Mode analysis for a half-loaded rectangular waveguide

This demo is implemented in two files, one for defining and solving the eigenvalue problem for a half-loaded electromagnetic waveguide with perfect electric conducting walls, and one for verifying if the numerical eigenvalues are consistent with the analytical modes of the problem.

The demo shows how to:

Setup an eigenvalue problem for Maxwell’s equations
Use SLEPc for solving eigenvalue problems in DOLFINx

Equations and problem definition in DOLFINx

In this demo, we are going to show how to solve the eigenvalue problem associated with a half-loaded rectangular waveguide with perfect electric conducting walls.

First of all, let’s import the modules we need for solving the problem:

import sys

import numpy as np
from analytical_modes import verify_mode

import ufl
from dolfinx import fem, io, plot
from dolfinx.mesh import (CellType, create_rectangle, exterior_facet_indices,
                          locate_entities)

from mpi4py import MPI
from petsc4py.PETSc import ScalarType

try:
    import pyvista
    have_pyvista = True
except ModuleNotFoundError:
    print("pyvista and pyvistaqt are required to visualise the solution")
    have_pyvista = False

try:
    from slepc4py import SLEPc
except ModuleNotFoundError:
    print("slepc4py is required to solve the problem")
    sys.exit(0)

Let’s now define our domain. It is a rectangular domain with width \(w\) and height \(h = 0.45w\), with the dielectric medium filling the lower-half of the domain, with a height of \(d=0.5h\).

w = 1
h = 0.45 * w
d = 0.5 * h
nx = 300
ny = int(0.4 * nx)

domain = create_rectangle(MPI.COMM_WORLD, np.array(
    [[0, 0], [w, h]]), np.array([nx, ny]), CellType.quadrilateral)

domain.topology.create_connectivity(
    domain.topology.dim - 1, domain.topology.dim)

Now we can define the dielectric permittivity \(\varepsilon_r\) over the domain as \(\varepsilon_r = \varepsilon_v = 1\) in the vacuum, and as \(\varepsilon_r = \varepsilon_d = 2.45\) in the dielectric:

eps_v = 1
eps_d = 2.45


def Omega_d(x):
    return x[1] <= d


def Omega_v(x):
    return x[1] >= d


D = fem.FunctionSpace(domain, ("DQ", 0))
eps = fem.Function(D)

cells_v = locate_entities(domain, domain.topology.dim, Omega_v)
cells_d = locate_entities(domain, domain.topology.dim, Omega_d)

eps.x.array[cells_d] = np.full_like(cells_d, eps_d, dtype=ScalarType)
eps.x.array[cells_v] = np.full_like(cells_v, eps_v, dtype=ScalarType)

In order to find the weak form of our problem, the starting point are Maxwell’s equation and the perfect electric conductor condition on the waveguide wall:

\[\begin{split} \begin{align} &\nabla \times \frac{1}{\mu_{r}} \nabla \times \mathbf{E}-k_{o}^{2} \epsilon_{r} \mathbf{E}=0 \quad &\text { in } \Omega\\ &\hat{n}\times\mathbf{E} = 0 &\text { on } \Gamma \end{align} \end{split}\]

with \(k_0\) and \(\lambda_0 = 2\pi/k_0\) being the wavevector and the wavelength, which we consider fixed at \(\lambda = h/0.2\). If we focus on non-magnetic material only, we can also use \(\mu_r=1\).

Now we can assume a known dependance on \(z\):

\[ \mathbf{E}(x, y, z)=\left[\mathbf{E}_{t}(x, y)+\hat{z} E_{z}(x, y)\right] e^{-jk_z z} \]

where \(\mathbf{E}_t\) is the component of the electric field transverse to the waveguide axis, and \(E_z\) is the component of the electric field parallel to the waveguide axis, and \(k_z\) represents our complex propagation constant.

In order to pose the problem as an eigenvalue problem, we need to make the following substitution:

\[\begin{split} \begin{align} & \mathbf{e}_t = k_z\mathbf{E}_t\\ & e_z = -jE_z \end{align} \end{split}\]

The final weak form can be written as:

\[\begin{split} \begin{aligned} F_{k_z}(\mathbf{e})=\int_{\Omega} &\left(\nabla_{t} \times \mathbf{e}_{t}\right) \cdot\left(\nabla_{t} \times \bar{\mathbf{v}}_{t}\right) -k_{o}^{2} \epsilon_{r} \mathbf{e}_{t} \cdot \bar{\mathbf{v}}_{t} \\ &+k_z^{2}\left[\left(\nabla_{t} e_{z}+\mathbf{e}_{t}\right) \cdot\left(\nabla_{t} \bar{v}_{z}+\bar{\mathbf{v}}_{t}\right)-k_{o}^{2} \epsilon_{r} e_{z} \bar{v}_{z}\right] \mathrm{d} x = 0 \end{aligned} \end{split}\]

Or, in a more compact form, as:

\[\begin{split} \left[\begin{array}{cc} A_{t t} & 0 \\ 0 & 0 \end{array}\right]\left\{\begin{array}{l} \mathbf{e}_{t} \\ e_{z} \end{array}\right\}=-k_z^{2}\left[\begin{array}{ll} B_{t t} & B_{t z} \\ B_{z t} & B_{z z} \end{array}\right]\left\{\begin{array}{l} \mathbf{e}_{t} \\ e_{z} \end{array}\right\} \end{split}\]

A problem of this kind is known as a generalized eigenvalue problem, where our eigenvalues are all the possible \( -k_z^2\). For further details about this problem, check Prof. Jin’s The Finite Element Method in Electromagnetics, third edition.

To write the weak form in DOLFINx, we need to specify our function space. For \(\mathbf{e}_t\), we can use RTCE elements (the equivalent of Nedelec elements on quadrilateral cells), while for \(e_z\) field we can use Lagrange elements. In DOLFINx, this hybrid formulation is implemented with MixedElement:

degree = 1
RTCE = ufl.FiniteElement("RTCE", domain.ufl_cell(), degree)
Q = ufl.FiniteElement("Lagrange", domain.ufl_cell(), degree)
V = fem.FunctionSpace(domain, ufl.MixedElement(RTCE, Q))

Now we can define our weak form:

lmbd0 = h / 0.2
k0 = 2 * np.pi / lmbd0

et, ez = ufl.TrialFunctions(V)
vt, vz = ufl.TestFunctions(V)

a_tt = (ufl.inner(ufl.curl(et), ufl.curl(vt)) - (k0**2)
        * eps * ufl.inner(et, vt)) * ufl.dx
b_tt = ufl.inner(et, vt) * ufl.dx
b_tz = ufl.inner(et, ufl.grad(vz)) * ufl.dx
b_zt = ufl.inner(ufl.grad(ez), vt) * ufl.dx
b_zz = (ufl.inner(ufl.grad(ez), ufl.grad(vz)) - (k0**2)
        * eps * ufl.inner(ez, vz)) * ufl.dx

a = fem.form(a_tt)
b = fem.form(b_tt + b_tz + b_zt + b_zz)

Let’s add the perfect electric conductor conditions on the waveguide wall:

bc_facets = exterior_facet_indices(domain.topology)

bc_dofs = fem.locate_dofs_topological(V, domain.topology.dim - 1, bc_facets)

u_bc = fem.Function(V)
with u_bc.vector.localForm() as loc:
    loc.set(0)
bc = fem.dirichletbc(u_bc, bc_dofs)

Solve the problem in SLEPc

Now we can solve the problem with SLEPc. First of all, we need to assemble our \(A\) and \(B\) matrices with PETSc in this way:

A = fem.petsc.assemble_matrix(a, bcs=[bc])
A.assemble()
B = fem.petsc.assemble_matrix(b, bcs=[bc])
B.assemble()

Now, we need to create the eigenvalue problem in SLEPc. Our problem is a linear eigenvalue problem, that in SLEPc is solved with the EPS module. We can initialize this solver in the following way:

eps = SLEPc.EPS().create(domain.comm)

We can pass to EPS our matrices by using the setOperators routine:

eps.setOperators(A, B)

If the matrices in the problem have known properties (e.g. hermiticity) we can use this information in SLEPc to accelerate the calculation with the setProblemType function. For this problem, there is no property that can be exploited, and therefore we define it as a generalized non-Hermitian eigenvalue problem with the SLEPc.EPS.ProblemType.GNHEP object:

eps.setProblemType(SLEPc.EPS.ProblemType.GNHEP)

Next, we need to specify a tolerance for the iterative solver, so that it knows when to stop:

tol = 1e-9
eps.setTolerances(tol=tol)

Now we need to set the eigensolver for our problem. SLEPc offers different built-in algorithms, and also wrappers to external libraries. Some of these can only solve Hermitian problems and/or problems with eigenvalues in a certain portion of the spectrum. However, the choice of the particular method to choose to solve a problem is a technical discussion that is out of the scope of this demo, and that is more comprehensively discussed in the SLEPc documentation. For our problem, we will use the Krylov-Schur method, which we can set by calling the setType function:

eps.setType(SLEPc.EPS.Type.KRYLOVSCHUR)

In order to accelerate the calculation of our solutions, we can also use a so-called spectral transformation, a technique which maps the original eigenvalues into another position of the spectrum without affecting the eigenvectors. In our case, we can use the shift-and-invert transformation with the SLEPc.ST.Type.SINVERT object:

# Get ST context from eps
st = eps.getST()

# Set shift-and-invert transformation
st.setType(SLEPc.ST.Type.SINVERT)

The spectral transformation needs a target value for the eigenvalues we are looking for. Since the eigenvalues for our problem can be complex numbers, we need to specify whether we are searching for specific values in the real part, in the imaginary part, or in the magnitude. In our case, we are interested in propagating modes, and therefore in real \(k_z\). For this reason, we can specify with the setWhichEigenpairs function that our target value will refer to the real part of the eigenvalue, with the SLEPc.EPS.Which.TARGET_REAL object:

eps.setWhichEigenpairs(SLEPc.EPS.Which.TARGET_REAL)

For specifying the target value, we can use the setTarget function. Even though we cannot know a good target value a priori, we can guess that \(k_z\) will be quite close to \(k_0\) in value, for instance \(k_z = 0.5k_0^2\). Therefore, we can set a target value of \(-(0.5k_0^2)\):

eps.setTarget(-(0.5 * k0)**2)

Then, we need to define the number of eigenvalues we want to calculate. We can do this with the setDimensions function, where we specify that we are looking for just one eigenvalue:

eps.setDimensions(nev=1)

We can finally solve the problem with the solve function. To gain a deeper insight over the simulation, we also print an output message from SLEPc by calling the view and errorView function:

eps.solve()
eps.view()
eps.errorView()

Now we can get the eigenvalues and eigenvectors calculated by SLEPc with the following code. We also verify if the numerical \(k_z\) are consistent with the analytical equations of the half-loaded waveguide modes, with the verify_mode() function defined in analytical_modes.py:

# Save the kz
vals = [(i, np.sqrt(-eps.getEigenvalue(i))) for i in range(eps.getConverged())]

# Sort kz by real part
vals.sort(key=lambda x: x[1].real)

eh = fem.Function(V)

kz_list = []

for i, kz in vals:

    # Save eigenvector in eh
    eps.getEigenpair(i, eh.vector)

    # Compute error for i-th eigenvalue
    error = eps.computeError(i, SLEPc.EPS.ErrorType.RELATIVE)

    # Verify, save and visualize solution
    if error < tol and np.isclose(kz.imag, 0, atol=tol):

        kz_list.append(kz)

        # Verify if kz is consistent with the analytical equations
        assert verify_mode(kz, w, h, d, lmbd0, eps_d, eps_v, threshold=1e-4)

        print(f"eigenvalue: {-kz**2}")
        print(f"kz: {kz}")
        print(f"kz/k0: {kz/k0}")

        eh.x.scatter_forward()

        eth, ezh = eh.split()
        eth = eh.sub(0).collapse()
        ez = eh.sub(1).collapse()

        # Transform eth, ezh into Et and Ez
        eth.x.array[:] = eth.x.array[:] / kz
        ezh.x.array[:] = ezh.x.array[:] * 1j

        V_dg = fem.VectorFunctionSpace(domain, ("DQ", degree))
        Et_dg = fem.Function(V_dg)
        Et_dg.interpolate(eth)

        # Save solutions
        with io.VTXWriter(domain.comm, f"sols/Et_{i}.bp", Et_dg) as f:
            f.write(0.0)

        with io.VTXWriter(domain.comm, f"sols/Ez_{i}.bp", ezh) as f:
            f.write(0.0)

        # Visualize solutions with Pyvista
        if have_pyvista:
            V_cells, V_types, V_x = plot.create_vtk_mesh(V_dg)
            V_grid = pyvista.UnstructuredGrid(V_cells, V_types, V_x)
            Et_values = np.zeros((V_x.shape[0], 3), dtype=np.float64)
            Et_values[:, : domain.topology.dim] = \
                Et_dg.x.array.reshape(V_x.shape[0], domain.topology.dim).real

            V_grid.point_data["u"] = Et_values

            pyvista.set_jupyter_backend("ipygany")
            plotter = pyvista.Plotter()

            plotter.add_mesh(V_grid.copy(), show_edges=False)
            plotter.view_xy()
            plotter.link_views()

            if not pyvista.OFF_SCREEN:
                plotter.show()
            else:
                pyvista.start_xvfb()
                plotter.screenshot("Et.png", window_size=[400, 400])

        if have_pyvista:
            V_lagr, lagr_dofs = V.sub(1).collapse()
            V_cells, V_types, V_x = plot.create_vtk_mesh(V_lagr)
            V_grid = pyvista.UnstructuredGrid(V_cells, V_types, V_x)

            V_grid.point_data["u"] = ezh.x.array.real[lagr_dofs]

            pyvista.set_jupyter_backend("ipygany")
            plotter = pyvista.Plotter()

            plotter.add_mesh(V_grid.copy(), show_edges=False)
            plotter.view_xy()
            plotter.link_views()

            if not pyvista.OFF_SCREEN:
                plotter.show()
            else:
                pyvista.start_xvfb()
                plotter.screenshot("Ez.png", window_size=[400, 400])