Differential/Radar Cross Section

As an extension of the Mie scattering example which involved computing the scattering cross section ($\sigma_{scat}$), we will compute the differential cross section ($\sigma_{diff}$) which is proportional to the radar cross section. Computing $\sigma_{diff}$ in a given direction involves three steps: (1) solve for the near fields on a closed box surrounding the object, (2) from the near fields, compute the far fields at a single point a large distance away (i.e., $R$ ≫ object diameter), and (3) calculate the Poynting flux of the far fields in the outward direction: $F = \hat{r}\cdot\Re[E^* \times H]$. The differential cross section in that direction is $R^2F$ divided by the incident intensity. The radar cross section is simply $\sigma_{diff}$ in the "backwards" direction (i.e., backscattering) multiplied by 4π.

The scattering cross section can be obtained by integrating the differential cross section over all spherical angles:

$$ \sigma_{scatt} = \int_0^{2\pi} d\phi \int_0^{\pi} \sigma_{diff}(\phi,\theta)\sin(\theta)d\theta $$

In this demonstration, we will verify this expression for the lossless dielectric sphere at a single wavelength by comparing with the analytic theory via PyMieScatt.

In [1]:
import meep as mp
import numpy as np
import PyMieScatt as ps

r = 1.0  # radius of sphere

frq_cen = 1.0

resolution = 20 # pixels/um

dpml = 0.5
dair = 1.5 # at least 0.5/frq_cen padding between source and near-field monitor

pml_layers = [mp.PML(thickness=dpml)]

s = 2*(dpml+dair+r)
cell_size = mp.Vector3(s,s,s)

# circularly-polarized source with propagation axis along x
# is_integrated=True necessary for any planewave source extending into PML
sources = [mp.Source(mp.GaussianSource(frq_cen,fwidth=0.2*frq_cen,is_integrated=True),
                     center=mp.Vector3(-0.5*s+dpml),
                     size=mp.Vector3(0,s,s),
                     component=mp.Ez),
           mp.Source(mp.GaussianSource(frq_cen,fwidth=0.2*frq_cen,is_integrated=True),
                     center=mp.Vector3(-0.5*s+dpml),
                     size=mp.Vector3(0,s,s),
                     component=mp.Ey,
                     amplitude=1j)]

sim = mp.Simulation(resolution=resolution,
                    cell_size=cell_size,
                    boundary_layers=pml_layers,
                    sources=sources,
                    k_point=mp.Vector3())

box_flux = sim.add_flux(frq_cen, 0, 1,
                        mp.FluxRegion(center=mp.Vector3(x=-2*r),size=mp.Vector3(0,4*r,4*r)))

nearfield_box = sim.add_near2far(frq_cen, 0, 1,
                                 mp.Near2FarRegion(center=mp.Vector3(x=-2*r),size=mp.Vector3(0,4*r,4*r),weight=+1),
                                 mp.Near2FarRegion(center=mp.Vector3(x=+2*r),size=mp.Vector3(0,4*r,4*r),weight=-1),
                                 mp.Near2FarRegion(center=mp.Vector3(y=-2*r),size=mp.Vector3(4*r,0,4*r),weight=+1),
                                 mp.Near2FarRegion(center=mp.Vector3(y=+2*r),size=mp.Vector3(4*r,0,4*r),weight=-1),
                                 mp.Near2FarRegion(center=mp.Vector3(z=-2*r),size=mp.Vector3(4*r,4*r,0),weight=+1),
                                 mp.Near2FarRegion(center=mp.Vector3(z=+2*r),size=mp.Vector3(4*r,4*r,0),weight=-1))

sim.run(until_after_sources=10)

input_flux = mp.get_fluxes(box_flux)[0]
nearfield_box_data = sim.get_near2far_data(nearfield_box)

sim.reset_meep()

n_sphere = 2.0
geometry = [mp.Sphere(material=mp.Medium(index=n_sphere),
                      center=mp.Vector3(),
                      radius=r)]

sim = mp.Simulation(resolution=resolution,
                    cell_size=cell_size,
                    boundary_layers=pml_layers,
                    sources=sources,
                    k_point=mp.Vector3(),
                    geometry=geometry)

nearfield_box = sim.add_near2far(frq_cen, 0, 1,
                                 mp.Near2FarRegion(center=mp.Vector3(x=-2*r),size=mp.Vector3(0,4*r,4*r),weight=+1),
                                 mp.Near2FarRegion(center=mp.Vector3(x=+2*r),size=mp.Vector3(0,4*r,4*r),weight=-1),
                                 mp.Near2FarRegion(center=mp.Vector3(y=-2*r),size=mp.Vector3(4*r,0,4*r),weight=+1),
                                 mp.Near2FarRegion(center=mp.Vector3(y=+2*r),size=mp.Vector3(4*r,0,4*r),weight=-1),
                                 mp.Near2FarRegion(center=mp.Vector3(z=-2*r),size=mp.Vector3(4*r,4*r,0),weight=+1),
                                 mp.Near2FarRegion(center=mp.Vector3(z=+2*r),size=mp.Vector3(4*r,4*r,0),weight=-1))

sim.load_minus_near2far_data(nearfield_box, nearfield_box_data)

sim.run(until_after_sources=100)

npts = 100     # number of points in [0,pi) range of polar angles to sample far fields along semi-circle
angles = np.pi/npts*np.arange(npts)

ff_r = 10000*r # radius of far-field semi-circle

E = np.zeros((npts,3),dtype=np.complex128)
H = np.zeros((npts,3),dtype=np.complex128)
for n in range(npts):
    ff = sim.get_farfield(nearfield_box, ff_r*mp.Vector3(np.cos(angles[n]),0,np.sin(angles[n])))
    E[n,:] = [np.conj(ff[j]) for j in range(3)]
    H[n,:] = [ff[j+3] for j in range(3)]

# compute Poynting flux Pr in the radial direction.  At large r,
# all of the flux is radial so we can simply compute the magnitude of the Poynting vector.
Px = np.real(np.multiply(E[:,1],H[:,2])-np.multiply(E[:,2],H[:,1]))
Py = np.real(np.multiply(E[:,2],H[:,0])-np.multiply(E[:,0],H[:,2]))
Pz = np.real(np.multiply(E[:,0],H[:,1])-np.multiply(E[:,1],H[:,0]))
Pr = np.sqrt(np.square(Px)+np.square(Py)+np.square(Pz))

intensity = input_flux/(4*r)**2
diff_cross_section = ff_r**2 * Pr / intensity
scatt_cross_section_meep = 2*np.pi * np.sum(diff_cross_section * np.sin(angles)) * np.pi/npts # trapezoidal rule integration
scatt_cross_section_theory = ps.MieQ(n_sphere,1000/frq_cen,2*r*1000,asDict=True,asCrossSection=True)['Csca']*1e-6 # units of um^2

print("scatt:, {:.16f}, {:.16f}".format(scatt_cross_section_meep,scatt_cross_section_theory))
-----------
Initializing structure...
time for choose_chunkdivision = 0.00184703 s
Working in 3D dimensions.
Computational cell is 6 x 6 x 6 with resolution 20
time for set_epsilon = 7.05405 s
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run 0 finished at t = 60.0 (2400 timesteps)
-----------
Initializing structure...
time for choose_chunkdivision = 0.000314951 s
Working in 3D dimensions.
Computational cell is 6 x 6 x 6 with resolution 20
     sphere, center = (0,0,0)
          radius 1
          dielectric constant epsilon diagonal = (4,4,4)
time for set_epsilon = 10.8833 s
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run 0 finished at t = 150.0 (6000 timesteps)
scatt:, 8.1554468215885674, 8.3429545590438750

The script is similar to the previous Mie scattering example with the main difference being the replacement of the add_flux with add_near2far objects. Instead of a linearly-polarized planewave, the source is circularly-polarized so that $\sigma_{diff}$ is invariant with the rotation angle $\phi$ around the axis of the incident direction (i.e., $x$). This way, the far fields need only be sampled with the polar angle $\theta$. A circularly-polarized planewave can be generated by overlapping two linearly-polarized planewaves ($E_y$ and $E_z$) which are 90° out of phase via specifying amplitude=1j for one of the two sources. Note, however, that there is no need to use complex fields (by specifying force_complex_fields=True in the Simulation object) which would double the floating-point memory consumption since only the real part of the source amplitude is used by default. The circularly-polarized source breaks the mirror symmetry which increases the size of the simulation. The size of the near-field monitor box surrounding the sphere is doubled so that it lies entirely within the homogeneous air region (a requirement of the near2far feature). After the near fields have been obtained for λ = 1 μm the far fields are computed for 100 points along a semi-circle with radius 10,000X that of the dielectric sphere. (Note: any such large radius would give the same $\sigma_{scat}$ to within discretization error). Finally, the scattered cross section is computed by numerically integrating the expression from above using the radial Poynting flux values.

The Meep results agree well with the analytic theory.

For resolution = 20, the error between the simulated and analytic result is 2.2%.

scatt:, 8.1554468215885674, 8.3429545590438750

For resolution = 25, the error decreases (as expected) to 1.5%.

scatt:, 8.2215435272741395, 8.3429545590438750