370
Views
7
CrossRef citations to date
0
Altmetric
Technical Papers

Convergence Studies on Nonlinear Coarse-Mesh Finite Difference Accelerations for Neutron Transport Analysis

&
Pages 136-149 | Received 19 Jan 2018, Accepted 07 Apr 2018, Published online: 21 May 2018
 

Abstract

Application of partial current–based coarse-mesh finite difference (pCMFD) acceleration to a one-node scheme is devised for stability enhancement of the parallel neutron transport calculation algorithm. Conventional one-node coarse-mesh finite difference (CMFD) allows parallel algorithms to be more tractable than two-node CMFD, but it has an inherent stability issue for some problems. In order to overcome this issue, pCMFD is modified to be fitted into the one-node scheme and is tested for both sequential and parallel calculations. The superior stability of the one-node pCMFD is shown by comparing results from analytic and numerical approaches. To investigate the convergence behavior of the acceleration methods in an analytic way, Fourier analysis is applied to an infinite homogeneous slab reactor configuration with the monoenergetic neutron flux assumption, and the spectral radius is calculated as a convergence factor. This paper carefully describes the process of the Fourier analysis on the parallel algorithm for neutron transport and compares it to that of the conventional sequential algorithm.

Acknowledgments

This work was supported by the National Research Foundation of Korea grant NRF-2016R1A5A1013919 funded by the Korean government.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 409.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.