Abstract
Monte Carlo (MC) shielding calculations often use weight windows (WWs) and biased sources formed from a deterministic estimate of the adjoint flux to improve the convergence rate of tallies. This requires a significant amount of computer memory, which can limit the memory available for high-resolution tally output. A new method is proposed for reducing these memory requirements by using singular value decomposition (SVD) in linear or logarithmic space to approximate the adjoint flux. This method's performance is evaluated using the Shift and Denovo codes for streaming and diffusion base case problems, followed by problems using the Westinghouse AP1000 and the Joint European Torus. The log SVD reduced WW memory requirements by an order of magnitude in all cases without a significant performance penalty. Additionally, the linear SVD reduced biased source memory requirements by an order of magnitude, but further investigation is needed to account for observed limitations.
| Original language | English |
|---|---|
| Article number | 107327 |
| Journal | Annals of Nuclear Energy |
| Volume | 141 |
| DOIs | |
| State | Published - Jun 15 2020 |
Funding
The authors would like to thank Eva Davidson for her advice on setting up the AP1000 excore problem and Katherine Royston for her help with JET. Thanks also go to Jonathan Naish for creating the MCNP model of JET and to Paola Batistoni for authorizing its use for this work. Special thanks go to Aaron Bevill for his insight in regards to performing SVD in log space. Work for this paper was supported by Oak Ridge National Laboratory, which is managed and operated by UT-Battelle, LLC, for the US Department of Energy under Contract No. DEAC05-00OR22725. This research was supported by the Exascale Computing Project (ECP), project number 17-SC-20-SC. The ECP is a collaborative effort of two DOE organizations, the Office of Science and the National Nuclear Security Administration, that are responsible for the planning and preparation of a capable exascale ecosystem—including software, applications, hardware, advanced system engineering, and early testbed platforms—to support the nation's exascale computing imperative. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the US Department of Energy under Contract No. DE-AC05-00OR22725. Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). The authors would like to thank Eva Davidson for her advice on setting up the AP1000 excore problem and Katherine Royston for her help with JET. Thanks also go to Jonathan Naish for creating the MCNP model of JET and to Paola Batistoni for authorizing its use for this work. Special thanks go to Aaron Bevill for his insight in regards to performing SVD in log space. Work for this paper was supported by Oak Ridge National Laboratory, which is managed and operated by UT-Battelle, LLC, for the US Department of Energy under Contract No. DEAC05-00OR22725. This research was supported by the Exascale Computing Project (ECP), project number 17-SC-20-SC. The ECP is a collaborative effort of two DOE organizations, the Office of Science and the National Nuclear Security Administration, that are responsible for the planning and preparation of a capable exascale ecosystem—including software, applications, hardware, advanced system engineering, and early testbed platforms—to support the nation’s exascale computing imperative. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the US Department of Energy under Contract No. DE-AC05-00OR22725.
| Funders | Funder number |
|---|---|
| DOE organizations | |
| U.S. Department of Energy | 17-SC-20-SC, DEAC05-00OR22725 |
| Office of Science | |
| National Nuclear Security Administration | |
| Oak Ridge National Laboratory |
Keywords
- Monte Carlo radiation transport
- Singular value decomposition
- Variance reduction
