def __init__(self, iotype, client, rpath):
ProxyMixin.__init__(self, client, rpath)
default = float(self._valstr)
desc = client.get(rpath+'.description')
as_units = client.get(rpath+'.units')
if as_units:
om_units = get_translation(as_units)
else:
om_units = None
if client.get(rpath+'.hasUpperBound') == 'true':
high = float(client.get(rpath+'.upperBound'))
else:
high = None
if client.get(rpath+'.hasLowerBound') == 'true':
low = float(client.get(rpath+'.lowerBound'))
else:
low = None
Float.__init__(self, default_value=default, iotype=iotype, desc=desc,
low=low, high=high, units=om_units)
def test_large_dataflow_nested_assys(self):
self.top = set_as_top(Assembly())
exp1 = ['y1 = 2.0*x1**2', 'y2 = 3.0*x1']
deriv1 = ['dy1_dx1 = 4.0*x1', 'dy2_dx1 = 3.0']
exp2 = ['y1 = 0.5*x1']
deriv2 = ['dy1_dx1 = 0.5']
exp3 = ['y1 = 3.5*x1']
deriv3 = ['dy1_dx1 = 3.5']
exp4 = ['y1 = x1 + 2.0*x2', 'y2 = 3.0*x1', 'y3 = x1*x2']
deriv4 = [
'dy1_dx1 = 1.0', 'dy1_dx2 = 2.0', 'dy2_dx1 = 3.0', 'dy2_dx2 = 0.0',
'dy3_dx1 = x2', 'dy3_dx2 = x1'
]
exp5 = ['y1 = x1 + 3.0*x2 + 2.0*x3']
deriv5 = ['dy1_dx1 = 1.0', 'dy1_dx2 = 3.0', 'dy1_dx3 = 2.0']
self.top.add('nest1', Assembly())
self.top.add('comp1', ExecCompWithDerivatives(exp1, deriv1))
self.top.nest1.add('comp2', ExecCompWithDerivatives(exp2, deriv2))
self.top.nest1.add('comp3', ExecCompWithDerivatives(exp3, deriv3))
self.top.nest1.add('comp4', ExecCompWithDerivatives(exp4, deriv4))
self.top.add('comp5', ExecCompWithDerivatives(exp5, deriv5))
self.top.add('driver', Driv())
self.top.driver.workflow.add(['comp1', 'nest1', 'comp5'])
self.top.nest1.driver.workflow.add(['comp2', 'comp3', 'comp4'])
self.top.driver.differentiator = ChainRule()
obj = 'comp5.y1'
con = 'comp5.y1-nest1.comp3.y1 > 0'
self.top.driver.add_parameter('comp1.x1',
low=-50.,
high=50.,
fd_step=.0001)
self.top.driver.add_objective(obj)
self.top.driver.add_constraint(con)
self.top.nest1.add('real_c2_x1',
Float(iotype='in', desc='I am really here'))
self.top.nest1.add('real_c3_x1',
Float(iotype='in', desc='I am really here'))
self.top.nest1.add('real_c4_y1',
Float(iotype='out', desc='I am really here'))
self.top.nest1.add('real_c4_y2',
Float(iotype='out', desc='I am really here'))
self.top.nest1.add('real_c4_y3',
Float(iotype='out', desc='I am really here'))
#self.top.connect('comp1.y1', 'nest1.comp2.x1')
self.top.connect('comp1.y1', 'nest1.real_c2_x1')
self.top.connect('comp1.y2', 'nest1.real_c3_x1')
self.top.nest1.connect('real_c2_x1', 'comp2.x1')
self.top.nest1.connect('real_c3_x1', 'comp3.x1')
self.top.nest1.connect('comp2.y1', 'comp4.x1')
self.top.nest1.connect('comp3.y1', 'comp4.x2')
self.top.nest1.connect('comp4.y1', 'real_c4_y1')
self.top.nest1.connect('comp4.y2', 'real_c4_y2')
self.top.nest1.connect('comp4.y3', 'real_c4_y3')
self.top.connect('nest1.real_c4_y1', 'comp5.x1')
self.top.connect('nest1.real_c4_y2', 'comp5.x2')
self.top.connect('nest1.real_c4_y3', 'comp5.x3')
#self.top.connect('nest1.comp4.y1', 'comp5.x1')
#self.top.connect('nest1.comp4.y3', 'comp5.x3')
self.top.comp1.x1 = 2.0
self.top.run()
self.top.driver.differentiator.calc_gradient()
grad = self.top.driver.differentiator.get_gradient(obj)
assert_rel_error(self, grad[0], 313.0, .001)
grad = self.top.driver.differentiator.get_gradient(
'comp5.y1-nest1.comp3.y1>0')
assert_rel_error(self, grad[0], -313.0 + 10.5, .001)
class OffshorePlot(TopfarmComponent):
wt_positions = Array(
[],
unit='m',
iotype='in',
desc='Array of wind turbines attached to particular positions')
baseline = Array(
[],
unit='m',
iotype='in',
desc='Array of wind turbines attached to particular positions')
borders = Array(iotype='in',
desc='The polygon defining the borders ndarray([n_bor,2])',
unit='m')
depth = Array(iotype='in',
desc='An array of depth ndarray([n_d, 2])',
unit='m')
foundations = Array(iotype='in',
desc='The foundation length ofeach wind turbine')
#wt_dist = Array(iotype='in', desc="""The distance between each turbines ndarray([n_wt, n_wt]).""", unit='m')
spiral_param = Float(5.0, iotype='in', desc='spiral parameter')
png_name = Str('wind_farm',
iotype='in',
desc='The base of the png name used to save the fig')
result_file = Str('wind_farm',
iotype='in',
desc='The base result name used to save the fig')
distribution = Str('spiral',
iotype='in',
desc='The type of distribution to plot')
elnet_layout = Dict(iotype='in')
inc = 0
fs = 15 #Font size
def __init__(self, add_inputs, title='', **kwargs):
super(OffshorePlot, self).__init__(**kwargs)
self.fig = plt.figure(num=None, facecolor='w',
edgecolor='k') #figsize=(13, 8), dpi=1000
self.shape_plot = self.fig.add_subplot(121)
self.objf_plot = self.fig.add_subplot(122)
self.targname = add_inputs
self.title = title
# Adding automatically the inputs
for i in add_inputs:
self.add(i, Float(0.0, iotype='in'))
#sns.set(style="darkgrid")
#self.pal = sns.dark_palette("skyblue", as_cmap=True)
plt.rc('lines', linewidth=1)
plt.ion()
self.force_execute = True
if not pa('fig').exists():
pa('fig').mkdir()
def execute(self):
plt.ion()
if self.inc == 0:
try:
pa(self.result_file + '.results').remove()
except:
pass
self.iterations = [self.inc]
self.targvalue = [[getattr(self, i) for i in self.targname]]
self.pre_plot()
else:
self.iterations.append(self.inc)
self.targvalue.append([getattr(self, i) for i in self.targname])
#print self.iterations,self.targvalue
#if self.inc % (2*self.wt_positions.shape[0]) == 0:
#self.refresh()
#plt.show()
self.save_plot('fig/' + self.png_name + 'layout%d.png' % (self.inc))
self.inc += 1
def pre_plot(self):
plt.ion()
#plt.show()
### Plot the water depth
N = 100
self.X, self.Y = plt.meshgrid(
plt.linspace(self.depth[:, 0].min(), self.depth[:, 0].max(), N),
plt.linspace(self.depth[:, 1].min(), self.depth[:, 1].max(), N))
self.Z = plt.griddata(self.depth[:, 0],
self.depth[:, 1],
self.depth[:, 2],
self.X,
self.Y,
interp='linear')
Zin = points_in_poly(self.X, self.Y, self.borders)
self.Z.mask = Zin.__neg__()
#Z.mask = False
#Z.data[Zin.__neg__()] = -20.0
display(plt.gcf())
# def refresh(self):
self.shape_plot.clear()
self.shape_plot.contourf(self.X,
self.Y,
self.Z,
10,
vmax=self.depth[:, 2].max()) #, cmap=self.pal
self.shape_plot.set_aspect('equal')
self.shape_plot.autoscale(tight=True)
Plot = lambda b, *args, **kwargs: self.shape_plot.plot(
b[:, 0], b[:, 1], *args, **kwargs)
if self.distribution == 'spiral':
spiral = lambda t_, a_, x_: [
a_ * t_**(1. / x_) * np.cos(t_), a_ * t_**
(1. / x_) * np.sin(t_)
]
spirals = lambda ts_, a_, x_: np.array(
[spiral(t_, a_, x_) for t_ in ts_])
for P in self.baseline:
Plot(P + spirals(plt.linspace(0., 10 * np.pi, 1000),
self.spiral_param, 1.),
'g-',
linewidth=0.1)
self.shape_plot.plot(self.borders[:, 0], self.borders[:, 1], 'k-')
self.posi = self.shape_plot.plot(self.wt_positions[:, 0],
self.wt_positions[:, 1], 'ro')
self.plotel = self.shape_plot.plot(
np.array([
self.baseline[[i, j], 0] for i, j in self.elnet_layout.keys()
]).T,
np.array([
self.baseline[[i, j], 1] for i, j in self.elnet_layout.keys()
]).T,
'y--',
linewidth=1)
#print self.plotel
self.objf_plot.clear()
targarr = np.array(self.targvalue)
self.posb = []
for i in range(targarr.shape[1]):
self.posb.append(
self.objf_plot.plot(self.iterations,
self.targvalue[0][i],
'.',
label=self.targname[i]))
print 'posb', self.posb
self.legend = self.objf_plot.legend(loc=3, bbox_to_anchor=(1.1, 0.0))
plt.title('Foundation = %8.2f' % (self.foundation_length))
plt.draw()
def save_plot(self, filename):
plt.ion()
targarr = np.array(self.targvalue)
self.posi[0].set_xdata(self.wt_positions[:, 0])
self.posi[0].set_ydata(self.wt_positions[:, 1])
while len(self.plotel) > 0:
self.plotel.pop(0).remove()
self.plotel = self.shape_plot.plot(
np.array([
self.wt_positions[[i, j], 0]
for i, j in self.elnet_layout.keys()
]).T,
np.array([
self.wt_positions[[i, j], 1]
for i, j in self.elnet_layout.keys()
]).T,
'y-',
linewidth=1)
for i in range(len(self.posb)):
self.posb[i][0].set_xdata(self.iterations)
self.posb[i][0].set_ydata(targarr[:, i])
self.legend.texts[i].set_text('%s = %8.2f' %
(self.targname[i], targarr[-1, i]))
self.objf_plot.set_xlim([0, self.iterations[-1]])
self.objf_plot.set_ylim([0.5, 1.2])
if not self.title == '':
plt.title('%s = %8.2f' % (self.title, getattr(self, self.title)))
plt.draw()
#print self.iterations[-1] , ': ' + ', '.join(['%s=%6.2f'%(self.targname[i], targarr[-1,i]) for i in range(len(self.targname))])
with open(self.result_file + '.results', 'a') as f:
f.write('%d:' % (self.inc) + ', '.join([
'%s=%6.2f' % (self.targname[i], targarr[-1, i])
for i in range(len(self.targname))
]) + '\n')
#plt.show()
#plt.savefig(filename)
display(plt.gcf())
#plt.show()
clear_output(wait=True)
def test_subassy_units(self):
self.top = set_as_top(Assembly())
self.top.add('nest1', Assembly())
self.top.add('comp1', CompFoot())
self.top.nest1.add('comp2', CompInch())
self.top.add('comp3', CompFoot())
self.top.nest1.add(
'nestx', Float(iotype='in', units='inch', desc='Legit connection'))
self.top.nest1.add(
'nesty', Float(iotype='out', units='inch',
desc='Legit connection'))
#self.top.connect('comp1.y', 'nest1.comp2.x')
self.top.connect('1.0*comp1.y', 'nest1.nestx')
self.top.nest1.connect('nestx', 'comp2.x')
#self.top.connect('nest1.comp2.y', 'comp3.x')
self.top.nest1.connect('comp2.y', 'nesty')
self.top.connect('1.0*nest1.nesty', 'comp3.x')
self.top.add('driver', Driv())
self.top.driver.workflow.add(['comp1', 'nest1', 'comp3'])
self.top.nest1.driver.workflow.add(['comp2'])
self.top.driver.differentiator = ChainRule()
obj = 'comp3.y'
con = 'nest1.nesty>0'
self.top.driver.add_parameter('comp1.x',
low=-50.,
high=50.,
fd_step=.0001)
self.top.driver.add_objective(obj)
self.top.driver.add_constraint(con)
self.top.comp1.x = 1.0
self.top.run()
self.top.driver.differentiator.calc_gradient()
grad = self.top.driver.differentiator.get_gradient(obj)
assert_rel_error(self, grad[0], 8.0, .001)
grad = self.top.driver.differentiator.get_gradient(con)
assert_rel_error(self, grad[0], -48.0, .001)
# Testing conversion at this boundary (ft instead of inch)
self.top.nest1.add(
'nestx', Float(iotype='in', units='ft', desc='Legit connection'))
self.top.nest1.add(
'nesty', Float(iotype='out', units='inch',
desc='Legit connection'))
self.top.comp1.x = 1.0
self.top.run()
self.top.driver.differentiator.calc_gradient()
grad = self.top.driver.differentiator.get_gradient(obj)
assert_rel_error(self, grad[0], 8.0, .001)
grad = self.top.driver.differentiator.get_gradient(con)
assert_rel_error(self, grad[0], -48.0, .001)
class HyperloopPod(Assembly):
#Design Variables
Mach_pod_max = Float(1.0, iotype="in", desc="travel Mach of the pod")
Mach_c1_in = Float(.6, iotype="in", desc="Mach number at entrance to the first compressor at design conditions")
Mach_bypass = Float(.95, iotype="in", desc="Mach in the air passing around the pod")
c1_PR_des = Float(12.47, iotype="in", desc="pressure ratio of first compressor at design conditions")
Ps_tube = Float(99, iotype="in", desc="static pressure in the tube", units="Pa", low=0)
#Parameters
solar_heating_factor = Float(.7, iotype="in",
desc="Fractional amount of solar radiation to consider in tube temperature calculations",
low=0, high=1)
tube_length = Float(563270, units = 'm', iotype='in', desc='Length of entire Hyperloop')
pwr_marg = Float(.3, iotype="in", desc="fractional extra energy requirement")
hub_to_tip = Float(.4, iotype="in", desc="hub to tip ratio for the compressor")
coef_drag = Float(2, iotype="in", desc="capsule drag coefficient")
n_rows = Int(14, iotype="in", desc="number of rows of seats in the pod")
length_row = Float(150, iotype="in", units="cm", desc="length of each row of seats")
def configure(self):
#Add Components
compress = self.add('compress', CompressionSystem())
mission = self.add('mission', Mission())
pod = self.add('pod', Pod())
flow_limit = self.add('flow_limit', TubeLimitFlow())
tube_wall_temp = self.add('tube_wall_temp', TubeWallTemp())
#Boundary Input Connections
#Hyperloop -> Compress
self.connect('Mach_pod_max', 'compress.Mach_pod_max')
self.connect('Ps_tube', 'compress.Ps_tube')
self.connect('Mach_c1_in','compress.Mach_c1_in') #Design Variable
self.connect('c1_PR_des', 'compress.c1_PR_des') #Design Variable
#Hyperloop -> Mission
self.connect('tube_length', 'mission.tube_length')
self.connect('pwr_marg','mission.pwr_marg')
#Hyperloop -> Flow Limit
self.connect('Mach_pod_max', 'flow_limit.Mach_pod')
self.connect('Ps_tube', 'flow_limit.Ps_tube')
self.connect('pod.radius_inlet_back_outer', 'flow_limit.radius_inlet')
self.connect('Mach_bypass','flow_limit.Mach_bypass')
#Hyperloop -> Pod
self.connect('Ps_tube', 'pod.Ps_tube')
self.connect('hub_to_tip','pod.hub_to_tip')
self.connect('coef_drag','pod.coef_drag')
self.connect('n_rows','pod.n_rows')
self.connect('length_row','pod.length_row')
#Hyperloop -> TubeWallTemp
self.connect('solar_heating_factor', 'tube_wall_temp.nn_incidence_factor')
self.connect('tube_length', 'tube_wall_temp.length_tube')
#Inter-component Connections
#Compress -> Mission
self.connect('compress.speed_max', 'mission.speed_max')
self.connect('compress.pwr_req', 'mission.pwr_req')
#Compress -> Pod
self.connect('compress.area_c1_in', 'pod.area_inlet_out')
self.connect('compress.area_inlet_in', 'pod.area_inlet_in')
self.connect('compress.rho_air', 'pod.rho_air')
self.connect('compress.F_net','pod.F_net')
self.connect('compress.speed_max', 'pod.speed_max')
#Compress -> TubeWallTemp
self.connect('compress.nozzle_Fl_O', 'tube_wall_temp.nozzle_air')
self.connect('compress.bearing_Fl_O', 'tube_wall_temp.bearing_air')
#Mission -> Pod
self.connect('mission.time','pod.time_mission')
self.connect('mission.energy', 'pod.energy')
#Add Solver
solver = self.add('solver',BroydenSolver())
solver.itmax = 50 #max iterations
solver.tol = .001
#Add Parameters and Constraints
solver.add_parameter('compress.W_in',low=-1e15,high=1e15)
solver.add_parameter('compress.c2_PR_des', low=-1e15, high=1e15)
solver.add_parameter(['compress.Ts_tube','flow_limit.Ts_tube','tube_wall_temp.temp_boundary'], low=-1e-15, high=1e15)
solver.add_parameter(['flow_limit.radius_tube', 'pod.radius_tube_inner'], low=-1e15, high=1e15)
solver.add_constraint('.01*(compress.W_in-flow_limit.W_excess) = 0')
solver.add_constraint('compress.Ps_bearing_residual=0')
solver.add_constraint('tube_wall_temp.ss_temp_residual=0')
solver.add_constraint('.01*(pod.area_compressor_bypass-compress.area_c1_out)=0')
driver = self.driver
driver.workflow.add('solver')
#driver.recorders = [CSVCaseRecorder(filename="hyperloop_data.csv")] #record only converged
#driver.printvars = ['Mach_bypass', 'Mach_pod_max', 'Mach_c1_in', 'c1_PR_des', 'pod.radius_inlet_back_outer',
# 'pod.inlet.radius_back_inner', 'flow_limit.radius_tube', 'compress.W_in', 'compress.c2_PR_des',
# 'pod.net_force', 'compress.F_net', 'compress.pwr_req', 'pod.energy', 'mission.time',
# 'compress.speed_max', 'tube_wall_temp.temp_boundary']
#Declare Solver Workflow
solver.workflow.add(['compress','mission','pod','flow_limit','tube_wall_temp'])
class PdcylComp(ExternalCode):
""" OpenMDAO component wrapper for PDCYL. """
icalc = Bool(False,
iotype='in',
desc='Print switch. Set to True for verbose output.')
title = Str("PDCYl Component", iotype='in', desc='Title of the analysis')
# Wing geometry
# --------------------
wsweep = Float(iotype='in',
units='deg',
desc='Wing sweep referenced to the leading edge')
war = Float(iotype='in', desc='Wing Aspect Ratio')
wtaper = Float(iotype='in', desc=' Wing taper ratio')
wtcroot = Float(iotype='in', desc=' Wing thickness-to-cord at root')
wtctip = Float(iotype='in', desc=' Wing thickness-to-cord at tip')
warea = Float(iotype='in', units='ft**2', desc='Wing planform area')
# Material properties
# --------------------
ps = Float(iotype='in', desc='Plasticity factor')
tmgw = Float(iotype='in',
units='inch',
desc='Min. gage thickness for the wing')
effw = Float(iotype='in', desc='Buckling efficiency of the web')
effc = Float(iotype='in', desc='Buckling efficiency of the covers')
esw = Float(iotype='in',
units='psi',
desc="Young's Modulus for wing material")
fcsw = Float(iotype='in',
units='psi',
desc='Ult. compressive strength of wing')
dsw = Float(iotype='in',
units='lb/inch**3',
desc=' Density of the wing material')
kdew = Float(iotype='in', desc="Knock-down factor for Young's Modulus")
kdfw = Float(iotype='in', desc='Knock-down factor for Ultimate strength')
# Geometric parameters
# --------------------
istama = Enum(
[1, 2],
iotype='in',
desc=' 1 - Position of wing is unknown; 2 - position is known')
cs1 = Float(
iotype='in',
desc=
'Position of structural wing box from leading edge as percent of root chord'
)
cs2 = Float(
iotype='in',
desc=
' Position of structural wing box from trailing edge as percent of root chord'
)
uwwg = Float(iotype='in',
units='lb/ft**2',
desc=' Wing Weight / Wing Area of baseline aircraft ')
xwloc1 = Float(iotype='in',
desc=' Location of wing as a percentage of body length')
# Structural Concept
# --------------------
claqr = Float(iotype='in', desc='Ratio of body lift to wing lift')
ifuel = Enum(
[1, 2],
iotype='in',
desc=
' 1 - No fuel is stored in the wing; 2 - Fuel is stored in the wing')
cwman = Float(iotype='in', desc='Design maneuver load factor')
cf = Float(iotype='in', desc="Shanley's const. for frame bending")
# Tails
# --------------------
itail = Enum(
[1, 2],
iotype='in',
desc=
' 1 - Control surfaces mounted on tail; 2 - Control surfaces mounted on wing'
)
uwt = Float(
iotype='in',
units='lb/ft**2',
desc='(Htail Weight + Vtail Weight) / Htail Area of baseline aircraft')
clrt = Float(iotype='in',
desc=' Location of tail as a percentage of body length')
harea = Float(iotype='in',
units='ft**2',
desc=' Location of tail as a percentage of body length')
# Fuselage geometry
# --------------------
frn = Float(
iotype='in',
desc='Fineness ratio of the nose section (length/diameter)')
frab = Float(
iotype='in',
desc='Fineness ratio of the after-body section (length/diameter)')
bodl = Float(iotype='in', units='ft', desc='Length of the fuselage ')
bdmax = Float(iotype='in', units='ft', desc='Maximum diameter of fuselage')
# These vars are listed in the pdcyl code, but they are never read in. Not sure
# what that's all about.
#vbod = Float(iotype='in', units='ft**3', desc='Fuselage total volume ')
#volnose = Float(iotype='in', units='ft**3', desc='Nose Volume')
#voltail = Float(iotype='in', units='ft**3', desc='Tail volume ')
# Structural Concept
# --------------------
ckf = Float(iotype='in', desc='Frame stiffness coefficient')
ec = Float(iotype='in',
desc='Power in approximation equation for buckling stability')
kgc = Float(
iotype='in',
desc=
'Buckling coefficient for component general buckling of stiffener web panel'
)
kgw = Float(
iotype='in',
desc='Buckling coefficient for component local buckling of web panel')
# KCONT(12) ! Structural Geometry Concept Top/Bottom
# KCONB(12) ! 2 - Simply stiffened shell, frames, sized for minimum weight in buckling
# ! 3 - Z-stiffened shell, frames, best buckling
# ! 4 - Z-stiffened shell, frames, buckling-minimum gage compromise
# ! 5 - Z-stiffened shell, frames, buckling-pressure compromise
# ! 6 - Truss-core sandwich, frames, best buckling
# ! 8 - Truss-core sandwich, no frames, best buckling
# ! 9 - Truss-core sandwich, no frames, buckling-min. gage-pressure compromise
kcont = Enum([2, 3, 4, 5, 6, 8, 9],
iotype='in',
desc='Structural Geometry Concept Top')
kconb = Enum([2, 3, 4, 5, 6, 8, 9],
iotype='in',
desc='Structural Geometry Concept Bottom')
# Material properties
# -------------------
ftst = Float(iotype='in', desc="Tensile Strength on Top")
ftsb = Float(iotype='in', desc="Tensile Strength on Bottom")
fcst = Float(iotype='in', desc="Compressive Strength on Top")
fcsb = Float(iotype='in', desc="Compressive Strength on Bottom")
est = Float(iotype='in', desc="Young's Modulus for the shells Top")
esb = Float(iotype='in', desc="Young's Modulus for the shells Bottom")
eft = Float(iotype='in', desc="Young's Modulus for the frames Top")
efb = Float(iotype='in', desc="Young's Modulus for the frames Bottom")
dst = Float(iotype='in', desc="Density of shell material on Top")
dsb = Float(iotype='in', desc="Density of shell material on Bottom")
dft = Float(iotype='in', desc="Density of frame material on Top")
dfb = Float(iotype='in', desc="Density of frame material on Bottom")
tmgt = Float(iotype='in', desc="Minimum gage thickness Top")
tmgb = Float(iotype='in', desc="Minimum gage thickness Bottom")
kde = Float(iotype='in', desc="Knock-down factor for modulus")
kdf = Float(iotype='in', desc="Knock-down factor for strength")
# Geometric parameters
# --------------------
clbr1 = Float(
iotype='in',
desc='Fuselage break point as a fraction of total fuselage length')
icyl = Enum(
[1, 0],
iotype='in',
desc=
' 1 - modeled with a mid-body cylinder, 0 - use two power-law bodies back to back'
)
# Engines
# --------------------
neng = Int(iotype='in', desc=' Total number of engines')
nengwing = Int(iotype='in', desc=' Number of engines on wing')
wfp = Float(iotype='in', desc='(Engine Weight * NENG) / WGTO')
clrw1 = Float(
iotype='in',
desc=' Location of first engine pair. Input 0 for centerline engine.')
clrw2 = Float(
iotype='in',
desc=' Location of second engine pair. measured from body centerline')
clrw3 = Float(
iotype='in',
desc=' Location of third engine pair. measured from body centerline')
# Loads
# --------------------
deslf = Float(iotype='in', desc='Design load factor')
ultlf = Float(iotype='in', desc='Ultimate load factor (usually 1.5*DESLF)')
axac = Float(iotype='in', desc='Axial acceleration')
cman = Float(iotype='in', desc=' Weight fraction at maneuver')
iload = Enum(
[1, 2, 3],
iotype='in',
desc=
'1 - Analyze maneuver only; 2 - Analyze maneuver and landing only; 3 - Analyze bump, landing and maneuver'
)
pgt = Float(iotype='in', desc="Fuselage gage pressure on top")
pgb = Float(iotype='in', desc="Fuselage gage pressure on bottom")
wfbump = Float(iotype='in', desc=' Weight fraction at bump')
wfland = Float(iotype='in', desc=' Weight fraction at landing')
# Landing Gear
# -------------------
vsink = Float(iotype='in',
units='ft/s',
desc='Design sink velocity at landing ')
stroke = Float(iotype='in', units='ft', desc=' Stroke of landing gear ')
clrg1 = Float(
iotype='in',
desc=
'Length fraction of nose landing gear measured as a fraction of total fuselage length'
)
clrg2 = Float(
iotype='in',
desc=
'Length fraction of main landing gear measured as a fraction of total fuselage length'
)
wfgr1 = Float(iotype='in', desc='Weight fraction of nose landing gear')
wfgr2 = Float(iotype='in', desc='Weight fraction of main landing gear')
igear = Enum(
[1, 2],
iotype='in',
desc=
'1 - Main landing gear located on fuselage,2 - Main landing gear located on wing'
)
gfrl = Float(
iotype='in',
desc=
'Ratio of force taken by nose landing gear to force taken by main gear at landing'
)
clrgw1 = Float(
iotype='in',
desc='Position of wing gear as a fraction of structural semispan')
clrgw2 = Float(
iotype='in',
desc=
'Position of second pair wing gear as a fraction of structural semispan'
)
# Weights
# -------------------
wgto = Float(iotype='in', units='lb', desc=' Gross takeoff weight')
wtff = Float(iotype='in', desc='Weight fraction of fuel')
cbum = Float(iotype='in', desc='Weight fraction at bump')
clan = Float(iotype='in', desc='Weight fraction at landing')
# Factors
# --------------------
ischrenk = Int(
iotype='in',
desc=
'1 - use Schrenk load distribution on wing,Else - use trapezoidal distribution'
)
icomnd = Enum(
[1, 2],
iotype='in',
desc=
'1 - print gross shell dimensions envelope,2 - print detailed shell geometry'
)
wgno = Float(
iotype='in',
desc='Nonoptimal factor for wing (including the secondary structure)')
slfmb = Float(iotype='in', desc='Static load factor for bumps')
wmis = Float(iotype='in', desc='Volume component of secondary structure')
wsur = Float(iotype='in',
desc='Surface area component of secondary structure')
wcw = Float(iotype='in',
desc='Factor in weight equation for nonoptimal weights')
wca = Float(iotype='in',
desc='Factor in weight equation for nonoptimal weights')
nwing = Int(iotype='in', desc='Number of wing segments for analysis')
# Outputs
# --------------------
wfuselaget = Float(iotype='out', units='lb', desc='Total fuselage weight')
wwingt = Float(iotype='out', units='lb', desc='Total wing weight')
def __init__(self):
"""Constructor for the PdcylComp component"""
super(PdcylComp, self).__init__()
# External Code public variables
self.stdin = 'PDCYL.in'
self.stdout = 'PDCYL.out'
self.stderr = 'PDCYL.err'
self.command = ['PDCYL']
self.external_files = [
FileMetadata(path=self.stdin, input=True),
FileMetadata(path=self.stdout),
FileMetadata(path=self.stderr),
]
# Dictionary contains location of every numeric scalar variable
fields = {}
fields[8] = 'wsweep'
fields[9] = 'war'
fields[10] = 'wtaper'
fields[11] = 'wtcroot'
fields[12] = 'wtctip'
fields[13] = 'warea'
fields[15] = 'ps'
fields[16] = 'tmgw'
fields[17] = 'effw'
fields[18] = 'effc'
fields[19] = 'esw'
fields[20] = 'fcsw'
fields[21] = 'dsw'
fields[22] = 'kdew'
fields[23] = 'kdfw'
fields[25] = 'istama'
fields[27] = 'cs1'
fields[28] = 'cs2'
fields[29] = 'uwwg'
fields[30] = 'xwloc1'
fields[32] = 'claqr'
fields[33] = 'ifuel'
fields[35] = 'cwman'
fields[36] = 'cf'
fields[40] = 'itail'
fields[42] = 'uwt'
fields[43] = 'clrt'
fields[44] = 'harea'
fields[49] = 'frn'
fields[50] = 'frab'
fields[51] = 'bodl'
fields[52] = 'bdmax'
fields[54] = 'ckf'
fields[55] = 'ec'
fields[56] = 'kgc'
fields[57] = 'kgw'
fields[58] = 'kcont'
fields[59] = 'kconb'
fields[67] = 'ftst'
fields[68] = 'ftsb'
fields[69] = 'fcst'
fields[70] = 'fcsb'
fields[71] = 'est'
fields[72] = 'esb'
fields[73] = 'eft'
fields[74] = 'efb'
fields[75] = 'dst'
fields[76] = 'dsb'
fields[77] = 'dft'
fields[78] = 'dfb'
fields[79] = 'tmgt'
fields[80] = 'tmgb'
fields[81] = 'kde'
fields[82] = 'kdf'
fields[84] = 'clbr1'
fields[85] = 'icyl'
fields[90] = 'neng'
fields[91] = 'nengwing'
fields[92] = 'wfp'
fields[93] = 'clrw1'
fields[95] = 'clrw2'
fields[96] = 'clrw3'
fields[100] = 'deslf'
fields[101] = 'ultlf'
fields[102] = 'axac'
fields[103] = 'cman'
fields[104] = 'iload'
fields[107] = 'pgt'
fields[108] = 'pgb'
fields[109] = 'wfbump'
fields[110] = 'wfland'
fields[114] = 'vsink'
fields[115] = 'stroke'
fields[116] = 'clrg1'
fields[117] = 'clrg2'
fields[118] = 'wfgr1'
fields[119] = 'wfgr2'
fields[120] = 'igear'
fields[122] = 'gfrl'
fields[123] = 'clrgw1'
fields[124] = 'clrgw2'
fields[129] = 'wgto'
fields[130] = 'wtff'
fields[131] = 'cbum'
fields[132] = 'clan'
fields[136] = 'ischrenk'
fields[138] = 'icomnd'
fields[140] = 'wgno'
fields[141] = 'slfmb'
fields[142] = 'wmis'
fields[143] = 'wsur'
fields[144] = 'wcw'
fields[145] = 'wca'
fields[146] = 'nwing'
self._fields = fields
def execute(self):
"""Run PDCYL."""
#Prepare the input file for PDCYL
self.generate_input()
#Run PDCYL via ExternalCode's execute function
super(PdcylComp, self).execute()
#Parse the outut file from PDCYL
self.parse_output()
def generate_input(self):
"""Creates the PDCYL custom input file."""
data = []
form = "%.15g %s\n"
# It turns out to be simple and quick to generate a new input file each
# time, rather than poking values into a template.
data.append("\n\n")
data.append(self.title)
data.append("\n\n")
if self.icalc == True:
icalc = 3
else:
icalc = 0
data.append("%d icalc print switch" % icalc)
data.append("\n\n\n")
data.append("Wing geometry:\n")
for nline in range(8, 14):
name = self._fields[nline]
data.append(form % (self.get(name), name))
data.append("Material properties:\n")
for nline in range(15, 24):
name = self._fields[nline]
data.append(form % (self.get(name), name))
data.append("Geometric properties:\n")
name = self._fields[25]
data.append(form % (self.get(name), name))
data.append("\n")
for nline in range(27, 31):
name = self._fields[nline]
data.append(form % (self.get(name), name))
data.append("Structural concept:\n")
for nline in range(32, 34):
name = self._fields[nline]
data.append(form % (self.get(name), name))
data.append("\n")
for nline in range(35, 37):
name = self._fields[nline]
data.append(form % (self.get(name), name))
data.append("\n\n")
data.append("Tails:\n")
name = self._fields[40]
data.append(form % (self.get(name), name))
data.append("\n")
for nline in range(42, 45):
name = self._fields[nline]
data.append(form % (self.get(name), name))
data.append("\n\n\n")
data.append("Fuselage geometry:\n")
for nline in range(49, 53):
name = self._fields[nline]
data.append(form % (self.get(name), name))
data.append("Structural concept:\n")
for nline in range(54, 60):
name = self._fields[nline]
data.append(form % (self.get(name), name))
data.append("\n\n\n\n\n\n")
data.append("Material properties:\n")
for nline in range(67, 83):
name = self._fields[nline]
data.append(form % (self.get(name), name))
data.append("Geometric parameters:\n")
for nline in range(84, 86):
name = self._fields[nline]
data.append(form % (self.get(name), name))
data.append("\n\n\n")
data.append("Engines:\n")
for nline in range(90, 94):
name = self._fields[nline]
data.append(form % (self.get(name), name))
data.append("\n")
for nline in range(95, 97):
name = self._fields[nline]
data.append(form % (self.get(name), name))
data.append("\n\n")
data.append("Loads:\n")
for nline in range(100, 105):
name = self._fields[nline]
data.append(form % (self.get(name), name))
data.append("\n\n")
for nline in range(107, 111):
name = self._fields[nline]
data.append(form % (self.get(name), name))
data.append("\n\n")
data.append("Landing gear:\n")
for nline in range(114, 121):
name = self._fields[nline]
data.append(form % (self.get(name), name))
data.append("\n\n")
for nline in range(122, 125):
name = self._fields[nline]
data.append(form % (self.get(name), name))
data.append("\n\n\n")
data.append("Weights:\n")
for nline in range(129, 133):
name = self._fields[nline]
data.append(form % (self.get(name), name))
data.append("\n\n")
data.append("Factors:\n")
name = self._fields[136]
data.append(form % (self.get(name), name))
data.append("\n")
name = self._fields[138]
data.append(form % (self.get(name), name))
data.append("\n")
for nline in range(140, 147):
name = self._fields[nline]
data.append(form % (self.get(name), name))
outfile = open(self.stdin, 'w')
outfile.writelines(data)
outfile.close()
def parse_output(self):
"""Parses the PCYL output file and extracts data."""
infile = FileParser()
infile.set_file(self.stdout)
self.wwingt = infile.transfer_keyvar("Total Wing Structural Weight", 1)
self.wfuselaget = infile.transfer_keyvar(
"Fuselage Total Structural Weight", 1)
def load_model(self, filename):
"""Reads in an existing PDCYL input file and populates the variable
tree with its values."""
infile = FileParser()
infile.set_file(filename)
# Title is a string
self.title = infile.transfer_line(2)
# Print flag becomes a Bool
if infile.transfer_var(4, 1) == 3:
self.icalc = True
else:
self.icalc = False
# Named variables in dictionary
for key, val in self._fields.iteritems():
self.set(val, infile.transfer_var(key, 1))
def __init__(self, *args, **kwargs):
super(MyComponent, self).__init__(*args, **kwargs)
self.add('cont', Container())
self.cont.add('dyntrait', Float(3.))
def __init__(self,
nTurbines,
nDirections,
optimize_position=False,
nSamples=0,
optimize_yaw=False,
datasize=0,
nSpeeds=False,
maxiter=100):
super(floris_assembly_opt_AEP, self).__init__()
if nSpeeds == False:
nSpeeds = nDirections
self.nTurbines = nTurbines
self.nSamples = nSamples
self.nDirections = nDirections
self.optimize_yaw = optimize_yaw
self.optimize_position = optimize_position
self.datasize = datasize
self.nSpeeds = nSpeeds
self.maxiter = maxiter
# wt_layout input variables
self.add(
'rotorDiameter',
Array(np.zeros(nTurbines),
dtype='float',
iotype='in',
units='m',
desc='rotor diameters of all turbine'))
self.add(
'axialInduction',
Array(np.zeros(nTurbines),
iotype='in',
dtype='float',
desc='axial induction of all turbines'))
self.add('hubHeight', Array(np.zeros(nTurbines), dtype='float', iotype='in', units='m', \
desc='hub heights of all turbines'))
# turbine properties for ccblade and pre-calculated controller
self.add(
'curve_CP',
Array(np.zeros(datasize), iotype='in', desc='pre-calculated CPCT'))
self.add(
'curve_CT',
Array(np.zeros(datasize), iotype='in', desc='pre-calculated CPCT'))
self.add(
'curve_wind_speed',
Array(np.zeros(datasize), iotype='in', desc='pre-calculated CPCT'))
self.add('initVelocitiesTurbines',
Array(np.zeros(nTurbines), iotype='in', units='m/s'))
self.add(
'generator_efficiency',
Array(np.zeros(nTurbines),
iotype='in',
dtype='float',
desc='generator efficiency of all turbines'))
self.add(
'turbineX',
Array(np.zeros(nTurbines),
iotype='in',
dtype='float',
desc='x positions of turbines in original ref. frame'))
self.add(
'turbineY',
Array(np.zeros(nTurbines),
iotype='in',
dtype='float',
desc='y positions of turbines in original ref. frame'))
if optimize_yaw:
for direction in range(0, nDirections):
self.add('yaw_%d' % direction, Array(np.zeros(nTurbines), iotype='in', dtype='float', \
desc='yaw of each turbine for each direction'))
else:
self.add('yaw', Array(np.zeros(nTurbines), iotype='in', dtype='float', \
desc='yaw of each turbine'))
# windrose input variables
self.add(
'windrose_directions',
Array(np.zeros(nDirections),
dtype='float',
iotype='in',
desc='windrose directions in degrees ccw from east'))
self.add(
'windrose_frequencies',
Array(
np.ones(nDirections),
dtype='float',
iotype='in',
desc='windrose frequencies corresponding to windrose_directions'
))
if nSpeeds == 1:
self.add(
'windrose_speeds',
Float(
iotype='in',
units='m/s',
desc=
'wind speeds for each direction given in windrose_directions'
))
else:
self.add(
'windrose_speeds',
Array(
np.zeros(nDirections),
dtype='float',
iotype='in',
units='m/s',
desc=
'wind speeds for each direction given in windrose_directions'
))
# Explicitly size output arrays
# variables added to test individual components
self.add(
'turbineXw',
Array(
np.zeros(nTurbines),
iotype='out',
units='m',
desc=
'X positions of turbines in the wind direction reference frame'
))
self.add(
'turbineYw',
Array(
np.zeros(nTurbines),
iotype='out',
units='m',
desc=
'Y positions of turbines in the wind direction reference frame'
))
self.add(
'wakeCentersYT',
Array(np.zeros(nTurbines),
dtype='float',
iotype='out',
units='m',
desc='centers of the wakes at each turbine'))
self.add(
'wakeDiametersT',
Array(np.zeros(nTurbines),
dtype='float',
iotype='out',
units='m',
desc='diameters of each of the wake zones for each of the \
wakes at each turbine'))
self.add(
'wakeOverlapTRel',
Array(np.zeros(nTurbines),
dtype='float',
iotype='out',
units='m',
desc='ratio of overlap area of each zone to rotor area'))
# standard output
self.add(
'velocitiesTurbines_directions',
Array(np.zeros([nDirections, nTurbines]),
iotype='out',
units='m/s',
dtype='float',
desc='effective windspeed at each turbine \
in each direction ccw from east using direction to'
))
self.add(
'wt_power_directions',
Array(np.zeros([nDirections, nTurbines]),
iotype='out',
units='kW',
dtype='float',
desc='power of each turbine in each direction ccw from \
east using direction to'))
self.add(
'power_directions',
Array(np.zeros(nDirections),
iotype='out',
units='kW',
desc='total windfarm power \
in each direction ccw from east using direction to'
))
if nSamples > 0:
# flow samples
self.add(
'ws_positionX',
Array(np.zeros(nSamples),
iotype='in',
units='m',
desc='X positions of sampling points'))
self.add(
'ws_positionY',
Array(np.zeros(nSamples),
iotype='in',
units='m',
desc='Y position of sampling points'))
self.add(
'ws_positionZ',
Array(np.zeros(nSamples),
iotype='in',
units='m',
desc='Z position of sampling points'))
for direction in range(0, nDirections):
self.add(
'ws_array_%d' % direction,
Array(np.zeros(nSamples),
iotype='out',
units='m/s',
desc='predicted wind speed at sampling points'))
class DumbVT(VariableTree):
v1 = Float(1., desc='vv1')
v2 = Float(2., desc='vv2')
vt2 = VarTree(DumbVT2(), iotype='in')
class DumbVT2(VariableTree):
x = Float(-1.)
y = Float(-2.)
vt3 = VarTree(DumbVT3())
class Input_List(Component):
SYS_list = ['VL_SYS', 'FWD_SYS', 'WING_SYS', 'ENG_SYS'] #a list of systems
ASPECT_list = ['VL_SYS_TYPE', 'VL_SYS_PROP', 'VL_SYS_DRV', 'VL_SYS_TECH', \
'FWD_SYS_PROP', 'FWD_SYS_DRV', 'FWD_SYS_TYPE',\
'WING_SYS_TYPE', \
'ENG_SYS_TYPE'] #list of system functional aspects
input_file = Str('', iotype='in', desc='input csv file with morph matrix data')
#options = List([], iotype='in', desc='list of integers representing options for a given alternative')
option1 = Float(1, iotype='in', low=0.5, high=6.5, desc='option selection for functional aspect VL_SYS_TYPE (1-6)')
option2 = Float(1, iotype='in', low=0.5, high=3.5, desc='option selection for functional aspect VL_SYS_PROP (1-3)')
option3 = Float(1, iotype='in', low=0.5, high=3.5, desc='option selection for functional aspect VL_SYS_DRV (1-3)')
option4 = Float(1, iotype='in', low=0.5, high=5.5, desc='option selection for functional aspect VL_SYS_TECH (1-5)')
option5 = Float(1, iotype='in', low=0.5, high=4.5, desc='option selection for functional aspect FWD_SYS_PROP (1-4)')
option6 = Float(1, iotype='in', low=0.5, high=3.5, desc='option selection for functional aspect FWD_SYS_DRV (1-3)')
option7 = Float(1, iotype='in', low=0.5, high=4.5, desc='option selection for functional aspect FWD_SYS_TYPE (1-4)')
option8 = Float(1, iotype='in', low=0.5, high=6.5, desc='option selection for functional aspect WING_SYS_TYPE (1-6)')
option9 = Float(1, iotype='in', low=0.5, high=4.5, desc='option selection for functional aspect ENG_SYS_TYPE (1-4)')
passthrough = Int(0, iotype='in', low=0, high=1, desc='passthrough flag for incompatible options')
### LIST INPUTS GENERATED (OUTPUTS) HERE ###
#PHI SYSTEM INPUTS:
#VL_SYS_TYPE_w = List(['VL_SYS_TYPE_w'], iotype='out', desc='')
#VL_SYS_TYPE_e_d = List(['VL_SYS_TYPE_e_d'], iotype='out', desc='')
#VL_SYS_PROP_w = List(['VL_SYS_PROP_w'], iotype='out', desc='')
#FWD_SYS_PROP_eta_p = List(['FWD_SYS_PROP_eta_p'], iotype='out', desc='')
#WING_SYS_TYPE_phi = List(['WING_SYS_TYPE_phi'], iotype='out', desc='')
#WING_SYS_TYPE_LD = List(['WING_SYS_TYPE_LD'], iotype='out', desc='')
#ENG_SYS_TYPE_phi = List(['ENG_SYS_TYPE_phi'], iotype='out', desc='')
#UPDATED
VL_SYS_TYPE_phi = List(['VL_SYS_TYPE_phi'], iotype='out', desc='')
VL_SYS_TYPE_w = List(['VL_SYS_TYPE_w'], iotype='out', desc='')
VL_SYS_TYPE_f = List(['VL_SYS_TYPE_f'], iotype='out', desc='')
VL_SYS_PROP_phi = List(['VL_SYS_PROP_phi' ], iotype='out', desc='')
VL_SYS_PROP_w = List(['VL_SYS_PROP_w' ], iotype='out', desc='')
VL_SYS_TECH_phi = List(['VL_SYS_TECH_phi'], iotype='out', desc='')
VL_SYS_TECH_w = List(['VL_SYS_TECH_w'], iotype='out', desc='')
VL_SYS_TECH_f = List(['VL_SYS_TECH_f'], iotype='out', desc='')
VL_SYS_TECH_LD = List(['VL_SYS_TECH_LD'], iotype='out', desc='')
FWD_SYS_PROP_eta_p = List(['FWD_SYS_PROP_eta_p'], iotype='out', desc='')
FWD_SYS_DRV_eta_d = List(['FWD_SYS_DRV_eta_d'], iotype='out', desc='')
FWD_SYS_TYPE_phi = List(['FWD_SYS_TYPE_phi'], iotype='out', desc='')
FWD_SYS_TYPE_TP = List(['FWD_SYS_TYPE_TP'], iotype='out', desc='')
WING_SYS_TYPE_LD = List(['WING_SYS_TYPE_LD'], iotype='out', desc='')
WING_SYS_TYPE_f = List(['WING_SYS_TYPE_f'], iotype='out', desc='')
#LoD SYSTEM INPUTS:
SYSTEM_f = List(['SYSTEM_f'], iotype='out', desc='average system flat plate drag (fuzzy gauss)')
WING_LoD = List(['WING_LoD'], iotype='out', desc='union of all LoD values (fuzzy gauss)')
#FoM SYSTEM INPUTS:
VL_SYS_w = List(['VL_SYS_w'], iotype='out', desc='')
VL_SYS_PROP_sigma = List(['VL_SYS_PROP_sigma'], iotype='out', desc='')
VL_SYS_e_d = List(['VL_SYS_e_d'], iotype='out', desc='')
VL_SYS_DRV_eta_d = List(['VL_SYS_DRV_eta_d'], iotype='out', desc='')
#Propulsive Efficiency INPUTS:
FWD_SYS_eta_p = List(['FWD_SYS_eta_p'], iotype='out', desc='')
FWD_DRV_eta_d = List(['FWD_DRV_eta_d'], iotype='out', desc='')
#RF System INPUTS:
WING_SYS_TYPE_WS = List(['WING_SYS_TYPE_WS'], iotype='out', desc='')
SYS_type = List(['VL_type'], iotype='out', desc='')
SYS_tech = List(['VL_tech'], iotype='out', desc='')
ENG_SYS_TYPE_SFC = List(['ENG_SYS_TYPE_SFC'], iotype='out', desc='')
#def __init__(self):
""" Initialize component """
#super(Input_List, self).__init__()
data = [] #list for input data
options = [] #place holder for selected options
###
def read_fuzzy_data(self):
"""
Read the (morph) input file and store as a list for pulling data from
"""
#input_file = self.input_file
print 'Reading', self.input_file, 'data file and translating inputs.'
with open(self.input_file, 'rU') as csvfile:
input_reader = csv.reader(csvfile, delimiter=',')
data = []
for row in input_reader: #read each row
if row[2] <> '': #if variable is there...
data_row = []
for x in row: #for each item in the row
if '[' in x: #check for '[min,max]' entry
y = x.strip(' []').split(',') #remove brackets from string and split at comma
if '.' in y: y[y.index('.')] = 0.0
data_row.append([ float(y[0]), float(y[1]) ]) #add data min/max as floats to list
else:
data_row.append(x)
data.append(data_row)
if len(data)>1: data.pop(0) #remove header line
self.data = data #save data as output var
print len(self.data), 'lines of input data read... inputs translated.'
def execute(self):
#print "Getting: ", [self.option1, self.option2, self.option3, self.option4, self.option5, self.option6, self.option7, self.option8, self.option9]
#if no data read in, read it in.
if len(self.data) == 0:
self.read_fuzzy_data()
#set options from individual functional attibutes
self.options = [int(self.option1), int(self.option2), int(self.option3),
int(self.option4), int(self.option5), int(self.option6),
int(self.option7), int(self.option8), int(self.option9)]
#print "Morph Opts:", self.options
#print 'Building input list from', len(self.data), 'lines of data...'
#get all inputs for selected options
#[ system, aspect, varname, [min,max], quant_option, qual_option ] for each line
var_list = []
for line in self.data:
var_list.append([ line[0], line[1], line[3], line[4], \
line[self.options[self.ASPECT_list.index(line[1])]+4], \
line[self.options[self.ASPECT_list.index(line[1])]+10] ])
### CALCULATE INPUTS: (all use quant data: qual data => q = 10)
q = 4 #for quant data, qual data in q=5
""" PHI SYSTEM: """
fuzzInType = 'gauss'
#VL_SYS_TYPE_phi:
_x = next( var[q] for var in var_list if all((var[2] == 'phi', var[1] == 'VL_SYS_TYPE')) )
self.VL_SYS_TYPE_phi = fuzzyOps.rangeToMF(_x, fuzzInType)
#VL_SYS_TYPE_w :
_x = next( var[q] for var in var_list if all((var[2] == 'w', var[1] == 'VL_SYS_TYPE')) )
self.VL_SYS_TYPE_w = fuzzyOps.rangeToMF(_x, fuzzInType)
#VL_SYS_TYPE_f
_x = next( var[q] for var in var_list if all((var[2] == 'f', var[1] == 'VL_SYS_TYPE')) )
self.VL_SYS_TYPE_f = fuzzyOps.rangeToMF(_x, fuzzInType)
#'VL_SYS_PROP_phi'
_x = next( var[q] for var in var_list if all((var[2] == 'phi', var[1] == 'VL_SYS_PROP')) )
self.VL_SYS_PROP_phi = fuzzyOps.rangeToMF(_x, fuzzInType)
#'VL_SYS_PROP_w'
_x = next( var[q] for var in var_list if all((var[2] == 'w', var[1] == 'VL_SYS_PROP')) )
self.VL_SYS_PROP_w = fuzzyOps.rangeToMF(_x, fuzzInType)
#'VL_SYS_TECH_phi'
_x = next( var[q] for var in var_list if all((var[2] == 'phi', var[1] == 'VL_SYS_TECH')) )
self.VL_SYS_TECH_phi = fuzzyOps.rangeToMF(_x, fuzzInType)
#'VL_SYS_TECH_w'
_x = next( var[q] for var in var_list if all((var[2] == 'w', var[1] == 'VL_SYS_TECH')) )
self.VL_SYS_TECH_w = fuzzyOps.rangeToMF(_x, fuzzInType)
#'VL_SYS_TECH_f'
_x = next( var[q] for var in var_list if all((var[2] == 'f', var[1] == 'VL_SYS_TECH')) )
self.VL_SYS_TECH_f = fuzzyOps.rangeToMF(_x, fuzzInType)
#'VL_SYS_TECH_LD'
_x = next( var[q] for var in var_list if all((var[2] == 'LD', var[1] == 'VL_SYS_TECH')) )
self.VL_SYS_TECH_LD = fuzzyOps.rangeToMF(_x, fuzzInType)
#FWD_SYS_PROP_eta_p : FWD system prop efficiency
_x = next( var[q] for var in var_list if all((var[2] == 'eta_p', var[1] == 'FWD_SYS_PROP')) )
self.FWD_SYS_PROP_eta_p = fuzzyOps.rangeToMF(_x, fuzzInType)
#FWD_SYS_DRV_eta_d : FWD drive efficiency
_x = next( var[q] for var in var_list if all((var[2] == 'eta_d', var[1] == 'FWD_SYS_DRV')) )
self.FWD_SYS_DRV_eta_d = fuzzyOps.rangeToMF(_x, fuzzInType)
#FWD_SYS_TYPE_phi
_x = next( var[q] for var in var_list if all((var[2] == 'phi', var[1] == 'FWD_SYS_TYPE')) )
self.FWD_SYS_TYPE_phi = fuzzyOps.rangeToMF(_x, fuzzInType)
#FWD_SYS_TYPE_TP
_x = next( var[q] for var in var_list if all((var[2] == 'TP', var[1] == 'FWD_SYS_TYPE')) )
self.FWD_SYS_TYPE_TP = fuzzyOps.rangeToMF(_x, fuzzInType)
#WING_SYS_TYPE_LD
_x = next( var[q] for var in var_list if all((var[2] == 'LD', var[1] == 'WING_SYS_TYPE')) )
self.WING_SYS_TYPE_LD = fuzzyOps.rangeToMF(_x, fuzzInType)
#WING_SYS_TYPE_f : WING system wing loading
_x = next( var[q] for var in var_list if all((var[2] == 'f', var[1] == 'WING_SYS_TYPE')) )
self.WING_SYS_TYPE_f = fuzzyOps.rangeToMF(_x, fuzzInType)
#ENG_SYS_TYPE_SFC
_x = next( var[q] for var in var_list if all((var[2] == 'SFC', var[1] == 'ENG_SYS_TYPE')) )
self.ENG_SYS_TYPE_SFC = fuzzyOps.rangeToMF(_x, fuzzInType)
""" LoD SYSTEM: """
fuzzInType = 'trap'
# SYSTEM_f : average system flat plate drag (fuzzy gauss)
_f = [var[q] for var in var_list if (var[2] == 'f') ]
data_range = [np.average([v[0] for v in _f]), np.average([v[1] for v in _f])] #get union
self.SYSTEM_f = fuzzyOps.rangeToMF(data_range, fuzzInType)
#WING_LoD : union of all LoD values (fuzzy gauss)
_ld = [var[q] for var in var_list if var[2] == 'LD']
data_range = [max([v[0] for v in _ld]), min([v[1] for v in _ld])] #get union
self.WING_LoD = fuzzyOps.rangeToMF(data_range, fuzzInType)
""" FOM SYSTEM: """
fuzzInType = 'gauss'
#VL_SYS_w
#_x = [var[q] for var in var_list if all((var[2] == 'w', var[0] == 'VL_SYS'))]
#_x = [ np.average([x[0] for x in _x]), np.average([x[1] for x in _x]) ]
_x1 = next( var[q] for var in var_list if all((var[2] == 'w', var[1] == 'VL_SYS_TYPE')) )
_x2 = next( var[q] for var in var_list if all((var[2] == 'w', var[1] == 'VL_SYS_PROP')) )
_x3 = next( var[q] for var in var_list if all((var[2] == 'w', var[1] == 'VL_SYS_TECH')) )
_x = [ max(np.average([_x1[0],_x2[0]]), _x3[0]), min(np.average([_x1[1],_x2[1]]),_x3[1]) ]
self.VL_SYS_w = fuzzyOps.rangeToMF(_x, fuzzInType)
_w = _x
#VL_SYS_PROP_sigma
_x = next( var[q] for var in var_list if all((var[2] == 'sigma', var[1] == 'VL_SYS_PROP')) )
self.VL_SYS_PROP_sigma = fuzzyOps.rangeToMF(_x, fuzzInType)
#x_2 = _x
#VL_SYS_e_d
_x = next( var[q] for var in var_list if all((var[2] == 'e_d', var[1] == 'VL_SYS_TYPE')) )
self.VL_SYS_e_d = fuzzyOps.rangeToMF(_x, fuzzInType)
_ed = _x
#VL_SYS_DRV_eta_d
_x = next( var[q] for var in var_list if all((var[2] == 'eta_d', var[1] == 'VL_SYS_DRV')) )
self.VL_SYS_DRV_eta_d = fuzzyOps.rangeToMF(_x, fuzzInType)
_eta = _x
#print 'w: %s, sigma: %s, e_d: %s, eta_d: %s' % (x_1, x_2, x_3, x_4)
""" etaP SYSTEM: """
fuzzInType = 'trap'
# FWD_SYS_eta_p : intersection of forward system propulsive efficiencies
_f = [var[q] for var in var_list if all((var[2] == 'eta_p', var[0] == 'FWD_SYS')) ]
data_range = [max([v[0] for v in _f]), min([v[1] for v in _f])] #get union
data_range = sorted(data_range)
self.FWD_SYS_eta_p = fuzzyOps.rangeToMF(data_range, fuzzInType)
#FWD_SYS_eta_d : foward system drive efficiency
_etad = next( var[q] for var in var_list if all((var[2] == 'eta_d', var[1] == 'FWD_SYS_DRV')) )
self.FWD_DRV_eta_d = fuzzyOps.rangeToMF(_etad, fuzzInType)
""" RF SYSTEMs (GWT/Pinst/VH): """
# SYSTEM_QUANT_PHI
# (from phi system)
# NEEDS TO BE QUANTIFIED
# VL_SYS_w
# self.VL_SYS_w (already calculatd with FoM system)
# WING_SYS_TYPE_WS : WING system wing loading
_x = next( var[q] for var in var_list if all((var[2] == 'WS', var[1] == 'WING_SYS_TYPE')) )
self.WING_SYS_TYPE_WS = fuzzyOps.rangeToMF(_x, 'gauss')
# sys_etaP: system propulsive efficiency
# (from etaP system)
# VL_SYS_DRV_eta_d : VL system drive efficiency
# VL_SYS_DRV_eta_d (already calcualted with FoM system)
# sys_FoM: system Figure of Merit
# (from FoM system)
# VL_SYS_e_d
# self.VL_SYS_e_d (already calcualted with FoM system)
# ENG_SYS_TYPE_SFC
# self.ENG_SYS_TYPE_SFC (already calcualted with phi system)
# NEEDS TO BE QUANTIFIED?
# SYS_TYPE (1-tilt, 2-compound, 3-other)
VL_SYS_TYPE = int(self.option1)
FWD_SYS_TYPE = int(self.option7)
if FWD_SYS_TYPE == 2:
T = 1 #tiling VL
else:
if VL_SYS_TYPE < 4:
T = 2 #compound
if VL_SYS_TYPE == 4 or VL_SYS_TYPE == 5:
T = 3 #other
if VL_SYS_TYPE == 6:
T = 1 #tilting tailsitter
self.SYS_type = fuzzyOps.rangeToMF([T,T], 'gauss')
# SYS_TECH (0 - None, 1 - varRPM, 2 - varDiameter, 3 - stop rotor, 4 - autogyro) (switch 2-3)
VL_SYS_TECH = int(self.option4)
if VL_SYS_TECH == 2: T = 3
elif VL_SYS_TECH == 3: T = 2
else: T = VL_SYS_TECH
self.SYS_tech = fuzzyOps.rangeToMF([T,T], 'gauss')
if self.passthrough == 1: #catch for incompatible options
return None
class SellarBLISS2000(Assembly):
""" Optimization of the Sellar problem using the BLISS2000 algorithm
Disciplines coupled with FixedPointIterator.
"""
# for a given iteration, x1_store holds the value of dis1.x1 obtained in the previous iteration. used to track convergence
x1_store = Float(0.0, iotype="in")
z1_store = Float(0.0, iotype="in")
z2_store = Float(0.0, iotype="in")
def configure(self):
""" Creates a new Assembly with this problem
Optimal Design at (1.9776, 0, 0)
Optimal Objective = 3.18339"""
# objective = '(dis1.x1)**2 + dis1.z2 + dis1.y1 + exp(-dis2.y2)'
# constraint1 = 'dis1.y1 > 3.16'
# constraint2 = 'dis2.y2 < 24.0'
# Metamodel for sellar discipline 1
self.add("meta_model_dis1", MetaModel())
self.meta_model_dis1.surrogates = {"y1": ResponseSurface()}
self.meta_model_dis1.model = SellarDiscipline1()
self.meta_model_dis1.recorder = DBCaseRecorder()
# Metamodel for sellar discipline 2
self.add("meta_model_dis2", MetaModel())
self.meta_model_dis2.surrogates = {"y2": ResponseSurface()}
self.meta_model_dis2.model = SellarDiscipline2()
self.meta_model_dis2.recorder = DBCaseRecorder()
# training metalmodel for disc1
# self.add("DOE_Trainer_dis2",NeighborhoodDOEdriver())
# self.DOE_Trainer_dis2.DOEgenerator = CentralComposite()
# self.DOE_Trainer_dis2.alpha = .1
# self.DOE_Trainer_dis2.add_parameter("meta_model_dis2.z1",low=-10,high=10,start=5.0)
# self.DOE_Trainer_dis2.add_parameter("meta_model_dis2.z2",low=0,high=10,start=2.0)
# self.DOE_Trainer_dis2.add_parameter("meta_model_dis2.y1",low=0,high=20)
# self.DOE_Trainer_dis2.add_event("meta_model_dis2.train_next")
# optimization of global objective function
self.add('sysopt', SLSQPdriver())
self.sysopt.add_objective(
'(meta_model_dis1.x1)**2 + meta_model_dis1.z2 + meta_model_dis1.y1 + math.exp(-meta_model_dis2.y2)'
)
self.sysopt.add_parameter(['meta_model_dis1.z1', 'meta_model_dis2.z1'],
low=-10,
high=10.0,
start=5.0)
self.sysopt.add_parameter(['meta_model_dis1.z2', 'meta_model_dis2.z2'],
low=0,
high=10.0,
start=2.0)
self.sysopt.add_parameter('meta_model_dis1.y2', low=-1e99, high=1e99)
self.sysopt.add_parameter('meta_model_dis2.y1', low=-1e99, high=1e99)
# feasibility constraints
self.sysopt.add_constraint('meta_model_dis1.y2 <= meta_model_dis2.y2')
self.sysopt.add_constraint('meta_model_dis1.y2 >= meta_model_dis2.y2')
self.sysopt.add_constraint('meta_model_dis2.y1 <= meta_model_dis1.y1')
self.sysopt.add_constraint('meta_model_dis2.y1 >= meta_model_dis1.y1')
self.sysopt.add_constraint('3.16 < meta_model_dis1.y1')
self.sysopt.add_constraint('meta_model_dis2.y2 < 24.0')
# optimization of discipline 1 (discipline 2 of the sellar problem has no local variables)
self.add('local_opt_dis1', SLSQPdriver())
self.local_opt_dis1.add_objective('meta_model_dis1.y1')
self.local_opt_dis1.add_parameter('meta_model_dis1.x1',
low=0,
high=10.0)
self.local_opt_dis1.add_constraint('3.16 < meta_model_dis1.y1')
self.local_opt_dis1.add_event('meta_model_dis1.train_next')
self.local_opt_dis1.workflow.add(['meta_model_dis1'])
# training metalmodel for disc1
self.add("DOE_Trainer_dis1", NeighborhoodDOEdriver())
self.DOE_Trainer_dis1.DOEgenerator = CentralComposite()
self.DOE_Trainer_dis1.alpha = .1
self.DOE_Trainer_dis1.add_parameter("meta_model_dis1.z1",
low=-10,
high=10,
start=5.0)
self.DOE_Trainer_dis1.add_parameter("meta_model_dis1.z2",
low=0,
high=10,
start=2.0)
self.DOE_Trainer_dis1.add_parameter("meta_model_dis1.y2",
low=-100,
high=100)
self.DOE_Trainer_dis1.add_event("meta_model_dis1.train_next")
self.DOE_Trainer_dis1.workflow.add("local_opt_dis1")
self.add('reset_train', Driver())
self.reset_train.add_event('meta_model_dis1.reset_training_data')
self.reset_train.add_event('meta_model_dis2.reset_training_data')
self.reset_train.workflow.add(['meta_model_dis1', 'meta_model_dis2'])
# build workflow for bliss2000
self.add('driver', FixedPointIterator())
# self.add('main_driver', IterateUntil())
# self.main_driver.max_iterations = 1
self.driver.tolerance = .0001
# self.driver.workflow.add(['local_opt_dis1','reset_train','DOE_Trainer_dis1','DOE_Trainer_dis2','sysopt'])
self.driver.workflow.add(['sysopt'])
self.driver.add_parameter('x1_store', low=0, high=10.0)
self.driver.add_constraint('meta_model_dis1.x1 = x1_store')
self.driver.add_parameter('z1_store', low=0, high=10.0)
self.driver.add_constraint('meta_model_dis1.z1 = z1_store')
self.driver.add_parameter('z2_store', low=0, high=10.0)
self.driver.add_constraint('meta_model_dis1.z2 = z2_store')
class Geometry(VariableTree):
"""Container of variables defining the mixer-ejector geometry"""
length = Float(10.0, units='ft', desc='Ejector width')
width = Float(6.0, units='ft', desc='Ejector width')
Apri = Float(6.00, units='ft**2', desc='Primary nozzle exit area')
Asec = Float(8.40, units='ft**2', desc='Secondary nozzle exit area')
Aexit = Float(13.68, units='ft**2', desc='Ejector exit area')
AsAp = Float(1.4,desc='Area ratio (secondary area/primary area)')
AR = Float(1.25, desc='Aspect ratio of the ejector at the inlet')
AeAt = Float(0.95, desc='Area ratio (exit area/total inlet area)')
ChuteAngles = Float(-10.0, units='deg', desc='Chute divergence angles. Outer tangent line relative to ejector flow surface at nozzle downstream of mixer exit')
Num_Lobes = Float(18.0, desc='Number of spokes or lobes of the mixer')
LhMh = Float(0.90, desc='Lobe height to mixing section height (from centerline) ratio (chute penetration)')
LhWave = Float(2.88, desc='Lobe height to wavelength ratio')
def calc_geom(self,length,Apri,AsAp,AR,AeAt,LhMh,LhWave):
self.length = length
self.Apri = Apri
self.AsAp = AsAp
self.AR = AR
self.AeAT = AeAt
self.LhMh = LhMh
self.LhWave = LhWave
self.Asec = AsAp*Apri
self.Aexit = AeAt*(Apri+self.Asec)
self.width = (AR*(Apri+self.Asec))**0.5
self.Num_Lobes = 4*self.width**2*LhWave/((Apri+self.Asec)*LhMh)
class Oneinp(Component):
""" A simple input component """
ratio1 = Float(1.00, iotype='in', desc='Float Variable',
units='mm') # not a pressure unit
ratio2 = Float(1.00, iotype='in', desc='Float Variable',
units='atm') # pressure in atm
ratio3 = Float(1.00, iotype='in', desc='Float Variable',
units='torr') # pressure in torr
ratio4 = Float(1.00, iotype='in', desc='Float Variable',
units='psi') # pressure in psi
ratio5 = Float(1.00, iotype='in', desc='Float Variable',
units='bar') # pressure in bar
ratio6 = Float(1.00,
iotype='in',
desc='Float variable ',
units='cal/(mol*degK)') #R, Gas constant
ratio7 = Float(1.00, iotype='in', desc='Float variable ',
units='m') # Meter
ratio8 = Float(1.00, iotype='in', desc='Float variable ',
units='mm') # millimeter
ratio9 = Float(1, iotype='in', desc='Float variable',
units='degR') # temp in R
ratio10 = Float(1, iotype='in', desc='Float variable',
units='degK') # temp in K
ratio11 = Float(1, iotype='in', desc='Float variable',
units='dyn') # force in dyn
ratio12 = Float(1, iotype='in', desc='Float variable',
units='N') # force in N
ratio13 = Float(1, iotype='in', desc='Float variable',
units='dyn') # force in dyn
ratio14 = Float(1, iotype='in', desc='Float variable',
units='lb') # mass in lb
ratio15 = Float(2, iotype='in', desc='Float variable') # no units defined
ratio16 = Float(2, iotype='in', desc='Float variable',
units='dyn') # force in dyn
ratio17 = Float(1, iotype='in', desc='Float variable',
units='deg') # angle in deg
def __init__(self):
super(Oneinp, self).__init__()
def execute(self):
"""
class DomeStatic(NastranComponent):
for i in range(1, 157):
cmd = "bar%d_init_area = 1.00" % i
exec(cmd)
for i in range(157, 253):
cmd = "tria%d_init_thickness = 1.00" % i
exec(cmd)
for i in range(1, 157):
cmd = 'bar%d_area = Float(bar%d_init_area, nastran_card="PROD",\
nastran_id=%d, \
nastran_field="A",\
iotype="in", units="inch*inch",\
desc="Cross-sectional area for bar %d")' % (i, i, i, i)
exec(cmd)
for i in range(157, 253):
cmd = 'tria%d_thickness = Float(tria%d_init_thickness, nastran_card="PSHELL",\
nastran_id=%d, \
nastran_field="T",\
iotype="in", units="inch*inch",\
desc="Membrane thickness for tria %d")' % (i, i, i, i)
exec(cmd)
# these are stresses that will be constrained
for i in range(1, 157):
cmd = "bar%d_stress = Float(0., iotype='out', units='lb/(inch*inch)', desc='Axial stress in element %d')" % (
i, i)
exec(cmd)
for i in range(157, 253):
cmd = "tria%d_stress = Float(0., iotype='out', units='lb/(inch*inch)', desc='Von Mises stress in element %d')" % (
i, i)
exec(cmd)
def mass(op2):
return op2.grid_point_weight.mass[0]
weight = Float(0.,
nastran_func=mass,
iotype='out',
units='lb',
desc='Weight of the structure')
def execute(self):
""" Simulates the analysis of a ten bar truss structure.
Force, Stress, Displacement,Frequency and Weight are returned at
the Ring output.
"""
super(DomeStatic, self).execute()
# get stresses from table with header
# S T R E S S E S I N R O D E L E M E N T S ( C R O D )
isubcase = 1
for i in range(len(self.op2.rodStress[isubcase].axial)):
stress = calculate_stress(
(self.op2.rodStress[isubcase].axial[i + 1],
self.op2.rodStress[isubcase].torsion[i + 1]))
cmd = "self.bar%d_stress = stress" % (i + 1)
exec(cmd)
# get stresses from table with header
# S T R E S S E S I N T R I A N G U L A R E L E M E N T S ( T R I A 3 )"
for i in range(157, 253):
biggest = self.op2.plateStress[isubcase].ovmShear[i]['CEN/3'][0]
cmd = "self.tria%d_stress = biggest" % i
exec(cmd)
class Postprocess_Fuzzy_Outputs(Component):
"""
Takes in some outputs from the fuzzy systems and creates crisp values by which
to find optimal solutions. Uses alpha cuts at the the given alpha_val level.
"""
# set up interface to the framework
#inputs are outputs from systems
fuzzSys_in_1 = VarTree(FuzzyMF(), iotype='in', desc='fuzzy system output')
fuzzSys_in_2 = VarTree(FuzzyMF(), iotype='in', desc='fuzzy system output')
fuzzSys_in_3 = VarTree(FuzzyMF(), iotype='in', desc='fuzzy system output')
fuzzSys_in_4 = VarTree(FuzzyMF(), iotype='in', desc='fuzzy system output')
fuzzSys_in_5 = VarTree(FuzzyMF(), iotype='in', desc='fuzzy system output')
fuzzSys_in_6 = VarTree(FuzzyMF(), iotype='in', desc='fuzzy system output')
fuzzSys_in_7 = VarTree(FuzzyMF(), iotype='in', desc='fuzzy system output')
fuzzSys_in_8 = VarTree(FuzzyMF(), iotype='in', desc='fuzzy system output')
fuzzSys_in_9 = VarTree(FuzzyMF(), iotype='in', desc='fuzzy system output')
alpha_val = Float(0.7, iotype='in', desc='alpha-cut to perform range post processing at')
goalVals = Dict({'sys_phi' :6.7,
'sys_FoM' :0.775,
'sys_LoD' :12.5,
'sys_etaP' :0.875,
'sys_Pin' :3500.0,
'sys_GWT' :10000,
'sys_VH' :325}, iotype='in', desc='crisp goals for each output')
PLOTMODE = Int(0, iotype='in', desc='Flag for plotting, 1 for plot')
printResults = Int(1, iotype='in', desc='print results each iteration?')
passthrough = Int(0, iotype='in', low=0, high=1, desc='catch flag for incompatible options')
incompatCount = Int(0, iotype='in', desc='count of incomatible options')
runFlag_in = Int(0, iotype='in', desc='test')
runFlag_out = Int(0, iotype='out', desc='test')
ranges_out = Dict({}, iotype='out', desc='alpha cuts for each fuzzy input')
response_1 = Float(0.0, iotype='out', desc='crisp measure 1 to perform optimization')
response_1_r = Float(0.0, iotype='out', desc='range for crisp measure 1')
response_1_POS = Float(0.0, iotype='out', desc='fuzzy POS measure (dominance to crisp goal)')
response_2 = Float(0.0, iotype='out', desc='crisp measure 2 to perform optimization')
response_2_r = Float(0.0, iotype='out', desc='range for crisp measure 2')
response_2_POS = Float(0.0, iotype='out', desc='fuzzy POS measure (dominance to crisp goal)')
response_3 = Float(0.0, iotype='out', desc='crisp measure 3 to perform optimization')
response_3_r = Float(0.0, iotype='out', desc='range for crisp measure 3')
response_3_POS = Float(0.0, iotype='out', desc='fuzzy POS measure (dominance to crisp goal)')
response_4 = Float(0.0, iotype='out', desc='crisp measure 4 to perform optimization')
response_4_r = Float(0.0, iotype='out', desc='range for crisp measure 4')
response_4_POS = Float(0.0, iotype='out', desc='fuzzy POS measure (dominance to crisp goal)')
response_5 = Float(0.0, iotype='out', desc='crisp measure 5 to perform optimization')
response_5_r = Float(0.0, iotype='out', desc='range for crisp measure 5')
response_5_POS = Float(0.0, iotype='out', desc='fuzzy POS measure (dominance to crisp goal)')
response_6 = Float(0.0, iotype='out', desc='crisp measure 6 to perform optimization')
response_6_r = Float(0.0, iotype='out', desc='range for crisp measure 6')
response_6_POS = Float(0.0, iotype='out', desc='fuzzy POS measure (dominance to crisp goal)')
response_7 = Float(0.0, iotype='out', desc='crisp measure 7 to perform optimization')
response_7_r = Float(0.0, iotype='out', desc='range for crisp measure 7')
response_7_POS = Float(0.0, iotype='out', desc='fuzzy POS measure (dominance to crisp goal)')
response_8 = Float(0.0, iotype='out', desc='crisp measure 8 to perform optimization')
response_8_r = Float(0.0, iotype='out', desc='range for crisp measure 8')
response_8_POS = Float(0.0, iotype='out', desc='fuzzy POS measure (dominance to crisp goal)')
response_9 = Float(0.0, iotype='out', desc='crisp measure 9 to perform optimization')
response_9_r = Float(0.0, iotype='out', desc='range for crisp measure 9')
response_9_POS = Float(0.0, iotype='out', desc='fuzzy POS measure (dominance to crisp goal)')
fuzzyPOS = Float(0.0, iotype='out', desc='Fuzzy Measure for POS (product of all POS measures')
def execute(self):
"""
Translate fuzzy inputs to crisp values to optimize system.
"""
inputs = [self.fuzzSys_in_1, self.fuzzSys_in_2, self.fuzzSys_in_3,
self.fuzzSys_in_4, self.fuzzSys_in_5, self.fuzzSys_in_6,
self.fuzzSys_in_7, self.fuzzSys_in_8, self.fuzzSys_in_9]
outs = [[self.response_1][0], [self.response_2][0], self.response_3,
self.response_4, self.response_5, self.response_6,
self.response_7, self.response_8, self.response_9]
outs_r = [self.response_1_r, self.response_2_r, self.response_3_r,
self.response_4_r, self.response_5_r, self.response_6_r,
self.response_7_r, self.response_8_r, self.response_9_r]
if self.passthrough == 1:
if self.printResults == 1: print "Incompatible combo found..."
self.response_1 = 0.0 #phi
self.response_1_r = 0.0
self.response_1_POS = -1.0
self.response_2 = 0.0 #FoM
self.response_2_r = 0.0
self.response_2_POS = -1.0
self.response_3 = 0.0 #LoD
self.response_3_r = 0.0
self.response_3_POS = -1.0
self.response_4 = 0.0 #etaP
self.response_4_r = 0.0
self.response_4_POS = -1.0
self.response_5 = 0.0 #GWT
self.response_5_r = 0.0
self.response_5_POS = -1.0
self.response_6 = -99999.0 #P
self.response_6_r = 0.0
self.response_6_POS = 0.0
self.response_7 = 0.0 #VH
self.response_7_r = 0.0
self.response_7_POS = -1.0
return None
else:
#get alpha cuts for crisp responses and ranges
for i in range(len(inputs)):
if inputs[i].mf_key <> '':
if len(inputs[i].mf_dict[inputs[i].mf_key]) == 1: #crisp value
self.ranges_out[inputs[i].mf_key] = [inputs[i].mf_dict[inputs[i].mf_key][0], inputs[i].mf_dict[inputs[i].mf_key][0]]
elif len(inputs[i].mf_dict[inputs[i].mf_key]) == 2: #fuzzy function
self.ranges_out[inputs[i].mf_key] = fuzzyOps.alpha_cut(self.alpha_val, inputs[i].mf_dict[inputs[i].mf_key])
#capture results for crisp measures
if self.ranges_out[inputs[i].mf_key] <> None: y = self.ranges_out[inputs[i].mf_key]
else: y = [0.0, 0.0]
if inputs[i].mf_key == 'sys_phi':
self.response_1 = fuzz.defuzz(inputs[i].mf_dict[inputs[i].mf_key][0],np.array(inputs[i].mf_dict[inputs[i].mf_key][1]),'centroid')
self.response_1 = self.response_1 * math.exp(-self.incompatCount)**0.5
self.response_1_r = max(y) - min(y)
self.response_1_POS = fuzzyOps.fuzzyPOS(inputs[i].mf_dict[inputs[i].mf_key][0],np.array(inputs[i].mf_dict[inputs[i].mf_key][1]), self.goalVals['sys_phi'])
if inputs[i].mf_key == 'sys_FoM':
self.response_2 = fuzz.defuzz(inputs[i].mf_dict[inputs[i].mf_key][0],np.array(inputs[i].mf_dict[inputs[i].mf_key][1]),'centroid')
self.response_2 = self.response_2 * math.exp(-self.incompatCount)**0.5
self.response_2_r = max(y) - min(y)
#if self.response_2 < 0.6:
# self.response_2_POS = fuzzyOps.fuzzyPOS(inputs[i].mf_dict[inputs[i].mf_key][0],np.array(inputs[i].mf_dict[inputs[i].mf_key][1]), self.goalVals['sys_FoM'], direction='max', plot=True)
self.response_2_POS = fuzzyOps.fuzzyPOS(inputs[i].mf_dict[inputs[i].mf_key][0],np.array(inputs[i].mf_dict[inputs[i].mf_key][1]), self.goalVals['sys_FoM'])
if inputs[i].mf_key == 'sys_LoD':
self.response_3 = fuzz.defuzz(inputs[i].mf_dict[inputs[i].mf_key][0],np.array(inputs[i].mf_dict[inputs[i].mf_key][1]),'centroid')
self.response_2 = self.response_3 * math.exp(-self.incompatCount)**0.5
self.response_3_r = max(y) - min(y)
self.response_3_POS = fuzzyOps.fuzzyPOS(inputs[i].mf_dict[inputs[i].mf_key][0],np.array(inputs[i].mf_dict[inputs[i].mf_key][1]), self.goalVals['sys_LoD'])
if inputs[i].mf_key == 'sys_etaP':
self.response_4 = fuzz.defuzz(inputs[i].mf_dict[inputs[i].mf_key][0],np.array(inputs[i].mf_dict[inputs[i].mf_key][1]),'centroid')
self.response_4 = self.response_4 * math.exp(-self.incompatCount)**0.5
self.response_4_r = max(y) - min(y)
self.response_4_POS = fuzzyOps.fuzzyPOS(inputs[i].mf_dict[inputs[i].mf_key][0],np.array(inputs[i].mf_dict[inputs[i].mf_key][1]), self.goalVals['sys_etaP'])
if inputs[i].mf_key == 'sys_GWT': #invert GWT to maximize all
self.response_5 = fuzz.defuzz(inputs[i].mf_dict[inputs[i].mf_key][0],np.array(inputs[i].mf_dict[inputs[i].mf_key][1]),'centroid')
self.response_5 = self.response_5 * math.exp(-self.incompatCount)**0.5
self.response_5_r = max(y) - min(y)
self.response_5_POS = fuzzyOps.fuzzyPOS(inputs[i].mf_dict[inputs[i].mf_key][0],np.array(inputs[i].mf_dict[inputs[i].mf_key][1]), self.goalVals['sys_GWT'], direction='min')
if inputs[i].mf_key == 'sys_P': #invert GWT to maximize all
self.response_6 = 0.0-fuzz.defuzz(inputs[i].mf_dict[inputs[i].mf_key][0],np.array(inputs[i].mf_dict[inputs[i].mf_key][1]),'centroid')
self.response_6 = self.response_6 * math.exp(-self.incompatCount)**0.5
self.response_6_r = max(y) - min(y)
self.response_6_POS = fuzzyOps.fuzzyPOS(inputs[i].mf_dict[inputs[i].mf_key][0],np.array(inputs[i].mf_dict[inputs[i].mf_key][1]), self.goalVals['sys_Pin'], direction='min')
if inputs[i].mf_key == 'sys_VH': #invert GWT to maximize all
self.response_7 = fuzz.defuzz(inputs[i].mf_dict[inputs[i].mf_key][0],np.array(inputs[i].mf_dict[inputs[i].mf_key][1]),'centroid')
self.response_7 = self.response_7 * math.exp(-self.incompatCount)**0.5
self.response_7_r = max(y) - min(y)
self.response_7_POS = fuzzyOps.fuzzyPOS(inputs[i].mf_dict[inputs[i].mf_key][0],np.array(inputs[i].mf_dict[inputs[i].mf_key][1]), self.goalVals['sys_VH'])
self.fuzzyPOS = self.response_1_POS*self.response_2_POS*self.response_3_POS*self.response_4_POS*self.response_6_POS
if self.printResults == 1: #print results
print "Alternative:", self.passthrough, ":",
print "PHI: %.1f, (%.3f)" % (self.response_1, self.response_1_POS),
print " FoM: %.3f, (%.3f)" % (self.response_2, self.response_2_POS),
print " L/D: %.1f, (%.3f)" % (self.response_3, self.response_3_POS),
print " etaP: %.3f, (%.3f)" % (self.response_4, self.response_4_POS),
print " GWT: %.0f, (%.3f)" % (self.response_5, self.response_5_POS),
print " Pinst: %.0f, (%.3f)" % (self.response_6, self.response_6_POS),
print " VH: %.0f, (%.3f)" % (self.response_7, self.response_7_POS),
print " FPOS: %.3f" % self.fuzzyPOS
#plotting for testing
if self.PLOTMODE == 1: #plot results
plt.figure()
i=1
for r in inputs:
plt.subplot(3,2,i)
if r.mf_key <> '':
if len(r.mf_dict[r.mf_key]) == 1: #crisp value
pass#self.ranges_out[r.mf_key] = [r.mf_dict[r.mf_key][0], r.mf_dict[r.mf_key][1]]
elif len(r.mf_dict[r.mf_key]) == 2: #fuzzy function
plt.plot(r.mf_dict[r.mf_key][0],r.mf_dict[r.mf_key][1])
i = i + 1
plt.show()
class floris_assembly_opt_AEP(Assembly):
""" Defines the connections between each Component used in the FLORIS model """
# general input variables
parameters = VarTree(FLORISParameters(), iotype='in')
verbose = Bool(False,
iotype='in',
desc='verbosity of FLORIS, False is no output')
# Flow property variables
air_density = Float(iotype='in',
units='kg/(m*m*m)',
desc='air density in free stream')
# output
AEP = Float(iotype='out', units='kW', desc='total windfarm AEP')
def __init__(self,
nTurbines,
nDirections,
optimize_position=False,
nSamples=0,
optimize_yaw=False,
datasize=0,
nSpeeds=False,
maxiter=100):
super(floris_assembly_opt_AEP, self).__init__()
if nSpeeds == False:
nSpeeds = nDirections
self.nTurbines = nTurbines
self.nSamples = nSamples
self.nDirections = nDirections
self.optimize_yaw = optimize_yaw
self.optimize_position = optimize_position
self.datasize = datasize
self.nSpeeds = nSpeeds
self.maxiter = maxiter
# wt_layout input variables
self.add(
'rotorDiameter',
Array(np.zeros(nTurbines),
dtype='float',
iotype='in',
units='m',
desc='rotor diameters of all turbine'))
self.add(
'axialInduction',
Array(np.zeros(nTurbines),
iotype='in',
dtype='float',
desc='axial induction of all turbines'))
self.add('hubHeight', Array(np.zeros(nTurbines), dtype='float', iotype='in', units='m', \
desc='hub heights of all turbines'))
# turbine properties for ccblade and pre-calculated controller
self.add(
'curve_CP',
Array(np.zeros(datasize), iotype='in', desc='pre-calculated CPCT'))
self.add(
'curve_CT',
Array(np.zeros(datasize), iotype='in', desc='pre-calculated CPCT'))
self.add(
'curve_wind_speed',
Array(np.zeros(datasize), iotype='in', desc='pre-calculated CPCT'))
self.add('initVelocitiesTurbines',
Array(np.zeros(nTurbines), iotype='in', units='m/s'))
self.add(
'generator_efficiency',
Array(np.zeros(nTurbines),
iotype='in',
dtype='float',
desc='generator efficiency of all turbines'))
self.add(
'turbineX',
Array(np.zeros(nTurbines),
iotype='in',
dtype='float',
desc='x positions of turbines in original ref. frame'))
self.add(
'turbineY',
Array(np.zeros(nTurbines),
iotype='in',
dtype='float',
desc='y positions of turbines in original ref. frame'))
if optimize_yaw:
for direction in range(0, nDirections):
self.add('yaw_%d' % direction, Array(np.zeros(nTurbines), iotype='in', dtype='float', \
desc='yaw of each turbine for each direction'))
else:
self.add('yaw', Array(np.zeros(nTurbines), iotype='in', dtype='float', \
desc='yaw of each turbine'))
# windrose input variables
self.add(
'windrose_directions',
Array(np.zeros(nDirections),
dtype='float',
iotype='in',
desc='windrose directions in degrees ccw from east'))
self.add(
'windrose_frequencies',
Array(
np.ones(nDirections),
dtype='float',
iotype='in',
desc='windrose frequencies corresponding to windrose_directions'
))
if nSpeeds == 1:
self.add(
'windrose_speeds',
Float(
iotype='in',
units='m/s',
desc=
'wind speeds for each direction given in windrose_directions'
))
else:
self.add(
'windrose_speeds',
Array(
np.zeros(nDirections),
dtype='float',
iotype='in',
units='m/s',
desc=
'wind speeds for each direction given in windrose_directions'
))
# Explicitly size output arrays
# variables added to test individual components
self.add(
'turbineXw',
Array(
np.zeros(nTurbines),
iotype='out',
units='m',
desc=
'X positions of turbines in the wind direction reference frame'
))
self.add(
'turbineYw',
Array(
np.zeros(nTurbines),
iotype='out',
units='m',
desc=
'Y positions of turbines in the wind direction reference frame'
))
self.add(
'wakeCentersYT',
Array(np.zeros(nTurbines),
dtype='float',
iotype='out',
units='m',
desc='centers of the wakes at each turbine'))
self.add(
'wakeDiametersT',
Array(np.zeros(nTurbines),
dtype='float',
iotype='out',
units='m',
desc='diameters of each of the wake zones for each of the \
wakes at each turbine'))
self.add(
'wakeOverlapTRel',
Array(np.zeros(nTurbines),
dtype='float',
iotype='out',
units='m',
desc='ratio of overlap area of each zone to rotor area'))
# standard output
self.add(
'velocitiesTurbines_directions',
Array(np.zeros([nDirections, nTurbines]),
iotype='out',
units='m/s',
dtype='float',
desc='effective windspeed at each turbine \
in each direction ccw from east using direction to'
))
self.add(
'wt_power_directions',
Array(np.zeros([nDirections, nTurbines]),
iotype='out',
units='kW',
dtype='float',
desc='power of each turbine in each direction ccw from \
east using direction to'))
self.add(
'power_directions',
Array(np.zeros(nDirections),
iotype='out',
units='kW',
desc='total windfarm power \
in each direction ccw from east using direction to'
))
if nSamples > 0:
# flow samples
self.add(
'ws_positionX',
Array(np.zeros(nSamples),
iotype='in',
units='m',
desc='X positions of sampling points'))
self.add(
'ws_positionY',
Array(np.zeros(nSamples),
iotype='in',
units='m',
desc='Y position of sampling points'))
self.add(
'ws_positionZ',
Array(np.zeros(nSamples),
iotype='in',
units='m',
desc='Z position of sampling points'))
for direction in range(0, nDirections):
self.add(
'ws_array_%d' % direction,
Array(np.zeros(nSamples),
iotype='out',
units='m/s',
desc='predicted wind speed at sampling points'))
def configure(self):
# rename options
nTurbines = self.nTurbines
nDirections = self.nDirections
optimize_position = self.optimize_position
optimize_yaw = self.optimize_yaw
datasize = self.datasize
nSamples = self.nSamples
nSpeeds = self.nSpeeds
maxiter = self.maxiter
# add driver so the workflow is not overwritten later
if optimize_position or optimize_yaw:
self.add('driver', SLSQPdriver())
# add AEP component first so it can be connected to
F6 = self.add('floris_AEP', AEP(nDirections=nDirections))
F6.missing_deriv_policy = 'assume_zero'
self.connect('windrose_frequencies', 'floris_AEP.windrose_frequencies')
self.connect('floris_AEP.AEP', 'AEP')
self.connect('floris_AEP.power_directions_out', 'power_directions')
# set up constraints
self.add('floris_dist_const', dist_const(nTurbines=nTurbines))
self.connect('turbineX', 'floris_dist_const.turbineX')
self.connect('turbineY', 'floris_dist_const.turbineY')
if nSamples > 0:
samplingNonSampling = ['', 'Sampling_']
else:
samplingNonSampling = ['']
for i in range(0, nDirections):
# add fixed point iterator
self.add('FPIdriver_%d' % i, FixedPointIterator())
self.add(
'rotor_CPCT_%d' % i,
CPCT_Interpolate(nTurbines=self.nTurbines,
datasize=self.datasize))
CP = 'rotor_CPCT_%d.CP' % i
CT = 'rotor_CPCT_%d.CT' % i
CPCT = 'rotor_CPCT_%d' % i
# add components of floris to assembly
F2 = self.add('floris_windframe_%d' % i,
floris_windframe(nTurbines=nTurbines))
F2.missing_deriv_policy = 'assume_zero'
self.add('floris_wcent_wdiam_%d' % i,
floris_wcent_wdiam(nTurbines=nTurbines))
F4 = self.add('floris_overlap_%d' % i,
floris_overlap(nTurbines=nTurbines))
F4.missing_deriv_policy = 'assume_zero'
self.add('floris_power_%d' % i, floris_power(nTurbines=nTurbines))
# add visualization components of floris to assembly
if nSamples > 0:
self.add(
'Sampling_floris_windframe_%d' % i,
floris_windframe(nTurbines=nTurbines, nSamples=nSamples))
self.add(
'Sampling_floris_wcent_wdiam_%d' % i,
floris_wcent_wdiam(nTurbines=nTurbines, nSamples=nSamples))
self.add('Sampling_floris_overlap_%d' % i,
floris_overlap(nTurbines=nTurbines))
self.add('Sampling_floris_power_%d' % i,
floris_power(nTurbines=nTurbines, nSamples=nSamples))
# connect inputs to components
self.connect('curve_CP', 'rotor_CPCT_%d.windSpeedToCPCT.CP' % i)
self.connect('curve_CT', 'rotor_CPCT_%d.windSpeedToCPCT.CT' % i)
self.connect('curve_wind_speed',
'rotor_CPCT_%d.windSpeedToCPCT.wind_speed' % i)
self.connect('parameters.pP', 'rotor_CPCT_%d.pP' % i)
for ssn in samplingNonSampling:
self.connect('parameters', [
'%sfloris_wcent_wdiam_%d.parameters' % (ssn, i),
'%sfloris_power_%d.parameters' % (ssn, i)
])
self.connect('verbose', [
'%sfloris_windframe_%d.verbose' % (ssn, i),
'%sfloris_wcent_wdiam_%d.verbose' % (ssn, i),
'%sfloris_power_%d.verbose' % (ssn, i)
])
self.connect('turbineX',
'%sfloris_windframe_%d.turbineX' % (ssn, i))
self.connect('turbineY',
'%sfloris_windframe_%d.turbineY' % (ssn, i))
self.connect('rotorDiameter', [
'%sfloris_wcent_wdiam_%d.rotorDiameter' % (ssn, i),
'%sfloris_overlap_%d.rotorDiameter' % (ssn, i),
'%sfloris_power_%d.rotorDiameter' % (ssn, i)
])
self.connect('axialInduction',
'%sfloris_power_%d.axialInduction' % (ssn, i))
self.connect(
'generator_efficiency',
'%sfloris_power_%d.generator_efficiency' % (ssn, i))
if nSamples > 0:
# connections needed for visualization
self.connect('ws_positionX',
'Sampling_floris_windframe_%d.ws_positionX' % i)
self.connect('ws_positionY',
'Sampling_floris_windframe_%d.ws_positionY' % i)
self.connect('ws_positionZ',
'Sampling_floris_windframe_%d.ws_positionZ' % i)
self.connect('hubHeight',
'Sampling_floris_wcent_wdiam_%d.hubHeight' % i)
if optimize_yaw:
yawToConnect = 'yaw_%d' % i
else:
yawToConnect = 'yaw'
self.connect(yawToConnect, '%s.yaw' % CPCT)
for ssn in samplingNonSampling:
self.connect(yawToConnect, [
'%sfloris_wcent_wdiam_%d.yaw' % (ssn, i),
'%sfloris_power_%d.yaw' % (ssn, i)
])
for ssn in samplingNonSampling:
self.connect('air_density',
'%sfloris_power_%d.air_density' % (ssn, i))
self.connect('windrose_directions[%d]' % i,
'%sfloris_windframe_%d.wind_direction' % (ssn, i))
# for satisfying the verbosity in windframe
for ssn in samplingNonSampling:
self.connect(CT, '%sfloris_windframe_%d.Ct' % (ssn, i))
self.connect(CP, '%sfloris_windframe_%d.Cp' % (ssn, i))
self.connect(yawToConnect,
'%sfloris_windframe_%d.yaw' % (ssn, i))
self.connect('axialInduction',
'%sfloris_windframe_%d.axialInduction' % (ssn, i))
# ############### Connections between components ##################
# connections from CtCp calculation to other components
for ssn in samplingNonSampling:
self.connect(CT, [
'%sfloris_wcent_wdiam_%d.Ct' % (ssn, i),
'%sfloris_power_%d.Ct' % (ssn, i)
])
self.connect(CP, '%sfloris_power_%d.Cp' % (ssn, i))
# connections from floris_windframe to floris_wcent_wdiam
self.connect('%sfloris_windframe_%d.turbineXw' % (ssn, i),
'%sfloris_wcent_wdiam_%d.turbineXw' % (ssn, i))
self.connect('%sfloris_windframe_%d.turbineYw' % (ssn, i),
'%sfloris_wcent_wdiam_%d.turbineYw' % (ssn, i))
# connections from floris_wcent_wdiam to floris_overlap
self.connect(
'%sfloris_wcent_wdiam_%d.wakeCentersYT' % (ssn, i),
'%sfloris_overlap_%d.wakeCentersYT' % (ssn, i))
self.connect(
'%sfloris_wcent_wdiam_%d.wakeDiametersT' % (ssn, i),
'%sfloris_overlap_%d.wakeDiametersT' % (ssn, i))
# connections from floris_windframe to floris_overlap
self.connect('%sfloris_windframe_%d.turbineXw' % (ssn, i),
'%sfloris_overlap_%d.turbineXw' % (ssn, i))
self.connect('%sfloris_windframe_%d.turbineYw' % (ssn, i),
'%sfloris_overlap_%d.turbineYw' % (ssn, i))
# connections from floris_windframe to floris_power
self.connect('%sfloris_windframe_%d.turbineXw' % (ssn, i),
'%sfloris_power_%d.turbineXw' % (ssn, i))
# connections from floris_overlap to floris_power
self.connect('%sfloris_overlap_%d.wakeOverlapTRel' % (ssn, i),
'%sfloris_power_%d.wakeOverlapTRel' % (ssn, i))
# additional connections needed for visualization
if nSamples > 0:
self.connect('Sampling_floris_windframe_%d.wsw_position' % i, [
'Sampling_floris_wcent_wdiam_%d.wsw_position' % i,
'Sampling_floris_power_%d.wsw_position' % i
])
self.connect('Sampling_floris_wcent_wdiam_%d.wakeCentersY' % i,
'Sampling_floris_power_%d.wakeCentersY' % i)
self.connect('Sampling_floris_wcent_wdiam_%d.wakeCentersZ' % i,
'Sampling_floris_power_%d.wakeCentersZ' % i)
self.connect(
'Sampling_floris_wcent_wdiam_%d.wakeDiameters' % i,
'Sampling_floris_power_%d.wakeDiameters' % i)
self.connect('Sampling_floris_power_%d.ws_array' % i,
'ws_array_%d' % i)
# connections from floris_power to floris_AEP
self.connect('floris_power_%d.power' % i,
'floris_AEP.power_directions[%d]' % i)
# #################################################################
# add to workflow
exec(
"self.FPIdriver_%d.workflow.add(['rotor_CPCT_%d', 'floris_windframe_%d', \
'floris_wcent_wdiam_%d', 'floris_overlap_%d', 'floris_power_%d'])"
% (i, i, i, i, i, i))
exec(
"self.FPIdriver_%d.add_parameter('rotor_CPCT_%d.wind_speed_hub', low=0., high=100.)"
% (i, i))
exec(
"self.FPIdriver_%d.add_constraint('rotor_CPCT_%d.wind_speed_hub = \
floris_power_%d.velocitiesTurbines')" % (i, i, i))
self.driver.workflow.add('FPIdriver_%d' % i)
if nSamples > 0:
self.driver.workflow.add([
'Sampling_floris_windframe_%d' % i,
'Sampling_floris_wcent_wdiam_%d' % i,
'Sampling_floris_overlap_%d' % i,
'Sampling_floris_power_%d' % i
])
if nSpeeds > 1:
for i in range(0, nSpeeds):
for ssn in samplingNonSampling:
self.connect('windrose_speeds[%d]' % i,
'%sfloris_power_%d.wind_speed' % (ssn, i))
self.connect('windrose_speeds[%d]' % i,
'%sfloris_windframe_%d.wind_speed' % (ssn, i))
else:
for i in range(0, nDirections):
for ssn in samplingNonSampling:
self.connect('windrose_speeds',
'%sfloris_power_%d.wind_speed' % (ssn, i))
self.connect('windrose_speeds',
'%sfloris_windframe_%d.wind_speed' % (ssn, i))
# add AEP calculations to workflow
self.driver.workflow.add(['floris_AEP', 'floris_dist_const'])
if optimize_position or optimize_yaw:
# set up driver
self.driver.iprint = 3
self.driver.accuracy = 1.0e-12
self.driver.maxiter = maxiter
self.driver.add_objective('-floris_AEP.AEP')
if optimize_position:
self.driver.add_parameter('turbineX',
low=7 * 126.4,
high=np.sqrt(self.nTurbines) * 7 *
126.4)
self.driver.add_parameter('turbineY',
low=7 * 126.4,
high=np.sqrt(self.nTurbines) * 7 *
126.4)
self.driver.add_constraint(
'floris_dist_const.separation > 2*rotorDiameter[0]')
if optimize_yaw:
for direction in range(0, self.nDirections):
self.driver.add_parameter('yaw_%d' % direction,
low=-30.,
high=30.,
scaler=1.)
class DumbVT3(VariableTree):
a = Float(1., units='ft')
b = Float(12., units='inch')
class LogisticRegression(HasTraits):
implements(ISurrogate)
alpha = Float(.1, low=0, iotype='in', desc='L2 regularization strength')
def __init__(self, X=None, Y=None, alpha=.1):
# must call HasTraits init to set up Traits stuff
super(LogisticRegression, self).__init__()
self.m = None #number of independents
self.n = None #number of training points
self.alpha = alpha
self.degenerate = False
if X is not None and Y is not None:
self.train(X, Y)
def lik(self, betas):
""" Likelihood of the data under the current settings of parameters. """
# Data likelihood
l = 0
for i in range(self.n):
l += log(sigmoid(self.Y[i] * \
np.dot(betas, self.X[i,:])))
# Prior likelihood
for k in range(1, self.X.shape[1]):
l -= (self.alpha / 2.0) * self.betas[k]**2
#multiply by -1 so the optimizer will maxize
return -1 * l
def get_uncertain_value(self, value):
"""Returns the value iself. Logistic regressions don't have uncertainty."""
return value
def train(self, X, Y):
""" Define the gradient and hand it off to a scipy gradient-based
optimizer. """
#normalize all Y data to be between -1 and 1
low = min(Y)
high = max(Y)
#there was no data to predict on, so just degenerate to predicting True all the time
self.degenerate = False
if high == low:
self.degenerate = high
return
self.m = 2.0 / (high - low)
self.b = (2.0 * low / (low - high)) - 1
#constants for unscaling the output
self.z = high - low
self.w = low
self.X = np.array(X)
self.Y = self.m * np.array(Y) + self.b
self.n = len(X)
self.betas = np.zeros(len(X[0]))
# Define the derivative of the likelihood with respect to beta_k.
# Need to multiply by -1 because we will be minimizing.
dB_k = lambda B, k : (k > 0) * self.alpha * B[k] - np.sum([ \
self.Y[i] * self.X[i, k] * \
sigmoid(-self.Y[i] *\
np.dot(B, self.X[i,:])) \
for i in range(self.n)])
# The full gradient is just an array of componentwise derivatives
dB = lambda B : np.array([dB_k(B, k) \
for k in range(self.X.shape[1])])
# Optimize
self.betas = fmin_bfgs(self.lik, self.betas, fprime=dB, disp=False)
def predict(self, new_x):
"""Calculates a predicted value of the response based on the current
trained model for the supplied list of inputs.
"""
if self.degenerate: return self.degenerate
return self.z * sigmoid(np.dot(self.betas, np.array(new_x))) + self.w
class EGO(Architecture):
initial_DOE_size = Int(10,
iotype="in",
desc="Number of initial training points to use.")
sample_iterations = Int(10,
iotype="in",
desc="Number of adaptively sampled points to use.")
EI_PI = Enum(
"PI",
values=["EI", "PI"],
iotype="in",
desc="Switch to decide between EI or PI for infill criterion.")
min_ei_pi = Float(
0.001,
iotype="in",
desc="EI or PI to use for stopping condition of optimization.")
def __init__(self, *args, **kwargs):
super(EGO, self).__init__(*args, **kwargs)
# the following variables determine the behavior of check_config
self.param_types = ['continuous']
self.num_allowed_objectives = 1
self.has_coupling_vars = False
def configure(self):
self._tdir = mkdtemp()
self.comp_name = None
#check to make sure no more than one component is being referenced
compnames = set()
for param in self.parent.get_parameters().values():
compnames.update(param.get_referenced_compnames())
if len(compnames) > 1:
self.parent.raise_exception(
'The EGO architecture can only be used on one'
'component at a time, but parameters from %s '
'were added to the problem formulation.' % compnames,
ValueError)
self.comp_name = compnames.pop()
#change name of component to add '_model' to it.
# lets me name the metamodel as the old name
self.comp = getattr(self.parent, self.comp_name)
self.comp.name = "%s_model" % self.comp_name
#add in the metamodel
meta_model = self.parent.add(
self.comp_name,
MetaModel()) #metamodel now replaces old component with same name
meta_model.default_surrogate = KrigingSurrogate()
meta_model.model = self.comp
meta_model_recorder = DBCaseRecorder(
os.path.join(self._tdir, 'trainer.db'))
meta_model.recorder = meta_model_recorder
meta_model.force_execute = True
EI = self.parent.add("EI", ExpectedImprovement())
self.objective = self.parent.get_objectives().keys()[0]
EI.criteria = self.objective
pfilter = self.parent.add("filter", ParetoFilter())
pfilter.criteria = [self.objective]
pfilter.case_sets = [
meta_model_recorder.get_iterator(),
]
pfilter.force_execute = True
#Driver Configuration
DOE_trainer = self.parent.add("DOE_trainer", DOEdriver())
DOE_trainer.sequential = True
DOE_trainer.DOEgenerator = OptLatinHypercube(
num_samples=self.initial_DOE_size)
for name, param in self.parent.get_parameters().iteritems():
DOE_trainer.add_parameter(param)
DOE_trainer.add_event("%s.train_next" % self.comp_name)
DOE_trainer.case_outputs = [self.objective]
DOE_trainer.recorders = [DBCaseRecorder(':memory:')]
EI_opt = self.parent.add("EI_opt", Genetic())
EI_opt.opt_type = "maximize"
EI_opt.population_size = 100
EI_opt.generations = 10
#EI_opt.selection_method = "tournament"
for name, param in self.parent.get_parameters().iteritems():
EI_opt.add_parameter(param)
EI_opt.add_objective("EI.%s" % self.EI_PI)
retrain = self.parent.add("retrain", Driver())
retrain.recorders = self.data_recorders
retrain.add_event("%s.train_next" % self.comp_name)
iter = self.parent.add("iter", IterateUntil())
iter.max_iterations = self.sample_iterations
iter.add_stop_condition('EI.PI <= %s' % self.min_ei_pi)
#Data Connections
self.parent.connect("filter.pareto_set", "EI.best_case")
self.parent.connect(self.objective, "EI.predicted_value")
#Iteration Heirarchy
self.parent.driver.workflow.add(['DOE_trainer', 'iter'])
#DOE_trainer.workflow.add(self.comp_name)
iter.workflow = SequentialWorkflow()
iter.workflow.add(['filter', 'EI_opt', 'retrain'])
#EI_opt.workflow.add([self.comp_name,'EI'])
retrain.workflow.add(self.comp_name)
def cleanup(self):
shutil.rmtree(self._tdir, ignore_errors=True)
class NEWSUMTdriver(DriverUsesDerivatives):
""" Driver wrapper of Fortran version of NEWSUMT.
.. todo:: Check to see if this itmax variable is needed.
NEWSUMT might handle it for us.
"""
implements(IHasParameters, IHasIneqConstraints, IHasObjective, IOptimizer)
itmax = Int(10,
iotype='in',
desc='Maximum number of iterations before \
termination.')
default_fd_stepsize = Float(0.01, iotype='in', desc='Default finite ' \
'difference stepsize. Parameters with ' \
'specified values override this.')
ilin = Array(dtype=numpy_int,
default_value=zeros(0, 'i4'),
iotype='in',
desc='Array designating whether each constraint is linear.')
# Control parameters for NEWSUMT.
# NEWSUMT has quite a few parameters to give the user control over aspects
# of the solution.
epsgsn = Float(0.001,
iotype='in',
desc='Convergence criteria \
of the golden section algorithm used for the \
one dimensional minimization.')
epsodm = Float(0.001,
iotype='in',
desc='Convergence criteria \
of the unconstrained minimization.')
epsrsf = Float(0.001,
iotype='in',
desc='Convergence criteria \
for the overall process.')
g0 = Float(0.1,
iotype='in',
desc='Initial value of the transition \
parameter.')
ra = Float(1.0,
iotype='in',
desc='Penalty multiplier. Required if mflag=1')
racut = Float(0.1,
iotype='in',
desc='Penalty multiplier decrease ratio. \
Required if mflag=1.')
ramin = Float(1.0e-13,
iotype='in',
desc='Lower bound of \
penalty multiplier. \
Required if mflag=1.')
stepmx = Float(2.0,
iotype='in',
desc='Maximum bound imposed on the \
initial step size of the one-dimensional \
minimization.')
iprint = Int(0,
iotype='in',
desc='Print information during NEWSUMT \
solution. Higher values are more verbose. If 0,\
print initial and final designs only.',
high=4,
low=0)
lobj = Int(0, iotype='in', desc='Set to 1 if linear objective function.')
maxgsn = Int(20,
iotype='in',
desc='Maximum allowable number of golden \
section iterations used for 1D minimization.')
maxodm = Int(6,
iotype='in',
desc='Maximum allowable number of one \
dimensional minimizations.')
maxrsf = Int(15,
iotype='in',
desc='Maximum allowable number of \
unconstrained minimizations.')
mflag = Int(0,
iotype='in',
desc='Flag for penalty multiplier. \
If 0, initial value computed by NEWSUMT. \
If 1, initial value set by ra.')
def __init__(self, *args, **kwargs):
super(NEWSUMTdriver, self).__init__(*args, **kwargs)
self.iter_count = 0
# Save data from common blocks into the driver
self.contrl = _contrl()
self.countr = _countr()
# define the NEWSUMTdriver's private variables
# note, these are all resized in config_newsumt
# basic stuff
self.design_vals = zeros(0, 'd')
self.constraint_vals = []
# temp storage
self.__design_vals_tmp = zeros(0, 'd')
self._ddobj = zeros(0)
self._dg = zeros(0)
self._dh = zeros(0)
self._dobj = zeros(0)
self._g = zeros(0)
self._gb = zeros(0)
self._g1 = zeros(0)
self._g2 = zeros(0)
self._g3 = zeros(0)
self._s = zeros(0)
self._sn = zeros(0)
self._x = zeros(0)
self._iik = zeros(0, dtype=int)
self._lower_bounds = zeros(0)
self._upper_bounds = zeros(0)
self._iside = zeros(0)
self.fdcv = zeros(0)
# Just defined here. Set elsewhere
self.n1 = self.n2 = self.n3 = self.n4 = 0
# Ready inputs for NEWSUMT
self._obj = 0.0
self._objmin = 0.0
self.isdone = False
self.resume = False
self.uses_Hessians = False
def start_iteration(self):
"""Perform the optimization."""
# Flag used to figure out if we are starting a new finite difference
self.baseline_point = True
# set newsumt array sizes and more...
self._config_newsumt()
self.iter_count = 0
# get the values of the parameters
# check if any min/max constraints are violated by initial values
for i, val in enumerate(self.get_parameters().values()):
value = val.evaluate(self.parent)
self.design_vals[i] = value
# next line is specific to NEWSUMT
self.__design_vals_tmp[i] = value
# Call the interruptible version of SUMT in a loop that we manage
self.isdone = False
self.resume = False
def continue_iteration(self):
"""Returns True if iteration should continue."""
return not self.isdone and self.iter_count < self.itmax
def pre_iteration(self):
"""Checks or RunStopped and evaluates objective."""
super(NEWSUMTdriver, self).pre_iteration()
if self._stop:
self.raise_exception('Stop requested', RunStopped)
def run_iteration(self):
""" The NEWSUMT driver iteration."""
self._load_common_blocks()
try:
( fmin, self._obj, self._objmin, self.design_vals,
self.__design_vals_tmp, self.isdone, self.resume) = \
newsumtinterruptible.newsuminterruptible(user_function,
self._lower_bounds, self._upper_bounds,
self._ddobj, self._dg, self._dh, self._dobj,
self.fdcv, self._g,
self._gb, self._g1, self._g2, self._g3,
self._obj, self._objmin,
self._s, self._sn, self.design_vals, self.__design_vals_tmp,
self._iik, self.ilin, self._iside,
self.n1, self.n2, self.n3, self.n4,
self.isdone, self.resume, analys_extra_args = (self,))
except Exception, err:
self._logger.error(str(err))
raise
self._save_common_blocks()
self.iter_count += 1
# Update the parameters and run one final time with what it gave us.
# This update is needed because I obeserved that the last callback to
# user_function is the final leg of a finite difference, so the model
# is not in sync with the final design variables.
if not self.continue_iteration():
dvals = [float(val) for val in self.design_vals]
self.set_parameters(dvals)
super(NEWSUMTdriver, self).run_iteration()
self.record_case()
class Something(VariableTree):
data = Float(10.0)
class Datain(Container):
# def __init__(self,name):
# Container.__init__(self, name)
# """ namelist 'DATAIN' for AXOD. """
ttin = Float(iotype='in', units='degR',
desc='Inlet total temperature (radially constant).')
ptin = Float(iotype='in', units='psi',
desc='Inlet total pressure (radially constant).')
pfind = Float(iotype='in',
desc='Turbine total pressure ratio to be searched for')
rpm = Float(iotype='in', units=('1/min'), desc='Stage rotative speed.')
#seta = Float(iotype='in', desc='Stator efficiency, decimal.')
seta = Array(_ZEROS5,dtype=float32,shape=(5,),iotype='in')
#scf = Float(iotype='in', desc='Stator flow coefficient, decimal.')
scf = Array(_ZEROS5,dtype=float32,shape=(5,),iotype='in')
#reta = Float(iotype='in', desc='Rotor efficiency, decimal.')
reta = Array(_ZEROS5,dtype=float32,shape=(5,),iotype='in')
#rcf = Float(iotype='in', desc='Rotor flow coefficient, decimal.')
rcf = Array(_ZEROS5,dtype=float32,shape=(5,),iotype='in')
#ttinh = Float(iotype='in', units= 'degR', \
# desc='Inlet total temperature radial distribution')
ttinh = Array(_ZEROS5,dtype=float32,shape=(5,),iotype='in')
#ptinh = Float(iotype='in', units= 'psi',
# desc='Inlet total pressure distribution')
ptinh = Array(_ZEROS5,dtype=float32,shape=(5,),iotype='in')
#alf0h = Float(iotype='in', units= 'deg',
# desc='Inlet flow angle radial distribution')
alf0h = Array(_ZEROS5,dtype=float32,shape=(5,),iotype='in')
wair = Float(iotype='in', desc='Inlet water/air ratio')
fair = Float(iotype='in', desc='Inlet fuel/air ratio')
ptps = Float(iotype='in',
desc='Starting value of first-stator meanline Inlet total to exit-static pressure ratio')
delc = Float(iotype='in', desc='ptps increment to initial blade-row choke')
dell = Float(iotype='in', desc='ptps increment from initial to last blade-row choke')
dela = Float(iotype='in', desc='ptps increment from last blade-row choke to exit-annulus choke')
stg = Float(iotype='in', desc='Number of stages, max 8')
sect = Float(iotype='in', desc='Number of radial sectors, max 6')
expn = Float(iotype='in', desc='Negative-incidence exponent, default=4.0')
rg = Float(iotype='in', units='(ft*lbf)/(lbm*degR)',desc='Gas constant')
paf = Float(iotype='in',desc='Profile (temp & press) averaging switch for next stage inlet')
sli = Float(iotype='in',desc='Stage loss-value for srec, seta, scf, rrec, reta, rcf, rtf')
aacs = Float(iotype='in',desc='Turbine-exit mach number for termination of speed line')
vctd = Float(iotype='in',desc='Output Switch (1.0-overall performance plus all variable values)')
#pcnh = Float(iotype='in',desc='Sector height distribution, fraction of annulus height')
pcnh = Array(_ZEROS5,dtype=float32,shape=(5,),iotype='in')
wg = Float(iotype='in',units='lb/s',desc='Mass flow rate')
epr = Float(iotype='in',desc='switch for high pressure-ratio correction to blade row efficiency')
wtol = Float(iotype='in',desc='Tolerance for mass-flow rate convergence, default=1.0e-5')
rhotol = Float(iotype='in',desc='Tolerance for density convergence, default=1.0e-4')
prtol = Float(iotype='in',desc='Tolerance for presuure ratio convergenc, default=1.0e-8')
trloop = Float(iotype='in',desc='Debug output switch for iteration control variables')
pfind = Float(iotype='in',desc='Selected value of turbine total pressure to be searched for')
dhfind = Float(iotype='in',desc='Selected value of turbine specific work to be searched for')
iar = Int(iotype='in',desc='Switch for axial chord length, default=0')
icyl = Int(iotype='in',desc='Switch for blading angle definition,default=0')
icf = Int(iotype='in',desc='Switch for flow coefficient variation, default=0')
endplt = Float(iotype='in',desc='Switch for writing a map file in NEPP format,default=0')
endjob = Float(iotype='in',desc='Switch for last case, default=0')
stage = Float(iotype='in',desc='Stage Number')
#gamg = Float(iotype='in',desc='Specific heat ratio')
gamg = Array(_ZEROS5,dtype=float32,shape=(5,),iotype='in')
#dr = Float(iotype='in',units='inch',desc='Hub diameter')
dr = Array(_ZEROS5,dtype=float32,shape=(5,),iotype='in')
#dt = Float(iotype='in',units='inch',desc='Tip diameter')
dt = Array(_ZEROS5,dtype=float32,shape=(5,),iotype='in')
#rwg = Float(iotype='in',desc='Ratio of Station mass-flow rate to turbine inlet mass-flow rate')
rwg = Array(_ZEROS5,dtype=float32,shape=(5,),iotype='in')
#twg = Float(iotype='in',units='degR',desc='Temperature of the Cooolant specified by rwg')
twg = Array(_ZEROS5,dtype=float32,shape=(5,),iotype='in')
#pwg = Float(iotype='in',units='psi',desc='Pressure of the Cooolant specified by pwg')
pwg = Array(_ZEROS5,dtype=float32,shape=(5,),iotype='in')
#sdia = Float(iotype='in',units='deg',desc='Stator vane inlet angle')
sdia = Array(_ZEROS5,dtype=float32,shape=(5,),iotype='in')
#sdea = Float(iotype='in',units='deg',desc='Stator vane exit angle')
sdea = Array(_ZEROS5,dtype=float32,shape=(5,),iotype='in')
#spa = Float(iotype='in',units='inch**2',desc='Stator throat area per unit height')
spa = Array(_ZEROS5,dtype=float32,shape=(5,),iotype='in')
sesth = Float(iotype='in',desc='Ratio of blade height at stator exit to blade height at stator throat')
#rdia = Float(iotype='in',units='deg',desc='Rotor blade inlet angle')
rdia = Array(_ZEROS5,dtype=float32,shape=(5,),iotype='in')
#rdea = Float(iotype='in',units='deg',desc='Rotor blade exit angle')
rdea = Array(_ZEROS5,dtype=float32,shape=(5,),iotype='in')
#rpa = Float(iotype='in',units='inch**2',desc='Rotor throat area per unit height')
rpa = Array(_ZEROS5,dtype=float32,shape=(5,),iotype='in')
rerth = Float(iotype='in',desc='Ratio of blade height at rotor exit to blade height at rotor throat')
#srec = Float(iotype='in',desc='Stator inlet recovery efficiency, decimal')
srec = Array(_ZEROS5,dtype=float32,shape=(5,),iotype='in')
#rrec = Float(iotype='in',desc='Rotor inlet recovery efficiency, decimal')
rrec = Array(_ZEROS5,dtype=float32,shape=(5,),iotype='in')
#rtf = Float(iotype='in',desc='Rotor test factor, decimal')
rtf = Array(_ZEROS5,dtype=float32,shape=(5,),iotype='in')
#rvu1 = Float(iotype='in',units='inch*ft/s',desc='Design Stator-exit angular momentum')
rvu1 = Array(_ZEROS5,dtype=float32,shape=(5,),iotype='in')
#rvu2 = Float(iotype='in',units='inch*ft/s',desc='Design rotor-exit angular momentum')
rvu2 = Array(_ZEROS5,dtype=float32,shape=(5,),iotype='in')
endstg = Float(iotype='in',desc='Switch for last stage, 1.0=last stage')
expp = Float(iotype='in',desc='positive-incidence exponent def=3.0 ')
def __init__(self):
super(Datain, self).__init__()
self._inputs = ['ttin', 'ptin', 'pfind', 'rpm', 'seta', 'scf', 'reta', 'rcf', \
'ttinh','ptinh','alf0h','wair','fair','ptps','delc','dell','dela', \
'stg','sect','expn','expp','rg','paf','sli','aacs','vctd','epr', \
'wtol','rhotol','prtol','trloop','pfind','dhfind','iar','icyl', \
'icf','endplt','endjob','pwg','spa','sesth','trdiag',\
'rpa','rerth','rvu1','rvu2','endstg','stage','rdia', \
'pcnh','gamg','sdia','srec','rtf','rrec','rwg','twg','sdea','rdea',
'wg','dr','dt']
self.ind = 0
self._nml_modified = []
self.on_trait_change(self._trait_change, '+iotype')
def _trait_change(self, obj, name, old, new):
"""
Track *changes* so we don't output more than intended.
This will *not* get called when the value is set but not changed.
"""
if name not in self._nml_modified:
self._nml_modified.append(name)
def read(self,stream):
# reads the namelist datain..................
#print self._inputs
# for name in self._inputs:
self._nml_modified = []
line = stream.readline().strip()
while line:
#print 'line=',line
if '/' in line: return True
if '&END' in line: return True
if '$END' in line: return True
#print 'line=',line,' before = in line'
fields = line.split('=')
name0 = fields[0].strip().lower()
for name in self._inputs:
if name == name0 :
if name not in self._nml_modified:
self._nml_modified.append(name)
value0 = fields[-1]
#print "'%s' found in '%s'" % (name0, value0)
val_field = value0.split(',')
#print 'fields in val_field =', len(val_field)
if len(val_field) > 2:
i = 1
while i < len(val_field):
#print 'name0=',name0,' val_field[',i-1,']=',val_field[i-1]
if ('*' in val_field[i-1]):
fld = val_field[i-1].split('*')
ndi = int(fld[0])
#print ' ndi =',ndi
id = 1
while id < ndi:
val = float(fld[1])
#print 'self.set', name, val, i-1
self.set(name,val,[i-1])
#print ' val = ',val,' in * part'
value = float(val)
#print ' value[',i,'] = ',value,' in * part'
id = id + 1
i = i + 1
else:
#print ' after else ******* 1'
val = float(val_field[i-1])
#print 'self.set', name, val, i-1
self.set(name,val,[i-1])
#name0[i] = name
value = float(val)
#print ' value[',i-1,'] = ',value,' in else part'
#print 'name =',name,' value=',value
i = i + 1
#print ' i =',i,' end of if ...'
else:
#print ' after else ******* 2'
#val = value0.replace(',','')
val = val_field[0]
#print ' name=',name,' val =',val,' after array........'
if ('*' in val):
fld = val.split('*')
nfld = int(fld[0])
val1 = float(fld[-1])
value = float(val1)
#print 'name=',name,' value= ',value,' for * variable'
ii = 0
while ii < nfld:
self.set(name,value,[ii])
ii = ii + 1
else:
#print ' name=',name,' val =',val,' befoe attrval........'
attrval = getattr(self,name)
if isinstance(attrval,int):
#print 'name =',name,' val =',val
value = int(val)
setattr(self, name, value)
else:
value = float(val)
setattr(self, name, value)
# parse the value and then setattr(self, name, value)
#print ' reading a new line******************'
line = stream.readline().strip()
# if ('/' in line) or ('&END' in line) or ('$END'in line) : return
return False
def write(self,stream):
# reads the namelist datain..................
#print 'in write .... datain ....'
stream.write(' &DATAIN\n')
for name in self._nml_modified:
#print 'name =', name
value = getattr(self, name)
if isinstance(value, ndarray):
stream.write(' %s = ' % name.upper())
for val in value:
stream.write('%s,' % val)
stream.write('\n')
else:
stream.write(' %s = %s,\n' % (name.upper(), value))
stream.write(' /\n')
return
class Vis3DObject(Container):
""" Non-root object of 3D visualization model. """
colorby_palette = Str()
symmetry = Str()
symmetry_axis = Str()
symmetry_angle = Float(low=0, exclude_low=True, high=180)
symmetry_instances = Int(1, low=1)
visible = Bool(True)
def __init__(self):
super(Vis3DObject, self).__init__()
self._vis_name = None
@property
def vis_name(self):
""" Returns name in visualization hierarchy. """
if not self._vis_name:
name = self.get_pathname()
parent = self.parent
while parent and not isinstance(parent, Vis3D):
parent = parent.parent
if parent is None:
return self.name
else:
pname = parent.get_pathname()
if pname:
self._vis_name = name[len(pname)+1:]
else:
self._vis_name = name
return self._vis_name
def clone(self, offset):
""" Return a copy of ourselves, adjusting for block `offset`. """
obj = copy.copy(self) # Must be shallow!
obj.parent = None
obj._vis_name = None
return obj
def write_ensight(self, stream):
""" Writes Ensight commands to `stream`. """
if not self.visible:
stream.write("""
# Make invisible.
part: select_byname_begin
"%(name)s"
part: select_byname_end
part: modify_begin
part: visible OFF
part: modify_end
""" % {'name':self.vis_name})
if self.symmetry == 'rotational':
stream.write("""
# Rotational symmetry.
part: select_byname_begin
"%(name)s"
part: select_byname_end
part: modify_begin
part: symmetry_type rotational
part: symmetry_axis %(axis)s
part: symmetry_angle %(angle)g
part: symmetry_rinstances %(instances)d
part: modify_end
""" % {'name':self.vis_name, 'axis':self.symmetry_axis,
'angle':self.symmetry_angle, 'instances':self.symmetry_instances})
if self.colorby_palette:
stream.write("""
# Color palette.
part: select_byname_begin
"%(name)s"
part: select_byname_end
part: modify_begin
part: colorby_palette %(palette)s
part: modify_end
""" % {'name':self.vis_name, 'palette':self.colorby_palette})
def test_find_edges(self):
# Verifies that we don't chain derivatives for inputs that are
# connected in a parent assembly, but are not germain to our subassy
# derivative.
self.top = set_as_top(Assembly())
exp1 = ['y1 = 2.0*x1**2', 'y2 = 3.0*x1']
deriv1 = ['dy1_dx1 = 4.0*x1', 'dy2_dx1 = 3.0']
exp2 = ['y1 = 0.5*x1']
deriv2 = ['dy1_dx1 = 0.5']
exp3 = ['y1 = 3.5*x1']
deriv3 = ['dy1_dx1 = 3.5']
exp4 = ['y1 = x1 + 2.0*x2', 'y2 = 3.0*x1', 'y3 = x1*x2']
deriv4 = [
'dy1_dx1 = 1.0', 'dy1_dx2 = 2.0', 'dy2_dx1 = 3.0', 'dy2_dx2 = 0.0',
'dy3_dx1 = x2', 'dy3_dx2 = x1'
]
exp5 = ['y1 = x1 + 3.0*x2 + 2.0*x3']
deriv5 = ['dy1_dx1 = 1.0', 'dy1_dx2 = 3.0', 'dy1_dx3 = 2.0']
self.top.add('nest1', Assembly())
self.top.add('comp1', ExecCompWithDerivatives(exp1, deriv1))
self.top.nest1.add('driver', Driv())
self.top.nest1.add('comp2', ExecCompWithDerivatives(exp2, deriv2))
self.top.nest1.add('comp3', ExecCompWithDerivatives(exp3, deriv3))
self.top.nest1.add('comp4', ExecCompWithDerivatives(exp4, deriv4))
self.top.add('comp5', ExecCompWithDerivatives(exp5, deriv5))
self.top.driver.workflow.add(['comp1', 'nest1', 'comp5'])
self.top.nest1.driver.workflow.add(['comp2', 'comp3', 'comp4'])
self.top.nest1.driver.differentiator = ChainRule()
obj = 'comp4.y1'
con = 'comp3.y1-comp3.y1 > 0'
self.top.nest1.driver.add_parameter('comp2.x1',
low=-50.,
high=50.,
fd_step=.0001)
self.top.nest1.driver.add_objective(obj)
self.top.nest1.driver.add_constraint(con)
self.top.nest1.add('real_c3_x1',
Float(iotype='in', desc='I am really here'))
self.top.nest1.add('real_c4_y2',
Float(iotype='out', desc='I am really here'))
self.top.connect('comp1.y1', 'nest1.comp2.x1')
#self.top.connect('comp1.y1', 'nest1.comp2.x1')
self.top.connect('comp1.y2', 'nest1.real_c3_x1')
self.top.nest1.connect('real_c3_x1', 'comp3.x1')
self.top.nest1.connect('comp2.y1', 'comp4.x1')
self.top.nest1.connect('comp3.y1', 'comp4.x2')
self.top.nest1.connect('comp4.y2', 'real_c4_y2')
self.top.connect('nest1.real_c4_y2', 'comp5.x2')
self.top.connect('nest1.comp4.y1', 'comp5.x1')
self.top.connect('nest1.comp4.y3', 'comp5.x3')
#self.top.connect('nest1.comp4.y2 + 1.0*nest1.comp4.y3', 'comp5.x3')
self.top.comp1.x1 = 2.0
self.top.run()
self.top.nest1.driver.differentiator.calc_gradient()
edge_dict = self.top.nest1.driver.differentiator.edge_dicts[
'nest1.driver']
self.assertTrue('x1' in edge_dict['comp2'][0])
self.assertTrue('x1' not in edge_dict['comp3'][0])
self.assertTrue('y1' in edge_dict['comp4'][1])
self.assertTrue('y2' not in edge_dict['comp4'][1])
class DREA(ExternalCode):
"""OpenMDAO component wrapper for DREA."""
# Variables from MEflows and Geometry variable trees
# -------------------------
flow_in = VarTree(MEflows(), iotype='in')
flow_out = VarTree(MEflows(), iotype='out')
geo_in = VarTree(Geometry(), iotype='in')
geo_out = VarTree(Geometry(), iotype='out')
# NOTE: All commented out variables below are now found in the variable
# trees listed above.
#mode parameter used to replace ist and ifab in control.in
# -------------------------
mode = Enum("Auto", ["Auto", "Fabri", "Subsonic"], iotype='in', desc='Auto mode: try Fabri choke solution, if failed, try subsonic solution.'
'Fabri mode: Fabri choke solution only, Subsonic mode: Subsonic solution only')
# Variables for control.in
# -------------------------
icnvl = Enum(0, [0, 1], iotype='in', desc='Control variable; 0=inviscid and viscous mixing solutions, 1=inviscid (control volume) only')
ieject = Enum(1, [0, 1], iotype='in', desc='Control variable; 0=mixer solution, 1=ejector solution')
#ist = Enum(1, [0, 1], iotype='in', desc='Control variable; 0=subsonic solution, 1=supersonic solution')
#ifab = Enum(1, [0, 1], iotype='in', desc='Control variable; 0=back pressure constrained solution, 1=Fabri choke solution')
ispm = Enum(0, [0, 1], iotype='in', desc='Control variable; 0=direct solution, 1=iterative closure inlet static pressure matching')
iprnt = Int(2, iotype='in', desc='Number of streamwise (x) station printer control, 2=print every station, etc.')
ipw = Int(1, iotype='in', desc='Number of cross-stream (y) station printer control, 1=print every variable, etc.')
nmax = Int(6, iotype='in', desc='Maximum number of summations used in analytical (Greens function) expansion for marching analytical/numerical decomposition')
# Variables for flocond.in
# -------------------------
#p01d = Float(8467.2, iotype='in', units='lbf/ft**2', desc='Primary stream total pressure')
#p02d = Float(2116.8, iotype='in', units='lbf/ft**2', desc='Secondary stream total pressure')
#t01d = Float(1037.38, iotype='in', units='degR', desc='Primary stream total temperature')
#t02d = Float(518.69, iotype='in', units='degR', desc='Secondary stream total temperature')
#rm1 = Float(1.50, iotype='in', desc='Primary stream Mach number')
#rm2 = Float(0.4, iotype='in', desc='Secondary stream Mach number')
a1d = Float(6.00, units='ft**2', desc='Primary inlet stream cross-sectional area')
a2d = Float(8.40, units='ft**2', desc='Secondary inlet stream cross-sectional area')
a3d = Float(13.68, units='ft**2', desc='Exit plane cross-sectional area')
rg = Float(1718., iotype='in', units='ft*lb/slug/degR', desc='Real gas constant (air) ((ft lb)/(slug deg R))')
#gam = Float(1.4, iotype='in', desc='Specific heat ratio')
#pinf = Float(2116.8, iotype='in', units='lbf/ft**2', desc='Ambient static pressure')
rec1 = Float(1.0, iotype='in', desc='Primary stream nozzle pressure recovery')
rec2 = Float(0.98, iotype='in', desc='Secondary stream nozzle pressure recovery')
# Variables for expnd.in
# -------------------------
rm1s = Float(1.8, iotype='in', desc='Expanded primary stream Mach number')
rm2s = Float(0.8, iotype='in', desc='Expanded secondary stream Mach number')
dxe = Float(0.1, iotype='in', desc='Jacobian permutation for Broyden solver, approximately 0.1')
relx = Float(1., iotype='in', desc='Relaxation constant (normally not used, set equal to 1.0)')
errm = Float(1.E-6, iotype='in', desc='Maximum error in expand routines')
nmx = Int(500, iotype='in', desc='Maximum number of iterations in Broyden solver')
intt = Int(100, iotype='in', desc='Number of intervals chosen to search for static pressure constrained expansion problem')
# Variables for zrdmix.in
# -------------------------
BWID = Float(6.0, units='ft', desc='Width of 2-d ejector mixing section')
#RLD = Float(10.0, iotype='in', units='ft', desc='Length of mixing section')
RLPRNT = Float(0.3333, iotype='in', units='ft', desc='Streamwise (x) location for cross-stream profile print out to file yprmw.out')
PR = Float(1.E0, iotype='in', desc='Turbulent Prandtl Number')
CGR = Float(1., iotype='in', desc='Streamwise (x) variable grid control parameter: CGR > 1 cluster points in near field, CGR < 1 cluster points in far field, and CGR= 1 constant grid spacing')
REVRT = Float(2.0E0, iotype='in', desc='Circulation Reynolds Number')
H0LM = Float(2.88, iotype='in', desc='Lobe height to wavelength ratio')
H0HY = Float(0.90, iotype='in', desc='Lobe height to mixing section height (from centerline) ratio (chute penetration)')
#ALP1 = Float(-10.0, iotype='in', desc='Primary flow angle off of mixing chutes')
#ALP2 = Float(-10.0, iotype='in', desc='Secondary flow angle off of mixing chutes')
IMAX = Int(5, iotype='in', desc='Number of streamwise (x) grid points')
JMAX = Int(20, iotype='in', desc='Number of cross-stream (y) grid points, maximum=30')
# Variables for hwall.in
# -------------------------
geom = Array(array([[0.0,2.4],[10.0,2.28]]), dtype=numpy_float, iotype='in', desc='x,y nozzle geometry coordinates')
# Output variables
# -------------------------
GrossThrust = Float(iotype='out', units='lbf', desc='Overall gross thrust')
ExitMassFlow = Float(iotype='out', units='lbm/s', desc='Exit mass flow')
ExitVelocity = Float(iotype='out', units='ft/s', desc='Exit plane ideally mixed velocity')
ExitMach = Float(iotype='out', desc='Exit plane ideally mixed Mach number')
ExitStaticTemp = Float(iotype='out', units='degR', desc='Exit plane ideally mixed static temperature')
ExitTotalTemp = Float(iotype='out', units='degR', desc='Exit plane ideally mixed total temperature')
PumpingRatio = Float(iotype='out', desc='Entrainment ratio w2/w1')
CFG = Float(iotype='out', desc='Gross thrust coefficient')
PrimaryVelocity = Float(iotype='out', units='ft/s', desc='Primary nozzle velocity')
SecondaryVelocity = Float(iotype='out', units='ft/s', desc='Secondary nozzle velocity')
PrimaryMassFlow = Float(iotype='out', units='slug/s', desc='Primary nozzle mass flow')
SecondaryMassFlow = Float(iotype='out', units='slug/s', desc='Secondary nozzle mass flow')
SecondaryMach = Float(iotype='out', desc='Secondary nozzle Mach number')
DegreeOfMixing = Float(iotype='out', desc='Degree of mixing in pressure constraint')
NPR = Float(iotype='out', desc='Nozzle pressure ratio')
def __init__(self):
super(DREA,self).__init__()
self.command = ['drea']
self.ist = None
self.ifab = None
self.external_files = [
FileMetadata(path='control.in', input=True),
FileMetadata(path='flocond.in', input=True),
FileMetadata(path='expnd.in', input=True),
FileMetadata(path='zrdmix.in', input=True),
FileMetadata(path='hwall.in', input=True),
FileMetadata(path='ejectd.out'),
FileMetadata(path=self.stderr),
]
def _runDREA(self, FabriOrSub):
self.generate_input(FabriOrSub)
#Remove existing primary output file before execution
if os.path.exists("ejectd.out"):
os.remove("ejectd.out")
#Execute the component
super(DREA, self).execute()
#Parse output file
self.parse_output(FabriOrSub)
def setup(self):
""" Uses some values in our variable tables to fill in some derived
paramters needed by DREA. This is application-specific."""
# Copy Flow parameters from flow_in to flow_out
self.flow_out = self.flow_in.copy()
self.geo_out = self.geo_in.copy()
# Perform area calculations based on input AsAp, AeAt and AR
# Note that DREA only uses half the area as it assumes a plane of symmetry
self.a1d = self.geo_in.Apri/2
self.a2d = self.geo_in.AsAp*self.geo_in.Apri/2
self.a3d = self.geo_in.AeAt*(self.geo_in.Apri+self.geo_in.Asec)/2
self.BWID = (self.geo_in.AR*(self.geo_in.Apri+self.geo_in.Asec))**0.5
def execute(self):
""" Executes our file-wrapped component. """
self.setup()
#Prepare the input files for DREA
if self.mode != 'Auto':
self._runDREA(self.mode)
else:
#Try Fabri choke solution
try:
self._runDREA('Fabri')
except RuntimeError, err:
if "EJECTOR SOLUTION" in str(err):
self._runDREA('Subsonic')
else:
raise(err)
class DistributeSpiral(TopfarmComponent):
borders = Array(iotype='in',
desc='The polygon defining the borders ndarray([n_bor,2])',
unit='m')
spiral_param = Float(5.0, iotype='in', desc='spiral parameter')
wt_positions = Array(
[],
unit='m',
iotype='out',
desc='Array of wind turbines attached to particular positions')
inc = 0
def __init__(self, wt_layout, **kwargs):
"""
baseline = Array(unit='m', iotype='in', desc='Array of wind turbines attached to particular positions')
pos = Array(iotype='in', desc='[n_wt]')
:param wt_layout:
:param borders:
:param spiral_param:
:return:
"""
super(DistributeSpiral, self).__init__(**kwargs)
self.original_wt_layout = wt_layout
self.wt_names = wt_layout.wt_names
self.baseline = wt_layout.wt_positions
for wt_name in self.wt_names:
self.add(wt_name, Float(0.0, iotype='in'))
def list_design_variables(self):
return [{
'name': wt_name,
'start': 0.0,
'low': 0.0,
'high': 1.0,
'fd_step': 0.005
} for wt_name in self.wt_names]
def execute(self):
#if self.inc == 0:
# self.polyfill = PolyFill(self.borders, 100, 100)
# self.old_positions = self.baseline
#self.wt_layout = self.baseline
#self.wt_layout.wt_positions += self.pos * scaling
#new_positions = self.baseline + self.pos * scaling_dist
new_positions = self.baseline.copy()
for i, wt_name in enumerate(self.wt_names):
opos = getattr(self.original_wt_layout, wt_name).position
parameter = getattr(self, wt_name)
new_positions[i, :] = opos + spiral(parameter * 10 * np.pi,
self.spiral_param, 1.)
inc = 0.0
while not point_in_poly(new_positions[i, 0], new_positions[i, 1],
self.borders):
inc += 0.01
new_positions[i, :] = opos + spiral(
(parameter + inc) * 10 * np.pi, self.spiral_param, 1.)
self.wt_positions = new_positions
#self.wt_positions = self.polyfill.update(self.old_positions, new_positions)
self.old_positions = self.wt_positions.copy()
self.inc += 1
class CrossSection(XMLContainer):
""" XML parameters specific to a cabin cross-section. """
XMLTAG = 'Cabin_Cross_Sections'
xs1_p1x = Float(iotype='in', xmltag='Cabin_Point_XS1_P1X', desc='')
xs1_p1y = Float(iotype='in', xmltag='Cabin_Point_XS1_P1Y', desc='')
xs1_p1z = Float(iotype='in', xmltag='Cabin_Point_XS1_P1Z', desc='')
xs1_p2x = Float(iotype='in', xmltag='Cabin_Point_XS1_P2X', desc='')
xs1_p2y = Float(iotype='in', xmltag='Cabin_Point_XS1_P2Y', desc='')
xs1_p2z = Float(iotype='in', xmltag='Cabin_Point_XS1_P2Z', desc='')
xs1_p3x = Float(iotype='in', xmltag='Cabin_Point_XS1_P3X', desc='')
xs1_p3y = Float(iotype='in', xmltag='Cabin_Point_XS1_P3Y', desc='')
xs1_p3z = Float(iotype='in', xmltag='Cabin_Point_XS1_P3Z', desc='')
xs1_p4x = Float(iotype='in', xmltag='Cabin_Point_XS1_P4X', desc='')
xs1_p4y = Float(iotype='in', xmltag='Cabin_Point_XS1_P4Y', desc='')
xs1_p4z = Float(iotype='in', xmltag='Cabin_Point_XS1_P4Z', desc='')
xs2_p1x = Float(iotype='in', xmltag='Cabin_Point_XS2_P1X', desc='')
xs2_p1y = Float(iotype='in', xmltag='Cabin_Point_XS2_P1Y', desc='')
xs2_p1z = Float(iotype='in', xmltag='Cabin_Point_XS2_P1Z', desc='')
xs2_p2x = Float(iotype='in', xmltag='Cabin_Point_XS2_P2X', desc='')
xs2_p2y = Float(iotype='in', xmltag='Cabin_Point_XS2_P2Y', desc='')
xs2_p2z = Float(iotype='in', xmltag='Cabin_Point_XS2_P2Z', desc='')
xs2_p3x = Float(iotype='in', xmltag='Cabin_Point_XS2_P3X', desc='')
xs2_p3y = Float(iotype='in', xmltag='Cabin_Point_XS2_P3Y', desc='')
xs2_p3z = Float(iotype='in', xmltag='Cabin_Point_XS2_P3Z', desc='')
xs2_p4x = Float(iotype='in', xmltag='Cabin_Point_XS2_P4X', desc='')
xs2_p4y = Float(iotype='in', xmltag='Cabin_Point_XS2_P4Y', desc='')
xs2_p4z = Float(iotype='in', xmltag='Cabin_Point_XS2_P4Z', desc='')
def __init__(self):
super(CrossSection, self).__init__(self.XMLTAG)
class PrintOutputs(TopfarmComponent):
wt_positions = Array(
[],
unit='m',
iotype='in',
desc='Array of wind turbines attached to particular positions')
baseline = Array(
[],
unit='m',
iotype='in',
desc='Array of wind turbines attached to particular positions')
borders = Array(iotype='in',
desc='The polygon defining the borders ndarray([n_bor,2])',
unit='m')
depth = Array(iotype='in',
desc='An array of depth ndarray([n_d, 2])',
unit='m')
foundation_length = Float(
iotype='in', desc='The total foundation length of the wind farm')
foundations = Array(iotype='in',
desc='The foundation length ofeach wind turbine')
wt_dist = Array(
iotype='in',
desc="""The distance between each turbines ndarray([n_wt, n_wt]).""",
unit='m')
spiral_param = Float(5.0, iotype='in', desc='spiral parameter')
png_name = Str('wind_farm',
iotype='in',
desc='The base of the png name used to save the fig')
result_file = Str('wind_farm',
iotype='in',
desc='The base result name used to save the fig')
net_aep = Float(iotype='in', desc='')
distribution = Str('spiral',
iotype='in',
desc='The type of distribution to plot')
elnet_layout = Dict(iotype='in')
elnet_length = Float(iotype='in')
inc = 0
fs = 15 #Font size
def execute(self):
dist_min = np.array(
[self.wt_dist[i] for i in range(self.wt_dist.shape[0])]).min()
dist_mean = np.array(
[self.wt_dist[i] for i in range(self.wt_dist.shape[0])]).mean()
if self.inc == 0:
try:
pa(self.result_file + '.results').remove()
except:
pass
self.iterations = [self.inc]
self.targvalue = [[
self.foundation_length, self.elnet_length, dist_mean, dist_min,
self.net_aep
]]
else:
self.iterations.append(self.inc)
self.targvalue.append([
self.foundation_length, self.elnet_length, dist_mean, dist_min,
self.net_aep
])
self.targname = [
'Foundation length', 'El net length', 'Mean WT Dist',
'Min WT Dist', 'AEP'
]
targarr = np.array(self.targvalue)
output = '%d:' % (self.inc) + ', '.join([
'%s=%6.2f' % (self.targname[i], targarr[-1, i])
for i in range(len(self.targname))
]) + '\n' # + str(self.wt_positions)
print output
with open(self.result_file + '.results', 'a') as f:
f.write(output)
self.inc += 1
class RegionVT(VariableTree):
c0 = Float()
c1 = Float()
class SellarBLISS(Assembly):
""" Optimization of the Sellar problem using the BLISS algorithm
Disciplines coupled with FixedPointIterator.
"""
z_store = Array([0, 0], dtype=Float)
x1_store = Float(0.0)
def configure(self):
""" Creates a new Assembly with this problem
Optimal Design at (1.9776, 0, 0)
Optimal Objective = 3.18339"""
# Disciplines
self.add('dis1', sellar.Discipline1())
self.add('dis2', sellar.Discipline2())
objective = '(dis1.x1)**2 + dis1.z2 + dis1.y1 + exp(-dis2.y2)'
constraint1 = 'dis1.y1 > 3.16'
constraint2 = 'dis2.y2 < 24.0'
# Top level is Fixed-Point Iteration
self.add('driver', FixedPointIterator())
self.driver.add_parameter('dis1.x1', low=0.0, high=10.0, start=1.0)
self.driver.add_parameter(['dis1.z1', 'dis2.z1'],
low=-10.0,
high=10.0,
start=5.0)
self.driver.add_parameter(['dis1.z2', 'dis2.z2'],
low=0.0,
high=10.0,
start=2.0)
self.driver.add_constraint('x1_store = dis1.x1')
self.driver.add_constraint('z_store[0] = dis1.z1')
self.driver.add_constraint('z_store[1] = dis1.z2')
self.driver.max_iteration = 50
self.driver.tolerance = .001
# Multidisciplinary Analysis
self.add('mda', BroydenSolver())
self.mda.add_parameter('dis1.y2', low=-9.e99, high=9.e99, start=0.0)
self.mda.add_constraint('dis2.y2 = dis1.y2')
self.mda.add_parameter('dis2.y1', low=-9.e99, high=9.e99, start=3.16)
self.mda.add_constraint('dis2.y1 = dis1.y1')
# Discipline 1 Sensitivity Analysis
self.add('sa_dis1', SensitivityDriver())
self.sa_dis1.workflow.add(['dis1'])
self.sa_dis1.add_parameter('dis1.x1', low=0.0, high=10.0, fd_step=.001)
self.sa_dis1.add_constraint(constraint1)
self.sa_dis1.add_constraint(constraint2)
self.sa_dis1.add_objective(objective, name='obj')
self.sa_dis1.differentiator = FiniteDifference()
self.sa_dis1.default_stepsize = 1.0e-6
# Discipline 2 Sensitivity Analysis
# dis2 has no local parameter, so there is no need to treat it as
# a subsystem.
# System Level Sensitivity Analysis
# Note, we cheat here and run an MDA instead of solving the
# GSE equations. Have to put this on the TODO list.
self.add('ssa', SensitivityDriver())
self.ssa.workflow.add(['mda'])
self.ssa.add_parameter(['dis1.z1', 'dis2.z1'], low=-10.0, high=10.0)
self.ssa.add_parameter(['dis1.z2', 'dis2.z2'], low=0.0, high=10.0)
self.ssa.add_constraint(constraint1)
self.ssa.add_constraint(constraint2)
self.ssa.add_objective(objective, name='obj')
self.ssa.differentiator = FiniteDifference()
self.ssa.default_stepsize = 1.0e-6
# Discipline Optimization
# (Only discipline1 has an optimization input)
self.add('bbopt1', CONMINdriver())
self.bbopt1.add_parameter('x1_store', low=0.0, high=10.0, start=1.0)
self.bbopt1.add_objective(
'sa_dis1.F[0] + sa_dis1.dF[0][0]*(x1_store-dis1.x1)')
self.bbopt1.add_constraint(
'sa_dis1.G[0] + sa_dis1.dG[0][0]*(x1_store-dis1.x1) < 0')
#this one is technically unncessary
self.bbopt1.add_constraint(
'sa_dis1.G[1] + sa_dis1.dG[1][0]*(x1_store-dis1.x1) < 0')
self.bbopt1.add_constraint('(x1_store-dis1.x1)<.5')
self.bbopt1.add_constraint('(x1_store-dis1.x1)>-.5')
self.bbopt1.iprint = 0
self.bbopt1.linobj = True
# Global Optimization
self.add('sysopt', CONMINdriver())
self.sysopt.add_parameter('z_store[0]',
low=-10.0,
high=10.0,
start=5.0)
self.sysopt.add_parameter('z_store[1]', low=0.0, high=10.0, start=2.0)
self.sysopt.add_objective(
'ssa.F[0]+ ssa.dF[0][0]*(z_store[0]-dis1.z1) + ssa.dF[0][1]*(z_store[1]-dis1.z2)'
)
self.sysopt.add_constraint(
'ssa.G[0] + ssa.dG[0][0]*(z_store[0]-dis1.z1) + ssa.dG[0][1]*(z_store[1]-dis1.z2) < 0'
)
self.sysopt.add_constraint(
'ssa.G[1] + ssa.dG[1][0]*(z_store[0]-dis1.z1) + ssa.dG[1][1]*(z_store[1]-dis1.z2) < 0'
)
self.sysopt.add_constraint('z_store[0]-dis1.z1<.5')
self.sysopt.add_constraint('z_store[0]-dis1.z1>-.5')
self.sysopt.add_constraint('z_store[1]-dis1.z2<.5')
self.sysopt.add_constraint('z_store[1]-dis1.z2>-.5')
self.sysopt.iprint = 0
self.sysopt.linobj = True
self.driver.workflow = SequentialWorkflow()
self.driver.workflow.add(['ssa', 'sa_dis1', 'bbopt1', 'sysopt'])
def __init__(self, **metadata):
self._vals = {}
Float.__init__(self, **metadata)
self._metadata['type'] = 'property' # Just to show correct type.