New Efficient Implicit Time Integration Method for DGTD Applied to Sequential Multidomain and Multiscale Problems.
The discontinuous Galerkin time-domain (DGTD) methods are promising in transient analysis of large and multiscale problems [1-9]. Based on the idea of domain decomposition, the DG methods can handle problems too large to be solved by conventional numerical techniques. Basically, the DG methods divide an original computational domain into several well designed subdomains. Within each subdomain, the sizes of the elements are at the same level. In this way, a large system matrix is divided into several smaller and balanced matrices. Thus, once the spatial discretization is defined, an optimal time integration method is crucial.
Time integration in transient multiscale modeling can be very challenging using explicit schemes; small cells needed for capturing fine details (e.g., a multiscale package-to- chip structure in Figure 1) will lead to an extremely small time step [DELTA]t, according to the Courant- Friedrichs-Lewy (CFL) condition . Consequently, the large number of calculation in time integration is unaffordable. On the other hand, implicit schemes can surpass the CFL limits because they are unconditionally stable. Thus, subdomains with electrically small structures can use a larger time step interval. This advantage is at the cost of inversion of matrices. Unlike explicit schemes, the matrices in an implicit scheme are not positive definite, and thus usually become more costly to invert. Several works have proposed algorithms to accelerate the implicit time stepping, such as the Block-Thomas (BT) algorithm introduced by .
This work presents an algorithm, based on the Crank-Nicholson method [11-15], that exploits the sequential way in which the subdomains are usually placed for layered structures. The coupling between different subdomains leads to a linear block tri-diagonal system. This system is transformed into a block LDU (Lower-Diagonal-Upper) decomposition, resulting in a non-iterative and efficient algorithm. Numerical experiments show the benefits of this new method for sequential systems, in comparison to algorithms based on LU decomposition or the Block-Thomas algorithm .
2. GOVERNING EQUATIONS
The DGTD method developed here is based on the first-order Maxwell's equations with electric field E and magnetic field H as variables
[epsilon] [partial derivative]E/[partial derivative]t = [nabla] x H - [[sigma].sub.e] E - [J.sub.s] (1)
[mu] [partial derivative]H/[partial derivative]t = [nabla] x E - [[sigma].sub.m] H - [M.sub.s] (1)
where [J.sub.S] and [M.sub.S] are electric and magnetic current densities; and, [epsilon], [mu], [[sigma].sub.e] and [[sigma].sub.m] represent material's permittivity, permeability, electric conductivity, and magnetic conductivity, respectively.
For the spatial discretization, we assume the ith subdomain as the local one and the jth as an adjacent one, and use the curl-conforming basis functions [16-19] [[PHI].sup.(i)] and [[PSI].sup.(i)] to represent E and H of the local subdomain, respectively. The discontinuous Galerkin's weak forms of (1) and (2) are
[mathematical expression not reproducible] (3)
[mathematical expression not reproducible] (4)
where [[OMEGA].sub.i] and [partial derivative][[OMEGA].sub.i] denote the volume of the ith subdomain and its boundary, respectively; n is the outward unit normal vector, and n x E and n x H represent the fluxes on the boundary surface, which are evaluated using the central flux or the Riemann solver [20,21]. Note that for the ith subdomain, the boundary surface may include the perfect electric conductor (PEC), perfect magnetic conductor (PMC) and shared interface with adjacent subdomains. Only the shared interface contributes to the surface integral terms in (3) and (4), as on PEC or PMC the tangential field components become zero and thus have no contribution.
Assuming N sequential subdomains in total, the discretized system will be a set of matrix equations
[M.sup.(i)] d[u.sup.(i)]/dt = - [i+1.summation over (j=i-1)] [L.sup.(i,j)] [u.sup.(j)] + [q.sup.(i)] (5)
where i = 1,..., N; [u.sup.(i)] is a vector of unknowns; [q.sup.(i)] is a vector of sources; [M.sup.(i)] is the mass matrix; and [L.sup.(i,j)] contains stiffness, damping, and interface matrices, which can be understood as transmission and reflection matrices. We note here that the whole linear system has a block diagonal matrix on the left hand side, and a block tridiagonal matrix on the right, and this characteristic will be exploited to improve the time integration, as explained in the following section.
3. IMPLICIT TIME INTEGRATION METHODS
The Crank-Nicholson method is employed here as the basic implicit scheme to surpass the CFL stability condition of explicit schemes. The solution in the (n + 1)th time step is obtained by solving the below block tridiagonal system:
[mathematical expression not reproducible].(6)
[mathematical expression not reproducible] (7)
The Block-Thomas algorithm presented in  was designed to accelerate the process of solving (6). It is based on a block LU decomposition, with forward and backward substitutions. This algorithm is very accurate and fast as it is free of iteration. However, the drawback of the method is the dense matrices appearing in the block matrix U. These matrices are memory expensive which limit the size of the EM problems to be solved. We can clarify this point with the following two-subdomain system
[mathematical expression not reproducible] (8)
where the matrix [G.sup.1] is a dense one.
Thus, to solve this issue, an LDU decomposition with a reordering of unknowns is implemented here. In this way, the linear system is lighter than that used in BT. The following is the LDU decomposition for two subdomains:
[mathematical expression not reproducible] (9)
[mathematical expression not reproducible]
[W.sup.i] are communication matrices between interface unknowns and volume unknowns of the ith subdomain; [u.sup.i.sub.V] are volume unknowns in the ith subdomain; and, [mathematical expression not reproducible] are unknowns on the interfaces of the ith subdomain connected with the (i - 1)th and (i + 1)th neighbor subdomains. As we can see, the solution of this system includes forward and backward subsitutions for L and U block matrices. For the block diagonal matrix D, we solve the volume and interface unknowns separately. Furthermore, the subsystem for the interface unknowns also presents a block tridiagonal form, and thus the Block-Thomas algorithm can apply.
4. NUMERICAL RESULTS
The first evaluation of the proposed algorithm is a simple PEC cavity with multiple subdomains, all with the same number of unknowns of 7000. With these models we can verify the performance of the LDU decomposition with respect to the previous implicit BT method and the classic explicit Runge-Kutta method of 4th order (exRK). Three meshes used in this analysis are shown in Figure 2, with four, eight and sixteen subdomains, respectively. Comparisons of the electric field are presented in Figure 3 for the sixteen subdomains case. We can see a perfect agreement between the implicit LDU, BT and explicit Runge-Kutta methods. Note that for the explicit RK method, only one single domain is divided as a reference (1SD exRK) to compare with the DG based LDU method. This figure shows that the new algorithm does not decrease the accuracy of the solution from the BT algorithm and also presents good consistency with the single domain explicit RK method.
In Figure 4, the memory cost and CPU time for the new algorithm are compared to the BT algorithm and explicit RK for different numbers of subdomains, ranging from 4 to 128. Due to the limitation of computer resource, cases with even larger number of subdomains are not shown, but instead we present the trendlines (the dashed lines) for the three methods based on data fitting. From the figuire, we can observe an obvious reduction in memory consumption with LDU (the line with circles), compared to BT (the line with squares). This advantage provides the feasibility of solving large EM problems. With respect to explicit RK (the line with diamonds), however, the LDU method would consume more memory. This is reasonable because the matrix to be inverted for the implicit method is no longer a positive definite one as in the explicit method. Thus it requires more memory. In terms of CPU time per timestep, the LDU method does not show advantage over BT and explicit RK. Especially as the number of subdomains increases, it will take even longer time for each timestep. This drawback, however, can be complemented to some degree by its strengths in other aspects. Compared to BT, although LDU is less efficient in time, it can solve larger problems with more subdomains with the same computer resource. As in this case, LDU can handle up to 128 subdomains, while BT shows difficulty when the subdomain number reaches 64. Compared to explicit RK, even though the LDU method takes longer time for each timestep, it can require much fewer timesteps to complete the simulation, since it is unconditional stable and thereby a large timestep size can be employed. The explicit RK, on the contrary, may require an extremely small timestep size to maintain stability, thus resulting in millions of timesteps, especially for multiscale problems with fine structures.
The next case is designed to verify and evaluate the performance of the LDU method when solving cases with large multiscale factor. The structure is shown in Figure 5, in which the ith layer is scaled as one tenth of the (i - 1)th layer. In this way, the dimension of the layers reduces dramatically as the number of layers increases. The last layer has a subdomain used to evaluate chips with very small structures; in this case, simple conductors with width of 0.15 pm are used. As the dimension of the largest layer is 1cm, the multiscale factor for this case is close to 67 thousand. Finally, eight lumped ports are deployed in each conductor of the bottom layer, with 50 [OMEGA] impedance. The lumped port number 1 is selected as active, where a first derivative of BHW pulse  is generated with a central frequency of 8.5 GHz.
Transient solutions and comparison to FDTD solutions are presented in Figure 6, with good agreement. Similarly, Figure 7 shows the S-parameters [S.sub.11], [S.sub.21], [S.sub.51], and [S.sub.61], which indicate the good connection between ports 1 and 5, and mutual coupling with ports 2 and 6. The results show some disagreements in high frequencies, and this could be associated with insufficient spatial sampling density in the FDTD method.
Finally, some computational costs are shown in Table 1. The spatial discretization based on DGTD allows a reduction of unknowns in 25 times. The CN based LDU scheme allows larger time step interval with respect to the explicit FDTD, even with very fine structures; in this case, the difference in [DELTA]t between the two methods is 5700 times. Finally, an impressive reduction of 110 times of CPU time shows the power of the proposed method.
The discontinuous Galerkin time domain method (DGTD) works accurately and efficiently for highly multiscale EM problems when an optimal time integration method is implemented. Particularly for sequential structure cases, the proposed block Lower-Diagonal-Upper (LDU) decomposition method reduces memory costs and CPU time with respect to the previously implemented Block-Thomas algorithm for the discontinuous Galerkin time domain method. For this reason, the LDU-DGTD method can solve larger cases than traditional single domain methods and the Block- Thomas method. Finally, the LDU-DGTD method also shows important improvements in computational costs with respect to the explicit FDTD, which demonstrates the advantages and potentials of the newly proposed method.
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Luis E. Tobon (1), Qiang Ren (2), Qingtao Sun (2), Jiefu Chen (3), and Qing Huo Liu (2), *
Received 22 November 2014, Accepted 13 February 2015, Scheduled 8 March 2015
* Corresponding author: [[OMEGA].sub.i]ng Huo Liu (firstname.lastname@example.org).
(1) Pontificia Universidad Javeriana-Cali, Colombia. 2 Duke University, USA. 3 Weatherford International, USA.
Caption: Figure 1. Multiscale package-to-chip structure.
Caption: Figure 2. Cavities with four, eight and sixteen subdomains used for verification.
Caption: Figure 3. Electric field in x, y, and 2 direction, case with sixteen subdomains.
Caption: Figure 4. Comparison in memory cost and CPU time for the proposed method.
Caption: Figure 5. General multiscale package-to-chip structure. (a) 3D view, and (b) top view of the 1st, the ith and the Nth layers.
Caption: Figure 6. Comparison of transient signals in ports 1 Figure and 5.
Caption: Figure 7. Comparison of S-parameters.
Table 1. Computational costs. Explicit FDTD Implicit DGTD Gain Number of unknowns 3.5 millions 138514 25 [DELTA]t 0.36 fs 2 ps 5700 Number of steps for 8 ns 22.8 millions 4000 5700 CPU time per time step (s) 0.0117 0.6 0.02 Total CPU time (s) 265712 2400 110 Memory (MB) 56 1340 0.04
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|Author:||Tobon, Luis E.; Ren, Qiang; Sun, Qingtao; Chen, Jiefu; Liu, Qing Huo|
|Publication:||Progress In Electromagnetics Research|
|Date:||Mar 1, 2015|
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