def calc_perf(theta_0=360,
plot=False,
verbose=True,
inertial=False,
export_csv=True):
t, torque, drag = foampy.load_all_torque_drag()
_t, theta, omega = foampy.load_theta_omega(t_interp=t)
reached_theta_0 = True
if theta.max() < theta_0:
theta_0 = 1
reached_theta_0 = False
# Compute tip speed ratio
tsr = omega * R / U_infty
# Compute mean TSR
meantsr = np.mean(tsr[theta >= theta_0])
if inertial:
inertia = 3 # guess from SolidWorks model
inertial_torque = inertia * fdiff.second_order_diff(omega, t)
torque -= inertial_torque
# Compute power coefficient
power = torque * omega
cp = power / (0.5 * rho * area * U_infty**3)
meancp = np.mean(cp[theta >= theta_0])
# Compute drag coefficient
cd = drag / (0.5 * rho * area * U_infty**2)
meancd = np.mean(cd[theta >= theta_0])
if export_csv:
df = pd.DataFrame()
df["theta_deg"] = theta
df["tsr"] = tsr
df["cp"] = cp
df["cd"] = cd
df.to_csv("processed/perf.csv", index=False)
if verbose:
print("Performance from {:.1f}--{:.1f} degrees:".format(
theta_0, theta.max()))
print("Mean TSR = {:.3f}".format(meantsr))
print("Mean C_P = {:.3f}".format(meancp))
print("Mean C_D = {:.3f}".format(meancd))
if plot:
plt.close('all')
plt.plot(theta[5:], cp[5:])
plt.title(r"$\lambda = %1.1f$" % meantsr)
plt.xlabel(r"$\theta$ (degrees)")
plt.ylabel(r"$C_P$")
#plt.ylim((0, 1.0))
plt.tight_layout()
plt.show()
if reached_theta_0:
return {"C_P": meancp, "C_D": meancd, "TSR": meantsr}
else:
return {"C_P": "nan", "C_D": "nan", "TSR": "nan"}
def calc_perf(theta_0=360, plot=False, verbose=True, inertial=False):
t, torque, drag = foampy.load_all_torque_drag()
_t, theta, omega = foampy.load_theta_omega(t_interp=t)
reached_theta_0 = True
if theta.max() < theta_0:
theta_0 = 1
reached_theta_0 = False
# Compute tip speed ratio
tsr = omega*R/U_infty
# Compute mean TSR
meantsr = np.mean(tsr[theta >= theta_0])
if inertial:
inertia = 3 # guess from SolidWorks model
inertial_torque = inertia*fdiff.second_order_diff(omega, t)
torque -= inertial_torque
# Compute power coefficient
power = torque*omega
cp = power/(0.5*rho*area*U_infty**3)
meancp = np.mean(cp[theta >= theta_0])
# Compute drag coefficient
cd = drag/(0.5*rho*area*U_infty**2)
meancd = np.mean(cd[theta >= theta_0])
if verbose:
print("Performance from {:.1f}--{:.1f} degrees:".format(theta_0,
theta.max()))
print("Mean TSR = {:.3f}".format(meantsr))
print("Mean C_P = {:.3f}".format(meancp))
print("Mean C_D = {:.3f}".format(meancd))
if plot:
plt.close('all')
plt.plot(theta[5:], cp[5:])
plt.title(r"$\lambda = %1.1f$" %meantsr)
plt.xlabel(r"$\theta$ (degrees)")
plt.ylabel(r"$C_P$")
#plt.ylim((0, 1.0))
plt.tight_layout()
plt.show()
if reached_theta_0:
return {"C_P" : meancp,
"C_D" : meancd,
"TSR" : meantsr}
else:
return {"C_P" : "nan",
"C_D" : "nan",
"TSR" : "nan"}
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 8 09:22:09 2013
@author: pete
"""
from __future__ import division, print_function
import matplotlib.pyplot as plt
import numpy as np
import foampy
from gendynmeshdict import meantsr
t, torque, drag = foampy.load_all_torque_drag(torque_axis="x")
# Compute tip speed ratio
R = 0.13
U = 1.0
omega = -meantsr*U/R # direction is reversed
theta = -omega*t*180.0/np.pi
print("Mean tsr:", meantsr)
# Pick an index to start from for mean calculations and plotting
# (allow turbine to reach steady state)
try:
i = np.where(np.round(theta) == 100)[0][0]
except IndexError:
i = 5
i2 = -1
def test_load_all_torque_drag():
t, torque, drag = foampy.load_all_torque_drag(casedir="test")
assert t.max() == 4.0
