Abstract
We derive and analyze a symmetric interior penalty discontinuous Galerkin scheme for the approximation of the second-order form of the radiative transfer equation in slab geometry. Using appropriate trace lemmas, the analysis can be carried out as for more standard elliptic problems. Supporting examples show the accuracy and stability of the method also numerically, for different polynomial degrees. For discretization, we employ quad-tree grids, which allow for local refinement in phase-space, and we show exemplary that adaptive methods can efficiently approximate discontinuous solutions. We investigate the behavior of hierarchical error estimators and error estimators based on local averaging.
1 Introduction
We consider the numerical solution of the radiative transfer equation in slab geometry, which has several applications such as atmospheric science [27], oceanography [5], pharmaceutical powders [9] or solid state lighting [38]; see also [10] for a recent introduction.
The radiative transfer equation in slab geometry describes the equilibrium distribution of specific intensity ϕ in a three-dimensional background medium
Here,
Assuming a strictly positive total cross section
Using (1.6) in (1.4) and in (1.2)–(1.3), and writing
Here,
Due to the product structure of Ω, it seems natural to use separate discretization techniques for the spatial variable z and the angular variable μ. This is for instance done in the spherical harmonics method, in which a truncated Legendre polynomial expansion is employed to discretize μ (see [18]). The resulting coupled system of Legendre moments, which still depend on z, is then discretized for instance by finite differences or finite elements [18]. Another class of approximations consists of discrete ordinates methods which perform a collocation in μ and the integral in (1.7) is approximated by a quadrature rule [18]. The resulting system of transport equations is then discretized for instance by finite differences [18] or discontinuous Galerkin methods [26, 24], and also spatially adaptive schemes have been used [41].
A major drawback of the independent discretization of the two variables z and μ is that a local refinement in phase-space is not possible.
Such local refinement is generally necessary to achieve optimal schemes.
For instance, the solution can be non-smooth in the two points
Phase-space discretizations have been used successfully for radiative transfer in several applications, see, e.g., [15, 35, 36, 37] for slab geometry, [32] for geometries with spherical symmetries, or [21, 33] for more general geometries. Let us also refer to [31] for a phase-space discontinuous Galerkin method for the nonlinear Boltzmann equation. A non-tensor product discretization that combines ideas of discrete ordinates to discretize the angular variable with a discontinuous Petrov-Galerkin method to discretize the spatial variable has been developed in [13].
In this work, we aim to develop a numerical method for (1.7)–(1.8) that allows for local mesh refinement in phase-space and that allows for a relatively simple analysis and implementation. To accomplish this, we base our discretization on a partition of Ω such that each element in that partition is the Cartesian product of two intervals. Local approximations are then constructed from products of polynomials defined on the respective intervals. In order to easily handle hanging nodes, which such partitions generally contain, we use globally discontinuous approximations. In case the resulting linear systems are very large, iterative solution techniques with small additional memory requirements may be employed for their numerical solution, such as the conjugate gradient method, which, however, requires the linear system to be symmetric positive definite. Therefore, we employ a symmetric interior penalty discontinuous Galerkin formulation. Besides the proper treatment of traces, which requires the inclusion of a weight function in our case, the analysis of the overall scheme is along the standard steps for the analysis of discontinuous Galerkin methods [16]. As a result, we obtain a scheme that enjoys an abstract quasi-best approximation property in a mesh-dependent energy norm. Our choice of meshes also allows to explicitly estimate the constants in auxiliary tools, such as inverse estimates and discrete trace inequalities. As a result, we can give an explicit lower bound on the penalty parameter required for discrete stability. This lower bound for the penalty parameter depends only on the polynomial degree for the approximation in the z-variable and is relatively simple to compute; see [20] for the estimation of the penalty parameter in the context of standard elliptic problems. Our theoretical results about accuracy and stability of the method are confirmed by numerical examples, which show optimal convergence rates for different polynomial degrees assuming sufficient regularity of the solution. Moreover, we show that adaptively refined grids are able to efficiently construct approximations to non-smooth solutions.
For the local adaptation of the grid we investigate several error estimators. First, we consider two hierarchical error estimators, which either use polynomials of higher degree or the discrete solution on a uniformly refined mesh, respectively. Such estimators have been investigated in the elliptic context, e.g., in [7, 30]. Our numerical results show that these error indicators can be used to refine the mesh towards the singularity of the solution. A drawback of these estimators is that an additional global problem has to be solved in every step. Since the solutions to (1.7)–(1.8) can be discontinuous in μ, the proofs developed for elliptic equations to show that the global estimator is equivalent to a locally computable quantity, see, e.g., [30], do not apply. To overcome the computational complexity of building estimators that require to solve a global problem, we propose an a posteriori estimator based on a local averaging procedure. This cheap estimator shows a similar performance compared to the more expensive hierarchical ones mentioned before.
The outline of the rest of the manuscript is as follows. In Section 2 we introduce notation and collect technical tools, such as trace theorems. In Section 3 we derive and analyze the discontinuous Galerkin scheme. Section 4 presents numerical examples confirming the theoretical results of Section 3. Section 5 shows that our scheme works well with adaptively refined grids. We introduce here two hierarchical error estimators and one based on local post-processing. The paper closes with some conclusions in Section 6.
2 Preliminaries
We denote by
Furthermore, we introduce the Hilbert space
which consists of square integrable functions for which the weighted derivative is also square integrable; see [2, Section 2.2]. We endow the space V with the graph norm
To treat the boundary condition (1.8), let us introduce the following inner product:
and the corresponding space
According to [2, Theorem 2.8] and its proof, functions in V have a trace on Γ and
and the trace operator mapping V to
Lemma 1.
Let
Proof.
Without loss of generality, we assume that
Multiplication by μ, integration over K and an application of the triangle inequality yields that
Setting
which concludes the proof. ∎
2.1 Weak Formulation and Solvability
Performing the usual integration-by-parts, see, e.g., [6, 39], the weak formulation of (1.7)–(1.8) is as follows: find
with bilinear form
Here, for ease of notation, we use the scattering operator
Using the Cauchy–Schwarz inequality, we deduce that
for some
If
Since the trace operator is surjective from V to
3 Discontinuous Galerkin Scheme
In the following we will derive the numerical scheme to approximate solutions to (2.2). After introducing a suitable partition of Ω using quad-tree grids and corresponding broken polynomial spaces, we can essentially follow the standard procedure for elliptic problems, cf. [16]. One notable difference is that we need to incorporate the weight function μ on the faces.
3.1 Mesh and Broken Polynomial Spaces
In order to simplify the presentation, and subsequently the implementation, we consider quad-tree meshes [23] as follows.
Let
for illustration see Figure 1. We denote the local mesh size by
Next, let us introduce some standard notation.
Denote
with
such that
and
In order to take into account local variations in the mesh size and diffusion coefficient
where
We note that the inclusion in (3.3) can be strict in the case of hanging nodes, see for instance Figure 1.
Combining Lemma 1 with common inverse inequalities, cf. [8, Sect. 4.5],
i.e., for any
we obtain the following discrete trace lemma.
Lemma 2 (Discrete Trace Inequality).
Let
where
Proof.
Using Lemma 1, we have that
Using (3.4), we estimate the weighted derivative term as follows:
Using that
which concludes the proof. ∎
Remark 1.
The value of
where
for a basis
3.2 Derivation of the DG Scheme
In order to extend the bilinear form defined in (2.3) to the broken space
for
which is defined on
where we used the identity
Since
Hence, a consistent bilinear form is given by
which, for
which is again well-defined on
with
The discrete variational problem is formulated as follows: Find
3.3 Analysis
For the analysis of (3.7), let us introduce mesh-dependent norms
(3.8)
In order to show discrete stability and boundedness of
Lemma 3 (Auxiliary Lemma).
Let
with
Proof.
By the definition of the average, we have that
where
where we used Lemma 2 applied to
which, in view of (3.2), concludes the proof. ∎
The auxiliary lemma allows to bound the consistency terms in
Lemma 4 (Discrete Stability).
For any
provided that
Proof.
Let
Using Lemma 3, and the fact that each sub-element
Hence, by choosing
from which we obtain the assertion. ∎
Discrete stability implies that the scheme (3.7) is well-posed, cf. [16, Lemma 1.30].
Theorem 1 (Discrete Well-Posedness).
Let
Proof.
The space
To proceed with an abstract error estimate, we need the following boundedness result.
Lemma 5 (Boundedness).
For any
where
Proof.
We have that
The first two terms can be estimated using the Cauchy–Schwarz inequality as follows:
For the third term, we use Lemma 3 to obtain
To separate the terms that include u and v, respectively, we apply the Cauchy–Schwarz inequality once more and use again that each sub-element
which concludes the proof as
Before continuing, an inspection of the previous proof shows that we have the following corollary stating boundedness of
Corollary 1 (Discrete Boundedness).
For any
where
Combining consistency, stability and boundedness ensures that the discrete solution
Theorem 2 (Error Estimate).
Let
provided that
Remark 2.
Note that
Remark 3.
Assuming that the exact solution is sufficiently regular, say
Remark 4.
In view of Remark 1, the value of
Remark 5.
Instead of using the symmetric bilinear form
with parameter
4 Numerical Examples
In the following we confirm the theoretical statements about stability and convergence of Section 3 numerically [43].
Let
Here,
For our computations we use the spaces
For the numerical solution of the resulting linear systems, we use a fixed-point iteration [1]: Introducing the auxiliary bilinear form
The fixed-point iteration converges linearly with a rate
Table 1 shows the
N | |||||||
16 | 64 | 256 | 1 024 | 4 096 | 16 384 | 65 536 | |
|
7.07e
|
3.53e
|
1.76e
|
8.81e
|
4.40e
|
2.20e
|
1.10e
|
|
5.51e
|
1.38e
|
3.44e
|
8.60e
|
2.15e
|
5.37e
|
1.34e
|
|
2.77e
|
3.47e
|
4.33e
|
5.41e
|
6.77e
|
8.46e
|
1.06e
|
|
1.38e
|
8.69e
|
5.44e
|
3.40e
|
2.16e
|
4.20e
|
4.16e
|
Since the coefficients are smooth, we may expect higher order convergence in the
N | |||||||
16 | 64 | 256 | 1 024 | 4 096 | 16 384 | 65 536 | |
|
5.75e
|
1.49e
|
3.78e
|
9.46e
|
2.37e
|
5.92e
|
1.48e
|
|
2.13e
|
2.60e
|
3.22e
|
4.02e
|
5.02e
|
6.27e
|
7.84e
|
|
9.43e
|
6.03e
|
3.79e
|
2.37e
|
1.53e
|
3.86e
|
3.79e
|
|
3.11e
|
9.64e
|
3.03e
|
3.85e
|
3.75e
|
3.75e
|
3.92e
|
N | |||||||
16 | 64 | 256 | 1 024 | 4 096 | 16 384 | 65 536 | |
|
4.46e
|
1.10e
|
2.74e
|
6.84e
|
1.71e
|
4.27e
|
1.07e
|
|
5.26e
|
1.17e
|
2.80e
|
6.93e
|
1.73e
|
4.31e
|
1.08e
|
|
1.08e
|
6.67e
|
4.16e
|
2.59e
|
1.65e
|
3.84e
|
3.82e
|
|
6.27e
|
3.31e
|
1.96e
|
1.30e
|
3.89e
|
3.74e
|
3.93e
|
5 Adaptivity
In this section we show, by examples, that hierarchical error estimators, see, e.g., [30] for the elliptic case, as well as estimators based on averaging the approximate solutions are a possible choice to adaptively construct optimal partitions
for some universal constant
For a justification of the saturation assumption in the context of elliptic problems we refer to [11].
In the following numerical experiments, we use the norm
to investigate the behavior of two hierarchical error indicators for different test cases. The local error contributions are then given by
where
where
The choice of
5.1 Hierarchical p-Error Estimator
Setting
We note that
Figure 2 and Figure 3 show the convergence rates for adaptively refined meshes using the
For the manufactured solution
5.2 Hierarchical h-Error Estimator
Using once again the test cases (5.3) and (5.4), we now keep
Some comments are due for the computation of
Comparing the penalty terms in
Figure 4 and Figure 5 show the optimality of the estimator for the manufactured solution
5.3 Error Estimator Based on the Solution of Local Problems
Since the computation of the global error estimators ζ presented in Section 5.1 and Section 5.2 is in general expensive, we investigate also an error estimator based on the solution of local problems, as presented in [22, 30] for corresponding elliptic problems.
In this approach, the computed solution
For each
where
where
Let
At this point we observe that (3.7), (5.7) and (5.11) imply, for all
(5.12)
Eventually, we introduce the functions
Each
Lemma 6.
We have that
Proof.
We first rewrite (5.13) in terms of the estimator
Plugging
where we used Corollary 1 in the last step.
Coercivity of
Combining (5.16) and (5.17), we have eventually
which concludes the proof. ∎
Due to the lack of appropriate interpolation operators for functions in the space V, one cannot adapt the proofs given in [30] in a straight-forward fashion to show a bound of ζ in terms of the local contributions η. In fact, some preliminary numerical tests, based on the broken
5.4 Error Estimator Based on Averaging the Approximate Solution
In the context of a posteriori error estimation and adaptive mesh refinement, ZZ-error estimators named after Zienkiewicz
and Zhu [45] are widely used in practice. Compared to the previously mentioned hierarchical error estimators, their major advantage is the fact that no further mesh nor a further solution is required.
We consider the case
If a regular node
holds for each regular node
where the local contributions are used to refine the mesh using Dörfler marking as described above.
Figure 7 shows the convergence rates for adaptively refined meshes using the averaging indicator for the test (5.4).
The indicator behaves correctly and replicates the curve of the actual error. These curves have the same slope as the optimal rate
In comparison to the hierarchical estimators, cf. Figure 3 and Figure 5, the averaging error estimator follows the actual error curve more closely.
6 Conclusions and Discussion
We developed and analyzed a discontinuous Galerkin approximation for the radiative transfer equation in slab geometry. The use of quad-tree grids allowed for a relatively simple analysis with similar arguments as for more standard elliptic problems. While such grids allow for local mesh refinement in phase-space, the implementation of the numerical scheme is straightforward. For sufficiently regular solutions, we showed optimal rates of convergence.
We showed by example that non-smooth solutions can be approximated well by adaptively refined grids.
The ability to easily adapt the computational mesh can also be useful when complicated geometries must be resolved, which may occur in higher-dimensional situations.
Also more general elements could be employed at the expense of a more complicated notation and analysis; we leave this to future research.
In order to automate the mesh adaptation procedure, an error estimator is required.
We investigated numerically hierarchical error estimators and estimators based on local averaging in a post-processing step. All three estimators closely follow the actual error, and, in the case of point singularities, they can be used to obtain optimal convergence rates. We note that the hierarchical error estimators require to solve global problems, and it is left for future research to investigate whether a localization is possible.
Upper bounds for the error can be derived for consistent approximations using duality theory [25]. Rigorous a posteriori error estimation has also been done using discontinuous Petrov-Galerkin discretizations [13]. We leave it to future research to analyze the error estimators for the discontinuous Galerkin scheme considered here and to generalize the present method to a corresponding
While the solution of the linear systems for uniformly refined grids can be implemented using the established preconditioned iterative solvers [1, 39], the structure of the linear systems for adaptively refined grids is more complex because the equations do not fully decouple in μ; compare the situations in Figure 1. One possible direction is to develop nested solvers, or to adapt the methodology of [40]. We leave this for future research.
Another direction of future research entails the regularity of the right-hand side f in (1.7) and g in (1.8). If f and g define only an element in the dual space of V, see (2.2), then the flux
Funding source: Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Award Identifier / Grant number: OCENW.KLEIN.183
Funding statement: Riccardo Bardin and Matthias Schlottbom acknowledge support by the Dutch Research Council (NWO) via grant OCENW.KLEIN.183.
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