'n_points': 200,
'lc': 0.16739,
'Ymax': 1000,
'yi': 10,
'alpha': 1.5,
'Re': 500,
'variables': 'v_eta',
'equation': 'LNS',
'mapping': ['semi_infinite_PB', [0, (46.7/13.8)]],
'Froude': 0.02,
'slope': 1.3e-5}
# In[3]:
g = sa.fluid(option)
g.diff_matrix()
g.integ_matrix()
# In[4]:
import numpy as np
# In[5]:
g.option['mapping'] = ['finite', [-5, 7]]
# In[6]:
option = {
'flow': 'DATA/G.txt',
'n_points': 200,
'lc': 0.16739,
'Ymax': 1000,
'yi': 5,
'alpha': 0.1,
'Re': 1274,
'variables': 'p_u_v',
'equation': 'LNS',
'mapping': ['semi_infinite_PB', [0, (46.7 / 13.8)]],
'Froude': 0.02,
'slope': 1.3e-5
}
f = sa.fluid(option)
f.diff_matrix()
f.integ_matrix()
#f.read_velocity_profile()
f.mapping()
f.set_blasius(f.y)
#f.interpolate()
# f.infinite_mapping()
# f.set_hyptan()
# f.set_poiseuille()
f.set_operator_variables()
def validation(self, pos, amp, gamma, eig_idx, shape):
""" check if the sensitivity of an eigenvalue is the same with the
adjoint procedure, or with a simple superposition of the base flow
plus the random perturbation:
dc = c(U+dU) - c(U) = dc(adjoint) """
self.get_perturbation(pos, amp, gamma, shape) # call the perturbation creator
self.U = self.U + self.perturb
# after the u_pert() call the self.delta_U property is accessible
d_delta_U = np.gradient(self.perturb) / np.gradient(self.y)
dd_delta_U = np.gradient(d_delta_U) / np.gradient(self.y)
self.dU = self.dU + d_delta_U
self.ddU = self.ddU + dd_delta_U
"""self.cd_pert(pos, amp)
self.aCD = self.aCD + self.delta_cd"""
mpl.rc('xtick', labelsize=40)
mpl.rc('ytick', labelsize=40)
# JUST A LITTLE VISUAL TEST TO SEE IF THE ADDITION OF
# THE VELOCITY WORKS
"""fig, ay = plt.subplots(figsize=(10, 10), dpi=100)
lines = ay.plot(self.U, self.y, 'b', self.dU, self.y, 'g',
self.ddU, self.y, 'r', self.aCD, self.y, 'm',
self.daCD, self.y, 'c', lw=2)
ay.set_ylabel(r'$y$', fontsize=32)
lgd = ay.legend((lines),
(r'$U$', r'$\partial U$',
r'$\partial^2 U$', r'$a^* C_D$',
r'$\partial a^* C_D$'),
loc=3, ncol=3, bbox_to_anchor=(0, 1), fontsize=32)
ay.set_ylim([0,5])
ay.grid()
plt.show()"""
dic = dict(zip(self.sim_param_keys, self.sim_param_values))
f = sa.fluid(dic)
# pdb.set_trace()
f.diff_matrix()
f.integ_matrix()
f.mapping()
f.y = self.y
f.U = self.U
f.dU = self.dU
f.ddU = self.ddU
f.aCD = self.aCD
f.daCD = self.daCD
f.set_operator_variables()
f.solve_eig()
'''fig, ay = plt.subplots(figsize=(20, 20), dpi=50)
lines = ay.plot(np.real(f.eigv),
np.imag(f.eigv), 'r*', markersize=20)
ay.set_ylabel(r'$c_i$', fontsize=32)
ay.set_xlabel(r'$c_r$', fontsize=32)
plt.show()'''
# remove the infinite and nan eigenvectors, and their eigenfunctions
selector = np.isfinite(f.eigv)
f.eigv = f.eigv[selector]
eigv_im = np.imag(f.eigv)
idx_new = np.argmax(eigv_im)
eigv_new = f.eigv[idx_new]
print ('new:', eigv_new)
print ('old:', self.eigv[eig_idx])
print ('diff:', eigv_new-self.eigv[eig_idx])
#self.get_perturbation(pos, amp, gamma)
print ('adj:', self.c_per())
print ('diff %', np.real((eigv_new-self.eigv[eig_idx]) -self.c_per())/np.real(self.c_per()) *100, np.imag((eigv_new-self.eigv[eig_idx]) -self.c_per())/np.imag(self.c_per()) *100)
return( eigv_new)
