问题
Normally when I use my optimization group I include it in a problem. Then, I can set it's component properties:
# import modules, prepare data for Problem setup
...
# Initialize problem with my group
prob = Problem(impl=impl, root=AEPGroup(nTurbines=10,
nDirections=5,
minSpacing=2))
# Configure driver, desvars, and constraints
prob.driver = pyOptSparseDriver()
prob.driver.add_desvar('turbineX', lower=np.ones(nTurbs)*min(turbineX), upper=np.ones(nTurbs)*max(turbineX), scaler=1E-2)
prob.driver.add_objective('obj', scaler=1E-8)
# run setup()
prob.setup(check=True)
# Now I set several specifications
prob['turbineX'] = turbineX
....
Please see my example below (adapted from test_brute_force.py). In line 204 I want to run AEPGroup as a group inside of another group. Is there a analogous way to configure specifications like turbineX within the subgroup?
from __future__ import print_function
from florisse.floris import AEPGroup
import unittest
from florisse.GeneralWindFarmComponents import calculate_boundary
from six.moves import range
from six import iteritems
import numpy as np
from openmdao.api import Problem, Group, ParallelGroup, \
Component, IndepVarComp, ExecComp, \
Driver, ScipyOptimizer, SqliteRecorder
from openmdao.test.sellar import *
from openmdao.test.util import assert_rel_error
from openmdao.core.mpi_wrap import MPI
if MPI:
from openmdao.core.petsc_impl import PetscImpl as impl
else:
from openmdao.api import BasicImpl as impl
# load wind rose data
windRose = np.loadtxt('./input_files/windrose_amalia_directionally_averaged_speeds.txt')
indexes = np.where(windRose[:, 1] > 0.1)
#print ("ypppp indexes are ", indexes)
indexes = [[8]]
#print ("ypppp indexes are ", indexes) ; quit()
windDirections = windRose[indexes[0], 0]
windSpeeds = windRose[indexes[0], 1]
windFrequencies = windRose[indexes[0], 2]
nDirections = len(windDirections)
# load turbine positions
locations = np.loadtxt('./input_files/layout_amalia.txt')
turbineX = locations[:, 0]
turbineY = locations[:, 1]
# generate boundary constraint
boundaryVertices, boundaryNormals = calculate_boundary(locations)
nVertices = boundaryVertices.shape[0]
# define turbine size
rotor_diameter = 126.4 # (m)
# initialize input variable arrays
nTurbines = turbineX.size
rotorDiameter = np.zeros(nTurbines)
axialInduction = np.zeros(nTurbines)
Ct = np.zeros(nTurbines)
Cp = np.zeros(nTurbines)
generatorEfficiency = np.zeros(nTurbines)
yaw = np.zeros(nTurbines)
minSpacing = 2. # number of rotor diameters
# define initial values
for turbI in range(0, nTurbines):
rotorDiameter[turbI] = rotor_diameter # m
axialInduction[turbI] = 1.0/3.0
Ct[turbI] = 4.0*axialInduction[turbI]*(1.0-axialInduction[turbI])
Cp[turbI] = 0.7737/0.944 * 4.0 * 1.0/3.0 * np.power((1 - 1.0/3.0), 2)
generatorEfficiency[turbI] = 0.944
yaw[turbI] = 0. # deg.
# Define flow properties
air_density = 1.1716 # kg/m^3
class Randomize(Component):
""" add random uncertainty to params and distribute
Args
----
n : number of points to generate for each param
params : collection of (name, value, std_dev) specifying the params
that are to be randommized.
"""
def __init__(self, n=0, params=[]):
super(Randomize, self).__init__()
self.dists = {}
for name, value, std_dev in params:
# add param
self.add_param(name, val=value)
# add an output array var to distribute the modified param values
if isinstance(value, np.ndarray):
shape = (n, value.size)
else:
shape = (n, 1)
# generate a standard normal distribution (size n) for this param
self.dists[name] = np.random.normal(0.0, std_dev, n*shape[1]).reshape(shape)
#self.dists[name] = std_dev*np.random.normal(0.0, 1.0, n*shape[1]).reshape(shape)
self.add_output('dist_'+name, val=np.zeros(shape))
def solve_nonlinear(self, params, unknowns, resids):
""" add random uncertainty to params
"""
for name, dist in iteritems(self.dists):
unknowns['dist_'+name] = params[name] + dist
def linearize(self, params, unknowns, resids):
""" derivatives
"""
J = {}
for u in unknowns:
name = u.split('_', 1)[1]
for p in params:
shape = (unknowns[u].size, params[p].size)
if p == name:
J[u, p] = np.eye(shape[0], shape[1])
else:
J[u, p] = np.zeros(shape)
return J
class Collector(Component):
""" collect the inputs and compute the mean of each
Args
----
n : number of points to collect for each input
names : collection of `Str` specifying the names of the inputs to
collect and the resulting outputs.
"""
def __init__(self, n=10, names=[]):
super(Collector, self).__init__()
self.names = names
# create n params for each input
for i in range(n):
for name in names:
self.add_param('%s_%i' % (name, i), val=0.)
# create an output for the mean of each input
for name in names:
self.add_output(name, val=0.)
def solve_nonlinear(self, params, unknowns, resids):
""" compute the mean of each input
"""
inputs = {}
for p in params:
name = p.split('_', 1)[0]
if name not in inputs:
inputs[name] = data = [0.0, 0.0]
else:
data = inputs[name]
data[0] += 1
data[1] += params[p]
for name in self.names:
unknowns[name] = inputs[name][1]/inputs[name][0]
def linearize(self, params, unknowns, resids):
""" derivatives
"""
J = {}
for p in params:
name, idx = p.split('_', 1)
for u in unknowns:
if u == name:
J[u, p] = 1
else:
J[u, p] = 0
return J
class BruteForceSellarProblem(Problem):
""" Performs optimization on the AEP problem.
Applies a normal distribution to the design vars and runs all of the
samples, then collects the values of all of the outputs, calculates
the mean of those and stuffs that back into the unknowns vector.
This is the brute force version that just stamps out N separate
AEP models in a parallel group and sets the input of each
one to be one of these random design vars.
Args
----
n : number of randomized points to generate for each input value
derivs : if True, use user-defined derivatives, else use Finite Difference
"""
def __init__(self, n=10, derivs=False):
super(BruteForceSellarProblem, self).__init__(impl=impl)
root = self.root = Group()
if not derivs:
root.deriv_options['type'] = 'fd'
sellars = root.add('sellars', ParallelGroup())
for i in range(n):
name = 'sellar%i' % i
sellars.add(name, AEPGroup(nTurbines=nTurbines, nDirections=nDirections,
differentiable=True,
use_rotor_components=False))
#sellars.add(name, SellarDerivatives())
root.connect('dist_air_density', 'sellars.'+name+'.air_density', src_indices=[i])
#root.connect('yaw0', 'sellars.'+name+'.yaw0')#, src_indices=[i])
#root.connect('dist_z', 'sellars.'+name+'.z', src_indices=[i*2, i*2+1])
root.connect('sellars.'+name+'.AEP', 'collect.obj_%i' % i)
#root.connect('sellars.'+name+'.con1', 'collect.con1_%i' % i)
#root.connect('sellars.'+name+'.con2', 'collect.con2_%i' % i)
root.add('indep', IndepVarComp([
('air_density', 1.0),
('z', np.array([5.0, 2.0]))
]),
promotes=['air_density', 'z'])
root.add('random', Randomize(n=n, params=[
# name, value, std dev
('air_density', 1.0, 1e-2),
('z', np.array([5.0, 2.0]), 1e-2)
]),
promotes=['z', 'dist_air_density', 'dist_z'])
#promotes=['x', 'z', 'dist_x', 'dist_z'])
root.add('collect', Collector(n=n, names=['obj', 'con1', 'con2']),
promotes=['obj', 'con1', 'con2'])
# top level driver setup
self.driver = ScipyOptimizer()
self.driver.options['optimizer'] = 'SLSQP'
self.driver.options['tol'] = 1.0e-8
self.driver.options['maxiter'] = 50
self.driver.options['disp'] = False
self.driver.add_desvar('z', lower=np.array([-10.0, 0.0]),
upper=np.array([ 10.0, 10.0]))
#self.driver.add_desvar('x', lower=0.0, upper=10.0)
self.driver.add_objective('obj')
self.driver.add_constraint('con1', upper=0.0)
self.driver.add_constraint('con2', upper=0.0)
prob = BruteForceSellarProblem(100, derivs=False)
prob.setup(check=False)
prob.run()
print (prob["obj"])
回答1:
since you're not doing any variable promotion when you call
sellars.add(name, AEPGroup(nTurbines=nTurbines, nDirections=nDirections,
differentiable=True,
use_rotor_components=False))
You can just set the variable names as
prob['sellars.sellar0.turbineX'] = turbineX
You just adjust your variable path name to account for the presence of the additional parent group and the fact that your AEPGroup is now named sellar0 (or whatever index you need to set).
来源：https://stackoverflow.com/questions/41624671/openmdao-how-to-set-subgroup-properties