def main():
if __name__ == '__main__':
from py_wake.examples.data.iea37._iea37 import IEA37Site
from py_wake.examples.data.iea37._iea37 import IEA37_WindTurbines
from py_wake.superposition_models import LinearSum
from py_wake.wind_farm_models import All2AllIterative
from py_wake.deficit_models.no_wake import NoWakeDeficit
from py_wake.deficit_models.vortexcylinder import VortexCylinder
from py_wake.deficit_models.vortexdipole import VortexDipole
import matplotlib.pyplot as plt
from py_wake import HorizontalGrid
from timeit import default_timer as timer
# setup site, turbines and wind farm model
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
d = windTurbines.diameter()
ra = Rathmann()
grid = HorizontalGrid(x=np.linspace(-6, 6, 100) * d, y=np.linspace(0, 4, 100) * d)
noj_ra = All2AllIterative(site, windTurbines, wake_deficitModel=NoWakeDeficit(),
superpositionModel=LinearSum(), blockage_deficitModel=ra)
noj_vc = All2AllIterative(site, windTurbines, wake_deficitModel=NoWakeDeficit(),
superpositionModel=LinearSum(), blockage_deficitModel=VortexCylinder())
noj_vd = All2AllIterative(site, windTurbines, wake_deficitModel=NoWakeDeficit(),
superpositionModel=LinearSum(), blockage_deficitModel=VortexDipole())
t1 = timer()
flow_map = noj_ra(x=[0], y=[0], wd=[270], ws=[10]).flow_map(grid=grid)
t2 = timer()
flow_map_vc = noj_vc(x=[0], y=[0], wd=[270], ws=[10]).flow_map(grid=grid)
t3 = timer()
flow_map_vd = noj_vd(x=[0], y=[0], wd=[270], ws=[10]).flow_map(grid=grid)
t4 = timer()
print(t2 - t1, t3 - t2, t4 - t3)
plt.figure()
clevels = np.array([.6, .7, .8, .9, .95, .98, .99, .995, .998, .999, 1., 1.005, 1.01, 1.02, 1.05]) * 10.
flow_map.plot_wake_map(levels=clevels)
plt.contour(flow_map.x, flow_map.y, flow_map.WS_eff[:, :, 0, -1, 0], levels=clevels, colors='k', linewidths=1)
plt.contour(flow_map.x, flow_map.y, flow_map_vc.WS_eff[:, :, 0, -1, 0], levels=clevels, colors='r', linewidths=1, linestyles='dashed')
plt.contour(flow_map.x, flow_map.y, flow_map_vd.WS_eff[:, :, 0, -1, 0], levels=clevels, colors='b', linewidths=1, linestyles='dotted')
plt.title('Rathmann')
plt.ylabel("Crosswind distance [y/R]")
plt.xlabel("Downwind distance [x/R]")
plt.show()
# run wind farm simulation
sim_res = noj_ra(x, y, wd=[0, 30, 45, 60, 90], ws=[5, 10, 15])
# calculate AEP
aep = sim_res.aep().sum()
# plot wake map
plt.figure()
print(noj_ra)
flow_map = sim_res.flow_map(wd=0, ws=10)
flow_map.plot_wake_map(levels=clevels, plot_colorbar=False)
plt.title('Rathmann model, AEP: %.3f GWh' % aep)
plt.show()
def main():
if __name__ == '__main__':
import matplotlib.pyplot as plt
from py_wake.examples.data.hornsrev1 import Hornsrev1Site
from py_wake.examples.data import hornsrev1
from py_wake.superposition_models import LinearSum
from py_wake.wind_farm_models import All2AllIterative
site = Hornsrev1Site()
windTurbines = hornsrev1.HornsrevV80()
ws = 10
D = 80
R = D / 2
WS_ilk = np.array([[[ws]]])
D_src_il = np.array([[D]])
ct_ilk = np.array([[[.8]]])
ss = SelfSimilarityDeficit()
x, y = -np.arange(200), np.array([0])
deficit = ss.calc_deficit(WS_ilk=WS_ilk,
D_src_il=D_src_il,
dw_ijlk=x.reshape((1, len(x), 1, 1)),
cw_ijlk=y.reshape((1, len(y), 1, 1)),
ct_ilk=ct_ilk)
plt.title('Fig 11 from [1]')
plt.xlabel('x/R')
plt.ylabel('a')
plt.plot(x / R, deficit[0, :, 0, 0] / ws)
plt.figure()
x, y = np.array([-2 * R]), np.arange(200)
deficit = ss.calc_deficit(WS_ilk=WS_ilk,
D_src_il=D_src_il,
dw_ijlk=x.reshape((1, len(x), 1, 1)),
cw_ijlk=y.reshape((1, len(y), 1, 1)),
ct_ilk=ct_ilk)
plt.title('Fig 10 from [1]')
r12 = np.sqrt(ss.lambda_ * (ss.eta + (x / R)**2)) # Eq. (13) from [1]
print(x, r12)
plt.xlabel('y/R12 (epsilon)')
plt.ylabel('f')
plt.plot((y / R) / r12, deficit[0, :, 0, 0] / deficit[0, 0, 0, 0])
plt.figure()
noj_ss = All2AllIterative(site,
windTurbines,
wake_deficitModel=NoWakeDeficit(),
superpositionModel=LinearSum(),
blockage_deficitModel=ss)
flow_map = noj_ss(x=[0], y=[0], wd=[270], ws=[10]).flow_map()
flow_map.plot_wake_map()
flow_map.plot_windturbines()
plt.show()
def main():
if __name__ == '__main__':
from py_wake.examples.data.iea37._iea37 import IEA37Site
from py_wake.examples.data.iea37._iea37 import IEA37_WindTurbines
from py_wake.superposition_models import LinearSum
from py_wake.wind_farm_models import All2AllIterative
from py_wake.deficit_models.no_wake import NoWakeDeficit
import matplotlib.pyplot as plt
# setup site, turbines and wind farm model
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
hi = HybridInduction()
plt.figure()
noj_hi = All2AllIterative(site,
windTurbines,
wake_deficitModel=NoWakeDeficit(),
superpositionModel=LinearSum(),
blockage_deficitModel=hi)
flow_map = noj_hi(x=[0], y=[0], wd=[270], ws=[10]).flow_map()
clevels = np.array([
.6, .7, .8, .9, .95, .98, .99, .995, .998, .999, 1., 1.01, 1.02
]) * 10.
flow_map.plot_wake_map(levels=clevels)
plt.title('Vortex Dipole (far-field) + Self-Similar (near-rotor)')
plt.ylabel("Crosswind distance [y/R]")
plt.xlabel("Downwind distance [x/R]")
plt.show()
# run wind farm simulation
sim_res = noj_hi(x, y, wd=[0, 30, 45, 60, 90], ws=[5, 10, 15])
# calculate AEP
aep = sim_res.aep().sum()
# plot wake map
plt.figure()
print(noj_hi)
flow_map = sim_res.flow_map(wd=0, ws=10)
flow_map.plot_wake_map(levels=clevels, plot_colorbar=False)
plt.title(
'Vortex Dipole (far-field) + Self-Similar (near-rotor), AEP: %.3f GWh'
% aep)
plt.show()
def pywake_run(blockage_models):
grid = HorizontalGrid(x=np.linspace(-10 * d, 10 * d, 100),
y=np.linspace(-15, 2 * d, 100))
res = {}
out = {}
for nam, blockage_model in blockage_models:
print(nam, blockage_model)
# for dum, rotor_avg_model in rotor_avg_models:
wm = All2AllIterative(site,
wt,
wake_deficitModel=NoWakeDeficit(),
superpositionModel=LinearSum(),
blockage_deficitModel=blockage_model,
rotorAvgModel=RotorCenter())
res[nam] = wm(wt_x, wt_y, wd=[180., 195., 210., 225.], ws=[1.])
tic = time.perf_counter()
flow_map = res[nam].flow_map(grid=grid, ws=[1.], wd=225.)
toc = time.perf_counter()
elapsed_time = toc - tic
out[nam] = flow_map['WS_eff'] / flow_map['WS']
out[nam]['time'] = elapsed_time
return out, res
def main():
if __name__ == '__main__':
from py_wake.examples.data.iea37._iea37 import IEA37Site
from py_wake.examples.data.iea37._iea37 import IEA37_WindTurbines
from py_wake.site._site import UniformSite
from py_wake.wind_turbines import OneTypeWindTurbines
from py_wake.superposition_models import LinearSum
from py_wake.wind_farm_models import All2AllIterative
from py_wake.deficit_models.no_wake import NoWakeDeficit
from py_wake.rotor_avg_models import RotorCenter
from py_wake.deficit_models.vortexcylinder import VortexCylinder
from py_wake.deficit_models.vortexdipole import VortexDipole
import matplotlib.pyplot as plt
from py_wake import HorizontalGrid
from timeit import default_timer as timer
import time
# setup site, turbines and wind farm model
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
d = windTurbines.diameter()
ra = Rathmann()
ras = RathmannScaled()
grid = HorizontalGrid(x=np.linspace(-6, 6, 100) * d,
y=np.linspace(0, 4, 100) * d)
noj_ra = All2AllIterative(site,
windTurbines,
wake_deficitModel=NoWakeDeficit(),
superpositionModel=LinearSum(),
blockage_deficitModel=ra)
noj_ras = All2AllIterative(site,
windTurbines,
wake_deficitModel=NoWakeDeficit(),
superpositionModel=LinearSum(),
blockage_deficitModel=ras)
noj_vc = All2AllIterative(site,
windTurbines,
wake_deficitModel=NoWakeDeficit(),
superpositionModel=LinearSum(),
blockage_deficitModel=VortexCylinder())
noj_vd = All2AllIterative(site,
windTurbines,
wake_deficitModel=NoWakeDeficit(),
superpositionModel=LinearSum(),
blockage_deficitModel=VortexDipole())
t1 = timer()
flow_map = noj_ra(x=[0], y=[0], wd=[270], ws=[10]).flow_map(grid=grid)
t2 = timer()
flow_map_ras = noj_ras(x=[0], y=[0], wd=[270],
ws=[10]).flow_map(grid=grid)
t3 = timer()
flow_map_vc = noj_vc(x=[0], y=[0], wd=[270],
ws=[10]).flow_map(grid=grid)
t4 = timer()
flow_map_vd = noj_vd(x=[0], y=[0], wd=[270],
ws=[10]).flow_map(grid=grid)
t5 = timer()
print(t2 - t1, t3 - t2, t4 - t3, t5 - t4)
plt.figure()
clevels = np.array([
.6, .7, .8, .9, .95, .98, .99, .995, .998, .999, 1., 1.005, 1.01,
1.02, 1.05
]) * 10.
flow_map.plot_wake_map(levels=clevels)
plt.contour(flow_map.x,
flow_map.y,
flow_map.WS_eff[:, :, 0, -1, 0],
levels=clevels,
colors='k',
linewidths=1)
plt.contour(flow_map.x,
flow_map.y,
flow_map_ras.WS_eff[:, :, 0, -1, 0],
levels=clevels,
colors='g',
linewidths=1,
linestyles='dashed')
plt.contour(flow_map.x,
flow_map.y,
flow_map_vc.WS_eff[:, :, 0, -1, 0],
levels=clevels,
colors='r',
linewidths=1,
linestyles='dashed')
plt.contour(flow_map.x,
flow_map.y,
flow_map_vd.WS_eff[:, :, 0, -1, 0],
levels=clevels,
colors='b',
linewidths=1,
linestyles='dotted')
plt.plot([0, 0], [0, 0], 'k-', label='Rathmann')
plt.plot([0, 0], [0, 0], 'g--', label='scaled Rathmann')
plt.plot([0, 0], [0, 0], 'r--', label='vortex cylinder')
plt.plot([0, 0], [0, 0], 'b:', label='vortex dipole')
plt.title('Rathmann')
plt.ylabel("Crosswind distance [y/R]")
plt.xlabel("Downwind distance [x/R]")
plt.legend()
plt.show()
# run wind farm simulation
sim_res = noj_ra(x, y, wd=[0, 30, 45, 60, 90], ws=[5, 10, 15])
# calculate AEP
aep = sim_res.aep().sum()
# plot wake map
plt.figure()
print(noj_ra)
flow_map = sim_res.flow_map(wd=0, ws=10)
flow_map.plot_wake_map(levels=clevels, plot_colorbar=False)
plt.title('Rathmann model, AEP: %.3f GWh' % aep)
plt.show()
# run wind farm simulation
sim_res = noj_ras(x, y, wd=[0, 30, 45, 60, 90], ws=[5, 10, 15])
# calculate AEP
aep = sim_res.aep().sum()
# plot wake map
plt.figure()
print(noj_ras)
flow_map = sim_res.flow_map(wd=0, ws=10)
flow_map.plot_wake_map(levels=clevels, plot_colorbar=False)
plt.title('Rathmann model, AEP: %.3f GWh' % aep)
plt.show()
class epfl_model_wt(OneTypeWindTurbines):
def __init__(self):
OneTypeWindTurbines.__init__(self,
'NREL 5MW',
diameter=2,
hub_height=1,
ct_func=self._ct,
power_func=self._power,
power_unit='W')
def _ct(self, u):
ct = 0.798
return ct * u / u
def _power(self, u):
cp = 0.5
A = np.pi
rho = 1.225
return 0.5 * rho * u**3 * A * cp
wt = epfl_model_wt()
d = wt.diameter()
h = wt.hub_height()
wt_x = np.array([-6. * d, -3. * d, 0. * d, 3. * d, 6. * d])
wt_y = np.array([0., 0., 0., 0., 0.])
class epfl_wt(UniformSite):
def __init__(self):
p_wd = [1]
ws = [1]
ti = [0.06]
UniformSite.__init__(self, p_wd=p_wd, ti=ti, ws=ws)
self.initial_position = np.array([wt_x, wt_y]).T
site = epfl_wt()
blockage_models = [('Rathmann', Rathmann()),
('RathmannScaled', RathmannScaled())]
def pywake_run(blockage_models):
grid = HorizontalGrid(x=np.linspace(-10 * d, 10 * d, 100),
y=np.linspace(-15, 2 * d, 100))
res = {}
out = {}
for nam, blockage_model in blockage_models:
print(nam, blockage_model)
# for dum, rotor_avg_model in rotor_avg_models:
wm = All2AllIterative(site,
wt,
wake_deficitModel=NoWakeDeficit(),
superpositionModel=LinearSum(),
blockage_deficitModel=blockage_model,
rotorAvgModel=RotorCenter())
res[nam] = wm(wt_x, wt_y, wd=[180., 195., 210., 225.], ws=[1.])
tic = time.perf_counter()
flow_map = res[nam].flow_map(grid=grid, ws=[1.], wd=225.)
toc = time.perf_counter()
elapsed_time = toc - tic
out[nam] = flow_map['WS_eff'] / flow_map['WS']
out[nam]['time'] = elapsed_time
return out, res
out, res = pywake_run(blockage_models)
# CFD data from Meyer Forsting et al. 2017
p_cfd = np.array([[
-1.12511646713429, 0.268977884040651, 0.712062872514373,
1.08033923355738, 1.97378837188847
],
[
-0.610410399845213, 0.355339771667814,
0.670255435929930, 0.915154608331424,
1.52808830519513
],
[
-0.0988002822865217, 0.451698279664756,
0.636987630794206, 0.751760283763044,
1.03629687168984
],
[
0.477918399858401, 0.628396438496795,
0.663799054750132, 0.628396438496795,
0.479688468006036
]])
plt.figure()
lines = ['.-', 'o--', '^', '-.', '.-', '.-']
theta = [0, 15, 30, 45]
viridis = plt.cm.viridis(np.linspace(0, 0.9, 5))
jj = 0
tno = np.arange(1, 6, 1)
for i in range(4):
ii = len(theta) - i - 1
plt.plot(tno,
((p_cfd[ii, :] / 100. + 1.) /
(p_cfd[ii, 0] / 100. + 1.) - 1.) * 100,
's:',
color=viridis[i],
label='CFD: ' + str(theta[i]) + 'deg',
lw=2)
for nam, blockage_model in blockage_models:
for i in range(4):
if i == 3:
plt.plot(
tno,
(res[nam].Power[:, i, 0] - res[nam].Power[0, i, 0]) /
res[nam].Power[0, i, 0] * 100,
lines[jj],
label=nam,
color=viridis[i],
lw=1,
alpha=0.8)
else:
plt.plot(
tno,
(res[nam].Power[:, i, 0] - res[nam].Power[0, i, 0]) /
res[nam].Power[0, i, 0] * 100,
lines[jj],
color=viridis[i],
lw=1,
alpha=0.8)
jj += 1
plt.grid(alpha=0.2)
plt.xlabel('Turbine no.')
plt.ylabel('Power change, $(P-P_1)/P_1$ [%]')
plt.xticks([0, 1, 2, 3, 4, 5])
plt.xlim([.9, 5.1])
plt.legend(fontsize=11)
plt.show()
def main():
if __name__ == '__main__':
import matplotlib.pyplot as plt
from py_wake.examples.data.hornsrev1 import Hornsrev1Site
from py_wake.examples.data import hornsrev1
from py_wake.superposition_models import LinearSum
from py_wake.wind_farm_models import All2AllIterative
site = Hornsrev1Site()
windTurbines = hornsrev1.HornsrevV80()
ws = 10
D = 80
R = D / 2
WS_ilk = np.array([[[ws]]])
D_src_il = np.array([[D]])
ct_ilk = np.array([[[.8]]])
ss = SelfSimilarityDeficit()
ss20 = SelfSimilarityDeficit2020()
x, y = -np.arange(200), np.array([0])
# original model
deficit = ss.calc_deficit(WS_ilk=WS_ilk,
D_src_il=D_src_il,
dw_ijlk=x.reshape((1, len(x), 1, 1)),
cw_ijlk=y.reshape((1, len(y), 1, 1)),
ct_ilk=ct_ilk)
# updated method
deficit20 = ss20.calc_deficit(WS_ilk=WS_ilk,
D_src_il=D_src_il,
dw_ijlk=x.reshape((1, len(x), 1, 1)),
cw_ijlk=y.reshape((1, len(y), 1, 1)),
ct_ilk=ct_ilk)
plt.figure()
plt.title('Fig 11 from [1]')
plt.xlabel('x/R')
plt.ylabel('a')
plt.plot(x / R, deficit[0, :, 0, 0] / ws, label='original')
plt.plot(x / R, deficit20[0, :, 0, 0] / ws, '--', label='updated')
plt.legend()
plt.figure()
x, y = np.array([-2 * R]), np.arange(200)
deficit = ss.calc_deficit(WS_ilk=WS_ilk,
D_src_il=D_src_il,
dw_ijlk=x.reshape((1, len(x), 1, 1)),
cw_ijlk=y.reshape((1, len(y), 1, 1)),
ct_ilk=ct_ilk)
deficit20 = ss20.calc_deficit(WS_ilk=WS_ilk,
D_src_il=D_src_il,
dw_ijlk=x.reshape((1, len(x), 1, 1)),
cw_ijlk=y.reshape((1, len(y), 1, 1)),
ct_ilk=ct_ilk)
plt.title('Fig 10 from [1]')
r12 = ss.r12(x / R)
r12_20 = ss20.r12(x / R)
plt.xlabel('y/R12 (epsilon)')
plt.ylabel('f')
plt.plot((y / R) / r12,
deficit[0, :, 0, 0] / deficit[0, 0, 0, 0],
label='original')
plt.plot((y / R) / r12_20,
deficit20[0, :, 0, 0] / deficit20[0, 0, 0, 0],
'--',
label='updated')
plt.legend()
plt.figure()
noj_ss = All2AllIterative(site,
windTurbines,
wake_deficitModel=NoWakeDeficit(),
superpositionModel=LinearSum(),
blockage_deficitModel=ss)
noj_ss20 = All2AllIterative(site,
windTurbines,
wake_deficitModel=NoWakeDeficit(),
superpositionModel=LinearSum(),
blockage_deficitModel=ss20)
flow_map = noj_ss(x=[0], y=[0], wd=[270], ws=[10]).flow_map()
flow_map20 = noj_ss20(x=[0], y=[0], wd=[270], ws=[10]).flow_map()
clevels = [.9, .95, .98, .99, .995, .998, .999, 1., 1.01, 1.02, 1.03]
flow_map.plot_wake_map()
plt.contour(flow_map.x,
flow_map.y,
flow_map.WS_eff[:, :, 0, -1, 0] / 10,
levels=clevels,
colors='k',
linewidths=0.5)
plt.contour(flow_map.x,
flow_map.y,
flow_map20.WS_eff[:, :, 0, -1, 0] / 10,
levels=clevels,
colors='r',
linewidths=0.5)
plt.title('Original (black) vs updated (red)')
plt.show()
from py_wake.examples.data.iea37._iea37 import IEA37Site
from py_wake.examples.data.iea37._iea37 import IEA37_WindTurbines
# setup site, turbines and wind farm model
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
noj_ss = All2AllIterative(site,
windTurbines,
wake_deficitModel=NoWakeDeficit(),
superpositionModel=LinearSum(),
blockage_deficitModel=ss)
noj_ss20 = All2AllIterative(site,
windTurbines,
wake_deficitModel=NoWakeDeficit(),
superpositionModel=LinearSum(),
blockage_deficitModel=ss20)
# run wind farm simulation
sim_res = noj_ss(x, y, wd=[0, 30, 45, 60, 90], ws=[5, 10, 15])
sim_res20 = noj_ss20(x, y, wd=[0, 30, 45, 60, 90], ws=[5, 10, 15])
# calculate AEP
aep = sim_res.aep().sum()
aep20 = sim_res20.aep().sum()
# plot wake map
fig, (ax1, ax2) = plt.subplots(1,
2,
figsize=(9, 4.5),
tight_layout=True)
levels = np.array(
[.9, .95, .98, .99, .995, .998, .999, 1., 1.01, 1.02, 1.03]) * 10.
print(noj_ss)
flow_map = sim_res.flow_map(wd=30, ws=10.)
flow_map.plot_wake_map(levels=levels, ax=ax1, plot_colorbar=False)
ax1.set_title('Original Self-Similar, AEP: %.3f GWh' % aep)
# plot wake map
print(noj_ss20)
flow_map = sim_res20.flow_map(wd=30, ws=10.)
flow_map.plot_wake_map(levels=levels, ax=ax2, plot_colorbar=False)
ax2.set_title('Self-Similar 2020, AEP: %.3f GWh' % aep20)
plt.show()