def test_double_wind_farm_model_All2AllIterative():
"""Check that a new wind farm model does not change results of previous"""
site = IEA37Site(64)
x, y = site.initial_position.T
x, y = wt_x, wt_y
windTurbines = IEA37_WindTurbines()
wfm = All2AllIterative(site, windTurbines, wake_deficitModel=IEA37SimpleBastankhahGaussianDeficit())
aep_ref = wfm(x, y).aep().sum()
All2AllIterative(site, windTurbines, wake_deficitModel=NoWakeDeficit())(x, y)
aep = wfm(x, y).aep().sum()
npt.assert_array_equal(aep, aep_ref)
def test_own_turbulence_is_zero(turbulenceModel):
site = Hornsrev1Site()
windTurbines = IEA37_WindTurbines()
wf_model = All2AllIterative(site, windTurbines, wake_deficitModel=IEA37SimpleBastankhahGaussianDeficit(),
turbulenceModel=turbulenceModel())
sim_res = wf_model([0], [0])
npt.assert_array_equal(sim_res.TI_eff, sim_res.TI.broadcast_like(sim_res.TI_eff))
def test_blockage_map(setup):
site, windTurbines, ss = setup
wm = All2AllIterative(site,
windTurbines,
wake_deficitModel=NoWakeDeficit(),
superpositionModel=LinearSum(),
blockage_deficitModel=ss)
flow_map = wm(x=[0], y=[0], wd=[270], ws=[10]).flow_map()
X_j, Y_j = flow_map.XY
WS_eff = flow_map.WS_eff_xylk[:, :, 0, 0]
if 0:
plt.contourf(X_j, Y_j, WS_eff)
plt.plot(X_j[200, ::50], Y_j[200, ::50], '.-')
plt.plot(X_j[250, ::50], Y_j[250, ::50], '.-')
print(list(np.round(WS_eff[200, ::50], 6)))
print(list(np.round(WS_eff[250, ::50], 6)))
ss.windTurbines.plot([0], [0], wd=[270])
plt.show()
npt.assert_array_almost_equal(WS_eff[200, ::50], [
9.940967, 9.911659, 9.855934, 9.736016, 9.44199, 10.0, 10.0, 10.0,
10.0, 10.0
])
npt.assert_array_almost_equal(WS_eff[250, ::50], [
9.937601, 9.90397, 9.834701, 9.659045, 9.049764, 10.0, 10.0, 10.0,
10.0, 10.0
])
def test_wake_and_blockage(setup):
site, windTurbines, ss = setup
noj_ss = All2AllIterative(site,
windTurbines,
wake_deficitModel=NOJDeficit(),
blockage_deficitModel=ss,
superpositionModel=LinearSum())
flow_map = noj_ss(x=[0], y=[0], wd=[270], ws=[10]).flow_map()
X_j, Y_j = flow_map.XY
WS_eff = flow_map.WS_eff_xylk[:, :, 0, 0]
npt.assert_array_almost_equal(WS_eff[200, ::50], [
9.940967, 9.911659, 9.855934, 9.736016, 9.44199, 4.560631, 5.505472,
6.223921, 6.782925, 7.226399
])
npt.assert_array_almost_equal(WS_eff[250, ::50], [
9.937601, 9.90397, 9.834701, 9.659045, 9.049764, 4.560631, 5.505472,
6.223921, 6.782925, 7.226399
])
if 0:
plt.contourf(X_j, Y_j, WS_eff)
plt.plot(X_j[200, ::50], Y_j[200, ::50], '.-')
plt.plot(X_j[250, ::50], Y_j[250, ::50], '.-')
print(list(np.round(WS_eff[200, ::50], 6)))
print(list(np.round(WS_eff[250, ::50], 6)))
ss.windTurbines.plot([0], [0], wd=[270])
plt.show()
def test_wake_blockage_split(upstream_only, ref):
class MyWakeModel(WakeDeficitModel):
args4deficit = []
def calc_deficit(self, dw_ijlk, **kwargs):
return np.ones_like(dw_ijlk) * 2
class MyBlockageModel(BlockageDeficitModel):
args4deficit = []
def calc_deficit(self, dw_ijlk, **kwargs):
return np.ones_like(dw_ijlk)
site = Hornsrev1Site()
windTurbines = IEA37_WindTurbines()
wf_model = All2AllIterative(
site,
windTurbines,
wake_deficitModel=MyWakeModel(),
blockage_deficitModel=MyBlockageModel(upstream_only=upstream_only))
sim_res = wf_model([0], [0], ws=10, wd=270)
fm = sim_res.flow_map(XYGrid(x=[-100, 100], y=[-100, 0, 100]))
if 0:
sim_res.flow_map().plot_wake_map()
print(fm.WS_eff.values.squeeze().T)
plt.show()
npt.assert_array_equal(fm.WS_eff.values.squeeze().T, ref)
def test_blockage_map(setup, blockage_model, center_ref, side_ref):
site, windTurbines = setup
wm = All2AllIterative(site,
windTurbines,
wake_deficitModel=NoWakeDeficit(),
superpositionModel=LinearSum(),
blockage_deficitModel=blockage_model())
xy = np.linspace(-200, 200, 500)
flow_map = wm(x=[0], y=[0], wd=[270],
ws=[10]).flow_map(XYGrid(x=xy[::50], y=xy[[190, 250]]))
X_j, Y_j = flow_map.XY
WS_eff = flow_map.WS_eff_xylk[:, :, 0, 0]
if debug:
flow_map_full = wm(x=[0], y=[0], wd=[270], ws=[10]).flow_map()
X_j_full, Y_j_full = flow_map_full.XY
WS_eff_full = flow_map_full.WS_eff_xylk[:, :, 0, 0]
plt.contourf(X_j_full, Y_j_full, WS_eff_full)
plt.plot(X_j.T, Y_j.T, '.-')
print(list(np.round(np.array(WS_eff[0]), 6)))
print(list(np.round(np.array(WS_eff[1]), 6)))
plt.title(blockage_model.__name__)
plt.show()
npt.assert_array_almost_equal(WS_eff[0], center_ref)
npt.assert_array_almost_equal(WS_eff[1], side_ref)
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 __init__(self, LUT_path, site, windTurbines, turbulenceModel=None, convergence_tolerance=1e-6):
"""
Parameters
----------
LUT_path : str
path to look up tables
site : Site
Site object
windTurbines : WindTurbines
WindTurbines object representing the wake generating wind turbines
deflectionModel : DeflectionModel
Model describing the deflection of the wake due to yaw misalignment, sheared inflow, etc.
turbulenceModel : TurbulenceModel
Model describing the amount of added turbulence in the wake
"""
fuga_deficit = FugaDeficit(LUT_path)
All2AllIterative.__init__(self, site, windTurbines, wake_deficitModel=fuga_deficit,
superpositionModel=LinearSum(), blockage_deficitModel=fuga_deficit,
turbulenceModel=turbulenceModel, convergence_tolerance=convergence_tolerance)
def test_deficit_symmetry(wake_deficitModel, blockage_deficitModel):
site = Hornsrev1Site()
windTurbines = IEA37_WindTurbines()
wfm = All2AllIterative(site, windTurbines, wake_deficitModel=wake_deficitModel,
superpositionModel=LinearSum(),
blockage_deficitModel=blockage_deficitModel,
deflectionModel=None, turbulenceModel=None)
power = wfm([0, 0, 500, 500], [0, 500, 0, 500], wd=[0], ws=[8]).power_ilk[:, 0, 0]
npt.assert_array_almost_equal(power[:2], power[2:])
def test_own_deficit_is_zero():
for deficitModel in get_models(WakeDeficitModel):
site = Hornsrev1Site()
windTurbines = IEA37_WindTurbines()
wf_model = All2AllIterative(site,
windTurbines,
wake_deficitModel=deficitModel(),
turbulenceModel=STF2017TurbulenceModel())
sim_res = wf_model([0], [0])
npt.assert_array_equal(sim_res.WS_eff,
sim_res.WS.broadcast_like(sim_res.WS_eff))
def test_NOJ_Nibe_result():
# Replicate result from: Jensen, Niels Otto. "A note on wind generator interaction." (1983).
site = UniformSite([1], 0.1)
x_i = [0, 0, 0]
y_i = [0, -40, -100]
h_i = [50, 50, 50]
wfm = All2AllIterative(site,
NibeA0,
wake_deficitModel=NOJDeficit(),
superpositionModel=LinearSum())
WS_eff_ilk = wfm.calc_wt_interaction(x_i, y_i, h_i, [0, 1, 1], 0.0, 8.1)[0]
npt.assert_array_almost_equal(WS_eff_ilk[:, 0, 0], [8.1, 4.35, 5.7])
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()
def __init__(self,
LUT_path,
site,
windTurbines,
rotorAvgModel=RotorCenter(),
deflectionModel=None,
turbulenceModel=None,
convergence_tolerance=1e-6,
remove_wriggles=False):
"""
Parameters
----------
LUT_path : str
path to look up tables
site : Site
Site object
windTurbines : WindTurbines
WindTurbines object representing the wake generating wind turbines
rotorAvgModel : RotorAvgModel
Model defining one or more points at the down stream rotors to
calculate the rotor average wind speeds from.\n
Defaults to RotorCenter that uses the rotor center wind speed (i.e. one point) only
deflectionModel : DeflectionModel
Model describing the deflection of the wake due to yaw misalignment, sheared inflow, etc.
turbulenceModel : TurbulenceModel
Model describing the amount of added turbulence in the wake
"""
fuga_deficit = FugaDeficit(LUT_path, remove_wriggles=remove_wriggles)
All2AllIterative.__init__(self,
site,
windTurbines,
wake_deficitModel=fuga_deficit,
rotorAvgModel=rotorAvgModel,
superpositionModel=LinearSum(),
deflectionModel=deflectionModel,
blockage_deficitModel=fuga_deficit,
turbulenceModel=turbulenceModel,
convergence_tolerance=convergence_tolerance)
def test_aep_two_turbines(setup, blockage_model, blockage_loss):
site, windTurbines = setup
nwm_ss = All2AllIterative(site, windTurbines, wake_deficitModel=NoWakeDeficit(),
blockage_deficitModel=blockage_model(), superpositionModel=LinearSum())
sim_res = nwm_ss(x=[0, 80 * 3], y=[0, 0])
aep_no_blockage = sim_res.aep_ilk(with_wake_loss=False).sum(2)
aep = sim_res.aep_ilk().sum(2)
# blockage reduce aep(wd=270) by .5%
npt.assert_almost_equal((aep_no_blockage[0, 270] - aep[0, 270]) / aep_no_blockage[0, 270] * 100, blockage_loss)
if debug:
plt.plot(sim_res.WS_eff_ilk[:, :, 7].T)
plt.title(blockage_model.__name__)
plt.show()
def test_stf(turbulence_model, ref_ti):
# setup site, turbines and wind farm model
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
for wake_model in [
NOJ(site, windTurbines, turbulenceModel=turbulence_model),
All2AllIterative(site,
windTurbines,
wake_deficitModel=NOJDeficit(),
superpositionModel=SquaredSum(),
turbulenceModel=turbulence_model)
]:
res = wake_model(x, y)
# print(np.round(res.TI_eff_ilk[:, 0, 0], 3).tolist())
npt.assert_array_almost_equal(res.TI_eff_ilk[:, 0, 0], ref_ti, 3)
def test_RotorGridAvg_deficit():
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wfm = IEA37SimpleBastankhahGaussian(site,
windTurbines)
flow_map = wfm([0, 500], [0, 0], wd=270, ws=10).flow_map(HorizontalGrid(x=[500], y=np.arange(-100, 100)))
plt.plot(flow_map.Y[:, 0], flow_map.WS_eff_xylk[:, 0, 0, 0])
R = windTurbines.diameter() / 2
for name, rotorAvgModel, ref1 in [
('RotorCenter', RotorCenter(), 7.172723970425709),
('RotorGrid2', EqGridRotorAvg(2), 7.495889360682771),
('RotorGrid3', EqGridRotorAvg(3), 7.633415167369133),
('RotorGrid4', EqGridRotorAvg(4), 7.710215921858325),
('RotorGrid100', EqGridRotorAvg(100), 7.820762402628349),
('RotorGQGrid_4,3', GQGridRotorAvg(4, 3), 7.826105012683896),
('RotorCGI4', CGIRotorAvg(4), 7.848406907726826),
('RotorCGI4', CGIRotorAvg(7), 7.819900693605533),
('RotorCGI4', CGIRotorAvg(9), 7.82149363932618),
('RotorCGI4', CGIRotorAvg(21), 7.821558905416136)]:
# test with PropagateDownwind
wfm = IEA37SimpleBastankhahGaussian(site,
windTurbines,
rotorAvgModel=rotorAvgModel)
sim_res = wfm([0, 500], [0, 0], wd=270, ws=10)
npt.assert_almost_equal(sim_res.WS_eff_ilk[1, 0, 0], ref1)
# test with All2AllIterative
wfm = All2AllIterative(site, windTurbines,
IEA37SimpleBastankhahGaussianDeficit(),
rotorAvgModel=rotorAvgModel,
superpositionModel=SquaredSum())
sim_res = wfm([0, 500], [0, 0], wd=270, ws=10)
npt.assert_almost_equal(sim_res.WS_eff_ilk[1, 0, 0], ref1)
plt.plot([-R, R], [sim_res.WS_eff_ilk[1, 0, 0]] * 2, label=name)
if 0:
plt.legend()
plt.show()
plt.close('all')
def test_models_with_noj(turbulence_model, ref_ti):
# setup site, turbines and wind farm model
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
for wake_model in [NOJ(site, windTurbines, turbulenceModel=turbulence_model),
All2AllIterative(site, windTurbines,
wake_deficitModel=NOJDeficit(),
superpositionModel=SquaredSum(),
turbulenceModel=turbulence_model),
]:
res = wake_model(x, y)
# print(turbulence_model.__class__.__name__, np.round(res.TI_eff_ilk[:, 0, 0], 3).tolist())
if 0:
res.flow_map(wd=0).plot_ti_map()
plt.show()
npt.assert_array_almost_equal(res.TI_eff_ilk[:, 0, 0], ref_ti, 3)
def test_RotorGridAvg_ti():
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wfm = IEA37SimpleBastankhahGaussian(site,
windTurbines,
turbulenceModel=STF2017TurbulenceModel())
flow_map = wfm([0, 500], [0, 0], wd=270, ws=10).flow_map(HorizontalGrid(x=[500], y=np.arange(-100, 100)))
plt.plot(flow_map.Y[:, 0], flow_map.TI_eff_xylk[:, 0, 0, 0])
R = windTurbines.diameter() / 2
for name, rotorAvgModel, ref1 in [
('RotorCenter', RotorCenter(), 0.22292190804089568),
('RotorGrid2', EqGridRotorAvg(2), 0.2111162769995657),
('RotorGrid3', EqGridRotorAvg(3), 0.2058616982653193),
('RotorGrid4', EqGridRotorAvg(4), 0.2028701990648858),
('RotorGrid100', EqGridRotorAvg(100), 0.1985255601976247),
('RotorGQGrid_4,3', GQGridRotorAvg(4, 3), 0.1982984399750206)]:
# test with PropagateDownwind
wfm = IEA37SimpleBastankhahGaussian(site,
windTurbines,
rotorAvgModel=rotorAvgModel,
turbulenceModel=STF2017TurbulenceModel())
sim_res = wfm([0, 500], [0, 0], wd=270, ws=10)
npt.assert_almost_equal(sim_res.TI_eff_ilk[1, 0, 0], ref1)
# test with All2AllIterative
wfm = All2AllIterative(site, windTurbines,
IEA37SimpleBastankhahGaussianDeficit(),
rotorAvgModel=rotorAvgModel,
superpositionModel=SquaredSum(),
turbulenceModel=STF2017TurbulenceModel())
sim_res = wfm([0, 500], [0, 0], wd=270, ws=10)
npt.assert_almost_equal(sim_res.TI_eff_ilk[1, 0, 0], ref1)
plt.plot([-R, R], [sim_res.TI_eff_ilk[1, 0, 0]] * 2, label=name)
if 0:
plt.legend()
plt.show()
plt.close('all')
def test_deficitModel_wake_map_convection_all2all(deficitModel, ref):
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wf_model = All2AllIterative(site,
windTurbines,
wake_deficitModel=deficitModel,
superpositionModel=WeightedSum(),
blockage_deficitModel=VortexDipole(),
turbulenceModel=STF2017TurbulenceModel())
x_j = np.linspace(-1500, 1500, 200)
y_j = np.linspace(-1500, 1500, 100)
flow_map = wf_model(x, y, wd=0, ws=9).flow_map(HorizontalGrid(x_j, y_j))
X, Y = flow_map.X, flow_map.Y
Z = flow_map.WS_eff_xylk[:, :, 0, 0]
mean_ref = [
3.2, 4.9, 8., 8.2, 7.9, 7.4, 7., 7., 7.4, 7.9, 8.1, 8.1, 8., 7.8, 7.9,
8.1, 8.4
]
if 0:
flow_map.plot_wake_map()
plt.plot(X[49, 100:133:2], Y[49, 100:133:2], '.-')
windTurbines.plot(x, y)
plt.figure()
plt.plot(Z[49, 100:133:2], label='Actual')
plt.plot(ref, label='Reference')
plt.plot(mean_ref, label='Mean ref')
plt.legend()
plt.show()
# check that ref is reasonable
npt.assert_allclose(ref[2:], mean_ref[2:], atol=2.6)
npt.assert_array_almost_equal(Z[49, 100:133:2], ref, 2)
def test_RotorAvg_deficit():
site = IEA37Site(16)
windTurbines = IEA37_WindTurbines()
wfm = IEA37SimpleBastankhahGaussian(site,
windTurbines,
turbulenceModel=STF2017TurbulenceModel())
flow_map = wfm([0, 500], [0, 0], wd=270, ws=10).flow_map(HorizontalGrid(x=[500], y=np.arange(-100, 100)))
plt.plot(flow_map.Y[:, 0], flow_map.TI_eff_xylk[:, 0, 0, 0])
R = windTurbines.diameter() / 2
for name, rotorAvgModel, ref1 in [
('None', None, 0.22292190804089568),
('RotorCenter', RotorCenter(), 0.22292190804089568),
('RotorGrid100', EqGridRotorAvg(100), 0.1989725533174574),
('RotorGQGrid_4,3', GQGridRotorAvg(4, 3), 0.19874837617113356),
('RotorCGI4', CGIRotorAvg(4), 0.19822024411411204),
('RotorCGI4', CGIRotorAvg(21), 0.1989414764606653)]:
# test with PropagateDownwind
wfm = IEA37SimpleBastankhahGaussian(site,
windTurbines,
turbulenceModel=STF2017TurbulenceModel(rotorAvgModel=rotorAvgModel))
sim_res = wfm([0, 500], [0, 0], wd=270, ws=10)
npt.assert_almost_equal(sim_res.TI_eff_ilk[1, 0, 0], ref1, err_msg=name)
# test with All2AllIterative
wfm = All2AllIterative(site, windTurbines,
IEA37SimpleBastankhahGaussianDeficit(),
turbulenceModel=STF2017TurbulenceModel(rotorAvgModel=rotorAvgModel),
superpositionModel=SquaredSum())
sim_res = wfm([0, 500], [0, 0], wd=270, ws=10)
npt.assert_almost_equal(sim_res.TI_eff_ilk[1, 0, 0], ref1)
plt.plot([-R, R], [sim_res.WS_eff_ilk[1, 0, 0]] * 2, label=name)
if 0:
plt.legend()
plt.show()
plt.close()
def test_blockage_map(setup, blockage_model, center_ref, side_ref):
site, windTurbines = setup
wm = All2AllIterative(site,
windTurbines,
wake_deficitModel=NoWakeDeficit(),
superpositionModel=LinearSum(),
blockage_deficitModel=blockage_model())
flow_map = wm(x=[0], y=[0], wd=[270], ws=[10]).flow_map()
X_j, Y_j = flow_map.XY
WS_eff = flow_map.WS_eff_xylk[:, :, 0, 0]
if debug:
plt.contourf(X_j, Y_j, WS_eff)
plt.plot(X_j[190, ::50], Y_j[190, ::50], '.-')
plt.plot(X_j[250, ::50], Y_j[250, ::50], '.-')
print(list(np.round(np.array(WS_eff[190, ::50]), 6)))
print(list(np.round(np.array(WS_eff[250, ::50]), 6)))
plt.title(blockage_model.__name__)
plt.show()
npt.assert_array_almost_equal(WS_eff[190, ::50], center_ref)
npt.assert_array_almost_equal(WS_eff[250, ::50], side_ref)
def test_superposition_model_indices(superpositionModel, sum_func):
class WTSite(UniformSite):
def local_wind(self,
x_i=None,
y_i=None,
h_i=None,
wd=None,
ws=None,
wd_bin_size=None,
ws_bins=None):
lw = UniformSite.local_wind(self,
x_i=x_i,
y_i=y_i,
h_i=h_i,
wd=wd,
ws=ws,
wd_bin_size=wd_bin_size,
ws_bins=ws_bins)
lw['WS'] = xr.DataArray(
lw.WS_ilk + np.arange(len(x_i))[:, np.newaxis, np.newaxis],
[('wt', [0, 1, 2]), ('wd', np.atleast_1d(wd)),
('ws', np.atleast_1d(ws))])
return lw
site = WTSite([1], 0.1)
x_i = [0, 0, 0]
y_i = [0, -40, -100]
h_i = [50, 50, 50]
# WS_ilk different at each wt position
WS_ilk = site.local_wind(x_i, y_i, h_i, wd=0, ws=8.1).WS_ilk
npt.assert_array_equal(WS_ilk, np.reshape([8.1, 9.1, 10.1], (3, 1, 1)))
for wake_model in [
PropagateDownwind(site,
NibeA0,
wake_deficitModel=NOJDeficit(),
superpositionModel=superpositionModel),
All2AllIterative(site,
NibeA0,
wake_deficitModel=NOJDeficit(),
superpositionModel=superpositionModel)
]:
# No wake (ct = 0), i.e. WS_eff == WS
WS_eff_ilk = wake_model.calc_wt_interaction(x_i, y_i, h_i, [1, 1, 1],
0.0, 8.1)[0]
npt.assert_array_equal(WS_eff_ilk, WS_ilk)
ref = WS_ilk - np.reshape(
[0, 3.75, sum_func([2.4, 3.58974359])], (3, 1, 1))
# full wake (CT=8/9)
WS_eff_ilk = wake_model.calc_wt_interaction(x_i, y_i, h_i, [0, 0, 0],
0.0, 8.1)[0]
npt.assert_array_almost_equal(WS_eff_ilk, ref)
sim_res = wake_model(x_i, y_i, h_i, [0, 0, 0], 0.0, 8.1)
WS_eff_ilk = sim_res.flow_map(HorizontalGrid(x=[0], y=y_i,
h=50)).WS_eff_xylk[:, 0]
npt.assert_array_almost_equal(WS_eff_ilk, ref)
def get_wfms(wt, site=Hornsrev1Site(), wake_model=NOJDeficit(), superpositionModel=SquaredSum()):
wfm1 = PropagateDownwind(site, wt, wake_model, superpositionModel=superpositionModel)
wfm2 = All2AllIterative(site, wt, wake_model, superpositionModel=superpositionModel)
wfm2.verbose = False
wfm1.verbose = False
return wfm1, wfm2
