def __init__(self, site, windTurbines, rotorAvgModel=RotorCenter(), superpositionModel=LinearSum(),
deflectionModel=None, turbulenceModel=None):
PropagateDownwind.__init__(self, site, windTurbines, wake_deficitModel=GCLDeficit(),
rotorAvgModel=rotorAvgModel, superpositionModel=superpositionModel,
deflectionModel=deflectionModel, turbulenceModel=turbulenceModel)
def test_check_model():
check_model(LinearSum(), SuperpositionModel)
def get_wf_model(cls):
return cls(site, NibeA0, wake_deficitModel=NoWakeDeficit(),
superpositionModel=LinearSum(),
turbulenceModel=STF2017TurbulenceModel())
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()