def run(inputs, parameters = None, W = 0.2*9.8, alpha = 0., V = 10):
"""Function to be callled by DOE and optimization. Design Variables are
the only inputs.
:param inputs: {'sma', 'linear', 'sigma_o'}
:param W: weight (N)
:param alpha: angle of attack(radians)
:param V: velocity (m/s)
"""
def thickness(x, t, chord):
y = af.Naca00XX(chord, t, [x], return_dict = 'y')
thickness_at_x = y['u'] - y['l']
return thickness_at_x
if parameters != None:
eng = parameters[0]
import_matlab = False
else:
eng = None
import_matlab = True
sma = inputs['sma']
linear = inputs['linear']
sigma_o = 400e6
airfoil = "naca0012"
chord = 1.#0.6175
t = 0.12*chord
J = {'x':0.75, 'y':0.}
# need to transform normalized coordiantes in to global coordinates
sma['y+'] = sma['y+']*thickness(sma['x+'], t, chord)/2.
sma['y-'] = sma['y-']*thickness(sma['x-'], t, chord)/2.
linear['y+'] = linear['y+']*thickness(linear['x+'], t, chord)/2.
linear['y-'] = linear['y-']*thickness(linear['x-'], t, chord)/2.
#Adding the area key to the dictionaries
sma['area'] = math.pi*0.00025**2
linear['area'] = 0.001
# Design constants
#arm length to center of gravity
r_w = 0.15
#Aicraft weight (mass times gravity)
altitude = 10000. #feet
# Temperature
T_0 = 220.
T_final = 420.
#Initial martensitic volume fraction
MVF_init = 1.
# Number of steps and cycles
n = 200
n_cycles = 0
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#Parameters to select how to output stuff
all_outputs = False
save_data = False
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
if all_outputs:
eps_s, eps_l, theta, sigma, MVF, T, eps_t, theta, F_l, k, L_s = flap(airfoil,
chord, J, sma, linear, sigma_o,
W, r_w, V, altitude, alpha, T_0,
T_final, MVF_init, n, all_outputs = True,
import_matlab = import_matlab, eng=eng,
n_cycles = n_cycles)
import matplotlib.pyplot as plt
plt.figure()
plt.plot(theta, eps_s, lw=2., label = "$\epsilon_s$")
plt.plot(theta, eps_l, 'b--',lw=2, label = "$\epsilon_l$")
# plt.scatter(theta, eps_s, c = 'b')
# plt.scatter(theta, eps_l, c = 'b')
plt.ylabel('$\epsilon$', fontsize=24)
plt.xlabel(r'$\theta ({}^{\circ})$', fontsize=20)
plt.legend(loc = 'best', fontsize = 'x-large')
plt.grid()
print len(T), len(eps_s), len(eps_l), len(theta), len(eps_t)
plt.figure()
plt.plot(theta, eps_t, lw=2.)
# plt.scatter(theta, eps_t, c = 'b')
plt.ylabel('$\epsilon_t$', fontsize=24)
plt.xlabel(r'$\theta ({}^{\circ})$', fontsize=20)
plt.legend(loc = 'best', fontsize = 'x-large')
plt.grid()
plt.figure()
plt.plot(theta, MVF, lw=2.)
# plt.scatter(theta, MVF, c = 'b')
plt.ylabel('$MVF$', fontsize=24)
plt.xlabel(r'$\theta ({}^{\circ})$', fontsize=20)
plt.legend(loc = 'best', fontsize = 'x-large')
plt.grid()
plt.figure()
plt.plot(T, MVF, lw=2.)
# plt.scatter(T, MVF, c = 'b')
plt.ylabel('$MVF$', fontsize=24)
plt.xlabel('$T (K)$', fontsize=20)
plt.legend(loc = 'best', fontsize = 'x-large')
plt.grid()
plt.figure()
plt.plot(T, sigma, lw=2.)
# plt.scatter(T, sigma, c = 'b')
plt.ylabel('$\sigma$', fontsize=24)
plt.xlabel('$T (K)$', fontsize=20)
plt.legend(loc = 'best', fontsize = 'x-large')
plt.grid()
plt.figure()
plt.plot(T, eps_s, 'b', lw=2., label = "$\epsilon_s$")
plt.plot(T, eps_l, 'b--',lw=2, label = "$\epsilon_l$")
# plt.scatter(T, eps_s, c = 'b')
# plt.scatter(T, eps_l, c = 'b')
plt.xlabel('$T (K)$', fontsize=20)
plt.ylabel('$\epsilon$', fontsize=24)
plt.legend(loc = 'best', fontsize = 'x-large')
plt.grid()
plt.figure()
plt.plot(T, theta, lw=2.)
# plt.scatter(T, theta, c = 'b')
plt.xlabel('$T (K)$', fontsize=20)
plt.ylabel(r'$\theta ({}^{\circ})$', fontsize=20)
plt.grid()
F_s = []
for i in range(len(sigma)):
F_s.append(sigma[i]*sma['area'])
# sigma_MPa = []
# for sigma_i in sigma:
# sigma_MPa.append(sigma_i/1e6)
plt.figure()
plt.plot(theta, F_s, 'b', lw=2., label = "$F_s$")
plt.plot(theta, F_l, 'b--', lw=2., label = "$F_l$")
# plt.scatter(theta, F_s, c = 'b')
# plt.scatter(theta, F_l, c = 'b')
plt.ylabel('$F (N)$', fontsize=20)
plt.xlabel(r'$\theta ({}^{\circ})$', fontsize=20)
plt.legend(loc = 'best', fontsize = 'x-large')
plt.grid()
else:
theta, k= flap(airfoil, chord, J, sma, linear, sigma_o,
W, r_w, V, altitude, alpha, T_0,
T_final, MVF_init, n, all_outputs = False,
import_matlab = import_matlab, eng=eng,
n_cycles = n_cycles)
if save_data == True:
Data = {'theta': theta, 'eps_s': eps_s, 'eps_l': eps_l,
'sigma': sigma, 'xi': MVF, 'T': T, 'eps_t': eps_t,
'F_l': F_l, 'k': k, 'L_s':L_s}
pickle.dump(Data, open( "data.p", "wb" ) )
return {'theta': theta, 'k': k}
def run(inputs, parameters = None):
"""Function to be callled by DOE and optimization. Design Variables are
the only inputs.
:param inputs: {'sma', 'linear', 'sigma_o'}"""
def thickness(x, t, chord):
y = af.Naca00XX(chord, t, [x], return_dict = 'y')
thickness_at_x = y['u'] - y['l']
return thickness_at_x
if parameters != None:
eng = parameters[0]
import_matlab = False
else:
eng = None
import_matlab = True
sma = inputs['sma']
linear = inputs['linear']
sigma_o = 100e6
airfoil = "naca0012"
chord = 1.#0.6175
t = 0.12*chord
J = {'x':0.75, 'y':0.}
# need to transform normalized coordiantes in to global coordinates
sma['y+'] = sma['y+']*thickness(sma['x+'], t, chord)/2.
sma['y-'] = sma['y-']*thickness(sma['x-'], t, chord)/2.
linear['y+'] = linear['y+']*thickness(linear['x+'], t, chord)/2.
linear['y-'] = linear['y-']*thickness(linear['x-'], t, chord)/2.
#Adding the area key to the dictionaries
sma['area'] = math.pi*(0.000381/2.)**2
linear['area'] = 0.001
# Design constants
#arm length to center of gravity
r_w = 0.1
#Aicraft weight (mass times gravity)
W = 0.0523*9.8 #0.06*9.8
alpha = 0.
V = 10 #m/s
altitude = 10000. #feet
# Temperature
T_0 = 273.15 + 30
T_final = 273.15 + 140
#Initial martensitic volume fraction
MVF_init = 1.
# Number of steps and cycles
n = 200
n_cycles = 0
#~~~~~~~~~~~~~~~~~~~~~bb~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#Parameters to select how to output stuff
all_outputs = True
save_data = True
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
if all_outputs:
# print "SMA: real y dimensions: ", sma['y-'], sma['y+'],sma['y+']*thickness(sma['x+'], t, chord)/2., sma['y-']*thickness(sma['x-'], t, chord)/2.
# print "linear: real y dimensions: ", linear['y-'], linear['y+'], linear['y+']*thickness(linear['x+'], t, chord)/2., linear['y-']*thickness(linear['x-'], t, chord)/2.
eps_s, eps_l, theta, sigma, MVF, T, eps_t, theta, F_l, k, L_s, H_cur = flap(airfoil,
chord, J, sma, linear, sigma_o,
W, r_w, V, altitude, alpha, T_0,
T_final, MVF_init, n, all_outputs = True,
import_matlab = import_matlab, eng=eng,
n_cycles = n_cycles)
import matplotlib.pyplot as plt
plt.figure()
plt.plot(np.rad2deg(theta), eps_s, lw=2., label = "$\epsilon_s$")
plt.plot(np.rad2deg(theta), eps_l, 'b--',lw=2, label = "$\epsilon_l$")
# plt.scatter(theta, eps_s, c = 'b')
# plt.scatter(theta, eps_l, c = 'b')
plt.ylabel('$\epsilon$', fontsize=24)
plt.xlabel(r'$\theta ({}^{\circ})$', fontsize=20)
plt.legend(loc = 'best', fontsize = 'x-large')
plt.grid()
print len(T), len(eps_s), len(eps_l), len(theta), len(eps_t)
plt.figure()
plt.plot(np.rad2deg(theta), eps_t, lw=2.)
# plt.scatter(theta, eps_t, c = 'b')
plt.ylabel('$\epsilon_t$', fontsize=24)
plt.xlabel(r'$\theta ({}^{\circ})$', fontsize=20)
plt.legend(loc = 'best', fontsize = 'x-large')
plt.grid()
plt.figure()
plt.plot(T, H_cur, lw=2.)
plt.ylabel('$H_{cur}$', fontsize=24)
plt.xlabel('T', fontsize=20)
plt.legend(loc = 'best', fontsize = 'x-large')
plt.grid()
sigma_crit = 0
H_max = 0.0550
H_min = 0.0387
k = 4.6849e-09
plt.figure()
plt.plot(sigma, H_cur, lw=2.)
plt.plot(sigma, H_min + (H_max - H_min)*(1. - np.exp(-k*(abs(np.array(sigma)) - \
sigma_crit))), 'k', lw=2.)
plt.ylabel('$H_{cur}$', fontsize=24)
plt.xlabel('$\sigma$', fontsize=20)
plt.legend(loc = 'best', fontsize = 'x-large')
plt.grid()
plt.figure()
plt.plot(np.rad2deg(theta), MVF, lw=2.)
# plt.scatter(theta, MVF, c = 'b')
plt.ylabel('$MVF$', fontsize=24)
plt.xlabel(r'$\theta ({}^{\circ})$', fontsize=20)
plt.legend(loc = 'best', fontsize = 'x-large')
plt.grid()
plt.figure()
plt.plot(T, MVF, lw=2.)
# plt.scatter(T, MVF, c = 'b')
plt.ylabel('$MVF$', fontsize=24)
plt.xlabel('$T (K)$', fontsize=20)
plt.legend(loc = 'best', fontsize = 'x-large')
plt.grid()
plt.figure()
plt.plot(T, sigma, lw=2.)
# plt.scatter(T, sigma, c = 'b')
plt.ylabel('$\sigma$', fontsize=24)
plt.xlabel('$T (K)$', fontsize=20)
plt.legend(loc = 'best', fontsize = 'x-large')
plt.grid()
plt.figure()
plt.plot(T, eps_s, 'b', lw=2., label = "$\epsilon_s$")
plt.plot(T, eps_l, 'b--',lw=2, label = "$\epsilon_l$")
# plt.scatter(T, eps_s, c = 'b')
# plt.scatter(T, eps_l, c = 'b')
plt.xlabel('$T (K)$', fontsize=20)
plt.ylabel('$\epsilon$', fontsize=24)
plt.legend(loc = 'best', fontsize = 'x-large')
plt.grid()
plt.figure()
plt.plot(T, np.rad2deg(theta), lw=2.)
# plt.scatter(T, theta, c = 'b')
plt.xlabel('$T (K)$', fontsize=20)
plt.ylabel(r'$\theta ({}^{\circ})$', fontsize=20)
plt.grid()
F_s = []
for i in range(len(sigma)):
F_s.append(sigma[i]*sma['area'])
# sigma_MPa = []
# for sigma_i in sigma:
# sigma_MPa.append(sigma_i/1e6)
plt.figure()
plt.plot(np.rad2deg(theta), F_s, 'b', lw=2., label = "$F_s$")
plt.plot(np.rad2deg(theta), F_l, 'b--', lw=2., label = "$F_l$")
# plt.scatter(theta, F_s, c = 'b')
# plt.scatter(theta, F_l, c = 'b')
plt.ylabel('$F (N)$', fontsize=20)
plt.xlabel(r'$\theta ({}^{\circ})$', fontsize=20)
plt.legend(loc = 'best', fontsize = 'x-large')
plt.grid()
else:
theta, k= flap(airfoil, chord, J, sma, linear, sigma_o,
W, r_w, V, altitude, alpha, T_0,
T_final, MVF_init, n, all_outputs = False,
import_matlab = import_matlab, eng=eng,
n_cycles = n_cycles)
if save_data == True:
Data = {'theta': theta, 'eps_s': eps_s, 'eps_l': eps_l,
'sigma': sigma, 'xi': MVF, 'T': T, 'eps_t': eps_t,
'F_l': F_l, 'k': k, 'L_s':L_s}
pickle.dump(Data, open( "data.p", "wb" ) )
return {'theta': theta, 'k': k}
