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.append(np.flip(expansion_factors), 1.0)
conv_tols = np.array([1E-2, 1E-2, 1E-2, 1E-2, 1E-2, 1E-2, 1E-3])
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
if opt_algorithm == "ps":
expansion_factors = np.array([1.0])
conv_tols = np.array([1E-3])
else:
expansion_factors = np.array([1.0, 1.0])
conv_tols = np.array([1E-2, 1E-3])
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_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])
if show_start:
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 == '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 == '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 == '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 == '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
}
nVertices = 0
if MODELS[model] == 'BPA':
# initialize problem
prob = om.Problem(
model=OptAEP(nTurbines=nTurbs,
nDirections=windDirections.size,
nVertices=nVertices,
minSpacing=minSpacing,
differentiable=differentiable,
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=OptAEP(nTurbines=nTurbs,
nDirections=windDirections.size,
nVertices=nVertices,
minSpacing=minSpacing,
differentiable=differentiable,
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=OptAEP(nTurbines=nTurbs, nDirections=windDirections.size, nVertices=nVertices,
# minSpacing=minSpacing, differentiable=False, use_rotor_components=False,
# wake_model=jensen_wrapper,
# params_IdepVar_func=add_jensen_params_IndepVarComps,
# params_IdepVar_args={},
# 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'] = np.float(1e-3)
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['stopIters'] = 10
# prob.driver.opt_settings['minInnerIter'] = 100
prob.driver.opt_settings['SwarmSize'] = 25
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'] = 0.01 # 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=1E-4)
# select design variables
prob.model.add_design_var(
'turbineX',
scaler=1E0,
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=1E0,
lower=np.ones(nTurbines) * (boundary_y[0] + rotor_diameter / 2.),
upper=np.ones(nTurbines) * (boundary_y[1] - rotor_diameter / 2.))
# prob.driver.recording_options['include'] = ['obj']
# 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)
driver_recorder = om.SqliteRecorder(output_directory +
'recorded_data_driver_%s.sql' %
(run_number))
# model_recorder = om.SqliteRecorder(output_directory + 'recorded_data_model_%s.sql' %(run_number))
prob.driver.add_recorder(driver_recorder)
# prob.model.add_recorder(model_recorder)
prob.driver.recording_options['record_constraints'] = False
prob.driver.recording_options['record_derivatives'] = False
prob.driver.recording_options['record_desvars'] = True
prob.driver.recording_options['record_inputs'] = False
prob.driver.recording_options['record_model_metadata'] = True
prob.driver.recording_options['record_objectives'] = True
prob.driver.recording_options['includes'] = ['AEP']
prob.driver.recording_options['record_responses'] = False
#
# prob_recorder = om.SqliteRecorder(output_directory + 'recorded_data_prob_%s.sql' %(run_number))
# prob.add_recorder(prob_recorder)
# prob.recording_options['includes'] = []
# prob.recording_options['record_objectives'] = True
# prob.model.recording_options['record_constraints'] = False
# prob.model.recording_options['record_derivatives'] = False
# prob.model.recording_options['record_desvars'] = False
# prob.model.recording_options['record_inputs'] = False
# prob.model.recording_options['record_model_metadata'] = False
# prob.model.recording_options['record_objectives'] = True
# prob.model.recording_options['includes'] = ['AEP']
# prob.model.recording_options['record_responses'] = False
# prob.set_solver_print(0)
# prob.record_iteration('all')
# 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] == '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)
modelruns = 0
prob.run_model(case_prefix='ModelRun%i' % modelruns)
AEP_init_calc = np.copy(prob['AEP'])
print(AEP_init_calc * 1E-6)
if MODELS[model] == 'BPA':
prob['model_params:ti_calculation_method'] = np.copy(ti_opt_method)
prob['model_params:calc_k_star'] = np.copy(calc_k_star_opt)
modelruns += 1
prob.run_model(case_prefix='ModelRun%i' % modelruns)
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
driverruns = 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)
calc_k_star_opt = True
print("starting run with exp. fac = ", expansion_factor)
if opt_algorithm == 'snopt':
# adjust convergence tolerance
prob.driver.opt_settings['Major optimality tolerance'] = float(
conv_tols[i])
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] == '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)
# 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(case_prefix='DriverRun%i' % driverruns)
driverruns += 1
# 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] == '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)
modelruns += 1
prob.run_model(case_prefix='ModelRun%i' % modelruns)
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
#
# cr = om.CaseReader(output_directory + 'recorded_data.sql')
#
# driver_cases = cr.list_cases('driver')
# nits = len(driver_cases)
# objectives = np.zeros(nits)
# calls = np.zeros(nits)
# for i in np.arange(0, nits):
# case = cr.get_case(driver_cases[i])
# print(case)
# objectives[i] = np.copy(case['obj'])
# calls[i] = i
# plt.plot(objectives)
# plt.scatter(calls, objectives)
# plt.show()
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)
calc_k_star_opt = True
if opt_algorithm == 'snopt':
prob.driver.opt_settings['Major optimality tolerance'] = float(
conv_tols[i])
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] == '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(case_prefix='DriverRun%i' % driverruns)
driverruns += 1
# 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] == '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)
modelruns += 1
prob.run_model(case_prefix='ModelRun%i' % modelruns)
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)
print('Final AEP (kWh): %s' % AEP_run_calc)
print('AEP improvement: %s' % (AEP_run_calc / AEP_init_calc))
if show_end:
plot_square_farm(turbineX_end,
turbineY_end,
rotor_diameter,
boundary_x,
boundary_y,
boundary_x[1] - boundary_x[0],
show_start=show_start)
prob.cleanup()
if MODELS[model] == 'BPA':
prob['model_params:wec_factor'] = 1.0
prob['model_params:wec_spreading_angle'] = 0.0
prob['model_params:ti_calculation_method'] = 4
prob['model_params:calc_k_star'] = True
cr = om.CaseReader(output_directory +
'recorded_data_driver_%s.sql' % run_number)
driver_cases = cr.list_cases('driver')
nits = len(driver_cases)
objectives = np.zeros(nits)
AEPopt = np.zeros(nits)
AEPcalc = np.zeros(nits)
calls = np.zeros(nits)
for i in np.arange(0, nits):
case = cr.get_case(driver_cases[i])
# print(case)
AEPopt[i] = case['AEP']
objectives[i] = case.get_objectives()['obj']
prob['turbineX'] = np.copy(case['turbineX'])
prob['turbineY'] = np.copy(case['turbineY'])
if opt_algorithm == "snopt":
prob.run_model(case_prefix='ProcessingRun')
AEPcalc[i] = np.copy(prob['AEP'])
else:
AEPcalc[i] = case['AEP']
calls[i] = i
def set_up_prob():
layout_number = 0
wec_method_number = 0
wake_model = 1
opt_alg_number = 2
max_wec = 3
nsteps = 6
record = False
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)
if opt_algorithm == 'ps':
expansion_factors = np.append(np.flip(expansion_factors), 1.0)
else:
expansion_factors = np.append(np.flip(expansion_factors), 1.0)
conv_tols = np.array([9E-3, 9E-3, 9E-3, 9E-3, 9E-3, 9E-3, 1E-3])
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
if opt_algorithm == 'ps':
expansion_factors = np.array([1.0])
conv_tols = np.array([1E-4])
else:
expansion_factors = np.array([1.0, 1.0])
conv_tols = np.array([9E-3, 1E-3])
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' and wec_method == 'none':
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_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)
# 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 == '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 == '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 == '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 == '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
}
if MODELS[model] == 'BPA':
# initialize problem
prob = om.Problem(
model=OptAEP(nTurbines=nTurbs,
nDirections=windDirections.size,
nVertices=nVertices,
minSpacing=minSpacing,
differentiable=differentiable,
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=OptAEP(nTurbines=nTurbs,
nDirections=windDirections.size,
nVertices=nVertices,
minSpacing=minSpacing,
differentiable=differentiable,
use_rotor_components=False,
wake_model=floris_wrapper,
params_IdepVar_func=add_floris_params_IndepVarComps,
params_IdepVar_args={},
record_function_calls=True))
else:
ValueError(
'The %s model is not currently available. Please select BPA or FLORIS'
% (MODELS[model]))
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'] = np.float(1e-3)
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-4) # ,
# active_tol=(2. * rotor_diameter) ** 2)
prob.model.add_constraint('boundaryDistances',
lower=(np.zeros(1 * turbineX.size)),
scaler=1E-4) # ,
# 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-4)
prob.model.add_constraint('boundaryDistances',
lower=(np.zeros(1 * turbineX.size)),
scaler=1E-4)
elif opt_algorithm == 'ps':
prob.driver.options['optimizer'] = 'ALPSO'
prob.driver.opt_settings[
"SwarmSize"] = 30 # Number of Particles (Depends on Problem dimensions)
# prob.driver.opt_settings["maxOuterIter"] = OuterIter # Maximum Number of Outer Loop Iterations (Major Iterations)
# prob.driver.opt_settings["maxInnerIter"] = InnerIter # Maximum Number of Inner Loop Iterations (Minor Iterations)
# prob.driver.opt_settings["minInnerIter"] = InnerIter # Minimum Number of Inner Loop Iterations (Dynamic Inner Iterations)
# prob.driver.opt_settings["dynInnerIter"] = 0 # Dynamic Number of Inner Iterations Flag
prob.driver.opt_settings[
"stopCriteria"] = 0 # Stopping Criteria Flag (0 - maxIters, 1 - convergence)
prob.driver.opt_settings[
"stopIters"] = 5 # Consecutive Number of Iterations for which the Stopping Criteria must be Satisfied
prob.driver.opt_settings[
"etol"] = 1e-3 # Absolute Tolerance for Equality constraints
prob.driver.opt_settings[
"itol"] = 1e-3 # Absolute Tolerance for Inequality constraints
# 'ltol':[float, 1e-2], # Absolute Tolerance for Lagrange Multipliers
prob.driver.opt_settings[
"rtol"] = 1e-6 # Relative Tolerance for Lagrange Multipliers
prob.driver.opt_settings[
"atol"] = 1e-6 # 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[
"printOuterIters"] = 0 # Number of Iterations Before Print Outer Loop Information
prob.driver.opt_settings[
"printInnerIters"] = 0 # Number of Iterations Before Print Inner Loop Information
prob.driver.opt_settings["rinit"] = 1.0 # Initial Penalty Factor
prob.driver.opt_settings[
"xinit"] = 1 # Initial Position Flag (0 - no position, 1 - position given)
prob.driver.opt_settings[
"vinit"] = 1.0 # Initial Velocity of Particles in Normalized [-1, 1] Design Space
prob.driver.opt_settings[
"vmax"] = 2.0 # Maximum Velocity of Particles in Normalized [-1, 1] Design Space
prob.driver.opt_settings["c1"] = 2.0 # Cognitive Parameter
prob.driver.opt_settings["c2"] = 1.0 # Social Parameter
prob.driver.opt_settings["w1"] = 0.99 # Initial Inertia Weight
prob.driver.opt_settings["w2"] = 0.55 # Final Inertia Weight
prob.driver.opt_settings[
"ns"] = 15 # Number of Consecutive Successes in Finding New Best Position of Best Particle Before Search Radius will be Increased (GCPSO)
prob.driver.opt_settings[
"nf"] = 5 # Number of Consecutive Failures in Finding New Best Position of Best Particle Before Search Radius will be Increased (GCPSO)
prob.driver.opt_settings["dt"] = 1.0 # Time step
prob.driver.opt_settings[
"vcrazy"] = 1e-2 # Craziness Velocity (Added to Particle Velocity After Updating the Penalty Factors and Langangian Multipliers)
prob.driver.opt_settings[
"fileout"] = 1 # Flag to Turn On Output to filename
# prob.driver.opt_settings["filename"] = "ALPSO.out" # We could probably remove fileout flag if filename or fileinstance is given
prob.driver.opt_settings[
"seed"] = 0.0 # Random Number Seed (0 - Auto-Seed based on time clock)
prob.driver.opt_settings[
"HoodSize"] = 5 # Number of Neighbours of Each Particle
prob.driver.opt_settings[
"HoodModel"] = "gbest" # Neighbourhood Model (dl/slring - Double/Single Link Ring, wheel - Wheel, Spatial - based on spatial distance, sfrac - Spatial Fraction)
prob.driver.opt_settings[
"HoodSelf"] = 1 # Selfless Neighbourhood Model (0 - Include Particle i in NH i, 1 - Don't Include Particle i)
prob.driver.opt_settings[
"Scaling"] = 1 # Design Variables Scaling Flag (0 - no scaling, 1 - scaling between [-1, 1])
# prob.driver.opt_settings["parallelType"] = 'EXT' # Type of parallelization ('' or 'EXT')
prob.model.add_constraint('sc',
lower=np.zeros(
int(((nTurbs - 1.) * nTurbs / 2.))),
scaler=1E-7)
prob.model.add_constraint('boundaryDistances',
lower=(np.zeros(1 * turbineX.size)),
scaler=1E-7)
# prob.driver.add_objective('obj', scaler=1E0)
prob.model.add_objective('obj', scaler=1E-9)
# select design variables
prob.model.add_design_var('turbineX',
scaler=1E-4,
lower=np.zeros(nTurbines),
upper=np.ones(nTurbines) * 2. * boundary_radius)
prob.model.add_design_var('turbineY',
scaler=1E-4,
lower=np.zeros(nTurbines),
upper=np.ones(nTurbines) * 2. * boundary_radius)
if record:
driver_recorder = om.SqliteRecorder(output_directory +
'recorded_data_driver_%s.sql' %
(run_number))
# model_recorder = om.SqliteRecorder(output_directory + 'recorded_data_model_%s.sql' %(run_number))
prob.driver.add_recorder(driver_recorder)
# prob.model.add_recorder(model_recorder)
prob.driver.recording_options['record_constraints'] = False
prob.driver.recording_options['record_derivatives'] = False
prob.driver.recording_options['record_desvars'] = True
prob.driver.recording_options['record_inputs'] = False
prob.driver.recording_options['record_model_metadata'] = True
prob.driver.recording_options['record_objectives'] = True
prob.driver.recording_options['includes'] = ['AEP']
prob.driver.recording_options['record_responses'] = False
# print("almost time for setup")
tic = time.time()
# print("entering setup at time = ", tic)
prob.setup(check=False)
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] == '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)
# prob.run_model()
# AEP_init_calc = np.copy(prob['AEP'])*1E3 # convert to Whr
# print("initial AEP: ", AEP_init_calc)
return prob
prob['model_params:wake_combination_method'] = wake_combination_method
prob['model_params:ti_calculation_method'] = ti_calculation_method
prob['model_params:wake_model_version'] = wake_model_version
prob['model_params:opt_exp_fac'] = 1.0
prob['model_params:calc_k_star'] = calc_k_star_calc
prob['model_params:sort'] = sort_turbs
prob['model_params:z_ref'] = z_ref
prob['model_params:z_0'] = z_0
prob['model_params:ky'] = k_calc
prob['model_params:kz'] = k_calc
prob['model_params:print_ti'] = print_ti
prob['model_params:shear_exp'] = shear_exp
prob['model_params:I'] = TI
prob['model_params:sm_smoothing'] = sm_smoothing
if nRotorPoints > 1:
prob['model_params:RotorPointsY'], prob['model_params:RotorPointsZ'] = sunflower_points(nRotorPoints)
prob.run_once()
AEP_init_calc = prob['AEP']
mpi_print(prob, AEP_init_calc * 1E-6)
if MODELS[model] is 'BPA':
prob['model_params:ti_calculation_method'] = ti_opt_method
prob['model_params:calc_k_star'] = calc_k_star_opt
prob.run_once()
AEP_init_opt = prob['AEP']
AEP_run_opt = np.copy(AEP_init_opt)
mpi_print(prob, AEP_init_opt * 1E-6)
config.obj_func_calls_array[:] = 0.0
def setup_probs():
nTurbines = 2
nDirections = 1
rotorDiameter = 126.4
rotorArea = np.pi * rotorDiameter * rotorDiameter / 4.0
axialInduction = 1.0 / 3.0
# CP = 0.7737/0.944 * 4.0 * 1.0/3.0 * np.power((1 - 1.0/3.0), 2)
CP = 4.0 * 1.0 / 3.0 * np.power((1 - 1.0 / 3.0), 2)
# CP =0.768 * 4.0 * 1.0/3.0 * np.power((1 - 1.0/3.0), 2)
CT = 4.0 * axialInduction * (1.0 - axialInduction)
generator_efficiency = 0.944
# Define turbine characteristics
axialInduction = np.array([axialInduction, axialInduction])
rotorDiameter = np.array([rotorDiameter, rotorDiameter])
generatorEfficiency = np.array(
[generator_efficiency, generator_efficiency])
yaw = np.array([0., 0.])
hubHeight = np.array([90.0, 90.0])
# Define site measurements
wind_direction = 270. - 0.523599 * 180. / np.pi
wind_speed = 8. # m/s
air_density = 1.1716
Ct = np.array([CT, CT])
Cp = np.array([CP, CP])
nRotorPoints = 4
rotor_pnt_typ = 0
location = 0.69
filename = "./input_files/NREL5MWCPCT_dict.p"
# filename = "../input_files/NREL5MWCPCT_smooth_dict.p"
import cPickle as pickle
data = pickle.load(open(filename, "rb"))
ct_curve_wind_speed = data['wind_speed']
ct_curve_ct = data['CT']
gauss_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,
'verbose': False
}
# gauss_prob = Problem(root=AEPGroup(nTurbines=nTurbines, nDirections=nDirections, use_rotor_components=False,
# wake_model=gauss_wrapper, datasize=0, minSpacing=2.0,
# params_IdepVar_func=add_gauss_params_IndepVarComps, wake_model_options=gauss_model_options,
# params_IndepVar_args={'nRotorPoints': nRotorPoints}))
gauss_prob = Problem(
root=AEPGroup(nTurbines=nTurbines,
nDirections=nDirections,
use_rotor_components=False,
wake_model=gauss_wrapper,
datasize=0,
params_IdepVar_func=add_gauss_params_IndepVarComps,
wake_model_options=gauss_model_options,
params_IndepVar_args={'nRotorPoints': nRotorPoints}))
floris_options = {
'differentiable': True,
'nSamples': 0,
'use_rotor_components': False
}
floris_prob_orig = Problem(
root=OptAEP(nTurbines=nTurbines,
nDirections=nDirections,
use_rotor_components=False,
wake_model=floris_wrapper,
wake_model_options=floris_options,
datasize=0,
params_IdepVar_func=add_floris_params_IndepVarComps,
params_IndepVar_args={}))
floris_prob_tuned = Problem(
root=OptAEP(nTurbines=nTurbines,
nDirections=nDirections,
use_rotor_components=False,
wake_model=floris_wrapper,
wake_model_options=floris_options,
datasize=0,
params_IdepVar_func=add_floris_params_IndepVarComps,
params_IndepVar_args={}))
probs = [gauss_prob, floris_prob_orig, floris_prob_tuned]
for prob in probs:
prob.setup()
if prob is floris_prob_orig or prob is floris_prob_tuned:
prob['model_params:useWakeAngle'] = True
turbineX = np.array([1118.1, 1881.9])
turbineY = np.array([1279.5, 1720.5])
# prob['gen_params:CTcorrected'] = False
# prob['gen_params:CPcorrected'] = False
prob['turbineX'] = turbineX
prob['turbineY'] = turbineY
prob['rotorDiameter'] = rotorDiameter
prob['axialInduction'] = axialInduction
prob['generatorEfficiency'] = generatorEfficiency
prob['air_density'] = air_density
prob['Cp_in'] = Cp
prob['Ct_in'] = Ct
prob['windSpeeds'] = np.array([wind_speed])
prob['windDirections'] = np.array([wind_direction])
prob['hubHeight'] = hubHeight
if prob is gauss_prob:
sort_turbs = True
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 = 5 # 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]
calc_k_star = True
z_ref = 90.0
z_0 = 0.001
k_calc = 0.065
# tuned with 1 rotor point: error_turbine2: 380593.475508 ky: 0.0147484983033 kz: 0.0365360001244 I: 1.0 shear_exp: 0.0804912726779
# tuned with 500 rotor points: error_turbine2: 505958.824163 ky: 0.010239469297 kz: 0.0187826477801 I: 0.5 shear_exp: 0.115698347406
# tuned with 1000 rotor points: error_turbine2: 440240.45048 ky: 0.0132947699754 kz: 0.0267832386866 I: 0.149427342515 shear_exp: 0.107996557048
# tuned with k_star and 1000 rotor points: error_turbine2: 759565.303289 ky: 0.065 kz: 0.065 I: 0.0765060707278 shear_exp: 0.104381464423
# using NPA to calculate initial spreading, but then letting BPA adjust it with TI after that. 1000 rotor points
# error_turbine2: 759565.279351 ky: 0.0330333796913 kz: 0.0330333796913 I: 0.0765060716478 shear_exp: 0.104381467026
# using NPA to calculate initial spreading, but then letting BPA adjust it with TI after that. 16 rotor points
# error_turbine2: 642639.730582 ky: 0.0307280539404 kz: 0.0307280539404 I: 0.0704979253074 shear_exp: 0.108435318499
# tuning only shear_exp with 16 rotor points: error_turbine2: 779216.077341 ky: 0.0267 kz: 0.0267 I: 0.06 shear_exp: 0.161084449732
I = .063 # + 0.04
# I = .06
ky = 0.3837 * I + 0.003678
# ky = 0.022
kz = 0.3837 * I + 0.003678
# kz = 0.022
# shear_exp = 0.161084449732
shear_exp = 0.11
prob[
'model_params:wake_combination_method'] = wake_combination_method
prob['model_params:ti_calculation_method'] = ti_calculation_method
prob['model_params:calc_k_star'] = calc_k_star
prob['model_params:sort'] = sort_turbs
prob['model_params:z_ref'] = z_ref
prob['model_params:z_0'] = z_0
prob['model_params:ky'] = ky
prob['model_params:kz'] = kz
prob['model_params:I'] = I
prob['model_params:shear_exp'] = shear_exp
print "in gauss setup"
if nRotorPoints > 1:
if rotor_pnt_typ == 0:
points = circumference_points(nRotorPoints, location)
elif rotor_pnt_typ == 1:
points = sunflower_points(nRotorPoints)
print points
prob['model_params:RotorPointsY'], prob[
'model_params:RotorPointsZ'] = points
print "setting rotor points"
return probs
ax2.plot(blade3X * c1 + 0.,
blade3Y * c1 + H,
linewidth=1 * 1.5,
color='C0',
alpha=0.5)
x_sample = np.array([0.69 * R, 0., -0.69 * R, 0.])
y_sample = np.array([0., 0.69 * R, 0., -0.69 * R]) + H
ax1.plot(x_sample, y_sample, 'o', color='C2', alpha=0.5, markersize=3)
ax1.text(0.69 * R, H + 7., '(0.69,0)', horizontalalignment='center')
ax1.text(0., H + 0.69 * R + 7., '(0,0.69)', horizontalalignment='center')
ax1.text(-0.69 * R, H + 7., '(-0.69,0)', horizontalalignment='center')
ax1.text(0., H - 0.69 * R + 7., '(0,-0.69)', horizontalalignment='center')
x_sample, y_sample = sunflower_points(100)
x_sample = x_sample * R
y_sample = y_sample * R + H
ax2.plot(x_sample, y_sample, 'o', color='C2', alpha=0.5, markersize=3)
ax1.axis('equal')
ax1.axis('off')
ax2.axis('equal')
ax2.axis('off')
ax1.set_ylim(-15., H + R + 5)
ax2.set_ylim(-15., H + R + 5)
ax1.text(0., -10., 'optimize', horizontalalignment='center')
ax2.text(0., -10., 'evaluate', horizontalalignment='center')
def setup_probs():
nTurbines = 2
nDirections = 1
rotorDiameter = 126.4
rotorArea = np.pi*rotorDiameter*rotorDiameter/4.0
axialInduction = 1.0/3.0
# CP = 0.7737/0.944 * 4.0 * 1.0/3.0 * np.power((1 - 1.0/3.0), 2)
CP = 4.0 * 1.0/3.0 * np.power((1 - 1.0/3.0), 2)
# CP =0.768 * 4.0 * 1.0/3.0 * np.power((1 - 1.0/3.0), 2)
CT = 4.0*axialInduction*(1.0-axialInduction)
generator_efficiency = 0.944
# Define turbine characteristics
axialInduction = np.array([axialInduction, axialInduction])
rotorDiameter = np.array([rotorDiameter, rotorDiameter])
generatorEfficiency = np.array([generator_efficiency, generator_efficiency])
yaw = np.array([0., 0.])
hubHeight = np.array([90.0, 90.0])
# Define site measurements
wind_direction = 270.-0.523599*180./np.pi
wind_speed = 8. # m/s
air_density = 1.1716
Ct = np.array([CT, CT])
Cp = np.array([CP, CP])
nRotorPoints = 4
rotor_pnt_typ = 0
location = 0.69
filename = "./input_files/NREL5MWCPCT_dict.p"
# filename = "../input_files/NREL5MWCPCT_smooth_dict.p"
import cPickle as pickle
data = pickle.load(open(filename, "rb"))
ct_curve_wind_speed = data['wind_speed']
ct_curve_ct= data['CT']
gauss_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,
'verbose': False}
# gauss_prob = Problem(root=AEPGroup(nTurbines=nTurbines, nDirections=nDirections, use_rotor_components=False,
# wake_model=gauss_wrapper, datasize=0, minSpacing=2.0,
# params_IdepVar_func=add_gauss_params_IndepVarComps, wake_model_options=gauss_model_options,
# params_IndepVar_args={'nRotorPoints': nRotorPoints}))
gauss_prob = Problem(root=AEPGroup(nTurbines=nTurbines, nDirections=nDirections, use_rotor_components=False,
wake_model=gauss_wrapper, datasize=0,
params_IdepVar_func=add_gauss_params_IndepVarComps,
wake_model_options=gauss_model_options,
params_IndepVar_args={'nRotorPoints': nRotorPoints}))
floris_options = {'differentiable': True, 'nSamples': 0, 'use_rotor_components': False}
floris_prob_orig = Problem(root=OptAEP(nTurbines=nTurbines, nDirections=nDirections, use_rotor_components=False,
wake_model=floris_wrapper, wake_model_options=floris_options, datasize=0,
params_IdepVar_func=add_floris_params_IndepVarComps,
params_IndepVar_args={}))
floris_prob_tuned = Problem(root=OptAEP(nTurbines=nTurbines, nDirections=nDirections, use_rotor_components=False,
wake_model=floris_wrapper, wake_model_options=floris_options, datasize=0,
params_IdepVar_func=add_floris_params_IndepVarComps,
params_IndepVar_args={}))
probs = [gauss_prob, floris_prob_orig, floris_prob_tuned]
for prob in probs:
prob.setup()
if prob is floris_prob_orig or prob is floris_prob_tuned:
prob['model_params:useWakeAngle'] = True
turbineX = np.array([1118.1, 1881.9])
turbineY = np.array([1279.5, 1720.5])
# prob['gen_params:CTcorrected'] = False
# prob['gen_params:CPcorrected'] = False
prob['turbineX'] = turbineX
prob['turbineY'] = turbineY
prob['rotorDiameter'] = rotorDiameter
prob['axialInduction'] = axialInduction
prob['generatorEfficiency'] = generatorEfficiency
prob['air_density'] = air_density
prob['Cp_in'] = Cp
prob['Ct_in'] = Ct
prob['windSpeeds'] = np.array([wind_speed])
prob['windDirections'] = np.array([wind_direction])
prob['hubHeight'] = hubHeight
if prob is gauss_prob:
sort_turbs = True
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 = 5 # 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]
calc_k_star = True
z_ref = 90.0
z_0 = 0.001
k_calc = 0.065
# tuned with 1 rotor point: error_turbine2: 380593.475508 ky: 0.0147484983033 kz: 0.0365360001244 I: 1.0 shear_exp: 0.0804912726779
# tuned with 500 rotor points: error_turbine2: 505958.824163 ky: 0.010239469297 kz: 0.0187826477801 I: 0.5 shear_exp: 0.115698347406
# tuned with 1000 rotor points: error_turbine2: 440240.45048 ky: 0.0132947699754 kz: 0.0267832386866 I: 0.149427342515 shear_exp: 0.107996557048
# tuned with k_star and 1000 rotor points: error_turbine2: 759565.303289 ky: 0.065 kz: 0.065 I: 0.0765060707278 shear_exp: 0.104381464423
# using NPA to calculate initial spreading, but then letting BPA adjust it with TI after that. 1000 rotor points
# error_turbine2: 759565.279351 ky: 0.0330333796913 kz: 0.0330333796913 I: 0.0765060716478 shear_exp: 0.104381467026
# using NPA to calculate initial spreading, but then letting BPA adjust it with TI after that. 16 rotor points
# error_turbine2: 642639.730582 ky: 0.0307280539404 kz: 0.0307280539404 I: 0.0704979253074 shear_exp: 0.108435318499
# tuning only shear_exp with 16 rotor points: error_turbine2: 779216.077341 ky: 0.0267 kz: 0.0267 I: 0.06 shear_exp: 0.161084449732
I = .063 # + 0.04
# I = .06
ky = 0.3837*I + 0.003678
# ky = 0.022
kz = 0.3837*I + 0.003678
# kz = 0.022
# shear_exp = 0.161084449732
shear_exp = 0.11
prob['model_params:wake_combination_method'] = wake_combination_method
prob['model_params:ti_calculation_method'] = ti_calculation_method
prob['model_params:calc_k_star'] = calc_k_star
prob['model_params:sort'] = sort_turbs
prob['model_params:z_ref'] = z_ref
prob['model_params:z_0'] = z_0
prob['model_params:ky'] = ky
prob['model_params:kz'] = kz
prob['model_params:I'] = I
prob['model_params:shear_exp'] = shear_exp
print "in gauss setup"
if nRotorPoints > 1:
if rotor_pnt_typ == 0:
points = circumference_points(nRotorPoints, location)
elif rotor_pnt_typ == 1:
points = sunflower_points(nRotorPoints)
print points
prob['model_params:RotorPointsY'], prob['model_params:RotorPointsZ'] = points
print "setting rotor points"
return probs
# location = 0.625 #0.5*1.25
location = (0.7353288267358161 + 0.6398349246319044) / 2.
location = 0.69
print(location)
rotor_pnt_typ = 1 # can be [0: circumference points,
# 1: sunflower points
# 2: horizontal line at hub height
fig, ax = plt.subplots()
if nRotorPoints > 1:
if rotor_pnt_typ == 0:
x, y = circumference_points(nRotorPoints, location=location)
# ax.set_xlim([-70, 70])
elif rotor_pnt_typ == 1:
x, y = sunflower_points(nRotorPoints)
# ax.plot(x, y)
elif rotor_pnt_typ == 2:
x, y = line_points(nRotorPoints)
else:
[x, y] = [0.0, 0.0]
print(x, y)
# x, y = sunflower_points(nRotorPoints)
x *= 0.5 * rotor_diameter
y *= 0.5 * rotor_diameter
ax.scatter(x, y)
else:
[x, y] = np.array([0.0, 0.0])
plt.scatter(x, y)
def run_opt(layout_number, wec_method_number, wake_model, opt_alg_number,
max_wec, nsteps, xs, ys):
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 = True
save_end = True
save_locations = True
save_aep = True
save_time = True
rec_func_calls = True
input_directory = "../project-code/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.append(np.flip(expansion_factors), 1.0)
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, 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_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,
save_start=save_start,
save_file="farm_layout.pdf")
# 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
}
if MODELS[model] == 'BPA':
# initialize problem
prob = om.Problem(
model=OptAEP(nTurbines=nTurbs,
nDirections=windDirections.size,
nVertices=nVertices,
minSpacing=minSpacing,
differentiable=differentiable,
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=OptAEP(nTurbines=nTurbs,
nDirections=windDirections.size,
nVertices=nVertices,
minSpacing=minSpacing,
differentiable=differentiable,
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=OptAEP(nTurbines=nTurbs, nDirections=windDirections.size, nVertices=nVertices,
# minSpacing=minSpacing, differentiable=False, use_rotor_components=False,
# wake_model=jensen_wrapper,
# params_IdepVar_func=add_jensen_params_IndepVarComps,
# params_IdepVar_args={},
# 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=False)
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_opt_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)
xx, yy = np.meshgrid(xs, ys)
vel_out = np.zeros((xs.size, ys.size))
for i in np.arange(0, xs.size):
prob['turbineX'][0] = np.copy(xs[i])
print(i)
for j in np.arange(0, ys.size):
if np.sqrt((ys[j] - boundary_center_y)**2 +
(xs[i] - boundary_center_x)**2) > boundary_radius:
vel_out[i, j] = np.nan
continue
prob['turbineY'][0] = np.copy(ys[j])
prob.run_model()
vel_out[i, j] = np.copy(prob['AEP'])
# print(i*j+j)
fig = plt.figure()
ax = plt.axes(projection='3d')
# Hide grid lines
ax.grid(False)
plt.axis("off")
# Hide axes ticks
ax.set_xticks([])
ax.set_yticks([])
ax.set_zticks([])
# make the panes transparent
ax.xaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
ax.yaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
ax.zaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
# make the grid lines transparent
ax.xaxis._axinfo["grid"]['color'] = (1, 1, 1, 0)
ax.yaxis._axinfo["grid"]['color'] = (1, 1, 1, 0)
ax.zaxis._axinfo["grid"]['color'] = (1, 1, 1, 0)
print(np.max(vel_out))
ax.plot_surface(xx,
yy,
vel_out,
vmin=np.nanmin(vel_out) - 10,
vmax=np.nanmax(vel_out) + 10,
cmap="hot",
rstride=1,
cstride=1,
linewidth=0,
antialiased=False)
# ax.set_zlim(0, nTurbines*2E3)
plt.savefig('multimodal.pdf', transparent=True)
plt.show()
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
def setUp(self):
from plantenergy.utilities import sunflower_points
self.tolerance = 1E-1
self.wake_combination_method = 1
self.wake_model_version = 2016
self.rotor_diameter = 80.
self.hub_height = 70.
self.ct = 0.6
self.alpha = 2.32
self.beta = 0.154
self.expratemultiplier = 1.0
self.wec_factor = 1.0
self.wind_speed = 8.0
self.z_ref = self.hub_height
self.z_0 = 0.0002
self.shear_exp = 0.15
self.yaw = 0.0
self.wtVelocity = np.array([self.wind_speed])
self.TI = 0.077
self.ky = 0.3837*self.TI + 0.003678 # np.array([0.3837*TIturbs[0] + 0.003678])
self.kz = 0.3837*self.TI + 0.003678 # np.array([0.3837*TIturbs[0] + 0.003678])
rotorpoints = sunflower_points(100)
self.RotorPointsY = rotorpoints[0] #np.array([0, .5, 1.0, 0., 0.0, -.5, -1.0, 0., 0.])
self.RotorPointsZ = rotorpoints[1] #np.array([0, 0., 0., .5, 1.0, 0., 0.0, -0.5, -1.])
# self.RotorPointsY = np.array([0])
# self.RotorPointsZ = np.array([0])
self.RotorPointsY = np.array([0, .5, 1.0, 0., 0.0, -.5, -1.0, 0., 0.])
self.RotorPointsZ = np.array([0, 0., 0., .5, 1.0, 0., 0.0, -0.5, -1.])
self.TI_calculation_method = 4
self.calc_k_star = True
self.print_ti = False
self.interp_type = 1
self.sm_smoothing = 700.
loc_data = np.loadtxt('input_files/horns_rev_locations.txt', delimiter=',')
turbineXw = loc_data[:, 0] * self.rotor_diameter
turbineYw = loc_data[:, 1] * self.rotor_diameter
turbineZ = np.ones_like(turbineXw) * self.hub_height
sorted_x_idx = np.argsort(turbineXw, kind='heapsort')
rotorDiameter = np.ones_like(turbineXw) * self.rotor_diameter
Ct = np.ones_like(turbineXw) * self.ct
yaw = np.ones_like(turbineXw) * self.yaw
TI_turbs = np.ones_like(turbineXw) * self.TI
use_ct_curve = True
# ct_data = np.loadtxt('input_files/predicted_ct_vestas_v80_niayifar2016.txt', delimiter=',')
ct_data = np.loadtxt('input_files/mfg_ct_vestas_v80_niayifar2016.txt', delimiter=',')
ct_curve_wind_speed = ct_data[:, 0]
ct_curve_ct = ct_data[:, 1]
CalculateFlowField=False
wtVelocity, _ = porteagel_analyze(turbineXw, sorted_x_idx, turbineYw, turbineZ,
rotorDiameter, Ct, self.wind_speed,
yaw, self.ky, self.kz, self.alpha, self.beta, TI_turbs, self.RotorPointsY,
self.RotorPointsZ, np.array([0]), np.array([0]), np.array([0]),
self.z_ref, self.z_0, self.shear_exp, self.wake_combination_method,
self.TI_calculation_method, self.calc_k_star, self.wec_factor, self.print_ti,
self.wake_model_version, self.interp_type, use_ct_curve,
ct_curve_wind_speed, ct_curve_ct, self.sm_smoothing,
self.expratemultiplier, CalculateFlowField)
free_stream_power = power_func_v80(self.wind_speed)
wtPower = power_func_v80(wtVelocity)
self.norm_pow_ave_by_row = np.zeros(10)
for i in np.arange(0, self.norm_pow_ave_by_row.size):
pow_ave_row = np.average([wtPower[40 + i], wtPower[50 + i], wtPower[60 + i]])
self.norm_pow_ave_by_row[i] = pow_ave_row / free_stream_power
