skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Eigenvalue Solvers for Modeling Nuclear Reactors on Leadership Class Machines

Journal Article · · Nuclear Science and Engineering
ORCiD logo [1];  [1];  [2];  [2];  [2]
  1. Univ. of California, Berkeley, CA (United States). Nuclear Engineering Dept.
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Radiation Transport and Criticality Group

In this paper, three complementary methods have been implemented in the code Denovo that accelerate neutral particle transport calculations with methods that use leadership-class computers fully and effectively: a multigroup block (MG) Krylov solver, a Rayleigh quotient iteration (RQI) eigenvalue solver, and a multigrid in energy (MGE) preconditioner. The MG Krylov solver converges more quickly than Gauss Seidel and enables energy decomposition such that Denovo can scale to hundreds of thousands of cores. RQI should converge in fewer iterations than power iteration (PI) for large and challenging problems. RQI creates shifted systems that would not be tractable without the MG Krylov solver. It also creates ill-conditioned matrices. The MGE preconditioner reduces iteration count significantly when used with RQI and takes advantage of the new energy decomposition such that it can scale efficiently. Each individual method has been described before, but this is the first time they have been demonstrated to work together effectively. The combination of solvers enables the RQI eigenvalue solver to work better than the other available solvers for large reactors problems on leadership-class machines. Using these methods together, RQI converged in fewer iterations and in less time than PI for a full pressurized water reactor core. These solvers also performed better than an Arnoldi eigenvalue solver for a reactor benchmark problem when energy decomposition is needed. The MG Krylov, MGE preconditioner, and RQI solver combination also scales well in energy. Finally, this solver set is a strong choice for very large and challenging problems.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); University of California, Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC); USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
1427595
Journal Information:
Nuclear Science and Engineering, Vol. 190, Issue 1; ISSN 0029-5639
Publisher:
American Nuclear Society - Taylor & FrancisCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 8 works
Citation information provided by
Web of Science

References (16)

A Multigrid Tutorial, Second Edition book January 2000
GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems journal July 1986
Matrix Algorithms book January 2001
Inverse Iteration, Ill-Conditioned Equations and Newton’s Method journal July 1979
Application of Quadruple Range Quadratures to Three-Dimensional Model Shielding Problems journal November 2009
The Rayleigh quotient iteration and some generalizations for nonnormal matrices journal September 1974
Fast iterative methods for discrete-ordinates particle transport calculations journal January 2002
Denovo: A New Three-Dimensional Parallel Discrete Ordinates Code in SCALE journal August 2010
An overview of the Trilinos project journal September 2005
Modified Gram-Schmidt (MGS), Least Squares, and Backward Stability of MGS-GMRES journal January 2006
The inverse power method for calculation of multiplication factors journal May 2002
Preconditioning Techniques for Large Linear Systems: A Survey journal November 2002
Multigrid in energy preconditioner for Krylov solvers journal June 2013
An S n Algorithm for the Massively Parallel CM-200 Computer journal March 1998
Numerical Linear Algebra book January 1997
Massively Parallel, Three-Dimensional Transport Solutions for the k -Eigenvalue Problem journal June 2014

Figures / Tables (7)