476 lines
17 KiB
Python
476 lines
17 KiB
Python
from typing import Dict, List
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import numpy
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import scipy.sparse as sparse
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from . import vec, unvec, dx_lists_t, vfield_t, field_t
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from . import operators, waveguide, functional
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from .eigensolvers import signed_eigensolve, rayleigh_quotient_iteration
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def solve_waveguide_mode_2d(mode_number: int,
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omega: complex,
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dxes: dx_lists_t,
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epsilon: vfield_t,
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mu: vfield_t = None,
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wavenumber_correction: bool = True,
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) -> Dict[str, complex or field_t]:
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"""
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Given a 2d region, attempts to solve for the eigenmode with the specified mode number.
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:param mode_number: Number of the mode, 0-indexed.
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:param omega: Angular frequency of the simulation
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:param dxes: Grid parameters [dx_e, dx_h] as described in meanas.types
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:param epsilon: Dielectric constant
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:param mu: Magnetic permeability (default 1 everywhere)
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:param wavenumber_correction: Whether to correct the wavenumber to
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account for numerical dispersion (default True)
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:return: {'E': List[numpy.ndarray], 'H': List[numpy.ndarray], 'wavenumber': complex}
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"""
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'''
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Solve for the largest-magnitude eigenvalue of the real operator
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'''
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dxes_real = [[numpy.real(dx) for dx in dxi] for dxi in dxes]
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A_r = waveguide.operator(numpy.real(omega), dxes_real, numpy.real(epsilon), numpy.real(mu))
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eigvals, eigvecs = signed_eigensolve(A_r, mode_number+3)
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v = eigvecs[:, -(mode_number + 1)]
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'''
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Now solve for the eigenvector of the full operator, using the real operator's
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eigenvector as an initial guess for Rayleigh quotient iteration.
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'''
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A = waveguide.operator(omega, dxes, epsilon, mu)
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eigval, v = rayleigh_quotient_iteration(A, v)
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# Calculate the wave-vector (force the real part to be positive)
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wavenumber = numpy.sqrt(eigval)
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wavenumber *= numpy.sign(numpy.real(wavenumber))
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e, h = waveguide.normalized_fields(v, wavenumber, omega, dxes, epsilon, mu)
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'''
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Perform correction on wavenumber to account for numerical dispersion.
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See Numerical Dispersion in Taflove's FDTD book.
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This correction term reduces the error in emitted power, but additional
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error is introduced into the E_err and H_err terms. This effect becomes
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more pronounced as the wavenumber increases.
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'''
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if wavenumber_correction:
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dx_mean = (numpy.hstack(dxes[0]) + numpy.hstack(dxes[1])).mean() / 2 #TODO figure out what dx to use here
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wavenumber -= 2 * numpy.sin(numpy.real(wavenumber * dx_mean / 2)) / dx_mean - numpy.real(wavenumber)
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shape = [d.size for d in dxes[0]]
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fields = {
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'wavenumber': wavenumber,
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'E': unvec(e, shape),
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'H': unvec(h, shape),
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}
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return fields
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def solve_waveguide_mode(mode_number: int,
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omega: complex,
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dxes: dx_lists_t,
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axis: int,
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polarity: int,
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slices: List[slice],
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epsilon: field_t,
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mu: field_t = None,
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wavenumber_correction: bool = True
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) -> Dict[str, complex or numpy.ndarray]:
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"""
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Given a 3D grid, selects a slice from the grid and attempts to
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solve for an eigenmode propagating through that slice.
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:param mode_number: Number of the mode, 0-indexed
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:param omega: Angular frequency of the simulation
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:param dxes: Grid parameters [dx_e, dx_h] as described in meanas.types
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:param axis: Propagation axis (0=x, 1=y, 2=z)
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:param polarity: Propagation direction (+1 for +ve, -1 for -ve)
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:param slices: epsilon[tuple(slices)] is used to select the portion of the grid to use
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as the waveguide cross-section. slices[axis] should select only one
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:param epsilon: Dielectric constant
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:param mu: Magnetic permeability (default 1 everywhere)
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:param wavenumber_correction: Whether to correct the wavenumber to
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account for numerical dispersion (default True)
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:return: {'E': List[numpy.ndarray], 'H': List[numpy.ndarray], 'wavenumber': complex}
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"""
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if mu is None:
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mu = numpy.ones_like(epsilon)
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slices = tuple(slices)
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'''
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Solve the 2D problem in the specified plane
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'''
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# Define rotation to set z as propagation direction
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order = numpy.roll(range(3), 2 - axis)
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reverse_order = numpy.roll(range(3), axis - 2)
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# Reduce to 2D and solve the 2D problem
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args_2d = {
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'dxes': [[dx[i][slices[i]] for i in order[:2]] for dx in dxes],
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'epsilon': vec([epsilon[i][slices].transpose(order) for i in order]),
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'mu': vec([mu[i][slices].transpose(order) for i in order]),
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'wavenumber_correction': wavenumber_correction,
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}
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fields_2d = solve_waveguide_mode_2d(mode_number, omega=omega, **args_2d)
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'''
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Apply corrections and expand to 3D
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'''
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# Scale based on dx in propagation direction
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dxab_forward = numpy.array([dx[order[2]][slices[order[2]]] for dx in dxes])
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# Adjust for propagation direction
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fields_2d['E'][2] *= polarity
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fields_2d['H'][2] *= polarity
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# Apply phase shift to H-field
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d_prop = 0.5 * sum(dxab_forward)
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fields_2d['H'] *= numpy.exp(-polarity * 1j * 0.5 * fields_2d['wavenumber'] * d_prop)
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# Expand E, H to full epsilon space we were given
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E = numpy.zeros_like(epsilon, dtype=complex)
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H = numpy.zeros_like(epsilon, dtype=complex)
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for a, o in enumerate(reverse_order):
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E[(a, *slices)] = fields_2d['E'][o][:, :, None].transpose(reverse_order)
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H[(a, *slices)] = fields_2d['H'][o][:, :, None].transpose(reverse_order)
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results = {
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'wavenumber': fields_2d['wavenumber'],
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'H': H,
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'E': E,
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}
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return results
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def compute_source(E: field_t,
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H: field_t,
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wavenumber: complex,
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omega: complex,
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dxes: dx_lists_t,
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axis: int,
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polarity: int,
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slices: List[slice],
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mu: field_t = None,
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) -> field_t:
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"""
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Given an eigenmode obtained by solve_waveguide_mode, returns the current source distribution
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necessary to position a unidirectional source at the slice location.
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:param E: E-field of the mode
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:param H: H-field of the mode (advanced by half of a Yee cell from E)
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:param wavenumber: Wavenumber of the mode
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:param omega: Angular frequency of the simulation
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:param dxes: Grid parameters [dx_e, dx_h] as described in meanas.types
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:param axis: Propagation axis (0=x, 1=y, 2=z)
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:param polarity: Propagation direction (+1 for +ve, -1 for -ve)
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:param slices: epsilon[tuple(slices)] is used to select the portion of the grid to use
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as the waveguide cross-section. slices[axis] should select only one
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:param mu: Magnetic permeability (default 1 everywhere)
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:return: J distribution for the unidirectional source
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"""
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if mu is None:
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mu = numpy.ones(3)
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J = numpy.zeros_like(E, dtype=complex)
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M = numpy.zeros_like(E, dtype=complex)
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src_order = numpy.roll(range(3), -axis)
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exp_iphi = numpy.exp(1j * polarity * wavenumber * dxes[1][axis][slices[axis]])
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J[src_order[1]] = +exp_iphi * H[src_order[2]] * polarity
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J[src_order[2]] = -exp_iphi * H[src_order[1]] * polarity
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rollby = -1 if polarity > 0 else 0
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M[src_order[1]] = +numpy.roll(E[src_order[2]], rollby, axis=axis)
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M[src_order[2]] = -numpy.roll(E[src_order[1]], rollby, axis=axis)
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m2j = functional.m2j(omega, dxes, mu)
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Jm = m2j(M)
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Jtot = J + Jm
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return Jtot
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def compute_overlap_e(E: field_t,
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H: field_t,
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wavenumber: complex,
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omega: complex,
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dxes: dx_lists_t,
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axis: int,
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polarity: int,
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slices: List[slice],
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mu: field_t = None,
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) -> field_t:
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"""
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Given an eigenmode obtained by solve_waveguide_mode, calculates overlap_e for the
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mode orthogonality relation Integrate(((E x H_mode) + (E_mode x H)) dot dn)
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[assumes reflection symmetry].
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overlap_e makes use of the e2h operator to collapse the above expression into
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(vec(E) @ vec(overlap_e)), allowing for simple calculation of the mode overlap.
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:param E: E-field of the mode
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:param H: H-field of the mode (advanced by half of a Yee cell from E)
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:param wavenumber: Wavenumber of the mode
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:param omega: Angular frequency of the simulation
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:param dxes: Grid parameters [dx_e, dx_h] as described in meanas.types
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:param axis: Propagation axis (0=x, 1=y, 2=z)
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:param polarity: Propagation direction (+1 for +ve, -1 for -ve)
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:param slices: epsilon[tuple(slices)] is used to select the portion of the grid to use
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as the waveguide cross-section. slices[axis] should select only one
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:param mu: Magnetic permeability (default 1 everywhere)
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:return: overlap_e for calculating the mode overlap
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"""
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slices = tuple(slices)
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cross_plane = [slice(None)] * 4
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cross_plane[axis + 1] = slices[axis]
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cross_plane = tuple(cross_plane)
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# Determine phase factors for parallel slices
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a_shape = numpy.roll([-1, 1, 1], axis)
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a_E = numpy.real(dxes[0][axis]).cumsum()
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a_H = numpy.real(dxes[1][axis]).cumsum()
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iphi = -polarity * 1j * wavenumber
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phase_E = numpy.exp(iphi * (a_E - a_E[slices[axis]])).reshape(a_shape)
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phase_H = numpy.exp(iphi * (a_H - a_H[slices[axis]])).reshape(a_shape)
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# Expand our slice to the entire grid using the calculated phase factors
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Ee = phase_E * E[cross_plane]
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He = phase_H * H[cross_plane]
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# Write out the operator product for the mode orthogonality integral
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domain = numpy.zeros_like(E[0], dtype=int)
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domain[slices] = 1
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npts = E[0].size
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dn = numpy.zeros(npts * 3, dtype=int)
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dn[0:npts] = 1
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dn = numpy.roll(dn, npts * axis)
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e2h = operators.e2h(omega, dxes, mu)
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ds = sparse.diags(vec([domain]*3))
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h_cross_ = operators.poynting_h_cross(vec(He), dxes)
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e_cross_ = operators.poynting_e_cross(vec(Ee), dxes)
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overlap_e = dn @ ds @ (-h_cross_ + e_cross_ @ e2h)
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# Normalize
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dx_forward = dxes[0][axis][slices[axis]]
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norm_factor = numpy.abs(overlap_e @ vec(Ee))
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overlap_e /= norm_factor * dx_forward
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return unvec(overlap_e, E[0].shape)
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def solve_waveguide_mode_cylindrical(mode_number: int,
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omega: complex,
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dxes: dx_lists_t,
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epsilon: vfield_t,
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r0: float,
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wavenumber_correction: bool = True,
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) -> Dict[str, complex or field_t]:
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"""
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Given a 2d (r, y) slice of epsilon, attempts to solve for the eigenmode
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of the bent waveguide with the specified mode number.
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:param mode_number: Number of the mode, 0-indexed
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:param omega: Angular frequency of the simulation
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:param dxes: Grid parameters [dx_e, dx_h] as described in meanas.types.
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The first coordinate is assumed to be r, the second is y.
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:param epsilon: Dielectric constant
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:param r0: Radius of curvature for the simulation. This should be the minimum value of
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r within the simulation domain.
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:param wavenumber_correction: Whether to correct the wavenumber to
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account for numerical dispersion (default True)
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:return: {'E': List[numpy.ndarray], 'H': List[numpy.ndarray], 'wavenumber': complex}
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"""
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'''
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Solve for the largest-magnitude eigenvalue of the real operator
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'''
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dxes_real = [[numpy.real(dx) for dx in dxi] for dxi in dxes]
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A_r = waveguide.cylindrical_operator(numpy.real(omega), dxes_real, numpy.real(epsilon), r0)
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eigvals, eigvecs = signed_eigensolve(A_r, mode_number + 3)
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v = eigvecs[:, -(mode_number+1)]
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'''
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Now solve for the eigenvector of the full operator, using the real operator's
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eigenvector as an initial guess for Rayleigh quotient iteration.
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'''
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A = waveguide.cylindrical_operator(omega, dxes, epsilon, r0)
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eigval, v = rayleigh_quotient_iteration(A, v)
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# Calculate the wave-vector (force the real part to be positive)
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wavenumber = numpy.sqrt(eigval)
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wavenumber *= numpy.sign(numpy.real(wavenumber))
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'''
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Perform correction on wavenumber to account for numerical dispersion.
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See Numerical Dispersion in Taflove's FDTD book.
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This correction term reduces the error in emitted power, but additional
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error is introduced into the E_err and H_err terms. This effect becomes
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more pronounced as the wavenumber increases.
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'''
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if wavenumber_correction:
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wavenumber -= 2 * numpy.sin(numpy.real(wavenumber / 2)) - numpy.real(wavenumber)
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shape = [d.size for d in dxes[0]]
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v = numpy.hstack((v, numpy.zeros(shape[0] * shape[1])))
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fields = {
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'wavenumber': wavenumber,
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'E': unvec(v, shape),
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# 'E': unvec(e, shape),
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# 'H': unvec(h, shape),
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}
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return fields
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def compute_source_q(E: field_t,
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H: field_t,
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wavenumber: complex,
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omega: complex,
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dxes: dx_lists_t,
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axis: int,
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polarity: int,
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slices: List[slice],
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mu: field_t = None,
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) -> field_t:
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A1f = functional.curl_h(dxes)
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A2f = functional.curl_e(dxes)
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J = A1f(H)
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M = A2f(-E)
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m2j = functional.m2j(omega, dxes, mu)
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Jm = m2j(M)
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Jtot = J + Jm
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return Jtot, J, M
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def compute_source_e(QE: field_t,
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omega: complex,
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dxes: dx_lists_t,
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axis: int,
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polarity: int,
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slices: List[slice],
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epsilon: field_t,
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mu: field_t = None,
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) -> field_t:
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"""
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Want (AQ-QA) E = -iwJ, where Q is a mask
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If E is an eigenmode, AE = 0 so just AQE = -iwJ
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Really only need E in 4 cells along axis (0, 0, Emode1, Emode2), find AE (1 fdtd step), then use center 2 cells as src
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"""
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slices = tuple(slices)
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# Trim a cell from each end of the propagation axis
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slices_reduced = list(slices)
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slices_reduced[axis] = slice(slices[axis].start + 1, slices[axis].stop - 1)
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slices_reduced = tuple(slice(None), *slices_reduced)
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# Don't actually need to mask out E here since it needs to be pre-masked (QE)
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A = functional.e_full(omega, dxes, epsilon, mu)
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J4 = A(QE) / (-1j * omega) #J4 is 4-cell result of -iwJ = A QE
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J = numpy.zeros_like(J4)
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J[slices_reduced] = J4[slices_reduced]
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return J
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def compute_source_wg(E: field_t,
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wavenumber: complex,
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omega: complex,
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dxes: dx_lists_t,
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axis: int,
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polarity: int,
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slices: List[slice],
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epsilon: field_t,
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mu: field_t = None,
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) -> field_t:
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slices = tuple(slices)
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Etgt, _slices2 = compute_overlap_ce(E=E, wavenumber=wavenumber,
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dxes=dxes, axis=axis, polarity=polarity,
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slices=slices)
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slices4 = list(slices)
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slices4[axis] = slice(slices[axis].start - 4 * polarity, slices[axis].start)
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slices4 = tuple(slices4)
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J = compute_source_e(QE=Etgt,
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omega=omega, dxes=dxes, axis=axis,
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polarity=polarity, slices=slices4,
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epsilon=epsilon, mu=mu)
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return J
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def compute_overlap_ce(E: field_t,
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wavenumber: complex,
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dxes: dx_lists_t,
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axis: int,
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polarity: int,
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slices: List[slice],
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) -> field_t:
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slices = tuple(slices)
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Ee = expand_wgmode_e(E=E, wavenumber=wavenumber,
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dxes=dxes, axis=axis, polarity=polarity,
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slices=slices)
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start, stop = sorted((slices[axis].start, slices[axis].start - 2 * polarity))
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slices2 = list(slices)
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slices2[axis] = slice(start, stop)
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slices2 = tuple(slice(None), slices2)
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Etgt = numpy.zeros_like(Ee)
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Etgt[slices2] = Ee[slices2]
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Etgt /= (Etgt.conj() * Etgt).sum()
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return Etgt, slices2
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def expand_wgmode_e(E: field_t,
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wavenumber: complex,
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dxes: dx_lists_t,
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axis: int,
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polarity: int,
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slices: List[slice],
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) -> field_t:
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slices = tuple(slices)
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# Determine phase factors for parallel slices
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a_shape = numpy.roll([1, -1, 1, 1], axis)
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a_E = numpy.real(dxes[0][axis]).cumsum()
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r_E = a_E - a_E[slices[axis]]
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iphi = polarity * 1j * wavenumber
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phase_E = numpy.exp(iphi * r_E).reshape(a_shape)
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# Expand our slice to the entire grid using the phase factors
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Ee = numpy.zeros_like(E)
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slices_exp = list(slices)
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slices_exp[axis] = slice(E.shape[axis + 1])
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slices_exp = (slice(None), *slices_exp)
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slices_in = tuple(slice(None), *slices)
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Ee[slices_exp] = phase_E * numpy.array(E)[slices_in]
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return Ee
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