def __init__(self,
site,
windTurbines,
rotorAvgModel=RotorCenter(),
k=.1,
superpositionModel=SquaredSum(),
deflectionModel=None,
turbulenceModel=None):
"""
Parameters
----------
site : Site
Site object
windTurbines : WindTurbines
WindTurbines object representing the wake generating wind turbines
k : float, default 0.1
wake expansion factor
superpositionModel : SuperpositionModel, default SquaredSum
Model defining how deficits sum up
blockage_deficitModel : DeficitModel, default None
Model describing the blockage(upstream) deficit
deflectionModel : DeflectionModel, default None
Model describing the deflection of the wake due to yaw misalignment, sheared inflow, etc.
turbulenceModel : TurbulenceModel, default None
Model describing the amount of added turbulence in the wake
"""
PropagateDownwind.__init__(self,
site,
windTurbines,
wake_deficitModel=NOJDeficit(k),
rotorAvgModel=rotorAvgModel,
superpositionModel=superpositionModel,
deflectionModel=deflectionModel,
turbulenceModel=turbulenceModel)
def test_with_all_deficit_models(WFM, deficitModel):
site = IEA37Site(16)
windTurbines = IEA37_WindTurbines()
wfm = WFM(site,
windTurbines,
wake_deficitModel=deficitModel,
rotorAvgModel=RotorCenter(),
superpositionModel=LinearSum(),
deflectionModel=None,
turbulenceModel=STF2017TurbulenceModel())
wfm2 = WFM(site,
windTurbines,
wake_deficitModel=deficitModel,
rotorAvgModel=EqGridRotorAvg(1),
superpositionModel=LinearSum(),
deflectionModel=None,
turbulenceModel=STF2017TurbulenceModel())
kwargs = {
'x': [0, 0, 500, 500],
'y': [0, 500, 0, 500],
'wd': [0],
'ws': [8]
}
npt.assert_equal(wfm.aep(**kwargs), wfm2.aep(**kwargs))
def __init__(self,
LUT_path,
site,
windTurbines,
rotorAvgModel=RotorCenter(),
deflectionModel=None,
turbulenceModel=None):
"""
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
"""
PropagateDownwind.__init__(self,
site,
windTurbines,
wake_deficitModel=FugaDeficit(LUT_path),
rotorAvgModel=rotorAvgModel,
superpositionModel=LinearSum(),
deflectionModel=deflectionModel,
turbulenceModel=turbulenceModel)
def __init__(self,
site,
windTurbines,
rotorAvgModel=RotorCenter(),
superpositionModel=SquaredSum(),
deflectionModel=None,
turbulenceModel=None):
"""
Parameters
----------
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
superpositionModel : SuperpositionModel, default SquaredSum
Model defining how deficits sum up
deflectionModel : DeflectionModel, default None
Model describing the deflection of the wake due to yaw misalignment, sheared inflow, etc.
turbulenceModel : TurbulenceModel, default None
Model describing the amount of added turbulence in the wake
"""
PropagateDownwind.__init__(
self,
site,
windTurbines,
wake_deficitModel=IEA37SimpleBastankhahGaussianDeficit(),
rotorAvgModel=rotorAvgModel,
superpositionModel=superpositionModel,
deflectionModel=deflectionModel,
turbulenceModel=turbulenceModel)
def __init__(self, site, windTurbines, rotorAvgModel=RotorCenter(), superpositionModel=LinearSum(),
deflectionModel=None, turbulenceModel=None):
PropagateDownwind.__init__(self, site, windTurbines,
wake_deficitModel=GCLDeficit(use_effective_ws=True, use_effective_ti=True),
rotorAvgModel=rotorAvgModel, superpositionModel=superpositionModel,
deflectionModel=deflectionModel, turbulenceModel=turbulenceModel)
def __init__(self,
site,
windTurbines,
wake_deficitModel,
rotorAvgModel=RotorCenter(),
superpositionModel=LinearSum(),
blockage_deficitModel=None,
deflectionModel=None,
turbulenceModel=None,
groundModel=None,
convergence_tolerance=1e-6):
"""Initialize flow model
Parameters
----------
site : Site
Site object
windTurbines : WindTurbines
WindTurbines object representing the wake generating wind turbines
wake_deficitModel : DeficitModel
Model describing the wake(downstream) deficit
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
superpositionModel : SuperpositionModel
Model defining how deficits sum up
blockage_deficitModel : DeficitModel
Model describing the blockage(upstream) deficit
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
convergence_tolerance : float
maximum accepted change in WS_eff_ilk [m/s]
"""
EngineeringWindFarmModel.__init__(
self,
site,
windTurbines,
wake_deficitModel,
rotorAvgModel,
superpositionModel,
blockage_deficitModel=blockage_deficitModel,
deflectionModel=deflectionModel,
turbulenceModel=turbulenceModel,
groundModel=groundModel)
self.convergence_tolerance = convergence_tolerance
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_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 __init__(self,
site,
windTurbines,
wake_deficitModel,
rotorAvgModel=RotorCenter(),
superpositionModel=LinearSum(),
deflectionModel=None,
turbulenceModel=None,
groundModel=None):
"""Initialize flow model
Parameters
----------
site : Site
Site object
windTurbines : WindTurbines
WindTurbines object representing the wake generating wind turbines
wake_deficitModel : DeficitModel
Model describing the wake(downstream) deficit
rotorAvgModel : RotorAvgModel, optional
Model defining one or more points at the down stream rotors to
calculate the rotor average wind speeds from.
Default is RotorCenter, i.e. one point at rotor center
superpositionModel : SuperpositionModel
Model defining how deficits sum up
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
"""
EngineeringWindFarmModel.__init__(self,
site,
windTurbines,
wake_deficitModel,
rotorAvgModel,
superpositionModel,
blockage_deficitModel=None,
deflectionModel=deflectionModel,
turbulenceModel=turbulenceModel,
groundModel=groundModel)
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 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.turbulence_models.stf import STF2017TurbulenceModel
import matplotlib.pyplot as plt
# setup site, turbines and wind farm model
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wf_model = NOJ(site, windTurbines)
wf_model_local = NOJLocal(site,
windTurbines,
turbulenceModel=STF2017TurbulenceModel())
wf_model_turbo = PropagateDownwind(
site,
windTurbines,
rotorAvgModel=RotorCenter(),
wake_deficitModel=TurboNOJDeficit(use_effective_ws=True,
use_effective_ti=False),
superpositionModel=LinearSum(),
turbulenceModel=STF2017TurbulenceModel())
# wf_model_turbo = NOJLocal(
# site, windTurbines, turbulenceModel=STF2017TurbulenceModel())
# run wind farm simulation
sim_res = wf_model(x, y)
sim_res_local = wf_model_local(x, y)
sim_res_turbo = wf_model_turbo(x, y)
# calculate AEP
aep = sim_res.aep().sum()
aep_local = sim_res_local.aep().sum()
aep_turbo = sim_res_turbo.aep().sum()
# plot wake map
fig, (ax1, ax2, ax3) = plt.subplots(1,
3,
figsize=(11, 4.5),
tight_layout=True)
levels = np.arange(0, 10.5, 0.5)
print(wf_model)
flow_map = sim_res.flow_map(wd=30, ws=9.8)
flow_map.plot_wake_map(levels=levels, ax=ax1, plot_colorbar=False)
flow_map.plot_windturbines(ax=ax1)
ax1.set_title('Original Jensen, AEP: %.2f GWh' % aep)
# plot wake map
print(wf_model_local)
flow_map = sim_res_local.flow_map(wd=30, ws=9.8)
flow_map.plot_wake_map(levels=levels, ax=ax2, plot_colorbar=False)
flow_map.plot_windturbines(ax=ax2)
ax2.set_title('Local Jensen, AEP: %.2f GWh' % aep_local)
# plot wake map
print(wf_model_turbo)
flow_map = sim_res_turbo.flow_map(wd=30, ws=9.8)
flow_map.plot_wake_map(levels=levels, ax=ax3, plot_colorbar=False)
flow_map.plot_windturbines(ax=ax3)
ax3.set_title('Turbo Jensen, AEP: %.2f GWh' % aep_turbo)
plt.figure()
flow_map.plot_ti_map()
plt.title('TI map for NOJLocal with STF2017 turbulence model')
plt.show()
# plot wake width as in Nygaard 2020
D = 1
D_src_il = np.array([[D]])
x = np.linspace(0, 60, 100)
dw_ijlk = x[na, :, na, na]
noj = NOJDeficit(k=0.04)
noj_wr = noj.wake_radius(D_src_il, dw_ijlk)
ct_ilk = np.array([[[8 / 9]]]) # thrust coefficient
TI_ilk = np.array([[[0.06]]])
TI_eff_ilk = np.array([[[0.06]]])
tj = TurboNOJDeficit()
tj_wr = tj.wake_radius(D_src_il,
dw_ijlk,
ct_ilk=ct_ilk,
TI_ilk=TI_ilk,
TI_eff_ilk=TI_eff_ilk)
plt.figure()
plt.title(
'Wake width comparison, NOJ orig and TurboNOJ (Nygaard2020) TI=6%')
plt.plot(x, noj_wr[0, :, 0, 0], label='NOJ, k=0.04')
plt.plot(x, tj_wr[0, :, 0, 0], label='TurboNOJ')
plt.xlabel('x/D')
plt.ylabel('y/D')
plt.grid()
plt.legend()
plt.show()
