from src.divers.pretty_print import print_lampam, print_ss, print_list_ss #============================================================================== # Targets #============================================================================== # Total number of plies n_plies = 61 # Stacking sequence target ss_target = np.array([45, 90, -45, 0, 0], dtype='int16') print_ss(ss_target, 200) # Calculation of target lamination parameters lampam = calc_lampam_2(ss_target) print_lampam(lampam) targets = Targets(n_plies=n_plies, lampam=lampam, stack=ss_target) #============================================================================== # Type of optimisations #============================================================================== optimisation_type = 'A' # only in-plane lamination parameters optimised optimisation_type = 'D' # only out-of-plane lamination parameters optimised optimisation_type = 'AD' # in- and out-of-plane lamination parameters optimised #============================================================================== # Design guidelines #============================================================================== ### Set of design and manufacturing constraints: constraints_set = 'C0' constraints_set = 'C1' # C0: - No design and manufacturing constraints other than symmetry
# print('''
#The number of plies of the last group (parameters.group_size_max) is
#recommended to be equal to or greater than 5.
#''')
if n_groups > maxi:
raise PartitioningError('''
No partition possible of the plies into groups of smaller size
parameters.group_size_minand bigger size parameters.group_size_max.
Increase parameters.group_size_max or decrease parameters.group_size_min.
''')
return n_plies_in_groups, pos_first_ply_groups, n_groups
if __name__ == "__main__":
import sys
sys.path.append(r'C:\LAYLA')
from src.LAYLA_V02.constraints import Constraints
from src.LAYLA_V02.parameters import Parameters
from src.LAYLA_V02.targets import Targets
constraints = Constraints(sym=False, bal=True)
targets = Targets(n_plies=200)
parameters = Parameters(
constraints=constraints,
n_outer_step=5,
group_size_min=3,
group_size_max=6)
n_plies_in_groups, pos_first_ply_groups, n_groups \
= divide_laminate_asym(parameters, targets)
print(n_plies_in_groups)
print(pos_first_ply_groups)
print(n_groups)
[1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0])
else:
lampam_to_be_optimised = np.array(
[1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1])
# Lamination parameters sensitivities from the first-lebel optimiser
first_level_sensitivities = np.ones((12, ), float)
parameters = Parameters(
constraints=constraints,
p_A=p_A,
n_D1=n_D1,
n_D2=n_D2,
n_D3=n_D3,
first_level_sensitivities=first_level_sensitivities,
lampam_to_be_optimised=lampam_to_be_optimised,
repair_membrane_switch=repair_membrane_switch,
repair_flexural_switch=repair_flexural_switch)
targets = Targets(n_plies=ss.size, lampam=lampam_target)
ss, completed, n_obj_func_D_calls = repair_ss(ss,
constraints,
parameters,
targets,
count_obj=True)
print('Repair successful?', completed)
print_ss(ss, 20)
print('n_obj_func_D_calls', n_obj_func_D_calls)
check_lay_up_rules(ss, constraints)
B11_target = data.loc[i, 'B11']
B22_target = data.loc[i, 'B22']
B12_target = data.loc[i, 'B12']
B66_target = data.loc[i, 'B66']
B16_target = data.loc[i, 'B16']
B26_target = data.loc[i, 'B26']
D11_target = data.loc[i, 'D11']
D22_target = data.loc[i, 'D22']
D12_target = data.loc[i, 'D12']
D66_target = data.loc[i, 'D66']
D16_target = data.loc[i, 'D16']
D26_target = data.loc[i, 'D26']
targets = Targets(n_plies=n_plies_lam,
lampam=lampam_target,
stack=ss_target)
# print('target', ss_target)
### Algorithm run
print(f'Algorithm running.')
print('Laminate type: ', constraints.laminate_scheme)
print('Laminate type of the target stacking sequences: ',
constraints.laminate_scheme_test)
t = time.time()
result = LAYLA_optimiser(parameters, constraints, targets, mat_prop)
elapsed1 = time.time() - t
### Results processing and display
if not result.completed:
pos_mom_areas = np.array([
(abs(pos_top[-1]) + abs(pos_bot[-1])) / 2,
(abs(pos_top[-1]**2) + abs(pos_bot[-1]**2)) / 2,
(abs(pos_top[-1]**3) + abs(pos_bot[-1]**3)) / 2
])
cummul_mom_areas[-1, :] += pos_mom_areas
mom_areas_ply_group += pos_mom_areas
group_mom_areas[-1, :] += mom_areas_ply_group
return mom_areas, cummul_mom_areas, group_mom_areas
if __name__ == "__main__":
print('*** Test for the functions calc_moment_of_areas ***\n')
import sys
sys.path.append(r'C:\LAYLA')
from src.LAYLA_V02.constraints import Constraints
from src.LAYLA_V02.targets import Targets
from src.LAYLA_V02.ply_order import calc_ply_order
constraints = Constraints(sym=True)
targets = Targets(n_plies=21)
ply_order = calc_ply_order(constraints, targets)
n_plies_in_groups = np.array([5, 6])
mom_areas, cummul_mom_areas, group_mom_areas = calc_mom_of_areas(
constraints, targets, ply_order, n_plies_in_groups)
print(mom_areas)
print(cummul_mom_areas)
print(group_mom_areas, sum(group_mom_areas))
INPUTS
- ply_order: array of the ply indices sorted in the order in which plies
are optimised (middle ply of symmetric laminates included)
- n_plies_in_groups: number of plies in each ply group
- n_groups: number of ply groups
"""
levels_in_groups = [None] * n_groups
for ind_group in range(n_groups):
levels_in_groups[ind_group] = []
ind_all_plies = 0
for ind_group in range(n_groups):
for ind_plies in range(n_plies_in_groups[ind_group]):
levels_in_groups[ind_group].append(ply_order[ind_all_plies])
ind_all_plies += 1
return levels_in_groups
if __name__ == "__main__":
import sys
sys.path.append(r'C:\LAYLA')
from src.LAYLA_V02.parameters import Parameters
from src.LAYLA_V02.constraints import Constraints
from src.LAYLA_V02.targets import Targets
constraints = Constraints(sym=False)
targets = Targets(n_plies=6)
ply_order = calc_ply_order(constraints, targets)
print(ply_order)