Engineering topics

Bergmann, Ryan M.

Publications and source records attributed to Bergmann, Ryan M..

3 records

DOE OSTI2017

Recent Improvements To CINDER2008 and Activation Analysis Tools

CINDER2008 is a transmutation code that calculates induced radioactivity for either reactor- or accelerator-driven radiation sources. Several Perl scripts have been developed to treat multi-cell problems in combination with either the radiation transport code MCNPX or a special version of MCNPX that writes residual nuclide tallies from model-based physics directly in the MCNPX output file. These scripts simplify the preparation of CINDER input, and can use CINDER output to prepare the source term for a subsequent MCNPX run to calculate dose rates due to activated material. In addition, these scripts support calculations with the transmutation codes SP-FISPACT and ORIHET-3. A number of improvements have been made to CINDER, its data libraries, and the Perl scripts, including (1) substantial corrections to the data libraries, (2) inclusion of recent revisions to USDOE HC-2/HC-3 thresholds and the corresponding thresholds for regulatory bodies in other countries, (3) the ability to run the CINDER accident analysis tool through the script input file, and (4) an easier way to write the script input file when treating a large number of cells. Some features of running CINDER and a number of example problems are discussed.

Micklich, Bradley J.↗

DOE OSTI2017

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↗

DOE OSTI2017

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.↗