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일반적으로 최적화 그룹을 사용할 때 문제에 포함시킵니다. 그 후, 나는 그것이 구성 요소 속성입니다 설정할 수 있습니다OpenMDAO 하위 그룹 속성을 설정하는 방법은 무엇입니까?
# 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
....
는 아래 내 예제를 참조하십시오 (test_brute_force.py에서 적응). 204 행에서 다른 그룹의 그룹으로 AEPGroup을 실행하고 싶습니다. 하위 그룹 내에 turbineX과 같은 사양을 구성하는 유사한 방법이 있습니까? 당신은 당신은 당신이 방금 계정에 변수 경로 이름을 조정
prob['sellars.sellar0.turbineX'] = turbineX
로 변수 이름을 설정할 수 있습니다
sellars.add(name, AEPGroup(nTurbines=nTurbines, nDirections=nDirections,
differentiable=True,
use_rotor_components=False))
를 호출 할 때 어떤 변수 승진을 안하고 있기 때문에
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"])