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Division Spotlight
Accelerator Applications
The division was organized to promote the advancement of knowledge of the use of particle accelerator technologies for nuclear and other applications. It focuses on production of neutrons and other particles, utilization of these particles for scientific or industrial purposes, such as the production or destruction of radionuclides significant to energy, medicine, defense or other endeavors, as well as imaging and diagnostics.
Meeting Spotlight
ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Latest News
Norway’s Halden reactor takes first step toward decommissioning
The government of Norway has granted the transfer of the Halden research reactor from the Institute for Energy Technology (IFE) to the state agency Norwegian Nuclear Decommissioning (NND). The 25-MWt Halden boiling water reactor operated from 1958 to 2018 and was used in the research of nuclear fuel, reactor internals, plant procedures and monitoring, and human factors.
Sterling M. Harper, Paul K. Romano, Benoit Forget, Kord S. Smith
Nuclear Science and Engineering | Volume 194 | Number 11 | November 2020 | Pages 1009-1015
Technical Paper | doi.org/10.1080/00295639.2020.1719765
Articles are hosted by Taylor and Francis Online.
Monte Carlo (MC) transport codes offer high-fidelity modeling of particle transport physics, but their high computational cost makes them impractical for many applications. For some applications such as multiphysics and depletion that use finely discretized geometries, a large portion of this computational cost is attributable to ray tracing. Neighbor lists are a well-known method for accelerating ray-tracing calculations in a MC code, but despite their prevalence, little work has been published on the details of their implementation. The fine details can have a significant impact on performance, particularly when using shared-memory parallelism. This paper addresses these details of implementation with a discussion of different neighbor list schemes and their impact on software runtime.
Performance tests were run by using OpenMC on a pin-cell problem discretized with up to 200 axial regions. The results demonstrate that switching from surface-based to cell-based neighbor lists leads to a 10 faster calculation rate for the most fine discretization. Furthermore, using a threadsafe shared-memory data structure results in a 20% faster calculation rate versus simple threadprivate neighbor lists. Results here show that a data structure that is contiguous in memory improves performance by only 1% to 2% over noncontiguous linked lists.