def _setup_model(self, solver_class):
prob = om.Problem()
model = prob.model
model.add_subsystem('px', om.IndepVarComp('x', 1.0), promotes=['x'])
model.add_subsystem('pz',
om.IndepVarComp('z', np.array([5.0, 2.0])),
promotes=['z'])
proms = [
'x', 'z', 'y1', 'state_eq.y2_actual', 'state_eq.y2_command',
'd1.y2', 'd2.y2'
]
sub = model.add_subsystem('sub', om.Group(), promotes=proms)
subgrp = sub.add_subsystem(
'state_eq_group',
om.Group(),
promotes=['state_eq.y2_actual', 'state_eq.y2_command'])
subgrp.add_subsystem('state_eq', StateConnection())
sub.add_subsystem('d1',
SellarDis1withDerivatives(),
promotes=['x', 'z', 'y1'])
sub.add_subsystem('d2',
SellarDis2withDerivatives(),
promotes=['z', 'y1'])
model.connect('state_eq.y2_command', 'd1.y2')
model.connect('d2.y2', 'state_eq.y2_actual')
model.add_subsystem('obj_cmp',
om.ExecComp('obj = x**2 + z[1] + y1 + exp(-y2)',
z=np.array([0.0, 0.0]),
x=0.0,
y1=0.0,
y2=0.0),
promotes=['x', 'z', 'y1', 'obj'])
model.connect('d2.y2', 'obj_cmp.y2')
model.add_subsystem('con_cmp1',
om.ExecComp('con1 = 3.16 - y1'),
promotes=['con1', 'y1'])
model.add_subsystem('con_cmp2',
om.ExecComp('con2 = y2 - 24.0'),
promotes=['con2'])
# splice a group containing discrete vars into the model
model.add_subsystem('discrete_g', InternalDiscreteGroup())
model.connect('d2.y2', 'discrete_g.x')
model.connect('discrete_g.y', 'con_cmp2.y2')
model.nonlinear_solver = solver_class()
prob.set_solver_print(level=0)
prob.setup()
return prob
def test_sellar_specify_linear_solver(self):
prob = om.Problem()
model = prob.model
model.add_subsystem('px', om.IndepVarComp('x', 1.0), promotes=['x'])
model.add_subsystem('pz', om.IndepVarComp('z', np.array([5.0, 2.0])), promotes=['z'])
proms = ['x', 'z', 'y1', 'state_eq.y2_actual', 'state_eq.y2_command', 'd1.y2', 'd2.y2']
sub = model.add_subsystem('sub', om.Group(), promotes=proms)
subgrp = sub.add_subsystem('state_eq_group', om.Group(),
promotes=['state_eq.y2_actual', 'state_eq.y2_command'])
subgrp.linear_solver = om.ScipyKrylov()
subgrp.add_subsystem('state_eq', StateConnection())
sub.add_subsystem('d1', SellarDis1withDerivatives(), promotes=['x', 'z', 'y1'])
sub.add_subsystem('d2', SellarDis2withDerivatives(), promotes=['z', 'y1'])
model.connect('state_eq.y2_command', 'd1.y2')
model.connect('d2.y2', 'state_eq.y2_actual')
model.add_subsystem('obj_cmp', om.ExecComp('obj = x**2 + z[1] + y1 + exp(-y2)',
z=np.array([0.0, 0.0]), x=0.0, y1=0.0, y2=0.0),
promotes=['x', 'z', 'y1', 'obj'])
model.connect('d2.y2', 'obj_cmp.y2')
model.add_subsystem('con_cmp1', om.ExecComp('con1 = 3.16 - y1'), promotes=['con1', 'y1'])
model.add_subsystem('con_cmp2', om.ExecComp('con2 = y2 - 24.0'), promotes=['con2'])
model.connect('d2.y2', 'con_cmp2.y2')
model.nonlinear_solver = om.NewtonSolver(solve_subsystems=False)
# Use bad settings for this one so that problem doesn't converge.
# That way, we test that we are really using Newton's Lin Solver
# instead.
model.linear_solver = om.ScipyKrylov()
model.linear_solver.options['maxiter'] = 1
# The good solver
model.nonlinear_solver.linear_solver = om.ScipyKrylov()
prob.set_solver_print(level=0)
prob.setup()
prob.run_model()
assert_near_equal(prob.get_val('y1'), 25.58830273, .00001)
assert_near_equal(prob['state_eq.y2_command'], 12.05848819, .00001)
# Make sure we aren't iterating like crazy
self.assertLess(model.nonlinear_solver._iter_count, 8)
self.assertEqual(model.linear_solver._iter_count, 0)
self.assertGreater(model.nonlinear_solver.linear_solver._iter_count, 0)
def test_implicit_iter(self):
prob = Problem()
model = prob.model
model.add_subsystem('indep', IndepVarComp('y', 1.0))
model.add_subsystem('statecomp', StateConnection())
model.connect('indep.y', 'statecomp.y2_actual')
# provide iterative solvers for implicit group
model.linear_solver = LinearBlockGS()
model.nonlinear_solver = NonlinearBlockGS()
# perform setup with checks but don't run model
testlogger = TestLogger()
prob.setup(check=True, logger=testlogger)
prob.final_setup()
# should not trigger any solver warnings
warnings = testlogger.get('warning')
self.assertEqual(len(warnings), 0)
