def test_complex_step_multipoint(self):
E = 1.
L = 1.
b = 0.1
volume = 0.01
num_cp = 5
num_elements = 50
num_load_cases = 2
model = MultipointBeamGroup(E=E, L=L, b=b, volume=volume,
num_elements=num_elements, num_cp=num_cp,
num_load_cases=num_load_cases)
prob = om.Problem(model=model)
prob.driver = om.ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1e-9
prob.driver.options['disp'] = True
prob.setup(force_alloc_complex=True)
prob.run_model()
derivs = prob.check_totals(method='cs', out_stream=None)
assert_rel_error(self, derivs[('obj_sum.obj', 'inputs_comp.h_cp')]['rel error'][0],
0.0, 1e-8)
assert_rel_error(self, derivs[('volume_comp.volume', 'inputs_comp.h_cp')]['rel error'][0],
0.0, 1e-8)
derivs = prob.check_partials(method='cs', out_stream=None)
assert_check_partials(derivs, rtol=1e-15)
def test_initial_value(self):
E = 1.
L = 1.
b = 0.1
volume = 0.01
num_cp = 5
num_elements = 50
num_load_cases = 2
prob = om.Problem(
model=MultipointBeamGroup(E=E,
L=L,
b=b,
volume=volume,
num_elements=num_elements,
num_cp=num_cp,
num_load_cases=num_load_cases))
prob.driver = om.ScipyOptimizeDriver(optimizer='SLSQP', tol=1e-9)
prob.setup()
prob.run_driver()
h = prob['interp.h']
expected = np.array([
0.14122705, 0.14130706, 0.14154096, 0.1419107, 0.14238706,
0.14293095, 0.14349514, 0.14402636, 0.1444677, 0.14476123,
0.14485062, 0.14468388, 0.14421589, 0.1434107, 0.14224356,
0.14070252, 0.13878952, 0.13652104, 0.13392808, 0.13105565,
0.1279617, 0.12471547, 0.1213954, 0.11808665, 0.11487828,
0.11185599, 0.10900669, 0.10621949, 0.10338308, 0.10039485,
0.09716531, 0.09362202, 0.08971275, 0.08540785, 0.08070168,
0.07561313, 0.0701851, 0.06448311, 0.05859294, 0.05261756,
0.0466733, 0.04088557, 0.03538417, 0.03029845, 0.02575245,
0.02186027, 0.01872173, 0.01641869, 0.0150119, 0.01453876
])
assert np.linalg.norm(h - expected) < 1e-6
def check_initial_value(subsys, parallel=False):
"""
check that 'initial_value' is indicated for variables under a parallel group
"""
if subsys['type'] == 'subsystem':
# Group or Component, recurse to children
parallel = parallel or subsys['class'] == 'ParallelGroup'
for child in subsys['children']:
check_initial_value(child, parallel)
else:
# input or output, check for 'initial_value' flag
if parallel:
assert ('initial_value' in subsys
and subsys['initial_value'] is True)
else:
assert ('initial_value' not in subsys)
model_data = _get_viewer_data(prob)
for subsys in model_data['tree']['children']:
check_initial_value(subsys)
def setup(self, problem, ndv, nstate, nproc, flag):
E = 1.
L = 1.
b = 0.1
volume = 0.01
num_elements = 50 * nstate
num_cp = ndv * 4
num_load_cases = 32
problem.model = MultipointBeamGroup(E=E, L=L, b=b, volume=volume, num_elements=num_elements,
num_cp=num_cp, num_load_cases=num_load_cases)
def test_multipoint(self):
import numpy as np
import openmdao.api as om
from openmdao.test_suite.test_examples.beam_optimization.multipoint_beam_group import MultipointBeamGroup
E = 1.
L = 1.
b = 0.1
volume = 0.01
num_cp = 5
num_elements = 50
num_load_cases = 2
model = MultipointBeamGroup(E=E,
L=L,
b=b,
volume=volume,
num_elements=num_elements,
num_cp=num_cp,
num_load_cases=num_load_cases)
prob = om.Problem(model=model)
prob.driver = om.ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1e-9
prob.driver.options['disp'] = True
prob.setup()
prob.run_driver()
assert_rel_error(self, prob['interp.h'][0], [
0.14122705, 0.14130706, 0.14154096, 0.1419107, 0.14238706,
0.14293095, 0.14349514, 0.14402636, 0.1444677, 0.14476123,
0.14485062, 0.14468388, 0.14421589, 0.1434107, 0.14224356,
0.14070252, 0.13878952, 0.13652104, 0.13392808, 0.13105565,
0.1279617, 0.12471547, 0.1213954, 0.11808665, 0.11487828,
0.11185599, 0.10900669, 0.10621949, 0.10338308, 0.10039485,
0.09716531, 0.09362202, 0.08971275, 0.08540785, 0.08070168,
0.07561313, 0.0701851, 0.06448311, 0.05859294, 0.05261756,
0.0466733, 0.04088557, 0.03538417, 0.03029845, 0.02575245,
0.02186027, 0.01872173, 0.01641869, 0.0150119, 0.01453876
], 1e-4)
def benchmark_beam_np1(self):
E = 1.
L = 1.
b = 0.1
volume = 0.01
num_elements = 50 * 32
num_cp = 4
num_load_cases = 32
prob = Problem(model=MultipointBeamGroup(E=E, L=L, b=b, volume=volume,
num_elements=num_elements, num_cp=num_cp,
num_load_cases=num_load_cases))
prob.setup(vector_class=PETScVector)
prob.run_model()
def setup(self, problem, ndv, nstate, nproc, flag):
E = 1.
L = 1.
b = 0.1
volume = 0.01
num_elements = 50 * nstate
num_cp = ndv * 40
num_load_cases = 32
problem.model = MultipointBeamGroup(E=E,
L=L,
b=b,
volume=volume,
num_elements=num_elements,
num_cp=num_cp,
num_load_cases=num_load_cases)
problem.driver = pyOptSparseDriver()
problem.driver.options['optimizer'] = 'SNOPT'
def test_multipoint_stress(self):
import numpy as np
import openmdao.api as om
from openmdao.test_suite.test_examples.beam_optimization.multipoint_beam_stress import MultipointBeamGroup
E = 1.
L = 1.
b = 0.1
volume = 0.01
max_bending = 100.0
num_cp = 5
num_elements = 25
num_load_cases = 2
model = MultipointBeamGroup(E=E,
L=L,
b=b,
volume=volume,
max_bending=max_bending,
num_elements=num_elements,
num_cp=num_cp,
num_load_cases=num_load_cases)
prob = om.Problem(model=model)
prob.driver = om.ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1e-9
prob.driver.options['disp'] = True
prob.setup(mode='rev')
prob.run_driver()
assert_rel_error(self, prob['interp.h'][0], [
0.45632323, 0.45612552, 0.45543324, 0.45397058, 0.45134629,
0.44714397, 0.4410258, 0.43283139, 0.42265378, 0.41087801,
0.3981731, 0.3854358, 0.37369202, 0.36342186, 0.35289066,
0.34008777, 0.32362887, 0.30300358, 0.27867837, 0.25204063,
0.22519409, 0.20063906, 0.18088818, 0.16807856, 0.16364104
], 1e-4)
def main():
E = 1.
L = 1.
b = 0.1
volume = 0.01
num_elements = 50 * 32
num_cp = 4
num_load_cases = 32
prob = Problem(model=MultipointBeamGroup(E=E,
L=L,
b=b,
volume=volume,
num_elements=num_elements,
num_cp=num_cp,
num_load_cases=num_load_cases))
prob.setup()
prob.run_model()
PETScVector = None
if __name__ == '__main__' and PETScVector is not None:
E = 1.
L = 1.
b = 0.1
volume = 0.01
num_cp = 5
num_elements = 50
num_load_cases = 2
prob = om.Problem(model=MultipointBeamGroup(E=E,
L=L,
b=b,
volume=volume,
num_elements=num_elements,
num_cp=num_cp,
num_load_cases=num_load_cases))
prob.driver = om.ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1e-9
prob.driver.options['disp'] = True
prob.setup()
prob.run_driver()
h = prob['interp.h']
expected = np.array([
