-
Notifications
You must be signed in to change notification settings - Fork 641
/
Copy pathabsorbed_power_density.py
113 lines (94 loc) · 2.74 KB
/
absorbed_power_density.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
import matplotlib
import numpy as np
matplotlib.use("agg")
import matplotlib.pyplot as plt
from meep.materials import SiO2
import meep as mp
resolution = 100 # pixels/um
dpml = 1.0
pml_layers = [mp.PML(thickness=dpml)]
r = 1.0 # radius of cylinder
dair = 2.0 # air padding thickness
s = 2 * (dpml + dair + r)
cell_size = mp.Vector3(s, s)
wvl = 1.0
fcen = 1 / wvl
# is_integrated=True necessary for any planewave source extending into PML
sources = [
mp.Source(
mp.GaussianSource(fcen, fwidth=0.1 * fcen, is_integrated=True),
center=mp.Vector3(-0.5 * s + dpml),
size=mp.Vector3(0, s),
component=mp.Ez,
)
]
symmetries = [mp.Mirror(mp.Y)]
geometry = [mp.Cylinder(material=SiO2, center=mp.Vector3(), radius=r, height=mp.inf)]
sim = mp.Simulation(
resolution=resolution,
cell_size=cell_size,
boundary_layers=pml_layers,
sources=sources,
k_point=mp.Vector3(),
symmetries=symmetries,
geometry=geometry,
)
dft_fields = sim.add_dft_fields(
[mp.Dz, mp.Ez],
fcen,
0,
1,
center=mp.Vector3(),
size=mp.Vector3(2 * r, 2 * r),
yee_grid=True,
)
# closed box surrounding cylinder for computing total incoming flux
flux_box = sim.add_flux(
fcen,
0,
1,
mp.FluxRegion(center=mp.Vector3(x=-r), size=mp.Vector3(0, 2 * r), weight=+1),
mp.FluxRegion(center=mp.Vector3(x=+r), size=mp.Vector3(0, 2 * r), weight=-1),
mp.FluxRegion(center=mp.Vector3(y=+r), size=mp.Vector3(2 * r, 0), weight=-1),
mp.FluxRegion(center=mp.Vector3(y=-r), size=mp.Vector3(2 * r, 0), weight=+1),
)
sim.run(until_after_sources=100)
Dz = sim.get_dft_array(dft_fields, mp.Dz, 0)
Ez = sim.get_dft_array(dft_fields, mp.Ez, 0)
absorbed_power_density = 2 * np.pi * fcen * np.imag(np.conj(Ez) * Dz)
dxy = 1 / resolution**2
absorbed_power = np.sum(absorbed_power_density) * dxy
absorbed_flux = mp.get_fluxes(flux_box)[0]
err = abs(absorbed_power - absorbed_flux) / absorbed_flux
print(
f"flux:, {absorbed_power} (dft_fields), {absorbed_flux} (dft_flux), {err} (error)"
)
plt.figure()
sim.plot2D()
plt.savefig("power_density_cell.png", dpi=150, bbox_inches="tight")
plt.figure()
x = np.linspace(-r, r, Dz.shape[0])
y = np.linspace(-r, r, Dz.shape[1])
plt.pcolormesh(
x,
y,
np.transpose(absorbed_power_density),
cmap="inferno_r",
shading="gouraud",
vmin=0,
vmax=np.amax(absorbed_power_density),
)
plt.xlabel("x (μm)")
plt.xticks(np.linspace(-r, r, 5))
plt.ylabel("y (μm)")
plt.yticks(np.linspace(-r, r, 5))
plt.gca().set_aspect("equal")
plt.title(
"absorbed power density"
+ "\n"
+ "SiO2 Labs(λ={} μm) = {:.2f} μm".format(
wvl, wvl / np.imag(np.sqrt(SiO2.epsilon(fcen)[0][0]))
)
)
plt.colorbar()
plt.savefig("power_density_map.png", dpi=150, bbox_inches="tight")