def test_values_compare(self):
uq_systemsize = 6
mu_orig = np.array(
[mean_Ma, mean_TSFC, mean_W0, mean_E, mean_G, mean_mrho])
rv_dict = {
'Mach_number': mean_Ma,
'CT': mean_TSFC,
'W0': mean_W0,
'E': mean_E, # surface RV
'G': mean_G, # surface RV
'mrho': mean_mrho, # surface RV
}
input_dict = {
'n_twist_cp': 3,
'n_thickness_cp': 3,
'n_CM': 3,
'n_thickness_intersects': 10,
'n_constraints': 1 + 10 + 1 + 3 + 3,
'ndv': 3 + 3 + 2,
'mesh_dict': mesh_dict,
'rv_dict': rv_dict
}
QoI = examples.OASScanEagleWrapper(uq_systemsize,
input_dict,
include_dict_rv=True)
fuel_burn_val = QoI.eval_QoI(mu_orig, np.zeros(uq_systemsize))
ks_val = QoI.p['oas_scaneagle.AS_point_0.wing_perf.failure']
print()
print('fuel burn val = ', fuel_burn_val[0])
print('ks_val = ', ks_val[0])
def test_deterministic_model(self):
# Check if the quantity of interest is being computed as expected
uq_systemsize = 6
mu_orig = np.array(
[mean_Ma, mean_TSFC, mean_W0, mean_E, mean_G, mean_mrho])
std_dev = np.diag([0.005, 0.00607 / 3600, 0.2, 5.e9, 1.e9, 50])
jdist = cp.MvNormal(mu_orig, std_dev)
rv_dict = {
'Mach_number': mean_Ma,
'CT': mean_TSFC,
'W0': mean_W0,
'E': mean_E, # surface RV
'G': mean_G, # surface RV
'mrho': mean_mrho, # surface RV
}
input_dict = {
'n_twist_cp': 3,
'n_thickness_cp': 3,
'n_CM': 3,
'n_thickness_intersects': 10,
'n_constraints': 1 + 10 + 1 + 3 + 3,
'ndv': 3 + 3 + 2,
'mesh_dict': mesh_dict,
'rv_dict': rv_dict
}
QoI = examples.OASScanEagleWrapper(uq_systemsize,
input_dict,
include_dict_rv=True)
# Check the value at the starting point
fval = QoI.eval_QoI(mu_orig, np.zeros(uq_systemsize))
true_val = 5.229858093218218
err = abs(fval - true_val)
self.assertTrue(err < 1.e-6)
# Check if plugging back value also yields the expected results
QoI.p['oas_scaneagle.wing.twist_cp'] = np.array([2.60830137, 10., 5.])
QoI.p['oas_scaneagle.wing.thickness_cp'] = np.array(
[0.001, 0.001, 0.001])
QoI.p['oas_scaneagle.wing.sweep'] = [18.89098985]
QoI.p['oas_scaneagle.alpha'] = [2.19244059]
fval = QoI.eval_QoI(mu_orig, np.zeros(uq_systemsize))
true_val = 4.735819672292367
err = abs(fval - true_val)
self.assertTrue(err < 1.e-6)
def test_variable_update(self):
uq_systemsize = 6
mu_orig = np.array(
[mean_Ma, mean_TSFC, mean_W0, mean_E, mean_G, mean_mrho])
std_dev = np.diag([0.005, 0.00607 / 3600, 0.2, 5.e9, 1.e9, 50])
jdist = cp.MvNormal(mu_orig, std_dev)
rv_dict = {
'Mach_number': mean_Ma,
'CT': mean_TSFC,
'W0': mean_W0,
'E': mean_E, # surface RV
'G': mean_G, # surface RV
'mrho': mean_mrho, # surface RV
}
input_dict = {
'n_twist_cp': 3,
'n_thickness_cp': 3,
'n_CM': 3,
'n_thickness_intersects': 10,
'n_constraints': 1 + 10 + 1 + 3 + 3,
'ndv': 3 + 3 + 2,
'mesh_dict': mesh_dict,
'rv_dict': rv_dict
}
QoI = examples.OASScanEagleWrapper(uq_systemsize,
input_dict,
include_dict_rv=True)
mu_pert = mu_orig + np.diagonal(std_dev)
QoI.update_rv(mu_pert)
QoI.p.final_setup()
self.assertEqual(mu_pert[0], QoI.p['Mach_number'])
self.assertEqual(mu_pert[1], QoI.p['CT'])
self.assertEqual(mu_pert[2], QoI.p['W0'])
self.assertEqual(mu_pert[3], QoI.p['E'])
self.assertEqual(mu_pert[4], QoI.p['G'])
self.assertEqual(mu_pert[5], QoI.p['mrho'])
def test_dfuelburn_drv(self):
uq_systemsize = 6
mu_orig = np.array(
[mean_Ma, mean_TSFC, mean_W0, mean_E, mean_G, mean_mrho])
std_dev = np.diag([0.005, 0.00607 / 3600, 0.2, 5.e9, 1.e9, 50])
jdist = cp.MvNormal(mu_orig, std_dev)
rv_dict = {
'Mach_number': mean_Ma,
'CT': mean_TSFC,
'W0': mean_W0,
'E': mean_E, # surface RV
'G': mean_G, # surface RV
'mrho': mean_mrho, # surface RV
}
input_dict = {
'n_twist_cp': 3,
'n_thickness_cp': 3,
'n_CM': 3,
'n_thickness_intersects': 10,
'n_constraints': 1 + 10 + 1 + 3 + 3,
'ndv': 3 + 3 + 2,
'mesh_dict': mesh_dict,
'rv_dict': rv_dict
}
QoI = examples.OASScanEagleWrapper(uq_systemsize,
input_dict,
include_dict_rv=True)
dJdrv = QoI.eval_QoIGradient(mu_orig, np.zeros(uq_systemsize))
true_val = np.array([
-83.76493292024509, 74045.31234313066, 0.44175879007053753,
-7.34403789212763e-13, -2.527193348815028e-13, 0.8838194148741767
])
err = abs(dJdrv - true_val) / true_val
self.assertTrue((err < 1.e-6).all())
def __init__(self, uq_systemsize, all_rv=False):
# Total number of nodes to use in the spanwise (num_y) and
# chordwise (num_x) directions. Vary these to change the level of fidelity.
num_y = 21
num_x = 3
mesh_dict = {
'num_y': num_y,
'num_x': num_x,
'wing_type': 'rect',
'symmetry': True,
'span_cos_spacing': 0.5,
'span': 3.11,
'root_chord': 0.3,
}
rv_dict = {
'Mach_number': mean_Ma,
'CT': mean_TSFC,
'W0': mean_W0,
'E': mean_E, # surface RV
'G': mean_G, # surface RV
'mrho': mean_mrho, # surface RV
}
dv_dict = {
'n_twist_cp': 3,
'n_thickness_cp': 3,
'n_CM': 3,
'n_thickness_intersects': 10,
'n_constraints': 1 + 10 + 1 + 3 + 3,
'ndv': 3 + 3 + 2,
'mesh_dict': mesh_dict,
'rv_dict': rv_dict
}
self.jdist = cp.MvNormal(mu, std_dev)
self.QoI = examples.OASScanEagleWrapper(uq_systemsize,
dv_dict,
include_dict_rv=all_rv)
dfuelburn_dict = {
'dv': {
'dQoI_func': self.QoI.eval_ObjGradient_dv,
'output_dimensions': dv_dict['ndv'],
}
}
dcon_dict = {
'dv': {
'dQoI_func': self.QoI.eval_ConGradient_dv,
'output_dimensions': dv_dict['ndv']
}
}
dcon_failure_dict = {
'dv': {
'dQoI_func': self.QoI.eval_ConFailureGradient_dv,
'output_dimensions': dv_dict['ndv'],
}
}
self.QoI_dict = {
'fuelburn': {
'QoI_func': self.QoI.eval_QoI,
'output_dimensions': 1,
'deriv_dict': dfuelburn_dict
},
# 'constraints' : {'QoI_func' : self.QoI.eval_AllConstraintQoI,
# 'output_dimensions' : dv_dict['n_constraints'],
# 'deriv_dict' : dcon_dict
# },
# 'con_failure' : {'QoI_func' : self.QoI.eval_confailureQoI,
# 'output_dimensions' : 1,
# 'deriv_dict' : dcon_failure_dict
# }
}
def __init__(self, uq_systemsize, all_rv=False):
# Default values
mean_Ma = 0.071
mean_TSFC = 9.80665 * 8.6e-6
mean_W0 = 10.0
mean_E = 85.e9
mean_G = 25.e9
mean_mrho = 1600
# Total number of nodes to use in the spanwise (num_y) and
# chordwise (num_x) directions. Vary these to change the level of fidelity.
num_y = 21
num_x = 3
mesh_dict = {
'num_y': num_y,
'num_x': num_x,
'wing_type': 'rect',
'symmetry': True,
'span_cos_spacing': 0.5,
'span': 3.11,
'root_chord': 0.3,
}
rv_dict = {
'Mach_number': mean_Ma,
'CT': mean_TSFC,
'W0': mean_W0,
'E': mean_E, # surface RV
'G': mean_G, # surface RV
'mrho': mean_mrho, # surface RV
}
dv_dict = {
'n_twist_cp': 3,
'n_thickness_cp': 3,
'n_CM': 3,
'n_thickness_intersects': 10,
'n_constraints': 1 + 10 + 1 + 3 + 3,
'ndv': 3 + 3 + 2,
'mesh_dict': mesh_dict,
'rv_dict': rv_dict
}
mu = np.array([mean_Ma, mean_TSFC, mean_W0, mean_E, mean_G, mean_mrho])
std_dev = np.diag([0.005, 0.00607 / 3600, 0.2, 5.e9, 1.e9, 50])
self.jdist = cp.MvNormal(mu, std_dev)
self.QoI = examples.OASScanEagleWrapper(uq_systemsize,
dv_dict,
include_dict_rv=all_rv)
self.QoI.p['oas_scaneagle.wing.thickness_cp'] = 1.e-3 * np.array([
5.5, 5.5, 5.5
]) # This setup is according to the one in the scaneagle paper
self.QoI.p['oas_scaneagle.wing.twist_cp'] = 2.5 * np.ones(3)
self.QoI.p.final_setup()
# self.dominant_space = DimensionReduction(n_arnoldi_sample=uq_systemsize+1,
# exact_Hessian=False)
# self.dominant_space.getDominantDirections(self.QoI, self.jdist, max_eigenmodes=1)
dfuelburn_dict = {
'dv': {
'dQoI_func': self.QoI.eval_ObjGradient_dv,
'output_dimensions': dv_dict['ndv'],
}
}
dcon_dict = {
'dv': {
'dQoI_func': self.QoI.eval_ConGradient_dv,
'output_dimensions': dv_dict['ndv']
}
}
dcon_failure_dict = {
'dv': {
'dQoI_func': self.QoI.eval_ConFailureGradient_dv,
'output_dimensions': dv_dict['ndv'],
}
}
self.QoI_dict = {
'fuelburn': {
'QoI_func': self.QoI.eval_QoI,
'output_dimensions': 1,
'deriv_dict': dfuelburn_dict
},
'constraints': {
'QoI_func': self.QoI.eval_AllConstraintQoI,
'output_dimensions': dv_dict['n_constraints'],
'deriv_dict': dcon_dict
},
'con_failure': {
'QoI_func': self.QoI.eval_confailureQoI,
'output_dimensions': 1,
'deriv_dict': dcon_failure_dict
}
}
'mrho': mean_mrho, # surface RV
}
dv_dict = {
'n_twist_cp': 3,
'n_thickness_cp': 3,
'n_CM': 3,
'n_thickness_intersects': 10,
'n_constraints': 1 + 10 + 1 + 3 + 3,
'ndv': 3 + 3 + 2,
'mesh_dict': mesh_dict,
'rv_dict': rv_dict
}
QoI = examples.OASScanEagleWrapper(uq_systemsize,
dv_dict,
include_dict_rv=True)
QoI.p['oas_scaneagle.wing.thickness_cp'] = 1.e-3 * np.array([
5.5, 5.5, 5.5
]) # This setup is according to the one in the scaneagle paper
QoI.p['oas_scaneagle.wing.twist_cp'] = 2.5 * np.ones(3)
QoI.p.final_setup()
print("twist = ", QoI.p['oas_scaneagle.wing.geometry.twist'])
print("thickness =", QoI.p['oas_scaneagle.wing.thickness'])
print("sweep = ", QoI.p['oas_scaneagle.wing.sweep'])
print("aoa = ", QoI.p['oas_scaneagle.alpha'])
print()
"""
# Dictionary for the collocation object
QoI_dict = {'fuelburn' : {'QoI_func' : QoI.eval_QoI,