from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import fmin_l_bfgs_b, fmin_slsqp, differential_evolution
from synthesis import sythesize
def parsec_airfoil(airfoil):
n_points = airfoil.shape[0]
func = lambda x: np.linalg.norm(sythesize(x, n_points) - airfoil)
bounds = [(0.001, 0.1), # rle
(1e-4, 0.5), # x_pre
(-0.1, 0.0), # y_pre
(-0.5, 0.5), # d2ydx2_pre
(-10, 10), # th_pre
(1e-4, 0.5), # x_suc
(0.0, 0.1), # y_suc
(-0.5, 0.5), # d2ydx2_suc
(-10, 10) # th_suc
]
bounds = np.array(bounds)
n_restarts = 10
opt_x = None
opt_f = np.inf
x0s = np.random.uniform(bounds[:,0], bounds[:,1], size=(n_restarts, bounds.shape[0]))
for x0 in x0s:
x, f, _ = fmin_l_bfgs_b(func, x0, approx_grad=1, bounds=bounds, disp=1)
if f < opt_f:
opt_x = x
opt_f = f
# res = differential_evolution(func, bounds=bounds, disp=1)
# opt_x = res.x
# opt_f = res.fun
print(opt_x)
print(opt_f)
return opt_x
if __name__ == "__main__":
data_path = '../initial_airfoil/naca0012.dat'
init_airfoil = np.loadtxt(data_path, skiprows=1)
# x = parsec_airfoil(init_airfoil)
x = [0.0147, 0.2996, -0.06, 0.4406, 7.335, 0.3015, 0.0599, -0.4360, -7.335] # NACA 0012
init_airfoil_fitted = sythesize(x, n_points=192)
plt.figure()
plt.plot(init_airfoil[:,0], init_airfoil[:,1], 'bo', ms=3, alpha=0.7, label='Original Airfoil')
plt.plot(init_airfoil_fitted[:,0], init_airfoil_fitted[:,1], 'r-', lw=1, alpha=0.7, label='PARSEC Approximation')
plt.legend()
plt.axis('equal')
plt.xlim(-0.1, 1.1)
plt.savefig('../initial_airfoil/initial_parsec.svg')
plt.close()
