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Division members promote the advancement of mathematical and computational methods for solving problems arising in all disciplines encompassed by the Society. They place particular emphasis on numerical techniques for efficient computer applications to aid in the dissemination, integration, and proper use of computer codes, including preparation of computational benchmark and development of standards for computing practices, and to encourage the development on new computer codes and broaden their use.
2020 ANS Virtual Winter Meeting
November 15–19, 2020
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UWC 2020: A call for transformational change
Bowing to current COVID-19 realities but buoyed by the success of June’s virtual Annual Meeting, ANS event planners returned to the virtual realm for this year’s Utility Working Conference. Originally scheduled for August 9–12 at Marco Island, Fla., the condensed event was held Wednesday, August 11, wherever registrants’ computer devices happened to be located.
In addition to 26 educational sessions and workshops, UWC 2020 featured an opening plenary session titled “Achieving Transformational Change: A leadership discussion,” moderated by Bob Coward, MPR Associates principal officer and ANS past president (2017–2018). Plenary panelists included representatives from three utilities—Arizona Public Service (APS), Exelon, and Xcel Energy—plus the Institute of Nuclear Power Operations (INPO) and the Nuclear Regulatory Commission.
R. N. Slaybaugh, M. Ramirez-Zweiger, Tara Pandya, Steven Hamilton, T. M. Evans
Nuclear Science and Engineering | Volume 190 | Number 1 | April 2018 | Pages 31-44
Technical Paper | dx.doi.org/10.1080/00295639.2017.1413875
Articles are hosted by Taylor and Francis Online.
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. This solver set is a strong choice for very large and challenging problems.