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Rowland, Kelly L.

Publications and source records attributed to Rowland, Kelly L..

Assessment of the Lagrange Discrete Ordinates Equations for Three-Dimensional Neutron Transport

The Lagrange Discrete Ordinates (LDO) equations, developed by Ahrens as an alternative to the traditional discrete ordinates formulation, have been implemented in Denovo, a threedimensional radiation transport code developed by Oak Ridge National Laboratory. The LDO equations retain the formal structure of the classical discrete ordinates equations but treat particle scattering in a different way. Solutions of the LDO equations have an interpolatory structure such that the angular flux can be naturally evaluated at directions other than the discrete ordinates used in arriving at the solutions, and the ordinates themselves may be chosen in a strategic way for the problem under consideration. Of particular interest is that the LDO equations have been shown to mitigate ray effects at increased angular resolutions. In this paper we present scalar flux solutions of the LDO equations for a small number of test cases of interest and compare the results against flux solutions generated using standard quadrature types. The LDO equations’ flux solutions were found to be comparable to those resultant from the standard quadrature types in value; results from the LDO equations were also found to be commensurate with those of standard quadrature types when comparing the flux solutions in the context of the experimental benchmark test case examined.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Performance and accuracy of criticality calculations performed using WARP – A framework for continuous energy Monte Carlo neutron transport in general 3D geometries on GPUs

In this companion paper to "Algorithmic Choices in WARP - A Framework for Continuous Energy Monte Carlo Neutron Transport in General 3D Geometries on GPUs" (doi:10.1016/j.anucene.2014.10.039), the WARP Monte Carlo neutron transport framework for graphics processing units (GPUs) is benchmarked against production-level central processing unit (CPU) Monte Carlo neutron transport codes for both performance and accuracy. We compare neutron flux spectra, multiplication factors, runtimes, speedup factors, and costs of various GPU and CPU platforms running either WARP, Serpent 2.1.24, or MCNP 6.1. WARP compares well with the results of the production-level codes, and it is shown that on the newest hardware considered, GPU platforms running WARP are between 0.8 to 7.6 times as fast as CPU platforms running production codes. Also, the GPU platforms running WARP were between 15% and 50% as expensive to purchase and between 80% to 90% as expensive to operate as equivalent CPU platforms performing at an equal simulation rate.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

WARP

WARP, which can stand for ``Weaving All the Random Particles,'' is a three-dimensional (3D) continuous energy Monte Carlo neutron transport code developed at UC Berkeley to efficiently execute on NVIDIA graphics processing unit (GPU) platforms. WARP accelerates Monte Carlo simulations while preserving the benefits of using the Monte Carlo method, namely, that very few physical and geometrical simplifications are applied. WARP is able to calculate multiplication factors, neutron flux distributions (in both space and energy), and fission source distributions for time-independent neutron transport problems. It can run in both criticality or fixed source modes, but fixed source mode is currently not robust, optimized, or maintained in the newest version. WARP can transport neutrons in unrestricted arrangements of parallelepipeds, hexagonal prisms, cylinders, and spheres. The goal of developing WARP is to investigate algorithms that can grow into a full-featured, continuous energy, Monte Carlo neutron transport code that is accelerated by running on GPUs. The crux of the effort is to make Monte Carlo calculations faster while producing accurate results. Modern supercomputers are commonly being built with GPU coprocessor cards in their nodes to increase their computational efficiency and performance. GPUs execute efficiently on data-parallel problems, but most CPU codes, including those for Monte Carlo neutral particle transport, are predominantly task-parallel. WARP uses a data-parallel neutron transport algorithm to take advantage of the computing power GPUs offer.

Bergmann, Ryan M.↗