Research on CMFD Preconditioner for Two-dimensional MOC Krylov Subspace Iteration

Zhang Guangchun; Zhang Haochun

doi:10.13832/j.jnpe.2023.S2.0120

Volume 44 Issue S2

Dec. 2023

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Zhang Guangchun, Zhang Haochun. Research on CMFD Preconditioner for Two-dimensional MOC Krylov Subspace Iteration[J]. Nuclear Power Engineering, 2023, 44(S2): 120-125. doi: 10.13832/j.jnpe.2023.S2.0120

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Zhang Guangchun, Zhang Haochun. Research on CMFD Preconditioner for Two-dimensional MOC Krylov Subspace Iteration[J]. Nuclear Power Engineering, 2023, 44(S2): 120-125. doi: 10.13832/j.jnpe.2023.S2.0120

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Research on CMFD Preconditioner for Two-dimensional MOC Krylov Subspace Iteration

doi: 10.13832/j.jnpe.2023.S2.0120

School of Energy Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China

Received Date: 2023-07-11
Rev Recd Date: 2023-09-13
Publish Date: 2023-12-30

Abstract

Abstract

To improve the efficiency of the Krylov subspace iteration for two-dimensional method of characteristics (MOC), a preconditioner based on the coarse-mesh finite difference (CMFD) matrix is proposed. Firstly, the CMFD acceleration method is linearized and the linear CMFD preconditioner is derived. Secondly, the linear CMFD preconditioner is applied to the Krylov subspace method to solve the two-dimensional MOC equation. Finally, the acceleration performance of the linear CMFD preconditioner is tested using the IAEA LWR and 2-D C5G7 benchmarks. The results show that, after applying the CMFD preconditioner, the iteration count for the IAEA LWR benchmark is reduced by 52.7%, and the computational time is decreased by 41.8%. For the 2-D C5G7 benchmark, the iteration count is reduced by 20.3% and the computational time is reduced by 13.2%. The study also finds that the CMFD preconditioner works well for problems with weak local heterogeneities, but its performance decreases for problems with strong local heterogeneities.
- MOC,
- Krylov subspace iteration,
- CMFD,
- Preconditioner

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References(15)

References

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