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update to new fdtd approach

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This commit is contained in:
Jan Petykiewicz 2022-10-04 14:32:54 -07:00
parent faecc79179
commit 71d6ceec1e
1 changed files with 10 additions and 15 deletions
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25
examples/fdtd.py
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@ -11,6 +11,7 @@ import numpy
import h5py import h5py
from meanas import fdtd from meanas import fdtd
from meanas.fdtd import cpml_params, updates_with_cpml
from masque import Pattern, shapes from masque import Pattern, shapes
import gridlock import gridlock
import pcgen import pcgen
@ -127,19 +128,15 @@ def main():
e = [numpy.zeros_like(epsilon[0], dtype=dtype) for _ in range(3)] e = [numpy.zeros_like(epsilon[0], dtype=dtype) for _ in range(3)]
h = [numpy.zeros_like(epsilon[0], dtype=dtype) for _ in range(3)] h = [numpy.zeros_like(epsilon[0], dtype=dtype) for _ in range(3)]
update_e = fdtd.maxwell_e(dt) dxes = [grid.dxyz, grid.autoshifted_dxyz()]
update_h = fdtd.maxwell_h(dt)
# PMLs in every direction # PMLs in every direction
pml_e_funcs = [] pml_params = [[cpml_params(axis=dd, polarity=pp, dt=dt,
pml_h_funcs = [] thickness=pml_thickness, epsilon_eff=1.0**2)
pml_fields = {} for pp in (-1, +1)]
for d in (0, 1, 2): for dd in range(3)]
for p in (-1, 1): update_E, update_H = updates_with_cpml(cpml_params=pml_params, dt=dt,
ef, hf, psis = fdtd.cpml(direction=d, polarity=p, dt=dt, epsilon=epsilon, epsilon_eff=n_slab**2, dtype=dtype) dxes=dxes, epsilon=epsilon)
pml_e_funcs.append(ef)
pml_h_funcs.append(hf)
pml_fields.update(psis)
# Source parameters and function # Source parameters and function
w = 2 * numpy.pi * dx / wl w = 2 * numpy.pi * dx / wl
@ -155,12 +152,10 @@ def main():
output_file = h5py.File('simulation_output.h5', 'w') output_file = h5py.File('simulation_output.h5', 'w')
start = time.perf_counter() start = time.perf_counter()
for t in range(max_t): for t in range(max_t):
[f(e, h, epsilon) for f in pml_e_funcs] update_E(e, h, epsilon)
update_e(e, h, epsilon)
e[1][tuple(grid.shape//2)] += field_source(t) e[1][tuple(grid.shape//2)] += field_source(t)
[f(e, h) for f in pml_h_funcs] update_H(e, h)
update_h(e, h)
print('iteration {}: average {} iterations per sec'.format(t, (t+1)/(time.perf_counter()-start))) print('iteration {}: average {} iterations per sec'.format(t, (t+1)/(time.perf_counter()-start)))
sys.stdout.flush() sys.stdout.flush()