def generate_amalia_layouts(nLayouts,
nTurbines,
rotor_diameter,
base_spacing,
output_directory=None,
show=False):
if nLayouts > 10 and show == True:
raise ValueError("do you really want to see %i plots in serial?" %
nLayouts)
locations = np.loadtxt('layout_amalia.txt')
x = locations[:, 0]
y = locations[:, 1]
center = [np.max(x) - np.min(x), np.max(y) - np.min(y)]
x, y = rotate(center, locations, -90)
x, y = flip(x, y, 'x')
turbineX = x - np.min(x)
turbineY = y
for i in np.arange(0, turbineX.size):
locations[i, 0] = turbineX[i]
locations[i, 1] = turbineY[i]
boundaryVertices, boundaryNormals = calculate_boundary(locations)
nVertices = boundaryVertices.shape[0]
np.savetxt(output_directory + 'nTurbs%i_spacing%i_layout_0.txt' %
(nTurbines, base_spacing),
np.c_[turbineX / rotor_diameter, turbineY / rotor_diameter],
header="turbineX, turbineY")
plot_farm(turbineX,
turbineY,
rotor_diameter,
boundaryVertices,
show_start=show)
if nLayouts > 1:
for L in np.arange(1, nLayouts):
print(L)
turbineX, turbineY = amalia_farm_random_start(turbineX.size,
boundaryVertices,
boundaryNormals,
rotor_diameter,
min_spacing=1)
# print turbineX, turbineY
np.savetxt(output_directory + "nTurbs%i_spacing%i_layout_%i.txt" %
(nTurbines, base_spacing, L),
np.c_[turbineX / rotor_diameter,
turbineY / rotor_diameter],
header="turbineX, turbineY")
plot_farm(turbineX,
turbineY,
rotor_diameter,
boundaryVertices,
show_start=show)
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([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])
else:
expansion_factors = np.array([1.0, 1.0])
conv_tols = np.array([1E-3, 1E-4])
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 = 'amalia' # 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/amalia_60turbs/nTurbs60_spacing5_layout_%i.txt" %
layout_number)
turbineX = np.copy(layout_data[:, 0]) * rotor_diameter
turbineY = np.copy(layout_data[:, 1]) * rotor_diameter
# generate boundary constraint
boundaryVertices, boundaryNormals = calculate_boundary(layout_data *
rotor_diameter)
nVertices = boundaryVertices.shape[0]
turbineX_init = np.copy(turbineX)
turbineY_init = np.copy(turbineY)
nTurbines = turbineX.size
# 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
}
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-4) # ,
# active_tol=(2. * rotor_diameter) ** 2)
prob.model.add_constraint('boundaryDistances',
lower=(np.zeros(nVertices * 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(nVertices * turbineX.size)),
scaler=1E-4)
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'] = 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-4)
prob.model.add_constraint('boundaryDistances',
lower=(np.zeros(nVertices * turbineX.size)),
scaler=1E-4)
# 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.zeros(nTurbines),
upper=np.ones(nTurbines) * 62. * rotor_diameter)
prob.model.add_design_var('turbineY',
scaler=1E0,
lower=np.zeros(nTurbines),
upper=np.ones(nTurbines) * 50. * rotor_diameter)
# 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
# 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':
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
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))
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
# load wind rose data
windRose = np.loadtxt('./input_files/windrose_amalia_directionally_averaged_speeds.txt')
indexes = np.where(windRose[:, 1] > 0.1)
windDirections = windRose[indexes[0], 0]
windSpeeds = windRose[indexes[0], 1]
windFrequencies = windRose[indexes[0], 2]
nDirections = len(windDirections)
# load turbine positions
locations = np.loadtxt('./input_files/layout_amalia.txt')
turbineX = locations[:, 0]
turbineY = locations[:, 1]
# generate boundary constraint
boundaryVertices, boundaryNormals = calculate_boundary(locations)
nVertices = boundaryVertices.shape[0]
# define turbine size
rotor_diameter = 126.4 # (m)
# initialize input variable arrays
nTurbines = turbineX.size
rotorDiameter = np.zeros(nTurbines)
axialInduction = np.zeros(nTurbines)
Ct = np.zeros(nTurbines)
Cp = np.zeros(nTurbines)
generatorEfficiency = np.zeros(nTurbines)
yaw = np.zeros(nTurbines)
minSpacing = 2. # number of rotor diameters
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/amalia_60turbs/nTurbs60_spacing5_layout_%i.txt" %
layout_number)
turbineX = np.copy(layout_data[:, 0]) * rotor_diameter
turbineY = np.copy(layout_data[:, 1]) * rotor_diameter
# generate boundary constraint
boundaryVertices, boundaryNormals = calculate_boundary(layout_data *
rotor_diameter)
nVertices = boundaryVertices.shape[0]
# print(nVertices)
# quit()
turbineX_init = np.copy(turbineX)
turbineY_init = np.copy(turbineY)
nTurbines = turbineX.size
# quit()
# initialize input variable arrays
nTurbs = nTurbines
rotorDiameter = np.zeros(nTurbs)
hubHeight = np.zeros(nTurbs)