def make_prob(self, transcription, n_segs, order, compressed):
p = om.Problem(model=om.Group())
gd = GridData(num_segments=n_segs,
segment_ends=np.arange(n_segs + 1),
transcription=transcription,
transcription_order=order,
compressed=compressed)
state_options = {
'x': {
'units': 'm',
'shape': (1, ),
'fix_initial': True,
'fix_final': False,
'solve_segments': True,
'connected_initial': False,
'connected_final': False
},
'v': {
'units': 'm/s',
'shape': (3, 2),
'fix_initial': False,
'fix_final': True,
'solve_segments': True,
'connected_initial': False,
'connected_final': False
}
}
subsys = StateIndependentsComp(grid_data=gd,
state_options=state_options)
p.model.add_subsystem('defect_comp', subsys=subsys)
self.state_idx_map = {}
for state_name, options in state_options.items():
self._make_state_idx_map(state_name, options, gd,
self.state_idx_map)
subsys.configure_io(self.state_idx_map)
p.setup()
p.final_setup()
return p
def make_prob(self, transcription, n_segs, order, compressed):
gd = GridData(num_segments=n_segs,
segment_ends=np.arange(n_segs + 1),
transcription=transcription,
transcription_order=order,
compressed=compressed)
state_options = {
'x': {
'units': 'm',
'shape': (1, ),
'fix_initial': True,
'fix_final': False,
'solve_segments': True,
'connected_initial': False
},
'v': {
'units': 'm/s',
'shape': (3, 2),
'fix_initial': True,
'fix_final': False,
'solve_segments': True,
'connected_initial': False
}
}
num_col_nodes = gd.subset_num_nodes['col']
num_col_nodes_per_seg = gd.subset_num_nodes_per_segment['col']
p = om.Problem(model=om.Group())
indep_comp = om.IndepVarComp()
p.model.add_subsystem('indep', indep_comp, promotes_outputs=['*'])
indep_comp.add_output('dt_dstau',
val=np.repeat(
np.arange(n_segs) + 1,
num_col_nodes_per_seg))
indep_comp.add_output('f_approx:x',
val=np.ones((num_col_nodes, 1)),
units='m')
indep_comp.add_output('f_computed:x',
val=np.ones((num_col_nodes, 1)) * 2,
units='m')
indep_comp.add_output('f_approx:v',
val=np.ones((num_col_nodes, 3, 2)),
units='m/s')
indep_comp.add_output('f_computed:v',
val=np.ones((num_col_nodes, 3, 2)) * 2,
units='m/s')
p.model.add_subsystem('defect_comp',
subsys=CollocationComp(
grid_data=gd, state_options=state_options))
indep = StateIndependentsComp(grid_data=gd,
state_options=state_options)
p.model.add_subsystem('state_indep', indep, promotes_outputs=['*'])
p.model.connect('f_approx:x', 'defect_comp.f_approx:x')
p.model.connect('f_approx:v', 'defect_comp.f_approx:v')
p.model.connect('f_computed:x', 'defect_comp.f_computed:x')
p.model.connect('f_computed:v', 'defect_comp.f_computed:v')
p.model.connect('dt_dstau', 'defect_comp.dt_dstau')
p.model.connect('defect_comp.defects:x', 'state_indep.defects:x')
p.model.connect('defect_comp.defects:v', 'state_indep.defects:v')
p.setup(force_alloc_complex=True)
return p
def make_prob(self, transcription, n_segs, order, compressed):
p = om.Problem(model=om.Group())
gd = GridData(num_segments=n_segs,
segment_ends=np.arange(n_segs + 1),
transcription=transcription,
transcription_order=order,
compressed=compressed)
state_options = {
'x': {
'units': 'm',
'shape': (1, ),
'fix_initial': True,
'fix_final': False,
'solve_segments': False,
'connected_initial': False
},
'v': {
'units': 'm/s',
'shape': (3, 2),
'fix_initial': False,
'fix_final': True,
'solve_segments': True,
'connected_initial': False
}
}
indep_comp = om.IndepVarComp()
p.model.add_subsystem('indep', indep_comp, promotes_outputs=['*'])
indep_comp.add_output('dt_dstau',
val=np.zeros((gd.subset_num_nodes['col'])))
indep_comp.add_output('f_approx:x',
val=np.zeros((gd.subset_num_nodes['col'], 1)),
units='m')
indep_comp.add_output('f_computed:x',
val=np.zeros((gd.subset_num_nodes['col'], 1)),
units='m')
indep_comp.add_output('f_approx:v',
val=np.zeros((gd.subset_num_nodes['col'], 3, 2)),
units='m/s')
indep_comp.add_output('f_computed:v',
val=np.zeros((gd.subset_num_nodes['col'], 3, 2)),
units='m/s')
p.model.add_subsystem('defect_comp',
subsys=CollocationComp(
grid_data=gd, state_options=state_options))
indep = StateIndependentsComp(grid_data=gd,
state_options=state_options)
p.model.add_subsystem('state_indep', indep, promotes_outputs=['*'])
p.model.connect('f_approx:x', 'defect_comp.f_approx:x')
p.model.connect('f_approx:v', 'defect_comp.f_approx:v')
p.model.connect('f_computed:x', 'defect_comp.f_computed:x')
p.model.connect('f_computed:v', 'defect_comp.f_computed:v')
p.model.connect('dt_dstau', 'defect_comp.dt_dstau')
p.model.connect('defect_comp.defects:v', 'state_indep.defects:v')
p.setup(force_alloc_complex=True)
p['dt_dstau'] = np.random.random(gd.subset_num_nodes['col'])
p['f_approx:x'] = np.random.random((gd.subset_num_nodes['col'], 1))
p['f_approx:v'] = np.random.random((gd.subset_num_nodes['col'], 3, 2))
p['f_computed:x'] = np.random.random((gd.subset_num_nodes['col'], 1))
p['f_computed:v'] = np.random.random(
(gd.subset_num_nodes['col'], 3, 2))
p.run_model()
p.model.run_apply_nonlinear()
# p.model.list_outputs(residuals=True, print_arrays=True)
return p
