def __init__(self):
rotor_diameter = 126.4
self.rotor_diameter = rotor_diameter
# define turbine locations in global reference frame
turbineX = np.array([0.0, 3.*rotor_diameter, 7.*rotor_diameter])
turbineY = np.array([-2.*rotor_diameter, 2.*rotor_diameter, 0.0])
hubHeight = np.zeros_like(turbineX)+90.
# import matplotlib.pyplot as plt
# plt.plot(turbineX, turbineY, 'o')
# plt.plot(np.array([0.0, ]))
# plt.show()
# 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)
# 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
nDirections = 1
wind_speed = 8.0 # m/s
air_density = 1.1716 # kg/m^3
wind_direction = 270. # deg (N = 0 deg., using direction FROM, as in met-mast data)
wind_frequency = 1. # probability of wind in this direction at this speed
# set up problem
wake_model_options = {'nSamples': 0}
prob = Problem(root=AEPGroup(nTurbines=nTurbines, nDirections=nDirections, wake_model=gauss_wrapper,
wake_model_options=wake_model_options, datasize=0, use_rotor_components=False,
params_IdepVar_func=add_gauss_params_IndepVarComps, differentiable=True,
params_IndepVar_args={}))
# initialize problem
prob.setup(check=True)
# assign values to turbine states
prob['turbineX'] = turbineX
prob['turbineY'] = turbineY
prob['hubHeight'] = hubHeight
prob['yaw0'] = yaw
# assign values to constant inputs (not design variables)
prob['rotorDiameter'] = rotorDiameter
prob['axialInduction'] = axialInduction
prob['generatorEfficiency'] = generatorEfficiency
prob['windSpeeds'] = np.array([wind_speed])
prob['model_params:z_ref'] = 90.
prob['air_density'] = air_density
prob['windDirections'] = np.array([wind_direction])
prob['windFrequencies'] = np.array([wind_frequency])
prob['Ct_in'] = Ct
prob['Cp_in'] = Cp
prob['model_params:wec_factor'] = 1.0
prob['model_params:wec_spreading_angle'] = 0.0
prob['model_params:wake_model_version'] = '2016'
prob['model_params:ti_calculation_method'] = 4
prob['model_params:I'] = 0.1
# run the problem
prob.run()
self.prob = prob
self.label = ''
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
nDirections = 1
wind_speed = 8.1 # m/s
air_density = 1.1716 # kg/m^3
wind_direction = 270.-0.523599*180./np.pi # deg (N = 0 deg., using direction FROM, as in met-mast data)
wind_frequency = 1. # probability of wind in this direction at this speed
# set up problem
wake_model_options = None
prob = Problem(root=AEPGroup(nTurbines, nDirections, wake_model=jensen_wrapper, wake_model_options=wake_model_options,
params_IdepVar_func=add_jensen_params_IndepVarComps,
params_IndepVar_args={'use_angle': False}))
# initialize problem
prob.setup(check=True)
# assign values to turbine states
prob['turbineX'] = turbineX
prob['turbineY'] = turbineY
prob['yaw0'] = yaw
# assign values to constant inputs (not design variables)
prob['rotorDiameter'] = rotorDiameter
prob['axialInduction'] = axialInduction
prob['generatorEfficiency'] = generatorEfficiency
prob['windSpeeds'] = np.array([wind_speed])
def setUp(self):
try:
from plantenergy.gauss import gauss_wrapper, add_gauss_params_IndepVarComps
self.working_import = True
except:
self.working_import = False
# define turbine locations in global reference frame
turbineX = np.array([1164.7, 947.2, 1682.4, 1464.9, 1982.6, 2200.1])
turbineY = np.array([1024.7, 1335.3, 1387.2, 1697.8, 2060.3, 1749.7])
hubHeight = np.zeros_like(turbineX) + 90.
# import matplotlib.pyplot as plt
# plt.plot(turbineX, turbineY, 'o')
# plt.plot(np.array([0.0, ]))
# plt.show()
# 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)
# define initial values
for turbI in range(0, nTurbines):
rotorDiameter[turbI] = 126.4 # 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
nDirections = 1
wind_speed = 8.0 # m/s
air_density = 1.1716 # kg/m^3
wind_direction = 270. - 0.523599 * 180. / np.pi # deg (N = 0 deg., using direction FROM, as in met-mast data)
wind_frequency = 1. # probability of wind in this direction at this speed
# set up problem
wake_model_options = {'nSamples': 0}
prob = Problem(
root=AEPGroup(nTurbines=nTurbines,
nDirections=nDirections,
wake_model=gauss_wrapper,
wake_model_options=wake_model_options,
datasize=0,
use_rotor_components=False,
params_IdepVar_func=add_gauss_params_IndepVarComps,
differentiable=True,
params_IndepVar_args={}))
# initialize problem
prob.setup(check=True)
# assign values to turbine states
prob['turbineX'] = turbineX
prob['turbineY'] = turbineY
prob['hubHeight'] = hubHeight
prob['yaw0'] = yaw
# assign values to constant inputs (not design variables)
prob['rotorDiameter'] = rotorDiameter
prob['axialInduction'] = axialInduction
prob['generatorEfficiency'] = generatorEfficiency
prob['windSpeeds'] = np.array([wind_speed])
prob['model_params:z_ref'] = 90.
prob['air_density'] = air_density
prob['windDirections'] = np.array([wind_direction])
prob['windFrequencies'] = np.array([wind_frequency])
prob['Ct_in'] = Ct
prob['Cp_in'] = Cp
# run the problem
prob.run()
self.prob = prob
def setUpClass(self):
super(test_jensen, self).setUpClass()
try:
from plantenergy.jensen import jensen_wrapper, add_jensen_params_IndepVarComps
self.working_import = True
except:
self.working_import = False
# define turbine locations in global reference frame
turbineX = np.array([1164.7, 947.2, 1682.4, 1464.9, 1982.6, 2200.1])
turbineY = np.array([1024.7, 1335.3, 1387.2, 1697.8, 2060.3, 1749.7])
# 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)
# define initial values
for turbI in range(0, nTurbines):
rotorDiameter[turbI] = 126.4 # 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
nDirections = 1
wind_speed = 8.1 # m/s
air_density = 1.1716 # kg/m^3
wind_direction = 270. - 0.523599 * 180. / np.pi # deg (N = 0 deg., using direction FROM, as in met-mast data)
wind_frequency = 1. # probability of wind in this direction at this speed
# set up problem
wake_model_options = None
# prob = Problem(root=AEPGroup(nTurbines, nDirections, wake_model=jensen_wrapper, wake_model_options=wake_model_options,
# params_IdepVar_func=add_jensen_params_IndepVarComps,
# params_IndepVar_args={'use_angle': False}))
prob = Problem(
root=AEPGroup(nTurbines,
nDirections,
wake_model=jensen_wrapper,
wake_model_options=wake_model_options,
params_IdepVar_func=add_jensen_params_IndepVarComps,
params_IndepVar_args={'use_angle': False}))
# initialize problem
prob.setup(check=True)
# assign values to turbine states
prob['turbineX'] = turbineX
prob['turbineY'] = turbineY
prob['yaw0'] = yaw
# assign values to constant inputs (not design variables)
prob['rotorDiameter'] = rotorDiameter
prob['axialInduction'] = axialInduction
prob['generatorEfficiency'] = generatorEfficiency
prob['windSpeeds'] = np.array([wind_speed])
prob['air_density'] = air_density
prob['windDirections'] = np.array([wind_direction])
prob['windFrequencies'] = np.array([wind_frequency])
prob['Ct_in'] = Ct
prob['Cp_in'] = Cp
# prob['model_params:spread_angle'] = 20.0
# prob['model_params:alpha'] = 0.1
# run the problem
prob.run()
self.prob = prob
def run_opt(layout_number, wec_method_number, wake_model, opt_alg_number,
max_wec, nsteps):
OPENMDAO_REQUIRE_MPI = False
run_number = layout_number
model = wake_model
# set model
MODELS = ['FLORIS', 'BPA', 'JENSEN', 'LARSEN']
print(MODELS[model])
# select optimization approach/method
opt_algs = ['snopt', 'ga', 'ps']
opt_algorithm = opt_algs[opt_alg_number]
# select wec method
wec_methods = ['none', 'diam', 'angle', 'hybrid']
wec_method = wec_methods[wec_method_number]
# pop_size = 760
# save and show options
show_start = False
show_end = False
save_start = False
save_end = False
save_locations = True
save_aep = True
save_time = True
rec_func_calls = True
input_directory = "../../../input_files/"
# set options for BPA
print_ti = False
sort_turbs = True
# turbine_type = 'NREL5MW' #can be 'V80' or 'NREL5MW'
turbine_type = 'V80' # can be 'V80' or 'NREL5MW'
wake_model_version = 2016
WECH = 0
if wec_method == 'diam':
output_directory = "../output_files/%s_wec_diam_max_wec_%i_nsteps_%.3f/" % (
opt_algorithm, max_wec, nsteps)
relax = True
# expansion_factors = np.array([3, 2.75, 2.5, 2.25, 2.0, 1.75, 1.5, 1.25, 1.0, 1.0])
expansion_factors = np.linspace(1.0, max_wec, nsteps)
expansion_factors = np.flip(expansion_factors)
elif wec_method == 'angle':
output_directory = "../output_files/%s_wec_angle_max_wec_%i_nsteps_%.3f/" % (
opt_algorithm, max_wec, nsteps)
relax = True
# expansion_factors = np.array([50, 40, 30, 20, 10, 0.0, 0.0])
expansion_factors = np.linspace(0.0, max_wec, nsteps)
expansion_factors = np.append(np.flip(expansion_factors), 0.0)
elif wec_method == 'hybrid':
expansion_factors = np.linspace(1.0, max_wec, nsteps)
expansion_factors = np.append(np.flip(expansion_factors), 1.0)
output_directory = "../output_files/%s_wec_hybrid_max_wec_%i_nsteps_%.3f/" % (
opt_algorithm, max_wec, nsteps)
relax = True
WECH = 1
elif wec_method == 'none':
relax = False
expansion_factors = np.array([1.0])
output_directory = "../output_files/%s/" % opt_algorithm
else:
raise ValueError('wec_method must be diam, angle, hybrid, or none')
# create output directory if it does not exist yet
import distutils.dir_util
distutils.dir_util.mkpath(output_directory)
differentiable = True
# for expansion_factor in np.array([5., 4., 3., 2.75, 2.5, 2.25, 2.0, 1.75, 1.5, 1.25, 1.0]):
# for expansion_factor in np.array([20., 15., 10., 5., 4., 3., 2.5, 1.25, 1.0]):
# expansion_factors = np.array([20., 10., 5., 2.5, 1.25, 1.0])
wake_combination_method = 1 # can be [0:Linear freestreem superposition,
# 1:Linear upstream velocity superposition,
# 2:Sum of squares freestream superposition,
# 3:Sum of squares upstream velocity superposition]
ti_calculation_method = 4 # can be [0:No added TI calculations,
# 1:TI by Niayifar and Porte Agel altered by Annoni and Thomas,
# 2:TI by Niayifar and Porte Agel 2016,
# 3:TI by Niayifar and Porte Agel 2016 with added soft max function,
# 4:TI by Niayifar and Porte Agel 2016 using area overlap ratio,
# 5:TI by Niayifar and Porte Agel 2016 using area overlap ratio and SM function]
if wec_method_number > 0:
ti_opt_method = 0 # can be [0:No added TI calculations,
# 1:TI by Niayifar and Porte Agel altered by Annoni and Thomas,
# 2:TI by Niayifar and Porte Agel 2016,
# 3:TI by Niayifar and Porte Agel 2016 with added soft max function,
# 4:TI by Niayifar and Porte Agel 2016 using area overlap ratio,
# 5:TI by Niayifar and Porte Agel 2016 using area overlap ratio and SM function]
else:
ti_opt_method = 0
final_ti_opt_method = 5
if opt_algorithm == 'ps':
ti_opt_method = ti_calculation_method
sm_smoothing = 700.
if ti_calculation_method == 0:
calc_k_star_calc = False
else:
calc_k_star_calc = True
if ti_opt_method == 0:
calc_k_star_opt = False
else:
calc_k_star_opt = True
nRotorPoints = 1
wind_rose_file = 'nantucket' # can be one of: 'amalia', 'nantucket', 'directional
TI = 0.108
k_calc = 0.3837 * TI + 0.003678
# k_calc = 0.022
# k_opt = 0.04
shear_exp = 0.31
# air_density = 1.1716 # kg/m^3
air_density = 1.225 # kg/m^3 (from Jen)
if turbine_type == 'V80':
# define turbine size
rotor_diameter = 80. # (m)
hub_height = 70.0
z_ref = 80.0 # m
z_0 = 0.0
# load performance characteristics
cut_in_speed = 4. # m/s
cut_out_speed = 25. # m/s
rated_wind_speed = 16. # m/s
rated_power = 2000. # kW
generator_efficiency = 0.944
ct_curve_data = np.loadtxt(input_directory +
'mfg_ct_vestas_v80_niayifar2016.txt',
delimiter=",")
ct_curve_wind_speed = ct_curve_data[:, 0]
ct_curve_ct = ct_curve_data[:, 1]
# air_density = 1.1716 # kg/m^3
Ar = 0.25 * np.pi * rotor_diameter**2
# cp_curve_wind_speed = ct_curve[:, 0]
power_data = np.loadtxt(input_directory +
'niayifar_vestas_v80_power_curve_observed.txt',
delimiter=',')
# cp_curve_cp = niayifar_power_model(cp_curve_wind_speed)/(0.5*air_density*cp_curve_wind_speed**3*Ar)
cp_curve_cp = power_data[:, 1] * (1E6) / (0.5 * air_density *
power_data[:, 0]**3 * Ar)
cp_curve_wind_speed = power_data[:, 0]
cp_curve_spline = UnivariateSpline(cp_curve_wind_speed,
cp_curve_cp,
ext='const')
cp_curve_spline.set_smoothing_factor(.0001)
elif turbine_type == 'NREL5MW':
# define turbine size
rotor_diameter = 126.4 # (m)
hub_height = 90.0
z_ref = 80.0 # m
z_0 = 0.0
# load performance characteristics
cut_in_speed = 3. # m/s
cut_out_speed = 25. # m/s
rated_wind_speed = 11.4 # m/s
rated_power = 5000. # kW
generator_efficiency = 0.944
filename = input_directory + "NREL5MWCPCT_dict.p"
# filename = "../input_files/NREL5MWCPCT_smooth_dict.p"
import pickle
data = pickle.load(open(filename, "rb"), encoding='latin1')
ct_curve = np.zeros([data['wind_speed'].size, 2])
ct_curve_wind_speed = data['wind_speed']
ct_curve_ct = data['CT']
# cp_curve_cp = data['CP']
# cp_curve_wind_speed = data['wind_speed']
loc0 = np.where(data['wind_speed'] < 11.55)
loc1 = np.where(data['wind_speed'] > 11.7)
cp_curve_cp = np.hstack([data['CP'][loc0], data['CP'][loc1]])
cp_curve_wind_speed = np.hstack(
[data['wind_speed'][loc0], data['wind_speed'][loc1]])
cp_curve_spline = UnivariateSpline(cp_curve_wind_speed,
cp_curve_cp,
ext='const')
cp_curve_spline.set_smoothing_factor(.000001)
else:
raise ValueError("Turbine type is undefined.")
# load starting locations
layout_directory = input_directory
layout_data = np.loadtxt(
layout_directory +
"layouts/round_38turbs/nTurbs38_spacing5_layout_%i.txt" %
layout_number)
# layout_data = np.loadtxt(layout_directory + "layouts/grid_16turbs/nTurbs16_spacing5_layout_%i.txt" % layout_number)
# layout_data = np.loadtxt(layout_directory+"layouts/nTurbs9_spacing5_layout_%i.txt" % layout_number)
# turbineX = layout_data[:, 0] * rotor_diameter + rotor_diameter / 2.
# turbineY = layout_data[:, 1] * rotor_diameter + rotor_diameter / 2.
turbineX = np.array([0.0])
turbineY = np.array([0.0])
turbineX_init = np.copy(turbineX)
turbineY_init = np.copy(turbineY)
nTurbines = turbineX.size
# create boundary specifications
boundary_radius = 0.5 * (rotor_diameter * 4000. / 126.4 - rotor_diameter
) # 1936.8
center = np.array([boundary_radius, boundary_radius]) + rotor_diameter / 2.
start_min_spacing = 5.
nVertices = 1
boundary_center_x = center[0]
boundary_center_y = center[1]
xmax = np.max(turbineX)
ymax = np.max(turbineY)
xmin = np.min(turbineX)
ymin = np.min(turbineY)
boundary_radius_plot = boundary_radius + 0.5 * rotor_diameter
plot_round_farm(turbineX,
turbineY,
rotor_diameter, [boundary_center_x, boundary_center_y],
boundary_radius,
show_start=show_start)
# quit()
# initialize input variable arrays
nTurbs = nTurbines
rotorDiameter = np.zeros(nTurbs)
hubHeight = np.zeros(nTurbs)
axialInduction = np.zeros(nTurbs)
Ct = np.zeros(nTurbs)
Cp = np.zeros(nTurbs)
generatorEfficiency = np.zeros(nTurbs)
yaw = np.zeros(nTurbs)
minSpacing = 2. # number of rotor diameters
# define initial values
for turbI in range(0, nTurbs):
rotorDiameter[turbI] = rotor_diameter # m
hubHeight[turbI] = hub_height # m
axialInduction[turbI] = 1.0 / 3.0
Ct[turbI] = 4.0 * axialInduction[turbI] * (1.0 - axialInduction[turbI])
# print(Ct)
Cp[turbI] = 4.0 * 1.0 / 3.0 * np.power((1 - 1.0 / 3.0), 2)
generatorEfficiency[turbI] = generator_efficiency
yaw[turbI] = 0. # deg.
# Define flow properties
if wind_rose_file is 'nantucket':
# windRose = np.loadtxt(input_directory + 'nantucket_windrose_ave_speeds.txt')
windRose = np.loadtxt(input_directory +
'nantucket_wind_rose_for_LES.txt')
windDirections = windRose[:, 0]
windSpeeds = windRose[:, 1]
windFrequencies = windRose[:, 2]
size = np.size(windDirections)
elif wind_rose_file is 'amalia':
windRose = np.loadtxt(
input_directory +
'windrose_amalia_directionally_averaged_speeds.txt')
windDirections = windRose[:, 0]
windSpeeds = windRose[:, 1]
windFrequencies = windRose[:, 2]
size = np.size(windDirections)
elif wind_rose_file is 'directional':
windRose = np.loadtxt(input_directory + 'directional_windrose.txt')
windDirections = windRose[:, 0]
windSpeeds = windRose[:, 1]
windFrequencies = windRose[:, 2]
size = np.size(windDirections)
elif wind_rose_file is '1d':
windDirections = np.array([270.])
windSpeeds = np.array([8.0])
windFrequencies = np.array([1.0])
size = np.size(windDirections)
else:
size = 20
windDirections = np.linspace(0, 270, size)
windFrequencies = np.ones(size) / size
wake_model_options = {
'nSamples': 0,
'nRotorPoints': nRotorPoints,
'use_ct_curve': True,
'ct_curve_ct': ct_curve_ct,
'ct_curve_wind_speed': ct_curve_wind_speed,
'interp_type': 1,
'use_rotor_components': False,
'differentiable': differentiable,
'verbose': False,
'variant': "CosineFortran"
}
if MODELS[model] == 'BPA':
# initialize problem
prob = om.Problem(
model=AEPGroup(nTurbines=nTurbs,
nDirections=windDirections.size,
use_rotor_components=False,
wake_model=gauss_wrapper,
params_IdepVar_func=add_gauss_params_IndepVarComps,
params_IdepVar_args={'nRotorPoints': nRotorPoints},
wake_model_options=wake_model_options,
cp_points=cp_curve_cp.size,
cp_curve_spline=cp_curve_spline,
record_function_calls=True,
runparallel=False))
elif MODELS[model] == 'FLORIS':
# initialize problem
prob = om.Problem(
model=AEPGroup(nTurbines=nTurbs,
nDirections=windDirections.size,
use_rotor_components=False,
wake_model=floris_wrapper,
params_IdepVar_func=add_floris_params_IndepVarComps,
params_IdepVar_args={},
record_function_calls=True))
elif MODELS[model] == 'JENSEN':
# initialize problem
prob = om.Problem(
model=AEPGroup(nTurbines=nTurbs,
nDirections=windDirections.size,
use_rotor_components=False,
wake_model=jensen_wrapper,
wake_model_options=wake_model_options,
params_IdepVar_func=add_jensen_params_IndepVarComps,
cp_points=cp_curve_cp.size,
cp_curve_spline=cp_curve_spline,
params_IdepVar_args={},
runparallel=False,
record_function_calls=True))
else:
ValueError(
'The %s model is not currently available. Please select BPA or FLORIS'
% (MODELS[model]))
print("almost time for setup")
tic = time.time()
print("entering setup at time = ", tic)
prob.setup(check=True)
toc = time.time()
print("setup complete at time = ", toc)
# print the results
print(('Problem setup took %.03f sec.' % (toc - tic)))
# assign initial values to design variables
prob['turbineX'] = np.copy(turbineX)
prob['turbineY'] = np.copy(turbineY)
for direction_id in range(0, windDirections.size):
prob['yaw%i' % direction_id] = yaw
# assign values to constant inputs (not design variables)
prob['rotorDiameter'] = rotorDiameter
prob['hubHeight'] = hubHeight
prob['axialInduction'] = axialInduction
prob['generatorEfficiency'] = generatorEfficiency
prob['windSpeeds'] = windSpeeds
prob['air_density'] = air_density
prob['windDirections'] = windDirections
prob['windFrequencies'] = windFrequencies
prob['Ct_in'] = Ct
prob['Cp_in'] = Cp
prob['cp_curve_cp'] = cp_curve_cp
prob['cp_curve_wind_speed'] = cp_curve_wind_speed
cutInSpeeds = np.ones(nTurbines) * cut_in_speed
prob['cut_in_speed'] = cutInSpeeds
ratedPowers = np.ones(nTurbines) * rated_power
prob['rated_power'] = ratedPowers
# assign values to turbine states
prob['cut_in_speed'] = np.ones(nTurbines) * cut_in_speed
prob['cut_out_speed'] = np.ones(nTurbines) * cut_out_speed
prob['rated_power'] = np.ones(nTurbines) * rated_power
prob['rated_wind_speed'] = np.ones(nTurbines) * rated_wind_speed
prob['use_power_curve_definition'] = True
if MODELS[model] is 'BPA':
prob['model_params:wake_combination_method'] = np.copy(
wake_combination_method)
prob['model_params:ti_calculation_method'] = np.copy(
ti_calculation_method)
prob['model_params:wake_model_version'] = np.copy(wake_model_version)
prob['model_params:wec_factor'] = 1.0
prob['model_params:wec_spreading_angle'] = 0.0
prob['model_params:calc_k_star'] = np.copy(calc_k_star_calc)
prob['model_params:sort'] = np.copy(sort_turbs)
prob['model_params:z_ref'] = np.copy(z_ref)
prob['model_params:z_0'] = np.copy(z_0)
prob['model_params:ky'] = np.copy(k_calc)
prob['model_params:kz'] = np.copy(k_calc)
prob['model_params:print_ti'] = np.copy(print_ti)
prob['model_params:shear_exp'] = np.copy(shear_exp)
prob['model_params:I'] = np.copy(TI)
prob['model_params:sm_smoothing'] = np.copy(sm_smoothing)
prob['model_params:WECH'] = WECH
if nRotorPoints > 1:
prob['model_params:RotorPointsY'], prob[
'model_params:RotorPointsZ'] = sunflower_points(nRotorPoints)
if MODELS[model] is 'JENSEN':
prob['model_params:alpha'] = 0.1
prob['model_params:wec_factor'] = 1.0
prob.run_model()
AEP_init_calc = np.copy(prob['AEP'])
print(AEP_init_calc * 1E-6)
if MODELS[model] is 'BPA':
prob['model_params:ti_calculation_method'] = np.copy(ti_opt_method)
prob['model_params:calc_k_star'] = np.copy(calc_k_star_opt)
prob.run_model()
AEP_init_opt = np.copy(prob['AEP'])
AEP_run_opt = np.copy(AEP_init_opt)
print(AEP_init_opt * 1E-6)
config.obj_func_calls_array[:] = 0.0
config.sens_func_calls_array[:] = 0.0
if prob.model.comm.rank == 0:
print('turbine X positions in wind frame (m): %s' % prob['turbineX'])
print('turbine Y positions in wind frame (m): %s' % prob['turbineY'])
print('Initial AEP (kWh): %s' % AEP_init_opt)
return 0
def get_jensen_problem(modelvariant):
# define turbine size
rotor_diameter = 40.0 # (m)
hub_height = 90.0
# define turbine locations in global reference frame
turbineX = np.array([0.0, 6.0, 20.0]) * rotor_diameter
turbineY = np.array([0.0, 0.0, 0.0])
z_ref = 90.0 # m
z_0 = 0.0
# load performance characteristics
cut_in_speed = 3. # m/s
cut_out_speed = 25. # m/s
rated_wind_speed = 11.4 # m/s
rated_power = 5000. # kW
generator_efficiency = 0.944
input_directory = "../project-code/input_files/"
filename = input_directory + "NREL5MWCPCT_dict.txt"
# filename = "../input_files/NREL5MWCPCT_smooth_dict.p"
data = np.loadtxt(filename)
ct_curve = np.zeros([data[:, 0].size, 2])
ct_curve_wind_speed = data[:, 0]
ct_curve_ct = data[:, 2]
# cp_curve_cp = data['CP']
# cp_curve_wind_speed = data['wind_speed']
loc0 = np.where(data[:, 0] < 11.55)
loc1 = np.where(data[:, 0] > 11.7)
cp_curve_cp = np.hstack([data[:, 1][loc0], data[:, 1][loc1]])
cp_curve_wind_speed = np.hstack([data[:, 0][loc0], data[:, 0][loc1]])
# 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)
# 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] = 1.0
yaw[turbI] = 0. # deg.
# Define flow properties
nDirections = 1
wind_speed = 8.0 # m/s
air_density = 1.1716 # kg/m^3
wind_direction = 270.0 # deg (N = 0 deg., using direction FROM, as in met-mast data)
wind_frequency = 1. # probability of wind in this direction at this speed
# set up problem
wake_model_options = {
'nSamples': 0,
'nRotorPoints': 1,
'use_ct_curve': False,
'ct_curve_ct': ct_curve_ct,
'ct_curve_wind_speed': ct_curve_wind_speed,
'interp_type': 1,
'use_rotor_components': False,
'differentiable': False,
'verbose': False,
'variant': modelvariant
}
prob = om.Problem(
model=AEPGroup(nTurbines=nTurbines,
nDirections=nDirections,
wake_model=jensen_wrapper,
wake_model_options=wake_model_options,
params_IdepVar_func=add_jensen_params_IndepVarComps,
cp_points=cp_curve_cp.size,
params_IdepVar_args={'use_angle': False}))
# initialize problem
prob.setup(check=False)
# assign values to turbine states
prob['turbineX'] = turbineX
prob['turbineY'] = turbineY
prob['yaw0'] = yaw
# assign values to constant inputs (not design variables)
prob['rotorDiameter'] = rotorDiameter
prob['axialInduction'] = axialInduction
prob['generatorEfficiency'] = generatorEfficiency
prob['windSpeeds'] = np.array([wind_speed])
prob['air_density'] = air_density
prob['windDirections'] = np.array([wind_direction])
prob['windFrequencies'] = np.array([wind_frequency])
prob['Ct_in'] = Ct
prob['Cp_in'] = Cp
prob['cp_curve_cp'] = cp_curve_cp
prob['cp_curve_wind_speed'] = cp_curve_wind_speed
# prob['model_params:spread_angle'] = 20.0
# prob['model_params:alpha'] = 0.1
# assign values to turbine states
prob['cut_in_speed'] = np.ones(nTurbines) * cut_in_speed
prob['cut_out_speed'] = np.ones(nTurbines) * cut_out_speed
prob['rated_power'] = np.ones(nTurbines) * rated_power
prob['rated_wind_speed'] = np.ones(nTurbines) * rated_wind_speed
prob['use_power_curve_definition'] = True
prob['gen_params:CTcorrected'] = True
prob['gen_params:CPcorrected'] = True
# run the problem
prob.run_model()
return prob
def run_opt(layout_number, wec_method_number, wake_model, opt_alg_number,
max_wec, nsteps):
OPENMDAO_REQUIRE_MPI = False
run_number = layout_number
model = wake_model
# set model
MODELS = ['FLORIS', 'BPA', 'JENSEN', 'LARSEN']
print(MODELS[model])
# select optimization approach/method
opt_algs = ['snopt', 'ga', 'ps']
opt_algorithm = opt_algs[opt_alg_number]
# select wec method
wec_methods = ['none', 'diam', 'angle', 'hybrid']
wec_method = wec_methods[wec_method_number]
# pop_size = 760
# save and show options
show_start = True
show_end = True
save_start = False
save_end = False
save_locations = True
save_aep = True
save_time = True
rec_func_calls = True
input_directory = "../../../input_files/"
# set options for BPA
print_ti = False
sort_turbs = True
# turbine_type = 'NREL5MW' #can be 'V80' or 'NREL5MW'
turbine_type = 'V80' # can be 'V80' or 'NREL5MW'
wake_model_version = 2016
WECH = 0
if wec_method == 'diam':
output_directory = "../output_files/%s_wec_diam_max_wec_%i_nsteps_%.3f/" % (
opt_algorithm, max_wec, nsteps)
relax = True
# expansion_factors = np.array([3, 2.75, 2.5, 2.25, 2.0, 1.75, 1.5, 1.25, 1.0, 1.0])
expansion_factors = np.linspace(1.0, max_wec, nsteps)
expansion_factors = np.flip(expansion_factors)
elif wec_method == 'angle':
output_directory = "../output_files/%s_wec_angle_max_wec_%i_nsteps_%.3f/" % (
opt_algorithm, max_wec, nsteps)
relax = True
# expansion_factors = np.array([50, 40, 30, 20, 10, 0.0, 0.0])
expansion_factors = np.linspace(0.0, max_wec, nsteps)
expansion_factors = np.append(np.flip(expansion_factors), 0.0)
elif wec_method == 'hybrid':
expansion_factors = np.linspace(1.0, max_wec, nsteps)
expansion_factors = np.append(np.flip(expansion_factors), 1.0)
output_directory = "../output_files/%s_wec_hybrid_max_wec_%i_nsteps_%.3f/" % (
opt_algorithm, max_wec, nsteps)
relax = True
WECH = 1
elif wec_method == 'none':
relax = False
expansion_factors = np.array([1.0])
output_directory = "../output_files/%s/" % opt_algorithm
else:
raise ValueError('wec_method must be diam, angle, hybrid, or none')
# create output directory if it does not exist yet
import distutils.dir_util
distutils.dir_util.mkpath(output_directory)
differentiable = True
# for expansion_factor in np.array([5., 4., 3., 2.75, 2.5, 2.25, 2.0, 1.75, 1.5, 1.25, 1.0]):
# for expansion_factor in np.array([20., 15., 10., 5., 4., 3., 2.5, 1.25, 1.0]):
# expansion_factors = np.array([20., 10., 5., 2.5, 1.25, 1.0])
wake_combination_method = 1 # can be [0:Linear freestreem superposition,
# 1:Linear upstream velocity superposition,
# 2:Sum of squares freestream superposition,
# 3:Sum of squares upstream velocity superposition]
ti_calculation_method = 4 # can be [0:No added TI calculations,
# 1:TI by Niayifar and Porte Agel altered by Annoni and Thomas,
# 2:TI by Niayifar and Porte Agel 2016,
# 3:TI by Niayifar and Porte Agel 2016 with added soft max function,
# 4:TI by Niayifar and Porte Agel 2016 using area overlap ratio,
# 5:TI by Niayifar and Porte Agel 2016 using area overlap ratio and SM function]
if wec_method_number > 0:
ti_opt_method = 0 # can be [0:No added TI calculations,
# 1:TI by Niayifar and Porte Agel altered by Annoni and Thomas,
# 2:TI by Niayifar and Porte Agel 2016,
# 3:TI by Niayifar and Porte Agel 2016 with added soft max function,
# 4:TI by Niayifar and Porte Agel 2016 using area overlap ratio,
# 5:TI by Niayifar and Porte Agel 2016 using area overlap ratio and SM function]
else:
ti_opt_method = 0
final_ti_opt_method = 5
if opt_algorithm == 'ps':
ti_opt_method = ti_calculation_method
sm_smoothing = 700.
if ti_calculation_method == 0:
calc_k_star_calc = False
else:
calc_k_star_calc = True
if ti_opt_method == 0:
calc_k_star_opt = False
else:
calc_k_star_opt = True
nRotorPoints = 1
wind_rose_file = 'directional' # can be one of: 'amalia', 'nantucket', 'directional'
TI = 0.108
k_calc = 0.3837 * TI + 0.003678
# k_calc = 0.022
# k_opt = 0.04
shear_exp = 0.31
# air_density = 1.1716 # kg/m^3
air_density = 1.225 # kg/m^3 (from Jen)
if turbine_type == 'V80':
# define turbine size
rotor_diameter = 80. # (m)
hub_height = 70.0
z_ref = 80.0 # m
z_0 = 0.0
# load performance characteristics
cut_in_speed = 4. # m/s
cut_out_speed = 25. # m/s
rated_wind_speed = 16. # m/s
rated_power = 2000. # kW
generator_efficiency = 0.944
ct_curve_data = np.loadtxt(input_directory +
'mfg_ct_vestas_v80_niayifar2016.txt',
delimiter=",")
ct_curve_wind_speed = ct_curve_data[:, 0]
ct_curve_ct = ct_curve_data[:, 1]
# air_density = 1.1716 # kg/m^3
Ar = 0.25 * np.pi * rotor_diameter**2
# cp_curve_wind_speed = ct_curve[:, 0]
power_data = np.loadtxt(input_directory +
'niayifar_vestas_v80_power_curve_observed.txt',
delimiter=',')
# cp_curve_cp = niayifar_power_model(cp_curve_wind_speed)/(0.5*air_density*cp_curve_wind_speed**3*Ar)
cp_curve_cp = power_data[:, 1] * (1E6) / (0.5 * air_density *
power_data[:, 0]**3 * Ar)
cp_curve_wind_speed = power_data[:, 0]
cp_curve_spline = UnivariateSpline(cp_curve_wind_speed,
cp_curve_cp,
ext='const')
cp_curve_spline.set_smoothing_factor(.0001)
elif turbine_type == 'NREL5MW':
# define turbine size
rotor_diameter = 126.4 # (m)
hub_height = 90.0
z_ref = 80.0 # m
z_0 = 0.0
# load performance characteristics
cut_in_speed = 3. # m/s
cut_out_speed = 25. # m/s
rated_wind_speed = 11.4 # m/s
rated_power = 5000. # kW
generator_efficiency = 0.944
filename = input_directory + "NREL5MWCPCT_dict.p"
# filename = "../input_files/NREL5MWCPCT_smooth_dict.p"
import pickle
data = pickle.load(open(filename, "rb"), encoding='latin1')
ct_curve = np.zeros([data['wind_speed'].size, 2])
ct_curve_wind_speed = data['wind_speed']
ct_curve_ct = data['CT']
# cp_curve_cp = data['CP']
# cp_curve_wind_speed = data['wind_speed']
loc0 = np.where(data['wind_speed'] < 11.55)
loc1 = np.where(data['wind_speed'] > 11.7)
cp_curve_cp = np.hstack([data['CP'][loc0], data['CP'][loc1]])
cp_curve_wind_speed = np.hstack(
[data['wind_speed'][loc0], data['wind_speed'][loc1]])
cp_curve_spline = UnivariateSpline(cp_curve_wind_speed,
cp_curve_cp,
ext='const')
cp_curve_spline.set_smoothing_factor(.000001)
else:
raise ValueError("Turbine type is undefined.")
# load starting locations
layout_directory = input_directory
# layout_data = np.loadtxt(layout_directory + "layouts/round_38turbs/nTurbs38_spacing5_layout_%i.txt" % layout_number)
layout_data = np.loadtxt(
layout_directory +
"layouts/grid_16turbs/nTurbs16_spacing5_layout_%i.txt" % layout_number)
# layout_data = np.loadtxt(layout_directory+"layouts/nTurbs9_spacing5_layout_%i.txt" % layout_number)
# turbineX = layout_data[:, 0] * rotor_diameter
# turbineY = layout_data[:, 1] * rotor_diameter
turbineX = np.array([0.0])
turbineY = np.array([0.0])
turbineX_init = np.copy(turbineX)
turbineY_init = np.copy(turbineY)
nTurbines = turbineX.size
boundary_x = np.array(
[0.0, 5. * rotor_diameter * (np.sqrt(nTurbines) - 1) + rotor_diameter])
boundary_y = np.array(
[0.0, 5. * rotor_diameter * (np.sqrt(nTurbines) - 1) + rotor_diameter])
plot_square_farm(turbineX_init,
turbineY_init,
rotor_diameter,
boundary_x,
boundary_y,
boundary_x[1] - boundary_x[0],
show_start=show_start)
# plot_round_farm(turbineX, turbineY, rotor_diameter, [boundary_center_x, boundary_center_y], boundary_radius,
# show_start=show_start)
# quit()
# initialize input variable arrays
nTurbs = nTurbines
rotorDiameter = np.zeros(nTurbs)
hubHeight = np.zeros(nTurbs)
axialInduction = np.zeros(nTurbs)
Ct = np.zeros(nTurbs)
Cp = np.zeros(nTurbs)
generatorEfficiency = np.zeros(nTurbs)
yaw = np.zeros(nTurbs)
minSpacing = 2. # number of rotor diameters
# define initial values
for turbI in range(0, nTurbs):
rotorDiameter[turbI] = rotor_diameter # m
hubHeight[turbI] = hub_height # m
axialInduction[turbI] = 1.0 / 3.0
Ct[turbI] = 4.0 * axialInduction[turbI] * (1.0 - axialInduction[turbI])
# print(Ct)
Cp[turbI] = 4.0 * 1.0 / 3.0 * np.power((1 - 1.0 / 3.0), 2)
generatorEfficiency[turbI] = generator_efficiency
yaw[turbI] = 0. # deg.
# Define flow properties
if wind_rose_file is 'nantucket':
# windRose = np.loadtxt(input_directory + 'nantucket_windrose_ave_speeds.txt')
windRose = np.loadtxt(input_directory +
'nantucket_wind_rose_for_LES.txt')
windDirections = windRose[:, 0]
windSpeeds = windRose[:, 1]
windFrequencies = windRose[:, 2]
size = np.size(windDirections)
elif wind_rose_file is 'amalia':
windRose = np.loadtxt(
input_directory +
'windrose_amalia_directionally_averaged_speeds.txt')
windDirections = windRose[:, 0]
windSpeeds = windRose[:, 1]
windFrequencies = windRose[:, 2]
size = np.size(windDirections)
elif wind_rose_file is 'directional':
windRose = np.loadtxt(input_directory + 'directional_windrose.txt')
windDirections = windRose[:, 0]
windSpeeds = windRose[:, 1]
windFrequencies = windRose[:, 2]
size = np.size(windDirections)
elif wind_rose_file is '1d':
windDirections = np.array([270.])
windSpeeds = np.array([8.0])
windFrequencies = np.array([1.0])
size = np.size(windDirections)
else:
size = 20
windDirections = np.linspace(0, 270, size)
windFrequencies = np.ones(size) / size
wake_model_options = {
'nSamples': 0,
'nRotorPoints': nRotorPoints,
'use_ct_curve': True,
'ct_curve_ct': ct_curve_ct,
'ct_curve_wind_speed': ct_curve_wind_speed,
'interp_type': 1,
'use_rotor_components': False,
'differentiable': differentiable,
'verbose': False,
'variant': "CosineFortran"
}
nVertices = 0
if MODELS[model] == 'BPA':
# initialize problem
prob = om.Problem(
model=AEPGroup(nTurbines=nTurbs,
nDirections=windDirections.size,
use_rotor_components=False,
wake_model=gauss_wrapper,
params_IdepVar_func=add_gauss_params_IndepVarComps,
params_IdepVar_args={'nRotorPoints': nRotorPoints},
wake_model_options=wake_model_options,
cp_points=cp_curve_cp.size,
cp_curve_spline=cp_curve_spline,
record_function_calls=True,
runparallel=False))
elif MODELS[model] == 'FLORIS':
# initialize problem
prob = om.Problem(
model=AEPGroup(nTurbines=nTurbs,
nDirections=windDirections.size,
use_rotor_components=False,
wake_model=floris_wrapper,
wake_model_options=wake_model_options,
params_IdepVar_func=add_floris_params_IndepVarComps,
params_IdepVar_args={},
record_function_calls=True))
elif MODELS[model] == 'JENSEN':
# initialize problem
prob = om.Problem(
model=AEPGroup(nTurbines=nTurbs,
nDirections=windDirections.size,
use_rotor_components=False,
wake_model=jensen_wrapper,
wake_model_options=wake_model_options,
params_IdepVar_func=add_jensen_params_IndepVarComps,
cp_points=cp_curve_cp.size,
cp_curve_spline=cp_curve_spline,
params_IdepVar_args={},
runparallel=False,
record_function_calls=True))
else:
ValueError(
'The %s model is not currently available. Please select BPA or FLORIS'
% (MODELS[model]))
# prob.model.deriv_options['type'] = 'fd'
# prob.model.deriv_options['form'] = 'central'
# prob.model.deriv_options['step_size'] = 1.0e-8
# prob.model.linear_solver = om.LinearBlockGS()
# prob.model.linear_solver.options['iprint'] = 0
# prob.model.linear_solver.options['maxiter'] = 5
#
# prob.model.nonlinear_solver = om.NonlinearBlockGS()
# prob.model.nonlinear_solver.options['iprint'] = 0
# prob.model.linear_solver = om.DirectSolver()
#
# prob.driver = om.pyOptSparseDriver()
#
# if opt_algorithm == 'snopt':
# # set up optimizer
# prob.driver.options['optimizer'] = 'SNOPT'
# prob.driver.options['gradient method'] = 'snopt_fd'
#
# # set optimizer options
# prob.driver.opt_settings['Verify level'] = 1
# # set optimizer options
# prob.driver.opt_settings['Major optimality tolerance'] = 1e-4
#
# prob.driver.opt_settings[
# 'Print file'] = output_directory + 'SNOPT_print_multistart_%iturbs_%sWindRose_%idirs_%sModel_RunID%i.out' % (
# nTurbs, wind_rose_file, size, MODELS[model], run_number)
# prob.driver.opt_settings[
# 'Summary file'] = output_directory + 'SNOPT_summary_multistart_%iturbs_%sWindRose_%idirs_%sModel_RunID%i.out' % (
# nTurbs, wind_rose_file, size, MODELS[model], run_number)
#
# prob.model.add_constraint('sc', lower=np.zeros(int(((nTurbs - 1.) * nTurbs / 2.))), scaler=1E-2) # ,
# # active_tol=(2. * rotor_diameter) ** 2)
# # prob.model.add_constraint('boundaryDistances', lower=(np.zeros(1 * turbineX.size)), scaler=1E-2) # ,
# # active_tol=2. * rotor_diameter)
#
# prob.driver.options['dynamic_derivs_sparsity'] = True
#
# elif opt_algorithm == 'ga':
#
# prob.driver.options['optimizer'] = 'NSGA2'
#
# prob.driver.opt_settings['PrintOut'] = 1
#
# prob.driver.opt_settings['maxGen'] = 50000
#
# prob.driver.opt_settings['PopSize'] = 10 * nTurbines * 2
#
# # prob.driver.opt_settings['pMut_real'] = 0.001
#
# prob.driver.opt_settings['xinit'] = 1
#
# prob.driver.opt_settings['rtol'] = 1E-4
#
# prob.driver.opt_settings['atol'] = 1E-4
#
# prob.driver.opt_settings['min_tol_gens'] = 200
#
# prob.driver.opt_settings['file_number'] = run_number
#
# prob.model.add_constraint('sc', lower=np.zeros(int(((nTurbs - 1.) * nTurbs / 2.))), scaler=1E-2)
# # prob.model.add_constraint('boundaryDistances', lower=(np.zeros(1 * turbineX.size)), scaler=1E-2)
#
#
# elif opt_algorithm == 'ps':
#
# prob.driver.options['optimizer'] = 'ALPSO'
#
# prob.driver.opt_settings['fileout'] = 1
#
# prob.driver.opt_settings[
#
# 'filename'] = output_directory + 'ALPSO_summary_multistart_%iturbs_%sWindRose_%idirs_%sModel_RunID%i.out' % (
#
# nTurbs, wind_rose_file, size, MODELS[model], run_number)
#
# prob.driver.opt_settings['maxOuterIter'] = 10000
#
# prob.driver.opt_settings['SwarmSize'] = 24
#
# prob.driver.opt_settings['xinit'] = 1 # Initial Position Flag (0 - no position, 1 - position given)
#
# prob.driver.opt_settings[
# 'Scaling'] = 1 # Design Variables Scaling Flag (0 - no scaling, 1 - scaling between [-1, 1])
#
# # prob.driver.opt_settings['rtol'] = 1E-3 # Relative Tolerance for Lagrange Multipliers
# #
# # prob.driver.opt_settings['atol'] = 1E-2 # Absolute Tolerance for Lagrange Function
# #
# # prob.driver.opt_settings['dtol'] = 1E-1 # Relative Tolerance in Distance of All Particles to Terminate (GCPSO)
# #
# # prob.driver.opt_settings['itol'] = 1E-3 # Absolute Tolerance for Inequality constraints
# #
# # prob.driver.opt_settings['dynInnerIter'] = 1 # Dynamic Number of Inner Iterations Flag
#
# prob.model.add_constraint('sc', lower=np.zeros(int(((nTurbs - 1.) * nTurbs / 2.))), scaler=1E-2)
# # prob.model.add_constraint('boundaryDistances', lower=(np.zeros(1 * turbineX.size)), scaler=1E-2)
#
# # prob.driver.add_objective('obj', scaler=1E0)
# prob.model.add_objective('obj', scaler=1E0)
#
# # select design variables
# prob.model.add_design_var('turbineX', scaler=1E1, lower=np.ones(nTurbines) * (boundary_x[0] + rotor_diameter / 2.),
# upper=np.ones(nTurbines) * (boundary_x[1] - rotor_diameter / 2.))
# prob.model.add_design_var('turbineY', scaler=1E1, lower=np.ones(nTurbines) * (boundary_y[0] + rotor_diameter / 2.),
# upper=np.ones(nTurbines) * (boundary_y[1] - rotor_diameter / 2.))
# prob.model.ln_solver.options['single_voi_relevance_reduction'] = True
# prob.model.ln_solver.options['mode'] = 'rev'
# if run_number == 0:
# # set up recorder
# recorder = SqliteRecorder(output_directory+'recorder_database_run%i' % run_number)
# recorder.options['record_params'] = True
# recorder.options['record_metadata'] = False
# recorder.options['record_unknowns'] = True
# recorder.options['record_derivs'] = False
# recorder.options['includes'] = ['turbineX', 'turbineY', 'AEP']
# prob.driver.add_recorder(recorder)
# recorder = om.SqliteRecorder(output_directory + 'recorded_data.sql')
# # prob.driver.add_recorder(recorder)
# # prob.driver.recording_options['includes'] = ['']
# # prob.driver.recording_options['excludes'] = ['*']
# # prob.driver.recording_options['record_constraints'] = False
# # prob.driver.recording_options['record_derivatives'] = False
# # prob.driver.recording_options['record_desvars'] = False
# # prob.driver.recording_options['record_inputs'] = False
# # prob.driver.recording_options['record_model_metadata'] = True
# # prob.driver.recording_options['record_objectives'] = False
# # prob.driver.recording_options['record_responses'] = False
# set up profiling
# from plantenergy.GeneralWindFarmComponents import WindFarmAEP
# methods = [
# ('*', (WindFarmAEP,))
# ]
#
# iprofile.setup(methods=methods)
print("almost time for setup")
tic = time.time()
print("entering setup at time = ", tic)
prob.setup(check=True)
toc = time.time()
print("setup complete at time = ", toc)
# print the results
print(('Problem setup took %.03f sec.' % (toc - tic)))
# assign initial values to design variables
prob['turbineX'] = np.copy(turbineX)
prob['turbineY'] = np.copy(turbineY)
for direction_id in range(0, windDirections.size):
prob['yaw%i' % direction_id] = yaw
# assign values to constant inputs (not design variables)
prob['rotorDiameter'] = rotorDiameter
prob['hubHeight'] = hubHeight
prob['axialInduction'] = axialInduction
prob['generatorEfficiency'] = generatorEfficiency
prob['windSpeeds'] = windSpeeds
prob['air_density'] = air_density
prob['windDirections'] = windDirections
prob['windFrequencies'] = windFrequencies
prob['Ct_in'] = Ct
prob['Cp_in'] = Cp
prob['cp_curve_cp'] = cp_curve_cp
prob['cp_curve_wind_speed'] = cp_curve_wind_speed
cutInSpeeds = np.ones(nTurbines) * cut_in_speed
prob['cut_in_speed'] = cutInSpeeds
ratedPowers = np.ones(nTurbines) * rated_power
prob['rated_power'] = ratedPowers
# assign values to turbine states
prob['cut_in_speed'] = np.ones(nTurbines) * cut_in_speed
prob['cut_out_speed'] = np.ones(nTurbines) * cut_out_speed
prob['rated_power'] = np.ones(nTurbines) * rated_power
prob['rated_wind_speed'] = np.ones(nTurbines) * rated_wind_speed
prob['use_power_curve_definition'] = True
# assign boundary values
# prob['boundary_center'] = np.array([boundary_center_x, boundary_center_y])
# prob['boundary_radius'] = boundary_radius
if MODELS[model] is 'BPA':
prob['model_params:wake_combination_method'] = np.copy(
wake_combination_method)
prob['model_params:ti_calculation_method'] = np.copy(
ti_calculation_method)
prob['model_params:wake_model_version'] = np.copy(wake_model_version)
prob['model_params:wec_factor'] = 1.0
prob['model_params:wec_spreading_angle'] = 0.0
prob['model_params:calc_k_star'] = np.copy(calc_k_star_calc)
prob['model_params:sort'] = np.copy(sort_turbs)
prob['model_params:z_ref'] = np.copy(z_ref)
prob['model_params:z_0'] = np.copy(z_0)
prob['model_params:ky'] = np.copy(k_calc)
prob['model_params:kz'] = np.copy(k_calc)
prob['model_params:print_ti'] = np.copy(print_ti)
prob['model_params:shear_exp'] = np.copy(shear_exp)
prob['model_params:I'] = np.copy(TI)
prob['model_params:sm_smoothing'] = np.copy(sm_smoothing)
prob['model_params:WECH'] = WECH
if nRotorPoints > 1:
prob['model_params:RotorPointsY'], prob[
'model_params:RotorPointsZ'] = sunflower_points(nRotorPoints)
if MODELS[model] is 'JENSEN':
prob['model_params:alpha'] = 0.1
prob['model_params:wec_factor'] = 1.0
prob.run_model()
AEP_init_calc = np.copy(prob['AEP'])
print(AEP_init_calc * 1E-6)
if MODELS[model] is 'BPA':
prob['model_params:ti_calculation_method'] = np.copy(ti_opt_method)
prob['model_params:calc_k_star'] = np.copy(calc_k_star_opt)
prob.run_model()
AEP_init_opt = np.copy(prob['AEP'])
AEP_run_opt = np.copy(AEP_init_opt)
print(AEP_init_opt * 1E-6)
#
# config.obj_func_calls_array[:] = 0.0
# config.sens_func_calls_array[:] = 0.0
#
# expansion_factor_last = 0.0
#
# tict = time.time()
# if relax:
# for expansion_factor, i in zip(expansion_factors, np.arange(0, expansion_factors.size)): # best so far
# # print("func calls: ", config.obj_func_calls_array, np.sum(config.obj_func_calls_array))
# # print("grad func calls: ", config.sens_func_calls_array, np.sum(config.sens_func_calls_array))
# # AEP_init_run_opt = prob['AEP']
#
# if expansion_factor_last == expansion_factor:
# ti_opt_method = np.copy(final_ti_opt_method)
#
# print("starting run with exp. fac = ", expansion_factor)
#
# if opt_algorithm == 'snopt':
# prob.driver.opt_settings['Print file'] = output_directory + \
# 'SNOPT_print_multistart_%iturbs_%sWindRose_%idirs_%sModel_RunID%i_EF%.3f_TItype%i.out' % (
# nTurbs, wind_rose_file, size, MODELS[model], run_number,
# expansion_factor, ti_opt_method)
#
# prob.driver.opt_settings['Summary file'] = output_directory + \
# 'SNOPT_summary_multistart_%iturbs_%sWindRose_%idirs_%sModel_RunID%i_EF%.3f_TItype%i.out' % (
# nTurbs, wind_rose_file, size, MODELS[model], run_number,
# expansion_factor, ti_opt_method)
# elif opt_algorithm == 'ps':
# prob.driver.opt_settings[
# 'filename'] = output_directory + 'ALPSO_summary_multistart_%iturbs_%sWindRose_%idirs_%sModel_RunID%i.out' % (
# nTurbs, wind_rose_file, size, MODELS[model], run_number)
#
# turbineX = np.copy(prob['turbineX'])
# turbineY = np.copy(prob['turbineY'])
# prob['turbineX'] = np.copy(turbineX)
# prob['turbineY'] = np.copy(turbineY)
#
# if MODELS[model] is 'BPA':
# prob['model_params:ti_calculation_method'] = np.copy(ti_opt_method)
# prob['model_params:calc_k_star'] = np.copy(calc_k_star_opt)
# if wec_method == 'diam':
# prob['model_params:wec_factor'] = np.copy(expansion_factor)
# elif wec_method == "hybrid":
# prob['model_params:wec_factor'] = np.copy(expansion_factor)
# elif wec_method == 'angle':
# prob['model_params:wec_spreading_angle'] = np.copy(expansion_factor)
# if MODELS[model] is 'JENSEN':
# prob['model_params:wec_factor'] = np.copy(expansion_factor)
#
# # run the problem
# print('start %s run' % (MODELS[model]))
# tic = time.time()
# # iprofile.start()
# config.obj_func_calls_array[prob.comm.rank] = 0.0
# config.sens_func_calls_array[prob.comm.rank] = 0.0
# prob.run_driver()
# # quit()
# toc = time.time()
# obj_calls = np.copy(config.obj_func_calls_array[0])
# sens_calls = np.copy(config.sens_func_calls_array[0])
# # iprofile.stop()
# toc = time.time()
# # print(np.sum(config.obj_func_calls_array))
# # print(np.sum(config.sens_func_calls_array))
# print('end %s run' % (MODELS[model]))
#
# run_time = toc - tic
# # print(run_time, expansion_factor)
#
# AEP_run_opt = np.copy(prob['AEP'])
# # print("AEP improvement = ", AEP_run_opt / AEP_init_opt)
#
# if MODELS[model] is 'BPA':
# prob['model_params:wec_factor'] = 1.0
# prob['model_params:wec_spreading_angle'] = 0.0
# prob['model_params:ti_calculation_method'] = np.copy(ti_calculation_method)
# prob['model_params:calc_k_star'] = np.copy(calc_k_star_calc)
# if MODELS[model] is 'JENSEN':
# prob['model_params:wec_factor'] = 1.0
#
#
# prob.run_model()
# AEP_run_calc = np.copy(prob['AEP'])
# # print("compare: ", aep_run, prob['AEP'])
# print("AEP calc improvement = ", AEP_run_calc / AEP_init_calc)
#
# if prob.model.comm.rank == 0:
# # if save_aep:
# # np.savetxt(output_directory + '%s_multistart_aep_results_%iturbs_%sWindRose_%idirs_%sModel_RunID%i_EF%.3f.txt' % (
# # opt_algorithm, nTurbs, wind_rose_file, size, MODELS[model], run_number, expansion_factor),
# # np.c_[AEP_init, prob['AEP']],
# # header="Initial AEP, Final AEP")
# if save_locations:
# np.savetxt(
# output_directory + '%s_multistart_locations_%iturbs_%sWindRose_%idirs_%s_run%i_EF%.3f_TItype%i.txt' % (
# opt_algorithm, nTurbs, wind_rose_file, size, MODELS[model], run_number, expansion_factor,
# ti_opt_method),
# np.c_[turbineX_init, turbineY_init, prob['turbineX'], prob['turbineY']],
# header="initial turbineX, initial turbineY, final turbineX, final turbineY")
# # if save_time:
# # np.savetxt(output_directory + '%s_multistart_time_%iturbs_%sWindRose_%idirs_%s_run%i_EF%.3f.txt' % (
# # opt_algorithm, nTurbs, wind_rose_file, size, MODELS[model], run_number, expansion_factor),
# # np.c_[run_time],
# # header="run time")
# if save_time and save_aep and rec_func_calls:
# output_file = output_directory + '%s_multistart_rundata_%iturbs_%sWindRose_%idirs_%s_run%i.txt' \
# % (opt_algorithm, nTurbs, wind_rose_file, size, MODELS[model], run_number)
# f = open(output_file, "a")
#
# if i == 0:
# header = "run number, exp fac, ti calc, ti opt, aep init calc (kW), aep init opt (kW), " \
# "aep run calc (kW), aep run opt (kW), run time (s), obj func calls, sens func calls"
# else:
# header = ''
#
# np.savetxt(f, np.c_[run_number, expansion_factor, ti_calculation_method, ti_opt_method,
# AEP_init_calc, AEP_init_opt, AEP_run_calc, AEP_run_opt, run_time,
# obj_calls, sens_calls],
# header=header)
# f.close()
# expansion_factor_last = expansion_factor
# else:
# for expansion_factor, i in zip(expansion_factors, np.arange(0, expansion_factors.size)): # best so far
# # print("func calls: ", config.obj_func_calls_array, np.sum(config.obj_func_calls_array))
# # print("grad func calls: ", config.sens_func_calls_array, np.sum(config.sens_func_calls_array))
# # AEP_init_run_opt = prob['AEP']
#
# if expansion_factor_last == expansion_factor:
# ti_opt_method = np.copy(final_ti_opt_method)
#
# if opt_algorithm == 'snopt':
# prob.driver.opt_settings['Print file'] = output_directory + \
# 'SNOPT_print_multistart_%iturbs_%sWindRose_%idirs_%sModel_RunID%i_TItype%i.out' % (
# nTurbs, wind_rose_file, size, MODELS[model], run_number, ti_opt_method)
#
# prob.driver.opt_settings['Summary file'] = output_directory + \
# 'SNOPT_summary_multistart_%iturbs_%sWindRose_%idirs_%sModel_RunID%i_TItype%i.out' % (
# nTurbs, wind_rose_file, size, MODELS[model], run_number, ti_opt_method)
#
# print("starting run with exp. fac = ", expansion_factor)
# # run the problem
# print('start %s run' % (MODELS[model]))
# # cProfile.run('prob.run_driver()')
# if MODELS[model] is 'BPA':
# # prob['model_params:wec_factor'] = 1.
# prob['model_params:ti_calculation_method'] = np.copy(ti_opt_method)
# prob['model_params:calc_k_star'] = np.copy(calc_k_star_opt)
#
# tic = time.time()
# # cProfile.run('prob.run_driver()')
# config.obj_func_calls_array[prob.comm.rank] = 0.0
# config.sens_func_calls_array[prob.comm.rank] = 0.0
# prob.run_driver()
# # quit()
# toc = time.time()
# obj_calls = np.copy(config.obj_func_calls_array[0])
# sens_calls = np.copy(config.sens_func_calls_array[0])
#
# run_time = toc - tic
#
# AEP_run_opt = np.copy(prob['AEP'])
# # print("AEP improvement = ", AEP_run_calc / AEP_init_calc)
#
# if MODELS[model] is 'BPA':
# prob['model_params:wec_factor'] = 1.0
# prob['model_params:wec_spreading_angle'] = 0.0
# prob['model_params:ti_calculation_method'] = np.copy(ti_calculation_method)
# prob['model_params:calc_k_star'] = np.copy(calc_k_star_calc)
# if MODELS[model] is 'JENSEN':
# prob['model_params:wec_factor'] = 1.0
#
# prob.run_model()
# AEP_run_calc = np.copy(prob['AEP'])
#
# if prob.model.comm.rank == 0:
#
# if save_locations:
# np.savetxt(output_directory + '%s_multistart_locations_%iturbs_%sWindRose_%idirs_%s_run%i_TItype%i.txt' % (
# opt_algorithm, nTurbs, wind_rose_file, size, MODELS[model], run_number, ti_opt_method),
# np.c_[turbineX_init, turbineY_init, prob['turbineX'], prob['turbineY']],
# header="initial turbineX, initial turbineY, final turbineX, final turbineY")
#
# if save_time and save_aep and rec_func_calls:
# output_file = output_directory + '%s_multistart_rundata_%iturbs_%sWindRose_%idirs_%s_run%i_TItype%i.txt' \
# % (opt_algorithm, nTurbs, wind_rose_file, size, MODELS[model], run_number, ti_opt_method)
# f = open(output_file, "a")
#
# header = "run number, ti calc, ti opt, aep init calc (kW), aep init opt (kW), " \
# "aep run calc (kW), aep run opt (kW), run time (s), obj func calls, sens func calls"
#
# np.savetxt(f, np.c_[run_number, ti_calculation_method, ti_opt_method,
# AEP_init_calc, AEP_init_opt, AEP_run_calc, AEP_run_opt, run_time,
# obj_calls, sens_calls],
# header=header)
# f.close()
# expansion_factor_last = expansion_factor
#
# turbineX_end = np.copy(prob['turbineX'])
# turbineY_end = np.copy(prob['turbineY'])
#
# toct = time.time()
# total_time = toct - tict
if prob.model.comm.rank == 0:
# print the results
# print(('Opt. calculation took %.03f sec.' % (toct - tict)))
# for direction_id in range(0, windDirections.size):
# print('yaw%i (deg) = ' % direction_id, prob['yaw%i' % direction_id])
print('turbine X positions in wind frame (m): %s' % prob['turbineX'])
print('turbine Y positions in wind frame (m): %s' % prob['turbineY'])
# print('wind farm power in each direction (kW): %s' % prob['dirPowers'])
print('Initial AEP (kWh): %s' % AEP_init_opt)
# plot_square_farm(turbineX_end, turbineY_end, rotor_diameter, boundary_x, boundary_y, boundary_x[1] - boundary_x[0],
# show_start=show_start)
return 0
def setUp(self):
try:
from plantenergy.gauss import gauss_wrapper, add_gauss_params_IndepVarComps
self.working_import = True
except:
self.working_import = False
# define turbine locations in global reference frame
# this is the 5 deg rotated from free stream wind farm from Gebraad 2014 CFD study
# wind to 30 deg from east, farm rotated to 35 deg from east
turbineX = np.array([1164.7, 947.2, 1682.4, 1464.9, 1982.6, 2200.1])
turbineY = np.array([1024.7, 1335.3, 1387.2, 1697.8, 2060.3, 1749.7])
hubHeight = np.zeros_like(turbineX)+90.
# import matplotlib.pyplot as plt
# plt.plot(turbineX, turbineY, 'o')
# print np.arctan((turbineY[5]-turbineY[0])/(turbineX[5]-turbineX[0]))*180./np.pi
# print 0.523599*180./np.pi
# plt.show()
# quit()
# plt.plot(turbineX, turbineY, 'o')
# plt.plot(np.array([0.0, ]))
# plt.show()
# 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)
# define initial values
for turbI in range(0, nTurbines):
rotorDiameter[turbI] = 126.4 # 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] = 1.#0.944
yaw[turbI] = 0. # deg.
# Define flow properties
nDirections = 1
wind_speed = 8.0 # m/s
air_density = 1.1716 # kg/m^3
wind_direction = 270.-0.523599*180./np.pi # deg (N = 0 deg., using direction FROM, as in met-mast data)
wind_frequency = 1. # probability of wind in this direction at this speed
# set up problem
nRotorPoints = 100
# define turbine size
rotor_diameter = 126.4 # (m)
hub_height = 90.0
z_ref = 80.0 # m
z_0 = 0.0
# load performance characteristics
cut_in_speed = 3. # m/s
rated_power = 5000. # kW
generator_efficiency = 0.944
input_directory = "./input_files/"
filename = input_directory + "NREL5MWCPCT_dict.p"
# filename = "../input_files/NREL5MWCPCT_smooth_dict.p"
import cPickle as pickle
data = pickle.load(open(filename, "rb"))
ct_curve = np.zeros([data['wind_speed'].size, 2])
ct_curve_wind_speed = data['wind_speed']
ct_curve_ct = data['CT']
# cp_curve_cp = data['CP']
# cp_curve_wind_speed = data['wind_speed']
loc0 = np.where(data['wind_speed'] < 11.55)
loc1 = np.where(data['wind_speed'] > 11.7)
cp_curve_cp = np.hstack([data['CP'][loc0], data['CP'][loc1]])
cp_curve_wind_speed = np.hstack([data['wind_speed'][loc0], data['wind_speed'][loc1]])
cp_curve_spline = UnivariateSpline(cp_curve_wind_speed, cp_curve_cp, ext='const')
cp_curve_spline.set_smoothing_factor(.000001)
wake_model_options = {'nSamples': 0,
'nRotorPoints': nRotorPoints,
'use_ct_curve': True,
'ct_curve_ct': ct_curve_ct,
'ct_curve_wind_speed': ct_curve_wind_speed,
'interp_type': 1,
'use_rotor_components': False,
'differentiable': True,
'verbose': False}
prob = Problem(root=AEPGroup(nTurbines=nTurbines, nDirections=nDirections, wake_model=gauss_wrapper,
wake_model_options=wake_model_options, datasize=0, use_rotor_components=False,
params_IdepVar_func=add_gauss_params_IndepVarComps, differentiable=True,
params_IndepVar_args={'nRotorPoints': nRotorPoints}))
# initialize problem
prob.setup(check=True)
if nRotorPoints > 1:
from plantenergy.utilities import sunflower_points
prob['model_params:RotorPointsY'], prob['model_params:RotorPointsZ'] = sunflower_points(nRotorPoints)
# assign values to turbine states
prob['turbineX'] = turbineX
prob['turbineY'] = turbineY
prob['hubHeight'] = hubHeight
prob['yaw0'] = yaw
# assign values to constant inputs (not design variables)
prob['rotorDiameter'] = rotorDiameter
prob['axialInduction'] = axialInduction
prob['generatorEfficiency'] = generatorEfficiency
prob['windSpeeds'] = np.array([wind_speed])
prob['model_params:I'] = 0.06
prob['model_params:z_ref'] = 90.
prob['model_params:z_0'] = 0.001
prob['model_params:wake_model_version'] = 2016.
prob['air_density'] = air_density
prob['windDirections'] = np.array([wind_direction])
prob['windFrequencies'] = np.array([wind_frequency])
prob['cp_curve_cp'] = cp_curve_cp
prob['cp_curve_wind_speed'] = cp_curve_wind_speed
cutInSpeeds = np.ones(nTurbines) * cut_in_speed
prob['cut_in_speed'] = cutInSpeeds
ratedPowers = np.ones(nTurbines) * rated_power
prob['rated_power'] = ratedPowers
prob['model_params:wake_combination_method'] = 1
prob['model_params:ti_calculation_method'] = 4
prob['model_params:wake_model_version'] = 2016
prob['model_params:wec_factor'] = 1.0
prob['model_params:calc_k_star'] = True
prob['model_params:sort'] = True
prob['model_params:z_ref'] = z_ref
prob['model_params:z_0'] = z_0
prob['model_params:ky'] = 0.022
prob['model_params:kz'] = 0.022
prob['model_params:print_ti'] = False
prob['model_params:shear_exp'] = 0.15
prob['model_params:I'] = 0.06
prob['model_params:sm_smoothing'] = 700
if nRotorPoints > 1:
prob['model_params:RotorPointsY'], prob['model_params:RotorPointsZ'] = sunflower_points(nRotorPoints)
prob['model_params:wec_spreading_angle'] = 0.0
prob['Ct_in'] = Ct
prob['Cp_in'] = Cp
# run the problem
prob.run()
self.prob = prob