f.write("ax.set_aspect('{0}')\n".format(aspect))
f.write("if add_title:\n")
f.write(
" ax.set_title(r'Abaqus, $PL=20 N$, $F_{{C}}=50 kN$, $w_{{PL}}=\\beta$, $mm$')\n"
)
f.write("if clean:\n")
f.write(" ax.xaxis.set_ticks_position('none')\n")
f.write(" ax.yaxis.set_ticks_position('none')\n")
f.write(" ax.xaxis.set_ticklabels([])\n")
f.write(" ax.yaxis.set_ticklabels([])\n")
f.write(" ax.set_frame_on(False)\n")
f.write("else:\n")
f.write(" ax.xaxis.set_ticks_position('bottom')\n")
f.write(" ax.yaxis.set_ticks_position('left')\n")
f.write(" ax.xaxis.set_ticks([{0}, 0, {1}])\n".format(
-self.rbot * np.pi, self.rbot * np.pi))
f.write(
" ax.xaxis.set_ticklabels([r'$-\\pi$', '$0$', r'$+\\pi$'])\n")
f.write("plt.savefig(pngname, transparent=True,\n")
f.write(" bbox_inches='tight', pad_inches=0.05, dpi=400)\n")
f.write("plt.show()\n")
log('')
log('Output exported to "{0}"'.format(npzname))
log('Please run the file "{0}" to plot the output'.format(pyname))
log('')
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 plot_field_data(self, x, y, field, create_npz_only, ax, figsize, save_png,
aspect, clean, outpath, pngname, npzname, pyname, num_levels,
show_colorbar, lines):
npzname = npzname.split('.npz')[0] + '.npz'
pyname = pyname.split('.py')[0] + '.py'
pngname = pngname.split('.png')[0] + '.png'
npzname = os.path.join(outpath, npzname)
pyname = os.path.join(outpath, pyname)
pngname = os.path.join(outpath, pngname)
if lines: # not empty or None
# Concatenate all lines into a single matrix, separated by 'sep'
sep = np.array([[float('NaN')], [float('NaN')]])
all_lines = (2*len(lines) - 1)*[sep]
all_lines[::2] = [np.array(l, copy=False) for l in lines]
line = np.hstack(all_lines)
else:
line = np.empty((2, 0))
if not create_npz_only:
try:
import matplotlib.pyplot as plt
import matplotlib
except:
create_npz_only = True
if not create_npz_only:
levels = np.linspace(field.min(), field.max(), num_levels)
if ax is None:
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
else:
if isinstance(ax, matplotlib.axes.Axes):
ax = ax
fig = ax.figure
save_png = False
else:
raise ValueError('"ax" must be an Axes object')
contours = ax.contourf(x, y, field, levels=levels)
if show_colorbar:
plt.colorbar(contours, ax=ax, shrink=0.5, format='%.4g', use_gridspec=True)
ax.plot(line[0,:], line[1,:], 'k-', linewidth=0.5)
ax.grid(False)
ax.set_aspect(aspect)
#ax.set_title(
#r'$PL=20 N$, $F_{{C}}=50 kN$, $w_{{PL}}=\beta$, $mm$')
if clean:
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
ax.set_frame_on(False)
else:
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks([-self.rbot*np.pi, 0, self.rbot*np.pi])
ax.xaxis.set_ticklabels([r'$-\pi$', '$0$', r'$+\pi$'])
if save_png:
log('')
log('Plot saved at: {0}'.format(pngname))
plt.tight_layout()
plt.savefig(pngname, transparent=True,
bbox_inches='tight', pad_inches=0.05,
dpi=400)
else:
log('Matplotlib cannot be imported from Abaqus')
np.savez(npzname, x=x, y=y, field=field, line=line)
with open(pyname, 'w') as f:
f.write("import os\n")
f.write("\n")
f.write("import numpy as np\n")
f.write("import matplotlib.pyplot as plt\n")
f.write("from mpl_toolkits.axes_grid1 import make_axes_locatable\n")
f.write("\n")
f.write("add_title = False\n")
f.write("tmp = np.load(r'{0}')\n".format(basename(npzname)))
f.write("pngname = r'{0}'\n".format(basename(pngname)))
f.write("x = tmp['x']\n")
f.write("y = tmp['y']\n")
f.write("field = tmp['field']\n")
f.write("line = tmp['line']\n")
f.write("clean = {0}\n".format(clean))
f.write("show_colorbar = {0}\n".format(show_colorbar))
f.write("plt.figure(figsize={0})\n".format(figsize))
f.write("ax = plt.gca()\n")
f.write("levels = np.linspace(field.min(), field.max(), {0})\n".format(
num_levels))
f.write("contours = ax.contourf(x, y, field, levels=levels)\n")
f.write("if show_colorbar:\n")
f.write(" divider = make_axes_locatable(ax)\n")
f.write(" cax = divider.append_axes('right', size='5%', pad=0.05)\n")
f.write(" cbar = plt.colorbar(contours, cax=cax, format='%.2f', use_gridspec=True,\n")
f.write(" ticks=[field.min(), field.max()])\n")
f.write(" cbar.outline.remove()\n")
f.write(" cbar.ax.tick_params(labelsize=14, pad=0., tick2On=False)\n")
f.write("ax.plot(line[0,:], line[1,:], 'k-', linewidth=0.5)\n")
f.write("ax.grid(False)\n")
f.write("ax.set_aspect('{0}')\n".format(aspect))
f.write("if add_title:\n")
f.write(" ax.set_title(r'Abaqus, $PL=20 N$, $F_{{C}}=50 kN$, $w_{{PL}}=\\beta$, $mm$')\n")
f.write("if clean:\n")
f.write(" ax.xaxis.set_ticks_position('none')\n")
f.write(" ax.yaxis.set_ticks_position('none')\n")
f.write(" ax.xaxis.set_ticklabels([])\n")
f.write(" ax.yaxis.set_ticklabels([])\n")
f.write(" ax.set_frame_on(False)\n")
f.write("else:\n")
f.write(" ax.xaxis.set_ticks_position('bottom')\n")
f.write(" ax.yaxis.set_ticks_position('left')\n")
f.write(" ax.xaxis.set_ticks([{0}, 0, {1}])\n".format(
-self.rbot*np.pi, self.rbot*np.pi))
f.write(" ax.xaxis.set_ticklabels([r'$-\\pi$', '$0$', r'$+\\pi$'])\n")
f.write("plt.savefig(pngname, transparent=True,\n")
f.write(" bbox_inches='tight', pad_inches=0.05, dpi=400)\n")
f.write("plt.show()\n")
log('')
log('Output exported to "{0}"'.format(npzname))
log('Please run the file "{0}" to plot the output'.format(
pyname))
log('')
