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dae51_polarfits.py
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dae51_polarfits.py
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"dae51_polarfits.py"
from __future__ import print_function
from builtins import zip
from builtins import range
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from gpfit.fit import fit
import sys
from gpkitmodels.GP.aircraft.wing.wing import Wing
from gpkitmodels.GP.aircraft.prop.propeller import ActuatorProp
import inspect
import os
GENERATE = True
plt.rcParams.update({'font.size':15})
def text_to_df(filename):
"parse XFOIL polars and concatente data in DataFrame"
lines = list(open(filename))
for i, l in enumerate(lines):
lines[i] = l.split("\n")[0]
for j in 10-np.arange(9):
if " "*j in lines[i]:
lines[i] = lines[i].replace(" "*j, " ")
if "---" in lines[i]:
start = i
data = {}
titles = lines[start-1].split(" ")[1:]
for t in titles:
data[t] = []
for l in lines[start+1:]:
for i, v in enumerate(l.split(" ")[1:]):
data[titles[i]].append(v)
df = pd.DataFrame(data)
return df
def fit_setup(Re_range):
"set up x and y parameters for gp fitting"
CL = []
CD = []
RE = []
fig, ax = plt.subplots()
for r in Re_range:
dataf = text_to_df("dae51.ncrit09.Re%dk.pol" % r)
if r < 150:
cl = dataf["CL"].values.astype(np.float)
cd = dataf["CD"].values.astype(np.float)
CL.append(np.hstack([cl[cl >= 1.0]]*1))
CD.append(np.hstack([cd[cl >= 1.0]]*1))
elif r < 200:
cl = dataf["CL"].values.astype(np.float)
cd = dataf["CD"].values.astype(np.float)
CL.append(cl[cl >= 0.9])
CD.append(cd[cl >= 0.9])
else:
CL.append(dataf["CL"].values.astype(np.float))
CD.append(dataf["CD"].values.astype(np.float))
ax.plot(dataf["CL"].values.astype(np.float), dataf["CD"].values.astype(np.float))
ax.legend(["%d" % re for re in Re_range])
RE.append([r*1000.0]*len(CL[-1]))
fig.savefig("polarstest.pdf")
u1 = np.hstack(CL)
u2 = np.hstack(RE)
w = np.hstack(CD)
u = [u1, u2]
xx = np.log(u2)
x = np.log(u)
y = np.log(w)
return x, y
def return_fit(cl, re):
"polar fit for the dae51 airfoil"
cd = (3.0902e14*cl**-.763161*re**-5.00624
+ 8.06832e-09*cl**6.32059*re**-0.810816
+ 2.65501*cl**42.1738*re**-2.87665 )**(1/6.1539)
# SMA function, K=3, max RMS error = 0.09734
return cd
def plot_fits(re, cnstr, x, y):
"plot fit compared to data"
# colors = ["k", "m", "b", "g", "y"]
colors = ["#084081", "#0868ac", "#2b8cbe", "#4eb3d3", "#7bccc4"]
assert len(re) == len(colors)
lre, ind = np.unique(X[1], return_index=True)
xre = np.exp(lre)
xcl = [np.exp(X[0][ind[i-1]:ind[i]]) for i in range(1, len(ind))]
cds = [np.exp(Y[ind[i-1]:ind[i]]) for i in range(1, len(ind))]
yfit = cnstr.evaluate(x)
cdf = [np.exp(yfit[ind[i-1]:ind[i]]) for i in range(1, len(ind))]
fig, ax = plt.subplots()
i = 0
for r, cl, cd, fi in zip(xre, xcl, cds, cdf):
roundre = int(np.round(r)/1000)
if roundre in re:
ax.plot(cl, cd, "o", mec=colors[i], mfc="none", mew=1.5)
ax.plot(cl, fi, c=colors[i], label="Re = %dk" % roundre, lw=2)
i += 1
ax.set_xlabel("$C_L$")
ax.set_ylabel("$c_{d_p}$")
ax.legend(loc=2)
ax.grid()
return fig, ax
if __name__ == "__main__":
Re = np.array([50, 75, 125, 150, 200, 300, 400, 500, 600, 700])
X, Y = fit_setup(Re) # call fit(X, Y, 4, "SMA") to get fit
np.random.seed(0)
cn, err = fit(X, Y, 3, "SMA")
print("RMS error: %.5f" % err)
df = cn.get_dataframe()
if GENERATE:
path = os.path.dirname(inspect.getfile(ActuatorProp))
df.to_csv(path + os.sep + "dae51_fitdata.csv", index=False)
else:
df.to_csv("dae51fitdata.csv", index=False)
# replot = np.array([150, 200, 300, 350, 400])
replot = np.array([300, 350, 400, 450, 500])
F, A = plot_fits(replot, cn, X, Y)
if len(sys.argv) > 1:
path = sys.argv[1]
F.savefig(path + "dae51polarfit1.eps", bbox_inches="tight")
else:
F.savefig("dae51polarfit1.eps", bbox_inches="tight")