def ext_helmholtz_convg(qbx_order, k):
"""
Tests external Helmholtz solution by placing monopoles inside domain
"""
alpha = k # CFIE parameter
sp, dp, qbx_exp_slp, qbx_exp_dlp, _, _, _, _ = eval_sp_dp_QBX(qbx_order, k)
xs = 0.75
ys = 1.75
test_targets = np.array([[-1, 0], [4, 0], [0, -1], [0, 4]]).T
# test_targets = np.array([[-5, 0], [5, 0], [0, -5], [0, 5]]).T
exact_test = sp(test_targets[0], test_targets[1], xs, ys)
plotset()
n_panels = np.arange(10, 60, 5)
convg_err = []
for n in n_panels:
domain = QuadratureInfo(n, show_bound=0)
rhs = sp(domain.curve_nodes[0], domain.curve_nodes[1], xs, ys)
soln = bvp(n, k, domain, alpha, qbx_exp_slp, qbx_exp_dlp, rhs)
num_test = eval_target(
test_targets,
domain.curve_nodes,
domain.curve_weights,
domain.normals,
soln,
sp,
dp,
alpha,
)
er = la.norm(
np.abs(num_test - exact_test) / la.norm(np.abs(exact_test)))
convg_err.append(er)
plt.figure(1)
plt.loglog(n_panels,
np.array(convg_err),
"-o",
label="QBX" + str(qbx_order))
plt.loglog(
n_panels,
5e4 * np.power(n_panels.astype(float), -qbx_order - 1),
label="$N^{-5}$",
)
plt.loglog(n_panels,
2e3 * np.power(n_panels.astype(float), -qbx_order),
label="$N^{-4}$")
title = "Helmholtz exterior problem: target evaluation convergence"
plt.xlabel("Number of panels", size=25)
plt.ylabel("$|u - u_e| / |u_e|$", size=25)
plt.title(title, size=25)
plt.legend(loc="upper right", ncol=1, frameon=True)
plt.show()
slope, _, _, _, _ = stats.linregress(np.log(n_panels),
np.log(np.array(convg_err)))
print(slope)
def main():
k = 10
alpha = k # CFIE parameter
sp, dp, qbx_exp_slp, qbx_exp_dlp, _, _, _, _ = eval_sp_dp_QBX(4, k)
npanels = 30
domain = QuadratureInfo(npanels, show_bound=1)
xs = 0.75
ys = 1.75
tg_size = 101
x = np.linspace(-2, 5, tg_size)
y = np.linspace(-2, 5, tg_size)
X, Y = np.meshgrid(x, y)
test_targets = np.array((X.reshape(-1), Y.reshape(-1)))
rhs = sp(domain.curve_nodes[0], domain.curve_nodes[1], xs, ys)
soln1 = bvp(npanels, k, domain, alpha, qbx_exp_slp, qbx_exp_dlp, rhs)
num_test = eval_target(
test_targets,
domain.curve_nodes,
domain.curve_weights,
domain.normals,
soln1,
sp,
dp,
alpha,
).reshape(tg_size, -1)
exact_test = sp(test_targets[0], test_targets[1], xs, ys).reshape(tg_size, -1)
err = np.abs(num_test - exact_test) / np.abs(exact_test)
# plt.contourf(X, Y, np.real(exact_test), cmap='magma', levels=100)
v = np.linspace(-0.15, 0.15, 100, endpoint=True)
plt.figure(1)
plt.contourf(X, Y, np.real(num_test), v, cmap="twilight")
plt.plot(xs, ys, "ro")
plt.fill(domain.curve_nodes[0], domain.curve_nodes[1], "w")
show_targets = np.array([[-1, 0], [4, 0], [0, -1], [0, 4]]).T
plt.plot(show_targets[0], show_targets[1], "go")
plt.xlabel("X", size=10)
plt.ylabel("Y", size=10)
plt.colorbar()
plt.figure(2)
plt.contourf(X, Y, np.log10(err), cmap="magma", levels=200)
plt.fill(domain.curve_nodes[0], domain.curve_nodes[1], "w")
plt.plot(xs, ys, "ro")
plt.plot(show_targets[0], show_targets[1], "go")
plt.xlabel("X", size=10)
plt.ylabel("Y", size=10)
plt.colorbar()
plt.show()
import matplotlib.pyplot as plt
import numpy as np
# import numpy.linalg as la
from kernels import eval_sp_dp_QBX, sommerfeld, Images_Integral
plt.gca().set_aspect("equal")
k = 10.2
alpha = k # CFIE parameter
beta = 2.04
interval = 20
xs = 0
ys = 5
m = int(np.ceil(np.log(k / ys) / np.log(2)))
(sp, dp, qbx_exp_slp, qbx_exp_dlp, img_sp, img_dp, _, _) = eval_sp_dp_QBX(4, k)
som_sp, som_dp = sommerfeld(k, beta, interval, "far")
imgs = Images_Integral(m, beta, img_sp, img_dp)
tg_size = 101
x = np.linspace(-5, 5, tg_size)
y = np.linspace(0, 10, tg_size)
X, Y = np.meshgrid(x, y)
test_targets = np.array((X.reshape(-1), Y.reshape(-1)))
exact_test = sp(test_targets[0], test_targets[1], xs, ys).reshape(tg_size, -1)
som_test = som_sp(test_targets[0], test_targets[1], xs,
ys).reshape(tg_size, -1)
imag_sp, imag_dp = imgs.eval_integral(test_targets, (xs, ys), 0.0, 0.0)
imag_sp = imag_sp.reshape(tg_size, -1)
v = np.linspace(-0.15, 0.15, 100, endpoint=True)
plt.figure(1)
import matplotlib.pyplot as plt
import numpy as np
from plotset import plotset
import numpy.linalg as la
from kernels import eval_sp_dp_QBX
from kernels import Images_Integral
plotset()
xs = -2
ys = 0.1
k = 10
alpha = k # CFIE parameter
beta = 2
(_, _, _, _, img_sp, img_dp, _, _) = eval_sp_dp_QBX(4, k)
convg_err = []
test_targets = np.array([[-2, 0.01], [0, 5], [-2, 4]]).T
C = 1
m = int(np.floor(np.log(k / ys * C) / np.log(2)))
for x in range(1, m + 1):
imgs = Images_Integral(x, beta, img_sp, img_dp)
num_test, _ = imgs.eval_integral(test_targets, (xs, ys), 0.0, 0.0)
convg_err.append(num_test)
print(x)
print(la.norm(num_test))
convg = np.array((convg_err))
num_fine = convg[-1]
convg = convg[:-1] - num_fine
convg_norm = []
for x in convg:
import matplotlib.pyplot as plt
import numpy as np
# import numpy.linalg as la
from kernels import eval_sp_dp_QBX, sommerfeld
plt.gca().set_aspect("equal")
k = 10
alpha = k # CFIE parameter
beta = 0
interval = 10
xs = 0
ys = 5
sp, dp, _, _, _, _, _, _ = eval_sp_dp_QBX(4, k)
som_sp, _ = sommerfeld(k, beta, interval, "full")
tg_size = 101
x = np.linspace(-5, 5, tg_size)
y = np.linspace(0, 10, tg_size)
X, Y = np.meshgrid(x, y)
test_targets = np.array((X.reshape(-1), Y.reshape(-1)))
exact_test = sp(test_targets[0], test_targets[1], xs, ys).reshape(tg_size, -1)
som_test = som_sp(test_targets[0], test_targets[1], xs,
ys).reshape(tg_size, -1)
v = np.linspace(-0.15, 0.15, 100, endpoint=True)
# plt.contourf(X, Y, np.real(exact_test), v,
# cmap='magma', extend='both')
plt.figure(1)
plt.contourf(X, Y, np.real(exact_test), v, cmap="twilight")
plt.colorbar()