def magneplane_brachistochrone(solver='SLSQP', num_seg=3, seg_ncn=3):
prob = Problem()
traj = prob.add_traj(Trajectory("traj0"))
if solver == 'SNOPT':
if pyOptSparseDriver is None:
raise ValueError('Requested SNOPT but pyoptsparse is not available')
driver=pyOptSparseDriver()
driver.options['optimizer'] = solver
driver.opt_settings['Major iterations limit'] = 1000
driver.opt_settings['iSumm'] = 6
driver.opt_settings['Major step limit'] = 0.5
driver.opt_settings["Major feasibility tolerance"] = 1.0E-6
driver.opt_settings["Major optimality tolerance"] = 1.0E-6
driver.opt_settings["Minor feasibility tolerance"] = 1.0E-4
driver.opt_settings['Verify level'] = 3
else:
driver=ScipyOptimizer()
driver.options['tol'] = 1.0E-6
driver.options['disp'] = True
driver.options['maxiter'] = 500
prob.driver = driver
dynamic_controls = [ {'name':'g','units':'m/s**2'},
{'name':'T','units':'N'},
{'name':'D','units':'N'},
{'name':'mass','units':'kg'},
{'name':'psi','units':'rad'},
{'name':'theta', 'units':'rad'},
{'name':'phi','units':'rad'} ]
phase0 = CollocationPhase(name='phase0',rhs_class=MagneplaneRHS,num_seg=num_seg,seg_ncn=seg_ncn,rel_lengths="equal",
dynamic_controls=dynamic_controls,static_controls=None)
traj.add_phase(phase0)
phase0.set_state_options('x', lower=0,upper=10,ic_val=0,ic_fix=True,fc_val=10,fc_fix=True,defect_scaler=0.1)
phase0.set_state_options('y', lower=0,upper=0,ic_val=0,ic_fix=True,fc_val=0,fc_fix=True,defect_scaler=0.1)
phase0.set_state_options('z', lower=-10,upper=0,ic_val=-10,ic_fix=True,fc_val=-5,fc_fix=True,defect_scaler=0.1)
phase0.set_state_options('v', lower=0, upper=np.inf,ic_val=0.0,ic_fix=True,fc_val=10.0,fc_fix=False,defect_scaler=0.1)
phase0.set_dynamic_control_options(name='psi', val=phase0.node_space(0.0, 0.0), opt=False)
phase0.set_dynamic_control_options(name='theta', val=phase0.node_space(-.46,-.46),opt=True,lower=-1.57,upper=1.57,scaler=1.0)
phase0.set_dynamic_control_options(name='phi', val=phase0.node_space(0.0, 0.0), opt=False)
phase0.set_dynamic_control_options(name='g', val=phase0.node_space(9.80665, 9.80665), opt=False)
phase0.set_dynamic_control_options(name='T', val=phase0.node_space(0.0, 0.0), opt=False)
phase0.set_dynamic_control_options(name='D', val=phase0.node_space(0.0, 0.0), opt=False)
phase0.set_dynamic_control_options(name='mass', val=phase0.node_space(1000.0, 1000.0), opt=False)
phase0.set_time_options(t0_val=0,t0_lower=0,t0_upper=0,tp_val=2.0,tp_lower=0.5,tp_upper=10.0)
traj.add_objective(name="t",phase="phase0",place="end",scaler=1.0)
return prob
def test_empty_jacobian(self):
prob = Problem(root=Group())
root = prob.root
root.add(name="ivc_a",
system=IndepVarComp(name="a", val=5.0),
promotes=["a"])
root.add(name="ivc_b",
system=IndepVarComp(name="b", val=10.0),
promotes=["b"])
root.add(name="g", system=MyGroup())
root.connect("a", "g.input.A")
root.connect("b", "g.input.B")
prob.driver = ScipyOptimizer()
prob.driver.options["disp"] = False
prob.driver.add_objective("g.exec.y")
prob.driver.add_desvar(name="a")
prob.driver.add_desvar(name="b")
prob.setup(check=False)
prob.run()
def test_undefined_jacobian(self):
prob = Problem(root=Group())
root = prob.root
root.add(name="ivc_a",
system=IndepVarComp(name="a", val=5.0),
promotes=["a"])
root.add(name="ivc_b",
system=IndepVarComp(name="b", val=10.0),
promotes=["b"])
root.add(name="g", system=MyGroup3())
root.connect("a", "g.input.A")
root.connect("b", "g.input.B")
prob.driver = ScipyOptimizer()
prob.driver.options["disp"] = False
prob.driver.add_objective("g.exec.y")
prob.driver.add_desvar(name="a")
prob.driver.add_desvar(name="b")
prob.setup(check=False)
try:
prob.run()
except ValueError as err:
self.assertEqual(str(err),
"No derivatives defined for Component 'g.input'")
else:
self.fail("expecting ValueError due to undefined linearize")
def test_driver_unicode_variable(self):
# this tests that unicode design variables and objectives works in python 2.
prob = Problem(root=Group())
root = prob.root
# simple paraboloid example from tutorial
root.add('p1', IndepVarComp('x0', 3.0), promotes=['*'])
root.add('p2', IndepVarComp('y0', -4.0), promotes=['*'])
root.add(
'p',
ExecComp('f_xy = (x0 - 3.0)**2 + x0 * y0 + (y0 + 4.0)**2 - 3.0'),
promotes=['*'])
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.add_desvar(u'x0', lower=-50, upper=50)
prob.driver.add_desvar(u'y0', lower=-50, upper=50)
prob.driver.add_objective(u'f_xy')
prob.driver.options['disp'] = False
prob.setup(check=False)
prob['x0'] = 3.0
prob['y0'] = -4.0
prob.run()
assert_rel_error(self, prob['f_xy'], -27.33333, 1e-3)
def test_array_scaler_bug(self):
class Paraboloid(Component):
def __init__(self):
super(Paraboloid, self).__init__()
self.add_param('X', val=np.array([0.0, 0.0]))
self.add_output('f_xy', val=0.0)
def solve_nonlinear(self, params, unknowns, resids):
X = params['X']
x = X[0]
y = X[1]
unknowns['f_xy'] = (1000. * x - 3.)**2 + (1000. * x) * (
0.01 * y) + (0.01 * y + 4.)**2 - 3.
def linearize(self, params, unknowns, resids):
""" Jacobian for our paraboloid."""
X = params['X']
J = {}
x = X[0]
y = X[1]
J['f_xy', 'X'] = np.array([[
2000000.0 * x - 6000.0 + 10.0 * y,
0.0002 * y + 0.08 + 10.0 * x
]])
return J
top = Problem()
root = top.root = Group()
root.deriv_options['type'] = 'fd'
root.add('p1', IndepVarComp('X', np.array([3.0, -4.0])))
root.add('p', Paraboloid())
root.connect('p1.X', 'p.X')
top.driver = ScipyOptimizer()
top.driver.options['optimizer'] = 'SLSQP'
top.driver.options['tol'] = 1e-12
top.driver.add_desvar('p1.X',
lower=np.array([-1000.0, -1000.0]),
upper=np.array([1000.0, 1000.0]),
scaler=np.array([1000., 0.01]))
top.driver.add_objective('p.f_xy')
top.setup(check=False)
top.run()
# Optimal solution (minimum): x = 6.6667; y = -7.3333
# Note: this scaling isn't so great, but at least we know it works
# and the bug is fixed.
assert_rel_error(self, top['p1.X'][0], 6.666667 / 1000.0, 1e-3)
assert_rel_error(self, top['p1.X'][1], -7.333333 / 0.01, 1e-3)
if __name__ == "__main__":
top = Problem()
root = top.root = Group()
root.add('p1', ParamComp('x', 3.0))
root.add('p2', ParamComp('y', -4.0))
root.add('p', Paraboloid())
root.connect('p1.x', 'p.x')
root.connect('p2.y', 'p.y')
top.driver = ScipyOptimizer()
top.driver.options['optimizer'] = 'SLSQP'
top.driver.add_param('p1.x', low=-50, high=50)
top.driver.add_param('p2.y', low=-50, high=50)
top.driver.add_objective('p.f_xy')
top.setup()
top.run()
print('\n')
print('Minimum of %f found at (%f, %f)' %
(top['p.f_xy'], top['p.x'], top['p.y']))
# Expected Output
# Minimum of -27.333333 found at (6.666667, -7.333333)
def magneplane_brachistochrone(solver='SLSQP', num_seg=3, seg_ncn=3):
prob = Problem()
traj = prob.add_traj(Trajectory("traj0"))
if solver == 'SNOPT':
if pyOptSparseDriver is None:
raise ValueError(
'Requested SNOPT but pyoptsparse is not available')
driver = pyOptSparseDriver()
driver.options['optimizer'] = solver
driver.opt_settings['Major iterations limit'] = 1000
driver.opt_settings['iSumm'] = 6
driver.opt_settings['Major step limit'] = 0.5
driver.opt_settings["Major feasibility tolerance"] = 1.0E-6
driver.opt_settings["Major optimality tolerance"] = 1.0E-6
driver.opt_settings["Minor feasibility tolerance"] = 1.0E-4
driver.opt_settings['Verify level'] = 3
else:
driver = ScipyOptimizer()
driver.options['tol'] = 1.0E-6
driver.options['disp'] = True
driver.options['maxiter'] = 500
prob.driver = driver
dynamic_controls = [{
'name': 'g',
'units': 'm/s**2'
}, {
'name': 'T',
'units': 'N'
}, {
'name': 'D',
'units': 'N'
}, {
'name': 'mass',
'units': 'kg'
}, {
'name': 'psi',
'units': 'rad'
}, {
'name': 'theta',
'units': 'rad'
}, {
'name': 'phi',
'units': 'rad'
}]
phase0 = CollocationPhase(name='phase0',
rhs_class=MagneplaneRHS,
num_seg=num_seg,
seg_ncn=seg_ncn,
rel_lengths="equal",
dynamic_controls=dynamic_controls,
static_controls=None)
traj.add_phase(phase0)
phase0.set_state_options('x',
lower=0,
upper=10,
ic_val=0,
ic_fix=True,
fc_val=10,
fc_fix=True,
defect_scaler=0.1)
phase0.set_state_options('y',
lower=0,
upper=0,
ic_val=0,
ic_fix=True,
fc_val=0,
fc_fix=True,
defect_scaler=0.1)
phase0.set_state_options('z',
lower=-10,
upper=0,
ic_val=-10,
ic_fix=True,
fc_val=-5,
fc_fix=True,
defect_scaler=0.1)
phase0.set_state_options('v',
lower=0,
upper=np.inf,
ic_val=0.0,
ic_fix=True,
fc_val=10.0,
fc_fix=False,
defect_scaler=0.1)
phase0.set_dynamic_control_options(name='psi',
val=phase0.node_space(0.0, 0.0),
opt=False)
phase0.set_dynamic_control_options(name='theta',
val=phase0.node_space(-.46, -.46),
opt=True,
lower=-1.57,
upper=1.57,
scaler=1.0)
phase0.set_dynamic_control_options(name='phi',
val=phase0.node_space(0.0, 0.0),
opt=False)
phase0.set_dynamic_control_options(name='g',
val=phase0.node_space(9.80665, 9.80665),
opt=False)
phase0.set_dynamic_control_options(name='T',
val=phase0.node_space(0.0, 0.0),
opt=False)
phase0.set_dynamic_control_options(name='D',
val=phase0.node_space(0.0, 0.0),
opt=False)
phase0.set_dynamic_control_options(name='mass',
val=phase0.node_space(1000.0, 1000.0),
opt=False)
phase0.set_time_options(t0_val=0,
t0_lower=0,
t0_upper=0,
tp_val=2.0,
tp_lower=0.5,
tp_upper=10.0)
traj.add_objective(name="t", phase="phase0", place="end", scaler=1.0)
return prob
def magneplane_brachistochrone(solver="SLSQP", num_seg=3, seg_ncn=3):
prob = Problem()
traj = prob.add_traj(Trajectory("traj0"))
if solver == "SNOPT":
if pyOptSparseDriver is None:
raise ValueError("Requested SNOPT but pyoptsparse is not available")
driver = pyOptSparseDriver()
driver.options["optimizer"] = solver
driver.opt_settings["Major iterations limit"] = 1000
driver.opt_settings["iSumm"] = 6
driver.opt_settings["Major step limit"] = 0.5
driver.opt_settings["Major feasibility tolerance"] = 1.0e-6
driver.opt_settings["Major optimality tolerance"] = 1.0e-6
driver.opt_settings["Minor feasibility tolerance"] = 1.0e-4
driver.opt_settings["Verify level"] = 3
else:
driver = ScipyOptimizer()
driver.options["tol"] = 1.0e-6
driver.options["disp"] = True
driver.options["maxiter"] = 500
prob.driver = driver
dynamic_controls = [
{"name": "g", "units": "m/s**2"},
{"name": "T", "units": "N"},
{"name": "D", "units": "N"},
{"name": "mass", "units": "kg"},
{"name": "psi", "units": "rad"},
{"name": "theta", "units": "rad"},
{"name": "phi", "units": "rad"},
]
phase0 = CollocationPhase(
name="phase0",
rhs_class=MagneplaneRHS,
num_seg=num_seg,
seg_ncn=seg_ncn,
rel_lengths="equal",
dynamic_controls=dynamic_controls,
static_controls=None,
)
traj.add_phase(phase0)
phase0.set_state_options("x", lower=0, upper=10, ic_val=0, ic_fix=True, fc_val=10, fc_fix=True, defect_scaler=0.1)
phase0.set_state_options("y", lower=0, upper=0, ic_val=0, ic_fix=True, fc_val=0, fc_fix=True, defect_scaler=0.1)
phase0.set_state_options(
"z", lower=-10, upper=0, ic_val=-10, ic_fix=True, fc_val=-5, fc_fix=True, defect_scaler=0.1
)
phase0.set_state_options(
"v", lower=0, upper=np.inf, ic_val=0.0, ic_fix=True, fc_val=10.0, fc_fix=False, defect_scaler=0.1
)
phase0.set_dynamic_control_options(name="psi", val=phase0.node_space(0.0, 0.0), opt=False)
phase0.set_dynamic_control_options(
name="theta", val=phase0.node_space(-0.46, -0.46), opt=True, lower=-1.57, upper=1.57, scaler=1.0
)
phase0.set_dynamic_control_options(name="phi", val=phase0.node_space(0.0, 0.0), opt=False)
phase0.set_dynamic_control_options(name="g", val=phase0.node_space(9.80665, 9.80665), opt=False)
phase0.set_dynamic_control_options(name="T", val=phase0.node_space(0.0, 0.0), opt=False)
phase0.set_dynamic_control_options(name="D", val=phase0.node_space(0.0, 0.0), opt=False)
phase0.set_dynamic_control_options(name="mass", val=phase0.node_space(1000.0, 1000.0), opt=False)
phase0.set_time_options(t0_val=0, t0_lower=0, t0_upper=0, tp_val=2.0, tp_lower=0.5, tp_upper=10.0)
traj.add_objective(name="t", phase="phase0", place="end", scaler=1.0)
return prob
unknowns['radiating_area'] = unknowns['convection_area']
unknowns['Qradiated_per_area'] = params['sb_const'] * params['emissivity'] * (params['temp_boundary'] ** 4 - params['temp_ambient'] ** 4) # P / A = SB * emmisitivity * (T ** 4 - To ** 4)
unknowns['Qradiated_tot'] = unknowns['radiating_area'] * unknowns['Qradiated_per_area']
unknowns['Qout_tot'] = unknowns['Qradiated_tot'] + unknowns['Qradiated_nat_convection_per_area']
unknowns['Qin_tot'] = unknowns['Qsolar_tot'] + unknowns['heat_rate_tot']
unknowns['Q_resid'] = abs(unknowns['Qout_tot'] - unknowns['Qin_tot'])
if __name__ == '__main__':
from openmdao.core.group import Group
from openmdao.core.problem import Problem
from openmdao.drivers.scipy_optimizer import ScipyOptimizer
from openmdao.components.param_comp import ParamComp
from openmdao.components.constraint import ConstraintComp
g = Group()
p = Problem(root=g, driver=ScipyOptimizer())
p.driver.options['optimizer'] = 'COBYLA'
g.add('temp_boundary', ParamComp('T', 340.0))
g.add('tube_wall', TubeWallTemp())
g.connect('temp_boundary.T', 'tube_wall.temp_boundary')
g.add('con', ConstraintComp('temp_boundary > 305.7', out='out'))
g.connect('temp_boundary.T', 'con.temp_boundary')
p.driver.add_param('temp_boundary.T', low=0.0, high=10000.0)
p.driver.add_objective('tube_wall.Q_resid')
p.driver.add_constraint('con.out')
p.setup()
g.tube_wall.params['flow_nozzle:in:Tt'] = 1710.0