def run_open_mdao():
if USE_SCALING:
# prepare scaling
global offset_weight
global offset_stress
global scale_weight
global scale_stress
runner = MultiRun(use_calcu=not USE_ABA, use_aba=USE_ABA, non_liner=False, project_name_prefix=PROJECT_NAME_PREFIX,
force_recalc=False)
sur = Surrogate(use_abaqus=USE_ABA, pgf=False, show_plots=False, scale_it=False)
res, surro = sur.auto_run(SAMPLE_HALTON, 16, SURRO_POLYNOM, run_validation=False)
p = runner.new_project_r_t(range_rib[0], range_shell[0])
offset_weight = p.calc_wight()
p = runner.new_project_r_t(range_rib[1], range_shell[1])
max_weight = p.calc_wight()
offset_stress = surro.predict([range_rib[1], range_shell[1]])
max_stress = surro.predict([range_rib[0], range_shell[0]])
scale_weight = (max_weight - offset_weight)
scale_stress = (max_stress - offset_stress)
write_mdao_log('iter,time,ribs(float),ribs,shell,stress,weight')
model = Group()
indeps = IndepVarComp()
indeps.add_output('ribs', (22 - offset_rib) / scale_rib)
indeps.add_output('shell', (0.0024 - offset_shell) / scale_shell)
model.add_subsystem('des_vars', indeps)
model.add_subsystem('wing', WingStructureSurro())
model.connect('des_vars.ribs', ['wing.ribs', 'con_cmp1.ribs'])
model.connect('des_vars.shell', 'wing.shell')
# design variables, limits and constraints
model.add_design_var('des_vars.ribs', lower=(range_rib[0] - offset_rib) / scale_rib, upper=(range_rib[1] - offset_rib) / scale_rib)
model.add_design_var('des_vars.shell', lower=(range_shell[0] - offset_shell) / scale_shell, upper=(range_shell[1] - offset_shell) / scale_shell)
# objective
model.add_objective('wing.weight', scaler=0.0001)
# constraint
print('constrain stress: ' + str((max_shear_strength - offset_stress) / scale_stress))
model.add_constraint('wing.stress', upper=(max_shear_strength - offset_stress) / scale_stress)
model.add_subsystem('con_cmp1', ExecComp('con1 = (ribs * '+str(scale_rib)+') - int(ribs[0] * '+str(scale_rib)+')'))
model.add_constraint('con_cmp1.con1', upper=.5)
prob = Problem(model)
# setup the optimization
if USE_PYOPTSPARSE:
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
prob.driver.opt_settings['SwarmSize'] = 8
prob.driver.opt_settings['stopIters'] = 5
else:
prob.driver = ScipyOptimizeDriver()
prob.driver.options['optimizer'] = OPTIMIZER # ['Nelder-Mead', 'Powell', 'CG', 'BFGS', 'Newton-CG', 'L-BFGS-B', 'TNC', 'COBYLA', 'SLSQP']
prob.driver.options['tol'] = TOL
prob.driver.options['disp'] = True
prob.driver.options['maxiter'] = 1000
#prob.driver.opt_settings['etol'] = 100
prob.setup()
prob.set_solver_print(level=0)
prob.model.approx_totals()
prob.setup(check=True, mode='fwd')
prob.run_driver()
print('done')
print('ribs: ' + str((prob['wing.ribs'] * scale_rib) + offset_rib))
print('shell: ' + str((prob['wing.shell'] * scale_shell) + offset_shell) + ' m')
print('weight= ' + str((prob['wing.weight'] * scale_weight) + offset_weight))
print('stress= ' + str((prob['wing.stress'] * scale_stress) + offset_stress) + ' ~ ' + str(prob['wing.stress']))
print('execution counts wing: ' + str(prob.model.wing.executionCounter))
def test_recording_remote_voi(self):
# Create a parallel model
model = Group()
model.add_subsystem('par', ParallelGroup())
model.par.add_subsystem('G1', Mygroup())
model.par.add_subsystem('G2', Mygroup())
model.connect('par.G1.y', 'Obj.y1')
model.connect('par.G2.y', 'Obj.y2')
model.add_subsystem('Obj', ExecComp('obj=y1+y2'))
model.add_objective('Obj.obj')
# Configure driver to record VOIs on both procs
driver = ScipyOptimizeDriver(disp=False)
driver.recording_options['record_desvars'] = True
driver.recording_options['record_responses'] = True
driver.recording_options['record_objectives'] = True
driver.recording_options['record_constraints'] = True
driver.recording_options['includes'] = ['par.G1.y', 'par.G2.y']
driver.add_recorder(self.recorder)
# Create problem and run driver
prob = Problem(model, driver)
prob.setup()
t0, t1 = run_driver(prob)
prob.cleanup()
# Since the test will compare the last case recorded, just check the
# current values in the problem. This next section is about getting those values
# These involve collective gathers so all ranks need to run this
expected_outputs = prob.driver.get_design_var_values()
expected_outputs.update(prob.driver.get_objective_values())
expected_outputs.update(prob.driver.get_constraint_values())
# includes for outputs are specified as promoted names but we need absolute names
prom2abs = model._var_allprocs_prom2abs_list['output']
abs_includes = [
prom2abs[n][0] for n in prob.driver.recording_options['includes']
]
# Absolute path names of includes on this rank
rrank = model.comm.rank
rowned = model._owning_rank
local_includes = [n for n in abs_includes if rrank == rowned[n]]
# Get values for all vars on this rank
inputs, outputs, residuals = model.get_nonlinear_vectors()
# Get values for includes on this rank
local_vars = {n: outputs[n] for n in local_includes}
# Gather values for includes on all ranks
all_vars = model.comm.gather(local_vars, root=0)
if prob.comm.rank == 0:
# Only on rank 0 do we have all the values. The all_vars variable is a list of
# dicts from all ranks 0,1,... In this case, just ranks 0 and 1
dct = all_vars[-1]
for d in all_vars[:-1]:
dct.update(d)
expected_includes = {
'par.G1.Cy.y': dct['par.G1.Cy.y'],
'par.G2.Cy.y': dct['par.G2.Cy.y'],
}
expected_outputs.update(expected_includes)
coordinate = [0, 'ScipyOptimize_SLSQP', (driver.iter_count - 1, )]
expected_data = ((coordinate, (t0, t1), expected_outputs, None), )
assertDriverIterDataRecorded(self, expected_data, self.eps)
model.connect('CT2Comp.CT2','CT1Comp.CT2')
model.add_subsystem('ATComp',ATComp(num_panel=airfoil.NUM_SAMPLES))
model.connect('CT1Comp.CT1','ATComp.CT1')
model.connect('CT2Comp.CT2','ATComp.CT2')
model.add_subsystem('VelocityComp',VelocityComp(num_panel=airfoil.NUM_SAMPLES,aoa=airfoil.aoa))
model.connect('GammaComp.gamma','VelocityComp.gamma')
model.connect('thetaComp.theta','VelocityComp.theta')
model.connect('ATComp.AT','VelocityComp.AT')
model.add_subsystem('LiftComp',LiftComp(num_panel=airfoil.NUM_SAMPLES))
model.connect('VelocityComp.V','LiftComp.V')
model.connect('arcComp.S','LiftComp.S')
model.add_design_var('input.y')
model.add_objective('LiftComp.CL')
leftmost_id = int(airfoil.NUM_SAMPLES/2)
model.add_constraint('input.y',indices=[0,leftmost_id,-1],equals=[airfoil.boundaryPoints_Y[0],airfoil.boundaryPoints_Y[leftmost_id],airfoil.boundaryPoints_Y[-1]])
prob = Problem(model=model)
prob.driver = ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['maxiter'] = 300
prob.driver.options['tol'] = 1e-6
prob.set_solver_print(level=0)
prob.model.approx_totals()
prob.setup()
prob.run_driver()
def test_recording_remote_voi(self):
# Create a parallel model
model = Group()
model.add_subsystem('par', ParallelGroup())
model.par.add_subsystem('G1', Mygroup())
model.par.add_subsystem('G2', Mygroup())
model.connect('par.G1.y', 'Obj.y1')
model.connect('par.G2.y', 'Obj.y2')
model.add_subsystem('Obj', ExecComp('obj=y1+y2'))
model.add_objective('Obj.obj')
# Configure driver to record VOIs on both procs
driver = ScipyOptimizeDriver(disp=False)
driver.recording_options['record_desvars'] = True
driver.recording_options['record_responses'] = True
driver.recording_options['record_objectives'] = True
driver.recording_options['record_constraints'] = True
driver.recording_options['includes'] = ['par.G1.y', 'par.G2.y']
driver.add_recorder(self.recorder)
# Create problem and run driver
prob = Problem(model, driver)
prob.add_recorder(self.recorder)
prob.setup()
t0, t1 = run_driver(prob)
prob.record_iteration('final')
t2 = time()
prob.cleanup()
# Since the test will compare the last case recorded, just check the
# current values in the problem. This next section is about getting those values
# These involve collective gathers so all ranks need to run this
expected_outputs = driver.get_design_var_values()
expected_outputs.update(driver.get_objective_values())
expected_outputs.update(driver.get_constraint_values())
# includes for outputs are specified as promoted names but we need absolute names
prom2abs = model._var_allprocs_prom2abs_list['output']
abs_includes = [prom2abs[n][0] for n in prob.driver.recording_options['includes']]
# Absolute path names of includes on this rank
rrank = model.comm.rank
rowned = model._owning_rank
local_includes = [n for n in abs_includes if rrank == rowned[n]]
# Get values for all vars on this rank
inputs, outputs, residuals = model.get_nonlinear_vectors()
# Get values for includes on this rank
local_vars = {n: outputs[n] for n in local_includes}
# Gather values for includes on all ranks
all_vars = model.comm.gather(local_vars, root=0)
if prob.comm.rank == 0:
# Only on rank 0 do we have all the values. The all_vars variable is a list of
# dicts from all ranks 0,1,... In this case, just ranks 0 and 1
dct = all_vars[-1]
for d in all_vars[:-1]:
dct.update(d)
expected_includes = {
'par.G1.Cy.y': dct['par.G1.Cy.y'],
'par.G2.Cy.y': dct['par.G2.Cy.y'],
}
expected_outputs.update(expected_includes)
coordinate = [0, 'ScipyOptimize_SLSQP', (driver.iter_count-1,)]
expected_data = ((coordinate, (t0, t1), expected_outputs, None),)
assertDriverIterDataRecorded(self, expected_data, self.eps)
expected_data = (('final', (t1, t2), expected_outputs),)
assertProblemDataRecorded(self, expected_data, self.eps)
# add_eq('')
add_var('hv_RPM') # hover RMP
add_var('cr_P') # cruise power
add_var('hv_P') # hover power
add_var('cr_T') # cruise Thrust
add_var('hv_T') # hover Thrust
add_var('cr_Q') # cruise Torque?
add_var('hv_Q') # hover Torque?
group = Group()
group.add_subsystem('indep_var_comp', indep_var_comp, promotes=['*'])
group.add_subsystem('equations_group', equations_group, promotes=['*'])
group.add_objective('x', scaler=1)
#group.add_constraint('b',lower=0.5,upper = 1,) # this is how you add a contraint
prob = Problem(model=group)
prob.driver = ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.setup()
prob.run_model()
#print(prob['Con_q'])
prob.run_driver()
prob.model.list_outputs()
# print(prob.list_problem_vars())
group.add_subsystem('vy_comp', ExecComp('vy = v * sin(theta)'))
group.add_subsystem('integrator_group', integrator)
group.connect('final_time_comp.final_time', 'integrator_group.final_time')
group.connect('theta_comp.theta', 'vx_comp.theta')
group.connect('theta_comp.theta', 'vy_comp.theta')
group.connect('v_comp.v', 'vx_comp.v')
group.connect('v_comp.v', 'vy_comp.v')
group.connect('vx_comp.vx', 'integrator_group.initial_condition:vx')
group.connect('vy_comp.vy', 'integrator_group.initial_condition:vy')
group.add_design_var('final_time_comp.final_time', lower=1e-3)
group.add_design_var('theta_comp.theta', lower=0., upper=np.pi / 2.)
group.add_constraint('integrator_group.state:y', indices=[-1], equals=0.)
group.add_objective('integrator_group.state:x', index=-1, scaler=-1.)
prob = Problem()
prob.model = group
prob.driver = ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'SLSQP'
# from openmdao.api import pyOptSparseDriver
# prob.driver = driver = pyOptSparseDriver()
# driver.options['optimizer'] = 'SNOPT'
# driver.opt_settings['Verify level'] = 0
# driver.opt_settings['Major iterations limit'] = 200 #1000
# driver.opt_settings['Minor iterations limit'] = 1000
# driver.opt_settings['Iterations limit'] = 100000
# driver.opt_settings['Major step limit'] = 2.0
# driver.opt_settings['Major feasibility tolerance'] = 1.0e-6
