def test_key_error1():
"""
"""
param = read_parameters('input.yaml')
# missing parameter, should throw KeyError
s = seal(param)
def run_example():
"""
"""
# output filename
param = read_parameters('Kanki01_input.yaml')
s = seal(param)
s.solve_zeroth()
s.plot_res()
def main():
"""
to call run *.py file containing class as script
"""
# output filename
param = read_parameters('Kanki01_input.yaml')
s = seal(param)
s.solve_zeroth()
s.plot_res()
def run_example():
"""
test01 of seal class
baseline test of zeroth-order solution
- coarse grid
- "standard" relaxation factors
"""
# output filename
param = read_parameters('Kanki01_input.yaml')
s = seal(param)
s.solve_zeroth()
s.plot_res()
def main():
relax_uv = np.array([0.3, 0.5, 0.7, 0.9])
q = np.zeros(relax_uv.size, dtype=np.float64)
param = read_parameters('Kanki01_input.yaml')
s = seal(param)
for idx, val in enumerate(relax_uv):
s.relax_uv = val
s.solve_zeroth()
q[idx] = s.q
for idx, val in enumerate(relax_uv):
print("relax : {a:g} , leakage [cm^3/s] : {b:g}".format(a=val, b= q[idx]/s.rho_s*1.e6) )
def test_key_error2():
"""
"""
param = read_parameters('input.yaml')
# try-except block execution
# KeyError asserted with try, so except block executed
try:
s = seal(param)
except:
param['uv_src_method'] = 0
s = seal(param)
# run zeroth order solve for a few iterations
s.max_it = 5
s.solve_zeroth()
def main():
# experimental results
q_exp = 4634.0 # leakage [cm^3/s]
kxx_exp = 3.59
kyx_exp = 10.8
dxx_exp = 147.0
dyx_exp = 55.3
mxx_exp = 221.5
# setup and solve zeroth-order problem
param = read_parameters('input04b.yaml')
s = seal(param)
s.solve_zeroth()
# solve first-order problem for several perturbation frequencies
# sub-synchronous frequencies considered
#pertFreq = np.array([0.0, 25.0, 50.0, 75.0])
pertFreq = np.array([0.0, 25.0, 50.0, 75.0, 100.0, 125.0, 150.0])
# arrays for saving complex forces
fx = np.zeros((2, pertFreq.size), dtype=np.complex128)
fy = np.zeros((2, pertFreq.size), dtype=np.complex128)
for idx_freq, freq in enumerate(pertFreq):
s.whirl_f = freq
s.solve_first()
fx[0, idx_freq] = s.fx1
fy[0, idx_freq] = s.fy1
# functions used if curve-fitting
def linear_func(x, b):
# damping, linear w.r.t. frequency
return (b * x)
def quad_func(x, a, b):
# dynamic stiffness, added mass term exhibits frequency^2 behavior
return (a + b * x**2.0)
# curve-fit real and imaginary parts of forces
# fitting coefficients are the dynamic coefficients
px_r, cov = optimize.curve_fit(quad_func,
pertFreq,
np.real(fx[0, :]),
p0=[0.0, 0.0])
py_r, cov = optimize.curve_fit(quad_func,
pertFreq,
np.real(fy[0, :]),
p0=[0.0, 0.0])
px_i, cov = optimize.curve_fit(linear_func,
pertFreq,
np.imag(fx[0, :]),
p0=[0.0])
py_i, cov = optimize.curve_fit(linear_func,
pertFreq,
np.imag(fy[0, :]),
p0=[0.0])
# print leakage and dynamic coefficients
print('---Values (model | exp)---')
print("leakage [cm^3/s] : {a:g} | {b:g}".format(a=s.q / s.rho_s * 1.e6,
b=q_exp))
print("K_xx [MN/m] : {a:g} | {b:g}".format(a=px_r[0] / 1.e6,
b=kxx_exp))
print("K_yx [MN/m] : {a:g} | {b:g}".format(a=py_r[0] / 1.e6,
b=kyx_exp))
print("D_xx [kN.s/m] : {a:g} | {b:g}".format(a=px_i[0] / 1.e3,
b=dxx_exp))
print("D_yx [kN.s/m] : {a:g} | {b:g}".format(a=py_i[0] / 1.e3,
b=dyx_exp))
print("M_xx [kg] : {a:g} | {b:g}".format(a=px_r[1], b=mxx_exp))
# print relative errors in leakage and dynamic coefficients
print('---Relative errors---')
print("leakage [%] : {a:g}".format(a=(s.q / s.rho_s * 1.e6 - q_exp) /
q_exp * 100.0))
print("K_xx [%] : {a:g}".format(a=(px_r[0] / 1.e6 - kxx_exp) / kxx_exp *
100.0))
print("K_yx [%] : {a:g}".format(a=(py_r[0] / 1.e6 - kyx_exp) / kyx_exp *
100.0))
print("D_xx [%] : {a:g}".format(a=(px_i[0] / 1.e3 - dxx_exp) / dxx_exp *
100.0))
print("D_yx [%] : {a:g}".format(a=(py_i[0] / 1.e3 - dyx_exp) / dyx_exp *
100.0))
print("M_xx [%] : {a:g}".format(a=(px_r[1] - mxx_exp) / mxx_exp *
100.0))
# generate figures of forces as a function of perturbation frequency
# include data and curve-fits
# curve-fit coefficients correspond to dynamic coefficients
plt.figure()
plt.scatter(pertFreq, np.real(fx[0, :]) / 1.e6, label='fx-data')
plt.plot(pertFreq,
quad_func(pertFreq, *px_r) / 1.e6,
'g--',
label='fit: a=%5.3f, b=%5.3f' % tuple(px_r))
plt.scatter(pertFreq, np.real(fy[0, :]) / 1.e6, label='fy-data')
plt.plot(pertFreq,
quad_func(pertFreq, *py_r) / 1.e6,
'r--',
label='fit: a=%5.3f, b=%5.3f' % tuple(py_r))
plt.xlabel(r'Pert. Freq. [rad/s]')
plt.ylabel(r'Re(Force) [MN/m]')
plt.legend(loc='best')
plt.tight_layout()
plt.savefig('force_real_' + str(s.Nx) + '_' + str(s.Ny) + 'bc.png')
plt.close()
plt.figure()
plt.scatter(pertFreq, np.imag(fx[0, :]) / 1.e6, label='fx-data')
plt.plot(pertFreq,
linear_func(pertFreq, *px_i) / 1.e6,
'g--',
label='fit: a=%5.3f' % tuple(px_i))
plt.scatter(pertFreq, np.imag(fy[0, :]) / 1.e6, label='fy-data')
plt.plot(pertFreq,
linear_func(pertFreq, *py_i) / 1.e6,
'r--',
label='fit: a=%5.3f' % tuple(py_i))
plt.xlabel(r'Pert. Freq. [rad/s]')
plt.ylabel(r'Im(Force) [MN/m]')
plt.legend(loc='best')
plt.tight_layout()
plt.savefig('force_imag_' + str(s.Nx) + '_' + str(s.Ny) + 'bc.png')
plt.close()
def main():
# functions used if curve-fitting
def linear_func(x, b):
# damping, linear w.r.t. frequency
return (b * x)
def quad_func(x, a, b):
# dynamic stiffness, added mass term exhibits frequency^2 behavior
return (a + b * x**2.0)
# experimental results
q_exp = 4634.0 # leakage [cm^3/s]
K_exp = np.array([[3.30, 11.3], [-10.3, 3.89]])
D_exp = np.array([[147.0, 52.9], [-57.7, 147.0]])
M_exp = np.array([[229.0, 16.0], [32.0, 214.0]])
K = np.zeros((2, 2))
D = np.zeros((2, 2))
M = np.zeros((2, 2))
Nx = np.array([5, 8, 10, 15, 20, 25])
Ny = np.array([10, 15, 20, 30, 40, 50])
# Nx = np.array([4, 5, 6, 7])
# Ny = np.array([8, 9, 10, 11])
# arrays for plotting
Kxx_p = np.zeros(Nx.size)
Kxy_p = np.zeros(Nx.size)
Dxx_p = np.zeros(Nx.size)
Dxy_p = np.zeros(Nx.size)
Mxx_p = np.zeros(Nx.size)
q_p = np.zeros(Nx.size)
param = read_parameters('Kanki01_input.yaml')
s = seal(param)
#pert_dirs = ['X','Y'] # for testing symmetry
pert_dirs = ['X']
#pertFreq = np.array([200.0])
pertFreq = np.array([0.0, 25.0, 50.0, 75.0, 100.0])
fx = np.zeros(pertFreq.size, dtype=np.complex128)
fy = np.zeros(pertFreq.size, dtype=np.complex128)
for idx, nx in enumerate(Nx):
s.Nx = Nx[idx]
s.Ny = Ny[idx]
print(s.Nx)
print(s.Ny)
s.update_mesh()
s.restart_seal()
s.solve_zeroth()
q_p[idx] = s.q / s.rho_s * 1.e6
for pert in pert_dirs:
s.pert_dir = pert
#s.update_seal() # need to clean up seal updates, this overwrites zeroth bcs which impacts stiffness
print(s.pert_dir)
# arrays for saving complex forces
#fx = fx * 0.0
#fy = fy * 0.0
for idx_freq, freq in enumerate(pertFreq):
s.whirl_f = freq
s.solve_first()
fx[idx_freq] = s.fx1
fy[idx_freq] = s.fy1
print(freq)
# curve-fit real and imaginary parts of forces
# fitting coefficients are the dynamic coefficients
px_r, cov = optimize.curve_fit(quad_func,
pertFreq,
np.real(fx),
p0=[0.0, 0.0])
py_r, cov = optimize.curve_fit(quad_func,
pertFreq,
np.real(fy),
p0=[0.0, 0.0])
px_i, cov = optimize.curve_fit(linear_func,
pertFreq,
np.imag(fx),
p0=[0.0])
py_i, cov = optimize.curve_fit(linear_func,
pertFreq,
np.imag(fy),
p0=[0.0])
if pert == 'X':
K[0, 0] = px_r[0] / 1.e6
K[1, 1] = K[0, 0]
K[1, 0] = py_r[0] / 1.e6
K[0, 1] = -K[1, 0]
D[0, 0] = px_i[0] / 1.e3
D[1, 1] = D[0, 0]
D[1, 0] = -py_i[0] / 1.e3
D[0, 1] = -D[1, 0]
M[0, 0] = px_r[1]
M[1, 1] = M[0, 0]
M[1, 0] = py_r[1]
M[0, 1] = -M[1, 0]
elif pert == 'Y':
K[0, 1] = px_r[0] / 1.e6
K[1, 1] = py_r[0] / 1.e6
D[0, 1] = px_i[0] / 1.e3
D[1, 1] = py_i[0] / 1.e3
M[0, 1] = px_r[1]
M[1, 1] = py_r[1]
Kxx_p[idx] = K[0, 0]
Kxy_p[idx] = K[0, 1]
Dxx_p[idx] = D[0, 0]
Dxy_p[idx] = D[0, 1]
Mxx_p[idx] = M[0, 0]
# print('---------------')
# print('--predicted--')
# print(K)
# print('--Exp.--')
# print(K_exp)
# print('---------------')
# print('--predicted--')
# print(D)
# print('--Exp.--')
# print(D_exp)
# print('---------------')
# print('--predicted--')
# print(M)
# print('--Exp.--')
# print(M_exp)
# print('---------------')
# if param["pert_dir"] == 'X':
# # # print leakage and dynamic coefficients
# print('---Values (model | exp)---')
# print("leakage [cm^3/s] : {a:g} | {b:g}".format(a=s.q/s.rho_s*1.e6, b=q_exp))
# print("K_xx [MN/m] : {a:g} | {b:g}".format(a=px_r[0]/1.e6, b=K_exp[0,0]))
# print("K_yx [MN/m] : {a:g} | {b:g}".format(a=py_r[0]/1.e6, b=K_exp[1,0]))
# print("D_xx [kN.s/m] : {a:g} | {b:g}".format(a=px_i[0]/1.e3, b=D_exp[0,0]))
# print("D_yx [kN.s/m] : {a:g} | {b:g}".format(a=py_i[0]/1.e3, b=D_exp[1,0]))
# print("M_xx [kg] : {a:g} | {b:g}".format(a=px_r[1], b=M_exp[0,0]))
# print("M_yx [kg] : {a:g} | {b:g}".format(a=py_r[1], b=M_exp[1,0]))
# # # print relative errors in leakage and dynamic coefficients
# # # print('---Relative errors---')
# # # print("leakage [%] : {a:g}".format(a=(s.q/s.rho_s*1.e6-q_exp) / q_exp * 100.0 ))
# # # print("K_xx [%] : {a:g}".format(a=(px_r[0]/1.e6-kxx_exp) / kxx_exp * 100.0 ))
# # # print("K_yx [%] : {a:g}".format(a=(py_r[0]/1.e6+kxy_exp) / kyx_exp * 100.0 ))
# # # print("D_xx [%] : {a:g}".format(a=(px_i[0]/1.e3-dxx_exp) / dxx_exp * 100.0 ))
# # # print("D_yx [%] : {a:g}".format(a=(py_i[0]/1.e3+dxy_exp) / dyx_exp * 100.0 ))
# # # print("M_xx [%] : {a:g}".format(a=(px_r[1]-mxx_exp) / mxx_exp * 100.0 ))
# elif param["pert_dir"] == 'Y':
# print('---Values (model | exp)---')
# print("leakage [cm^3/s] : {a:g} | {b:g}".format(a=s.q/s.rho_s*1.e6, b=q_exp))
# print("K_xy [MN/m] : {a:g} | {b:g}".format(a=px_r[0]/1.e6, b=K_exp[0,1]))
# print("K_yy [MN/m] : {a:g} | {b:g}".format(a=py_r[0]/1.e6, b=K_exp[1,1]))
# print("D_xy [kN.s/m] : {a:g} | {b:g}".format(a=px_i[0]/1.e3, b=D_exp[0,1]))
# print("D_yy [kN.s/m] : {a:g} | {b:g}".format(a=py_i[0]/1.e3, b=D_exp[1,1]))
# print("M_xy [kg] : {a:g} | {b:g}".format(a=px_r[1], b=M_exp[0,1]))
# print("M_yy [kg] : {a:g} | {b:g}".format(a=py_r[1], b=M_exp[1,1]))
# print relative errors in leakage and dynamic coefficients
# print('---Relative errors---')
# print("leakage [%] : {a:g}".format(a=(s.q/s.rho_s*1.e6-q_exp) / q_exp * 100.0 ))
# print("K_xx [%] : {a:g}".format(a=(px_r[0]/1.e6-kxx_exp) / kxx_exp * 100.0 ))
# print("K_yx [%] : {a:g}".format(a=(py_r[0]/1.e6-kyx_exp) / kyx_exp * 100.0 ))
# print("D_xx [%] : {a:g}".format(a=(px_i[0]/1.e3-dxx_exp) / dxx_exp * 100.0 ))
# print("D_yx [%] : {a:g}".format(a=(py_i[0]/1.e3-dyx_exp) / dyx_exp * 100.0 ))
# print("M_xx [%] : {a:g}".format(a=(px_r[1]-mxx_exp) / mxx_exp * 100.0 ))
plt.figure()
plt.plot(Nx * Ny, q_p)
plt.axhline(y=q_exp, color='r', linestyle='-')
plt.xlabel(r'$N_x \times N_y$')
plt.ylabel(r'$Leakage [cm$^3$/s]')
plt.tight_layout()
plt.savefig('q_grid.png')
plt.close()
plt.figure()
plt.plot(Nx * Ny, Kxx_p)
plt.axhline(y=np.mean(K_exp[0, 0]), color='r', linestyle='-')
plt.xlabel(r'$N_x \times N_y$')
plt.ylabel(r'$K_{xx}=K_{yy}$ [MN/m]')
plt.tight_layout()
plt.savefig('Kxx_grid.png')
plt.close()
plt.figure()
plt.plot(Nx * Ny, Kxy_p)
plt.axhline(y=np.mean(K_exp[0, 1]), color='r', linestyle='-')
plt.xlabel(r'$N_x \times N_y$')
plt.ylabel(r'$K_{xy}=-K_{yx}$ [MN/m]')
plt.tight_layout()
plt.savefig('Kxy_grid.png')
plt.close()
plt.figure()
plt.plot(Nx * Ny, Dxx_p)
plt.axhline(y=np.mean(D_exp[0, 0]), color='r', linestyle='-')
plt.xlabel(r'$N_x \times N_y$')
plt.ylabel(r'$D_{xx}=D_{yy}$ [kN.s/m]')
plt.tight_layout()
plt.savefig('Dxx_grid.png')
plt.close()
plt.figure()
plt.plot(Nx * Ny, Dxy_p)
plt.axhline(y=np.mean(D_exp[0, 1]), color='r', linestyle='-')
plt.xlabel(r'$N_x \times N_y$')
plt.ylabel(r'$D_{xy}=-D_{yx}$ [kN.s/m]')
plt.tight_layout()
plt.savefig('Dxy_grid.png')
plt.close()
plt.figure()
plt.plot(Nx * Ny, Mxx_p)
plt.axhline(y=np.mean(M_exp[0, 0]), color='r', linestyle='-')
plt.xlabel(r'$N_x \times N_y$')
plt.ylabel(r'$M_{xx}=M_{yy}$ [kg]')
plt.tight_layout()
plt.savefig('Mxx_grid.png')
plt.close()
def main():
# functions used if curve-fitting
def linear_func(x, b):
# damping, linear w.r.t. frequency
return (b * x)
def quad_func(x, a, b):
# dynamic stiffness, added mass term exhibits frequency^2 behavior
return (a + b * x**2.0)
# experimental results
q_exp = 4634.0 # leakage [cm^3/s]
K_exp = np.array([[3.30, 11.3], [-10.3, 3.89]])
D_exp = np.array([[147.0, 52.9], [-57.7, 147.0]])
M_exp = np.array([[229.0, 16.0], [32.0, 214.0]])
K = np.zeros((2, 2))
D = np.zeros((2, 2))
M = np.zeros((2, 2))
#Nx = np.array([5, 8, 10, 15, 20, 25])
#Ny = np.array([10, 15, 20, 30, 40, 50])
Nx = np.array([20])
Ny = np.array([40])
# arrays for plotting
Kxx_p = np.zeros(Nx.size)
Kxy_p = np.zeros(Nx.size)
Dxx_p = np.zeros(Nx.size)
Dxy_p = np.zeros(Nx.size)
Mxx_p = np.zeros(Nx.size)
q_p = np.zeros(Nx.size)
param = read_parameters('Kanki01_input.yaml')
s = seal(param)
#pert_dirs = ['X','Y'] # for testing symmetry
pert_dirs = ['X']
#pertFreq = np.array([200.0])
pertFreq = np.array([0.0, 25.0, 50.0, 75.0, 100.0])
fx = np.zeros(pertFreq.size, dtype=np.complex128)
fy = np.zeros(pertFreq.size, dtype=np.complex128)
for idx, nx in enumerate(Nx):
s.Nx = Nx[idx]
s.Ny = Ny[idx]
print(s.Nx)
print(s.Ny)
s.update_mesh()
s.restart_seal()
s.solve_zeroth()
q_p[idx] = s.q / s.rho_s * 1.e6
for pert in pert_dirs:
s.pert_dir = pert
#s.update_seal() # need to clean up seal updates, this overwrites zeroth bcs which impacts stiffness
print(s.pert_dir)
# arrays for saving complex forces
#fx = fx * 0.0
#fy = fy * 0.0
for idx_freq, freq in enumerate(pertFreq):
s.whirl_f = freq
s.solve_first()
fx[idx_freq] = s.fx1
fy[idx_freq] = s.fy1
print(freq)
# curve-fit real and imaginary parts of forces
# fitting coefficients are the dynamic coefficients
px_r, cov = optimize.curve_fit(quad_func,
pertFreq,
np.real(fx),
p0=[0.0, 0.0])
py_r, cov = optimize.curve_fit(quad_func,
pertFreq,
np.real(fy),
p0=[0.0, 0.0])
px_i, cov = optimize.curve_fit(linear_func,
pertFreq,
np.imag(fx),
p0=[0.0])
py_i, cov = optimize.curve_fit(linear_func,
pertFreq,
np.imag(fy),
p0=[0.0])
if pert == 'X':
K[0, 0] = px_r[0] / 1.e6
K[1, 1] = K[0, 0]
K[1, 0] = py_r[0] / 1.e6
K[0, 1] = -K[1, 0]
D[0, 0] = px_i[0] / 1.e3
D[1, 1] = D[0, 0]
D[1, 0] = -py_i[0] / 1.e3
D[0, 1] = -D[1, 0]
M[0, 0] = px_r[1]
M[1, 1] = M[0, 0]
M[1, 0] = py_r[1]
M[0, 1] = -M[1, 0]
elif pert == 'Y':
K[0, 1] = px_r[0] / 1.e6
K[1, 1] = py_r[0] / 1.e6
D[0, 1] = px_i[0] / 1.e3
D[1, 1] = py_i[0] / 1.e3
M[0, 1] = px_r[1]
M[1, 1] = py_r[1]
Kxx_p[idx] = K[0, 0]
Kxy_p[idx] = K[0, 1]
Dxx_p[idx] = D[0, 0]
Dxy_p[idx] = D[0, 1]
Mxx_p[idx] = M[0, 0]
print('----q [cm^3/s]-------')
print('--predicted--')
print(q_p[0])
print('--Exp.--')
print(q_exp)
print('----K [MN/m]-------')
print('--predicted--')
print(K)
print('--Exp.--')
print(K_exp)
print('----D [kN.s/m]-------')
print('--predicted--')
print(D)
print('--Exp.--')
print(D_exp)
print('----M [kg]-------')
print('--predicted--')
print(M)
print('--Exp.--')
print(M_exp)
print('---------------')