def process_tare_torque(nrun, plot=False):
"""Process a single tare torque run."""
print("Processing tare torque run", nrun)
times = {0 : (35, 86),
1 : (12, 52),
2 : (11, 32),
3 : (7, 30)}
nidata = loadhdf("Data/Raw/Tare-torque/" + str(nrun) + "/nidata.h5")
# Compute RPM
time_ni = nidata["time"]
angle = nidata["turbine_angle"]
rpm_ni = fdiff.second_order_diff(angle, time_ni)/6.0
rpm_ni = ts.smooth(rpm_ni, 8)
try:
t1, t2 = times[nrun]
except KeyError:
t1, t2 = times[3]
meanrpm, _ = ts.calcstats(rpm_ni, t1, t2, 2000)
torque = nidata["torque_trans"]
meantorque, _ = ts.calcstats(torque, t1, t2, 2000)
print("Tare torque =", meantorque, "Nm at", meanrpm, "RPM")
if plot:
plt.figure()
plt.plot(time_ni, torque)
plt.xlabel("Time (s)")
plt.ylabel("Torque (Nm)")
plt.tight_layout()
plt.show()
return meanrpm, -meantorque
def process_strut_torque(nrun, zero_torque=0.0, plot=False, covers=False,
verbose=False):
"""Process a single strut torque run."""
testplan = pd.read_csv("Config/Test plan/Strut-torque.csv",
index_col="run")
ref_speed = testplan.ref_speed.iloc[nrun]
tsr_nom = testplan.tsr.iloc[nrun]
revs = testplan.revs.iloc[nrun]
rpm_nom = tsr_nom*ref_speed/R/(2*np.pi)*60
dur = revs/rpm_nom*60
if covers:
if verbose:
print("Processing strut torque with covers run", nrun)
nidata = loadhdf("Data/Raw/Strut-torque-covers/" + str(nrun) + \
"/nidata.h5")
else:
if verbose:
print("Processing strut torque run", nrun)
nidata = loadhdf("Data/Raw/Strut-torque/" + str(nrun) + "/nidata.h5")
# Compute RPM
time_ni = nidata["time"]
angle = nidata["turbine_angle"]
rpm_ni = fdiff.second_order_diff(angle, time_ni)/6.0
rpm_ni = ts.smooth(rpm_ni, 8)
t1, t2 = 9, dur
meanrpm, _ = ts.calcstats(rpm_ni, t1, t2, 2000)
torque = nidata["torque_trans"]
torque += calc_tare_torque(rpm_ni)
meantorque, _ = ts.calcstats(torque, t1, t2, 2000)
tsr_ref = meanrpm/60.0*2*np.pi*R/ref_speed
if verbose:
print("Reference TSR =", np.round(tsr_ref, decimals=4))
print("Strut torque =", meantorque, "Nm at", meanrpm, "RPM")
if plot:
plt.figure()
plt.plot(time_ni, torque)
plt.xlabel("Time (s)")
plt.ylabel("Torque (Nm)")
plt.tight_layout()
plt.show()
meantorque -= zero_torque
ct = meantorque/(0.5*rho*A*R*ref_speed**2)
cp = ct*tsr_ref
summary = pd.Series()
summary["run"] = nrun
summary["tsr_ref"] = tsr_ref
summary["cp"] = cp
summary["mean_torque"] = meantorque
summary["mean_rpm"] = meanrpm
return summary
def process_tare_drag(nrun, plot=False):
"""Process a single tare drag run."""
print("Processing tare drag run", nrun)
times = {0.2: (15, 120),
0.3: (10, 77),
0.4: (10, 56),
0.5: (8, 47),
0.6: (10, 40),
0.7: (8, 33),
0.8: (5, 31),
0.9: (8, 27),
1.0: (6, 24),
1.1: (9, 22),
1.2: (8, 21),
1.3: (7, 19),
1.4: (6, 18)}
rdpath = os.path.join(raw_data_dir, "Tare-drag", str(nrun))
with open(os.path.join(rdpath, "metadata.json")) as f:
metadata = json.load(f)
speed = float(metadata["Tow speed (m/s)"])
nidata = loadhdf(os.path.join(rdpath, "nidata.h5"))
time_ni = nidata["time"]
drag = nidata["drag_left"] + nidata["drag_right"]
drag = drag - np.mean(drag[:2000])
t1, t2 = times[speed]
meandrag, x = ts.calcstats(drag, t1, t2, 2000)
print("Tare drag =", meandrag, "N at", speed, "m/s")
if plot:
plt.figure()
plt.plot(time_ni, drag, 'k')
plt.show()
return speed, meandrag
