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improve pml specification

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jan 2017-10-01 12:34:11 -07:00
commit d02cd18403
4 changed files with 69 additions and 54 deletions
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opencl_fdtd/simulation.py
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@ -29,7 +29,8 @@ class Simulation(object):
After constructing this object, call the (update_E, update_H, update_S) members
to perform FDTD updates on the stored (E, H, S) fields:
sim = Simulation(grid.grids, do_poynting=True, pml_thickness=8)
pmls = [{'axis': a, 'polarity': p} for a in 'xyz' for p in 'np']
sim = Simulation(grid.grids, do_poynting=True, pmls=pmls)
with open('sources.c', 'w') as f:
f.write('{}'.format(sim.sources))
@ -73,31 +74,34 @@ class Simulation(object):
def __init__(self,
epsilon: List[numpy.ndarray],
pmls: List[Dict[str, int or float]],
dt: float = .99/numpy.sqrt(3),
initial_E: List[numpy.ndarray] = None,
initial_H: List[numpy.ndarray] = None,
context: pyopencl.Context = None,
queue: pyopencl.CommandQueue = None,
float_type: numpy.float32 or numpy.float64 = numpy.float32,
pmls: List[List[str]] = None,
pml_thickness: int = 10,
do_poynting: bool = True):
"""
Initialize the simulation.
:param epsilon: List containing [eps_r,xx, eps_r,yy, eps_r,zz], where each element is a Yee-shifted ndarray
spanning the simulation domain. Relative epsilon is used.
:param dt: Time step. Default is the Courant factor.
:param pmls: List of dicts with keys:
'axis': One of 'x', 'y', 'z'.
'direction': One of 'n', 'p'.
'thickness': Number of layers, default 8.
'epsilon_eff': Effective epsilon to match to. Default 1.0.
'mu_eff': Effective mu to match to. Default 1.0.
'ln_R_per_layer': Desired (ln(R) / thickness) value. Default -1.6.
'm': Polynomial grading exponent. Default 3.5.
'ma': Exponent for alpha. Default 1.
:param dt: Time step. Default is .99/sqrt(3).
:param initial_E: Initial E-field (default is 0 everywhere). Same format as epsilon.
:param initial_H: Initial H-field (default is 0 everywhere). Same format as epsilon.
:param context: pyOpenCL context. If not given, pyopencl.create_some_context(False) is called.
:param queue: pyOpenCL command queue. If not given, pyopencl.CommandQueue(context) is called.
:param float_type: numpy.float32 or numpy.float64. Default numpy.float32.
:param pmls: List of [axis, direction] pairs which specify simluation boundaries to be
'coated' with a PML (absorbing layer). Axis should be one of 'x', 'y', 'z', and
direction should be one of 'n', 'p' (i.e., negative, positive).
Default is to apply PMLs to all six boundaries.
:param pml_thickness: Thickness of any PMLs, in number of grid cells. Default 10.
:param do_poynting: If true, enables calculation of the poynting vector, S.
Poynting vector calculation adds the following computational burdens:
* During update_H, ~6 extra additions/cell are performed in order to spatially
@ -154,8 +158,13 @@ class Simulation(object):
Exception('Initial_H list elements must have same shape as epsilon elements')
self.H = pyopencl.array.to_device(self.queue, vec(H).astype(float_type))
if pmls is None:
pmls = [[d, p] for d in 'xyz' for p in 'np']
for pml in pmls:
pml.setdefault('thickness', 8)
pml.setdefault('epsilon_eff', 1.0)
pml.setdefault('mu_eff', 1.0)
pml.setdefault('ln_R_per_layer', -1.6)
pml.setdefault('m', 3.5)
pml.setdefault('ma', 1)
ctype = type_to_C(self.arg_type)
@ -176,7 +185,6 @@ class Simulation(object):
)
jinja_args = {
'common_header': common_source,
'pml_thickness': pml_thickness,
'pmls': pmls,
'do_poynting': do_poynting,
}
@ -201,35 +209,39 @@ class Simulation(object):
'''
PML
'''
m = (3.5, 1)
sigma_max = 0.8 * (m[0] + 1) / numpy.sqrt(1.0) # TODO: epsilon_eff (not 1.0)
alpha_max = 0 # TODO: Decide what to do about non-zero alpha
def par(x):
sigma = ((x / pml_thickness) ** m[0]) * sigma_max
alpha = ((1 - x / pml_thickness) ** m[1]) * alpha_max
p0 = numpy.exp(-(sigma + alpha) * dt)
p1 = sigma / (sigma + alpha) * (p0 - 1)
return p0, p1
xen, xep, xhn, xhp = (numpy.arange(1, pml_thickness + 1, dtype=float_type)[::-1] for _ in range(4))
xep -= 0.5
xhn -= 0.5
pml_p_names = [['p' + a + eh + np for np in 'np' for a in '01'] for eh in 'eh']
pml_e_fields = OrderedDict()
pml_h_fields = OrderedDict()
for ne, nh, pe, ph in zip(*pml_p_names, par(xen) + par(xep), par(xhn) + par(xhp)):
pml_e_fields[ptr(ne)] = pyopencl.array.to_device(self.queue, pe)
pml_h_fields[ptr(nh)] = pyopencl.array.to_device(self.queue, ph)
for pml in pmls:
uv = 'xyz'.replace(pml[0], '')
psi_base = 'Psi_' + ''.join(pml) + '_'
a = 'xyz'.find(pml['axis'])
sigma_max = -pml['ln_R_per_layer'] / 2 * (pml['m'] + 1) / \
numpy.sqrt(pml['epsilon_eff'] * pml['mu_eff'])
alpha_max = 0 # TODO: Nonzero alpha
def par(x):
sigma = ((x / pml['thickness']) ** pml['m']) * sigma_max
alpha = ((1 - x / pml['thickness']) ** pml['ma']) * alpha_max
p0 = numpy.exp(-(sigma + alpha) * dt)
p1 = sigma / (sigma + alpha) * (p0 - 1)
return p0, p1
xe, xh = (numpy.arange(1, pml['thickness'] + 1, dtype=float_type)[::-1] for _ in range(2))
if pml['polarity'] == 'p':
xe -= 0.5
elif pml['polarity'] == 'n':
xh -= 0.5
pml_p_names = [['p' + pml['axis'] + i + eh + pml['polarity'] for i in '01'] for eh in 'eh']
for name_e, name_h, pe, ph in zip(pml_p_names[0], pml_p_names[1], par(xe), par(xh)):
pml_e_fields[ptr(name_e)] = pyopencl.array.to_device(self.queue, pe)
pml_h_fields[ptr(name_h)] = pyopencl.array.to_device(self.queue, ph)
uv = 'xyz'.replace(pml['axis'], '')
psi_base = 'Psi_' + pml['axis'] + pml['polarity'] + '_'
psi_names = [[psi_base + eh + c for c in uv] for eh in 'EH']
psi_shape = list(epsilon[0].shape)
psi_shape['xyz'.find(pml[0])] = pml_thickness
psi_shape[a] = pml['thickness']
for ne, nh in zip(*psi_names):
pml_e_fields[ptr(ne)] = pyopencl.array.zeros(self.queue, tuple(psi_shape), dtype=self.arg_type)