When the numerical grid does not extend to infinity, e.g., when solving for a hyperbolic PDE, the
boundary defined by is a smooth surface, on which boundary conditions are much easier to
impose. Finally, spherical harmonics, which are strongly linked with these coordinates, can simplify a lot the
solution of Poisson-like or wave-like equations. On the other hand, there are some technical problems linked
with this set of coordinates, as detailed hereafter, but spectral methods can handle them in a very efficient
way.
The transformation from spherical to Cartesian coordinates
is obtained by
In addition, the -dependence translates into a Taylor series near the origin, with the same
parity as
. More details in the case of polar (2D) coordinates are given in Chapter 18 of
Boyd [48].
If we go back to the evaluation of the Laplace operator (93), it is now clear that the result is always
regular, at least for
and
. We detail the cases of
and
, using the fact that
spherical harmonics are eigenfunctions of the angular part of the Laplace operator (see Equation (103
)).
For
the scalar field
is reduced to a Taylor series of only even powers of
, therefore the
first derivative contains only odd powers and can be safely divided by
. Once decomposed
on spherical harmonics, the angular part of the Laplace operator (93
) acting on the
component reads
, which is a problem only for the first term of the Taylor expansion.
On the other hand, this term cancels with the
, providing a regular result. This is the
general behavior of many differential operators in spherical coordinates: when applied to a
regular field, the full operator gives a regular result, but single terms of this operator may give
singular results when computed separately, the singularities canceling between two different
terms.
As this may seem an argument against the use of spherical coordinates, let us stress that
spectral methods are very powerful in evaluating such operators, keeping everything finite. As an
example, we use Chebyshev polynomials in for the expansion of the field
,
being a positive constant. From the Chebyshev polynomial recurrence relation (46
), one has
Spherical harmonics are the pure angular functions
where The first property makes the description of scalar fields on spheres very easy: spherical harmonics are
used as a decomposition basis within spectral methods, for instance in geophysics or meteorology, and by
some groups in numerical relativity [21, 109
, 219
]. However, they could be more broadly used in numerical
relativity, for example for Cauchy-characteristic evolution or matching [228, 15], where a single coordinate
chart on the sphere might help in matching quantities. They can also help to describe star-like
surfaces being defined by
as event or apparent horizons [153, 23
, 2]. The search for
apparent horizons is also made easier: since the function
verifies a two-dimensional Poisson-like
equation, the linear part can be solved directly, just by dividing by
in the coefficient
space.
The second property makes the Poisson equation,
very easy to solve (see Section 1.3). If the source The use of spherical-harmonics decomposition can be regarded as a basic spectral method, like Fourier
decomposition. There are, therefore, publicly available “spherical harmonics transforms”, which consist of a
Fourier transform in the -direction and a successive Fourier and Legendre transform in the
-direction. A rather efficient one is the SpharmonicsKit/S2Kit [152], but writing one’s own functions is
also possible [99
].
All the discussion in Sections 3.2.1 – 3.2.2 has been restricted to scalar fields. For vector, or more generally
tensor fields in three spatial dimensions, a vector basis (triad) must be specified to express the components.
At this point, it is very important to stress that the choice of the basis is independent of the choice of
coordinates. Therefore, the most straightforward and simple choice, even if one is using spherical
coordinates, is the Cartesian triad . With this basis, from a numerical
point of view, all tensor components can be regarded as scalars and therefore, a regular tensor
can be defined as a tensor field, whose components with respect to this Cartesian frame are
expandable in powers of
and
(as in Bardeen and Piran [19
]). Manipulations and solutions
of PDEs for such tensor fields in spherical coordinates are generalizations of the techniques
for scalar fields. In particular, when using the multidomain approach with domains having
different shapes and coordinates, it is much easier to match Cartesian components of tensor
fields. Examples of use of Cartesian components of tensor fields in numerical relativity include
the vector Poisson equation [109
] or, more generally, the solution of elliptic systems arising in
numerical relativity [172
]. In the case of the evolution of the unconstrained Einstein system, the
use of Cartesian tensor components is the general option, as it is done by the Caltech/Cornell
group [127
, 189
].
The use of an orthonormal spherical basis (see. Figure 19
) requires
more care. The interested reader can find more details in the work of Bonazzola et al. [44, 37
].
Nevertheless, there are systems in general relativity in which spherical components of tensors can be
useful:
Problems arise because of the singular nature of the basis itself, in addition to the spherical coordinate
singularities. The consequences are first that each component is a multivalued function at the origin
or on the
-axis, and then that components of a given tensor are not independent from one another,
meaning that one cannot, in general, specify each component independently or set it to zero, keeping the
tensor field regular. As an example, we consider the gradient
of the scalar field
, where
is the usual first Cartesian coordinate field. This gradient expressed in Cartesian components is
a regular vector field
. The spherical components of
read
The other drawback of spherical coordinates is that the usual partial differential operators mix the
components. This is due to the nonvanishing connection coefficients associated with the spherical flat
metric [37]. For example, the vector Laplace operator (
) reads
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