2015.09.24

In [6]:

    

from __future__ import division, print_function
import os
if os.path.split(os.getcwd())[-1] == "Lab notebooks":
    os.chdir("../../")
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import Modules.processing as pr
import Modules.plotting as pl
%load_ext autoreload
%autoreload 2
%matplotlib inline


    

In [26]:

    

r = pr.Run("Perf", 2)
print(r.mean_tsr)
print(r.mean_cp)
print(r.mean_cd)
s = 5
plt.plot(r.angle[:s*2000], r.cp[:s*2000])
plt.show()


    

    




2.00109375829
0.256487206631
0.967965271976






    

In [27]:

    

pr.process_latest_run("Perf")


    

    




Processing latest run in Perf

Summary for Perf run 4:
run                 4.000000
tow_speed_nom       1.000000
tsr_nom             2.800000
mean_tow_speed      0.999769
std_tow_speed       0.010182
t1                 15.000000
t2                 30.700000
n_blade_pass       42.000000
n_revs             14.000000
mean_tsr            2.800778
mean_cp             0.100351
mean_cd             1.062450
std_tsr             0.044355
std_cp              0.066167
std_cd              0.436838
std_tsr_per_rev     0.004151
std_cp_per_rev      0.014371
std_cd_per_rev      0.019537
exp_unc_tsr         0.009306
exp_unc_cp          0.032552
exp_unc_cd          0.043806
dof_tsr            13.000167
dof_cp             13.974549
dof_cd             13.010691
dtype: float64

In [30]:

    

s = pr.Section("Perf")
s.process()


    

In [33]:

    

omega = s.data.rpm*2*np.pi/60
s.data["tsr"] = omega*pr.R/1
power = s.data.torque*omega
s.data["cp"] = power/(0.5*1000*1*1**3)
s.data["cd"] = s.data.drag/(0.5*1000*1*1**2)
s.data


    

    
Out[33]:






  

    

      

      
tow_speed
      
rpm
      
torque
      
drag
      
tsr
      
cp
      
cd
    
    

      
run
      

      

      

      

      

      

      

    
  
  

    

      
0
      
 0.999737
      
 22.911482
      
 29.297704
      
 377.696892
      
 1.199642
      
 0.140587
      
 0.755394
    
    

      
1
      
 0.999723
      
 30.576568
      
 36.566590
      
 440.679143
      
 1.600985
      
 0.234170
      
 0.881358
    
    

      
2
      
 0.999749
      
 38.214584
      
 31.466900
      
 483.666696
      
 2.000911
      
 0.251850
      
 0.967333
    
    

      
3
      
 0.999736
      
 45.828674
      
 20.867135
      
 512.859426
      
 2.399584
      
 0.200290
      
 1.025719
    
    

      
4
      
 0.999769
      
 53.483139
      
  8.960410
      
 530.745002
      
 2.800371
      
 0.100370
      
 1.061490
    
  

In [35]:

    

plt.figure()
plt.plot(s.data.tsr, s.data.cp, "-o")
plt.xlabel(r"$\lambda$")
plt.ylabel(r"$C_P$")

plt.figure()
plt.plot(s.data.tsr, s.data.cd, "-s")
plt.xlabel(r"$\lambda$")
plt.ylabel(r"$C_D$")
plt.show()