from abc import abstractmethod
from numpy import newaxis as na
from py_wake import np
from py_wake.superposition_models import SuperpositionModel, LinearSum, WeightedSum, CumulativeWakeSum
from py_wake.wind_farm_models.wind_farm_model import WindFarmModel
from py_wake.deflection_models.deflection_model import DeflectionModel
from py_wake.utils.gradients import cabs
from py_wake.rotor_avg_models.rotor_avg_model import RotorAvgModel, RotorCenter, NodeRotorAvgModel
from py_wake.turbulence_models.turbulence_model import TurbulenceModel
from py_wake.deficit_models.deficit_model import ConvectionDeficitModel, BlockageDeficitModel, WakeDeficitModel
from tqdm import tqdm
from py_wake.wind_turbines._wind_turbines import WindTurbines
from py_wake.utils.model_utils import check_model, fix_shape
from py_wake.utils.gradients import hypot
import warnings
from py_wake.input_modifier_models.input_modifier_model import InputModifierModel
from py_wake.deficit_models.no_wake import NoWakeDeficit
class EngineeringWindFarmModel(WindFarmModel):
"""
Base class for engineering wake models
General suffixes:
- i: turbines ordered by id
- j: downstream points/turbines
- k: wind speeds
- l: wind directions
Arguments available for calc_deficit (specifiy in args4deficit):
- WS_ilk: Local wind speed without wake effects
- TI_ilk: Local turbulence intensity without wake effects
- TI_std_ilk: Standard deviation of local turbulence intensity
- WS_eff_ilk: Local wind speed with wake effects
- TI_eff_ilk: Local turbulence intensity with wake effects
- D_src_il: Diameter of source turbine
- D_dst_ijl: Diameter of destination turbine
- dw_ijlk: Downwind distance from turbine i to point/turbine j
- hcw_ijlk: Horizontal cross wind distance from turbine i to point/turbine j
- dh_ijl: vertical distance from turbine i to point/turbine j
- cw_ijlk: Cross wind(horizontal and vertical) distance from turbine i to point/turbine j
- ct_ilk: Thrust coefficient
"""
default_grid_resolution = 500
def __init__(self, site, windTurbines: WindTurbines, wake_deficitModel, superpositionModel, rotorAvgModel=None,
blockage_deficitModel=None, deflectionModel=None, turbulenceModel=None, inputModifierModels=[]):
WindFarmModel.__init__(self, site, windTurbines)
if not isinstance(inputModifierModels, (list, tuple)):
inputModifierModels = [inputModifierModels]
for model, cls, name in ([(wake_deficitModel, WakeDeficitModel, 'wake_deficitModel'),
(superpositionModel, SuperpositionModel, 'superpositionModel'),
(blockage_deficitModel, BlockageDeficitModel, 'blockage_deficitModel'),
(deflectionModel, DeflectionModel, 'deflectionModel'),
(turbulenceModel, TurbulenceModel, 'turbulenceModel')] +
[(imm, InputModifierModel, 'inputModificierModels') for imm in inputModifierModels]):
check_model(model, cls, name)
if model is not None:
setattr(model, 'windFarmModel', self)
setattr(self, name, model)
self.inputModifierModels = inputModifierModels
if isinstance(superpositionModel, (WeightedSum, CumulativeWakeSum)):
assert isinstance(wake_deficitModel, ConvectionDeficitModel)
wake_rotorAvgModel = rotorAvgModel or self.wake_deficitModel.rotorAvgModel
assert wake_rotorAvgModel is None or isinstance(wake_rotorAvgModel, NodeRotorAvgModel), \
"WeightedSum and CumulativeWakeSum only works with NodeRotorAvgModel-based rotor average models"
# TI_eff requires a turbulence model
assert 'TI_eff_ilk' not in wake_deficitModel.args4deficit or turbulenceModel
self.wake_deficitModel = wake_deficitModel
if rotorAvgModel is not None:
warnings.warn("""The rotorAvgModel-argument of WindFarmModel is ambiguous and therefore deprecated.
Set the rotorAvgModel of the wake_deficitModel, the blockage_deficitModel and/or turbulenceModel instead.
Until removed, the rotorAvgModel of WindFarmModel will apply the rotorAvgModel to the wake_deficitModel only
if a rotorAvgModel has not already been specified for the wake_deficitModel""",
DeprecationWarning, stacklevel=2)
check_model(rotorAvgModel, RotorAvgModel, 'rotorAvgModel')
self.wake_deficitModel.rotorAvgModel = self.wake_deficitModel.rotorAvgModel or rotorAvgModel
self.superpositionModel = superpositionModel
self.blockage_deficitModel = blockage_deficitModel
self.deflectionModel = deflectionModel
self.turbulenceModel = turbulenceModel
# wake expansion continuation (wake-width scale factor) see
self.wec = 1
# Thomas, J. J. and Ning, A., "A Method for Reducing Multi-Modality in the Wind Farm Layout Optimization Problem,"
# Journal of Physics: Conference Series, Vol. 1037, The Science of Making
# Torque from Wind, Milano, Italy, jun 2018, p. 10.
self.deficit_initalized = False
self.args4deficit = self.wake_deficitModel.args4deficit
# self.args4deficit = set(self.args4deficit) | {'yaw_ilk'}
if self.blockage_deficitModel:
self.args4deficit = set(self.args4deficit) | set(self.blockage_deficitModel.args4deficit)
self.args4all = set(self.args4deficit)
if self.turbulenceModel:
self.args4all |= set(self.turbulenceModel.args4model)
if self.deflectionModel:
self.args4all |= set(self.deflectionModel.args4deflection)
for input_modifier in self.inputModifierModels:
self.args4all |= set(input_modifier.args4model)
def __str__(self):
def name(o):
return o.__class__.__name__
models = [self.__class__.__bases__[0].__name__, "%s-wake" % name(self.wake_deficitModel)]
if self.blockage_deficitModel:
models.append("%s-blockage" % name(self.blockage_deficitModel))
models.append("%s-superposition" % (name(self.superpositionModel)))
if self.deflectionModel:
models.append("%s-deflection" % name(self.deflectionModel))
if self.turbulenceModel:
models.append("%s-turbulence" % name(self.turbulenceModel))
return "%s(%s)" % (name(self), ", ".join(models))
def _init_deficit(self, **kwargs):
"""Calculate layout dependent wake (and blockage) deficit terms"""
self.wake_deficitModel.calc_layout_terms(**kwargs)
self.wake_deficitModel.deficit_initalized = True
if self.blockage_deficitModel:
if self.blockage_deficitModel != self.wake_deficitModel:
self.blockage_deficitModel.calc_layout_terms(**kwargs)
self.blockage_deficitModel.deficit_initalized = True
def _reset_deficit(self):
self.wake_deficitModel.deficit_initalized = False
if self.blockage_deficitModel:
self.blockage_deficitModel.deficit_initalized = False
def _add_blockage(self, deficit, dw_ijlk, **kwargs):
# the split line between wake and blockage is set slightly upstream to handle
# numerical inaccuracy in the trigonometric functions that calculates dw_ijlk
rotor_pos = -1e-10
if self.blockage_deficitModel is None:
deficit *= (dw_ijlk > rotor_pos)
blockage = None
elif (self.blockage_deficitModel != self.wake_deficitModel):
blockage = self.blockage_deficitModel.calc_blockage_deficit(
dw_ijlk=dw_ijlk, WS_ref_ilk=kwargs[self.blockage_deficitModel.WS_key], **kwargs)
deficit *= (dw_ijlk > rotor_pos)
else:
# Same model for both wake and blockage
# keep blockage in deficit and set blockage to zero
blockage = np.zeros_like(deficit)
return deficit, blockage
def _calc_deficit(self, dw_ijlk, **kwargs):
"""Calculate wake (and blockage) deficit"""
deficit = self.wake_deficitModel(dw_ijlk=dw_ijlk, **kwargs)
deficit, blockage = self._add_blockage(deficit, dw_ijlk, **kwargs)
return deficit, blockage
def _calc_deficit_convection(self, dw_ijlk, **kwargs):
"""Calculate wake convection deficit (and blockage)"""
deficit, uc, sigma_sqr = self.wake_deficitModel.calc_deficit_convection(dw_ijlk=dw_ijlk, **kwargs)
deficit, blockage = self._add_blockage(deficit, dw_ijlk, **kwargs)
return deficit, uc, sigma_sqr, blockage
def _calc_wt_interaction_args(self, kwargs):
"""Used for parallel execution"""
return self.calc_wt_interaction(**kwargs)
def calc_wt_interaction(self, x_ilk, y_ilk, h_i=None, type_i=0, wd=None, ws=None, time=False,
WS_eff_ilk=None,
n_cpu=1, wd_chunks=None, ws_chunks=1,
**kwargs):
"""See WindFarmModel.calc_wt_interaction and additional parameters below
Parameters
----------
n_cpu : int or None, optional
Number of CPUs to be used for execution.
If 1 (default), the execution is not parallized
If None, the available number of CPUs are used
wd_chunks : int or None, optional
If n_cpu>1, the wind directions are divided into <wd_chunks> chunks and executed in parallel.
If wd_chunks is None, wd_chunks is set to the available number of CPUs
ws_chunks : int or None, optional
If n_cpu>1, the wind speeds are divided into <ws_chunks> chunks and executed in parallel.
If ws_chunks is None, ws_chunks is set to 1
"""
h_i, D_i = self.windTurbines.get_defaults(len(x_ilk), type_i, h_i)
wd, ws = self.site.get_defaults(wd, ws)
I, L, K, = len(x_ilk), len(wd), (1, len(ws))[time is False]
kwargs.update(dict(x_ilk=x_ilk, y_ilk=y_ilk, h_ilk=h_i[:, na, na], wd=wd, ws=ws, time=time,
type_i=np.zeros_like(D_i) + type_i))
for inputModifierModel in self.inputModifierModels:
kwargs.update(inputModifierModel.setup(**kwargs))
# Find local wind speed, wind direction, turbulence intensity and probability
lw = self.site.local_wind(x=np.mean(x_ilk, (1, 2)), y=np.mean(y_ilk, (1, 2)), h=h_i,
wd=kwargs['wd'], ws=kwargs['ws'], time=kwargs['time'])
for k in ['WS_ilk', 'WD_ilk', 'TI_ilk']:
if k in kwargs:
lw.add_ilk(k, kwargs.pop(k))
ri, oi = self.windTurbines.function_inputs
kwargs.update({'WD_ilk': lw.WD_ilk,
'WS_ilk': lw.WS_ilk,
'WS_eff_ilk': WS_eff_ilk,
'D_i': D_i,
'I': I, 'L': L, 'K': K,
**{k + '_ilk': self.site.interp(self.site.ds[k], lw) for k in ri + oi if k in self.site.ds},
})
if hasattr(lw, 'TI_ilk'):
kwargs['TI_ilk'] = lw.TI_ilk
kwargs['TI_eff_ilk'] = lw.TI_ilk + 0. # autograd-friendly copy
self._check_input(kwargs)
if n_cpu != 1 or wd_chunks or ws_chunks > 1:
# parallel execution
map_func, arg_lst, wd_chunks, ws_chunks = self._multiprocessing_chunks(
n_cpu=n_cpu, wd_chunks=wd_chunks, ws_chunks=ws_chunks,
**kwargs)
WS_eff_ilk, TI_eff_ilk, power_ilk, ct_ilk, _, kwargs = list(
zip(*map_func(self._calc_wt_interaction_args, arg_lst)))
def concatenate(v_ilk):
v_ilk = [fix_shape(v, WS_eff.shape) for v, WS_eff in zip(v_ilk, WS_eff_ilk)]
if kwargs[0]['time'] is False:
return np.concatenate([np.concatenate(v_ilk[i::ws_chunks], axis=1)
for i in range(ws_chunks)], axis=2)
else:
return np.concatenate(v_ilk, axis=1)
return ([concatenate(v) for v in [WS_eff_ilk, TI_eff_ilk, power_ilk, ct_ilk]] +
[lw,
{'type_i': kwargs[0]['type_i'],
**{k: concatenate([wt_i[k] for wt_i in kwargs]) for k in kwargs[0] if k.endswith('_ilk')}}])
# Calculate down-wind and cross-wind distances
self.site.distance.setup(kwargs['x_ilk'], kwargs['y_ilk'], kwargs['h_ilk'])
WS_eff_ilk, TI_eff_ilk, ct_ilk, kwargs = self._calc_wt_interaction(**kwargs)
power_ilk = self.windTurbines.power(ws=WS_eff_ilk, **self.get_wt_kwargs(TI_eff_ilk, kwargs))
kwargs.update({'time': time, 'type_i': type_i})
return WS_eff_ilk, TI_eff_ilk, power_ilk, ct_ilk, lw, kwargs
@abstractmethod
def _calc_wt_interaction(self, **kwargs):
"""calculate WT interaction"""
def get_map_args(self, x_j, y_j, h_j, sim_res_data, D_dst=0):
wt_d_i = self.windTurbines.diameter(sim_res_data.type)
wd, ws = [np.atleast_1d(sim_res_data[k].values) for k in ['wd', 'ws']]
time = sim_res_data.get('time', False)
wt_x_ilk = sim_res_data['x'].ilk()
WD_il = sim_res_data.WD.ilk()
lw_j = self.site.local_wind(x=x_j, y=y_j, h=h_j, wd=wd, ws=ws, time=time)
I, J, L, K = [len(x) for x in [wt_x_ilk, x_j, wd, ws]]
def get_ilk(k):
v = sim_res_data[k].ilk()
def wrap(l):
l_ = [l, slice(0, 1)][v.shape[1] == 1]
return v[:, l_]
return wrap
map_arg_funcs = {k.replace('CT', 'ct') + '_ilk': get_ilk(k)
for k in sim_res_data if k not in ['wd_bin_size', 'ws_l', 'ws_u']}
map_arg_funcs.update({
'D_src_il': lambda l: wt_d_i[:, na],
'D_dst_ijl': lambda l: np.zeros((1, 1, 1)) + D_dst,
'IJLK': lambda l=slice(None), I=I, J=J, L=L, K=K: (I, J, len(np.arange(L)[l]), K)})
for k in ['WS', 'WD', 'TI']:
if k in sim_res_data:
if k + '_ilk' in sim_res_data.localWind.overwritten:
if 'wt' not in sim_res_data[k].dims and 'i' not in sim_res_data[k].dims:
lw_j.add_ilk(k + "_ilk", sim_res_data[k])
else:
warnings.warn(
f"The WT dependent {k} that was provided for the simulation is not available at the flow map points and therefore ignored")
return map_arg_funcs, lw_j, wd, WD_il
def _get_flow_l(self, model_kwargs, l, wt_x_ilk, wt_y_ilk, wt_h_ilk, lw_j, wd, WD_ilk):
self.site.distance.setup(wt_x_ilk, wt_y_ilk, wt_h_ilk, (lw_j.x, lw_j.y, lw_j.h))
dw_ijlk, hcw_ijlk, dh_ijlk = self.site.distance(wd_l=wd, WD_ilk=WD_ilk)
WS_jlk = lw_j.WS_ilk[:, [l, slice(0, 1)][lw_j.WS_ilk.shape[1] == 1]]
TI_jlk = lw_j.TI_ilk[:, [l, slice(0, 1)][lw_j.TI_ilk.shape[1] == 1]]
if self.wec != 1:
hcw_ijlk = hcw_ijlk / self.wec
if self.deflectionModel:
dw_ijlk, hcw_ijlk, dh_ijlk = self.deflectionModel.calc_deflection(
dw_ijlk=dw_ijlk, hcw_ijlk=hcw_ijlk, dh_ijlk=dh_ijlk, z_ijlk=wt_h_ilk[:, na] + dh_ijlk,
**model_kwargs)
model_kwargs.update({'dw_ijlk': dw_ijlk, 'hcw_ijlk': hcw_ijlk, 'dh_ijlk': dh_ijlk,
'x_ilk': wt_x_ilk, 'y_ilk': wt_y_ilk})
if 'cw_ijlk' in self.args4all:
model_kwargs['cw_ijlk'] = hypot(dh_ijlk, hcw_ijlk)
if 'wake_radius_ijlk' in self.args4all:
model_kwargs['wake_radius_ijlk'] = self.wake_deficitModel.wake_radius(**model_kwargs)
if 'wake_radius_ijl' in self.args4all:
model_kwargs['wake_radius_ijl'] = self.wake_deficitModel.wake_radius(**model_kwargs)[..., 0]
if 'z_ijlk' in self.args4all:
model_kwargs['z_ijlk'] = wt_h_ilk[:, na] + dh_ijlk
if 'WS_jlk' in self.args4all:
model_kwargs['WS_jlk'] = WS_jlk
# ===============================================================================================================
# Calculate deficit
# ===============================================================================================================
if isinstance(self.superpositionModel, (WeightedSum, CumulativeWakeSum)):
deficit_ijlk, uc_ijlk, sigma_sqr_ijlk, blockage_ijlk = self._calc_deficit_convection(**model_kwargs)
else:
deficit_ijlk, blockage_ijlk = self._calc_deficit(**model_kwargs)
# ===============================================================================================================
# Calculate added Turbulence
# ===============================================================================================================
if self.turbulenceModel:
add_turb_ijlk = self.turbulenceModel.calc_added_turbulence(**model_kwargs)
# ===============================================================================================================
# Sum up deficits
# ===============================================================================================================
sp_kwargs = dict(deficit_jxxx=deficit_ijlk)
if isinstance(self.superpositionModel, (WeightedSum, CumulativeWakeSum)):
if isinstance(self.superpositionModel, WeightedSum):
sp_kwargs.update({'WS_xxx': WS_jlk, 'convection_velocity_jxxx': uc_ijlk})
else:
sp_kwargs.update({'WS0_xxx': model_kwargs['WS_ilk'] * np.ones_like(model_kwargs['WS_eff_ilk']),
'WS_eff_xxx': model_kwargs['WS_eff_ilk'], 'ct_xxx': model_kwargs['ct_ilk'],
'D_xx': model_kwargs['D_src_il']})
sigma_sqr_ijlk = sigma_sqr_ijlk * (dw_ijlk > 1e-10)
sp_kwargs.update(dict(sigma_sqr_jxxx=sigma_sqr_ijlk, cw_jxxx=model_kwargs['cw_ijlk'],
hcw_jxxx=hcw_ijlk, dh_jxxx=dh_ijlk))
WS_eff_jlk = WS_jlk - self.superpositionModel.superpose_deficit(**sp_kwargs)
if self.blockage_deficitModel:
if isinstance(self.superpositionModel, (WeightedSum, CumulativeWakeSum)):
blockage_superpositionModel = self.blockage_deficitModel.superpositionModel or LinearSum()
else:
blockage_superpositionModel = self.blockage_deficitModel.superpositionModel or self.superpositionModel
WS_eff_jlk -= blockage_superpositionModel(blockage_ijlk)
# ===============================================================================================================
# Sum up added Turbulence
# ===============================================================================================================
if self.turbulenceModel:
TI_eff_jlk = self.turbulenceModel.calc_effective_TI(TI_jlk, add_turb_ijlk)
else:
TI_eff_jlk = None
return WS_eff_jlk, TI_eff_jlk
def _aep_map(self, x_j, y_j, h_j, type_j, sim_res_data):
lw_j, WS_eff_jlk, _ = self._flow_map(x_j, y_j, h_j, sim_res_data)
power_kwargs = {}
if 'type' in (self.windTurbines.powerCtFunction.required_inputs +
self.windTurbines.powerCtFunction.optional_inputs):
power_kwargs['type'] = type_j
power_jlk = self.windTurbines.power(WS_eff_jlk, **power_kwargs)
aep_j = (power_jlk * lw_j.P_ilk).sum((1, 2))
return aep_j * 365 * 24 * 1e-9
def _flow_map(self, x_j, y_j, h_j, sim_res_data, D_dst=0):
"""call this function via SimulationResult.flow_map"""
arg_funcs, lw_j, wd, WD_il = self.get_map_args(x_j, y_j, h_j, sim_res_data, D_dst=D_dst)
I, J, L, K = arg_funcs['IJLK']()
if I == 0:
return (lw_j, np.broadcast_to(lw_j.WS_ilk, (len(x_j), L, K)).astype(float),
np.broadcast_to(lw_j.TI_ilk, (len(x_j), L, K)).astype(float))
size_gb = I * J * L * K * 8 / 1024**3
wd_chunks = int(np.minimum(np.maximum(int(size_gb // 1), 1), L))
wd_i = np.round(np.linspace(0, L, wd_chunks + 1)).astype(int)
l_iter = tqdm([slice(i0, i1) for i0, i1 in zip(wd_i[:-1], wd_i[1:])], disable=L <= 1 or not self.verbose,
desc='Calculate flow map', unit='wd')
wt_x_ilk, wt_y_ilk, wt_h_ilk = [sim_res_data[k].ilk() for k in ['x', 'y', 'h']]
WS_eff_jlk, TI_eff_jlk = zip(*[self._get_flow_l(
{k: arg_funcs[k](l) for k in arg_funcs},
l,
*[(v, v[:, l])[np.shape(v)[1] == L] for v in [wt_x_ilk, wt_y_ilk, wt_h_ilk]],
lw_j, wd[l], WD_il[:, l])
for l in l_iter])
WS_eff_jlk = np.concatenate(WS_eff_jlk, 1)
if self.turbulenceModel:
TI_eff_jlk = np.concatenate(TI_eff_jlk, 1)
else:
TI_eff_jlk = np.zeros_like(WS_eff_jlk) + lw_j.TI_ilk
return lw_j, WS_eff_jlk, TI_eff_jlk
def _check_input(self, kwargs):
x_ilk, y_ilk, h_ilk = kwargs['x_ilk'], kwargs['y_ilk'], kwargs['h_ilk']
i1, i2, *_ = np.where((cabs(x_ilk[:, na] - x_ilk[na]) +
cabs(y_ilk[:, na] - y_ilk[na]) +
cabs(h_ilk[:, na] - h_ilk[na]) +
np.eye(len(x_ilk))[:, :, na, na]) == 0)
if len(i1):
msg = "\n".join(["Turbines %d and %d are at the same position" % (i1[i], i2[i]) for i in range(len(i1))])
raise ValueError(msg)
for k in self.args4all:
if k.endswith('_ilk') and k not in ['ct_ilk'] and k not in kwargs:
n = k.replace('_ilk', '')
needed_by = str(self)
for model in [self.wake_deficitModel, self.superpositionModel, self.blockage_deficitModel,
self.deflectionModel, self.turbulenceModel] + self.inputModifierModels:
if ((hasattr(model, 'args4model') and k in model.args4model) or
(hasattr(model, 'args4deficit') and k in model.args4deficit)):
needed_by = model.__class__.__name__
break
raise ValueError(f"'{n}' needed by {needed_by} is missing")
ri, oi = self.windTurbines.function_inputs
for k in kwargs:
n = k.replace('_ilk', '').replace('_i', '')
if (n not in ri + oi and k not in self.args4all and
n not in {'x', 'y', 'h', 'wd', 'ws', 'time', 'type', 'D', 'WD', 'WS',
'WS_eff', 'TI', 'TI_eff', 'I', 'L', 'K'}):
raise ValueError(f"WindFarmModel an got unexpected keyword argument: '{n}'")
class PropagateUpDownIterative(EngineeringWindFarmModel):
"""Downstream wake deficits calculated and propagated in downstream direction.
Very fast, but ignoring blockage effects
"""
def __init__(self, site, windTurbines, wake_deficitModel,
superpositionModel=LinearSum(),
blockage_deficitModel=None,
deflectionModel=None, turbulenceModel=None, rotorAvgModel=None,
inputModifierModels=[], 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, optional
Model defining one or more points at the down stream rotors to
calculate the rotor average wind speeds from.\n
if None, default, the wind speed at the rotor center is used
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, superpositionModel, rotorAvgModel,
blockage_deficitModel=blockage_deficitModel, deflectionModel=deflectionModel,
turbulenceModel=turbulenceModel, inputModifierModels=inputModifierModels)
msg = 'PropagateUpDownIterative requires a wake deficit model that scales with the effective wind speed. For most models this can be achieved by setting the argument use_effective_ws=True'
assert isinstance(wake_deficitModel, NoWakeDeficit) or wake_deficitModel.WS_key == 'WS_eff_ilk', msg
msg = "PropagateUpDownIterative only works with blockage deficit models that scales with the effective wind speed. For most models this can be achieved by setting the argument use_effective_ws=True"
assert blockage_deficitModel is None or blockage_deficitModel.WS_key == 'WS_eff_ilk', msg
self.convergence_tolerance = convergence_tolerance
def _calc_wt_interaction(self, wd, WS_eff_ilk,
**kwargs):
WS_ilk = kwargs.pop('WS_ilk')
blockage_deficit = WS_ilk * 0.
I = kwargs['I']
dw_order_indices_ld = self.site.distance.dw_order_indices(wd)[:, 0]
if self.blockage_deficitModel:
# use linear sum as default blockage superpositionModel
alt_model = [self.superpositionModel, LinearSum()][isinstance(
self.superpositionModel, (WeightedSum, CumulativeWakeSum))]
self.blockage_superpositionModel = self.blockage_deficitModel.superpositionModel or alt_model
WS_eff_ilk_last = WS_ilk
for j in tqdm(range(I), disable=I <= 1 or not self.verbose, desc="Calculate flow interaction", unit="wt"):
# wake deficit
self.direction = 'down'
WS_eff_wake_ilk, TI_eff_ilk, ct_ilk, res_kwargs = self._propagate_deficit(
wd, dw_order_indices_ld,
WS_ilk - blockage_deficit, **kwargs)
wake_deficit = (WS_ilk - blockage_deficit) - WS_eff_wake_ilk
# blockage deficit
self.direction = 'up'
WS_eff_blockage_ilk = self._propagate_deficit(wd, dw_order_indices_ld[:, ::-1],
WS_ilk - wake_deficit, **kwargs)[0]
blockage_deficit = (WS_ilk - wake_deficit) - WS_eff_blockage_ilk
WS_eff_ilk = WS_ilk - wake_deficit - blockage_deficit
# Check if converged
diff_ilk = cabs(WS_eff_ilk_last - WS_eff_ilk)
max_diff = np.max(diff_ilk.max(0))
if max_diff < 1e-6:
break
WS_eff_ilk_last = WS_eff_ilk
self.direction = 'down'
return WS_eff_ilk, TI_eff_ilk, ct_ilk, res_kwargs
def _calc_deficit(self, dw_ijlk, **kwargs):
"""Calculate wake (and blockage) deficit"""
if self.direction == 'up':
deficit = dw_ijlk * 0
deficit, blockage = self._add_blockage(deficit, dw_ijlk, **kwargs)
else:
deficit, blockage = EngineeringWindFarmModel._calc_deficit(self, dw_ijlk=dw_ijlk, **kwargs)
return deficit, blockage
def _propagate_deficit(self, wd,
wt_order_indices_ld,
WS_ilk,
TI_eff_ilk,
D_i,
I, L, K, **kwargs):
"""
Additional suffixes:
- m: turbines and wind directions (il.flatten())
- n: from_turbines, to_turbines and wind directions (iil.flatten())
"""
deficit_nk = []
blockage_nk = []
uc_nk = []
sigma_sqr_nk = []
cw_nk = []
hcw_nk = []
dh_nk = []
def ilk2mk(v_ilk):
dtype = (float, np.complex128)[np.iscomplexobj(v_ilk)]
_K = np.shape(v_ilk)[2]
return np.broadcast_to(np.asarray(v_ilk).astype(dtype), (I, L, _K)).reshape((I * L, _K))
WS_mk = ilk2mk(WS_ilk)
WD_mk, TI_mk, h_mk = [ilk2mk(kwargs[k + '_ilk']) for k in ['WD', 'TI', 'h']]
WS_eff_mk, TI_eff_mk = [], []
yaw_mk = ilk2mk(kwargs.get('yaw_ilk', [[[0]]]))
tilt_mk = ilk2mk(kwargs.get('tilt_ilk', [[[0]]]))
modified_input_dict_mk = []
WS_free_mk = []
D_mk = []
ct_jlk = []
if self.turbulenceModel:
add_turb_nk = []
i_wd_l = np.arange(L).astype(int)
wt_kwargs = self.get_wt_kwargs(TI_eff_ilk, kwargs)
# Iterate over turbines in down wind order
for j in tqdm(range(I), disable=I <= 1 or not self.verbose, desc="Calculate flow interaction", unit="wt"):
i_wt_l = wt_order_indices_ld[:, j]
# current wt (j'th most upstream wts for all wdirs)
m = i_wt_l * L + i_wd_l
# Calculate effectiv wind speed at current turbines(all wind directions and wind speeds) and
# look up power and thrust coefficient
if j == 0: # Most upstream turbines (no wake)
WS_eff_lk = WS_mk[m]
WS_eff_mk.append(WS_eff_lk)
if self.turbulenceModel:
TI_eff_lk = TI_mk[m]
TI_eff_mk.append(np.broadcast_to(TI_eff_lk, (L, K)))
else: # 2..n most upstream turbines (wake)
def get_value2WT(value_nk):
"""Get value input to current turbine, j
value_nk triangular is a list j elements.
First element contains e.g. the defict to
"""
return np.array([d_nk2[i] for d_nk2, i in zip(value_nk, range(j)[::-1])])
sp_kwargs = {'deficit_jxxx': get_value2WT(deficit_nk)}
if isinstance(self.superpositionModel, (WeightedSum, CumulativeWakeSum)):
sp_kwargs.update({k: get_value2WT(v_nk) for k, v_nk in [('sigma_sqr_jxxx', sigma_sqr_nk),
('cw_jxxx', cw_nk),
('hcw_jxxx', hcw_nk),
('dh_jxxx', dh_nk)]})
if isinstance(self.superpositionModel, WeightedSum):
sp_kwargs.update({'WS_xxx': WS_mk[m],
'convection_velocity_jxxx': get_value2WT(uc_nk)})
else:
sp_kwargs.update({'WS0_xxx': np.array(WS_free_mk),
'WS_eff_xxx': np.array(WS_eff_mk),
'ct_xxx': np.array(ct_jlk),
'D_xx': np.array(D_mk)})
WS_eff_lk = WS_mk[m] - self.superpositionModel.superpose_deficit(**sp_kwargs)
if self.blockage_deficitModel:
WS_eff_lk -= self.blockage_superpositionModel(get_value2WT(blockage_nk))
WS_eff_mk.append(WS_eff_lk)
if self.turbulenceModel:
add_turb2WT = np.array([d_nk2[i] for d_nk2, i in zip(add_turb_nk, range(j)[::-1])])
TI_eff_lk = self.turbulenceModel.calc_effective_TI(TI_mk[m], add_turb2WT)
TI_eff_mk.append(TI_eff_lk)
# assemble free wind speed (ask mmpe why it is not) and diameter to allow cumulative superposition
WS_free_mk.append(WS_mk[m])
D_mk.append(D_i[i_wt_l])
# Calculate Power/CT
def mask(k, v):
if len(np.squeeze(v).shape) == 0:
return np.squeeze(v)
v = np.asarray(v)
if v.shape[:2] == (I, L):
return v[i_wt_l, i_wd_l]
elif v.shape[0] == I:
return v[i_wt_l].flatten()
else:
assert v.shape[1] == L
return v[0, i_wd_l]
_wt_kwargs = {k: mask(k, v) for k, v in wt_kwargs.items()}
if 'TI_eff' in _wt_kwargs:
_wt_kwargs['TI_eff'] = TI_eff_mk[-1]
ct_lk = self.windTurbines.ct(WS_eff_lk, **_wt_kwargs)
ct_jlk.append(ct_lk)
if j < I - 1 or len(self.inputModifierModels):
i_dw = wt_order_indices_ld[:, j + 1:]
# Calculate required args4deficit parameters
arg_funcs = {'WS_ilk': lambda: WS_mk[m][na],
'WS_jlk': lambda: np.moveaxis([WS_ilk[(slice(0, 1), j)[WS_ilk.shape[0] > 1],
(0, l)[WS_ilk.shape[1] > 1]]
for j, l in zip(i_dw, i_wd_l)], 0, 1),
'WS_eff_ilk': lambda: WS_eff_mk[-1][na],
'TI_ilk': lambda: TI_mk[m][na],
'TI_eff_ilk': lambda: TI_eff_mk[-1][na],
'D_src_il': lambda: D_i[i_wt_l][na],
'yaw_ilk': lambda: yaw_mk[m][na],
'tilt_ilk': lambda: tilt_mk[m][na],
'D_dst_ijl': lambda: D_i[wt_order_indices_ld[:, j + 1:]].T[na],
'h_ilk': lambda: h_mk[m][na],
'ct_ilk': lambda: ct_lk[na],
'IJLK': lambda: (1, i_dw.shape[1], L, K),
'WD_ilk': lambda: WD_mk[m][na],
**{k + '_ilk': lambda k=k: ilk2mk(kwargs[k + '_ilk'])[m][na] for k in 'xyh'},
'type_il': lambda: kwargs['type_i'][i_wt_l][na]
}
model_kwargs = {k: arg_funcs[k]() for k in self.args4all if k in arg_funcs}
# custom model arguments
custom_args = (set([k for k in self.args4all if k.endswith('_ilk')]) - set(model_kwargs)) & set(kwargs)
model_kwargs.update({k: ilk2mk(kwargs[k])[m][na] for k in custom_args})
dw_ijlk, hcw_ijlk, dh_ijlk = self.site.distance(
wd_l=wd, WD_ilk=WD_mk[m][na], src_idx=i_wt_l, dst_idx=i_dw.T)
for inputModidifierModel in self.inputModifierModels:
modified_input_dict = inputModidifierModel(**model_kwargs)
modified_input_dict_mk.append(modified_input_dict)
model_kwargs.update(modified_input_dict)
if self.wec != 1:
hcw_ijlk = hcw_ijlk / self.wec
if self.deflectionModel:
dw_ijlk, hcw_ijlk, dh_ijlk = self.deflectionModel.calc_deflection(
dw_ijlk=dw_ijlk, hcw_ijlk=hcw_ijlk, dh_ijlk=dh_ijlk, **model_kwargs)
model_kwargs.update({'dw_ijlk': dw_ijlk, 'hcw_ijlk': hcw_ijlk, 'dh_ijlk': dh_ijlk})
if 'z_ijlk' in self.args4all:
model_kwargs['z_ijlk'] = h_mk[m][na, na] + dh_ijlk
hcw_nk.append(hcw_ijlk[0])
dh_nk.append(dh_ijlk[0])
if 'cw_ijlk' in self.args4all:
# sqrt(a**2+b**2) as hypot does not support complex numbers
model_kwargs['cw_ijlk'] = np.sqrt(dh_ijlk**2 + hcw_ijlk**2)
cw_nk.append(model_kwargs['cw_ijlk'][0])
if 'wake_radius_ijl' in self.args4all:
model_kwargs['wake_radius_ijl'] = self.wake_deficitModel.wake_radius(**model_kwargs)[..., 0]
if 'wake_radius_ijlk' in self.args4all:
model_kwargs['wake_radius_ijlk'] = self.wake_deficitModel.wake_radius(**model_kwargs)
# ======================================================================================================
# Calculate deficit
# ======================================================================================================
if isinstance(self.superpositionModel, (WeightedSum, CumulativeWakeSum)):
# only cw needs to be rotor averaged as remaining super position input is
# the same all over the rotor
if self.wake_deficitModel.rotorAvgModel:
cw_nk[-1] = (self.wake_deficitModel.rotorAvgModel(lambda ** kwargs: kwargs['cw_ijlk'],
**model_kwargs))[0]
if isinstance(self.superpositionModel, WeightedSum):
deficit, uc, sigma_sqr, _ = self._calc_deficit_convection(**model_kwargs)
uc_nk.append(uc[0])
sigma_sqr_nk.append(sigma_sqr[0])
elif isinstance(self.superpositionModel, CumulativeWakeSum):
# only sigma needed in cumulative wake model, centerline deficit computed inside superpostion model
# sigma set to zero upstream to ensure downwind activation only
sigma_sqr = (self.wake_deficitModel.sigma_ijlk(**model_kwargs))**2 * (dw_ijlk > 1e-10)
sigma_sqr_nk.append(sigma_sqr[0])
deficit = np.zeros_like(sigma_sqr)
else:
deficit, blockage = self._calc_deficit(**model_kwargs)
deficit_nk.append(deficit[0])
if self.blockage_deficitModel:
blockage_nk.append(blockage[0])
if self.turbulenceModel:
# Calculate added turbulence
add_turb_nk.append(self.turbulenceModel(**model_kwargs)[0])
WS_eff_jlk, ct_jlk = np.array(WS_eff_mk), np.array(ct_jlk)
wt_inv_indices = (np.argsort(wt_order_indices_ld, 1).T * L + np.arange(L).astype(int)[na]).flatten()
WS_eff_ilk = WS_eff_jlk.reshape((I * L, K))[wt_inv_indices].reshape((I, L, K))
ct_ilk = ct_jlk.reshape((I * L, K))[wt_inv_indices].reshape((I, L, K))
if self.turbulenceModel:
TI_eff_jlk = np.array(TI_eff_mk)
TI_eff_ilk = TI_eff_jlk.reshape((I * L, K))[wt_inv_indices].reshape((I, L, K))
if len(self.inputModifierModels):
for k in modified_input_dict_mk[0].keys():
mi_jlk = np.array([mi_dict[k] for mi_dict in modified_input_dict_mk])
kwargs[k] = mi_jlk.reshape((I * L, K))[wt_inv_indices].reshape((I, L, K))
return WS_eff_ilk, TI_eff_ilk, ct_ilk, kwargs
[docs]class PropagateDownwind(PropagateUpDownIterative):
"""Downstream wake deficits calculated and propagated in downstream direction.
Very fast, but ignoring blockage effects
"""
[docs] def __init__(self, site, windTurbines, wake_deficitModel,
superpositionModel=LinearSum(),
deflectionModel=None, turbulenceModel=None, rotorAvgModel=None,
inputModifierModels=[]):
"""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.\n
if None, default, the wind speed at the rotor center is used
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, superpositionModel, rotorAvgModel,
blockage_deficitModel=None, deflectionModel=deflectionModel,
turbulenceModel=turbulenceModel, inputModifierModels=inputModifierModels)
def _calc_deficit(self, dw_ijlk, **kwargs):
return EngineeringWindFarmModel._calc_deficit(self, dw_ijlk, **kwargs)
def _calc_wt_interaction(self, wd, WS_eff_ilk, **kwargs):
WS_ilk = kwargs.pop('WS_ilk')
dw_order_indices_ld = self.site.distance.dw_order_indices(wd)[:, 0]
return self._propagate_deficit(wd, dw_order_indices_ld, WS_ilk, **kwargs)
[docs]class All2AllIterative(EngineeringWindFarmModel):
"""Wake and blockage deficits calculated from all wt to all points of interest (wt/map points).
The calculations are iteratively repeated until convergence (change of effective wind speed < convergence_tolerance)"""
[docs] def __init__(self, site, windTurbines, wake_deficitModel,
superpositionModel=LinearSum(),
blockage_deficitModel=None, deflectionModel=None, turbulenceModel=None,
convergence_tolerance=1e-6, rotorAvgModel=None, inputModifierModels=[]):
"""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.\n
if None, default, the wind speed at the rotor center is used
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 or None
if float: maximum accepted change in WS_eff_ilk [m/s]
if None: return after first iteration. This only makes sense for benchmark studies where CT,
wakes and blockage are independent of effective wind speed WS_eff_ilk
"""
EngineeringWindFarmModel.__init__(self, site, windTurbines, wake_deficitModel, superpositionModel, rotorAvgModel,
blockage_deficitModel=blockage_deficitModel, deflectionModel=deflectionModel,
turbulenceModel=turbulenceModel, inputModifierModels=inputModifierModels)
self.convergence_tolerance = convergence_tolerance
def _calc_wt_interaction(self, ws, wd, WD_ilk, WS_ilk, TI_ilk,
WS_eff_ilk, TI_eff_ilk,
D_i, time,
I, L, K, **kwargs):
if any([np.iscomplexobj(v) for v in ([kwargs.get(k, 0)
for k in ['x_ilk', 'y_ilk', 'h_ilk', 'D_i', 'yaw_ilk', 'tilt_ilk']] +
[ws, wd])]):
dtype = np.complex128
else:
dtype = float
WS_ILK = np.broadcast_to(WS_ilk, (I, L, K))
# calculate WS_eff without blockage as a first guess
if WS_eff_ilk is None:
# Initialize with PropagateDownwind
blockage_deficitModel = self.blockage_deficitModel
self.blockage_deficitModel = None
dw_order_indices_ld = self.site.distance.dw_order_indices(wd)[:, 0]
WS_eff_ilk = PropagateUpDownIterative._propagate_deficit(
self, wd, dw_order_indices_ld, WD_ilk=WD_ilk, WS_ilk=WS_ilk, TI_ilk=TI_ilk,
WS_eff_ilk=WS_eff_ilk, TI_eff_ilk=TI_eff_ilk, D_i=D_i, I=I, L=L, K=K, **kwargs)[0]
self.blockage_deficitModel = blockage_deficitModel
elif np.all(WS_eff_ilk == 0):
WS_eff_ilk = WS_ILK + 0.
WS_eff_ilk = WS_eff_ilk.astype(dtype)
WS_eff_ilk_last = WS_eff_ilk + 0 # fast autograd-friendly copy
diff_lk = np.zeros((L, K))
diff_lk_last = None
dw_iilk, hcw_iilk, dh_iilk = self.site.distance(wd_l=wd, WD_ilk=WD_ilk)
kwargs['WD_ilk'] = WD_ilk
wt_kwargs = self.get_wt_kwargs(TI_eff_ilk, kwargs)
ct_ilk = self.windTurbines.ct(ws=WS_ILK, **wt_kwargs)
try:
ct_ilk_idle = self.windTurbines.ct(ws=0.1 * np.ones_like(WS_ILK), **wt_kwargs)
except BaseException:
ct_ilk_idle = 0
unstable_lk = np.zeros((L, K), dtype=bool)
ioff = np.broadcast_to(ct_ilk, (I, L, K)) < -1 # index of off/idling turbines
D_src_il = D_i[:, na]
model_kwargs = {'WS_ilk': WS_ilk,
'WS_eff_ilk': WS_eff_ilk,
'WD_ilk': WD_ilk,
'TI_ilk': TI_ilk,
'TI_eff_ilk': TI_eff_ilk,
'D_src_il': D_src_il,
'D_dst_ijl': D_src_il[na],
'dw_ijlk': dw_iilk,
'hcw_ijlk': hcw_iilk,
'cw_ijlk': np.sqrt(hcw_iilk**2 + dh_iilk**2),
'dh_ijlk': dh_iilk,
'z_ijlk': kwargs['h_ilk'][:, na] + dh_iilk,
'IJLK': (I, I, L, K),
'type_il': kwargs['type_i'][:, na],
** kwargs,
}
if 'wake_radius_ijl' in self.args4all:
model_kwargs['wake_radius_ijl'] = self.wake_deficitModel.wake_radius(**model_kwargs)[:, :, :, 0]
if not self.deflectionModel:
self._init_deficit(**model_kwargs)
cw_iilk = np.sqrt(hcw_iilk**2 + dh_iilk**2)
i2i_zero = ~np.eye(I).astype(bool)[:, :, na, na]
if self.blockage_deficitModel:
# use linear sum as default blockage superpositionModel
alt_model = [self.superpositionModel, LinearSum()][isinstance(
self.superpositionModel, (WeightedSum, CumulativeWakeSum))]
blockage_superpositionModel = self.blockage_deficitModel.superpositionModel or alt_model
# Iterate until convergence
for j in tqdm(range(I), disable=I <= 1 or not self.verbose,
desc="Calculate flow interaction", unit="Iteration"):
ct_ilk = self.windTurbines.ct(np.maximum(WS_eff_ilk, 0), **wt_kwargs)
ioff |= (unstable_lk)[na] & (ct_ilk <= ct_ilk_idle)
model_kwargs.update(dict(ct_ilk=ct_ilk, WS_eff_ilk=WS_eff_ilk))
if self.inputModifierModels:
# x_ilk, y_ilk and h_ilk is may be updated by an inputModifierModel and
# must be reset in every iterations
model_kwargs.update(dict(x_ilk=kwargs['x_ilk'], y_ilk=kwargs['y_ilk'], h_ilk=kwargs['h_ilk']))
if self.deflectionModel:
model_kwargs.update(dict(
# dw_ijlk, hcw_ijlk and dh_ijlk is updated by deflection model and must be reset in every iterations
dw_ijlk=dw_iilk,
hcw_ijlk=hcw_iilk,
cw_ijlk=cw_iilk,
dh_ijlk=dh_iilk,
z_ijlk=kwargs['h_ilk'][:, na] + dh_iilk))
for inputModidifierModel in self.inputModifierModels:
modified_input_dict = inputModidifierModel(**model_kwargs)
model_kwargs.update(modified_input_dict)
if any([k in modified_input_dict for k in ['x_ilk', 'y_ilk']]):
self.site.distance.setup(model_kwargs['x_ilk'], model_kwargs['y_ilk'], model_kwargs['h_ilk'])
model_kwargs.update({k: v for k, v in zip(['dw_ijlk', 'hcw_ijlk', 'dh_ijlk'],
self.site.distance(wd_l=wd, WD_ilk=WD_ilk))})
model_kwargs['cw_ijlk'] = hypot(model_kwargs['dh_ijlk'], model_kwargs['hcw_ijlk'])
if not self.deflectionModel:
self._init_deficit(**model_kwargs)
if self.deflectionModel:
dw_ijlk, hcw_ijlk, dh_ijlk = self.deflectionModel.calc_deflection(**model_kwargs)
model_kwargs.update({'dw_ijlk': dw_ijlk, 'hcw_ijlk': hcw_ijlk, 'dh_ijlk': dh_ijlk,
'cw_ijlk': hypot(dh_ijlk, hcw_ijlk)})
self._reset_deficit()
if 'wake_radius_ijlk' in self.args4all:
model_kwargs['wake_radius_ijlk'] = self.wake_deficitModel.wake_radius(**model_kwargs)
if self.turbulenceModel:
model_kwargs['TI_eff_ilk'] = TI_eff_ilk
# Calculate deficit
if isinstance(self.superpositionModel, WeightedSum):
deficit_iilk, uc_iilk, sigmasqr_iilk, blockage_iilk = self._calc_deficit_convection(**model_kwargs)
elif isinstance(self.superpositionModel, CumulativeWakeSum):
sigmasqr_iilk = (self.wake_deficitModel.sigma_ijlk(**model_kwargs))**2 * \
(model_kwargs['dw_ijlk'] > 1e-10)
deficit_iilk, blockage_iilk = self._calc_deficit(**model_kwargs)
else:
deficit_iilk, blockage_iilk = self._calc_deficit(**model_kwargs)
# set own deficit to 0
deficit_iilk *= i2i_zero
if blockage_iilk is not None:
blockage_iilk *= i2i_zero
sp_kwargs = {'deficit_jxxx': deficit_iilk}
if isinstance(self.superpositionModel, (WeightedSum, CumulativeWakeSum)):
cw_ijlk = model_kwargs['cw_ijlk']
if self.wake_deficitModel.rotorAvgModel:
cw_ijlk = self.wake_deficitModel.rotorAvgModel(lambda **kwargs: kwargs['cw_ijlk'], **model_kwargs)
sp_kwargs.update({'sigma_sqr_jxxx': sigmasqr_iilk,
'cw_jxxx': cw_ijlk,
'hcw_jxxx': model_kwargs['hcw_ijlk'],
'dh_jxxx': dh_iilk})
if isinstance(self.superpositionModel, WeightedSum):
sp_kwargs.update({'WS_xxx': WS_ilk,
'convection_velocity_jxxx': uc_iilk})
else:
sp_kwargs.update({'WS0_xxx': WS_ILK,
'WS_eff_xxx': model_kwargs['WS_eff_ilk'],
'ct_xxx': model_kwargs['ct_ilk'],
'D_xx': model_kwargs['D_src_il']})
WS_eff_ilk = WS_ilk.astype(dtype) - self.superpositionModel.superpose_deficit(**sp_kwargs)
if self.blockage_deficitModel:
WS_eff_ilk -= blockage_superpositionModel(blockage_iilk)
# ensure idling wt in unstable flow cases do not cutin even if ws increases due to speedup
# this helps to converge
# WS_eff_ilk[ioff] = np.minimum(WS_eff_ilk[ioff], WS_eff_ilk_last[ioff])
WS_eff_ilk = np.minimum(WS_eff_ilk, WS_eff_ilk_last, out=WS_eff_ilk, where=ioff)
if self.turbulenceModel:
add_turb_ijlk = self.turbulenceModel(**model_kwargs)
add_turb_ijlk *= i2i_zero
TI_eff_ilk = self.turbulenceModel.calc_effective_TI(TI_ilk, add_turb_ijlk)
# Check if converged
diff_ilk = cabs(WS_eff_ilk_last - WS_eff_ilk)
diff_lk = diff_ilk.mean(0)
max_diff = np.max(diff_ilk.max(0))
if (self.convergence_tolerance is None or
(self.convergence_tolerance and max_diff < self.convergence_tolerance)):
break
# i_, l_, k_ = list(zip(*np.where(diff_ilk == max_diff)))[0]
# wsi, wsl, wsk = WS_ilk.shape
# wsi, wsl, wsk = WS_ilk.shape
# print("Iteration: %d, max diff_ilk: %.8f, WT: %d, WD: %d, WS: %f, WS_eff: %f" %
# (j, max_diff, i_, wd[l_],
# WS_ilk[min(i_, wsi - 1), min(l_, wsl - 1), min(k_, wsk - 1)],
# WS_eff_ilk[i_, l_, k_]))
# print(j, diff_ilk.mean(0), WS_eff_ilk.squeeze())
# assume flow case to be unstable if mean difference of two iterations increases
if j > 1:
unstable_lk |= diff_lk_last < diff_lk
WS_eff_ilk_last = WS_eff_ilk + 0 # fast autograd-friendly copy
diff_lk_last = diff_lk
# print("All2AllIterative converge after %d iterations" % (j + 1))
self.iterations = j + 1
self.WS_eff_ilk_last = getattr(WS_eff_ilk, '_value', WS_eff_ilk)
self._reset_deficit()
if len(self.inputModifierModels):
kwargs.update({k: modified_input_dict[k] for k in modified_input_dict})
return WS_eff_ilk, np.broadcast_to(TI_eff_ilk, (I, L, K)), ct_ilk, kwargs
class All2All(All2AllIterative):
def __init__(self, site, windTurbines, wake_deficitModel,
superpositionModel=LinearSum(),
blockage_deficitModel=None, deflectionModel=None, turbulenceModel=None,
rotorAvgModel=None):
All2AllIterative.__init__(self, site, windTurbines, wake_deficitModel, superpositionModel=superpositionModel,
blockage_deficitModel=blockage_deficitModel, deflectionModel=deflectionModel,
turbulenceModel=turbulenceModel, convergence_tolerance=None,
rotorAvgModel=rotorAvgModel)
def _calc_wt_interaction(self, WS_eff_ilk, **kwargs):
return All2AllIterative._calc_wt_interaction(self, WS_eff_ilk=0, **kwargs)
def main():
if __name__ == '__main__':
from py_wake.examples.data.iea37 import IEA37Site, IEA37_WindTurbines
from py_wake.deficit_models.selfsimilarity import SelfSimilarityDeficit
from py_wake.deficit_models.gaussian import ZongGaussianDeficit
from py_wake.turbulence_models.stf import STF2017TurbulenceModel
from py_wake.flow_map import XYGrid
import matplotlib.pyplot as plt
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
from py_wake.deficit_models.noj import NOJDeficit
from py_wake.superposition_models import SquaredSum
# NOJ wake model
noj = PropagateDownwind(site, windTurbines, wake_deficitModel=NOJDeficit(), superpositionModel=SquaredSum())
# NOJ wake and selfsimilarity blockage
noj_ss = All2AllIterative(site, windTurbines, wake_deficitModel=NOJDeficit(), superpositionModel=SquaredSum(),
blockage_deficitModel=SelfSimilarityDeficit())
# Zong convection superposition
zongp = PropagateDownwind(site, windTurbines, wake_deficitModel=ZongGaussianDeficit(), superpositionModel=WeightedSum(),
turbulenceModel=STF2017TurbulenceModel())
# Zong convection superposition
zong_ss = All2AllIterative(site, windTurbines, wake_deficitModel=ZongGaussianDeficit(), superpositionModel=WeightedSum(),
blockage_deficitModel=SelfSimilarityDeficit(), turbulenceModel=STF2017TurbulenceModel())
# Cumulativ wake superposition
cwp = PropagateDownwind(site, windTurbines, wake_deficitModel=ZongGaussianDeficit(), superpositionModel=CumulativeWakeSum(),
turbulenceModel=STF2017TurbulenceModel())
# Cumulativ wake superposition
cw_ss = All2AllIterative(site, windTurbines, wake_deficitModel=ZongGaussianDeficit(), superpositionModel=CumulativeWakeSum(),
blockage_deficitModel=SelfSimilarityDeficit(), turbulenceModel=STF2017TurbulenceModel())
for wm in [noj, noj_ss, zongp, zong_ss, cwp, cw_ss]:
sim = wm(x=x, y=y, wd=[30], ws=[9])
plt.figure()
sim.flow_map(XYGrid(resolution=200)).plot_wake_map(levels=np.linspace(0, 1, 21) * 9.)
plt.title(' AEP: %.3f GWh' % sim.aep().sum())
plt.show()
main()