def test_min_ws_eff_line():
site = IEA37Site(16)
x, y = [0, 600, 1200], [0, 0, 0] # site.initial_position[:2].T
windTurbines = IEA37_WindTurbines()
wfm = IEA37SimpleBastankhahGaussian(
site, windTurbines, deflectionModel=JimenezWakeDeflection())
yaw_ilk = np.reshape([-30, 30, 0], (3, 1, 1))
plt.figure(figsize=(14, 3))
fm = wfm(x, y, yaw=yaw_ilk, wd=270, ws=10).flow_map(
XYGrid(x=np.arange(-100, 2000, 10), y=np.arange(-500, 500, 10)))
min_ws_line = fm.min_WS_eff()
if 0:
fm.plot_wake_map()
min_ws_line.plot()
print(np.round(min_ws_line[::10], 2))
plt.show()
npt.assert_array_almost_equal(min_ws_line[::10], [
np.nan, np.nan, 11.6, 21.64, 30.42, 38.17, 45.09, 51.27, -8.65, -18.66,
-27.51, -35.37, -42.38, -48.58, -1.09, -1.34, -1.59, -1.83, -2.07,
-2.31, -2.56
], 2)
def test_yaw_tilt(yaw, tilt, cx, cz):
site = IEA37Site(16)
x, y = [0], [0]
windTurbines = V80()
D = windTurbines.diameter()
wfm = IEA37SimpleBastankhahGaussian(
site, windTurbines, deflectionModel=JimenezWakeDeflection())
x_lst = np.linspace(-200, 1000, 100)
y_lst = np.linspace(-100, 100, 201)
z_lst = np.arange(10, 200, 1)
fm_yz = wfm(x, y, wd=270, ws=10, yaw=yaw,
tilt=tilt).flow_map(YZGrid(x=5 * D, y=y_lst, z=z_lst))
fm_xz = wfm(x, y, wd=180, ws=10, yaw=yaw,
tilt=tilt).flow_map(YZGrid(x=0, y=x_lst, z=z_lst))
fm_xy = wfm(x, y, wd=270, ws=10, yaw=yaw,
tilt=tilt).flow_map(XYGrid(x=x_lst))
if 0:
axes = plt.subplots(3, 1)[1].flatten()
fm_xy.plot_wake_map(ax=axes[0])
axes[0].axvline(5 * D)
fm_xz.plot_wake_map(ax=axes[1])
axes[1].axvline(5 * D)
fm_yz.plot_wake_map(ax=axes[2])
plt.show()
wake_center = fm_yz.WS_eff.where(fm_yz.WS_eff == fm_yz.WS_eff.min(),
drop=True).squeeze()
npt.assert_allclose(wake_center.y, cx, atol=.1)
npt.assert_allclose(wake_center.h, cz, atol=.24)
def test_All2AllIterativeDeflection():
site = IEA37Site(16)
windTurbines = IEA37_WindTurbines()
wf_model = All2AllIterative(site, windTurbines,
wake_deficitModel=NOJDeficit(),
superpositionModel=SquaredSum(),
deflectionModel=JimenezWakeDeflection())
wf_model([0], [0])
def test_str():
site = IEA37Site(16)
windTurbines = IEA37_WindTurbines()
wf_model = All2AllIterative(site, windTurbines,
wake_deficitModel=NOJDeficit(),
superpositionModel=SquaredSum(),
blockage_deficitModel=SelfSimilarityDeficit(),
deflectionModel=JimenezWakeDeflection(),
turbulenceModel=STF2005TurbulenceModel())
assert str(wf_model) == "All2AllIterative(EngineeringWindFarmModel, NOJDeficit-wake, SelfSimilarityDeficit-blockage, RotorCenter-rotor-average, SquaredSum-superposition, JimenezWakeDeflection-deflection, STF2005TurbulenceModel-turbulence)"
def test_All2AllIterativeDeflection():
site = IEA37Site(16)
windTurbines = IEA37_WindTurbines()
wf_model = All2AllIterative(site,
windTurbines,
wake_deficitModel=NOJDeficit(),
superpositionModel=SquaredSum(),
deflectionModel=JimenezWakeDeflection())
sim_res = wf_model([0, 500], [0, 0], wd=270, ws=10, yaw=30)
if 0:
sim_res.flow_map(
XYGrid(x=np.linspace(-200, 1000, 100))).plot_wake_map()
plt.show()
wf_model = All2AllIterative(site,
windTurbines,
wake_deficitModel=NOJDeficit(),
superpositionModel=SquaredSum(),
blockage_deficitModel=SelfSimilarityDeficit(),
deflectionModel=JimenezWakeDeflection(),
turbulenceModel=STF2005TurbulenceModel())
assert str(
wf_model
) == "All2AllIterative(EngineeringWindFarmModel, NOJDeficit-wake, SelfSimilarityDeficit-blockage, RotorCenter-rotor-average, SquaredSum-superposition, JimenezWakeDeflection-deflection, STF2005TurbulenceModel-turbulence)"
@pytest.mark.parametrize(
'wake_deficitModel,deflectionModel,superpositionModel',
[(NOJDeficit(), None, SquaredSum()),
(IEA37SimpleBastankhahGaussianDeficit(), JimenezWakeDeflection(),
WeightedSum())])
def test_huge_flow_map(wake_deficitModel, deflectionModel, superpositionModel):
site = IEA37Site(16)
windTurbines = IEA37_WindTurbines()
wake_model = PropagateDownwind(site,
windTurbines,
wake_deficitModel=wake_deficitModel,
superpositionModel=superpositionModel,
deflectionModel=deflectionModel,
turbulenceModel=STF2005TurbulenceModel())
n_wt = 2
flow_map = wake_model(*site.initial_position[:n_wt].T,
wd=[0, 90]).flow_map(HorizontalGrid(resolution=1000))
# check that deficit matrix > 10MB (i.e. it enters the memory saving loop)
assert (np.prod(flow_map.WS_eff_xylk.shape) * n_wt * 8 / 1024**2) > 10
site = IEA37Site(16)
windTurbines = IEA37_WindTurbines()
wf_model = All2AllIterative(site,
windTurbines,
wake_deficitModel=NOJDeficit(),
superpositionModel=SquaredSum(),
blockage_deficitModel=SelfSimilarityDeficit(),
deflectionModel=JimenezWakeDeflection(),
turbulenceModel=STF2005TurbulenceModel())
assert str(
wf_model
) == "All2AllIterative(EngineeringWindFarmModel, NOJDeficit-wake, SelfSimilarityDeficit-blockage, SquaredSum-superposition, JimenezWakeDeflection-deflection, STF2005TurbulenceModel-turbulence)"
@pytest.mark.parametrize('deflectionModel', [(None),
(JimenezWakeDeflection())])
def test_huge_flow_map(deflectionModel):
site = IEA37Site(16)
windTurbines = IEA37_WindTurbines()
wake_model = NOJ(site,
windTurbines,
deflectionModel=deflectionModel,
turbulenceModel=STF2005TurbulenceModel())
n_wt = 2
flow_map = wake_model(*site.initial_position[:n_wt].T,
wd=0).flow_map(HorizontalGrid(resolution=1000))
# check that deficit matrix > 10MB (i.e. it enters the memory saving loop)
assert (np.prod(flow_map.WS_eff_xylk.shape) * n_wt * 8 / 1024**2) > 10
assert flow_map.WS_eff_xylk.shape == (1000, 1000, 1, 1)
def test_str():
site = IEA37Site(16)
windTurbines = IEA37_WindTurbines()
wf_model = All2AllIterative(site, windTurbines,
wake_deficitModel=NOJDeficit(),
superpositionModel=SquaredSum(),
blockage_deficitModel=SelfSimilarityDeficit(),
deflectionModel=JimenezWakeDeflection(),
turbulenceModel=STF2005TurbulenceModel())
assert str(wf_model) == "All2AllIterative(EngineeringWindFarmModel, NOJDeficit-wake, SelfSimilarityDeficit-blockage, RotorCenter-rotor-average, SquaredSum-superposition, JimenezWakeDeflection-deflection, STF2005TurbulenceModel-turbulence)"
@pytest.mark.parametrize('wake_deficitModel,deflectionModel,superpositionModel',
[(NOJDeficit(), None, SquaredSum()),
(IEA37SimpleBastankhahGaussianDeficit(), JimenezWakeDeflection(), WeightedSum())])
def test_huge_flow_map(wake_deficitModel, deflectionModel, superpositionModel):
site = IEA37Site(16)
windTurbines = IEA37_WindTurbines()
wake_model = PropagateDownwind(site, windTurbines, wake_deficitModel=wake_deficitModel,
superpositionModel=superpositionModel, deflectionModel=deflectionModel,
turbulenceModel=STF2005TurbulenceModel())
n_wt = 2
flow_map = wake_model(*site.initial_position[:n_wt].T, wd=0).flow_map(HorizontalGrid(resolution=1000))
# check that deficit matrix > 10MB (i.e. it enters the memory saving loop)
assert (np.prod(flow_map.WS_eff_xylk.shape) * n_wt * 8 / 1024**2) > 10
assert flow_map.WS_eff_xylk.shape == (1000, 1000, 1, 1)
def test_aep():
site = UniformSite([1], ti=0)
from py_wake.deflection_models.jimenez import JimenezWakeDeflection
from py_wake.superposition_models import MaxSum
from py_wake.wind_turbines.power_ct_functions import SimpleYawModel
site = LillgrundSite()
x, y = site.initial_position.T
#keeping only every second turbine as lillegrund turbines are approx. half the size of the iea 3.4MW
x = x[::2]
y = y[::2]
# # Wind turbines and wind farm model definition
windTurbines = IEA34_130_1WT_Surrogate() #additional_models=[SimpleYawModel()]
wfm = IEA37SimpleBastankhahGaussian(
site,
windTurbines,
deflectionModel=JimenezWakeDeflection(),
turbulenceModel=STF2017TurbulenceModel(
addedTurbulenceSuperpositionModel=MaxSum()))
load_signals = [
'del_blade_flap', 'del_blade_edge', 'del_tower_bottom_fa',
'del_tower_bottom_ss', 'del_tower_top_torsion'
]
wsp = np.asarray([10, 15])
wdir = np.asarray([90])
n_wt = x.size
i = n_wt
k = wsp.size
l = wdir.size