def test_fuga_table_edges():
wts = HornsrevV80()
path = tfp + 'fuga/2MW/Z0=0.03000000Zi=00401Zeta0=0.00E+00/'
site = hornsrev1.Hornsrev1Site()
fuga = FugaBlockage(path, site, wts)
D = 80
flow_map_dw = fuga([0], [0], wd=270, ws=10).flow_map(
HorizontalGrid(np.arange(-200 * D, 450 * D), y=[0]))
flow_map_cw = fuga([0], [0], wd=270, ws=10).flow_map(
HorizontalGrid([0], np.arange(-20 * D, 20 * D)))
flow_map = fuga([0], [0], wd=270, ws=10).flow_map(
HorizontalGrid(np.arange(-150, 400) * D,
np.arange(-20, 21) * D))
if 0:
plt.plot(flow_map_dw.x / D, flow_map_dw.WS_eff.squeeze())
plt.grid()
plt.ylim([9.9, 10.1])
plt.figure()
plt.plot(flow_map_cw.y / D, flow_map_cw.WS_eff.squeeze())
plt.grid()
plt.ylim([9.9, 10.1])
plt.figure()
flow_map.WS_eff.plot()
plt.show()
npt.assert_array_equal(flow_map.WS_eff.squeeze()[[0, -1], :], 10)
npt.assert_array_equal(flow_map.WS_eff.squeeze()[:, [0, -1]], 10)
def test_fuga_wriggles():
wts = HornsrevV80()
path = tfp + 'fuga/2MW/Z0=0.03000000Zi=00401Zeta0=0.00E+00/'
site = hornsrev1.Hornsrev1Site()
fuga = PropagateDownwind(site, wts, FugaDeficit(path,
remove_wriggles=True))
D = 80
flow_map_cw = fuga([0], [0], wd=270, ws=10).flow_map(
HorizontalGrid([0], np.arange(-20 * D, 20 * D)))
y = np.linspace(-5 * D, 5 * D, 100)
dw_lst = range(10)
flow_map_cw_lst = np.array([
fuga([0], [0], wd=270,
ws=10).flow_map(HorizontalGrid([dw * D], y)).WS_eff.squeeze()
for dw in dw_lst
])
if 0:
for flow_map_cw, dw in zip(flow_map_cw_lst, dw_lst):
plt.plot(y, flow_map_cw, label="%dD" % dw)
plt.xlabel('y [m]')
plt.ylabel('ws [m/s')
plt.ylim([9.9, 10.1])
plt.grid()
plt.legend(loc=1)
plt.show()
assert np.all(flow_map_cw_lst > 0)
def test_fuga_new_format():
# move turbine 1 600 300
wt_x = [-250, 600, -500, 0, 500, -250, 250]
wt_y = [433, 300, 0, 0, 0, -433, -433]
wts = HornsrevV80()
path = tfp + 'fuga/2MW/Z0=0.00408599Zi=00400Zeta0=0.00E+00/'
site = UniformSite([1, 0, 0, 0], ti=0.075)
wake_model = Fuga(path, site, wts)
res = wake_model(x=wt_x, y=wt_y, wd=[30], ws=[10])
npt.assert_array_almost_equal(res.WS_eff_ilk.flatten(), [
10.00647891, 10., 8.21713928, 10.03038884, 9.36889964, 8.23084088,
7.80662141
], 8)
npt.assert_array_almost_equal(res.ct_ilk.flatten(), [
0.79265014, 0.793, 0.80621714, 0.791359, 0.80183541, 0.80623084,
0.80580662
], 8)
x_j = np.linspace(-1500, 1500, 500)
y_j = np.linspace(-1500, 1500, 300)
wake_model = Fuga(path, site, wts)
sim_res = wake_model(wt_x, wt_y, wd=[30], ws=[10])
flow_map70 = sim_res.flow_map(HorizontalGrid(x_j, y_j, h=70))
flow_map73 = sim_res.flow_map(HorizontalGrid(x_j, y_j, h=73))
X, Y = flow_map70.XY
Z70 = flow_map70.WS_eff_xylk[:, :, 0, 0]
Z73 = flow_map73.WS_eff_xylk[:, :, 0, 0]
if 0:
flow_map70.plot_wake_map(levels=np.arange(6, 10.5, .1))
plt.plot(X[0], Y[140])
plt.figure()
plt.plot(X[0], Z70[140, :], label="Z=70m")
plt.plot(X[0], Z73[140, :], label="Z=73m")
plt.plot(X[0, 100:400:10], Z70[140, 100:400:10], '.')
print(list(np.round(Z70.values[140, 100:400:10], 4)))
print(list(np.round(Z73.values[140, 100:400:10], 4)))
plt.legend()
plt.show()
npt.assert_array_almost_equal(Z70[140, 100:400:10], [
10.0384, 10.042, 10.044, 10.0253, 9.7194, 7.7561, 6.7421, 9.2308,
9.9894, 10.0413, 10.0499, 10.0579, 10.0437, 9.1626, 7.2334, 9.1208,
10.0396, 10.0322, 10.0276, 9.9504, 9.2861, 7.8375, 6.6608, 8.3343,
9.9756, 10.0229, 10.0136, 10.0142, 10.0118, 10.0094
], 4)
npt.assert_array_almost_equal(Z73[140, 100:400:10], [
10.0384, 10.042, 10.044, 10.0253, 9.7194, 7.7561, 6.7421, 9.2308,
9.9894, 10.0413, 10.0499, 10.0579, 10.0437, 9.1626, 7.2334, 9.1208,
10.0396, 10.0322, 10.0276, 9.9504, 9.2861, 7.8375, 6.6608, 8.3343,
9.9756, 10.0229, 10.0136, 10.0142, 10.0118, 10.0094
], 4)
def test_fuga_new_format():
# move turbine 1 600 300
wt_x = [-250, 600, -500, 0, 500, -250, 250]
wt_y = [433, 300, 0, 0, 0, -433, -433]
wts = HornsrevV80()
path = tfp + 'fuga/2MW/Z0=0.00014617Zi=00399Zeta0=0.00E+0/'
site = UniformSite([1, 0, 0, 0], ti=0.075)
wake_model = Fuga(path, site, wts)
res = wake_model(x=wt_x, y=wt_y, wd=[30], ws=[10])
npt.assert_array_almost_equal(res.WS_eff_ilk.flatten(), [
10.00725165, 10., 7.92176401, 10.02054952, 9.40501317, 7.92609363,
7.52384558
], 8)
npt.assert_array_almost_equal(res.ct_ilk.flatten(), [
0.79260841, 0.793, 0.80592176, 0.79189033, 0.80132982, 0.80592609,
0.80552385
], 8)
x_j = np.linspace(-1500, 1500, 500)
y_j = np.linspace(-1500, 1500, 300)
wake_model = Fuga(path, site, wts)
sim_res = wake_model(wt_x, wt_y, wd=[30], ws=[10])
flow_map70 = sim_res.flow_map(HorizontalGrid(x_j, y_j, h=70))
flow_map73 = sim_res.flow_map(HorizontalGrid(x_j, y_j, h=73))
X, Y = flow_map70.XY
Z70 = flow_map70.WS_eff_xylk[:, :, 0, 0]
Z73 = flow_map73.WS_eff_xylk[:, :, 0, 0]
if 0:
flow_map70.plot_wake_map(levels=np.arange(6, 10.5, .1))
plt.plot(X[0], Y[140])
plt.figure()
plt.plot(X[0], Z70[140, :], label="Z=70m")
plt.plot(X[0], Z73[140, :], label="Z=73m")
plt.plot(X[0, 100:400:10], Z70[140, 100:400:10], '.')
print(list(np.round(Z70.values[140, 100:400:10], 4)))
print(list(np.round(Z73.values[140, 100:400:10], 4)))
plt.legend()
plt.show()
npt.assert_array_almost_equal(Z70[140, 100:400:10], [
10.0458, 10.0309, 10.065, 10.0374, 9.7865, 7.7119, 6.4956, 9.2753,
10.0047, 10.0689, 10.0444, 10.0752, 10.0699, 9.1852, 6.9783, 9.152,
10.0707, 10.0477, 10.0365, 9.9884, 9.2867, 7.5714, 6.4451, 8.3276,
9.9976, 10.0251, 10.0264, 10.023, 10.0154, 9.9996
], 4)
npt.assert_array_almost_equal(Z73[140, 100:400:10], [
10.0458, 10.0309, 10.065, 10.0374, 9.7865, 7.7119, 6.4956, 9.2753,
10.0047, 10.0689, 10.0444, 10.0752, 10.0699, 9.1852, 6.9783, 9.152,
10.0707, 10.0477, 10.0365, 9.9884, 9.2867, 7.5714, 6.4451, 8.3276,
9.9976, 10.0251, 10.0264, 10.023, 10.0154, 9.9996
], 4)
def test_wec():
# move turbine 1 600 300
wt_x = [-250, 600, -500, 0, 500, -250, 250]
wt_y = [433, 300, 0, 0, 0, -433, -433]
wts = HornsrevV80()
site = UniformSite([1, 0, 0, 0], ti=0.075)
wake_model = Fuga(LUT_path_2MW_z0_0_03, site, wts)
x_j = np.linspace(-1500, 1500, 500)
y_j = np.linspace(-1500, 1500, 300)
flow_map_wec1 = wake_model(wt_x, wt_y, 70, wd=[30],
ws=[10]).flow_map(HorizontalGrid(x_j, y_j))
Z_wec1 = flow_map_wec1.WS_eff_xylk[:, :, 0, 0]
wake_model.wec = 2
flow_map_wec2 = wake_model(wt_x, wt_y, 70, wd=[30],
ws=[10]).flow_map(HorizontalGrid(x_j, y_j))
X, Y = flow_map_wec1.XY
Z_wec2 = flow_map_wec2.WS_eff_xylk[:, :, 0, 0]
if 0:
print(list(np.round(Z_wec1[140, 100:400:10].values, 4)))
print(list(np.round(Z_wec2[140, 100:400:10].values, 4)))
flow_map_wec1.plot_wake_map(levels=np.arange(6, 10.5, .1),
plot_colorbar=False)
plt.plot(X[0], Y[140])
wts.plot(wt_x, wt_y)
plt.figure()
c = flow_map_wec2.plot_wake_map(levels=np.arange(6, 10.5, .1),
plot_colorbar=False)
plt.colorbar(c)
plt.plot(X[0], Y[140])
wts.plot(wt_x, wt_y)
plt.figure()
plt.plot(X[0], Z_wec1[140, :], label="Z=70m")
plt.plot(X[0], Z_wec2[140, :], label="Z=70m")
plt.plot(X[0, 100:400:10], Z_wec1[140, 100:400:10], '.')
plt.plot(X[0, 100:400:10], Z_wec2[140, 100:400:10], '.')
plt.legend()
plt.show()
npt.assert_array_almost_equal(Z_wec1[140, 100:400:10], [
10.0467, 10.0473, 10.0699, 10.0093, 9.6786, 7.8589, 6.8539, 9.2199,
9.9837, 10.036, 10.0796, 10.0469, 10.0439, 9.1866, 7.2552, 9.1518,
10.0449, 10.0261, 10.0353, 9.9256, 9.319, 8.0062, 6.789, 8.3578,
9.9393, 10.0332, 10.0183, 10.0186, 10.0191, 10.0139
], 4)
npt.assert_array_almost_equal(Z_wec2[140, 100:400:10], [
10.0297, 9.9626, 9.7579, 9.2434, 8.2318, 7.008, 6.7039, 7.7303, 9.0101,
9.6877, 9.9068, 9.7497, 9.1127, 7.9505, 7.26, 7.9551, 9.2104, 9.7458,
9.6637, 9.1425, 8.2403, 7.1034, 6.5109, 7.2764, 8.7653, 9.7139, 9.9718,
10.01, 10.0252, 10.0357
], 4)
def test_wec():
# move turbine 1 600 300
wt_x = [-250, 600, -500, 0, 500, -250, 250]
wt_y = [433, 300, 0, 0, 0, -433, -433]
wts = HornsrevV80()
site = UniformSite([1, 0, 0, 0], ti=0.075)
wfm = BastankhahGaussian(site, wts)
x_j = np.linspace(-1500, 1500, 500)
y_j = np.linspace(-1500, 1500, 300)
flow_map_wec1 = wfm(wt_x, wt_y, 70, wd=[30],
ws=[10]).flow_map(HorizontalGrid(x_j, y_j))
Z_wec1 = flow_map_wec1.WS_eff_xylk[:, :, 0, 0]
wfm.wec = 2
flow_map_wec2 = wfm(wt_x, wt_y, 70, wd=[30],
ws=[10]).flow_map(HorizontalGrid(x_j, y_j))
X, Y = flow_map_wec1.XY
Z_wec2 = flow_map_wec2.WS_eff_xylk[:, :, 0, 0]
if 0:
print(list(np.round(Z_wec1[140, 100:400:10].values, 2)))
print(list(np.round(Z_wec2[140, 100:400:10].values, 2)))
flow_map_wec1.plot_wake_map(levels=np.arange(6, 10.5, .1),
plot_colorbar=False)
plt.plot(X[0], Y[140])
wts.plot(wt_x, wt_y)
plt.figure()
c = flow_map_wec2.plot_wake_map(levels=np.arange(6, 10.5, .1),
plot_colorbar=False)
plt.colorbar(c)
plt.plot(X[0], Y[140])
wts.plot(wt_x, wt_y)
plt.figure()
plt.plot(X[0], Z_wec1[140, :], label="Z=70m")
plt.plot(X[0], Z_wec2[140, :], label="Z=70m")
plt.plot(X[0, 100:400:10], Z_wec1[140, 100:400:10], '.')
plt.plot(X[0, 100:400:10], Z_wec2[140, 100:400:10], '.')
plt.legend()
plt.show()
npt.assert_array_almost_equal(Z_wec1[140, 100:400:10], [
10.0, 10.0, 10.0, 9.99, 9.8, 6.52, 1.47, 9.44, 9.98, 10.0, 10.0, 10.0,
10.0, 9.05, 0.03, 9.11, 10.0, 10.0, 10.0, 9.97, 9.25, 7.03, 2.35, 6.51,
9.99, 10.0, 10.0, 10.0, 10.0, 10.0
], 2)
npt.assert_array_almost_equal(Z_wec2[140, 100:400:10], [
9.99, 9.96, 9.84, 9.47, 7.82, 2.24, 0.21, 6.21, 9.22, 9.82, 9.98, 9.92,
9.05, 4.45, 0.01, 4.53, 9.35, 9.95, 9.75, 9.13, 7.92, 5.14, 0.32, 2.2,
8.38, 9.94, 10.0, 10.0, 10.0, 10.0
], 2)
def test_fuga():
# move turbine 1 600 300
wt_x = [-250, 600, -500, 0, 500, -250, 250]
wt_y = [433, 300, 0, 0, 0, -433, -433]
wts = HornsrevV80()
path = tfp + 'fuga/2MW/Z0=0.03000000Zi=00401Zeta0=0.00E+0/'
site = UniformSite([1, 0, 0, 0], ti=0.075)
wake_model = Fuga(path, site, wts)
res, _ = timeit(wake_model.__call__, verbose=0, line_profile=0,
profile_funcs=[FugaDeficit.interpolate, LUTInterpolator.__call__, GridInterpolator.__call__])(x=wt_x, y=wt_y, wd=[30], ws=[10])
npt.assert_array_almost_equal(res.WS_eff_ilk.flatten(),
[10.002683492812844, 10.0, 8.413483643142389, 10.036952526815286,
9.371203842245153, 8.437429367715435, 8.012759083790058], 8)
npt.assert_array_almost_equal(res.ct_ilk.flatten(), [0.79285509, 0.793, 0.80641348, 0.79100456,
0.80180315, 0.80643743, 0.80601276], 8)
x_j = np.linspace(-1500, 1500, 500)
y_j = np.linspace(-1500, 1500, 300)
wake_model = Fuga(path, site, wts)
sim_res = wake_model(wt_x, wt_y, wd=[30], ws=[10])
flow_map70 = sim_res.flow_map(HorizontalGrid(x_j, y_j, h=70))
flow_map73 = sim_res.flow_map(HorizontalGrid(x_j, y_j, h=73))
X, Y = flow_map70.XY
Z70 = flow_map70.WS_eff_xylk[:, :, 0, 0]
Z73 = flow_map73.WS_eff_xylk[:, :, 0, 0]
if 0:
flow_map70.plot_wake_map(levels=np.arange(6, 10.5, .1))
plt.plot(X[0], Y[140])
plt.figure()
plt.plot(X[0], Z70[140, :], label="Z=70m")
plt.plot(X[0], Z73[140, :], label="Z=73m")
plt.plot(X[0, 100:400:10], Z70[140, 100:400:10], '.')
print(list(np.round(Z70.data[140, 100:400:10], 4)))
print(list(np.round(Z73.data[140, 100:400:10], 4)))
plt.legend()
plt.show()
npt.assert_array_almost_equal(
Z70[140, 100:400:10],
[10.0467, 10.0473, 10.0699, 10.0093, 9.6786, 7.8589, 6.8539, 9.2199, 9.9837, 10.036, 10.0796,
10.0469, 10.0439, 9.1866, 7.2552, 9.1518, 10.0449, 10.0261, 10.0353, 9.9256, 9.319, 8.0062,
6.789, 8.3578, 9.9393, 10.0332, 10.0183, 10.0186, 10.0191, 10.0139], 4)
npt.assert_array_almost_equal(
Z73[140, 100:400:10],
[10.0463, 10.0468, 10.0688, 10.0075, 9.6778, 7.9006, 6.9218, 9.228, 9.9808, 10.0354, 10.0786,
10.0464, 10.0414, 9.1973, 7.3099, 9.1629, 10.0432, 10.0257, 10.0344, 9.9236, 9.3274, 8.0502,
6.8512, 8.3813, 9.9379, 10.0325, 10.018, 10.0183, 10.019, 10.0138], 4)
def test_deficitModel_wake_map_convection(deficitModel, ref):
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wf_model = PropagateDownwind(site, windTurbines, wake_deficitModel=deficitModel, superpositionModel=WeightedSum(),
turbulenceModel=GCLTurbulence())
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 flow_map(self, grid=None, wd=None, ws=None):
"""Return a FlowMap object with WS_eff and TI_eff of all grid points
Parameters
----------
grid : Grid or tuple(X, Y, x, y, h)
Grid, e.g. HorizontalGrid or\n
tuple(X, Y, x, y, h) where X, Y is the meshgrid for visualizing data\n
and x, y, h are the flattened grid points
See Also
--------
pywake.wind_farm_models.flow_map.FlowMap
"""
if grid is None:
grid = HorizontalGrid()
if isinstance(grid, HorizontalGrid):
grid = grid(self.x_i, self.y_i, self.h_i)
if wd is None:
wd = self.wd
else:
assert np.all(np.isin(
wd, self.wd)), "All wd=%s not in simulation result" % wd
if ws is None:
ws = self.ws
else:
assert np.all(np.isin(
ws, self.ws)), "All ws=%s not in simulation result (ws=%s)" % (
ws, self.ws)
wd, ws = np.atleast_1d(wd), np.atleast_1d(ws)
l_indices = np.argwhere(wd[:, None] == self.wd)[:, 1]
# wd对应函数参数3， self.wd是对应classSimulationResult初始化后的结果
k_indices = np.argwhere(ws[:, None] == self.ws)[:, 1]
X, Y, x_j, y_j, h_j = grid
# 为了搞清楚中间变量进行了修改
lWD = self.localWind.WD_ilk[:,
l_indices][:, :, :] # 源代码lWD = self.localWind.WD_ilk[:, l_indices][:, :, k_indices]
lWS = self.localWind.WS_ilk[:, l_indices][:, :, k_indices]
lTI = self.localWind.TI_ilk[:, l_indices][:, :, k_indices]
WSe = self.WS_eff_ilk[:, l_indices][:, :, k_indices]
TIe = self.TI_eff_ilk[:, l_indices][:, :, k_indices]
cti = self.ct_ilk[:, l_indices][:, :, k_indices]
lw_j, WS_eff_jlk, TI_eff_jlk = self.windFarmModel._flow_map(
x_j, y_j, h_j, self.x_i, self.y_i, self.h_i, self.type_i,
self.yaw_ilk, lWD, lWS, lTI, WSe, TIe, cti, wd, ws)
if self.yaw_ilk is not None:
yaw_ilk = self.yaw_ilk[:, l_indices][:, :, k_indices]
else:
yaw_ilk = None
return FlowMap(self,
X,
Y,
lw_j,
WS_eff_jlk,
TI_eff_jlk,
wd,
ws,
yaw_ilk=yaw_ilk)
def test_interpolation_speed():
import xarray as xr
da = xr.DataArray(np.sin(0.3 * np.arange(20).reshape(5, 4)),
[('x', np.arange(5)), ('y', [0.1, 0.2, 0.3, 0.4])])
x = xr.DataArray([0.5, 1.5, 2.5], dims='z')
y = xr.DataArray([0.15, 0.25, 0.35], dims='z')
da.interp(x=x, y=y)
site = ParqueFicticioSite()
x, y = site.initial_position.T
X, Y, x_j, y_j, h_j = HorizontalGrid()(x, y, 70)
wd = [270] # site.default_wd
ws = site.default_ws
res1, t_lst = timeit(site.interp_funcs['A'])(
(x_j, y_j, h_j, x_j * 0 + 270))
print(res1.shape)
res2, t_lst = timeit(lambda x, y, z, sec: site._ds.A.interp(
x=xr.DataArray(x, dims='z'),
y=xr.DataArray(y, dims='z'),
z=xr.DataArray(z, dims='z'),
sec=xr.DataArray(sec, dims='z')).data)(x_j, y_j, h_j, x_j * 0 + 10)
npt.assert_array_almost_equal(res1, res2)
if 0:
c = plt.contourf(X, Y, res1.reshape(X.shape))
plt.colorbar(c)
plt.figure()
c = plt.contourf(X, Y, res2.reshape(X.shape))
plt.colorbar(c)
plt.show()
def test_distance_over_rectangle():
x, y = [-100, 50], [200, -100]
windTurbines = IEA37_WindTurbines()
site = Rectangle(height=200, width=100, distance_resolution=100)
wf_model = NOJ(site, windTurbines)
sim_res = wf_model(x, y, wd=[270], ws=[9])
x_j = np.linspace(-100, 500, 100)
y_j = np.linspace(-200, 300, 100)
flow_map = sim_res.flow_map(HorizontalGrid(x_j, y_j))
Z = flow_map.WS_eff_xylk[:, :, 0, 0]
X, Y = flow_map.X, flow_map.Y
my = np.argmin(np.abs(Y[:, 0] - 200))
my2 = np.argmin(np.abs(Y[:, 0] + 100))
if 0:
import matplotlib.pyplot as plt
flow_map.plot_wake_map()
H = site.elevation(X, Y)
plt.plot(X[my], Z[my] * 10, label='wsp*10')
plt.plot(X[my2], Z[my2] * 10, label='wsp*10')
plt.contour(X, Y, H)
plt.plot(X[my, :50:4], Z[my, :50:4] * 10, '.')
plt.plot(x_j, site.elevation(x_j, x_j * 0), label='terrain level')
plt.legend()
plt.show()
ref = [
9., 3.42, 3.8, 6.02, 6.17, 6.31, 6.43, 7.29, 7.35, 7.41, 7.47, 7.53,
7.58
]
npt.assert_array_almost_equal(Z[my, :50:4], ref, 2)
def test_wake_radius(deficitModel, wake_radius_ref):
mean_ref = [105, 68, 135, 93, 123]
# check that ref is reasonable
npt.assert_allclose(wake_radius_ref, mean_ref, rtol=.5)
npt.assert_array_almost_equal(deficitModel.wake_radius(
D_src_il=np.reshape([100, 50, 100, 100, 100], (5, 1)),
dw_ijlk=np.reshape([500, 500, 1000, 500, 500], (5, 1, 1, 1)),
ct_ilk=np.reshape([.8, .8, .8, .4, .8], (5, 1, 1)),
TI_ilk=np.reshape([.1, .1, .1, .1, .05], (5, 1, 1)),
TI_eff_ilk=np.reshape([.1, .1, .1, .1, .05], (5, 1, 1)))[:, 0, 0, 0],
wake_radius_ref)
# Check that it works when called from WindFarmModel
site = IEA37Site(16)
windTurbines = IEA37_WindTurbines()
wfm = PropagateDownwind(site, windTurbines, wake_deficitModel=deficitModel, turbulenceModel=GCLTurbulence())
wfm(x=[0, 500], y=[0, 0], wd=[30], ws=[10])
if 0:
sim_res = wfm([0], [0], wd=[270], ws=10)
sim_res.flow_map(HorizontalGrid(x=np.arange(-100, 1500, 10))).WS_eff.plot()
x = np.arange(0, 1500, 10)
wr = deficitModel.wake_radius(
D_src_il=np.reshape([130], (1, 1)),
dw_ijlk=np.reshape(x, (1, len(x), 1, 1)),
ct_ilk=sim_res.CT.values,
TI_ilk=np.reshape(sim_res.TI.values, (1, 1, 1)),
TI_eff_ilk=sim_res.TI_eff.values)[0, :, 0, 0]
plt.title(deficitModel.__class__.__name__)
plt.plot(x, wr)
plt.plot(x, -wr)
plt.axis('equal')
plt.show()
def aep4smart_start(X, Y, wt_x, wt_y, type=0):
sim_res = self.windFarmModel(wt_x, wt_y, type=type, wd=wd, ws=ws)
x = np.sort(np.unique(X))
y = np.sort(np.unique(Y))
aep_map = sim_res.flow_map(HorizontalGrid(x, y)).aep_xy()
if isinstance(aep_map, xr.DataArray):
aep_map = aep_map.values[:, :, 0]
return RegularGridInterpolator((y, x), aep_map)(np.array([Y, X]).T)
def test_power_xylk():
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
# NOJ wake model
wind_farm_model = IEA37SimpleBastankhahGaussian(site, windTurbines)
simulation_result = wind_farm_model(x, y)
fm = simulation_result.flow_map(grid=HorizontalGrid(resolution=3))
npt.assert_array_almost_equal(fm.power_xylk(with_wake_loss=False)[:, :, 0, 0] * 1e-6, 3.35)
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_NOJ_Nibe_result_wake_map():
# Replicate result from: Jensen, Niels Otto. "A note on wind generator interaction." (1983).
def ct_func(_):
return 8 / 9
def power_func(*_):
return 0
windTurbines = NibeA0
site = UniformSite([1], 0.1)
wake_model = NOJ(site, windTurbines)
sim_res = wake_model(x=[0], y=[0], wd=[0], ws=[8.1])
WS_eff_xy = sim_res.flow_map(HorizontalGrid(
x=[0], y=[0, -40, -100], h=50)).WS_eff_xylk.mean(['wd', 'ws'])
npt.assert_array_almost_equal(WS_eff_xy[:, 0], [8.1, 4.35, 5.7])
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 test_superposition_model_indices():
class WTSite(UniformSite):
def local_wind(self, x_i=None, y_i=None, h_i=None, wd=None,
ws=None, time=False, 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['TI'] = xr.DataArray(lw.TI_ilk + np.arange(len(x_i))[:, np.newaxis, np.newaxis] * .1,
[('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
TI_ilk = site.local_wind(x_i, y_i, h_i, wd=0, ws=8.1).TI_ilk
npt.assert_array_almost_equal(TI_ilk, np.reshape([0.1, 0.2, 0.3], (3, 1, 1)), 10)
def get_wf_model(cls):
return cls(site, NibeA0, wake_deficitModel=NoWakeDeficit(),
superpositionModel=LinearSum(),
turbulenceModel=STF2017TurbulenceModel())
for wake_model in [get_wf_model(PropagateDownwind),
get_wf_model(All2AllIterative)]:
# No wake (ct = 0), i.e. WS_eff == WS
TI_eff_ilk = wake_model.calc_wt_interaction(x_i, y_i, h_i, [1, 1, 1], 0.0, 8.1)[1]
npt.assert_array_equal(TI_eff_ilk, TI_ilk)
# full wake (CT=8/9)
ref_TI_eff_ilk = TI_ilk + np.reshape([0, 0.33738364, np.sum([0.19369135, 0.21239116])], (3, 1, 1))
TI_eff_ilk = wake_model.calc_wt_interaction(x_i, y_i, h_i, [0, 0, 0], 0.0, 8.1)[1]
npt.assert_array_almost_equal(TI_eff_ilk, ref_TI_eff_ilk)
sim_res = wake_model(x_i, y_i, h_i, [0, 0, 0], 0.0, 8.1)
TI_eff_ilk = sim_res.flow_map(HorizontalGrid(x=[0], y=y_i, h=50)).TI_eff_xylk[:, 0]
npt.assert_array_almost_equal(TI_eff_ilk, ref_TI_eff_ilk)
def test_wake_map():
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml')
wake_model = NOJ(site, windTurbines)
x_j = np.linspace(-1500, 1500, 200)
y_j = np.linspace(-1500, 1500, 100)
flow_map = wake_model(x, y, wd=[0],
ws=[9]).flow_map(HorizontalGrid(x_j, y_j, 110))
Z = flow_map.WS_eff_xylk.mean((2, 3))
if 0:
flow_map.plot_wake_map()
plt.show()
ref = [
3.27, 3.27, 9.0, 7.46, 7.46, 7.46, 7.46, 7.31, 7.31, 7.31, 7.31, 8.3,
8.3, 8.3, 8.3, 8.3, 8.3
]
npt.assert_array_almost_equal(Z[49, 100:133:2], ref, 2)
def test_IEA37SimpleBastankhahGaussian_wake_map():
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml')
wake_model = IEA37SimpleBastankhahGaussian(site, windTurbines)
x_j = np.linspace(-1500, 1500, 200)
y_j = np.linspace(-1500, 1500, 100)
flow_map = wake_model(x, y, wd=0, ws=9).flow_map(HorizontalGrid(x_j, y_j))
X, Y, Z = flow_map.X, flow_map.Y, flow_map.WS_eff_xylk[:, :, 0, 0]
# test that the result is equal to last run (no evidens that these number are correct)
ref = [
3.32, 4.86, 7.0, 8.1, 7.8, 7.23, 6.86, 6.9, 7.3, 7.82, 8.11, 8.04,
7.87, 7.79, 7.85, 8.04, 8.28
]
if 0:
flow_map.plot_wake_map()
plt.plot(X[49, 100:133:2], Y[49, 100:133:2], '.-')
plt.show()
npt.assert_array_almost_equal(Z[49, 100:133:2], ref, 2)
def flow_map(self, grid=None, wd=None, ws=None):
"""Return a FlowMap object with WS_eff and TI_eff of all grid points
Parameters
----------
grid : Grid or tuple(X, Y, x, y, h)
Grid, e.g. HorizontalGrid or\n
tuple(X, Y, x, y, h) where X, Y is the meshgrid for visualizing data\n
and x, y, h are the flattened grid points
See Also
--------
pywake.wind_farm_models.flow_map.FlowMap
"""
if grid is None:
grid = HorizontalGrid()
if isinstance(grid, Grid):
if isinstance(grid, HorizontalGrid):
plane = "XY", self.h
if isinstance(grid, YZGrid):
plane = 'YZ', grid.x
if isinstance(grid, Points):
plane = 'xyz', None
grid = grid(x_i=self.x,
y_i=self.y,
h_i=self.h,
d_i=self.windFarmModel.windTurbines.diameter(
self.type))
else:
plane = (None, )
wd, ws = self._wd_ws(wd, ws)
X, Y, x_j, y_j, h_j = grid
lw_j, WS_eff_jlk, TI_eff_jlk = self.windFarmModel._flow_map(
x_j, y_j, h_j, self.sel(wd=wd, ws=ws))
return FlowMap(self, X, Y, lw_j, WS_eff_jlk, TI_eff_jlk, plane=plane)
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 import IEA37Site, IEA37_WindTurbines
from py_wake import IEA37SimpleBastankhahGaussian
import matplotlib.pyplot as plt
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wind_farm_model = IEA37SimpleBastankhahGaussian(site, windTurbines)
simulation_result = wind_farm_model(x, y)
fm = simulation_result.flow_map(wd=30)
fm.plot_wake_map()
plt.figure()
fm.plot(fm.power_xylk().sum(['wd', 'ws']) * 1e-3, "Power [kW]")
fm = simulation_result.flow_map(grid=HorizontalGrid(resolution=50))
plt.figure()
fm.plot(fm.aep_xy(), "AEP [GWh]")
plt.show()
def test_BastankhahGaussian_wake_map():
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml')
wake_model = BastankhahGaussian(site, windTurbines)
x_j = np.linspace(-1500, 1500, 200)
y_j = np.linspace(-1500, 1500, 100)
flow_map = wake_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]
# test that the result is equal to last run (no evidens that these number are correct)
ref = [
0.18, 3.6, 7.27, 8.32, 7.61, 6.64, 5.96, 6.04, 6.8, 7.69, 8.08, 7.87,
7.59, 7.46, 7.55, 7.84, 8.19
]
if 0:
flow_map.plot_wake_map()
plt.plot(X[49, 100:133:2], Y[49, 100:133:2], '.-')
windTurbines.plot(x, y)
plt.show()
npt.assert_array_almost_equal(Z[49, 100:133:2], ref, 2)
def test_fuga():
# move turbine 1 600 300
wt_x = [-250, 600, -500, 0, 500, -250, 250]
wt_y = [433, 300, 0, 0, 0, -433, -433]
wts = HornsrevV80()
path = tfp + 'fuga/2MW/Z0=0.03000000Zi=00401Zeta0=0.00E+00/'
site = UniformSite([1, 0, 0, 0], ti=0.075)
wake_model = Fuga(path, site, wts)
res, _ = timeit(wake_model.__call__,
verbose=0,
line_profile=0,
profile_funcs=[
FugaDeficit.interpolate, LUTInterpolator.__call__,
GridInterpolator.__call__
])(x=wt_x, y=wt_y, wd=[30], ws=[10])
npt.assert_array_almost_equal(res.WS_eff_ilk.flatten(), [
10.00669629, 10., 8.47606501, 10.03143097, 9.37288077, 8.49301941,
8.07462708
], 8)
npt.assert_array_almost_equal(res.ct_ilk.flatten(), [
0.7926384, 0.793, 0.80647607, 0.79130273, 0.80177967, 0.80649302,
0.80607463
], 8)
x_j = np.linspace(-1500, 1500, 500)
y_j = np.linspace(-1500, 1500, 300)
wake_model = Fuga(path, site, wts)
sim_res = wake_model(wt_x, wt_y, wd=[30], ws=[10])
flow_map70 = sim_res.flow_map(HorizontalGrid(x_j, y_j, h=70))
flow_map73 = sim_res.flow_map(HorizontalGrid(x_j, y_j, h=73))
X, Y = flow_map70.XY
Z70 = flow_map70.WS_eff_xylk[:, :, 0, 0]
Z73 = flow_map73.WS_eff_xylk[:, :, 0, 0]
if 0:
flow_map70.plot_wake_map(levels=np.arange(6, 10.5, .1))
plt.plot(X[0], Y[140])
plt.figure()
plt.plot(X[0], Z70[140, :], label="Z=70m")
plt.plot(X[0], Z73[140, :], label="Z=73m")
plt.plot(X[0, 100:400:10], Z70[140, 100:400:10], '.')
print(list(np.round(Z70.data[140, 100:400:10], 4)))
print(list(np.round(Z73.data[140, 100:400:10], 4)))
plt.legend()
plt.show()
npt.assert_array_almost_equal(Z70[140, 100:400:10], [
10.0407, 10.0438, 10.0438, 10.013, 9.6847, 7.8787, 6.9561, 9.2251,
9.9686, 10.0382, 10.0498, 10.0569, 10.0325, 9.1787, 7.4004, 9.1384,
10.0329, 10.0297, 10.0232, 9.9265, 9.3163, 8.0768, 6.8858, 8.3754,
9.9592, 10.0197, 10.0118, 10.0141, 10.0118, 10.0095
], 4)
npt.assert_array_almost_equal(Z73[140, 100:400:10], [
10.0404, 10.0435, 10.0433, 10.0113, 9.6836, 7.9206, 7.0218, 9.2326,
9.9665, 10.0376, 10.0494, 10.0563, 10.0304, 9.1896, 7.4515, 9.15,
10.0317, 10.0294, 10.0226, 9.9245, 9.3252, 8.1192, 6.9462, 8.3988,
9.9574, 10.0194, 10.0117, 10.014, 10.0117, 10.0094
], 4)
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 wake_map(self,
x_j=None,
y_j=None,
height_level=None,
wt_x=[],
wt_y=[],
wt_type=0,
wt_height=None,
wd=None,
ws=None):
"""Calculate wake(effective wind speed) map
Parameters
----------
x_j : array_like or None, optional
X position map points
y_j : array_like
Y position of map points
height_level : int, float or None, optional
Height of wake map\n
If None, default, the mean hub height is used
wt_x : array_like, optional
X position of wind turbines
wt_y : array_like, optional
Y position of wind turbines
wt_type : array_like or None, optional
Type of the wind turbines
wt_height : array_like or None, optional
Hub height of the wind turbines\n
If None, default, the standard hub height is used
wd : int, float, array_like or None
Wind directions(s)\n
If None, default, the wake is calculated for site.default_wd
ws : int, float, array_like or None
Wind speed(s)\n
If None, default, the wake is calculated for site.default_ws
Returns
-------
X_j : array_like
2d array of map x positions
Y_j : array_like
2d array of map y positions
WS_eff_avg : array_like
2d array of average effective local wind speed taking into account
the probability of wind direction and speed
See Also
--------
plot_wake_map
"""
sim_res = self.wake_model(x=wt_x,
y=wt_y,
type=wt_type,
h=wt_height,
wd=wd,
ws=ws)
flow_map = sim_res.flow_map(
HorizontalGrid(x=x_j, y=y_j, h=height_level))
X, Y = flow_map.XY
return X, Y, flow_map.WS_eff_xylk.mean((2, 3))
def aep_map(self,
x_j=None,
y_j=None,
type_j=None,
wt_x=[],
wt_y=[],
wt_type=0,
wt_height=None,
wd=None,
ws=None):
"""Calculate AEP map
The map represents the of AEP produced by a new turbine at the specified positions
Parameters
----------
x_j : array_like or None, optional
X position map points (potential turbine positions)
y_j : array_like
Y position of map points (potential turbine positions)
type_j : int, float or None, optional
Type of potential turbine positions\n
If None, default, first turbine type(0) is used
wt_x : array_like, optional
X position of the current wind turbines
wt_y : array_like, optional
Y position of the current wind turbines
wt_type : array_like or None, optional
Type of the current wind turbines
wt_height : array_like or None, optional
Hub height of the current wind turbines\n
If None, default, the standard hub height is used
wd : int, float, array_like or None
Wind directions(s)\n
If None, default, the wake is calculated for site.default_wd
ws : int, float, array_like or None
Wind speed(s)\n
If None, default, the wake is calculated for site.default_ws
Returns
-------
X_j : array_like
2d array of map x positions
Y_j : array_like
2d array of map y positions
WS_eff_avg : array_like
2d array of average effective local wind speed taking into account
the probability of wind direction and speed
"""
h_j = self.windTurbines.hub_height(type_j)
sim_res = self.wake_model(x=wt_x,
y=wt_y,
type=wt_type,
h=wt_height,
wd=wd,
ws=ws)
flow_map = sim_res.flow_map(HorizontalGrid(x=x_j, y=y_j, h=h_j))
X, Y = flow_map.XY
aep_xy = flow_map.aep_xy(normalize_probabilities=True)
return X, Y, aep_xy
def test_fuga():
# move turbine 1 600 300
wt_x = [-250, 600, -500, 0, 500, -250, 250]
wt_y = [433, 300, 0, 0, 0, -433, -433]
wts = HornsrevV80()
path = tfp + 'fuga/2MW/Z0=0.03000000Zi=00401Zeta0=0.00E+0/'
site = UniformSite([1, 0, 0, 0], ti=0.075)
wake_model = Fuga(path, site, wts)
res = wake_model(x=wt_x, y=wt_y, wd=[30], ws=[10])
npt.assert_array_almost_equal(res.WS_eff_ilk.flatten(), [
10.002683492812844, 10.0, 8.413483643142389, 10.036952526815286,
9.371203842245153, 8.437429367715435, 8.012759083790058
], 8)
npt.assert_array_almost_equal(res.ct_ilk.flatten(), [
0.79285509, 0.793, 0.80641348, 0.79100456, 0.80180315, 0.80643743,
0.80601276
], 8)
x_j = np.linspace(-1500, 1500, 500)
y_j = np.linspace(-1500, 1500, 300)
wake_model = Fuga(path, site, wts)
sim_res = wake_model(wt_x, wt_y, wd=[30], ws=[10])
flow_map70 = sim_res.flow_map(HorizontalGrid(x_j, y_j, h=70))
flow_map73 = sim_res.flow_map(HorizontalGrid(x_j, y_j, h=73))
X, Y = flow_map70.XY
Z70 = flow_map70.WS_eff_xylk[:, :, 0, 0]
Z73 = flow_map73.WS_eff_xylk[:, :, 0, 0]
if 0:
import matplotlib.pyplot as plt
flow_map70.plot_wake_map(levels=np.arange(6, 10.5, .1))
plt.plot(X[0], Y[140])
plt.figure()
plt.plot(X[0], Z70[140, :], label="Z=70m")
plt.plot(X[0], Z73[140, :], label="Z=73m")
plt.plot(X[0, 100:400:10], Z70[140, 100:400:10], '.')
print(list(np.round(Z70[140, 100:400:10], 4)))
print(list(np.round(Z73[140, 100:400:10], 4)))
plt.legend()
plt.show()
# npt.assert_array_almost_equal(
# Z70[140, 100:400:10],
# [10.0547, 10.0519, 10.0741, 10.0099, 9.6774, 7.8538, 6.8484, 9.2134, 9.9749, 10.0232, 10.0658, 10.0189, 10.0187,
# 9.1496, 7.2077, 9.1154, 10.0183, 10.0008, 10.0146, 9.8838, 9.2848, 7.9681, 6.7412, 8.3149, 9.9114, 10.0119,
# 10.0011, 9.9979, 10.0002, 9.9981], 4)
npt.assert_array_almost_equal(Z70[140, 100:400:10], [
10.0547, 10.0519, 10.0718, 10.0093, 9.6786, 7.8589, 6.8539, 9.2199,
9.9837, 10.036, 10.0796, 10.0469, 10.0439, 9.1866, 7.2552, 9.1518,
10.0449, 10.0261, 10.0353, 9.9256, 9.319, 8.0062, 6.789, 8.3578,
9.9393, 10.0332, 10.0191, 10.0186, 10.0191, 10.0139
], 4)
npt.assert_array_almost_equal(Z73[140, 100:400:10], [
10.0542, 10.0514, 10.0706, 10.0075, 9.6778, 7.9006, 6.9218, 9.228,
9.9808, 10.0354, 10.0786, 10.0464, 10.0414, 9.1973, 7.3099, 9.1629,
10.0432, 10.0257, 10.0344, 9.9236, 9.3274, 8.0502, 6.8512, 8.3813,
9.9379, 10.0325, 10.0188, 10.0183, 10.019, 10.0138
], 4)
