from numpy import newaxis as na
import numpy as np
from py_wake.deficit_models.deficit_model import DeficitModel, WakeDeficitModel, BlockageDeficitModel
from py_wake.superposition_models import LinearSum
from py_wake.tests.test_files import tfp
from py_wake.utils.fuga_utils import FugaUtils
from py_wake.wind_farm_models.engineering_models import PropagateDownwind, All2AllIterative
from scipy.interpolate import RectBivariateSpline
from py_wake.utils import gradients
from py_wake.utils.gradients import cabs
class FugaDeficit(WakeDeficitModel, BlockageDeficitModel, FugaUtils):
ams = 5
invL = 0
args4deficit = ['WS_ilk', 'WS_eff_ilk', 'dw_ijlk', 'hcw_ijlk', 'dh_ijlk', 'h_il', 'ct_ilk', 'D_src_il']
def __init__(self, LUT_path=tfp + 'fuga/2MW/Z0=0.03000000Zi=00401Zeta0=0.00E+00/', remove_wriggles=False,
method='linear', groundModel=None):
"""
Parameters
----------
LUT_path : str
Path to folder containing 'CaseData.bin', input parameter file (*.par) and loop-up tables
remove_wriggles : bool
The current Fuga loop-up tables have significan wriggles.
If True, all deficit values after the first zero crossing (when going from the center line
and out in the lateral direction) is set to zero.
This means that all speed-up regions are also removed
"""
DeficitModel.__init__(self, groundModel=groundModel)
BlockageDeficitModel.__init__(self, upstream_only=True)
FugaUtils.__init__(self, LUT_path, on_mismatch='input_par')
self.remove_wriggles = remove_wriggles
x, y, z, du = self.load()
err_msg = "Method must be 'linear' or 'spline'. Spline is supports only height level only"
assert method == 'linear' or (method == 'spline' and len(z) == 1), err_msg
if method == 'linear':
self.lut_interpolator = LUTInterpolator(x, y, z, du)
else:
du_interpolator = RectBivariateSpline(x, y, du[0].T)
def interp(xyz):
x, y, z = xyz
assert np.all(z == self.z[0]), f'LUT table contains z={self.z} only'
return du_interpolator.ev(x, y)
self.lut_interpolator = interp
def zeta0_factor(self):
def psim(zeta):
return self.ams * zeta
if not self.zeta0 >= 0: # pragma: no cover
# See Colonel.u2b.psim
raise NotImplementedError
return 1 / (1 - (psim(self.zHub * self.invL) - psim(self.zeta0)) / np.log(self.zHub / self.z0))
def load(self):
mdUL = self.load_luts(['UL'])[0]
du = -np.array(mdUL, dtype=np.float32) * self.zeta0_factor() # minus because it is deficit
if self.remove_wriggles:
# remove all positive and negative deficits after first zero crossing in lateral direction
du *= (np.cumsum(du < 0, 1) == 0)
# smooth edges to zero
n = 250
du[:, :, :n] = du[:, :, n][:, :, na] * np.arange(n) / n
du[:, :, -n:] = du[:, :, -n][:, :, na] * np.arange(n)[::-1] / n
n = 50
du[:, -n:, :] = du[:, -n, :][:, na, :] * np.arange(n)[::-1][na, :, na] / n
return self.x, self.y, self.z, du
def interpolate(self, x, y, z):
# self.grid_interplator(np.array([zyx.flatten() for zyx in [z, y, x]]).T, check_bounds=False).reshape(x.shape)
return self.lut_interpolator((x, y, z))
def _calc_layout_terms(self, dw_ijlk, hcw_ijlk, h_il, dh_ijlk, D_src_il, **_):
self.mdu_ijlk = self.interpolate(dw_ijlk, cabs(hcw_ijlk), (h_il[:, na, :, na] + dh_ijlk)) * \
~((dw_ijlk == 0) & (hcw_ijlk <= D_src_il[:, na, :, na]) # avoid wake on itself
)
def calc_deficit(self, WS_ilk, WS_eff_ilk, dw_ijlk, hcw_ijlk, dh_ijlk, h_il, ct_ilk, D_src_il, **kwargs):
if not self.deficit_initalized:
self._calc_layout_terms(dw_ijlk, hcw_ijlk, h_il, dh_ijlk, D_src_il, **kwargs)
return self.mdu_ijlk * (ct_ilk * WS_eff_ilk**2 / WS_ilk)[:, na]
def wake_radius(self, D_src_il, dw_ijlk, **_):
# Set at twice the source radius for now
return np.zeros_like(dw_ijlk) + D_src_il[:, na, :, na]
class FugaYawDeficit(FugaDeficit):
args4deficit = ['WS_ilk', 'WS_eff_ilk', 'dw_ijlk', 'hcw_ijlk', 'dh_ijlk', 'h_il', 'ct_ilk', 'D_src_il', 'yaw_ilk']
def __init__(self, LUT_path=tfp + 'fuga/2MW/Z0=0.00408599Zi=00400Zeta0=0.00E+00/',
remove_wriggles=False, method='linear', groundModel=None):
"""
Parameters
----------
LUT_path : str
Path to folder containing 'CaseData.bin', input parameter file (*.par) and loop-up tables
remove_wriggles : bool
The current Fuga loop-up tables have significan wriggles.
If True, all deficit values after the first zero crossing (when going from the center line
and out in the lateral direction) is set to zero.
This means that all speed-up regions are also removed
"""
DeficitModel.__init__(self, groundModel=groundModel)
BlockageDeficitModel.__init__(self, upstream_only=True)
FugaUtils.__init__(self, LUT_path, on_mismatch='input_par')
self.remove_wriggles = remove_wriggles
x, y, z, dUL = self.load()
mdUT = self.load_luts(['UT'])[0]
dUT = np.array(mdUT, dtype=np.float32) * self.zeta0_factor()
dU = np.concatenate([dUL[:, :, :, na], dUT[:, :, :, na]], 3)
err_msg = "Method must be 'linear' or 'spline'. Spline is supports only height level only"
assert method == 'linear' or (method == 'spline' and len(z) == 1), err_msg
if method == 'linear':
self.lut_interpolator = LUTInterpolator(x, y, z, dU)
else:
UL_interpolator = RectBivariateSpline(x, y, dU[0, :, :, 0].T)
UT_interpolator = RectBivariateSpline(x, y, dU[0, :, :, 1].T)
def interp(xyz):
x, y, z = xyz
assert np.all(z == self.z[0]), f'LUT table contains z={self.z} only'
return np.moveaxis([UL_interpolator.ev(x, y), UT_interpolator.ev(x, y)], 0, -1)
self.lut_interpolator = interp
def _calc_layout_terms(self, dw_ijlk, hcw_ijlk, h_il, dh_ijlk, D_src_il, **_):
self.mdu_ijlk = (self.interpolate(dw_ijlk, cabs(hcw_ijlk), (h_il[:, na, :, na] + dh_ijlk)) *
~((dw_ijlk == 0) & (hcw_ijlk <= D_src_il[:, na, :, na]))[..., na] # avoid wake on itself
)
def calc_deficit_downwind(self, WS_ilk, WS_eff_ilk, dw_ijlk, hcw_ijlk,
dh_ijlk, h_il, ct_ilk, D_src_il, yaw_ilk, **_):
mdUL_ijlk, mdUT_ijlk = np.moveaxis(self.interpolate(
dw_ijlk, cabs(hcw_ijlk), (h_il[:, na, :, na] + dh_ijlk)), -1, 0)
mdUT_ijlk = np.negative(mdUT_ijlk, out=mdUT_ijlk, where=hcw_ijlk < 0) # UT is antisymmetric
theta_ilk = np.deg2rad(yaw_ilk)
mdu_ijlk = (mdUL_ijlk * np.cos(theta_ilk)[:, na] - mdUT_ijlk * np.sin(theta_ilk)[:, na])
# avoid wake on itself
mdu_ijlk *= ~((dw_ijlk == 0) & (hcw_ijlk <= D_src_il[:, na, :, na]))
return mdu_ijlk * (ct_ilk * WS_eff_ilk**2 / WS_ilk)[:, na]
def calc_deficit(self, **kwargs):
# fuga result is already downwind
return self.calc_deficit_downwind(**kwargs)
class LUTInterpolator(object):
# Faster than scipy.interpolate.interpolate.RegularGridInterpolator
def __init__(self, x, y, z, V):
self.x = x
self.y = y
self.z = z
self.V = V
self.nx = nx = len(x)
self.ny = ny = len(y)
self.nz = nz = len(z)
assert V.shape[:3] == (nz, ny, nx)
self.dx, self.dy = [xy[1] - xy[0] for xy in [x, y]]
self.x0 = x[0]
self.y0 = y[0]
Ve = np.concatenate((V, V[-1:]), 0)
Ve = np.concatenate((Ve, Ve[:, -1:]), 1)
Ve = np.concatenate((Ve, Ve[:, :, -1:]), 2)
self.V000 = np.array([V,
Ve[:-1, :-1, 1:],
Ve[:-1, 1:, :-1],
Ve[:-1, 1:, 1:],
Ve[1:, :-1, :-1],
Ve[1:, :-1, 1:],
Ve[1:, 1:, :-1],
Ve[1:, 1:, 1:]])
if V.shape == (nz, ny, nx, 2):
# Both UL and UT
self.V000 = self.V000.reshape((8, nz * ny * nx, 2))
else:
self.V000 = self.V000.reshape((8, nz * ny * nx))
def __call__(self, xyz):
xp, yp, zp = xyz
xp = np.maximum(np.minimum(xp, self.x[-1]), self.x[0])
yp = np.maximum(np.minimum(yp, self.y[-1]), self.y[0])
xif, xi0 = gradients.modf((xp - self.x0) / self.dx)
yif, yi0 = gradients.modf((yp - self.y0) / self.dy)
zif, zi0 = gradients.modf(gradients.interp(zp, self.z, np.arange(self.nz)))
nx, ny = self.nx, self.ny
idx = zi0 * nx * ny + yi0 * nx + xi0
v000, v001, v010, v011, v100, v101, v110, v111 = self.V000[:, idx]
if len(self.V000.shape) == 3:
# Both UL and UT
xif = xif[..., na]
yif = yif[..., na]
zif = zif[..., na]
v_00 = v000 + (v100 - v000) * zif
v_01 = v001 + (v101 - v001) * zif
v_10 = v010 + (v110 - v010) * zif
v_11 = v011 + (v111 - v011) * zif
v__0 = v_00 + (v_10 - v_00) * yif
v__1 = v_01 + (v_11 - v_01) * yif
return (v__0 + (v__1 - v__0) * xif)
# # Slightly slower
# xif1, yif1, zif1 = 1 - xif, 1 - yif, 1 - zif
# w = np.array([xif1 * yif1 * zif1,
# xif * yif1 * zif1,
# xif1 * yif * zif1,
# xif * yif * zif1,
# xif1 * yif1 * zif,
# xif * yif1 * zif,
# xif1 * yif * zif,
# xif * yif * zif])
#
# return np.sum(w * self.V01[:, zi0, yi0, xi0], 0)
class Fuga(PropagateDownwind):
def __init__(self, LUT_path, site, windTurbines,
rotorAvgModel=None, deflectionModel=None, turbulenceModel=None, 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
"""
PropagateDownwind.__init__(self, site, windTurbines,
wake_deficitModel=FugaDeficit(LUT_path, remove_wriggles=remove_wriggles),
rotorAvgModel=rotorAvgModel, superpositionModel=LinearSum(),
deflectionModel=deflectionModel, turbulenceModel=turbulenceModel)
class FugaBlockage(All2AllIterative):
def __init__(self, LUT_path, site, windTurbines, rotorAvgModel=None,
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 main():
if __name__ == '__main__':
from py_wake.examples.data.iea37._iea37 import IEA37Site
from py_wake.examples.data.iea37._iea37 import IEA37_WindTurbines
import matplotlib.pyplot as plt
# setup site, turbines and wind farm model
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
path = tfp + 'fuga/2MW/Z0=0.03000000Zi=00401Zeta0=0.00E+00/'
for wf_model in [Fuga(path, site, windTurbines),
FugaBlockage(path, site, windTurbines)]:
plt.figure()
print(wf_model)
# run wind farm simulation
sim_res = wf_model(x, y)
# calculate AEP
aep = sim_res.aep().sum()
# plot wake map
flow_map = sim_res.flow_map(wd=30, ws=9.8)
flow_map.plot_wake_map()
flow_map.plot_windturbines()
plt.title('AEP: %.2f GWh' % aep)
plt.show()
plt.show()
main()