def test_twotype_windturbines():
v80 = V80Deprecated()
v88 = OneTypeWindTurbines(
'V88', 88, 77, lambda ws: np.interp(ws, hornsrev1.ct_curve[:, 0],
hornsrev1.ct_curve[:, 1]),
lambda ws: np.interp(ws, hornsrev1.power_curve[:, 0], hornsrev1.
power_curve[:, 1] * 1.1), 'w')
wts = DeprecatedWindTurbines.from_WindTurbines([v80, v88])
ws = np.reshape([4, 6, 8, 10] * 2, (2, 4))
types = np.asarray([0, 1])
p = wts.power(ws, type=types)
npt.assert_array_equal(p[0], p[1] / 1.1)
def from_case_dict(name, case_dict):
site = UniformSite(p_wd=[1],
ti=case_dict['TItot'] / 0.8,
ws=case_dict['U0'])
site.default_wd = np.linspace(-30, 30, 61) % 360
windTurbines = OneTypeWindTurbines(
name="",
diameter=case_dict['D'],
hub_height=case_dict['zH'],
ct_func=lambda ws, ct=case_dict['CT']: ws * 0 + ct,
power_func=lambda ws: ws * 0 + 1,
power_unit='W')
xD = case_dict['xDown']
x = xD * case_dict['sDown']
return SingleWakeValidationCase(name, site, windTurbines, xD, x)
def deficitPlotSingleWakeCases(SingleWakecases, site, linewidth, cLES, cRANS,
colors):
# Plot velocity deficit for each single wake case
UdefCases = []
for case in SingleWakecases:
def ct(ws):
# ct function for wind turbine
ct = case['CT']
return ct
wt = OneTypeWindTurbines(name=case['name'],
diameter=case['D'],
hub_height=case['zH'],
ct_func=ct,
power_func=p,
power_unit='W')
if case['name'] == 'Nordtank-500':
xDown = case['xDown'] * 40.0
else:
xDown = case['xDown'] * wt._diameters
wds = np.linspace(-30, 30, 61)
kNOJ = NOJ_k_from_location(case['location'])
kGAU = Gau_k_from_Iu(case['TItot'] / 0.8)
print(case['name'], case['TItot'], kNOJ, kGAU)
wakemodels = [
NOJ(site, wt, k=kGAU),
BastankhahGaussian(site, wt, k=kGAU)
]
wake_ws = np.zeros((len(wakemodels), len(wds), len(xDown)))
for k in range(len(wakemodels)):
aep_calc = AEPCalculator(wakemodels[k])
# The velocity deficit at an arc is calculated by running the iwake_map for each point.
# because it is only possible to provide a x and y array that define a rectangular plane.
# This should be improved, where one can use a list of points and run wake_map once.
for i in range(len(wds)):
for j in range(len(xDown)):
x = xDown[j] * np.cos(wds[i] / 180.0 * np.pi)
y = xDown[j] * np.sin(wds[i] / 180.0 * np.pi)
X, Y, WS_eff = aep_calc.wake_map(x_j=x,
y_j=y,
wt_x=[0.0],
wt_y=[0.0],
wd=[270.0],
ws=case['U0'])
wake_ws[k, i, j] = WS_eff
lines = []
fig, ax = plt.subplots(1,
len(xDown),
sharey=False,
figsize=(3 * len(xDown), 3))
Udef = np.zeros((len(xDown), len(wakemodels) + 3))
Udef[:, :] = np.nan
for j in range(len(xDown)):
if case['xDown'][j] % 2 == 0 or (case['xDown'][j] + 1) % 2 == 0:
xDownlabel = str(int(case['xDown'][j]))
else:
xDownlabel = str(case['xDown'][j]).replace('.', 'p')
# References, based on field measurements
if case['name'] == 'Wieringermeer-West' and j == 1:
data = np.genfromtxt('data/' + case['name'] + '_data_' +
xDownlabel + 'D.dat')
ldata = ax[j].errorbar(data[:, 0] - 315,
data[:, 1] / case['U0'],
yerr=data[:, 2] / case['U0'] /
np.sqrt(data[:, 3]),
color='k',
elinewidth=1.0,
linewidth=0,
marker='o',
zorder=0,
markersize=4)
fdata = interp1d(data[:, 0] - 315, data[:, 1])
Udef[j, 0] = integrate_velocity_deficit_arc(
wds, fdata(wds), case['xDown'][j], case['U0'])
elif case['name'] == 'Wieringermeer-East' and j == 0:
data = np.genfromtxt('data/' + case['name'] + '_data_' +
xDownlabel + 'D.dat')
ldata = ax[j].errorbar(data[:, 0] - 31,
data[:, 1] / case['U0'],
yerr=data[:, 2] / case['U0'] /
np.sqrt(data[:, 3]),
color='k',
elinewidth=1.0,
linewidth=0,
marker='o',
zorder=0,
markersize=4)
fdata = interp1d(data[:, 0] - 31, data[:, 1])
Udef[j, 0] = integrate_velocity_deficit_arc(
wds, fdata(wds), case['xDown'][j], case['U0'])
elif case['name'] == 'Nibe':
# No standard deviation of the 10 min. available.
data = np.genfromtxt('data/' + case['name'] + '_data_' +
xDownlabel + 'D.dat')
ldata = ax[j].scatter(data[:, 0],
data[:, 1],
color='k',
marker='o',
zorder=0,
s=10)
fdata = interp1d(data[:, 0], data[:, 1])
Udef[j, 0] = integrate_velocity_deficit_arc(
wds,
fdata(wds) * case['U0'], case['xDown'][j], case['U0'])
elif case['name'] == 'Nordtank-500' and j < 2:
data = np.genfromtxt('data/' + case['name'] + '_data_' +
xDownlabel + 'D.dat')
ldata = ax[j].errorbar(data[:, 0],
data[:, 2],
yerr=data[:, 3] / np.sqrt(74.0),
color='k',
elinewidth=1.0,
linewidth=0,
marker='o',
zorder=0,
markersize=4)
# LES, based on EllipSys3D AD
LES = np.genfromtxt('data/' + case['name'] + '_LES_' + xDownlabel +
'D.dat')
# Shaded area represent the standard error of the mean
lLES = ax[j].fill_between(
LES[:, 0],
LES[:, 1] - LES[:, 2] / np.sqrt(LES[:, 3]),
LES[:, 1] + LES[:, 2] / np.sqrt(LES[:, 3]),
color=cLES,
alpha=0.5)
# RANS, based on EllipSys3D AD k-epsilon-fP
RANS = np.genfromtxt('data/' + case['name'] + '_RANS_' +
xDownlabel + 'D.dat')
lRANS, = ax[j].plot(RANS[:, 0],
RANS[:, 1],
color=cRANS,
linewidth=linewidth)
for k in range(len(wakemodels)):
l1, = ax[j].plot(wds,
wake_ws[k, :, j] / case['U0'],
color=colors[k],
linewidth=linewidth)
lines.append(l1)
if case['name'] == 'Nordtank-500':
title = '%s %g %s' % ('$x=', case['xDown'][j], 'D^*$')
else:
title = '%s %g %s' % ('$x=', case['xDown'][j], 'D$')
ax[j].set_title(title)
ax[j].set_xticks(np.arange(min(wds), max(wds) + 10.0, 10.0))
if case['xDown'][j] < 7.0:
ax[j].set_xlim(-30, 30)
else:
ax[j].set_xlim(-20, 20)
ax[j].grid(True)
ax[j].set_ylim(None, ymax=1.1)
# Momemtum deficit wake models
for k in range(len(wakemodels)):
Udef[j, 3 + k] = integrate_velocity_deficit_arc(
wds, wake_ws[k, :, j], case['xDown'][j], case['U0'])
Udef[j,
1] = integrate_velocity_deficit_arc(LES[:, 0],
LES[:, 1] * case['U0'],
case['xDown'][j],
case['U0'])
Udef[j,
2] = integrate_velocity_deficit_arc(RANS[:, 0],
RANS[:, 1] * case['U0'],
case['xDown'][j],
case['U0'])
ax[0].set_ylabel('$U/U_0$', rotation=0)
ax[0].yaxis.labelpad = 20
ax[1].set_xlabel('Relative wind direction [deg]')
fig.tight_layout(rect=[0, 0, 1, 0.9])
NOJlabel = 'NOJ ($k=%g$)' % (kNOJ)
GAUlabel = 'GAU ($k=%g$)' % (kGAU)
if case['name'] in [
'Nibe', 'Nordtank-500', 'Wieringermeer-West',
'Wieringermeer-East'
]:
fig.legend((ldata, lLES, lRANS, lines[0], lines[1]),
('Data', 'LES', 'RANS', NOJlabel, GAUlabel),
ncol=5,
loc='upper center',
bbox_to_anchor=(0, 0, 1, 1),
numpoints=1,
scatterpoints=1)
else:
fig.legend((lLES, lRANS, lines[0], lines[1]),
('LES', 'RANS', NOJlabel, GAUlabel),
ncol=4,
loc='upper center',
bbox_to_anchor=(0, 0, 1, 1),
numpoints=1,
scatterpoints=1)
filename = case['name'] + '.pdf'
fig.savefig(filename)
os.system('mv ' + filename + ' report/figures/')
UdefCases.append(Udef)
return UdefCases