Diffusion synthetic acceleration with the fine mesh rebalance of the subcell balance method with tetrahedral meshes for SN transport calculationsopen access
- Authors
- Muhammad, Habib; Hong, Ser Gi
- Issue Date
- Mar-2020
- Publisher
- KOREAN NUCLEAR SOC
- Keywords
- Neutron transport equation; Diffusion equation; DFEMLDEM-SCB; Tetrahedral mesh
- Citation
- NUCLEAR ENGINEERING AND TECHNOLOGY, v.52, no.3, pp.485 - 498
- Indexed
- SCIE
SCOPUS
KCI
- Journal Title
- NUCLEAR ENGINEERING AND TECHNOLOGY
- Volume
- 52
- Number
- 3
- Start Page
- 485
- End Page
- 498
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/1989
- DOI
- 10.1016/j.net.2019.08.021
- ISSN
- 17385733
- Abstract
- A diffusion synthetic acceleration (DSA) technique for the SN transport equation discretized with the linear discontinuous expansion method with subcell balance (LDEM-SCB) on unstructured tetrahedral meshes is presented. The LDEM-SCB scheme solves the transport equation with the discrete ordinates method by using the subcell balances and linear discontinuous expansion of the flux. Discretized DSA equations are derived by consistently discretizing the continuous diffusion equation with the LDEM-SCB method, however, the discretized diffusion equations are not fully consistent with the discretized transport equations. In addition, a fine mesh rebalance (FMR) method is devised to accelerate the discretized diffusion equation coupled with the preconditioned conjugate gradient (CG) method. The DSA method is applied to various test problems to show its effectiveness in speeding up the iterative convergence of the transport equation. The results show that the DSA method gives small spectral radii for the tetrahedral meshes having various minimum aspect ratios even in highly scattering dominant mediums for the homogeneous test problems. The numerical tests for the homogeneous and heterogeneous problems show that DSA with FMR (with preconditioned CG) gives significantly higher speedups and robustness than the one with the Gauss-Seidel-like iteration.
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